Identification of B and T cell epitope based peptide vaccine from IGF-1 receptor in breast cancer

The insulin-like growth factor-1 receptor (IGF-1R) plays a key role in proliferation, growth, differentiation, and development of several human malignancies including breast and pancreatic adenocarcinoma. IGF-1R targeted immunotherapeutic approaches are particularly attractive, as they may potentially elicit even stronger antitumor responses than traditional targeted approaches. Cancer peptide vaccines can produce immunologic responses against cancer cells by triggering helper T cell (Th) or cytotoxic T cells (CTL) in association with Major Histocompatibility Complex (MHC) class I or II molecules on the cell surface of antigen presenting cells. In our previous study, we set a technique based on molecular docking in order to find the best MHC class I and II binder peptides using GOLD. In the present work, molecular docking analyses on a library consisting of 30 peptides mimicking discontinuous epitopes from IGF-1R extracellular domain identified peptides 249 and 86, as the best MHC binder peptides to both MHC class I and II molecules. The receptors most often targeted by peptide 249 are HLA-DR4, HLA-DR3 and HLA-DR2 and those most often targeted by peptide 86 are HLA-DR4, HLA-DP2 and HLA-DR3. These findings, based on bioinformatics analyses, can be conducted in further experimental analyses in cancer therapy and vaccine design.     

Automated classification and analysis of the calcium response of single T lymphocytes using a neural network approach

The gene activities in T lymphocytes that regulate immune responses are influenced by Ca/sup 2+/ ([Ca/sup 2+/]/sub i/). The intracellular calcium signals are highly heterogeneous and vitally important in determining the immune outcome. The signals in individual cells can be measured using fluorescence microscopy but to group the cells into classes with similar signal kinetics is currently laborious. Here, we demonstrate a method for the automated classification of the responses into four categories formerly identified by an expert's inspection. This method comprises characterising the response by a second-order model, performing frequency analysis, and using derived features as inputs to two multilayer perceptron neural networks (NNs). We compare the algorithm's performance on an example data set against the human classification: it was found to classify identically more than 70% of the data, despite small sample sizes in two categories and significant overlap between the other two classes. The group characterized by an oscillating signal showed the presence of a number of frequencies, which may be important in determining gene activation. A classification threshold enables the automatic identification of patterns with a low-classification certainty. Future refinement of the algorithm may allow the identification of more classes, which may be important in different immune responses associated with disease.     

Quantitative Physiological Assessment of Stress Via Altered Immune Functioning Following Interaction With Differing Automotive Interface Technologies

Technology can enhance or diminish a user's psycho-physiological stress level; the ability to quantify these responses can help evaluate and refine design. The capability of drivers to accomplish basic tasks utilizing differing sensory modalities while maintaining lane discipline within a computer-simulated environment was assessed. Fifteen healthy subjects provided capillary blood samples before and after using three human–machine interface designs—touch-screen, voice control, and multimodal. Using a chemiluminescent technique termed Leukocyte Coping Capacity, the ability of leukocytes to produce reactive oxygen species in vitro was assessed. Significant poststressor changes in leukocyte activity of varying magnitude were observed following the use of all interfaces; with the multimodal interface provoking the most pronounced response and voice control the least. Although still requiring further research, the results support the proposition for using immune responsiveness as a means for quantifying psychological stress during assessment of ergonomic design and psycho-physiological and social interaction.     

Label-free continuous lateral magneto-dielectrophoretic microseparators for highly efficient enrichment of circulating nucleated cells from peripheral blood

We have developed a continuous lateral magneto-dielectrophoretic (MAP-DEP) microseparator for the highly efficient enrichment of circulating nucleated cells from peripheral blood, based on native magnetic and dielectric properties of blood cells. Lateral magnetophoretic (MAP) force is achieved using a high-gradient magnetic field, caused by a ferromagnetic wire array inlaid on the bottom glass substrate. Lateral dielectrophoretic (DEP) force is achieved using a planar interdigitated electrode array, patterned on the top glass substrate. Red blood cells in peripheral blood are primarily driven by the lateral MAP force, while white blood cells are primarily forced by the lateral DEP force, the direction of which is opposite to that of the lateral MAP force. These lateral MAP and DEP forces are produced evenly on the whole area of the microchannel, thereby achieving highly efficient enrichment. The experimental results showed that the lateral MAP-DEP microseparator can continuously enrich circulating nucleated cells by 20,000-fold from peripheral blood simply by using an external magnetic flux of 0.3 T and a 2-MHz sinusoidal voltage of 4 Vp-p. Additionally, by using the intrinsic magnetic and dielectric properties of blood cells, we eliminated the need for laborious sample preparation procedures before and after enrichment, and also reduced the cost.     

人类免疫系统仿真与建模研究综述

人类免疫系统是具有自组织、自适应性质的复杂系统.它由许多不同的分子、细胞、组织和器官组成.免疫细胞互相合作涌现出智能性.人工免疫系统是人工智能研究领域,计算模型和概念模型是计算机免疫学中仿真免疫系统的两类主要的模型.文中介绍了位符串模型、遗传算法、多主体等免疫系统的仿真方法,特别讨论了微分方程和细胞自动机两类代表模型的缺点和优点,提出免疫系统仿真方法可以用于研究免疫系统智能性,对设计新的人工免疫系统方法很有意义.     

血细胞动画素材的创建

人体血细胞的数字化在许多领域都有所应用,在动画制作中“血细胞”成为常用的素材。文章介绍了采用通用的3DsMax创作平台。实现血细胞从模型、材质、动画到渲染输出整个素材创建的过程和方法,通过实例验证了这一方法能够有效提高项目制作的速度。     

Automated White Blood Cell Disease Recognition Using Lightweight Deep Learning

White blood cells (WBC) are immune system cells, which is why they are also known as immune cells. They protect the human body from a variety of dangerous diseases and outside invaders. The majority of WBCs come from red bone marrow, although some come from other important organs in the body. Because manual diagnosis of blood disorders is difficult, it is necessary to design a computerized technique. Researchers have introduced various automated strategies in recent years, but they still face several obstacles, such as imbalanced datasets, incorrect feature selection, and incorrect deep model selection. We proposed an automated deep learning approach for classifying white blood disorders in this paper. The data augmentation approach is initially used to increase the size of a dataset. Then, a Darknet-53 pre-trained deep learning model is used and fine-tuned according to the nature of the chosen dataset. On the fine-tuned model, transfer learning is used, and features engineering is done on the global average pooling layer. The retrieved characteristics are subsequently improved with a specified number of iterations using a hybrid reformed binary grey wolf optimization technique. Following that, machine learning classifiers are used to classify the selected best features for final classification. The experiment was carried out using a dataset of increased blood diseases imaging and resulted in an improved accuracy of over 99%.     

Interactive component extraction from fEEG, fNIRS and peripheral biosignals for affective brain-machine interfacing paradigms

This paper investigates whether some well understood principles of human behavioral analysis can be used to design novel paradigms for affective brain-computer/machine interfaces. This is achieved by using the visual, audio, and audiovisual stimuli representing human emotions. The analysis of brain responses to such stimuli involves several challenges related to the conditioning of brain electrical responses, extraction of the responses to stimuli and mutual information between the several physiological recording modalities used. This is achieved in the time-frequency domain, using multichannel empirical mode decomposition (EMD), which proves very accurate in the joint analysis of neurophysiological and peripheral body signals. Our results indicate the usefulness of such an approach and confirm the possibility of using affective brain-computer/machine interfaces.     

基于动态疫苗提取的免疫遗传算法求解TSP问题

针对免疫系统能通过注射疫苗来达到快速识别抗原的特性,对免疫遗传算法进行改进.根据抗体的优劣来从中提取不同长度的疫苗,并在此基础上提出一种基于动态疫苗提取的免疫遗传算法(IGAB),将其用于TSP问题的求解中,实验结果表明,IGAB能够抑制遗传算法在迭代过程中出现的退化现象,提高算法的收敛速度.     

结合先天和适应性免疫的蠕虫检测免疫模型

现有的蠕虫检测方法大多通过关闭不安全的端口,切断感染主机与未感染主机之间通信等方法延缓蠕虫传播而达到将损害减少到最低程度的目的.实际上在实施这些方法时往往有许多障碍需要克服,其中的最大障碍就是存在错误检测率高的问题.现将免疫危险理论中的DCs(树突状细胞,Dendritic Cells)-T细胞协同机制用于蠕虫检测,其中DCs属于先天免疫系统细胞,T细胞属于适应性免疫系统细胞.本模型将蠕虫进程触发的系统调用序列当作抗原,将感染蠕虫导致的主机和网络异常当作危险信号.在该模型中,DCs负责危险信号的收集检测并提呈与该危险信号关联的抗原给T细胞检测器进行抗原结构检测.理论分析说明,这样的双重检测方法可以降低伪肯定率和伪否定率,并且记忆T细胞检测器的采用能使系统对类似蠕虫的再次感染反应更加迅速.     

Intelligent agent based artificial immune system for computer security—a review

Since its introduction in the 1990s the internet has proliferated in the life of human kind in many numbers of ways. The two by-products of the internet are intelligent agents and intrusions which are far away from each other in the intention of their creation while similar in their characteristics. With automated code roaming the network intruding the users on one side as worms, viruses, and Trojans and autonomous agents tending to help the users on the other side, the internet has given great research challenges to the computer scientists. The greatest challenge of the internet is intrusion, which has increased and never decreased. There are various security systems for the internet. As the Human Immune System protects human body from external attacks, these security systems tend to protect the internet from intruders. Thus the internet security systems are comparable with human immune systems in which autonomous cells move throughout the body to protect it while learning to tackle new threats and keeping them in their memory for the future. These properties are comparable with that of autonomous agents in the internet. Thus intelligent agent technology combined with ideas from human immune system is a great area of research which is still in its developing phase. In this paper, state of the art of security systems which use both these technologies of intelligent agents and artificial immune system i.e., Agent Based Artificial Immune System (ABAIS) for security are reviewed, paying special attention to features of human immune system used in the system, the role of the agents in the ABAIS and the security mechanisms provided against intrusions.     

An automatic algorithm for the detection of Trypanosoma cruzi parasites in blood sample images

Chagas disease is a tropical parasitic disease caused by the flagellate protozoan Trypanosoma cruzi (T. cruzi) and currently affecting large portions of the Americas. One of the standard laboratory methods to determine the presence of the parasite is by direct visualization in blood smears stained with some colorant. This method is time-consuming, requires trained microscopists and is prone to human mistakes. In this article we propose a novel algorithm for the automatic detection of T. cruzi parasites, in microscope digital images obtained from peripheral blood smears treated with Wright's stain. Our algorithm achieved a sensitivity of 0.98 and specificity of 0.85 when evaluated against a dataset of 120 test images. Experimental results show the versatility of the method for parasitemia determination.     

Information fusion for anomaly detection with the dendritic cell algorithm

Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system, providing the initial detection of pathogenic invaders. Research into this family of cells has revealed that they perform information fusion which directs immune responses. We have derived a dendritic cell algorithm based on the functionality of these cells, by modelling the biological signals and differentiation pathways to build a control mechanism for an artificial immune system. We present algorithmic details in addition to experimental results, when the algorithm was applied to anomaly detection for the detection of port scans. The results show the dendritic cell algorithm is successful at detecting port scans.     

Noise- and compression-robust biological features for texture classification

Texture classification is an important aspect of many digital image processing applications such as surface inspection, content-based image retrieval, and biomedical image analysis. However, noise and compression artifacts in images cause problems for most texture analysis methods. This paper proposes the use of features based on the human visual system for texture classification using a semisupervised, hierarchical approach. The texture feature consists of responses of cells which are found in the visual cortex of higher primates. Classification experiments on different texture libraries indicate that the proposed features obtain a very high classification near 97%. In contrast to other well-established texture analysis methods, the experiments indicate that the proposed features are more robust to various levels of speckle and Gaussian noise. Furthermore, we show that the classification rate of the textures using the presented biologically inspired features is hardly affected by image compression techniques.     

Toward the Proteome of the Human Peripheral Blood Eosinophil

Eosinophils (EOSs) are granular leukocytes that have significant roles in many inflammatory and immunoregulatory responses, especially asthma and allergic diseases. We have undertaken a fairly comprehensive proteomic analysis of purified peripheral blood EOSs from normal human donors primarily employing 2‐DE with protein spot identification by MALDI‐MS. Protein subfractionation methods employed included IEF (Zoom^® Fractionator) and subcellular fractionation using differential protein solubilization. We have identified 3141 proteins, which had Mascot expectation scores of 10^?3 or less. Of these 426 were unique and non‐redundant of which 231 were novel proteins not previously reported to occur in EOSs. Ingenuity Pathway Analysis showed that some 70% of the non‐redundant proteins could be subdivided into categories that are clearly related to currently known EOS biological activities. Cytoskeletal and associated proteins predominated among the proteins identified. Extensive protein posttranslational modifications were evident, many of which have not been previously reported that reflected the dynamic character of the EOS. This data set of eosinophilic proteins will prove valuable in comparative studies of disease versus normal states and for studies of gender differences and polymorphic variation among individuals.     

Exercise thermoregulation and hyperprolactinaemia

The anterior pituitary hormone prolactin (PRL), measured in the peripheral blood circulation, reflects alterations in central brain 5-hydroxytryptamine (serotonin) and dopaminergic activity and is used as a marker of 'central fatigue' during active heat exposure. Significant correlations have consistently been found between PRL and core temperature (T(CORE)) during prolonged exercise. There has been no investigation into the relationship between PRL and other key thermoregulatory variables during exercise, such as weighted mean skin (T(SK)) and mean body temperature (T(B)), heat storage (HS), thermal gradient (T(GRAD)), heart rate (HR) and skin blood flow (cutaneous vascular conductance, CVC). Therefore, the aim of this study was to ascertain if a significant relationship exists between PRL and these thermoregulatory variables during prolonged exercise. Nine active male subjects conducted three trials of approximately 60% VO(2peak) at 70-80 rpm for 45 min on a semi-recumbent cycle ergometer at three different ambient temperatures [6 degrees C (Cold), 18 degrees C (Neutral) and 30 degrees C (Hot)] to elicit varying levels of thermoregulatory stress during exercise. Significant differences existed in T(SK), T(B), HS, T(GRAD) and CVC across the environmental conditions (p < 0.001). Core temperature (T(CORE)), HR and PRL were significantly elevated only in Hot (p < 0.05). Moderate correlations were found for T(CORE), T(SK), T(B), HS, T(GRAD), HR and CVC with post-exercise PRL (rho = 0.358-0.749). The end-of-exercise <38.0 degrees C T(CORE) responses were not (rho = -0.129, p > 0.05) but the >38.0 degrees C T(CORE) responses were (rho = 0.845, p < 0.001) significantly related to their corresponding PRL responses. The significant relationships between PRL release and T(SK), T(B), HS, T(GRAD), HR and CVC have extended previous research on T(CORE) and PRL release and indicate an association between these thermoregulatory variables, as well as T(CORE), and serotonergic/dopaminergic activity during prolonged exercise.     

免疫原理在入侵检测中的应用

简要介绍入侵检测系统和人体免疫系统的基本概念.基于人体免疫系统的3个进化阶段,将两者有机结合,提出了一个免疫网络入侵检测系统模型,对模型主要功能模块进行了描述.     

一种新的求解最小权三角划分的免疫算法

提出了一种基于自适应免疫遗传算法的求解最小权三角划分(MWT)问题的方案,通过自适应地调整疫苗库的进化和有选择地注射疫苗,提高了新算法的收敛速度和全局搜索能力,结合具体的MWT问题,给出了疫苗更新与注射算子构造的具体方案。仿真实验表明,新算法能产生比免疫算法更好的划分效果,尤其适合大规模点集,有较大的实用价值。     

应用免疫原理的无线传感器网络入侵检测系统

为了提高无线传感器网络的安全性,将生物免疫原理应用到无线传感器网络安全问题中,设计了一个基于生物免疫原理的轻量级入侵检测系统。该系统主要完成检测器的生成和抗原检测,在检测器的生成过程中,通过离散r-连续位匹配算法简化否定选择算法;通过提取记忆免疫细胞疫苗对抗体进行接种,加快免疫算法的收敛性;通过聚类算法对记忆免疫细胞集合进行分类优化,提高了抗体的多样性。仿真实验表明,系统具有较好的检测率和较低的能耗。