Cytogenetic analysis of chorionic villi for prenatal diagnosis: An ACC collaborative study of U.K. data

A prospective 3-year collaborative study was undertaken in 1987 to collect cytogenetic data from diagnostic chorionic villus samples (CVS) in the U.K. in order to determine the predictive value of the chromosome abnormalities encountered. Twenty-seven laboratories contributed a total number of 7595 cases, of which 97·6 per cent were successful. Excluding single cell anomalies, a total of 480 cytogenetic abnormalities were reported, of which 137 were familial structural rearrangements and 343 were de novo problems. Non-mosaic trisomies of chromosomes 13, 18, and 21 (n=157), non-mosaic sex chromosome abnormalities (n=33), and triploidy (n=6) were all confirmed in cells of fetal origin where follow-up information was available. Of the nine remaining non-mosaics including tetraploidy, trisomies of other autosomes, and extra markers, only a trisomy 16 and a case of a supernumerary marker proved genuine. Eighty-eight cases of mosaicism were reported to the study, of which only nine were confirmed as genuine: two cases involving chromosome 13, one trisomy 18, two examples of extra marker chromosomes, three 45,X, and one 47,XXX. There were no reports of false-negative findings. Presumptive maternal cell contamination was encountered in 39 cases, a detected incidence of 0·5 per cent. Four cases of presumptive ‘vanishing twin’ were recorded: in three of these, direct preparations showed a female karyotype, whereas cultures indicated a male (with male fetuses in two cases). The fourth case was of a female fetus with male and female cells in the CVS cultures. Subtle structural chromosome abnormalities were missed in three instances. Accurate prediction of the fetal karyotype was shown to require detailed knowledge of both the nature and the distribution of abnormal cells in the extra-embryonic tissues. In many cases, this could only be made where results from direct preparations and cultured cells were available. A number of conclusions were reached from these and similar data in the literature regarding the reliability of chromosome findings in CVS.     

ASSAULT PREVENTION AS SOCIAL CONTROL: WOMEN AND SEXUAL ASSAULT PREVENTION ON URBAN COLLEGE CAMPUSES

Sexual assault on U.S.A. college campuses is increasingly recognized as an urgent and pervasive national problem. To prevent assault, school administrators and individual women promote and adopt safety strategies that often compromise women's free and independent use of the campus environment. Such strategies may fail to correspond to the actual nature of sexual assault on campus. Based on open-ended interviews with college personnel and women students, this study examines sexual assault and personal crime prevention strategies at two midwestern, urban universities. School and individual strategies are characterized according to type, goals, and orientation. Findings explore the relationship of strategies to sexual assault on campus, and implications of strategies for women's use of public space. Conclusions suggest means to increase real safety while enhancing women's freedom and mobility on and near campus.     

Effect of different irrigation methods on yield of red hot pepper and plant mortality caused by Phytophthora capsici Leon

This experiment was conducted in 1999 and 2000 in a field naturally infected by Phytophthora capsici to determine the effects of different irrigation methods, namely, basin (B), closed-end furrow (F), drip (D) and sprinkler (S) irrigation on dry yield of chili pepper (Capsium annuum L. K. Maras Type). Water use efficiency (WUE) and plant mortality were also measured. The averages of total applied water to the B, F, D and S plots for the two years were 937.7 mm, 920.6 mm, 886.5 and 913.4 mm, respectively. Thus, the mean seasonal water use by pepper varied from 1020.7 to 1109.7 mm. The highest water use was measured in B, followed by use for S, D and F methods. The highest mean potential dry yield with 1.58 t ha(-1) was obtained using D method. This was followed by S (1.36 t ha(-1)), B (1.13 t ha(-1)), and F (0.81 t ha(-1)) methods. In terms of plant mortality, crops having B irrigation had the highest (93.9%), followed by lessen amount to the S (3.2%), F (3.1%), and D methods (1.7%). Only the B irrigation method stimulated the development of P. capsici disease. On the other hand, mean WUEs varied between 0.7 and 1.7 kg ha(-1)mnm(-1) for Irrigation methods. WUE for D method was slightly higher than S method, but considerably higher than B and F methods.     

Modeling of SO<Subscript>2</Subscript> distribution in Istanbul using artificial neural networks

Urban air pollution is a growing problem in developing countries. Some compounds especially sulphur dioxide (SO₂) is considered as typical indicators of the urban air quality. Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently, various stochastic image-processing algorithms such as Artificial Neural Network (ANN) are applied to environmental engineering. ANN structure employs input, hidden and output layers. Due to the complexity of the problem, as the number of input–output parameters differs, ANN model settings such as the number of neurons of these layers changes. The ability of ANN models to learn, particularly capability of handling large amounts (or sets) of data simultaneously as well as their fast response time, are invariably the characteristics desired for predictive and forecasting purposes. In this paper, ANN models have been used to predict air pollutant parameter in meteorological considerations. We have especially focused on modeling of SO₂ distribution and predicting its future concentration in Istanbul, Turkey. We have obtained data sets including meteorological variables and SO₂ concentrations from Istanbul-Florya meteorological station and Istanbul-Yenibosna air pollution station. We have preferred three-layer perceptron type of ANN which consists of 10, 22 and 1 neurons for input, hidden and output layers, respectively. All considered parameters are measured as daily mean. The input parameters are: SO₂ concentration, pressure, temperature, humidity, wind direction, wind speed, strength of sunshine, sunshine, cloudy, rainfall and output parameter is the future prediction of SO₂. To evaluate the performance of ANN model, our results are compared to classical nonlinear regression methods. The over all system finds an optimum correlation between input–output variables. Here, the correlation parameter, r is 0.999 and 0.528 for training and test data. Thus in our model, the trend of SO₂ is well estimated and seasonal effects are well represented. As a result, we conclude that ANN is one of the compromising methods in estimation of environmental complex air pollution problems.     

Estimation of stream temperature in firtina creek (Rize-Turkiye) using artificial neural network model

Water temperature is one of the most important environmental variables in aquatic ecosystem. Temperature changes may have positive or negative effects on organisms. High water temperatures have caused mortalities in salmonid fishes. Therefore, monitoring and prediction of potential adverse changes in water temperature is very important. Here, we have developed and tested an artificial neural network (ANN) model to predict stream temperature of Firtina Creekin Black Sea region; using local water temperature, dissolved oxygen, pH and other available meteorological data (air temperature, rainfall). Thus, enabling define suitable habitat for native Sea Trout (Salmo trutta labrax, Pallas 1811) under past drought or other adverse envIronmental conditions.