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The genome sequence of V. cholerae O1 Biovar Eltor strain N16961 has revealed a putative antibiotic resistance (var) regulon that is predicted to encode a transcriptional activator (VarR), which is divergently transcribed relative to the putative resistance genes for both a metallo-β-lactamase (VarG) and an antibiotic efflux-pump (VarABCDEF). We sought to test whether these genes could confer antibiotic resistance and are organised as a regulon under the control of VarR. VarG was overexpressed and purified and shown to have β-lactamase activity against penicillins, cephalosporins and carbapenems, having the highest activity against meropenem. The expression of VarABCDEF in the Escherichia coli (ΔacrAB) strain KAM3 conferred resistance to a range of drugs, but most significant resistance was to the macrolide spiramycin. A gel-shift analysis was used to determine if VarR bound to the promoter regions of the resistance genes. Consistent with the regulation of these resistance genes, VarR binds to three distinct intergenic regions, varRG, varGA and varBC located upstream and adjacent to varG, varA and varC, respectively. VarR can act as a repressor at the varRG promoter region; whilst this repression was relieved upon addition of β-lactams, these did not dissociate the VarR/varRG-DNA complex, indicating that the de-repression of varR by β-lactams is indirect. Considering that the genomic arrangement of VarR-VarG is strikingly similar to that of AmpR-AmpC system, it is possible that V. cholerae has evolved a system for resistance to the newer β-lactams that would prove more beneficial to the bacterium in light of current selective pressures.

Peer-to-peer lending (P2P lending) has proliferated in recent years thanks to Fintech and big data advancements. However, P2P lending platforms are not tightly governed by relevant laws yet, as their development speed has far exceeded that of regulations. Therefore, P2P lending operations are still subject to risks. This paper proposes prediction models to mitigate the risks of default and asymmetric information on P2P lending platforms. Specifically, we designed sophisticated procedures to pre-process mass data extracted from Lending Club in 2018 Q3–2019 Q2. After that, three statistical models, namely, Logistic Regression, Bayesian Classifier, and Linear Discriminant Analysis (LDA), and five AI models, namely, Decision Tree, Random Forest, LightGBM, Artificial Neural Network (ANN), and Convolutional Neural Network (CNN), were utilized for data analysis. The loan statuses of Lending Club’s customers were rationally classified. To evaluate the models, we adopted the confusion matrix series of metrics, AUC-ROC curve, Kolmogorov–Smirnov chart (KS), and Student’s t-test. Empirical studies show that LightGBM produces the best performance and is 2.91% more accurate than the other models, resulting in a revenue improvement of nearly USD 24 million for Lending Club. Student’s t-test proves that the differences between models are statistically significant.

This study examined the factorial structure of the World Health Organization Quality of Life Questionnaire—Brief Version in a community sample of Canadians with spinal cord injuries. A confirmatory factor analysis provides evidence that the instrument is a multidimensional measure of quality of life. Additionally, the questionnaire is correlated in the predicted directions with education, income, time since injury, self-esteem, and acceptance of disability. Implications of its use in rehabilitation counseling practice and research are discussed.

This paper studies the reliability evaluation of a stochastic manufacturing system with multiple production lines in parallel. Multiple repairs and different failure rates, never simultaneously addressed in earlier works, are taken into account. First, a revised graphical methodology integrating transformation and decomposition is utilized to construct the stochastic manufacturing system as a multi-state manufacturing network （MSMN）. In particular, a ＂predecessor-set＂ technique is proposed to deal with multiple repairs. An algorithm is proposed to generate the lowest capacity vectors （LCVs） that stations should provide to satisfy the workloads. Subsequently, the system reliability of the MSMN, which is defined as the probability of demand satisfaction, is calculated in terms of the LCVs. A real case of a printed circuit board manufacturing system is utilized to demonstrate how the system reliability can be evaluated. A further decision making issue is addressed based on the derived system reliability.

This paper presents an algorithm to evaluate estimated and exact system reliabilities for a computer network in the cloud computing environment.From the quality of service（QOS） viewpoint,the computer network should be maintained when falling to a specific state such that it cannot afford enough capacity to satisfy demand.Moreover,the transmission time should be concerned as well.Thus,the data can be sent through several disjoint minimal paths simultaneously to shorten the transmission time.Under the maintenance budget B and time constraint T,we evaluate the system reliability that d units of data can be sent from the cloud to the client through multiple paths.Two procedures are integrated in the proposed algorithm-an estimation procedure for estimated system reliability and an adjusting procedure utilizing the branch-and-bound approach for exact system reliability.Subsequently,the estimated system reliability with lower bound and upper bound,and exact system reliability are computed by applying the recursive sum of disjoint products（RSDP） algorithm.

This paper proposes a graphical-based methodology to evaluate the performance of a manufacturing system in terms of network model.We focus on a manufacturing system which consists of multiple distinct production lines.A transformation technique is developed to build the manufacturing system as a manufacturing network.In such a manufacturing network,the capacity of each machine is multistate due to failure,partial failure,or maintenance.Thus,this manufacturing network is also regarded as a multistate network.We evaluate the probability that the manufacturing network can meet a given demand,where the probability is referred to as the system reliability.A simple algorithm integrating decomposition technique is proposed to generate the minimal capacity vectors that machines should provide to eventually satisfy demand.The system reliability is derived in terms of such capacity vectors afterwards.A practical application in the context of IC card manufacturing system is utilized to demonstrate the performance evaluation procedure.

Cytosine methylation of genomic DNA controls gene expression and maintains genome stability. How a specific DNA sequence is targeted for methylation by a methyitransferase is largely unknown. Here, we show that histone H3 tails lacking iysine 4 （K4） methylation function as an allosteric activator for methyltransferase Dnmt3a by binding to its plant homeodomain （PHD）. In vitro, histone H3 peptides stimulated the methylation activity of Dnmt3a up to 8-fold, in a manner reversely correlated with the level of K4 methylation. The biological significance of allosteric regulation was manifested by molecular modeling and identification of key residues in both the PHD and the catalytic domain of Dnmt3a whose mutations impaired the stimulation of methylation activity by H3 peptides but not the binding of H3 peptides. Significantly, these mutant Dnmt3a proteins were almost inactive in DNA methylation when expressed in mouse embryonic stem cells while their recruitment to genomic targets was unaltered. We therefore propose a two-step mechanism for de novo DNA methylation - first recruitment of the methyltransferase probably assisted by a chromatin- or DNA-binding factor, and then allosteric activation depending on the interaction between Dnmt3a and the histone tails - the latter might serve as a checkpoint for the methylation activity.

The genome sequence of V. cholerae O1 Biovar Eltor strain N16961 has revealed a putative antibiotic resistance (var) regulon that is predicted to encode a transcriptional activator (VarR), which is divergently transcribed relative to the putative resistance genes for both a metallo-[beta]-lactamase (VarG) and an antibiotic efflux-pump (VarABCDEF). We sought to test whether these genes could confer antibiotic resistance and are organised as a regulon under the control of VarR. VarG was overexpressed and purified and shown to have [beta]-lactamase activity against penicillins, cephalosporins and carbapenems, having the highest activity against meropenem. The expression of VarABCDEF in the Escherichia coli ([DELTA]acrAB) strain KAM3 conferred resistance to a range of drugs, but most significant resistance was to the macrolide spiramycin. A gel-shift analysis was used to determine if VarR bound to the promoter regions of the resistance genes. Consistent with the regulation of these resistance genes, VarR binds to three distinct intergenic regions, varRG, varGA and varBC located upstream and adjacent to varG, varA and varC, respectively. VarR can act as a repressor at the varRG promoter region; whilst this repression was relieved upon addition of [beta]-lactams, these did not dissociate the VarR/varRG-DNA complex, indicating that the de-repression of varR by [beta]-lactams is indirect. Considering that the genomic arrangement of VarR-VarG is strikingly similar to that of AmpR-AmpC system, it is possible that V. cholerae has evolved a system for resistance to the newer [beta]-lactams that would prove more beneficial to the bacterium in light of current selective pressures.