甘蓝型油菜抗菌核病研究进展

由真菌核盘菌（Sclerotinia sclerotiorum）引起的油菜菌核病是世界范围内最严重的病害之一,也是影响油菜高产稳产的丰要生物逆境.选育抗菌核病油菜品种并在生产上大面积种植是防控菌核病最经济有效和环保的途径.本文从核盘菌的致病机理、油菜抗菌核病的遗传控制、抗病基因表达谱和抗性相关基因的应用等方面综述了油菜抗菌核病研究有关的最新进展.     

口蹄疫与猪水疱病的鉴别诊断

本文从病毒的结构形态、血清型别、抗原性、传染途径、临床特征、灭活消毒、诊断方法等方面,详细说明了口蹄疫和猪水疱病的鉴别诊断.     

猪口蹄疫与猪水泡病的检验与区别

介绍猪口蹄疫与猪水泡病的病因、症状以及鉴别方法.     

基于HHT-MFCC和短时能量的慢性阻塞性肺病患者呼吸声识别

为了优化梅尔频率倒谱系数(MFCC)特征提取算法,提高对呼吸声信号识别的准确率,实现识别慢性阻塞性肺病(COPD)的目的,提出了基于希尔伯特黄变换(HHT)的MFCC与短时能量(Energy)融合的特征提取算法HHT-MFCC+Energy.首先,经预处理的呼吸声信号通过HHT计算出Hilbert边际谱和边际谱能量;其...     

GANAB and N-Glycans Substrates Are Relevant in Human Physiology,Polycystic Pathology and Multiple Sclerosis: A Review

Glycans are one of the four fundamental macromolecular components of living matter, and they are highly regulated in the cell. Their functions are metabolic, structural and modulatory. In particular, ER resident N-glycans participate with the Glc₃Man₉GlcNAc₂ highly conserved sequence, in protein folding process, where the physiological balance between glycosylation/deglycosylation on the innermost glucose residue takes place, according GANAB/UGGT concentration ratio. However, under abnormal conditions, the cell adapts to the glucose availability by adopting an aerobic or anaerobic regimen of glycolysis, or to external stimuli through internal or external recognition patterns, so it responds to pathogenic noxa with unfolded protein response (UPR). UPR can affect Multiple Sclerosis (MS) and several neurological and metabolic diseases via the BiP stress sensor, resulting in ATF6, PERK and IRE1 activation. Furthermore, the abnormal GANAB expression has been observed in MS, systemic lupus erythematous, male germinal epithelium and predisposed highly replicating cells of the kidney tubules and bile ducts. The latter is the case of Polycystic Liver Disease (PCLD) and Polycystic Kidney Disease (PCKD), where genetically induced GANAB loss affects polycystin-1 (PC1) and polycystin-2 (PC2), resulting in altered protein quality control and cyst formation phenomenon. Our topics resume the role of glycans in cell physiology, highlighting the N-glycans one, as a substrate of GANAB, which is an emerging key molecule in MS and other human pathologies.     

卷积神经网络应用于先心病心音信号分类研究

心脏听诊是先心病初诊和筛查的主要手段。传统心音分类算法普适性差，过程复杂，不利于将来实时化决策。采用1 800个心音信号对几种时间序列分类的主流深度学习网络进行训练，结果显示循环神经网络易出现过拟合；长短时记忆网络分类损失值0.257，准确率0.872；卷积神经网络损失值0.25，准确率0.896。实验表明卷积神经网络相比较其他两种网络具备更大的潜力。基于卷积神经网络的先心病分类算法，因训练样本量大，使网络普适性得到了保证。与其他分类器相比，CNN的另一个优势是其可自动提取特征。该研究有望用于机器辅助听诊。     

Framework for Evaluating Measures to Control Nosocomial Tuberculosis Transmission

Abstract Diverse control measures can be applied to reduce tuberculosis infection risk in health-care facilities. Selecting optimal controls requires methods for predicting the dependence of infections risk on underlying parameters. A common model for infection risk only explicitly accounts for control by ventilation. This paper proposes a more complete model for evaluating tuberculosis infection control methods in health-care settings. An infection risk parameter is defined as the probable number of infectious droplet nuclei inhaled by all susceptible persons from a single infectious person. Algebraic model equations are presented for two exposure cases. In one, the susceptible and infectious persons are together in a well-mixed indoor environment; in the socond, the infectious person is in respiratory isolation. Model equations are used to explore many common tuberculosis control measures: identification, isolation and treatment of tuberculosis cases; surgical masks and treatment booths applied at the source; environmental controls such as ventilation, air filtration, and ultraviolet germicidal irradiation; and respiratory protection for susceptible persons. Experimental data are limited or lacking on some key variables, such as emissions of infectious droplet nuclei by contagious persons and air leakage rates from isolation rooms. Methods are outlined for collecting additional data.     

住屋与人类健康──“房屋病”初探

住屋与人类健康休戚相关。本文通过剖析住屋环境与方位，提出了“房屋病”这个新概念，指出不利的环境和方位对人体的“信息作用”是产生“房屋病”的源由，从而以此用来指导建筑设计人员和住屋的使用者，真正使人们安其居、乐其业。     

新冠肺炎疫情背景下聚集性传染风险智能监测模型

新型冠状病毒肺炎疫情严重威胁人们的生命安全,对于聚集性人群密度及口罩佩戴情况的监管是控制病毒扩散的重要途经。公共场所具有人流密集且流动性大的特点,人工监测易增加感染风险,而现有基于深度学习的口罩检测算法存在功能及场景单一的问题,不能在多场景下实现多类别检测,同时精度也有待提升。提出Cascade-Attention R-CNN目标检测算法,实现对聚集区域、行人和口罩佩戴情况的自动检测。针对任务中目标尺度变化过大的问题,选取高精度两阶段Cascade R-CNN目标检测算法作为基础检测框架。通过设计多个级联的候选分类-回归网络并加入空间注意力机制,突出候选区域特征中的重要特征并抑制噪声特征,从而提高检测精度。在此基础上,构建聚集性传染风险智能监测模型,结合Cascade-Attention R-CNN算法的输出结果确定传染风险等级。实验结果表明,该模型对于不同场景和视角的多类别目标图片具有较高的准确性和鲁棒性,Cascade-Attention R-CNN算法平均精度均值达到89.4%,较原始Cascade RCNN算法提升2.6个百分点,较经典的两阶段目标检测算法Faster R-CNN和单阶段目标检测框架RetinaNet分别提升10.1和8.4个百分点。     

Detection of Wheat Stripe Rust Using Solar-induced Chlorophyll Fluorescence and Reflectance Spectral Indices

Detection of wheat stripe rust is important for agriculture management and decision,this paper aims to improve detection accuracy of the disease severity of wheat stripe rust by integrating the advantages of reflectance spectroscopy in the detection of crop biochemical parameters and the advantages of chlorophyll fluorescence in photosynthetic physiology diagnosis.Firstly,the solar-induced chlorophyll fluorescence (SIF) at O₂-A band (760 nm) was calculated using the 3FLD algorithm,and seven spectral indices sensitive to wheat stripe rust were investigated for estimating the disease severity.Then,three classic statistical modelling methods,including Support Vector Machine (SVM),Stepwise Regression (SR) and BP neural network (BP),were used to quantitatively investigated the performance of the spectral indices and SIF for detection of winter wheat stripe rust severity.The results show that:(1) there is a significantly negative correlation between SIF and the severity of wheat stripe rust.The relationship between SIF and DI can be effectively applied to detect wheat stripe rust.(2) the spectral models based on SIF combined with spectral indices are more accurate than those based on spectral indices.SIF can significantly improve the detection accuracy of the disease severity of winter wheat stripe rust.(3) compared to the SVM and SR methods,the training model constructed by the BP neural network has the highest prediction accuracy whether using the spectral indices or SIF combined spectral indices.However,the verification results show that the disease severity prediction model constructed by SVM and SR method have a better prediction.