高通量TCR免疫组库技术在艾滋病中的应用及前景分析

T细胞受体(TCR)免疫组库是机体所有TCR的总和,TCR免疫组库的多样性与HIV识别和免疫逃逸密切相关。高通量免疫组库测序技术能够检测分析TCR组库的所有序列,真实反映所有TCR的遗传信息,全面揭示TCR组库的复杂性和多样性。通过高通量测序技术分析TCR免疫组库变化可监测HIV患者诊疗情况、疾病进展及预后,同时有助于疫苗研发、病毒库清除及临床疗效评价,从而为丰富HIV/AIDS防治思路和靶点提供依据。

逆锁键非线性协同CD8塑造TCR抗原识别的特异性

目的自然进化的T细胞受体(TCR)能在动态复杂的物理化学微环境中特异性地识别外来抗原,同时很好地控制对自身抗原的交叉反应性。然而,大多体外改造且亲和力成熟的TCR易交叉于自身抗原,诱发非目标毒性,其机制目前仍不清楚。方法通过单分子力谱技术、分子动力学模拟、体外功能实验以及系统仿真,展开了详细的研究。结果(1)天然TCR能协同利用机械力和CD8识别外来抗原,表现出中等强度(最优)的逆锁键;这种机械协同互作依次诱导了p MHC构象变化和CD8配体结合域(CD8 LBD)的旋转,形成了柔性的TCR-p MHC结合界面,将天然TCR特异性提高超过10倍。(2)改造且高亲和力TCR识别外来抗原虽表现出超强的逆锁键,其紧密的结合界面反而限制了p MHC构象变化及CD8 LBD旋转,同时与自身抗原结合时也形成较强逆锁键,从而削弱了TCR特异性。(3)通过整合以上TCR识别抗原的动力学及结构特征,构建了一种依赖于力学参数的动力学图谱,能够帮助快速区分有无功能性以及潜在引起毒副作用的TCR-p MHC互作对。结论本研究揭示了天然与改造的TCRs其特异性的动态塑造过程,为未来筛选出高潜能、高特异性且对自身抗原交叉反应小的TCR-T提供了新策略。

基于TCR模型的台风极值降雨强度预测

近些年台风带来的强降雨与洪水灾害给沿海城市造成严重的经济损失和人员伤亡,极端降雨环境多数是在台风作用下产生的,且极值降雨强度是城市防洪排涝、排水规划和工程设计的雨量计算依据。为更加合理准确地预测极值降雨,文章引入热带气旋降雨(tropical cyclone rainfall,TCR)模型,利用蒙特卡罗(Monte Carlo)技术模拟香港地区的极值降雨强度。对比相关规范与历史数据,该方法能较好地预测极值降雨强度,为后续台风降雨灾害危险性分析和排水工程建设研究提供一定的参考。

辅受体CD4介导TCR抗原识别的力学机制研究

目的辅受体CD4增强T细胞受体(TCR)/p MHC-II抗原识别的特异性与灵敏度,在激活CD4+T细胞介导的适应性免疫应答过程中具有重要的意义。但是,由于通过传统方法一直无法直接检测到CD4与p MHCII的互作,使得CD4参与并增强TCR抗原识别的分子机制至今都未被解决。方法为了揭示CD4介导TCR抗原识别的分子机制,综合运用单分子生物力学、结构及计算生物学、免疫学等多学科研究手段,从原子到细胞水平多尺度研究生物力如何动态调控CD4参与增强TCR抗原识别这一问题。结果(1)生物力能够动态调控CD4的构象变化,从而帮助CD4与p MHC-II直接结合,并延长TCR与p MHC-II互作的结合时间,从而增强TCR抗原识别能力;(2)生物力能够调控CD4与TCR之间的距离,使得CD4与CD3结合,最终增强TCR的激活。结论本研究解析了TCR/p MHC-II/CD4互作的动力学特性和结构变化规律,揭示了其力学-化学耦合调控CD4介导TCR抗原识别的分子机制。

单细胞TCR测序在肿瘤免疫治疗中的应用

T细胞受体(TCR)序列可作为分辨T细胞特异性的唯一标签,因其多样性的特征,在特定时间内个体循环系统中所有TCR构成的TCR组库可以反映个体的抗肿瘤免疫状态。单细胞TCR测序技术能够在单细胞水平上检测编码TCR双链的基因序列和表达量等信息,包括获得TCR的α链和β链的配对信息,从而在细胞水平上更为准确地探索TCR组库的异质性,还可结合其他技术将TCR序列与基因组、转录组、蛋白质组等多组学信息成套配对,精准展现细胞水平上的多角度信息。因该技术具有高通量、高分辨率的优势而被广泛应用于肿瘤免疫治疗相关研究,为探索TME中T细胞功能、发展T细胞受体工程化T细胞(TCR-T)疗法、预测免疫检查点抑制剂(ICI)疗效等提供重要依据,成为重要的筛选工具,期待该技术的发展使更多肿瘤患者受益。

TCR DNA疫苗在自身免疫病中的研究进展

自身免疫病(AID)是由于机体对自身抗原失去耐受,免疫系统攻击自身组织或细胞引起组织器官或细胞损伤,并出现一定临床表现的疾病。研究证明,在AID的治疗中,致病性T细胞可以被TCR疫苗选择性抑制或杀伤,应用前景很广阔。本文从TCR疫苗的作用机制、相关疾病以及疫苗的安全性等方面,对其在AID领域的研究现状进行简要综述。

基于双模块卷积神经网络的TCR-多肽结合位点预测

TCR(T细胞受体)-多肽结合位点的准确预测对免疫治疗和相关药物发现具有重要意义。文章综合多个文献及数据库整理了一个TCR-多肽结合位点数据集,并引入了一种基于卷积神经网络的预测方法Propep-TCR。该方法综合考虑了输入TCR的序列特征和结构特征,通过采用残基可变滑动窗口方法提取每个目标残基的特征向量。为解决数据集中正负样本不平衡的问题,还采用了改进的损失函数和过采样技术。实验结果表明,Propep-TCR可以成功预测出TCR序列中的潜在结合位点,取得了优于传统算法的性能,其预测准确度达到0.98,AUROC达到了0.95。

Exploring the therapeutic potential of precision T-Cell Receptors (TCRs) in targeting KRAS G12D cancer through in vitro development

Objectives:The Kirsten rat sarcoma virus(KRAS)G12D oncogenic mutation poses a significant challenge in treating solid tumors due to the lack of specific and effective therapeutic interventions.This study aims to explore innovative approaches in T cell receptor(TCR)engineering and characterization to target the KRAS G12D7-16 mutation,providing potential strategies for overcoming this therapeutic challenge.Methods:In this innovative study,we engineered and characterized two T cell receptors(TCRs),KDA11-01 and KDA11-02 with high affinity for the KRAS G12D7-16 mutation.These TCRs were isolated from tumor-infiltrating lymphocytes(TILs)derived from tumor tissues of patients with the KRAS G12D mutation.We assessed their specificity and anti-tumor activity in vitro using various cancer cell lines.Results:KDA11-01 and KDA11-02 demonstrated exceptional specificity for the HLA-A*11:01-restricted KRAS G12D7-16 epitope,significantly inducing IFN-γrelease and eliminating tumor cells without cross-reactivity or alloreactivity.Conclusions:The successful development of KDA11-01 and KDA11-02 introduces a novel and precise TCR-based therapeutic strategy against KRAS G12D mutation,showing potential for significant advancements in cancer immunotherapy.

单细胞TCR测序分析SARS-CoV-2感染患者不同感染时期免疫组库特征

目的:采用单细胞测序的方法,分析SARS-CoV-2感染患者不同感染时期免疫组库特征,探讨其可能的发病机制。方法:利用NCBI数据库获得数据,设置对照组、进展组和康复组。使用RStudio、Origin、Hipliot和Excel软件对单细胞测序数据进行分析。结果:TCR在识别抗原和清除病毒的过程中发挥重要作用,免疫组库特征在不同感染时期有很大差异。细胞的克隆数量均超过26.92%。α链CDR3长度分布集中在14个氨基酸上,而β链集中在15个氨基酸上。V和J区基因片段使用频率不同,TRAV13-1、TRAJ20、TRBV20-1、TRBJ2-1等基因的使用频率差异有统计学意义(P<0.05)。进一步V-J基因组合分析显示,对照组和进展组、对照组和康复组的单链V-J对使用频率差异均有统计学意义(P<0.05)。对照组αβ双链V-J对使用频率最高的是TRAV19-J34-TRBV4-1-J2-1,进展组αβ双链V-J对使用频率最高的是TRAV12-1-J30-TRBV19-1-J2-1,康复组αβ双链V-J对使用频率最高的是TRAV12-2-J52-TRBV7-9-J1-5。结论:本研究分析免疫组库T细胞的全局特征,有助于理解SARSCoV-2感染患者的发病机制,使患者在感染初期及时得到有效的治疗。

Chain Elongation Using Native Soil Inocula:Exceptional n-Caproate Biosynthesis Performance and Microbial Mechanisms

This study demonstrates the feasibility and effectiveness of utilizing native soils as a resource for inocula to produce n-caproate through the chain elongation(CE)platform,offering new insights into anaerobic soil processes.The results reveal that all five of the tested soil types exhibit CE activity when supplied with high concentrations of ethanol and acetate,highlighting the suitability of soil as an ideal source for n-caproate production.Compared with anaerobic sludge and pit mud,the native soil CE system exhibited higher selectivity(60.53%),specificity(82.32%),carbon distribution(60.00%),electron transfer efficiency(165.00%),and conductivity(0.59 ms∙cm^(-1)).Kinetic analysis further confirmed the superiority of soil in terms of a shorter lag time and higher yield.A microbial community analysis indicated a positive correlation between the relative abundances of Pseudomonas,Azotobacter,and Clostridium and n-caproate production.Moreover,metagenomics analysis revealed a higher abundance of functional genes in key microbial species,providing direct insights into the pathways involved in n-caproate formation,including in situ CO_(2)utilization,ethanol oxidation,fatty acid biosynthesis(FAB),and reverse beta-oxidation(RBO).The numerous functions in FAB and RBO are primarily associated with Pseudomonas,Clostridium,Rhodococcus,Stenotrophomonas,and Geobacter,suggesting that these genera may play roles that are involved or associated with the CE process.Overall,this innovative inoculation strategy offers an efficient microbial source for n-caproate production,underscoring the importance of considering CE activity in anaerobic soil microbial ecology and holding potential for significant economic and environmental benefits through soil consortia exploration.

Mathematical Modeling of Possibility Markov Chains by Possibility Theory

Statistical regression models are input-oriented estimation models that account for observation errors. On the other hand, an output-oriented possibility regression model that accounts for system fluctuations is proposed. Furthermore, the possibility Markov chain is proposed, which has a disidentifiable state (posterior) and a nondiscriminable state (prior). In this paper, we first take up the entity efficiency evaluation problem as a case study of the posterior non-discriminable production possibility region and mention Fuzzy DEA with fuzzy constraints. Next, the case study of the ex-ante non-discriminable event setting is discussed. Finally, we introduce the measure of the fuzzy number and the equality relation and attempt to model the possibility Markov chain mathematically. Furthermore, we show that under ergodic conditions, the direct sum state can be decomposed and reintegrated using fuzzy OR logic. We had already constructed the Possibility Markov process based on the indifferent state of this world. In this paper, we try to extend it to the indifferent event in another world. It should be noted that we can obtain the possibility transfer matrix by full use of possibility theory.

Research on Alliance Decision of Dual-Channel Remanufacturing Supply Chain Considering Bidirectional Free-Riding and Cost-Sharing

This study delves into the formation dynamics of alliances within a closed-loop supply chain(CLSC)that encom-passes a manufacturer,a retailer,and an e-commerce platform.It leverages Stackelberg game for this exploration,contrasting the equilibrium outcomes of a non-alliance model with those of three differentiated alliance models.The non-alliance model acts as a crucial benchmark,enabling the evaluation of the motivations for various supply chain entities to engage in alliance formations.Our analysis is centered on identifying the most effective alliance strategies and establishing a coordination within these partnerships.We thoroughly investigate the consequences of diverse alliance behaviors,bidirectional free-riding and cost-sharing,and the resultant effects on the optimal decision-making among supply chain actors.The findings underscore several pivotal insights:(1)The behavior of alliances within the supply chain exerts variable impacts on the optimal pricing and demand of its members.In comparison to the non-alliance(D)model,the manufacturer-retailer(MR)and manufacturer-e-commerce platform(ME)alliances significantly lower both offline and online resale prices for new and remanufactured goods.This adjustment leads to an enhanced demand for products via the MR alliance’s offline outlets and the ME alliance’s online platforms,thereby augmenting the profits for those within the alliance.Conversely,retailer-e-commerce platform(ER)alliance tends to increase the optimal retail price and demand across both online and offline channels.Under specific conditions,alliance behavior can also increase the profits of non-alliance members,and the profits derived through alliance channels also exceed those from non-alliance channels.(2)The prevalence of bidirectional free-riding behavior largely remains constant across different alliance configurations.Across these models,bidirectional free-riding typically elevates the equilibrium prices in offline channel while negatively affecting the equilibrium prices in other channel.(3)The effect of cost-sharing shows relative uniformity across the various alliance models.Across all configurations,cost-sharing tends to reduce the manufacturer’s profits.Nonetheless,alliances initiated by the manufacturer can counteract these negative impacts,providing a strategic pathway to bolster CLSC profitability.

Supply Chain Management Analysis and Design for a Variety of Economic Scenarios, Including Data and System Administration

Due to the rapid progress of information technology, organizations anticipate significant changes in the planning, scheduling, and optimization aspects of operation and supply chain management (SCM) shortly. Two primary types of risk have an impact on supply chain management and design. The first group deals with the difficulties in matching supply and demand, whereas the second group deals with disruptions to regular business operations. The essay offers a theoretical framework that combines the cooperative efforts of risk assessment and mitigation, which are critical for effectively handling potential supply chain interruptions. This content provides insightful viewpoints on the strategic resources and operational structure needed to improve organizational success. We utilized the partial least squares (PLS) method to address the problem of multicollinearity and measurement mistakes in examining cause-and-effect constructs. The statistical method, Least Squares (PLS), used in structural equation modeling, is based on partial variance. The Partial Least Squares (PLS) strategy uses a two-stage estimate procedure to calculate weights, loadings, and route estimations. Initially, several simple and complex regressions were performed with the provided model. The procedure was repeated until a solution was found, resulting in a set of weights used to determine the latent variable scores. In the second step, non-iterative PLS regression yields loadings, path coefficients, mean scores, and location parameters. According to the structural study, implementing Sustainable Supply Chain Management (SSCM) can significantly improve a business’s operational and financial performance. The findings offer a comprehensive understanding of several elements of supply chain management (SSCM), including information systems, organizational configurations, supply chain network architecture (SCND), and supply chain strategy (SCS). The supply chain is essential for effectively moving goods over great distances and encouraging cooperation between parties. Therefore, these connections are established precisely, quickly, and cheaply via a knowledgeable and efficient supply chain. Two key components are necessary for a supply chain (SC) to be successful: efficient collaboration and the smooth integration of information-sharing platforms.

A Tutorial Review of the Solar Power Curve: Regressions, Model Chains, and Their Hybridization and Probabilistic Extensions

Owing to the persisting hype in pushing toward global carbon neutrality,the study scope of atmospheric science is rapidly expanding.Among numerous trending topics,energy meteorology has been attracting the most attention hitherto.One essential skill of solar energy meteorologists is solar power curve modeling,which seeks to map irradiance and auxiliary weather variables to solar power,by statistical and/or physical means.In this regard,this tutorial review aims to deliver a complete overview of those fundamental scientific and engineering principles pertaining to the solar power curve.Solar power curves can be modeled in two primary ways,one of regression and the other of model chain.Both classes of modeling approaches,alongside their hybridization and probabilistic extensions,which allow accuracy improvement and uncertainty quantification,are scrutinized and contrasted thoroughly in this review.

Durable poly(binaphthyl-co-p-terphenyl piperidinium)-based anion exchange membranes with dual side chains

Building well-developed ion-conductive highways is highly desirable for anion exchange membranes(AEMs).Grafting side chain is a highly effective approach for constructing a well-defined phaseseparated morphological structure and forming unblocked ion pathways in AEMs for fast ion transport.Fluorination of side chains can further enhance phase separation due to the superhydrophobic nature of fluorine groups.However,their electronic effect on the alkaline stability of side chains and membranes is rarely reported.Here,fluorine-containing and fluorine-free side chains are introduced into the polyaromatic backbone in proper configuration to investigate the impact of the fluorine terminal group on the stability of the side chains and membrane properties.The poly(binaphthyl-co-p-terphenyl piperidinium)AEM(QBNp TP)has the highest molecular weight and most dimensional stability due to its favorable backbone arrangement among ortho-and meta-terphenyl based AEMs.Importantly,by introducing both a fluorinated piperidinium side chain and a hexane chain into the p-terphenyl-based backbone,the prepared AEM(QBNp TP-QFC)presents an enhanced conductivity(150.6 m S cm^(-1))and a constrained swelling at 80℃.The electronic effect of fluorinated side chains is contemplated by experiments and simulations.The results demonstrate that the presence of strong electro-withdrawing fluorine groups weakens the electronic cloud of adjacent C atoms,increasing OH^(-)attack on the C atom and improving the stability of piperidinium cations.Hence QBNp TP-QFC possesses a robust alkaline stability at 80℃(95.3%conductivity retention after testing in 2 M Na OH for 2160 h).An excellent peak power density of 1.44 W cm^(-2)and a remarkable durability at 80℃(4.5%voltage loss after 100 h)can be observed.

Aligned Ion Conduction Pathway of Polyrotaxane‑Based Electrolyte with Dispersed Hydrophobic Chains for Solid‑State Lithium–Oxygen Batteries

A critical challenge hindering the practical application of lithium–oxygen batteries(LOBs)is the inevitable problems associated with liquid electrolytes,such as evaporation and safety problems.Our study addresses these problems by proposing a modified polyrotaxane(mPR)-based solid polymer electrolyte(SPE)design that simultaneously mitigates solvent-related problems and improves conductivity.mPR-SPE exhibits high ion conductivity(2.8×10^(−3)S cm^(−1)at 25℃)through aligned ion conduction pathways and provides electrode protection ability through hydrophobic chain dispersion.Integrating this mPR-SPE into solid-state LOBs resulted in stable potentials over 300 cycles.In situ Raman spectroscopy reveals the presence of an LiO_(2)intermediate alongside Li_(2)O_(2)during oxygen reactions.Ex situ X-ray diffraction confirm the ability of the SPE to hinder the permeation of oxygen and moisture,as demonstrated by the air permeability tests.The present study suggests that maintaining a low residual solvent while achieving high ionic conductivity is crucial for restricting the sub-reactions of solid-state LOBs.

Global Industrial and Supply Chain Restructuring:Background,Characteristics and Development Trends

The restructuring of global industrial and supply chains is a hot issue in today’s world,and as a result of the once-in-a-century changes and intensified geopolitical and economic competition,the demand for global industrial and supply chain security has risen sharply,with government departments and transnational corporations(TNCs)becoming the most critical stakeholders.Governments carefully calibrate industrial and trade policies from the perspective of national strategies to create industrial chains that support national competitive advantages,and multinational corporations respond to geopolitical risks by prioritizing industrial and supply chain security and sustainability over capital and efficiency.Against the backdrop of changes unseen in a century,decelerating globalization and global economic downturn,the characteristics and future trends of global industrial and supply chain restructuring are worth paying attention to.

Supply Chain Cooperation between China and ASEAN:A Top-Down Approach

The significant relocation of supply chains from China to ASEAN countries did not start with the pandemic.China has been steadily increasing its investment in ASEAN's low valueadded manufacturing sector due to the region's comparative advantage in labor costs.In recent years,changing geopolitics and the restructuring of regional economies in the Asia-Pacific region have accelerated this relocation.

A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

International Supply Chain Cooperation and China’s Role

In recent years,the challenges facing the development of international supply chains have been increasing.Major country competition,COVID pandemic and geopolitical conflicts have impacted the development of international supply chains and continue to have negative effects.Against this background,the international community should cooperate to deal with the risks and challenges.