Revisions to USDA biotechnology regulations: The SECURE rule

In keeping with the directive in Executive Order 13874 (Modernizing the Regulatory Framework for Agricultural Biotechnology Products) to adopt regulatory approaches that are proportionate to risk and avoid arbitrary distinctions across like products, the US Department of Agriculture (USDA) revised its biotechnology regulations by promulgating the Sustainable, Ecological, Consistent, Uniform, Responsible, and Efficient (SECURE) rule. Specifically, the SECURE rule 1) establishes exemptions for plants modified by genetic engineering where the modification could otherwise have been made through conventional breeding, 2) uses risk posed by the introduced trait to determine whether an organism is regulated, rather than relying on whether the organism was developed using a plant pest, and 3) provides a mechanism for a rapid initial review to efficiently distinguish plants developed using genetic engineering that do not pose plausible pathways to increased plant pest risk from those that do. As a result of the focused oversight on potentially riskier crops developed using genetic engineering, USDA is expected to improve the efficiency and effectiveness of its oversight program. The reduced regulatory burden is expected to promote innovation by expanding the number and diversity of developers to include smaller businesses and academics and to increase the number and variety of traits being developed through biotechnology.     

Quantitative visualization of passive transport across bilayer lipid membranes

The ability to predict and interpret membrane permeation coefficients is of critical importance, particularly because passive transport is crucial for the effective delivery of many pharmaceutical agents to intracellular targets. We present a method for the quantitative measurement of the permeation coefficients of protonophores by using laser confocal scanning microscopy coupled to microelectrochemistry, which is amenable to precise modeling with the finite element method. The technique delivers well defined and high mass transport rates and allows rapid visualization of the entire pH distribution on both the cis and trans side of model bilayer lipid membranes (BLMs). A homologous series of carboxylic acids was investigated as probe molecules for BLMs composed of soybean phosphatidylcholine. Significantly, the permeation coefficient decreased with acyl tail length contrary to previous work and to Overton's rule. The reasons for this difference are considered, and we suggest that the applicability of Overton's rule requires re-evaluation.     

Learning to Synchronize: Midfrontal Theta Dynamics during Rule Switching

In recent years, several hierarchical extensions of well-known learning algorithms have been proposed. For example, when stimulus-action mappings vary across time or context, the brain may learn two or more stimulus-action mappings in separate modules, and additionally (at a hierarchically higher level) learn to appropriately switch between those modules. However, how the brain mechanistically coordinates neural communication to implement such hierarchical learning remains unknown. Therefore, the current study tests a recent computational model that proposed how midfrontal theta oscillations implement such hierarchical learning via the principle of binding by synchrony (Sync model). More specifically, the Sync model uses bursts at theta frequency to flexibly bind appropriate task modules by synchrony. The 64-channel EEG signal was recorded while 27 human subjects (female: 21, male: 6) performed a probabilistic reversal learning task. In line with the Sync model, postfeedback theta power showed a linear relationship with negative prediction errors, but not with positive prediction errors. This relationship was especially pronounced for subjects with better behavioral fit (measured via Akaike information criterion) of the Sync model. Also consistent with Sync model simulations, theta phase-coupling between midfrontal electrodes and temporoparietal electrodes was stronger after negative feedback. Our data suggest that the brain uses theta power and synchronization for flexibly switching between task rule modules, as is useful, for example, when multiple stimulus-action mappings must be retained and used.SIGNIFICANCE STATEMENT Everyday life requires flexibility in switching between several rules. A key question in understanding this ability is how the brain mechanistically coordinates such switches. The current study tests a recent computational framework (Sync model) that proposed how midfrontal theta oscillations coordinate activity in hierarchically lower task-related areas. In line with predictions of this Sync model, midfrontal theta power was stronger when rule switches were most likely (strong negative prediction error), especially in subjects who obtained a better model fit. Additionally, also theta phase connectivity between midfrontal and task-related areas was increased after negative feedback. Thus, the data provided support for the hypothesis that the brain uses theta power and synchronization for flexibly switching between rules.     

Prospective comparison of three validated prediction rules for prognosis in community-acquired pneumonia

PURPOSE: We assessed the performance of 3 validated prognostic rules in predicting 30-day mortality in community-acquired pneumonia: the 20 variable Pneumonia Severity Index and the easier to calculate CURB (confusion, urea nitrogen, respiratory rate, blood pressure) and CURB-65 severity scores. SUBJECTS AND METHODS: We prospectively followed 3181 patients with community-acquired pneumonia from 32 hospital emergency departments (January-December 2001) and assessed mortality 30 days after initial presentation. Patients were stratified into Pneumonia Severity Index risk classes (I-V) and CURB (0-4) and CURB-65 (0-5) risk strata. We compared the discriminatory power (area under the receiver operating characteristic curve) of these rules to predict mortality and their accuracy based on sensitivity, specificity, predictive values, and likelihood ratios. RESULTS: The Pneumonia Severity Index (risk classes I-III) classified a greater proportion of patients as low risk (68% [2152/3181]) than either a CURB score <1 (51% [1635/3181]) or a CURB-65 score <2 (61% [1952/3181]). Low-risk patients identified based on the Pneumonia Severity Index had a slightly lower mortality (1.4% [31/2152]) than patients classified as low-risk based on the CURB (1.7% [28/1635]) or the CURB-65 (1.7% [33/1952]). The area under the receiver operating characteristic curve was higher for the Pneumonia Severity Index (0.81) than for either the CURB (0.73) or CURB-65 (0.76) scores (P <0.001, for each pairwise comparison). At comparable cut-points, the Pneumonia Severity Index had a higher sensitivity and a somewhat higher negative predictive value for mortality than either CURB score. CONCLUSIONS: The more complex Pneumonia Severity Index has a higher discriminatory power for short-term mortality, defines a greater proportion of patients at low risk, and is slightly more accurate in identifying patients at low risk than either CURB score.     

数据挖掘分析王晖治疗肺癌用药规律

<正>王晖主任中医师是第三批全国老中医药专家学术经验继承工作指导老师,享受国务院特殊津贴,拥有宁波市首个国家中医药管理局全国名老中医专家传承工作室,临床经验丰富,对肺癌的治疗有独到见解。笔者通过收集、整理王老师治疗的肺癌病例,采用数据挖掘的方法,分析处方中药物的使用频次、关联规则,摸索处方规律及用药经验,提炼经验为临床诊治打下前期基础。1资料与方法1.1研究对象及诊断标准按原发性支气管肺癌诊     

以“四则四法”论治肺间质纤维化

[目的]通过数据挖掘方法,探讨分析宋康教授治疗间质性肺病的中医组方特色和用药规律,总结其临证经验。[方法]收集2014年1月至2018年12月于浙江中医药大学第一附属医院宋康教授门诊治疗的间质性肺病患者病案,提取所有患者处方药物,建立处方数据库;采用关联规则分析、系统聚类分析及中医复杂网络分析等数据挖掘方法进行分析总结,归纳宋康教授辨治间质性肺病的学术经验及用药规律。[结果]本研究共纳入处方1 114首,涉及药物203味。使用频次居前3位的单味药物为浙贝、桔梗、苏子,总结出核心药对3个、药组3组,并得出治疗间质性肺病的核心处方,由前胡、苏子、浙贝、桔梗、半枝莲、半边莲、虎杖、蛇舌草、鱼腥草、紫草、茜草、甘草组成。方中前胡、苏子、浙贝宣肺降气、化痰平喘,桔梗、半枝莲、半边莲、虎杖、蛇舌草、鱼腥草清热解毒散结,紫草、茜草走血分,有解毒祛瘀之功效,甘草调和诸药。[结论]宋康教授在治疗间质性肺病时以"痰""毒""瘀"为切入点,以清热解毒、化痰散瘀、化痰止咳平喘为主,重视固本祛邪、调畅气机,并擅用药对,精于配伍。     

基于数据挖掘和网络药理学的失眠中药配方规律及作用机制研究

[目的]基于数据挖掘和网络药理学方法,研究《中国知网中药方剂知识库》《中国方剂数据库》《方剂现代应用数据库》中治疗失眠的中药配伍用药规律,并探讨所得高频药组的作用机制。[方法]通过Microsoft Excel 2019软件统计中药频次,SPSS Modeler 18.0软件中的Apriori算法进行关联分析,SPSS Statistics 25.0软件进行聚类分析,运用中药分子作用机制生物信息学分析工具(bioinformatics analysis tool for molecular mechanism of TCM, BATMAN-TCM)构建“高频中药成分-靶点-疾病”网络,并对其作用靶点进行基因本体(gene ontology,GO)和京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)功能富集分析。[结果]筛选有效方剂共853首,含469味中药,核心药物有人参、茯苓、酸枣仁、当归、甘草,常用中药类型为补虚药、安神药、清热药、化痰止咳平喘药、解表药。关联规则分析得到6个药对、13个药组,聚类分析得到8个药对、7个药组,发掘得到远志、酸枣仁、当归等高频药组,其主要通过影响神经活动配体-受体相互作用,调控多巴胺能突触和γ-氨基丁酸(γ-aminobutyric acid,GABA)能突触,并通过调节氨基酸等基础代谢以及免疫功能起到抗失眠作用。[结论]关联规则与聚类分析以及网络分析结果表明,中药治疗失眠以安神益智、交通心肾、补脾益气等为主要治则,远志、酸枣仁、当归可能通过调控多巴胺能突触和GABA能突触,调节机体基础代谢和神经活动配体-受体相互作用、钙信号通路、磷酸肌醇-3-激酶-蛋白激酶β(phosphatidylinositol-3-kinase-protein kinase B,PI3K-Akt)信号通路等多条信号通路发挥治疗失眠的作用。     

基于数据挖掘和网络药理学的肥胖用药规律及潜在作用分析

[目的]基于数据挖掘分析中药治疗肥胖的组方用药规律,并通过网络药理学方法分析高频药物治疗肥胖的潜在作用机制。[方法]以中国知识资源总库(CNKI)、中国生物医学文献数据库(CBMdisc)、中文科技期刊数据库(VIP)、万方数据资源系统为资料来源。使用Excel 2016,统计学软件R3.5.2,对数据进行频数分析、关联规则分析、聚类分析及主成分分析,使用BATMAN-TCM系统对高频药物所含化合物进行检索,获取这些化合物对应靶点,与Genecard数据库中检索obesity相关靶点相映射并取交集,通过STRING 11.0构建PPI网络,对其进行网络拓扑分析,筛选出高频药物治疗肥胖病的核心靶点,通过DAVID 6.8数据库进行KEGG通路分析获取高频药物治疗肥胖病的重要通路。[结果]筛选出符合排除与纳入标准的文献共143篇,其中收录中药组方148首、药物172味。通过分析处方中药物的使用频次、并进行关联规则分析、聚类分析以及主成分分析,得出药物使用频率≥10%的药物共21种,药物关联规则按支持度、置信度、提升度排序各6个药物组合,以及2个聚类药物群和主成分分析中4个主要维度的代表用药。通过网络药理学方法发现茯苓治疗肥胖病的核心靶点10个以及和肥胖病关系密切的通路20条。[结论]中药治疗肥胖主要从"气虚""痰湿""血瘀"3个方面进行调治。以健脾利湿化痰为主要治则,故用药多以具有益气健脾,燥湿化痰功效的中药为主,辅以具有化瘀消脂作用之泽泻、山楂、丹参、荷叶等。初步验证和预测了高频药物茯苓治疗肥胖的核心靶点和重要通路,为深入研究其作用机制提供参考。     

Interestingness measures and strategies for mining multi-ontology multi-level association rules from gene ontology annotations for the discovery of new GO relationships

The Gene Ontology (GO), a set of three sub-ontologies, is one of the most popular bio-ontologies used for describing gene product characteristics. GO annotation data containing terms from multiple sub-ontologies and at different levels in the ontologies is an important source of implicit relationships between terms from the three sub-ontologies. Data mining techniques such as association rule mining that are tailored to mine from multiple ontologies at multiple levels of abstraction are required for effective knowledge discovery from GO annotation data. We present a data mining approach, Multi-ontology data mining at All Levels (MOAL) that uses the structure and relationships of the GO to mine multi-ontology multi-level association rules. We introduce two interestingness measures: Multi-ontology Support (MOSupport) and Multi-ontology Confidence (MOConfidence) customized to evaluate multi-ontology multi-level association rules. We also describe a variety of post-processing strategies for pruning uninteresting rules. We use publicly available GO annotation data to demonstrate our methods with respect to two applications (1) the discovery of co-annotation suggestions and (2) the discovery of new cross-ontology relationships.