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关键词： 父母心理控制   欺负行为   敌意归因   冷酷无情  

摘要： 采用班级整群抽样法选取1847名3~6年级小学生(男生987名;平均年龄10.73±1.16岁)及其家长为被试,采用问卷法考察父母心理控制与小学生欺负行为的关系,同时探讨敌意归因和冷酷无情的中介作用。结果发现:(1)父母心理控制显著正向预测小学生的欺负行为;(2)冷酷无情在父母心理控制与小学生欺负行为间的关系中存在中介作用,敌意归因不发挥中介作用;(3)父母心理控制对小学生欺负行为影响的中介机制不存在显著的性别差异和学段差异。本研究结果表明,冷酷无情是父母心理控制影响小学生欺负行为的重要机制,但敌意归因不是。研究者和实践者应注重对欺负者情感加工能力的关注和干预。

关键词： 急性脑梗死   中医证型   检验指标  

摘要： 目的观察急性脑梗死(ACI)患者不同中医证型与检验指标差别及规律。方法收集2018年11月—2019年10月在中国中医科学院西苑医院脑病科就诊的ACI患者405例,依照中医辨证分型为风痰阻络组159例、痰浊上扰组81例、阴虚风动组37例、痰瘀阻络组45例、阴虚阳亢组38例和气虚血瘀组45例,分析不同中医证型ACI患者检验指标变化,绘制受试者工作特征(ROC)曲线评价所观察指标对中医证型的鉴别诊断价值。结果痰瘀阻络组PLT水平低于气虚血瘀组、阴虚阳亢组和风痰阻络组(P<0.01);痰瘀阻络组TCH和Apo-B水平均低于气虚血瘀组(P<0.01);痰浊上扰组PLT水平低于阴虚阳亢组(P<0.05);痰浊上扰组UA水平高于气虚血瘀组(P<0.05);痰浊上扰组Apo-A1水平高于阴虚风动组(P<0.05)。UA在鉴别气虚血瘀组和痰浊上扰组时曲线下面积(AUC)是0.662;PLT在鉴别风痰阻络组和痰瘀阻络组时AUC是0.615;PLT在鉴别气虚血瘀组和痰瘀阻络组时AUC是0.694;PLT在鉴别阴虚阳亢组和痰瘀阻络组时AUC是0.661;Apo-A1在鉴别痰浊上扰组和阴虚风动组时AUC是0.7。结论 PLT、TCH、Apo-B、UA和Apo-A1等指标,可为辨证诊治ACI提供可量化参考。

关键词： 糖尿病肾病   早期诊断   生化检验   精准性  

摘要： 目的讨论糖尿病肾病早期诊断中临床生化检验的作用。方法随机抽取本院2016年10月~2018年6月期间收治的120例糖尿病肾病患者,纳入观察组;同期抽取于本院体检者120例,纳入健康组,通过血糖检查、生化检验的方式,对其检测结果予以鉴别。结果观察组FPG、2hPG、HbAlc等血糖指标明显高于健康组,两组患者各数据间比较,存在统计学差异(P<0.05)。观察组FPG指标为(10.9±4.3)mmol/L、2hPG指标为(14.3±4.8)mmol/L;健康组FPG指标为(4.8±2.5)mmol/L、2hPG指标为(8.7±2.0)mmol/L。健康组CRP、CysC、UALB/Cr、α1-MG等生化指标值低于观察组,两组患者各数据间比较,存在统计学差异(P<0.05)。健康组CRP指标为(1.2±0.4)mg/L、UALB/Cr指标为(4.2±1.6)mg/g;观察组CRP指标为(8.7±4.7)mg/L、UALB/Cr指标为(65.8±6.8)mg/g。结论在糖尿病肾病患者早期诊断时,生化检验能够保证诊断结果的精准性,便于患者尽早治疗,保证良好的预后效果。

关键词： 环境样品   挥发性石油烃   分类检测   吹扫捕集-气相色谱质谱法  

摘要： 该文采用吹扫捕集-气相色谱质谱法研究环境样品中挥发性石油烃的分类检测技术,挥发性石油烃经高纯氮气(或氦气)吹扫后吸附于捕集阱中,将捕集阱加热并以高纯氮气反吹,被热脱附出来的挥发性组分经气相色谱分离,质谱检测器检测。根据2-甲基戊烷、癸烷、甲苯和1,2,4-三甲苯保留时间窗定性,在确定的时间窗口内提取特定离子,分别检测脂肪族和芳香族石油烃,外标法定量。实验结果表明,在10~300μg/L线性范围内,脂肪族和芳香族线性关系良好,相关系数大于0.999,水质样品中挥发性石油烃的方法检出限为0.8~0.9μg/L,土壤和沉积物中挥发性石油烃的方法检出限为0.7mg/kg,样品的加标回收率在70%~90%,精密度测试RSD值均小于10%。

关键词： Microfibers   Microplastic   Stimulated Raman scattering   SRS microscopy   Vibrational microspectroscopy   MICROPLASTICS   IDENTIFICATION   WATER   CONTAMINATION   ACCUMULATION   PARTICLES   RELEASE   FIBERS  

摘要： Microfibers are reported as the most abundant microparticle type in the environment. Their small size and light weight allow easy and fast distribution, but also make it challenging to determine their chemical composition. Vibrational microspectroscopy methods as infrared and spontaneous Raman microscopy have been widely used for the identification of environmental microparticles. However, only few studies report on the identification of microfibers, mainly due to difficulties caused by their small diameter. Here we present the use of Stimulated Raman Scattering (SRS) microscopy for fast and reliable classification of microfibers from environmental samples. SRS microscopy features high sensitivity and has the potential to be faster than other vibrational microspectroscopy methods. As a proof of principle, we analyzed fibers extracted from the fish gastrointestinal (GIT) tract, deep-sea and coastal sediments, surface seawater and drinking water. Challenges were faced while measuring fibers from the fish GIT, due to the acidic degradation they undergo. However, the main vibrational peaks were still recognizable and sufficient to determine the natural or synthetic origin of the fibers. Notably, our results are in accordance to other recent studies showing that the majority of the analyzed environmental fibers has a natural origin. Our findings suggest that advanced spectroscopic methods must be used for estimation of the plastic fibers concentration in the environment. (c) 2020 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： FATTY LIVER-DISEASE   PREVALENCE   RISK   LIGHT  

摘要： Objective: To study the interaction of alcohol consumption with body mass index (BMI) in the development of hepatic steatosis and mortality. Participants and Methods: We conducted a retrospective cohort study of 18,506 participants without fatty liver disease or cirrhosis at enrollment in the Mayo Clinic Biobank from April 9, 2009, through March 31, 2016. Participants were classified by self-reported alcohol consumption status (nondrinkers, moderate drinkers [0 to 2 drinks per day], and heavy drinkers [>2 drinks per day]). The primary outcome of interest was the incidence of hepatic steatosis, identified by International Classification of Diseases, Ninth Revision code and confirmed with imaging. The secondary outcome of interest was all-cause mortality. Multivariate Cox regression analysis determined the impact of alcohol consumption stratified by BMI on outcomes compared with nondrinkers. Results: The cohort (mean SD age, 55.8 +/- 16.9 years; 63.8% female; mean +/- SD BMI, 28.8 +/- 6.1 kg/m(2)) of 18,506 participants included 3657 (19.8%) nondrinkers, 14,236 (76.9%) moderate drinkers, and 613 (3.3%) heavy drinkers at enrollment. After a median follow-up of 5.8 years (interquartile range, 3.8 to 7.2 years), 684 participants had development of hepatic steatosis and 968 died. In moderate drinkers, the risk of hepatic steatosis development was high in the obese group (adjusted hazard ratio [AHR], 1.31; 95% CI, 1.03 to 1.67), insignificant in the overweight group (AHR, 0.86; 95% CI, 0.58 to 1.26), and decreased in the normal-BMI group (AHR, 0.48; 95% CI, 0.26 to 0.90). Heavy drinkers had an increased risk of hepatic steatosis irrespective of BMI. Moderate alcohol use was associated with decreased mortality in the normal-weight (AHR, 0.44; 95% CI, 0.34 to 0.58) and overweight (AHR, 0.70; 95% CI, 0.56 to 0.88) groups but not in the obese group (AHR, 0.80; 95% CI, 0.64 to 1.00). Conclusion: In obese individuals, even moderate alcohol use is associated with the development

关键词： ICA   PCA   Speech   Stress   Classification   EMOTION RECOGNITION   COMPONENT ANALYSIS   SPEECH   ALGORITHMS   FEATURES   SCHEMES  

摘要： Emotion detection is a hot topic nowadays for its potential application to intelligent systems in different fields such as neuromarketing, dialogue systems, friendly robotics, vending platforms and amiable banking. Nevertheless, the lack of a benchmarking standard makes it difficult to compare results produced by different methodologies, which could help the research community improve existing approaches and design new ones. Besides, there is the added problem of accurate dataset production. Most of the emotional speech databases and associated documentation are either privative or not publicly available. Therefore, in this work, two stress-elicited databases containing speech from male and female speakers were recruited, and four classification methods are compared in order to detect and classify speech under stress. Results from each method are presented to show their quality performance, besides the final scores attained, in what is a novel approach to the field of study.

摘要： Introduction Large efforts have been made to divide bladder cancer into prognostic or predictive subtypes by unsupervised or semi-supervised clustering of mRNA data. To validate and make use of subtypes, classifiers must be constructed that accurately predicts subtypes in new samples. Variations of nearest centroid classification is commonly used, where each class is represented as a vector of mean fold changes of selected genes. The similarity to these vectors is then used to predict the class of a new sample. Using relative fold change values means that classification becomes related to both the composition of the training cohort and the cohort the new sample originates from. Without thorough data preprocessing, this can result in major classification errors. Optimally, we want to classify a new sample in isolation in a manner robust to preprocessing, sample purity, and mRNA measurement technology. Methods We evaluated single sample classification methods based on using sets of gene-pair rules (e.g. Gene A>Gene B indicates Subtype X), using the R packages AIMS and SwitchBox, and a custom iterative RandomForest (RF) approach, as well as the single sample centroid approach used by Kamoun et. el. These were tested on Lund, TCGA, and IMvigor210 data. Results Rule-based and centroid based single sample classifiers were able to achieve similar accuracy in predicting LundTaxonomy subtypes in the datasets. The single sample centroid classifier based on raw expression values suffered from poor separation between predictions (delta between best and second-best prediction), which becomes worse as more genes were included. Rule-based methods, particularly RF, had much more distinct subtype calls. The custom RF approach outperformed the published AIMS and SwitchBox methods. Conclusions Each single sample classifier method had strengths and limitations. The RF based approach emerged as a promising new method, being robust even when using very few genes, while also allowi

关键词： Dalbergia wood   Species characterization   Geolocation   Classification   SUPPORT VECTOR MACHINES   PARTIAL LEAST-SQUARES   NEAR-INFRARED SPECTROSCOPY   PATTERN-RECOGNITION   ISOTOPE   DISCRIMINATION   WOODS   MEAT  

摘要： New regulations concerning the trade in protected tropical hardwoods require a way to assess the species and the country of origin for wood samples. Rosewoods from the Dalbergia genus are heavily traded and are now one of the wood sources that are controlled. While may efforts have been reported on characterization of the species of Dalbergia samples, none has investigated the feasibility of providing probable provenance of the sample by locating the country where the tree grew. This study reports use of several well-known classifiers applied to data from an extensive elemental analysis and isotope ratio analysis of 239 curated samples of wood from the genus Dalbergia. The species and the country of origin can be identified with about 90% balanced accuracy.

关键词： Ascending thoracic aortic aneurysm   Aortic stenosis   Shear stress   CFD   Finite-element analysis   FLUID-DYNAMICS   SHEAR-STRESS   VALVE   MORPHOLOGY   AORTOPATHY   DISSECTION   PATIENT  

摘要： An ascending thoracic aortic aneurysm (ATAA) is a life-threatening cardiovascular consequence of vessel dilatation that portends adverse events and death. From a clinical perspective, ATAA should not be treated as an isolated disease, and surgery is often carried out in the presence of AS, aortic insufficiency or a calcified valve leaflet. Aortic stenosis (AS) is common in ATAAs and leads to both vessel rigidity and left ventricular (LV) impairment. In this study, lumped-parameter modeling and computational analysis were used to assess the change in the wall shear stress (WSS) and intramural wall stress of patient-specific ATAA models with different degrees of AS (i.e., mild to severe). The ATAAs of four patients were reconstructed from imaging data and AS was simulated virtually using the lumped-based CircAdapt tool using clinical and echocardiographic data. Results show that LV work derived from pressure-volume loops increased with the severity of AS. Post-stenotic hemodynamic and structural variables markedly increased with AS severity, with WSS showing a 10-fold increase for the most severe AS model as compared to the baseline model with a well-functioning aortic valve. Most importantly, the increase in WSS and aortic wall stress was associated with pronounced values of valvulo-arterial impedance as an indicator of LV dysfunction. This study provides novel insights into progression of AS in patients with ATAAs at high risk of adverse events, and the potential value of valvulo-arterial impedance to predict changes in hemodynamic and structural parameters with the severity of AS. (c) 2020 Elsevier Ltd. All rights reserved.