Madison County, Kentucky Hazardous Materials Commodity Flow Analysis

This report presents the results of a Commodity Flow Analysis of Hazardous Materials for Interstate-75 (I-75) (North and South Bound lanes) conducted by Western Kentucky University in partnership with the Madison County (Kentucky) Local Emergency Planning Committee (LEPC). The only Kentucky County within the study area is Madison County as shown in Figure 1.1. The purpose of report is to give information on patterns of hazardous materials being transported along I-75 as observed from July 25th 2011 to August 5th 2011. A secondary purpose is to summarize incidents involving hazardous materials over the previous 6 years (January 2006 – June 2011). Finally, this report assesses survey information collected from fixed facilities that ship and receive hazardous materials in the I-75 highway. Commodity flow analysis is necessary in order for the LEPC to prepare for future hazardous material releases that may occur along this section of I-75. Data collected from this study will aid the emergency planning process for specific hazardous materials that were observed to frequent the study area during the study period

Common cancer biomarkers of breast and ovarian types identified through artificial intelligence

Biomarkers can offer great promise for improving prevention and treatment of complex diseases such as cancer, cardiovascular diseases, and diabetes. These can be used as either diagnostic or predictive or as prognostic biomarkers. The revolution brought about in biological big data analytics by artificial intelligence (AI) has the potential to identify a broader range of genetic differences and support the generation of more robust biomarkers in medicine. AI is invigorating biomarker research on various fronts, right from the cataloguing of key mutations driving the complex diseases like cancer to the elucidation of molecular networks underlying diseases. In this study, we have explored the potential of AI through machine learning approaches to propose that these methods can act as recommendation systems to sort and prioritize important genes and finally predict the presence of specific biomarkers. Essentially, we have utilized microarray datasets from open‐source databases, like GEO, for breast, lung, colon, and ovarian cancer. In this context, different clustering analyses like hierarchical and k‐means along with random forest algorithm have been utilized to classify important genes from a pool of several thousand genes. To this end, network centrality and pathway analysis have been implemented to identify the most potential target as CREB1

Common cancer biomarkers of breast and ovarian types identified through artificial intelligence

Biomarkers can offer great promise for improving prevention and treatment of complex diseases such as cancer, cardiovascular diseases, and diabetes. These can be used as either diagnostic or predictive or as prognostic biomarkers. The revolution brought about in biological big data analytics by artificial intelligence (AI) has the potential to identify a broader range of genetic differences and support the generation of more robust biomarkers in medicine. AI is invigorating biomarker research on various fronts, right from the cataloguing of key mutations driving the complex diseases like cancer to the elucidation of molecular networks underlying diseases. In this study, we have explored the potential of AI through machine learning approaches to propose that these methods can act as recommendation systems to sort and prioritize important genes and finally predict the presence of specific biomarkers. Essentially, we have utilized microarray datasets from open‐source databases, like GEO, for breast, lung, colon, and ovarian cancer. In this context, different clustering analyses like hierarchical and k‐means along with random forest algorithm have been utilized to classify important genes from a pool of several thousand genes. To this end, network centrality and pathway analysis have been implemented to identify the most potential target as CREB1