Oak-Associated Negativicute Equipped with Ancestral Aromatic Polyketide Synthase Produces Antimycobacterial Dendrubins

Anaerobic bacteria have only recently been recognized as a source of antibiotics; yet, the metabolic potential of Negativicutes (Gram-negative staining Firmicutes) such as the oak-associated Dendrosporobacter quercicolus has remained unknown. Genome mining of D. quercicolus and phylogenetic analyses revealed a gene cluster for a type II polyketide synthase (PKS) complex that belongs to the most ancestral enzyme systems of this type. Metabolic profiling, NMR analyses, and stable-isotope labeling led to the discovery of a new family of anthraquinone-type polyphenols, the dendrubins, which are diversified by acylation, methylation, and dimerization. Dendrubin A and B were identified as strong antibiotics against a range of clinically relevant, human-pathogenic mycobacteria.     

Phosphate recognition in structural biology

Drug-discovery research in the past decade has seen an increased selection of targets with phosphate recognition sites, such as protein kinases and phosphatases, in the past decade. This review attempts, with the help of database-mining tools, to give an overview of the most important principles in molecular recognition of phosphate groups by enzymes. A total of 3003 X-ray crystal structures from the RCSB Protein Data Bank with bound organophosphates has been analyzed individually, in particular for H-bonding interactions between proteins and ligands. The various known binding motifs for phosphate binding are reviewed, and similarities to phosphate complexation by synthetic receptors are highlighted. An analysis of the propensities of amino acids in various classes of phosphate-binding enzymes showed characteristic distributions of amino acids used for phosphate binding. This review demonstrates that structure-based lead development and optimization should carefully address the phosphate-binding-site environment and also proposes new alternatives for filling such sites.     

Urinary exposure marker discovery for toxicants using ultra-high pressure liquid chromatography coupled with Orbitrap high resolution mass spectrometry and three untargeted metabolomics approaches

Human biomonitoring is the assessment of actual internal contamination of chemicals by measuring exposure markers, chemicals or their metabolites, in human urine, blood, serum, and other body fluids. However, the metabolism of chemicals within an organism is extremely complex. Therefore, the identification of metabolites is often difficult and laborious. Several untargeted metabolomics methods have been developed to perform objective searching/filtering of accurate-mass-based LC-MS data to facilitate metabolite identification. In this study, three metabolomics data processing approaches were used for chemical exposure marker discovery in urine with an LTQ-Orbitrap high-resolution mass spectrometry (HRMS) dataset; di-isononyl phthalate (DINP) was used as an example. The data processing techniques included the SMAIT, mass defect filtering (MDF), and XCMS Online. Sixteen, 83, and 139 probable DINP metabolite signals were obtained using the SMAIT, MDF, and XCMS procedures, respectively. Fourteen probable metabolite signals mined simultaneously by the three metabolomics approaches were confirmed as DINP metabolites by structural information provided by LC-MS/MS. Among them, 13 probable metabolite signals were validated as exposure-related markers in a rat model. Six (m/z 319.155, 361.127, 373.126, 389.157, 437.112 and 443.130) of the 13 exposure-related DINP metabolite signals have not previously been reported in the literature. Our data indicate that SMAIT provided an efficient method to discover effectively and systematically urinary exposure markers of toxicant. The DINP metabolism information can provide valuable information for further investigations of DINP toxicity, toxicokinetics, exposure assessment, and human health effects.     

基于效用的备品备件优化库存控制法

 在(s,Q) 的故障替换概率分布模型基础上,提出了基于最小补充周期效用的优化补充周期定义算法和基于最小订货周期效用的优化订购周期定义算法,在保持了(s,Q)机制强调费用节省和可用性优点的同时,克服了它对资金积压、技术折旧损失和缺货损失、冗余库存维护损失缺乏调控能力的缺点.仿真结果很好验证了本机制的优点.     

一种时间序列频繁模式挖掘算法及其在WSAN行为预测中的应用

该文提出FPM(Frequent Pattern Mining)算法充分考虑频繁模式在时间序列中出现次数和分布。基于这些不同分布的频繁模式扩展MAMC(Mixed memory Aggregation Markov Chain)模型提出FMAMC(Frequent pattern based Mixed memory Aggregation Markov Chain)模型。将FPM和FMAMC应用到实际的智能楼宇项目中,证明和现有算法相比FPM算法具有较好的时间性能,FMAMC模型能够比MAMC模型更准确的预测WSAN节点行为。     

模糊因子下知识产权保护与侵犯的动态博弈分析

针对知识产权屡禁不止的侵权行为现状,运用模糊数学、数理统计方法和博弈论原理,从知识产权保护和侵犯双方效用及模型出发,以系统和动态的视角对双方行为进了博弈分析.研究结果表明保护与侵犯构成为一对均衡策略状态,其主要原因在于一定条件下,保护方期望的效用均衡处于低水平态势,从而提出在效用模糊因子的调整中不断提高保护方的动态效用水平是推进有效保护知识产权的动力途径.     

Deductibles in health insurance

This study is an extension to a simulation study that has been developed to determine ruin probabilities in health insurance. The study concentrates on inpatient and outpatient benefits for customers of varying age bands. Loss distributions are modelled through the Allianz tool pack for different classes of insureds. Premiums at different levels of deductibles are derived in the simulation and ruin probabilities are computed assuming a linear loading on the premium. The increase in the probability of ruin at high levels of the deductible clearly shows the insufficiency of proportional loading in deductible premiums. The PH-transform pricing rule developed by Wang is analyzed as an alternative pricing rule. A simple case, where an insured is assumed to be an exponential utility decision maker while the insurer’s pricing rule is a PH-transform is also treated.     

Size of random Galois lattices and number of closed frequent itemsets

Given a sample of binary random vectors with i.i.d. Bernoulli(p) components, that is equal to 1 (resp. 0) with probability p (resp. 1−p), we first establish a formula for the mean of the size of the random Galois lattice built from this sample, and a more complex one for its variance. Then, noticing that closed α-frequent itemsets are in bijection with closed α-winning coalitions, we establish similar formulas for the mean and the variance of the number of closed α-frequent itemsets. This can be interesting for the study of the complexity of some data mining problems such as association rule mining, sequential pattern mining and classification.     

A Novel Rule Induction Algorithm

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Optimal proportional reinsurance and investment with multiple risky assets and no-shorting constraint

In this paper, the basic claim process is assumed to follow a Brownian motion with drift. In addition, the insurer is allowed to invest in a risk-free asset and n risky assets and to purchase proportional reinsurance. Under the constraint of no-shorting, we consider two optimization problems: the problem of maximizing the expected exponential utility of terminal wealth and the problem of minimizing the probability of ruin. By solving the corresponding Hamilton–Jacobi–Bellman equations, explicit expressions for their optimal value functions and the corresponding optimal strategies are obtained. In particular, when there is no risk-free interest rate, the results indicate that the optimal strategies, under maximizing the expected exponential utility and minimizing the probability of ruin, are equivalent for some special parameter. This validates Ferguson’s longstanding conjecture about the relation between the two problems.