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关键词： 副会长单位   分类分级   工业数据   京山轻机   美盈森   (中国)   联合体   宣贯  

摘要： 4月28日,中国包装联合会召开工业数据分类分级宣贯线上会议。会议由王跃中主持,MPA中国联合体联席主席兼首席专家镇锡惠主讲,MPA中国联合体联席主席高振刚、国家工业信息安全发展研究中心李红飞出席。高斯(中国)、美盈森、浙江大胜达、东经科技、北京博源包装、华兴玻璃、厦门合兴、奥瑞金、京山轻机、河南银金达、中荣印刷等中国包联副会长单位代表及中国包联相关负责人出席会议。

关键词： 理化检验质量控制   突发公共卫生事件   控制效果  

摘要： 目的分析理化检验质量控制在突发公共卫生事件中的应用价值。方法以2015年10月~2019年10月在皇姑区疾病预防控制中心登记的82例突发公共卫生事件为研究对象,将2017年11月前未实施理化检验质量控制的41例作为参照组,将2017年11月~2019年10月实施理化检验质量控制的41例作为观察组,分析理化检验质量控制的应用价值。结果观察组报告发出时间(4.09±1.02 h)、有效控制时间(22.08±3.95 h)及质量控制满意度均优于参照组(7.26±1.63 h、30.27±4.58 h),差异显著(P<0.05)。结论将理化检验质量控制应用于突发公共卫生事件控制当中,可提升控制效率、民众满意度。

关键词： Machine learning   Multi-temporal change detection   Landsat satellite imagery   Label-noise robust active learning   Multi-date classification   Automatic sample collection   SUPPORT VECTOR MACHINES   IMAGES  

摘要： Timely and reliable land-use/land-cover (LULC) change dynamic monitoring is the basis of urban understanding and planning. However, either the training sample shortage or the error accumulation in the multi-temporal processing inevitably restricts the monitoring performance. In this paper, to overcome these problems, we present a label-noise robust active learning method, which automatically collects reliable and informative samples from the images and builds a unified classification system with these augmented samples. In more detail, a Bayesian sample collection process that fuses the unsupervised transition information and the multitemporal land-cover information is designed to provide candidate samples with "from-to" labels. A reliability-based multi-classifier active learning method is then proposed to adaptively allocate the more reliable samples to the classes that are difficulty to classify. Finally, a fusion of the multiple multi-date classifications trained by the selected samples is implemented to identify the change type of interest. The dynamic monitoring results for Shanghai, Shenzhen, and Shiyan in China, two megacities with rapid and obvious urbanization and a small city with relatively slow urbanization, indicate that the proposed method achieves a significantly higher accuracy than the current state-of-the art methods. The sample accuracy verified by the high spatial resolution reference maps endorses the applicability of the sample collection, while the reliability-based active learning further ensures the robustness of the proposed method in the label-noise situation. The presented method was tested in two difficult situations (a small training sample case and a training sample set without joint labeling), so that the robustness and accuracy of the approach can be expected to be of a similar or better quality in cases with more training samples. Given its effectiveness and robustness, the proposed method could be widely applied in LULC change dyn

关键词： Ensemble learning   Active learning   Cancer classification   Gene expression data   Fuzzy set   Rough set   GENE-EXPRESSION DATA   TUMOR CLASSIFICATION   CLUSTER-ANALYSIS   PREDICTION   ALGORITHM   SELECTION   SVM  

摘要： Background and Objective: Classification of cancer from gene expression data is one of the major research areas in the field of machine learning and medical science. Generally, conventional supervised methods are not able to produce desired classification accuracy due to inadequate training samples present in gene expression data to train the system. Ensemble-based active learning technique in this situation can be effective as it determines few informative samples by all the base classifiers and ensemble the decisions of all the base classifiers to get the most informative samples. Most informative samples are labeled by the subject experts and those are added to the training set, which can improve the classification accuracy. Method: We propose a novel ensemble-based active learning using fuzzy-rough approach for cancer sample classification from microarray gene expression data. The proposed method is able to deal with the uncertainty, overlap and indiscernibility usually present in the subtype classes of the gene expression data and can improve the accuracy of the individual base classifier in presence of limited training samples. Results: The proposed method is validated using eight microarray gene expression datasets. The performance of the proposed method in terms of classification accuracy, precision, recall, F-1-measures and kappa is compared with six other methods. The improvements in accuracy achieved by the proposed method compared to its nearest competitive methods are 2.96%, 9.34%, 0.93%, 3.69%, 7.2% and 4.53% respectively for Colon cancer, Prostate cancer, SRBCT, Ovarian cancer, DLBCL and Central nervous system datasets. Results of the paired t-test justify the statistical relevance of the results in favor of the proposed method for most of the datasets. Conclusion: The proposed method is an effective general purpose ensemble-based active learning adopting the fuzzy-rough concept and therefore can be applied for other classification problem in future.

关键词： Metagenomics   Skin microbiome   Taxonomic profiling   16S sequencing   WGS sequencing   RIBOSOMAL-RNA   MICROBIAL-POPULATIONS   IDENTIFICATION   BACTERIA   DATABASE   GENES   MICROORGANISMS   METAGENOMICS   COMMUNITIES   INSIGHTS  

摘要： The assessment of microbiome biodiversity is the most common application of metagenomics. While 16S sequencing remains standard procedure for taxonomic profiling of metagenomic data, a growing number of studies have clearly demonstrated biases associated with this method. By using Whole Genome Shotgun sequencing (WGS) metagenomics, most of the known restrictions associated with 16S data are alleviated. However, due to the computationally intensive data analyses and higher sequencing costs, WGS based metagenomics remains a less popular option. Selecting the experiment type that provides a comprehensive, yet manageable amount of information is a challenge encountered in many metagenomics studies. In this work, we created a series of artificial bacterial mixes, each with a different distribution of skin-associated microbial species. These mixes were used to estimate the resolution of two different metagenomic experiments - 16S and WGS - and to evaluate several different bioinformatics approaches for taxonomic read classification. In all test cases, WGS approaches provide much more accurate results, in terms of taxa prediction and abundance estimation, in comparison to those of 16S. Furthermore, we demonstrate that a 16S dataset, analysed using different state of the art techniques and reference databases, can produce widely different results. In light of the fact that most forensic metagenomic analysis are still performed using 16S data, our results are especially important.

关键词： Hyperspectral imaging   Deoxynivalenol   Near infrared   Cereal sorting   Contamination prediction   FUSARIUM-DAMAGED KERNELS   HEAD BLIGHT   DEOXYNIVALENOL   FOOD   SPECTROSCOPY   DURUM  

摘要： Near infrared hyperspectral imaging (HSI-NIR) is considered a promising technique able to replace time-consuming, costly and destructive classic methods to predict and classify deoxynivalenol (DON) contaminated wheat kernels or samples by its concentration and level of contamination, respectively. The main objective of the present study was to standardise the HSI-NIR image acquisition method in naturally contaminated whole wheat kernels to obtain a high accuracy method to quantify and classify samples according to DON levels. To confirm the results, wheat samples were analysed by high performance liquid chromatography as the reference method to determine their DON levels. Hyperspectral images for single kernels and whole samples were obtained and spectral data were processed by multivariate analysis software. The initial work revealed that HSI-NIR was able to overcome kernel orientation, position and pixel selection. The subsequent developed Partial Least Squares (PIS) prediction achieved a RMSEP (Root Mean Square Error of Prediction) of 405 mu g/kg and 1174 mu g/kg for a cross-validated model and an independent set validated model, respectively. Moreover, the classification accuracy obtained by Linear Discriminant Analysis (LDA) was 62.7% for two categories depending on the EU maximum level (1250 mu g/kg). Despite of the results are not accurate enough for DON quantification and sample classification, they can be considered a starting point for further improved protocols for DON management.