类器官组织在感染性疾病研究中的应用

类器官(organoid)是体外3D培养组织干细胞所形成的多功能细胞团,具有自身增殖和多向分化的能力,在空间和结构上与来源器官组织的基因、结构和功能相似,可用于模拟体内组织细胞生长、分化及器官形成过程,在药物筛选与评价、生物医学材料及组织工程等方面具有重要的应用潜能.当前在感染性疾病研究方面,越来越多数据表明不同类器官...     

Neural network‐derived Potts models for structure‐based protein design using backbone atomic coordinates and tertiary motifs

Designing novel proteins to perform desired functions, such as binding or catalysis, is a major goal in synthetic biology. A variety of computational approaches can aid in this task. An energy‐based framework rooted in the sequence‐structure statistics of tertiary motifs (TERMs) can be used for sequence design on predefined backbones. Neural network models that use backbone coordinate‐derived features provide another way to design new proteins. In this work, we combine the two methods to make neural structure‐based models more suitable for protein design. Specifically, we supplement backbone‐coordinate features with TERM‐derived data, as inputs, and we generate energy functions as outputs. We present two architectures that generate Potts models over the sequence space: TERMinator, which uses both TERM‐based and coordinate‐based information, and COORDinator, which uses only coordinate‐based information. Using these two models, we demonstrate that TERMs can be utilized to improve native sequence recovery performance of neural models. Furthermore, we demonstrate that sequences designed by TERMinator are predicted to fold to their target structures by AlphaFold. Finally, we show that both TERMinator and COORDinator learn notions of energetics, and these methods can be fine‐tuned on experimental data to improve predictions. Our results suggest that using TERM‐based and coordinate‐based features together may be beneficial for protein design and that structure‐based neural models that produce Potts energy tables have utility for flexible applications in protein science.     

中国区域可持续发展定量研究进展

基于CNKI中出现区域可持续发展定量研究以来的核心期刊文献视角,对文献的年度分布、期刊分布进行统计分析,继而从社会-经济学方法、生态学方法、系统学方法、新兴方法4个方面透视区域可持续发展定量研究方法的变动过程及趋势.分析表明,伴随文献的逐年递增,定量研究所涉及的学科领域不断拓展;方法体系由单调走向丰富,但模型的作用有被夸大的趋势;研究内容以水平评价为主,其余评价内容较少涉及;多尺度及动态的时间序列研究全面铺开,但时空结合研究有待深入.系统科学、复杂性理论及空间信息技术的综合运用将促进区域可持续发展定量研究的进一步发展和完善.     

基于源-库互反馈的温室青椒坐果时空动态模拟

基于源-库互反馈的温室青椒坐果时空动态模拟     

人工湿地去污模型的统一结构特征

通过对典型人工湿地去污模型的分析比较,提出了基于各模型微分方程而建立的统一去污模型。该模型能够将典型去污模型作为特例而导出,并能很好地解释这些模型之间的过渡关系。以潜流湿地中NH4^＋和BOD5的降解为例,对统一去污模型的应用情况进行了简单探讨,表明利用统一去污模型结构有助于深入揭示去污机理,从而提出更为精确的去污模型。     

A study of cultural transmission in Taiwan

Our study of cultural transmission in Taiwan is based on a survey of 1000 students, their families, and friends, for characters ranging from religion to various customs and beliefs, as well as entertainment and hygienic habits. The effects of father, mother, and an older sib on a propositus are tested by an additive model of transmission, using a novel statistical procedure, and compared with correlations with friends. For many traits there exist significant influences; older sibs are almost as important as father and mother, with effects differing somewhat with their sex. Formulas for recurrences and equilibrium frequency of a cultural character for which father, mother, and sib are active in transmission are given in the appendices along with formulas for estimation of their effects from real data.This research was supported in part by grant NIH GM 20467 and NIH GM 20816. In early part of this investigation K.H.C. was supported by a grant to SIMS from the Alfred P. Sloan Foundation.     

The severity of psychiatric disorders

The issue of the severity of psychiatric disorders has great clinical importance. For example, severity influences decisions about level of care, and affects decisions to seek government assistance due to psychiatric disability. Controversy exists as to the efficacy of antidepressants across the spectrum of depression severity, and whether patients with severe depression should be preferentially treated with medication rather than psychotherapy. Measures of severity are used to evaluate outcome in treatment studies and may be used as meaningful endpoints in clinical practice. But, what does it mean to say that someone has a severe illness? Does severity refer to the number of symptoms a patient is experiencing? To the intensity of the symptoms? To symptom frequency or persistence? To the impact of symptoms on functioning or on quality of life? To the likelihood of the illness resulting in permanent disability or death? Putting aside the issue of how severity should be operationalized, another consideration is whether severity should be conceptualized similarly for all illnesses or be disorder specific. In this paper, we examine how severity is characterized in research and contemporary psychiatric diagnostic systems, with a special focus on depression and personality disorders. Our review shows that the DSM‐5 has defined the severity of various disorders in different ways, and that researchers have adopted a myriad of ways of defining severity for both depression and personality disorders, although the severity of the former was predominantly defined according to scores on symptom rating scales, whereas the severity of the latter was often linked with impairments in functioning. Because the functional impact of symptom‐defined disorders depends on factors extrinsic to those disorders, such as self‐efficacy, resilience, coping ability, social support, cultural and social expectations, as well as the responsibilities related to one's primary role function and the availability of others to assume those responsibilities, we argue that the severity of such disorders should be defined independently from functional impairment.     

Dynamic conditionally linear mixed models for longitudinal data

We develop a new class of models, dynamic conditionally linear mixed models, for longitudinal data by decomposing the within-subject covariance matrix using a special Cholesky decomposition. Here 'dynamic' means using past responses as covariates and 'conditional linearity' means that parameters entering the model linearly may be random, but nonlinear parameters are nonrandom. This setup offers several advantages and is surprisingly similar to models obtained from the first-order linearization method applied to nonlinear mixed models. First, it allows for flexible and computationally tractable models that include a wide array of covariance structures; these structures may depend on covariates and hence may differ across subjects. This class of models includes, e.g., all standard linear mixed models, antedependence models, and Vonesh-Carter models. Second, it guarantees the fitted marginal covariance matrix of the data is positive definite. We develop methods for Bayesian inference and motivate the usefulness of these models using a series of longitudinal depression studies for which the features of these new models are well suited.     

Semiparametric modeling of longitudinal measurements and time-to-event data--a two-stage regression calibration approach

SUMMARY: In this article we investigate regression calibration methods to jointly model longitudinal and survival data using a semiparametric longitudinal model and a proportional hazards model. In the longitudinal model, a biomarker is assumed to follow a semiparametric mixed model where covariate effects are modeled parametrically and subject-specific time profiles are modeled nonparametrially using a population smoothing spline and subject-specific random stochastic processes. The Cox model is assumed for survival data by including both the current measure and the rate of change of the underlying longitudinal trajectories as covariates, as motivated by a prostate cancer study application. We develop a two-stage semiparametric regression calibration (RC) method. Two variations of the RC method are considered, risk set regression calibration and a computationally simpler ordinary regression calibration. Simulation results show that the two-stage RC approach performs well in practice and effectively corrects the bias from the naive method. We apply the proposed methods to the analysis of a dataset for evaluating the effects of the longitudinal biomarker PSA on the recurrence of prostate cancer.