Molecular Characterization of a Debilitation-Associated Partitivirus Infecting the Pathogenic Fungus Aspergillus flavus

The opportunistic human pathogenic fungus Aspergillus flavus is known to be infected with mycoviruses. In this study, we report a novel mycovirus A. flavus partitivirus 1 (AfPV1) that was originally isolated from the abnormal colonial morphology isolate LD-3-8 of A. flavus. AfPV1 has spherical virus-like particles about 40 nm in diameter, and three double-stranded RNA (dsRNA) segments (dsRNA1, 2, and 3 with lengths of 1.7, 1.4, and 1.1 kbp, respectively) were packaged in the virions. dsRNA1, dsRNA2, and dsRNA3 each contained a single open reading frame and potentially encoded 62, 42, and 32 kDa proteins, respectively. The dsRNA1 encoded protein shows similarity to the RNA-dependent RNA polymerase (RdRp) of partitiviruses, and the dsRNA2 product has no significant similarity to any other capsid protein (CP) in the GenBank databases, beside some homology with the hypothetical “capsid” protein of a few partitiviruses. The dsRNA3 encodes a protein with no similarity to any protein in the GenBank database. SDS-PAGE and polypeptide mass fingerprint-mass spectrum (PMF-MS) analyses indicated that the CP of the AfPV1 was encoded by dsRNA2. Phylogenetic analysis showed that the AfPV1 and relative viruses were found in an unclassified group inside the Partitiviridae family. AfPV1 seems to result in debilitation symptoms, but had no significant effects to murine pathogenicity. These findings provide new insights into the partitiviruses taxonomy and the interactions between viruses and A. flavus

The longitudinal association between internet addiction and depressive and anxiety symptoms among Chinese adolescents before and during the COVID-19 pandemic

BackgroundThe COVID-19 pandemic and related prevention policies, such as home quarantine or online courses, could increase the risks of experiencing internet addiction and mental health problems among Chinese adolescents. There is a lack of longitudinal evidence to show the association between internet addiction symptoms and psychological consequences (e.g., depressive and anxiety symptoms).ObjectiveThis study aimed to explore the association between internet addiction and depressive and anxiety symptoms before and during the coronavirus disease 2019 (COVID-19) pandemic.MethodsAn effective sample of 7,958 Chinese adolescents was recruited for this two-wave longitudinal survey conducted over a six-month interval. All participants completed two-wave surveys before and during the COVID-19 pandemic. A longitudinal cross-lagged path model was used to analyze the associations between internet addiction and depressive and anxiety symptoms after controlling for four covariates (i.e., age, sex, minority, and COVID-19 influence).ResultsHigher depressive and anxiety symptoms before COVID-19 significantly predicted severe internet addiction during COVID-19. Results showed a significant bidirectional relationship between internet addiction and depressive symptoms. Furthermore, the prevalence of internet addiction displayed an increasing trend over the two waves. Conversely, a reduced prevalence of anxiety and depressive symptoms was observed over the two waves.ConclusionThis current study provided valuable evidence that psychological problems and internet addiction significantly influenced each other before and during the COVID-19 outbreak. Consequently, the presence of psychological problems before and during the COVID-19 outbreak could indicate internet addiction. Thus, depression- and anxiety-related psychotherapies should be developed to prevent internet addiction among Chinese adolescents

Toward robust linear SLAM

This paper presents a robust solution to the linear pose-graph SLAM problem based on local submap joining. This algorithm aims to converge toward correct solutions by detecting and eliminating the passive impacts from the failed loop closures. In the linear SLAM problem, the information matrix of each submap becomes non-diagonal due to nonlinear coordinate transformations. It is naive to make a least square operation between two constraints, when there isn't enough information to make a decision whether outlier loop closures exist. We thereby apply a delayed optimization to process the observations and pass them to the next level submap. To detect the outlier loop closure, we treat each loop closure as a random variable with an additional weight computed according to Expectation Maximization. By investing the feature of information matrix, the corrupted information matrix can be recovered efficiently. Experimental results based on publicly synthetic and real-world datasets show that this robust approach can effectively deal with incorrect loop closures

Application of Allele Specific PCR in Identifying Offspring Genotypes of Bi-Allelic SbeIIb Mutant Lines in Rice

Bi-allelic mutant lines induced by clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated (Cas) systems are important genetic materials. It is very important to establish a rapid and cheap method in identifying homozygous mutant plants from offspring segregation populations of bi-allelic mutant lines. In this study, the offspring genotypes of rice bi-allelic starch branching enzyme IIb mutant lines were identified using the allele specific PCR (AS-PCR) method. The target sequences of two alleles were aligned from their 5′ to 3′ ends, and the first different bases were used as the 3′ ends of mismatch primers. Another mismatched base was introduced at the third nucleotide from the 3′ end of mismatch primer. The PCR reaction mixture and amplification program were optimized according to the differences of mutation target sequence and mismatch primers. The offspring plant genotypes of bi-allelic mutant lines could be accurately identified using the amplified DNA fragments by agarose gel electrophoresis. This study could provide a method reference for the rapid screening of homozygous mutant plants from offspring segregation population of heterozygous and bi-allelic mutant lines

Robust Predictive Functional Control for Flight Vehicles Based on Nonlinear Disturbance Observer

A novel robust predictive functional control based on nonlinear disturbance observer is investigated in order to address the control system design for flight vehicles with significant uncertainties, external disturbances, and measurement noise. Firstly, the nonlinear longitudinal dynamics of the flight vehicle are transformed into linear-like state-space equations with state-dependent coefficient matrices. And then the lumped disturbances are considered in the linear structure predictive model of the predictive functional control to increase the precision of the predictive output and resolve the intractable mismatched disturbance problem. As the lumped disturbances cannot be derived or measured directly, the nonlinear disturbance observer is applied to estimate the lumped disturbances, which are then introduced to the predictive functional control to replace the unknown actual lumped disturbances. Consequently, the robust predictive functional control for the flight vehicle is proposed. Compared with the existing designs, the effectiveness and robustness of the proposed flight control are illustrated and validated in various simulation conditions

A Traffic-Aware Federated Imitation Learning Framework for Motion Control at Unsignalized Intersections with Internet of Vehicles

The wealth of data and the enhanced computation capabilities of Internet of Vehicles (IoV) enable the optimized motion control of vehicles passing through an intersection without traffic lights. However, more intersections and demands for privacy protection pose new challenges to motion control optimization. Federated Learning (FL) can protect privacy via model interaction in IoV, but traditional FL methods hardly deal with the transportation issue. To address the aforementioned issue, this study proposes a Traffic-Aware Federated Imitation learning framework for Motion Control (TAFI-MC), consisting of Vehicle Interactors (VIs), Edge Trainers (ETs), and a Cloud Aggregator (CA). An Imitation Learning (IL) algorithm is integrated into TAFI-MC to improve motion control. Furthermore, a loss-aware experience selection strategy is explored to reduce communication overhead between ETs and VIs. The experimental results show that the proposed TAFI-MC outperforms imitated rules in the respect of collision avoidance and driving comfort, and the experience selection strategy can reduce communication overheads while ensuring convergence

Rapid Identification of Potential Drugs for Diabetic Nephropathy Using Whole-Genome Expression Profiles of Glomeruli

Objective. To investigate potential drugs for diabetic nephropathy (DN) using whole-genome expression profiles and the Connectivity Map (CMAP). Methodology. Eighteen Chinese Han DN patients and six normal controls were included in this study. Whole-genome expression profiles of microdissected glomeruli were measured using the Affymetrix human U133 plus 2.0 chip. Differentially expressed genes (DEGs) between late stage and early stage DN samples and the CMAP database were used to identify potential drugs for DN using bioinformatics methods. Results. (1) A total of 1065 DEGs (FDR < 0.05 and fold change > 1.5) were found in late stage DN patients compared with early stage DN patients. (2) Piperlongumine, 15d-PGJ2 (15-delta prostaglandin J2), vorinostat, and trichostatin A were predicted to be the most promising potential drugs for DN, acting as NF-κB inhibitors, histone deacetylase inhibitors (HDACIs), PI3K pathway inhibitors, or PPARγ agonists, respectively. Conclusion. Using whole-genome expression profiles and the CMAP database, we rapidly predicted potential DN drugs, and therapeutic potential was confirmed by previously published studies. Animal experiments and clinical trials are needed to confirm both the safety and efficacy of these drugs in the treatment of DN