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摘要： The article focuses on the pact of Memry Corp. and Biomer Technology Ltd. They have entered into an agreement to jointly develop proprietary drug-loadable coatings for stents and possibly other medical devices. Memry has made a $400,000 initial investment in Runcon, England-based Biomer for the development of technologies relating to Memry's Nitinol medical devices and will finance a $200,000 joint development program for the same technology, said Philippe Poncet, Memry's vice president of business development.

关键词： 高校   图书馆   信息用户   违约责任   用益权   法律地位  

摘要： 高等学校是事业单位法人,图书馆是其隶属的一个部门。学生利用图书馆藏书是学生和学校之间合同关系的应有之义。逾期还书罚款的法律根据不足,应改为违约金,丢书赔款方法和标准应由法律法规明确规定,不同的图书赔偿额要有区别,以体现合理、等价有偿的民事法律关系。

关键词： 保险合同   保险合同的成立   保险单   要式合同  

摘要： 保险合同的成立对于判断保险合同是否存在、认定保险合同是否有效、判定有关当事人应当承担什么样的责任有着重要意义。保险合同成立后,双方当事人就受保险合同的约束。保险合同的成立应当适用承诺生效的标准。当投保人提出保险要求,保险人同意承保,双方就合同的条款达成协议,保险合同成立。保险合同应是非要式合同,不应规定为要式合同。保险单是保险合同成立的书面凭证,除非双方当事人有约定,否则保险单不应作为合同成立的要件,保险单可在合同成立之时或之后签发。

关键词： 标本   活动微粒   革兰氏染色   微生物检验  

摘要： 目的 :阐述涂片直接观察在微生物检验中的作用。方法 :用涂片直接观察结合革兰氏染色与培养结果进行综合分析。结果 :171份标本中 ,细菌培养的总阳性率为 2 1.6 % (37/ 171) ,革兰氏染色找到细菌的阳性率为 2 7.5 %(47/ 171) ,有较多活动微粒和白细胞的阳性率为 75 .4 % (12 9/ 171) ;在 12 9份观察到有较多活动微粒和白细胞的标本中 ,革兰氏染色找到细菌的阳性率为 36 .4 % (47/ 12 9) ,细菌培养阳性率为 2 9.5 % (37/ 12 9) ;在革兰氏染色未找到细菌的 82份标本中 ,细菌培养的阳性率为 1.2 % (1/ 82 )。结论 :涂片直接观察结合革兰氏染色检查 ,可以对标本的性质(是细菌性的还是非细菌性的 )作出快速的初步判断 ;涂片直接观察还可以对粘附力差的标本的革兰氏染色是否成功作出判断 ;涂片直接观察还有利于发现异形细胞、活动的原虫、寄生虫等。

关键词： 监管模式体系   检验检疫实验室   对外经济贸易   工矿产品   专项检验   型式试验  

摘要： 从检验检疫实验室 (以下简称实验室 )工作角度出发 ,通过对实验室在“进出口工矿产品检验监管模式体系”(以下简称“模式体系”)中的地位和作用的分析 ,探讨了实验室改革和模式转变之间相互结合的问题 ,并对“模式体系”运行和模式转变提出了自己的意见和建议。

关键词： 头油   酱油   检验   合格   氨基酸态氮   质量技术指标  

摘要： 在食品质量安全准入市场制度下,国家规定酱油出厂要作8项以上检验合格方准入市。按国家标准GB18186-2000准确检验头油中氨基酸态氮含量占酱油出厂8项检验合格具有十分重要作用与地位,特别是它与4项质量技术指标检验合格还具有纲举目张的连带关系。

关键词： data mining   k-nearest neighbor   support vector machine   feature selection   P-tree   gene expression   machine learning  

摘要： Classification analysis of microarray gene expression data has been widely used to uncover biological features and to distinguish closely related cell types that often appear in the diagnosis of cancer. However, the number of dimensions of gene expression data is often very high, e.g., in the hundreds or thousands. Accurate and efficient classification of such high-dimensional data remains a contemporary challenge. In this paper, we propose a comprehensive vertical sample-based KNN/LSVM classification approach with weights optimized by genetic algorithms for high-dimensional data. Experiments on common gene expression datasets demonstrated that our approach can achieve high accuracy and efficiency at the same time. The improvement of speed is mainly related to the vertical data representation, P-tree,(1) and its optimized logical algebra. The high accuracy is due to the combination of a KNN majority voting approach and a local support vector machine approach that makes optimal decisions at the local level. As a result, our approach could be a powerful tool for high-dimensional gene expression data analysis. (C) 2004 Elsevier Inc. All rights reserved.

关键词： DECOMPOSITION (Chemistry)   NUCLEAR magnetic resonance   PATTERN perception   ALGORITHMS   DIAGNOSIS   DRUGS -- Side effects  

摘要： H-1 NMR spectra of biofluids provides a wealth of biochemical information on the metabolic status of an organism. Through the application of pattern recognition and classification algorithms, the data have been shown to provide information on disease diagnosis and the beneficial and adverse effects of potential therapeutics. Here, a novel approach is described for identifying subsets of spectral patterns in databases of NMR spectra, and it is shown that the intensities of these spectral patterns can be related to the onset and recovery from a toxic lesion in both a time-related and dose-related fashion. These patterns form a new type of combination biomarker for the biological effect under study. The approach is illustrated with a study of liver toxicity in rats using NMR spectra of urine following administration of a model hepatotoxin hydrazine.

关键词： 2D gel electrophoresis   principal component analysis   neuroblastoma   proteomics   multivariate analysis  

摘要： Background: The adrenal gland is the election organ forming primary neuroblastoma (NB) tumours, the most common extracranial solid tumours of infancy and childhood. Methods: Samples of adrenal gland belonging to healthy and diseased nude mouse were analysed by 2D gel-electrophoresis. The resulting 2D-PAGE maps were digitized by PDQuest and investigated by principal component analysis (PCA). Results: The analysis of the loadings of the first principal component (PC) permitted the evaluation of the spots characterising each class of samples. Moreover, the soft-independent model of class analogy (SIMCA) method confirmed the separation of the samples in the two classes and allowed the identification of the modelling and discriminating spots. Very good correlation was found between the data obtained by analysis of 2D maps via the commercial software PDQuest and the present PCA analysis. In both cases, the comparison between such maps showed up- and down-regulation of 84 polypeptide chains, out of a total of 700 spots detected by a fluorescent stain, Sypro Ruby. Spots that were differentially expressed between the two groups were analysed by matrix-assisted laser desorption time-of-flight (MALDI-TOF) mass spectrometry and 14 of these spots were identified so far. (C) 2004 Elsevier B.V. All tights reserved.

关键词： FT-IR spectroscopy   principal component analysis   outliers   glycerol   wine  

摘要： Principal component analysis (PCA) was used to identify the main sources of variation in the Fourier transform infrared (FT-IR) spectra of 329 wines of various styles. The FT-IR spectra were gathered using a specialized WineScan instrument. The main sources of variation included the reducing sugar and alcohol content of the samples, as well as the stage of fermentation and the maturation period of the wines. The implications of the variation between the different wine styles for the design of calibration models with accurate predictive abilities were investigated using glycerol calibration in wine as a model system. PCA enabled the identification and interpretation of samples that were poorly predicted by the calibration models, as well as the detection of individual samples in the sample set that had atypical spectra (i.e., outlier samples). The Soft Independent Modeling of Class Analogy (SIMCA) approach was used to establish a model for the classification of the outlier samples. A glycerol calibration for wine was developed (reducing sugar content < 30 g/L, alcohol > 8% v/v) with satisfactory predictive ability (SEP = 0.40 g/L). The RPD value (ratio of the standard deviation of the data to the standard error of prediction) was 5.6, indicating that the calibration is suitable for quantification purposes. A calibration for glycerol in special late harvest and noble late harvest wines (RS 31-147 g/L, alcohol > 11.6% v/v) with a prediction error SECV = 0.65 g/L, was also established. This study yielded an analytical strategy that combined the careful design of calibration sets with measures that facilitated the early detection and interpretation of poorly predicted samples and outlier samples in a sample set. The strategy provided a powerful means of quality control, which is necessary for the generation of accurate prediction data and therefore for the successful implementation of FT-IR in the routine analytical laboratory.