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2,900 | Learning and generalising object extraction skill for contact-rich disassembly tasks: an introductory study | Remanufacturing automation must be designed to be flexible and robust enough to overcome the uncertainties, conditions of the products, and complexities in the planning and operation of the processes. Machine learning methods, in particular reinforcement learning, are presented as techniques to learn, improve, and generalise the automation of many robotic manipulation tasks (most of them related to grasping, picking, or assembly). However, not much has been exploited in remanufacturing, in particular in disassembly tasks. This work presents the state of the art of contact-rich disassembly using reinforcement learning algorithms and a study about the generalisation of object extraction skills when applied to contact-rich disassembly tasks. The generalisation capabilities of two state-of-the-art reinforcement learning agents (trained in simulation) are tested and evaluated in simulation, and real world while perform a disassembly task. Results show that at least one of the agents can generalise the contact-rich extraction skill. Besides, this work identifies key concepts and gaps for the reinforcement learning algorithms' research and application on disassembly tasks. |
2,901 | Mobile Visual Search Compression With Grassmann Manifold Embedding | With the increasing popularity of mobile phones and tablets, the explosive growth of query-by-capture applications calls for a compact representation of the query image feature. Compact descriptors for visual search (CDVS) is a recently released standard from the ISO/IEC moving pictures experts group, which achieves state-of-the-art performance in the context of image retrieval applications. However, they did not consider the matching characteristics in local space in a large-scale database, which might deteriorate the performance. In this paper, we propose a more compact representation with scale invariant feature transform (SIFT) descriptors for the visual query based on Grassmann manifold. Due to the drastic variations in image content, it is not sufficient to capture all the information using a single transform. To achieve more efficient representations, a SIFT manifold partition tree (SMPT) is initially constructed to divide the large dataset into small groups at multiple scales, which aims at capturing more discriminative information. Grassmann manifold is then applied to prune the SMPT and search for the most distinctive transforms. The experimental results demonstrate that the proposed framework achieves state-of-the-art performance on the standard benchmark CDVS dataset. |
2,902 | Transferred Multi-Perception Attention Networks for Remote Sensing Image Super-Resolution | Image super-resolution (SR) reconstruction plays a key role in coping with the increasing demand on remote sensing imaging applications with high spatial resolution requirements. Though many SR methods have been proposed over the last few years, further research is needed to improve SR processes with regard to the complex spatial distribution of the remote sensing images and the diverse spatial scales of ground objects. In this paper, a novel multi-perception attention network (MPSR) is developed with performance exceeding those of many existing state-of-the-art models. By incorporating the proposed enhanced residual block (ERB) and residual channel attention group (RCAG), MPSR can super-resolve low-resolution remote sensing images via multi-perception learning and multi-level information adaptive weighted fusion. Moreover, a pre-train and transfer learning strategy is introduced, which improved the SR performance and stabilized the training procedure. Experimental comparisons are conducted using 13 state-of-the-art methods over a remote sensing dataset and benchmark natural image sets. The proposed model proved its excellence in both objective criterion and subjective perspective. |
2,903 | GourmetNet: Food Segmentation Using Multi-Scale Waterfall Features with Spatial and Channel Attention | We propose GourmetNet, a single-pass, end-to-end trainable network for food segmentation that achieves state-of-the-art performance. Food segmentation is an important problem as the first step for nutrition monitoring, food volume and calorie estimation. Our novel architecture incorporates both channel attention and spatial attention information in an expanded multi-scale feature representation using our advanced Waterfall Atrous Spatial Pooling module. GourmetNet refines the feature extraction process by merging features from multiple levels of the backbone through the two attention modules. The refined features are processed with the advanced multi-scale waterfall module that combines the benefits of cascade filtering and pyramid representations without requiring a separate decoder or post-processing. Our experiments on two food datasets show that GourmetNet significantly outperforms existing current state-of-the-art methods. |
2,904 | Listen and Look: Audio-Visual Matching Assisted Speech Source Separation | Source permutation, i.e., assigning separated signal snippets to wrong sources over time, is a major issue in the state-of-the-art speaker-independent speech source separation methods. In addition to auditory cues, humans also leverage visual cues to solve this problem at cocktail parties: matching lip movements with voice fluctuations helps humans to better pay attention to the speaker of interest. In this letter, we propose an audio-visual matching network to learn the correspondence between voice fluctuations and lip movements. We then propose a framework to apply this network to address the source permutation problem and improve over audio-only speech separation methods. The modular design of this frame work makes it easy to apply the matching network to any audio-only speech separation method. Experiments on two-talker mixtures show that the proposed approach significantly improves the separation quality over the state-of-the-art audio-only method. This improvement is especially pronounced on mixtures that the audio-only method fails, in which the speakers often have similar voice characteristics. |
2,905 | Robust Deep Learning for IC Test Problems | Numerous machine learning (ML), and more recently, deep-learning (DL)-based approaches, have been proposed to tackle scalability issues in electronic design automation, including those in integrated circuit (IC) test. This article examines state-of-the-art DL for IC test and highlights two critical unaddressed challenges. The first challenge involves identifying fit-for-purpose statistical metrics to train and evaluate ML model performance and usefulness in IC test. Our work shows that current metrics do not reflect how well ML models have learned to generalize and perform in the domain-specific context. From this insight, we propose and evaluate alternative metrics that better capture a model's likely usefulness in the IC test problem. The second challenge is to choose an appropriate input abstraction so as to enable an ML model to learn robust and reliable features. We investigate how well DL for IC test techniques generalize by exploring their robustness to perturbations that alter a netlist's structure but do not alter its functionality. This article provides insights into challenges via empirical evaluation of the state-of-the-art and offers guidance for future work. |
2,906 | E2BNet: MAC-free yet accurate 2-level binarized neural network accelerator for embedded systems | Deep neural networks are widely used in computer vision, pattern recognition, and speech recognition and achieve high accuracy at the cost of remarkable computation. High computational complexity and memory accesses of such networks create a big challenge for using them in resource-limited and low-power embedded systems. Several binary neural networks have been proposed that exploit only 1-bit values for both weights and activations. Binary neural networks substitute complex multiply-accumulation operations with bitwise logic operations to reduce computations and memory usage. However, these quantized neural networks suffer from accuracy loss, especially in big datasets. In this paper, we introduce a quantized neural network with 2-bit weights and activations that is more accurate compared to the state-of-the-art quantized neural networks, and also the accuracy is close to the full precision neural networks. Moreover, we propose E2BNet, an efficient MAC-free hardware architecture that increases power efficiency and throughput/W about 3.6 x and 1.5 x , respectively, compared to the state-of-the-art quantized neural networks. E2BNet processes more than 500 images/s on the ImageNet dataset that not only meet real-time requirements of images/video processing but also can be deployed on high frame rate video applications. |
2,907 | LPCVD silicon nitride uniformity improvement using adaptive real-time temperature control | Art effective approach to improve silicon nitride thickness uniformity has been demonstrated on a batch LPCVD furnace platform. Implementation of adapthe real time temperature control provides accurate, real-time estimation of substrate temperature profiles that enables model-based optimization of Process temperature. optimization of a 200-nm silicon nitride deposition yielded long-term, overall nitride thickness uniformity of 0.79% 1sigma over a seven-week period, compared to 1-24% for an equivalent PID-tuned process. Three sequential silicon nitride deposition iterations were implemented in the proem recipe to enable increased temperature ramp rates, for more efficient optimization of within wafer uniformity The optimized process requalified quickly after major and minor equipment maintenance, and is suitable for use in a manufacturing environment The ART-optimized temperature ramp intervals used in this study are comparable to temperature deltas often used to offset dichlorosilane depletion effects encountered in some large-batch vertical furnace depositions. SIMS depth profiling of ART-optimized silicon nitride does reveal small oxygen and chlorine peaks, indicating slight interface formation between deposition steps. |
2,908 | SAGE: Steering the Adversarial Generation of Examples With Accelerations | To generate image adversarial examples, state-of-the-art black-box attacks usually require thousands of queries. However, massive queries will introduce additional costs and exposure risks in the real world. Towards improving the attack efficiency, we carefully design an acceleration framework SAGE for existing black-box methods, which is composed of sLocator (initial point optimization) and sRudder (search process optimization). The core idea of SAGE in terms of 1) saliency map can guide the perturbations towards the most adversarial direction and 2) exploiting bounding box (bbox) to capture those salient pixels in the black-box attack. Meanwhile, we provide a series of observations and experiments that demonstrate bbox holds model invariance and process invariance. We extensively evaluate SAGE on four state-of-the-art black-box attacks involving three popular datasets (MNIST, CIFAR10, and ImageNet). The results show that SAGE could present fundamental improvements even against robust models that use adversarial training. Specifically, SAGE could reduce > 20% of queries and improve the success rate of attacks to 95%similar to 100%. Compared with the other acceleration framework, SAGE fulfills the more significant effect in a flexible, stable, and low-overhead manner. Moreover, our practical evaluation (Google Cloud Vision API) shows SAGE can be applied to real-world scenarios. |
2,909 | Moving Toward Intelligence: Detecting Symbols on 5G Systems Through Deep Echo State Network | Due to the nonlinear distortion caused by radio-frequency (RF) components in the transceiver, detecting transmitted symbols for multiple-input and multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems can be challenging and resource consuming. In this work, we introduce a Deep Echo State Network (DESN) to serve as the symbol detector for 5G communication networks. Our DESN employs memristive synapses as the dynamic reservoir layer to accelerate the learning algorithm and computation. By cascading multiple dynamic reservoir layers in a hierarchical processing structure, our DESN processes received signal in both spatial and temporal domains. The resulting hybrid memristor-CMOS co-design provides the nonlinear computation required by the reservoir layer while significantly reduces the power consumption. From the benchmark on nonlinear system prediction, our DESN exhibits 10.31 X reduction on the prediction error compared to state-of-the-art neural network designs. Moreover, our DESN records a bit error rate (BER) of 5.76x10(-2) on the high-speed transmitted symbol detection task for MIMO-OFDM systems, yielding 47.73% more precise than state-of-the-art techniques in the literate for 5G communication networks. |
2,910 | Propagation of Lamb waves in a metal plate with an abrupt change in thickness using Peridynamics and laser Doppler velocimetry | Plate-like structures can be characterized by a variety of abrupt geometric changes affecting the Lamb wave propagation, similarly to damage occurring in service. Therefore, a deep knowledge of phenomena involved in the interaction between guide waves and discontinuities is required. For this purpose, an experimental investigation is carried out considering an isotropic plate where an abrupt thickness change is present. The fundamental modes excitation is operated by a piezoelectric transducer while the signal sensing in multiple locations, also across the discontinuity, is performed by a scanning laser Doppler vibrometer. The investigation reveals mode conversion and highlights how the effects on the wave propagation depend upon the discontinuity geometrical characteristics. A peridynamics-based model representing the examined problem is also defined and its effectiveness to simulate the observed phenomena is proven. |
2,911 | Sex-specific newborn screening for X-linked adrenoleukodystrophy | Males with X-linked adrenoleukodystrophy (ALD) are at high risk for developing adrenal insufficiency and/or progressive leukodystrophy (cerebral ALD) at an early age. Pathogenic variants in ABCD1 result in elevated levels of very long-chain fatty acids (VLCFA), including C26:0-lysophosphatidylcholine (C26:0-LPC). Newborn screening for ALD enables prospective monitoring and timely therapeutic intervention, thereby preventing irreversible damage and saving lives. The Dutch Health Council recommended to screen only male newborns for ALD without identifying untreatable conditions associated with elevated C26:0-LPC, like Zellweger spectrum disorders and single peroxisomal enzyme defects. Here, we present the results of the SCAN (Screening for ALD in the Netherlands) study which is the first sex-specific newborn screening program worldwide. Males with ALD are identified based on elevated C26:0-LPC levels, the presence of one X-chromosome and a variant in ABCD1, in heel prick dried bloodspots. Screening of 71 208 newborns resulted in the identification of four boys with ALD who, following referral to the pediatric neurologist and confirmation of the diagnosis, enrolled in a long-term follow-up program. The results of this pilot show the feasibility of employing a boys-only screening algorithm that identifies males with ALD without identifying untreatable conditions. This approach will be of interest to countries that are considering ALD newborn screening but are reluctant to identify girls with ALD because for girls there is no direct health benefit. We also analyzed whether gestational age, sex, birth weight and age at heel prick blood sampling affect C26:0-LPC concentrations and demonstrate that these covariates have a minimal effect. |
2,912 | Probabilistic Sparse Matching for Robust 3D/3D Fusion in Minimally Invasive Surgery | Classical surgery is being overtaken by minimally invasive and transcatheter procedures. As there is no direct view or access to the affected anatomy, advanced imaging techniques such as 3D C-arm computed tomography (CT) and C-arm fluoroscopy are routinely used in clinical practice for intraoperative guidance. However, due to constraints regarding acquisition time and device configuration, intraoperative modalities have limited soft tissue image quality and reliable assessment of the cardiac anatomy typically requires contrast agent, which is harmful to the patient and requires complex acquisition protocols. We propose a probabilistic sparse matching approach to fuse high-quality preoperative CT images and nongated, noncontrast intraoperative C-arm CT images by utilizing robust machine learning and numerical optimization techniques. Thus, high-quality patient-specific models can be extracted from the preoperative CT and mapped to the intraoperative imaging environment to guide minimally invasive procedures. Extensive quantitative experiments on 95 clinical datasets demonstrate that our model-based fusion approach has an average execution time of 1.56 s, while the accuracy of 5.48 mm between the anchor anatomy in both images lies within expert user confidence intervals. In direct comparison with image-to-image registration based on an open-source state-of-the-art medical imaging library and a recently proposed quasi-global, knowledge-driven multi-modal fusion approach for thoracic-abdominal images, our model-based method exhibits superior performance in terms of registration accuracy and robustness with respect to both target anatomy and anchor anatomy alignment errors. |
2,913 | miRNome-transcriptome analysis unveils the key regulatory pathways involved in the tumorigenesis of tongue squamous cell carcinoma | Tongue squamous cell carcinoma (TSCC) is considered the most common malignant tumor among the oral squamous cell carcinomas with a poor prognosis. Understanding the underlying molecular mechanisms that underpin TSCC and its treatments is the focus of the research. Deregulated expression of microRNAs (miRNAs) has recently been implicated in various biological processes linked to cancer. Therefore, in this study, we attempted to investigate miRNAs and their targets expressed in TSCC, which could be involved in its oncogenesis. We performed next-generation sequencing of small RNAs and transcriptomes in H357 TSCC cell line and human oral keratinocytes as a control to find miRNAs and mRNAs that are differentially expressed (DE), which were then supplemented with additional expression datasets from databases, yielding 269 DE miRNAs and 2094 DE genes. The target prediction followed by pathway and disease function analysis revealed that the DE targets were significantly associated with the key processes and pathways, such as apoptosis, epithelial-mesenchymal transition, endocytosis and vascular endothelial growth factor signaling pathways. Furthermore, the top 12 DE targets were chosen based on their involvement in more than one cancer-related pathway, of which 6 genes are targeted by miR-128-3p. Real-time quantitative PCR validation of this miRNA and its targets in H357 and SCC9 TSCC cells confirmed their possible targeting from their reciprocal expression, with MAP2K7 being a critical target that might be involved in oncogenesis and progression of TSCC by acting as a tumor suppressor. Further research is underway to understand how miR-128-3p regulates oncogenesis in TSCC via MAP2K7 and associated pathways. |
2,914 | The Impact of Cache and Dynamic Memory Management in Static Dataflow Applications | Dataflow is a parallel and generic model of computation that is agnostic of the underlying multi/many-core architecture executing it. State-of-the-art frameworks allow fast development of dataflow applications providing memory, communicating, and computing optimizations by design time exploration. However, the frameworks usually do not consider cache memory behavior when generating code. A generally accepted idea is that bigger and multi-level caches improve the performance of applications. This work evaluates such a hypothesis in a broad experiment campaign adopting different multi-core configurations related to the number of cores and cache parameters (size, sharing, controllers). The results show that bigger is not always better, and the foreseen future of more cores and bigger caches do not guarantee software-free better performance for dataflow applications. Additionally, this work investigates the adoption of two memory management strategies for dataflow applications: Copy-on-Write (CoW) and Non-Temporal Memory transfers (NTM). Experimental results addressing state-of-the-art applications show that NTM and CoW can contribute to reduce the execution time to -5.3% and -15.8%, respectively. CoW, specifically, shows improvements up to -21.8% in energy consumption with -16.8% of average among 22 different cache configurations. |
2,915 | Does the concept of "ultra-processed foods" help inform dietary guidelines, beyond conventional classification systems? Debate consensus | The participants in this debate agree that food processing vitally affects human health, and that the extent of food processing significantly affects diet quality and health outcomes. They disagree on the significance of ultra-processing, as defined within the Nova food classification system. The YES position holds that the concept is well-founded, clear, and supported by a wealth of investigations, as demonstrated by systematic association between ultra-processed food (UPF) intake and various diseases and disorders, and the persistence of these associations with control for critical nutrients. The NO position argues that the concept of UPF is poorly defined; gives rise to misclassification of foods; is without clear mechanisms of action; and that the observed associations with obesity are likely confounded. The YES position argues that the Nova system is therefore crucial to inform dietary guidelines and also public policies designed to reduce production and consumption of UPFs, whereas the NO position argues that the system adds no value to conventional nutrient metrics and existing nutrient profiling systems, pointing instead to the need to develop an evidence-based system to characterize obesogenic foods. |
2,916 | Hematopoietic Stem and Progenitor Cell Identification and Transplantation in Zebrafish | The zebrafish as a model organism is well known for its versatile genetics, rapid development, and straightforward live imaging. It is an excellent model to study hematopoiesis because of its highly conserved ontogeny and gene regulatory networks. Recently developed highly specific transgenic reporter lines have allowed direct imaging and tracking of hematopoietic stem and progenitor cells (HSPCs) in live zebrafish. These reporter lines can also be used for fluorescence-activated cell sorting (FACS) of HSPCs. Similar to mammalian models, HSPCs can be transplanted to reconstitute the entire hematopoietic system of zebrafish recipients. However, the zebrafish provides unique advantages to study HSPC biology, such as transplants into embryos and high-throughput chemical screening. This chapter will outline the methods needed to identify, isolate, and transplant HSPCs in zebrafish. |
2,917 | Molecular Epidemiology of Methicillin-Resistant Staphylococcus aureus in a Tertiary Hospital from the Comunidad Valenciana (Spain) | To reduce the high rates of morbidity and mortality caused by methicillin-resistant Staphylococcus aureus (MRSA) strains, it is essential to prevent their transmission. This can be achieved through molecular surveillance of the infecting strains, for which the detection of the entry of new strains, the analysis of antimicrobial resistance, and their containment are essential. In this study, we have analyzed 190 MRSA isolates obtained at the Consorcio Hospital General Universitario de Valencia (Spain) from 2013 to 2018 with three approaches: Multilocus Sequence Typing, spa, and SCCmec typing. Although the incidence of S. aureus infections detected in the hospital increased in the study period, the frequency of MRSA isolates decreased from 33% to 18%. One hundred seventy-two MRSA isolates were resistant to three or more classes of antimicrobials, especially to fluoroquinolones. No relevant temporal trend in the distribution of antibiotic susceptibility was observed. The combination of the three typing schemes allowed the identification of 74 different clones, of which the combination ST125-t067-IV was the most abundant in the study (27 cases). Members of three clonal complexes, CC5, CC8, and CC22, comprised 91% of the isolates, and included 32 STs and 32 spa types. The emergence of low incidence strains throughout the study period and a large number of isolates resistant to different classes of antibiotics shows the need for epidemiological surveillance of this pathogen. Our study demonstrates that epidemiological and molecular surveillance is a powerful tool to detect the emergence of clinically important MRSA clones. |
2,918 | Method development for the determination of seven ginsenosides in three Panax ginseng reference materials via liquid chromatography with tandem mass spectrometry | A new liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed for the analysis of ginsenosides in three Panax ginseng reference materials (RMs). Extraction procedures were optimized to recover neutral and malonyl-ginsenosides using a methanol-water extraction under basic conditions. Optimized mass fragmentation transitions were obtained for the development of a multiple reaction monitoring (MRM) detection method with electrospray ionization in negative and positive ion mode. Mass fraction values were determined for ginsenosides Rb1, Rb2, Rc, Rd, Re, Rf, and Rg1 in the three ginseng materials (rhizomes, extract, and an oral dosage form). Quantitation of these seven compounds was accomplished with 4-methylestradiol and SRM 3389 Ginsenoside Calibration Solution serving as an internal standard (IS) and calibration standards, respectively. Mass fraction values for the seven ginsenosides ranged from 1.27 mg/g to 21.42 mg/g, 3.25 mg/g to 35.81 mg/g, and 0.56 mg/g to 2.51 mg/g for SRM 3384, SRM 3385, and RM 8664, respectively. |
2,919 | The influence of journal submission guidelines on authors' reporting of statistics and use of open research practices: Five years later | Changes in statistical practices and reporting have been documented by Giofrè et al. PLOS ONE 12(4), e0175583 (2017), who investigated ten statistical and open practices in two high-ranking journals (Psychological Science [PS] and Journal of Experimental Psychology-General [JEPG]): null hypothesis significance testing; confidence or credible intervals; meta-analysis of the results of multiple experiments; confidence interval interpretation; effect size interpretation; sample size determination; data exclusion; data availability; materials availability; and preregistered design and analysis plan. The investigation was based on an analysis of all papers published in these journals between 2013 and 2015. The aim of the present study was to follow up changes in both PS and JEPG in subsequent years, from 2016 to 2020, adding code availability as a further open practice. We found improvement in most practices, with some exceptions (i.e., confidence interval interpretation and meta-analysis). Despite these positive changes, our results indicate a need for further improvements in statistical practices and adoption of open practices. |
2,920 | Bag of words KAZE (BoWK) with two-step classification for high-resolution remote sensing images | The bag-of-words (BoW) model has been widely used for scene classification in recent state-of-the-art methods. However, inter-class similarity among scene categories and very high spatial resolution imagery makes its performance limited in the remote-sensing domain. Therefore, this research presents a new KAZE-based image descriptor that makes use of the BoW approach to substantially increase classification performance. Specifically, a novel multi-neighbourhood KAZE is proposed for small image patches. Secondly, the spatial pyramid matching and BoW representation can be adopted to use the extracted features and make an innovative BoW KAZE (BoWK) descriptor. Third, two bags of multi-neighbourhood KAZE features are selected in which each bag is regarded as separated feature descriptors. Next, canonical correlation analysis is introduced as a feature fusion strategy to further refine the BOWK features, which allows a more effective and robust fusion approach than the traditional feature fusion strategies. Experiments on three challenging remote-sensing data sets show that the proposed BoWK descriptor not only surpasses the conventional KAZE descriptor but also yields significantly higher classification performance than the state-of-the-art methods used now. Moreover, the proposed BoWK approach produces rich informative features to describe the scene images with low-computational cost and a much lower dimension. |
2,921 | Resistance to Multiple Insecticide Classes in the Vinegar Fly Drosophila melanogaster (Diptera: Drosophilidae) in Michigan Vineyards | Vinegar flies are vectors of pathogens causing fruit rots of grapes, so control of these insects is important for preventing vineyard yield loss. Recent outbreaks of sour rots may be linked to greater challenges controlling vinegar flies, so we investigated the insecticide susceptibility of populations collected from commercial vineyards across Michigan. We first determined the discriminating concentration for phosmet, malathion, methomyl, and zeta-cypermethrin using a laboratory susceptible (Canton-S) strain of D. melanogaster females. The discriminating concentrations were determined as 252.08, 2.58, 0.96, and 1.68 ppm of the four insecticides, respectively. These concentrations were first tested in 2020 against populations from the two major counties for grape production. In 2021, we expanded monitoring to twenty-three populations collected from vineyards across six counties. All populations had significantly lower sensitivity to all four insecticides compared with Canton-S strain, with up to 98.8% lower mortality for phosmet. The LC50, LC90, and LC99 values of the four insecticides for the two populations tested in 2020 were 7-1,157-fold higher than the Canton-S strain. For the twenty-three populations collected in 2021, mortality ranged from 56.3 to 100% when the flies were screened using a 10x concentration of the discriminating concentration of the insecticides, whereas it ranged from 82.4 to 100% when the flies were screened using a 20x concentration. Our results suggest variable levels of resistance to insecticides from multiple chemical classes in D. melanogaster populations in Michigan vineyards, highlighting the need to implement integrated sour rot management approaches that are less dependent on insecticides for control of this species. |
2,922 | Multifeature Landmark-Free Active Appearance Models: Application to Prostate MRI Segmentation | Active shape models (ASMs) and active appearance models (AAMs) are popular approaches for medical image segmentation that use shape information to drive the segmentation process. Both approaches rely on image derived landmarks (specified either manually or automatically) to define the object's shape, which require accurate triangulation and alignment. An alternative approach to modeling shape is the levelset representation, defined as a set of signed distances to the object's surface. In addition, using multiple image derived attributes (IDAs) such as gradient information has previously shown to offer improved segmentation results when applied to ASMs, yet little work has been done exploring IDAs in the context of AAMs. In this work, we present a novel AAM methodology that utilizes the levelset implementation to overcome the issues relating to specifying landmarks, and locates the object of interest in a new image using a registration based scheme. Additionally, the framework allows for incorporation of multiple IDAs. Our multifeature landmark-free AAM(MFLAAM) utilizes an efficient, intuitive, and accurate algorithm for identifying those IDAs that will offer the most accurate segmentations. In this paper, we evaluate our MFLAAM scheme for the problem of prostate segmentation from T2-w MRI volumes. On a cohort of 108 studies, the levelset MFLAAM yielded a mean Dice accuracy of 88% +/- 5%, and a mean surface error of 1.5 mm +/-.8 mm with a segmentation time of 150/s per volume. In comparison, a state of the art AAM yielded mean Dice and surface error values of 86% +/- 9% and 1.6 mm +/- 1.0 mm, respectively. The differences with respect to our levelset-basedMFLAAM model are statistically significant (p < .05). In addition, our results were in most cases superior to several recent state of the art prostate MRI segmentation methods. |
2,923 | Molecular phenotyping of malignant canine mammary tumours: Detection of high-risk group and its relationship with clinicomolecular characteristics | Canine mammary gland tumours (CMTs) constitute the most common cancer in female dogs and comprise approximately 50% of all canine cancers. With the advent of high-throughput technologies such as microarray and next-generation sequencing, the molecular phenotyping (classification) of various cancers has been extensively developed. The present study used a canine RNA-sequencing dataset, namely GSE119810, to classify 113 malignant CMTs and 64 matched normal samples via an unsupervised hierarchical algorithm with a view to evaluating the association between the resulting subtypes (clusters) (n = 4) and clinical and molecular characteristics. Finally, a molecular classifier was developed, and it detected 1 high-risk molecular subtype in the training dataset (GSE119810) and 2 independent validation datasets (GSE20718 and GSE22516). Our results revealed four molecular subtypes (C2-C5) in malignant CMTs. Furthermore, the normal samples constituted a distinct group in the clustering analysis. Marked significant associations were observed between the molecular subtypes (especially C5) and clinical/molecular features, including positive lymphatic invasion, high tumour grades, histopathology diagnoses, short survival and high TP53 mutation rates (ps <.05). The high-risk subtype (C5) was further characterized through the development of a cell cycle-based gene signature, which comprised 37 proliferation-related genes according to the support vector machine algorithm. This signature identified the high-risk group in both training and validation datasets (ps <.001). In the validation analysis, our potential classifier robustly predicted patients with positive lymphatic invasion, metastases and short survival. |
2,924 | Self-Attention Context Network: Addressing the Threat of Adversarial Attacks for Hyperspectral Image Classification | Deep learning models have shown their great capability for the hyperspectral image (HSI) classification task in recent years. Nevertheless, their vulnerability towards adversarial attacks could not be neglected. In this study, we systematically analyze the influence of adversarial attacks on the HSI classification task for the first time. While existing research of adversarial attacks focuses on the generation of adversarial examples in the RGB domain, the experiments in this study show such adversarial examples could also exist in the hyperspectral domain. Although the difference between the generated adversarial image and the original hyperspectral data is imperceptible to the human visual system, most of the existing state-of-the-art deep learning models could be fooled by the adversarial image to make wrong predictions. To address this challenge, a novel self-attention context network (SACNet) is further proposed. We discover that the global context information contained in HSI can significantly improve the robustness of deep neural networks when confronted with adversarial attacks. Extensive experiments on three benchmark HSI datasets demonstrate that the proposed SACNet possesses stronger resistibility towards adversarial examples compared with the existing state-of-the-art deep learning models. |
2,925 | Weakly Supervised Liver Tumor Segmentation Using Couinaud Segment Annotation | Automatic liver tumor segmentation is of great importance for assisting doctors in liver cancer diagnosis and treatment planning. Recently, deep learning approaches trained with pixel-level annotations have contributed many breakthroughs in image segmentation. However, acquiring such accurate dense annotations is time-consuming and labor-intensive, which limits the performance of deep neural networks for medical image segmentation. We note that Couinaud segment is widely used by radiologists when recording liver cancer-related findings in the reports, since it is well-suited for describing the localization of tumors. In this paper, we propose a novel approach to train convolutional networks for liver tumor segmentation using Couinaud segment annotations. Couinaud segment annotations are image-level labels with values ranging from 1 to 8, indicating a specific region of the liver. Our proposed model, namely CouinaudNet, can estimate pseudo tumor masks from the Couinaud segment annotations as pixel-wise supervision for training a fully supervised tumor segmentation model, and it is composed of two components: 1) an inpainting network with Couinaud segment masks which can effectively remove tumors for pathological images by filling the tumor regions with plausible healthy-looking intensities; 2) a difference spotting network for segmenting the tumors, which is trained with healthy-pathological pairs generated by an effective tumor synthesis strategy. The proposed method is extensively evaluated on two liver tumor segmentation datasets. The experimental results demonstrate that our method can achieve competitive performance compared to the fully supervised counterpart and the state-of-the-art methods while requiring significantly less annotation effort. |
2,926 | Bio-surfaces and geometric references for mass customization in bio-interface design | Mass customization of products that interface with the human body poses unique problems due to the complexities of bio-interface design, the lack of biomechanical techniques in traditional mechanical design, and the absence of specific parametric strategies. Current biomechanical design often follows craftsman-like design processes using less than state-of-the-art tools and techniques. Thus, products that interface with the human body are not readily parameterized or automated. This paper presents a strategy for implementing mass customization in the design of mechanical devices that interface with the human body. This strategy is based on three methods that include: a method for capturing and representing the human body so that the model can be used with state-of-the-art tools and solid modeling techniques, a design methodology based on feature structure planning allowing the design process to be reused and automated, and a strategy for identifying parametric variables tied to the human body. A case study is presented to illustrate the proposed process. |
2,927 | Exploiting the Largest Available Zone: A Proactive Approach to Adaptive Random Testing by Exclusion | Adaptive random testing (ART) has been proposed to enhance the effectiveness of random testing (RT) through more even spreading of the test cases. In particular, restricted random testing (RRT) is an ART algorithm based on the intuition of skipping all the candidate test cases that are within the neighborhoods (or zones) of previously executed test cases. RRT has higher effectiveness than RT in terms of failure detection but incurs a higher time cost. In this paper, we aim to further reduce the time costs for RRT and improve the effectiveness for RT and ART methods. We propose a proactive technique known as & x201C;RRT by largest available zone & x201D; (RRT-LAZ). Like RRT, RRT-LAZ first defines an exclusion zone around every executed test case in order to determine the available zones. Unlike the original RRT, RRT-LAZ then compares all the available zones to proactively pick the largest one, from which the next test case is randomly generated. Both simulation analyses and empirical studies have been employed to investigate the efficiency and effectiveness of RRT-LAZ in relation to RT and related ART algorithms. The results show that RRT-LAZ has significantly lower time costs than RRT. Furthermore, RRT-LAZ is more effective than RT and related ART methods for block failure patterns in low-dimensional input spaces. In general, since RRT-LAZ employs a proactive technique instead of a passive one in generating next cases, it is much more cost-effective than RRT. RRT-LAZ is also more cost-effective than RT and other ART methods that we have studied. |
2,928 | A PID controller for synchronization between master-slave neurons in fractional-order of neocortical network model | Modeling of the biological neurons is a way to understand the architecture of neural networks of the brain. A complex brain network includes the synchronization between some groups of neurons. The dynamic behavior of interactions between groups of slave-master neurons in the neocortical network is unpredictable and challenging. The purpose of synchronizing a neural interaction is to reduce the synchronization error between the chaotic slave-master neurons. This paper uses a proportional-integral-derivative (PID) controller to synchronize master-slave neurons in the fractional-order of the neocortical network model based on dendritic spike frequency adaptation (DSFA) uncertainties and unknown disturbance effects. The purpose of this article is in two parts: First, we implemented the effect of previous states of the neuron conditions by fractional-order of the differential equations in the neocortical network model. Second, by synchronizing the FO neocortical master-slave model by PID controller, we investigated the connection strength of the complex network in chaotic point of view. The optimized PID coefficients and fractional-order were calculated using root mean square error (RMSE) criteria to control the membrane voltage synchronization. The chaotic behavior of the system was evaluated by numerical techniques such as attractor analysis and time series diagrams. The optimal RMSE value for master-slave neurons occurred at fractional-orders 0.89. It is shown that the synchronization of master-slave neurons improves over time, and eventually they are fully synchronized while the controller error is reduced. |
2,929 | Synthesis, Biochemical, and Cellular Evaluation of HDAC6 Targeting Proteolysis Targeting Chimeras | Histone deacetylases are considered promising epigenetic targets for chemical protein degradation due to their diverse roles in physiological cellular functions and in the diseased state. Proteolysis-targeting chimeras (PROTACs) are bifunctional molecules that hijack the cell's ubiquitin-proteasome system (UPS). One of the promising targets for this approach is histone deacetylase 6 (HDAC6), which is highly expressed in several types of cancers and is linked to the aggressiveness of tumors. In the present work, we describe the synthesis of HDAC6 targeting PROTACs based on previously synthesized benzohydroxamates selectively inhibiting HDAC6 and how to assess their activities in different biochemical in vitro assays and in cellular assays. HDAC inhibition was determined using fluorometric assays, while the degradation ability of the PROTACs was assessed using western blot analysis. |
2,930 | Wide/Multiband Linearization of TWTAs Using Predistortion | This paper discusses the state of the art in achieving wideband (>1 GHz) linearization of TWTAs. Multioctave bandwidth linearization has been achieved at frequencies from L- to Ka-band. The trades between using a single predistortion linearizer and multiband linearizers are discussed. The effect of harmonies (both even and odd-order-distortion products) and their correction for applications that cover more than an octave are considered. |
2,931 | Sound Coding Color to Improve Artwork Appreciation by People with Visual Impairments | The recent development of color coding in tactile pictograms helps people with visual impairments (PVI) appreciate the visual arts. The auditory sense, in conjunction with (or possibly as an alternative to) the tactile sense, would allow PVI to perceive colors in a way that would be difficult to achieve with just a tactile stimulus. Sound coding colors (SCCs) can replicate three characteristics of colors, i.e., hue, chroma, and value, by matching them with three characteristics of sound, i.e., timbre, intensity, and pitch. This paper examines relationships between sound (melody) and color mediated by tactile pattern color coding and provides sound coding for hue, chroma, and value to help PVI deepen their relationship with visual art. Our two proposed SCC sets use melody to improve upon most SCC sets currently in use by adding more colors (18 colors in 6 hues). User experience and identification tests were conducted with 12 visually impaired and 8 sighted adults, and the results suggest that the SCC sets were helpful for the participants. |
2,932 | Burn Depth Analysis Using Multidimensional Scaling Applied to Psychophysical Experiment Data | In this paper a psychophysical experiment and a multidimensional scaling (MDS) analysis are undergone to determine the physical characteristics that physicians employ to diagnose a burn depth. Subsequently, these characteristics are translated into mathematical features, correlated with these physical characteristics analysis. Finally, a study to verify the ability of these mathematical features to classify burns is performed. In this study, a space with axes correlated with the MDS axes has been developed. 74 images have been represented in this space and a k-nearest neighbor classifier has been used to classify these 74 images. A success rate of 66.2% was obtained when classifying burns into three burn depths and a success rate of 83.8% was obtained when burns were classified as those which needed grafts and those which did not. Additional studies have been performed comparing our system with a principal component analysis and a support vector machine classifier. Results validate the ability of the mathematical features extracted from the psychophysical experiment to classify burns into their depths. In addition, the method has been compared with another state-of-the-art method and the same database. |
2,933 | Efficient Frequency Scaling Algorithm for Short-Range 3-D Holographic Imaging Based on a Scanning MIMO Array | Millimeter-wave (MMW) holographic imaging technology is widely used in plenty of short-range applications like security and medical diagnosis. When combining with multiple-input-multiple-output (MIMO) array, such a technology can acquire precise reconstruction with wider field of view and higher dynamic range. However, to focus the higher dimensional data set obtained from MIMO architecture, the complicated iteration or interpolation employed by the previous state-of-the-art focusing techniques prevents the real-time operation of such an imaging system under a general computation power. It is more economical to increase the operational speed by improving the algorithm efficiency. Hence, a novel fast imaging algorithm that uses multistatic frequency scaling technique is proposed in this article for achieving real-time 3-D imaging on a 1-D MIMO scanning system. Only fast Fourier transform (FFT)/inverse FFT (IFFT) and multiplications are employed in the algorithm, which can be easily implemented. Compared with the previous state-of-the-art techniques, the proposed algorithm has the lower computation complexity. Practical experiments with self-developed MMW MIMO scanning radar prove the accuracy and efficiency of the algorithm. On a common laptop without any acceleration technology, the proposed algorithm requires less than one tenth of the time as required by the previous state-of-the-art techniques. |
2,934 | Modeling public holidays in load forecasting: a German case study | We address the issue of public or bank holidays in electricity load modeling and forecasting. Special characteristics of public holidays such as their classification into fixed-date and weekday holidays are discussed in detail. We present state-of-the-art techniques to deal with public holidays such as removing them from the data set, treating them as Sunday dummy or introducing separate holiday dummies. We analyze pros and cons of these approaches and provide a large load forecasting study for Germany that compares the techniques using standard performance and significance measures. Finally, we give general recommendations for the treatment of public holidays in energy forecasting to suggest how the drawbacks particular to most of the state-of-the-art methods can be mitigated. This is especially useful, as the incorporation of holiday effects can improve the forecasting accuracy during public holidays periods by more than 80%, but even for non-holidays periods, the forecasting error can be reduced by approximately 10%. |
2,935 | Glutathione S-transferase P1 gene rs4147581 polymorphism predicts overall survival of patients with hepatocellular carcinoma: evidence from an enlarged study | As the most important detoxifying enzymes in liver, glutathione S-transferases (GSTs) can protect hepatocytes against carcinogens. We conducted a large cohort study to investigate the prognostic value of single nucleotide polymorphisms (SNPs) in seven encoding genes of GSTs for hepatocellular carcinoma (HCC). Twelve SNPs were genotyped and correlated with overall survival in 469 HCC patients. The median follow-up time of all patients was 21 (range 3-60) months, and the median survival time was 22 months. By the end of the study, 135 (28.8 %) patients were alive. Only rs4147581 in GSTP1 gene exhibited a significant association with survival of HCC patients (P = 0.006), with its mutant allele bearing a significantly lower risk of death (hazard ratio, 0.71; 95 % confidence interval 0.53-0.90), compared with the homozygous wide-type. A longer median survival time in patients with rs4147581 mutant allele was noticed than those homozygous wide-type (P = 0.03), and there was a marked adverse effect on survival conferred by smoking exposure in these patients. Conclusively, our findings provide supporting evidence for a contributory role of GSTP1 rs4147581 polymorphism in predicting the prognosis of HCC. |
2,936 | Functional Brain Network Classification Based on Deep Graph Hashing Learning | Brain network classification using resting-state functional magnetic resonance imaging (rs-fMRI) is an effective analytical method for diagnosing brain diseases. In recent years, brain network classification methods based on deep learning have attracted increasing attention. However, these methods only consider the spatial topological characteristics of the brain network but ignore its proximity relationships in semantic space. To overcome this problem, we propose a novel brain network classification method based on deep graph hashing learning named BNC-DGHL. Specifically, we first extract the deep features of the brain network and then learn a graph hash function based on clinical phenotype labels and the similarity of diagnostic labels. Secondly, we use the learned graph hash function to convert deep features into hash codes, which can maintain the original semantic spatial relationships. Finally, we calculate the distance between hash codes to obtain the predicted category of the brain network. Experimental results on ABIDE I, ABIDE II, and ADHD-200 datasets demonstrate that our method achieves better classification performance of brain diseases compared with some state-of-the-art methods, and the abnormal functional connectivities between brain regions identified may serve as biomarkers associated with related brain diseases. |
2,937 | Super Resolution Wide Aperture Automotive Radar | State-of-the-art automotive radars have an angular resolution of 1 degrees, which is insufficient for estimating the objects shape and boundaries at long distance. In this paper, we design a novel automotive radar with 1m aperture, which is an order of magnitude larger aperture than state-of-the-art automotive radars, and thus achieves super high angular resolution of 0.1 degrees. The radar is designed to overcomes the major technical challenges of a wide aperture radar, which are: non-robust phase coherency over a wide aperture, spanning a wide aperture with sparse antenna elements, simultaneous non-interfering transmissions from a relatively large number of transmit antennas with unambiguous target speed estimation, and high complexity of near-field beamforming. We demonstrate the super high resolution performance of the radar in automotive scenarios, and show that it attains comparable results to a high resolution LIDAR at short range, and outperforms the LIDAR at long ranges. The wide aperture radar has the potential to enable autonomous driving at higher speed, and also to increase the operation of autonomous driving to more regions and climates. |
2,938 | Extracellular production of Ulp1403-621 in leaky E. coli and its application in antimicrobial peptide production | Small ubiquitin-like modifier (SUMO) tag is widely used to promote soluble expression of exogenous proteins, which can then be cleaved by ubiquitin-like protease 1 (Ulp1) to obtain interested protein. But the application of Ulp1 in large-scale recombinant protein production is limited by complicated purification procedures and high cost. In this study, we describe an efficient and simple method of extracellular production of Ulp1403-621 using a leaky Escherichia coli BL21(DE3), engineered by deleting the peptidoglycan-associated outer membrane lipoprotein (pal) gene. Ulp1403-621 was successfully leaked into extracellular supernatant by the BL21(DE3)-Δpal strain after IPTG induction. The addition of 1% glycine increased the extracellular production of Ulp1403-621 approximately four fold. Moreover, extracellular Ulp1403-621 without purification had high activities for cleaving SUMO fusion proteins, and antimicrobial peptide pBD2 obtained after cleavage can inhibit the growth of Staphylococcus aureus. The specific activity of extracellular Ulp1403-621 containing 1 mM EDTA and 8 mM DTT reached 2.0 × 106 U/L. Another commonly used protease, human rhinovirus 3C protease, was also successfully secreted by leaky E. coli strains. In conclusion, extracellular production of tool enzymes is an attractive way for producing large-scale active recombinant proteins at a lower cost for pharmaceutical, industrial, and biotechnological applications. KEY POINTS: • First report of extracellular production of Ulp1403-621 in leaky Escherichia coli BL21(DE3) strain. • One percent glycine addition into cultivation medium increased the extracellular production of Ulp1403-621 approximately four fold. • The specific activity of extracellular Ulp1403-621 produced in this study reached 2.0 × 106 U/L. |
2,939 | Highly effective remediation of high arsenic-bearing wastewater using aluminum-containing waste residue | Wastewater from non-ferrous metal smelting is known as one of the most dangerous sources of arsenic (As) due to its high acidity and high arsenic content. Herein, we propose a new environmental protection process for the efficient purification and removal of arsenic from wastewater by the formation of an AlAsO4@silicate core-shell structure based on the characteristics of aluminum-containing waste residue (AWR). At room temperature, the investigation with AWR almost achieved 100% As removal efficiency from wastewater, reducing the arsenic concentration from 5500 mg/L to 52 μg/L. With Al/As molar ratio of 3.5, the structural properties of AWR provided good adsorption sites for arsenic adsorption, leading to the formation of arsenate and insoluble aluminum arsenate with As. As-containing AWR silicate shells were produced under alkaline conditions, resulting in an arsenic leaching concentration of 1.32 mg/L in the TCLP test. AWR, as an efficient As removal and fixation agent, shows great potential in the treatment of copper smelting wastewater, and is expected to achieve large-scale industrial As removal. |
2,940 | Occurrences, distribution and risk assessment of polar pesticides in Niger River valley and its tributary the Mekrou River (Niger Republic) | The increase in food needs due to high population growth in Niger has led to the intensification of urban agriculture and the increased use of pesticides. The objective of this study is primarily to assess the polar pesticide contamination (mainly herbicides) of the Niger River and its tributary, the Mekrou River, in Niger, using both grab sampling and POCIS (Polar Organic Chemical Integrative Samplers), and then to evaluate the risk to the aquatic environment. Two water sampling campaigns were carried out during the wet and dry seasons. The polar pesticides were analyzed by liquid chromatography coupled with tandem mass spectrometry, which allowed the identification of compounds with concentrations in the grab samples above the WHO guide values and the EU directive: diuron with 2221 ng/L (EU quality guideline: 200 ng/L), atrazine with 742 ng/L (EU quality guideline: 600 ng/L) and acetochlor with 238 ng/L (EU quality guideline: 100 ng/L). The risk assessment study indicated that diuron and atrazine present a high risk for the aquatic environment during the wet season. The main source of water contamination is the intensive use of pesticides in urban agriculture near the city of Niamey, and the intensive cotton farming in the Benin. Moreover, the surveys (30 producers interviewed) showed that 70% of the pesticides used are not approved by the Interstate Committee for Drought Control in the Sahel (CILSS) and some are prohibited in Niger. The inventory of pesticides sold in the zone showed that active ingredients used by producers are 48% insecticides, 45% herbicides, and 7% fungicides. |
2,941 | Cross-Scale Cost Aggregation for Stereo Matching | This paper proposes a generic framework that enables a multiscale interaction in the cost aggregation step of stereo matching algorithms. Inspired by the formulation of image filters, we first reformulate cost aggregation from a weighted least-squares (WLS) optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels. Our key motivation is that while the human stereo vision system processes information at both coarse and fine scales interactively for the correspondence search, state-of-the-art approaches aggregate costs at the finest scale of the input stereo images only, ignoring inter-consistency across multiple scales. This motivation leads us to introduce an interscale regularizer into the WLS optimization objective to enforce the consistency of the cost volume among the neighboring scales. The new optimization objective with the inter-scale regularization is convex, and thus, it is easily and analytically solved. Minimizing this new objective leads to the proposed framework. Since the regularization term is independent of the similarity kernel, various cost aggregation approaches, including discrete and continuous parameterization methods, can be easily integrated into the proposed framework. We show that the cross-scale framework is important as it effectively and efficiently expands state-of-the-art cost aggregation methods and leads to significant improvements, when evaluated on Middlebury, Middlebury Third, KITTI, and New Tsukuba data sets. |
2,942 | Comparison of the Clinical and Genotypic Characteristics of Uropathogenic Escherichia coli Strains According to Sex in Korea | In this study, we compared the microbiological, genotypic, and antibiotic resistance characteristics of uropathogenic Escherichia coli (UPEC) strains in patients with pyelonephritis in Korea according to sex based on data corresponding to the February 2015 to June 2018 period. Based on Escherichia coli phylogenetic group analysis, gene virulence detection, and subgroup analyses by sex, we observed that the antibiotic resistance percentages and proportions corresponding to extended-spectrum beta-lactamase producing UPEC were higher in males than in females. In addition, phylogenetic group B2 showed predominance in both the male and female groups, which further showed similar adhesion molecule distributions. Toxin-associated factors, hlyA and cnf1, were more common in males. In clinical presentations, urinary predisposing factors, complicated urinary tract infections (UTIs), concomitant bacteremia, and persistent fever were also more common with males. Although females and males showed UPEC genotypic differences, there were no differences between them with respect to poor outcomes. Persistent fever was associated with community-acquired infection and bacteremic UTI and relapsed UTI within 3 months was associated with urinary tract stone. In future, it will be necessary to conduct multicenter studies, involving more cases on UPEC to validate our results. |
2,943 | Martial Arts Routine Difficulty Action Technology VR Image Target Real-Time Extraction Simulation | With the gradual increase in the difficulty of competitive martial arts, athletes must complete fine, stable, high-quality and difficult movements in order to achieve excellent performance. The real-time extraction of martial arts movements is a topic that many martial arts enthusiasts care about. This study mainly discusses the real-time extraction and simulation of VR image target in the difficult movement technology of martial arts routine. Considering the complex characteristics of martial arts movements, this article will analyze the preprocessing content of existing images. This includes image enhancement and image filtering, and uses median filtering methods to enhance the characteristics of the collected images. In this way, the visual effect of the original image can be improved, and the processed image will contribute to the subsequent segmentation. A new image segmentation method is proposed for the color model of the image. According to the H component of the HSV model representing the characteristics of chromaticity, the color image is transformed into the HSV model, and the H component is extracted. The histogram concept applies to H components. Based on the histogram of the H component, the segmentation threshold is determined, and the cropping target in the image is detected. Because the model space is very sensitive to color, VR technology is used to automatically determine the segmentation target. Combined with the above division methods, the automatic extraction of objects in the image is completed. The method of using VR technology for image extraction processing has high precision, and the error value is 3.92%<5%. The research results show that the method has good segmentation results, and is suitable for image segmentation under complex background and automatic image extraction under complex background. |
2,944 | Diagnosis of Benign and Malignant Pulmonary Ground-Glass Nodules Using Computed Tomography Radiomics Parameters | Objective: To assess the clinical value of a radiomics model based on low-dose computed tomography (LDCT) in diagnosing benign and malignant pulmonary ground-glass nodules. Methods: A retrospective analysis was performed on 274 patients who underwent LDCT scanning with the identification of pulmonary ground-glass nodules from January 2018 to March 2021. All patients had complete clinical and pathological data. The cases were randomly divided into 191 cases in a training set and 83 cases in a validation set using the random sampling method and a 7:3 ratio. Based on the predictor sources, we established clinical, radiomics, and combined prediction models in the training set. A receiver operating characteristic (ROC) curve was generated for the training and validation sets, the predictive abilities of the different models for benign and malignant nodules were compared according to the area under the curve (AUC), and the model with the best predictive ability was selected. A calibration curve was plotted to test the good-of-fitness of the model in the validation set. Results: Of the 274 patients (84 males and 190 females), 156 had malignant, and 118 had benign nodules. The univariate analysis showed a statistically significant difference in nodule position between benign nodules and lung adenocarcinoma in both data sets (P <.001 and .021). In the training set, when the nodule diameter was >8 mm, the probability of nodule malignancy increased (P < .001). The results showed that the combined model had a higher prediction ability than the other two models. The combined model could distinguish between benign and malignant pulmonary nodules in the training set (AUC: 0.711; 95%CI: 0.634-0.787; ACC: 0.696; sensitivity: 0.617; specificity: 0.816; PPV:0.835; NPV: 0.585). Moreover, this model could predict benign and malignant nodules in the validation set (AUC: 0.695; 95%CI: 0.574-0.816; ACC: 9.747; sensitivity: 0.694; specificity: 0.824; PPV: 0.850; NPV: 0.651). The calibration curve had a P value of 0.775, indicating that in the validation set, there was no difference between the value predicted by the combined model and the actual observed value and that the result was a good fit. Conclusion: The prediction model combining clinical information and radiomics parameters had a good ability to distinguish benign and malignant pulmonary ground-glass nodules. |
2,945 | Robotic Surgery: A Narrative Review | In general surgery, the use of robotic and laparoscopic methods has increased. Robotic surgery that requires the least incision has advanced over the years in a short period of time, benefitting both the patient and the surgeon. According to this, robotic platforms and tools are now being used and improved more commonly in general surgery. In a quickly growing and dynamic environment of research and development, the goal of this review is to explore the present and emerging surgical robotic technologies. Future progress in robotics will focus primarily on more durable haptic systems that would provide tactile and kinesthetic input, miniaturisation and micro-robotics, better visual feedback with higher fidelity detail and magnification, and autonomous robots. It is recommended to develop a structured training course with benchmarks for success and evidence-based training strategies. This usually includes a step-by-step progression starting with observation, case aid in programming and manipulation of surgical instruments, learning the basics of robotics in a dry and wet lab setting, attaining non-technical skills on an individual and team level, and monitored modular console training, accompanied by autonomous practice. Prior to independent practice, basic robotics skills and procedural activities must be performed safely and effectively as part of robotic surgical training. It is advised to create a systematic training programme with performance indicators and research-based instructional techniques. |
2,946 | Tissue-specific mitochondrial HIGD1C promotes oxygen sensitivity in carotid body chemoreceptors | Mammalian carotid body arterial chemoreceptors function as an early warning system for hypoxia, triggering acute life-saving arousal and cardiorespiratory reflexes. To serve this role, carotid body glomus cells are highly sensitive to decreases in oxygen availability. While the mitochondria and plasma membrane signaling proteins have been implicated in oxygen sensing by glomus cells, the mechanism underlying their mitochondrial sensitivity to hypoxia compared to other cells is unknown. Here, we identify HIGD1C, a novel hypoxia-inducible gene domain factor isoform, as an electron transport chain complex IV-interacting protein that is almost exclusively expressed in the carotid body and is therefore not generally necessary for mitochondrial function. Importantly, HIGD1C is required for carotid body oxygen sensing and enhances complex IV sensitivity to hypoxia. Thus, we propose that HIGD1C promotes exquisite oxygen sensing by the carotid body, illustrating how specialized mitochondria can be used as sentinels of metabolic stress to elicit essential adaptive behaviors. |
2,947 | Human action recognition by means of subtensor projections and dense trajectories | In last years, most human action recognition works have used dense trajectories features, to achieve state-of-the-art results. Histograms of Oriented Gradients (HOG), Histogram of Optical Flow (HOF) and Motion Boundary Histograms (MBH) features are extracted from regions and being tracked across the frames. The goal of this paper is to improve the performance obtained by means of Improved Dense Trajectories (IDTs), adding new features based on temporal templates. We construct these templates considering a video sequence as a third-order tensor and computing three different projections. We use several functions for projecting the fibers from the video sequences, and combined them by means of sum pooling. As a first contribution of our work, we present in detail the method based on tensor projections. First, we have assessed the results obtained using only template based action recognition. Next, in order to achieve state-of-art recognition rates, we have fused our features with those of IDTs. This is the second contribution of the article. Experiments on four different public datasets have shown that this technique improves IDTs performance and that the results outperform the ones obtained by most of the state-of-the-art techniques for action recognition. (C) 2018 Elsevier Ltd. All rights reserved. |
2,948 | Association of environmental exposure to perchlorate, nitrate, and thiocyanate with overweight/obesity and central obesity among children and adolescents in the United States of America using data from the National Health and Nutrition Examination Survey (NHANES) 2005-2016 | The association of overweight/obesity, and central obesity with thiocyanate (SCN), perchlorate (CIO), and nitrate (NO) in childhood and adolescence is unclear. Therefore, this study aimed to explore this association in 4447 participants comprising children and adolescents (aged 6-19 years) using data from the United States National Health and Nutrition Examination Survey 2005-2016. SCN level was positively associated with overweight/obesity in both children and adolescents, while CIO level was negatively associated with overweight/obesity only in children; however, no significant association was found for NO level. Similar associations were found between SCN level and central obesity. Thus, our results suggest that SCN exposure was associated with overweight/obesity and central obesity in both children and adolescents, while a negative association was observed for CIO in children. Strategies to monitor the exposure levels and the mechanisms underlying the relationship between exposure and the weight parameters are recommended. |
2,949 | Automatic Parameter Selection for Multimodal Image Registration | Over the past ten years similarity measures based on intensity distributions have become state-of-the-art in automatic multimodal image registration. An implementation for clinical usage has to support a plurality of images. However, a generally applicable parameter configuration for the number and sizes of histogram bins, optimal Parzen-window kernel widths or background thresholds cannot be found. This explains why various research groups present partly contradictory empirical proposals for these parameters. This paper proposes a set of data-driven estimation schemes for a parameter-free implementation that eliminates major caveats of heuristic trial and error. We present the following novel approaches: a new coincidence weighting scheme to reduce the influence of background noise on the similarity measure in combination with Max-Lloyd requantization, and a tradeoff for the automatic estimation of the number of histogram bins. These methods have been integrated into a state-of-the-art rigid registration that is based on normalized mutual information and applied to CT-MR, PET-MR, and MR-MR image pairs of the RIRE 2.0 database. We compare combinations of the proposed techniques to a standard implementation using default parameters, which can be found in the literature, and to a manual registration by a medical expert. Additionally, we analyze the effects of various histogram sizes, sampling rates, and error thresholds for the number of histogram bins. The comparison of the parameter selection techniques yields 25 approaches in total, with 114 registrations each. The number of bins has no significant influence on the proposed implementation that performs better than both the manual and the standard method in terms of acceptance rates and target registration error (TRE). The overall mean TRE is 2.34 mm compared to 2.54 mm for the manual registration and 6.48 mm for a standard implementation. Our results show a significant TRE reduction for distortion-corrected magnetic resonance images. |
2,950 | Collective response of fish to combined manipulations of illumination and flow | Collective behavior is ubiquitous among fish, yet, its hows and whys are yet to be completely elucidated. It is known that several environmental factors can dramatically influence collective behavior, by eliciting behavioral adaptations in the individuals and altering physical pathways of social interactions in the group. Yet, empirical research has mostly focused on the quantification of the role of one factor at a time, with a paucity of studies designed to explore the multi-sensory basis of collective behavior. We investigated collective behavior of zebrafish (Danio rerio) pairs swimming in a water channel under combined manipulations of illumination (bright and dark) and flow conditions (absence and presence). The ability of the pair to orient and school increased in the presence of the flow and when fish were allowed to visually interact under bright illumination. Shoaling, instead, was only modulated by the illumination, so that fish swam at higher relative distances in the dark, irrespective of the flow. We also found evidence in favor of a modulatory effect of flow and illumination on the formation of the pair. Specifically, in the bright illumination, fish swam more side-by-side against a flow than in placid water; likewise, in the presence of a flow, they spent more time side-by-side in the bright illumination than in the dark. These findings point at a rich interplay between flow and illumination, whose alterations have profound effects on collective behavior. |
2,951 | Design and Properties of Antimicrobial Biomaterials Surfaces | Emergence of antibiotic-resistance pathogens has caused serious health issues and if the current trend is to continue, treatment of the infection will become complicated and even unsuccessful due to new antimicrobial resistance (AMR). Therefore, there is a global drive to identify new methods to treat infection and develop better antibacterial materials and therapy. Although new and more potent antibiotics have aided the fight against microbes, they only offer a temporary solution because future bacteria strains may become resistant to these antibiotics and drugs. Recently, application of non-biological methods such as, electrical currents and photothermal/dynamic therapies to kill bacteria, reveal new approaches to design antimicrobial biomaterials, as complications stemming from drug-resistant bacteria can be obviated. Furthermore, recent research has focused on mimicking the surface patterns on plants and insects such as lotus leaves and dragonfly wings. Bio-inspired micro/nano patterns have been replicated on a variety of biomaterials to improve the bacterial resistance and other properties with good success. This is an exciting research area with immense practical and clinical potentials. In this review, recent advances in the application of chemical/biological approaches to combat bacterial infection and AMR are summarized and the related mechanisms are discussed. |
2,952 | Outcomes of Robot-Assisted Laparoscopic Pyeloplasty Based on Degree of Obstruction on Preoperative Tc-99 MAG-3 Renal Scintigraphy | Objective: Management of symptomatic ureteropelvic junction (UPJ) obstruction with hydronephrosis and discordant Tc-99 mercaptoacetyltriglycine (MAG-3) renal scintigraphy is challenging. In this study we describe long-term outcomes of patients who underwent robot-assisted laparoscopic pyeloplasty for the correction of symptomatic UPJ obstruction with discordant preoperative Tc-99m MAG-3 renal scintigraphy. Methods: Patients undergoing robot-assisted laparoscopic pyeloplasty for symptomatic UPJ obstruction at a single academic center from 2009 to 2021 were retrospectively reviewed. Patients were categorized into three groups with varying degrees of obstruction based on preoperative MAG-3 imaging: Group 1: no obstruction (Lasix T1/2 clearance <10 minutes), Group 2: equivocal obstruction (Lasix T1/2 clearance 10-20 minutes), and Group 3: obstruction (Lasix T1/2 clearance >20 minutes. Pyeloplasty success was defined as resolution of symptoms and improvement/stable computed tomography (CT) imaging or MAG-3 scintigraphy. Failure was defined as persistence of symptoms with either obstruction on functional imaging, worsening hydronephrosis, or subsequent intervention. Results: A total of 125 cases were identified, with a median patient age of 35 years. Dismembered pyeloplasty technique was performed in 98.4% of cases. Median preoperative split renal function on MAG-3 scintigraphy was the only statistically significant (p = 0.003) difference in preoperative characteristics between the three groups. There were 15 postoperative complications, with a rate of Clavien-Dindo grade 3 or higher complications of 4.8%. Overall pyeloplasty success was 92.8%, with success rates of 100% (15/15) and 97% (32/33) in the no obstruction and equivocal obstruction groups, respectively. Median time to pyeloplasty failure was 20.4 months. Conclusion: Robot-assisted laparoscopic pyeloplasty is a safe and effective surgical intervention for correcting UPJ obstruction. Patients with symptoms of UPJ obstruction and discordant functional imaging studies demonstrate similar or improved success rates after pyeloplasty compared with patients with documented high-grade obstruction. Based on these findings preoperative renal scan may not be reliable in appropriate selection of candidacy for pyeloplasty. |
2,953 | An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era | Speech is the fundamental mode of human communication, and its synthesis has long been a core priority in human-computer interaction research. In recent years, machines have managed to master the art of generating speech that is understandable by humans. However, the linguistic content of an utterance encompasses only a part of its meaning. Affect, or expressivity, has the capacity to turn speech into a medium capable of conveying intimate thoughts, feelings, and emotions-aspects that are essential for engaging and naturalistic interpersonal communication. While the goal of imparting expressivity to synthesized utterances has so far remained elusive, following recent advances in text-to-speech synthesis, a paradigm shift is well under way in the fields of affective speech synthesis and conversion as well. Deep learning, as the technology that underlies most of the recent advances in artificial intelligence, is spearheading these efforts. In this overview, we outline ongoing trends and summarize state-of-the-art approaches in an attempt to provide a broad overview of this exciting field. |
2,954 | Towards Personalized Statistical Deformable Model and Hybrid Point Matching for Robust MR-TRUS Registration | Registration and fusion of magnetic resonance (MR) and 3D transrectal ultrasound (TRUS) images of the prostate gland can provide high-quality guidance for prostate interventions. However, accurate MR-TRUS registration remains a challenging task, due to the great intensity variation between two modalities, the lack of intrinsic fiducials within the prostate, the large gland deformation caused by the TRUS probe insertion, and distinctive biomechanical properties in patients and prostate zones. To address these challenges, a personalized model-to-surface registration approach is proposed in this study. The main contributions of this paper can be threefold. First, a new personalized statistical deformable model (PSDM) is proposed with the finite element analysis and the patient-specific tissue parameters measured from the ultrasound elastography. Second, a hybrid point matching method is developed by introducing the modality independent neighborhood descriptor (MIND) to weight the Euclidean distance between points to establish reliable surface point correspondence. Third, the hybrid point matching is further guided by the PSDM for more physically plausible deformation estimation. Eighteen sets of patient data are included to test the efficacy of the proposed method. The experimental results demonstrate that our approach provides more accurate and robust MR-TRUS registration than state-of-the-art methods do. The averaged target registration error is 1.44 mm, which meets the clinical requirement of 1.9 mm for the accurate tumor volume detection. It can be concluded that the presented method can effectively fuse the heterogeneous image information in the elastography, MR, and TRUS to attain satisfactory image alignment performance. |
2,955 | An improved reversible watermarking scheme using weighted prediction and watermarking simulation | For the reversible watermarking scheme using the prediction error expansion and histogram shifting (PEE-HS), improving the prediction accuracy facilitates performance enhancement, which still remains a challenging problem in this field. To this end, the paper improves the state-of-the-art local predictor (LP) by designing the following approaches: 1) enlarging the prediction context; 2) partitioning the prediction block surrounding the target pixel into the watermarked and original regions, and imposing different weights on prediction values from these two regions to generate the final prediction for the target pixel; and 3) conducting watermarking simulation on the original region via random noises to further enhance the prediction performance. These three approaches are then integrated to result in an improved LP using weighted prediction and watermarking simulation (LP-WPWS). By exploiting the LP-WPWS for prediction error generation, we thus construct a new PEE-HS-based reversible watermarking scheme. Extensive simulation shows that the proposed scheme outperforms the state-of-the-art LP and is comparable to the excellent methods exploiting the sorting, multiple histograms modification, and hybrid dimensional histogram generation with adaptive mapping selection. |
2,956 | Discovering Hidden Topical Hubs and Authorities Across Multiple Online Social Networks | Finding influential users in online social networks (OSNs) is an important problem with many possible useful applications. Many methods have been proposed to identify influential users in OSNs. PageRank and HITs are two well known examples that determine influential users through link analysis. In recent years, new models that consider both content and social network links have been developed. The Hub and Authority Topic (HAT) model is one that extends HITS to identify topic-specific hubs and authorities by jointly learning hubs, authorities, and topical interests from users' relationship and textual content. However, many of the previous works are confined to identifying influential users within a single OSN. These models, when applied to multiple OSNs, could not learn influential users under a common set of topics nor address platform preferences. In this paper, we therefore propose the MPHAT model, an extension of HAT, to jointly model the topic-specific hub users, authority users, their topical interests and platform preferences. We evaluate MPHAT against several existing state-of-the-art methods in three tasks: (i) modeling of topics, (ii) platform choice prediction, and (iii) link recommendation. Based on our extensive experiments in multiple OSNs settings using synthetic datasets and real-world datasets from Twitter and Instagram, we show that MPHAT is comparable to state-of-the-art topic models in learning topics but outperforms the state-of-the-art models in platform prediction and link recommendation tasks. We also empirically demonstrate the ability of MPHAT to determine influential users within and across multiple OSNs. |
2,957 | Intramolecular Cyclization of Carbonate and Thiocarbonate Derivatives of myo-Inositol in the Solid State: Implications for Acyl Group Transfer Reactions in Molecular Crystals | Racemic 4-O-phenoxycarbonyl and 4-O-phenoxythiocarbonyl derivatives of myo-inositol orthoformate undergo thermal intramolecular cyclization in the solid state to yield the corresponding 4,6-bridged carbonates and thiocarbonates, respectively. The thermal cyclization also occurs in the solution and molten states, but less efficiently, suggesting that these cyclization reactions are aided by molecular pre-organization, although not strictly topochemically controlled. Crystal structures of two carbonates and a thiocarbonate clearly revealed that the relative orientation of the electrophile and the nucleophile in the crystal lattice facilitates the intramolecular cyclization reaction and forbids the intermolecular reaction. The correlation observed between the chemical reactivity and the non-covalent interactions in the crystal of the reactants provides a way to estimate the chemical stability of analogous molecules in the solid state. |
2,958 | Antecedent blood pressure as a predictor of cardiovascular disease | Elevated blood pressure (BP) is associated with greater risk of cardiovascular disease (CVD), and evidence suggests that prior BP levels may be at least as important as current BP in prediction models. We analyzed the determinants of CVD risk in Offspring Framingham Heart Study participants (n = 3344). The baseline Cox model included the traditional risk factors and current systolic BP to predict 20-year risk of CVD (643 events). Current systolic BP was significant, and the associated hazard ratio was 1.09 for 10 mm Hg (confidence interval [CI] 95%: 1.04-1.15). A second model used the traditional risk factors plus antecedent BP (hazard ratio [HR] = 1.19; CI 95%: 1.10-1.24). In a third model that included traditional risk factors and both current and antecedent BP, the antecedent BP was significant (HR = 1.18; CI 95%: 1.08-1.23), but the current BP was not statistically significant (HR = 1.01; CI 95%: 0.97-1.09). Antecedent BP showed a significantly stronger effect on risk of CVD than current BP. |
2,959 | Recognition of Chinese artists via windowed and entropy balanced fusion in classification of their authored ink and wash paintings (IWPs) | As one of the most important cultural heritages, ink and wash paintings (IWPs) play an important role in the world of traditional Chinese arts. In comparison with western arts, the Chinese IWPs have the unique feature that the art form is primarily populated with limited number of content elements, such as stones, mountains, flowers, and animals etc. and hence most likely different art pieces share similar content, making it difficult to differentiate in terms of content alone. In this paper, we propose to extract histogram-based local feature and global feature to characterize different aspects of art styles, and such features are applied to drive neural networks to complete the classification of IWPs in terms of individual artistic descriptors. We then propose a windowed and entropy balanced fusion scheme to make integrated decisions to optimize the final classification and recognition results. Extensive evaluation via experiments is also reported, which supports that the proposed algorithm achieves good performances, outperforming the existing benchmark techniques and hence providing an excellent potential for computerized analysis and management of traditional Chinese IWPs. Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved. |
2,960 | Scalable Probabilistic Similarity Ranking in Uncertain Databases | This paper introduces a scalable approach for probabilistic top-k similarity ranking on uncertain vector data. Each uncertain object is represented by a set of vector instances that is assumed to be mutually exclusive. The objective is to rank the uncertain data according to their distance to a reference object. We propose a framework that incrementally computes for each object instance and ranking position, the probability of the object falling at that ranking position. The resulting rank probability distribution can serve as input for several state-of-the-art probabilistic ranking models. Existing approaches compute this probability distribution by applying the Poisson binomial recurrence technique of quadratic complexity. In this paper, we theoretically as well as experimentally show that our framework reduces this to a linear-time complexity while having the same memory requirements, facilitated by incremental accessing of the uncertain vector instances in increasing order of their distance to the reference object. Furthermore, we show how the output of our method can be used to apply probabilistic top-k ranking for the objects, according to different state-of-the-art definitions. We conduct an experimental evaluation on synthetic and real data, which demonstrates the efficiency of our approach. |
2,961 | Single Image Super-Resolution via Multiple Mixture Prior Models | Example learning-based single image super-resolution (SR) is a promising method for reconstructing a high-resolution (HR) image from a single-input low-resolution (LR) image. Lots of popular SR approaches are more likely either time-or space-intensive, which limit their practical applications. Hence, some research has focused on a subspace view and delivered state-of-the-art results. In this paper, we utilize an effective way with mixture prior models to transform the large nonlinear feature space of LR images into a group of linear subspaces in the training phase. In particular, we first partition image patches into several groups by a novel selective patch processing method based on difference curvature of LR patches, and then learning the mixture prior models in each group. Moreover, different prior distributions have various effectiveness in SR, and in this case, we find that student-t prior shows stronger performance than the well-known Gaussian prior. In the testing phase, we adopt the learned multiple mixture prior models to map the input LR features into the appropriate subspace, and finally reconstruct the corresponding HR image in a novel mixed matching way. Experimental results indicate that the proposed approach is both quantitatively and qualitatively superior to some state-of-the-art SR methods. |
2,962 | Identification of polymorphisms in GDF9 and BMP15 genes in Jamunapari and crossbred goats in Bangladesh | Polymorphisms in growth differentiation factor 9 (GDF9) and bone morphogenetic protein 15 (BMP15) genes have been found to be associated with litter size in goats across the globe. Our previous study detected single-nucleotide polymorphisms (SNPs) in GDF9 and BMP15 genes associated with litter size in Black Bengal, Bangladesh's primary native goat breed. However, Jamunapari and crossbred goats in Bangladesh are yet to be investigated for litter size-associated polymorphisms. In this study, we screened Jamunapari and crossbred (50% Black Bengal × 50% Jamunapari) goats to identify polymorphisms in the GDF9 and BMP15 genes and to assess the association between identified SNPs and litter size. The genomic DNA from 100 female goats (50 Jamunapari and 50 crossbred) was used in polymerase chain reactions (PCRs) to amplify exon 2 of the GDF9 and exon 2 of the BMP15 genes. PCR products were sequenced employing the BigDye Terminator cycle sequencing protocol to identify SNPs. We used a generalized linear model to perform the association analysis for identified SNPs and litter size. Seven SNPs were identified, of which four, C818CT, G1073A, G1189A, and G1330T, were in the GDF9 gene and three, G616T, G735A, and G811A, were in the BMP15 gene. G735A was a synonymous SNP, whereas the remaining were non-synonymous SNPs. Identified SNP loci in GDF9 were low polymorphic (PIC < 0.25), while loci in BMP15 were moderately polymorphic (PIC ≥ 0.25). The genotypes at the G1330T locus had a significant (p < 0.05) difference in litter size in Jamunapari goats, but no significant difference was observed for all genotypes at other loci. Therefore, the G1330T loci could be useful as a marker in marker-assisted selection for litter size traits in goats of Bangladesh. |
2,963 | Unravelling the pharmacological properties of cryptolepine and its derivatives: a mini-review insight | Cryptolepine (1,5-methyl-10H-indolo[3,2-b]quinoline), an indoloquinoline alkaloid, found in the roots of Cryptolepis sanguinolenta (Lindl.) Schltr (family: Periplocaceae), is associated with the suppression of cancer and protozoal infections. Cryptolepine also exhibits anti-bacterial, anti-fungal, anti-hyperglycemic, antidiabetic, anti-inflammatory, anti-hypotensive, antipyretic, and antimuscarinic properties. This review of the latest research data can be exploited to create a basis for the discovery of new cryptolepine-based drugs and their analogues in the near future. PubMed, Scopus, and Google Scholar databases were searched to select and collect data from the existing literature on cryptolepine and their pharmacological properties. Several in vitro studies have demonstrated the potential of cryptolepine A as an anticancer and antimalarial molecule, which is achieved through inhibiting DNA synthesis and topoisomerase II. This review summarizes the recent developments of cryptolepine pharmacological properties and functional mechanisms, providing information for future research on this natural product. |
2,964 | Superficial femoral vein transposition as a solution for hemodialysis vascular access | The significant growth in the number of individuals dependent on hemodialysis for renal replacement therapy and unrestricted use of short and long-term catheters have challenged vascular surgeons in search of solutions for patients whose options for access via the upper limbs have been exhausted and for the increasing rates of central venous stenosis in these patients. When access via the upper limbs is impossible, exceptional techniques can be used and the lower limbs offer feasible alternative vascular access sites for hemodialysis. This article reports a case of superficial femoral vein transposition to make a loop arteriovenous fistula in a patient with no possibility of access via the upper limbs and presents a literature review on this technique that remains little used. |
2,965 | Recurrent Tissue-Aware Network for Deformable Registration of Infant Brain MR Images | Deformable registration is fundamental to longitudinal and population-based image analyses. However, it is challenging to precisely align longitudinal infant brain MR images of the same subject, as well as cross-sectional infant brain MR images of different subjects, due to fast brain development during infancy. In this paper, we propose a recurrently usable deep neural network for the registration of infant brain MR images. There are three main highlights of our proposed method. (i) We use brain tissue segmentation maps for registration, instead of intensity images, to tackle the issue of rapid contrast changes of brain tissues during the first year of life. (ii) A single registration network is trained in a one-shot manner, and then recurrently applied in inference for multiple times, such that the complex deformation field can be recovered incrementally. (iii) We also propose both the adaptive smoothing layer and the tissue-aware anti-folding constraint into the registration network to ensure the physiological plausibility of estimated deformations without degrading the registration accuracy. Experimental results, in comparison to the state-of-the-art registration methods, indicate that our proposed method achieves the highest registration accuracy while still preserving the smoothness of the deformation field. The implementation of our proposed registration network is available online https://github.com/Barnonewdm/ACTA-Reg-Net. |
2,966 | MASK: Practical Source and Path Verification Based on Multi-AS-Key | The source and path verification in Path-Aware Networking considers the two critical issues: (1) end hosts could verify that the network follows their forwarding decisions, and (2) both on-path routers and destination host could authenticate the source of packets and filter the malicious traffic. Unfortunately, the state-of-the-art mechanisms require heavy communication overhead in the network and computation overhead in the router; moreover, it is difficult to meet the dynamic requirements of the end host. We propose a user-driven mechanism, source and path verification based on Multi-AS-Key (MASK). MASK decreases the communication overhead by a short additional packet header and reduces the computation overhead by separating the control and data plane in terms of the cryptographic operation. Furthermore, it utilizes the stateful user to instruct the stateless routers to process the packet with a user-driven policy, thus satisfying the user's requirements such as detecting the packet drop and replay attack. With the plausible design, the communication overhead for realistic path lengths is 1/2 to 1/10 compared with the state-of-the-art mechanisms. We implement MASK in the BMv2 environment and commodity Barefoot Tofino programmable switch, testify that MASK introduces significantly less overhead than the state-of-the-art mechanisms, and demonstrate that MASK could achieve the verification in the programmable switch at line rate. |
2,967 | Emotional Framework of Marine Graphic Design Art in the Era of Interactive Design | With the current design art entering the era of interactive design, the expression of emotion and the expression of emotional resonance of ocean graphic art design based on interactive design reconstruct the design concept and design emotion, which is helpful to realize the continuous improvement of emotional framework of ocean graphic design art. This paper first analyzes the extension of the concept of interactive design and the consideration of marine graphic art design. Secondly, it studies the motivation of emotional expression of marine graphic design in the era of interactive design. Finally, it clarifies the emotional communication of marine graphic design in the era of interactive design. |
2,968 | Making visible the invisible. A microarchaeology approach and an Archaeology of Color perspective for rock art paintings from the southern cone of South America | From the literature research review of studies that involved the physicochemical characterization of rock art paintings in Argentina and Chile, we evaluate the impact of this analytic approach in our understanding of these visual and material practices in the southern region of South America. We identify the techniques, protocols and sample preparation, the information obtained, and archaeological questions addressed with these analyses. Consequently, we propose the need for a microarchaeological approach. We stress the materiality and particularities of the rock art practice, as an action performed over continuously altered walls, which forms complex microstratigraphies. Moreover, we highlight the benefits of obtaining comparable results with the use of paintings on different supports and contexts to hold an Archaeology of Color that allows studying not only the meaning, but also understand the exploitation, production, and consumption of color, being the painted rock art one form of the final stage of a complex sequence related to color materials. |
2,969 | Women's mental health and climate change Part II: Socioeconomic stresses of climate change and eco-anxiety for women and their children | Climate change is a significant public health crisis that is both rooted in pre-existing inequitable socioeconomic and racial systems and will further worsen these social injustices. In the face of acute and slow-moving natural disasters, women, and particularly women of color, will be more susceptible to gender-based violence, displacement, and other socioeconomic stressors, all of which have adverse mental health outcomes. Among the social consequences of climate change, eco-anxiety resulting from these negative impacts is also increasingly a significant factor in family planning and reproductive justice, as well as disruptions of the feminine connection to nature that numerous cultures historically and currently honor. This narrative review will discuss these sociologic factors and also touch on ways that practitioners can become involved in climate-related advocacy for the physical and mental well-being of their patients. |
2,970 | Occupational exposure to physicians working with a Zero-Gravity (TM) protection system in haemodynamic and electrophysiology labs and the assessment of its performance against a standard ceiling suspended shield | A two centre clinical study was performed to analyse exposure levels of cardiac physicians performing electrophysiology and haemodynamic procedures with the use of state of the art Zero-Gravity (TM) radiation protective system (ZG). The effectiveness of ZG was compared against the commonly used ceiling suspended lead shield (CSS) in a haemodynamic lab. The operator's exposure was assessed using thermoluminescent dosimeters (TLDs) during both ablation (radiofrequency ablation (RFA) and cryoablation (CRYA)) and angiography and angioplasty procedures (CA/PCI). The dosimeters were placed in multiple body regions: near the left eye, on the left side of the neck, waist and chest, on both hands and ankles during each measurement performed with the use of ZG. In total 29 measurements were performed during 105 procedures. To compare the effectiveness of ZG against CSS an extra 80 measurements were performed with the standard lead apron, thyroid collar and ceiling suspended lead shield during CA/PCI procedures. For ZG, the upper values for the average eye lens and whole body doses per procedure were 4 mu Sv and 16 mu Sv for the left eye lens in electrophysiology lab (with additionally used CSS) and haemodynamic lab (without CSS), respectively, and about 10 mu Sv for the remaining body parts (neck, chest and waist) in both labs. The skin doses to hands and ankles non-protected by the ZG were 5 mu Sv for the most exposed left finger and left ankle in electrophysiology lab, while in haemodynamic lab 150 mu Sv and 17 mu Sv, respectively. The ZG performance was 3 times (p < 0.05) and at least 15 times (p < 0.05) higher for the eye lenses and thoracic region, respectively, compared to CSS (with dosimeters on the apron/collar). However, when only ZG was used slightly higher normalised doses were observed for the left finger compared to CSS (5.88e - 2 Sv/Gym(2) vs. 4.31 e - 2 Sv/Gym(2), p = 0.016). The study results indicate that ZG performance is superior to CSS. It can be simultaneously used with the ceiling suspended lead shield to ensure the protection to the hands as long as this is not obstructive for the work. |
2,971 | Regeneration enhancers: a field in development | The ability to regenerate tissues and organs following damage is not equally distributed across metazoans, and even highly related species can vary considerably in their regenerative capacity. Studies of animals with high regenerative potential have shown that factors expressed during normal development are often reactivated upon damage and required for successful regeneration. As such, regenerative potential may not be dictated by the presence or absence of the necessary genes, but whether such genes are appropriately activated following injury. The identification of damage-responsive enhancers that regulate regenerative gene expression in multiple species and tissues provides possible mechanistic insight into this phenomenon. Enhancers that are reused from developmental programs, and those that are potentially unique to regeneration, have been characterized individually and at a genome-wide scale. A better understanding of the regulatory events that, direct and in some cases limit, regenerative capacity is an important step in developing new methods to manipulate and augment regeneration, particularly in tissues that do not have this ability, including those of humans. |
2,972 | An efficient and robust negotiating strategy in bilateral negotiations over multiple items | Multi-item negotiations surround our daily life and usually involve two parties that share common or conflicting interests. Effective automated negotiation techniques should enable the agents to adaptively adjust their behaviors depending on the characteristics of their negotiating partners and negotiation scenarios. This is complicated by the fact that the negotiation agents are usually unwilling to reveal their information (strategies and preferences) to avoid being exploited during negotiation. In this paper, we propose an adaptive negotiation strategy, called ABiNeS, which can make effective negotiations against different types of negotiating partners. The ABiNeS strategy employs the non-exploitation point to adaptively adjust the appropriate time to stop exploiting the negotiating partner and also predicts the optimal offer for the negotiating partner based on the reinforcement-learning based approach. Simulation results show that the ABiNeS strategy can perform more efficient exploitations against different types of negotiating partners, and thus achieve higher overall payoffs compared with the state-of-the-art strategies under negotiation tournaments. We also provide a detailed analysis of why the ABiNeS strategy can negotiate more efficiently compared with other existing state-of-the-art negotiation strategies focusing on two major components. Lastly, we propose adopting the single-agent best deviation principle to analyze the robustness of different negotiation strategies based on model checking techniques. Through our analysis, the ABiNeS strategy is shown to be very robust against other state-of-the-art strategies under different negotiation contexts. (C) 2014 Elsevier Ltd. All rights reserved. |
2,973 | GASTRONOMY AS A SOCIAL CATALYST IN THE CREATIVE PLACE-MAKING PROCESS | This paper examines the integration of gastronomy in the creative place-making process. The study is based on the interviews with the organizers of five gastronomy events: a cooking workshop, a gastronomy festival, a series of gastronomy events at museums, a gastronomy theatre performance and an intimate dinner event. The contextual analysis shows that gastronomy events can contribute to five important features defining quality of place: diversity, liveliness, innovativeness, creativity and openness/tolerance. The final phase of our study brings comparison with findings in art-based place-making studies and discusses on diversity, integration in development policies and replicability potential of analyzed gastronomy events. |
2,974 | A mini-review on bio-inspired polymer self-assembly: single-component and interactive polymer systems | Biology demonstrates meticulous ways to control biomaterials self-assemble into ordered and disordered structures to carry out necessary bioprocesses. Empowering the synthetic polymers to self-assemble like biomaterials is a hallmark of polymer physics studies. Unlike protein engineering, polymer science demystifies self-assembly by purposely embedding particular functional groups into the backbone of the polymer while isolating others. The polymer field has now entered an era of advancing materials design by mimicking nature to a very large extend. For example, we can make sequence-specific polymers to study highly ordered mesostructures similar to studying proteins, and use charged polymers to study liquid-liquid phase separation as in membraneless organelles. This mini-review summarizes recent advances in studying self-assembly using bio-inspired strategies on single-component and multi-component systems. Sequence-defined techniques are used to make on-demand hybrid materials to isolate the effects of chirality and chemistry in synthetic block copolymer self-assembly. In the meantime, sequence patterning leads to more hierarchical assemblies comprised of only hydrophobic and hydrophilic comonomers. The second half of the review discusses complex coacervates formed as a result of the associative charge interactions of oppositely charged polyelectrolytes. The tunable phase behavior and viscoelasticity are unique in studying liquid macrophase separation because the slow polymer relaxation comes primarily from charge interactions. Studies of bio-inspired polymer self-assembly significantly impact how we optimize user-defined materials on a molecular level. |
2,975 | Influence of citrate/tartrate on chromite crystallization behavior and its potential environmental implications | The ferrite process has been developed to purify wastewater containing heavy metal ions and recycle valuable metals by forming chromium ferrite. However, organic matter has an important influence on the crystallization behavior and stability of chromite synthesized from chromium-containing wastewater. We focused on the influence and effect mechanism of two typical organic acid salts (citrate (CA) and tartrate (TA)) on the process of chromium mineralization. It was found that the presence of organic matter leads to the increase of the residual content of Cr in CA system (0.50 mmol/L) and TA system (0.61 mmol/L) in the solution, and the removal of chromium was mainly due to the surface adsorption of Fe(III) hydrolysate. The decreased crystallinity of mineralized products is ascribed to the completion of organic compounds with Fe(II) and Fe(III), which hinders the formation of ferrite precursors. There was bidentate and monodentate chelation between -COO- and metal ions in the CA system and TA system respectively, which resulted in a stronger affinity between CA and iron. This study provides the underlying mechanism for Cr(III) solid oxidation by the ferrite method in an organic matter environment and is of great significance to prevent and control chromium pollution in the environment. |
2,976 | Segment 2D and 3D Filaments by Learning Structured and Contextual Features | We focus on the challenging problem of filamentary structure segmentation in both 2D and 3D images, including retinal vessels and neurons, among others. Despite the increasing amount of efforts in learning based methods to tackle this problem, there still lack proper data-driven feature construction mechanisms to sufficiently encode contextual labelling information, which might hinder the segmentation performance. This observation prompts us to propose a data-driven approach to learn structured and contextual features in this paper. The structured features aim to integrate local spatial label patterns into the feature space, thus endowing the follow-up tree classifiers capability to grouping training examples with similar structure into the same leaf node when splitting the feature space, and further yielding contextual features to capture more of the global contextual information. Empirical evaluations demonstrate that our approach outperforms state-of-the arts on well-regarded testbeds over a variety of applications. Our code is also made publicly available in support of the open-source research activities. |
2,977 | LSTM multichannel neural networks in mental task classification | Purpose The purpose of this paper is to apply recurrent neural networks (RNNs) and more specifically long-short term memory (LSTM)-based ones for mental task classification in terms of BCI systems. The authors have introduced novel LSTM-based multichannel architecture model which proved to be highly promising in other fields, yet was not used for mental tasks classification. Design/methodology/approach Validity of the multichannel LSTM-based solution was confronted with the results achieved by a non-multichannel state-of-the-art solutions on a well-recognized data set. Findings The results demonstrated evident advantage of the introduced method. The best of the provided variants outperformed most of the RNNs approaches and was comparable with the best state-of-the-art methods. Practical implications - The approach presented in the manuscript enables more detailed investigation of the electroencephalography analysis methods, invaluable for BCI mental tasks classification. Originality/value The new approach to mental task classification, exploiting LSTM-based RNNs with multichannel architecture, operating on spatial features retrieving filters, has been adapted to mental tasks with noticeable results. To the best of the authors' knowledge, such an approach was not present in the literature before. |
2,978 | Sparse Reverberant Audio Source Separation via Reweighted Analysis | We propose a novel algorithm for source signals estimation from an underdetermined convolutive mixture assuming known mixing filters. Most of the state-of-the-art methods are dealing with anechoic or short reverberant mixture, assuming a synthesis sparse prior in the time-frequency domain and a narrowband approximation of the convolutive mixing process. In this paper, we address the source estimation of convolutive mixtures with a new algorithm based on i) an analysis sparse prior, ii) a reweighting scheme so as to increase the sparsity, iii) a wideband data-fidelity term in a constrained form. We show, through theoretical discussions and simulations, that this algorithm is particularly well suited for source separation of realistic reverberation mixtures. Particularly, the proposed algorithm outperforms state-of-the-art methods on reverberant mixtures of audio sources by more than 2 dB of signal-to-distortion ratio on the BSS Oracle dataset. |
2,979 | First Report of Anthracnose Crown Rot on Strawberry (Fragaria × ananassa Duch.) Caused by Colletotrichum pandanicola in Yunnan Provence, China | Strawberry (Fragaria × ananassa Duch.), a widely grown octoploid species, is one of the most important economic fruit crops and has been widely cultivated in the world, including China. In December 2021, a serious crown rot disease (approximately 50% incidence) was observed in strawberry (cultivar Miaoxiang) plantations in Qujing City, Yunnan Province, China. Symptoms observed on aboveground part withered rapidly, reddish-brown marbled necrosis on crown. The roots were healthy and strong, but the plants finally died. To isolate the causal agent of this disease, crown tissues from five strawberry plants showing typical symptoms were cut into pieces of 5×5 mm, and the pieces were surface-sterilized with 75% ethanol for 45 s followed by 2.5% NaClO for 3 min and rinsed thrice with sterile water, and then placed onto potato dextrose agar (PDA) for 7 days at 25 ºC. After 3 to 4 days, extended single hyphal tips from the tissues were transferred to PDA and incubated for 7 days at 25 ºC. The colonies were initially white, later became somewhat zonate, velvety, cyan gray on the upper side and cyan ink pigment ring on the reverse side of plates, with concentric rings of salmon sporodochia. Many yellowish or orange creamy conidial droplets formed on PDA after 14 days at 25 ºC. Fifty-nine isolates were obtained, and three isolates QLYRR1, QLMCR9, and QLMCR39 were selected for further experiments. Conidia were hyaline, cylindrical with rounded ends, 12.17-19.35×3.71-6.30 μm (average±SD, 15.24±1.37×5.09±0.45 μm, n=150), L/W ratio = 2.99. The three isolates were molecularly identified using the genomic regions of internal transcribed spacer (ITS), actin (ACT), chitin synthase (CHS-1), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and beta-tubulin (TUB2) genes, and the sequences were deposited in GenBank (accession nos. QLYRR1, QLMCR9, QLMCR39: ON668272, ON668256, ON668257[ITS], ON684302, ON684300, ON684301[ACT], ON684316, ON684314, ON684315[CHS-1], ON684292, ON684290, ON684291[GAPDH], ON684286, ON684284, ON684285[TUB2]). The phylogenetic analysis of experimental strains was performed by Maximum-likelihood (ML) tree and Bayesian inference (BI) method. Nucleotide sequences exhibited three isolates were clustered with the ex-type strain C. pandanicola strain MFLUCC 170571T found in Thailand, C. pandanicola strains (SAUCC201152, SAUCC200204) found in Shandong Province, and the holotype stain C. parvisporum YMF 1.06942T found in Guangxi Province, China. Morphologically, isolates were easily distinguished from C. parvisporum by the colony on PDA and the size of conidia (Yu et al. 2022). Morphological characteristics and phylogenetic analyses revealed that QLYRR1, QLMCR9, and QLMCR39 belong to C. pandanicola, the members of the C. gloeosporioides species complex (Tibpromma et al. 2018; Mu et al. 2021). Koch's postulates were tested by strawberry plants (two cultivars, Akihime and Miaoxiang) in vivo, strawberry plants were tested for the three isolates by spraying 1×106 conidia/mL suspension on three seedlings. Three seedlings sprayed with sterile distilled water were served as control. All of the plants were transferred to a glasshouse with a 28/20 °C day/night temperature range and natural sunlight. After 6 weeks, QLYRR1-, QLMCR9-, and QLMCR39-sprayed seedlings were stunted and developed typical wilt symptoms similar to those observed in the field with the incidence for 3, 3, and 3 seedlings, respectively. The negative control remained asymptomatic. The fungi were reisolated again from lesions of diseased plants and leaves with 100% frequency, and morphological characteristics and tested gene sequences were identical to the original isolates in this note, thus fulfilling Koch's postulates. C. pandanicola was described from the healthy leaves of Pandanus sp. and the lesion fruits of Juglans regia. To our knowledge, this is the first report confirming C. pandanicola causes anthracnose crown rot on strawberries in China. C. pandanicola has the potential for causing serious losses to the strawberry industry, and research is needed on management strategies to minimize losses. |
2,980 | Improving Lateral Resolution in 3-D Imaging With Micro-beamforming Through Adaptive Beamforming by Deep Learning | There is an increased desire for miniature ultrasound probes with small apertures to provide volumetric images at high frame rates for in-body applications. Satisfying these increased requirements makes simultaneous achievement of a good lateral resolution a challenge. As micro-beamforming is often employed to reduce data rate and cable count to acceptable levels, receive processing methods that try to improve spatial resolution will have to compensate the introduced reduction in focusing. Existing beamformers do not realize sufficient improvement and/or have a computational cost that prohibits their use. Here we propose the use of adaptive beamforming by deep learning (ABLE) in combination with training targets generated by a large aperture array, which inherently has better lateral resolution. In addition, we modify ABLE to extend its receptive field across multiple voxels. We illustrate that this method improves lateral resolution both quantitatively and qualitatively, such that image quality is improved compared with that achieved by existing delay-and-sum, coherence factor, filtered-delay-multiplication-and-sum and Eigen-based minimum variance beamformers. We found that only in silica data are required to train the network, making the method easily implementable in practice. |
2,981 | Deep Mining External Imperfect Data for Chest X-Ray Disease Screening | Deep learning approaches have demonstrated remarkable progress in automatic Chest X-ray analysis. The data-driven feature of deep models requires training data to cover a large distribution. Therefore, it is substantial to integrate knowledge from multiple datasets, especially for medical images. However, learning a disease classification model with extra Chest X-ray (CXR) data is yet challenging. Recent researches have demonstrated that performance bottleneck exists in joint training on different CXR datasets, and few made efforts to address the obstacle. In this paper, we argue that incorporating an external CXR dataset leads to imperfect training data, which raises the challenges. Specifically, the imperfect data is in two folds: domain discrepancy, as the image appearances vary across datasets; and label discrepancy, as different datasets are partially labeled. To this end, we formulate the multi-label thoracic disease classification problem as weighted independent binary tasks according to the categories. For common categories shared across domains, we adopt task-specific adversarial training to alleviate the feature differences. For categories existing in a single dataset, we present uncertainty-aware temporal ensembling of model predictions to mine the information from the missing labels further. In this way, our framework simultaneously models and tackles the domain and label discrepancies, enabling superior knowledge mining ability. We conduct extensive experiments on three datasets with more than 360,000 Chest X-ray images. Our method outperforms other competing models and sets state-of-the-art performance on the official NIH test set with 0.8349 AUC, demonstrating its effectiveness of utilizing the external dataset to improve the internal classification. |
2,982 | Nucleoporin 35 regulates cardiomyocyte pH homeostasis by controlling Na+-H+ exchanger-1 expression | The mammalian nuclear pore complex is comprised of ∼ 30 different nucleoporins (Nups). It governs the nuclear import of gene expression modulators and the export of mRNAs. In cardiomyocytes, Na(+)-H(+) exchanger-1 (NHE1) is an integral membrane protein that exclusively regulates intracellular pH (pHi) by exchanging one intracellular H(+) for one extracellular Na(+). However, the role of Nups in cardiac NHE1 expression remains unknown. We herein report that Nup35 regulates cardiomyocyte NHE1 expression by controlling the nucleo-cytoplasmic trafficking of nhe1 mRNA. The N-terminal domain of Nup35 determines nhe1 mRNA nuclear export by targeting the 5'-UTR (-412 to -213 nt) of nhe1 mRNA. Nup35 ablation weakens the resistance of cardiomyocytes to an acid challenge by depressing NHE1 expression. Moreover, we identify that Nup35 and NHE1 are simultaneously downregulated in ischemic cardiomyocytes both in vivo and in vitro. Enforced expression of Nup35 effectively counteracts the anoxia-induced intracellular acidification. We conclude that Nup35 selectively regulates cardiomyocyte pHi homeostasis by posttranscriptionally controlling NHE1 expression. This finding reveals a novel regulatory mechanism of cardiomyocyte pHi, and may provide insight into the therapeutic strategy for ischemic cardiac diseases. |
2,983 | THM-GCMS and FTIR for the study of binding media in Yellow Islands by Jackson Pollock and Break Point by Fiona Banner | Throughout the 20th century a number of binding media, including synthetic resins, have been employed in paints. It is only very recently that thermally assisted hydrolysis and methylation-gas chromatography-mass spectrometry (THM-GCMS) has been used for the identification of a wide variety of binding media employed in the 20th century art. This paper shows as THM-GCMS was successfully used in conjunction with Fourier Transform Infrared Spectroscopy for the study of binding media in samples from Yellon, Islands by Jackson Pollock [Tate Collection, T00436] and Break Point by Fiona Banner [Tate Collection, T07501]. (C) 2003 Elsevier B.V. All rights reserved. |
2,984 | Techno-economic assessment of microbial electrosynthesis from CO2 and/or organics: An interdisciplinary roadmap towards future research and application | Microbial electrosynthesis (MES) allows carbon-waste and renewable electricity valorization into industrially-relevant chemicals. MES has received much attention in laboratory-scale research, although a technoeconomic-driven roadmap towards validation and large-scale demonstration of the technology is lacking. In this work, two main integrated systems were modelled, centered on (1) MES-from-CO2 and (2) MES from short-chain carboxylates, both for the production of pure, or mixture of, acetate, n-butyrate, and n-caproate. Twenty eight key parameters were identified, and their impact on techno-economic feasibility of the systems assessed. The main capital and operating costs were found to be the anode material cost (59%) and the electricity consumption (up to 69%), respectively. Under current state-of-the-art MES performance and economic conditions, these systems were found non-viable. However, it was demonstrated that sole improvement of MES performance, independent of improvement of non-technological parameters, would result in profitability. In otherwise state-of-the-art conditions, an improved electron selectivity (>= 36%) towards n-caproate, especially at the expense of acetate, was showed to result in positive net present values (i.e. profitability; NPV). Cell voltage, faradaic efficiency, and current density also have significant impact on both the capital and operating costs. Variation in electricity cost on overall process feasibility was also investigated, with a cost lower than 0.045 (sic) kWh(-1) resulting in positive NPV of the state-of-the-art scenario. Maximum purification costs were also determined to assess the integration of a product's separation unit, which was showed possible at positive NPV. Finally, we briefly discuss CO2 electroreduction versus MES, and their potential market complementarities. |
2,985 | An Exploratory Trial of Brief Mindfulness-Based Zentangle Art Workshops in Family Social Services during COVID-19: Transitioning from Offline to Online | Mindfulness-based art therapy has shown to improve psychological well-being. Zentangle is an easy-to-learn, mindfulness-based art therapy suitable for everyone. We reported the transition from face-to-face to online Zentangle workshops in family social services during COVID-19. We explored feedback from face-to-face workshops and the acceptability of an online approach utilizing information communication technology (ICT) to achieve greater service reach, satisfaction, and knowledge and related outcomes. Under the Hong Kong Jockey Club SMART Family-Link Project and in collaboration with Caritas Integrated Family Service Centre-Aberdeen, this study was conducted in two phases: a four-session, face-to-face workshop (phase one) and eleven online single-session workshops (phase two) from September 2019 to September 2020. A total of 305 participants joined the workshops. Phase one participants (n = 11) reported high satisfaction (4.7 out of 5), increases in knowledge (4.2/5) and confidence (3.9/5) towards managing stress, increases in knowledge (4.1/5) and confidence (3.9/5) in showing support and care towards family members, and an increase in knowledge towards strengthening family relationships (4.0/5). Phase two participants (n = 294) also reported high satisfaction (4.7/5) and strongly agreed that ICT helped with learning Zentangle more conveniently, that they had increased knowledge and interest in Zentangle (all 4.7/5), and would definitely join the workshop again (4.8/5). The qualitative data supported the quantitative findings. We are the first to report on the utilization of ICT in an exploratory trial of brief, online Zentangle art workshops targeting the general public, with high satisfaction and positive participant experiences with ICT integration, learning Zentangle, and enhanced psychological and family well-being. This study provided preliminary evidence on the use of ICT to successfully transition face-to-face to online workshops and reach a wider audience. |
2,986 | Symbolic expression in Pleistocene Sahul, Sunda, and Wallacea | The pace of research undertaken in Sunda (Southeast Asia) through to Sahul (Greater Australia) has increased exponentially over the last three decades, resulting in spectacular discoveries ranging from new hominin species, significant extension to the age for first human occupation in the region, as well as the identification of what is currently the oldest known rock art in the world. These breakthroughs cast the archaeological record of complexity in Sunda, Sahul, and Wallacea in an entirely different light to that of several decades ago, placing it on an equal footingto that of Africa, Asia, and Europe. The archaeological record of these regions now points to rich and diverse early modern human (Homo sapien) societies engaged in complex symbolic and technological behaviours demonstrating capacities for innovation and self-expression found in all modern human groups now around the globe. Here we provide a comprehensive review of all Pleistocene symbolic evidence reported for Sahul, Sunda, and Wallacea to date. We explore how recent findings have changed our perceptions of the first modern human colonists and our understanding of the origins and development of the rich and diverse cultures that arose in each region through time. (C) 2019 Elsevier Ltd. All rights reserved. |
2,987 | A Quantitative Measure of Conformational Changes in Apo, Holo and Ligand-Bound Forms of Enzymes | Determination of the native geometry of the enzymes and ligand complexes is a key step in the process of structure-based drug designing. Enzymes and ligands show flexibility in structural behavior as they come in contact with each other. When ligand binds with active site of the enzyme, in the presence of cofactor some structural changes are expected to occur in the active site. Motivation behind this study is to determine the nature of conformational changes as well as regions where such changes are more pronounced. To measure the structural changes due to cofactor and ligand complex, enzyme in apo, holo and ligand-bound forms is selected. Enzyme data set was retrieved from protein data bank. Fifteen triplet groups were selected for the analysis of structural changes based on selection criteria. Structural features for selected enzymes were compared at the global as well as local region. Accessible surface area for the enzymes in entire triplet set was calculated, which describes the change in accessible surface area upon binding of cofactor and ligand with the enzyme. It was observed that some structural changes take place during binding of ligand in the presence of cofactor. This study will helps in understanding the level of flexibility in protein-ligand interaction for computer-aided drug designing. |
2,988 | Gradual Machine Learning for Entity Resolution | Usually considered as a classification problem, entity resolution (ER) can be very challenging on real data due to the prevalence of dirty values. The state-of-the-art solutions for ER were built on a variety of learning models (most notably deep neural networks), which require lots of accurately labeled training data. Unfortunately, high-quality labeled data usually require expensive manual work, and are therefore not readily available in many real scenarios. In this paper, we propose a novel learning paradigm for ER, called gradual machine learning, which aims to enable effective machine labeling without the requirement for manual labeling effort. It begins with some easy instances in a task, which can be automatically labeled by the machine with high accuracy, and then gradually labels more challenging instances by iterative factor graph inference. In gradual machine learning, the hard instances in a task are gradually labeled in small stages based on the estimated evidential certainty provided by the labeled easier instances. Our extensive experiments on real data have shown that the performance of the proposed approach is considerably better than its unsupervised alternatives, and highly competitive compared to the state-of-the-art supervised techniques. Using ER as a test case, we demonstrate that gradual machine learning is a promising paradigm potentially applicable to other challenging classification tasks requiring extensive labeling effort. |
2,989 | A General Framework for First Story Detection Utilizing Entities and Their Relations | News portals, such as Yahoo News or Google News, collect large amounts of news articles from a variety of sources on a daily basis. Only a small portion of these documents can be selected and displayed on the homepage. Thus, there is a strong preference for major, recent events. In this work, we propose a scalable First Story Detection (FSD) pipeline that identifies fresh news. This pipeline is used in order to instantiate a variety of FSD approaches. In addition we suggest a novel FSD technique that in comparison to existing systems, relies on relation extraction algorithms and exploits the named entities and their relations in order to decide about the freshness of an article. We evaluate our technique by instantiating existing state of art FSD techniques within our generic pipeline. As ground truth we use multiple datasets that cover different categories. Experimental results demonstrate that our FSD method in many cases provides an improvement over state-of-the-art techniques. In addition, we show using a large synthetic dataset that our general FSD pipeline has constant space and time requirements and is suitable for very high volume streams. |
2,990 | Rock art of the upper Lluta valley, northernmost of Chile (South Central Andes): A visual approach to socio-economic changes between Archaic and Formative periods (6,000-1,500 years BP) | Though they are generally characterized on the basis of faunal remains or lithic industries, in the highlands of northernmost Chile, the cultural aspects of the socio-economic changes, between Archaic and Formative periods (6000-1500 years BP), from hunter-gatherer to pastoral modes of life, a consequence of the domestication of camelids, can be discussed through the numerous scenes painted on the stone surfaces of rock shelters. The originality of these representations lies in the precision with which certain practices are represented, and in the socio-economic and symbolic relationships that between humans and animals, specifically with the camelids of the Andes. The present study is based on the analysis of these scenes, with the human-animal relationship, and the graphic superpositions, at six rock-art sites in the upper Lluta valley in precordillera or andean foothill, of the northernmost of Chile. We observe that the technical investment and the objective of the scenes become increasingly complex and focused on the control and possible protection of the animal. In the absence of archaeological contexts related to domestication in this region of the South Central Andes, this new study provides an innovative approach to the progressive changes of practices related to animal management, several hundreds of miles from the area where in situ domestication is evidenced. (C) 2016 Elsevier Ltd and INQUA. All rights reserved. |
2,991 | Strengthened removal of emerging contaminants over S/Fe codoped activated carbon fabricated by a mild one-step thermal transformation scheme | Thermal transformation of carbonized materials to functional activated carbon (AC) is a simplified, economical and eco-friendly strategy, which has great potential in the practical applications of water purification. Herein, a S/Fe codoped activated carbon (S/Fe@AC) with only 0.90 wt% S and 0.76 wt% Fe was creatively fabricated by one synchronous method of physical activation, carbothermal reduction and sulfidation in the solid phase. The formed iron sulfide shell significantly enhances the antioxidation ability of nanoscale zero-valent iron (NZVI, >180 d) and dramatically improves the hydrophobicity of the composite. Meanwhile, the doped thiophenic S in AC enhances the hydrophobicity and increases the specific surface area to 1194.14 m2 g-1. Incorporating with AC in turn greatly strengthens the dispersibility and stability of sulfurized NZVI particles. Compared to NZVI@AC, AC and NZVI, the removal capacity of S/Fe@AC for the representative hydrophobic contaminant-triclosan (TCS) increases to 519.68 mg g-1 by 66.60%, 78.60% and 981.21%, respectively, outperforming most of the previously reported materials. The strong hydrophobic and π-π interactions, and weak hydrogen bonding and electrostatic repulsion are responsible for the excellent removal performance for TCS. More importantly, the improved chemical property (29.38%) of the composite caused by the doped S/Fe has a greater effect on TCS removal compared with the changed physical structure (14.56%). Furthermore, the stable S/Fe@AC shows strong anti-interference capability and exceptional regenerability. These intriguing discoveries provide new insights into the design of advanced and sustainable adsorbing materials for emerging contaminants. |
2,992 | Reusable Security Requirements Repository Implementation Based on Application/System Components | Forming high quality requirements has a direct impact on project success. Gathering security requirements could be challenging, since it demands a multidisciplinary approach and security expertise. Security requirements repository enables an effective alternative for addressing this challenge. The main objective of this paper is to present the design of a practical repository model for reusable security requirements, which is easy to use and understand for even non-security experts. The paper also portrays an approach and a software tool for using this model to determine subtle security requirements for improved coverage. Proposed repository consists of attributes determined by examining common security problems covered in state-of-the-art publications. A test repository was prepared using specification files and Common Criteria documents. The outcomes of applying the proposed model were compared with the sample requirement sets included in the state-of-the-art publications. The results reveal that in the absence of a security requirements repository, key security points can be missed. Repository improves the completeness of the security terms with reasonable effort. |
2,993 | Categorizing paintings in art styles based on qualitative color descriptors, quantitative global features and machine learning (QArt-Learn) | The QArt-Learn approach for style painting categorization based on Qualitative Color Descriptors (QCD), color similarity (SimQCD), and quantitative global features (i.e. average of brightness, hue, saturation and lightness and brightness contrast) is presented in this paper. k-Nearest Neighbor (k-NN) and support vector machine (SVM) techniques have been used for learning the features of paintings from the Baroque, Impressionism and Post-Impressionism styles. Specifically two classifiers are built, and two different parameterizations have been applied for the QCD. For testing QArt-Learn approach, the Painting-91 dataset has been used, from which the paintings corresponding to Velazquez, Vermeer, Monet, Renoir, van Gogh and Gauguin were extracted, resulting in a set of 252 paintings. The results obtained have shown categorization accuracies higher than 65%, which are comparable to accuracies obtained in the literature. However, QArt-Learn uses qualitative color names which can describe style color palettes linguistically, so that they can be better understood by non-experts in art since QCDs are aligned with human perception. (C) 2017 Elsevier Ltd. All rights reserved. |
2,994 | Unsupervised densely attention network for infrared and visible image fusion | Integrating the information of infrared and visible images without human supervision is a long-standing problem. A key technical challenge in this domain is how to extract features from heterogeneous data-sources and fuse them appropriately. Prior deep learning works either extract the middle layers information or use costly training step to improve fusion performance, which limited their performances in cluttered scenes and real-time applications. In this paper, we introduce a novel and pragmatic unsupervised infrared and visible image fusion method based on a pre-trained deep network, which employs a densely connection structure and incorporates the attention mechanism to achieve high fusion performance. Furthermore, we propose to use the cross-dimensional weighting and aggregation to compute the attention map for infrared and visible image fusion. The attention map enables more efficient feature extraction and captures more structure information from source images. We evaluate our method and compare it with ten typical state-of-the-art fusion methods. Extensive experimental results demonstrate that our method achieves state-of-the-art fusion performance in both subjective and objective evaluation. |
2,995 | Dietary zinc intake and body mass index as modifiers of the association between household pesticide exposure and infertility among US women: a population-level study | Clinical studies on the relationship between pesticide exposure at home and infertility in the general population are scarce. Whether the antioxidant nutrients or other health-related factors affect the pesticide-infertility relationship remains unknown. This nationwide study screened 29,400 participants of the National Health and Nutrition Examination Surveys conducted between 2013 and 2018. The participants were subdivided according to dietary zinc intake based on the recommended dietary allowances as the low-zinc and high-zinc groups (< 8 and ≥ 8 mg/day, respectively), and according to body mass index (BMI; cut-off 28 kg/m2) as the low-BMI and high-BMI groups. Participants who were exposed to pesticides at home had an increased risk of infertility (odds ratio [OR] = 1.56, 95% confidence intervals [CI]: 1.06-2.29). The incidence of infertility differed in low-zinc and high-zinc groups (OR, 95% CI: 2.38, 1.40-4.06 vs. 0.98, 0.53-1.79, respectively), indicating an interaction between pesticide exposure and zinc intake in households (P = 0.047), which suggests that a zinc-rich diet may reduce the risk of pesticide-induced infertility. Similarly, the relationship between pesticide exposure and infertility risk differed in the low-BMI and high-BMI groups (OR, 95% CI: 0.90, 0.42-1.93 vs. 2.23, 1.39-3.58, respectively; P = 0.045), suggesting that high BMI may intensify the infertility risk caused by pesticide exposure. These new findings reveal the antagonistic and synergistic effect of zinc and obesity, respectively, in pesticide-induced infertility risk and suggest that individuals who are obese and on a low-zinc diet may be more susceptible to infertility induced by household pesticide exposure. |
2,996 | SPHARM-Net: Spherical Harmonics-Based Convolution for Cortical Parcellation | We present a spherical harmonics-based convolutional neural network (CNN) for cortical parcellation, which we call SPHARM-Net. Recent advances in CNNs offer cortical parcellation on a fine-grained triangle mesh of the cortex. Yet, most CNNs designed for cortical parcellation employ spatial convolution that depends on extensive data augmentation and allows only predefined neighborhoods of specific spherical tessellation. On the other hand, a rotation-equivariant convolutional filter avoids data augmentation, and rotational equivariance can be achieved in spectral convolution independent of a neighborhood definition. Nevertheless, the limited resources of a modern machine enable only a finite set of spectral components that might lose geometric details. In this paper, we propose (1) a constrained spherical convolutional filter that supports an infinite set of spectral components and (2) an end-to-end framework without data augmentation. The proposed filter encodes all the spectral components without the full expansion of spherical harmonics. We show that rotational equivariance drastically reduces the training time while achieving accurate cortical parcellation. Furthermore, the proposed convolution is fully composed of matrix transformations, which offers efficient and fast spectral processing. In the experiments, we validate SPHARM-Net on two public datasets with manual labels: Mindboggle-101 (N=101) and NAMIC (N=39). The experimental results show that the proposed method outperforms the state-of-the-art methods on both datasets even with fewer learnable parameters without rigid alignment and data augmentation. Our code is publicly available at https://github.com/Shape-Lab/SPHARM-Net. |
2,997 | Carotid wall segmentation in longitudinal ultrasound images using structured random forest | Edge detection is a primary image processing technique used for object detection, data extraction, and image segmentation. Recently, edge-based segmentation using structured classifiers has been receiving increasing attention. The intima media thickness (IMT) of the common carotid artery is mainly used as a primitive indicator for the development of cardiovascular disease. For efficient measurement of the IMT, we propose a fast edge-detection technique based on a structured random forest classifier. The accuracy of IMT measurement is degraded owing to the speckle noise found in carotid ultrasound images. To address this issue, we propose the use of a state-of-the-art denoising method to reduce the speckle noise, followed by an enhancement technique to increase the contrast. Furthermore, we present a novel approach for an automatic region of interest extraction in which a pre-trained structured random forest classifier algorithm is applied for quantifying the IMT. The proposed method exhibits IMTmean +/- standard deviation of 0.66mm +/- 0.14, which is closer to the ground truth value 0.67mm +/- 0.15 as compared to the state-of-the-art techniques. (C) 2018 Elsevier Ltd. All rights reserved. |
2,998 | Segmentation of agriculture products images by fuzzy ART | Computer vision has proven successful for online measurement of several agriculture products with applications ranging from routine inspection to the complex vision guided robotic control. Agriculture products image segmentation is a major step of computer vision tasks. This paper proposes a segmentation method of agriculture products images by combining fuzzy clustering with wavelet transform. The new approach employs fuzzy art and color texture characterization. The experimental results have also shown that the proposed method can obtain satisfactory results of agriculture products image segmentation, for the subsequent automatic inspection systems and computer vision systems. |
2,999 | A Miniaturized 0.78-mW/cm(2) Autonomous Thermoelectric Energy-Harvesting Platform for Biomedical Sensors | In order to use thermoelectric energy harvesters (TEHs) as a truly autonomous energy source for size-limited sensing applications, it is essential to improve the power conversion efficiency and energy density. This study presents a thin-film, arraybasedTEHwith a surface area of 0.83 cm(2). TheTEHautonomously supplies a power management IC fabricated in a 65-nm CMOS technology. The IC utilizes a single-inductor topology with integrated analog maximum power point tracking (MPPT), resulting in a 68% peak end-to-end efficiency (92% converter efficiency) and less than 20-ms MPPT. In an in-vivo test, a 645-mu Wregulated output power (effective 3.5 K of temperature gradient) was harvested from a rat implanted with our TEH, demonstrating true energy independence in a real environment while showing a 7.9 x improvement in regulated power density compared to the state-ofthe-art. The system showed autonomous operation down to 65-mV TEH input. |
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