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关键词： 数字普惠金融   贫困减缓   直接效应   间接效应  

摘要： 目前我国正处于脱贫攻坚关键时期,数字普惠金融对于减缓贫困、缩小城乡差距具有独特优势,是实现乡村振兴的重要支撑力量。我国2011—2018年31个省市有关经济指标的实证检验结果表明,数字普惠金融能够显著地减缓贫困,且具有直接效应和间接效应,即数字普惠金融能够直接减缓贫困,还可通过推动经济增长和改善收入分配间接减缓贫困。另外,数字普惠金融减贫的总效应中绝大部分来自于直接作用的贡献,并且通过促进经济增长减缓贫困的间接效应远大于通过改善收入分配减缓贫困的间接效应。因此,应提高数字普惠金融服务水平,充分发挥数字普惠金融减贫的直接作用,同时推动经济增长,改善收入分配,充分发挥数字普惠金融减贫的间接作用。

关键词： 微生物   检验   合理   用药   医院感染   作用  

摘要： 目的总结并归纳探究微生物检验对临床合理用药及医院感染发生率的作用和影响。方法文章挑选本院2018年2月-2019年2月服用药物的200例患者为对象,将其纳入此次研究。将药物按照时间先后分为对照组(未采用微生物检验)、观察组(采用微生物检验)各100份。对比两组用药结局(医院感染、临床合理用药率)。结果观察两组用药结局,采用微生物检验的观察组医院感染发生率(3.0%)低于对照组(12.0%),临床合理用药率(98.0%)高于对照组(75.0%),有分析价值(χ^2=4.816 9,4.613 7;P=0.031 7,0.021 9 <0.05)。结论微生物检验有利于对临床药物合理性、有效性、是否感染等进行准确掌握,有利于合理用药,预防治疗期间发生医院感染,符合社会要求,有助于医院发展。

关键词： 肝硬化疾病患者   凝血酶原时间   血小板   检验方式  

摘要： 目的主要探讨在诊断是否患有肝硬化疾病患者时,应用凝血酶原时间测定与血小板检验方式是否具有价值,观察并总结经验。方法在我院选取2017年1月至2019年1月间收治的100例肝硬化疾病患者作为观察组,并将同一时间段于我院进行体检后显示健康的人群100例设置为对照组,两组均分别给予凝血酶原时间测定与血小板检验方式。结果观察组血小板分布宽度、凝血酶原时间均高于对照组,血小板计数、血小板分布宽度、血小板压积低于对照组,差异显著(P <0.05)。结论在对肝硬化疾病患者进行诊断时运用凝血酶原时间测定与血小板检验方式所得结果较为显著,具有一定的诊断价值。

关键词： dual-branch structure   hyperspectral images (HSIs)   local and global constraints   spatial-spectral feature extraction  

摘要： The convolutional neural network (CNN) has been gradually applied to the hyperspectral images (HSIs) classification, but the lack of training samples caused by the difficulty of HSIs sample marking and ignoring of correlation between spatial and spectral information seriously restrict the HSIs classification accuracy. In an attempt to solve these problems, this paper proposes a dual-branch extraction and classification method under limited samples of hyperspectral images based on deep learning (DBECM). At first, a sample augmentation method based on local and global constraints in this model is designed to augment the limited training samples and balance the number of different class samples. Then spatial-spectral features are simultaneously extracted by the dual-branch spatial-spectral feature extraction method, which improves the utilization of HSIs data information. Finally, the extracted spatial-spectral feature fusion and classification are integrated into a unified network. The experimental results of two typical datasets show that the DBECM proposed in this paper has certain competitive advantages in classification accuracy compared with other public HSIs classification methods, especially in the Indian pines dataset. The parameters of the overall accuracy (OA), average accuracy (AA), and Kappa of the method proposed in this paper are at least 4.7%, 5.7%, and 5% higher than the existing methods.

关键词： Chemometrics   Ethanol   Ion mobility spectrometry   Food quality   Olive oil classification  

摘要： The usefulness of headspace (HS) -gas chromatography (GC)-ion mobility spectrometry (IMS) has been explored for quantitative purposes, focusing on ethanol in olive oils. For that, since this emerging analytical tool provides a 2D analysis, different calibration curves were built using the height, area and volume of the detected species, i.e. a protonated monomer and a proton-bound dimer of ethanol. The methodology was validated and the quantification results compared to those obtained by GC coupled to more conventional detectors. Once then, the content of ethanol was determined in 81 olive oil samples. The ethanol content was lower than 12 mg/kg for 89% of extra virgin olive oil samples, whereas it was higher than 12 mg/kg for around 90% of virgin olive oil samples. This concentration value was used as a limit to support the decision-making process for olive oil classification into extra virgin, virgin, and lampante olive oil by complementary methods, sensory analysis and chemometrics. In conclusion, the proposed HS-GC-IMS method requires no sample pretreatment, shows low limits of detection (0.095 mg/kg) and quantification (0.100 mg/kg) and is faster enough to monitor the content of this marker in olive oils in the industry.

关键词： Bipartite network   Ranking algorithm   Knowledge structure   Knowledge discovery   Biological entity relation  

摘要： The current study has two objectives. First, we explore the characteristics of biological entities, such as drugs, and their side effects using an author-entity pair bipartite network. Second, we use the constructed network to examine whether there are outstanding features of relations between drugs and side effects. We extracted drug and side effect names from 169,766 PubMed abstracts published between 2010 to 2014 and constructed author-entity pair bipartite networks after ambiguous author names were processed. We propose a new ranking algorithm that takes into consideration the characteristics of bipartite networks to identify top-ranked biological drug and side effect pairs. To investigate the relationship between a particular drug and a side effect, we compared the drug and side effect pairs obtained from the network containing both drug and side effect with those observed in SIDER, a human expert-curated database. The results of this study indicate that our approach was able to identify a wide range of patterns of drug-side effect relations from the perspective of authors' research interests. Further, our approach also identified the unique characteristics of the relation of biomedical entities obtained using an author-entity pair bipartite network. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： deep learning   hyperspectral image classification   few-shot learning   quadruplet loss   dense network   dilated convolutional network  

摘要： This study proposes a deep quadruplet network (DQN) for hyperspectral image classification given the limitation of having a small number of samples. A quadruplet network is designed, which makes use of a new quadruplet loss function in order to learn a feature space where the distances between samples from the same class are shortened, while those from a different class are enlarged. A deep 3-D convolutional neural network (CNN) with characteristics of both dense convolution and dilated convolution is then employed and embedded in the quadruplet network to extract spatial-spectral features. Finally, the nearest neighbor (NN) classifier is used to accomplish the classification in the learned feature space. The results show that the proposed network can learn a feature space and is able to undertake hyperspectral image classification using only a limited number of samples. The main highlights of the study include: (1) The proposed approach was found to have high overall accuracy and can be classified as state-of-the-art;(2) Results of the ablation study suggest that all the modules of the proposed approach are effective in improving accuracy and that the proposed quadruplet loss contributes the most;(3) Time-analysis shows the proposed methodology has a similar level of time consumption as compared with existing methods.

关键词： Hydrocarbons   Reaction products   Carbon   Flavonoids   Mathematical methods  

摘要： Propolis samples collected from five areas in Mato Grosso do Sul state, Midwest Brazil, comprising portions of the Cerrado, Pantanal, and Atlantic Forest ecosystems, were investigated for metabolomic profiles and evaluated for antioxidant and antitumor potential. Chemical profiles were determined by HPLC-DAD-MS/MS data and evaluated using principal component analysis and hierarchical clustering analysis to discern chemical composition patterns. Based on phytogeographical origin and chemical composition, 20 potential markers were identified and five groups were distinguished: (I) Cerrado/Central, (II) Atlantic Forest/South, (III) Cerrado-Pantanal transition area/Northwest, (IV) Cerrado/North, and (V) Pantanal/West. Drawing on HPLC-DAD-MS/MS and NMR data, 47 compounds were successfully or tentatively identified, including prenylated phenylpropanoids, flavonoids, isoflavonoids, and di- and triterpenoids, among other constituents. Isoflavonoids, typically found in red propolis from Northeast Brazil, are being reported for the first time in a propolis sample from the Midwest. A new prenylated aromatic compound, (E)-3-[4-hydroxy-3-(2-hydroxy-3-methylbut-3-en-1-yl)-5-(3-methylbut-2-en-1-yl)phenyl]propenoic acid, was obtained. Samples in group II exhibited promising antitumor potential against prostate and breast carcinoma cells, as did samples in groups III and IV against the latter cell line. The sample in group I, despite containing the highest amount of total phenolic compounds and being the only sample to exhibit scavenging activity against DPPH, was not the most cytotoxic against the cell lines tested.

关键词： sparse representation   information entropy   pseudo-labeling   intrinsic image decomposition   hyperspectral image classification  

摘要： Hyperspectral image classification methods may not achieve good performance when a limited number of training samples are provided. However, labeling sufficient samples of hyperspectral images to achieve adequate training is quite expensive and difficult. In this paper, we propose a novel sample pseudo-labeling method based on sparse representation (SRSPL) for hyperspectral image classification, in which sparse representation is used to select the purest samples to extend the training set. The proposed method consists of the following three steps. First, intrinsic image decomposition is used to obtain the reflectance components of hyperspectral images. Second, hyperspectral pixels are sparsely represented using an overcomplete dictionary composed of all training samples. Finally, information entropy is defined for the vectorized sparse representation, and then the pixels with low information entropy are selected as pseudo-labeled samples to augment the training set. The quality of the generated pseudo-labeled samples is evaluated based on classification accuracy, i.e., overall accuracy, average accuracy, and Kappa coefficient. Experimental results on four real hyperspectral data sets demonstrate excellent classification performance using the new added pseudo-labeled samples, which indicates that the generated samples are of high confidence.

关键词： Crystallographic sphere packing   Hyperbolic reflection groups   Arithmetic groups   Coxeter diagram   Vinberg's algorithm  

摘要： This paper seeks to catalogue and examine crystallographic sphere packings as defined by Kontorovich and Nakamura. There exist at least three sources which give rise to crystallographic packings, namely polyhedra, reflective extended Bianchi groups, and various higher dimensional quadratic forms. When applied in conjunction with the Koebe-Andreev-Thurston Theorem, Kontorovich and Nakamura's Structure Theorem guarantees crystallographic packings to be generated from polyhedra in n = 2. The Structure Theorem similarly allows us to generate packings from the reflective extended Bianchi groups in n = 2 by applying Vinberg's algorithm to obtain the appropriate Coxeter diagrams. In n > 2, the Structure Theorem when used with Vinberg's algorithm allows us to explore whether certain Coxeter diagrams in Hn+1 for a given quadratic form admit a packing at all. Kontorovich and Nakamura's Finiteness Theorem shows that there exist only finitely many classes of superintegral such packings, all of which exist in dimensions n <= 20. In this work, we systematically determine all known examples of crystallographic sphere packings. (C) 2019 Elsevier Inc. All rights reserved.