Hypergraph Modelling for Geometric Model Fitting

In this paper, we propose a novel hypergraph based method (called HF) to fit and segment multi-structural data. The proposed HF formulates the geometric model fitting problem as a hypergraph partition problem based on a novel hypergraph model. In the hypergraph model, vertices represent data points and hyperedges denote model hypotheses. The hypergraph, with large and "data-determined" degrees of hyperedges, can express the complex relationships between model hypotheses and data points. In addition, we develop a robust hypergraph partition algorithm to detect sub-hypergraphs for model fitting. HF can effectively and efficiently estimate the number of, and the parameters of, model instances in multi-structural data heavily corrupted with outliers simultaneously. Experimental results show the advantages of the proposed method over previous methods on both synthetic data and real images.Comment: Pattern Recognition, 201

Assessment of radiation exposure and public health before and after the operation of Sanmen nuclear power plant

IntroductionSanmen nuclear power plant (SNPP) operates the first advanced passive (AP1000) nuclear power unit in China.MethodsTo assess the radiological impacts of SNPP operation on the surrounding environment and the public health, annual effective dose (AED) and excess risk (ER) were estimated based on continuous radioactivity monitoring in drinking water and ambient dose before and after its operation during 2014–2021. In addition, the residents' cancer incidence was further analyzed through authorized health data collection.ResultsThe results showed that the gross α and gross β radioactivity in all types of drinking water were ranged from 0.008 to 0.017 Bq/L and 0.032 to 0.112 Bq/L, respectively. The cumulative ambient dose in Sanmen county ranged from 0.254 to 0.460 mSv/y, with an average of 0.354 ± 0.075 mSv/y. There is no statistical difference in drinking water radioactivity and ambient dose before and after the operation of SNPP according to Mann–Whitney U test. The Mann-Kendall test also indicates there is neither increasing nor decreasing trend during the period from 2014 to 2021. The age-dependent annual effective doses due to the ingestion of drinking water or exposure to the outdoor ambient environment are lower than the recommended threshold of 0.1 mSv/y. The incidence of cancer (include leukemia and thyroid cancer) in the population around SNPP is slightly higher than that in other areas, while it is still in a stable state characterized by annual percentage changes.DiscussionThe current comprehensive results show that the operation of SNPP has so far no evident radiological impact on the surrounding environment and public health, but continued monitoring is still needed in the future

FWAlgaeDB, an integrated genome database of freshwater algae

Algal genomics research contributes to a deeper understanding of algal evolution and provides useful genomics inferences correlated with various functions. Published algal genome sequences are very limited owing to genome assembly challenges. Because genome data of freshwater algae are rapidly increasing with the recent boom in next-generation sequencing and bioinformatics, an interface to store, interlink, and display these data is needed. To provide a substantial genomic resource specifically for freshwater algae, we developed the Freshwater Algae Database (FWAlgaeDB), a user-friendly, constantly updated online repository for integrating genomic data and annotation information. This database, which includes information on 204 freshwater algae, allows easy access to gene repertoires and gene clusters of interest and facilitates potential applications. Three functional modules are integrated into FWAlgaeDB: a Basic Local Alignment Search Tool tool for similarity analyses, a Search tool for rapid data retrieval, and a Download function for data downloads. This database tool is freely available at http://www.fwalagedb.com/#/home. To demonstrate the utility of FWAlgaeDB, we also individually mapped metagenomic sequencing reads of 10 water samples to FWAlgaeDB and Nt algae databases we constructed to obtain taxonomic composition information. According to the mapping results, FWAlgaeDB may be a better choice for identifying algal species in freshwater samples, with fewer potential false positives because of its focus on freshwater algal species. FWAlgaeDB can therefore serve as an open-access, sustained platform to provide genomic data and molecular analysis tools specifically for freshwater algae

Cell transcriptomic atlas of the non-human primate Macaca fascicularis.

Studying tissue composition and function in non-human primates (NHPs) is crucial to understand the nature of our own species. Here we present a large-scale cell transcriptomic atlas that encompasses over 1 million cells from 45 tissues of the adult NHP Macaca fascicularis. This dataset provides a vast annotated resource to study a species phylogenetically close to humans. To demonstrate the utility of the atlas, we have reconstructed the cell-cell interaction networks that drive Wnt signalling across the body, mapped the distribution of receptors and co-receptors for viruses causing human infectious diseases, and intersected our data with human genetic disease orthologues to establish potential clinical associations. Our M. fascicularis cell atlas constitutes an essential reference for future studies in humans and NHPs.We thank W. Liu and L. Xu from the Huazhen Laboratory Animal Breeding Centre for helping in the collection of monkey tissues, D. Zhu and H. Li from the Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory) for technical help, G. Guo and H. Sun from Zhejiang University for providing HCL and MCA gene expression data matrices, G. Dong and C. Liu from BGI Research, and X. Zhang, P. Li and C. Qi from the Guangzhou Institutes of Biomedicine and Health for experimental advice or providing reagents. This work was supported by the Shenzhen Basic Research Project for Excellent Young Scholars (RCYX20200714114644191), Shenzhen Key Laboratory of Single-Cell Omics (ZDSYS20190902093613831), Shenzhen Bay Laboratory (SZBL2019062801012) and Guangdong Provincial Key Laboratory of Genome Read and Write (2017B030301011). In addition, L.L. was supported by the National Natural Science Foundation of China (31900466), Y. Hou was supported by the Natural Science Foundation of Guangdong Province (2018A030313379) and M.A.E. was supported by a Changbai Mountain Scholar award (419020201252), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA16030502), a Chinese Academy of Sciences–Japan Society for the Promotion of Science joint research project (GJHZ2093), the National Natural Science Foundation of China (92068106, U20A2015) and the Guangdong Basic and Applied Basic Research Foundation (2021B1515120075). M.L. was supported by the National Key Research and Development Program of China (2021YFC2600200).S

Vehicular-Network-Intrusion Detection Based on a Mosaic-Coded Convolutional Neural Network

With the development of Internet of Vehicles (IoV) technology, the car is no longer a closed individual. It exchanges information with an external network, communicating through the vehicle-mounted network (VMN), which, inevitably, gives rise to security problems. Attackers can intrude on the VMN, using a wireless network or vehicle-mounted interface devices. To prevent such attacks, various intrusion-detection methods have been proposed, including convolutional neural network (CNN) ones. However, the existing CNN method was not able to best use the CNN’s capability, of extracting two-dimensional graph-like data, and, at the same time, to reflect the time connections among the sequential data. Therefore, this paper proposed a novel CNN model, based on two-dimensional Mosaic pattern coding, for anomaly detection. It can not only make full use of the ability of a CNN to extract grid data but also maintain the sequential time relationship of it. Simulations showed that this method could, effectively, distinguish attacks from the normal information on the vehicular network, improve the reliability of the system’s discrimination, and, at the same time, meet the real-time requirement of detection

Vehicular-Network-Intrusion Detection Based on a Mosaic-Coded Convolutional Neural Network

With the development of Internet of Vehicles (IoV) technology, the car is no longer a closed individual. It exchanges information with an external network, communicating through the vehicle-mounted network (VMN), which, inevitably, gives rise to security problems. Attackers can intrude on the VMN, using a wireless network or vehicle-mounted interface devices. To prevent such attacks, various intrusion-detection methods have been proposed, including convolutional neural network (CNN) ones. However, the existing CNN method was not able to best use the CNN’s capability, of extracting two-dimensional graph-like data, and, at the same time, to reflect the time connections among the sequential data. Therefore, this paper proposed a novel CNN model, based on two-dimensional Mosaic pattern coding, for anomaly detection. It can not only make full use of the ability of a CNN to extract grid data but also maintain the sequential time relationship of it. Simulations showed that this method could, effectively, distinguish attacks from the normal information on the vehicular network, improve the reliability of the system’s discrimination, and, at the same time, meet the real-time requirement of detection

Bioinformatics and system biology analysis revealed the crosstalk between COVID‐19 and osteoarthritis

Abstract Background The global coronavirus disease 2019 (COVID‐19) outbreak has significantly impacted public health. Moreover, there has been an association between the incidence and severity of osteoarthritis (OA) and the onset of COVID‐19. However, the optimal diagnosis and treatment strategies for patients with both diseases remain uncertain. Bioinformatics is a novel approach that may help find the common pathology between COVID‐19 and OA. Methods Differentially expressed genes (DEGs) were screened by R package “limma.” Functional enrichment analyses were performed to find key biological functions. Protein–protein interaction (PPI) network was constructed by STRING database and then Cytoscape was used to select hub genes. External data sets and OA mouse model validated and identified the hub genes in both mRNA and protein levels. Related transcriptional factors (TF) and microRNAs (miRNAs) were predicted with miRTarBase and JASPR database. Candidate drugs were obtained from Drug Signatures database. The immune infiltration levels of COVID‐19 and OA were evaluated by CIBERSORT and scRNA‐seq. Results A total of 74 common DEGs were identified between COVID‐19 and OA. Receiver operating characteristic curves validated the effective diagnostic values (area under curve > 0.7) of four hub genes (matrix metalloproteinases 9, ATF3, CCL4, and RELA) in both the training and validation data sets of COVID‐19 and OA. Quantitative polymerase chain reaction and Western Blot showed significantly higher hub gene expression in OA mice than in healthy controls. A total of 84 miRNAs and 28 TFs were identified to regulate the process of hub gene expression. The top 10 potential drugs were screened including “Simvastatin,” “Hydrocortisone,” and “Troglitazone” which have been proven by Food and Drug Administration. Correlated with hub gene expression, Macrophage M0 was highly expressed while Natural killer cells and Mast cells were low in both COVID‐19 and OA. Conclusion Four hub genes, disease‐related miRNAs, TFs, drugs, and immune infiltration help to understand the pathogenesis and perform further studies, providing a potential therapy target for COVID‐19 and OA

Study on Insulation Breakdown Characteristics of Printed Circuit Board under Continuous Square Impulse Voltage

The widely distributed interconnects in printed circuit boards (PCBs) easily couple with high voltage under the action of electromagnetic pulses, which leads to insulation failure. In this study, the dielectric breakdown characteristics of four typical PCBs are studied under continuous square impulse voltage conditions. First, the electric field distribution in the four electrode models is simulated with the ANSYS software (ANSYS Maxwell 17.0). Electric field simulation results show the weak area of electric field distribution. On this basis, the possible breakdown patterns of PCB are analyzed. Second, the influence of factors, such as temperature, pulse duty ratio, interconnect insulation distance, and air pressure, on PCB breakdown voltage is studied through a breakdown test on the PCBs. Results show that the discharge between the single-layer electrodes of the PCBs is surface discharge, and the breakdown is that of a “gas⁻solid composite medium„. Meanwhile, the breakdown of a double-layer PCB is solid breakdown. Finally, scanning electron microscopy (SEM) produced by Tescan (Brno, Czech Republic) is performed to study the carbonization channel after PCB breakdown. SEM results reveal that the PCB carbonization channel is influenced by temperature and pressure in varying degrees

A New Method for Automatic Detection of Defects in Selective Laser Melting Based on Machine Vision

Selective laser melting (SLM) is a forming technology in the field of metal additive manufacturing. In order to improve the quality of formed parts, it is necessary to monitor the selective laser melting forming process. At present, most of the research on the monitoring of the selective laser melting forming process focuses on the monitoring of the melting pool, but the quality of forming parts cannot be controlled in real-time. As an indispensable link in the SLM forming process, the quality of powder spreading directly affects the quality of the formed parts. Therefore, this paper proposes a detection method for SLM powder spreading defects, mainly using industrial cameras to collect SLM powder spreading surfaces, designing corresponding image processing algorithms to extract three common powder spreading defects, and establishing appropriate classifiers to distinguish different types of powder spreading defects. It is determined that the multilayer perceptron (MLP) is the most accurate classifier. This detection method has high recognition rate and fast detection speed, which cannot only meet the SLM forming efficiency, but also improve the quality of the formed parts through feedback control