Computational biology in topical bioactive peptide discovery for cosmeceutical application:a concise review

Regenerative medicine and anti-aging research have made great strides at the molecular and cellular levels in dermatology and the medical aesthetic field,targeting potential treatments with skin therapeutic and intervention pathways,which make it possible to develop effective skin regeneration and repair ingredients.With the rapid development of computational biology,bioinformatics as well as artificial intelligence(A.I.),the development of new ingredients for regenerative medicine has been greatly accelerated,and the success rate has been improved.Some application cases have appeared in topical skin regeneration and repair scenarios.This review will briefly introduce the application of bioactive peptides in skin repair and anti-aging as emerging ingredients in cosmeceutics and emphasize how A.I.based computational biology technology may accelerate the development of innovative peptide molecules and ultimately translate them into potential skin regenerative and anti-aging scenarios.Typically,two research routines have been summarized and current limitations as well as directions were discussed for border applications in future research.

Computer-aided Translation Technology and Its Applications

This article begins with a brief analysis of the significance of translation technology in different spheres of modern life,followed by a distinction between machine translation(MT)and computer-aided translation(CAT).It then describes some translation resources and tools and examines the negative and positive aspects of computer-aided translations.Finally it comes to a conclusion that it would be greatly efficient and productive for the translators to acquire the new skills in the translation workplace.

Applications—Influence of Biology on Engineering

Examples are presented showing the way in which biological systems produce a range of functions which can be implemented in engineering, such as feedback-control of stiffness （muscles and nervous system）, the design of fault-free structures （trees） and damage-tolerant materials （wood） and high performance insulation （penguin feathers） and shock absorbers （hedgehog spines）.

北京师范大学系统科学学院团队研究成果在生物类Top期刊《Plos Computational Biology》作为封面文章发表

北京师范大学系统科学学院韩战钢教授团队与法国图卢兹大学Guy Theraulaz教授合作完成的关于生物集群行为研究的文章＂Identifying influential neighbors in animal flocking＂近日被生物类Top期刊《Plos Computational Biology》接收并在线发表.

Computational biology approach to uncover hepatitis C virus helicase operation

Hepatitis C virus(HCV)helicase is a molecular motor that splits nucleic acid duplex structures during viral replication,therefore representing a promising target for antiviral treatment.Hence,a detailed understanding of the mechanism by which it operates would facilitate the development of efficient drug-assisted therapies aiming to inhibit helicase activity.Despite extensive investigations performed in the past,a thorough understanding of the activity of this important protein was lacking since the underlying internal conformational motions could not be resolved.Here we review investigations that have been previously performed by us for HCV helicase.Using methods of structure-based computational modelling it became possible to follow entire operation cycles of this motor protein in structurally resolved simulations and uncover the mechanism by which it moves along the nucleic acid and accomplishes strand separation.We also discuss observations from that study in the light of recent experimental studies that confirm our findings.

Preface to special issue on recent advances in computational seismology and its applications

Computational seismology is a relatively new interdisciplinary field spanning computational techniques in theoretical and observational seismology. It studies numerical methods and their implementation in various theoretical and applied problems in seismology.

Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

In the smart city paradigm, the deployment of Internet of Things(IoT) services and solutions requires extensive communication and computingresources to place and process IoT applications in real time, which consumesa lot of energy and increases operational costs. Usually, IoT applications areplaced in the cloud to provide high-quality services and scalable resources.However, the existing cloud-based approach should consider the above constraintsto efficiently place and process IoT applications. In this paper, anefficient optimization approach for placing IoT applications in a multi-layerfog-cloud environment is proposed using a mathematical model (Mixed-Integer Linear Programming (MILP)). This approach takes into accountIoT application requirements, available resource capacities, and geographicallocations of servers, which would help optimize IoT application placementdecisions, considering multiple objectives such as data transmission, powerconsumption, and cost. Simulation experiments were conducted with variousIoT applications (e.g., augmented reality, infotainment, healthcare, andcompute-intensive) to simulate realistic scenarios. The results showed thatthe proposed approach outperformed the existing cloud-based approach interms of reducing data transmission by 64% and the associated processingand networking power consumption costs by up to 78%. Finally, a heuristicapproach was developed to validate and imitate the presented approach. Itshowed comparable outcomes to the proposed model, with the gap betweenthem reach to a maximum of 5.4% of the total power consumption.

Edge Cloud Selection in Mobile Edge Computing(MEC)-Aided Applications for Industrial Internet of Things(IIoT)Services

In many IIoT architectures,various devices connect to the edge cloud via gateway systems.For data processing,numerous data are delivered to the edge cloud.Delivering data to an appropriate edge cloud is critical to improve IIoT service efficiency.There are two types of costs for this kind of IoT network:a communication cost and a computing cost.For service efficiency,the communication cost of data transmission should be minimized,and the computing cost in the edge cloud should be also minimized.Therefore,in this paper,the communication cost for data transmission is defined as the delay factor,and the computing cost in the edge cloud is defined as the waiting time of the computing intensity.The proposed method selects an edge cloud that minimizes the total cost of the communication and computing costs.That is,a device chooses a routing path to the selected edge cloud based on the costs.The proposed method controls the data flows in a mesh-structured network and appropriately distributes the data processing load.The performance of the proposed method is validated through extensive computer simulation.When the transition probability from good to bad is 0.3 and the transition probability from bad to good is 0.7 in wireless and edge cloud states,the proposed method reduced both the average delay and the service pause counts to about 25%of the existing method.

Deep Learning Applied to Computational Mechanics:A Comprehensive Review,State of the Art,and the Classics

Three recent breakthroughs due to AI in arts and science serve as motivation:An award winning digital image,protein folding,fast matrix multiplication.Many recent developments in artificial neural networks,particularly deep learning(DL),applied and relevant to computational mechanics(solid,fluids,finite-element technology)are reviewed in detail.Both hybrid and pure machine learning(ML)methods are discussed.Hybrid methods combine traditional PDE discretizations with ML methods either(1)to help model complex nonlinear constitutive relations,(2)to nonlinearly reduce the model order for efficient simulation(turbulence),or(3)to accelerate the simulation by predicting certain components in the traditional integration methods.Here,methods(1)and(2)relied on Long-Short-Term Memory(LSTM)architecture,with method(3)relying on convolutional neural networks.Pure ML methods to solve(nonlinear)PDEs are represented by Physics-Informed Neural network(PINN)methods,which could be combined with attention mechanism to address discontinuous solutions.Both LSTM and attention architectures,together with modern and generalized classic optimizers to include stochasticity for DL networks,are extensively reviewed.Kernel machines,including Gaussian processes,are provided to sufficient depth for more advanced works such as shallow networks with infinite width.Not only addressing experts,readers are assumed familiar with computational mechanics,but not with DL,whose concepts and applications are built up from the basics,aiming at bringing first-time learners quickly to the forefront of research.History and limitations of AI are recounted and discussed,with particular attention at pointing out misstatements or misconceptions of the classics,even in well-known references.Positioning and pointing control of a large-deformable beam is given as an example.

基于RNA测序和生物信息学分析鉴定椎旁肌退变中关键的铁死亡基因

目的探究椎旁肌退变(PMD)中的基因表达谱,并鉴定关键的铁死亡基因。方法选取正常和PMD患者各3例,分别取椎旁肌组织进行RNA测序,获得差异表达的基因。通过蛋白-蛋白相互作用(PPI)和基因功能富集分析,与铁死亡基因取交集,鉴定与铁死亡相关的关键中枢基因。通过受试者工作特征(ROC)曲线分析铁死亡基因对PMD疾病的诊断价值。结果在PMD中共鉴定出292个差异表达的基因,其中125个显著下调,167个显著上调。生物信息学分析发现14个差异表达的基因与铁死亡相关,其中铁死亡基因MUC1、ATF3、CDKN1A是关键的中枢基因,对诊断PMD具有良好的敏感度和特异度。功能富集分析发现它们可能通过调控细胞凋亡、铁死亡和骨骼肌组织发育、分化等介导PMD的发生与进展。结论铁死亡基因MUC1、ATF3、CDKN1A可作为诊断PMD的生物标志物,为解码PMD的病理机制及开发新的药物提供理论依据。

生物信息学在食用菌研究中的应用

食用菌因其富含多种氨基酸及微量元素等物质,具有较高的营养价值和药用价值,越来越受到人们的关注和喜爱。我国作为最重要的食用菌生产国,食用菌生产规模不断扩大,产量也在逐年提高。为了更好地发展食用菌产业,迫切需要在传统的食用菌产业链,如优良品种选育及栽培生产中融入新技术。生物信息学作为一门研究分析生物生命结构的技术门类,通过运用数学、计算机科学等工具揭示了数据所蕴含的生物学意义,极大地促进了生命科学研究的发展,也为食用菌更深入的研究与应用提供了技术保障。本文从食用菌育种及种质资源调查、病虫害防治、基因组学、食用菌安全等几方面阐述了生物信息学在食用菌领域的具体应用,对生物信息学在食用菌及农业领域的发展进行了展望,以期为促进食用菌研究和生产发展提供参考。

大数据、云计算及中台技术在能源企业门户项目模板化的设计应用

在能源企业数字化转型、信息化补强工程背景下,结合能源企业信息门户新模板研发课题要求、采用大数据、云计算及中台技术,优化技术路线,行业互补,打通最后一公里,形成新型模板方案。该模板全套技术高度实现国产化,具备人机融合生产环境、网络化项目建设、数字化运维等优势,适用范围广,模板化方案可高效赋能项目建设与运营全生命周期应用,对能源企业数字化转型领域的新改扩信息化、数字化、智慧化项目补强短板和深化应用有一定效果。

信息技术教育与创新创业教育融合的课程体系研究——以牡丹江大学“计算机应用技术”专业为例

本文以国务院办公厅出台的《关于深化高等学校创新创业教育改革的实施意见》为指导思想,并以牡丹江大学正在进行的双高专业群建设项目为契机,针对“计算机应用技术”专业的课程体系进行了改革与创新研究,从课程设置、职业面向、培养目标及规格、课程体系与实践教学体系的创新等几个方面进行了论述。

基于云计算的软件工程方法在通信网络中的应用与效益

文章探讨了云计算与软件工程方法的结合,以及这种结合在通信网络中的应用和效益。首先,概述了云计算的基本概念和软件工程方法,分析了两者的结合点。其次,深入探讨了基于云计算的软件工程方法如何满足通信网络的软件需求并解决存在的问题,并评估了这种方法带来的效益,包括提高工作效率、降低运营成本,以及提高网络的可靠性和安全性等。最后,总结了软件工程方法的未来发展趋势,并对未来通信网络的发展趋势和预期效益进行了预测。

螺旋CT三维扫描重建联合3D打印用于髋臼骨折手术的价值研究

目的:探讨螺旋CT三维扫描重建联合3D打印技术在髋臼骨折外科手术中的应用价值。方法:选取2018年1月至2020年12月新疆维吾尔自治区人民医院收治的71例髋臼骨折并接受手术治疗的患者,依据治疗方式的不同将其分为观察组(38例)和对照组(33例),观察组采用螺旋CT三维重建,并以3D打印辅助内固定治疗,对照组采用常规手术治疗。比较两组患者手术效果及恢复情况。结果:观察组骨折复位质量评分及分项百分制髋功能评分高于对照组,术中出血量、术中透视次数、手术用时及住院时间均明显低于对照组,差异均有统计学意义(t=23.426、4.216、3.425、17.783,P<0.05);观察组和对照组骨折复位优良率分别为94.74%和78.79%,差异有统计学意义(x^(2)=4.059,P<0.05);观察组患者髋关节功能评分中疼痛、功能及关节活动均高于对照组,差异有统计学意义(t=-4.873、-5.776、-6.247,P<0.05);观察组并发症发生率明显低于对照组,差异有统计学意义(x^(2)=4.059,P<0.05)。结论:在髋臼骨折围术期应用螺旋CT三维重建结合3D打印可进一步保证手术的安全性和可行性,提高手术质量和效率,促进术后恢复。

应用实践导向的机器学习导论课程教学探讨

机器学习技术有很强的理论性和实用性。为提高学生对机器学习课程的兴趣和主动性,以计算机科学与技术专业机器学习导论教学为例,探究应用实践导向的机器学习导论课程教学改革,对每个机器学习算法设置对应的应用实践项目,构建1学时机器学习理论知识对应1学时项目实践的教学方式,将机器学习算法融入到实践项目中。教学实践证明,应用实践导向的机器学习导论教学模式能够明显提高理论和实践结合教学方法的效率,提高学生的积极性和主动性,培养学生解决实际问题的能力。

面向微控制器的卷积神经网络加速器设计

针对目前嵌入式微控制器的性能难以满足实时图像识别任务的问题,提出一种适用于微控制器的卷积神经网络加速器。该加速器在卷积层设计了无阻塞的行并行乘法-加法树结构,获得了更高的硬件利用率;为了满足行并行的数据吞吐量,设计了卷积专用SRAM存储器。加速器将池化和激活单元融入数据通路,有效减少数据重复存取带来的时间开销。FPGA原型验证表明加速器的性能达到92.2 GOPS@100 MHz;基于TSMC 130 nm工艺节点进行逻辑综合,加速器的动态功耗为33 mW,面积为90 764.2μm^(2),能效比高达2 793 GOPS/W,比FPGA加速器方案提高了约100倍。该加速器低功耗、低成本的特性,有利于实现嵌入式系统在目标检测、人脸识别等机器视觉领域的广泛应用。

高职《计算机应用基础》课程的线上线下混合式分层教学研究与实践

在信息化时代,《计算机应用基础》是培养学生信息技术及素养必不可少的基础课程。该文针对高职计算机基础教学中教学内容偏重技术训练、教学模式难以支撑多层次教学需求、教学评价激励及指导作用不足等问题,通过线上线下混合式分层教学进行“德技融通、分层施教、数字赋能”的课堂革命,对教学内容、教学模式、教学评价进行全面改革,为高职《计算机应用基础》课程创新实践提供了借鉴的方案。

“智能+”视域下体育院校计算机应用基础课程思政设计与实践研究

课程思政建设是落实高校全面发展育人的重要抓手。体育院校“计算机应用基础”课程是各个专业的公共基础课,在培养体育人才的过程中起着举足轻重的作用。以“智能+”赋能课程思政教学改革为切入点,从课程思政目标精准设定、课程思政元素智能挖掘、课程思政内容智能推荐和课程思政评价体系构建等方面来探究体育院校“计算机应用基础”课程思政设计思路,充分发挥“智能+”在课程思政教学改革中的助溶剂作用,以期为体育院校同类课程的思政教学改革提供有益参考。

超大规模数据处理中并行计算技术的应用研究

随着人工智能和大数据时代的到来,超大规模数据处理成了一个重要的研究领域。该文主要探讨并行计算技术在超大规模数据处理中的应用,首先详细阐述并行计算和超大规模数据处理的基本理论与概念,特别是并行计算的编程模型与工具,最后通过分析并行计算在搜索引擎、气象预报和金融分析等中的实际案例,阐述并行计算技术在超大规模数据处理中的实际应用。