Genetic and epigenetic factors associated with depression: An updated overview

Depression is a complex psychiatric disturbance involving many environmental, genetic, and epigenetic factors. Until now, genetic, and non-genetic studies are still on the way to understanding the complex mechanism of this disease, and there are still many questions that have not yet been answered. Depression includes a large spectrum of heterogeneous symptoms correlated to the deficit of a range of psychological, cognitive, and emotional processes, and it affects various age groups. It is classified into several types according to the severity of symptoms, time of occurrence, and time. Following the World Health Organization (WHO), depression attacks near 350 million persons globally. Several factors overlap in causing depression, including genetic and epigenetic factors, environmental conditions, various stresses, lack of some nutrients to which people are exposed, and excessive stress and abuse in childhood. This study included conducting surveys on depression and new treatment trends based on epigenetic factors associated with the occurrence of the disease. Epigenetic factors provide a completely novel dimension to therapeutic approaches as most diseases are not monogenic, and it is likely that the environment has a significant contribution. Epigenetic inheritance is included in many mental and psychiatric disorders such as depression. In general, epigenetic modifications could be summarized in 3 major points: DNA methylation, histone modification, and non-mediated regulation of RNA (ncRNA). This study also describes some genes associated with one of the depressive disorders using bioinformatics tools and gene bank and had the genes: SLC6A4, COMT, TPH2, FKBP5, MDD1, HTR2A, and MDD2. As in this study, the awareness of Saudi society about depression and its genetic and non-genetic causes was estimated. The results showed that an encouraging percentage of more than half of the research sample possessed correct information about this disorder.     

微生物连续发酵酶催化动力系统的鲁棒性与并行计算

依据甘油连续发酵生产1,3-丙二醇的非线性酶催化动力系统.针对1,3-丙二醇可能存在的跨膜运输方式建立相应的动力学模型,提出了酶催化动力系统的定量鲁棒性定义,并建立了以鲁棒性为性能指标、非线性动力系统为主要约束的参数辨识模型.由于求解该辨识问题的数值计算量大,在普通的PC机上难以完成,因此本文构建了相应的并行算法.根据数值结果推断出1,3-丙二醇最有可能的跨膜运输方式,这对于进一步研究甘油连续发酵的机理具有重要的参考价值.     

Towards molecular computing: co-development of microfluidic devices and chemical reaction media

Microfluidics provides a powerful technology for both the production of molecular computing components and for the implementation of molecular computing architectures. The potential commercial applications of microfluidics drive rapid progress in this field-but at the same time focus interest on materials that are compatible with physiological aqueous conditions. For engineering applications that consider a broader range of physico-chemical conditions the narrow set of established materials for microfluidics can be a challenge. As a consequence of the large surface to volume ratio inherent in microfluidic technology the material of the device can greatly affect the chemistry in the channels of the device. In practice it is necessary to co-develop the chemical medium to be used in the device together with the microfluidic devices. We describe this process for a molecular computing architecture that makes use of excitable lipid-coated droplets of Belousov-Zhabotinsky reaction medium as its active processing components. We identify fluoropolymers with low melting temperature as a suitable substrate for microfluidics to be used in conjunction with Belousov-Zhabotinsky droplets in decane.     

A security mechanism based on evolutionary game in fog computing

Fog computing is a distributed computing paradigm at the edge of the network and requires cooperation of users and sharing of resources. When users in fog computing open their resources, their devices are easily intercepted and attacked because they are accessed through wireless network and present an extensive geographical distribution. In this study, a credible third party was introduced to supervise the behavior of users and protect the security of user cooperation. A fog computing security mechanism based on human nervous system is proposed, and the strategy for a stable system evolution is calculated. The MATLAB simulation results show that the proposed mechanism can reduce the number of attack behaviors effectively and stimulate users to cooperate in application tasks positively.     

A System for Distributed Storage and Analysis of Genome Information

A distributed computing system is developed to search and analyze genetic databases using parallel computing technologies. Queries are processed by a local network PC cluster. A universal task and data exchange format is developed for effective query processing. A multilevel hierarchic task batching procedure is elaborated to generate multiple subtasks and distribute them over cluster units under dynamic priority levels and with dynamic distribution of replicated source data subbases. Primary source data preparation and generation of annotation word indices are used to significantly reduce query processing time.     

A Network Model of Motion Processing in Area MT of Primates

A simple and biologically plausible model is proposed to simulatethe visual motion processing taking place in the middle temporal (MT) areaof the visual cortex in the primate brain. The model is ahierarchical neural network composed of multiple competitive learninglayers. The input layer of the network simulates the neurons in the primaryvisual cortex (V1), which are sensitive to the orientation and motionvelocity of the visual stimuli, and the middle and output layers of thenetwork simulate the component MT and pattern MT neurons, which areselectively responsive to local and global motions, respectively. Thenetwork model was tested with various simulated motion patterns (random dotsof different direction correlations, transparent motion, grating and plaidpatterns, and so on). The response properties of the model closely resemblemany of the known features of the MT neurons found neurophysiologically.These results show that the sophisticated response behaviors of the MTneurons can emerge naturally from some very simple models, such as acompetitive learning network.     

Meta-QC-Chain: Comprehensive and Fast Quality Control Method for Metagenomic Data

Next-generation sequencing(NGS) technology has revolutionized and significantly impacted metagenomic research.However,the NGS data usually contains sequencing artifacts such as low-quality reads and contaminating reads,which will significantly compromise downstream analysis.Many quality control(QC) tools have been proposed,however,few of them have been verified to be suitable or efficient for metagenomic data,which are composed of multiple genomes and are more complex than other kinds of NGS data.Here we present a metagenomic data QC method named Meta-QC-Chain.Meta-QC-Chain combines multiple QC functions:technical tests describe input data status and identify potential errors,quality trimming filters poor sequencing-quality bases and reads,and contamination screening identifies higher eukaryotic species,which are considered as contamination for metagenomic data.Most computing processes are optimized based on parallel programming.Testing on an 8-GB real dataset showed that Meta-QC-Chain trimmed low sequencing-quality reads and contaminating reads,and the whole quality control procedure was completed within 20 min.Therefore,Meta-QC-Chain provides a comprehensive,useful and high-performance QC tool for metagenomic data.Meta-QC-Chain is publicly available for free at:http://computationalbioenergy.org/meta-qc-chain.html.     

Workload characterization in a high-energy data grid and impact on resource management

The analysis of data usage in a large set of real traces from a high-energy physics collaboration revealed the existence of an emergent grouping of files that we coined “filecules”. This paper presents the benefits of using this file grouping for prestaging data and compares it with previously proposed file grouping techniques along a range of performance metrics. Our experiments with real workloads demonstrate that filecule grouping is a reliable and useful abstraction for data management in science Grids; that preserving time locality for data prestaging is highly recommended; that job reordering with respect to data availability has significant impact on throughput; and finally, that a relatively short history of traces is a good predictor for filecule grouping. Our experimental results provide lessons for workload modeling and suggest design guidelines for data management in data-intensive resource-sharing environments.

AutoMate: Enabling Autonomic Applications on the Grid

The increasing complexity, heterogeneity, and dynamism of emerging pervasive Grid environments and applications has necessitated the development of autonomic self-managing solutions, that are inspired by biological systems and deal with similar challenges of complexity, heterogeneity, and uncertainty. This paper introduces Project AutoMate and describes its key components. The overall goal of Project Automate is to investigate conceptual models and implementation architectures that can enable the development and execution of such self-managing Grid applications. Illustrative autonomic scientific and engineering Grid applications enabled by AutoMate are presented. The research presented in this paper is supported in part by the National Science Foundation via grants numbers ACI 9984357, EIA 0103674, EIA 0120934, ANI 0335244, CNS 0305495, CNS 0426354 and IIS 0430826. The authors would like to acknowledge the contributions of M. Agarwal, V. Bhat and N. Jiang to this research.