Journal of Computer Science & Systems Biology

During 2015-2017 we have conducted experiments in Japan to test the capacity of microbial fuel cells (MFC) to treat different types of wastewaters (swine farm, domestic, yeast fermentation, winery, etc.) and concomitantly collecting DNA samples from MFC anodic and planktonic bacterial communities. W e analyzed these metagenomes in UK, using our new bioinformatics tool (ASAR) that allow integration of phylogenetic and functional data. Characteristic MFC communities and the associated functional signatures were shown to reflect effective waste water treatment. We also found that the fraction of opportunistic pathogenic bacteria DNA was reduced in metagenomes from MFC communities during swine waste treatment. The highest loss was recorded for Enterobacteriaceae family (such as Yersinia, Vibrio, and Shigella). The abundance of virulent genes responsible for adhesion, secretion systems, invasion and intracellular survival, as well as antibiotic resistance, associated with Firmicutes and Actinobacteria phyla of Gram-positive bacteria, also decreased in the MFC residential metagenomes. Key metabolic functions were redistributed among bacteria on the anode and archaea in plankton. We propose to use MFC, inoculated with electroactive bacterial communities, for waste disinfection, and potentially for development of novel antibacterial therapies. This approach promises to be effective and economically justified, especially in cases of epidemics of enterobacteria-associated diseases, and common residential hospital pathogens such as Enterococcus.

In this case study, we validate a personalized approach to gut microbiome (GM) analysis and interpretation based on published association studies. We apply our ASAR data annotation and clustering package to a series of 10 sequenced GM’s from individuals of different ethnical and geographical backgrounds, age and health groups. The differentially presented and detectable taxonomic and functional signatures in each GM metagenome are used to predict the hosts’ characteristics via correlations established in published studies, and the predictions are being validated by available individual-associated metadata. We also test sensitivity of the routine annotation and data clustering pipeline to an individual and family-linked signatures in GM structure and functionalities, when applied to a limited number of varying samples. The number of samples was sufficient to demonstrate 2 main types of GM composition, based on Bacteroides or Prevotella as main abundant genera, limitation of a variety of taxa as a result of antibiotics application, clustering of family members’ GM metagenomes both in taxonomic and in functional space, individual signatures related to chronic diseases and pharmacological interventions, and elements of ethnicityrelated characteristics in the metagenomes. The method and logical algorithms of the analysis applied here may be utilized in rather computational pipeline for a personalized microbiome analyses, and their potential useful outputs and limitations are being discussed.

Artificial Intelligence (AI) is as a promising tool for supporting the healthcare administration and it is fundamentally changing medicine. AI mainly refers to doctors and hospitals analyzing vast data sets of potentially life-saving information through AI algorithms. These algorithms have several applications in hospitals, clinical laboratories, and research facilities. In this review, we will provide an overview of applications of AI in improving patient flow to the hospital and patient transfer within a hospital.