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关键词： 媒体   检验检疫   信息传递渠道   领域  

摘要： 就检验检疫基层来说,新媒体畅通了检验检疫基层与高层之间的信息传递渠道,搭建了检验检疫与企业之间的沟通桥梁,新媒体在检验检疫基层建设中有着良好的推动作用。当前,移动互联网高速发展,人们的行为方式已发生了根本性的改变。以微博、微信、移动客户端为代表的新媒体正逐渐进入我们的生活,从改变人们的阅读习惯开始,对生活的方方面面产生着潜移默化的影响。新媒体打破了时空限制,人们可以在任何时间、任何地点使用终端获取信息,传递信息。

关键词： 规避法律   约定   合同   保护   公司   银行   案情  

摘要： 基本案情： 2013年12月26日,D公司与E银行签署《委贷合同》约定D公司委托E银行向B置业公司发放贷款1.95亿元。

关键词： 血液分析仪   白血病  

摘要： 对2015年1月~2016年1月我院确诊急慢性白血病患者48例临床资料进行回顾性分析,将其分为急性白血病与慢性白血病组,其中急性30例,慢性18例;同时选取在我院体检的健康患者48例做对照组,利用血液分析仪检验空腹静脉血标本,比较红细胞、白细胞及血红蛋白差异。急慢性白血病患者RBC及HB水平低于对照组,差异有统计学意义（P〈0.05）;急慢性白血病患者WBC高于对照组,差异有统计学意义（P〈0.05）;急、慢性白血病患者RBC、WBC、HB之间差异无统计学意义（P〉0.05）。血液分析仪诊断急慢性白血病有重要意义,可作为其首诊方法,但同时应结合人工显微镜检查更为准确。

关键词： 干化学法   尿沉渣检测   尿常规检测  

摘要： 目的观察与探讨干化学法与尿沉渣联合用于尿常规检验的临床效果,为临床尿常规检验方法提供参考依据。方法随机选取2014年5月至2015年5月间于该院进行尿常规检查的患者300例,随机分为3组,每组100例,分别采用干化学法、尿沉渣法及干化学法与尿沉渣联合检测,对3组患者进行尿常规检测,对比3组患者尿常规检测结果、误诊率、漏诊率等指标的差异,评价干化学法与尿沉渣联合检测的效果,对尿常规的检测方法提出参考意见。结果干化学法检测定量红细胞阳性率显著高于尿沉渣法,尿沉渣法测定白细胞阳性率显著高于干化学法,差异有统计学意义(P<0.05);干化学法与尿沉渣联合检测能使红细胞和白细胞阳性率显著提高,差异有统计学意义(P<0.05);干化学法与尿沉渣联合检测误诊率为4.0%,漏诊率为3.0%,总错误率为7.0%,总错误率显著高于单独采用干化学法或尿沉渣法,差异有统计学意义(P<0.05)。结论干化学法与尿沉渣联合检测方法能显著提高红细胞和白细胞阳性率,减少误诊率和漏诊率,使得检验结果出错的概率大大减少,推荐将其用于尿常规检查。

关键词： Partial least squares   RBF neural network   Genetic algorithm   PLS-GA-RBF algorithm   Small sample classification  

摘要： Radial basis function (RBF) neural network can use linear learning algorithm to complete the work formerly handled by nonlinear learning algorithm, and maintain the high precision of the nonlinear algorithm. However, the results of RBF would be slightly unsatisfactory when dealing with small sample which has higher feature dimension and fewer numbers. Higher feature dimension will influence the design of neural network, and fewer numbers of samples will cause network training incomplete or over-fitted, both of which restrict the recognition precision of the neural network. RBF neural network has some drawbacks, for example, it is hard to determine the numbers, center and width of the hidden layer's neurons, which constrain the success of training. To solve the above problems, partial least squares (PLS) and genetic algorithm(GA)are introduced into RBF neural network, and better recognition precision will be obtained, because PLS is good at dealing with the small sample data, it can reduce feature dimension and make low-dimensional data more interpretative. In addition, GA can optimize the network architecture, the weights between hidden layer and output layer of the RBF neural network can ease non-complete network training, the way of hybrid coding and simultaneous evolving is adopted, and then an accurate algorithm is established. By these two consecutive optimizations, the RBF neural network classification algorithm based on PLS and GA (PLS-GA-RBF) is proposed, in order to solve some recognition problems caused by small sample. Four experiments and comparisons with other four algorithms are carried out to verify the superiority of the proposed algorithm, and the results indicate a good picture of the PLS-GA-RBF algorithm, the operating efficiency and recognition accuracy are improved substantially. The new small sample classification algorithm is worthy of further promotion. (C) 2016 Elsevier B.V. All rights reserved.

关键词： transformation matrix   AKR1B10 gene   Dataset   optimum performance   Image classification   GTF2E1 gene   EIS IMPLEMENTATION PLANS   Finite element method   Iron   hyperspectral imagery  

摘要： Locality preserving projection (LPP) is a popular unsupervised feature extraction (FE) method. In this paper, the spatial-spectral LPP (SSLPP) method is proposed, which uses both the spectral and spatial information of hyperspectral image (HSI) for FE. The proposed method consists of two parts. In the first part, unlabelled samples are selected in a spatially homogeneous neighbourhood from filtered HSI. In the second part, the transformation matrix is calculated by an LPP-based method and by using the spectral and spatial information of the selected unlabelled samples. Experimental results on Indian Pines (IP), Kennedy Space Center (KSC), and Pavia University (PU) datasets show that the performance of SSLPP is superior to spectral unsupervised, supervised, and semi-supervised FE methods in small and large sample size situations. Moreover, the proposed method outperforms other spatial-spectral semi-supervised FE methods for PU dataset, which has high spatial resolution. For IP and KSC datasets, spectral regularized local discriminant embedding (SSRLDE) has the best performance by using spectral and spatial information of labelled and unlabelled samples, and SSLPP is ranked just behind it. Experiments show that SSLPP is an efficient unsupervised FE method, which does not use training samples as preparation of them is so difficult, costly, and sometimes impractical. SSLPP results are much better than LPP. Also, it decreases the storage and calculation costs using less number of unlabelled samples.

关键词： Sparse coding   Low-rank   Dictionary learning   Image classification   Structured sparsity  

摘要： In this paper, we propose a novel Structure-Constrained Low-Rank and Partial Sparse Representation algorithm for image classification. First, a Structure-Constrained Low-Rank Dictionary Learning (SCLRDL) algorithm is proposed, which imposes both structure and low-rank restriction on the coefficient matrix. Second, under the assumption that the coefficient of test sample is sparse and correlated with the learned representation of training samples, we propose a Low-Rank and Partial Sparse Representation (LRPSR) algorithm which concatenates training samples and test sample to form a data matrix and finds a low-rank and sparse representation of the data matrix over learned dictionary by low-rank matrix recovery technique. Finally, we design a Sample Selection (SS) procedure to accelerate LRPSR. Experimental results on Caltech 101 and Caltech 256 show that our method outperforms most sparse or low rank based image classification algorithm proposed recently. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： writing as a method of inquiry   qualitative health research   active interviews   GLBT issues & theory   reflexivity  

摘要： The adequate presentation of empirical research findings poses an essential, yet often neglected challenge in qualitative methodology. This article contributes to the debate by proposing the research vignette as a mediating position between conventional and experimental forms of writing. On the basis of an illustrating example from an interview study on the psychosocial dynamics of HIV-related sexual risk behavior of gay men in Germany, the methodological foundations of the research vignette are outlined and practical applications for qualitative research are discussed. The research vignette provides the opportunity to conduct a psychoanalytically informed interpretation in which the presentation of findings is woven into a critical reflection of the interaction dynamic in the research encounter and substantiating theoretical considerations.

关键词： Hyperspectral classification   Co-training   Tracking-Learning-Detection  

摘要： We present a novel semi-supervised algorithm for classification of hyperspectral data from remote sensors. Our method is inspired by the Tracking-Learning-Detection (TLD) framework, originally applied for tracking objects in a video stream. TLD introduced the co-training approach called P-N learning, making use of two independent 'experts' (or learners) that scored samples in different feature spaces. In a similar fashion, we formulated the hyperspectral classification task as a co-training problem, that can be solved with the P-N learning scheme. Our method uses both spatial and spectral features of data, extending a small set of initial labelled samples during the process of region growing. We show that this approach is stable and achieves very good accuracy even for small training sets. We analyse the algorithm's performance on several publicly available hyperspectral data sets. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.

关键词： BRCAl   BRCA2   variants of unknown significance   cDNA   splicing   cancer genetic counseling  

摘要： Ascertaining the clinical consequences of BRCA1 and BRCA2 variants of uncertain significance (VUS) is currently indispensable for providing effective genetic counseling and preventive actions for families with hereditary breast and ovarian cancer (HBOC). To this end, we conducted a combination of in silico prediction and cDNA splicing analyses of 13 BRCA1 and 10 BRCA2VUS identified in our cohort as well as a case-control analysis in a population-based sample of 10 recurrent VUS. We observed consistent results between the in silico predictions and sequencing analyses for all analyzed VUS. An abnormal cDNA pattern was observed for variants c.212+1G>A and c.52781G>A in BRCA1 and c.516+2T>A and c.8168A>G in BRCA2 according to in silico splicing prediction. A case-control study of VUS confirmed the polymorphisms of the c.67+62A>G, c.7008-62A>G and c.8851G>A BRCA2 variants previously published. c.4068G>A in the BRCA2 gene can also be considered a polymorphism due to its occurrence at a frequency greater than 1% in our population. Our study shows that employing population-based analysis and a combination of several in silico methods yields highly accurate information, resulting in a reliable tool for selecting variants for cDNA sequencing analysis in routine cancer genetic counseling units.