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Polymerase squence of events using conjunctival swab biological materials with regard to sensing Leishmania DNA in dogs.
Objective Ubiquitous technologies can be leveraged to construct ecologically relevant metrics that complement traditional psychological assessments. This study aims to determine the feasibility of smartphone-derived real-world keyboard metadata to serve as digital biomarkers of mood. Materials and methods BiAffect, a real-world observation study based on a freely available iPhone app, allowed the unobtrusive collection of typing metadata through a custom virtual keyboard that replaces the default keyboard. User demographics and self-reports for depression severity (Patient Health Questionnaire-8) were also collected. Using >14 million keypresses from 250 users who reported demographic information and a subset of 147 users who additionally completed at least 1 Patient Health Questionnaire, we employed hierarchical growth curve mixed-effects models to capture the effects of mood, demographics, and time of day on keyboard metadata. Results We analyzed 86 541 typing sessions associated with a total of 543 Patient Health Questionnaires. Results showed that more severe depression relates to more variable typing speed (P less then .001), shorter session duration (P less then .001), and lower accuracy (P less then .05). Additionally, typing speed and variability exhibit a diurnal pattern, being fastest and least variable at midday. Older users exhibit slower and more variable typing, as well as more pronounced slowing in the evening. The effects of aging and time of day did not impact the relationship of mood to typing variables and were recapitulated in the 250-user group. Conclusions Keystroke dynamics, unobtrusively collected in the real world, are significantly associated with mood despite diurnal patterns and effects of age, and thus could serve as a foundation for constructing digital biomarkers.Motivation Although long non-coding RNAs (lncRNAs) have limited capacity for encoding proteins, they have been verified as biomarkers in the occurrence and development of complex diseases. Recent wet-lab experiments have shown that lncRNAs function by regulating the expression of protein-coding genes (PCGs), which could also be the mechanism responsible for causing diseases. Currently, lncRNA-related biological data is increasing rapidly. Whereas, no computational methods have been designed for predicting the novel target genes of lncRNA. Results In this study, we present a graph convolutional network (GCN) based method, named DeepLGP, for prioritizing target PCGs of lncRNA. First, gene and lncRNA features were selected, these included their location in the genome, expression in 13 tissues, and miRNA-mediated lncRNA-gene pairs. find more Next, GCN was applied to convolve a gene interaction network for encoding the features of genes and lncRNAs. Then, these features were used by the convolutional neural network (CNN) for prioritizing target genes of lncRNAs. In 10-cross validations on two independent datasets, DeepLGP obtained high AUCs (0.90, 0.98) and AUPRs (0.91, 0.98). We found that lncRNA pairs with high similarity had more overlapped target genes. Further experiments showed that genes targeted by the same lncRNA sets had a strong likelihood of causing the same diseases, which could help in identifying disease-causing PCGs. Availability and implementation https//github.com/zty2009/LncRNA-target-gene. Supplementary information Supplementary data are available at Bioinformatics online.Cold seeps, characterized by the methane, hydrogen sulfide, and other hydrocarbon chemicals, foster one of the most widespread chemosynthetic ecosystems in deep sea that are densely populated by specialized benthos. However, scarce genomic resources severely limit our knowledge about the origin and adaptation of life in this unique ecosystem. Here, we present a genome of a deep-sea limpet Bathyacmaea lactea, a common species associated with the dominant mussel beds in cold seeps. We yielded 54.6 gigabases (Gb) of Nanopore reads and 77.9-Gb BGI-seq raw reads, respectively. Assembly harvested a 754.3-Mb genome for B. lactea, with 3,720 contigs and a contig N50 of 1.57 Mb, covering 94.3% of metazoan Benchmarking Universal Single-Copy Orthologs. In total, 23,574 protein-coding genes and 463.4 Mb of repetitive elements were identified. We analyzed the phylogenetic position, substitution rate, demographic history, and TE activity of B. lactea. We also identified 80 expanded gene families and 87 rapidly evolving Gene Ontology categories in the B. lactea genome. Many of these genes were associated with heterocyclic compound metabolism, membrane-bounded organelle, metal ion binding, and nitrogen and phosphorus metabolism. The high-quality assembly and in-depth characterization suggest the B. lactea genome will serve as an essential resource for understanding the origin and adaptation of life in the cold seeps.The paper describes the results of an ESC Covid survey.Negative symptoms are a critical, but poorly understood, aspect of schizophrenia. Measurement of negative symptoms primarily relies on clinician ratings, an endeavor with established reliability and validity. There have been increasing attempts to digitally phenotype negative symptoms using objective biobehavioral technologies, eg, using computerized analysis of vocal, speech, facial, hand and other behaviors. Surprisingly, biobehavioral technologies and clinician ratings are only modestly inter-related, and findings from individual studies often do not replicate or are counterintuitive. In this article, we document and evaluate this lack of convergence in 4 case studies, in an archival dataset of 877 audio/video samples, and in the extant literature. We then explain this divergence in terms of "resolution"-a critical psychometric property in biomedical, engineering, and computational sciences defined as precision in distinguishing various aspects of a signal. We demonstrate how convergence between clinical ratings and biobehavioral data can be achieved by scaling data across various resolutions. Clinical ratings reflect an indispensable tool that integrates considerable information into actionable, yet "low resolution" ordinal ratings. This allows viewing of the "forest" of negative symptoms. Unfortunately, their resolution cannot be scaled or decomposed with sufficient precision to isolate the time, setting, and nature of negative symptoms for many purposes (ie, to see the "trees"). Biobehavioral measures afford precision for understanding when, where, and why negative symptoms emerge, though much work is needed to validate them. Digital phenotyping of negative symptoms can provide unprecedented opportunities for tracking, understanding, and treating them, but requires consideration of resolution.
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