Using data mining techniques to isolate chemical intrusion in water distribution systems.

The security of water distribution systems has become the subject of an increasing volume of research over the last decade. Data analysis and machine learning are linked to hydraulic and quality modeling for improving the capacity of water utilities to save lives when faced with the contamination of water networks. This research applies k-nearest neighbor and random forest algorithms to estimate the location of contamination sources at near-real time. Epanet and Epanet-MSX software are used to simulate intrusions of pesticide into water distribution system and the interaction with compounds already present in water bulk. Different pesticide concentrations are considered in the simulations, and chlorine monitoring occurs through placed quality sensors. The results show that random forest can localize [Formula: see text] of contamination scenarios, while the KNN algorithm found [Formula: see text]. Finally, an assessment of contamination spread is made for a better understanding of the impacts of non-localized contamination.

Chromatin Landscape Distinguishes the Genomic Loci of Hundreds of Androgen-Receptor-Associated LincRNAs From the Loci of Non-associated LincRNAs.

Cell signaling events triggered by androgen hormone in prostate cells is dependent on activation of the androgen receptor (AR) transcription factor. Androgen hormone binding to AR promotes its displacement from the cytoplasm to the nucleus and AR binding to DNA motifs, thus inducing activatory and inhibitory transcriptional programs through a complex regulatory mechanism not yet fully understood. In this work, we performed RNA-seq deep-sequencing of LNCaP prostate cancer cells and found over 7000 expressed long intergenic non-coding RNAs (lincRNAs), of which ∼4000 are novel lincRNAs, and 258 lincRNAs have their expression activated by androgen. Immunoprecipitation of AR, followed by large-scale sequencing of co-immunoprecipitated RNAs (RIP-Seq) has identified in the LNCaP cell line a total of 619 lincRNAs that were significantly enriched (FDR < 10%, DESeq2) in the anti-Androgen Receptor (antiAR) fraction in relation to the control fraction (non-specific IgG), and we named them Androgen-Receptor-Associated lincRNAs (ARA-lincRNAs). A genome-wide analysis showed that protein-coding gene neighbors to ARA-lincRNAs had a significantly higher androgen-induced change in expression than protein-coding genes neighboring lincRNAs not associated to AR. To find relevant epigenetic signatures enriched at the ARA-lincRNAs' transcription start sites (TSSs) we used a machine learning approach and identified that the ARA-lincRNA genomic loci in LNCaP cells are significantly enriched with epigenetic marks that are characteristic of in cis enhancer RNA regulators, and that the H3K27ac mark of active enhancers is conspicuously enriched at the TSS of ARA-lincRNAs adjacent to androgen-activated protein-coding genes. In addition, LNCaP topologically associating domains (TADs) that comprise chromatin regions with ARA-lincRNAs exhibit transcription factor contents, epigenetic marks and gene transcriptional activities that are significantly different from TADs not containing ARA-lincRNAs. This work highlights the possible involvement of hundreds of lincRNAs working in synergy with the AR on the genome-wide androgen-induced gene regulatory program in prostate cells.