人尿中1-羟基芘浓度与多环芳烃日暴露量的关系

采集并分析了 10 0名不吸烟成人 2 4h的呼吸带空气 (PM10 和气相 )、食物及尿液样品 ,建立了尿中1 羟基芘与多环芳烃日暴露量的关系 结果表明 :晨尿 1 羟基芘 (μmol·mol肌酐 -1)浓度与芘、苯并 (a )芘等 13种多环芳烃日暴露量的对数线性回归方程拟合度R2 达 0 12— 0 6 7,方程各项参数也均达到了显著性水平 (P <0 0 0 1)     

应用模式识别法研究芳腈化合物结构与活性的关系

基于２３个芳腈衍生物对发光菌的毒性疚和一批结构参数，用模式识别法研究了结构与活性的关系。在化合物的生物活性类别与模式特征之间建立的３个类别判别函数对化合物活性类别的判别正确率达９１．３％，并为进一步的研究工作合理地挑选了４个代表性化合物。     

有机化合物厌氧生物降解性的测定和预测

测定有机物厌氧生物降解性的方法包括非特性参数和特性参数测定法。本文着重介绍有机物厌氧生物降解性的筛选测定法，以基团贡献法为基础，不外加其它理化参数的有机物结构与生物降解性关系的预测已经由简单的线性模型发展至专家系统和人工神经网络模型，并显示出极好的应用前景。     

Review and comparison of methods to study the contribution of variables in artificial neural network models

Convinced by the predictive quality of artificial neural network (ANN) models in ecology, we have turned our interests to their explanatory capacities. Seven methods which can give the relative contribution and/or the contribution profile of the input factors were compared: (i) the ‘PaD’ (for Partial Derivatives) method consists in a calculation of the partial derivatives of the output according to the input variables; (ii) the ‘Weights’ method is a computation using the connection weights; (iii) the ‘Perturb’ method corresponds to a perturbation of the input variables; (iv) the ‘Profile’ method is a successive variation of one input variable while the others are kept constant at a fixed value; (v) the ‘classical stepwise’ method is an observation of the change in the error value when an adding (forward) or an elimination (backward) step of the input variables is operated; (vi) ‘Improved stepwise a’ uses the same principle as the classical stepwise, but the elimination of the input occurs when the network is trained, the connection weights corresponding to the input variable studied is also eliminated; (vii) ‘Improved stepwise b’ involves the network being trained and fixed step by step, one input variable at its mean value to note the consequences on the error. The data tested in this study concerns the prediction of the density of brown trout spawning redds using habitat characteristics. The PaD method was found to be the most useful as it gave the most complete results, followed by the Profile method that gave the contribution profile of the input variables. The Perturb method allowed a good classification of the input parameters as well as the Weights method that has been simplified but these two methods lack stability. Next came the two improved stepwise methods (a and b) that both gave exactly the same result but the contributions were not sufficiently expressed. Finally, the classical stepwise methods gave the poorest results.     

QSAR计算中的分子连接性指数法

本文介绍了分子连接性指数法和原理及各阶指数的计算方法，以及在ＱＳＡＲ计算中的应用实例，并对特殊连接性指数的用法作了分析。     

The Importance of Being Spatial (and Reserved): Assessing Northern Spotted Owl Habitat Relationships with Hierarchical Bayesian Models

Abstract:  Regional conservation planning increasingly draws on habitat suitability models to support decisions regarding land allocation and management. Nevertheless, statistical techniques commonly used for developing such models may give misleading results because they fail to account for 3 factors common in data sets of species distribution: spatial autocorrelation, the large number of sites where the species is absent (zero inflation), and uneven survey effort. We used spatial autoregressive models fit with Bayesian Markov Chain Monte Carlo techniques to assess the relationship between older coniferous forest and the abundance of Northern Spotted Owl nest and activity sites throughout the species' range. The spatial random-effect term incorporated in the autoregressive models successfully accounted for zero inflation and reduced the effect of survey bias on estimates of species–habitat associations. Our results support the hypothesis that the relationship between owl distribution and older forest varies with latitude. A quadratic relationship between owl abundance and older forest was evident in the southern portion of the range, and a pseudothreshold relationship was evident in the northern portion of the range. Our results suggest that proposed changes to the network of owl habitat reserves would reduce the proportion of the population protected by up to one-third, and that proposed guidelines for forest management within reserves underestimate the proportion of older forest associated with maximum owl abundance and inappropriately generalize threshold relationships among subregions. Bayesian spatial models can greatly enhance the utility of habitat analysis for conservation planning because they add the statistical flexibility necessary for analyzing regional survey data while retaining the interpretability of simpler models.     

EFFECTS OF SIMULATED CLOUD SEEDING ON STREAMFLOW OF SELECTED WATERSHEDS IN PENNSYLVANIA1

A mathematical model was developed to simulate the hydrologic behavior of five small watersheds in central Pennsylvania. Continuous hydrographs for the 6-month period, April to September 1964, were simulated. Synthesized rainfall cycles consisting of increasing rainfall by 10, 20, and 30 percent to simulate the effects of cloud seeding were processed through the watershed model to determine the effects on low flow augmentation. Other rainfall cycles used consisted of increasing every third storm by 30 percent and of developing a rainfall cycle by processing daily radiosonde data through a mathematical cumulus cloud model to obtain a prediction of rainfall following seeding. A comparison of actual and predicted hydrographs indicated that simulated cloud seeding resulted in significant monthly and seasonal water yields. In general, the results of the study appear to indicate that on a theoretical basis cloud seeding would be a feasible method of augmenting low stream-flow during the summer months on watersheds in the northern Appalachian region.     

A SELF-VERIFYING HYBRID COMPUTER MODEL OF RIVER-BASIN HYDROLOGY1

ABSTRACT. As demands upon available water supplies increase, there is an accompanying increase in the need to assess the downstream consequences resulting from changes at specific locations within a hydrologic system. The problem is approached in this study by hybrid computer simulation of the hydrologic system. Modeling concepts are based upon the development of basic relationships which describe the various hydrologic processes. Within a system these relationships are linked by the continuity-of-mass principle. Spatial resolution is achieved by considering the modeled areas as a series of subbasins. The time increment adopted for the model is one month, so that time varying quantities are expressed in terms of mean monthly values. The model is general in nature and is applied to a particular hydrologic system through a programmed verification procedure whereby model coefficients are evaluated for the particular system. In this study the model is applied to the Bear River basin of western Wyoming, southern Idaho, and northern Utah. Comparisons between observed and computed outflow hydrographs show good agreement. The utility of the model is demonstrated by predicting the effects of various possible water resource management alternatives. The verified hybrid computer program can be digitized for application to the digital computer.     

CHLOROPHYLL-BIOMASS-NUTRIENT RELATIONSHIPS FOR NATURAL ASSEMBLAGES OF FLORIDA PHYTOPLANKTON1

Parallel determination of phytoplankton biomass and chlorophyll a concentration were made on spring and summer phytoplankton samples collected from 165 Florida lakes. There was a significant correlation between chlorophyll a concentration and phytoplankton biomass (r=0.80; P < 0.01). Chlorophyll content per unit phytoplankton biomass ranged over two orders of magnitude. Nitrogen seemed to be a major factor influencing the chlorophyll content of Florida algae. Multiple regression analyses indicated that phytoplankton biomass was dependent on both the total phosphorus and total nitrogen concentration. Nutrient-phytoplankton and Secchi-phytoplankton relationships for the Florida lakes had higher coefficients of determination if chlorophyll a concentrations rather than phytoplankton biomass data were used in regression analyses.     

Changes in interacting species with disturbance

Human-influenced changes in the diversity and abundance of native wildlife in a southern boreal forest area, which became a national park in 1975, are used to develop working hypotheses for predicting and subsequently measuring the effects of disturbance or restoration programs on groups of interacting species. Changes from presettlement conditions began with early 1900 hunting, which eliminated woodland caribou (Rangifer tarandus) and elk (Cervus elaphus), and reduced moose (Alces alces) to the low numbers which still persist. Increases in white-tailed deer (Odocoileus virginianus), as these other cervid species became less abundant or absent, provided enough alternative food to sustain the system's carnivores until plant succession on previously burned or logged areas also caused deer to decline. With increased competition for reduced food, carnivore species also became less abundant or absent and overexploited some prey populations. The abilities of interacting species to maintain dynamically stable populations or persist varied with their different capacities to compensate for increased exploitation or competition. These relationships suggested a possible solution to the problem of predicting the stability of populations in disturbed systems. For the 1976–1985 period, a hypothesis that the increased protection of wildlife from exploitation in a national park would restore a more diverse, abundant, and productive fauna had to be rejected.