Duckworthsmed2345
Direct link between metabolism and cell and organism phenotype in health and disease makes metabolomics, a high throughput study of small molecular metabolites, an essential methodology for understanding and diagnosing disease development and progression. Machine learning methods have seen increasing adoptions in metabolomics thanks to their powerful prediction abilities. However, the "black-box" nature of many machine learning models remains a major challenge for wide acceptance and utility as it makes the interpretation of decision process difficult. This challenge is particularly predominant in biomedical research where understanding of the underlying decision making mechanism is essential for insuring safety and gaining new knowledge.
In this article, we proposed a novel computational framework, Systems Metabolomics using Interpretable Learning and Evolution (SMILE), for supervised metabolomics data analysis. Our methodology uses an evolutionary algorithm to learn interpretable predictive models and tbility and explainability in machine learning, and contributes to more transparent and powerful applications of machine learning in bioinformatics.
Abdominal obesity as a predominant comorbidity has played a key role in the incidence and worsening of heart failure with preserved ejection fraction (HFpEF), and waist-to-height ratio (WHtR) behaves better than waist circumference or body mass index in evaluating abdominal obesity. While the association between WHtR and all-cause death in Chinese patients with HFpEF remains unclear.
Patients with stable HFpEF (N = 2041) who presented to our hospital from January 2008 to July 2019 were divided into low-WHtR (< 0.5, N = 378) and high-WHtR (≥ 0.5, N = 1663). selleck chemical Multivariable Cox proportional-hazard models were used to examine the association of WHtR with all-cause death.
The average age was 76.63 ± 11.44years, and the mean follow-up was 4.53years. During follow-up, 185 patients (9.06%) reached the primary outcome of all-cause death. As for the secondary outcome, 79 patients (3.87%) experienced cardiovascular death, 106 (5.19%) had non-cardiovascular death, and 94 (4.61%) had heart failure rehospitalization. After multivariable adjustment, a higher WHtR was significantly associated with the increased risks of all-cause death [adjusted hazard ratios (HR) 1.91, 95% confidence interval (CI) 1.06-3.45, p = 0.032], cardiovascular death (adjusted HR 2.58; 95% CI 1.01-6.67, p = 0.048), and HF rehospitalization (adjusted HR 3.04; 95% CI 1.26-7.31, p = 0.013).
Higher WHtR is an independent risk factor for all-cause death in Chinese patients with HFpEF.
Higher WHtR is an independent risk factor for all-cause death in Chinese patients with HFpEF.
Chromosomes are organized into units called topologically associated domains (TADs). TADs dictate regulatory landscapes and other DNA-dependent processes. Even though various factors that contribute to the specification of TADs have been proposed, the mechanism is not fully understood. Understanding the process for specification and maintenance of these units is essential in dissecting cellular processes and disease mechanisms.
In this study, we report a genome-wide study that considers the idea of long noncoding RNAs (lncRNAs) mediating chromatin organization using lncRNADNA triplex-forming sites (TFSs). By analyzing the TFSs of expressed lncRNAs in multiple cell lines, we find that they are enriched in TADs, their boundaries, and loop anchors. However, they are evenly distributed across different regions of a TAD showing no preference for any specific portions within TADs. No relationship is observed between the locations of these TFSs and CTCF binding sites. However, TFSs are located not just in promoter regions but also in intronic, intergenic, and 3'UTR regions. We also show these triplex-forming sites can be used as predictors in machine learning models to discriminate TADs from other genomic regions. Finally, we compile a list of important "TAD-lncRNAs" which are top predictors for TADs identification.
Our observations advocate the idea that lncRNADNA TFSs are positioned at specific areas of the genome organization and are important predictors for TADs. LncRNADNA triplex formation most likely is a general mechanism of action exhibited by some lncRNAs, not just for direct gene regulation but also to mediate 3D chromatin organization.
Our observations advocate the idea that lncRNADNA TFSs are positioned at specific areas of the genome organization and are important predictors for TADs. LncRNADNA triplex formation most likely is a general mechanism of action exhibited by some lncRNAs, not just for direct gene regulation but also to mediate 3D chromatin organization.
The formation of the Isthmus of Panama and final closure of the Central American Seaway (CAS) provides an independent calibration point for examining the rate of DNA substitutions. This vicariant event has been widely used to estimate the substitution rate across mitochondrial genomes and to date evolutionary events in other taxonomic groups. Nuclear sequence data is increasingly being used to complement mitochondrial datasets for phylogenetic and evolutionary investigations; these studies would benefit from information regarding the rate and pattern of DNA substitutions derived from the nuclear genome.
To estimate the genome-wide neutral mutation rate (µ), genotype-by-sequencing (GBS) datasets were generated for three transisthmian species pairs in Alpheus snapping shrimp. A range of bioinformatic filtering parameters were evaluated in order to minimize potential bias in mutation rate estimates that may result from SNP filtering. Using a Bayesian coalescent approach (G-PhoCS) applied to 44,960 GBS loci, at generated lower mutation rate estimates and influenced demographic parameters, serving as a cautionary tale for the adherence to conservative bioinformatic strategies when generating reduced-representation datasets at the species level. To our knowledge this is the first use of transisthmian species pairs to calibrate the rate of molecular evolution from GBS data.
