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关键词： 过程控制   质量控制   资源管理   质量分析  

摘要： 本文阐述了实验室信息管理系统(LIMS)在实验室质量管理中的作用,分析了LIMS在运行中的质量控制、资源管理、质量分析等问题,旨在强化和提升检验检测机构工作人员对LIMS系统的认识,提高人员素质、规范管理,主动学习和运用好LIMS系统,从基础上保证和维护检验机构的检验检测能力。

关键词： 急性脑梗死   缺血性修饰白蛋白   脂蛋白相关磷脂酶A2   超敏C-反应蛋白   心电图  

摘要： 目的:探讨缺血性修饰白蛋白(IMA)、脂蛋白相关磷脂酶A2(Lp-PLA2)、超敏C-反应蛋白(hs-CRP)和心电图(ECG)联合检验对急性脑梗死患者的作用和意义。方法:选取160例第一作者医院收取的急性脑梗死患者,按照OCSP分型分为完全前循环梗死(TACI)组、部分前循环梗死(PACI)组、后循环梗死(POCI)组和腔隙性梗死(LACI)组,每组40例。同时选取同期本院健康体检者40例为对照组。分析实验对象的IMA、Lp-PLA2和hs-CRP的含量,并分析其ECG差异。结果:随着脑梗死患者疾病程度的加重,TACI组、PACI组、POCI组和LACI组患者血液中IMA、Lp-PLA2、hs-CRP的含量显著上升; ECG异常人数明显增多,差异具有统计学意义(P <0. 05)。结论:患者ECG异常率,IMA、Lp-PLA2和hs-CRP含量随分型病情程度加重而增高,可用于临床监测患者急性脑梗死的病情。

关键词： 尿液标本   显微镜检查   全自动尿沉渣分析仪   作用  

摘要： 目的探讨和分析在尿沉渣检验中全自动尿沉渣分析仪作用。方法此次抽取2016年3月至2018年5月在我医院做尿沉渣检查的尿液标本(96例)当做分析的对象,此次研究所有标本均进行显微镜检查、全自动尿沉渣分析仪检查,把镜检结果当做诊断标准,比较两种方法的检验结果。结果在此次研究的96例标本中,镜检管型阳性是7例、白细胞阳性是31例、红细胞阳性是27例。全自动尿沉渣分析仪管型阳性是13例,检验假阳性是6.74%,检验准确率是93.26%;白细胞阳性是33例,检验假阳性是3.08%,检验准确率是96.92%;红细胞阳性是31例,检验假阳性是5.80%,检验准确率是94.20%。结论在尿沉渣检验中,全自动尿沉渣分析仪对于尿沉渣进行检验的准确率高,但是也存在一定的假阳性情况,所以在尿沉渣检验中,应镜检联合全自动尿沉渣分析仪进行检查,确保检验准确和可靠。

关键词： 国际危规   腐蚀性物质   分类   运输   包装  

摘要： 从定义、分类标准和分类方法等方面介绍了国际危规中8类腐蚀性物质的分类方式,主要服务于检测机构的分类工作,对企业如何根据准确的分类结果选择合适的包装,并进行规范的托运,确保运输的安全性给出建议。

关键词： Metabolomics   Reliability   Bootstrap   Uncertainty estimation   Chemometrics   Biomarker discovery  

摘要： Recent progress in metabolomics has been aided by the development of analysis techniques such as gas and liquid chromatography coupled with mass spectrometry (GC-MS and LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. The vast quantities of data produced by these techniques has resulted in an increase in the use of machine algorithms that can aid in the interpretation of this data, such as principal components analysis (PCA) and partial least squares (PLS). Techniques such as these can be applied to biomarker discovery, interlaboratory comparison, and clinical diagnoses. However, there is a lingering question whether the results of these studies can be applied to broader sets of clinical data, usually taken from different data sources. In this work, we address this question by creating a metabolomics workflow that combines a previously published consensus analysis procedure with PCA and PLS models using uncertainty analysis based on bootstrapping. This workflow is applied to NMR data that come from an interlaboratory comparison study using synthetic and biologically obtained metabolite mixtures. The consensus analysis identifies trusted laboratories, whose data are used to create classification models that are more reliable than without. With uncertainty analysis, the reliability of the classification can be rigorously quantified, both for data from the original set and from new data that the model is analyzing.

关键词： plasma target interaction   plasma dielectric interaction   plasma jet   electric field   surface charges   Mueller polarimetry   electro-optic crystals  

摘要： Numerical simulations and experiments are performed to better understand the interaction between a pulsed helium plasma jet and a dielectric target. The focus of this work lies on the volume and surface charge influence on the electric field distribution. Experimentally, the electric field due to surface charges is measured inside an electro-optic target under exposure of a plasma jet, using the optical technique called Mueller polarimetry. For the first time, the time-resolved spatial distributions of both the axial and radial components of electric field inside the target are obtained simultaneously. A 2D fluid model is used in a complementary way to the experiments in order to study separately the contribution of volume charges and surface charges to the spatio-temporal evolutions of the electric field during the plasma-surface interaction. The experimental investigation shows that the average axial and radial components of electric field inside the dielectric target, only due to surface charges, are lower than generally reported for electric field values in the plasma plume. Thanks to the phenomenological comparison with experiments, simulations show that during the plasma-surface interaction two effects sequentially determine the electric field inside the target: firstly, a relatively high electric field is observed due to the proximity of the ionization front;afterwards, in longer timescales, lower electric fields are induced due to the contribution of both leftover volume charges close to the target and surface charges deposited on its surface. The experimental technique provides a unique way to examine this second phase of the plasma-surface interaction.

关键词： N mineralization   Nematodes   Soil biome   Microorganismos de la montana   Local effect microorganisms  

摘要： Culturing and using effective microorganisms from locally derived microorganisms has become a common practice employed by farmers throughout Central America as well as parts of South America and Southeast Asia to control foul odors and flies around compost piles and livestock facilities. These local, effective microorganisms (LEM) are applied in a concentrated solution fermented from carbohydrate-rich substrates inoculated with locally-sourced, actively decomposing leaf litter. Despite its growing prevalence among small-scale farmers in some parts of the world, there is little published research that explores the beneficial or detrimental effects of changing the biome of animal-based nutrients and the soils to which LEM are applied. The objectives of this study were to evaluate the effect of combining LEM with swine effluent to fertilize annual ryegrass (Lolium multiflontm) on plant-available nitrogen, nematode community structure, and forage productivity. Forage performance was good across all of the treatments - with relative feed quality (RFQ) values above 200 and 100 in the March and May harvests respectively. In the first, second and third sampling dates, soils from 0 to 5 cm depth showed more nitrogen mineralized in LEM-treated soils than in control soils (p = 0.178, p < 0.0001 and p = 0.027) respectively. After nutrient applications, soil-nematode community structure (0-10 cm) at the trophic group level changed over time. Most changes were similar between treatments, however, plant parasitic nematode populations were found to be significantly higher in LEM-treated plots in all but one of the sampling dates. LEM is gaining popularly as a natural amendment and although we have shown that it does increase N availability and changes trophic-level nematode community structure, more research is needed to optimize the potential of these benefits.

关键词： Object-based classifiers   Pixel-based classifiers   Landsat-8 imagery   Training sample size  

摘要： In this study, we used Landsat-8 imagery to test object- and pixel-based image classification approaches in an urban fringe area. For object-based classification, we applied four machine learning classifiers: decision tree (DT), naive Bayes (NB), random trees (RT), and support vector machine (SVM). For pixel-based classification, we utilized the maximum likelihood classifier (MLC). Specifically, we explored the influence of repeated sampling on classification results with different training sample sizes. We found that (1) except the overall accuracy of NB, those of the other four classifiers increased as the training sample size increased;(2) repeated sampling had a significant effect on classification accuracy, especially for the DT and NB classifiers;and (3) SVM achieved the best classification accuracy. In addition, the performance of the object-based classifiers was superior to that of the pixel-based classifier. The results of this study can provide guidance on the training sample size and classifier selection.

关键词： Visual aesthetic quality assessment   Aesthetic understanding   Sample-specific weighting  

摘要： Currently image aesthetic estimation using deep learning has achieved great success compared with the traditional methods by hand-crafted features. Similar to recognition problem, aesthetic estimation categorizes images into visually appealing or not. Nevertheless, it is desirable to understand why certain images are visually more appealing, in specific, which part of the image is contributing to the aesthetic preference. In fact, most traditional approaches adopting hand-crafted feature are, to some extent, able to understand part of image's aesthetic and content information while few studies have been conducted in the context of deep learning. Moreover, we discover that aesthetic rating is ambiguous so that many examples are uncertain in aesthetic level. This has caused a highly imbalanced distribution of aesthetic ratings. To tackle all these issues, we propose an end-to-end convolutional neural network (CNN) model which simultaneously implements aesthetic classification and understanding. To overcome the imbalanced aesthetic ratings, a sample-specific classification method that re-weights samples' importance is proposed. We find that dropping out ambiguous image, as common adopted by recent deep learning models, is a special case of the sample-specific method, and also figure out that as the weights of the non-ambiguous images increase, the performance is positively affected. In order to understand what is learned in the deep model, global average pooling (GAP) following the last feature map is employed to generate aesthetic activation map (AesAM) and attribute activation map (AttAM). AesAM and AttAM respectively represent the likelihood of aesthetic level for spatial location, and the likelihood of different attribute information. In particular, AesAM mainly accounts for what is learned in deep model. Experiments are carried out on public aesthetic datasets and state-of-the-art performance is achieved. Thanks to the introduction of AttAM, the aesthetic preferen

关键词： Electronic tongue   Sensor array   Pharmaceuticals   Pattern recognition techniques   Taste-masking  

摘要： This work reports a critical evaluation of performance of various pattern recognition techniques applied to the classification of pharmaceutical taste-masked samples. Data obtained by potentiometric electronic tongue equipped with 16 ion-selective electrodes (ISEs) were processed by the most frequently used techniques in the analysis of electronic tongue data. Principal component analysis, partial least squares discriminant analysis, soft independent modelling of class analogy, principal component regression, support vector machine discriminant analysis, 3-way partial least squares, K-nearest neighbours as well as combination of principal components analysis and back propagation neural networks were tested. In order to compare their ability to estimate class affinity of pharmaceutical samples, sensitivity, precision, percent of correct classification (%cc) and root mean square error (RMSE) were calculated. Additionally, 4 different kinds of data matrices: dynamic responses, stationary responses, combinations of them both, CPA values (change of the membrane potential caused by adsorption) were processed by pattern recognition techniques for the determination of the influence of the extraction of the data on the classification results. SVM-DA is proved to exhibit the best performance for the most commonly applied data extraction i.e. the steady-state response of the sensor array. Furthermore, it is shown, that including dynamic responses in the data matrix better classification abilities of the majority of the studied pattern recognition techniques are obtained. It must be underlined, that the presented findings are based on studying 399 models for whom all performance factors (sensitivity, precision, %cc, RMSE) were determined for both train and test sets to obtain reliable and repeatable results. (C) 2018 Elsevier B.V. All rights reserved.