Supramolecular Glyco‐nanoparticles Toward Immunological Applications

Glyco‐mimicking nanoparticles (glyco‐NPs) with Förster resonance energy transfer (FRET) donor and acceptor groups formed via dynamic covalent bond of benzoboroxole and sugar from two complementary polymers are prepared. The glyco‐NPs are proved to be quite stable under physiological conditions but sensitive to pH. So the glyco‐NPs can be internalized by dendritic cells with integrity and nontoxicity and then dissociate within the acidic organelles. This particle dissociation is directly observed and visualized in vitro, for the first time via the FRET measurements and fluorescent microscopy. This feature makes controlled release of drug or protein by glyco‐NPs possible, i.e., when model antigen Ovalbumin is loaded in the glyco‐NPs, the released Ovalbumin in dendritic cells stimulates T cells more efficiently than the free Ovalbumin itself as a result of the enhanced antigen processing and presentation. Thus, the results enlighten a bright future of the glyco‐NPs in immunotherapy.     

COVID-19 hospitalization rates rise exponentially with age,inversely proportional to thymic T-cell production

Here, we report that COVID-19 hospitalization rates follow an exponential relationship with age, doubling for every 16 years of age or equivalently increasing by 4.5% per year of life (R² = 0.98). This mirrors the well-studied exponential decline of both thymus volume and T-cell production, which halve every 16 years. COVID-19 can therefore be added to the list of other diseases with this property, including those caused by methicillin-resistant Staphylococcus aureus, MERS-CoV, West Nile virus, Streptococcus pneumoniae and certain cancers, such as chronic myeloid leukaemia and brain cancers. In addition, the incidence of severe disease and mortality due to COVID-19 are both higher in men, consistent with the degree to which thymic involution (and the decrease in T-cell production with age) is more severe in men compared to women. Since these properties are shared with some non-contagious diseases, we hypothesized that the age dependence does not come from social-mixing patterns, i.e. that the probability of hospitalization given infection rises exponentially, doubling every 16 years. A Bayesian analysis of daily hospitalizations, incorporating contact matrices, found that this relationship holds for every age group except for the under 20s. While older adults have fewer contacts than young adults, our analysis suggests that there is an approximate cancellation between the effects of fewer contacts for the elderly and higher infectiousness due to a higher probability of developing severe disease. Our model fitting suggests under 20s have 49–75% additional immune protection beyond that predicted by strong thymus function alone, consistent with increased juvenile cross-immunity from other viruses. We found no evidence for differences between age groups in susceptibility to infection or infectiousness to others (given disease state), i.e. the only important factor in the age dependence of hospitalization rates is the probability of hospitalization given infection. These findings suggest the existence of a T-cell exhaustion threshold, proportional to thymic output and that clonal expansion of peripheral T-cells does not affect disease risk. The strikingly simple inverse relationship between risk and thymic T-cell output adds to the evidence that thymic involution is an important factor in the decline of the immune system with age and may also be an important clue in understanding disease progression, not just for COVID-19 but other diseases as well.     

基于免疫原理的非法系统调用的检测*

入侵检测系统依赖于大量的数据检测以区别合法和非法的行为.通过检测操作系统内核的系统调用序列,应用免疫原理中的否定选择算法,以区分合法与非法的系统调用.实验验证了该方法的可行性和有效性.     

高维数据环境下网络异常检测的改进否定选择算法

人工免疫中的否定选择算法目前已成功地应用于异常检测的低维数据集上,但在高维数据集上的效果不大理想。为了改善算法的性能,分析了现有否定选择算法在网络异常检测中的不足,对带变长检测器的否定选择算法进行了修改,提出了一种改进的否定选择算法,新算法中通过移动检测器控制检测器覆盖情况。仿真实验结果表明了改进算法在网络异常检测中高维数据集上的有效性,可以取得较高的检测率和较低的误报率。     

基于模式识别的入侵检测模型

生物免疫进化理论揭示了完美自然免疫机制的关键在于基于模式的病原体的识别。免疫系统中模式识别的最基本的好处是：基于模式的比较和识别比模式中个成员的比较和识别更加高效。计算机网络安全系统在功能性上与生物免疫系统非常相似。将模式识别机制应用于计算机网络安全领域,主要讨论了多元化静态分析法的应用和遗传算法的应用。     

基于免疫的入侵检测系统中检测子集分布策略

为了提高现有的入侵检测系统的鲁棒性及对未知入侵的检测能力,根据免疫的原理、体系结构,建立了一种新的基于免疫原理的分布式入侵检测系统模型.在该模型中,检测子集如何在各个分布式节点上合理的分配将在很大程度上影响系统的性能.在分析了影响检测子集分布空间因素的基础上,从理论上研究了分布策略的优劣,给出了一种可行的将具有部分重复检测空间的检测子尽量分配到不同检测节点的方法.实验结果表明,与单纯的顺序分配相比该策略能在部分检测节点失效时系统依然维持较高的检测率.     

计算机免疫识别规则的演化挖掘

识别器的构造是计算机免疫学的一个重要研究方向。提出一种构造识别器的演化算法,其核心思想是采用数据挖掘的方法从Self集和nonself集中挖掘识别规则,这些识别规则能够反映出Self和nonself的一些内在特征,因此有很好的识别能力。