Avaliação genética por técnicas de modelo animal da duração da primeira lactação e produção média diária de leite da raça Caracu.

El objetivo de este estudio fue estimar los efectos genéticos y ambientales sobre la duración de la primera lactancia (DL) y la producción diaria de leche (PDL) de las vacas Caracu Criollo del hato de la Finca Recreio en Poços de Caldas, Estado de Minas Gerais. Los datos de 1 323 lactancias de vacas que parieron entre 1983 y 1990 se analizaron mediante métodos de mínimos cuadrados para obtener datos con números de subclase desiguales. Las medias de mínimos cuadrados y los errores estándar fueron 289,7 ± 7 días para DL y 4,95 ± 0,12 kg para PDL. Los efectos fijos de la temporada y el año del parto fueron significativos (P <0.01) para ambos rasgos. Las estimaciones de componentes de varianza y heredabilidades, obtenidas por el algoritmo Derivative Free Maximum Likelihood (DFREML), fueron: 0.24 ± 0.08 para DL y 0.57 ± 0.09 para PDL.The objective of this study was to estimate genetic and environmental effects on first lactation length (DL) and daily milk production (PDL) of Caracu Criollo cows of the Recreio Farm herd in Poços de Caldas, State of Minas Gerais. Data from 1 323 lactations of cows that calved from 1983 to 1990, were analysed by least squares methods for data with unequal subclass numbers. Least squares means and standard errors were 289.7 ± 7 days for DL and 4.95 ± 0.12 kg for PDL. The fixed effects of season and year of parturition were significant (P<0.01) for both traits. Estimates of variance components and heritabilities, obtained by the algorhythm Derivative Free Maximum Likelihood (DFREML), were: 0.24 ± 0.08 for DL and 0.57 ± 0.09 for PDL.O objetivo deste estudo foi estimar os efeitos genéticos e ambientais sobre a duração da primeira lactação (DL) e a produção diária de leite (PDL) de vacas Caracu Crioulo do rebanho da Fazenda Recreio em Poços de Caldas, Estado de Minas Gerais. Dados de 1.323 lactações de vacas que pariram de 1983 a 1990 foram analisados por métodos de quadrados mínimos para dados com números de subclasse desiguais. As médias dos mínimos quadrados e os erros padrão foram 289,7 ± 7 dias para DL e 4,95 ± 0,12 kg para PDL. Os efeitos fixos de estação e ano de parto foram significativos (P <0,01) para ambas as características. As estimativas dos componentes de variância e herdabilidades, obtidas pelo algoritmo Derivative Free Maximum Likelihood (DFREML), foram: 0,24 ± 0,08 para DL e 0,57 ± 0,09 para PDL

TextEssence: a tool for interactive analysis of semantic shifts between corpora

Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora and how they relate to one another. We introduce TextEssence, an interactive system designed to enable comparative analysis of corpora using embeddings. TextEssence includes visual, neighbor-based, and similarity-based modes of embedding analysis in a lightweight, web-based interface. We further propose a new measure of embedding confidence based on nearest neighborhood overlap, to assist in identifying high-quality embeddings for corpus analysis. A case study on COVID-19 scientific literature illustrates the utility of the system. TextEssence can be found at https://textessence.github.io

PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model.

MotivationElectronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge.ResultsWe present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes.Availability and implementationPatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu.Supplementary informationSupplementary data are available at Bioinformatics online

Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

Routinely collected data in hospital Electronic Medical Records (EMR) is rich and abundant but often not linked or analysed for purposes other than direct patient care. We have created a methodology to integrate patient-centric data from different EMR systems into clinical pathways that represent the history of all patient interactions with the hospital during the course of a disease and beyond. In this paper, the literature in the area of data visualisation in healthcare is reviewed and a method for visualising the journeys that patients take through care is discussed. Examples of the hidden knowledge that could be discovered using this approach are explored and the main application areas of visualisation tools are identified. This paper also highlights the challenges of collecting and analysing such data and making the visualisations extensively used in the medical domain. This paper starts by presenting the state-of-the-art in visualisation of clinical and other health related data. Then, it describes an example clinical problem and discusses the visualisation tools and techniques created for the utilisation of these data by clinicians and researchers. Finally, we look at the open problems in this area of research and discuss future challenges

A visual analytics approach for understanding biclustering results from microarray data

Abstract Background Microarray analysis is an important area of bioinformatics. In the last few years, biclustering has become one of the most popular methods for classifying data from microarrays. Although biclustering can be used in any kind of classification problem, nowadays it is mostly used for microarray data classification. A large number of biclustering algorithms have been developed over the years, however little effort has been devoted to the representation of the results. Results We present an interactive framework that helps to infer differences or similarities between biclustering results, to unravel trends and to highlight robust groupings of genes and conditions. These linked representations of biclusters can complement biological analysis and reduce the time spent by specialists on interpreting the results. Within the framework, besides other standard representations, a visualization technique is presented which is based on a force-directed graph where biclusters are represented as flexible overlapped groups of genes and conditions. This microarray analysis framework (BicOverlapper), is available at http://vis.usal.es/bicoverlapper Conclusion The main visualization technique, tested with different biclustering results on a real dataset, allows researchers to extract interesting features of the biclustering results, especially the highlighting of overlapping zones that usually represent robust groups of genes and/or conditions. The visual analytics methodology will permit biology experts to study biclustering results without inspecting an overwhelming number of biclusters individually.</p