水资源短缺风险因子的筛选模型

水资源短缺评价首先需要确定指标体系,投影寻踪模型与灰色关联度法相结合,可以有效地筛选出水资源短缺风险因子,为进一步进行风险评价奠定了基础,同时也为制定风险的防范措施和对策提供了理论依据.综合应用这两种模型,对北京市2001～2009年的水资源短缺风险进行研究,结果表明:降水量、日照时数、雨日数、单位GDP污水排放量、人均GDP、工业总产值占有率、污水处理率和水循环使用率这8个指标是北京市水资源短缺的主要风险因子.     

Relationship between selectivity and average resolution in comprehensive two-dimensional separations with spectroscopic detection

The average value of the multivariate selectivity (SEL) of randomly positioned peaks in a multi-component separation is shown to equal the average fraction of peaks that are singlets, as predicted by statistical-overlap theory (SOT). This equality is the basis for proposing a useful metric, specifically the average minimum resolution of nearest-neighbor peaks, for the performance of comprehensive two-dimensional (2D) separations. Furthermore this metric was computed both without ancillary spectroscopic information and with the assistance of such help, specifically multi-wavelength UV-vis spectra, acquired during the separation. Separations are simulated with randomly positioned peaks over wide ranges of total number of peaks, first- and second-dimension peak capacity, dimensionless first-dimension sampling time, and spectral diversity. The specific version of the general multivariate selectivity concept that is used here--identified as SEL--gives the relative precision of quantification when using the PARAFAC (parallel factor analysis) method, a popular curve resolution algorithm. The SEL values of all peaks were calculated, averaged, and compared to the predictions of SOT. In the absence of auxiliary spectral data, the SEL-based average minimum resolution required to separate two peaks in a 2D separation is 0.256, compared to resolution of 0.5 if no chemometric assistance is available. This was found to be valid over a wide range of conditions and is essentially independent of peak crowding. With the assistance of the spectral data, the requisite minimum resolution substantially improves, that is, it decreases, especially when peak crowding is severe. The requisite minimum resolution decreases even further, up to a limit, as the spectral diversity is increased. In contrast, the SEL-based average under-sampling correction factor is virtually independent of the presence of the additional spectral data, and additionally is about the same as calculated with SOT from the average number of maxima in closely analogous simulations. The use of selectivity greatly increases the fraction of peaks that are singlets, relative to the number of singlet maxima, especially when spectral assistance is added. The insensitivity of the under-sampling correction factor to either the use of selectivity or added spectral data simplifies optimization of the corrected peak capacity in on-line comprehensive 2D separations.     

One target, different effects: a comparison of distinct therapeutic antibodies against the same targets

To date, more than 30 antibodies have been approved worldwide for therapeutic use. While the monoclonal antibody market is rapidly growing, the clinical use of therapeutic antibodies is mostly limited to treatment of cancers and immunological disorders. Moreover, antibodies against only five targets (TNF-α, HER2, CD20, EGFR, and VEGF) account for more than 80 percent of the worldwide market of therapeutic antibodies. The shortage of novel, clinically proven targets has resulted in the development of many distinct therapeutic antibodies against a small number of proven targets, based on the premise that different antibody molecules against the same target antigen have distinct biological and clinical effects from one another. For example, four antibodies against TNF-α have been approved by the FDA -- infliximab, adalimumab, golimumab, and certolizumab pegol -- with many more in clinical and preclinical development. The situation is similar for HER2, CD20, EGFR, and VEGF, each having one or more approved antibodies and many more under development. This review discusses the different binding characteristics, mechanisms of action, and biological and clinical activities of multiple monoclonal antibodies against TNF-α, HER-2, CD20, and EGFR and provides insights into the development of therapeutic antibodies.