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

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： Hyperspectral imaging   Deoxynivalenol   Near infrared   Cereal sorting   Contamination prediction  

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.

关键词： Validation   Minisequencing   SNaPshot   Biogeographic origin   Highly degraded samples   Forensic laboratories  

摘要： Mitochondrial DNA (mtDNA) is a useful tool in forensic investigation as it provides information about the matrilineal ancestry of individuals. In addition, mtDNA can be analyzed when the analysis of other nuclear markers is underperforming. Recently, we developed a minisequencing panel for the simultaneous analysis of 52 mtDNA SNPs to classify maternal lineages into the main haplogroups and their phylogeographic origin. In order to make this panel suitable for forensic genetics laboratories, a validation study has been performed in accordance with the Scientific Working Group on DNA Analysis Methods (SWGDAM) guidelines, including species specificity, reproducibility, sensitivity, and stability tests. The results demonstrate that the panel of 52 mtDNA SNPs is highly sensitive, since it enables to obtain complete genetic profiles of samples containing minimal amounts of DNA (1 pg). Furthermore, it provides sufficient genetic information to detect the matrilineal biogeographical origin of highly degraded samples, i.e., ancient dating skeletal remains, and samples with the presence of inhibitors, such as hematin and humic acid. In addition, this panel can detect mixtures in samples whose mtDNA haplogroups of contributors are different. Overall, the results of this study demonstrate the suitability of this minisequencing panel of 52 mtDNA SNPs to be used in forensic cases, with samples of low amount or degraded DNA.

关键词： Ensemble learning   Active learning   Cancer classification   Gene expression data   Fuzzy set   Rough set  

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  

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： Emotional solidarity   social distance   place attachment   contact   festival   World Heritage Site  

摘要： Building on common identity theory and intergroup contact theory, this study sought to further understanding of people-place relationships by developing a holistic theoretical model to scrutinize place attachment as an antecedent of social distance, mediated by emotional solidarity and moderated by frequency of contact between tourists and residents. Visitor data analyzed with SEM revealed that place dependence is a significant predictor of social distance given it affects affinity positively and avoidance negatively, both of which are mediated by the three dimensions of emotional solidarity. Furthermore, the mediated relationships (via emotional solidarity) between place attachment and social distance vary by level of visitors' frequency of interaction with residents. This study expands current theorization by examining the merits of emotional solidarity as an affective link in a tourist cognitive-behavioral model. From a practical standpoint, DMOs need to understand these construct linkages and include residents in their marketing strategies to increase repeat visitation.

关键词： 卫生理化检验   疾病   预防控制   作用  

摘要： 研究卫生理化检验在疾病预防控制中的作用。方法:取2018年10月-2019年12月41例不同疾病,施行卫生理化检验,同时选取2017年8月-2018年10月41例不同疾病,未施行卫生理化检验,对比实施前后疾病控制情况和公众满意程度。结果:在卫生理化检验施行后,患者疾病控制时间明显降低,同时可以提升患者公众满意程度,和施行前比较差异有意义(P<0.05)。结论:对疾病进行预防控制时,卫生理化检验具有较高作用,可以缩短其控制时间,同时可以改善公众满意情况,可以充分发挥其重要作用,需要对其给予高度重视。

关键词： 化工分析   化工检验   现状   重要作用  

摘要： 我国经济科技快速发展,各个行业在发展过程向想现代化转变,人们生活质量提升对各方面的要求不断增加,化工生产中为了保证产品的质量满足人们的需求,需要在实际生产过程中重视化工分析和化工检验,从更本上提升身产技术并且保证产品质量,提升整体发展水平。本篇文章首先就化工分析与化工检验进行简单概述,根据当前的实际情况分析化工分析与化工检验的现状,深入探讨化工分析与化工检验的重要作用,希望可以为实践发展提供借鉴。

关键词： 食品安全   合同评审   质量控制  

摘要： 近年来,食品安全工作是全国上下特别重视、特别关注、特别支持的工作之一。食品检验检测机构作为食品安全建设工作的直接参与者,担当起了重要的社会责任。合同评审作为食品检验检测机构展开技术工作的“龙头”工序,对整个检验检测技术工作起着至关重要的作用,直接决定了整个检验检测工作是否能够顺利完成和完成的质量如何,本文将阐述合同评审的作用和关键控制点。

关键词： 特种设备检验机构   特种设备   安全管理方面   作用  

摘要： 经济的快速发展为我国科技的发展创造良好的条件,这极大程度的增加了特种设备的发展和普及进程。但大部分的特种设备本身危险性相对较高,在实际应用时,一旦发生异常现象或者问题,就可能给人们带来较为严重的经济损失和不可控伤害。为了有效的避免这些现象的出现,特种设备检验机构就必须根据规定来对特种设备的安全状况进行全面检验。本文首先分析了特种设备检验机构与特种设备安全管理,然后分析了特种设备检验机构在特种设备安全管理方面的重要作用,希望能对相关的研究有所助益。