A tutorial on dynamic risk prediction of a binary outcome based on a longitudinal biomarker

Dynamic risk predictions based on all available information are useful in timely identification of high-risk patients. However, in contrast with time to event outcomes, there is still a lack of studies that clearly demonstrate how to obtain and update predictions for a future binary outcome using a repeatedly measured biomarker. The aim of this study is to give an illustrative overview of four approaches to obtain such predictions: likelihood based two-stage method (2SMLE), likelihood based joint model (JMMLE), Bayesian two-stage method (2SB), and Bayesian joint model (JMB). We applied the approaches to provide weekly updated predictions of post–molar gestational trophoblastic neoplasia (GTN) based on age and repeated measurements of human chorionic gonadotropin (hCG). Discrimination and calibration measures were used to compare the accuracy of the weekly predictions. Internal validation of the models was conducted using bootstrapping. The four approaches resulted in the same predictive and discriminative performance in predicting GTN. A simulation study showed that the joint models outperform the two-stage methods when we increase the within- and the between-patients variability of the biomarker. The applicability of these models to produce dynamic predictions has been illustrated through a comprehensive explanation and accompanying syntax (R and SAS^®).     

基于随机森林算法的中国西南地区林火发生预测模型构建及驱动因子

林火直接破坏森林资源,改变森林的结构与功能,影响局地甚至全球气候状况并威胁人类生命和财产安全,在气候变暖背景下林火将更加频发,因此开展林火预测/预报研究至关重要。利用MODIS （Moderate-resolution Imaging Spectroradiometer）的温度异常/火产品（MOD14A1）获取逐日林火数据,分析了2001-2018年中国西南地区林火时空分布特征;采用随机森林算法,综合考虑气象、地形、可燃物状况及植被等林火驱动因子,构建了中国西南地区干、湿季林火发生预测模型,系统分析了西南地区干湿季林火发生的主要驱动因子。结果表明：（1）中国西南地区林火主要集中分布于云南大部、四川西南部及贵州南部地区,并呈集聚分布特征;林火多发于干季,占林火发生总次数的96.5%,年林火发生次数呈阶段性变化特征,2001-2014年呈现显著增加趋势,随后表现为不显著减少趋势;（2）构建的干、湿季林火发生预测模型能较准确地模拟林火发生状况：训练期模型准确率分别处于82.94%-83.99%与85.12%-90.31%之间,AUC （Area Under Curve）值分别处于0.908-0.914与0.922-0.965之间;测试期模型准确率分别为79.73%和83.27%,AUC值分别为0.886和0.855;（3）海拔是西南地区林火发生最关键的限制因子,林火多集中于中海拔区,而在低海拔和高海拔地区林火不易发生,这与人类活动密切相关。当日的气象条件是干季林火发生次重要的驱动因子,可燃物的温湿度状况则是湿季林火发生次重要的驱动因子。FWI系统指标（Fire Weather Index）在西南地区有较好的适用性且对于区域干湿季林火发生均有重要的影响,因此在西南地区林火预测/预报工作中有必要引入FWI系统指标。     

Generalized comparative modeling (GENECOMP): a combination of sequence comparison, threading, and lattice modeling for protein structure prediction and refinement

An improved generalized comparative modeling method, GENECOMP, for the refinement of threading models is developed and validated on the Fischer database of 68 probe-template pairs, a standard benchmark used to evaluate threading approaches. The basic idea is to perform ab initio folding using a lattice protein model, SICHO, near the template provided by the new threading algorithm PROSPECTOR. PROSPECTOR also provides predicted contacts and secondary structure for the template-aligned regions, and possibly for the unaligned regions by garnering additional information from other top-scoring threaded structures. Since the lowest-energy structure generated by the simulations is not necessarily the best structure, we employed two structure-selection protocols: distance geometry and clustering. In general, clustering is found to generate somewhat better quality structures in 38 of 68 cases. When applied to the Fischer database, the protocol does no harm and in a significant number of cases improves upon the initial threading model, sometimes dramatically. The procedure is readily automated and can be implemented on a genomic scale.     

Comparison of breeding value prediction for two traits in a Nellore-Angus crossbred population using different Bayesian modeling methodologies

The objectives of this study were to 1) compare four models for breeding value prediction using genomic or pedigree information and 2) evaluate the impact of fixed effects that account for family structure. Comparisons were made in a Nellore-Angus population comprising F₂, F₃ and half-siblings to embryo transfer F₂ calves with records for overall temperament at weaning (TEMP; n = 769) and Warner-Bratzler shear force (WBSF; n = 387). After quality control, there were 34,913 whole genome SNP markers remaining. Bayesian methods employed were BayesB ($\tilde{\pi }$ = 0.995 or 0.997 for WBSF or TEMP, respectively) and BayesC (π = 0 and $\tilde{\pi }$), where $\tilde{\pi }$ is the ideal proportion of markers not included. Direct genomic values (DGV) from single trait Bayesian analyses were compared to conventional pedigree-based animal model breeding values. Numerically, BayesC procedures (using $\tilde{\pi }$) had the highest accuracy of all models for WBSF and TEMP ($\widehat{\rho }$_gĝ = 0.843 and 0.923, respectively), but BayesB had the least bias (regression of performance on prediction closest to 1, $\widehat{\beta }$_y,x = 2.886 and 1.755, respectively). Accounting for family structure decreased accuracy and increased bias in prediction of DGV indicating a detrimental impact when used in these prediction methods that simultaneously fit many markers.     

Construction of prediction models for growth traits of soybean cultivars based on phenotyping in diverse genotype and environment combinations

As soybean cultivars are adapted to a relatively narrow range of latitude, the effects of climate changes are estimated to be severe. To address this issue, it is important to improve our understanding of the effects of climate change by applying the simulation model including both genetic and environmental factors with their interactions (G×E). To achieve this goal, we conducted the field experiments for soybean core collections using multiple sowing times in multi-latitudinal fields. Sowing time shifts altered the flowering time (FT) and growth phenotypes, and resulted in increasing the combinations of genotypes and environments. Genome-wide association studies for the obtained phenotypes revealed the effects of field and sowing time to the significance of detected alleles, indicating the presence of G×E. By using accumulated phenotypic and environmental data in 2018 and 2019, we constructed multiple regression models for FT and growth pattern. Applicability of the constructed models was evaluated by the field experiments in 2020 including a novel field, and high correlation between the predicted and measured values was observed, suggesting the robustness of the models. The models presented here would allow us to predict the phenotype of the core collections in a given environment.     

Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non‐native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species–environment relationships for Parthenium hysterophorus L. (Asteraceae) with four modeling methods run with multiple scenarios of (i) sources of occurrences and geographically isolated background ranges for absences, (ii) approaches to drawing background (absence) points, and (iii) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e., into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g., boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post hoc test conducted on a new Parthenium dataset from Nepal validated excellent predictive performance of our ‘best’ model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for parthenium. However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed.     

Potential for assessing quality of protein structure based on contact number prediction

We developed a novel knowledge-based residue environment potential for assessing the quality of protein structures in protein structure prediction. The potential uses the contact number of residues in a protein structure and the absolute contact number of residues predicted from its amino acid sequence using a new prediction method based on a support vector regression (SVR). The contact number of an amino acid residue in a protein structure is defined by the number of residues around a given residue. First, the contact number of each residue is predicted using SVR from an amino acid sequence of a target protein. Then, the potential of the protein structure is calculated from the probability distribution of the native contact numbers corresponding to the predicted ones. The performance of this potential is compared with other score functions using decoy structures to identify both native structure from other structures and near-native structures from nonnative structures. This potential improves not only the ability to identify native structures from other structures but also the ability to discriminate near-native structures from nonnative structures.     

基于Logistic回归模型的黄河三角洲淡水恢复湿地大型底栖生物种群分布模拟

掌握大型底栖生物种群分布的时空变化对正确把握湿地生态修复效率、揭示湿地生态演替过程具有重要理论与实践意义。选择黄河三角洲地区一千二自然保护区的淡水恢复湿地为研究区,在2014—2015年大型底栖生物野外采样和优势物种的基础上,选择了琥珀刺沙蚕、中华蜾蠃蜚、摇蚊幼虫作为典型优势物种,构建了基于Logistic回归的淡水恢复湿地大型底栖生物种群分布模拟模型。其中,琥珀刺沙蚕和摇蚊幼虫的模拟结果较好,模拟准确率分别为84.9%和77.9%,而中华蜾蠃蜚的模拟结果不甚理想。对比生态补水前后大型底栖生物的模拟分布结果发现,琥珀刺沙蚕主要集中在潮间带区域,且在春、秋两季的生存概率分布差异不显著;而淡水恢复湿地中摇蚊幼虫的分布概率显著提高,其中高于分割值0.5的栖息面积增长了9.9—10.8倍,表明退化湿地生境正处于向淡水湿地演替进程中。     

Two-stage functional mixed models for evaluating the effect of longitudinal covariate profiles on a scalar outcome

The Daily Hormone Study, a substudy of the Study of Women's Health Across the Nation (SWAN) consisting of more than 600 pre- and perimenopausal women, includes a scalar measure of total hip bone mineral density (BMD) together with repeated measures of creatinine-adjusted follicle stimulating hormone (FSH) assayed from daily urine samples collected over one menstrual cycle. It is of scientific interest to investigate the effect of the FSH time profile during a menstrual cycle on total hip BMD, adjusting for age and body mass index. The statistical analysis is challenged by several features of the data: (1) the covariate FSH is measured longitudinally and its effect on the scalar outcome BMD may be complex; (2) due to varying menstrual cycle lengths, subjects have unbalanced longitudinal measures of FSH; and (3) the longitudinal measures of FSH are subject to considerable among- and within-subject variations and measurement errors. We propose a measurement error partial functional linear model, where repeated measures of FSH are modeled using a functional mixed effects model and the effect of the FSH time profile on BMD is modeled using a partial functional linear model by treating the unobserved true subject-specific FSH time profile as a functional covariate. We develop a two-stage nonparametric regression calibration method using period smoothing splines. Using the connection between smoothing splines and mixed models, we show that a key feature of our approach is that estimation at both stages can be conveniently cast into a unified mixed model framework. A simple testing procedure for constant functional covariate effect is also proposed. The proposed methods are evaluated using simulation studies and applied to the SWAN data.     

基于ARIMA模型的生态足迹动态模拟和预测——以甘肃省为例

生态足迹(EF)是一种定量测量人类对自然利用程度的方法。然而目前对其发展趋势准确的定量分析尚不多见。可采用自回归综合移动平均模型(ARIMA)来模拟并预测区域生态足迹。综合运用生态足迹方法和ARIMA模型对甘肃省1949-2009年的生态足迹和生态承载力进行了动态模拟和分析,在此基础上预测了2010-2015年的生态足迹变化趋势。结果表明:1949-2009年,人均生态足迹呈现上升趋势,预计2010-2015年上升趋势明显加快,2015年会增加到2.6051 hm²/人,是2009年的1.67倍;1997-2004年人均生态承载力逐年减少,2005年之后逐年增加,预计2010-2015年仍会增加;预计2010-2015年所有人均生态足迹组成类型均呈现上升趋势,尤以人均化石能源生态足迹增长显著;1997-2009年人均生态承载力均小于人均生态足迹,导致生态赤字,甘肃省生态环境处于不可持续状态,预计2010-2015年人均生态承载力略有增长,但仍小于人均生态足迹,生态赤字不断增大,预计2015年增长到-2.0468 hm²/人,约为2009年(-1.0262 hm²/人)的两倍,甘肃省生态环境不断恶化;经济的发展依赖于化石能源的消耗而造成对自然资源的过度利用,大量耕地转换为建设用地,草地荒漠化是引起甘肃省生态赤字的主要原因;改变经济发展和资源消费模式,控制人口规模,减少人均生态足迹消耗,优化配置和集约节约利用自然资源,提高生态承载力是促进社会经济和资源环境可持续发展的有效途经。     

Comparison of a culture‐based and a PCR‐based methods for estimating bacterial abundance on eggshells,with comments on statistical analyses

Field ornithologists have used traditional culture‐based techniques to determine the presence and abundance of microbes on surfaces such as eggshells, but culture‐independent PCR‐based methods have recently been introduced. We compared the traditional culture‐based and the real‐time PCR‐based methods for detecting and quantifying Escherichia coli on the eggshells of Eurasian Magpies (Pica pica). PCR estimates of bacterial abundance were ～10 times higher than culture‐based estimates, and the culture‐based technique failed to detect bacteria at lower densities. When both methods detected bacteria, bacterial densities determined by the two methods were positively correlated, indicating that both methods can be used to study factors affecting bacterial densities. The difference between the two methods is consistent with generally acknowledged higher sensitivity of the PCR method, but the extent of the difference in our study (10×) may have been influenced by both a PCR‐based overestimation and culture‐based underestimation of bacterial densities. Our results also illustrate that bacterial counts may sometimes produce left‐censored data (i.e., we did not detect E. coli in 62% of our samples using the culture‐based method). Specific statistical methods have been developed for analyzed left‐censored data, but, to our knowledge, have not been used by ornithologists. In future studies, investigators studying bacterial loads should provide information about the possible degree of left censoring and should justify their choice of statistical methods from the broad set of available methods, including those explicitly designed for censored data.     

基于景观生态风险格局的盐碱地分区规划防治研究——以黑龙江省林甸县为例

盐碱地分区规划防治是提高区域盐碱综合治理效果,降低治理成本的有效途径,对实现区域生态恢复、土地可持续利用和保障粮食安全具有重要实践价值。当前,在区域景观生态风险分析基础上,进行盐碱地规划防治研究的研究尚显不足。以大庆市林甸县为研究区,以景观生态学理论为视角,基于RS和GIS技术平台,综合运用最小累积阻力模型和空间分析方法,分析了生态流和生态阻力面,构建了区域景观生态风险格局;结合关键景观与盐碱地的生态廊道分析等方法,确定盐碱地防治分区;并进一步确定了防治分区中的核心防治区域。结果表明:林甸县盐碱地面积为263 km~2,其景观生态风险格局呈现西部和南部的景观生态风险等级较高,东北部、中部和西南部较低的特征,其中,高危区、重度危险区占地区总面积41.24%,集中分布在地区下游的沼泽湿地周围,说明保护沼泽湿地迫在眉睫。林甸县的两条土地盐碱化防治景观生态廊道是以最低防治成本实现土地盐碱化预防和治理的最佳区域,其主要分布在15个村(镇)上,未来应将此地区作为规划防治的核心。基于景观生态风险格局的盐碱地分区规划防治研究,为大尺度土地盐碱化综合防治提供了新的思路和方法,研究结果对林甸县未来开展土地盐碱化预防和治理提供了科学依据。