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4,600 | Towards Pointsets Representation Learning via Self-Supervised Learning and Set Augmentation | Deep metric learning is a supervised learning paradigm to construct a meaningful vector space to represent complex objects. A successful application of deep metric learning to pointsets means that we can avoid expensive retrieval operations on objects such as documents and can significantly facilitate many machine learning and data mining tasks involving pointsets. We propose a self-supervised deep metric learning solution for pointsets. The novelty of our proposed solution lies in a self-supervision mechanism that makes use of a distribution distance for set ranking called the Earth's Mover Distance (EMD) to generate pseudo labels and a pointset augmentation method for supporting the learning solution. Our experimental studies on documents, graphs, and point clouds datasets show that our proposed solutions outperform baselines and state-of-the-art approaches under the unsupervised settings. The learned self-supervised representation can also be used as a pre-trained model, which can boost downstream tasks with a fine-tuning step and outperform state-of-the-art language models. |
4,601 | Sketch4Image: a novel framework for sketch-based image retrieval based on product quantization with coding residuals | Sketch-based Image Retrieval (SBIR) is one important branch of Content-based Image Retrieval (CBIR). SBIR means dealing with retrieval using simple edge or contour images. However, SBIR is more difficult than CBIR due to the lack of visual information, this makes the Bag-of-Words (BoW) or codebook in SBIR hard to construct. In this paper, we propose a novel SBIR framework based on Product Quantization (PQ) with sparse coding (SC) to construct an optimized codebook. By using state-of-the-art local descriptors, we transform sketch images into features and then build the optimized codebook using PQ-based SC. In the retrieval stage, we can obtain a better representation of the query sketch and testing images by the optimized codebook with coding quantization residuals, by which the information loss during feature encoding process can be reduced; similarity computing is implemented by comparing the feature histograms between a query sketch and the testing data for the final results. We demonstrate the superiority and effectiveness of the proposed SBIR by comparing it with several state-of-the-art methods on three public sketch datasets. |
4,602 | Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers | Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision regarding the imaging operator to enforce data consistency. To reduce supervision requirements, the recent deep image prior framework instead conjoins untrained MRI priors with the imaging operator during inference. Yet, canonical convolutional architectures are suboptimal in capturing long-range relationships, and priors based on randomly initialized networks may yield suboptimal performance. To address these limitations, here we introduce a novel unsupervised MRI reconstruction method based on zero-Shot Learned Adversarial TransformERs (SLATER). SLATER embodies a deep adversarial network with cross-attention transformers to map noise and latent variables onto coil-combined MR images. During pre-training, this unconditional network learns a high-quality MRI prior in an unsupervised generative modeling task. During inference, a zero-shot reconstruction is then performed by incorporating the imaging operator and optimizing the prior to maximize consistency to undersampled data. Comprehensive experiments on brain MRI datasets clearly demonstrate the superior performance of SLATER against state-of-the-art unsupervised methods. |
4,603 | Wavelet kernel learning | This paper addresses the problem of optimal feature extraction from a wavelet representation. Our work aims at building features by selecting wavelet coefficients resulting from signal or image decomposition on an adapted wavelet basis. For this purpose, we jointly learn in a kernelized large-margin context the wavelet shape as well as the appropriate scale and translation of the wavelets, hence the name "wavelet kernel learning". This problem is posed as a multiple kernel learning problem, where the number of kernels can be very large. For solving such a problem, we introduce a novel multiple kernel learning algorithm based on active constraints methods. We furthermore propose some variants of this algorithm that can produce approximate solutions more efficiently. Empirical analysis show that our active constraint MKL algorithm achieves state-of-the art efficiency. When used for wavelet kernel learning, our experimental results show that the approaches we propose are competitive with respect to the state-of-the-art on brain-computer interface and Brodatz texture datasets. (C) 2011 Elsevier Ltd. All rights reserved. |
4,604 | Static Grid Equivalent Models Based on Artificial Neural Networks | Power systems are rapidly and significantly changing due to the increasing penetration of distributed energy resources (DERs) and the rapid growth of widespread grid interconnections. An increasing number of grid operators is thus interested in the reduced equivalent representation of a large, interconnected power system to reduce the amount of required computational resources and data exchange, e.g., between grid operators. However, state-of-the-art grid equivalents become more and more inapplicable since they are analytically calculated for one specific grid state. They cannot properly be adapted to grid state changes and the behavior of the increasingly used controllers, such as reactive power controllers of DERs. Therefore, an innovative grid equivalent based on artificial neural networks (ANN) is proposed which overcomes the drawbacks of the state-of-the-art grid equivalents as follows: 1) Using supervised ANNs with feedforward and recurrent architectures, power systems can be equivalently represented adaptively and thus more accurately. 2) A feature selection method identifies the elements in the grid with high sensitivity on the boundary enabling a reduction of grid data required for the ANN-based equivalent. 3) To guarantee data confidentiality and cybersecurity, an additional unsupervised ANN, an Autoencoder, is used for obfuscation of the data which is required to be exchanged among grid operators, while the relevant information of the original data is preserved, maintaining the estimation accuracy. The ANN-based approach is analyzed and evaluated with two German benchmark grids and representative scenarios. The results demonstrate that the proposed approach outperforms the state-of-the-art radial equivalent independent method. |
4,605 | Digital Image Decoder for Efficient Hardware Implementation | Increasing the resolution of digital images and the frame rate of video sequences leads to an increase in the amount of required logical and memory resources necessary for digital image and video decompression. Therefore, the development of new hardware architectures for digital image decoder with a reduced amount of utilized logical and memory resources become a necessity. In this paper, a digital image decoder for efficient hardware implementation, has been presented. Each block of the proposed digital image decoder has been described. Entropy decoder, decoding probability estimator, dequantizer and inverse subband transformer (parts of the digital image decoder) have been developed in such way which allows efficient hardware implementation with reduced amount of utilized logic and memory resources. It has been shown that proposed hardware realization of inverse subband transformer requires 20% lower memory capacity and uses less logic resources compared with the best state-of-the-art realizations. The proposed digital image decoder has been implemented in a low-cost FPGA device and it has been shown that it requires at least 32% less memory resources in comparison to the other state-of-the-art decoders which can process high-definition frame size. The proposed solution also requires effectively lower memory size than state-of-the-art architectures which process frame size or tile size smaller than high-definition size. The presented digital image decoder has maximum operating frequency comparable with the highest maximum operating frequencies among the state-of-the-art solutions. |
4,606 | Dynamic multivariate analysis for pollution assessment and river habitat conservation in the Vietnamese La Buong watershed | Analysis of temporal patterns of high-dimensional time-series water quality data is essential for pollution management worldwide. This study has applied dynamic factor analysis (DFA) and cluster analysis (CA) to analyze time-series water quality data monitored at the five stations installed along the La Buong river in Southern Vietnam. Application of the DFA identified two types of temporal patterns, one of the run-off driven parameters (total suspended solid (TSS), turbidity, and iron) and the other of diffuse source pollution. The association of the variables like BOD5 and COD at most stations to the run-off-driven parameters revealed their sharing of drivers. On the contrary, separating variables like phosphate (PO43) at the three upstream stations from the run-off patterns suggested their local point-source origin. The DFA-derived factors were later used in the time-point CA to explore the seasonality of water quality parameters and their pollution intensities compared to regulatory levels. The result suggested intensification in wet season of Fe, TSS, BOD5, and COD concentrations at most sites, which are unobservable in run-off detached parameters like reactive nitrogen, phosphate (PO43-), and E. coli. These findings generated robust insights to support water quality management for river habitat conservation. |
4,607 | Novel Sensor/Access-Point Coverage-Area Maximization for Arbitrary Indoor Polygonal Geometries | Nowadays, sensor/access-point coverage is an essential problem for wireless communication and sensor systems, which will significantly impact the quality of access, monitoring, and surveillance. Indoor sensor/access-point placement still remains very challenging as the regions of interest (ROIs) or underlying geometries may be in an arbitrary polygonal shape. In this work, we would like to study how to place a sensor/access-point to maximize the coverage area within an arbitrary polygonal ROI. Our novel approach is based on finding the maximum-area clique over the visibility graph corresponding to the indoor geometry. According to many examples, our proposed optimal sensor/access-point scheme can lead to larger coverage efficiencies than the existing solution to the art gallery problem (AGP) and the conventional Delaunay triangulation method. Our new scheme is of great practical value as it can be applied for not only both convex and nonconvex simply-connected polygons but also both convex and nonconvex multiply-connected polygons with internal holes. |
4,608 | Enhanced Single Shot Small Object Detector for Aerial Imagery Using Super-Resolution, Feature Fusion and Deconvolution | One common issue of object detection in aerial imagery is the small size of objects in proportion to the overall image size. This is mainly caused by high camera altitude and wide-angle lenses that are commonly used in drones aimed to maximize the coverage. State-of-the-art general purpose object detector tend to under-perform and struggle with small object detection due to loss of spatial features and weak feature representation of the small objects and sheer imbalance between objects and the background. This paper aims to address small object detection in aerial imagery by offering a Convolutional Neural Network (CNN) model that utilizes the Single Shot multi-box Detector (SSD) as the baseline network and extends its small object detection performance with feature enhancement modules including super-resolution, deconvolution and feature fusion. These modules are collectively aimed at improving the feature representation of small objects at the prediction layer. The performance of the proposed model is evaluated using three datasets including two aerial images datasets that mainly consist of small objects. The proposed model is compared with the state-of-the-art small object detectors. Experiment results demonstrate improvements in the mean Absolute Precision (mAP) and Recall values in comparison to the state-of-the-art small object detectors that investigated in this study. |
4,609 | The Psychological Consequences of Envying Influencers on Instagram | This study examines how being envious toward social media influencers (SMIs) relates to users' affective well-being. An online survey was conducted in which 305 U.S. participants viewed to 20 posts of an SMI and subsequently measured their affective well-being, envy, and inspiration toward the SMI, and self-esteem levels. The results revealed a direct negative relationship between envy and affective well-being, but a positive indirect effect through inspiration. Furthermore, individuals' self-esteem moderates the relationship such that the positive relationship between envy and affective well-being through inspiration is stronger among those with high levels of self-esteem. Moreover, inspiration varies between different influencer categories, that is, participants who viewed fitness influencers reported the greatest amount of inspiration, followed by fashion, beauty, and entertainment influencers. |
4,610 | Examining the Preliminary Efficacy of a Dating Violence Prevention Program for Hispanic Adolescents | The purpose of this study is to evaluate the preliminary efficacy of a dating violence (DV) prevention program for Cuban American adolescents (JOVEN/YOUTH: Juntos Opuestos a la Violence Entre Novios/Together Against Dating Violence). A randomized-controlled experimental design with a delayed condition was used to evaluate the effects on DV victimization and perpetration (N = 82). Self-administrated assessments were completed at baseline, 1 week, 3 months, and 12 months after the intervention to assess for psychological victimization and perpetration and physical and sexual victimization and perpetration. Effect sizes were estimated, and generalized estimating equations were generated to test intervention effects over time and potential gender interactions. The intervention had medium to strong effects on DV victimization and perpetration for male participants but not for females. However, intervention effects were not statistically significant over time. More research is needed to enhance intervention effects of JOVEN on DV outcomes and to evaluate these effects among a larger and more diverse sample. |
4,611 | Two novel mutations in the NR5A1 gene as a cause of disorders of sex development in a Pakistani cohort of 46,XY patients | NR5A1 plays a central role in gonadal development and regulation by transcriptional regulation of key modulators involved in steroidogenesis. Mutations in human NR5A1 are frequently associated with 46,XY disorders of sex development (DSD). We analysed a Pakistani cohort of patients with 46,XY DSD, presenting with variable degrees of gonadal dysgenesis, for NR5A1 mutations. The study identified three mutations (p.Tyr03X, p.Glu07X and p.Gln299HisfsX386), of which two are novel, in these patients with 46,XY DSD. The mutations, p.Tyr03X and novel p.Glu07X, are located in the coding region of the gene, corresponding to DNA-binding domain of the predicted protein. In silico analysis for the novel homozygous p.Gln299HisfsX386 mutation in ligand-binding domain of NR5A1 revealed subtle changes in overall tertiary conformation which is predicted to affect the normal physiology of this mutant protein. This study reveals two novel mutations with altered NR5A1 protein in twenty patients with 46,XY DSD, highlighting the critical role of NR5A1 protein in gonadal development and differentiation. In conclusion, the current and previous studies suggest that the NR5A1 mutations are present in around 8-15% of patients with 46,XY DSD presenting with gonadal dysgenesis. For the clinical utility of NR5A1 gene mutations, more comprehensive studies with large 46,XY DSD patient series in different populations are suggested. |
4,612 | Variation consistency of attributes-based postverification method for copy image retrieval | The state-of-the-art approaches of copy image retrieval are based on the bag-of-visual-words model, which represents an image with a set of visual words obtained by quantizing local features. However, the quantization process reduces local features' discriminative power and thus causes many false matches of local features between images. As a consequence, this brings down the effectiveness of copy image retrieval in large-scale image dataset. In order to handle this problem, postverification methods have been proposed to reject false matches. Previous works of the postverification method focused mainly on geometric relationship consistency among matches of local feature between query image and its candidate for rejecting false candidates. The variation consistency of local feature's attributes is proposed to verify if two pairs of matches are consistent. The matching reliability of local features can be measured by a voting-based method, which is based on the number of consistent matches between two images. This method can easily integrate more attributes of local feature, such as dominant orientation, position, and scale, rather than position of local feature. Experiments on the large-scale datasets demonstrate the effectiveness and efficiency of the proposed approach and show it outperforms the state-of-the-art postverification approaches. (C) 2018 SPIE and IS&T |
4,613 | Circular economy-driven ammonium recovery from municipal wastewater: State of the art, challenges and solutions forward | In current biological nitrogen removal (BNR) processes, most of ammonium in municipal wastewater is biologically transformed to nitrogen gas, making ammonium recovery impossible. Thus, this article aims to provide a holistic review with in-depth discussions on (i) current BNR processes for municipal wastewater treatment, (ii) environmental and economic costs behind ammonium in municipal wastewater, (iii) state of the art of ammonium recovery from municipal wastewater including anaerobic membrane bioreactor turning municipal wastewater to a liquid fertilizer, capturing ammonium in phototrophic biomass, waste activated sludge for land application, bioelectrochemical systems, biological conversion of ammonium to nitrous oxide as a fuel oxidizer, and adsorption, (iv) feasibility and challenge of adsorption for ammonium recovery from municipal wastewater and (v) innovative municipal wastewater reclamation processes coupled with ammonium recovery. Moving forward, municipal wastewater reclamation and resource recovery should be addressed under the framework of circular economy. |
4,614 | Learning Rotation Domain Deep Mutual Information Using Convolutional LSTM for Unsupervised PolSAR Image Classification | Deep learning can archive state-of-the-art performance in polarimetric synthetic aperture radar (PolSAR) image classification with plenty of labeled data. However, obtaining large number of accurately labeled samples of PolSAR data is very hard, which limits the practical use of deep learning. Therefore, unsupervised PolSAR image classification is worthy of further investigation that is based on deep learning. Inspired by the superior performance of deep mutual information in natural image feature learning and clustering, an end-to-end Convolutional Long Short Term Memory (ConvLSTM) network is used in order to learn the deep mutual information of polarimetric coherent matrices in the rotation domain with different polarimetric orientation angles (POAs) for unsupervised PolSAR image classification. First, for each pixel, paired "POA-spatio" samples are generated from the polarimetric coherent matrices with different POAs. Second, a special designed ConvLSTM network, along with deep mutual information losses, is used in order to learn the discriminative deep mutual information feature representation of the paired data. Finally, the classification results can be output directly from the trained network model. The proposed method is trained in an end-to-end manner and does not have cumbersome pipelines. Experiments on four real PolSAR datasets show that the performance of proposed method surpasses some state-of-the-art deep learning unsupervised classification methods. |
4,615 | An enhanced fast mode decision model for spatial enhancement layers in scalable video coding | Recently, the H.264/AVC standard has been extended to incorporate Scalable Video Coding (SVC). SVC offers the advantage of scalable (layered) coding, but has the disadvantage of a highly increased computational complexity at the encoder side when dealing with spatial scalability. To restrict the increase in required processing power, fast mode decision models for spatial enhancement layers have been proposed in literature. We propose a novel generic fast mode decision model for spatial enhancement layers for both P and B frames based on both the quantization of the enhancement layer and the correlation between the macroblock type in the enhancement layer and the co-located macroblock in the reference layer. In this paper, an evaluation of the proposed model and comparison with a state-of-the-art model is given. Results show that the proposed technique performs exceptionally well for spatial scalability. For both dyadic and non-dyadic spatial scalability, we achieve an average time saving of 75%, while only a slight bit rate increase of 2.23% and a minor PSNR decrease of 0.46 dB are measured. Compared with state of the art techniques, we further halve the complexity while having comparable rate-distortion results. |
4,616 | The problem of art teaching based on interactive multimedia assisted instruction platform | When digital technology reproduces and simulates real scenes, it can realistically multi-dimensionally present and simulate real-life situations. This solves the problem that the design methods that are difficult to avoid in art design teaching are out of touch with the real space environment, and the design concept and function are out of touch. The openness of the teaching knowledge of art design in interactive teaching platform is reflected in the informationization of resources and the development of diverse knowledge. This paper proposes an architecture scheme of multimedia remote interactive teaching system composed of network multimedia teaching platform, general network teaching platform, digital resource management platform, streaming media resource recording platform and interactive distance learning platform. The system architecture has the characteristics that the underlying data are mutually integrated and the platforms complement each other. The essential characteristics of e-Learning are provided in the interaction characteristics, the dynamic resource scheduling function is enabled, and the multimedia-assisted remote interactive teaching in the future m-Learning and u-learning eras The system proposes a vision. |
4,617 | Motion Estimation by Deep Learning in 2D Echocardiography: Synthetic Dataset and Validation | Motion estimation in echocardiography plays an important role in the characterization of cardiac function, allowing the computation of myocardial deformation indices. However, there exist limitations in clinical practice, particularly with regard to the accuracy and robustness of measurements extracted from images. We therefore propose a novel deep learning solution for motion estimation in echocardiography. Our network corresponds to a modified version of PWC-Net which achieves high performance on ultrasound sequences. In parallel, we designed a novel simulation pipeline allowing the generation of a large amount of realistic B-mode sequences. These synthetic data, together with strategies during training and inference, were used to improve the performance of our deep learning solution, which achieved an average endpoint error of 0.07 +/- 0.06 mm per frame and 1.20 +/- 0.67 mm between ED and ES on our simulated dataset. The performance of our method was further investigated on 30 patients from a publicly available clinical dataset acquired from a GE system. The method showed promise by achieving a mean absolute error of the global longitudinal strain of 2.5 +/- 2.1% and a correlation of 0.77 compared to GLS derived from manual segmentation, much better than one of the most efficient methods in the state-of-the-art (namely the FFT-Xcorr block-matching method). We finally evaluated our method on an auxiliary dataset including 30 patients from another center and acquired with a different system. Comparable results were achieved, illustrating the ability of our method to maintain high performance regardless of the echocardiographic data processed. |
4,618 | Effect of Natural Forest Fires on Regional Weather Conditions in Siberia | Effects of forest fires on regional weather conditions were analyzed for Central and Eastern Siberia after warm and dry weather conditions in summer 2019 using COSMO-Ru (COnsortium for Small-scale MOdeling; Ru-Russia) and COSMO-RuART (ART-Aerosols and Reactive Trace gases) model systems. Four series of numerical experiments were conducted (one control experiment and three forest fire experiments assuming total vegetation destruction within the burned areas) to evaluate possible effects of forest fires on surface albedo and vegetation properties as well as their influence on air chemistry and aerosol concentration in the atmosphere. The modeling results showed significant influence of forest fires on regional weather conditions that occurred over large areas situated even away from burnt regions. Decreased surface albedo and reduced latent heat fluxes due to fire-induced destruction of forest cover lead to higher near-surface air temperature and lower air humidity in both burned and surrounding unburned forest areas. On the other hand, reduced incoming solar radiation due to smoke from forest fire plumes decreased land surface temperatures and increased thermal atmospheric stability resulting in reduced regional precipitation. |
4,619 | Recurrent pleomorphic adenoma: unusual cause of isolated sphenoid sinus lesion | Pleomorphic adenoma is the most common benign salivary gland tumour of the head and neck region, most commonly seen in parotid glands. These may arise also from minor salivary glands of the upper aerodigestive tract comprises the upper lip, cheek, floor of the mouth and rarely from mucoserous glands in the nasal cavity and paranasal sinuses. A middle-aged man, who had undergone surgery for a nasal mass 14 years ago, presented with headache and visual complaints from a recurrent mass originating from the sphenoid sinus. Isolated sphenoid sinus space-occupying lesions have always been a diagnostic challenge. The mass was biopsied and diagnosed as pleomorphic adenoma on histopathology, and then excised endoscopically. Early and accurate diagnosis on a biopsy may result in complete excision of the tumour and prevent recurrence. The endoscopic route is preferred for surgical excision and the patient should be followed up clinically and radiologically to detect early recurrence. |
4,620 | A phosphate starvation response-regulated receptor-like kinase, OsADK1, is required for mycorrhizal symbiosis and phosphate starvation responses | Most land plants associate with arbuscular mycorrhizal (AM) fungi to secure mineral nutrient acquisition, especially that of phosphorus. A phosphate starvation response (PHR)-centered network regulates AM symbiosis. Here, we identified 520 direct target genes for the rice transcription factor OsPHR1/2/3 during AM symbiosis using transcriptome deep sequencing and DNA affinity purification sequencing. These genes were involved in strigolactone biosynthesis, transcriptional reprogramming, and bidirectional nutrient exchange. Moreover, we identified the receptor-like kinase, Arbuscule Development Kinase 1 (OsADK1), as a new target of OsPHR1/2/3. Electrophoretic mobility shift assays and transactivation assays showed that OsPHR2 can bind directly to the P1BS elements within the OsADK1 promoter to activate its transcription. OsADK1 appeared to be required for mycorrhizal colonization and arbuscule development. In addition, hydroponic experiments suggested that OsADK1 may be involved in plant Pi starvation responses. Our findings validate a role for OsPHR1/2/3 as master regulators of mycorrhizal-related genes involved in various stages of symbiosis, and uncover a new RLK involved in AM symbiosis and plant Pi starvation responses. |
4,621 | A new dominance-relation metric balancing convergence and diversity in multi- and many-objective optimization | Maintaining a good balance between convergence and diversity in many-objective optimization is a key challenge for most Pareto dominance-based multi-objective evolutionary algorithms. In most existing multi-objective evolutionary algorithms, a certain fixed metric is used in the selection operation, no matter how far the solutions are from the Pareto front. Such a selection scheme directly affects the performance of the algorithm, such as its convergence, diversity or computational complexity. In this paper, we use a more structured metric, termed augmented penalty boundary intersection, which acts differently on each of the non-dominated fronts in the selection operation, to balance convergence and diversity in many-objective optimization problems. In diversity maintenance, we apply a distance-based selection scheme to each non-dominated front. The performance of our proposed algorithm is evaluated on a variety of benchmark problems with 3 to 15 objectives and compared with five state-of-the-art multi-objective evolutionary algorithms. The empirical results demonstrate that our proposed algorithm has highly competitive performance on almost all test instances considered. Furthermore, the combination of a special mate selection scheme and a clustering-based selection scheme considerably reduces the computational complexity compared to most state-of-the-art multi-objective evolutionary algorithms. (C) 2019 Elsevier Ltd. All rights reserved. |
4,622 | A Task Decomposing and Cell Comparing Method for Cervical Lesion Cell Detection | Automatic detection of cervical lesion cells or cell clumps using cervical cytology images is critical to computer-aided diagnosis (CAD) for accurate, objective, and efficient cervical cancer screening. Recently, many methods based on modern object detectors were proposed and showed great potential for automatic cervical lesion detection. Although effective, several issues still hinder further performance improvement of such known methods, such as large appearance variances between single-cell and multi-cell lesion regions, neglecting normal cells, and visual similarity among abnormal cells. To tackle these issues, we propose a new task decomposing and cell comparing network, called TDCC-Net, for cervical lesion cell detection. Specifically, our task decomposing scheme decomposes the original detection task into two subtasks and models them separately, which aims to learn more efficient and useful feature representations for specific cell structures and then improve the detection performance of the original task. Our cell comparing scheme imitates clinical diagnosis of experts and performs cell comparison with a dynamic comparing module (normal-abnormal cells comparing) and an instance contrastive loss (abnormal-abnormal cells comparing). Comprehensive experiments on a large cervical cytology image dataset confirm the superiority of our method over state-of-the-art methods. |
4,623 | A hybrid ART-GRNN online learning neural network with a epsilon-insensitive loss function | In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models. |
4,624 | A Gentle Introduction to Bayesian Network Meta-Analysis Using an Automated R Package | Network meta-analysis is an extension of standard meta-analysis. It allows researchers to build a network of evidence to compare multiple interventions that may have not been compared directly in existing publications. With a Bayesian approach, network meta-analysis can be used to obtain a posterior probability distribution of all the relative treatment effects, which allows for the estimation of relative treatment effects to quantify the uncertainty of parameter estimates, and to rank all the treatments in the network. Ranking treatments using both direct and indirect evidence can provide guidance to policy makers and clinicians for making decisions. The purpose of this paper is to introduce fundamental concepts of Bayesian network meta-analysis (BNMA) to researchers in psychology and social sciences. We discuss several essential concepts of BNMA, including the assumptions of homogeneity and consistency, the fixed and random effects models, prior specification, and model fit evaluation strategies, while pointing out some issues and areas where researchers should use caution in the application of BNMA. Additionally, using an automated R package, we provide a step-by-step demonstration on how to conduct and report the findings of BNMA with a real dataset of psychological interventions extracted from PubMed. |
4,625 | Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images | Annotating cancerous regions in whole-slide images (WSIs) of pathology samples plays a critical role in clinical diagnosis, biomedical research, and machine learning algorithms development. However, generating exhaustive and accurate annotations is labor-intensive, challenging, and costly. Drawing only coarse and approximate annotations is a much easier task, less costly, and it alleviates pathologists' workload. In this paper, we study the problem of refining these approximate annotations in digital pathology to obtain more accurate ones. Some previous works have explored obtaining machine learning models from these inaccurate annotations, but few of them tackle the refinement problemwhere themislabeled regions should be explicitly identified and corrected, and all of them require a - often very large - number of training samples. We present a method, named Label Cleaning Multiple Instance Learning (LC-MIL), to refine coarse annotations on a single WSI without the need for external training data. Patches cropped from a WSI with inaccurate labels are processed jointly within a multiple instance learning framework, mitigating their impact on the predictive model and refining the segmentation. Our experiments on a heterogeneous WSI set with breast cancer lymph node metastasis, liver cancer, and colorectal cancer samples show that LC-MIL significantly refines the coarse annotations, outperforming state-of-the-art alternatives, even while learning from a single slide. Moreover, we demonstrate how real annotations drawn by pathologists can be efficiently refined and improved by the proposed approach. All these results demonstrate that LC-MIL is a promising, lightweight tool to provide fine-grained annotations from coarsely annotated pathology sets. |
4,626 | A Wireless Interconnection Framework for Seamless Inter and Intra-Chip Communication in Multichip Systems | Computing modules in typical datacenter nodes or server racks consist of several multicore chips either on a board or in a System-in-Package (SiP) environment. State-of-the-art inter-chip communication over wireline channels require data signals to travel from internal nets to the peripheral I/O ports and then get routed over the inter-chip channels to the I/O port of the destination chip. Following this, the data is finally routed from the I/O to internal nets of the destination chip over a wireline interconnect fabric. This multihop communication increases energy consumption while decreasing data bandwidth in a multichip system. Also, traditional I/O does not scale well with technology generations due to limitations of pitch. Moreover, intra-chip and inter-chip communication protocol within such a multichip system is often decoupled to facilitate design flexibility. However, a seamless interconnection between on-chip and off-chip data transfer can improve the communication efficiency significantly. Here, we propose the design of a seamless hybrid wired and wireless interconnection network for multichip systems with dimensions spanning up to tens of centimeters with on-chip wireless transceivers. We demonstrate with cycle accurate simulations that such a design increases the bandwidth and reduces the energy consumption in comparison to state-of-the-art wireline I/O based multichip communication. |
4,627 | A Hybrid Deep Model for Recognizing Arabic Handwritten Characters | Handwriting recognition for computer systems has been in research for a long time, with different researchers having an extensive variety of methods at their disposal. The problem is that most of these experiments are done in English, as it is the most spoken language in the world. But other languages such as Arabic, Mandarin, Spanish, French, and Russian also need research done on them since there are millions of people who speak them. In this work, recognizing and developing Arabic handwritten characters is proposed by cleaning the state-of-the-art Arabic dataset called Hijaa, developing Conventional Neural Network (CNN) with a hybrid model using Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) classifiers. The CNN is used for feature extraction of the Arabic character images, which are then passed on to the Machine Learning classifiers. A recognition rate of up to 96.3% for 29classes is achieved, far surpassing the already state-of-the-art results of the Hijaa dataset. |
4,628 | Robust Visual Odometry Leveraging Mixture of Manhattan Frames in Indoor Environments | We propose a robust RGB-Depth (RGB-D) Visual Odometry (VO) system to improve the localization performance of indoor scenes by using geometric features, including point and line features. Previous VO/Simultaneous Localization and Mapping (SLAM) algorithms estimate the low-drift camera poses with the Manhattan World (MW)/Atlanta World (AW) assumption, which limits the applications of such systems. In this paper, we divide the indoor environments into two different scenes: MW and non-MW scenes. The Manhattan scenes are modeled as a Mixture of Manhattan Frames, in which each Manhattan Frame in itself defines a Manhattan World of a specific orientation. Moreover, we provide a method to detect Manhattan Frames (MFs) using the dominant directions extracted from the parallel lines. Our approach is designed with lower computational complexity than existing techniques using planes to detect Manhattan Frame (MF). For MW scenes, we separately estimate rotational and translational motion. A novel method is proposed to estimate the drift-free rotation using MF observations, unit direction vectors of lines, and surface normal vectors. Then, the translation part is recovered from point-line tracking. In non-MW scenes, the tracked and matched dominant directions are combined with the point and line features to estimate the full 6 degree of freedom (DoF) camera poses. Additionally, we exploit the rotation constraints generated from the multi-view dominant directions observations. The constraints are combined with the reprojection errors of points and lines to refine the camera pose through local map bundle adjustment. Evaluations on both synthesized and real-world datasets demonstrate that our approach outperforms state-of-the-art methods. On synthesized datasets, average localization accuracy is 1.5 cm, which is equivalent to state-of-the-art methods. On real-world datasets, the average localization accuracy is 1.7 cm, which outperforms the state-of-the-art methods by 43%. Our time consumption is reduced by 36%. |
4,629 | A linear wavelet filter for parametric imaging with dynamic PET | This paper describes a new filter for parametric images obtained from dynamic positron emission tomography (PET) studies. The filter is based on the wavelet transform following the heuristics of a previously published method that are here developed into a rigorous theoretical framework. It is shown that the space-time problem of modeling a dynamic PET sequence reduces to the classical one of estimation of a normal multivariate vector of independent wavelet coefficients that, under least-squares risk, can be solved by straightforward application of well established theory. From the study of the distribution of wavelet coefficients of PET images, it is inferred that a James-Stein linear estimator is more suitable for the problem than traditional nonlinear procedures that are incorporated in standard wavelet filters. This is confirmed by the superior performance of the James-Stein filter in simulation studies compared to a state-of-the-art nonlinear wavelet filter and a nonstationary filter selected from literature. Finally, the formal framework is interpreted for the practitioner's point of view and advantages and limitations of the method are discussed. |
4,630 | EEG Microstate Analysis and the EEG Inverse Problem Solution as a Tool for Diagnosing Cognitive Dysfunctions in Individuals Who Have Had a Mild Form of COVID-19 | The term "postcovid syndrome" is firmly entrenched in medical terminology, however, many aspects of its clinical manifestations are not well understood. The aim of this work was to find the causes of the development of cognitive dysfunctions in individuals who had a mild form of SARS-CoV-2 using high-density EEG technology and solving an inverse neurophysiological problem. A dynamic study was conducted of 38 people who had COVID-19 and returned to work. Neurophysiological studies were carried out using the EGI-GES-300 system (128 channels). The descriptive characteristics of electroencephalograms were built on the method of studying the spectral density of the EEG signal on the surface of the scalp, and the dynamic characteristics of the signal were studied by fixing EEG microstates, using the method of D. Lehmann and T. Koenig (2018). In the study, a relatively new diagnostic technique for studying cognitive impairments based on the analysis of EEG microstates was implemented, which made it possible to identify signs of functional restructuring of the neuronal macronetworks of the brain and trace the characteristic adaptation of a person during the period of convalescence. The results obtained made it possible to detect a violation of the implementation of the speech function, as a violation of the perception system (ventral information flow system), as well as the connection between the fields of Wernicke's center and Broca's center (dorsal information flow system), leading to the development of communicative dysfunctions that cause characteristic clinical symptoms due to impaired perception of new information and difficulties in implementing the solution. Thus, the survey showed that SARS-Co-V2 causes objective changes in the functional activity of the brain, which are manifested by the syndrome of cognitive dysfunction and require the development of more sensitive clinical tests than currently used. |
4,631 | CLDA: an adversarial unsupervised domain adaptation method with classifier-level adaptation | Domain adaptation is an active and important research field in transfer learning. Unsupervised domain adaptation, which is better in line with real-world scenarios than supervised and semi-supervised domain adaptation, has attracted much attention and research. Inspired by generative adversarial networks (GANs), adversarial unsupervised domain adaptation methods are proposed in recent years, which are shown to achieve state-of-the-art performance. Existing adversarial unsupervised domain adaptation methods generally adopt feature-level adaptation to reduce the cross-domain shifts, which is shown to have some limitations in related research. In this paper, we propose a classifier-level adaptation approach to further reducing the cross-domain shifts. The classifier-level adaptation uses two different but related classifiers for source domain and target domain, different from existing adversarial unsupervised domain adaptation methods. In addition, not only domain-invariant feature representations but also auxiliary information of class labels is used to exploit the joint distribution of category information and extracted features. Based on the above-mentioned approaches, a classifier-level domain adaptation (CLDA) method is proposed. Experimental results show that the proposed CLDA method outperforms state-of-the-art unsupervised domain adaptation methods on Digits and Office-31 datasets. |
4,632 | GPU-based exhaustive algorithms processing kNN queries | Efficient kNN search, or k-nearest neighbors search, is useful, among other fields, in multimedia information retrieval, data mining and pattern recognition problems. A distance function determines how similar the objects are to a given kNN query object. As finding the distance between any given pair of objects (i.e., high-dimensional vectors) is known to be a computationally expensive operation, using parallel computation techniques is an effective way of reducing running times to acceptable values in large databases. In the present work, we offer novel GPU approaches to solving kNN (k-nearest neighbor) queries using exhaustive algorithms based on the Selection Sort, Quicksort and state-of-the-art algorithms. We show that the best approach depends on the k value of the kNN query and achieve a speedup up to 86.4 better than the sequential counterpart. We also propose a multi-core algorithm to be used as reference for the experiments and a hybrid algorithm which combines the proposed algorithms with a state-of-the-art heaps-based method, in which the best performance is obtained with high k values. We also extend our algorithms to be able to deal with large databases that do not fit in GPU memory and whose performance does not deteriorate as database size increases. |
4,633 | Progressive Joint Modeling in Unsupervised Single-Channel Overlapped Speech Recognition | Unsupervised single-channel overlapped speech recognition is one of the hardest problems in automatic speech recognition (ASR). Permutation invariant training (PIT) is a state of the art model-based approach, which applies a single neural network to solve this single-input, multiple-output modeling problem. We propose to advance the current state of the art by imposing a modular structure on the neural network, applying a progressive pretraining regimen, and improving the objective function with transfer learning and a discriminative training criterion. The modular structure splits the problem into three subtasks: frame-wise interpreting, utterance-level speaker tracing, and speech recognition. The pretraining regimen uses these modules to solve progressively harder tasks. Transfer learning leverages parallel clean speech to improve the training targets for the network. Our discriminative training formulation is a modification of standard formulations that also penalizes competing outputs of the system. Experiments are conducted on the artificial overlapped switchboard and hub5e-swb dataset. The proposed framework achieves over 30% relative improvement of word error rate over both a strong jointly trained system, PIT for ASR, and a separately optimized system, PIT for speech separation with clean speech ASR model. The improvement comes from better model generalization, training efficiency, and the sequence level linguistic knowledge integration. |
4,634 | Urban Public Space as a Didactic Platform: Raising Awareness of Climate Change through Experiencing Arts | This paper investigates the meanings of urban public space, both as a didactic platform and as a way to spread awareness of climate change through art. What are the roles of public space? How do artworks intervene in urban public space? How can public art contribute to "sustainability" issues? I have argued that the intervention of art in urban public space offers effective ways of developing climate change art, which is understood to be an educator. Public space can be categorized into three different types: everyday, social, and symbolic spaces. These can be used as a platform for opening discussion and learning about the increased issues of the global crisis in contemporary society. I have drawn upon the representative case studies about climate change to explore how they intervene in urban public space and how they engage viewers to spread awareness, which is one of the fundamental aspects of this paper. It also stimulates viewers' perceptions and awareness of a more sustainable future through phenomenological and emotional experiences. Thus, this paper contributes to the understanding and knowledge of the relationship between art and public space with respect to raising awareness about climate change and considering how art intervenes in urban public space to create an eco-didactic platform. |
4,635 | Fast Georeferenced Aerial Image Stitching With Absolute Rotation Averaging and Planar- Restricted Pose Graph | Accurate digital orthophoto map generation from high-resolution aerial images is important in various applications. Compared with the existing commercial software and the current state-of-the-art mosaicing systems, a novel fast georeferenced orthophoto mosaicing framework is proposed in this study. The framework can adapt to the challenging requirements of high-accuracy orthoimage generations with relatively fast speed, even if the overlap rate is low. We provide appearance and spatial correlation-constrained fast low-overlap neighbor candidate query and matching. On the basis of GPS information, we introduce an absolute position and rotation-averaging strategy for global pose initialization, which is essential for the high convergence and efficiency of nonconvex pose optimization of every image. We also propose a planar-restricted global pose graph optimization method. The optimization is extremely efficient and robust considering that point clouds are parameterized to planes. Finally, we apply a matching graph-based exposure compensation and region reduction algorithm for large-scale and high-resolution image fusion with high efficiency and novel precision. Experimental results demonstrate that our method can achieve the state-of-the-art performance while maintaining high precision and robustness. |
4,636 | Symbolic territories in pre-Magdalenian art? | The legacy of specialists in Upper Paleolithic art shows a common point: a more or less clear separation between Magdalenian art and earlier symbolic manifestations. One of principal difficulty is due to little data firmly dated in the chronology for the "ancient" periods, even if recent studies precise chronological framework. There is a variability of the symbolic traditions from the advent of monumental art in Europe, and there are graphic elements crossing regional limits and asking the question of real symbolic territories existence. The different thematic choices also allows to raise territorial kinships between various caves and various regions. The object of this paper is to define where these rich and varied symbolic records appear, and how graphic traditions are distributed in the Western European Paleolithic area, throughout these 15 000 years. To provide some elements of response to this question, we will draw on the one hand, on the formal approaches in the figures, and on the other - on the thematic range used by Paleolithics. (c) 2017 Published by Elsevier Ltd. |
4,637 | Crystal Facet-Modulated WO3 Nanoplate Photoanode for Photoelectrochemical Glyoxal Semi-oxidation into Glyoxylic Acid | Transforming glyoxal to value-added glyoxylic acid (GA) is highly desirable but challenging due to the uncontrollable over-oxidation. In this work, we report on a first demonstration of semi-oxidation of glyoxal with high selectivity (86.5%) and activity on WO3 nanoplate photoanode through the photoelectrochemical strategy. The optimization of reactivity was achieved via crystal facet regulation, showing a satisfactory GA production rate of 308.4 mmol m-2 h-2, 84.0% faradaic efficiency, and 4.3% total solar-to-glyoxylic acid efficiency on WO3 with enriched {200} facets at 1.6 V versus RHE. WO3 with a high {200} facet ratio exhibits more efficient electron-hole transfer kinetics, resulting in the facilitated formation of hydroxyl radicals (•OH) and glyoxal radicals. Meanwhile, the theoretical calculation results indicate that the high selectivity and activity come from the strong adsorption ability for glyoxal and the low reaction energy for glyoxal radical generation on the (200) facets of WO3. Moreover, the high energy demand toward oxalic acid production on WO3 leads to the exciting semi-oxidation process. |
4,638 | Occlusion Boundary: A Formal Definition & Its Detection via Deep Exploration of Context | Occlusion boundaries contain rich perceptual information about the underlying scene structure and provide important cues in many visual perception-related tasks such as object recognition, segmentation, motion estimation, scene understanding, and autonomous navigation. However, there is no formal definition of occlusion boundaries in the literature, and state-of-the-art occlusion boundary detection is still suboptimal. With this in mind, in this paper we propose a formal definition of occlusion boundaries for related studies. Further, based on a novel idea, we develop two concrete approaches with different characteristics to detect occlusion boundaries in video sequences via enhanced exploration of contextual information (e.g, local structural boundary patterns, observations from surrounding regions, and temporal context) with deep models and conditional random fields. Experimental evaluations of our methods on two challenging occlusion boundary benchmarks (CMU and VSB100) demonstrate that our detectors significantly outperform the current state-of-the-art. Finally, we empirically assess the roles of several important components of the proposed detectors to validate the rationale behind these approaches. |
4,639 | High-Voltage Integrated Circuits: History, State of the Art, and Future Prospects | High-voltage ICs (HVICs) are used in many applications, including ac/dc conversion, off-line LED lighting, and gate drivers for power modules. This paper describes the technologies most commonly used in commercial HVICs, including junction-isolation, thin silicon-on-insulator (SOI), and thick SOI approaches. Emerging technologies such as thin silicon membrane are also discussed. |
4,640 | Circular Dichroism of Amino Acids: Following the Structural Formation of Phenylalanine | Circular dichroism (CD) is frequently used to assess the secondary structure of peptides and proteins, whereas less attention has been given to their building blocks, that is, single amino acids, as they do not possess a secondary structure. Here, we follow the CD signal of amino acids and reveal that several acids exhibit a unique CD pattern as a function of their concentration. Accordingly, we propose an eight-level classification of the CD signal of the various amino acids. Special focus is given to the CD pattern of phenylalanine (Phe), for which we observe the formation of an ultra-narrow CD peak (full width at high maximum of only 5 nm). This CD peak can be attributed to the formation of Phe-based chiral structural features. Further support for the formation of an ordered structure is given by using NMR, and the additional self-assembly process of Phe to tubular structures. |
4,641 | Ultralow-Latency VLSI Architecture Based on a Linear Approximation Method for Computing Nth Roots of Floating-Point Numbers | State-of-the-art approaches that perform root computations based on the COordinate Rotation Digital Computer (CORDIC) algorithm suffer from high latency in performing multiple iterations. Therefore, root computations based on the CORDIC algorithm cannot meet the strict latency requirements of some applications. In this paper, we propose a methodology for performing Nth root computations on floating-point numbers based on the piecewise linear (PWL) approximation method. The proposed method divides an Nth root computation into several subtasks approximated by the PWL algorithm. It determines the widest segments of the subtasks and the smallest fractional width needed to satisfy the predefined maximum relative error Max_ Err(r). Our design is coded in Verilog HDL and synthesized under TSMC 40 nm CMOS technology. The synthesized results show that our design can reach the highest frequency of 2.703 GHz with an area consumption of 2608.84 mu m(2) and a power consumption of 2.4476 mW. Compared with one state-of-the-art architecture, our design saves 91.60%, 89.84%, and 63.33% of the area, power, and latency @1.89GHz frequency, respectively, while reducing Max_Err(r) by 57.30%. In addition, it saves 94.52%, 92.68%, and 73.17% of the area, power, and delay @1.89GHz frequency, respectively, and reduces Max_ Err(r) by 1.65% when compared with the other state-of-the-art design. |
4,642 | Phase II Trial of Reduced-Intensity Busulfan/Clofarabine Conditioning with Allogeneic Hematopoietic Stem Cell Transplantation for Patients with Acute Myeloid Leukemia, Myelodysplastic Syndromes, and Acute Lymphoid Leukemia | Clofarabine has potent antileukemia activity and its inclusion in reduced-intensity conditioning (RIC) allogeneic hematopoietic stem cell transplantation (HSCT) for acute leukemia could potentially improve outcomes. We conducted a phase II study of busulfan (.8 mg/kg i.v. twice daily on days -5, -4, -3, and -2) with clofarabine (40 mg/m(2) i.v. daily on days -5, -4, -3, and -2) conditioning before allogeneic 8/8 HLA-matched related or unrelated HSCT. The primary endpoint was donor neutrophil engraftment by day +40. Secondary endpoints included nonrelapse mortality (NRM), acute and chronic graft-versus-host disease (GVHD), progression-free survival (PFS), and overall survival (OS). Thirty-four patients (acute myeloid leukemia [AML], n = 25; myelodysplastic syndromes, n = 5; and acute lymphoid leukemia, n = 4) were enrolled. Day 40+ engraftment with donor chimerism was achieved in 33 of 34 patients with 1 patient dying before count recovery. Day 100 and 1-year NRM were 5.9% (95% confidence interval [CI], 1.0 to 17.4) and 24% (95% CI, 11 to 39), respectively. The 2-year relapse rate was 26% (95% CI, 13 to 42). Cumulative incidences of acute and chronic GVHD were 21% and 44%, respectively. The 2-year PFS was 50% (95% CI, 32 to 65) and OS was 56% (95% CI, 38 to 71). For patients with AML in first complete remission, 2-year PFS and OS were both 82% (95% CI, 55 to 94). RIC with busulfan and clofarabine leads to successful engraftment with acceptable rates of NRM and GVHD. |
4,643 | Switchable RAFT Polymerization Employing Photoresponsive HABI as a Mediator | Recently, considerable interest has been devoted to developing switchable reversible addition fragmentation chain transfer (RAFT) polymerizations via photoactivation methods. Herein, a photo-deactivation strategy is introduced to regulate RAFT polymerization using photoresponsive hexaarylbiimidozole (HABI) as a mediator, which leads to switchable RAFT polymerization by repeated ON/OFF experiments. In comparison with well-known PET-RAFT polymerization, photo-deactivation RAFT (PD-RAFT) polymerization can be temporally stopped with UV light ON, where photoresponsive HABI can reversibly quench propagating radicals, resulting in switchable RAFT polymerization. The proposed mechanism of PD-RAFT polymerization in the presence of HABI involving radical quenching is based on ESR, NMR, GPC, MALDI-TOF-MS, and kinetics studies. |
4,644 | Gaussian Process Regression for Estimating Chlorophyll Concentration in Subsurface Waters From Remote Sensing Data | In this letter, we explore the effectiveness of a novel regression method in the context of the estimation of biophysical parameters from remotely sensed imagery as an alternative to state-of-the-art regression methods like those based on artificial neural networks and support vector machines. This method, called Gaussian process (GP) regression, formulates the learning of the regressor within a Bayesian framework, where the regression model is derived by assuming the model variables follow a Gaussian prior distribution encoding the prior knowledge about the output function. One of its interesting properties, which gives it a key advantage over state-of-the-art regression methods, is the possibility to tune the free parameters of the model in an automatic way. Experiments were focused on the problem of estimating chlorophyll concentration in subsurface waters. The achieved results suggest that the GP regression method is very promising from both viewpoints of estimation accuracy and free parameter tuning. Moreover, it handles particularly well the problem of limited availability of training samples, typically encountered in biophysical parameter estimation applications. |
4,645 | Calcium-permeable AMPA receptors trigger vesicular glutamate release from Bergmann gliosomes | The Bergmann glia is equipped with Ca2+-permeable AMPA receptors for glutamate, indispensable for structural and functional relations between the Bergmann glia and parallel/climbing fibers-Purkinje cell synapses. To better understand roles for the Bergmann AMPA receptors, herein we investigate on gliotransmitter release and Ca2+ signals in isolated Bergmann glia processes obtained from adult rat cerebellum. We found that: 1) the rat cerebellar purified astrocyte processes (gliosomes) expressed astrocytic and Bergmann markers and exhibited negligible contamination by nerve terminals, microglia, or oligodendrocytes; 2) activation of Ca2+-permeable AMPA receptors caused Ca2+ signals in the processes, and the release of glutamate from the processes; 3) effectiveness of rose bengal, trypan blue or bafilomycin A1, indicated that activation of the AMPA receptors evoked vesicular glutamate release. Cerebellar purified nerve terminals appeared devoid of glutamate-releasing Ca2+-permeable AMPA receptors, indicating that neuronal contamination may not be the source of the signals detected. Ultrastructural analysis indicated the presence of vesicles in the cytoplasm of the processes; confocal imaging confirmed the presence of vesicular glutamate transporters in Bergmann glia processes. We conclude that: a vesicular mechanism for release of the gliotransmitter glutamate is present in mature Bergmann processes; entry of Ca2+ through the AMPA receptors located on Bergmann processes is coupled with vesicular glutamate release. The findings would add a new role for a well-known Bergmann target for glutamate (the Ca2+-permeable AMPA receptors) and a new actor (the gliotransmitter glutamate) at the cerebellar excitatory synapses onto Purkinje cells. |
4,646 | Using percentiles in the interpretation of Patient-Reported Outcomes Measurement Information System scores: Guidelines for autism | The objectives of this study were to (1) demonstrate the application of percentiles to advance the interpretation of patient-reported outcomes and (2) establish autism-specific percentiles for four Patient-Reported Outcomes Measurement Information System (PROMIS) measures. PROMIS measures were completed by parents of autistic children and adolescents ages 5-17 years as part of two studies (n = 939 parents in the first study and n = 406 parents in the second study). Data from the first study were used to develop autism-specific percentiles for PROMIS parent-proxy sleep disturbance, sleep-related impairment, fatigue, and anxiety. Previously established United States general population percentiles were applied to interpret PROMIS scores in both studies. Results of logistic regression models showed that parent-reported material hardship was associated with scoring in the moderate-severe range (defined as ≥75th percentile in the general population) on all four PROMIS measures (odds ratios 1.7-2.2). In the second study, the percentage of children with severe scores (defined as ≥95th percentile in the general population) was 30% for anxiety, 25% for sleep disturbance, and 17% for sleep-related impairment, indicating a high burden of these problems among autistic children. Few children had scores at or above the autism-specific 95th percentile on these measures (3%-4%), indicating that their scores were similar to other autistic children. The general population and condition-specific percentiles provide two complementary reference points to aid interpretation of PROMIS scores, including corresponding severity categories that are comparable across different PROMIS measures. |
4,647 | Activated Single-Phase Ti4 O7 Nanosheets with Efficient Use of Precious Metal for Inspired Oxygen Reduction Reaction | The oxygen reduction reaction (ORR) is central to modern energy storage and conversion technologies for grids such as fuel cells and electrolyzers, but challenges remain due to the lack of reliable, economic, and durable electrocatalysts. Here, we develop single-crystal conductive black titanium (Ti4 O7 ) nanosheets (NSs) as a new precious metal carrier based on sacrificial hard templates and ultrasonic-assisted peeling, and deposit Pt clusters on Ti4 O7 NSs induced by wetness impregnation under the irradiation of visible light (VI; 650 nm). Pt/Ti4 O7 NSs provide Ti3+ , Pt2+ , and Pt0+ continuous active sites for the ORR multielectron process, achieving synergy among them. The assistance of visible light not only makes a more uniform and smaller distribution of Pt nanoclusters, but also strengthens the charge transfer, thereby constructing a strong metal-support interaction interface. VI-Pt/Ti4 O7 NSs show superior initial oxidation potential and a mass activity of 1.61 A mg-1 Pt at a E1/2 =0.91 V, which is nine times higher than that of commercial Pt/C. This work provides an effective strategy for achieving high-value applications of titanium sub-oxides and further explores the enhanced interface in metals Tin O2n-1 by light radiation. |
4,648 | Multi-Oriented Object Detection in High-Resolution Remote Sensing Imagery Based on Convolutional Neural Networks with Adaptive Object Orientation Features | In high-resolution earth observation systems, object detection in high spatial resolution remote sensing images (HSRIs) is the key technology for automatic extraction, analysis and understanding of image information. With respect to the multi-angle features of object orientation in HSRIs object detection, this paper presents a novel HSRIs object detection method based on convolutional neural networks (CNN) with adaptive object orientation features. First, an adaptive object orientation regression method is proposed to obtain object regions in any direction. In the adaptive object orientation regression method, five coordinate parameters are used to regress the object region with any direction. Then, a CNN framework for object detection of HSRIs is designed using the adaptive object orientation regression method. Using multiple object detection datasets, the proposed method is compared with some state-of-the-art object detection methods. The experimental results show that the proposed method can more accurately detect objects with large aspect ratios and densely distributed objects than some state-of-the-art object detection methods using a horizontal bounding box, and obtain better object detection results for HSRIs. |
4,649 | Visual Grouping by Neural Oscillator Networks | Distributed synchronization is known to occur at several scales in the brain, and has been suggested as playing a key functional role in perceptual grouping. State-of-the-art visual grouping algorithms, however, seem to give comparatively little attention to neural synchronization analogies. Based on the framework of concurrent synchronization of dynamical systems, simple networks of neural oscillators coupled with diffusive connections are proposed to solve visual grouping problems. The key idea is to embed the desired grouping properties in the choice of the diffusive couplings, so that synchronization of oscillators within each group indicates perceptual grouping of the underlying stimulative atoms, while desynchronization between groups corresponds to group segregation. Compared with state-of-the-art approaches, the same algorithm is shown to achieve promising results on several classical visual grouping problems, including point clustering, contour integration, and image segmentation. |
4,650 | Reweighted sparse subspace clustering | Motion segmentation and human face clustering are two fundamental problems in computer vision. The state-of-the-art algorithms employ the subspace clustering scheme when processing the two problems. Among these algorithms, sparse subspace clustering (SSC) achieves the state-of-the-art clustering performance via solving a l(1) minimization problem and employing the spectral clustering technique for clustering data points into different subspaces. In this paper, we propose an iterative weighting (reweighted) l(1) minimization framework which largely improves the performance of the traditional l(1) minimization framework. The reweighted l(1) minimization framework makes a better approximation to the to minimization than tradition l(1) minimization framework. Following the reweighted l(1) minimization framework, we propose a new subspace clustering algorithm, namely, reweighted sparse subspace clustering (RSSC). Through an extensive evaluation on three benchmark datasets, we demonstrate that the proposed RSSC algorithm significantly reduces the clustering errors over the SSC algorithm while the additional reweighted step has a moderate impact on the computational cost. The proposed RSSC also achieves lowest clustering errors among recently proposed algorithms. On the other hand, as majority of the algorithms were evaluated on the Hopkins155 dataset, which is insufficient of non-rigid motion sequences, the dataset can hardly reflect the ability of the existing algorithms on processing non-rigid motion segmentation. Therefore, we evaluate the performance of the proposed RSSC and state-of-the-art algorithms on the Freiburg-Berkeley Motion Segmentation Dataset, which mainly contains non-rigid motion sequences. The performance of these state-of-the-art algorithms, as well as RSSC, will drop dramatically on this dataset with mostly non-rigid motion sequences. Though the proposed RSSC achieves the better performance than other algorithms, the results suggest that novel algorithms that focus on segmentation of non-rigid motions are still in need. (C) 2015 Elsevier Inc. All rights reserved. |
4,651 | Life cycle assessment comparison of emerging and traditional Titanium dioxide manufacturing processes | Titanium dioxide (TiO2) is used as pigment in a wide variety of domestic and industrial applications, and is becoming an increasingly valuable nanomaterial. TiO2 is manufactured by the traditional sulfate process or high temperature chloride process. Several hydrometallurgical processes for manufacturing TiO2 have recently emerged to reduce the environmental impact of TiO2 production. A new process is reported that features alkaline roasting of titania slag (ARTS), with subsequent washing, leaching, solvent extraction, hydrolysis, and calcination stages, and implements the recycling and regeneration of alkaline and acid process streams to minimize waste generation. A virtual ARTS processing plant is described in detail and is used to conduct an LCA comparison with the sulfate, chloride, and Altairnano processes. The cumulative energy demand (CED) and total CO2 emissions for the ARTS process are 92.6 MJ/kg TiO2 and 7.47 kg CO2/kg TiO2, respectively, which compares favorably with the traditional and Altairnano processes. (C) 2014 Elsevier Ltd. All rights reserved. |
4,652 | Automatic Multiorgan Segmentation via Multiscale Registration and Graph Cut | We propose an automatic multiorgan segmentation method for 3-D radiological images of different anatomical contents and modalities. The approach is based on a simultaneous multilabel graph cut optimization of location, appearance, and spatial configuration criteria of target structures. Organ location is defined by target-specific probabilistic atlases (PA) constructed from a training dataset using a fast (2+1)D SURF-based multiscale registration method involving a simple four-parameter transformation. PAs are also used to derive target-specific organ appearance models represented as intensity histograms. The spatial configuration prior is derived from shortest-path constraints defined on the adjacency graph of structures. Thorough evaluations on Visceral project benchmarks and training dataset, as well as comparisons with the stateof-the-art confirm that our approach is comparable to and often outperforms similar approaches in multiorgan segmentation, thus proving that the combination of multiple suboptimal but complementary information sources can yield very good performance. |
4,653 | Preprocedural Multimodality Imaging in Atrial Fibrillation | Atrial fibrillation (AF) is the most common arrhythmia worldwide and is associated with increased risk of heart failure, stroke, and death. In current medical practice, multimodality imaging is routinely used in the management of AF. Twenty-one years ago, the ACUTE trial (Assessment of Cardioversion Using Transesophageal Echocardiography) results were published, and the management of AF changed forever by incorporating transesophageal echocardiography guided cardioversion of patients in AF for the first time. Current applications of multimodality imaging in AF in 2022 include the use of transesophageal echocardiography and computed tomography before cardioversion to exclude left atrial thrombus and in left atrial appendage occlusion device implantation. Transesophageal echocardiography, cardiac computed tomography, and cardiac magnetic resonance are clinically used for AF ablation planning. The decision to use a particular imaging modality in AF is based on patient's characteristics, guideline recommendation, institutional preferences, expertise, and cost. In this first of 2-part review series, we discuss the preprocedural role of echocardiography, computed tomography, and cardiac magnetic resonance in the AF, with regard to their clinical applications, relevant outcomes data and unmet needs, and highlights future directions in this rapidly evolving field. |
4,654 | Quantum circuit-based modeling of continuous-variable quantum key distribution system | The second generation Continuous-Variable Quantum Key Distribution is one of the most promising application of quantum mechanics in communications. This paper investigates a state-of-the-art Continuous-Variable Quantum Key Distribution solution from circuit modeling point of view in order to build an appropriate simulation framework. This framework allows detailed evaluation of the system's performance and adjusting the system parameters. Copyright (c) 2017 John Wiley & Sons, Ltd. |
4,655 | Efficient Shapelet Discovery for Time Series Classification | Time-series shapelets are discriminative subsequences, recently found effective for time series classification (TSC). It is evident that the quality of shapelets is crucial to the accuracy of TSC. However, major research has focused on building accurate models from some shapelet candidates. To determine such candidates, existing studies are surprisingly simple, e.g., enumerating subsequences of some fixed lengths, or randomly selecting some subsequences as shapelet candidates. The major bulk of computation is then on building the model from the candidates. In this paper, we propose a novel efficient shapelet discovery method, called BSPCOVER, to discover a set of high-quality shapelet candidates for model building. Specifically, BSPCOVER generates abundant candidates via Symbolic Aggregate approXimation with sliding window, then prunes identical and highly similar candidates via Bloom filters, and similarity matching, respectively. We next propose a p-Cover algorithm to efficiently determine discriminative shapelet candidates that maximally represent each time-series class. Finally, any existing shapelet learning method can be adopted to build a classification model. We have conducted extensive experiments with well-known time-series datasets and representative state-of-the-art methods. Results show that BSPCOVER speeds up the state-of-the-art methods by more than 70 times, and the accuracy is often comparable to or higher than existing works. |
4,656 | A Unified Approach to Diffusion Direction Sensitive Slice Registration and 3-D DTI Reconstruction From Moving Fetal Brain Anatomy | This paper presents an approach to 3-D diffusion tensor image (DTI) reconstruction from multi-slice diffusion weighted (DW) magnetic resonance imaging acquisitions of the moving fetal brain. Motion scatters the slice measurements in the spatial and spherical diffusion domain with respect to the underlying anatomy. Previous image registration techniques have been described to estimate the between slice fetal head motion, allowing the reconstruction of 3D a diffusion estimate on a regular grid using interpolation. We propose Approach to Unified Diffusion Sensitive Slice Alignment and Reconstruction (AUDiSSAR) that explicitly formulates a process for diffusion direction sensitive DW-slice-to-DTI-volume alignment. This also incorporates image resolution modeling to iteratively deconvolve the effects of the imaging point spread function using the multiple views provided by thick slices acquired in different anatomical planes. The algorithm is implemented using a multi-resolution iterative scheme and multiple real and synthetic data are used to evaluate the performance of the technique. An accuracy experiment using synthetically created motion data of an adult head and an experiment using synthetic motion added to sedated fetal monkey dataset show a significant improvement in motion-trajectory estimation compared to current state-of-the-art approaches. The performance of the method is then evaluated on challenging but clinically typical in utero fetal scans of four different human cases, showing improved rendition of cortical anatomy and extraction of white matter tracts. While the experimental work focuses on DTI reconstruction (second-order tensor model), the proposed reconstruction framework can employ any 5-D diffusion volume model that can be represented by the spatial parameterizations of an orientation distribution function. |
4,657 | Estimation of Age Using Alveolar Bone Loss: Forensic and Anthropological Applications | The objective of this study was to utilize a new odontological methodological approach based on radiographic for age estimation. The study was comprised of 397 participants aged between 9 and 87 years. A clinical examination and a radiographic assessment of alveolar bone loss were performed. Direct measures of alveolar bone level were recorded using CT scans. A medical examination report was attached to the investigation file. Because of the link between alveolar bone loss and age, a model was proposed to enable simple, reliable, and quick age estimation. This work added new arguments for age estimation. This study aimed to develop a simple, standardized, and reproducible technique for age estimation of adults of actual populations in forensic medicine and ancient populations in funeral anthropology. |
4,658 | Dominant negative Ras attenuates pathological ventricular remodeling in pressure overload cardiac hypertrophy | The importance of the oncogene Ras in cardiac hypertrophy is well appreciated. The hypertrophic effects of the constitutively active mutant Ras-Val12 are revealed by clinical syndromes due to the Ras mutations and experimental studies. We examined the possible anti-hypertrophic effect of Ras inhibition in vitro using rat neonatal cardiomyocytes (NRCM) and in vivo in the setting of pressure-overload left ventricular (LV) hypertrophy (POH) in rats. Ras functions were modulated via adenovirus directed gene transfer of active mutant Ras-Val12 or dominant negative mutant N17-DN-Ras (DN-Ras). Ras-Val12 expression in vitro activates NFAT resulting in pro-hypertrophic and cardio-toxic effects on NRCM beating and Z-line organization. In contrast, the DN-Ras was antihypertrophic on NRCM, inhibited NFAT and exerted cardio-protective effects attested by preserved NRCM beating and Z line structure. Additional experiments with silencing H-Ras gene strategy corroborated the antihypertrophic effects of siRNA-H-Ras on NRCM. In vivo, with the POH model, both Ras mutants were associated with similar hypertrophy two weeks after simultaneous induction of POH and Ras-mutant gene transfer. However, LV diameters were higher and LV fractional shortening lower in the Ras-Val12 group compared to control and DN-Ras. Moreover, DN-Ras reduced the cross-sectional area of cardiomyocytes in vivo, and decreased the expression of markers of pathologic cardiac hypertrophy. In isolated adult cardiomyocytes after 2 weeks of POH and Ras-mutant gene transfer, DN-Ras improved sarcomere shortening and calcium transients compared to Ras-Val12. Overall, DN-Ras promotes a more physiological form of hypertrophy, suggesting an interesting therapeutic target for pathological cardiac hypertrophy. |
4,659 | The neuroprotective and antidiabetic effects of trigonelline: A review of signaling pathways and molecular mechanisms | The global epidemic of diabetes has brought heavy pressure on public health. New effective anti-diabetes strategies are urgently needed. Trigonelline is the main component of fenugreek, which has been proved to have a good therapeutic effect on diabetes and diabetic complications. Trigonelline achieves amelioration of diabetes, the mechanisms of which include the modulation of insulin secretion, a reduction in oxidative stress, and the improvement of glucose tolerance and insulin resistance. Besides, trigonelline has been reported to be a neuroprotective agent against many neurologic diseases including Alzheimer's disease, Parkinson's disease, stroke, and depression. Concerning the potential therapeutic effects of trigonelline, comprehensive clinical trials are warranted to evaluate this valuable molecule. |
4,660 | Malawi Stories: mapping an art-science collaborative process | This paper outlines a project drawing together an artist working on creative GIS, a geomatics scholar, an NGO leader, a rural geographer and soil scientist, an environmental geochemist, and a political geographer. With a shared interest in the social and physical processes affecting people's lives in Malawi, and the possibilities for interdisciplinary collaboration, the team engaged in practice-based mapping of our data sources and respective methodologies. The project relates to two sites in Malawi: Tikondwe Freedom Gardens and the Likangala River. The paper details our practices as we shared, debated, and repurposed our data as a means of situating these practices and data. Using paper and pen, whiteboard, PowerPoint, and web-design software, we note here our effort to map a 'space of experimentation' highlighting, and reflecting on, our diverse disciplinary orientations, training, instrumentation, recording, and reporting procedures, as well as bodily practices that enable and give animation to these factors. |
4,661 | Microbial ecology of the Southern Ocean | The Southern Ocean (SO) distributes climate signals and nutrients worldwide, playing a pivotal role in global carbon sequestration. Microbial communities are essential mediators of primary productivity and carbon sequestration, yet we lack a comprehensive understanding of microbial diversity and functionality in the SO. Here, we examine contemporary studies in this unique polar system, focusing on prokaryotic communities and their relationships with other trophic levels (i.e. phytoplankton and viruses). Strong seasonal variations and the characteristic features of this ocean are directly linked to community composition and ecosystem functions. Specifically, we discuss characteristics of SO microbial communities and emphasise differences from the Arctic Ocean microbiome. We highlight the importance of abundant bacteria in recycling photosynthetically derived organic matter. These heterotrophs appear to control carbon flux to higher trophic levels when light and iron availability favour primary production in spring and summer. Conversely, during winter, evidence suggests that chemolithoautotrophs contribute to prokaryotic production in Antarctic waters. We conclude by reviewing the effects of climate change on marine microbiota in the SO. |
4,662 | Toward a Universal Synthetic Speech Spoofing Detection Using Phase Information | In the field of speaker verification (SV) it is nowadays feasible and relatively easy to create a synthetic voice to deceive a speech driven biometric access system. This paper presents a synthetic speech detector that can be connected at the front-end or at the back-end of a standard SV system, and that will protect it from spoofing attacks coming from state-of-the-art statistical Text to Speech (TTS) systems. The system described is a Gaussian Mixture Model (GMM) based binary classifier that uses natural and copy-synthesized signals obtained from the Wall Street Journal database to train the system models. Three different state-of-the-art vocoders are chosen and modeled using two sets of acoustic parameters: 1) relative phase shift and 2) canonical Mel Frequency Cepstral Coefficients (MFCC) parameters, as baseline. The vocoder dependency of the system and multivocoder modeling features are thoroughly studied. Additional phase-aware vocoders are also tested. Several experiments are carried out, showing that the phase-based parameters perform better and are able to cope with new unknown attacks. The final evaluations, testing synthetic TTS signals obtained from the Blizzard challenge, validate our proposal. |
4,663 | Stabilizing peer-to-peer energy trading in prosumer coalition through computational efficient pricing | Load balancing issues in distribution networks have emerged alongside the large-scale deployment of distributed renewable generation sources. In light of this challenge, peer-to-peer (P2P) energy trading constitutes a promising approach for delivering secure and economic supply-demand balance when faced with variable load and intermittent renewable generation through matching energy demand and supply locally. However, state-of-the-art mechanisms for governing P2P energy trading either fail to suitably incentivize prosumers to participate in P2P trading or suffer severely from the curse of dimensionality with their computational complexity increase exponentially with the number of prosumers. In this paper, a P2P energy trading mechanism based on cooperative game theory is proposed to establish a grand energy coalition of prosumers and a computationally efficient pricing algorithm is developed to suitably incentivize prosumers for their sustainable participation in the grand coalition. The performance of the proposed algorithm is demonstrated by comparing it to state-of-the-art mechanisms through numerous case studies in a real-world scenario. The superior computational performance of the proposed algorithm is also validated. |
4,664 | An Efficient Audio Coding Scheme for Quantitative and Qualitative Large Scale Acoustic Monitoring Using the Sensor Grid Approach | The spreading of urban areas and the growth of human population worldwide raise societal and environmental concerns. To better address these concerns, the monitoring of the acoustic environment in urban as well as rural or wilderness areas is an important matter. Building on the recent development of low cost hardware acoustic sensors, we propose in this paper to consider a sensor grid approach to tackle this issue. In this kind of approach, the crucial question is the nature of the data that are transmitted from the sensors to the processing and archival servers. To this end, we propose an efficient audio coding scheme based on third octave band spectral representation that allows: (1) the estimation of standard acoustic indicators; and (2) the recognition of acoustic events at state-of-the-art performance rate. The former is useful to provide quantitative information about the acoustic environment, while the latter is useful to gather qualitative information and build perceptually motivated indicators using for example the emergence of a given sound source. The coding scheme is also demonstrated to transmit spectrally encoded data that, reverted to the time domain using state-of-the-art techniques, are not intelligible, thus protecting the privacy of citizens. |
4,665 | A clustering fuzzy approach for image segmentation | Segmentation is a fundamental step in image description or classification. In recent years, several computational models have been used to implement segmentation methods but without establishing a single analytic solution. However, the intrinsic properties of neural networks make them an interesting approach, despite some measure of inefficiency. This paper presents a clustering approach for image segmentation based on a modified fuzzy approach for image segmentation (ART) model. The goal of the proposed approach is to find a simple model able to instance a prototype for each cluster avoiding complex post-processing phases. Results and comparisons with other similar models presented in the literature (like self-organizing maps and original fuzzy ART) are also discussed. Qualitative and quantitative evaluations confirm the validity of the approach proposed. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved. |
4,666 | Multiscale texture retrieval based on low-dimensional and rotation-invariant features of curvelet transform | Multiscale-based texture retrieval algorithms use low-dimensional feature sets in general. However, they do not have as good retrieval performances as those of the state-of-the-art techniques in the literature. The main motivation of this study is to use low-dimensional multiscale features to provide comparable retrieval performances with the state-of-the-art techniques. The proposed features of this study are low-dimensional, robust against rotation, and have better performance than the earlier multiresolution-based algorithms and the state-of-the-art techniques with low-dimensional feature sets. They are obtained through curvelet transformation and have considerably small dimensions. The rotation invariance is provided by applying a novel principal orientation alignment based on cross energies of adjacent curvelet blocks. The curvelet block pair with the highest cross energy is marked as the principle orientation, and the rest of the blocks are cycle-shifted around the principle orientation. Two separate rotation-invariant feature vectors are proposed and evaluated in this study. The first feature vector has 84 elements and contains the mean and standard deviation of curvelet blocks at each angle together with a weighting factor based on the spatial support of the curvelet coefficients. The second feature vector has 840 elements and contains the kernel density estimation (KDE) of curvelet blocks at each angle. The first and the second feature vectors are used in the classification of textures based on nearest neighbor algorithm with Euclidian and Kullback-Leibler distance measures, respectively. The proposed method is evaluated on well-known databases such as, Brodatz, TC10, TC12-t184, and TC12-horizon of Outex, UIUCTex, and KTH-TIPS. The best performance is obtained for kernel density feature vector. Mean and standard deviation feature vector also provides similar performance and has less complexity due to its smaller feature dimension. The results are reported as both precision-recall curves and classification rates and compared with the existing state-of-the-art texture retrieval techniques. It is shown through several experiments that the proposed rotation-invariant feature vectors outperform earlier multiresolution-based ones and provide comparable performances with the rest of the literature even though they have considerably small dimensions. |
4,667 | Detection of the protistan parasite, Haplosporidium costale in Crassostrea gigas oysters from the French coast: A retrospective study | The parasite Haplosporidium costale is known to infect and cause mortality in the oyster Crassostrea virginica in the USA. Decades after its first description in the 1960s, this parasite was detected in Crassostrea gigas in the USA and China. However, it presented a low prevalence and no mortality was associated with it. More recently, in 2019, H. costale was detected in France in a batch of moribund oysters. In order to observe how long this parasite has been present on French coasts, from Normandy to Thau lagoon, a retrospective investigation was conducted on 871 adult and spat oyster batches from 2004 to 2020. To allow rapid detection on a large panel of samples, a real-time PCR for the H. costale actin gene was developed. This method allowed the detection of H. costale DNA in adults from 2005 and in spat from 2008. The H. costale prevalence in spat appeared higher than in adults over the years studied, 14.59 % compared to 6.50 %, respectively. All samples presenting positive results were then sequenced on two targets, H. costale rRNA and actin genes. The actin gene sequencing highlighted the presence of two H. costale strains. Adult C. gigas as well as spat batches coming from hatcheries and DNA controls from C. virginica all presented with the Profile 1 H. costale strain. The Profile 2 H. costale strain was detected only in C. gigas spat coming from natural sources. These observations suggest a correlation between the origin of oysters and H. costale strains which may have been caused by commercial imports between Japan, USA and France back to the 1970s. Over the positive samples studied, only few batches (n = 3) suffered mortalities which could be hypothesized to be caused by H. costale, all presenting the Profile 1 H. costale strain. |
4,668 | Histopathological Image Classification Using Discriminative Feature-Oriented Dictionary Learning | In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not available. |
4,669 | Snow Avalanche Segmentation in SAR Images With Fully Convolutional Neural Networks | Knowledge about frequency and location of snow avalanche activity is essential for forecasting and mapping of snow avalanche hazard. Traditional field monitoring of avalanche activity has limitations, especially when surveying large and remote areas. In recent years, avalanche detection in Sentinel-1 radar satellite imagery has been developed to improve monitoring. However, the current state-of-the-art detection algorithms, based on radar signal processing techniques, are still much less accurate than human experts. To reduce this gap, we propose a deep learning architecture for detecting avalanches in Sentinel-1 radar images. We trained a neural network on 6345 manually labeled avalanches from 117 Sentinel-1 images, each one consisting of six channels that include backscatter and topographical information. Then, we tested our trained model on a new synthetic aperture radar image. Comparing to the manual labeling (the gold standard), we achieved an F1 score above 66%, whereas the state-of-the-art detection algorithm sits at an F1 score of only 38%. A visual inspection of the results generated by our deep learning model shows that only small avalanches are undetected, whereas some avalanches that were originally not labeled by the human expert are discovered. |
4,670 | NICUface: Robust Neonatal Face Detection in Complex NICU Scenes | The development of non-contact patient monitoring applications for the neonatal intensive care unit (NICU) is an active research area, particularly in facial video analysis. Recent studies have used facial video data to estimate vital signs, assess pain from facial expression, differentiate sleep-wake status, detect jaundice, and in face recognition. These applications depend on an accurate definition of the patient's face as a region of interest (ROI). Most studies have required manual ROI definition, while others have leveraged automated face detectors developed for adult patients, without systematic validation for the neonatal population. To overcome these issues, this paper first evaluates the state-of-the-art in face detection in the NICU setting. Finding that such methods often fail in complex NICU environments, we demonstrate how fine-tuning can increase neonatal face detector robustness, resulting in our NICUface models. A large and diverse neonatal dataset was gathered from actual patients admitted to the NICU across three studies and gold standard face annotations were completed. In comparison to state-of-the-art face detectors, our NICUface models address NICU-specific challenges such as ongoing clinical intervention, phototherapy lighting, occlusions from hospital equipment, etc. These analyses culminate in the creation of robust NICUface detectors with improvements on our most challenging neonatal dataset of +36.14, +35.86, and +32.19 in AP30, AP50, and mAP respectively, relative to state-of-the-art CE-CLM, MTCNN, img2pose, RetinaFace, and YOLO5Face models. Face orientation estimation is also addressed, leading to an accuracy of 99.45%. Fine-tuned NICUface models, gold-standard face annotation data, and the face orientation estimation method are also released here. |
4,671 | An Application of Tumor-Associated Macrophages as Immunotherapy Targets: Sialic Acid-Modified EPI-Loaded Liposomes Inhibit Breast Cancer Metastasis | Breast cancer metastasis is an important cause of death in patients with breast cancer and is closely related to circulating tumor cells (CTCs) and the metastatic microenvironment. As the most infiltrating immune cells in the tumor microenvironment (TME), tumor-associated macrophages (TAMs), which highly express sialic acid (SA) receptor (Siglec-1), are closely linked to tumor progression and metastasis. Furthermore, the surface of CTCs also highly expressed receptor (Selectin) for SA. A targeting ligand (SA-CH), composed of SA and cholesterol, was synthesized and modified on the surface of epirubicin (EPI)-loaded liposomes (EPI-SL) as an effective targeting delivery system. Liposomes were evaluated for characteristics, stability, in vitro release, cytotoxicity, cellular uptake, pharmacokinetics, tumor targeting, and pharmacodynamics. In vivo and in vitro experiments showed that EPI-SL enhanced EPI uptake by TAMs. In addition, cellular experiments showed that EPI-SL could also enhance the uptake of EPI by 4T1 cells, resulting in cytotoxicity second only to that of EPI solution. Pharmacodynamic experiments have shown that EPI-SL has optimal tumor inhibition with minimal toxicity, which can be ascribed to the fact that EPI-SL can deliver drugs to tumor based on TAMs and regulate TME through the depletion of TAMs. Our study demonstrated the significant potential of SA-modified liposomes in antitumor metastasis. Schematic diagram of the role of SA-CH modified EPI-loaded liposomes in the model of breast cancer metastasis. |
4,672 | Abundance, composition, and distribution of microplastics in intertidal sediment and soft tissues of four species of Bivalvia from Southeast Brazilian urban beaches | In coastal areas, microplastics (MPs) can deposit in sediment, allowing it to be ingested by benthic organisms, like mussels, thus creating a possible transfer to humans. The aim of this study is to evaluate MPs pollution in sediment as a function of shoreline elevation in two urbanized beaches and to evaluate the abundance/frequency of MPs in 4 different species of bivalves commonly used in the human diet, such as the oyster Crassostrea brasiliana, the mussels Mytella strigata and Perna perna and the clam Tivela mactroides, and identify the polymers via μ-FTIR technique. A total of 3337 MPs were found in this study, of which 1488 were found in the sediment at the five sites analyzed, and 1849 in the bivalve tissues at the two sampling sites. MPs contamination was observed in all sediment samples and species of the pool and in each of the 10 specimens of the four species. Thus, the frequency of contamination by MPs reached 100 % for the analyzed samples. The number of filaments is higher than fragments in sediment samples and in each bivalve species. Regarding types and colors, the blue were greater than fragment-type in sediments and samples. In an effort to classify the polymers via μ-FTIR, our study was able to identify polypropylene, polyethylene and polyethylene terephthalate, besides a great number of cellulose fibers. |
4,673 | Early-immediate gene Egr1 is associated with TGFβ1 regulation of epigenetic reader Bromodomain-containing protein 4 via the canonical Smad3 signaling in hepatic stellate cells in vitro and in vivo | Upon chronic damage to the liver, multiple cytokines stimulate hepatic stellate cells (HSCs), causing the alterations of gene expression profiles and thus leading to HSC activation, a key step in liver fibrogenesis. Activated HSCs are the dominant contributors to liver fibrosis. Bromodomain containing protein 4 (BrD4), an important epigenetic reader, was demonstrated to concentrate on hundreds of enhancers associated with genes involved in multiple profibrotic pathways, thereby directing HSC activation and the fibrotic responses. The present studies were designed to examine the effect of transforming growth factor beta-1 (TGFβ1), the most potent pro-fibrotic cytokine, on BrD4 expression in HSCs and, if so, elucidated the underlying mechanisms in vitro and in vivo. The experiments employed the heterogeneous TGFβ1 knockout (TGFβ1+/- ) mice, gene knockdown in vivo, and a model of thioacetamide (TAA)-induced liver injury. The results revealed that TGFβ1 enhanced BrD4 expression in HSCs, which was mediated, at least, by Smad3 signaling and early-immediate gene Egr1 (early growth response-1). TGFβ1-induced Smad3 signaling increased Egr1 expression and promoted Egr1 binding to BrD4 promoter at a site around -111 bp, promoting BrD4 expression. Egr1 knockdown reduced BrD4 expression in HSCs in a mouse model of TAA-induced liver injury and lessened liver fibrosis. Double fluorescence staining demonstrated a strong increase in BrD4 expression in activated HSCs in fibrotic areas of the human livers, paralleling the upregulation of p-Smad3 and Egr1. This research suggested novel molecular events underlying the roles of the master pro-fibrotic cytokine TGFβ1 in HSC activation and liver fibrogenesis. |
4,674 | Three-Dimensional Covalent Organic Frameworks: From Synthesis to Applications | Three-dimensional covalent organic frameworks (3D COFs) with spatially periodic networks demonstrate significant advantages over their 2D counterparts, including enhanced specific surface areas, interconnected channels, and more sufficiently exposed active sites. Nevertheless, research on these materials has met an impasse due to serious problems in crystallization and stability, which must be solved for practical applications. In this Minireview, we first summarize some strategies for preparing functional 3D COFs, including crystallization techniques and functionalization methods. Hereafter, applications of these functional materials are presented, covering adsorption, separation, catalysis, fluorescence, sensing, and batteries. Finally, the future challenges and perspectives for the development of 3D COFs are discussed. |
4,675 | Triclosan Promotes Conjugative Transfer of Antibiotic Resistance Genes to Opportunistic Pathogens in Environmental Microbiome | Although triclosan, as a widely used antiseptic chemical, is known to promote the transmission of antibiotic resistance to diverse hosts in pure culture, it is still unclear whether and how triclosan could affect the transmission of broad-host-range plasmids among complex microbial communities. Here, bacterial culturing, fluorescence-based cell sorting, and high-throughput 16S rRNA gene amplicon sequencing were combined to investigate contributions of triclosan on the transfer rate and range of an IncP-type plasmid from a proteobacterial donor to an activated sludge microbiome. Our results demonstrate that triclosan significantly enhances the conjugative transfer of the RP4 plasmid among activated sludge communities at environmentally relevant concentrations. High-throughput 16S rRNA gene sequencing on sorted transconjugants demonstrates that triclosan not only promoted the intergenera transfer but also the intragenera transfer of the RP4 plasmid among activated sludge communities. Moreover, triclosan mediated the transfer of the RP4 plasmid to opportunistic human pathogens, for example, Legionella spp. The mechanism of triclosan-mediated conjugative transfer is primarily associated with excessive oxidative stress, followed by increased membrane permeability and provoked SOS response. Our findings offer insights into the impacts of triclosan on the dissemination of antibiotic resistance in the aquatic environmental microbiome. |
4,676 | Accelerating Semi-Supervised Text Classification by K-Way Projecting Networks | The state of the art semi-supervised learning framework has greatly shown its potential in making deep and complex language models such as BERT highly effective for text classification tasks when labeled data is limited. However, the large size and low inference speed of such models may hinder their application on resources-limited or real-time use cases. In this paper, we propose a new approach in semi-supervised learning framework to distill large complex teacher model into a fairly lightweight student model which has the ability of acquiring knowledge from different layers of teacher with the usage of K -way projecting networks. Across four English datasets in text classification benchmarks and one dataset collected from an Chinese online course, our experiment shows that this student model achieves comparable results with the state of the art Transformer-based semi-supervised text classification methods, while using only 0.156MB parameters and having an inference speed 785 times faster than the teacher model. |
4,677 | A Deep Learning Approach for the Design of Narrow Transition-Band FIR Filter | Deep neural network (DNN), being an important member of machine learning family, has been employed to serve a wide range of applications in the area of signal and image processing like pattern recognition, speech recognition, language processing, image segmentation, etc. To this aim, this paper concentrates on the design of a narrow transition-band finite impulse response (FIR) filter with the aid of back-propagation-based deep learning approach. The proposed deep learning-based approach offers a unified design framework for a variety of FIR filters. Convergence behaviour of the proposed algorithm has been proved analytically in situations when weights between adjacent layers are updated continuously. Simulation results have shown the frequency response characteristics of several FIR filters with narrow transition-band, designed with the help of proposed approach. Advantage of our design strategy has also been established in terms of magnitude response over a number of state-of-the-art techniques of recent interest. Simulation results have shown noticeable improvement in terms of transition bandwidth when compared with few existing works. Designed filter is subsequently implemented on Altera's Cyclone IV field programmable gate array (FPGA) chip, and hardware efficiency of the suggested design has strongly been established by correlating its hardware cost with many of the state-of-the-art FIR filters. |
4,678 | Breastfeeding duration is associated with higher adiposity at 6-8 months of age | Breastfeeding (BF) has been identified as a protective factor against childhood obesity. However, evidence of the association between BF duration and adiposity remains inconclusive. Few studies have been conducted among Southeast Asian infants that have measured body composition during infancy using the gold standard stable isotope method. This study aimed to evaluate the association between BF duration and body composition during infancy. Healthy full-term Thai infants aged 6-8 months (n = 60) receiving exclusive or predominant BF for at least 3 months were recruited. Skinfold thickness (SFT) was measured by well-trained investigators. Body composition was assessed by the deuterium dilution technique. Infants with longer BF duration (>6 months; mean 7.5 ± 0.5 months, n = 29) had a higher subscapular SFT z-score than those with shorter BF duration (≤6 months; mean 5.3± 0.9 months, n = 31) by 0.48 (95% confidence interval [CI]: 0.01-0.94). After adjustment for age and sex, BF duration and age at introduction of complementary feeding (CF) were positively associated with fat mass and fat mass index at 6-8 months. One month increase in BF duration and CF age was associated with a 0.37 (95% CI: 0.05, 0.69) kg/m2 and 0.76 (95% CI: 0.18, 1.34) kg/m2 increase in the fat mass index, respectively. After adjusting for infant body mass index (BMI) during the earlier infancy period, the strength of the association was attenuated. This finding may reflect reverse causality where infants with lower BMI received formula or CF earlier. A longitudinal study with follow-up into childhood is warranted to confirm the effects of BF on adiposity in infancy and childhood. |
4,679 | Financing Sustainability in the Arts Sector: The Case of the Art Bonus Public Crowdfunding Campaign in Italy | This paper addresses the conditions that can facilitate the long-term effectiveness of civic crowdfunding fundraising strategies. While previous studies have provided a broad picture of the possible conditions for fostering effective fundraising strategies, most have considered the implications of fundraising only for management or only for cultural policy, neglecting an integrated approach that contemplates the needs of both. Thus, this work integrates cultural management and cultural policy perspectives by discussing a specific exploratory case study: Art Bonus, a cultural patronage tax incentive strategy introduced by the Italian government in 2014, which also includes civic crowdfunding features. To the best of our knowledge, Art Bonus is the first national civic crowdfunding platform supported by a national government. As an innovative and unique platform, its analysis is particularly relevant. This work analyzes the system's functioning and the results obtained in its first years of operation (2014-2016) by accessing the public database relating to the donations transited through the platform. While the initiative effectively channeled more fundraising resources into the cultural sector, the results also illustrate potential points for improving such a system. |
4,680 | Recognizing Focal Liver Lesions in CEUS With Dynamically Trained Latent Structured Models | This work investigates how to automatically classify Focal Liver Lesions (FLLs) into three specific benign or malignant types in Contrast-Enhanced Ultrasound ( CEUS) videos, and aims at providing a computational framework to assist clinicians in FLL diagnosis. The main challenge for this task is that FLLs in CEUS videos often show diverse enhancement patterns at different temporal phases. To handle these diverse patterns, we propose a novel structured model, which detects a number of discriminative Regions of Interest ( ROIs) for the FLL and recognize the FLL based on these ROIs. Our model incorporates an ensemble of local classifiers in the attempt to identify different enhancement patterns of ROIs, and in particular, we make the model reconfigurable by introducing switch variables to adaptively select appropriate classifiers during inference. We formulate the model learning as a non-convex optimization problem, and present a principled optimization method to solve it in a dynamic manner: the latent structures ( e.g. the selections of local classifiers, and the sizes and locations of ROIs) are iteratively determined along with the parameter learning. Given the updated model parameters in each step, the data-driven inference is also proposed to efficiently determine the latent structures by using the sequential pruning and dynamic programming method. In the experiments, we demonstrate superior performances over the state-of-the-art approaches. We also release hundreds of CEUS FLLs videos used to quantitatively evaluate this work, which to the best of our knowledge forms the largest dataset in the literature. Please find more information at "http:/ vision.sysu.edu.cn/projects/fllrecog/". |
4,681 | Parvovirus Infection Leading to Severe Anemia in an Adult Patient With HIV Disease | Individuals with human immunodeficiency virus (HIV) disease frequently suffer from anemia. The causes include anemia of chronic disease, vitamin B12 and iron deficiency, opportunistic infections (Mycobacterium tuberculosis, Pneumocystis jiroveci), HIV-related bone marrow suppression, AIDS-associated malignancies, and antiretroviral therapy (ART), specifically zidovudine. In HIV patients with advanced immunodeficiency, failure to produce neutralizing antibodies can lead to chronic parvovirus B19 (B19) infection. Normally, in persons with intact immunity, the progression of B19 is self-limited. However, in chronic B19 infection, it can lead to pure red cell aplasia (PRCA) and chronic anemia. In human immunodeficiency virus (HIV)-infected patients, B19-related anemia is rare and underdiagnosed. It has a great response to intravenous immunoglobulin (IVIG) therapy. Hence, early diagnosis and prompt treatment can significantly reduce mortality. In this article, we described the case of a 25-year-old male with HIV infection who presented with a headache. He had severe normocytic anemia with a low reticulocyte count. The workup for blood loss, hemolysis, hemoglobinopathy, and iron deficiency was negative. Because of extremely low reticulocytopenia with severe anemia, the investigations favored multiple myeloma, parvovirus infection, and bone marrow aspiration biopsy. He was tested for parvovirus B19 deoxyribonucleic acid (DNA) polymerase chain reaction (PCR) test due to insufficient seroconversion. It turned out to be positive and he was treated with IVIG therapy. |
4,682 | Art through the Colors of Graffiti: From the Perspective of the Chromatic Structure | Graffiti is a general term that describes inscriptions on a wall, a practice with ancient origins, ranging from simple drawings and writings to elaborate pictorial representations. Nowadays, the term graffiti commonly describes the street art dedicated to wall paintings, which raises complex questions, including sociological, legal, political and aesthetic issues. Here we examine the aesthetics of graffiti colors by quantitatively characterizing and comparing their chromatic structure to that of traditional paintings in museums and natural scenes obtained by hyperspectral imaging. Two hundred twenty-eight photos of graffiti were taken in the city of SAo Paulo, Brazil. The colors of graffiti were represented in a color space and characterized by several statistical parameters. We found that graffiti have chromatic structures similar to those of traditional paintings, namely their preferred colors, distribution, and balance. In particular, they have color gamuts with the same degree of elongation, revealing a tendency for combining similar colors in the same proportions. Like more traditional artists, the preferred colors are close to the yellow-blue axis of color space, suggesting that graffiti artists' color choices also mimic those of the natural world. Even so, graffiti tend to have larger color gamuts due to the availability of a new generation of synthetic pigments, resulting in a greater freedom in color choice. A complementary analysis of graffiti from other countries supports the global generalization of these findings. By sharing their color structures with those of paintings, graffiti contribute to bringing art to the cities. |
4,683 | Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) Spectroscopy Sensitivity to the Thermal Decay of Bone Collagen | The analysis of collagen stability is of interest in forensics, archaeology, and molecular paleontology. Collagen decay rates are often measured by thermal kinetic studies that employ liquid chromatography mass spectrometry (LC-MS) to assay collagen quantities. However, these kinetic studies generally focus on measuring the decreasing levels of collagen instead of an exact molecular concentration of each sample. Thus, attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy can offer a simpler and less expensive alternative to LC-MS. The application of a new protocol to determine decreasing amounts of bone collagen in artificially decayed porcine and bovine bone was assessed. The protocol uses a forensic application of ATR FT-IR spectroscopy on size-restricted bone powder from three uniformly high temperature conditions. Also, for the first time, collagen-specific second-harmonic generation (SHG) imaging was also applied to artificially aged bone to add an independent, qualitative perspective to parallel FT-IR assessments. SHG images and ATR FT-IR spectra together reveal the same orderly bone collagen decay as found in previous thermal kinetic studies. Resulting Arrhenius plots with r2 values > 0.95 suggest that the ATR FT-IR-based protocol has potential as a precise and simple tool for measuring bone collagen decay rates. The results are significant for applications of thermal kinetic studies, and our protocol can serve as an inexpensive, precise, and pragmatic means of evaluating bone collagen stability within an array of conditions. |
4,684 | Tendoscopic-Assisted Repair of Distal Peroneus Longus Rupture: A Novel Approach and 2 Case Reports | Level IV: Case Series. |
4,685 | Unsupervised Reconstruction of Sea Surface Currents from AIS Maritime Traffic Data Using Trainable Variational Models | The estimation of ocean dynamics is a key challenge for applications ranging from climate modeling to ship routing. State-of-the-art methods relying on satellite-derived altimetry data can hardly resolve spatial scales below similar to 100 km. In this work we investigate the relevance of AIS data streams as a new mean for the estimation of the surface current velocities. Using a physics-informed observation model, we propose to solve the associated the ill-posed inverse problem using a trainable variational formulation. The latter exploits variational auto-encoders coupled with neural ODE to represent sea surface dynamics. We report numerical experiments on a real AIS dataset off South Africa in a highly dynamical ocean region. They support the relevance of the proposed learning-based AIS-driven approach to significantly improve the reconstruction of sea surface currents compared with state-of-the-art methods, including altimetry-based ones. |
4,686 | Structure orderliness assessment of grid development to improve the reliability of coal mine external electrical power supply | State-of-the-art reliability assessment methods require a large amount of initial statistical data, which does not always have the necessary degree of accuracy for accurate assessment. Moreover, this data is often difficult to obtain, which can ultimately delay the execution of calculations and increase error within results. This paper explores an alternative assessment method using the "structural orderliness indicator" and minimal initial data to calculate the change in reliability indicators in connection with reinforcement decisions. This paper includes the cost of reconstruction associated with the introduction of a new unit cost indicator for increasing the structure orderliness index. As a real-world application, the following discussion proposes improvements to external electrical power supply reliability for coal-mining enterprises within the Russian Federation, such as in the Kemerovo region (the biggest coal-producing region of Russia). In recent years, the number of production process suspensions and emergency shutdowns of life support facilities has increased due to external power supply disruptions. The performed calculations made possible qualitatively comparison the proposed method and state-of-the-art method based on the determination of reliability indices (SAIDI, SAIFI, etc.). |
4,687 | Business for ocean sustainability: Early responses of ocean governance in the private sector | A large sample of 1664 companies-69 directly working in the ocean economy-distributed across 19 industrial sectors was investigated to explore awareness and activation regarding direct and indirect pressures on the ocean, their responses to these pressures, and the disclosure tools used. We examined their accountability and disclosure practices on sustainable development goals (SDGs) using the drivers, pressures, state, welfare, and response accounting framework. Based on their 2019 sustainability reports, just 7% of the companies assessed disclosed on SDG14. However, 51% of these companies can be considered as aware, albeit to varying degrees, of the pressures their industries place on the oceans, 44% deploy mitigating activities, and 26% are aware and actively lead business responses to ocean challenges. Although we have seen just early responses in addressing ocean challenges, companies' awareness and activation must converge to achieve ocean sustainability and move businesses into a truly blue economy. |
4,688 | Relation-Induced Multi-Modal Shared Representation Learning for Alzheimer's Disease Diagnosis | The fusion of multi-modal data (e.g., magnetic resonance imaging (MRI) and positron emission tomography (PET)) has been prevalent for accurate identification of Alzheimer's disease (AD) by providing complementary structural and functional information. However, most of the existing methods simply concatenate multi-modal features in the original space and ignore their underlying associations which may provide more discriminative characteristics for AD identification. Meanwhile, how to overcome the overfitting issue caused by high-dimensional multi-modal data remains appealing. To this end, we propose a relation-induced multi-modal shared representation learning method for AD diagnosis. The proposed method integrates representation learning, dimension reduction, and classifier modeling into a unified framework. Specifically, the framework first obtains multi-modal shared representations by learning a bi-directional mapping between original space and shared space. Within this shared space, we utilize several relational regularizers (including feature-feature, feature-label, and sample-sample regularizers) and auxiliary regularizers to encourage learning underlying associations inherent in multi-modal data and alleviate overfitting, respectively. Next, we project the shared representations into the target space for AD diagnosis. To validate the effectiveness of our proposed approach, we conduct extensive experiments on two independent datasets (i.e., ADNI-1 and ADNI-2), and the experimental results demonstrate that our proposed method outperforms several state-of-the-art methods. |
4,689 | Epidemiology and risk factors for faecal extended-spectrum β-lactamase-producing Enterobacteriaceae (ESBL-E) carriage derived from residents of seven nursing homes in western Shanghai, China | Nursing homes (NHs) have been implicated as significant reservoirs of antibiotic-resistant organisms causing severe infectious disease. We investigated the prevalence and molecular epidemiology of, and risk factors for, faecal carriage of extended-spectrum β-lactamase-producing Enterobacteriaceae (ESBL-E). A multicentre cross-sectional study was conducted in seven NHs in Shanghai between March 2014 and May 2014. Antimicrobial susceptibility testing and polymerase chain reaction were used to detect genes coding for ESBLs and carbapenemases. NH records at individual-resident level and facility level were examined for potential risk factors. Four hundred and fifty-seven Enterobacteriaceae isolates were collected of which 183 (46·92%) were colonized by ESBL-E. CTX-M enzymes (198/200, 99%) predominated, with CTX-M-14 (84/200, 42%) the most common types. Two carbapenemase producers harboured blaKPC-2. Resistance rates to carbapenems, TZP, AK, FOS, CL and TGC were low. History of invasive procedures [odds ratio (OR) 2·384, 95% confidence interval (CI) 1·318-4·310, P = 0·004], narrow-spectrum cephalosporins (OR 1·635, 95% CI 1·045-2·558, P = 0·031) and broad-spectrum cephalosporins (OR 3·276, 95% CI 1·278-8·398, P = 0·014) were independently associated with ESBL-E carriage. In conclusion, NH residents have a very high prevalence of faecal carriage of ESBL-E. Continuous and active surveillance is important, as are prudent infection control measures and antibiotic use to prevent and control the spread of these antibiotic-resistant strains. |
4,690 | Stay on the road: from germ cell specification to gonadal colonization in mammals | The founder cells of the gametes are primordial germ cells (PGCs). In mammals, PGCs are specified early during embryonic development, at the boundary between embryonic and extraembryonic tissue, long before their later residences, the gonads, have developed. Despite the differences in form and behaviour when differentiated into oocytes or sperm cells, in the period between specification and gonadal colonization, male and female PGCs are morphologically indistinct and largely regulated by similar mechanisms. Here, we compare different modes and mechanisms that lead to the formation of PGCs, putting in context protocols that are in place to differentiate both human and mouse pluripotent stem cells into PGC-like cells. In addition, we review important aspects of the migration of PGCs to the gonadal ridges, where they undergo further sex-specific differentiation. Defects in migration need to be effectively corrected, as misplaced PGCs can become tumorigenic. Concluding, a combination of in vivo studies and the development of adequate innovative in vitro models, ensuring both robustness and standardization, are providing us with the tools for a greater understanding of the first steps of gametogenesis and to develop disease models to study the origin of germ cell tumours. This article is part of the theme issue 'Extraembryonic tissues: exploring concepts, definitions and functions across the animal kingdom'. |
4,691 | Point-of-care vertical flow immunoassay system for ultra-sensitive multiplex biothreat-agent detection in biological fluids | This paper presents simple, fast, and sensitive detection of multiple biothreat agents by paper-based vertical flow colorimetric sandwich immunoassay for detection of Yersinia pestis (LcrV and F1) and Francisella tularensis (lipopolysaccharide; LPS) antigens using a vertical flow immunoassay (VFI) prototype with portable syringe pump and a new membrane holder. The capture antibody (cAb) printing onto nitrocellulose membrane and gold-labelled detection antibody (dAb) were optimized to enhance the assay sensitivity and specificity. Even though the paper pore size was relaxed from previous 0.1 μm to the current 0.45 μm for serum samples, detection limits as low as 0.050 ng/mL for LcrV and F1, and 0.100 ng/mL for FtLPS have been achieved in buffer and similarly in diluted serum (with LcrV and F1 LODs remained the same and LPS LOD reduced to 0.250 ng/mL). These were 40, 80, and 50X (20X for LPS in serum) better than those from lateral flow configuration. Furthermore, the comparison of multiplex format demonstrated low cross-reactivity and equal sensitivity to that of the singleplex assay. The optimized VFI platform thus provides a portable and rapid on-site monitoring system for multiplex biothreat detection with the potential for high sensitivity, specificity, reproducibility, and multiplexing capability, supporting its utility in remote and resource-limited settings. |
4,692 | Antitumor Immunotherapy of Sialic Acid and/or GM1 Modified Coenzyme Q10 Submicron Emulsion | Immunotherapy is a novel therapeutic approach for controlling and killing tumor cells by stimulating or reconstituting the immune system, among which T cells serve as immune targets. Herein, we used coenzyme Q10 (CoQ10), which has both immune activation and avoids adverse reactions, as a model drug and developed four CoQ10 submicron emulsions modified with sialic acid (SA) and/or monosialotetrahexosyl ganglioside (GM1). On the one hand, SA interacts with L-selectins on the surface of T cells after entering the circulatory system, leading to activation of T cells and enhancement of antitumor immune responses. On the other hand, owing to its immune camouflage, GM1 can prolong the circulation time of the preparation in the body, thereby increasing the accumulation of the drug at the tumor site. In vitro and in vivo experiments showed that SA-modified preparations exhibited stronger immune activation and inhibition of tumor proliferation. Pharmacokinetic experiments showed that GM1-modified preparations have longer circulation times in vivo. However, SA and GM1 co-modification did not produce a synergistic effect on the preparation. In conclusion, the SA-modified CoQ10 submicron emulsion (Q10-SE) showed optimal antitumor efficacy when administered at a medium dose (6 mg CoQ10 kg-1). In this study, the submicron emulsion model was used as a carrier, and the tumor-bearing mice were used as animal models. In addition, CoQ10 submicron emulsion was modified with SA-CH with active targeting function and/or GM1 with long-circulation function to explore the antitumor effects of different doses of CoQ10 submicron emulsion, and to screen the best tumor immunotherapy formulations of CoQ10. |
4,693 | An Efficient High-Throughput Generic QAM Transmitter with Scalable Spiral FIR Filter | The need for efficient Finite Impulse Response (FIR) filters in high-speed applications such as telecommunications targets Field Programmable Gate Arrays (FPGAs) as an effective and flexible platform for digital implementation. Although FIR filter offers many advantages, its convolution nature poises a challenge in parallelization due to data dependency and computational complexity. To resolve this, we propose a novel FPGA-based reconfigurable filter architecture, which processes several data samples in parallel and breaks down data interdependency in a spiral fashion. Experimental results show a throughput of 7.2 GSPS with an operating frequency of only 450MHz for a filter length of 11 with 16 parallel inputs. With parallelization of 4, it is 4.44 times faster than the state-of-the-art solution for a filter length of 16 and a promising 1.04 GSPS throughput is achieved for a higher order of length 61. Incorporated into a generic Quadrature Amplitude Modulation (QAM) transmitter fitted with Forward Error Correction technique, a maximum throughput of 23 Gb/s is achieved by the system for processing 16 input samples in parallel. In comparison to the state-of-the-art mixed domain approach, a threefold performance gain, while utilizing comparatively less Look-up Tables (LUTs), registers and DSP48 slices with an average gain factor of 43.3x, 4.7x and 3.9x, respectively, is accomplished. |
4,694 | Interactive Few-Shot Learning: Limited Supervision, Better Medical Image Segmentation | Many known supervised deep learning methods for medical image segmentation suffer an expensive burden of data annotation for model training. Recently, few-shot segmentation methods were proposed to alleviate this burden, but such methods often showed poor adaptability to the target tasks. By prudently introducing interactive learning into the few-shot learning strategy, we develop a novel few-shot segmentation approach called Interactive Few-shot Learning (IFSL), which not only addresses the annotation burden of medical image segmentation models but also tackles the common issues of the known few-shot segmentation methods. First, we design a new few-shot segmentation structure, called Medical Prior-based Few-shot Learning Network (MPrNet), which uses only a few annotated samples (e.g., 10 samples) as support images to guide the segmentation of query images without any pretraining. Then, we propose an Interactive Learning-based Test Time Optimization Algorithm (IL-TTOA) to strengthen our MPrNet on the fly for the target task in an interactive fashion. To our best knowledge, our IFSL approach is the first to allow few-shot segmentation models to be optimized and strengthened on the target tasks in an interactive and controllable manner. Experiments on four few-shot segmentation tasks show that our IFSL approach outperforms the state-of-the-art methods by more than 20% in the DSC metric. Specifically, the interactive optimization algorithm (IL-TTOA) further contributes similar to 10% DSC improvement for the few-shot segmentation models. |
4,695 | Hace1 overexpression mitigates myocardial hypoxia/reoxygenation injury via the effects on Keap1/Nrf2 pathway | HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1 (Hace1) is a crucial mediator of multiple pathological disorders. However, there are few studies regarding the role of Hace1 in myocardial ischemia/reperfusion injury. Here, we studied the functional role of Hace1 on myocardial ischemia/reperfusion injury using hypoxia/reoxygenation (H/R)-injured cardiac cells in vitro. Reduced levels of Hace1 were observed in H/R-exposed cardiac cells. Hace1-overexpressed cardiac cells were resistant to H/R injuries with reduced apoptosis, lowered oxidative stress, and a suppressed inflammatory response. Subsequent analysis revealed that Hace1 overexpression enhanced the activation of nuclear translocation of nuclear factor (erythroid-derived 2)-like 2 (Nrf2) and increased the transcriptional activity of Nrf2 in H/R-exposed cardiac cells. The knockout of kelch-like ECH-associated protein 1 (Keap1) diminished the regulatory role of Hace1 on Nrf2 activation. Additionally, inhibiting Nrf2 reversed Hace1-elicited cardioprotective effects in H/R-injured cardiac cells. In short, these data demonstrated that Hace1 overexpression mitigated myocardial H/R injury by enhancing the Nrf2 pathway via Keap1. This work underlines a possible role of Hace1 in myocardial ischemia/reperfusion injury and suggests Hace1 as a candidate target for exploiting cardioprotective therapy. |
4,696 | Spallation reactions and accelerator-driven systems | The state-of-the-art in the field of neutron and nuclear-fragment emissions from spallation reactions initiated by protons in the GeV energy range with extended heavy targets is discussed. Such reactions are essential for the operation of accelerator-driven systems. (C) 2003 Elsevier Science Ltd. All rights reserved. |
4,697 | Object detection on remote sensing images using deep learning: an improved single shot multibox detector method | Remote sensing images recognition technology has great significance in many aspects, such as military navigation and environmental monitoring. We propose an improved single shot multibox detector approach by combining some strategies, including upsampling, focal loss, and proper calibration of key parameters. Comprehensive experiments on three remote sensing images datasets have demonstrated the effectiveness of the proposed approach in benchmarking with several state-of-the-art object detection methods. (C) 2019 SPIE and IS&T |
4,698 | First-Principles-Based Quantum Transport Simulations of High-Performance and Low-Power MOSFETs Based on Monolayer Ga2O3 | The electronic properties of monolayer (ML) Ga2O3 and transport properties of ML Ga2O3-based n-type metal-oxide-semiconductor field-effect transistors (MOSFETs) are investigated by first-principles calculations under the framework of density functional theory (DFT) coupled with the nonequilibrium Green's function (NEGF) formalism. The results show that ML Ga2O3 has a quasi-direct band gap of 4.92 eV, and the x- and y-directed electron mobilities are 1210 and 816 cm2 V-1 s-1 at 300 K, respectively, under the full consideration of phonon scattering. The electron-phonon scattering mechanism shows a temperature-dependent behavior, with the acoustic modes dominating below 300 K and optical modes dominating above 300 K. At a gate length of Lg = 5 nm, the on-current of ML Ga2O3 n-MOSFET for high-performance (HP) application is 2890 μA/μm, which is more than those of the most reported two-dimensional (2D) materials. The delay time as well as the power delay product of ML Ga2O3 MOSFETs can meet the demands of the latest International Technology Roadmap for Semiconductors (ITRS) for HP and low-power (LP) applications until Lg is less than 4 and 5 nm, respectively. Through underlap structure and doping optimization strategies, ML Ga2O3 n-MOSFET can further fulfill the ITRS requirements for 1 nm. At last, we compare the performance of the 32-bit arithmetic logic unit (ALU) built on ML Ga2O3 MOSFETs with the recently reported beyond-CMOS devices. Our results indicate that ML Ga2O3 can serve as a promising channel material in the post-silicon era. |
4,699 | A recent epidemiological cluster of acute hepatitis B genotype F1b infection in a restricted geographical area of Italy | In this study, by phylogenetic analysis, we identified an epidemiological cluster involving eight individuals diagnosed with acute hepatitis B virus (HBV) infection related to unprotected sexual intercourse in a restricted area of central Italy (time period: 2011-2014). Notably, these patients (six of eight Italians) were infected by subgenotype F1b, which is not commonly found in western countries. Ultra-deep pyrosequencing confirmed a superimposable composition of HBV quasi-species in these patients. Despite the availability of effective vaccination, this study highlights the importance of not underestimating the risk of HBV infection, of continuing to set up surveillance programmes for HBV infection, and of investigating the pathogenetic potential of these atypical genotypes. |
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