Recent advances in protein conformation sampling by combining machine learning with molecular simulation

The rapid advancement and broad application of machine learning(ML)have driven a groundbreaking revolution in computational biology.One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules.This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape.We first highlight the recent development of ML-aided enhanced sampling methods,including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential,or facilitate the exploration of the unsampled region of the energy landscape.Further,we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations.Lastly,we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights.Collectively,this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.

Experiment Study of Reverse Demulsifier on Simulated Water Sample Treatment at Different Oscillation Time and Different Concentrations of Drugging

In recent years, the water content of oilfield production fluid is high and there is a large amount of oily sewage. In order to improve the capability of sewage treatment, usually using demulsifier for oily sewage processing. This article uses simulated water sample to test the treatment effect of the optimized reverse demulsifier at different oscillation time. As the increase of action time and oscillation, the average size of droplets increases and the amount of the droplets under 1 μm decreases.

Sensitivity of Sample for Simulation-Based Reliability Analysis Methods

In structural reliability analysis,simulation methods are widely used.The statistical characteristics of failure probability estimate of these methods have been well investigated.In this study,the sensitivities of the failure probability estimate and its statistical characteristics with regard to sample,called‘contribution indexes’,are proposed to measure the contribution of sample.The contribution indexes in four widely simulation methods,i.e.,Monte Carlo simulation(MCS),importance sampling(IS),line sampling(LS)and subset simulation(SS)are derived and analyzed.The proposed contribution indexes of sample can provide valuable information understanding the methods deeply,and enlighten potential improvement of methods.It is found that the main differences between these investigated methods lie in the contribution indexes of the safety samples,which are the main factors to the efficiency of the methods.Moreover,numerical examples are used to validate these findings.

Construction of Network Fault Simulation Platform and Event Samples Acquisition Techniques for Event Correlation

Event correlation is one key technique in network fault management. For the event sample acquisition problem in event correlation, a novel approach is proposed to collect the samples by constructing network simulation platform. The platform designed can set kinds of network faults according to user's demand and generate a lot of network fault events, which will benefit the research on efficient event correlation techniques.

Simulation Program to Determine Sample Size and Power for a Multiple Logistic Regression Model with Unspecified Covariate Distributions

Binary logistic regression models are commonly used to assess the association between outcomes and covariates. Many covariates are inherently continuous, and have a variety of distributions, including those that are heavily skewed to the left or right. Existing theoretical formulas, criteria, and simulation programs cannot accurately estimate the sample size and power of non-standard distributions. Therefore, we have developed a simulation program that uses Monte Carlo methods to estimate the exact power of a binary logistic regression model. This power calculation can be used for distributions of any shape and covariates of any type (continuous, ordinal, and nominal), and can account for nonlinear relationships between covariates and outcomes. For illustrative purposes, this simulation program is applied to real data obtained from a study on the influence of smoking on 90-day outcomes after acute atherothrombotic stroke. Our program is applicable to all effect sizes and makes it possible to apply various statistical methods, logistic regression and related simulations such as Bayesian inference with some modifications.

The Development of a Simulated Sediment Dosing Apparatus for Deposition Research in Wastewater Collection Systems

Water conservation initiatives promote installation of water efficient and low-flow appliances in waste water collection systems. This has resulted in lower flow rates in those systems than the intended design loading, causing solid deposition and sedimentation in some areas. A joint UKWIR/EPSRC CASE grant (14440031) has funded the work described in this paper which investigates sedimentation and solid deposition in building drainage system pipes. The purpose of this paper is to detail the design, calibration and operation of a sediment dosing apparatus to simulate sedimentation rates and explore possible solutions to this issue with a full scale laboratory model based on real site data. The methodology adopted is an experimental approach, where tests have been conducted on the sediment dosing apparatus based on calculations and observations to determine an appropriate sediment dosing regime representative of typical systems. Further tests were conducted with the addition of everyday household products to investigate their effects on sedimentation. The results indicated that a suitable dosing rate was approximately 12% weight-to-volume (w/v) of a fine sand with a known particle size distribution, diluted 1:5 in a clean water base flow. It was also shown that the addition of the household products added to the problem of sedimentation within drainage systems. The results give excellent correlation to real site data, with deposition depth and distribution comparable to measured site data to within 10%. The deposition was achieved within three hours, which approximated six weeks deposition in the live site used in the study. This straightforward investigation details the design, construction and testing of a device to cause accelerated sedimentation in a full scale model of a building drainage system. This is the first step in the process of updating research underpinning our understanding of the behaviour of these systems under conditions of low flow rates caused by water conservation, sedimentation, and the use of common household additives. It will be used to improve simulation of water flow and solid transport in sediment-laden systems. Specifically, the results will be used to determine refinements required to a specific drainage simulation model (DRAINET), which currently has an unquantified sedimentation component. This work is part of a larger body of current research funded by two joint EPSRK/UKWIR grants.

Crowdsourced Sampling of a Composite Random Variable: Analysis, Simulation, and Experimental Test

A composite random variable is a product (or sum of products) of statistically distributed quantities. Such a variable can represent the solution to a multi-factor quantitative problem submitted to a large, diverse, independent, anonymous group of non-expert respondents (the “crowd”). The objective of this research is to examine the statistical distribution of solutions from a large crowd to a quantitative problem involving image analysis and object counting. Theoretical analysis by the author, covering a range of conditions and types of factor variables, predicts that composite random variables are distributed log-normally to an excellent approximation. If the factors in a problem are themselves distributed log-normally, then their product is rigorously log-normal. A crowdsourcing experiment devised by the author and implemented with the assistance of a BBC (British Broadcasting Corporation) television show, yielded a sample of approximately 2000 responses consistent with a log-normal distribution. The sample mean was within ~12% of the true count. However, a Monte Carlo simulation (MCS) of the experiment, employing either normal or log-normal random variables as factors to model the processes by which a crowd of 1 million might arrive at their estimates, resulted in a visually perfect log-normal distribution with a mean response within ~5% of the true count. The results of this research suggest that a well-modeled MCS, by simulating a sample of responses from a large, rational, and incentivized crowd, can provide a more accurate solution to a quantitative problem than might be attainable by direct sampling of a smaller crowd or an uninformed crowd, irrespective of size, that guesses randomly.

A SIMULATION STUDY ON DYNAMIC-SAMPLING-MODE FOR FLUORESCENCE MOLECULAR TOMOGRAPHY

Fluorescence tomography can obtain a sufficient dataset and optimal three-dimensional imageswhen projections are captured over 360◦ by CCD camera. Herein a non-stop dynamic samplingmode for fluorescence tomography is proposed in an attempt to improve the optical measurementspeed of the traditional imaging system and stability of the object to be imaged. A series ofsimulations are carried out to evaluate the accuracy of dataset acquired from the dynamicsampling mode. Reconstruction with the corresponding data obtained in the dynamic-modeprocess is also performed with the phantom. The results demonstrate the feasibility of suchan imaging mode when the angular velocity is set to the appropriate value, thus laying thefoundation for real experiments to verify the superiority in performance of this new imagingmode over the traditional one.

Impacts of sample size for stomach content analysis on the estimation of ecosystem indices

This study used Ecopath model of the Jiaozhou Bay as an example to evaluate the effect of stomach sample size of three fish species on the projection of this model. The derived ecosystem indices were classified into three categories:(1) direct indices, like the trophic level of species, influenced by stomach sample size directly;(2)indirect indices, like ecology efficiency(EE) of invertebrates, influenced by the multiple prey-predator relationships;and(3) systemic indices, like total system throughout(TST), describing the status of the whole ecosystem. The influences of different stomach sample sizes on these indices were evaluated. The results suggest that systemic indices of the ecosystem model were robust to stomach sample sizes, whereas specific indices related to species were indicated to be with low accuracy and precision when stomach samples were insufficient.The indices became more uncertain when the stomach sample sizes varied for more species. This study enhances the understanding of how the quality of diet composition data influences ecosystem modeling outputs. The results can also guide the design of stomach content analysis for developing ecosystem models.

The Probability of Pairwise Shared Ancestry and the Expected Number of Pairs of k-th Cousins in a Population Sample

An analytical solution is derived for the probability that a random pair of individuals from a panmictic population of size N will share ancestors who lived G generations previously. The analysis is extended to obtain 1) the probability that a sample of size s will contain at least one pair of (G - 1)th cousins;and 2) the expected number of pairs of (G - 1)th cousins in that sample. Solutions are given for both monogamous and promiscuous (non-monogamous) cases. Simulation results for a population size of N = 20,000 closely approximate the analytical expectations. Simulation results also agree very well with previously derived expectations for the proportion of unrelated individuals in a sample. The analysis is broadly consistent with genetic estimates of relatedness among a sample of 406 Danish school children, but suggests that a different genetic study of a heterogenous sample of Europeans overestimates the frequency of cousin pairs by as much as one order of magnitude.

Two-Stage Double Sampling by Variables and Its Simulation

The method to use double sampling on the two stage sampling was introduced and its characteristics were obtained through amalyzing and simusating.

Simulation Design of Baseband Optimal Digital Communication System with Non-Ideal Channel

This paper aims to discuss how to effectively suppress intersymbol interference by optimizing the filter design, so as to achieve a distortion-free output effect, and effectively compensate the transmission characteristics of the baseband transmission system in a non-ideal channel environment, so as to minimize the impact of intersymbol crosser. The simulation experiment model of digital optimal baseband transmission and the overall structure of the system are designed based on the Matlab simulation platform, and the parameters of each module in the simulation experiment model are set. The working process and performance of the digital optimal baseband transmission system are simulated, and the conditions and performance of the digital optimal baseband transmission system are verified according to the simulation results.

大鼠脑关键核团精准定位与快速取材虚拟仿真实验项目的建设及应用

虚拟仿真实验是智能环境下教育发展的必然选择。大鼠脑组织关键核团的精准定位和快速取材是医学专业学生必备的实验技能,但本科生操作实验成功率低、实验成本较高,亟需建设虚拟仿真实验。文章从实验教学目标、实验教学过程与方法、交互性操作步骤简介、实验教学特色等方面对大鼠脑关键核团精准定位与快速取材虚拟仿真实验项目的建设进行了介绍。该项目具有交互步骤清晰、沉浸式体验感好、可分步计分并附扣分说明等特点,有利于提升学生学习兴趣,提高学生的实验操作水平。小结部分简介了项目的应用,以期为医学院校其他虚拟仿真实验的建设提供参考。

中医院校大鼠脑关键核团精准定位及快速取材实验混合式教学设计

大鼠脑关键核团精确定位和快速取材是医学生常用的实验技能,但学习和掌握该技能具有较大的难度和挑战度。对此,团队采用线上线下混合式教学,遵循“以虚补实、虚实结合”的原则,让学生“先看图谱再虚拟,实验实操强记忆”。即在进行理论知识讲解和图谱线下学习之后,让学生自主通过虚拟仿真实验进行在线练习,学生反复训练后进行考核,考核合格再进入真实实验室完成整个实验。采用上述方式进行教学设计和授课,取得了较为满意的效果。

医用放射性废液取样容器尺寸的最优化研究

医用放射性废液中核素放射性活度必须经监测满足相关标准后,该放射性废液方可排放,采用LaBr3(Ce)晶体探测医用放射性废液的核素活度,废液样品体积及其在探测器周围的分布情况直接影响探测效率,为此利用Geant4工具建立LaBr3(Ce)晶体探测模型,探索最优马林杯样品盒尺寸参数的变化规律,并在实验室利用?25.4 mm×25.4 mm的LaBr3(Ce)探测器和3D打印光敏树脂样品盒作验证实验。结果显示:单纯增加样品体积不能提高探测效率,随着样品体积增大,探测效率随样品盒环状部分深度方向的变化趋势趋于平缓。研究得到马林杯最优尺寸比例:环状部分深度(h2)和顶部半径(r)分别约为探测器晶体长度和空心腔直径的两倍,顶部半径(r)和样品盒高度(H)之比约为0.5。在最优化尺寸下,全能峰探测效率实验值与模拟结果一致,相对偏差优于2.5%。研究结果将为后续医用放射性废液监测装置在探测器选择、采样容器设计、加工及放射性废液监测量值溯源方法等方面提供重要的技术借鉴。

基于特征样本的变参数模型估计方法研究

文章基于特征样本采样方法,通过对特征样本的重新组合,设计了一种新的变参数模型估计方法,实现经济社会问题中小样本情况下的变参数估计。该方法根据采样顺序将特征样本重组为时间序列各期的样本,通过分期回归实现各期的变参数估计,并对估计结果进行显著性和拟合度检验。该方法简便易行,且符合贝叶斯原理和蒙特卡罗原理,为变参数模型的估计提供了一种新的思路。

碳排放清单在天然气场站应用的不确定性研究

温室气体清单编制指南为有效推进节能减排措施提供了重要参考和政策支撑,但目前各清单标准不一、推荐缺省值的偏差是碳排放计算误差的主要来源。从计算方法的角度建立研究排放清单不确定性的模型,通过对比采用蒙特卡洛模拟法和拉丁超立方抽样法对不确定性因素进行模拟的结果;提出采用消减技术找到具有代表性典型数据的方法,改善仅采用平均值计算碳排放量带来的误差。将模型应用于阿克苏管理站2022年碳排放计算中,依据不同清单计算造成的碳排放不确定度在1%~4%,采用不确定性模型计算的碳排放结果比确定性模型计算的碳排放量少,更具代表性,有助于企业更早实现“双碳”目标。

基于降方差采样策略的随机重构法

在随机重构法的采样过程中,每个结构特征需要的采样数量是不相等且变化的。为了将拉丁超立方采样用于降低随机重构模型的方差,基于随机重构法采样过程的特征和拉丁超立方采样原理,提出了适用于随机重构法的新型拉丁超立方采样方法,探究了在多种分子数量设定情况下应用该方法对随机重构模型的方差和精度的影响。结果表明,应用该方法能够显著降低随机重构模型的方差,提高模型的精度,在分子数量为1000~50000范围内,新模型的标准差相较传统模型降低了71.36%~74.53%,目标函数值降低了1.69%~13.82%。综合模型精度和模拟过程的运算开销,选择4000~6000作为新模型最优的分子数量设定。

基于MATLAB的PN结形成过程蒙特卡罗程序研发

本文基于蒙特卡罗随机行走的思想,用概率和随机抽样模拟PN结形成过程中载流子的运动以及动态分布,解决平衡态PN结电势所满足的泊松方程过于复杂、难以计算以及模拟的问题.利用MATLAB软件将演化过程中的载流子的运动过程可视化,并且将平衡态PN结的电势分布可视化.该方法可通过模拟载流子的运动规律及分布了解PN结的形成过程,进而及时对物理模型进行修正.该程序实现手段简便,物理图像清晰,可以用于半导体物理等相关课程的课堂教学.

基于自动生成样本的优化Deeplabv3+网络速度建模方法

建立精确的速度模型对地震成像和解释十分重要,深度学习方法为建立精确的速度模型提供了新的途径,然而,目前可用于速度建模网络训练的样本非常有限。为此,提出了一种利用随机曲线模拟地下速度模型,自动生成大量样本用于深度学习训练的方法。该方法通过生成一组随机数,插值形成随机序列,利用三角函数将随机序列生成随机曲线来模拟地下界面,生成层状速度模型,在此基础上,通过添加断裂、速度异常体和地层倾角来模拟更复杂的速度模型;在速度预测网络构建方面,选取Deeplabv3+网络作为速度预测网络。通过增加卷积层的方法,优化了Deeplabv3+网络上采样后直接输出导致边界模糊的问题。将上述方法应用于模型数据和实际数据,测试了该方法在含噪声、不同子波频率和部分数据缺失情况下的稳定性。结果表明,该方法能够有效地应对噪声、不同子波频率和部分数据缺失的影响,具有可靠的泛化性和鲁棒性。