CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension

We present a Chinese judicial reading comprehension (CJRC) dataset which contains approximately 10K documents and almost 50K questions with answers. The documents come from judgment documents and the questions are annotated by law experts. The CJRC dataset can help researchers extract elements by reading comprehension technology. Element extraction is an important task in the legal field. However, it is difficult to predefine the element types completely due to the diversity of document types and causes of action. By contrast, machine reading comprehension technology can quickly extract elements by answering various questions from the long document. We build two strong baseline models based on BERT and BiDAF. The experimental results show that there is enough space for improvement compared to human annotators

The elicitor VP2 from Verticillium dahliae triggers defence response in cotton

Summary: Verticillium dahliae is a widespread and destructive soilborne vascular pathogenic fungus that causes serious diseases in dicot plants. Here, comparative transcriptome analysis showed that the number of genes upregulated in defoliating pathotype V991 was significantly higher than in the non‐defoliating pathotype 1cd3‐2 during the early response of cotton. Combined with analysis of the secretome during the V991–cotton interaction, an elicitor VP2 was identified, which was highly upregulated at the early stage of V991 invasion, but was barely expressed during the 1cd3‐2‐cotton interaction. Full‐length VP2 could induce cell death in several plant species, and which was dependent on NbBAK1 but not on NbSOBIR1 in N. benthamiana. Knock‐out of VP2 attenuated the pathogenicity of V991. Furthermore, overexpression of VP2 in cotton enhanced resistance to V. dahliae without causing abnormal plant growth and development. Several genes involved in JA, SA and lignin synthesis were significantly upregulated in VP2‐overexpressing cotton. The contents of JA, SA, and lignin were also significantly higher than in the wild‐type control. In summary, the identified elicitor VP2, recognized by the receptor in the plant membrane, triggers the cotton immune response and enhances disease resistance

Deep learning based CT images automatic analysis model for active/non-active pulmonary tuberculosis differential diagnosis

Active pulmonary tuberculosis (ATB), which is more infectious and has a higher mortality rate compared with non-active pulmonary tuberculosis (non-ATB), needs to be diagnosed accurately and timely to prevent the tuberculosis from spreading and causing deaths. However, traditional differential diagnosis methods of active pulmonary tuberculosis involve bacteriological testing, sputum culturing and radiological images reading, which is time consuming and labour intensive. Therefore, an artificial intelligence model for ATB differential diagnosis would offer great assistance in clinical practice. In this study, computer tomography (CT) scans images and corresponding clinical information of 1160 ATB patients and 1131 patients with non-ATB were collected and divided into training, validation, and testing sets. A 3-dimension (3D) Nested UNet model was utilized to delineate lung field regions in the CT images, and three different pre-trained deep learning models including 3D VGG-16, 3D EfficientNet and 3D ResNet-50 were used for classification and differential diagnosis task. We also collected an external testing set with 100 ATB cases and 100 Non-ATB cases for further validation of the model. In the internal and external testing set, the 3D ResNet-50 model outperformed other models, reaching an AUC of 0.961 and 0.946, respectively. The 3D ResNet-50 model reached even higher levels of diagnostic accuracy than experienced radiologists, while the CT images reading and diagnosing speed was 10 times faster than human experts. The model was also capable of visualizing clinician interpretable lung lesion regions important for differential diagnosis, making it a powerful tool assisting ATB diagnosis. In conclusion, we developed an auxiliary tool to differentiate active and non-active pulmonary tuberculosis, which would have broad prospects in the bedside

Polyethyleneimine-coated MXene quantum dots improve cotton tolerance to Verticillium dahliae by maintaining ROS homeostasis

Verticillium dahliae is a soil-borne hemibiotrophic fungal pathogen that threatens cotton production worldwide. In this study, we assemble the genomes of two V. dahliae isolates: the more virulence and defoliating isolate V991 and nondefoliating isolate 1cd3-2. Transcriptome and comparative genomics analyses show that genes associated with pathogen virulence are mostly induced at the late stage of infection (Stage II), accompanied by a burst of reactive oxygen species (ROS), with upregulation of more genes involved in defense response in cotton. We identify the V991-specific virulence gene SP3 that is highly expressed during the infection Stage II. V. dahliae SP3 knock-out strain shows attenuated virulence and triggers less ROS production in cotton plants. To control the disease, we employ polyethyleneimine-coated MXene quantum dots (PEI-MQDs) that possess the ability to remove ROS. Cotton seedlings treated with PEI-MQDs are capable of maintaining ROS homeostasis with enhanced peroxidase, catalase, and glutathione peroxidase activities and exhibit improved tolerance to V. dahliae. These results suggest that V. dahliae trigger ROS production to promote infection and scavenging ROS is an effective way to manage this disease. This study reveals a virulence mechanism of V. dahliae and provides a means for V. dahliae resistance that benefits cotton production

First operational BRDF, albedo nadir reflectance products from MODIS

With the launch of NASA’s Terra satellite and the MODerate Resolution Imaging Spectroradiometer (MODIS), operational Bidirectional Reflectance Distribution Function (BRDF) and albedo products are now being made available to the scientific community. The MODIS BRDF/Albedo algorithm makes use of a semiempirical kernel-driven bidirectional reflectance model and multidate, multispectral data to provide global 1-km gridded and tiled products of the land surface every 16 days. These products include directional hemispherical albedo (black-sky albedo), bihemispherical albedo (white-sky albedo), Nadir BRDF-Adjusted surface Reflectances (NBAR), model parameters describing the BRDF, and extensive quality assurance information. The algorithm has been consistently producing albedo and NBAR for the public since July 2000. Initial evaluations indicate a stable BRDF/Albedo Product, where, for example, the spatial and temporal progression of phenological characteristics is easily detected in the NBAR and albedo results. These early beta and provisional products auger well for the routine production of stable MODIS-derived BRDF parameters, nadir reflectances, and albedos for use by the global observation and modeling communities

Fusing R features and local features with context-aware kernels for action recognition

The performance of action recognition in video sequences depends significantly on the representation of actions and the similarity measurement between the representations. In this paper, we combine two kinds of features extracted from the spatio-temporal interest points with context-aware kernels for action recognition. For the action representation, local cuboid features extracted around interest points are very popular using a Bag of Visual Words (BOVW) model. Such representations, however, ignore potentially valuable information about the global spatio-temporal distribution of interest points. We propose a new global feature to capture the detailed geometrical distribution of interest points. It is calculated by using the 3D R transform which is defined as an extended 3D discrete Radon transform, followed by the application of a two-directional two-dimensional principal component analysis. For the similarity measurement, we model a video set as an optimized probabilistic hypergraph and propose a context-aware kernel to measure high order relationships among videos. The context-aware kernel is more robust to the noise and outliers in the data than the traditional context-free kernel which just considers the pairwise relationships between videos. The hyperedges of the hypergraph are constructed based on a learnt Mahalanobis distance metric. Any disturbing information from other classes is excluded from each hyperedge. Finally, a multiple kernel learning algorithm is designed by integrating the l2 norm regularization into a linear SVM classifier to fuse the R feature and the BOVW representation for action recognition. Experimental results on several datasets demonstrate the effectiveness of the proposed approach for action recognition

Prevalence of and risk factors for non-suicidal self-injury in rural China: Results from a nationwide survey in China

Background Non-suicidal self-injury (NSSI) is a highly prevalent and serious public health problem among adolescents worldwide. However, to date there were no studies assessing the prevalence of NSSI defined by suggested DSM-5 criteria among Chinese adolescents. We aimed to conduct a nationwide survey to explore the prevalence of and risk factors for NSSI among school-based adolescents in rural China. Methods A total sample of 15,623 adolescents in rural China were enrolled by using a multistage sampling method. Data was collected by self-report questionnaires including demographic characteristics, neglect, maltreatment, loneliness, resilience, social support and emotional management ability. NSSI was defined by suggested DSM-5 criteria, according to which the engagement in self-injury took place more than 5 times a year. Multinomial logistic regression models were used to estimate the association between risk factors and NSSI. Results There were 12.2% of adolescents (n = 1908) met the suggested DSM-5 criteria. Approximately 29% reported a history of NSSI at least once during the last year. Significant differences were found in several demographic factors including gender, ethnicity, grade, and family structure between adolescents with and without experiencing NSSI. The top three NSSI behaviors among adolescents with NSSI experience were hitting self, pinching, and pulling hair, with a prevalence rate of 16.7%, 14.1% and 11.2%, respectively. Female, Han ethnicity, fathers’ education level, neglect, maltreatment, loneliness, social support, suicidal behaviors and emotional management ability were significantly associated with NSSI by multivariate analysis. No significant relationship was found between resilience and risk of NSSI. Limitation The DSM-5 has proposed 6 groups of criteria for NSSI, we only used criteria on frequency given its more accepted feasibility and pragmatic application. Consequently, it may different from other prevalence that estimated by other criteria. Conclusion To the best of our knowledge, this is the first study reporting prevalence of NSSI defined by suggested DSM-5 criteria among adolescent in rural China. In comparison to finding from the similar samples of adolescents, Chinese rural adolescents seem to have a relative higher prevalence. The potential risk factors for NSSI include female, father's education, Han ethnicity, psychosocial factors and suicide behaviors. More evidence for further understanding of context of the occurrence, improving access to health care utilization, and identifying the role of psychosocial factors and family relationship, is needed for the prevention and management of NSSI.Published versio

The Integration of Multi-source Remotely-Sensed Data in Support of the Classification of Wetlands

Wetlands play a key role in regional and global environments, and are critically linked to major issues such as climate change, wildlife habitat, biodiversity, water quality protection, and global carbon and methane cycles. Remotely-sensed imagery provides a means to detect and monitor wetlands on large scales and with regular frequency. In this project, methodologies were developed to classify wetlands (Open Bog, Treed Bog, Open Fen, Treed Fen, and Swamps) from multi-source remotely sensed data using advanced classification algorithms. The data utilized included multispectral optical and thermal data (Landsat-5) and Radar imagery from RADARSAT-2 and Sentinel-1. The goals were to determine the best way to combine the aforementioned imagery to classify wetlands, and determine the most significant image features. Classification algorithms investigated in this study were Naive Bayes, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Random Forest (RF). Based on the test results in the study area in Northern Ontario, Canada (49°31′.34N, 80°43′37.04W), a RF based classification methodology produced the most accurate classification result (87.51%). SVM, in some cases, produced results of comparable or better accuracy than RF. Our work also showed that the use of surface temperature (an untraditional feature choice) could aid in the classification process if the image is from an abnormally warm spring. This study found that wetlands were best classified using the NDVI (Normalized Difference Vegetative Index) calculated from optical imagery obtained in the spring months, radar backscatter coefficients, surface temperature, and ancillary data such as surface slope, computed through either an RF or SVM classifier. It was also found that preselection of features using Log-normal or RF variable importance analysis was an effective way of identifying low quality features and to a lesser extent features which were of higher quality