Outlier detection methods are still effective even using virtual species created with the probabilistic approach

Liu et al. (Journal of Biogeography, 2018, 45 :164–176) presented an approach to detect outliers in species distribution data by developing virtual species created using the threshold approach. Meynard et al. (Journal of biogeography, 2019, 46 :2141–2144) raised concerns about this approach stating that ‘using a probabilistic approach … may significantly change results’. Here we provide a new series of simulations using the two approaches and demonstrate that the outlier detection approach based on pseudo species distribution models was still effective when using the probabilistic approach, although the detection rate was lower than when using the threshold approach.     

“互联网+”发酵工程实操与虚拟仿真中试实验室平台的建设与探索

发酵工程是理工科高校生物工程学科领域的核心课程之一,是一门应用性、实践性极强的专业课程。该课程传统的实践模式已无法满足当前高校对大学生工程素质教育的需求。随着信息技术、自控技术的飞速发展,多层次、跨学科的“互联网+”教学已成为现今高等教育人才培养的新模式。本文中的发酵工程实操与虚拟仿真中试实验室平台以工程学为技术手段,通过“互联网+”将虚拟现实(virtual reality,VR)技术、信息自动化控制技术、数据库与发酵过程控制有机地结合在一起,构建一个“虚实”结合的“多维”工程中试实验室平台,并以此作为抓手开展食品发酵技能训练课程工程素质教育教学的创新与探索。初步建设成果与前期教学效果表明,该实验室平台的建设对发酵工程及相关专业学生的实践动手能力有明显的提高,为后期建设积累了宝贵的经验及大量有价值的工程实训数据。     

林可霉素生产中三级种子罐的发酵工艺优化

【背景】林可霉素是一种在临床应用上占有重要地位的林可酰胺类抗生素,关于调控发酵生产中三级种子罐相关参数优化林可霉素发酵工艺的研究较少。【目的】优化林可霉素发酵工艺,提高林可霉素发酵效价及市场竞争力。【方法】对林可霉素生产中三级种子罐的培养基和接种量及三级种子移种菌龄进行优化。【结果】在三级种子罐培养基中葡萄糖、淀粉、玉米浆、黄豆饼粉和硫酸铵浓度分别为64.0、5.0、15.0、14.5和3.5 g/L,三级种子罐接种量为25%及三级种子移种菌龄为60 h的优化条件下,林可霉素四级发酵效价高达7 883 U/mL,比优化前效价提高了10%。【结论】对林可霉素生产中三级种子罐相关参数进行调控,初步优化了林可霉素发酵工艺,提高了发酵效价,为优化林可霉素发酵工艺提供了新思路。     

The impact of fossil stratigraphic ranges on tip-calibration,and the accuracy and precision of divergence time estimates

The molecular clock provides the only viable means of establishing realistic evolutionary timescales but it remains unclear how best to calibrate divergence time analyses. Calibrations can be applied to the tips and/or to the nodes of a phylogeny. Tip-calibration is an attractive approach since it allows fossil species to be included alongside extant relatives in molecular clock analyses. However, most fossil species are known from multiple stratigraphical horizons and it remains unclear how such age ranges should be interpreted to codify tip-calibrations. We use simulations and empirical data to explore the impact on precision and accuracy of different approaches to informing tip-calibrations. In particular, we focus on the effect of using tip-calibrations defined using the oldest vs youngest stratigraphic occurrences, the full stratigraphical range, as well as confidence intervals on these data points. The results of our simulations show that using different calibration approaches leads to different divergence-time estimates and demonstrate that concentrating tip-calibrations near the root of the dated phylogeny improves both precision and accuracy of estimated divergence times. Finally, our results indicate that the highest levels of accuracy and precision are achieved when fossil tips are calibrated based on the fossil occurrence from which the morphological data were derived. These trends were corroborated by analysis of an empirical dataset for Ursidae. Overall, we conclude that tip-dating analyses should, in particular, employ tip calibrations close to the root of the tree and they should be calibrated based on the age of the fossil used to inform the morphological data used in Total Evidence Dating.     

“新工科”背景下环境微生物学课程实践教学育人模式的探索

从"新工科"背景下环境微生物学课程实践教学改革的现实意义出发,阐述了构建基于"新工科"要求的环境微生物学课程实践教学模式。该实践教学模式从学科基础实验、校企合作实训、课程设计实践和教学实践德育等4个方面进行设计,要求以提升学生的思想道德素质和实践能力为目标,达到契合环境微生物行业发展需求的目的。实践教学环节有效地加强了师生间的互动交流,激发了学生的实践创新积极性,为培养符合"新工科"要求的复合型、实践型具有环境微生物学知识背景的工程师提供了良好的实践平台。     

Validation of discrete time-to-event prediction models in the presence of competing risks

Clinical prediction models play a key role in risk stratification, therapy assignment and many other fields of medical decision making. Before they can enter clinical practice, their usefulness has to be demonstrated using systematic validation. Methods to assess their predictive performance have been proposed for continuous, binary, and time-to-event outcomes, but the literature on validation methods for discrete time-to-event models with competing risks is sparse. The present paper tries to fill this gap and proposes new methodology to quantify discrimination, calibration, and prediction error (PE) for discrete time-to-event outcomes in the presence of competing risks. In our case study, the goal was to predict the risk of ventilator-associated pneumonia (VAP) attributed to Pseudomonas aeruginosa in intensive care units (ICUs). Competing events are extubation, death, and VAP due to other bacteria. The aim of this application is to validate complex prediction models developed in previous work on more recently available validation data.     

SIMEX for correction of dietary exposure effects with Box-Cox transformed data

Modelling dietary data, and especially 24-hr dietary recall (24HDR) data, is a challenge. Ignoring the inherent measurement error (ME) leads to biased effect estimates when the association between an exposure and an outcome is investigated. We propose an adapted simulation extrapolation (SIMEX) algorithm for modelling dietary exposures. For this purpose, we exploit the ME model of the NCI method where we assume the assumption of normally distributed errors of the reported intake on the Box-Cox transformed scale and of unbiased recalls on the original scale. According to the SIMEX algorithm, remeasurements of the observed data with additional ME are generated in order to estimate the association between the level of ME and the resulting effect estimate. Subsequently, this association is extrapolated to the case of zero ME to obtain the corrected estimate. We show that the proposed method fulfils the key property of the SIMEX approach, that is, that the MSE of the generated data will converge to zero if the ME variance converges to zero. Furthermore, the method is applied to real 24HDR data of the I.Family study to correct the effects of salt and alcohol intake on blood pressure. In a simulation study, the method is compared with the NCI method resulting in effect estimates with either smaller MSE or smaller bias in certain situations. In addition, we found our method to be more informative and easier to implement. Therefore, we conclude that the proposed method is useful to promote the dissemination of ME correction methods in nutritional epidemiology.     

Sample size recalculation in multicenter randomized controlled clinical trials based on noncomparative data

Many late-phase clinical trials recruit subjects at multiple study sites. This introduces a hierarchical structure into the data that can result in a power-loss compared to a more homogeneous single-center trial. Building on a recently proposed approach to sample size determination, we suggest a sample size recalculation procedure for multicenter trials with continuous endpoints. The procedure estimates nuisance parameters at interim from noncomparative data and recalculates the sample size required based on these estimates. In contrast to other sample size calculation methods for multicenter trials, our approach assumes a mixed effects model and does not rely on balanced data within centers. It is therefore advantageous, especially for sample size recalculation at interim. We illustrate the proposed methodology by a study evaluating a diabetes management system. Monte Carlo simulations are carried out to evaluate operation characteristics of the sample size recalculation procedure using comparative as well as noncomparative data, assessing their dependence on parameters such as between-center heterogeneity, residual variance of observations, treatment effect size and number of centers. We compare two different estimators for between-center heterogeneity, an unadjusted and a bias-adjusted estimator, both based on quadratic forms. The type 1 error probability as well as statistical power are close to their nominal levels for all parameter combinations considered in our simulation study for the proposed unadjusted estimator, whereas the adjusted estimator exhibits some type 1 error rate inflation. Overall, the sample size recalculation procedure can be recommended to mitigate risks arising from misspecified nuisance parameters at the planning stage.     

Flexible Mixture Model Approaches That Accommodate Footprint Size Variability for Robust Detection of Balancing Selection

Long-term balancing selection typically leaves narrow footprints of increased genetic diversity, and therefore most detection approaches only achieve optimal performances when sufficiently small genomic regions (i.e., windows) are examined. Such methods are sensitive to window sizes and suffer substantial losses in power when windows are large. Here, we employ mixture models to construct a set of five composite likelihood ratio test statistics, which we collectively term B statistics. These statistics are agnostic to window sizes and can operate on diverse forms of input data. Through simulations, we show that they exhibit comparable power to the best-performing current methods, and retain substantially high power regardless of window sizes. They also display considerable robustness to high mutation rates and uneven recombination landscapes, as well as an array of other common confounding scenarios. Moreover, we applied a specific version of the B statistics, termed B₂, to a human population-genomic data set and recovered many top candidates from prior studies, including the then-uncharacterized STPG2 and CCDC169–SOHLH2, both of which are related to gamete functions. We further applied B₂ on a bonobo population-genomic data set. In addition to the MHC-DQ genes, we uncovered several novel candidate genes, such as KLRD1, involved in viral defense, and SCN9A, associated with pain perception. Finally, we show that our methods can be extended to account for multiallelic balancing selection and integrated the set of statistics into open-source software named BalLeRMix for future applications by the scientific community.     

An in silico analysis of deleterious single nucleotide polymorphisms and molecular dynamics simulation of disease linked mutations in genes responsible for neurodegenerative disorder

Abstract

Mutation in two genes deglycase gene (DJ-1) and retromer complex component gene (VPS35) are linked with neurodegenerative disorder such as Parkinson's disease, Huntington's disease, and Alzheimer's disease. DJ-1 gene located at 1p36 chromosomal position and involved in PD pathogenesis through many pathways including mitochondrial dysfunction and oxidative injury. VPS35 gene located at 16q13-q21 chromosomal position and the two pathways, the Wnt signaling pathway, and retromer-mediated DMT1 missorting are proposed for basis of VPS35 related PD. The study focuses on identifying most deleterious SNPs through computational analysis. Result obtained from various bioinformatics tools shows that D149A is most deleterious in DJ-1 and A54W, R365H, and V717M are most deleterious in VPS35. To understand the functionality of protein comparative modeling of DJ-1 and VPS35 native and mutants was done by MODELLER. The generated structures are validated by two web servers–ProSa and RAMPAGE. Molecular dynamic simulation (MDS) analysis done for the most validated structures to know the functional and structural nature of native and mutants protein of DJ-1 and VPS35. Native structure of DJ-1 and VPS35 show more flexibility through MDS analysis. DJ-1 D149A mutant structures become more compact which shows the structural perturbation and loss of DJ-1 protein function which in turn are probable cause for PD. A54W, R365H, and V717M mutant protein of VPS35 also shows compactness which cause structure perturbation and absence of retromer function which likely to be linked to PD pathogenesis. This in silico study may provide a new insight for fundamental molecular mechanism involved in Parkinson’s disease.

Communicated by Ramaswamy H. Sarma