基于红外相机技术的野生白鹇活动规律

2013年11月起,在浙江凤阳山-百山祖国家级自然保护区核心区布设了27台红外相机,1年后收回。共获得有效独立照片2 580张,其中白鹇Lophura nycthemera 695张、870只次。对其月、日活动规律和不同栖息地的活动差异性分析表明:(1)夏季的月相对多度指数最高,6月和8月各有1个高峰,秋季次高,春、冬季较低;(2)上午和下午各有1个活动高峰,但各个季节出现时段不同。秋、冬季的活动时间比春、夏季缩短近2 h;(3)在西坡活动的频次最多,东坡和南坡次之,北坡最少。在北坡的活动频次与在其他坡向的差异均有统计学意义,其余坡向之间的差异均无统计学意义。在3种类型林分之间的活动频次差异无统计学意义。     

Prediction of Dengue Outbreaks Based on Disease Surveillance and Meteorological Data

Research is needed to create early warnings of dengue outbreaks to inform stakeholders and control the disease. This analysis composes of a comparative set of prediction models including only meteorological variables; only lag variables of disease surveillance; as well as combinations of meteorological and lag disease surveillance variables. Generalized linear regression models were used to fit relationships between the predictor variables and the dengue surveillance data as outcome variable on the basis of data from 2001 to 2010. Data from 2011 to 2013 were used for external validation purposed of prediction accuracy of the model. Model fit were evaluated based on prediction performance in terms of detecting epidemics, and for number of predicted cases according to RMSE and SRMSE, as well as AIC. An optimal combination of meteorology and autoregressive lag terms of dengue counts in the past were identified best in predicting dengue incidence and the occurrence of dengue epidemics. Past data on disease surveillance, as predictor alone, visually gave reasonably accurate results for outbreak periods, but not for non-outbreaks periods. A combination of surveillance and meteorological data including lag patterns up to a few years in the past showed most predictive of dengue incidence and occurrence in Yogyakarta, Indonesia. The external validation showed poorer results than the internal validation, but still showed skill in detecting outbreaks up to two months ahead. Prior studies support the fact that past meteorology and surveillance data can be predictive of dengue. However, to a less extent has prior research shown how the longer-term past disease incidence data, up to years, can play a role in predicting outbreaks in the coming years, possibly indicating cross-immunity status of the population.     

Network Based Prediction Model for Genomics Data Analysis

Biological networks, such as genetic regulatory networks and protein interaction networks, provide important information for studying gene/protein activities. In this paper, we propose a new method, NetBoosting, for incorporating a priori biological network information in analyzing high dimensional genomics data. Specially, we are interested in constructing prediction models for disease phenotypes of interest based on genomics data, and at the same time identifying disease susceptible genes. We employ the gradient descent boosting procedure to build an additive tree model and propose a new algorithm to utilize the network structure in fitting small tree weak learners. We illustrate by simulation studies and a real data example that, by making use of the network information, NetBoosting outperforms a few existing methods in terms of accuracy of prediction and variable selection.     

The Detection of Emerging Trends Using Wikipedia Traffic Data and Context Networks

Can online media predict new and emerging trends, since there is a relationship between trends in society and their representation in online systems? While several recent studies have used Google Trends as the leading online information source to answer corresponding research questions, we focus on the online encyclopedia Wikipedia often used for deeper topical reading. Wikipedia grants open access to all traffic data and provides lots of additional (semantic) information in a context network besides single keywords. Specifically, we suggest and study context-normalized and time-dependent measures for a topic’s importance based on page-view time series of Wikipedia articles in different languages and articles related to them by internal links. As an example, we present a study of the recently emerging Big Data market with a focus on the Hadoop ecosystem, and compare the capabilities of Wikipedia versus Google in predicting its popularity and life cycles. To support further applications, we have developed an open web platform to share results of Wikipedia analytics, providing context-rich and language-independent relevance measures for emerging trends.     

Computational Prediction of Broadly Neutralizing HIV-1 Antibody Epitopes from Neutralization Activity Data

Broadly neutralizing monoclonal antibodies effective against the majority of circulating isolates of HIV-1 have been isolated from a small number of infected individuals. Definition of the conformational epitopes on the HIV spike to which these antibodies bind is of great value in defining targets for vaccine and drug design. Drawing on techniques from compressed sensing and information theory, we developed a computational methodology to predict key residues constituting the conformational epitopes on the viral spike from cross-clade neutralization activity data. Our approach does not require the availability of structural information for either the antibody or antigen. Predictions of the conformational epitopes of ten broadly neutralizing HIV-1 antibodies are shown to be in good agreement with new and existing experimental data. Our findings suggest that our approach offers a means to accelerate epitope identification for diverse pathogenic antigens.     

Empirical Bayes Estimation and Prediction Using Summary-Level Information From External Big Data Sources Adjusting for Violations of Transportability

Large external data sources may be available to augment studies that collect data to address a specific research objective. In this article we consider the problem of building regression models for prediction based on individual-level data from an “internal” study while incorporating summary information from an “external” big data source. We extend the work of Chatterjee et al. (J Am Stat Assoc 111(513):107–117, 2006) by introducing an adaptive empirical Bayes shrinkage estimator that uses the external summary-level information and the internal data to trade bias with variance for protection against departures in the conditional probability distribution of the outcome given a set of covariates between the two populations. We use simulation studies and a real data application using external summary information from the Prostate Cancer Prevention Trial to assess the performance of the proposed methods in contrast to maximum likelihood estimation and the constrained maximum likelihood (CML) method developed by Chatterjee et al. (J Am Stat Assoc 111(513):107–117, 2006). Our simulation studies show that the CML method can be biased and inefficient when the assumption of a transportable covariate distribution between the external and internal populations is violated, and our empirical Bayes estimator provides protection against bias and loss of efficiency.     

Prediction of Protein Structural Features from Sequence Data Based on Shannon Entropy and Kolmogorov Complexity

While the genome for a given organism stores the information necessary for the organism to function and flourish it is the proteins that are encoded by the genome that perhaps more than anything else characterize the phenotype for that organism. It is therefore not surprising that one of the many approaches to understanding and predicting protein folding and properties has come from genomics and more specifically from multiple sequence alignments. In this work I explore ways in which data derived from sequence alignment data can be used to investigate in a predictive way three different aspects of protein structure: secondary structures, inter-residue contacts and the dynamics of switching between different states of the protein. In particular the use of Kolmogorov complexity has identified a novel pathway towards achieving these goals.     

预测转录单位基础上的原核生物启动子预测

启动子及转录单位预测对于了解基因间的功能及相互间的调节关系具有重要的作用 ,这方面的研究一直是生物信息学的一个重要方向 ,但预测的准确率一直都很有限。本文建立了在转录单位预测基础上进行原核生物启动子预测的新方法 ,首先根据基因间距离、功能关系及多基因比对结果来进行转录单位预测 ,得到了比较理想的结果 ,而且对于研究得比较透和研究得较少的基因组都适用。其后在转录单位预测结果基础上进行启动子预测则采用了隐Markov链模型 ,并在Markov链中考虑状态驻留时间。结果显示 ,该方法能有效地预测出启动子序列及其位置 ,准确率达到 70 %以上。     

Special Issue on "Big Data and Precision Medicine"

正The journal Genomics ProteomicsBioinformatics(GPB)is now inviting submissions for a special issue on the topic of‘‘Big Data and Precision Medicine’’to be published in the fall of 2016.For many complex diseases,the traditional‘‘one drug for one disease’’scenario may soon become history.The new concept of‘‘precision medicine’’will heavily rely on the collection of large amount of data from the population as well as patients of specific diseases.     

基于植保大数据的病虫害移动智能采集新设备

为提高农作物重大病虫害发生信息自动化、智能化采集能力,全面提升监测预警水平,笔者基于大数据、人工智能和深度学习技术,研发了一款农作物病虫害移动智能采集设备——智宝,主要实现了3个方面的功能:一是病虫害发生信息自动采集上报.通过该产品进行人工拍照,可实现对田间农作物重大病虫害发生图像、发生位置、发生数量、微环境因子等数据的实时采集和上报.二是自动识别计数.基于植保大数据与人工智能技术,通过构建病虫害自动识别系统,可实现重大病虫害精准识别与分析,只要拍摄照片,即可快速、精确地识别病虫害种类,并自动计数、上报到指定的测报系统.三是自动分析判别分级.针对拍摄采集上报的重大病虫害发生信息,系统可在自动识别和计数的基础上,进一步对病虫害发生严重程度进行智能判别分级,甚至根据相关预测模型,对病虫害的发生趋势进行辅助分析预测,提出预测建议.通过2016—2019年组织多地植保机构进行试验改进,该技术产品日趋成熟,有望在未来的农作物病虫害发生信息采集和预测预报工作中推广使用.     

Prediction of Pharmacokinetic Profile of Valsartan in Humans Based on in vitro Uptake‐Transport Data

The aim of this study was to evaluate a physiologically based pharmacokinetic (PBPK) model for predicting PK profiles in humans based on a model refined in rats and humans in vitro uptake‐transport data using valsartan as a probe substrate. Valsartan is eliminated unchanged, mostly through biliary excretion, both in humans and rats. It was, therefore, chosen as model compound to predict in vivo elimination based on in vitro hepatic uptake‐transport data using a fully mechanistic PBPK model. Plated rat and human hepatocytes, and cell lines overexpressing human OATP1B1 and OATP1B3 were used for in vitro uptake experiments. A mechanistic two‐compartment model was used to derive the active and passive transport parameters, namely uptake Michaelis–Menten parameters (V_max and K_m,u) together with passive diffusion (P_dif). These transport parameters were then used as input in a whole body physiologically based pharmacokinetic (PBPK) model. The uptake rate of valsartan was higher for rat hepatocytes (K_m,u=28.4±3.7 μM , V_max=1320±180 pmol/mg/min, and P_dif =1.21±0.42 μl/mg/min) compared to human hepatocytes (K_m,u=44.4±14.6 μM , V_max=304±85 pmol/mg/min, and P_dif=0.724±0.271 μl/mg/min). OATP1B1 and ‐1B3 parameters were correlated to human hepatocyte data, using experimentally established relative activity factors (RAF). Resulting PBPK simulations were compared for plasma‐ (humans and rats) and bile‐ (rats) concentration–time profiles following iv bolus administration of valsartan. Plasma clearances (CL_P) for rats and humans were predicted within twofold relative to predictions based on respective in vitro data. The simulations were extended to simulate the impact of either OATP1B1 or ‐1B3 inhibition on plasma profile. The limited data set indicates that the mechanistic model allowed for accurate evaluation of in vitro transport data; and the resulting hepatic uptake transport kinetic parameters enabled the prediction of in vivo PK profiles and plasma clearances, using PBPK modelling. Moreover, the interspecies difference in elimination rate observed in vivo was correctly reflected in the transport parameters determined in vitro.     

Understanding Sexual Activity and Chlamydia Testing Rate Based on Linked National Survey and Medicaid Claims Data

Background

Monitoring adherence to national recommendations for annual chlamydia screening of female adolescents and young adult women is important for targeting quality improvement interventions to improve low screening rates. However, accurate measurement of rates may vary depending on the data source used to determine eligible sexually-active women.

Methods

The 2001–2004 NHANES data linked with Medicaid administrative data by respondent’s unique identifier, the 2011–2012 NHANES data, and the 2004 and 2010 Medicaid data were used in this cross-sectional analysis. We defined self-reported sexual activity by self-reported sexual behaviors, claim-identified sexual activity by reproductive-related claims among women who had ≥ one healthcare claim, HEDIS-defined sexual activity by reproductive-related claims among women who were enrolled in Medicaid for ≥330 days and had ≥ one healthcare claim, and chlamydia tests by claims submitted in the 12 months prior to the survey interview.

Results

Of Medicaid women aged 18–25 years, 91.5% self-reported to be sexually-active. Of self-reported sexually-active women aged 18–25 years, 92.0% had ≥ one healthcare claim in the 12 months prior to the survey interview; of this subpopulation, only 58.8% were enrolled in Medicaid for ≥ 330 days in the 12 months prior to the survey interview; of this further subpopulation, 74.1% had healthcare claims identifying them as sexually-active in the 12 months prior to the survey interview. Of HEDIS-defined sexually-active women, 42.4% had chlamydia testing.

Conclusion

Our study suggests that the number of sexually-active women aged 18–25 years used as the denominator in the chlamydia testing measure could be significantly different, depending upon the definition applied and the data used. Our data highlight the limited representativeness of Medicaid population in the current HEDIS measure on chlamydia testing when a high proportion of women who were enrolled in Medicaid for <330 days had been excluded from the measure. The interventions that can improve the proportion of women who were enrolled in Medicaid for ≥ 330 days among all young Medicaid women are needed not only for improving health care services, but also for measuring quality of healthcare.     

基于统计模型的基因预测

基因预测是指预测DNA序列中编码蛋白质的部分。随着多数生物基因组的测序工作的完成 ,基因预测更显得尤为重要。基因预测主要包括两种方法 ,首先是同源方法 ,也称为“外在方法” ,其次是基因预测方法或称为“内在方法”。主要对隐马尔可夫模型、傅立叶变换、动态规划等几种“外在方法”进行介绍。     

The Next Decade of Big Data in Ecosystem Science

Ecosystem scientists will increasingly be called on to inform forecasts and define uncertainty about how changing planet conditions affect human well-being. We should be prepared to leverage the best tools available, including big data. Use of the term ‘big data’ implies an approach that includes capacity to aggregate, search, cross-reference, and mine large volumes of data to generate new understanding that can inform decision-making about emergent properties of complex systems. Although big-data approaches are not a panacea, there are large-scale environmental questions for which big data are well suited, even necessary. Ecosystems are complex biophysical systems that are not easily defined by any one data type, location, or time. Understanding complex ecosystem properties is data intensive along axes of volume (size of data), velocity (frequency of data), and variety (diversity of data types). Ecosystem scientists have employed impressive technology for generating high-frequency, large-volume data streams. Yet important challenges remain in both theoretical and infrastructural development to support visualization and analysis of large and diverse data. The way forward includes greater support for network science approaches, and for development of big-data infrastructure that includes capacity for visualization and analysis of integrated data products. Likewise, a new paradigm of cross-disciplinary training and professional evaluation is needed to increase the human capital to fully exploit big-data analytics in a way that is sustainable and adaptable to emerging disciplinary needs.