Hanna Hall (julyfir51)

This paper describes a simple and reproducible method for universal evaluation of the performance of electrical impedance tomography (EIT) systems using reconstructed images. To address the issues where common electrical parameters are not directly related to the quality of EIT images, based on objective full reference (FR) image quality assessment, the method provides a visually distinguishable hot colormap and two new FR metrics, the global and the more specific 'region of interest'. A passive 16 electrode EIT system using an application specific integrated circuit front-end was used to evaluate the proposed method. The measured results show, both visually and in terms of the proposed FR metrics, the impact on recorded EIT images with different design parameters and non-idealities. The paper also compares the image results of a passive electrode system with a matched 'single variable' active electrode system and demonstrates the merit of an active electrode system for noise interference. A figure of merit based on the FR metrics is proposed.Gastric cancer (GC) is the third leading cause of cancer death in the world. It is associated with the stimulation of microenvironment, aberrant epigenetic modification, and chronic inflammation. However, few researches discuss the GC molecular progression mechanisms from the perspective of the system level. In this study, we proposed a systems medicine design procedure to identify essential biomarkers and find corresponding drugs for GC. At first, we did big database mining to construct candidate protein-protein interaction network (PPIN) and candidate gene regulation network (GRN). Secondly, by leveraging the next-generation sequencing (NGS) data, we performed system modeling and applied system identification and model selection to obtain real genome-wide genetic and epigenetic networks (GWGENs). To make the real GWGENs easy to analyze, the principal network projection method was used to extract the core signaling pathways denoted by KEGG pathways. Subsequently, based on the identified biomarkers, we trained a deep neural network of drug-target interaction (DeepDTI) with supervised learning and filtered our candidate drugs considering drug regulation ability and drug sensitivity. With the proposed systematic strategy, we not only shed the light on the progression of GC but also suggested potential multiple-molecule drugs efficiently.With the development of sequencing technology, microbiological genome sequencing analysis has attracted extensive attention. For inexperienced users without sufficient bioinformatics skills, making sense of sequencing data for microbial identification, especially for bacterial identification, through reads analysis is still challenging. In order to address the challenge of effectively analyzing genomic information, in this paper, we develop an effective approach and automatic bioinformatics pipeline called PBGI for bacterial genome identification, performing automatedly and customized bioinformatics analysis using short-reads or long-reads sequencing data produced by multiple platforms such as Illumina, PacBio and Oxford Nanopore. An evaluation of the proposed approach on the practical data set is presented, showing that PBGI provides a user-friendly way to perform bacterial identification through short or long reads analysis, and could provide accurate analyzing results. The source code of the PBGI is freely available at https//github.com/lyotvincent/PBGI.In comparative genomics, one goal is to find similarities between the genomes of different organisms. Comparisons using genome features like genes, gene order, and regulatory sequences are carried out with this purpose in mind. Genome rearrangements are mutational events that affect large extensions of the genome. Reversal is one of the most studied genome rearrangement events. This event acts in a segment of the genome, inverting the position and the orientation of genes in it. Transposition is another