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关键词： 中医药   中医教育   质量控制   传承   检验医学  

摘要： 谢小兵:中医药是中国五千年历史文明的象征,是我国特有的、与人文充分融合的医学科学。中医药的传承与发展是实现民族文化伟大复兴的重要载体,对我国医药行业的发展有着不可替代的深远意义。随着现代医学的发展和普及,中医药传承与发展受到了极大的影响和冲击,中医药理论、中医医疗模式、中医药秘方及相关成果、中医药人才等面临严重挑战,特别是目前的中医药教育模式、科研模式、医疗模式、宏观管理等关键因素存在诸多矛盾和问题。

关键词： 产品质量检测   市场监管   检验方法  

摘要： 产品质量关系着一个国家经济增长和稳定发展,涉及国计民生问题。在经济发展新常态的今天,产品质量检测作为市场监督管理部门质量监督的重要手段,好的产晶质量不仅能够维持市场的繁荣稳定发展,也是加速转变经济发展方式的必然要求。

关键词： 尿液常规检验   生化常规检验   糖尿病   诊断  

摘要： 目的探究尿液常规检验与生化常规检验在糖尿病诊断中的作用效果。方法纳入此次研究中的65例疑似糖尿病患者均选自我院2018年6月-2019年6月,对其分别选择尿液常规检验和生化常规检验,比较两种方法的诊断效果。结果统计对比尿液常规检验和生化常规检验灵敏度以及正确度,组间数据差异显著,具有统计学意义(P<0.05);统计对比尿液常规检验和生化常规检验特异度,组间数据并无统计学意义(P>0.05)。结论生化常规检验对糖尿病进行诊断具有较高的灵敏度、特异度以及正确度,可将糖尿病检出率提升。

关键词： Autism spectrum disorder   fNIRS   Time series   Classification   Machine learning   Sample entropy   FRONTAL-CORTEX   CHILDREN   CONNECTIVITY   BOLD  

摘要： Objectives To assess the possibility of distinguishing autism spectrum disorder (ASD) based on the characteristic of spontaneous hemodynamic fluctuations and to explore the location of abnormality in the brain. Methods Using the sample entropy (SampEn) of functional near-infrared spectroscopy (fNIRS) from bilateral inferior frontal gyrus (IFG) and temporal cortex (TC) on 25 children with ASD and 22 typical development (TD) children, the pattern of mind-wandering was assessed. With the SampEn as feature variables, a machine learning classifier was applied to mark ASD and locate the abnormal area in the brain. Results The SampEn was generally lower for ASD than TD, indicating the fNIRS series from ASD was unstable, had low fluctuation, and high self-similarity. The classification between ASD and TD could reach 97.6% in accuracy. Conclusions The SampEn of fNIRS could accurately distinguish ASD. The abnormality in terms of the SampEn occurs more frequently in IFG than TC, and more frequently in the left than in the right hemisphere. Significance The results of this study may help to understand the cortical mechanism of ASD and provide a fNIRS-based diagnosis for ASD.

关键词： Land cover classification   In-situ reference data   LUCAS   Sentinel-2   Remote sensing   TIME-SERIES   SCALE   ACCURACY   OPENSTREETMAP   INDEX   MAPS   CROP  

摘要： In this study, we test the use of Land Use and Coverage Area frame Survey (LUCAS) in-situ reference data for classifying high-resolution Sentinel-2 imagery at a large scale. We compare several pre-processing schemes (PS) for LUCAS data and propose a new PS for a fully automated classification of satellite imagery on the national level. The image data utilizes a high-dimensional Sentinel-2-based image feature space. Key elements of LUCAS data pre-processing include two positioning approaches and three semantic selection approaches. The latter approaches differ in the applied quality measures for identifying valid reference points and by the number of LU/LC classes (7-12). In an iterative training process, the impact of the chosen PS on a Random Forest image classifier is evaluated. The results are compared to LUCAS reference points that are not pre-processed, which act as a benchmark, and the classification quality is evaluated by independent sets of validation points. The classification results show that the positional correction of LUCAS points has an especially positive effect on the overall classification accuracy. On average, this improves the accuracy by 3.7%. This improvement is lowest for the most rigid sample selection approach, PS2, and highest for the benchmark data set, PS0. The highest overall accuracy is 93.1% which is achieved by using the newly developed PS3; all PS achieve overall accuracies of 80% and higher on average. While the difference in overall accuracy between the PS is likely to be influenced by the respective number of LU/LC classes, we conclude that, overall, LUCAS in-situ data is a suitable source for reference information for large scale high resolution LC mapping using Sentinel-2 imagery. Existing sample selection approaches developed for Landsat imagery can be transferred to Sentinel-2 imagery, achieving comparable semantic accuracies while increasing the spatial resolution. The resulting LC classification product that uses the newly

关键词： cell type hierarchy   cell type identification   multiscale classification   sample size estimation   single-cell   RNA-SEQ DATA   VISUALIZATION   SIGNATURES   BIOLOGY   STATES  

摘要： Automated cell type identification is a key computational challenge in single-cellRNA-sequencing (scRNA-seq) data. To capitalise on the large collection of well-annotated scRNA-seq datasets, we developed scClassify, a multiscale classification framework based on ensemble learning and cell type hierarchies constructed from single or multiple annotated datasets as references. scClassify enables the estimation of sample size required for accurate classification of cell types in a cell type hierarchy and allows joint classification of cells when multiple references are available. We show that scClassify consistently performs better than other supervised cell type classification methods across 114 pairs of reference and testing data, representing a diverse combination of sizes, technologies and levels of complexity, and further demonstrate the unique components of scClassify through simulations and compendia of experimental datasets. Finally, we demonstrate the scalability of scClassify on large single-cell atlases and highlight a novel application of identifying subpopulations of cells from the Tabula Muris data that were unidentified in the original publication. Together, scClassify represents state-of-the-art methodology in automated cell type identification from scRNA-seq data.

关键词： Stable gene selection   Self-representation   Fuzzy classifier   UNSUPERVISED FEATURE-SELECTION   MICROARRAY   CANCER   PREDICTION  

摘要： In recent years, microarray technology and gene expression profiles have been widely used to detect, predict, or classify the samples of various diseases. The presence of large genes in these profiles and the small number of samples are known challenges in this field and are widely considered in previous papers. In previous studies, other topics such as the noise of microarray data or the dependence of selected genes on samples have been less considered. Therefore, we have tried to address these two issues by using a fuzzy classifier and stability index of selected genes, respectively. The proposed method is based on the regression function between the genes and class labels which is determined by the self-representing method. This regression function is determined individually for each class of the database. To minimize the effect of noise in microarray data, a fuzzy classifier is applied in the proposed model. Four databases of gene expression profiles are examined in this article, and the results indicate that the proposed model has a relative advantage over the previous methods.

关键词： Eigenvector spectra   Feature extraction   Limited training sample classification   Hyperspectral image   WEIGHTED FEATURE-EXTRACTION  

摘要： Classical supervised feature extraction methods, such as linear discriminant analysis (LDA) and nonparametric weighted feature extraction (NWFE), and search for projection directions through which the ratio of a between-class scatter matrix to a within-class scatter matrix can be maximized. The two feature extraction methods can obtain good classification results when training samples are sufficient; however, the effect is nonideal when samples are insufficient. In this study, the eigenvector spectra of LDA and NWFE are modified using spectral distribution information, which is locally unstable under the condition of a few samples. Experiments demonstrate that the proposed method outperforms several conventional feature extraction methods.

关键词： Cannabis   college students   driving under the influence   marijuana   riding with a high driver   theory of planned behavior   MARIJUANA LEGALIZATION   ALCOHOL   RISK   PASSENGERS   CONSEQUENCES   PREVALENCE   VIOLATIONS   DRINKING   EFFICACY   GENDER  

摘要： Objective: As marijuana use becomes more available to college students through increasing legal reform, this paper seeks to examine intentions for driving under the influence of cannabis (DUIC) and riding with a high driver (RWHD) through the lens of the theory of planned behavior (TPB) and assess potential interactions between personal attitudes, subjective norms, perceived behavioral control (PBC), and sex. Participants: Undergraduate college students (N = 311) completed online surveys in September, 2013. Method: Participants self-reported their attitudes toward DUIC, subjective norms, PBC, past DUIC and RWHD, and intentions to DUIC and RWHD. Results: Participants' attitudes toward DUIC, subjective norms, and PBC were strongly associated with intentions to DUIC and RWHD bivariately. In regression models, attitudes and PBC were both positively and significantly related to intentions to DUIC and RWHD. Conclusions: DUIC and RWHD are concerns for college populations. Targeting personal attitudes and perceived behavioral control via interventions may reduce these behaviors.