Determining the incidence and risk factors of medical device-related pressure injury in intensive care patients.

AIM: This study aims to determine the incidence and risk factors of Medical Device-Related Pressure Injury (MDRPI) in Intensive Care Unit (ICU) patients. MATERIAL AND METHODS: This descriptive cross-sectional study involved 300 patients who did not have an MDRPI at the time of admission to the ICU of a university hospital in Turkey. The data was collected using the Patient Information Form, the Medical Device-Related Pressure Injury Follow-Up Chart, and the Jackson/Cubbin Risk Assessment Scale. RESULTS: The mean age of the patients was 71.88 ± 14.82 years old. Precisely 31% of patients were found to be at risk for pressure injuries, and MDRPI occurred in 18% of them. It was found that patients most commonly experienced stage 1 MDRPI in the hand-finger region due to pulse oximetry. It was also determined that nasal cannulas caused MDRPI the fastest. Patients with MDRPI had low Jackson/Cubbin scores, low albumin and hematocrit levels, and longer hospitalization durations (p < .05). Dependence on -respiratory support device, bedridden, and experiencing non-device-related pressure injuries were associated with MDRPI (p < .05). CONCLUSION: It was found that factors causing non-device-related pressure injuries may also pose an MDRPI risk for patients in intensive care. It was also observed that devices used in ICU could cause pressure injuries even in very short periods in cases where necessary precautions are not taken. Periodic evaluation of the area the medical devices are in contact with, removal of unused devices as quickly as possible, and the use of prophylactic dressings can play an important role in preventing MDRPI.

Data on Machine Learning regenerated Lithium-ion battery impedance.

This paper describes and provides the data on the regenerated-impedance spectra that is computed from experimental results of electrochemical impedance spectroscopy measurements taken from a commercial Li-ion battery. The empirical impedance data of secondary coin type Li-ion batteries were collected in different states of charge ranging from empty to full state of charge configurations. This approach utilizes only a small seed (ex grano) experimental data set to first build an ensemble of weighted disparate models selected based on performance and non-correlative criteria ("co-modelling") then second to generate what would be the remaining experimental data synthetically. The "Cooperative Model Framework" demonstrates the efficacy of this approach by assessing the synthetically generated data.

Rapidly predicting Kohn-Sham total energy using data-centric AI.

Predicting material properties by solving the Kohn-Sham (KS) equation, which is the basis of modern computational approaches to electronic structures, has provided significant improvements in materials sciences. Despite its contributions, both DFT and DFTB calculations are limited by the number of electrons and atoms that translate into increasingly longer run-times. In this work we introduce a novel, data-centric machine learning framework that is used to rapidly and accurately predicate the KS total energy of anatase [Formula: see text] nanoparticles (NPs) at different temperatures using only a small amount of theoretical data. The proposed framework that we call co-modeling eliminates the need for experimental data and is general enough to be used over any NPs to determine electronic structure and, consequently, more efficiently study physical and chemical properties. We include a web service to demonstrate the effectiveness of our approach.

ccImpute: an accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data.

BACKGROUND: In recent years, the introduction of single-cell RNA sequencing (scRNA-seq) has enabled the analysis of a cell's transcriptome at an unprecedented granularity and processing speed. The experimental outcome of applying this technology is a [Formula: see text] matrix containing aggregated mRNA expression counts of M genes and N cell samples. From this matrix, scientists can study how cell protein synthesis changes in response to various factors, for example, disease versus non-disease states in response to a treatment protocol. This technology's critical challenge is detecting and accurately recording lowly expressed genes. As a result, low expression levels tend to be missed and recorded as zero - an event known as dropout. This makes the lowly expressed genes indistinguishable from true zero expression and different than the low expression present in cells of the same type. This issue makes any subsequent downstream analysis difficult. RESULTS: To address this problem, we propose an approach to measure cell similarity using consensus clustering and demonstrate an effective and efficient algorithm that takes advantage of this new similarity measure to impute the most probable dropout events in the scRNA-seq datasets. We demonstrate that our approach exceeds the performance of existing imputation approaches while introducing the least amount of new noise as measured by clustering performance characteristics on datasets with known cell identities. CONCLUSIONS: ccImpute is an effective algorithm to correct for dropout events and thus improve downstream analysis of scRNA-seq data. ccImpute is implemented in R and is available at https://github.com/khazum/ccImpute .

A new method for stranded whole transcriptome RNA-seq.

This report describes an improved protocol to generate stranded, barcoded RNA-seq libraries to capture the whole transcriptome. By optimizing the use of duplex specific nuclease (DSN) to remove ribosomal RNA reads from stranded barcoded libraries, we demonstrate improved efficiency of multiplexed next generation sequencing (NGS). This approach detects expression profiles of all RNA types, including miRNA (microRNA), piRNA (Piwi-interacting RNA), snoRNA (small nucleolar RNA), lincRNA (long non-coding RNA), mtRNA (mitochondrial RNA) and mRNA (messenger RNA) without the use of gel electrophoresis. The improved protocol generates high quality data that can be used to identify differential expression in known and novel coding and non-coding transcripts, splice variants, mitochondrial genes and SNPs (single nucleotide polymorphisms).

Gene networks in Drosophila melanogaster: integrating experimental data to predict gene function.

BACKGROUND: Discovering the functions of all genes is a central goal of contemporary biomedical research. Despite considerable effort, we are still far from achieving this goal in any metazoan organism. Collectively, the growing body of high-throughput functional genomics data provides evidence of gene function, but remains difficult to interpret. RESULTS: We constructed the first network of functional relationships for Drosophila melanogaster by integrating most of the available, comprehensive sets of genetic interaction, protein-protein interaction, and microarray expression data. The complete integrated network covers 85% of the currently known genes, which we refined to a high confidence network that includes 20,000 functional relationships among 5,021 genes. An analysis of the network revealed a remarkable concordance with prior knowledge. Using the network, we were able to infer a set of high-confidence Gene Ontology biological process annotations on 483 of the roughly 5,000 previously unannotated genes. We also show that this approach is a means of inferring annotations on a class of genes that cannot be annotated based solely on sequence similarity. Lastly, we demonstrate the utility of the network through reanalyzing gene expression data to both discover clusters of coregulated genes and compile a list of candidate genes related to specific biological processes. CONCLUSIONS: Here we present the the first genome-wide functional gene network in D. melanogaster. The network enables the exploration, mining, and reanalysis of experimental data, as well as the interpretation of new data. The inferred annotations provide testable hypotheses of previously uncharacterized genes.

Data-driven ontologies.

Gene networks are important tools in studying gene-gene relationships and gene function. Understanding the relationships within these networks is an important challenge. Ontologies are a critical tool in helping deal with these data. The use of the Gene Ontology, for example, has become routine in methods for validation, discovery, etc. Here we present a novel algorithm that synthesizes an ontology by considering both extant annotation terms and also the connections between genes in gene networks. The process is efficient and produces easily inspectable ontologies. Because the relationships drawn between terms are heavily influenced by data, we call these "Data-Driven" Ontologies. We apply this algorithm to both discover new relationships between biological processes and as a tool to compare sets of genes across microrarray experiments. Supplemental data and source code are available at: http://www.ddont.org

Data pushing: a fly-centric guide to bioinformatics tools.

Bioinformatics tools can be invaluable resources to Drosophila researchers; however, the sheer number of applications and databases can be overwhelming. We present a broad overview of common bioinformatics tasks and the resources used to do them, with a specific focus on resources for Drosophila. The topics covered include: Genome Databases, Sequence Analysis, Comparative Genomics, Gene Expression Databases and Analysis Tools, Function-Based Data and Analysis, Pathways, Networks, and Interactions; and finally, tools to stay current with resources and literature. We also present a compilation of URLs and short descriptions that correspond to the topics and resources mentioned in this review.

From protein-disease associations to disease informatics.

Advancements in high-throughput technology and computational power have brought about significant progress in our understanding of cellular processes, including an increased appreciation of the intricacies of disease. The computational biology community has made strides in characterizing human disease and implementing algorithms that will be used in translational medicine. Despite this progress, most of the identified biomarkers and proposed methodologies have still not achieved the sensitivity and specificity to be effectively used, for example, in population screening against various diseases. Here we review the current progress in computational methodology developed to exploit major high-throughput experimental platforms towards improved understanding of disease, and argue that an integrated model for biomarker discovery, predictive medicine and treatment is likely to be data-driven and personalized. In such an approach, major data collection is yet to be done and comprehensive computational models are yet to be developed.

iGibbs: improving Gibbs motif sampler for proteins by sequence clustering and iterative pattern sampling.

The motif prediction problem is to predict short, conserved subsequences that are part of a family of sequences, and it is a very important biological problem. Gibbs is one of the first successful motif algorithms and it runs very fast compared with other algorithms, and its search behavior is based on the well-studied Gibbs random sampling. However, motif prediction is a very difficult problem and Gibbs may not predict true motifs in some cases. Thus, the authors explored a possibility of improving the prediction accuracy of Gibbs while retaining its fast runtime performance. In this paper, the authors considered Gibbs only for proteins, not for DNA binding sites. The authors have developed iGibbs, an integrated motif search framework for proteins that employs two previous techniques of their own: one for guiding motif search by clustering sequences and another by pattern refinement. These two techniques are combined to a new double clustering approach to guiding motif search. The unique feature of their framework is that users do not have to specify the number of motifs to be predicted when motifs occur in different subsets of the input sequences since it automatically clusters input sequences into clusters and predict motifs from the clusters. Tests on the PROSITE database show that their framework improved the prediction accuracy of Gibbs significantly. Compared with more exhaustive search methods like MEME, iGibbs predicted motifs more accurately and runs one order of magnitude faster.

COMPAM :visualization of combining pairwise alignments for multiple genomes.

UNLABELLED: COMPAM is a tool for visualizing relationships among multiple whole genomes by combining all pairwise genome alignments. It displays shared conserved regions (blocks) and where these blocks occur (edges) as block relation graphs which can be explored interactively. An unannotated genome, e.g. can then be explored using information from well-annotated genomes, COG-based genome annotation and genes. COMPAM can run either as a stand-alone application or through an applet that is provided as service to PLATCOM, a toolset for whole genome comparative analysis, where a wide variety of genomes can be easily selected. Features provided by COMPAM include the ability to export genome relationship information into file formats that can be used by other existing tools. AVAILABILITY: http://bio.informatics.indiana.edu/projects/compam/