基于人工免疫系统的客户流失分析

针对目前客户流失分析中的过量抽样和规则不容易理解导致分类算法在实际应用中的效果不明显的问题,提出了一种抗体抗原交叉的规则归纳算法（IRAA)。该算法基于人工免疫思想,结合了Michigan方法模型的规则提取和分类方法,并且在其抗体的克隆选择过程中增加抗体与抗原的交叉。通过与传统分类算法的比较实验表明,IRAA提高了分类准确率,导出了容易理解的规则。     

基于System View的直接序列扩频通信系统仿真分析

为了研究直接序列扩频通信系统（DS—SS）的抗干扰性能,引用了System View仿真平台对其进行仿真研究,通过搭建直接序列扩频通信系统的仿真模型,并采用了三种扩频码的对比仿真,再结合仿真得到的图形推导出如下仿真结论：DS—SS的抗干扰能力远优于普通通信系统;还证明扩频码的选取对系统性能有一定的影响,因此在构建扩频系统时选取扩频码相当关键。     

基于生物免疫原理的抽油机井故障诊断研究

基于生物免疫原理提出了一种故障诊断方法。将故障作为抗原,首先应用反面选择算法产生初始抗体,然后基于生物免疫系统的克隆、变异、克隆选择、自我调节和免疫记忆等机理对初始抗体进行进化,产生能够表示和识别抗原即故障的记忆抗体,并以抽油机井为对象进行了仿真研究,结果表明,该方法能够取得很好的诊断效果。     

CD317 在肿瘤发生发展及治疗方面的研究进展

CD317(Tetherin、BST-2 或 HM1.24)是由 BST-2 基因编码的一个结构独特的 II 型跨膜糖蛋白, 在多种人体组织中组成型表达,也可被干扰素等细胞因子诱导。近年来,越来越多的研究表明,CD317 在多种肿瘤中表达上调,广泛参与肿瘤细胞增殖、迁移以及凋亡抵抗等生物学过程从而促进肿瘤的发 生发展,是肿瘤治疗的潜在靶点。该文聚焦肿瘤领域,总结 CD317 的表达模式、作用机制以及靶向策 略的相关进展,为肿瘤发病理论研究、治疗策略开发提供新的思考和方向。     

人工免疫网络模型的数据特征提取性能评价技术

人工免疫网络模型(aiNET)是一种用于提取数据特征的免疫智能信息处理模型,其核心是用小规模的记忆网络数据(抗体)映射输入数据(抗原),达到减少数据冗余的数据压缩和特征提取效果.由于aiNET模型中存在免疫克隆选择机制,记忆网络(抗体)不是输入抗原数据集的实际子集,同时,由于它没有实现维数简化,导致目前广泛使用的数据特征性能评价方法无法应用.本文给出了一种基于复杂网络的拓扑结构分析技术,利用描述网络结构稳定性的网络社区结构,通过对aiNET压缩前后的抗体和抗原网络社区的对比,达到对人工免疫网络(aiNET)的特征提取性能评价的目的.     

基于离散增量的免疫分类器在模式生物基因中应用*

首先把线虫、酵母和拟南芥三种模式生物基因组中的内含子、外显子和基因间序列归为三类, 滑动统计这些序列中64种三核苷的重复出现次数作为离散源的状态参数,这样就得到了这些序列的64维特征值,并将这些数据分成训练样本集和测试样本集。根据免疫进化网络理论,用离散增量作为抗体—抗原间的亲和度函数,把训练集看成抗原,不断刺激免疫网络向识别抗原的方向进化,构造了一个基于离散增量的免疫分类器。通过测试表明,该分类器性能优良,分类预测准确率达到了85％以上。     

基于免疫危险理论的网络安全态势评估

为实时定量评估网络安全态势,提出了一种基于免疫危险理论的网络安全态势评估方法.通过研究免疫运行机制,定义了网络安全问题中的抗原、抗体和免疫细胞,描述了危险信号的判断规则,准确识别出了抗原.在分析免疫应答机制和免疫平衡机制中抗体浓度变化原因的基础上,给出了抗体浓度的计算方法.最后,结合抗体浓度与危险程度的关系,建立了基于抗体浓度的危险感知模型以实时定量评估网络安全态势.仿真实验表明,所提方法计算出的抗体浓度准确地反映了系统面临的危险程度,能够为网络管理提供有效的决策支持.     

质量放大石英晶体微天平传感器用于人血清中IgM的测定

本文报道了一种质量放大石英晶体微天平免疫分析。抗体包被的石英晶振在待测怕溶液中孵育时,加入相应的抗体,待测抗原与抗体以复合物的形式结合到晶振上。这种抗原抗体多分子层与通常抗原单仓层结合相比克服好单分子层结合的抗原分子数较少的限制。应用该方法测定了人血清中的免疫球蛋白,在１０．２￣１６１．０μｇ／ｍｌ范围,响应频率与浓度有较好的线性关系,比较常规的石英晶体微天平免疫传感器方法,灵敏度提高了约３倍。     

药物-靶点亲和力预测的全局特征提取策略

为有效解决药物靶点亲和力预测中单模型提取特征种类受限问题,结合深度学习混合模型,提出一种深度并行全局特征提取策略.利用卷积神经网络(CNN)和特征存储融合层构建局部特征提取器,实现药物靶点序列局部特征的多层次提取、存储与压缩;利用卷积神经网络(CNN)和双向长短时记忆(BiLSTM)神经网络的串行混合模型构建上下文特征...     

基于胸腺肽调节机制的多机器人免疫任务分配

针对多机器人系统协作中的任务分配问题,提出一种基于胸腺肽的免疫任务分配算法(TPITAA).借鉴独特型免疫网络假设,将机器人作为B细胞,机器人行为作为抗体,机器人任务作为抗原,通过抗原与抗体间的激励和抑制机理构建免疫分配模型.为进一步提高分配效率,根据胸腺肽的免疫调节机理,定义基于机器人运动方向的胸腺肽反馈函数,实现免疫分配中的抗体激励水平及浓度自调节.仿真实验表明,新算法能实现任务的自动分配,减少任务完成时间,提高系统执行效率,较好地解决多机器人系统中的协作搬运问题.     

Construction of human monoclonal single-chain Fv antibodies against small-cell lung cancer by phage display libraries derived from cell-immunized SCID mice engrafted with human peripheral blood lymphocytes

In this study, we describe a phage display strategy to obtain human monoclonal single-chain Fv (scFv) antibodies binding target cancer cell surface proteins. By developing a cancer cell immunization protocol for SCID mice engrafted with human peripheral blood lymphocytes in combination with an antibody phage display method, we have isolated phage antibodies binding small-cell lung cancer cell line H889 by subtractive selection. One of the isolated scFv antibodies, 12EAb, recognized the E2 component of pyruvate dehydrogenase complex (PDC-E2) by immunoprecipitation according to MALDI-TOF MS analysis. Furthermore, we have confirmed the plasma membrane localization of PDC-E2 in small-cell lung cancer cells by immunocytochemistry and cell surface protein biotinylation, although PDC-E2 is usually located in the mitochondrial matrix. These results, including unique localization of identified antigens, were obtained by proteomic approaches. The present methods can be applied to generate human monoclonal scFv antibodies against tumor cells and to identify new molecular targets for immunotherapy and markers for diagnosis.     

An inverse docking approach for identifying new potential anti-cancer targets

Inverse docking is a relatively new technique that has been used to identify potential receptor targets of small molecules. Our docking software package MDock is well suited for such an application as it is both computationally efficient, yet simultaneously shows adequate results in binding affinity predictions and enrichment tests. As a validation study, we present the first stage results of an inverse-docking study which seeks to identify potential direct targets of PRIMA-1. PRIMA-1 is well known for its ability to restore mutant p53's tumor suppressor function, leading to apoptosis in several types of cancer cells. For this reason, we believe that potential direct targets of PRIMA-1 identified in silico should be experimentally screened for their ability to inhibit cancer cell growth. The highest-ranked human protein of our PRIMA-1 docking results is oxidosqualene cyclase (OSC), which is part of the cholesterol synthetic pathway. The results of two followup experiments which treat OSC as a possible anti-cancer target are promising. We show that both PRIMA-1 and Ro 48-8071, a known potent OSC inhibitor, significantly reduce the viability of BT-474 and T47-D breast cancer cells relative to normal mammary cells. In addition, like PRIMA-1, we find that Ro 48-8071 results in increased binding of p53 to DNA in BT-474 cells (which express mutant p53). For the first time, Ro 48-8071 is shown as a potent agent in killing human breast cancer cells. The potential of OSC as a new target for developing anticancer therapies is worth further investigation.     

Structural insights into a high affinity nanobody:antigen complex by homology modelling

Porphyromonas gingivalis is a major periodontitis-causing pathogens. P. gingivalis secrete a cysteine protease termed RgpB, which is specific for Arg-Xaa bonds in substrates. Recently, a nanobody-based assay was used to demonstrate that RgpB could represent a novel diagnostic target, thereby simplifying. P. gingivalis detection. The nanobody, VHH7, had a high binding affinity and was specific for RgpB, when tested towards the highly identical RgpA.In this study a homology model of VHH7 was build. The complementarity determining regions (CDR) comprising the paratope residues responsible for RgpB binding were identified and used as input to the docking. Furthermore, residues likely involved in the RgpB epitope was identified based upon RgpB:RgpA alignment and analysis of residue surface accessibility. CDR residues and putitative RgpB epitope residues were used as input to an information-driven flexible docking approach using the HADDOCK server. Analysis of the VHH7:RgpB model demonstrated that the epitope was found in the immunoglobulin-like domain and residue pairs located at the molecular paratope:epitope interface important for complex stability was identified.Collectively, the VHH7 homology model and VHH7:RgpB docking supplies knowledge of the residues involved in the high affinity interaction. This information could prove valuable in the design of an antibody-drug conjugate for specific RgpB targeting.     

Multidimensional protein identification technology analysis highlights mitoxantrone‐induced expression modulations in the primary prostate cancer cell proteome

Chemotherapeutic agents as they are used today have limited effectiveness against prostate cancer, but may potentially be used in new combinations with more efficacious results. Mitoxantrone, used for palliation of prostate cancer, has recently been found by our group to improve the susceptibility of primary prostate cancer cells to killing through the Fas‐mediated death pathway. Here we used a shotgun proteomics approach to first profile the entire prostate cancer proteome and then identify specific factors involved in this mitoxantrone response. Peptides derived from primary prostate cancer cells treated with or without 100 nM mitoxantrone were analyzed by multidimensional protein identification technology (MudPIT). Strict limits and data filtering hierarchies were applied to identify proteins with high confidence. We identified 1498 proteins belonging to the prostate cancer proteome, 83 of which were significantly upregulated and 27 of which were markedly downregulated following mitoxantrone treatment. These proteins perform diverse functions, including ceramide production, tumour suppression, and oxidative reduction. Detailed proteomic analyses of prostate cancer cells and their response to mitoxantrone will further our understanding of its mechanisms of action. Identification of proteins influenced by treatment with mitoxantrone or other compounds may lead to the development of more effective drug combinations against prostate cancer.     

SPR生物传感器在急性白血病髓系抗原CD33检测中的应用

采用自组装膜技术,在传感芯片表面修饰抗CD33单克隆抗体,利用自行构建的SPR生物传感器检测人骨髓血液中急性白血病髓系抗原CD33的表达.实验结果表明SPR生物传感器对AML患者骨髓血液的响应值远远大于对健康人骨髓血液的响应值.与传统的流式细胞术(FCM)方法相比,SPR方法具有能快速给出定量分析结果,操作简单等优点....     

Love wave immunosensor for antibody recognition using an innovative semicarbazide surface functionalization

A new novel and easy functionalization route of a Love wave acoustic sensor based on the α-oxo-semicarbazone bond is described. The interest is firstly to observe in real-time the immobilization of the peptide on the semicarbazide surface of the transducer and secondly to monitor the specific binding of antibodies. Site-specific immobilization of antigenic-peptides as well as binding of murine monoclonal antibodies has been shown by gravimetric measurements. A tetramethylrhodamine-labeled goat antibody directed against murine antibodies was used to further characterize the biomolecular interactions by fluorescence microscopy and surface analysis (by AFM). Our data show that the gravimetric monitoring developed from the prepared Love wave immunosensor is a promising alternative route to characterize chemical and biomolecular events.     

Automation of cytokine flow cytometry assays

Cytokine flow cytometry (CFC) is a multiparameter assay of antigen-specific T cell function, potentially useful in the monitoring of experimental vaccines and progression of infectious diseases and cancer. Automation of CFC assays would greatly facilitate their use in clinical trials and involves several components. We describe here the migration of these assays to 96-well plates, the use of sample-handling robotics, and the use of lyophilized antigen and antibody plates to help automate CFC. Together, these elements can produce an integrated system capable of walkaway automation of an entire assay, resulting in the reproducible processing of potentially hundreds of samples per day. Implementation of such systems has begun to be undertaken by our group and others.     

乙肝肝硬化的突变演化研究

为了研究乙型肝炎肝硬化的演化规律,从而制定出有针对性的治疗方案,提出了一种基于突变理论的乙型肝炎肝硬化演化模型。研究了乙肝病毒免疫模型中抗原、抗体、和B细胞作用机理,以及免疫模型中的突变特性。利用肝脏边缘粗糙度作为模型状态变量,抗原和抗体作为模型控制变量,建立了尖点型突变模型,并根据临床数据对模型参数进行了拟合。通过历史临床数据验证了模型的有效性。     

Using evolvable genetic cellular automata to model breast cancer

Cancer is an evolutionary process. Mutated cells undergo selection for abnormal growth and survival creating a tumor. We model this process with cellular automata that use a simplified genetic regulatory network simulation to control cell behavior and predict cancer etiology. Our genetic model gives us the ability to relate genetic mutation to cancerous outcomes. The simulation uses known histological morphology, cell types, and stochastic behavior to specifically model ductal carcinoma in situ (DCIS), a common form of non-invasive breast cancer. Using this model we examine the effects of hereditary predisposition on DCIS incidence and aggressiveness. Results show that we are able to reproduce in vivo pathological features to hereditary forms of breast cancer: earlier incidence and increased aggressiveness. We also show that a contributing factor to the different pathology of hereditary breast cancer results from the ability of progenitor cells to pass cancerous mutations on to offspring.     

Phosphoproteomics: A possible route to novel biomarkers of breast cancer

Proteomics is rapidly transforming the way that cancer and other pathologies are investigated. The ability to identify hundreds of proteins and to compare their abundance in different clinical samples presents a unique opportunity for direct identification of novel disease markers. Furthermore, recent advances allow us to analyse and compare PTMs. This gives an additional dimension for defining a new class of protein biomarker based not only on abundance and expression but also on the occurrence of covalent modifications specific to a disease state or therapy response. Such modifications are often a consequence of the activation/inactivation of a particular disease related pathway. In this review we evaluate the available information on breast cancer related protein-phosphorylation events, illustrating the rationale for investigating this PTM as a target for breast cancer research with eventual clinical relevance. We present a critical survey of the published experimental strategies to study protein phosphorylation on a system wide scale and highlight recent specific advances in breast cancer phosphoproteomics. Finally we discuss the feasibility of establishing novel biomarkers for breast cancer based on the detection of patterns of specific protein phosphorylation events.