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Multi-tenant applications have been proliferating in recent years, supported by the emergence of computing-as-service paradigms. Unfortunately, multi-tenancy induces new security vulnerabilities due to spatial or temporal co-location of applications with possibly malicious intent. In this article, we consider a special class of stealthy integrity attacks on multi-tenant deep learning accelerators. One interesting conclusion is that it is possible to perform targeted integrity attacks on kernel weights of deep learning systems such that it remains functional but mis-labels specific categories of input data through standard RowHammer attacks by only changing 0.0009% of the total weights. We develop an automated framework, AroMa , to evaluate the impact of multi-tenancy on security of deep learning accelerators against integrity attacks on memory systems. We present extensive evaluations on AroMa to demonstrate its effectiveness.

This paper proposes a design method of low temperature co-fired ceramic(LTCC) diplexer, which is used to separate GPS and ISM band signals, achieves high iso lation and miniaturization, and can be used in electronic consumer products. The diplexer is composed of a low-pass filter and a band-pass filter. The antenna sep arates the received mixed signals according to frequency bands, and enters their respective processing modules through two output ports. The size of the diplexer is 1.6mm*0.8mm*0.6mm, the insertion loss is less than 0.6dB for GPS 1.575GHz appli cation, the isolation is greater than 26dB, and the insertion loss is less than 0.6dB for ISM 2.4/5GHz application, the isolation is greater than 37dB. Compared with the reported LTCC diplexer, the features of this diplexer are that the lower channel occupies a smaller volume at lower frequency, and the higher channel maintains low insertion loss in a wider bandwidth, and the passbands of the two filters are close but still have high isolation.

A high-fidelity ssDNA circularisation via the pairing of five 3′-terminal bases was developed to assist LR-LAMP for genotyping of SNPs.

Canna yellow streak virus (CaYSV) is a potyvirus that causes severe damage to the ornamental plant canna in the United Kingdom and Brazil. Here we identified CaYSV in China by isolating total RNA from infected plant, amplifying the virus genome segments, cloning and sequencing the amplicons. After assembly, the full-length genome of the virus was obtained and uploaded to the NCBI database. Phylogenetic analysis results showed that the Guizhou isolate (OL546222) was most closley related to the KS isolate (MG545919.1). Virus detection is essential for virus disease control, but the subclinical infection of CaYSV on canna in its early development increases the difficulty of CaYSV diagnosis. The goal of this study was to develop an efficient method for detection of CaYSV. We designed the primers, optimized the reaction conditions, and finally established a one-step reverse-transcription loop-mediated isothermal amplification (RT-LAMP) method. The product of RT-LAMP can be analyzed by both agarose gel electrophoresis and visible color change. The established one-step RT-LAMP assay showed high specificity and sensitivity in detecting CaYSV. This RT-LAMP method was also applied in analysis of 61 field samples collected from Guizhou and Jiangsu provinces. The results showed that the infection rates of CaYSV on canna samples from these two provinces were very high (63% and 96% respectively).

BackgroundSurvival outcomes of early-stage T1-2N0M0 small cell lung cancer (SCLC) patients differ widely, and the existing Veterans Administration Lung Study Group (VALSG) or TNM staging system is inefficient at predicting individual prognoses. In our study, we developed and validated nomograms for individually predicting overall survival (OS) and lung cancer-specific survival (LCSS) in this special subset of patients.MethodsData on patients diagnosed with T1-2N0M0 SCLC between 2000 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. All enrolled patients were split into a training cohort and a validation cohort according to the year of diagnosis. Using multivariable Cox regression, significant prognostic factors were identified and integrated to develop nomograms for 1-, 3-, and 5-year OS and LCSS prediction. The prognostic performance of our new model was measured by the concordance index (C-index) and calibration curve. We compared our latest model and the 8th AJCC staging system using decision curve analyses (DCA). Kaplan–Meier survival analyses were applied to test the application of the risk stratification system.ResultsA total of 1,147 patients diagnosed from 2000 to 2011 were assigned to the training cohort, and 498 cases that were diagnosed from 2012 to 2015 comprised the validation cohort. Age, surgery, lymph node removal (LNR), and chemotherapy were independent predictors of LCSS. The variables of sex, age, surgery, LNR, and chemotherapy were identified as independent predictors of OS. The above-mentioned prognostic factors were entered into the nomogram construction of OS and LCSS. The C-index of this model in the training cohort was 0.663, 0.702, 0.733, and 0.658, 0.702, 0.733 for predicting 1-, 3-, and 5-year OS and LCSS, respectively. Additionally, in the validation cohort, there were 0.706, 0.707, 0.718 and 0.712, 0.691, 0.692. The calibration curve showed accepted prediction accuracy between nomogram-predicted survival and actual observed survival, regardless of OS or LCSS. In addition, there were significant distinctions in the survival curves of OS and LCSS between different risk groups stratified by prognostic scores. Compared with the 8th AJCC staging system, our new model also improved net benefits.ConclusionsWe developed and validated novel nomograms for individual prediction of OS and LCSS, integrating the characteristics of patients and tumors. The model showed superior reliability and may help clinicians make treatment strategies and survival predictions for early-stage T1-2N0M0 SCLC patients.

Background and purpose Skin tissue is the natural barrier that protects our body, the damage of which can be repaired by the epidermal stem cells (ESCs). However, external factors abolish the self-repair ability of ESCs by inducing oxidative stress and severe inflammation. Apremilast is a small molecular inhibitor of phosphodiesterase 4 that was approved for the treatment of psoriasis. In the present study, the protective property of Apremilast against IL-1α-induced dysfunction on epidermal stem cells, as well as the preliminary mechanism, will be investigated. Methods ESCs were isolated from neonatal mice. The expression levels of TNF-α, IL-8, IL-12, MMP-2, and MMP-9 were detected using real-time PCR and ELISA. MitoSOX Red assay was used to determine the level of mitochondrial reactive oxygen species (ROS). Western blot and real-time PCR were utilized to determine the expression levels of IL-1R1, Myd88, and TRAF6. Activation of NF-κB was assessed by measuring the p-NF-κB p65 and luciferase activity. Capacities of ESCs were evaluated by measuring the gene expressions of integrin β1 and Krt19 using real-time PCR. Results Firstly, the expression levels of TNF-α, IL-8, IL-12, MMP-2, MMP-9 and IL-1R1, as well as the ROS level, were significantly elevated by IL-1α but greatly suppressed by treatment with Apremilast. Subsequently, we found that the activated Myd88/TRAF6/NF-κB signaling pathway induced by stimulation with IL-1α was significantly inhibited by the introduction of Apremilast. As a result, Apremilast protected ESCs against IL-1α-induced impairment in capacities of ESCs, this was verified by the elevated expression levels of integrin β1 and Krt19. Conclusions Apremilast might ameliorate IL-1α-induced dysfunction in ESCs by mitigating oxidative stress and inflammation through inhibiting the activation of the Myd88/TRAF6/NF-κB signaling pathway.

Rhizoctonia solani is a widely distributed plant pathogen that can damage many crops. Here, we identified a novel mycovirus tentatively named Rhizoctonia solani partitivirus 433 (RsPV433) from an R. solani (AG-3) strain which caused tobacco target spot disease on flue-cured tobacco. RsPV433 was consisted of two dsRNA segments with lengths of 2450 and 2273 bp, which encoded an RNA-dependent RNA polymerase and a coat protein, respectively. BLASTP results of RsPV433 showed that the closest relative of RsPV433 was Sarcosphaera coronaria partitivirus (QLC36830.1), with an identity of 60.85% on the RdRp amino sequence. Phylogenetic analysis indicated that RsPV433 belonged to the Betapartitivirus genus in the Partitiviridae family. The virus transmission experiment revealed that RsPV433 can be transmitted horizontally. We further tested the biological effect of RsPV433 on R. solani strains and found that the RsPV433-infected R. solani strain grew slower than the RsPV433-free strain on the PDA medium and RsPV433 seemed to have no obvious impact on the lesion inducing ability of R. solani.

Androgenic alopecia (AGA) is a common disease that negatively affects patients' physical and mental health. AGA can be treated with drugs that improve the perifollicular microenvironment, such as 5α-reductase inhibitors (e.g., dutasteride [DUT]), androgen receptor blockers, and minoxidil. However, the efficacy of these treatments is limited. Therefore, this study aimed to show that nanoparticles are effective as stable carriers with high curative benefits and little adverse effects. The

The poor fidelity of T4 DNA ligase has always limited the simple detection of single-nucleotide polymorphisms (SNPs) and is only applicable to some special SNP types. This study developed a highly sensitive and specific detection method for SNPs based on high-fidelity single-stranded circularisation. It used T4 DNA ligase and rolling circle amplification (RCA) plus loop-mediated isothermal amplification (LAMP). Surprisingly, the cyclisation stage's efficiency greatly improved. The ligation fidelity was almost perfect

Poleroviruses are positive-sense, single-stranded viruses. In this study, we describe the identification of a novel polerovirus isolated from soybean displaying curled leaves. The complete viral genome sequence was identified using high-throughput sequencing and confirmed using rapid amplification of cDNA ends (RACE), RT-PCR and Sanger sequencing. Its genome organization is typical of the members of genus Polerovirus, containing seven putative open reading frames (ORFs). The full genome is composed of single-stranded RNA of 5822 nucleotides in length, with the highest nucleotide sequence identity (79.07% with 63% coverage) for cowpea polerovirus 2 (CPPV2). Amino acid sequence identities of the protein products between the virus and its relatives are below the threshold determined by the International Committee of Taxonomy of Viruses (ICTV) for species demarcation, and this strongly supports this virus’ status as a novel species, for which the name soybean chlorotic leafroll virus (SbCLRV) is proposed. Recombination analysis identified a recombination event in the ORF5 of the 3’ portion in the genome. Phylogenetic analyses of the genome and encoded protein sequences revealed that the new virus is closely related to phasey bean mild yellows virus, CPPV2 and siratro latent polerovirus. Subsequently, we demonstrated the infectivity of SbCLRV in Nicotiana benthamiana via infectious cDNA clone generation and agroinoculation.

BackgroundDrug–target interaction (DTI) prediction plays a crucial role in drug discovery. Although the advanced deep learning has shown promising results in predicting DTIs, it still needs improvements in two aspects: (1) encoding method, in which the existing encoding method, character encoding, overlooks chemical textual information of atoms with multiple characters and chemical functional groups; as well as (2) the architecture of deep model, which should focus on multiple chemical patterns in drug and target representations.ResultsIn this paper, we propose a multi-granularity multi-scaled self-attention (SAN) model by alleviating the above problems. Specifically, in process of encoding, we investigate a segmentation method for drug and protein sequences and then label the segmented groups as the multi-granularity representations. Moreover, in order to enhance the various local patterns in these multi-granularity representations, a multi-scaled SAN is built and exploited to generate deep representations of drugs and targets. Finally, our proposed model predicts DTIs based on the fusion of these deep representations. Our proposed model is evaluated on two benchmark datasets, KIBA and Davis. The experimental results reveal that our proposed model yields better prediction accuracy than strong baseline models.ConclusionOur proposed multi-granularity encoding method and multi-scaled SAN model improve DTI prediction by encoding the chemical textual information of drugs and targets and extracting their various local patterns, respectively.

The unmanned aerial vehicles (UAVs) significantly contribute to the convenience and intelligence of life. However, the large use of UAVs also leads to high security risk. Only detecting the small and flying UAVs can prevent the safety accidents. UAV detection task could be regarded as a branch of object detection in flied of image processing. The advanced object detection models are mainly data driven, which depend on large-scale databases. The well-labeled datasets have proved to be of profound value for the effectiveness and accuracy in various object detection tasks. Thus, the first step of detecting UAVs is to build up a dataset of UAVs. In this study, we collect and release a dataset for UAV detection, called UAVData. To maintain the universality and robustness of the trained models, balloons and 6 types of UAVs are recorded in the dataset which totally consists of 13,803 well-labeled and recognizable images. We further conduct strong benchmarks using several advanced deep detection models, including faster R-CNN, SSD, YOLOv3. In addition, we utilize 4 different convolutional neural network models as the backbone models of these object detection methods to learn UAV-related features in images. By providing this dataset and baselines, we hope to gather researchers in both UAVs detection and machine learning field to advance toward the application.

Cutaneous malignant melanoma (CMM) is responsible for ≥1/2 of skin cancer-related mortalities. The aberrant expression of long non-coding RNAs (lncRNAs) has been associated with the development of CMM. However, to the best of our knowledge, the role of the lncRNA TINCR ubiquitin domain containing (TINCR) in CMM has not been previously investigated, and thus, the current study aimed to evaluate this in vitro and in vivo. Reverse transcription-quantitative PCR (RT-qPCR) was used to analyze microRNA (miR)-424-5p expression, and RT-qPCR and western blotting were used to measure TINCR, large tumor suppressor kinase 1 (LATS1), cellular communication network factor 2 (CTGF), cellular communication network factor 1 (CCN1) and AXL receptor tyrosine kinase (AXL) mRNA and protein expression levels, respectively. Cell Counting Kit-8, flow cytometry and Transwell assays were used to detect the proliferation, apoptosis and invasion of CMM cell lines, respectively. The binding sites between TINCR and miR-424-5p were predicted using the miRDB database. A dual luciferase reporter assay and RT-qPCR were used to identify the relationship between TINCR and miR-424-5p in CMM cell lines. The bioinformatics analysis revealed that TINCR was one of the most significantly downregulated lncRNAs in CMM, and advanced stage CMM tissues showed the greatest decrease in TINCR expression. Moreover, in the collected CMM tissues and tested cell lines of the current study, TINCR expression was found to be downregulated compared with the respective controls. Notably, TINCR overexpression inhibited the expression levels of CTGF, CCN1 and AXL, decreased the proliferation and invasion, and induced the apoptosis of CMM cell lines. In addition, a mutual binding association was identified between miR-424-5p and TINCR in CMM cells. LATS1, a target of miR-424-5p, was found to be positively regulated by TINCR. TINCR activated Hippo signaling and repressed the activity of Yes 1 associated transcriptional regulator by regulating LATS1 expression, while LATS1 knockdown reversed the effect of TINCR overexpression on CMM cells. Collectively, the findings of the present study suggested that TINCR may attenuate the progression of CMM by regulating the miR-424-5p/LATS1 signaling axis. These results indicated that TINCR may play a tumor suppressive role in CMM.

Metal casting is an important manufacturing technology for efficiently producing massive components with complex shape. A large share of industrial castings is made from iron and steel alloys, combining attractive properties and low production cost. Upgrading of properties in cast iron and steel is mainly achieved by alloying and in fewer cases by heat treatment. Molybdenum is an important alloying element in that respect, increasing strength, hardness and toughness. It also facilitates particular heat treatments such as austempering. The paper describes the metallurgical functionality of molybdenum alloying in iron-based castings and demonstrates its effectiveness for applications in the automotive and mining industry.

Austempered ductile iron (ADI) is a heat-treated nodular iron variant that has for a given strength level much higher elongation than conventional iron with spheroidized graphite. This paper considers the effect of niobium addition on the graphite microstructure, bainite microstructure, bainite transformation process and properties such as hardness and impact toughness under given heat-treatment conditions. The resulting properties are significant with regard to the wear resistance of the material. The effects of niobium addition on the graphite morphology can be understood based on a detailed precipitation analysis of the niobium precipitation behavior in the liquid phase. Niobium influences the temperature and kinetics of pearlite as well as bainite transformation. Furthermore, niobium refines the bainite microstructure generated during austempering treatment. It was found that niobium addition in the range of 0.2–0.5 wt percent allows obtaining an optimum combination of hardness, impact toughness and wear resistance.

Bidens pilosa is a weed species that invades crop areas in tropical and subtropical regions. To date, only two potyviruses have been reported to infect B. pilosa. Here, we report the complete genome sequence of a tomato zonate spot tospovirus (TZSV) isolate from Bidens named TZSV-Bidens. The tripartite RNA of the TZSV-Bidens genome contains L, M, and S segments that are 8912, 4724, and 2997 nt in length, respectively. The genome contains five open reading frames (ORFs), with 92.23-95.01% amino acid sequence identity to the TZSV-YN isolate. Phylogenetic analysis based on amino acid sequences of members of the family Tospoviridae showed that TZSV-Bidens was grouped into a well-supported Eurasian cluster. The intergenic regions (IGRs) of the M and S RNAs are among the most variable regions and are far shorter than those of the TZSV-YN reference genome.

Based on the catastrophe theory, this paper will select 26 scene indicators for classification and stratification, use different methods to process the data, and then calculate these factors that affect the stock price to obtain the overall comprehensive evaluation of the stock market; used in the research process Eviews analysis software builds an ARIMA time series forecasting model to predict the stock trading information of Zoomlion from 2000 to 2019, and finally compare the models. After testing, the accuracy of the prediction method combined with the analysis result of catastrophe theory is relatively high.

Drug-target interaction (DTI) prediction has drawn increasing interest due to its substantial position in the drug discovery process. Many studies have introduced computational models to treat DTI prediction as a regression task, which directly predict the binding affinity of drug-target pairs. However, existing studies (i) ignore the essential correlations between atoms when encoding drug compounds and (ii) model the interaction of drug-target pairs simply by concatenation. Based on those observations, in this study, we propose an end-to-end model with multiple attention blocks to predict the binding affinity scores of drug-target pairs. Our proposed model offers the abilities to (i) encode the correlations between atoms by a relation-aware self-attention block and (ii) model the interaction of drug representations and target representations by the multi-head attention block. Experimental results of DTI prediction on two benchmark datasets show our approach outperforms existing methods, which are benefit from the correlation information encoded by the relation-aware self-attention block and the interaction information extracted by the multi-head attention block. Moreover, we conduct the experiments on the effects of max relative position length and find out the best max relative position length value \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$k \in \{3, 5\}$\end{document}. Furthermore, we apply our model to predict the binding affinity of Corona Virus Disease 2019 (COVID-19)-related genome sequences and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$3137$\end{document} FDA-approved drugs.

Correction for ‘High-efficiency and high-fidelity ssDNA circularisation via the pairing of five 3′-terminal bases to assist LR-LAMP for the genotyping of single-nucleotide polymorphisms’ by Taiwen Li et al., Analyst, 2022, https://doi.org/10.1039/d2an01042a.

Herein, electron beam irradiation (EBI) was employed to in situ construct Ag-based micro/nanostructures. The composition of AgX@Ag (X = Cl−, Br−, etc.) composites and the size/morphology of silver nanoparticles (Ag-NPs) were tuned by adjusting the electron beam current density (EBCD) and the feeding dose of electrons. A hypothesis of gas–solid fluid-bed composed of atomic gas and nanocrystals is suggested to discuss the nanoparticle constructions with EBI. The low/medium EBCD creates a fixed fluid-bed for AgX@Ag-NPs and piled-pore architecture of Ag-NPs (PPAAg-NPs), while a high EBCD sets up a stirred fluid-bed for monodispersed Ag-NPs. Hopefully, this strategy would promote nanotechnology.

One of the current challenges in the development of swarm robots is making them realize obstacle avoidance while keeping formation. This paper proposes a new obstacle avoidance method for multiple robots based on the aggregation and disaggregation approach. First, the formation-based multi-resolution models of swarm robots are presented and the formation attribute-based consistency mapping functions are designed. Then, obstacle avoidance behavior based on aggregation and disaggregation method is presented and the basic obstacle avoidance behaviors are designed. Finally, the simulation results are presented to show the feasibility of the proposed method.

The effects of niobium addition up to 0.11 wt% on the microstructure and tensile properties of as-cast ductile iron (ACDI) were investigated. Metallographic analyses by both optical microscopy (OM) and scanning electron microscopy (SEM) indicated that niobium (Nb) promoted the formation of pearlite, reduced pearlite lamellar spacing and decreased the extent of graphitization taking place in the Nb-alloyed ACDI. The nodularity and nodule counts of graphite changed insignificantly when the Nb content was less than 0.08 wt% in the ACDI. The analysis of precipitates by transmission electron microscopy (TEM) revealed that nano and micro sized (Nb, Ti)C carbides acted as nucleation site for graphites, and promoted the formation of large graphite nodules with low roundnesses as Nb content rose above 0.08 wt%. The results of tensile testing showed that the yield strength, ultimate tensile strength and elongation of the ACDI with 0.08 wt% Nb increased by 12.1%, 11.2% and 14.3% over those of the Nb-free ACDI, respectively. The optimum values of the yield strength, tensile strength and elongation of the Nb-alloyed ACDI were found to be 418 MPa, 746.0 MPa and 8.0%, respectively, at the Nb content of 0.08 wt%. The high strain hardening rates of the Nb-containing ACDIs implied that they were capable of spontaneously strengthening itself increasingly to a large extent, in response to a slight plastic deformation after yielding.

Today's big and fast data and the changing circumstance require fast training of Deep Neural Networks (DNN) in various applications. However, training a DNN with tons of parameters involves intensive computation. Enlightened by the fact that redundancy exists in DNNs and the observation that the ranking of the significance of the weights changes slightly during training, we propose Eager Pruning, which speeds up DNN training by moving pruning to an early stage. Eager Pruning is supported by an algorithm and architecture co-design. The proposed algorithm dictates the architecture to identify and prune insignificant weights during training without accuracy loss. A novel architecture is designed to transform the reduced training computation into performance improvement. Our proposed Eager Pruning system gains an average of 1.91x speedup over state-of-the-art hardware accelerator and 6.31x energy-efficiency over Nvidia GPUs.

Sarcandra glabra is a species of Chloranthaceae family and this family grow in the southern part of China, Japan, and Southeastern Asia (Li et al. 2019). It is a kind of precious Chinese herbal medicine, which occupies an important position in traditional Chinese herbal medicine. It plays an effective role in the treatment of cancer, rheumatism, pneumonia, digestive tract inflammations, traumatic injuries and fractures, anti-virus, anti-bacterial, antioxidant, etc. (Li et al. 2019; Zheng et al. 2003; Zhou et al. 2013). Since June 2020, we discovered a serious leaf disease in the S. glabra planting base of Shibing County (108.12E 27.03N), in Guizhou Province, with an incidence rate of 60% and yield losses of 40%. Initially, the symptoms developed as small specks where spots were purple with a dark brown halo margin, and round or oval. In later stages, the spots gradually expanded and became dry, whole severe leaf loss. To identify the pathogen, we collected the diseased leaves from S. glabra fields in Shibing County. Small tissue pieces from the edges of lesions were disinfected in 75% ethyl alcohol for 30 s and 1% hypochlorite for 1 min, rinsed five times in sterile water, plated on potato dextrose agar (PDA), and incubated at 28°C in lighted incubator for 3 days. Fungal colonies were consistently isolated and transferred to PDA for morphological characterization (Fang et al. 2007). Pathogenicity tests of the novel isolate HGUP CSH-2 were conducted by spraying spore suspensions with a concentration of 1.6×108 conidia/ml on surface-disinfected (70% ethyl alcohol, 30 s) leaves, while sterile distilled water was used as the control. Plants with inoculated leaves (three per treatment) were placed in lighted growth chambers at 28°C for 5 days and watered as needed (Light to dark ratio 1:1, RH=90%). Symptoms on inoculated leaves were similar to those described previously in the field. The same pathogenic fungus was re-isolated from the infected leaves but not from the non-inoculated leaves. Colonies on PDA attaining 70 mm diam after 7 d at 28°C, with pale honey-colored, sparse aerial mycelia on the surface with black, gregarious conidiomata. Conidiogenous cell discrete or integrated, ampulliform, clavate or subcylindrical, hyaline, smooth-walled, wide at base. Conidia fusoid, ellipsoid, straight to slightly curved, 4-septate, slightly constricted at septa, 22.26-27.17×6.9-8.22 µm (av.±SD: 24.68±1.57×7.68±0.38 µm; n=30). According to the colony and conidia characteristics, the isolate was initially identified as Pestalotiopsis spp. (Liu et al. 2017). The pathogen was confirmed by amplification and sequencing of the internal transcribed spacer region (ITS) gene, the translation elongation factor-1 (TEF1) gene and the β-tubulin (TUB2) gene (Liu et al. 2017) using ITS1/ITS4, Bt2a/T1 and EF1-526F/1567R primers, respectively. The sequences of the PCR products were deposited in GenBank with accession numbers MT919215 (ITS), MT939300 (TUB2) and MT939299 (TEF1). BLAST results of the obtained sequences of the ITS, TUB2 and TEF1 genes revealed 97.16% (479/493 nucleotides), 99.56% (675/678 nucleotides) and 99.89% (890/891 nucleotides) homology with those of Pestalotiopsis lushanensis in GenBank (MG726538, KY464157 and KX895223). Maximum Likelihood method was used for phylogenetic analysis. The result showed that HGUP CSH-2 was together with P. lushanensis with a support rate of 100%. According to the morphological characteristics and molecular phylogenetic analysis, the pathogen was identified as P. lushanensis. So far as we know, our research is the first report of brown leaf spot of S. glabra caused by P. lushanensis in China. Thus, identification of P. lushanensis for this disease is important for the advancement of effective prevention and control practises as future perspectives.

Learning to predict human body motion has emerged as a meaningful research in computer vision and artificial intelligence. This paper presents the study on predicting human body motion from video sequences. We propose a human body motion prediction network integrating the recent advanced 2D feature extraction and video sequences prediction. Based on the temporal characteristics extracted from video sequences, our network realizes the prediction of the human motion. We train the network using the video based human pose datasets and demonstrate good performance of our network on 2D human body motion prediction through quantitative and qualitative results. Experimental results prove the feasibility of our method.

Capsule network is a new type of deep learning method to improve the CNN module. Though it has performed quite well on classifying the MNIST dataset, there are few applications in other fields. Thus in this paper, we apply the capsule network on skeleton-based classification and propose a framework to explore the potential of it. Since the bottom layer of the capsule network is still based on convolution operation, we feed heatmap as well as raw skeleton data and reach good performance on convolution-based action recognition. Most researches take spatial and temporal features into consideration and they do help to recognition accuracy. We propose two different encapsulations to extract the spatial and temporal features of skeleton sequences. We perform our experiments on UT-Kinect and a portion of NTU RGB+D dataset, and we achieve best 87% accuracy on the NTU RGB+D dataset. We also find that the capsule network is suitable for the coarse-grained classification tasks. In a conclusion, not only the characteristics of capsule network are proved, but also an efficient method to recognize human action is realized.

Empirical discovery of the mixed effects of group-level social capital.Measurement of the group social capital in different dimensions.Extension of social capital theories by introducing institutional environment.Recommendation for mitigation of the negative effects of group social capital. We studied the relationship between individuals' group social capital and their lending outcomes in the online peer-to-peer financial credit market, where individual lenders make direct unsecured microloans to other individual borrowers. Despite its ability to facilitate economic exchange, social capital as public goods may also cause free-rider problems, particularly in an online environment. Based on the analyses of transaction data collected from one of the largest online peer-to-peer lending platform in the U.S., we found that the borrower's general group social capital (i.e., group membership) and relational social capital (i.e., group credibility and verifiability, and group trust) yielded inconsistent effects, and the borrower's structural social capital (i.e., group inclusiveness) had a negative impact on, his/her funding and repayment performance. We discuss the implications of our findings for reconciling two major but conflicting theoretical views of social capital and for improving institutional mechanism design in a decentralized online financial credit market.

Human body motion includes the complex spatiotemporal information and human body motion prediction is useful in the human-computer interaction. An Encoder-Multiple-Recurrent-Decoder (EMRD) model to learn human action from sensor data and predict the later ones is proposed in this paper. The kernel of this method is recurrent neural networks (RNN). The model is used to predict the next several frames of a set of sensor data, which is continuous data but is pre-processed by embedding method proposed in this paper. EMRD extends the previous Encoder-Recurrent-Decoder (ERD) models and Long Short Terms Memory (LSTM) model which are used in the video human body movement prediction.

Research on the mesh simplification algorithm has been very active in the past years. The color features are very important and can be perceived by human observers. Nevertheless, these features are ignored by most simplification methods. For this reason, this paper presents the salient color features preserving mesh simplification method. First, the multiscale method for the estimation of the color saliency of 3D colored meshes is proposed. Then, the mesh simplification process based on the salient color features is presented. Finally, the experiment results show that the salient color features could be detected efficiently and this color saliency can improve the preservation of salient color features during the mesh simplification.

Missing data is a widespread problem that can affect the ability to use data to construct effective prediction systems. We investigate a common machine learning technique that can tolerate missing values, namely C4.5, to predict cost using six real world software project databases. We analyze the predictive performance after using the k-NN missing data imputation technique to see if it is better to tolerate missing data or to try to impute missing values and then apply the C4.5 algorithm. For the investigation, we simulated three missingness mechanisms, three missing data patterns, and five missing data percentages. We found that the k-NN imputation can improve the prediction accuracy of C4.5. At the same time, both C4.5 and k-NN are little affected by the missingness mechanism, but that the missing data pattern and the missing data percentage have a strong negative impact upon prediction (or imputation) accuracy particularly if the missing data percentage exceeds 40%.

In this paper, against the actual operating condition of the low alloy CrMo wear-resistant steel liner plate, the influence of Nb on the microstructure and properties of the low alloy CrMo steel were investigated based on the reasonable chemical composition design, got excellent combination of hardness and impact toughness to reach the requirements of liner. It is found that, with the treatment of 920°C(8h)+550°C(20h), adding 0.03wt% Nb could refine and homogenize the grain structure and the lamella spacing of matrix is smaller. Tensile strength and elongation are similar in both alloys with and without Nb, while increases hardness by 16%, which is benefit to the wear resistance.

In this paper, ductile iron material with different content of niobium was prepared by a scientific melting method. Image Analysis Software was used to studied the effect of niobium microalloying on both pearlite content and pearlite lamellar spacing in ductile iron quantitatively. The results showed that the addition of a small amount of niobium could increase the pearlite content and decrease pearlite lamellar spacing of ductile iron effectively.