基于病毒传播网络的基因序列表示学习

基因序列数据中往往存在大量的非编码和缺失序列,现有的基因序列表示大多通过人工方法对高维的基因序列进行特征提取,不仅非常耗时且成功的预测很大程度依赖于生物学知识的正确利用.基于病毒传播网络构建了一种基于图上下文信息的基因序列表示方法,对目标节点病毒序列进行编码后,使用注意力机制对其邻居节点的序列信息进行聚合,从而得到目标节点病毒序列的新的低维表示.进而依据病毒传播网络中相邻节点的基因序列相似性高于不相邻节点的特征,对基因序列表示模型进行优化,训练后得到的新的表示不仅可以有效表达基因序列的特征,同时极大地降低了序列的维度,提高了计算效率.分别在仿真病毒传播网络、新型冠状病毒和艾滋病毒传播网络数据上训练基因序列表示模型,并在相应的网络上进行未采样感染者发现任务.实验结果充分验证了模型的有效性,与其他方法的比较证明了模型的高效性,模型可以有效地在病毒传播网络上发现未采样感染者,这在流行病调查领域也具有一定的实际意义.     

Consideration of the BDNF gene in relation to two phenotypes: Hoarding and obesity.

The gene coding for the brain derived neurotrophic factor (BDNF) has emerged as an interesting candidate for multiple brain and brain disorder-related phenomena. The primary aim of the present investigation was to consider the relationship between the BDNF Val66Met variant and two phenotypes: compulsive hoarding as a symptom dimension of obsessive–compulsive disorder (OCD), and body mass index (BMI). We examined the BDNF gene in a large (N = 301) clinical sample of probands with OCD. Participants were classified as hoarding or nonhoarding using a strict, multimeasure grouping approach. Results revealed that the Val/Val genotype was linked with hoarding classification and more severe hoarding behaviors, as well as greater BMI levels. Hoarding status was also associated with greater BMI scores, with individuals in the hoarding group being far more likely to be classified as obese compared with the nonhoarding group. Our findings may provide a distinct avenue through which hoarding and BMI could be linked. These findings are suggestive of a complex gene, body weight, and psychopathology relationship wherein a primitive, survival “thrifty gene” strategy may be conserved and represented in a subgroup of humans manifesting severe hoarding symptoms. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

A guiding framework and approach for implementation research in substance use disorders treatment.

This paper introduces readers to the concepts of implementation science, implementation theory, and implementation frameworks and models. A wide range of models has been published in the literature related to implementation. The paper will present an overview of the Consolidated Framework for Implementation Research (CFIR), which is a comprehensive typology that unifies and consolidates the array of constructs that influence implementation from the perspective of these models. The CFIR is then used to evaluate implementation models used in studies of substance use disorder (SUD) treatments. Implementation research is scarce, with few prospective studies of theory-driven implementation. We assert that future research in SUD needs to meet three overarching objectives to promote wider implementation of evidence-based practices: (a) differentiation of core versus adaptable components of evidence-based interventions need; (b) development of methods to design implementation strategies, effectively adapted to the broad context; and (c) design and testing of predictive models to assess likelihood of effective implementation and prospects for sustainability while taking into account salient contextual factors. A recommended strategy for accomplishing these objectives is described. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Implementation of evidence-based substance use disorder continuing care interventions.

Continuing care following initial substance use disorder treatment often is associated with improved treatment outcomes and evidence-based interventions (EBIs) have been developed in this area. However, rates of patient participation in continuing care treatment and mutual help groups (MHGs) are low and a large gap exists between the existing EBIs and actual clinical care. This paper uses the Consolidated Framework for Implementation Research (CFIR; Damschroder et al., 2009) to review the literature on continuing care treatment and monitoring, and mutual help-group promotion. Although existing research provides implications for implementing EBIs in continuing care, few direct implementation trials have been conducted. This literature indicates that EBIs in continuing care have been successfully modified for different settings, that they can be delivered using different modalities (e.g., individual, group, and telephone-based care), and that low cost options are available. Additionally, much is known about the differential effectiveness of continuing care with different populations that may guide treatment programs and providers in selecting the most effective interventions for their clients. One significant barrier to successful implementation of EBIs for continuing care is the lack of information about incentives for providing continuing care across what in the CFIR terminology is a program's outer setting (i.e., external economic, political, and social setting), and its inner setting (i.e., internal political, structural, and cultural contexts). Implications for implementation of EBIs in substance use disorder continuing care are discussed. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

基于邻域粗糙集的加权KNN肿瘤基因表达谱分类算法

肿瘤亚型的准确判别对肿瘤的治疗具有重要意义,对肿瘤的不同亚型进行准确判别是当前生物信息学研究的重要课题．本文首先利用Relief算法排序基因并选出初始的肿瘤信息基因子集,然后利用向基于邻域粗糙集模型的向前属性约减算法FARNeM来计算加权基因集合,最后用加权KNN算法对肿瘤对这些数据进行分析,从而发现有差异的基因表达。实验结果表明了上述方法的可行性和有效性。     

基于AWG/RTSA的PD雷达信号模拟和相参分析方法

针对脉冲多普勒(PD)雷达的回波特点和相参特点,分析了回波模拟的难点.在其传统回波模拟和相参分析方法基础上,结合现代电子测量仪器在任意波形产生和实时频谱技术的应用,提出了一种基于现代任意波信号发生器的PD雷达回波模拟方法和基于现代实时频谱分析仪的相参分析方法.经过实验验证,该方法可有效提高PD雷达的调试效率.     

浅谈高级别生物安全实验室活毒废水处理系统

从活毒废水处理装置的设计、安装、控制、系统参数、系统组成等多个方面对活毒废水处理系统做了详细的阐述,并详细介绍了序批式活毒废水处理系统的三种方式.     

基于Volterra 级数对火工品起爆过程的辨识

为解决火工品起爆过程中线性函数不能解决元件参数变化、不确定性和非线性强的问题,将Volterra 级 数模型与基因表达式编程(gene expression programing,GEP)相结合,设计一种新的火工品起爆过程辨识算法。利用 Volterra 级数能准确反应非线性系统的特征来描述火工品起爆过程,GEP 算法则克服传统辨识方法的不足,辨识出正 确的模型。仿真结果表明,该方法能精确、快速地辨识出火工品起爆过程。