Zobrazení zdroje stránky Mendozajernigan4297

The human gut microbiota performs functions that are essential for the maintenance of the host physiology. However, characterizing the functioning of microbial communities in relation to the host remains challenging in reference-based metagenomic analyses. Indeed, as taxonomic and functional analyses are performed independently, the link between genes and species remains unclear. Although a first set of species-level bins was built by clustering co-abundant genes, no reference bin set is established on the most used gut microbiota catalog, the Integrated Gene Catalog (IGC). With the aim to identify the best suitable method to group the IGC genes, we benchmarked nine taxonomy-independent binners implementing abundance-based, hybrid and integrative approaches. To this purpose, we designed a simulated non-redundant gene catalog (SGC) and computed adapted assessment metrics. Overall, the best trade-off between the main metrics is reached by an integrative binner. For each approach, we then compared the results of the best-performing binner with our expected community structures and applied the method to the IGC. The three approaches are distinguished by specific advantages, and by inherent or scalability limitations. Hybrid and integrative binners show promising and potentially complementary results but require improvements to be used on the IGC to recover human gut microbial species.Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers.Purpose This study aimed to compare performance on sand and a firm surface and to describe the physical capacity of male and female beach soccer players. Methods Sixty-six male and 29 female competitive beach soccer players voluntarily participated in this study. Firstly, within-subjects test scores were compared to scores on a firm surface (criterion validity; n = 15 men) and reconducted on a second occasion (reliability; n = 51 men). [https://www.selleckchem.com/products/bgb-283-bgb283.html Lifirafenib] Secondly, the best score on sand was retained to compare test performance between ages (classified as below 20, 20-30, and above 30 years) and sexes. Performance assessments included sprint time over 5 and 15 m (once on a firm surface and twice on sand), standing long jump (SLJ, once on a firm surface and twice on sand) and Yo-Yo intermittent recovery level 1 (Yo-Yo IR1, once on a firm surface and once on sand; only data for men were available). Results Five-m sprint and Yo-Yo IR1 performance on sand were not correlated to performance on a firm surface (P &gt; 0.05). Test-retest reliability was acceptable for the 15-m sprint and SLJ tests (ICC &gt; 0.90; CV less then 5%). Performance in 15-m sprint and maximal sprinting speed were moderately lower in male players aged above 30 years. compared to players aged below 30 years (d = 0.35-0.42; P less then 0.05). Irrespective of the age group, weight-bearing power-based performance mass was moderately to very largely higher in male players than in female players (d = 0.42-0.88; P less then 0.05). Conclusions The lack of a consistent relationship between performance on sand and on a firm surface might indicate the need to develop specific test batteries for sand-based athletes. Age-related differences in physical performance were evident only in sprint capacity. Further studies are warranted to elucidate our preliminary findings and to develop the sand specific tests.Abnormal scarring is a consequence of dysregulation in the wound healing process, with limited options for effective and noninvasive therapies. Given the ability of spherical nucleic acids (SNAs) to penetrate skin and regulate gene expression within, we investigated whether gold-core SNAs (AuSNAs) and liposome-core SNAs (LSNAs) bearing antisense oligonucleotides targeting transforming growth factor beta 1 (TGF-β1) can function as a topical therapy for scarring. Importantly, both SNA constructs appreciably downregulated TGF-β1 protein expression in primary hypertrophic and keloid scar fibroblasts in vitro. In vivo, topically applied AuSNAs and LSNAs downregulated TGF-β1 protein expression levels and improved scar histology as determined by the scar elevation index. These data underscore the potential of SNAs as a localized, self-manageable treatment for skin-related diseases and disorders that are driven by increased gene expression.<br /><br /> Trauma quality improvement programs and registries improve care and outcomes for injured patients. Designated trauma centers calculate injury scores using dedicated trauma registrars; however, many injuries arrive at nontrauma centers, leaving a substantial amount of data uncaptured. We propose automated methods to identify severe chest injury using machine learning (ML) and natural language processing (NLP) methods from the electronic health record (EHR) for quality reporting.<br /><br /> A level I trauma center was queried for patients presenting after injury between 2014 and 2018. Prediction modeling was performed to classify severe chest injury using a reference dataset labeled by certified registrars. Clinical documents from trauma encounters were processed into concept unique identifiers for inputs to ML models logistic regression with elastic net (EN) regularization, extreme gradient boosted (XGB) machines, and convolutional neural networks (CNN). The optimal model was identified by examining predictive and face validity metrics using global explanations.<br /><br /> Of 8952 encounters, 542 (6.1%) had a severe chest injury. CNN and EN had the highest discrimination, with an area under the receiver operating characteristic curve of 0.93 and calibration slopes between 0.88 and 0.97. CNN had better performance across risk thresholds with fewer discordant cases. Examination of global explanations demonstrated the CNN model had better face validity, with top features including "contusion of lung" and "hemopneumothorax."<br /><br /> The CNN model featured optimal discrimination, calibration, and clinically relevant features selected.<br /><br /> NLP and ML methods to populate trauma registries for quality analyses are feasible.<br /><br />NLP and ML methods to populate trauma registries for quality analyses are feasible.How clinicians utilize medically actionable genomic information, displayed in the electronic health record (EHR), in medical decision-making remains unknown. Participating sites of the Electronic Medical Records and Genomics (eMERGE) Network have invested resources into EHR integration efforts to enable the display of genetic testing data across heterogeneous EHR systems. To assess clinicians' engagement with unsolicited EHR-integrated genetic test results of eMERGE participants within a large tertiary care academic medical center, we analyzed automatically generated EHR access log data. We found that clinicians viewed only 1% of all the eMERGE genetic test results integrated in the EHR. Using a cluster analysis, we also identified different user traits associated with varying degrees of engagement with the EHR-integrated genomic data. These data contribute important empirical knowledge about clinicians limited and brief engagements with unsolicited EHR-integrated genetic test results of eMERGE participants. Appreciation for user-specific roles provide additional context for why certain users were more or less engaged with the unsolicited results. This study highlights opportunities to use EHR log data as a performance metric to more precisely inform ongoing EHR-integration efforts and decisions about the allocation of informatics resources in genomic research.<br /><br /> Concerns about patient privacy have limited access to COVID-19 datasets. Data synthesis is one approach for making such data broadly available to the research community in a privacy protective manner.<br /><br /> Evaluate the utility of synthetic data by comparing analysis results between real and synthetic data.<br /><br /> A gradient boosted classification tree was built to predict death using Ontario's 90514 COVID-19 case records linked with community comorbidity, demographic, and socioeconomic characteristics. Model accuracy and relationships were evaluated, as well as privacy risks. The same model was developed on a synthesized dataset and compared to one from the original data.<br /><br /> The AUROC and AUPRC for the real data model were 0.945 [95% confidence interval (CI), 0.941-0.948] and 0.34 (95% CI, 0.313-0.368), respectively. The synthetic data model had AUROC and AUPRC of 0.94 (95% CI, 0.936-0.944) and 0.313 (95% CI, 0.286-0.342) with confidence interval overlap of 45.05% and 52.02% when compared with the real data. The most important predictors of death for the real and synthetic models were in descending order age, days since January 1, 2020, type of exposure, and gender. The functional relationships were similar between the two data sets. Attribute disclosure risks were 0.0585, and membership disclosure risk was low.<br /><br /> This synthetic dataset could be used as a proxy for the real dataset.<br /><br />This synthetic dataset could be used as a proxy for the real dataset.<br /><br /> Seizure forecasting algorithms have become increasingly accurate and may reduce the morbidity and mortality caused by seizure unpredictability. Translating these benefits into meaningful health outcomes for people with epilepsy requires effective data visualization of algorithm outputs. To date, no studies have investigated patient and physician perspectives on effective translation of algorithm outputs into data visualizations through health information technology.<br /><br /> We developed front-end data visualizations as part of a Seizure Forecast Visualization Toolkit. We surveyed 627 people living with epilepsy and caregivers, and 28 epilepsy healthcare providers. Respondents scored each visualization in terms of international standardized software quality criteria for functionality, appropriateness, and usability.<br /><br /> People with epilepsy and caregivers ranked hourly radar charts highest for protecting against errors in interpreting forecasts, reducing anxiety from seizure unpredictability, and understanding seizuating standardized, quantitative methods for assessing the effectiveness of data visualization to translate seizure forecast algorithms into clinical practice.<br /><br /> Research &amp; Exploratory Analysis Driven Time-data Visualization (<br /><br /> ) is an open source R Shiny application for visualizing irregularly and regularly spaced longitudinal data. <br /><br /> provides unique filtering and changepoint analysis (CPA) features. The need for these analyses was motivated by research of surgical work-flow disruptions in operating room settings. Specifically, for the analysis of the causes and characteristics of periods of high disruption-rates, which are associated with adverse surgical outcomes.<br /><br /> is a graphical application, and the main component of a package of the same name. <br /><br /> generates and evaluates code to filter and visualize data. Users can view the visualization code from within the application, which facilitates reproducibility. The data input requirements are simple, a table with a time column with no missing values. The input can either be in the form of a file, or an in-memory dataframe- which is effective for rapid visualization during curation.<br /><br /> We used <br /><br /> to automatically detect surgical disruption cascades.