A new sequence based encoding for prediction of host–pathogen protein interactions

Pathogen–host interactions are very important to figure out the infection process at the molecular level, where pathogen proteins physically bind to human proteins to manipulate critical biological processes in the host cell. Data scarcity and data unavailability are two major problems for computational approaches in the prediction of pathogen–host interactions. Developing a computational method to predict pathogen–host interactions with high accuracy, based on protein sequences alone, is of great importance because it can eliminate these problems. In this study, we propose a novel and robust sequence based feature extraction method, named Location Based Encoding, to predict pathogen–host interactions with machine learning based algorithms. In this context, we use Bacillus Anthracis and Yersinia Pestis data sets as the pathogen organisms and human proteins as the host model to compare our method with sequence based protein encoding methods, which are widely used in the literature, namely amino acid composition, amino acid pair, and conjoint triad. We use these encoding methods with decision trees (Random Forest, j48), statistical (Bayesian Networks, Naive Bayes), and instance based (kNN) classifiers to predict pathogen–host interactions. We conduct different experiments to evaluate the effectiveness of our method. We obtain the best results among all the experiments with RF classifier in terms of F1, accuracy, MCC, and AUC.     

Inactivation of foodborne pathogens based on synergistic effects of ultrasound and natural compounds during fresh produce washing

Ultrasound has potential to be used for disinfection, and its antimicrobial effectiveness can be enhanced in presence of natural compounds. In this study, we compared the antimicrobial effects of ultrasound at 20 kHz (US 20 kHz) or 1 MHz (US 1 MHz) in combination with carvacrol, citral, cinnamic acid, geraniol, gallic acid, lactic acid, or limonene against E. coli K12 and Listeria innocua at a constant power density in water. Compared to the cumulative effect of the individual treatments, the combined treatment of US 1 MHz and 10 mM citral generated >1.5 log CFU/mL additional inactivation of E. coli K12. Similarly, combined treatments of US 1 MHz and 2 mM carvacrol (30 min), US 20 kHz and 2 mM carvacrol, 10 mM citral, or 5 mM geraniol (15 min) generated >0.5–2.0 log CFU/mL additional inactivation in L. innocua. The synergistic effect of citral, as a presentative compound, and US 20 kHz treatment was determined to be a result of enhanced dispersion of insoluble citral droplets in combination with physical impact on bacterial membrane structures, whereas the inactivation by US 1 MHz was likely due to generation of oxidative stress within the bacteria. Combined ultrasound and citral treatments improved the bacterial inactivation in simulated wash water in presence of organic matter or during washing of inoculated blueberries but only additive antimicrobial effects were observed. Findings in this study will be useful to enhance fresh produce safety and shelf-life and design other alternative ultrasound based sanitation processes.     

表面增强拉曼光谱在食源性致病微生物检测中的应用研究

食源性致病微生物导致的食源性疾病已成为全球化的公共卫生问题。快速、有效地检测食源性致病微生物是实现食源性疾病预防与控制的关键环节，也是保障食品安全的技术关键。表面增强拉曼光谱(SERS)具有简单、快速、灵敏度高等优点，在食品安全、生物医学、环境监控等领域展现出良好的应用前景。介绍了近年来SERS在食源性致病微生物检测中的应用研究进展。对SERS技术概况、SERS增强理论及SERS增强基底进行了简要介绍，重点回顾了SERS在食源性致病微生物检测中的应用和发展现状。在食品安全分析方面，利用SERS与模式识别方法相结合对食品中常见食源性致病微生物能实现快速、有效鉴别，部分研究已应用于不同食品样品的分析，体现了SERS作为“指纹图谱”的分析优势；在医学诊断方面，SERS可对病理样品(如血液、尿液等)中食源性致病微生物进行快速检测，缩短了样本分析时间，使食源性疾病的快速诊断成为可能；随着微流控技术的发展，微流控平台结合SERS技术被称为“芯片实验室”应用于食源性致病微生物的检测，可提高分析的可控性，稳定性，特异性和灵敏度。通过对比分析，发现不同研究可采用不同分离方法、不同基底、不同目标捕获方式等实现了食源性致病微生物的检测，展示了不同方法间的差异性。已有研究表明了SERS在食源性致病微生物检测中应用可克服传统方法耗时等缺点，实现灵敏快速分析，为食品安全实时监控，食源性疾病即时诊断提供了有效的分析工具。同时，指出了SERS技术应用于食源性致病微生物分析依然面临很大挑战，(1)大多数研究并没有聚焦于实际样品，而标准培养液和实际样品的SERS检测存在较大差异，实际样品组分会对SERS响应产生干扰；(2)不同方法结果有较大差异，主要是由于纳米增强基底差异，吸附方式原理的差异，稳定性的差异等，因此需要更多深入研究进一步优化条件；(3)期望建立标准化的SERS方法替代传统技术，充分展示SERS作为新兴分析工具快速、灵敏、简捷的优势应用于食品安全，医学诊断等领域。将来，随着研究的深入及相关学科的发展，SERS作为极具潜力的快速分析工具，将在食品安全，生物医学等领域具有更广阔的应用前景。     

Fluorescence Polarization Based Nucleic Acid Testing for Rapid and Cost‐Effective Diagnosis of Infectious Disease

A new nucleic acid detection method was developed for a rapid and cost‐effective diagnosis of infectious disease. This approach relies on the three unique elements: 1) detection probes that regulate DNA polymerase activity in response to the complementary target DNA; 2) universal reporters conjugated with a single fluorophore; and 3) fluorescence polarization (FP) detection. As a proof‐of‐concept, the assay was used to detect and sub‐type Salmonella bacteria with sensitivities down to a single bacterium in less than three hours.     

新型α-蒎烯基苯磺酰胺类化合物的合成及其抗真菌活性

以天然产物α蒎烯为原料,经多步反应合成了9个新型α-蒎烯基苯磺酰胺类化合物（7a~7i）,其结构经¹H NMR、 ¹³C NMR、 FT-IR和MS(ESI)表征。采用离体法测试了化合物的抗真菌活性。结果表明：在50 μg·mL^-1浓度下,目标化合物对黄瓜枯萎病菌、花生褐斑病菌、苹果轮纹病菌、小麦赤霉病菌以及番茄早疫病菌等5种植物病原菌有一定的抑制活性,其中化合物α-蒎烯基p-氯苯基磺酰胺(7d, R=p-Cl)和α-蒎烯基o-硝基苯基磺酰胺(7h, R=o-NO₂)对苹果轮纹病菌的抑制率分别为83.9%和79.6%（B级活性水平）,优于阳性对照百菌清（75.0%）;化合物α-蒎烯基m-甲基苯基磺酰胺(7b, R=m-Me)对番茄早疫病菌的抑制率为82.2%（B级活性水平）,优于阳性对照百菌清（73.9%）。-     

一类具有直接和间接传播的SIV传染病模型

传染病的传播途径是多种多样的,如:直接传播和间接传播等.在以往的传染病模型中,大多是只考虑直接传播对疾病的影响,而忽略了环境作用对疾病的影响,即间接传播的影响.其实,间接传播在传染病的传播中同样起着重要的作用,忽略间接传播的影响会低估基本再生数,从而错估传染病爆发的规模,给人类的健康带来更大的威胁.研究了一个既有直接传播又有间接传播的SⅣ传染病模型,通过研究模型的动力学行为,给出了无病平衡点和地方病平衡点全局渐近稳定的条件,并用数值模拟证实了该结果.     

游戏传播的动力学研究

随着网络游戏的快速发展,沉溺于网络游戏的人也越来越多.利用传染病模型,根据人们对某款游戏的沉迷程度,构建游戏传播的动力学模型.通过定性分析研究模型的平衡点和正平衡点的存在性,得出了游戏传播达到稳定状态时,各类人群的人数和游戏传播的阈值R_0.并证明当R_01时正平衡点的渐进稳定性,表明该游戏将会继续传播形成部分人群沉迷于该游戏;相反,当R_01时,游戏将无法传播.通过仿真,分析了不同参数对网络游戏传播的影响,为如何能更有效的控制管理游戏提供了合理的解释.     

基于显微共聚焦拉曼技术对三种食源性致病菌的快速鉴别研究

采用显微共聚焦拉曼技术,建立了对三种常见食源性致病菌快速鉴别的检测方法。使用XploRA PLUS共聚焦拉曼光谱仪,在激光功率为5 mW、积分时间为30s、积分次数为1次的条件下,对德尔卑沙门氏菌、副溶血性弧菌和金黄色葡萄球菌进行了拉曼光谱数据的采集。对拉曼光谱采用多项式平滑算法和荧光背底扣除后,采用主成分分析法(PCA)对预处理后的数据进行降维,提取出前三个主成分的累计方差贡献率达到了95.4％,样本明显的聚为了3类。同时结合Fisher判别分析法(FLD)构建分类模型,对三种样本进行交叉验证,分类准确率达到了100％。结果表明,采用显微共聚焦拉曼技术与PCA-FLD方法结合可实现对三种食源性致病菌的快速准确鉴别且模型检测精度高,方法具有一定的实用性及参考价值。     

The impact of human activity patterns on asymptomatic infectious processes in complex networks

The study of the impact of human activity patterns on network dynamics has attracted a lot of attention in recent years. However, individuals’ knowledge of their own physical states has rarely been incorporated into modeling processes. In real life, for certain infectious processes, infected agents may not have any visible or physical signs and symptoms; therefore, they may believe that they are uninfected even when they have been infected asymptomatically. This infection awareness factor is covered neither in the classical epidemic models such as SIS nor in network propagation studies. In this article, we propose a novel infectious process model that differentiates between the infection awareness states and the physical states of individuals and extend the SIS model to deal with both asymptomatic infection characteristics and human activity patterns. With regards to the latter, we focus particularly on individuals’ testing action, which is to determine whether an individual is infected by an epidemic. The simulation results show that less effort is required in controlling the disease when the transmission probability is either very small or large enough and that Poisson activity patterns are more effective than heavy-tailed patterns in controlling and eliminating asymptomatic infectious diseases due to the long-tail characteristic.     

跨界创新联盟关联信用风险传染模型研究

跨界创新联盟是企业创新过程中资源整合与共享的一种有效形式,但在带来企业创新资源共享效益的同时,联盟中复杂的关联关系也构成了联盟关联信用风险传染的新渠道。本文结合复杂网络理论与传染病模型,根据跨界创新联盟特点,在原有资产关联研究的基础上,综合考虑行业关联、资产关联和创新合作关联三种关联关系,改进了风险传播的传染病模型,并进行了数值仿真实验。结果表明:行业关联、资产关联和创新合作关联对跨界创新联盟关联信用风险传染阈值具有综合性影响,增加行业关联和创新合作关联因素,有利于提高关联信用风险传染阈值的精度。此外,资产关联度越高则感染成员密度越大;行业关联度越低,创新合作关联度越高则感染成员密度越小。