化合物构效关系的模糊极小极大神经网络识别研究

应用模糊极小极大神经网络研究了化合物复杂结构和性能（QSAR）之间的关系，用该法进行几组化合物致癌的识别，结果优于线性回归的方法，对此作出一些分析。     

一种EEG信号盲分离和分类的神经网络方法

提出一种采用多神经网络处理脑电(EEG)信号的方法。首先，对混有噪声的脑电信号给出一种盲分离的自适应神经算法。通过寻求采样时间序列线性组合的kurtosis系数的局部极值，得出该算法的模型和步骤。在盲分离的基础上，对分离出的估计信号进一步利用Kohonen网络进行分类。将该算法用于300个EEG样本处理，并给出处理结果。     

抗聚合人血清白蛋白的抗独特型抗体

一种针对兔抗聚合人血清白蛋白抗体(anti-PHSA,Ab_1)的抗独特型抗体(anti-anti-PHSA,Ab_2),已通过免疫同种动物制成。该Ab_2具有以下特性:(1)它既可与兔anti-PHSA反应,也可与鼠的单克隆的anti-PHSA反应。(2)它与鼠的单克隆的anti-PHSA的反应,可受到PHSA和乙型肝炎病毒(HBV)上的聚合人血清白蛋白受体(PHSA-R)的抑制。(3)它可在异种动物体内诱导产生具有anti-PHSA活性的抗-抗独特型抗体(anti-anti-anti-PHSA,Ab_3),而且在收获的Ab_2血清中也查见了Ab_3。本研究不仅表明了针对PHSA的免疫应答中独特型网络调节的客观存在,而且在其抗独特型抗体(anti-Id)中,的确存在具有PHSA内映像的成分。     

国人延髓动脉的观察

本文观察了110例成人脑标本的延髓外部动脉;采用动脉X线造影法和透明法,在20例人脑标本上观察了延髓内部动脉的形态和供应。将延髓表面分为四区,供应各区的外部动脉可分为相应的4群。前正中群和前外侧群动脉主要来自脊髓前动脉,前正中群动脉供应舌下神经核、内侧丘系及部分锥体束等中缝两侧结构;前外侧群动脉供应大部分锥体束;外侧群动脉来自椎动脉、小脑下后动脉、基底动脉和小脑下前动脉,主要供应网状结构、脊髓丘脑束、三叉神经脊束及核、迷走神经背核等;后群动脉来自脊髓后动脉和小脑下后动脉,主要供应薄、楔束核等。本文还讨论了延髓外侧区的血液供应及“终动脉”等问题。     

HOPFIELD网络的联想记忆性质

本文首先给出了有关联想记忆的一系列概念，通过计算机模拟验证了网络的某些固有性质，同时指出了它的固有问题，并仿真研究了Ｈｏｐｆｉｅｌｄ网络中参数间的关系。     

人体寄生虫学网络课程的应用与评价

目的对郧阳医学院人体寄生虫学网络课程的教学效果进行评价。方法选取2005级临床医学一系和二系分别作为实验组和对照组，对照组的人体寄生虫学采用传统教学方法教学，实验组采用传统教学和本课题组构建的人体寄生虫学网络课程平台进行辅助教学。结果实验组学生的人体寄生虫学理论课程考试成绩和实验课程考试成绩明显优于对照组学生（P〈0．05）。结论人体寄生虫学网络课程能够显著提高教学效果，对突破传统面授教学的局限性和帮助学生网上学习具有重要意义。     

Infrared spectroscopy: A new diagnostic tool in Alzheimer disease

Biological markers play an evolving role in the diagnosis of Alzheimer disease (AD). We compare conventional measurements of cerebrospinal fluid (CSF) tau and β-amyloid_1–42 proteins to a novel approach – Fourier transformed infrared (FT-IR) spectroscopy – a simple technique derived from chemical and physical sciences that characterizes intramolecular bonds. For automatic diagnostic analysis, we developed an artificial neural network (ANN). We examined 71 patients with a clinical diagnosis of AD and 66 controls. β-Amyloid_1–42 was decreased (sensitivity 80% and specificity 78%); tau was elevated (sensitivity 76% and specificity 88%) in CSF of AD patients. The combined tau/β-amyloid_1–42 quotient was able to distinguish healthy from diseased subjects with 99% sensitivity and 86% specificity. The ANN could separate FT-IR spectroscopy data with 88.5% sensitivity and 80% specificity. FT-IR spectroscopy proved to be cost-effective and simple to perform. Diagnostic sensitivity and specificity is in the range of CSF tau and β-amyloid_1–42 protein analysis. Larger sample numbers for ANN training and validation could increase diagnostic accuracy and thus prove to be a useful screening tool.     

利用神经网络集成预测MHC-Ⅰ类分子结合肽

目的：利用神经网络集成（NNE）预测MHC-Ⅰ类分子结合肽。 方法： 基于HLA-A*0201编码的MHC-Ⅰ类分子结合肽数据库（含有628个9聚物）及其结合能力分类，利用NNE分别对具有无、低、中和高4类亲合性的结合肽进行分类预测；同时还进一步利用T细胞真实表位集（含50个表位）评估了NNE的预测性能。 结果： 集成数为12的NNE对上述分类的平均预测命中率可达0.8,而且NNE对潜在T细胞表位的预测能力也较高，约84%的真实表位归于高和中等亲合性的潜在抗原肽一类。 结论： 可以利用神经网络集成预测MHC-Ⅰ类分子结合肽，并进而预测相应的T细胞表位。经适当修改，NNE预测工具可扩展为能涵盖任意长度的Ⅰ类分子结合肽甚至可扩展到Ⅱ类分子结合肽的预测。     

Control of feeding rhythm in the terrestrial slug Incilaria bilineata

A modulatory neuron of feeding rhythm was newly identified in the buccal ganglia of the isolated central nervous system (CNS) of the terrestrial slug Incilaria bilineata. This neuron was termed the “feeding rhythm modulator” (FRM). Its morphological and electrical properties were compared with those of the MGC (metacerebral giant cell, a cerebral modulatory neuron of feeding rhythm). There was no direct connection between FRM and MGC. In order to investigate the control mechanism of the buccal central pattern generator, feeding rhythm was observed by varying the activities of MGC and FRM simultaneously. At a lower level of activity of MGC, feeding rhythm was not only sensitive to the activity of MGC but also to that of FRM. As the level of activity of MGC increased, feeding rhythm was exclusively controlled by the activity of MGC, and became unaffected by the activity of FRM. This indicates that cerebral neurons such as MGC primarily control feeding rhythm and modulate the contribution of FRM in a hierarchical manner.     

The superspreading places of COVID-19 and the associated built-environment and socio-demographic features: A study using a spatial network framework and individual-level activity data

Previous studies observed that most COVID-19 infections were transmitted by a few individuals at a few high-risk places (e.g., bars or social gathering venues). These individuals, often called superspreaders, transmit the virus to an unexpectedly large number of people. Further, a small number of superspreading places (SSPs) where this occurred account for a large number of COVID-19 transmissions. In this study, we propose a spatial network framework for identifying the SSPs that disproportionately spread COVID-19. Using individual-level activity data of the confirmed cases in Hong Kong, we first identify the high-risk places in the first four COVID-19 waves using the space-time kernel density method (STKDE). Then, we identify the SSPs among these high-risk places by constructing spatial networks that integrate the flow intensity of the confirmed cases. We also examine what built-environment and socio-demographic features would make a high-risk place to more likely become an SSP in different waves of COVID-19 by using regression models. The results indicate that some places had very high transmission risk and suffered from repeated COVID-19 outbreaks over the four waves, and some of these high-risk places were SSPs where most (about 80%) of the COVID-19 transmission occurred due to their intense spatial interactions with other places. Further, we find that high-risk places with dense urban renewal buildings and high median monthly household rent-to-income ratio have higher odds of being SSPs. The results also imply that the associations between built-environment and socio-demographic features with the high-risk places and SSPs are dynamic over time. The implications for better policymaking during the COVID-19 pandemic are discussed.