疫情下数据结构课程线上教学模式探索与实践

为了在新冠肺炎疫情的影响下充分保障正常的教学秩序,全力做到“停课不停教、停课不停学”,落实“教师真在教、学生真在学”,数据结构教学团队利用超星“一平三端”智慧教学系统和钉钉直播相结合的方式,在教学准备、教学实施、学习评价和教学改进等四个方面开展线上教学模式探索和实践,实现了将线上线下混合式教学转变为纯线上教学模式,体现了“以学生为中心”的教学理念,达到线上与线下同质同效。     

基于智能计算的脑机制研究

解析脑认知原理和脑疾病的发病机制已是当下脑科学研究的热点。智能计算在脑影像领域的应用可为认知心理学研究、脑疾病的识别和干预以及类脑智能理论研究等提供借鉴和参考。本文介绍利用智能影像计算方法在识别脑认知网络成分、构建脑有向连接模型、预测认知行为变化、构建心理过程神经表征等方面的工作,并对未来相关研究进行展望。     

Pilot proteomic analysis of cerebrospinal fluid in Alzheimer's disease

Proteomic analysis of cerebrospinal fluid (CSF) holds great promise in understanding the progression of neurodegenerative diseases, including Alzheimer's disease (AD). As one of the primary reservoirs of neuronal biomolecules, CSF provides a window into the biochemical and cellular aspects of the neurological environment. CSF can be drawn from living participants allowing the potential alignment of clinical changes with these biochemical markers. Using cutting-edge mass spectrometry technologies, we perform a streamlined proteomic analysis of CSF. We quantify greater than 700 proteins across 10 pairs of age- and sex-matched participants in approximately one hour of analysis time each. Using the paired participant study structure, we identify a small group of biologically relevant proteins that show substantial changes in abundance between cognitive normal and AD participants, which were then analyzed at the peptide level using parallel reaction monitoring experiments. Our findings suggest the utility of fractionating a single sample and using matching to increase proteomic depth in cerebrospinal fluid, as well as the potential power of an expanded study.     

阿尔茨海默病药物治疗的研究进展

综述了阿尔茨海默病的药物治疗研究进展,包括乙酰胆碱酯酶抑制剂,NMDA受体拮抗剂,非甾体类抗炎药,抗氧化剂,免疫治疗以及针对Aβ的其它治疗手段等.     

基于文本分析的自动化疾病编码方法

对疾病进行编码是将疾病诊断名称转化为标准ICD(国际疾病分类)编码的过程.鉴于编码量庞大和人工编码效率低等原因,有必要实现疾病编码的自动化.提出一种自动化的疾病编码方法,使用一种文本建模方法将ICD表示为文本集,然后借助文本相关性度量,获取与待编码疾病诊断名称最相关的ICD编码.经实验验证,本文提出的自动化疾病编码方法准确率较高、效率优秀、分类层次变换灵活,可广泛应用于各种类型的数据分析场景.     

Mass spectrometric immunoassay and MRM as targeted MS-based quantitative approaches in biomarker development: Potential applications to cardiovascular disease and diabetes

Type 2 diabetes mellitus (T2DM) is an important risk factor for cardiovascular disease (CVD)—the leading cause of death in the United States. Yet not all subjects with T2DM are at equal risk for CVD complications; the challenge lies in identifying those at greatest risk. Therapies directed toward treating conventional risk factors have failed to significantly reduce this residual risk in T2DM patients. Thus newer targets and markers are needed for the development and testing of novel therapies. Herein we review two complementary MS-based approaches—mass spectrometric immunoassay (MSIA) and MS/MS as MRM—for the analysis of plasma proteins and PTMs of relevance to T2DM and CVD. Together, these complementary approaches allow for high-throughput monitoring of many PTMs and the absolute quantification of proteins near the low picomolar range. In this review article, we discuss the clinical relevance of the high density lipoprotein (HDL) proteome and Apolipoprotein A-I PTMs to T2DM and CVD as well as provide illustrative MSIA and MRM data on HDL proteins from T2DM patients to provide examples of how these MS approaches can be applied to gain new insight regarding cardiovascular risk factors. Also discussed are the reproducibility, interpretation, and limitations of each technique with an emphasis on their capacities to facilitate the translation of new biomarkers into clinical practice.     

冻融循环作用下盐渍土病害机理及改良技术综述

盐渍土在冻融循环作用下,主要表现出盐胀和冻胀特性,对土体结构造成破坏,土体发生变形,从而降低土体强度,给工程建设造成危害。为改良盐渍土、防治盐渍土病害,从盐渍土病害产生的机理出发,介绍了结晶压力是导致土体结构发生破坏、土体发生变形的本质因素,证实了结晶压力的大小与孔隙大小有关。水盐迁移会加剧盐胀和冻胀的发生。在冻融循环作用下,水分和盐分向土体表层发生运移。盐渍土的盐－胀特性受土体的干密度、含水量、含盐量、荷载和温度的影响,不同因素的影响程度不同,干密度的影响较含水量小。干密度越大、含水量和含盐量越高、降温速率越慢,盐－冻胀越强烈。荷载对盐－冻胀有抑制作用。通过对盐渍土病害机理的研究,结合盐渍土盐－冻胀的影响因素,从土质改良、加固土体、阻断水盐通道和温度控制四个方面阐述了冻融作用下盐渍土的改良方法,可供盐渍土地区的工程建设参考。目前关于结晶压力的研究主要是定性方面的,水盐迁移的研究以室内试验为主。未来对盐渍土病害机理的研究将主要集中在结晶压力的定量研究和水盐迁移的现场试验等方面。     

Feature extraction by autoregressive spectral analysis using maximum likelihood estimation: internal carotid arterial Doppler signals

Abstract: In this study, Doppler signals recorded from the internal carotid artery (ICA) of 97 subjects were processed by personal computer using classical and model-based methods. Fast Fourier transform (classical method) and autoregressive (model-based method) methods were selected for processing the ICA Doppler signals. The parameters in the autoregressive method were found by using maximum likelihood estimation. The Doppler power spectra of the ICA Doppler signals were obtained by using these spectral analysis techniques. The variations in the shape of the Doppler spectra as a function of time were presented in the form of sonograms in order to obtain medical information. These Doppler spectra and sonograms were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of stenosis and occlusion in the ICA. Reliable information on haemodynamic alterations in the ICA can be obtained by evaluation of these sonograms.     

AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction

Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (>94%) and comparably small prediction difference intervals (<6%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation.