Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification

Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease control. Host responses analysis can detect variform and sensitive host inflammatory responses to ascertain the presence and type of the infection. However, traditional host-derived inflammatory indicators are insufficient for clinical infection classification. Fingerprints-based omic analysis has attracted increasing attention globally for analyzing the complex host systemic immune response. A single type of fingerprints is not applicable for infection classification (area under curve (AUC) of 0.550–0.617). Herein, an infection classification platform based on deep learning of dual plasma fingerprints (DPFs-DL) is developed. The DPFs with high reproducibility (coefficient of variation <15%) are obtained at low sample consumption (550 nL native plasma) using inorganic nanoparticle and organic matrix assisted laser desorption/ionization mass spectrometry. A classifier (DPFs-DL) for viral versus bacterial infection discrimination (AUC of 0.775) and coronavirus disease 2019 (COVID-2019) diagnosis (AUC of 0.917) is also built. Furthermore, a metabolic biomarker panel of two differentially regulated metabolites, which may serve as potential biomarkers for COVID-19 management (AUC of 0.677–0.883), is constructed. This study will contribute to the development of precision clinical care for infectious diseases.     

酵素中具有抑菌能力乳酸菌的筛选及鉴定

该研究探讨了从酵素中分离纯化具有抗菌性能的12株乳酸菌，对其进行生理生化实验和API鉴定。利用牛津杯法对阪崎大肠杆菌、鼠伤寒沙门氏菌和宋内志贺氏菌、大肠杆菌、金黄色葡萄球菌进行抑菌特性研究，并探讨了乳酸菌是否产生物胺能力和偶氮还原酶。结果表明，酵素源的12株乳酸菌对肠道致病菌均具有不同程度的抑菌活性，其抑菌圈直径为14~22 mm，对大肠杆菌的抑菌效果较好，其抑菌圈直径为15~23 mm，对金黄色葡萄球菌的抑菌圈直径为14~24 mm，12株乳酸菌都不具有产生物胺和偶氮还原酶的能力。通过生理和API生化鉴定确定XZJ003、XZJ006、XZJ015均为副干酪乳杆菌，通过16S rDNA和同源性确证XZJ015为副干酪乳杆菌，该研究结果对今后益生菌的深入研究和功能性食品的开发具有一定的作用。     

浅谈彩色PDP的现状与发展

本文介绍了彩色ＰＤＰ的现有技术水平，着重对ＰＤＰ的技术难点，工艺，性能，以及市场前景进行了分析，并对ＰＤＰ将来的发展方向作了探讨。     

Risk-sensitive large-population linear-quadratic-Gaussian Games with major and minor agents

This paper studies large-population dynamic games involving a linear-quadratic-Gaussian (LQG) system with an exponential cost functional. The parameter in the cost functional can describe an investor's risk attitude. In the game, there are a major agent and a population of $N$ minor agents where $N$ is very large. The agents in the games are coupled via both their individual stochastic dynamics and their individual cost functions. The mean field methodology yields a set of decentralized controls, which are shown to be an $ϵ$-Nash equilibrium for a finite $N$ population system where $ϵ=O\left(\frac{1}{\sqrt{N}}\right)$. Numerical results are established to illustrate the impact of the population's collective behaviors and the consistency of the mean field estimation.