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关键词： Land cover classification   In-situ reference data   LUCAS   Sentinel-2   Remote sensing  

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 d

关键词： cell type hierarchy   cell type identification   multiscale classification   sample size estimation   single-cell  

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  

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  

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  

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.

关键词： 实地调研   上市公司违规   违规稽查  

摘要： 上市公司质量是一国经济发展和资本市场运行的基石。本文分别从事前和事后的视角考察了投资者实地调研与上市公司违规之间的作用机制及其作用效果。研究发现,在事前,投资者实地调研能够通过发挥信息效应与治理效应抑制公司违规行为;在事后,投资者实地调研还能够提高违规行为被稽查的效率。进一步地,对作用效果深入考察后发现对于违规空间更大、违规动机更强、所处行业可实地观察信息更多的公司,投资者实地调研对其违规行为产生的影响更加明显。研究结果为当前监管部门规范上市公司运作、完善投资者实地调研制度、加强投资者保护等方面提供了有益的启示。

关键词： 化学试剂分类   药品检验机构   化学试剂管理  

摘要： 目的浅析在药品检验机构化学试剂管理中化学试剂分类的作用。方法选取2018年3月至2019年3月某药品检验机构管理的21例管理人员为研究对象,比较化学试剂分类管理实施前后的管理质量。结果化学试剂分类管理实施后,管理质量评分明显高于管理前(P<0.05);学试剂分类管理实施后,差错率为4.76%、低于管理前的28.57%,安全事故率为0%、低于管理前的19.05%(P<0.05)。结论在药品检验机构化学试剂管理中应用化学试剂分类管理,能提高管理效率,保证管理质量,降低因管理不到位而造成的人身伤害或环境影响。

关键词： 违反规定   一次性筷子   垃圾分类   集贸市场   生活垃圾管理   洗漱用品   服务提供者   塑料袋  

摘要： 随着5月1日的到来,《北京市生活垃圾管理条例》正式执行。根据《条例》,北京超市、商场、集贸市场等商品零售场所不得免费提供塑料袋;餐饮经营者及配送服务提供者和旅馆经营单位不得主动向消费者提供一次性筷子、叉子、勺子、洗漱用品等。生活垃圾分类违反规定且屡教不改的个人将被处以50-200元罚款,单位罚金则高至1万-5万元,单位罚款下限比上海的5000元高一倍。

关键词： 临床微生物检验   控制医院感染   临床作用  

摘要： 目的:分析临床微生物检验在控制医院感染中的作用。方法:选取2015年6月-2017年3月住院患者200例,随机分为两组,各100例。观察组住院期间进行微生物检验和监测;对照组住院期间不采用微生物监测。比较两组医院感染情况。结果:观察组医院感染发生率及感染程度均显著低于对照组,差异有统计学意义(P<0.05)。结论:采用临床微生物检验能有效控制医院感染发生率,降低感染程度,有效提高患者临床治疗效果。