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关键词： INDUCED BREAKDOWN SPECTROSCOPY   PATTERN-RECOGNITION   IDENTIFICATION   CHEMOMETRICS   SPECTRA  

摘要： Despite the intrinsic elemental analysis capability and lack of sample preparation requirements, laser-induced breakdown spectroscopy (LIBS) has not been extensively used for real-world applications, e.g., quality assurance and process monitoring. Specifically, variability in sample, system, and experimental parameters in LIBS studies present a substantive hurdle for robust classification, even when standard multivariate chemometric techniques are used for analysis. Considering pharmaceutical sample investigation as an example, we propose the use of support vector machines (SVM) as a nonlinear classification method over conventional linear techniques such as soft independent modeling of class analogy (SIMCA) and partial least-squares discriminant analysis (PLS-DA) for discrimination based on LIBS measurements. Using over-the-counter pharmaceutical samples, we demonstrate that the application of SVM enables statistically significant improvements in prospective classification accuracy (sensitivity), because of its ability to address variability in LLBS sample ablation and plasma self-absorption behavior. Furthermore, our results reveal that SVM provides nearly 10% improvement in correct allocation rate and a concomitant reduction in misclassification rates of 75% (cf. PLS-DA) and 80% (cf. SIMCA)-when measurements from samples not included in the training set are incorporated in the test data-highlighting its robustness. While further studies on a wider matrix of sample types performed using different LIBS systems is needed to fully characterize the capability of SVM to provide superior predictions, we anticipate that the improved sensitivity and robustness observed here will facilitate application of the proposed LIBS-SVM toolbox for screening drugs and detecting counterfeit samples, as well as in related areas of forensic and biological sample analysis.

关键词： Opioid receptor   Tolerance   Dependence   Addiction   Trafficking   Endocytosis   PROTEIN-COUPLED RECEPTORS   VENTRAL TEGMENTAL AREA   MORPHINE-TOLERANCE   NMDA RECEPTOR   GLUCOCORTICOID-RECEPTORS   MOLECULAR-MECHANISMS   PHYSICAL-DEPENDENCE   REDUCES TOLERANCE   DRUG SEEKING   SPINAL-CORD  

摘要： Opioid drugs remain the gold standard for the treatment of severe pain, both acute/post-surgical and chronic. However, the utility of opioid drugs for the treatment of chronic pain is compromised by the development of analgesic tolerance which, in turn, leads to dose-escalation and increased likelihood of dangerous side effects, including dependence. Consequently, there remains resistance among clinicians and the general population to using opiates for pain management because of risk of "addiction." These fears are not unwarranted. More than 2.5 million people begin abusing opioid painkillers each year, and prescription opioid abuse is now the second most common type of illegal drug use after marijuana. Some abusers become dependent due to recreational use of prescription painkillers. However, many abusers are among the 40 million people suffering from chronic pain, and developed dependence while using the drugs for legitimate purposes. Both of these trends highlight the need to develop opioid therapeutics with a reduced liability to cause tolerance, dependence and addiction. Identifying the ideal properties of opioid drugs that would retain analgesia but reduce these side-effects has been a goal of my laboratory for more than a decade. During this time, we have proposed the novel hypothesis that opioid drugs that promote desensitization, endocytosis and recycling of the mu-opioid-receptor (MOR) will retain analgesic efficacy, but will have a reduced liability to cause tolerance, dependence and addiction. We have generated substantial data, both pharmacological and genetic to suggest that our hypothesis is a valid one. These data are summarized in this review. (C) 2011 Published by Elsevier Ireland Ltd.

关键词： 业主   合同解除   费用   处理   合同管理  

摘要： 某公路工程由于业主违约并提出解除合同,经过协商承包人同意有条件解除。本文重点介绍在合同解除时,监理工程师对承包人提出的主要合同问题的处理。

关键词： 抗肿瘤药   鼻咽癌   5-氟尿嘧啶   细胞周期检验点激酶   RNA干扰  

摘要： 目的探讨靶向抑制细胞周期检验点激酶2（Chk2）对5-氟尿嘧啶（5-FU）抗鼻咽癌鳞状上皮细胞SUNE-1的增敏作用。方法根据Chk2基因设计siRNA干扰序列，在mRNA和蛋白水平检测其干扰效果；通过细胞生长曲线观察Chk2siRNA对SUNE-1细胞增殖能力的影响；通过细胞毒性试验检测Chk2siRNA是否影响SUNE-1细胞对代谢类抗肿瘤药5-FU的敏感性；采用AnnexinV／PI双染方法检测细胞凋亡的变化情况。结果所采用的siRNA在mRNA和蛋白水平均显著降低SUNE-1细胞中Chk2表达，验证了该siRNA的干扰效果；Chk2siRNA对SUNE-1细胞增殖能力无显著影响，但能显著增强5-Fu的细胞毒作用（P〈0．05）；细胞凋亡检测结果显示，Chk2siRNA显著增加5-FU引起的SUNE-1细胞凋亡水平。结论尽管Chk2对SUNE-1细胞生存和增殖不发挥关键作用，但在5-Fu引起的细胞损伤中对细胞起保护作用，该作用与其抑制5-FU引起的细胞凋亡有关，因此Chk2可成为在鼻咽癌治疗中增强5-FU的疗效的靶点。

关键词： 计价结算货币   货币国际化   碳交易  

摘要： 与国际大宗商品特别是能源的计价结算绑定是一国货币崛起的起点。文章构建利润最大化模型,并结合Devereux et al.(2004)文中的定理,发现对能源(包括碳排放权)计价结算货币的绑定能够促使出口商在国际贸易中选择此货币计价,从而有利于此货币的国际化,这一结论在本文的实证分析中得到证实。最后,结合计价结算货币选择理论对如何推进碳交易人民币计价提出政策建议。

关键词： 痕迹检验技术   交通肇事案件  

摘要： 长期以来,技术人员在应用痕迹检验技术勘验和检验交通肇事案件时,往往只考虑到特殊痕迹检验中整体分离痕迹和轮胎印痕来认定肇事车辆,对于其他痕迹在确定案件的性质和侦查方向有着特殊作用却少有所知。本文通过一系列具体的交通肇事案件,说明整个痕迹检验技术在交通肇事案件现场中有着广泛应用,对交通肇事案件的侦破具有积极的意义。

关键词： Feature selection   Support vector machine (SVM)   Diseases classification   Recursive feature elimination (RFE)   VOXEL-BASED MORPHOMETRY   ALZHEIMERS-DISEASE   MATTER LOSS   FMRI   PREDICTION   REGRESSION   NETWORKS   PATTERNS  

摘要： There are growing numbers of studies using machine learning approaches to characterize patterns of anatomical difference discernible from neuroimaging data. The high-dimensionality of image data often raises a concern that feature selection is needed to obtain optimal accuracy. Among previous studies, mostly using fixed sample sizes, some show greater predictive accuracies with feature selection, whereas others do not. In this study, we compared four common feature selection methods. 1) Pre-selected region of interests (ROIs) that are based on prior knowledge. 2) Univariate t-test filtering. 3) Recursive feature elimination (RFE), and 4) t-test filtering constrained by ROIs. The predictive accuracies achieved from different sample sizes, with and without feature selection, were compared statistically. To demonstrate the effect, we used grey matter segmented from the T1-weighted anatomical scans collected by the Alzheimer's disease Neuroimaging Initiative (ADNI) as the input features to a linear support vector machine classifier. The objective was to characterize the patterns of difference between Alzheimer's disease (AD) patients and cognitively normal subjects, and also to characterize the difference between mild cognitive impairment (MCI) patients and normal subjects. In addition, we also compared the classification accuracies between MCI patients who converted to AD and MCI patients who did not convert within the period of 12 months. Predictive accuracies from two data-driven feature selection methods (t-test filtering and RFE) were no better than those achieved using whole brain data. We showed that we could achieve the most accurate characterizations by using prior knowledge of where to expect neurodegeneration (hippocampus and parahippocampal gyrus). Therefore, feature selection does improve the classification accuracies, but it depends on the method adopted. In general, larger sample sizes yielded higher accuracies with less advantage obtained by using knowle

关键词： POWER   ACCURACY   NUMBER  

摘要： Background: Supervised learning methods need annotated data in order to generate efficient models. Annotated data, however, is a relatively scarce resource and can be expensive to obtain. For both passive and active learning methods, there is a need to estimate the size of the annotated sample required to reach a performance target. Methods: We designed and implemented a method that fits an inverse power law model to points of a given learning curve created using a small annotated training set. Fitting is carried out using nonlinear weighted least squares optimization. The fitted model is then used to predict the classifier's performance and confidence interval for larger sample sizes. For evaluation, the nonlinear weighted curve fitting method was applied to a set of learning curves generated using clinical text and waveform classification tasks with active and passive sampling methods, and predictions were validated using standard goodness of fit measures. As control we used an un-weighted fitting method. Results: A total of 568 models were fitted and the model predictions were compared with the observed performances. Depending on the data set and sampling method, it took between 80 to 560 annotated samples to achieve mean average and root mean squared error below 0.01. Results also show that our weighted fitting method outperformed the baseline un-weighted method (p < 0.05). Conclusions: This paper describes a simple and effective sample size prediction algorithm that conducts weighted fitting of learning curves. The algorithm outperformed an un-weighted algorithm described in previous literature. It can help researchers determine annotation sample size for supervised machine learning.

关键词： 木材检验   工作   重要性  

摘要： 随着我国森林面积逐渐减少,林木的可采资源也随之不断下降,木材市场价格越来越高,当前木材生产仍然是林业企业的重要来源。木材检验工作是木材生产经营进程中不可缺少的重要工作,本文介绍了木材检验工作的重要性。

关键词： 交叉感染   综合预防   组织和管理   学生保健服务  

摘要： 高校是人口居住密集、有特定功能的自然社区。高校医疗机构长期为广大师生提供基本医疗、预防保健、计划免疫、健康教育等社区卫生服务工作[1]。但随着社会医疗保险制度的实施和高校后勤社会化改革的推行,为了适应社会发展的客观需要,高校医院可以对外开放以满足附近居民的健康服务需求。近年来,到高校医院就诊的人群逐渐发生变化,除教职工、大学生、离退休教师之外,还包括众多教职工家属、外来培训人员、学校周边居民、外来务工人员以及留学生和专家等。