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关键词： 生化免疫检验   肾脏疾病   诊治   满意度   准确率  

摘要： 目的探究肾脏疾病生化免疫检验的诊治的医学上的临床价值。方法在该院的所有患者中,符合实验要求的患者中,按照随机抽样的方式抽取160例作为该次研究的对象,诊治时间为2018年1月—2019年1月,该次研究的分组采用随机分组的随机数字表法为主要的方法,根据该院按照随机抽样的方式随机分组抽取的160例患者分为对照组80例和观察组80例。对照组患者采取免疫学检验,观察组患者采取生化免疫检验,根据实验目的观察观察组患者和对照组患者的治疗总有效率、观察组患者和对照组患者的诊断的准确率、观察组患者和对照组患者的检验满意度。结果观察组患者的治疗有效率为93.75%(75/80),对照组的治疗有效率为70.00%(56/80),观察组患者的治疗有效率经过对比分析得其有效率显著高于对照组,差异有统计学意义(χ^2=15.204,P<0.05);观察组中,非常满意有33例、基本满意有24例、满意有20例、不满意有3例,观察组患者的诊断满意度为96.25%(77/80);对照组中,非常满意有25例、基本满意有18例、满意有13例、不满意有24例,对照组的诊断满意度为70.00%(56/80),前者高于后者,数据之间差异有统计学意义(χ^2=19.649,P<0.05);观察组患者的诊断准确率为93.75%,对照组患者的诊断准确率为81.25%,前者高于后者,差异有统计学意义(χ^2=5.714,P<0.05)。结论肾脏疾病生化免疫检验的临床诊治作用显著,诊断准确率、满意度较高,可有效提高患者的治疗有效率。

关键词： 特种设备   安全管理   检验机构  

摘要： 随着社会的不断发展,各行各业都开始应用起了特种设备,并且取得了显著的效果,为了能够充分的发挥特种设备的功能,需要对特种设备的安全管理工作进行加强。想要使特种设备的安全管理水平得到提升,就需要检验机构加强其自身的工作,其工作内容主要是检验各受检单位的特种设备,及时对设备存在的故障在检验报告中进行记录,以此来避免各项安全事故的发生。

关键词： DNA   DNA self-assembled monolayer   DNA-drug binding   Electrochemical DNA melting   Cisplatin   Biosensor   SELF-ASSEMBLED MONOLAYERS   THERMAL-STABILITY   GOLD   HYBRIDIZATION   DENSITY   BINDING   MODEL   SERS   IMMOBILIZATION   OPTIMIZATION  

摘要： This paper reports on the electrostatic denaturation of double-stranded DNA monolayers in the presence of anti-cancer drug cis-Dicumninedichloroplatinum(Il) (cisplatin). Cisplatin binds DNA at purine bases, effecting its thermal stability and melting temperature. Here we show for the first time how cisplatin effects the stability of DNA monolayers in the presence of high electric fields generated at the electrode/solution interface, and how this behavior is effected by the method of monolayer preparation. A slight decrease in melting rate and a large decrease in the extent of melting are observed for electrodes prepared with methods that produce homogeneous DNA surface coverage where interstrand distances are maximized. On the other hand, heterogeneous and densely-packed monolayers are found to melt much slower and to a slightly greater extent with bound cisplatin. Three procedures for preparation of the mercaptohexanol/DNA mixed monolayers are compared, demonstrating the potential application of cisplatin, along with electrostatic denaturation analysis, to interrogate surface density and surface heterogeneity in self-assembled DNA monolayers.

关键词： 农业机械化   安全管理   年度检验  

摘要： 近年来,农田作业机械化程度也越来越高,在农业机械化快速发展的同时,也存在更多农机事故隐患,给广大人民群众生命财产安全造成了较大威胁.为了提高安全生产能力,降低事故隐患,杜绝重特大事故的发生,我们必须实施和开展好农业机械的年度检验工作.

关键词： land cover mapping   multiple classifier ensemble (MCE)   iterative classification (IC)   self-adaptive updating of samples   China-Pakistan economic corridor (CPEC)   remote sensing   OBJECT-BASED CLASSIFICATION   ACCURACY ASSESSMENT   SYSTEM   INDEX   INFORMATION   DESIGN   FOREST   SET   TM  

摘要： Land cover samples are usually the foundation for supervised classification. Unfortunately, for land cover mapping in large areas, only limited samples can be used due to the time-consuming and labor-intensive sample collection. A novel and practical Object-oriented Iterative Classification method based on Multiple Classifiers Ensemble (OIC-MCE) was proposed in this paper. It systematically integrated object-oriented segmentation, Multiple Classifier Ensemble (MCE), and Iterative Classification (IC). In this method, the initial training samples were updated self-adaptively during the iterative processes. Based on these updated training samples, the inconsistent regions (ICR) in the classification results of the MCE method were reclassified to reduce their uncertainty. Three typical case studies in the China-Pakistan Economic Corridor (CPEC) indicate that the overall accuracy of the OIC-MCE method is significantly higher than that of the single classifier. After five iterations, the overall accuracy of the OIC-MCE approach increased by 5.58%-8.38% compared to the accuracy of the traditional MCE method. The spatial distribution of newly added training samples generated by the OIC-MCE approach was relatively uniform. It was confirmed by ten repeated experiments that the OIC-MCE approach has good stability. More importantly, even if the initial sample size reduced by 65%, the quality of the final classification result based on the proposed OIC-MCE approach would not be greatly affected. Therefore, the proposed OIC-MCE approach provides a new solution for land cover mapping with limited samples. Certainly, it is also well suited for land cover mapping with abundant samples.

关键词： Rectal surgery   Laparoscopic   Volume   HOSPITAL VOLUME   CANCER-SURGERY   MORTALITY   RESECTION   QUALITY  

摘要： Background Laparoscopic rectal surgery is technically challenging and often low volume. Alternatively, colon resections utilize similar advanced laparoscopic skills and are more common but it is unknown whether this experience affects laparoscopic rectal surgery outcomes. The purpose of this paper is to determine the volume-outcome relationship between several colorectal procedures and laparoscopic rectal surgery outcomes. Methods This was a population-based retrospective cohort of all colorectal surgeries with primary anastomoses performed across Canada (excluding Quebec) between April 2008 and March 2015. Patient characteristics, comorbidities, procedures, and discharge details were collected from the Canadian Institute for Health Information. Volumes for common colorectal procedures were calculated for individual surgeons. All-cause morbidity, defined as complications arising during the index admission and contributing to an increased length of stay by more than 24 h, was the primary outcome examined. Results A total of 5323 laparoscopic rectal surgery cases and 108,034 colorectal cases, between 180 hospitals and 620 surgeons, were identified. Data analysis demonstrated that high-volume laparoscopic rectal surgeons (OR 0.77, CI 0.61-0.96, p = 0.020) and high-volume open rectal surgeons (OR 0.76, CI 0.61-0.93, p = 0.009) significantly reduced all-cause morbidity. Conversely, surgeon volumes for laparoscopic and open colon cases had no effect on laparoscopic rectal outcomes. Conclusion High-volume surgeon status in laparoscopic and open rectal surgery are important predictors of all-cause morbidity after laparoscopic rectal surgery, while laparoscopic colon surgery volumes did not impact outcomes. This may reflect more dissimilarity between colon and rectal cases and less transferability of advanced laparoscopic skills than previously thought.

关键词： Hyperspectral image classification   Collaborative learning   Lightweight convolutional neural networks   Dual-loss   Deep clustering   Limited training samples   ENSEMBLE CLASSIFICATION   RANDOM FOREST  

摘要： Deep learning provides excellent potentials for hyperspectral images (HSIs) classification, but it is infamous for requiring large amount of labeled samples while the collection of high-quality labels for HSIs is extremely expensive and time-consuming. Furthermore, when the limited training samples are available, deep learning methods may suffer from over-fitting. In this work, we propose a novel collaborative learning framework for semi-supervised HSI classification with joint deep convolutional neural networks and deep clustering. Specifically, a lightweight 3D convolutional neural network (CNN) with much less parameters compared with classical 3D CNNs is designed for deep discriminative feature learning and classification. Then a deep clustering method, that is approximate rank-order clustering (AROC) algorithm, is applied to cluster deep features to generate pseudo labels for abundant unlabeled samples. Finally, we fine-tune the lightweight 3D CNN by minimizing a dual-loss (softmax loss and center loss) using both true and pseudo labels. Experimental results on three challenging HSI datasets demonstrate that the proposed method can achieve better performance than other state-of-the-art deep learning based methods and traditional HSI classification methods methods.

关键词： automatic training samples generation   unsupervised segmentation   graph convolutional neural network   ALS point clouds   POINT CLOUDS   LIDAR DATA   URBAN   IMAGES  

摘要： The labeling of point clouds is the fundamental task in airborne laser scanning (ALS) point clouds processing. Many supervised methods have been proposed for the point clouds classification work. Training samples play an important role in the supervised classification. Most of the training samples are generated by manual labeling, which is time-consuming. To reduce the cost of manual annotating for ALS data, we propose a framework that automatically generates training samples using a two-dimensional (2D) topographic map and an unsupervised segmentation step. In this approach, input point clouds, at first, are separated into the ground part and the non-ground part by a DEM filter. Then, a point-in-polygon operation using polygon maps derived from a 2D topographic map is used to generate initial training samples. The unsupervised segmentation method is applied to reduce the noise and improve the accuracy of the point-in-polygon training samples. Finally, the super point graph is used for the training and testing procedure. A comparison with the point-based deep neural network Pointnet++ (average F1 score 59.4%) shows that the segmentation based strategy improves the performance of our initial training samples (average F1 score 65.6%). After adding the intensity value in unsupervised segmentation, our automatically generated training samples have competitive results with an average F1 score of 74.8% for ALS data classification while using the ground truth training samples the average F1 score is 75.1%. The result shows that our framework is feasible to automatically generate and improve the training samples with low time and labour costs.