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4,200 | Multi-Scale Dynamic Graph Convolution Network for Point Clouds Classification | Point clouds provide an efficient way for 3D geometric object representation. In order to deal with the classification and segmentation of point cloud, it is very important to design an efficient and intelligent model that can directly affect point cloud. Due to the irregularity of the data format, traditional convolutional neural networks cannot be applied to point clouds processing directly. Graph convolution network (GCN) has attracted more and more attention in recent years, especially in the field of non-Euclidean data processing. Point clouds processing with GCN models is an efficient and suitable method, a lot of GCN models have achieved state-of-the-art performance on irregular data processing challenges. In this paper, we propose a Multi-scale Dynamic GCN model for point clouds classification, a Farthest Point Sampling method is applied in our model firstly to efficiently cover the entire point set, it uses different scale k-NN group method to locate on k nearest neighborhood for each central node, Edge Convolution (EdgeConv) operation is used to extract and aggregate local features between neighbor connected nodes and central node. We use ModelNet40, ModelNet10 and ShapeNet part dataset to classify point clouds and segment them semantically. Experiments show that our model achieves a better performance on classification accuracy and model complexity than other state-of-the-art models. |
4,201 | Ensemble Optimization for Invasive Ductal Carcinoma (IDC) Classification Using Differential Cartesian Genetic Programming | The high cost of acquiring annotated histological slides for breast specimens entails exploiting an ensemble of models appropriately trained on small datasets. Histological Image Classification ensembles strive to accurately detect abnormal tissues in the breast samples by determining the correlation between the predictions of its weak learners. Nonetheless, the state-of-the-art ensemble methods, such as boosting and bagging, count merely on manipulating the dataset and lack intelligent ensemble decision making. Furthermore, the methods mentioned above are short of the diversity of the weak models of the ensemble. Likewise, other commonly used voting strategies, such as weighted averaging, are limited to how the classifiers' diversity and accuracy are balanced. Hence, In this paper, we assemble a Neural Network ensemble that integrates the models trained on small datasets by employing biologically-inspired methods. Our procedure is comprised of two stages. First, we train multiple heterogeneous pre-trained models on the benchmark Breast Histopathology Images for Invasive Ductal Carcinoma (IDC) classification dataset. In the second meta-training phase, we utilize the differential Cartesian Genetic Programming (dCGP) to generate a Neural Network that merges the trained models optimally. We compared our empirical outcomes with other state-of-the-art techniques. Our results demonstrate that improvising a Neural Network ensemble using Cartesian Genetic Programming transcended formerly published algorithms on slim datasets. |
4,202 | Resistance in health and healthcare: Applying Essex conceptualisation to a multiphased study on the experiences of Australian nurses and midwives who provide abortion care to people victimised by gender-based violence | In this article, we explore the act of resistance by nurses and midwives at the nexus of abortion care and gender-based violence. We commence with a brief overview of a multiphased extended grounded theory doctoral project that analysed the individual, situational and socio-political experiences of Australian nurses and midwives who provide abortion care to people victimised by gender-based violence. We then turn to Essex's conceptualisation of resistance in health and healthcare and draw upon these concepts to tell a unifying and cohesive story about how nurses and midwives exercise their politics. Vignettes taken from the three study phases are provided for demonstrative purposes. Finally, we discuss the potential of resistance in health and healthcare as a postmodern feminist research tool to analyse acts by nurses and midwives that could be categorised as political. |
4,203 | Characterization of hepatic inflammatory changes in a C57BL/6J mouse model of alpha1-antitrypsin deficiency | Alpha-1 antitrypsin deficiency (AATD) is a genetic disease caused by a hepatic accumulation of mutant alpha-1 antitrypsin (ZAAT). Individuals with AATD are prone to develop a chronic liver disease that remains undiagnosed until late stage of the disease. Here, we sought to characterize the liver pathophysiology of a human transgenic mouse model for AATD with a manifestation of liver disease compared with normal transgenic mice model. Male and female transgenic mice for normal (Pi*M) and mutant variant (Pi*Z) human alpha-1 antitrypsin at 3 and 6 mo of age were subjected to this study. The progression of hepatic ZAAT accumulation, hepatocyte injury, steatosis, liver inflammation, and fibrotic features were monitored by performing an in vivo study. We have also performed a Next-Gene transcriptomic analysis of the transgenic mice liver tissue 16 h after lipopolysaccharide (LPS) administration to delineate liver inflammatory response in Pi*Z mice as compared with Pi*M. Our results show hepatic ZAAT accumulation, followed by hepatocyte ballooning and liver steatosis developed at 3 mo in Pi*Z mice compared with the mice carrying normal variant of human alpha-1 antitrypsin. We observed higher levels of hepatic immune cell infiltrations in both 3- and 6-mo-old Pi*Z mice compared with Pi*M as an indication of liver inflammation. Liver fibrosis was observed as accumulation of collagen in 6-mo-old Pi*Z liver tissues compared with Pi*M control mice. Furthermore, the transcriptomic analysis revealed a dysregulated liver immune response to LPS in Pi*Z mice compared with Pi*M. Of particular interest for translational work, this study aims to establish a mouse model of AATD with a strong manifestation of liver disease that will be a valuable in vivo tool to study the pathophysiology of AATD-mediated liver disease. Our data suggest that the human transgenic mouse model of AATD could provide a suitable model for the evaluation of therapeutic approaches and preventive reagents against AATD-mediated liver disease.NEW & NOTEWORTHY We have characterized a mouse model of human alpha-1 antitrypsin deficiency with a strong manifestation of liver disease that can be used as an in vivo tool to test preventive and therapeutic reagents. Our data explores the altered immunophenotype of alpha-1 antitrypsin-deficient liver macrophages and suggests a relationship between acute inflammation, immune response, and fibrosis. |
4,204 | Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification | Imbalanced data problem is widely present in network intrusion detection, spam filtering, biomedical engineering, finance, science, being a challenge in many real-life data-intensive applications. Classifier bias occurs when traditional classification algorithms are used to deal with imbalanced data. As already known, the General Vector Machine (GVM) algorithm has good generalization ability, though it does not work well for the imbalanced classification. Additionally, the state-of-the-art Binary Ant Lion Optimizer (BALO) algorithm has high exploitability and fast convergence rate. Based on these facts, we have proposed in this paper a Cost-sensitive Feature selection General Vector Machine (CFGVM) algorithm based on GVM and BALO algorithms to tackle the imbalanced classification problem, delivering different cost weights to different classes of samples. In our method, the BALO algorithm determines the cost weights and extract more significant features to improve the classification performance. Experiments conducted on eleven imbalanced data sets have shown that the CFGVM algorithm significantly improves the classification performance of minority class samples. By comparing with similar algorithms and state-of-the-art algorithms, the proposed algorithm significantly outperforms in performance and produces better classification results. |
4,205 | Pyrrole-2-carbaldehydes with neuroprotective activities from Moringa oleifera seeds | Seven undescribed pyrrole-2-carbaldehydes (pyrrolemorines A-G), along with four known analogs, were isolated from the aqueous extract of Moringa oleifera seeds. The structures were elucidated by comprehensive spectroscopic and chemical analyses using HRMS and NMR spectra, acid hydrolysis, and Rh2(OCOCF3)4-induced ECD experiments. Pyrrolemorines A, E, and pyrrolemarumine displayed neuroprotective activities against oxygen-glucose deprivation/reperfusion injury in PC12 cells by regulating NF-κb and Nrf2. |
4,206 | High Dielectric Transparent Film Tailored by Acceptor and Donor Codoping | High dielectric constant materials are of particular current interests as indispensable components in transistors, capacitors, etc. In this context, there are emerging trends to exploit defect engineering in dielectric ceramics for enhancing the performance. However, demonstrations of similar high dielectric performance in integration-compatible crystalline films are rare. Herein, such a breakthrough via the functionalization of donor-acceptor dipoles by compositional tuning in GaCu codoped ZnO films is reported. The dielectric constant reaches ~200 at 1 kHz and the optical transmittance in visible light reaches ~80%. Importantly, by analyzing the impedance spectroscopy data, prominent relaxation mechanisms in correlation with the dipole properties, enabling consistent explanations of the dielectric constant as a function of frequency are discriminated. The atomistic nature of the dipoles is revealed by the systematic X-ray spectroscopy analysis. Spectacularly, similar trends for the dielectric properties are observed, while synthesizing samples by pulsed laser deposition and ion implantation, indicating the general character of the phenomena. |
4,207 | Con-Net: A Consolidated Light-Weight Image Restoration Network | There has been a considerable gap between the recent high-resolution display technologies and the short storage of its content. However, most of the existing restoration methods are restricted by local convolution operations and equal treatment of the diverse information in degraded image. These approaches being degradation-specific employ the same rigid spatial processing across different images ultimately resulting in high memory consumption. For overcoming this limitation we propose Con-Net, a network design capable of exploiting the non-uniformities of the degradations in spatial-domain with limited number of parameters (656k). Our proposed Con-Net comprises of basically two main components, (1) a spatial-degradation aware network for extracting the diverse information inherent in any degraded image, and (2) a holistic attention refinement network for exploiting the knowledge from the degradation aware network to selectively restore the degraded pixels. In a nutshell, our proposed method is generalizable for three applications: image denoising, super-resolution and real-world low-light enhancement. Extensive qualitative and quantitative comparison with prior arts on 8 benchmark datasets demonstrates the efficacy of our proposed Con-Net over existing state-of-the-art degradation-specific architectures, by huge parameter and FLOPs reduction in all the three tasks. |
4,208 | SSEM: A Novel Self-Adaptive Stacking Ensemble Model for Classification | In the past decades, the ensemble systems have been shown as an efficient method to increase the accuracy and stability of classification algorithms. However, how to get a valid combination of multiple base-classifiers is still an open question to be solved. In this paper, based on the genetic algorithm, a new self-adaptive stacking ensemble model (called SSEM) is proposed. Different from other ensemble learning classification algorithms, SSEM selectively integrates different base-classifiers, and automatically selects the optimal base-classifier combination and hyper-parameters of base-classifiers via the genetic algorithm. It is noted that all of machine learning methods can be the components of SSEM. In this work, based on two base-classifier selection principles (low complexity of base-classifier and high diversity between different base-classifiers), we select five state-of-art classifiers including Naive Bayes (NB), Extremely Randomized trees (ERT), Logistic, Random Forest (RF) and Classification and Regression Tree (CART) as the base-classifiers of SSEM. To demonstrate the efficiency of SSEM, we have applied it to nine different datasets. Compared with other 11 state-of-art classifiers (NB, ERT, Logistic, RF, CART, Back Propagation Network (BPN), Support Vector Machine (SVM), AdaBoost, Bagging, Convolutional Neural Networks (CNN) and Deep neural network (DNN)), SSEM always performs the best under the five evaluation indexes (Accuracy, Recall, AUC, F1-score and Matthews correlation coefficient (MCC)). Moreover, the significance test result shows that SSEM can achieve highly competitive performance against the other 11 state-of-art classifiers. Altogether, it is evident that SSEM can be a useful framework for classification. |
4,209 | Fractional Order PID Design based on Novel Improved Slime Mould Algorithm | This study attempts to maintain the terminal voltage level of an automatic voltage regulator (AVR) and control the speed of a direct current (DC) motor using a fractional order proportional integral derivative (FOPID) controller. The best parameters of the controller have been adjusted using a novel meta-heuristic algorithm called opposition-based hybrid slime mold with simulated annealing algorithm. The proposed algorithm aims to improve the original slime mold algorithm in terms of exploitation and exploration using simulated annealing and opposition-based learning, respectively. A time domain objective function was adopted as performance index to design the FOPID-based AVR and DC motor systems. The initial performance evaluation was carried out using unimodal and multimodal benchmark functions. The results confirmed the superior exploration and exploitation capabilities of the developed algorithm compared to the other state-of-the-art optimization algorithms. The performance of the proposed algorithm has also been assessed through statistical tests, time domain and frequency domain simulations along with robustness and disturbance rejection analyses for both DC motor and AVR systems. The proposed algorithm has shown superior capabilities for the respective systems compared to the other state-of-the-art optimization algorithms used for the same purpose. |
4,210 | Guggulsterone Attenuated Lipopolysaccharide-Induced Inflammatory Responses in Mouse Inner Medullary Collecting Duct-3 Cells | Guggulsterone (GS) is a phytosterol that has been used to treat inflammatory diseases such as colitis, obesity, and thrombosis. Although many previous studies have examined activities of GS, the effect of GS on lipopolysaccharide (LPS)-induced inflammatory responses in mouse inner medullary collecting duct-3 (mIMCD-3) cells have not been examined. Therefore, here, we investigated the anti-inflammatory action of GS on mIMCD-3 cells exposed to LPS. LPS treatment on mIMCD-3 cells produced pro-inflammatory molecules such as inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α) significantly; however, GS treatment significantly inhibited the production of pro-inflammatory molecules. In addition, GS inhibited the degradation of Iκ-Bα and translocation of NF-κB on mIMCD-3 cells. These results suggest that GS could inhibit inflammatory responses in collecting duct cells which could contribute to kidney injury during systemic infection. |
4,211 | Fast pixel-matching for video object segmentation | Video object segmentation, aiming to segment the foreground objects given the annotation of the first frame, has been attracting increasing attentions. Many state-of-the-art approaches have achieved great performance by relying on online model updating or mask-propagation techniques. However, most online models require high computational cost due to model fine-tuning during inference. Most mask-propagation based models are faster but with relatively low performance due to failure to adapt to object appearance variation. In this paper, we are aiming to design a new model to make a good balance between speed and performance. We propose a model, called NPMCA-net, which directly localizes foreground objects based on mask-propagation and non-local technique by matching pixels in reference and target frames. Since we bring in information of both first and previous frames, our network is robust to large object appearance variation, and can better adapt to occlusions. Extensive experiments show that our approach can achieve a new state-of-the-art performance with a fast speed at the same time (86.5% IoU on DAVIS-2016 and 72.2% IoU on DAVIS-2017, with speed of 0.11s per frame) under the same level comparison. Source code is available at https://github.com/siyueyu/NPMCA-net. |
4,212 | Speech Enhancement Using Multi-Stage Self-Attentive Temporal Convolutional Networks | Multi-stage learning is an effective technique for invoking multiple deep-learning modules sequentially. This paper applies multi-stage learning to speech enhancement by using a multi-stage structure, where each stage comprises a self-attention (SA) block followed by stacks of temporal convolutional network (TCN) blocks with doubling dilation factors. Each stage generates a prediction that is refined in a subsequent stage. A feature fusion block is inserted at the input of later stages to re-inject original information. The resulting multi-stage speech enhancement system, multi-stage SA-TCN, is compared with state-of-the-art deep-learning speech enhancement methods using the LibriSpeech and VCTK datasets. The multi-stage SA-TCN system's hyperparameters are fine-tuned, and the impact of the SA block, the feature fusion block, and the number of stages are determined. The use of a multi-stage SA-TCN system as a front-end for automatic speech recognition systems is also investigated. It is shown that the multi-stage SA-TCN systems perform well relative to other state-of-the-art systems in terms of speech enhancement and speech recognition scores. |
4,213 | Pseudomonas aeruginosa in the Cystic Fibrosis Lung | Cystic fibrosis is a common genetically inherited, multisystem disorder caused by loss of function of the cystic fibrosis transmembrane conductance regulator (CFTR) protein, an apically situated anion channel. In the lung, lack of CFTR leads to airway surface dehydration, mucociliary clearance failure and an acidic pH in which innate defence molecules are rendered ineffective. Infection occurs early in life, with P. aeruginosa dominating by adolescence. The characteristic features of the CF airway highlighted above encourage persistence of infection, but P. aeruginosa also possess an array of mechanisms with which they attack host defences and render themselves protected from antimicrobials. Early eradication is usually successful, but this is usually transient. Chronic infection is manifest by biofilm formation which is resistant to treatment. Outcomes for people with CF have improved greatly in the last few decades, but particularly so with the recent advent of small molecule CFTR modulators. However, despite impressive efficacy on lung function and exacerbation frequency, most people with chronic infection remain with their pathogens. There is an active pipeline of new treatments including anti-biofilm and anti-quorum sensing molecules and non-drug approaches such as bacteriophage. Studies are reviewed and challenges for future drug development considered. |
4,214 | Whole-blood immunoassay for gamma H2AX as a radiation biodosimetry assay with minimal sample preparation | The current state of the art in high-throughput minimally invasive radiation biodosimetry involves the collection of samples in the field and analysis at a centralized facility. We have developed a simple biological immunoassay for radiation exposure that could extend this analysis out of the laboratory into the field. Such a forward placed assay would facilitate triage of a potentially exposed population. The phosphorylation and localization of the histone H2AX at double-stranded DNA breaks has already been proven to be an adequate surrogate assay for reporting DNA damage proportional to radiation dose. Here, we develop an assay for phosphorylated H2AX directed against minimally processed sample lysates. We conduct preliminary verification of H2AX phosphorylation using irradiated mouse embryo fibroblast cultures. Additional dosimetry is performed using human blood samples irradiated ex vivo. The assay reports H2AX phosphorylation in human blood samples in response to ionizing radiation over a range of 0-5 Gy in a linear fashion, without requiring filtering, enrichment, or purification of the blood sample. |
4,215 | Characterizing the Rate-Memory Tradeoff in Cache Networks Within a Factor of 2 | We consider a basic caching system, where a single server with a database of N files (e.g., movies) is connected to a set of K users through a shared bottleneck link. Each user has a local cache memory with a size of M files. The system operates in two phases: a placement phase, where each cache memory is populated up to its size from the database, and a following delivery phase, where each user requests a file from the database, and the server is responsible for delivering the requested contents. The objective is to design the two phases to minimize the load (peak or average) of the bottleneck link. We characterize the rate-memory tradeoff of the above caching system within a factor of 2.00884 for both the peak rate and the average rate (under uniform file popularity), improving the state of the arts that are within a factor of 4 and 4.7, respectively. Moreover, in a practically important case where the number of files (N) is large, we exactly characterize the tradeoff for systems with no more than five users and characterize the tradeoff within a factor of 2 otherwise. To establish these results, we develop two new converse bounds that improve over the state of the art. |
4,216 | A dynamic conceptualization of power for sustainability research | This paper takes up the challenge of providing a conceptual power framework to be used in the context of sustainability research. First, challenges of sustainability research are discussed by focusing specifically on recent insights from Integrated Sustainability Assessment (ISA), and on that basis some requirements for concepts to be used in sustainability research are postulated. It is argued that two of the most important aspects of sustainability assessment research are the long-term dynamics of change and an interdisciplinary paradigm. Second, a dynamic power framework is presented that was developed in the context of research on socio-technical sustainability transitions, including the basics of this power framework as well as some empirical illustrations. Third, it is discussed how the presented power framework deals with time, change and long-term dynamics, and how this contributes to the state-of-the-art. Fourth, it is indicated how the power framework integrates interdisciplinary and 'interparadigmaticatic' research requirements, and how this contributes to the state-of-the art. In conclusion, the arguments are summarized and some challenges for future research are distilled. (C) 2010 Elsevier Ltd. All rights reserved. |
4,217 | Region-based multimodal image fusion using ICA bases | In this paper, we present a novel multimodal image fusion algorithm in the independent component analysis (ICA) domain. Region-based fusion of ICA coefficients is implemented, where segmentation is performed in the spatial domain and ICA coefficients from separate regions are fused separately. The ICA coefficients from given regions are consequently weighted using the Piella fusion metric in order to maximize the quality of the fused image. The proposed method exhibits significantly higher performance than the basic ICA algorithm and also shows improvement over other state-of-the-art algorithms. |
4,218 | Fast Volume Reconstruction From Motion Corrupted Stacks of 2D Slices | Capturing an enclosing volume of moving subjects and organs using fast individual image slice acquisition has shown promise in dealing with motion artefacts. Motion between slice acquisitions results in spatial inconsistencies that can be resolved by slice-to-volume reconstruction (SVR) methods to provide high quality 3D image data. Existing algorithms are, however, typically very slow, specialised to specific applications and rely on approximations, which impedes their potential clinical use. In this paper, we present a fast multi-GPU accelerated framework for slice-to-volume reconstruction. It is based on optimised 2D/3D registration, super-resolution with automatic outlier rejection and an additional (optional) intensity bias correction. We introduce a novel and fully automatic procedure for selecting the image stack with least motion to serve as an initial registration target. We evaluate the proposed method using artificial motion corrupted phantom data as well as clinical data, including tracked freehand ultrasound of the liver and fetal Magnetic Resonance Imaging. We achieve speed-up factors greater than 30 compared to a single CPU system and greater than 10 compared to currently available state-of-the-art multi-core CPU methods. We ensure high reconstruction accuracy by exact computation of the point-spread function for every input data point, which has not previously been possible due to computational limitations. Our framework and its implementation is scalable for available computational infrastructures and tests show a speed-up factor of 1.70 for each additional GPU. This paves the way for the online application of image based reconstruction methods during clinical examinations. The source code for the proposed approach is publicly available. |
4,219 | Can the KG1 cell line be used as a model of dendritic cells and discriminate the sensitising potential of chemicals? | The KG1 myeloid leukaemia was used as source of dendritic cells (DC) to discriminate between respiratory and contact sensitising chemicals. A cocktail of cytokines was used to differentiate KG1 to dendritic like cells (termed dKG1) and the effects of nine chemicals (respiratory and contact sensitisers) and an irritant control on surface marker expression, 'antigen presenting' function and cytokine expression investigated. The stability of these chemicals when dissolved was characterised using MALDI ToF MS. A Hill plot model was used with the cellular viability data to quantify the lethal dose 50% (LD50) and a maximum sub toxic concentration of each chemical defined. Cytokine expression by the treated dKG1 was quantified using multiplex immunobead analysis. Whilst dKG1 cells were morphologically similar to DCs, expression of specific surface markers was not typical for DCs derived from healthy precursor cells. When the chemicals were applied at defined sub toxic doses no effects on dKG1 phenotype, function, or cytokine expression, attributable to the sensitisation properties were discriminated. However, dKG1 cells were much more sensitive to the toxic effects of these chemicals compared to the parent KG1 cells. Only 4 of the 9 chemicals tested were stable when dissolved indicating that the effect of sensitising chemicals on antigen presenting cells may be related to species other than the parent compound. |
4,220 | A Comparison of Correlation Filter-Based Trackers and Struck Trackers | In recent years, two types of trackers, namely correlation filter-based tracker (CF tracker) and structured output tracker (struck), have exhibited the state-of-the-art performance. However, there seems to be a lack of analytic work on their relations in the computer vision community. In this paper, we investigate two state-of-the-art CF trackers, i.e., spatial regularization discriminative correlation filter (SRDCF) and correlation filter with limited boundaries (CFLB), and struck, and reveal their relations. Specifically, after extending the CFLB to its multiple channel versions, we prove the relation between SRDCF and CFLB on the condition that the spatial regularization factor of SRDCF is replaced by the masking matrix of CFLB. We also prove the asymptotical approximate relation between SRDCF and struck on the conditions that the spatial regularization factor of SRDCF is replaced by an indicator function of object bounding box, the weights of SRDCF in its loss item are replaced by those of struck, the linear kernel is employed by struck, and the search region tends to infinity. The extensive experiments on public benchmarks OTB50 and OTB100 are conducted to verify our theoretical results. Moreover, we explain how detailed differences among SRDCF, CFLB, and Struck would give rise to slightly different performances on visual sequences. |
4,221 | Efficient Particle Scale Space for Robust Tracking | Both siamese network and correlation filter (CF) based trackers have recently achieved superior performance in tracking scenarios with various challenging factors. For the challenging scale variations, most of these state-of-the-art trackers usually employ multiple patches with different bounding boxes to estimate the target size. However, these patches are fixedly generated by the hand-crafted bounding boxes in spatial domains, which may be suboptimal to cope with scale changes due to the lack of temporal scale information. In this letter, we tackle the problem of efficient scale estimation by presenting a generic scheme that allows the adaptive generation of bounding boxes in temporal domains and improves the tracking accuracy. Specifically, we introduce the novel particle scale space by refining the conventional particle filter and extend this space to many siamese and CF trackers for robust tracking. Extensive experiments are performed on the OTB2013, OTB50, OTB100 and UAVDT datasets. The proposed variants maintain at least almost identical frame-rates with baseline trackers and perform favorably against them, as well as other state-of-the-art trackers. |
4,222 | Printed Motes for IoT Wireless Networks: State of the Art, Challenges, and Outlooks | Although wireless sensor networks (WSNs) have been an active field of research for many years, the modules incorporated by WSN nodes have been mainly manufactured utilizing conventional fabrication techniques that are mostly subtractive, requiring significant amounts of materials and increased chemical waste. The new era of the Internet of Things (IoT) will see the fabrication of numerous small form factor devices for wireless sensing for a plurality of applications, including security, health, and environmental monitoring. The large volume of these devices will require new directions in terms of manufacturing cost and energy efficiency, which will be achieved with redesigned, energy-aware modules. This paper presents the state of the art of printed passives, sensors, energy harvesting modules, actives, and communication front ends, and summarizes the challenges of implementing modules that feature low power consumptions without compromising the low fabrication cost. The plethora of the modules presented herein will facilitate the implementation of low cost, additively manufactured, energy-aware IoT nodes that can be fabricated in large volumes with green processes. |
4,223 | Conceptualizing the State of the Art of Corporate Social Responsibility (CSR) in Green Construction and Its Nexus to Sustainable Development | The study has been investigated on conceptualizing the state of the art of cooperate social responsibility (CSR) in green construction and its nexus to sustainable development. The research objective is to find out the significant relationship between CSR and green construction and further link green construction with sustainable development. The data has been collected from the 319 respondents working on different projects in the construction industry of China. The study is quantitative by nature. SEM analysis with the help of Smart-PLS has been applied to test the hypothesis relationship and mediations between components of CSR, green construction, and sustainable development. Results define that green procurement as a component of green construction strongly mediates between corporate social responsibility and sustainable development, and green design and CO2 emission moderately mediates between corporate social responsibility and sustainable development. This research would add values, benefits, and knowledge toward reducing environmental temperature with the help of green construction occupying the effects of CSR on it. It would be helpful to change the trends in the construction industry to make environmental health protective and to boost the sustainable development. |
4,224 | Concepts and implementation of Spatial Division Multiplexing for guaranteed throughput in Networks-on-Chip | To ensure low power consumption while maintaining flexibility and performance, future Systems-on-Chip (SoC) will combine several types of processor cores and data memory units of widely different sizes. To interconnect the IPs of these heterogeneous platforms, Networks- on- Chip ( NoC) have been proposed as an efficient and scalable alternative to shared buses. NoCs can provide throughput and latency guarantees by establishing virtual circuits between source and destination. State- of- the- art NoCs currently exploit Time Division Multiplexing (TDM) to share network resources among virtual circuits, but this typically results in high network area and energy overhead with long circuit setup time. We propose an alternative solution based on Spatial Division Multiplexing (SDM). This paper describes our design of an SDM- based network, discusses design alternatives for network implementation, and shows why SDM can be better adapted to NoCs than TDM in a specific context. Our case study clearly illustrates the advantages of our technique over TDM in terms of energy consumption, area overhead, and flexibility. A comparison is also performed with a State- of- the- Art industrial reference NoC: Arteris. |
4,225 | Radio frequency ablation for the intrauterine treatment of giant placental chorioangioma associated with fetal compromise: A case report | Giant placental chorioangiomas associated with fetal hyperdynamic circulation complications are rare to see. Here, we summarized a case of giant placental chorioangioma associated with fetal anemia and heart failure treated by radiofrequency ablation (RFA) combined with cordocentesis and intrauterine transfusion. The sonographic appearance of the placental chorioangioma was atypical which was isoechoic with unclear boundary. RFA was performed successfully at 27 weeks of gestation, when the chorioangioma has increased to 17.0 × 10.6 × 12.3 cm3 . Unfortunately, intrauterine fetal demise was found on the first day after operation. After induction of labor, it was pathologically confirmed as placental chorioangioma. |
4,226 | SLIC Superpixels Compared to State-of-the-Art Superpixel Methods | Computer vision applications have come to rely increasingly on superpixels in recent years, but it is not always clear what constitutes a good superpixel algorithm. In an effort to understand the benefits and drawbacks of existing methods, we empirically compare five state-of-the-art superpixel algorithms for their ability to adhere to image boundaries, speed, memory efficiency, and their impact on segmentation performance. We then introduce a new superpixel algorithm, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels. Despite its simplicity, SLIC adheres to boundaries as well as or better than previous methods. At the same time, it is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation. |
4,227 | Language Accommodations for Limited English Proficient Patients in Rural Health Care | Over 25 million individuals living in America are limited English proficient, many of whom live in rural communities. Adequate language accommodations are critical to providing effective healthcare for these populations. An online questionnaire was delivered to 42 rural facilities in Washington State. It included questions about their demand for language services, modalities of interpretation, translated documentation and barriers to providing accommodations. Fifteen of 42 (35.7%) responded. Spanish, Russian, Vietnamese, Japanese, Ukrainian and Mam were encountered daily. Telephonic and virtual remote interpreter services were the most widely available. Not all facilities had vital documents translated to frequently encountered languages. Challenges to providing language access were reported by nearly all participants. The rural facilities surveyed all encountered LEP patient populations and offered oral interpretation. Overall, these facilities were meeting requirements for providing language accommodation services. Even so, many facilities reported experiencing barriers to providing these services. |
4,228 | An Efficient Semi-Supervised Multi-label Classifier Capable of Handling Missing Labels | Multi-label classification has received considerable interest in recent years. Multi-label classifiers usually need to address many issues including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments in the training set, considering correlations between labels, as well as exploiting unlabeled data to improve prediction performance. To tackle datasets with a large set of labels, embedding-based methods represent the label assignments in a low-dimensional space. Many state-of-the-art embedding-based methods use a linear dimensionality reduction to map the label assignments to a low-dimensional space. However, by doing so, these methods actually neglect the tail labels -labels that are infrequently assigned to instances. In this paper, we propose an embedding-based method that non-linearly embeds the label vectors using a stochastic approach, thereby predicting the tail labels more accurately. Moreover, the proposed method has excellent mechanisms for handling missing labels, dealing with large-scale datasets, as well as exploiting unlabeled data. Experiments on real-world datasets show that our method outperforms state-of-the-art multi-label classifiers by a large margin, in terms of prediction performance, as well as training time. Our implementation of the proposed method is available online at: https://github.com/Akbarnejad/ESMC_ Implementation. |
4,229 | Feeder cell-dependent primary culture of single blastula-derived embryonic cell lines from marine medaka (Oryzias dancena) | Fish embryonic stem cells (ESCs) are derived from blastomeres that have been cultured from blastula embryos. The most widely used method for derivation of fish ESCs is the culture of blastomeres that have been isolated from approximately 10 blastula embryos under feeder-free conditions. However, this method leads to intercellular genetic heterogeneity among the cultured cells, which is a major obstacle to the development of stable ESC culture conditions. In this study, to establish ESC lines with intercellular genetic homogeneity at the early stage of culture, we attempted to derive embryonic cell lines from single blastula-derived blastomeres of marine medaka (Oryzias dancena) in a feeder cell culture system. Using basic fibroblast growth factor-expressing feeder cells during primary culture, we successfully established 22 single blastula-derived embryonic cell lines that could be subcultured more than 20 times. In contrast, we were unable to efficiently derive cell lines using wild-type feeder cells and under feeder-free conditions. The established cell lines exhibited ESC-like cell characteristics in terms of alkaline phosphatase activity, pluripotency-related gene expression, and embryoid body formation. The results of this study will contribute to the development of methods for derivation of fish ESCs. |
4,230 | Modelling fingerprint ridge orientation using Legendre polynomials | The estimation of fingerprint ridge orientation is an essential step in every automatic fingerprint verification system. The importance of ridge orientation can be deflected from the fact that it is inevitably used for detecting, describing and matching fingerprint features such as minutiae and singular points. In this paper we propose a novel method for fingerprint ridge orientation modelling using Legendre polynomials. One of the main problems it addresses is smoothing orientation data while preserving details in high curvature areas, especially singular points. We show that singular points, which result in a discontinuous orientation field, can be modelled by the zero-poles of Legendre polynomials. The models parameters are obtained in a two staged optimization procedure. Another advantage of the proposed method is a very compact representation of the orientation field, using only 56 coefficients. We have carried out extensive experiments using a state-of-the-art fingerprint matcher and a singular point detector. Moreover, we compared the proposed method with other state-of-the-art fingerprint orientation estimation algorithms. We can report significant improvements in both singular point detection and matching rates. (C) 2009 Elsevier Ltd. All rights reserved. |
4,231 | Two-Level Approach for No-Reference Consumer Video Quality Assessment | Smartphones and other consumer devices capable of capturing video content and sharing it on social media in nearly real time are widely available at a reasonable cost. Thus, there is a growing need for no-reference video quality assessment (NR-VQA) of consumer produced video content, typically characterized by capture impairments that are qualitatively different from those observed in professionally produced video content. To date, most of the NR-VQA models in prior art have been developed for assessing coding and transmission distortions, rather than capture impairments. In addition, the most accurate NR-VQA methods known in prior art are often computationally complex, and therefore impractical for many real life applications. In this paper, we propose a new approach for learning-based video quality assessment, based on the idea of computing features in two levels so that low complexity features are computed for the full sequence first, and then high complexity features are extracted from a subset of representative video frames, selected by using the low complexity features. We have compared the proposed method against several relevant benchmark methods using three recently published annotated public video quality databases, and our results show that the proposed method can predict subjective video quality more accurately than the benchmark methods. The best performing prior method achieves nearly similar accuracy, but at substantially higher computational cost. |
4,232 | The Importance of Corporate Reputation for Sustainable Supply Chains: A Systematic Literature Review, Bibliometric Mapping, and Research Agenda | Corporate Reputation (CR) is essential to value generation and is co-created between a company and its stakeholders, including supply chain actors. Consequently, CR is a critical and valuable resource that should be managed carefully along supply chains. However, the current CR literature is fragmented, and a general definition of CR is elusive. Besides, the academic CR debate largely lacks a supply chain perspective. This is not surprising, as it is very difficult to collect reliable data along supply chains. When supply chains span the globe, data collection is especially challenging, as the chain consists of multiple suppliers and subcontractors, positioned at different tier levels. Recognizing this, the paper examines firstly the current state of CR research through a systematic literature review from a business perspective. The review is combined with a bibliometric mapping approach to show the most influential research clusters, representative of CR research streams and their contributors. This process highlights that the connection between CR and supply chain issues represents a major research gap. Consequently, this paper introduces a research agenda connecting these the two traditionally separated research fields. |
4,233 | Exergy analysis of underground coal gasification with simultaneous storage of carbon dioxide | Various options are considered to reduce CO2 emissions when utilizing deep coal by applying underground coal gasification (UCG), i.e., in combination with carbonation of synthetic minerals (CaO), conventional UCG followed by ex-situ separation of CO2 and upgrading the product gas using naturally occurring minerals (wollastonite). A chemical equilibrium model was used to analyze the effect of process parameters on product composition and use it for an exergy analysis. The result is presented in terms of theoretical (ideal unit operations), practical (state of the art technology), and zero-emission (applying current CO2 capture and sequestration technology (CCS) to all sources of CO2 emission) recovery factors. The results show that underground gasification of deep coal can optimally extract 52-68% of the coal chemical exergy, but zero-emission extraction gives a negative recovery indicating that it is not practical with current state of the art CCS technology. Using in-situ CaO, which will enhance the H-2 production, is theoretically feasible with a recovery factor around 80%, but is not exergetically feasible with the current state of technology. Ex-situ upgrading of the conventional UCG product gas with wollastonite is exergetically feasible for both practical and zero-emission cases according to the equilibrium model. (C) 2012 Elsevier Ltd. All rights reserved. |
4,234 | Spontaneous retroperitoneal haemorrhage from pancreatoduodenal artery (PDA) rupture and associated complications | Spontaneous retroperitoneal haemorrhage (SRH) is rare. It may present with abdominal or back pain with or without haemodynamic instability. Aggressive resuscitation while investigating the cause of bleeding and providing haemostasis are the standard of care. Subsequent close monitoring is necessary to identify early complications.This study reports three patients who presented to our institution within the last 5 years with SRH from a ruptured pancreaticoduodenal artery (PDA) aneurysm. Each patient had a unique presentation, complications and treatment demonstrating the variability and complexity of SRH. One patient presented with sudden abdominal pain and hypovolaemic shock, underwent angioembolisation and had an eventful recovery. Another patient presented similarly and was treated via angioembolisation but experienced gastric outlet obstruction and obstructive jaundice requiring surgical haematoma evacuation. Another patient had an incidental finding of haemoperitoneum during laparoscopic cholecystectomy that was subsequently diagnosed as SRH resulting from a PDA aneurysm rupture secondary to medial arcuate ligament syndrome. |
4,235 | Alterations in gut microbiota are related to metabolite profiles in spinal cord injury | Studies have shown that gut microbiota metabolites can enter the central nervous system via the blood-spinal cord barrier and cause neuroinflammation, thus constituting secondary injury after spinal cord injury. To investigate the correlation between gut microbiota and metabolites and the possible mechanism underlying the effects of gut microbiota on secondary injury after spinal cord injury, in this study, we established mouse models of T8-T10 traumatic spinal cord injury. We used 16S rRNA gene amplicon sequencing and metabolomics to reveal the changes in gut microbiota and metabolites in fecal samples from the mouse model. Results showed a severe gut microbiota disturbance after spinal cord injury, which included marked increases in pro-inflammatory bacteria, such as Shigella, Bacteroides, Rikenella, Staphylococcus, and Mucispirillum and decreases in anti-inflammatory bacteria, such as Lactobacillus, Allobaculum, and Sutterella. Meanwhile, we identified 27 metabolites that decreased and 320 metabolites that increased in the injured spinal cord. Combined with pathway enrichment analysis, five markedly differential amino acids (L-leucine, L-methionine, L-phenylalanine, L-isoleucine and L-valine) were screened out, which play a pivotal role in activating oxidative stress and inflammatory responses following spinal cord injury. Integrated correlation analysis indicated that the alteration of gut microbiota was related to the differences in amino acids, which suggests that disturbances in gut microbiota might participate in the secondary injury through the accumulation of partial metabolites that activate oxidative stress and inflammatory responses. Findings from this study provide a new theoretical basis for improving the secondary injury after spinal cord injury through fecal microbial transplantation. |
4,236 | A modified equilibrium optimizer using opposition-based learning and novel update rules | Equilibrium Optimizer (EO) is a newly developed physics-based metaheuristic algorithm that is based on control volume mass balance models, and has shown competitive performance with other state-of-the-art algorithms. However, the original EO has the disadvantages of a low exploitation ability, ease of falling into local optima, and an immature balance between exploration and exploitation. To address these shortcomings, this paper proposes a modified EO (m-EO) using opposition-based learning (OBL) and novel update rules that incorporates four main modifications: the definition of the concentrations of some particles based on OBL, a new nonlinear time control strategy, novel population update rules and a chaos-based strategy. Based on these modifications, the optimization precision and convergence speed of the original EO are greatly improved. The validity of m-EO is tested on 35 classical benchmark functions, 25 of which have variants belonging to multiple difficulty categories (Dim = 30, 100, 300, 500 and 1000). In addition, m-EO is used to solve three real-world engineering design problems. The experimental results and two different statistical tests demonstrate that the proposed m-EO shows higher performance than original EO and other state-of-the-art algorithms. |
4,237 | Haze Removal Using Radial Basis Function Networks for Visibility Restoration Applications | Restoration of visibility in hazy images is the first relevant step of information analysis in many outdoor computer vision applications. To this aim, the restored image must feature clear visibility with sufficient brightness and visible edges, while avoiding the production of noticeable artifacts. In this paper, we propose a haze removal approach based on the radial basis function (RBF) through artificial neural networks dedicated to effectively removing haze formation while retaining not only the visible edges but also the brightness of restored images. Unlike traditional haze-removal methods that consist of single atmospheric veils, the multiatmospheric veil is generated and then dynamically learned by the neurons of the proposed RBF networks according to the scene complexity. Through this process, more visible edges are retained in the restored images. Subsequently, the activation function during the testing process is employed to represent the brightness of the restored image. We compare the proposed method with the other state-of-the-art haze-removal methods and report experimental results in terms of qualitative and quantitative evaluations for benchmark color images captured in typical hazy weather conditions. The experimental results demonstrate that the proposed method is able to produce brighter and more vivid haze-free images with more visible edges than can the other state-of-the-art methods. |
4,238 | Silibinin Radiosensitizes EGF Receptor-knockdown Prostate Cancer Cells by Attenuating DNA Repair Pathways | Emergence of radioresistance in prostate cancer (PCa) cells is a major obstacle in cancer therapy and contributes to the relapse of the disease. EGF receptor (EGFR) signaling plays an important role in the development of radioresistance. Herein, we have assessed the modulatory effects of silibinin on radiation-induced resistance via DNA repair pathways in EGFR-knockdown DU145 cells. shRNA-based silencing of EGFR was done in radioresistant human PCa DU145 cells and effects of ionizing radiation (IR) and silibinin were assessed using clonogenic and trypan blue assays. Furthermore, radiosensitizing effects of silibinin on PCa in context with EGFR were analyzed using flow cytometry, comet assay, and immunoblotting. Silibinin decreased the colony formation ability with an increased death of DU145 cells exposed to IR (5 Gray), with a concomitant decrease in Rad51 protein expression. Silibinin (25 μM) augmented the IR-induced cytotoxic effect in EGFR-knockdown PCa cells, along with induction of G2/M phase cell cycle arrest. Further, we studied homologous recombination (HR) and non-homologous end joining (NHEJ) pathways in silibinin-induced DNA double-strand breaks in EGFR-knockdown DU145 cells. Silibinin down-regulated the expression of Rad51 and DNA-dependent protein kinase proteins without any considerable effect on Ku70 and Ku80 in IR-exposed EGFR-knockdown PCa cells. The pro-survival signaling proteins, phospho-extracellular signal-regulated kinases (ERK)1/2, phospho-Akt and phospho-STAT3 were decreased by silibinin in EGFR-deficient PCa cells. These findings suggest a novel mechanism of silibinin-induced radiosensitization of PCa cells by targeting DNA repair pathways, HR and NHEJ, and suppressing the pro-survival signaling pathways, ERK1/2, Akt and STAT3, in EGFR-knockdown PCa cells. |
4,239 | Harmonization of Multi-Center Diffusion Tensor Tractography in Neonates with Congenital Heart Disease: Optimizing Post-Processing and Application of ComBat | Advanced brain imaging of neonatal macrostructure and microstructure, which has prognosticating importance, is more frequently being incorporated into multi-center trials of neonatal neuroprotection. Multicenter neuroimaging studies, designed to overcome small sample sized clinical cohorts, are essential but lead to increased technical variability. Few harmonization techniques have been developed for neonatal brain microstructural (diffusion tensor) analysis. The work presented here aims to remedy two common problems that exist with the current state of the art approaches: 1) variance in scanner and protocol in data collection can limit the researcher's ability to harmonize data acquired under different conditions or using different clinical populations. 2) The general lack of objective guidelines for dealing with anatomically abnormal anatomy and pathology. Often, subjects are excluded due to subjective criteria, or due to pathology that could be informative to the final analysis, leading to the loss of reproducibility and statistical power. This proves to be a barrier in the analysis of large multi-center studies and is a particularly salient problem given the relative scarcity of neonatal imaging data. We provide an objective, data-driven, and semi-automated neonatal processing pipeline designed to harmonize compartmentalized variant data acquired under different parameters. This is done by first implementing a search space reduction step of extracting the along-tract diffusivity values along each tract of interest, rather than performing whole-brain harmonization. This is followed by a data-driven outlier detection step, with the purpose of removing unwanted noise and outliers from the final harmonization. We then use an empirical Bayes harmonization algorithm performed at the along-tract level, with the output being a lower dimensional space but still spatially informative. After applying our pipeline to this large multi-site dataset of neonates and infants with congenital heart disease (n= 398 subjects recruited across 4 centers, with a total of n=763 MRI pre-operative/post-operative time points), we show that infants with single ventricle cardiac physiology demonstrate greater white matter microstructural alterations compared to infants with bi-ventricular heart disease, supporting what has previously been shown in literature. Our method is an open-source pipeline for delineating white matter tracts in subject space but provides the necessary modular components for performing atlas space analysis. As such, we validate and introduce Diffusion Imaging of Neonates by Group Organization (DINGO), a high-level, semi-automated framework that can facilitate harmonization of subject-space tractography generated from diffusion tensor imaging acquired across varying scanners, institutions, and clinical populations. Datasets acquired using varying protocols or cohorts are compartmentalized into subsets, where a cohort-specific template is generated, allowing for the propagation of the tractography mask set with higher spatial specificity. Taken together, this pipeline can reduce multi-scanner technical variability which can confound important biological variability in relation to neonatal brain microstructure. |
4,240 | INFLUENCE OF ENVIRONMENTAL ISSUES INTO VISUAL ARTS CURRICULUM ON STUDENTS' ENVIRONMENTAL BEHAVIOUR | In recent years, when we are pursuing industrial development and economic growth, we do not know how to cherish our natural resources, and we are willing to destroy our nature, which leads to water pollution, soil loss, and extinction of animals and plants. We are facing the consequences of environmental pollution, and these natural counter-attacks remind us to pay attention to the crisis facing the environment. The most fundamental and efficient way is to start with education. Only by promoting environmental education we can change the way humans treat nature. In this study, the experimental design mode is used for experimental research. This study mainly focuses on Jiang Su Province Chang Zhou City College students as empirical objects, and a total of 222 students are experimental research objects, and experimental teaching research is conducted for 15 weeks, three hours a week (45 hours in total). In conclusion, it is expected that by integrating environmental issues into the visual arts curriculum, the school will implement relevant teaching activities in stages, to achieve the goal of environmental education and enable students to pay attention to, learn and participate in environmental issues for life. |
4,241 | Influenza subtype-specific maternal antibodies protect offspring against infection but inhibit vaccine-induced immunity and protection in mice | Following influenza A virus (IAV) infection or vaccination during pregnancy, maternal antibodies are transferred to offspring in utero and during lactation. The age and sex of offspring may differentially impact the transfer and effects of maternal immunity on offspring. To evaluate the effects of maternal IAV infection on immunity in offspring, we intranasally inoculated pregnant mice with sublethal doses of mouse-adapted (ma) H1N1, maH3N2, or media (mock) at embryonic day 10. In offspring of IAV-infected dams, maternal subtype-specific antibodies peaked at postnatal day (PND) 23, remained detectable through PND 50, and were undetectable by PND 105 in both sexes. When offspring were challenged with homologous IAV at PND 23, both male and female offspring had greater clearance of pulmonary virus and less morbidity and mortality than offspring from mock-inoculated dams. Inactivated influenza vaccination (IIV) against homologous IAV at PND 23 caused lower vaccine-induced antibody responses and protection following live virus challenge in offspring from IAV than mock-infected dams, with this effect being more pronounced among female than male offspring. At PND 105, there was no impact of maternal infection status, but vaccination induced greater antibody responses and protection against challenge in female than male offspring of both IAV-infected and mock-inoculated dams. To determine if maternal antibody or infection interfered with vaccine-induced immunity and protection in early life, offspring were vaccinated and challenged against a heterosubtypic IAV (i.e., different IAV group than dam) at PND 23 or 105. Heterosubtypic IAV maternal immunity did not affect antibody responses after IIV or protection after live IAV challenge of vaccinated offspring at either age. Subtype-specific maternal IAV antibodies, therefore, provide protection independent of offspring sex but interfere with vaccine-induced immunity and protection in offspring with more pronounced effects among females than males. |
4,242 | Choosing the right word: Using bidirectional LSTM tagger for writing support systems | Scientific writing is difficult. It is even harder for those for whom English is a second language (ESL learners). Scholars around the world spend a significant amount of time and resources proofreading their work before submitting it for review or publication. In this paper we present a novel machine learning based application for proper word choice task. Proper word choice is a generalization the lexical substitution (LS) and grammatical error correction (GEC) tasks. We demonstrate and evaluate the usefulness of applying bidirectional Long Short Term Memory (LSTM) tagger, for this task. While state-of-the-art grammatical error correction uses error-specific classifiers and machine translation methods, we demonstrate an unsupervised method that is based solely on a high quality text corpus and does not require manually annotated data. We use a bidirectional Recurrent Neural Network (RNN) with LSTM for learning the proper word choice based on a word's sentential context. We demonstrate and evaluate our application in various settings, including both a domain-specific (scientific), writing task and a general-purpose writing task. We perform both strict machine and human evaluation. We show that our domain-specific and general-purpose models outperform state-of-the-art general context learning. As an additional contribution of this research, we also share our code, pre-trained models, and a new ESL learner test set with the research community. |
4,243 | A Fully Embedded Adaptive Real-Time Hand Gesture Classifier Leveraging HD-sEMG and Deep Learning | This paper presents a real-time fine gesture recognition system for multi-articulating hand prosthesis control, using an embedded convolutional neural network (CNN) to classify hand-muscle contractions sensed at the forearm. The sensor consists in a custom non-intrusive, compact, and easy-to-install 32-channel high-density surface electromyography (HDsEMG) electrode array, built on a flexible printed circuit board (PCB) to allow wrapping around the forearm. The sensor provides a low-noise digitization interface with wireless data transmission through an industrial, scientific and medical (ISM) radio link. An original frequency-time-space cross-domain preprocessing method is proposed to enhance gesture-specific data homogeneity and generate reliable muscle activation maps, leading to 98.15% accuracy when using a majority vote over 5 subsequent inferences by the proposed CNN. The obtained real-time gesture recognition, within 100 to 200 ms, and CNN properties show reliable and promising results to improve on the state-of-the-art of commercial hand prostheses. Moreover, edge computing using a specialized embedded artificial intelligence (AI) platform ensures reliable, secure and low latency real-time operation as well as quick and easy access to training, fine-tuning and calibration of the neural network. Co-design of the signal processing, AI algorithms and sensing hardware ensures a reliable and power-efficient embedded gesture recognition system. |
4,244 | Cascade primer exchange reaction-based amplification strategy for sensitive and portable detection of amyloid β oligomer using personal glucose meters | Amyloid β oligomer (AβO) is an important biomarker for Almerzheimer's disease (AD) early diagnosis. In present study, cascade primer exchange reaction (PER) based amplification strategy was proposed for sensitive and portable detection of AβO using personal glucose meters (PGM). Two PER processes were employed here. In the primary PER, the hairpin template 1 (HT1) was designed with a primer binding domain, a primer extending domain and a blocking extending domain. The primers were designed to be modified on magbeads surface. Initially, the primer binding domain in HT1 was locked by AβO aptamer. When target AβO was present, aptamer bound with AβO and dissociated from HT1 to initiate the primary PER. The products acted as the primer of the secondary PER to hybridize with another hairpin template 2 (HT2), initiating the secondary PER and producing numerous ssDNA with repeated DNA-invertase binding sites. After binding with DNA-invertase, the obtained conjugates were magnetically separation to catalyze the conversion of sucrose to glucose, which were detected by a PGM. The strategy achieved a limit of detection of 0.22 pM with a linear ranged from 1 pM to 250 pM. Satisfactory reproducibility results were obtained in actual samples. This strategy provided a superior tool for sensitive and convenient detection of AβO, and showing a great potential in the early diagnosis of AD. |
4,245 | A Deep Reinforcement Learning Method for Pricing Electric Vehicles With Discrete Charging Levels | The effective pricing of electric vehicle (EV) charging by aggregators constitutes a key problem toward the realization of the significant EV flexibility potential in deregulated electricity systems and has been addressed by previous work through bi-level optimization formulations. However, the solution approach adopted in previous work cannot capture the discrete nature of the EV charging/discharging levels. Although reinforcement learning (RL) can tackle this challenge, state-of-the-art RL methods require discretization of state and/or action spaces and thus exhibit limitations in terms of solution optimality and computational requirements. This article proposes a novel deep reinforcement learning (DRL) method to solve the examined EV pricing problem, combining deep deterministic policy gradient (DDPG) principles with a prioritized experience replay (PER) strategy and setting up the problem in multi-dimensional continuous state and action spaces. Case studies demonstrate that the proposed method outperforms state-of-the-art RL methods in terms of both solution optimality and computational requirements and comprehensively analyze the economic impacts of smart-charging and vehicle-to-grid (V2G) flexibility on both aggregators and EV owners. |
4,246 | Drive-independent data recovery: The current state-of-the-art | The term "data recovery" herein refers to accessing logically and/or physically damaged storage media, for which no functioning backup exists. The state-of-the-art physical techniques for recovering data from failed hardware can all be described as "part replacement." To achieve high data density and high manufacturing yields, modern drives are "hyper-tuned" in the factory so that their data layout, zone frequencies, and various channel settings are optimized for each head, surface, and zone. This greatly complicates part re. placement because a transplanted headstack, for example, no longer matches the servo, preamp, and read channel parameters that were optimized for the original headstack. Methods and challenges are discussed for replacing, or "refreshing," firmware and system area information and for replacing all of the drive's electronics. The data recovery industry's point of view, limitations of current techniques, and some probable future directions in data recovery are also presented. It is predicted that data recovery will be more important in the future as drives are exposed to more extreme mobile environments. Drive manufacturers may be able to differentiate themselves from their competition by designing for recoverability. |
4,247 | Increasing energy efficiency of Massive-MIMO network via base stations switching using reinforcement learning and radio environment maps | Energy Efficiency (EE) is of high importance while considering Massive Multiple-Input Multiple-Output (M-MIMO) networks where base stations (BSs) are equipped with an antenna array composed of up to hundreds of elements. M-MIMO transmission, although highly spectrally efficient, results in high energy consumption growing with the number of antennas. This paper investigates EE improvement through switching on/off underutilized BSs. It is proposed to use the location-aware approach, where data about an optimal active BSs set is stored in a Radio Environment Map (REM). For efficient acquisition, processing and utilization of the REM data, reinforcement learning (RL) algorithms are used. State-of-the-art exploration/exploitation methods including epsilon-greedy, Upper Confidence Bound (UCB), and Gradient Bandit are evaluated. Then analytical action filtering, and an REM-based Exploration Algorithm (REM-EA) are proposed to improve the RL convergence time. Algorithms are evaluated using an advanced, system-level simulator of an M-MIMO Heterogeneous Network (HetNet) utilizing an accurate 3D-ray-tracing radio channel model. The proposed RL-based BSs switching algorithm is proven to provide 70% gains in EE over a state-of-the-art algorithm using an analytical heuristic. Moreover, the proposed action filtering and REM-EA can reduce RL convergence time in relation to the best-performing state-of-the-art exploration method by 60% and 83%, respectively. |
4,248 | Inhibition of NADPH Oxidase-ROS Signal using Hyaluronic Acid Nanoparticles for Overcoming Radioresistance in Cancer Therapy | Upregulation of NADPH oxidases (NOXs) in cancer cells leads to chronic increase in intracellular reactive oxygen species (ROS) and adaptation to a high ROS level for cell survival and, thereby, low sensitivity to radiotherapy. To overcome resistance to radiotherapy, we have developed a bioactive and CD44 targeted hyaluronic acid nanoparticle encapsulated with an NOX inhibitor, GKT831 (HANP/GKT831). We found that HANP/GKT831 had stronger inhibitory effects on ROS generation and cell proliferation than that of GKT831 alone in cancer cells. Systemic delivery of HANP/GKT831 led to the targeted accumulation in breast cancer patient derived xenograft (PDX) tumors in nude mice. Importantly, the combination of systemic delivery of HANP/GKT831 with a low dose of local radiotherapy significantly enhanced tumor growth inhibition in breast cancer PDX models. Our results showed that HANP/GKT831 primed tumor cells to radiation-induced DNA damage and cell death by downregulation of DNA repair function and oncogenic signal pathways. |
4,249 | Fused lasso for feature selection using structural information | Most state-of-the-art feature selection methods tend to overlook the structural relationship between a pair of samples associated with each feature dimension, which may encapsulate useful information for refining the performance of feature selection. Moreover, they usually consider candidate feature relevancy equivalent to selected feature relevancy, and therefore, some less relevant features may be misinterpreted as salient features. To overcome these issues, we propose a new feature selection method based on graph-based feature representations and the Fused Lasso framework in this paper. Unlike stateof-the-art feature selection approaches, our method has two main advantages. First, it can accommodate structural relationship between a pair of samples through a graph-based feature representation. Second, our method can enhance the trade-off between the relevancy of each individual feature on the one hand and its redundancy between pairwise features on the other. This is achieved through the use of a Fused Lasso framework applied to features reordered on the basis of their relevance with respect to the target feature. To effectively solve the optimization problem, an iterative algorithm is developed to identify the most discriminative features. Experiments demonstrate that our proposed approach can outperform its competitors on benchmark datasets. (c) 2021 Elsevier Ltd. All rights reserved. |
4,250 | A Spatially Separable Attention Mechanism for Massive MIMO CSI Feedback | Channel State Information (CSI) Feedback plays a crucial role in achieving higher gains through beamforming. However, for a massive MIMO system, this feedback overhead is huge and grows linearly with the number of antennas. To reduce the feedback overhead several compressive sensing (CS) techniques were implemented in recent years but these techniques are often iterative and are computationally complex to realize in power-constrained user equipment (UE). Hence, a data-based deep learning approach took over in these recent years introducing a variety of neural networks for CSI compression. Specifically, transformer-based networks have been shown to achieve state-of-the-art performance. However, the multi-head attention operation, which is at the core of transformers, is computationally complex making transformers difficult to implement on a UE. In this letter, we present a lightweight transformer named STNet which uses a spatially separable attention mechanism that is significantly less complex than the traditional full-attention. Equipped with this, STNet outperformed state-of-the-art models in some scenarios with approximately 1/10(th )of the resources. |
4,251 | A New Necessary Condition for Threshold Function Identification | This article proposes a new necessary condition and the corresponding speedup strategies to the threshold function (TF) identification problem. The state-of-the-art to this identification problem could be very time-consuming when the function-under-identification is a non-TF with the unateness property. The proposed new necessary condition can be seamlessly integrated into this identification algorithm. As compared with the state-of-the-art, the improved identification algorithm with the proposed necessary condition can more effectively and efficiently detect non-TFs. Furthermore, according to the experimental results, the ratio of CPU time overhead in the process of checking the proposed necessary condition for identifying all the 8-input TF is only 0.1%. |
4,252 | Self-Supervised Rigid Registration for Multimodal Retinal Images | The ability to accurately overlay one modality retinal image to another is critical in ophthalmology. Our previous framework achieved the state-of-the-art results for multimodal retinal image registration. However, it requires human-annotated labels due to the supervised approach of the previous work. In this paper, we propose a self-supervised multimodal retina registration method to alleviate the burdens of time and expense to prepare for training data, that is, aiming to automatically register multimodal retinal images without any human annotations. Specially, we focus on registering color fundus images with infrared reflectance and fluorescein angiography images, and compare registration results with several conventional and supervised and unsupervised deep learning methods. From the experimental results, the proposed self-supervised framework achieves a comparable accuracy comparing to the state-of-the-art supervised learning method in terms of registration accuracy and Dice coefficient. |
4,253 | Learning Soft Mask Based Feature Fusion with Channel and Spatial Attention for Robust Visual Object Tracking | We propose to improve the visual object tracking by introducing a soft mask based low-level feature fusion technique. The proposed technique is further strengthened by integrating channel and spatial attention mechanisms. The proposed approach is integrated within a Siamese framework to demonstrate its effectiveness for visual object tracking. The proposed soft mask is used to give more importance to the target regions as compared to the other regions to enable effective target feature representation and to increase discriminative power. The low-level feature fusion improves the tracker robustness against distractors. The channel attention is used to identify more discriminative channels for better target representation. The spatial attention complements the soft mask based approach to better localize the target objects in challenging tracking scenarios. We evaluated our proposed approach over five publicly available benchmark datasets and performed extensive comparisons with 39 state-of-the-art tracking algorithms. The proposed tracker demonstrates excellent performance compared to the existing state-of-the-art trackers. |
4,254 | BHIVA position statement on HIV, the law and the work of the clinical team | The purpose of this statement is to outline issues at the interface between HIV transmission and the law and provide guidance to healthcare professionals (HCPs) working in the field of HIV medicine. The guidance is to support work in the UK, and it is important to note that the law in England and Wales differs from that in Scotland and Northern Ireland. Approaches are suggested to deal with these issues consistently, within legal and General Medical Council (GMC) regulatory frameworks and in the context of the public health agenda. The guidance specifically addresses sexual transmission. |
4,255 | Detection of Prostate Cancer in Whole-Slide Images Through End-to-End Training With Image-Level Labels | Prostate cancer is the most prevalent cancer among men in Western countries, with 1.1 million new diagnoses every year. The gold standard for the diagnosis of prostate cancer is a pathologists' evaluation of prostate tissue. To potentially assist pathologists deep/learning/based cancer detection systems have been developed. Many of the state-of-the- art models are patch/based convolutional neural networks, as the use of entire scanned slides is hampered by memory limitations on accelerator cards. Patch-based systems typically require detailed, pixel-level annotations for effective training. However, such annotations are seldom readily available, in contrast to the clinical reports of pathologists, which contain slide-level labels. As such, developing algorithms which do not require manual pixel-wise annotations, but can learn using only the clinical report would be a significant advancement for the field. In this paper, we propose to use a streaming implementation of convolutional layers, to train a modern CNN (ResNet/34) with 21 million parameters end-to-end on 4712 prostate biopsies. Themethod enables the use of entire biopsy images at high-resolution directly by reducing the GPUmemory requirements by 2.4 TB. We show thatmodern CNNs, trained using our streaming approach, can extract meaningful features from high-resolution images without additional heuristics, reaching similar performance as state-of-the-art patch-based and multiple-instance learning methods. By circumventing the need for manual annotations, this approach can function as a blueprint for other tasks in histopathological diagnosis. The source code to reproduce the streaming models is available at https://github.com/DIAGNijmegen/ pathology-streaming-pipeline. |
4,256 | Face age classification based on a deep hybrid model | Face age estimation, a computer vision task facing numerous challenges due to its potential applications in identity authentication, human-computer interface, video retrieval and robot vision, has been attracting increasing attention. In recent years, the deep convolutional neural networks (DCNN) have achieved state-of-the-art performance in age classification of face images. We propose a deep hybrid framework for age classification by exploiting DCNN as the raw feature extractor along with several effective methods, including fine-tuning the DCNN into a fine-tuned deep age feature extraction (FDAFE) model, introducing a new method of feature extracting, applying the maximum joint probability classifier to age classification and a strategy to incorporate information from face images more effectively to improve estimation capabilities further. In addition, we pre-process the original image to represent age information more accurately. Based on the discriminative and compact framework, state-of-the-art performance on several face image data sets has been achieved in terms of classification accuracy. |
4,257 | The Risk of Fatal Arrhythmias in Post-Myocardial Infarction Depression in Association With Venlafaxine | Venlafaxine is a second line anti-depressant and the most commonly used in the treatment of selective serotonin reuptake inhibitor nonresponders in major depression; due to its effects on the noradrenergic and serotonergic systems as a serotonin and norepinephrine reuptake inhibitor, there has been considerable apprehension regarding its use in patients with cardiovascular diseases, particularly post-myocardial infarction depression, some of the feared adverse effects include QT prolongation, arrhythmias including torsades de pointes and sudden cardiac death. We tried to resolve the facts regarding the risks associated with venlafaxine use in cardiac patients. We have reviewed all the relevant information up to May 2022 regarding the risks of venlafaxine use in cardiovascular disease, particularly with a focus on post-myocardial infarction depression, and gathered around 350 articles in our research and narrowed it down to 49 articles. The database used was PubMed and the keywords used were venlafaxine, arrhythmia, major depression, post-myocardial infarction, and ventricular tachycardia. We carefully screened all relevant articles and found articles supporting and refuting the effects of venlafaxine in increasing cardiovascular morbidity and mortality. We have concluded that there is a significant variability due to confounding factors affecting individual cases. Overall there is no increased arrhythmia risk in comparison with other anti-depressants except in high-risk cases such as with pre-existing cardiovascular disease, certain genotypes, and other co-morbidities. Any patient with a high risk of arrhythmias due to any etiology should receive a screening electrocardiogram before venlafaxine prescription for baseline QT interval and periodically while on therapy to check for changes. We encourage further research, including randomized clinical trials and post-marketing surveillance regarding the use of venlafaxine in high-risk cases such as patients with multiple co-morbidities, elderly patients, or patients with certain genotypes. |
4,258 | The Mediating Role of Eco-Friendly Artwork for Urban Hotels to Attract Environmental Educated Consumers | The adoption and implementation of environmental marketing strategies is the hotel industry's new approach to maintaining a competitive advantage and attracting more green consumers. Indeed, hotels with more sustainable practices and eco-friendly artwork generate more trust and make green consumers more loyal and satisfied. However, there is little prior research which has suggested the mediating role of green artwork between customers' levels of green perception and their hotel satisfaction. For this reason, the current authors obtained a total of 659 responses from South Korean consumers and conducted the structural equation analysis (SEM) to identify the indirect effect explaining how green arts boosts green hotel consumers' satisfaction. Our statistical findings offer vital insights regarding the relationship between customers' green perceptions and their hotel satisfaction, with eco-friendly artwork in the hotel interior design as the mediating variable. Finally, the current study provides a detailed understanding of art infusion to urban hotels by highlighting the impact of art and its spillover effects on consumer satisfaction. |
4,259 | Depthwise Spatio-Temporal STFT Convolutiona Neural Networks for Human Action Recognition | Conventional 3D convolutional neural networks (CNNs) are computationally expensive, memory intensive, prone to overfitting, and most importantly, there is a need to improve their feature learning capabilities. To address these issues, we propose spatio-temporal short-term Fourier transform (STFT) blocks, a new class of convolutional blocks that can serve as an alternative to the 3D convolutional layer and its variants in 3D CNNs. An STFT block consists of non-trainable convolution layers that capture spatially and/or temporally local Fourier information using an STFT kernel at multiple low frequency points, followed by a set of trainable linear weights for learning channel correlations. The STFT blocks significantly reduce the space-time complexity in 3D CNNs. In general, they use 3.5 to 4.5 times less parameters and 1.5 to 1.8 times less computational costs when compared to the state-of-the-art methods. Furthermore, their feature learning capabilities are significantly better than the conventional 3D convolutional layer and its variants. Our extensive evaluation on seven action recognition datasets, including Something(2) v1 and v2, Jester, Diving-48, Kinetics-400, UCF 101, and HMDB 51, demonstrate that STFT blocks based 3D CNNs achieve on par or even better performance compared to the state-of-the-art methods. |
4,260 | Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval | In order to achieve efficient similarity searching, hash functions are designed to encode images into low-dimensional binary codes with the constraint that similar features will have a short distance in the projected Hamming space. Recently, deep learning-based methods have become more popular, and outperform traditional non-deep methods. However, without label information, most state-of-the-art unsupervised deep hashing (DH) algorithms suffer from severe performance degradation for unsupervised scenarios. One of the main reasons is that the ad-hoc encoding process cannot properly capture the visual feature distribution. In this paper, we propose a novel unsupervised framework that has two main contributions: 1) we convert the unsupervised DH model into supervised by discovering pseudo labels; 2) the framework unifies likelihood maximization, mutual information maximization, and quantization error minimization so that the pseudo labels can maximumly preserve the distribution of visual features. Extensive experiments on three popular data sets demonstrate the advantages of the proposed method, which leads to significant performance improvement over the state-of-the-art unsupervised hashing algorithms. |
4,261 | AFSense-ECG: Atrial Fibrillation Condition Sensing From Single Lead Electrocardiogram (ECG) Signals | In this paper, we propose AFSense-ECG, an intelligence-embedded single lead ECG sensor that is enabled with the ability of accurate detection of Atrial Fibrillation (AF) condition, which is the most common sustained cardiac arrhythmia and increased risk of stroke is higher with sub-clinical AF patients. AFSense-ECG acts like an early-warning sensor for AF condition detection. A processing unit (e.g., ESP32WROVERE microcontroller) integrated with off-the-shelf single lead ECG sensor like Alivecor or AD8232 embeds intelligence to the sensing system to augment for inferential sensing for empowering automated decision-making. AFSense-ECG captures the quasi-periodic nature of typical ECG signals with repetitive P-wave, QRS complex and T-wave patterns into its feature extraction and the representation learning process of model construction and learning rate optimization. Our empirical study validates the superiority of proposed ECG signal characteristics-based hyperparameter tuned ECG classification model construction. AFSense-ECG demonstrates F1-measure of 86.13%, where the current state-of-the-art methods report F1-measures of 83.70%, 83.10%, 82.90%, 82.60%, 82.50%, 81.00% over publicly available single lead ECG datasets of Physionet 2017 Challenge. Further, the proposed learning model for the inferential sensing is lean (approximately 25 times simpler in terms of total number of trainable parameters with reduced model size than relevant state-of-the-art model, where the state-of-the-art method with 83.70% F1-measure consists of 10474607 trainable parameters, and our proposed model consists of 433675 trainable parameters) and more effective (better F1-measure than the state-of-the-art methods), which enables us to construct affordable intelligent sensing system. |
4,262 | An Integrated Multi-Channel Biopotential Recording Analog Front-End IC With Area-Efficient Driven-Right-Leg Circuit | A multi-channel biopotential recording analog front-end (AFE) with a fully integrated area-efficient driven-right-leg (DRL) circuit is presented in this paper. The proposed AFE includes 10 channels of low-noise capacitive coupled instrumentation amplifier (CCIA), one shared 10-bit SAR ADC and a fully integrated DRL to enhance the system-level common-mode rejection ratio (CMRR). The proposed DRL circuit senses the common-mode at the CCIA output so that the AFE gain is reused as the DRL loop gain. Therefore, area efficient unit-gain buffer with small averaging capacitors can be used in DRL circuit to reduce the circuit area significantly. The proposed AFE has been implemented in a standard 0.18-mu m CMOS process. The DRL circuit achieved more than 85% chip area reduction compared to the state-of-art on-chip DRL circuits and maximum 60 dB enhancement to system-level CMRR. Measurement results show high/low AFE gain of 60 dB/54 dB respectively with 1 mu A/channel current consumption under 1.0 V power supply. The measured AFE input-referred noise in 1 Hz - 10k Hz range is 4.2 mu V-rms and the maximum system-level CMRR is 110 dB. |
4,263 | Detecting Covert Channels in Computer Networks Based on Chaos Theory | Covert channels via the widely used TCP/IP protocols have become a new challenging issue for network security. In this paper, we analyze the information hiding in TCP/IP protocols and propose a new effective method to detect the existence of hidden information in TCP initial sequence numbers (ISNs), which is known as one of the most difficult covert channels to be detected. Our method uses phase space reconstruction to create a processing space called reconstructed phase space, where a statistical model is proposed for detecting covert channels in TCP ISNs. Based on the model, a classification algorithm is developed to identify the existence of information hidden in ISNs. Simulation results have demonstrated that our proposed detection method outperforms the state-of-the-art technique in terms of high detection accuracy and greatly reduced computational complexity. Instead of offline processing as the state-of-the-art does, our new scheme can be used for online detection. |
4,264 | Effects of dietary calcium and phosphorus restrictions on growth performance, intestinal morphology, nutrient retention, and tibia characteristics in broiler chickens | 1. This study evaluated the effects of dietary calcium (Ca) and available phosphorus (aP) restrictions on growth performance, intestinal morphology, nutrient apparent total tract retention (ATTR), and tibia characteristics.2. A total of 1296, one-day-old male Ross-308 broilers were reared for 42 d. During the starter phase (1-10 d), all birds were fed a nutrient-adequate diet (C). Diets fed during the grower phase (11-24 d) included: 1. C; 2. 15% of the Ca and aP in C; 3. 30% of the Ca and aP in C. At the beginning of the finisher phase (25 d), chickens fed the C diet were divided into two subgroups including C, and C+ phytase (500 FTU/kg). Restricted treatments were divided into eight subgroups as 1. C; 2. 10% of the Ca and aP in C; 3. 20% of the Ca and aP in C; 4. 30% of the Ca and aP in C; 5. C+ phytase; 6. 10% of the Ca and aP in C+ phytase; 7. 20% of the Ca and aP in C+ phytase and 8. 30% of the Ca and aP in C+ phytase. 3. On d 24 and 42, ATTR of Ca and phytate phosphorus (pP) were linearly increased by decreasing Ca and aP levels (P < 0.05). Birds receiving phytase showed higher nutrient ATTR compared to those fed non-phytase supplemented diets (P < 0.05). Tibia Ca and P were linearly decreased at 24 d (P < 0.05) and tibial ash was linearly decreased (P < 0.05) at 42 d by decreasing levels of Ca and aP in finisher diets (without phytase). By decreasing the levels of Ca and aP in the finisher diets (with phytase) with a 30% reduction of Ca and aP in the grower phase, tibia ash linearly decreased (P < 0.05). Using 500 FTU/kg phytase improved tibia traits compared to non-phytase supplemented treatments (P < 0.05).4. In general, decreasing dietary Ca and aP (up to 30%) during grower and finisher phases increased ATTR of minerals and decreased Ca, P and breaking strength (BS) of tibia without any negative effect on growth performance or intestinal morphology. Reduced dietary Ca and aP decreased tibial ash content, although 500 FTU/kg phytase improved ATTR of minerals and tibia attributes. |
4,265 | Tuning the Roundabout of Four-Point-Star Tiles with the Core Arm Length of Three Half-Turns for 2D DNA Arrays | By rationally adjusting the weaving modes of point-star tiles, the curvature inherent in the tiles can be changed, and various DNA nanostructures can be assembled, such as planar wireframe meshes, perforated wireframe tubes, and curved wireframe polyhedra. Based on the weaving and tiling architectures for traditional point-star tiles with the core arm length at two DNA half-turns, we improved the weaving modes of our newly reported four-point-star tiles with the core arm length at three half-turns to adjust their curvature and rigidity for assembling 2D arrays of DNA grids and tubes. Following our previous terms and methods to analyze the structural details of E-tiling tubes, we used the chiral indices (n,m) to describe the most abundant tube of typical assemblies; especially, we applied both one-locus and/or dual-locus biotin/streptavidin (SA) labelling strategies to define the configurations of two specific tubes, along with the absolute conformations of their component tiles. Such structural details of the DNA tubes composed of tiles with addressable concave and convex faces and packing directions should help us understand their physio-chemical and biological properties, and therefore promote their applications in drug delivery, biocatalysis, biomedicine, etc. |
4,266 | Curriculum-Style Fine-Grained Adaption for Unsupervised Cross-Lingual Dependency Transfer | Unsupervised cross-lingual transfer has been shown great potentials for dependency parsing of the low-resource languages when there is no annotated treebank available. Recently, the self-training method has received increasing interests because of its state-of-the-art performance in this scenario. In this work, we advance the method further by coupling it with curriculum learning, which guides the self-training in an easy-to-hard manner. Concretely, we present a novel metric to measure the instance difficulty of a dependency parser which is trained mainly on a Treebank from a resource-rich source language. By using the metric, we divide a low-resource target language into several fine-grained sub-languages by their difficulties, and then apply iterative-self-training progressively on these sub-languages. To fully explore the auto-parsed training corpus from sub-languages, we exploit an improved parameter generation network to model the sub-languages for better representation learning. Experimental results show that our final curriculum-style self-training can outperform a range of strong baselines, leading to new state-of-the-art results on unsupervised cross-lingual dependency parsing. We also conduct detailed experimental analyses to examine the proposed approach in depth for comprehensive understandings. |
4,267 | Recognition of English calling cards by using enhanced fuzzy radial basis function neural networks | In this paper, we proposed the novel method for the recognition of English calling cards by using the contour tracking algorithm and the enhanced fuzzy RBF (Radial Basis Function) neural networks. The recognition of calling cards consists of the extraction phase of character areas and the recognition phase of extracted characters. In the extraction phase, first of all, noises are removed from the images of calling cards, and the feature areas including character strings are separated from the calling card images by using the horizontal smearing method and the 8-directional contour tracking method. And using the image projection method the feature areas are split into the areas of individual characters. We also proposed the enhanced fuzzy RBF neural network that organizes the middle layer effectively by using the enhanced fuzzy ART neural network adjusting the vigilance parameter dynamically according to the similarity between patterns. In the recognition phase, the proposed fuzzy neural network was applied to recognize individual characters. Our experiment result showed that the proposed recognition algorithm has higher success rate of recognition and faster learning time than the conventional RBF network based recognitions. |
4,268 | Computed Tomography of Chemiluminescence (CTC): Instantaneous 3D measurements and Phantom studies of a turbulent opposed jet flame | Time resolved 3D measurements are required to further the understanding of turbulent combustion and to support the development of advanced simulation techniques such as LES. The Computed Tomography of Chemiluminescence (CTC) technique reconstructs the 3D chemiluminescence field of a turbulent flame from a series of integral measurements (camera images). The resulting data can be analysed to obtain the flame surface density, wrinkling factor, flame normal direction and possibly heat release rate, and also to study transient phenomena. High resolution CTC requires measurements from many viewing angles, and the capabilities of recent machine vision cameras make this affordable. The present paper investigates CTC using such commodity cameras. CTC is implemented using five PicSight P32M cameras and mirrors to provide 10 simultaneous views of a premixed turbulent opposed jet (TOJ) flame. The reconstructions are then performed using a 3D Algebraic Reconstruction Technique (ART) algorithm. For the flame investigated, camera exposure times of only 250 mu s were found to provide more than sufficient signal-to-noise ratios for ART reconstruction with still shorter exposures times possible. All reconstructions capture the main features of the TOJ flame and were found to provide a useful spatial resolution, even with just 10 views. Detailed Phantom studies were performed to assess the resolution available from ART. The resolution was found to be object dependent but a good working estimate was obtained from a relation by Frieder and Herman (1971)[64]. Reconstructions of realistic LES Phantom data have shown that high resolution reconstructions, which resolve wavelengths of 0.035 object diameters, can be a achieved from only 20 views, with each view costing less than $1000. (C) 2010 The Combustion Institute. Published by Elsevier Inc. All rights reserved. |
4,269 | A paradigm shift in retinal detachment repair: The concept of integrity | The management of rhegmatogenous retinal detachment has rapidly evolved over recent decades. A range of surgical techniques exist, all of which can achieve retinal reattachment in most cases. In recent years there have also been vast technical advances in retinal imaging that have introduced novel ways of visualizing and studying the retinal macro and microstructural anatomy following retinal detachment repair. Recent clinical trial data demonstrates that functional and patient-reported outcomes of retinal reattachment differ with surgical technique, accompanied by differences in anatomic biomarkers of retinal recovery or 'integrity'. We discuss recent insights into the physiology of retinal reattachment gleaned from multimodal imaging, which shed light on the pathophysiology of various post-operative anatomic abnormalities. The ideal scenario is to achieve retinal reattachment as soon as possible, without retinal displacement, outer retinal folds or discontinuity of the external limiting membrane, ellipsoid zone and interdigitation zone, with an intact foveal bulge. To this end, we present an in-depth contemporary account of current concepts and mechanisms involved during retinal reattachment surgery, supported by clinical data and mathematical modelling, awareness of which can help the vitreoretinal surgeon achieve better post-operative outcomes. In this review we substantiate the case for a paradigm shift in rhegmatogenous retinal detachment repair; beyond the emphasis on single-operation reattachment rates, and instead striving to maximize functional outcomes using minimally invasive techniques. This can only be achieved if vitreoretinal surgeons embrace all of the available techniques, with individualized selection of surgical approach and the resolute goal of optimizing the 'integrity' of retinal reattachment. |
4,270 | Position-aware lightweight object detectors with depthwise separable convolutions | Recently, significant improvements have been achieved for object detection algorithm by increasing the size of convolutional neural network (CNN) models, but the resulting increase of computational complexity poses an obstacle to practical applications. And some of the lightweight methods fail to consider the characteristics of object detection into and suffer a huge loss of accuracy. In this paper, we design a multi-scale feature lightweight network structure and specific convolution module for object detection based on depthwise separable convolution, which not only reduces the computational complexity but also improves the accuracy by using the specific position information in object detection. Furthermore, in order to improve the detection accuracy for small objects, we construct a multi-channel position-aware map and propose training based on knowledge distillation for object detection to train the lightweight model effectively. Last, we propose a training strategy based on a key-layer guiding structure to balance performance with training time. The experimental results show that on the COCO dataset that takes the state-of-the-art object detection algorithm, YOLOv3, as the baseline, our model size is compressed to 1/11 while accuracy drops by 7.4 mmAP, and the computational latency on the GPU and ARM platforms are reduced to 43.7% and 0.29%, respectively. Compared with the state-of-the-art lightweight object detection model, MNet V2 + SSDLite, the accuracy of our model increases by 3.5 mmAP while the inferencing time stays nearly the same. On the PASCAL VOC2007 dataset, the accuracy of our model increases by 5.2 mAP compared to the state-of-the-art lightweight algorithm based on knowledge distillation. Therefore, in terms of accuracy, parameter count, and real-time performance, our algorithm has better performance than lightweight algorithms based on knowledge distillation or depthwise separable convolution. |
4,271 | Achieving mainstream anammox in biological aerated filter by regulating bacteria community structure | Although mainstream partial nitrification-anammox (PN-A) is a highly efficient and sustainable wastewater treatment process, it is difficult to achieve and stabilize due to the competition among functional bacteria. In this study, achieving one-stage mainstream anammox via regulating bacteria community structure was studied in a lab-scale biological aerated filter (BAF). The results showed that high free ammonia with 89.57 mg/L, nitrite nitrogen (NO2--N) competition between anammox bacteria (AnAOB) and nitrite oxidizing bacteria (NOB), and backwash regulated the bacteria community structure. After backwash, Candidatus Kuenenia became the dominant bacteria and the relative abundance increased to 5.56 %. In BAF, one-stage mainstream anammox with total nitrogen (TN) being lower than 15 mg/L in the effluent was achieved using lag-time of bacteria activity recovery caused by alternating operation of high and low ammonia nitrogen (NH4+-N), which have great potential applied in municipal wastewater treatment plants (MWWTPs). |
4,272 | Oligostilbenes from the seeds of Paeonia lactiflora as potent GLP-1 secretagogues targeting TGR5 receptor | One unusual stilbene trimer-flavonoid hybrid, paeonilactiflobenoid (1), together with six known stilbenes (2-7) were isolated from the seeds of Paeonia lactiflora. The structure of 1 was elucidated with the aid of HRESIMS, 1D and 2D NMR, [α]D spectroscopic data and ECD calculation. Compounds 2-7 showed stimulative effects on GLP-1 secretion with promoting rates of 79.8%-880.4% (25 μM) and 217.6%-1089.4% (50 μM), more potent than the positive control, oleoylethanolamide (250.2% at 50 μM). Moreover, compounds 4 and 6 exhibited agonistic activity on the G protein-coupled receptor (GPCR) TGR5 with stimulative ratios of 40.2% and 40.5% at 50 μM, and 54.2% and 49.1% at 100 μM, respectively. Docking study manifested that 6 well located in the catalytic pocket of TGR5 by hydrogen-bond and hydrophobic interactions. The GLP-1 promotion of 6 could be attenuated by IP3, Ca2+/CaMKII and MEK/ERK pathway inhibitors, suggesting that these pathways played important roles in GLP-1 secretion. Thus, stilbenes in peony seeds maybe regarded as potential GLP-1 secretagogues through TGR5-IP3-Ca2+/CaMKII-MEK/ERK pathways. |
4,273 | Photonic-Assisted Arbitrary Waveform Generation for Uplink Applications in Beyond 5G Taking Advantage of Low Frequency Technology | We present a photonic-assisted arbitrary waveform generation for uplink applications in beyond 5G taking advantage of low frequency technology and it has a potential to upgrade signal frequency from MHz to more than tens GHz retaining the high ENOB of conventional low frequency electrical technology competitive to the state of the arts of tens GHz class AWG. In machine-to-machine communications over uplink traffic as one of application scenario examples, massive multi-data should be accommodated and be conveyed to beyond GHz region in the era of beyond 5G. The proposed photonic assistance for temporal waveform compression has a potential to upgrade signal frequency from MHz to more than tens GHz retaining the high ENOB of the low frequency technology and surpass the state of the arts of tens GHz class AWG. We successfully demonstrate the principle of the proposed approach in experiment and the potential of achievable ENOB over 12 bit at 50 GHz in simulation. |
4,274 | Morphological and release characterization of nanoparticles formulated with poly (dl-lactide-co-glycolide) (PLGA) and lupeol: In vitro permeability and modulator effect on NF-κB in Caco-2 cell system stimulated with TNF-α | Lupeol exhibits anti-inflammatory effects; unfortunately it shows low water solubility. An alternative to overcome this is the development of nanomaterials. Several methods for nanomaterial production are available. One of them is emulsification/solvent-evaporation. The objective of the present work was to evaluate physical properties, transport and in vitro modulator effects on NF-κB of poly (lactide-co-glycolide) (PLGA) nanoparticles loaded with lupeol. Nanonutraceuticals were prepared with 16% (w/v) of lupeol. Size distribution and morphology were measured by particle size analyzer and TEM. In vitro release of lupeol was studied by three different models: Higuchi, Siepmann & Peppas, and Power law. Transport of nanonutraceutical was studied in a Caco-2 cell model and by GC-MS. Modulator effect on NK-κB was studied by western blot analysis. Nanonutraceuticals were 10% larger than the nanoparticles without lupeol (372 vs 337 nm) and presented a broader size distribution (0.28 vs 0.22). TEM results displayed spherical structures with a broader size distribution. Entrapment efficiency of lupeol was 64.54% and it in vitro release data fitted well to the Power law and Higuchi equation (R > 0.84-0.84). Strong regulation of NF-κB of nanonutraceutical was observed. It was not observed any transport across the Caco-2 cell model at the different experimental conditions. |
4,275 | Adaptive Greedy Dictionary Selection for Web Media Summarization | Initializing an effective dictionary is an indispensable step for sparse representation. In this paper, we focus on the dictionary selection problem with the objective to select a compact subset of basis from original training data instead of learning a new dictionary matrix as dictionary learning models do. We first design a new dictionary selection model via l(2,0) norm. For model optimization, we propose two methods: one is the standard forward-backward greedy algorithm, which is not suitable for large-scale problems; the other is based on the gradient cues at each forward iteration and speeds up the process dramatically. In comparison with the state-of-the-art dictionary selection models, our model is not only more effective and efficient, but also can control the sparsity. To evaluate the performance of our new model, we select two practical web media summarization problems: 1) we build a new data set consisting of around 500 users, 3000 albums, and 1 million images, and achieve effective assisted albuming based on our model and 2) by formulating the video summarization problem as a dictionary selection issue, we employ our model to extract keyframes from a video sequence in a more flexible way. Generally, our model outperforms the state-of-the-art methods in both these two tasks. |
4,276 | Gesture estimation for 3D martial arts based on neural network | A 3D martial arts gestures estimation method is proposed, for it is difficult to accurately describe the gestures, which is caused by the high degree of freedom and high similarity of 3D martial arts gestures. This method is combined with finger kinematics analysis model. And beyond that, it is based on the morphological topological structure of hand and combined with the CNN neural network. Firstly, the CNN is used to extract and classify 3D gesture features of martial arts. Then, using the morphological topological structure of hands to simulate the dependence of hand joints, and the 3D coordinates of hand joints were obtained. Finally, the attitude regression module is used to realize the attitude estimation of martial arts gesture action. Simulation results show that the accuracy of gesture estimation can be improved through cascade splicing in this research. Compared with existing 3D gesture estimation methods such as V2V, Pose-REN and CrossInfoNet, the proposed method performs better in MSE and FS indicators. It has lower estimation errors and an inference speed of 220.7 frames per second. In addition, the three-dimensional visualization results show that the predicted joint points obtained by the proposed estimation method coincide with the labeled joint points, and there is no occlusion. So it is proved that the proposed method is feasible, and it can be used to estimate martial arts gestures in the future. |
4,277 | Facilely Achieved Self-Biased Black Silicon Heterojunction Photodiode with Broadband Quantum Efficiency Approaching 100 | Photodiodes are fundamental components in modern optoelectronics. Heterojunction photodiodes, simply configured by two different contact materials, have been a hot research topic for many years. Currently reported self-biased heterojunction photodiodes routinely have external quantum efficiency (EQE) significantly below 100% due to optical and electrical losses. Herein, an approach that virtually overcomes this 100% EQE challenge via low-aspect-ratio nanostructures and drift-dominated photocarrier transport in a heterojunction photodiode is proposed. Broadband near-ideal EQE is achieved in nanocrystal indium tin oxide/black silicon (nc-ITO/b-Si) Schottky photodiodes. The b-Si comprises nanostalagmites which balance the antireflection effect and surface morphology. The built-in electric field is explored to match the optical generation profile, realizing enhanced photocarrier transport over a broadband of photogeneration. The devices exhibit unprecedented EQE among the reported leading-edge heterojunction photodiodes: average EQE surpasses ≈98% for wavelengths of 570-925 nm, while overall EQE is greater than ≈95% from 500 to 960 nm. Further, only elementary fabrication techniques are explored to achieve these excellent device properties. A heart rate sensor driven by nanowatt faint light is demonstrated, indicating the enormous potential of this near-ideal b-Si photodiode for low power consuming applications. |
4,278 | Remote sensing image super-resolution and object detection: Benchmark and state of the art | For the past two decades, there have been significant efforts to develop methods for object detection in Remote Sensing (RS) images. In most cases, the datasets for small object detection in remote sensing images are inadequate. Many researchers used scene classification datasets for object detection, which has its limitations; for example, the large-sized objects outnumber the small objects in object categories. Thus, they lack diversity; this further affects the detection performance of small object detectors in RS images. This paper reviews current datasets and object detection methods (deep learning-based) for remote sensing images. We also propose a largescale, publicly available benchmark Remote Sensing Super-resolution Object Detection (RSSOD) dataset. The RSSOD dataset consists of 1,759 hand-annotated images with 22,091 instances of very high-resolution (VHR) images with a spatial resolution of ~ 0.05 m. There are five classes with varying frequencies of labels per class; the images are annotated in You Only Look Once (YOLO) and Common Objects in Context (COCO) format. The image patches are extracted from satellite images, including real image distortions such as tangential scale distortion and skew distortion. The proposed RSSOD dataset will help researchers benchmark the state-of-the-art object detection methods across various classes, especially for small objects using image super-resolution. We also propose a novel Multi-class Cyclic super-resolution Generative adversarial network with Residual feature aggregation (MCGR) and auxiliary YOLOv5 detector to benchmark image super-resolution-based object detection and compare with the existing state-of-the-art methods based on image super-resolution (SR). The proposed MCGR achieved state-of-the-art performance for image SR with an improvement of 1.2 dB in peak signal-to-noise ratio (PSNR) compared to the current state-of-the-art non-local sparse network (NLSN). MCGR achieved best object detection mean average precisions (mAPs) of 0.758, 0.881, 0.841, and 0.983, respectively, for five-class, four-class, two-class, and single classes, respectively surpassing the performance of the state-of-the-art object detectors YOLOv5, EfficientDet, Faster RCNN, SSD, and RetinaNet. |
4,279 | Fast and Accurate Craniomaxillofacial Landmark Detection via 3D Faster R-CNN | Automatic craniomaxillofacial (CMF) landmark localization from cone-beam computed tomography (CBCT) images is challenging, considering that 1) the number of landmarks in the images may change due to varying deformities and traumatic defects, and 2) the CBCT images used in clinical practice are typically large. In this paper, we propose a two-stage, coarse-to-fine deep learning method to tackle these challenges with both speed and accuracy in mind. Specifically, we first use a 3D faster R-CNN to roughly locate landmarks in down-sampled CBCT images that have varying numbers of landmarks. By converting the landmark point detection problem to a generic object detection problem, our 3D faster R-CNN is formulated to detect virtual, fixed-size objects in small boxes with centers indicating the approximate locations of the landmarks. Based on the rough landmark locations, we then crop 3D patches from the high-resolution images and send them to a multi-scale UNet for the regression of heatmaps, from which the refined landmark locations are finally derived. We evaluated the proposed approach by detecting up to 18 landmarks on a real clinical dataset of CMF CBCT images with various conditions. Experiments show that our approach achieves state-of-the-art accuracy of 0.89 +/- 0.64 mm in an average time of 26.2 seconds per volume. |
4,280 | [FPGA Implementation of Digital Coordinate Conversion of Intravascular Ultrasound Imaging System Based on CORDIC Algorithm] | In order to facilitate doctors to better obtain cardiovascular images by using intravascular ultrasound imaging system and make a more accurate diagnosis, a digital coordinate conversion method of intravascular ultrasound imaging system based on CORDIC algorithm is proposed, it converts polar coordinates into rectangular coordinates through angular rotation and orientation calculation. The experimental simulation test is carried out on the platform of intravascular ultrasound imaging system by FPGA. Experimental simulation shows that, CORDIC algorithm can effectively output sine and cosine values, compared with the traditional table finding method, this algorithm has faster speed, stronger real-time performance and needs less hardware resources. It is more suitable for intravascular ultrasound imaging system. |
4,281 | Enabling Ultrasound In-Body Communication: FIR Channel Models and QAM Experiments | Ultrasound waves pose a promising alternative to the commonly used electromagnetic waves for intra-body communication. This due to the lower ultrasound wave attenuation, the reduced health risks, and the reduced external interference. Current state-of-the-art ultrasound designs, however, are limited in their practical in-body deployment and reliability. This stems from their use of bulky, focused transducers, the use of simple modulation schemes or the absence of a realistic test environment and corresponding realistic channel models. Therefore, this paper proposes a new, ultrasound, static emulation test bed consisting of small, omnidirectional transducers, and custom gelatin phantoms with additional scattering materials. Using this test bed, we investigate different in-body communication scenarios. Multiple communication channels were extracted and mapped onto finite impulse response (FIR) channel models, which are verified and open sourced for future research. Furthermore, a basic quadrature-amplitude modulation (QAM) modem was built to assess the communication performance under various modulation schemes. A link was established using 4-QAM and 200 kbit/s resulting in a BER <1e-4 at received Eb/No of 13dB. Identical results were obtained by computer simulations on the FIR channels, which makes the extracted FIR channels suitable for the design of future ultrasound in-body communication schemes. |
4,282 | Art Installation Design and Algorithm Research Oriented to Heterogeneous Computing Architecture and Particle Swarm Algorithm | Traditional high-performance computers generally use commercial general-purpose processors. When constructing large-scale parallel computing systems, they will face many challenges in system efficiency, power consumption, system maintenance, and cost. Heterogeneous architectures have begun to become the key to constructing supercomputer systems. In order to improve the optimization efficiency of the particle swarm algorithm, based on the simplified particle swarm algorithm, an improved strategy for fusion of population information is proposed. Only using the optimal position of the individual particle and the optimal position of the population to update the particle position makes the algorithm description simpler, construct a random term that depends on the population information to increase the diversity of the population, and design an adaptive control function equation to balance the algorithm's global detection and local detection. This article aims to study the design and algorithm of art installations for heterogeneous computing architecture and particle swarm algorithm. Aiming at the two heterogeneous system components of GPU and FPGA in heterogeneous computing systems to solve the dual heterogeneous problem between various computing components and applications, a task flow model for heterogeneous computing systems is proposed, which adopts heterogeneous systems. The structure focuses on the deterministic and nondeterministic calculation methods of particle swarm algorithm and carries out parallel research algorithms of particle transport data to analyze the design of art installations. The experimental results show that by making full use of the GPU hardware storage structure to improve memory access speed and reduce cache failure, the combination of particle swarm algorithm and heterogeneous computing system enables the calculation of the carrier components to obtain 3 times the acceleration effect; through the energy analysis model, Heterogeneous computing system components can obtain at least 1.5 times performance improvement. |
4,283 | High efficiency self-cleaning of nanocomposites ZnO with additional chitosan for helping electron and hole transport | ZnO/chitosan coated wool fabric has been applied as a self-cleaning which obtained through sol-gel method for various pH: 5, 7, and 9. The self-cleaning test was carried out by irradiating the cloth samples using UVA-UVB lamps up to 15 h with dye for the clothing stain. ZnO/chitosan composites were characterized using X-Ray Diffraction (XRD), Fourier Transform Infra-Red (FTIR), and UV-Visible spectroscopy. The diffraction spectra from chitosan for pH 9 is 19.56°. The vibration bond at the wavenumbers 422 cm-1- 621 cm-1 identified as ZnO bond and at the wavenumber 3495 cm-1 identified for stretching -OH and -NH2 from chitosan. Optical properties were analysed using the Kamers Kronig (KK) relation which was applied to the FTIR spectra and shows the highest distance between two optical photon vibration modes (∆ (LO-TO)) is 199 cm-1 was obtained when the pH 9. The pH 9 is the best self-cleaning performance for dye stain which completely lost after being exposed for 15 h irradiation. Samples pH 9 shows the best self-cleaning due to the smallest crystallite size and highest bandgap and (∆ (LO-TO)) indicated high potential for new self-cleaning material in future. |
4,284 | Framework and baseline examination of the German National Cohort (NAKO) | The German National Cohort (NAKO) is a multidisciplinary, population-based prospective cohort study that aims to investigate the causes of widespread diseases, identify risk factors and improve early detection and prevention of disease. Specifically, NAKO is designed to identify novel and better characterize established risk and protection factors for the development of cardiovascular diseases, cancer, diabetes, neurodegenerative and psychiatric diseases, musculoskeletal diseases, respiratory and infectious diseases in a random sample of the general population. Between 2014 and 2019, a total of 205,415 men and women aged 19-74 years were recruited and examined in 18 study centres in Germany. The baseline assessment included a face-to-face interview, self-administered questionnaires and a wide range of biomedical examinations. Biomaterials were collected from all participants including serum, EDTA plasma, buffy coats, RNA and erythrocytes, urine, saliva, nasal swabs and stool. In 56,971 participants, an intensified examination programme was implemented. Whole-body 3T magnetic resonance imaging was performed in 30,861 participants on dedicated scanners. NAKO collects follow-up information on incident diseases through a combination of active follow-up using self-report via written questionnaires at 2-3 year intervals and passive follow-up via record linkages. All study participants are invited for re-examinations at the study centres in 4-5 year intervals. Thereby, longitudinal information on changes in risk factor profiles and in vascular, cardiac, metabolic, neurocognitive, pulmonary and sensory function is collected. NAKO is a major resource for population-based epidemiology to identify new and tailored strategies for early detection, prediction, prevention and treatment of major diseases for the next 30 years. |
4,285 | A Graph Lattice Approach to Maintaining and Learning Dense Collections of Subgraphs as Image Features | Effective object and scene classification and indexing depend on extraction of informative image features. This paper shows how large families of complex image features in the form of subgraphs can be built out of simpler ones through construction of a graph lattice-a hierarchy of related subgraphs linked in a lattice. Robustness is achieved by matching many overlapping and redundant subgraphs, which allows the use of inexpensive exact graph matching, instead of relying on expensive error-tolerant graph matching to a minimal set of ideal model graphs. Efficiency in exact matching is gained by exploitation of the graph lattice data structure. Additionally, the graph lattice enables methods for adaptively growing a feature space of subgraphs tailored to observed data. We develop the approach in the domain of rectilinear line art, specifically for the practical problem of document forms recognition. We are especially interested in methods that require only one or very few labeled training examples per category. We demonstrate two approaches to using the subgraph features for this purpose. Using a bag-of-words feature vector we achieve essentially single-instance learning on a benchmark forms database, following an unsupervised clustering stage. Further performance gains are achieved on a more difficult dataset using a feature voting method and feature selection procedure. |
4,286 | Variable Parallelism Cyclic Redundancy Check Circuit for 3GPP-LTE/LTE-Advanced | Cyclic Redundancy Check (CRC) is often employed in data storage and communications to detect errors. The 3GPP-LTE wireless communication standard uses a 24-bit CRC with every turbo coded frame, thus, the CRC can be exploited to detect residual errors and to enable early stopping of iterations as well. The current state of the art lacks specific CRC implementations for this standard, and most current solutions adopt a fixed degree of parallelism, unsuitable for many turbo decoder architectures. This work proposes a variable parallelism circuit targeting the 3GPP-LTE/LTE-Advanced 24-bit CRC, that can adapt to input data of different sizes. Low complexity is achieved through careful functional sharing among the various parallelisms: comparison with the state of the art shows comparable or superior speed and extremely low complexity. |
4,287 | Hybrid SDN evolution: A comprehensive survey of the state-of-the-art | Software-Defined Networking (SDN) is an evolutionary networking paradigm which has been adopted by large network and cloud providers, among which are Tech Giants. However, embracing a new and futuristic paradigm as an alternative to well-established and mature legacy networking paradigm requires a lot of time along with considerable financial resources and technical expertise. Consequently, many enterprises cannot afford it. A compromise solution then is a hybrid networking environment (a.k.a. Hybrid SDN (hSDN)) in which SDN functionalities are leveraged while existing traditional network infrastructures are acknowledged. Recently, hSDN has been seen as a viable networking solution for a diverse range of businesses and organizations. Accordingly, the body of literature on hSDN research has improved remarkably. On this account, we present this paper as a comprehensive state-of-the-art survey which expands upon hSDN from many different perspectives. |
4,288 | Different degree of loss-of-function among four missense mutations in the EDAR gene responsible for autosomal recessive hypohidrotic ectodermal dysplasia may be associated with the phenotypic severity | Hypohidrotic ectodermal dysplasia is a rare condition characterized by hypohidrosis, hypodontia, and hypotrichosis. The disease can show X-linked recessive, autosomal dominant or autosomal recessive inheritance trait. Of these, the autosomal forms are caused by mutations in either EDAR or EDARADD. To date, the underlying pathomechanisms or genotype-phenotype correlations for autosomal forms have not completely been disclosed. In this study, we performed a series of in vitro studies for four missense mutations in the death domain of EDAR protein: p.R358Q, p.G382S, p.I388T, and p.T403M. The results revealed that p.R358Q- and p.T403M-mutant EDAR showed different expression patterns from wild-type EDAR in both western blots and immunostainings. NF-κB reporter assays demonstrated that all the mutant EDAR showed reduced activation of NF-κB, but the reduction by p.G382S- and p.I388T-mutant EDAR was moderate. Co-immunoprecipitation assays showed that p.R358Q- and p.T403M-mutant EDAR did not bind with EDARADD at all, whereas p.G382S- and p.I388T-mutant EDAR maintained the affinity to some extent. Furthermore, we demonstrated that all the mutant EDAR proteins analyzed aberrantly bound with TRAF6. Sum of the data suggest that the degree of loss-of-function is different among the mutant EDAR proteins, which may be associated with the severity of the disease. |
4,289 | Quinary, Senary, and Septenary High Entropy Alloy Nanoparticle Catalysts from Core@Shell Nanoparticles and the Significance of Intraparticle Heterogeneity | Colloidally prepared core@shell nanoparticles (NPs) were converted to monodisperse high entropy alloy (HEA) NPs by annealing, including quinary, senary, and septenary phases comprised of PdCuPtNi with Co, Ir, Rh, Fe, and/or Ru. Intraparticle heterogeneity, i.e., subdomains within individual NPs with different metal distributions, was observed for NPs containing Ir and Ru, with the phase stabilities of the HEAs studied by atomistic simulations. The quinary HEA NPs were found to be durable catalysts for the oxygen reduction reaction, with all but the PdCuPtNiIr NPs presenting better activities than commercial Pt. Density functional theory (DFT) calculations for PdCuPtNiCo and PdCuPtNiIr surfaces (the two extremes in performance) found agreement with experiment by weighting the adsorption energy contributions by the probabilities of each active site based on their DFT energies. This finding highlights how intraparticle heterogeneity, which we show is likely overlooked in many systems due to analytical limitations, can be leveraged toward efficient catalysis. |
4,290 | Learning Saliency From Single Noisy Labelling: A Robust Model Fitting Perspective | The advances made in predicting visual saliency using deep neural networks come at the expense of collecting large-scale annotated data. However, pixel-wise annotation is labor-intensive and overwhelming. In this paper, we propose to learn saliency prediction from a single noisy labelling, which is easy to obtain (e.g., from imperfect human annotation or from unsupervised saliency prediction methods). With this goal, we address a natural question: Can we learn saliency prediction while identifying clean labels in a unified framework? To answer this question, we call on the theory of robust model fitting and formulate deep saliency prediction from a single noisy labelling as robust network learning and exploit model consistency across iterations to identify inliers and outliers (i.e., noisy labels). Extensive experiments on different benchmark datasets demonstrate the superiority of our proposed framework, which can learn comparable saliency prediction with state-of-the-art fully supervised saliency methods. Furthermore, we show that simply by treating ground truth annotations as noisy labelling, our framework achieves tangible improvements over state-of-the-art methods. |
4,291 | Intranasal vaccination of recombinant H5N1 HA1 proteins fused with foldon and Fc induces strong mucosal immune responses with neutralizing activity: Implication for developing novel mucosal influenza vaccines | The highly pathogenic avian influenza (HPAI) H5N1 virus remains a threat to public health because of its continued spread in poultry in some countries and its ability to infect humans with high mortality rate, calling for the development of effective and safe vaccines against H5N1 infection. Here, we constructed 4 candidate vaccines by fusing H5N1 hemagglutinin 1 (HA1) with foldon (HA1-Fd), human IgG Fc (HA1-Fc), foldon and Fc (HA1-FdFc) or His-tag (HA1-His). We then compared their ability to induce mucosal immune responses and neutralizing antibodies in the presence or absence of Poly(I:C) and CpG adjuvants via the intranasal route. Without an adjuvant, HA1-FdFc could elicit appreciable humoral immune responses and local mucosal IgA antibodies in immunized mice, while other vaccine candidates only induced background immune responses. In the presence of Poly(I:C) and CpG, both HA1-Fd and HA1-Fc elicited much higher levels of serum IgG and local mucosal IgA antibodies than HA1-His. Poly(I:C) and CpG could also augment the neutralizing antibody responses induced by these 4 vaccine candidates in the order of HA1-FdFc > HA1-Fc > HA1-Fd > HA1-His. These results suggest that both Fd and Fc potentiate the immunogenicity of the recombinant HA1 protein and that Poly(I:C) and CpG serve as efficient mucosal adjuvants in promoting efficacy of these vaccine candidates to induce strong systemic and local antibody responses and potent neutralizing antibodies, providing a useful strategy to develop effective and safe mucosal H5N1 vaccines. |
4,292 | Using multisines to measure state-of-the-art analog-to-digital converters | Multisines have proven to be very useful for fast and accurate measurements, particularly when studying the nonlinear behavior of analog devices under test. This paper will deal with some problems that can arise when designing experiments for measuring state-of-the-art analog-to-digital converters (ADCs) with multisines. Special care has been taken to the measurement setup, particularly with the generation and verification of the test signals. The verification of the test signals requires either a more performant ADC or alternative measurement methods such as a spectrum analyzer. By means of real measurement examples, it is shown that most existing techniques used for analog measurements can be reused after an adaptation to the new proposed measurement setup. It will, e.g., be shown that the loss of information introduced by using a spectrum analyzer leads to bounds onto the nonlinear distortions instead of the absolute value. |
4,293 | An adversarial framework for op en-set human action recognition using skeleton data | Human action recognition is a fundamental problem which is applied in various domains, and it is widely studied in the literature. Majority of the studies model action recognition as a closed-set problem. However, in real-life applications it usually arises as an op en-set problem where a set of actions are not available during training but are introduced to the system during testing. In this study, we propose an op en-set action recognition system, human action recognition and novel action detection system (HARNAD), which consists of two stages and uses only 3D skeleton information. In the first stage, HARNAD recognizes a given action and in the second stage it decides whether the action really belongs to one of the a priori known classes or if it is a novel action. We evaluate the performance of the system experimentally both in terms of recognition and novelty detection. We also compare the system performance with state-of-the-art op en-set recognition methods. Our experiments show that HARNAD is compatible with state-of-the-art methods in novelty detection, while it is superior to those methods in recognition. |
4,294 | Thermal-Stable Separators: Design Principles and Strategies Towards Safe Lithium-Ion Battery Operations | Lithium-ion batteries (LIBs) are momentous energy storage devices, which have been rapidly developed due to their high energy density, long lifetime, and low self-discharge rate. However, the frequent occurrence of fire accidents in laptops, electric vehicles, and mobile phones caused by thermal runaway of the inside batteries constantly reminds us of the urgency in pursuing high-safety LIBs with high performance. To this end, this Review surveyed the state-of-the-art developments of high-temperature-resistant separators for highly safe LIBs with excellent electrochemical performance. Firstly, the basic properties of separators (e. g., thickness, porosity, pore size, wettability, mechanical strength, and thermal stability) in constructing commercialized LIBs were introduced. Secondly, the working mechanisms of advanced separators with different melting points acting in the thermal runaway stage were discussed in terms of improving battery safety. Thirdly, rational design strategies for constructing high-temperature-resistant separators for LIBs with high safety were summarized and discussed, including graft modification, blend modification, and multilayer composite modification strategies. Finally, the current obstacles and future research directions in the field of high-temperature-resistant separators were highlighted. These design ideas are expected to be applied to other types of high-temperature-resistant energy storage systems working under extreme conditions. |
4,295 | Radiation effects in low dielectric constant methyl-silsesquioxane films | The use of state-of-the-art microelectronic devices in space radiation environments faces new challenges with the adoption of low dielectric constant (low-k) materials as interlevel dielectrics. This is demonstrated in a preliminary study of methyl-silsesquioxane low-k films. We report radiation damage, induced by a 2-keV low- current-density (similar to2 x 10(6) s(-1) cm(-2)) positron beam, and observed by positron annihilation spectroscopy. |
4,296 | Proximal tubular Bmal1 protects against chronic kidney injury and renal fibrosis by maintaining of cellular metabolic homeostasis | Recent studies suggest that deletion of the core clock gene Bmal1 in the kidney has a significant influence on renal physiological functions. However, the role of renal Bmal1 in chronic kidney disease (CKD) remains poorly understood. Here by generating mice lacking Bmal1 in proximal tubule (Bmal1flox/flox-KAP-Cre+, ptKO) and inducing CKD with the adenine diet model, we found that lack of Bmal1 in proximal tubule did not alter renal water and electrolyte homeostasis. However, adenine-induced renal injury indexes, including blood urea nitrogen, serum creatinine, and proteinuria, were markedly augmented in the ptKO mice. The ptKO kidneys also developed aggravated tubulointerstitial fibrosis and epithelial-mesenchymal transformation. Mechanistically, RNAseq analysis revealed significant downregulation of the expression of genes related to energy and substance metabolism, in particular fatty acid oxidation and glutathione/homocysteine metabolism, in the ptKO kidneys. Consistently, the renal contents of ATP and glutathione were markedly reduced in the ptKO mice, suggesting the disruption of cellular metabolic homeostasis. Moreover, we demonstrated that Bmal1 can activate the transcription of cystathionine β-synthase (CBS), a key enzyme for homocysteine metabolism and glutathione biosynthesis, through direct recruitment to the E-box motifs of its promoter. Supporting the in vivo findings, knockdown of Bmal1 in cultured proximal tubular cells inhibited CBS expression and amplified albumin-induced cell injury and fibrogenesis, while glutathione supplementation remarkably reversed these changes. Taken together, we concluded that deletion of Bmal1 in proximal tubule may aggravate chronic kidney injury and exacerbate renal fibrosis, the mechanism is related to suppressing CBS transcription and disturbing glutathione related metabolic homeostasis. These findings suggest a protective role of Bmal1 in chronic tubular injury and offer a novel target for treating CKD. |
4,297 | Automatic Knowledge Discovery in Lecturing Videos via Deep Representation | In recent years, e-learning systems such as massive open online courses (MOOC) have been widely employed in academic institutions and have shown its power in enhancing the students' learning ability. Automatic detection and classification of knowledge points in lecture video would significantly enhance the performance of the online learning platform. Most of the previously presented approach for knowledge discovery focused on the text and audio documents, whereas the identification of knowledge points in videos still remains a challenge. To bridge this gap, we proposed a novel convolutional neural network which was designed for the characteristics of lecture video. It could both extract the temporal-spatial and semantic information from the multimedia record. To evaluate the performance of the proposed technique, we conducted comparison experiments between the state-of-the-art methods and ours. The experimental results demonstrated that the presented approach outperformed the state-of-the-art techniques and could be potentially invaluable for the accurate discovery of knowledge points within videos. |
4,298 | Microfluidics on foil: state of the art and new developments | The concept of microfluidics on foil opens up new opportunities for combining the advantages of having a flexible substrate with reel-to-reel processing, which has the potential to be the basis for extremely cheap micro products. To reach this goal, foil substrates must be combined with micro-manufacturing technologies that are well adapted to these substrates. Some technologies are already available, some are the subject of current research, and some still have to be conceived. In the current paper, technologies such as reel-to-reel embossing, reel-to-reel laminating, and laser ablation/cutting as well as laser welding will be discussed. The discussions include a brief review of the state of the art as well as a report on latest research results stemming from research by the present authors. Furthermore, this paper shows the vision of what can be achieved if foil-based technologies, such as polymer (opto-) electronics and microfluidics are combined. A polymer electronics-based alcohol sensor is presented as an example system. |
4,299 | Machine learning based false data injection in smart grid | Smart Grid is the seamless integration of advance digital communication network, state of the art control technologies, and power system infrastructure working together as an entity to ensure the reliability, sustainability, and stability of the power infrastructure. Digital communication network with is the key to the reliability of Smart Grid as all control actions are deemed upon the data transmitted by a communication network. With false data, however, the same digital communication network can lead to anomalies like abnormal disruptions, load shedding, malicious attacks and power theft. Robust False data injection attack methods proposed till now demand for the complete knowledge of interconnected power grid network topology. In this paper, three network topology independent techniques for false data injection into the smart grid are proposed based on linear regression, linear regression with time stamp, and by using delta thresholds. To make injected false data more unlikely to be detected, it is constructed to fill up the missing measurements in real-time data. The robustness of proposed attack algorithms are stated by state-of-the-art defence techniques, i.e. Bad Data Detection, AC State estimation, Support Vector Machine, and Temporal Behaviours based False data detection. |
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