Abstract: This report describes the results of a wet and dry season ecological baseline impact assessment study based on the context of a proposed Thermal Desorption Unit (TDU) development project in Eteo Eleme, Rivers State. Thermal Desorption Unit project of the magnitude of a waste management project must always have some negative effects on the quality and quantity of the environment. Following an environment impact assessment (EIA) a series of mitigation measures must be put in place to minimize the intensity of the negative effect of the project on the environment. The paper seeks to assess through baseline data the envisaged impact of the project on the existing conditions of the biophysical environment, to appraise the possible risk to the environment and mitigation measures adopted. It uses a combination of standard procedures of integrated data sources to qualitatively and quantitatively assess the floristic profile of the project study area. The result highlights most of the biophysical impact variables that will have negative effect on the environment. However, the study site still maintains the status of abundance, richness and evenness with obvious similarity in floristic composition and forest structure to that in tropical forest elsewhere in the world. It is obvious that the Eteo vegetation system is gradually under ecological succession resulting to secondary vegetation system without proper articulation of its wealth of flora diversity, but with high flora diversity in rainy season than dry season. The useful application of phyto-sociological indices in determining the status of its vegetation complex in terms of species abundance, density, important value index, diversity and distribution pattern is being recorded in the Table for both wet and dry seasons. The paper emphasis the need for sound environmental commitments to the project and to appraise their implementation. A proper balance between the expected benefits from the project and cost implication can only be obtained through impact studies and careful monitoring.Abstract: This report describes the results of a wet and dry season ecological baseline impact assessment study based on the context of a proposed Thermal Desorption Unit (TDU) development project in Eteo Eleme, Rivers State. Thermal Desorption Unit project of the magnitude of a waste management project must always have some negative effects on the quality a...Show More
Abstract: Various gene signatures of chemosensitivity in breast cancer have been identified. When used to build predictors of have chemosensitivity, many of them have their prediction accuracy around 80%. Identifying gene signatures to build high accuracy such predictors is a prerequisite for their clinical tests and applications. To elucidate the importance of each individual gene in a signature is another pressing need before such signature could be tested in clinical settings. In this study, Genetic Algorithms (GAs) and Sparse Logistic Regression (SLR) were employed to identify two signatures. The first had 28 probe sets selected by GA from the top 65 probe sets that were highly overexpressed between pathologic compete response (pCR) and residual disease (RD) and was used to build a SLR predictor of pCR (SLR-28). The second had 86 probe sets (Notch-86) selected by GA from Notch signaling pathway and was used to develop a SLR predictor of pCR (SLR-Notch-86). These two predictors tested on a training set (n=81) and validation set (n=52) had very precise predictions measured by accuracy, specificity, sensitivity, positive predictive value and negative predictive value with their corresponding P value all zero. Furthermore, these two predictors discovered 12 important genes in the 28 probe set signature and 14 important genes in the Notch-86 signature. Our two signatures produced superior performance over a signature in a previous study, demonstrating the potential of GA and SLR in identifying robust gene signatures in chemo response prediction in breast cancer.Abstract: Various gene signatures of chemosensitivity in breast cancer have been identified. When used to build predictors of have chemosensitivity, many of them have their prediction accuracy around 80%. Identifying gene signatures to build high accuracy such predictors is a prerequisite for their clinical tests and applications. To elucidate the importance...Show More
