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。

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。

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。

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.

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.

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。

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.

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.

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.

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.

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.

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.

Global Supply Chain Governance and the Future of Globalization

Over the past 30 years or so,globalization has been underpinned by ever-evolving supply chains.The heightened attention to supply chain issues in recent years has been due to a series of global events in the world that have had a significant and profound impact.Factors such as the COVID pandemic,the Ukraine Crisis and increased geopolitical tensions have led to supply chain crises globally or regionally.

Production Chain Length and PPI-CPI Divergence:Analysis Based on the Global Input-output Price Model

Productivity and international energy price shocks are reflected in PPI and CPI via industrial chains.China’s in-depth participation into the global value chains has increasingly lengthened its industrial production chains.The question is how the changing length of production chains will affect CPI and PPI,as well as CPI-PPI correlation?By constructing a global input-output price model,this paper offers a theoretical discussion on the impact of production chain length on the CPI-PPI divergence.Our findings suggest that the price shock of international bulk commodities has a greater impact on China’s PPI than that on CPI.The effects on both China’s PPI and CPI estimated by using the single-country input-output model are higher than the results estimated with the global input-output model.However,the difference between CPI and PPI variations estimated with the global input-output model is greater than the result estimated with the single-country input-output model,which supports the view that the lengthening of production chains,especially international production chains,leads to a divergence between CPI and PPI.Empirical results based on cross-national panel data also suggest that the lengthening of production chains has reduced the CPI-PPI correlation for countries,i.e.the lengthening of production chains has increased the PPI-CPI divergence.That is to say,policymakers should target not just CPI in maintaining price stability,but instead focus on the stability of both PPI and CPI.Efforts can be made to proactively adjust the price index system,and formulate the industrial chain price index.

Hepatomegaly and jaundice as the presenting symptoms of systemic light-chain amyloidosis: A case report

BACKGROUND Light chain(AL)amyloidosis is a plasma cell dyscrasia characterized by the pathologic production and extracellular tissue deposition of fibrillar proteins derived from immunoglobulin AL fragments secreted by a clone of plasma cells,which leads to progressive dysfunction of the affected organs.The two most commonly affected organs are the heart and kidneys,and liver is rarely the dominant affected organ with only 3.9%of cases,making them prone to misdia-gnosis and missed diagnosis.CASE SUMMARY A 65-year-old woman was admitted with a 3-mo history of progressive jaundice and marked hepatomegaly.Initially,based on enhanced computed tomography scan and angiography,Budd-Chiari syndrome was considered and balloon dilatation of significant hepatic vein stenoses was performed.However,addi-tional diagnostic procedures,including liver biopsy and bone marrow-exami-nation,revealed immunoglobulin kapa AL amyloidosis with extensive liver involvement and hepatic vascular compression.The disease course was progre-ssive and fatal,and the patient eventually died 5 mo after initial presentation of symptoms.CONCLUSION AL amyloidosis with isolated liver involvement is very rare,and can be easily misdiagnosed as a vascular disease.

Alkyl chain modulation of asymmetric hexacyclic fused acceptor synergistically with wide bandgap third component for high efficiency ternary organic solar cells

Herein,two asymmetric hexacyclic fused small molecule acceptors(SMAs),namely BP4F-HU and BP4F-UU,were synthesized.The elongated outside chains in the BP4F-UU molecule played a crucial role in optimizing the morphology of blend film,thereby improving charge mobility and reducing energy loss within the corresponding film.Notably,the PM6:BP4F-UU device exhibited a higher open-circuit voltage(V_(oc))of 0.878 V compared to the PM6:BP4F-HU device with a V_(oc)of 0.863 V.Further,a new wide bandgap SMA named BTP-TA was designed and synthesized as the third component to the PM6:BP4F-UU host binary devices,which showed an ideal complementary absorption spectrum in PM6:BP4F-UU system.In addition,BTP-TA can achieve efficient intermolecular energy transfer to BP4F-UU by fluorescence resonance energy transfer(FRET)pathway,due to the good overlap between the photoluminescence(PL)spectrum of BTP-TA and the absorption region of BP4F-UU.Consequently,ternary devices with 15wt%BTP-TA exhibits broader photon utilization,optimal blend morphology,and reduced charge recombination compared to the corresponding binary devices.Consequently,PM6:BP4F-UU:BTP-TA ternary device achieved an optimal power conversion efficiency(PCE)of 17.83%with simultaneously increased V_(oc)of 0.905 V,short-circuit current density(J_(sc))of 26.14 mA/cm^(2),and fill factor(FF)of 75.38%.

Establishment of Regional Green Innovation Value Chains:A Case Study on Tackling the Impeded Diffusion of Green Products

The green innovation value chain is a key step in transforming green,innovative scientific,and technological achievements into productive forces.The establishment of green innovation value chains based on value distribution rather than technical conditions can effectively overcome the common bottleneck faced by different nations during their green innovation endeavors,namely,the substitution of conventional products with green alternatives.This study investigates the impeded diffusion of green products and their underlying causes,analyzes the internal structure and mechanism of the green innovation value chain,and explores the establishment of regional green innovation value chains and the models available for value chain upgrading.

Service Function Chain Deployment Algorithm Based on Multi-Agent Deep Reinforcement Learning

Aiming at the rapid growth of network services,which leads to the problems of long service request processing time and high deployment cost in the deployment of network function virtualization service function chain(SFC)under 5G networks,this paper proposes a multi-agent deep deterministic policy gradient optimization algorithm for SFC deployment(MADDPG-SD).Initially,an optimization model is devised to enhance the request acceptance rate,minimizing the latency and deploying the cost SFC is constructed for the network resource-constrained case.Subsequently,we model the dynamic problem as a Markov decision process(MDP),facilitating adaptation to the evolving states of network resources.Finally,by allocating SFCs to different agents and adopting a collaborative deployment strategy,each agent aims to maximize the request acceptance rate or minimize latency and costs.These agents learn strategies from historical data of virtual network functions in SFCs to guide server node selection,and achieve approximately optimal SFC deployment strategies through a cooperative framework of centralized training and distributed execution.Experimental simulation results indicate that the proposed method,while simultaneously meeting performance requirements and resource capacity constraints,has effectively increased the acceptance rate of requests compared to the comparative algorithms,reducing the end-to-end latency by 4.942%and the deployment cost by 8.045%.