Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 14.113
Filtrar
1.
J Public Health (Oxf) ; 42(3): 483-485, 2020 08 18.
Artigo em Inglês | MEDLINE | ID: mdl-32880394

RESUMO

As a global crisis, COVID-19 has underscored the challenge of disseminating evidence-based public health recommendations amidst a rapidly evolving, often uncensored information ecosystem-one fueled in part by an unprecedented degree of connected afforded through social media. In this piece, we explore an underdiscussed intersection between the visual arts and public health, focusing on the use of validated infographics and other forms of visual communication to rapidly disseminate accurate public health information during the COVID-19 pandemic. We illustrate our arguments through our own experience in creating a validated infographic for patients, now disseminated through social media and other outlets across the world in nearly 20 translations. Visual communication offers a creative and practical medium to bridge critical health literacy gaps, empower diverse patient communities through evidence-based information and facilitate public health advocacy during this pandemic and the 'new normal' that lies ahead.


Assuntos
Recursos Audiovisuais , Betacoronavirus , Gráficos por Computador , Infecções por Coronavirus/epidemiologia , Educação em Saúde/métodos , Pandemias , Pneumonia Viral/epidemiologia , Humanos , Saúde Pública
2.
PLoS One ; 15(8): e0232384, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32750052

RESUMO

We propose a process graph (P-graph) approach to develop ecosystem networks from knowledge of the properties of the component species. Originally developed as a process engineering tool for designing industrial plants, the P-graph framework has key advantages over conventional ecological network analysis techniques based on input-output models. A P-graph is a bipartite graph consisting of two types of nodes, which we propose to represent components of an ecosystem. Compartments within ecosystems (e.g., organism species) are represented by one class of nodes, while the roles or functions that they play relative to other compartments are represented by a second class of nodes. This bipartite graph representation enables a powerful, unambiguous representation of relationships among ecosystem compartments, which can come in tangible (e.g., mass flow in predation) or intangible form (e.g., symbiosis). For example, within a P-graph, the distinct roles of bees as pollinators for some plants and as prey for some animals can be explicitly represented, which would not otherwise be possible using conventional ecological network analysis. After a discussion of the mapping of ecosystems into P-graph, we also discuss how this framework can be used to guide understanding of complex networks that exist in nature. Two component algorithms of P-graph, namely maximal structure generation (MSG) and solution structure generation (SSG), are shown to be particularly useful for ecological network analysis. These algorithms enable candidate ecosystem networks to be deduced based on current scientific knowledge on the individual ecosystem components. This method can be used to determine the (a) effects of loss of specific ecosystem compartments due to extinction, (b) potential efficacy of ecosystem reconstruction efforts, and (c) maximum sustainable exploitation of human ecosystem services by humans. We illustrate the use of P-graph for the analysis of ecosystem compartment loss using a small-scale stylized case study, and further propose a new criticality index that can be easily derived from SSG results.


Assuntos
Ecossistema , Algoritmos , Animais , Gráficos por Computador , Heurística Computacional , Cadeia Alimentar , Heurística , Humanos , Conceitos Matemáticos , Modelos Biológicos , Biologia de Sistemas , Teoria de Sistemas
3.
BMC Bioinformatics ; 21(1): 343, 2020 Aug 05.
Artigo em Inglês | MEDLINE | ID: mdl-32758139

RESUMO

BACKGROUND: Nanopore sequencing enables portable, real-time sequencing applications, including point-of-care diagnostics and in-the-field genotyping. Achieving these outcomes requires efficient bioinformatic algorithms for the analysis of raw nanopore signal data. However, comparing raw nanopore signals to a biological reference sequence is a computationally complex task. The dynamic programming algorithm called Adaptive Banded Event Alignment (ABEA) is a crucial step in polishing sequencing data and identifying non-standard nucleotides, such as measuring DNA methylation. Here, we parallelise and optimise an implementation of the ABEA algorithm (termed f5c) to efficiently run on heterogeneous CPU-GPU architectures. RESULTS: By optimising memory, computations and load balancing between CPU and GPU, we demonstrate how f5c can perform ∼3-5 × faster than an optimised version of the original CPU-only implementation of ABEA in the Nanopolish software package. We also show that f5c enables DNA methylation detection on-the-fly using an embedded System on Chip (SoC) equipped with GPUs. CONCLUSIONS: Our work not only demonstrates that complex genomics analyses can be performed on lightweight computing systems, but also benefits High-Performance Computing (HPC). The associated source code for f5c along with GPU optimised ABEA is available at https://github.com/hasindu2008/f5c .


Assuntos
Gráficos por Computador , Nanoporos , Processamento de Sinais Assistido por Computador , Algoritmos , Biologia Computacional , Bases de Dados como Assunto , Genoma Humano , Humanos , Análise de Sequência
4.
PLoS One ; 15(8): e0236883, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32817642

RESUMO

While it is still not possible to describe the neuronal-level connections of the human brain, we can map the human connectome with several hundred vertices, by the application of diffusion-MRI based techniques. In these graphs, the nodes correspond to anatomically identified gray matter areas of the brain, while the edges correspond to the axonal fibers, connecting these areas. In our previous contributions, we have described numerous graph-theoretical phenomena of the human connectomes. Here we map the frequent complete subgraphs of the human brain networks: in these subgraphs, every pair of vertices is connected by an edge. We also examine sex differences in the results. The mapping of the frequent subgraphs gives robust substructures in the graph: if a subgraph is present in the 80% of the graphs, then, most probably, it could not be an artifact of the measurement or the data processing workflow. We list here the frequent complete subgraphs of the human braingraphs of 413 subjects (238 women and 175 men), each with 463 nodes, with a frequency threshold of 80%, and identify 812 complete subgraphs, which are more frequent in male and 224 complete subgraphs, which are more frequent in female connectomes.


Assuntos
Gráficos por Computador , Conectoma , Algoritmos , Axônios/metabolismo , Feminino , Humanos , Masculino , Rede Nervosa/citologia , Rede Nervosa/diagnóstico por imagem
5.
Nat Protoc ; 15(9): 2837-2866, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32814837

RESUMO

The accurate resolution of the binding mechanism of a ligand to its molecular target is fundamental to develop a successful drug design campaign. Free-energy calculations, which provide the energy value of the ligand-protein binding complex, are essential for resolving the binding mode of the ligand. The accuracy of free-energy calculation methods is counteracted by their poor user-friendliness, which hampers their broad application. Here we present the Funnel-Metadynamics Advanced Protocol (FMAP), which is a flexible and user-friendly graphical user interface (GUI)-based protocol to perform funnel metadynamics, a binding free-energy method that employs a funnel-shape restraint potential to reveal the ligand binding mode and accurately calculate the absolute ligand-protein binding free energy. FMAP guides the user through all phases of the free-energy calculation process, from preparation of the input files, to production simulation, to analysis of the results. FMAP delivers the ligand binding mode and the absolute protein-ligand binding free energy as outputs. Alternative binding modes and the role of waters are also elucidated, providing a detailed description of the ligand binding mechanism. The entire protocol on the paradigmatic system benzamidine-trypsin, composed of ~105 k atoms, took ~2.8 d using the Cray XC50 piz Daint cluster at the Swiss National Supercomputing Centre.


Assuntos
Gráficos por Computador , Modelos Moleculares , Ligantes , Terapia de Alvo Molecular , Preparações Farmacêuticas/metabolismo , Ligação Proteica , Conformação Proteica , Termodinâmica , Fatores de Tempo , Interface Usuário-Computador
7.
BMC Bioinformatics ; 21(1): 297, 2020 Jul 10.
Artigo em Inglês | MEDLINE | ID: mdl-32650717

RESUMO

BACKGROUND: Stable isotope tracing has become an invaluable tool for probing the metabolism of biological systems. However, data analysis and visualization from metabolic tracing studies often involve multiple software packages and lack pathway architecture. A deep understanding of the metabolic contexts from such datasets is required for biological interpretation. Currently, there is no single software package that allows researchers to analyze and integrate stable isotope tracing data into annotated or custom-built metabolic networks. RESULTS: We built a standalone web-based software, Escher-Trace, for analyzing tracing data and communicating results. Escher-Trace allows users to upload baseline corrected mass spectrometer (MS) tracing data and correct for natural isotope abundance, generate publication quality graphs of metabolite labeling, and present data in the context of annotated metabolic pathways. Here we provide a detailed walk-through of how to incorporate and visualize 13C metabolic tracing data into the Escher-Trace platform. CONCLUSIONS: Escher-Trace is an open-source software for analysis and interpretation of stable isotope tracing data and is available at https://escher-trace.github.io/ .


Assuntos
Marcação por Isótopo/métodos , Redes e Vias Metabólicas , Software , Gráficos por Computador , Espectrometria de Massas/métodos
8.
PLoS Comput Biol ; 16(7): e1008007, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-32702019

RESUMO

Biomedical research is becoming increasingly data driven. New technologies that generate large-scale, complex data are continually emerging and evolving. As a result, there is a concurrent need for training researchers to use and understand new computational tools. Here we describe an efficient and effective approach to developing curriculum materials that can be deployed in a research environment to meet this need.


Assuntos
Biologia Computacional/educação , Currículo , Algoritmos , Pesquisa Biomédica/educação , Gráficos por Computador , Retroalimentação , Internet , Aprendizagem , Desenvolvimento de Programas , Reprodutibilidade dos Testes , Software
9.
PLoS One ; 15(7): e0236058, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32730259

RESUMO

A minimum cost spanning tree problem analyzes the way to efficiently connect individuals to a source. Hence the question is how to fairly allocate the total cost among these agents. Our approach, reinterpreting the spanning tree cost allocation as a claims problem defines a simple way to allocate the optimal cost with two main criteria: (1) each individual only pays attention to a few connection costs (the total cost of the optimal network and the cost of connecting himself to the source); and (2) an egalitarian criteria is used to share costs. Then, using claims rules, we define an egalitarian solution so that the total cost is allocated as equally as possible. We show that this solutions could propose allocations outside the core, a counter-intuitive fact whenever cooperation is necessary. Then we propose a modification to get a core selection, obtaining in this case an alternative interpretation of the Folk solution.


Assuntos
Algoritmos , Gráficos por Computador , Simulação por Computador
10.
PLoS One ; 15(7): e0235035, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32667924

RESUMO

This study aimed to clarify the cause of rugby head and spinal cord injuries through a network centrality analysis of 14-year (2004-2018) longitudinal data in Japan. The study hypothesis is that understanding the causal relationship among the occurrence of serious injuries, the quality of player experience and play situation as a network structure could be possible to obtain practical knowledge on injury prevention. In this study, bipartite graphs are used to make it easier to understand the situation of players and injuries. This would also help to elucidate more characteristic subgroup. A network bipartite graph and subgroup (cluster) analyses were performed to clarify the injured players' experience and the cause of injury. We used the algorithm of R program, IGRAPH, clustering edge betweenness. For subgroup extraction, the modularity Q value was used to determine which step to cut. The Japanese rugby population was 93,873 (2014-2018 average), and 27% were high school students. The data showed that careful attention would be particularly needed for groups of inexperienced Japanese high school players. Our study suggests that we should consider introducing rules that prohibit "head-on collisions" in youth rugby.


Assuntos
Traumatismos em Atletas/etiologia , Traumatismos Craniocerebrais/etiologia , Futebol Americano/lesões , Traumatismos da Coluna Vertebral/etiologia , Adolescente , Criança , Regras de Decisão Clínica , Gráficos por Computador , Feminino , Humanos , Japão , Estudos Longitudinais , Masculino , Estudos Retrospectivos , Instituições Acadêmicas , Adulto Jovem
11.
PLoS One ; 15(7): e0235596, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32716924

RESUMO

We propose a method to simplify textual Twitter data into understandable networks of terms that can signify important events and their possible changes over time. The method allows for common characteristics of the networks across time periods and each period can comprise multiple unknown sub-networks. The networks are described by Gaussian graphical models and their parameter values are estimated through a Bayesian approach with a fused lasso-type prior on the precision matrices of the underlying mixtures of the sub-models. A flexible data allocation scheme is at the heart of an MCMC algorithm to recover mean and covariance parameters of the mixture components. Several implementations of the outlined estimation procedure are studied and compared based on simulated data. The procedure with the highest predictive power is used for mining tweets regarding the 2009 Iranian presidential election.


Assuntos
Gráficos por Computador , Mídias Sociais/estatística & dados numéricos , Estatística como Assunto/métodos , Teorema de Bayes , Modelos Estatísticos
12.
Stud Health Technol Inform ; 270: 1329-1330, 2020 Jun 16.
Artigo em Inglês | MEDLINE | ID: mdl-32570643

RESUMO

Several drug databases exist, but sometimes differ in their content. Here, we propose a taxonomy of the variability we observed, and we present a tool for investigating the variability through the visual comparison of the properties of a given drug as represented in several sources or databases.


Assuntos
Bases de Dados Factuais , Aprendizagem , Gráficos por Computador , Preparações Farmacêuticas
13.
PLoS One ; 15(6): e0231169, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32502204

RESUMO

Known as a degenerative and progressive dementia, Alzheimer's disease (AD) affects about 25 million elderly people around the world. This illness results in a decrease in the productivity of people and places limits on their daily lives. Electroencephalography (EEG), in which the electrical brain activity is recorded in the form of time series and analyzed using signal processing techniques, is a well-known neurophysiological AD biomarker. EEG is noninvasive, low-cost, has a high temporal resolution, and provides valuable information about brain dynamics in AD. Here, we present an original approach based on the use of quantile graphs (QGs) for classifying EEG data. QGs map frequency, amplitude, and correlation characteristics of a time series (such as the EEG data of an AD patient) into the topological features of a network. The five topological network metrics used here-clustering coefficient, mean jump length, betweenness centrality, modularity, and Laplacian Estrada index-showed that the QG model can distinguish healthy subjects from AD patients, with open or closed eyes. The QG method also indicates which channels (corresponding to 19 different locations on the patients' scalp) provide the best discriminating power. Furthermore, the joint analysis of delta, theta, alpha, and beta wave results indicate that all AD patients under study display clear symptoms of the disease and may have it in its late stage, a diagnosis known a priori and supported by our study. Results presented here attest to the usefulness of the QG method in analyzing complex, nonlinear signals such as those generated from AD patients by EEGs.


Assuntos
Doença de Alzheimer/diagnóstico , Eletroencefalografia , Processamento de Sinais Assistido por Computador , Envelhecimento/fisiologia , Gráficos por Computador , Humanos
14.
PLoS One ; 15(6): e0233489, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32497055

RESUMO

Designing logos, typefaces, and other decorated shapes can require professional skills. In this paper, we aim to produce new and unique decorated shapes by stylizing ordinary shapes with machine learning. Specifically, we combined parametric and non-parametric neural style transfer algorithms to transfer both local and global features. Furthermore, we introduced a distance-based guiding to the neural style transfer process, so that only the foreground shape will be decorated. Lastly, qualitative evaluation and ablation studies are provided to demonstrate the usefulness of the proposed method.


Assuntos
Arte , Gráficos por Computador , Desenho Assistido por Computador , Aprendizado de Máquina , Emblemas e Insígnias , Humanos , Estatísticas não Paramétricas
15.
PLoS Comput Biol ; 16(6): e1007863, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32497138

RESUMO

Scientists are sequencing new genomes at an increasing rate with the goal of associating genome contents with phenotypic traits. After a new genome is sequenced and assembled, structural gene annotation is often the first step in analysis. Despite advances in computational gene prediction algorithms, most eukaryotic genomes still benefit from manual gene annotation. This requires access to good genome browsers to enable annotators to visualize and evaluate multiple lines of evidence (e.g., sequence similarity, RNA sequencing [RNA-Seq] results, gene predictions, repeats) and necessitates many volunteers to participate in the work. To address the technical barriers to creating genome browsers, the Genomics Education Partnership (GEP; https://gep.wustl.edu/) has partnered with the Galaxy Project (https://galaxyproject.org) to develop G-OnRamp (http://g-onramp.org), a web-based platform for creating UCSC Genome Browser Assembly Hubs and JBrowse genome browsers. G-OnRamp also converts a JBrowse instance into an Apollo instance for collaborative genome annotations in research and educational settings. The genome browsers produced can be transferred to the CyVerse Data Store for long-term access. G-OnRamp enables researchers to easily visualize their experimental results, educators to create Course-based Undergraduate Research Experiences (CUREs) centered on genome annotation, and students to participate in genomics research. In the process, students learn about genes/genomes and about how to utilize large datasets. Development of G-OnRamp was guided by extensive user feedback. Sixty-five researchers/educators from >40 institutions participated through in-person workshops, which produced >20 genome browsers now available for research and education. Genome browsers generated for four parasitoid wasp species have been used in a CURE engaging students at 15 colleges and universities. Our assessment results in the classroom demonstrate that the genome browsers produced by G-OnRamp are effective tools for engaging undergraduates in research and in enabling their contributions to the scientific literature in genomics. Expansion of such genomics research/education partnerships will be beneficial to researchers, faculty, and students alike.


Assuntos
Biologia Computacional/educação , Biologia Computacional/métodos , Genoma , Genômica/educação , Genômica/métodos , Anotação de Sequência Molecular , Software , Algoritmos , Animais , Sequência de Bases , Gráficos por Computador , Bases de Dados Genéticas , Drosophila melanogaster , Humanos , Análise de Sequência de RNA , Estudantes , Interface Usuário-Computador
16.
PLoS Comput Biol ; 16(6): e1007912, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32542031

RESUMO

Interactive data visualization is imperative in the biological sciences. The development of independent layers of interactivity has been in pursuit in the visualization community. We developed bigPint, a data visualization package available on Bioconductor under the GPL-3 license (https://bioconductor.org/packages/release/bioc/html/bigPint.html). Our software introduces new visualization technology that enables independent layers of interactivity using Plotly in R, which aids in the exploration of large biological datasets. The bigPint package presents modernized versions of scatterplot matrices, volcano plots, and litre plots through the implementation of layered interactivity. These graphics have detected normalization issues, differential expression designation problems, and common analysis errors in public RNA-sequencing datasets. Researchers can apply bigPint graphics to their data by following recommended pipelines written in reproducible code in the user manual. In this paper, we explain how we achieved the independent layers of interactivity that are behind bigPint graphics. Pseudocode and source code are provided. Computational scientists can leverage our open-source code to expand upon our layered interactive technology and/or apply it in new ways toward other computational biology tasks.


Assuntos
Big Data , Biologia Computacional/instrumentação , Gráficos por Computador , Conjuntos de Dados como Assunto , Análise de Sequência de RNA , Software
17.
Nucleic Acids Res ; 48(W1): W244-W251, 2020 07 02.
Artigo em Inglês | MEDLINE | ID: mdl-32484539

RESUMO

miRNet is an easy-to-use, web-based platform designed to help elucidate microRNA (miRNA) functions by integrating users' data with existing knowledge via network-based visual analytics. Since its first release in 2016, miRNet has been accessed by >20 000 researchers worldwide, with ∼100 users on a daily basis. While version 1.0 was focused primarily on miRNA-target gene interactions, it has become clear that in order to obtain a global view of miRNA functions, it is necessary to bring other important players into the context during analysis. Driven by this concept, in miRNet version 2.0, we have (i) added support for transcription factors (TFs) and single nucleotide polymorphisms (SNPs) that affect miRNAs, miRNA-binding sites or target genes, whilst also greatly increased (>5-fold) the underlying knowledgebases of miRNAs, ncRNAs and disease associations; (ii) implemented new functions to allow creation and visual exploration of multipartite networks, with enhanced support for in situ functional analysis and (iii) revamped the web interface, optimized the workflow, and introduced microservices and web application programming interface (API) to sustain high-performance, real-time data analysis. The underlying R package is also released in tandem with version 2.0 to allow more flexible data analysis for R programmers. The miRNet 2.0 website is freely available at https://www.mirnet.ca.


Assuntos
Redes Reguladoras de Genes , MicroRNAs/metabolismo , Software , Gráficos por Computador , Regulação da Expressão Gênica , Humanos , Bases de Conhecimento , Esclerose Múltipla/genética , Esclerose Múltipla/metabolismo , Polimorfismo de Nucleotídeo Único , Biologia de Sistemas , Fatores de Transcrição/metabolismo
18.
Nature ; 582(7810): 137-138, 2020 06.
Artigo em Inglês | MEDLINE | ID: mdl-32385367
19.
BMC Bioinformatics ; 21(1): 221, 2020 May 29.
Artigo em Inglês | MEDLINE | ID: mdl-32471392

RESUMO

BACKGROUND: The use of RNA-sequencing (RNA-seq) in molecular biology research and clinical settings has increased significantly over the past decade. Despite its widespread adoption, there is a lack of simple and interactive tools to analyze and explore RNA-seq data. Many established tools require programming or Unix/Bash knowledge to analyze and visualize results. This requirement presents a significant barrier for many researchers to efficiently analyze and present RNA-seq data. RESULTS: Here we present BEAVR, a Browser-based tool for the Exploration And Visualization of RNA-seq data. BEAVR is an easy-to-use tool that facilitates interactive analysis and exploration of RNA-seq data. BEAVR is developed in R and uses DESeq2 as its engine for differential gene expression (DGE) analysis, but assumes users have no prior knowledge of R or DESeq2. BEAVR allows researchers to easily obtain a table of differentially-expressed genes with statistical testing and then visualize the results in a series of graphs, plots and heatmaps. Users are able to customize many parameters for statistical testing, dealing with variance, clustering methods and pathway analysis to generate high quality figures. CONCLUSION: BEAVR simplifies analysis for novice users but also streamlines the RNA-seq analysis process for experts by automating several steps. BEAVR and its documentation can be found on GitHub at https://github.com/developerpiru/BEAVR. BEAVR is available as a Docker container at https://hub.docker.com/r/pirunthan/beavr.


Assuntos
RNA-Seq/métodos , Software , Análise por Conglomerados , Gráficos por Computador , Interpretação Estatística de Dados , Humanos
20.
Zhonghua Yu Fang Yi Xue Za Zhi ; 54(5): 586-592, 2020 May 06.
Artigo em Chinês | MEDLINE | ID: mdl-32388965

RESUMO

As an important method to study the phenotype and function of organisms, transcriptome has become one of hot topics in current research. The transcriptomics research usually accompanies with massive data. With the increase of the amount of data, the rules and features hidden in it are not easy to be found. Transforming big data into visual graphics is the most undoubtedly intuitive way to display the hidden information of big data. Several graphs commonly used in transcriptome study were introduced in this paper, such as Venn diagram, heat map, principal component analysis scatter plot, enrichment analysis plot, and time series analysis plot, in order to help readers to choose suitable graphics in future studies.


Assuntos
Biologia Computacional , Gráficos por Computador , Transcriptoma , Big Data , Software
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA