基于GM(1,1)组合模型的小麦条锈病预测方法研究

小麦条锈病的预测是制定小麦条锈病防治方案的重要组成部分.鉴于小麦条锈病病害系统的复杂性和灰色性,首先用Brown指数平滑法和灰色预测建立了甘肃天水地区1995年到2003年的普遍率的单项预测模型,然后采用预测残差方差最小的原则进行优化组合,从而获得更为精确的预测模型和预测值,拟合率达到94.5%.     

基于情景分析和改进IOWA组合预测模型的经济预测

为了反映不同情景下地区经济的增长情况,本文在情景分析的基础上,提出采用一种基于IOWA算子的改进组合预测模型预测GDP的增长。即为了克服传统基于IOWA算子的组合预测模型由于实际数据未知而不能求取预测期的误差而无法构造目标函数的缺陷,采用经济增长数理模型的拟合数据和预测数据作为基础数据,替代原始数据,并在情景分析的基础上对某市的GDP进行了预测,实验结果可以很好的反映该市各情景下的经济增长情况。     

大规模蛋白质功能预测方法的进展

全基因组测序的快速发展在获得大量序列信息的同时也迫切需要获取功能信息,用生物信息学方法进行大规模蛋白质功能预测在这种需求中获得发展。这些预测方法从基于序列同源性发展到基于genomic-context获得功能相关蛋白质对。基于genomic-context的方法具体有基因融合、染色体邻近、相似系统发生谱等。由于各种方法的偏向性,最新的趋势是整合多种方法的数据,组成蛋白质相互作用网络,通过分析网络的结构进行蛋白质功能预测。     

三种预测林木生长量方法的比较

本研究选择了三种方法灰色GM(1,1)模型法、回归法、灰色线性回归组合模型法,经过数据的加载,灰色GM(1,1)为Ⅱ级合格模型,发展系数|a|＜0.3可用于中长期预测;在回归法中选用5个模型进行拟合,以Richards方程的复相关系高,残差平方和最小,被选中;灰色线性回组合模型达到Ⅲ级勉强可用模型.用三个模型分别对32、34龄阶的生长量进行预测,其平均相对精度分别为94.64%、80.68%、92.41%.     

基于组合药物网络的复杂疾病药物组合模式研究

人体作为一个复杂的功能系统。疾病的发生和发展,尤其是复杂疾病,其病理过程往往涉及多环节、多系统。单一药物难以满足复杂疾病的治疗要求,组合药物成为未来药物发展的新趋势。本文在构建组合药物网络的基础上进行MCODE算法聚类,得到33个独立且内部联系紧密的药物模块。其中26组药物模块用于治疗单一复杂疾病。通过详细分析癌症、疼痛、银屑病、细菌感染、类风湿性关节炎、化疗呕吐这六种复杂疾病,归纳总结出这六种疾病的药物组合模式,从而提出复杂疾病多角度的治疗策略。     

松材线虫(Bursaphelenchus xylophilus)入侵对马尾松(Pinus massoniana)林分生长的影响及相关生长模型

松材线虫（Bursaphelenchus xylophilus）是一种松树上发生严重的有害生物,它不仅改变了生态系统的结构和功能,而且改变了系统内生物的原有特性和地理分布。松材线虫及其引起的松树萎蔫病已对中国马尾松林（Pinus massoniana）的树木成长产生了巨大影响。基于此,使用＂每木调查法＂和＂样方法＂,对松材线虫入侵后的马尾松林内松树的各项生长指标因子进行了调查分析,其结果表明：自松材线虫1996年入侵所调查地区的松林后,对于受害松树不管是伐倒木（被伐倒）还是倒木（自然倒地）,其对周围马尾松胸径生长的影响是显著的,而对树高生长的影响不显著。最后建立了一系列的灰色和灰色-马尔可夫链数学模型,其预测结果精度高,可用于今后受害和未受害区马尾松林分因子的生长预测。     

中国森林生物多样性动态的灰色预测

我国森林生物多样性动态分析具有少数据和贫信息带来的灰色不确定性, 灰色系统理论是进行相关研究的重要工具。在前人工作的基础上, 作者根据PSR(Pressure-State-Response)模型计算得到1973–1998年间我国5次森林资源连续清查期内的森林生物多样性指数序列, 包括压力指数、森林生态系统多样性指数和森林物种多样性指数, 以及由这3个指数建立的森林生物多样性总指数; 并建立了各个指数的GM(1,1)灰色模型, 预测我国森林生物多样性的动态。结果表明, 在未来2个森林资源连续清查期(大约10年), (1) 我国森林生物多样性指数将继续增加, 且与过去5个森林资源连续清查期相比其增加速度将有所提高; (2) 压力指数将维持继续增大的趋势不变; (3) 森林生态系统多样性指数将维持在当前水平, 有轻微波动; (4) 森林物种多样性指数将继续增加, 但与过去5个森林资源连续清查期相比其增加速度将渐趋平缓。研究表明, 根据PSR模型建立我国森林生物多样性动态的灰色预测模型, 适合我国森林资源管理的实际需要。     

基于支持向量机的枯草杆菌启动子预测方法

启动子预测是研究基因转录调控的重要环节,但现有算法的预测正确率偏低．在深入分析启动子生物特征的基础上,提出了一种基于支持向量机的枯草杆菌启动子预测算法,在启动子序列的组成特征、信号特征和结构特征中选取9种典型特征作为预测的依据,对于信号特征,除了利用保守模式的一致序列,还考虑了间隔距离的分布信息．首先通过特征描述模型分别计算每种特征在启动子序列和非启动子序列中的得分,将特征得分组合成9维特征向量,再利用支持向量机在特征向量集上进行训练和判别．对实际数据集进行的刀切法测试验证了算法的有效性．对σ启动予的预测,平均正确率达到了90．7％;对几种其它σ因子启动子的预测,平均正确率也超过了80％．算法不但有广泛的适用性,还有良好的可扩展性,能够方便的容纳新特征,使识别性能不断提高．     

模糊综合评判法在斜纹夜蛾预测预报上的应用

通过分析多年斜纹夜蛾灯下虫量、田间虫量以及气象资料,采用模糊综合评判的原理和方法,组建了斜纹夜蛾发生量模糊综合评判模型。应用该模型对2006年和2007年斜纹夜蛾田间发生量进行预测,预测结果与田间发生实况基本相符。     

Prediction of protein structure from ideal forms

For many years it has been accepted that the sequence of a protein can specify its three-dimensional structure. However, there has been limited progress in explaining how the sequence dictates its fold and no attempt to do this computationally without the use of specific structural data has ever succeeded for any protein larger than 100 residues. We describe a method that can predict complex folds up to almost 200 residues using only basic principles that do not include any elements of sequence homology. The method does not simulate the folding chain but generates many thousands of models based on an idealized representation of structure. Each rough model is scored and the best are refined. On a set of five proteins, the correct fold score well and when tested on a set of larger proteins, the correct fold was ranked highest for some proteins more than 150 residues, with others being close topological variants. All other methods that approach this level of success rely on the use of templates or fragments of known structures. Our method is unique in using a database of ideal models based on general packing rules that, in spirit, is closer to an ab initio approach.     

Financial hardship and obesity

There is a substantial correlation between household debt and health. Individuals with less healthy lifestyles are more likely to hold debt, yet there is little evidence as to whether this is merely a correlation or if financial hardship actually causes obesity. In this paper, we use data from the National Longitudinal Survey of Adolescent Health to test whether financial hardship affects body weight. We divide our sample into two groups: men and women, explore two different types of financial hardship: holding credit card debt and having trouble paying bills, and three outcomes: overweight, obese and body mass index (BMI). We use a variety of econometric techniques: Ordinary Least Squares, Propensity Score Matching, Sibling Fixed Effects, and Instrumental Variables to investigate the relationship that exists between financial hardship and body weight. In addition, we conduct several robustness checks. Although our OLS and PSM results indicate a correlation between financial hardship and body weight these results appear to be largely driven by unobservables. Our IV results suggest that there is no causal relationship between credit card debt and overweight or obesity for either men or women. However, we find suggestive evidence that having trouble paying bills may be a cause of obesity for women.     

放疗设备等中心精度分析与对策

等中心回转精度是医用电子直线加速器,钴60远距离治疗机,放疗模拟定位机,立体放射外科系统等医疗设备重要的综合技术性能指标之一。其精度高低将直接影响放射治疗的治愈率和有效率。本文通过对放疗模拟定位机的等中心回转误差分析。提出了一些对策措施。实践证明,这些方法是行之有效的。     

模糊综合评判在血液流变学指标归类判定中的应用研究

首次将模糊综合评判方法,应用到在脑血管疾病诊断中颇为重要的血液流变学指标的自动归类判别中．本文引入了模糊隶属函数,并在此基础上形成单因素评价矩阵．还讨论了模糊综合评判的失效问题,给出了失效后的处理方法．实验结果表明,提出的方法是快速而有效的．     

Drger麻醉呼吸设备的维修与探讨

介绍了Dr／tger麻醉机与呼吸机结构与工作原理。Fabius麻醉机潮气量不准与压力报警故障分析及排除方法;呼吸机FiO2低报警与开机后反复起动自检的故障分析及排除方法。     

医院计算机化医疗设备的维护管理

现代医疗设备大都采用了计算机化设计,分析了目前医院拥有的计算机现状、医疗设备的分类和管理。最后,提出了维护管理措施。     

利用人工智能预测癌症的易感性、复发性和生存期

癌症具有较高的发病率和致死率,对人类健康具有重大威胁。癌症预后分析可以有效避免过度治疗及医疗资源的浪费,为医务人员及家属进行医疗决策提供科学依据,已成为癌症研究的必要条件。随着近年来人工智能技术的迅速发展,对癌症患者的预后情况进行自动化分析成为可能。此外,随着医疗信息化的发展,智慧医疗的理念受到广泛关注。癌症患者作为智慧医疗的重要组成部分,对其进行有效的智能预后分析十分必要。本文综述现有基于机器学习的癌症预后方法。首先,对机器学习与癌症预后进行概述,介绍癌症预后及相关的机器学习方法,分析机器学习在癌症预后中的应用;然后,对基于机器学习的癌症预后方法进行归纳,包括癌症易感性预测、癌症复发性预测、癌症生存期预测,梳理了它们的研究现状、涉及到的癌症类型与数据集、用到的机器学习方法及预后性能、特点、优势与不足;最后,对癌症预后方法进行总结与展望。     

Prediction of biogas potentials using quick laboratory analyses: Upgrading previous models for application to heterogeneous organic matrices

This study presents an upgrading of the mathematical models to predict anaerobic biogasification potential (ABP) through quick laboratory analyses that have been presented in an earlier study. The aim is to widen the applicability of the models to heterogeneous organic substrates and to improve their reliability through a deeper statistical approach.Three multiple-step linear regressions were obtained using biomass oxygen demand in 20 h (OD₂₀) plus the volatile solids content (VS) of 23 new samples of heterogeneous organic matrices, of 46 samples presented in the earlier work and of the data set comprising all the 69 samples. The two variables chosen were found to be suitable for very heterogeneous materials. To judge the prediction quality, a validation procedure was performed with 12 new samples using model efficiency indexes. The proposed model had good prediction ability for a large variety of organic substrates, and allows the calculation of the ABP value within only 2-day’s laboratory work instead of the 60–90 days required to obtain ABP by anaerobic test.     

High‐accuracy modeling of antibody structures by a search for minimum‐energy recombination of backbone fragments

Current methods for antibody structure prediction rely on sequence homology to known structures. Although this strategy often yields accurate predictions, models can be stereo‐chemically strained. Here, we present a fully automated algorithm, called AbPredict, that disregards sequence homology, and instead uses a Monte Carlo search for low‐energy conformations built from backbone segments and rigid‐body orientations that appear in antibody molecular structures. We find cases where AbPredict selects accurate loop templates with sequence identity as low as 10%, whereas the template of highest sequence identity diverges substantially from the query's conformation. Accordingly, in several cases reported in the recent Antibody Modeling Assessment benchmark, AbPredict models were more accurate than those from any participant, and the models' stereo‐chemical quality was consistently high. Furthermore, in two blind cases provided to us by crystallographers prior to structure determination, the method achieved <1.5 Ångstrom overall backbone accuracy. Accurate modeling of unstrained antibody structures will enable design and engineering of improved binders for biomedical research directly from sequence. Proteins 2016; 85:30–38. © 2016 Wiley Periodicals, Inc.     

原核基因翻译起始位点预测的新方法

翻译起始位点（TIS,即基因5’端）的精确定位是原核生物基因预测的一个关键问题,而基因组GC含量和翻译起始机制的多样性是影响当前TIS预测水平的重要因素．结合基因组结构的复杂信息（包括GC含量、TIS邻近序列及上游调控信号、序列编码潜能、操纵子结构等）,发展刻画翻译起始机制的数学统计模型,据此设计TIS预测的新算法MED．StartPlus．并将MED．StartPlus与同类方法RBSfinder、GS．Finder、MED-Start、TiCo和Hon-yaku等进行系统地比较和评价．测试针对两种数据集进行：当前14个已知的TIS被确认的基因数据集,以及300个物种中功能已知的基因数据集．测试结果表明,MED-StartPlus的预测精度在总体上超过同类方法．尤其是对高GC含量基因组以及具有复杂翻译起始机制的基因组,MED-StartPlus具有明显的优势．