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1.
Int J Mol Sci ; 22(14)2021 Jul 13.
Artigo em Inglês | MEDLINE | ID: mdl-34299110

RESUMO

Molecular docking is widely used in computed drug discovery and biological target identification, but getting fast results can be tedious and often requires supercomputing solutions. AMIDE stands for AutoMated Inverse Docking Engine. It was initially developed in 2014 to perform inverse docking on High Performance Computing. AMIDE version 2 brings substantial speed-up improvement by using AutoDock-GPU and by pulling a total revision of programming workflow, leading to better performances, easier use, bug corrections, parallelization improvements and PC/HPC compatibility. In addition to inverse docking, AMIDE is now an optimized tool capable of high throughput inverse screening. For instance, AMIDE version 2 allows acceleration of the docking up to 12.4 times for 100 runs of AutoDock compared to version 1, without significant changes in docking poses. The reverse docking of a ligand on 87 proteins takes only 23 min on 1 GPU (Graphics Processing Unit), while version 1 required 300 cores to reach the same execution time. Moreover, we have shown an exponential acceleration of the computation time as a function of the number of GPUs used, allowing a significant reduction of the duration of the inverse docking process on large datasets.


Assuntos
Algoritmos , Ensaios de Triagem em Larga Escala/métodos , Simulação de Acoplamento Molecular , Preparações Farmacêuticas/química , Proteínas/química , Software , Gráficos por Computador , Humanos , Ligantes , Reprodutibilidade dos Testes , Fluxo de Trabalho
2.
Genes (Basel) ; 12(7)2021 06 29.
Artigo em Inglês | MEDLINE | ID: mdl-34209818

RESUMO

This study builds a coronavirus knowledge graph (KG) by merging two information sources. The first source is Analytical Graph (AG), which integrates more than 20 different public datasets related to drug discovery. The second source is CORD-19, a collection of published scientific articles related to COVID-19. We combined both chemo genomic entities in AG with entities extracted from CORD-19 to expand knowledge in the COVID-19 domain. Before populating KG with those entities, we perform entity disambiguation on CORD-19 collections using Wikidata. Our newly built KG contains at least 21,700 genes, 2500 diseases, 94,000 phenotypes, and other biological entities (e.g., compound, species, and cell lines). We define 27 relationship types and use them to label each edge in our KG. This research presents two cases to evaluate the KG's usability: analyzing a subgraph (ego-centered network) from the angiotensin-converting enzyme (ACE) and revealing paths between biological entities (hydroxychloroquine and IL-6 receptor; chloroquine and STAT1). The ego-centered network captured information related to COVID-19. We also found significant COVID-19-related information in top-ranked paths with a depth of three based on our path evaluation.


Assuntos
COVID-19 , Bases de Conhecimento , COVID-19/epidemiologia , COVID-19/etiologia , Cloroquina/farmacologia , Gráficos por Computador , Bases de Dados Factuais , Doença pelo Vírus Ebola/tratamento farmacológico , Humanos , Hidroxicloroquina/farmacologia , Reconhecimento Automatizado de Padrão , Peptidil Dipeptidase A/genética , PubMed , Receptores de Interleucina-6/sangue , SARS-CoV-2 , Fator de Transcrição STAT1
3.
Eur J Epidemiol ; 36(7): 659-667, 2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-34114186

RESUMO

Causal graphs provide a key tool for optimizing the validity of causal effect estimates. Although a large literature exists on the mathematical theory underlying the use of causal graphs, less literature exists to aid applied researchers in understanding how best to develop and use causal graphs in their research projects. We sought to understand why researchers do or do not regularly use DAGs by surveying practicing epidemiologists and medical researchers on their knowledge, level of interest, attitudes, and practices towards the use of causal graphs in applied epidemiology and health research. We used Twitter and the Society for Epidemiologic Research to disseminate the survey. Overall, a majority of participants reported being comfortable with using causal graphs and reported using them 'sometimes', 'often', or 'always' in their research. Having received training appeared to improve comprehension of the assumptions displayed in causal graphs. Many of the respondents who did not use causal graphs reported lack of knowledge as a barrier to using DAGs in their research. Causal graphs are of interest to epidemiologists and medical researchers, but there are several barriers to their uptake. Additional training and clearer guidance are needed. In addition, methodological developments regarding visualization of effect measure modification and interaction on causal graphs is needed.


Assuntos
Atitude do Pessoal de Saúde , Causalidade , Gráficos por Computador , Interpretação Estatística de Dados , Projetos de Pesquisa Epidemiológica , Epidemiologistas , Feminino , Humanos , Masculino , Pesquisa Qualitativa , Pesquisadores , Inquéritos e Questionários
4.
J Phys Chem A ; 125(25): 5633-5642, 2021 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-34142824

RESUMO

Computational approaches for predicting drug-target interactions (DTIs) play an important role in drug discovery since conventional screening experiments are time-consuming and expensive. In this study, we proposed end-to-end representation learning of a graph neural network with an attention mechanism and an attentive bidirectional long short-term memory (BiLSTM) to predict DTIs. For efficient training, we introduced a bidirectional encoder representations from transformers (BERT) pretrained method to extract substructure features from protein sequences and a local breadth-first search (BFS) to learn subgraph information from molecular graphs. Integrating both models, we developed a DTI prediction system. As a result, the proposed method achieved high performances with increases of 2.4% and 9.4% for AUC and recall, respectively, on unbalanced datasets compared with other methods. Extensive experiments showed that our model can relatively screen potential drugs for specific protein. Furthermore, visualizing the attention weights provides biological insight.


Assuntos
Biologia Computacional/métodos , Gráficos por Computador , Aprendizado Profundo , Descoberta de Drogas/métodos , Preparações Farmacêuticas/metabolismo , Proteínas/química , Proteínas/metabolismo , Sequência de Aminoácidos
5.
Nucleic Acids Res ; 49(W1): W153-W161, 2021 07 02.
Artigo em Inglês | MEDLINE | ID: mdl-34125897

RESUMO

As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration.


Assuntos
Doença/genética , Software , Esclerose Amiotrófica Lateral/genética , Gráficos por Computador , Genes , Humanos , Aprendizado de Máquina
6.
Int J Mol Sci ; 22(10)2021 May 14.
Artigo em Inglês | MEDLINE | ID: mdl-34069090

RESUMO

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated 'bioactive' 3D ligand conformation is constructed as a 'sophisticated guess' (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis-sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its 'dialects' have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the 'mainstream' algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.


Assuntos
Algoritmos , Relação Quantitativa Estrutura-Atividade , Gráficos por Computador , Modelos Moleculares , Conformação Molecular
7.
Nucleic Acids Res ; 49(W1): W46-W51, 2021 07 02.
Artigo em Inglês | MEDLINE | ID: mdl-34038559

RESUMO

With Aviator, we present a web service and repository that facilitates surveillance of online tools. Aviator consists of a user-friendly website and two modules, a literature-mining based general and a manually curated module. The general module currently checks 9417 websites twice a day with respect to their availability and stores many features (frontend and backend response time, required RAM and size of the web page, security certificates, analytic tools and trackers embedded in the webpage and others) in a data warehouse. Aviator is also equipped with an analysis functionality, for example authors can check and evaluate the availability of their own tools or those of their peers. Likewise, users can check the availability of a certain tool they intend to use in research or teaching to avoid including unstable tools. The curated section of Aviator offers additional services. We provide API snippets for common programming languages (Perl, PHP, Python, JavaScript) as well as an OpenAPI documentation for embedding in the backend of own web services for an automatic test of their function. We query the respective APIs twice a day and send automated notifications in case of an unexpected result. Naturally, the same analysis functionality as for the literature-based module is available for the curated section. Aviator can freely be used at https://www.ccb.uni-saarland.de/aviator.


Assuntos
Gráficos por Computador , Software , COVID-19/tratamento farmacológico , Reposicionamento de Medicamentos , Humanos , Internet , Melanoma/metabolismo , Receptores Odorantes/metabolismo , Transdução de Sinais
8.
PLoS Comput Biol ; 17(5): e1008859, 2021 05.
Artigo em Inglês | MEDLINE | ID: mdl-33983945

RESUMO

Simulating complex biological and physiological systems and predicting their behaviours under different conditions remains challenging. Breaking systems into smaller and more manageable modules can address this challenge, assisting both model development and simulation. Nevertheless, existing computational models in biology and physiology are often not modular and therefore difficult to assemble into larger models. Even when this is possible, the resulting model may not be useful due to inconsistencies either with the laws of physics or the physiological behaviour of the system. Here, we propose a general methodology for composing models, combining the energy-based bond graph approach with semantics-based annotations. This approach improves model composition and ensures that a composite model is physically plausible. As an example, we demonstrate this approach to automated model composition using a model of human arterial circulation. The major benefit is that modellers can spend more time on understanding the behaviour of complex biological and physiological systems and less time wrangling with model composition.


Assuntos
Simulação por Computador , Modelos Biológicos , Artérias/anatomia & histologia , Artérias/fisiologia , Circulação Sanguínea/fisiologia , Biologia Computacional , Gráficos por Computador , Humanos , Modelos Cardiovasculares , Semântica , Software
9.
Nucleic Acids Res ; 49(W1): W257-W262, 2021 07 02.
Artigo em Inglês | MEDLINE | ID: mdl-34037782

RESUMO

Networks have been an excellent framework for modeling complex biological information, but the methodological details of network-based tools are often described for a technical audience. We have developed Graphery, an interactive tutorial webserver that illustrates foundational graph concepts frequently used in network-based methods. Each tutorial describes a graph concept along with executable Python code that can be interactively run on a graph. Users navigate each tutorial using their choice of real-world biological networks that highlight the diverse applications of network algorithms. Graphery also allows users to modify the code within each tutorial or write new programs, which all can be executed without requiring an account. Graphery accepts ideas for new tutorials and datasets that will be shaped by both computational and biological researchers, growing into a community-contributed learning platform. Graphery is available at https://graphery.reedcompbio.org/.


Assuntos
Algoritmos , Modelos Biológicos , Software , Gráficos por Computador , Mapeamento de Interação de Proteínas , Transdução de Sinais
10.
PLoS Comput Biol ; 17(4): e1008887, 2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33872301

RESUMO

Mass Based Imaging (MBI) technologies such as Multiplexed Ion Beam Imaging by time of flight (MIBI-TOF) and Imaging Mass Cytometry (IMC) allow for the simultaneous measurement of the expression levels of 40 or more proteins in biological tissue, providing insight into cellular phenotypes and organization in situ. Imaging artifacts, resulting from the sample, assay or instrumentation complicate downstream analyses and require correction by domain experts. Here, we present MBI Analysis User Interface (MAUI), a series of graphical user interfaces that facilitate this data pre-processing, including the removal of channel crosstalk, noise and antibody aggregates. Our software streamlines these steps and accelerates processing by enabling real-time and interactive parameter tuning across multiple images.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Proteínas/metabolismo , Análise de Célula Única/métodos , Interface Usuário-Computador , Linhagem Celular Tumoral , Gráficos por Computador , Humanos , Proteínas/análise
11.
J Nurs Scholarsh ; 53(3): 323-332, 2021 05.
Artigo em Inglês | MEDLINE | ID: mdl-33811733

RESUMO

PURPOSE: To provide a summary of research on ontology development in the Centre of eIntegrated Care at Dublin City University, Ireland. DESIGN: Design science methods using Open Innovation 2.0. METHODS: This was a co-participatory study focusing on adoption of health informatics standards and translation of nursing knowledge to advance nursing theory through a nursing knowledge graph (NKG). In this article we outline groundwork research conducted through a focused analysis to advance structural interoperability and to inform integrated care in Ireland. We provide illustrated details on a simple example of initial research available through open access. FINDINGS: For this phase of development, the initial completed research is presented and discussed. CONCLUSIONS: We conclude by promoting the use of knowledge graphs for visualization of diverse knowledge translation, which can be used as a primer to gain valuable insights into nursing interventions to inform big data science in the future. CLINICAL RELEVANCE: In line with stated global policy, the uptake and use of health informatics standards in design science within the profession of nursing is a priority. Nursing leaders should initially focus on health informatics standards relating to structural interoperability to inform development of NKGs. This will provide a robust foundation to gain valuable insights into articulating the nursing contribution in relation to the design of digital health and progress the nursing contribution to targeted data sources for the advancement of United Nations Sustainable Development Goal Three.


Assuntos
Big Data , Gráficos por Computador , Conhecimento , Informática em Enfermagem , Humanos , Teoria de Enfermagem , Pesquisa Médica Translacional
12.
BMC Bioinformatics ; 22(1): 214, 2021 Apr 26.
Artigo em Inglês | MEDLINE | ID: mdl-33902456

RESUMO

BACKGROUND: Area-proportional Euler diagrams are frequently used to visualize data from Microarray experiments, but are also applied to a wide variety of other data from biosciences, social networks and other domains. RESULTS: This paper details Edeap, a new simple, scalable method for drawing area-proportional Euler diagrams with ellipses. We use a search-based technique optimizing a multi-criteria objective function that includes measures for both area accuracy and usability, and which can be extended to further user-defined criteria. The Edeap software is available for use on the web, and the code is open source. In addition to describing our system, we present the first extensive evaluation of software for producing area-proportional Euler diagrams, comparing Edeap to the current state-of-the-art; circle-based method, venneuler, and an alternative ellipse-based method, eulerr. CONCLUSIONS: Our evaluation-using data from the Gene Ontology database via GoMiner, Twitter data from the SNAP database, and randomly generated data sets-shows an ordering for accuracy (from best to worst) of eulerr, followed by Edeap and then venneuler. In terms of runtime, the results are reversed with venneuler being the fastest, followed by Edeap and finally eulerr. Regarding scalability, eulerr cannot draw non-trivial diagrams beyond 11 sets, whereas no such limitation is present in Edeap or venneuler, both of which draw diagrams up to the tested limit of 20 sets.


Assuntos
Gráficos por Computador , Software , Bases de Dados Factuais , Humanos , Projetos de Pesquisa
13.
Zootaxa ; 4963(3): zootaxa.4963.3.10, 2021 Apr 20.
Artigo em Inglês | MEDLINE | ID: mdl-33903547

RESUMO

Scientific illustration continues to remain a critical part of taxonomy. Illustrations often require lots of time and, in many cases, the results are not as expected. At present, taxonomy journals only accept high quality digital illustrations; thus, image manipulation programs using vector or bitmap graphics have become the new focus of attention. This paper provides a step-by-step guide to making illustrations using bitmap graphics in Autodesk SketchBook. This application provides an alternative to other known tools by allowing: 1) faster illustrations; 2) direct drawing with a wide range of tools that simulate traditional drawing; 3) more detailed illustrations; and 4) an easy interface and work-flow for novice illustrators, all while being completely free and compatible with multiple operating systems.


Assuntos
Classificação , Gráficos por Computador , Software , Animais , Classificação/métodos
14.
Neural Netw ; 140: 247-260, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-33831786

RESUMO

We introduce a novel adaptive version of the Neighborhood Retrieval Visualizer (NeRV). We maintain the advantages of the conventional NeRV method, while proposing an improvement of the data samples' neighborhood width calculation, in the input and output data space. In the standard NeRV, the data samples' neighborhood widths are determined in an arbitrary manner, in this way, inhibiting the possible quality of the resulting data visualization. We propose to compute the widths adaptively, on the basis of the input data scattering. Therefore, we first perform the preliminary input data clustering, next, we calculate the values of the inner-cluster variances, which convey the information on the input data scattering, then, we assign them to each data sample, and finally, we use them as the basis for the data samples' neighborhood widths determination. The results of the experiments conducted on the three different real datasets confirm the effectiveness and usefulness of the proposed approach.


Assuntos
Gráficos por Computador , Aprendizado de Máquina , Análise por Conglomerados
15.
J Biol Chem ; 296: 100554, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33744290

RESUMO

The structural study of icosahedral viruses has a long and impactful history in both crystallographic methodology and molecular biology. The evolution of the Protein Data Bank has paralleled and supported these studies providing readily accessible formats dealing with novel features associated with viral particle symmetries and subunit interactions. This overview describes the growth in size and complexity of icosahedral viruses from the first early studies of small RNA plant viruses and human picornaviruses up to the larger and more complex bacterial phage, insect, and human disease viruses such as Zika, hepatitis B, Adeno and Polyoma virus. The analysis of icosahedral viral capsid protein domain folds has shown striking similarities, with the beta jelly roll motif observed across multiple evolutionarily divergent species. The icosahedral symmetry of viruses drove the development of noncrystallographic symmetry averaging as a powerful phasing method, and the constraints of maintaining this symmetry resulted in the concept of quasi-equivalence in viral structures. Symmetry also played an important early role in demonstrating the power of cryo-electron microscopy as an alternative to crystallography in generating atomic resolution structures of these viruses. The Protein Data Bank has been a critical resource for assembling and disseminating these structures to a wide community, and the virus particle explorer (VIPER) was developed to enable users to easily generate and view complete viral capsid structures from their asymmetric building blocks. Finally, we share a personal perspective on the early use of computer graphics to communicate the intricacies, interactions, and beauty of these virus structures.


Assuntos
Bases de Dados de Proteínas , Vírion/química , Vírus/química , Gráficos por Computador , Vírus/genética
16.
J Biophotonics ; 14(7): e202000377, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-33733621

RESUMO

Monte Carlo (MC) modeling is a valuable tool to gain fundamental understanding of light-tissue interactions, provide guidance and assessment to optical instrument designs, and help analyze experimental data. It has been a major challenge to efficiently extend MC towards modeling of bulk-tissue Raman spectroscopy (RS) due to the wide spectral range, relatively sharp spectral features, and presence of background autofluorescence. Here, we report a computationally efficient MC approach for RS by adapting the massively-parallel Monte Carlo eXtreme (MCX) simulator. Simulation efficiency is achieved through "isoweight," a novel approach that combines the statistical generation of Raman scattered and Fluorescence emission with a lookup-table-based technique well-suited for parallelization. The MC model uses a graphics processor to produce dense Raman and fluorescence spectra over a range of 800 - 2000 cm-1 with an approximately 100× increase in speed over prior RS Monte Carlo methods. The simulated RS signals are compared against experimentally collected spectra from gelatin phantoms, showing a strong correlation.


Assuntos
Gráficos por Computador , Análise Espectral Raman , Simulação por Computador , Método de Monte Carlo , Imagens de Fantasmas
17.
Nature ; 591(7849): 234-239, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-33692557

RESUMO

The ability to present three-dimensional (3D) scenes with continuous depth sensation has a profound impact on virtual and augmented reality, human-computer interaction, education and training. Computer-generated holography (CGH) enables high-spatio-angular-resolution 3D projection via numerical simulation of diffraction and interference1. Yet, existing physically based methods fail to produce holograms with both per-pixel focal control and accurate occlusion2,3. The computationally taxing Fresnel diffraction simulation further places an explicit trade-off between image quality and runtime, making dynamic holography impractical4. Here we demonstrate a deep-learning-based CGH pipeline capable of synthesizing a photorealistic colour 3D hologram from a single RGB-depth image in real time. Our convolutional neural network (CNN) is extremely memory efficient (below 620 kilobytes) and runs at 60 hertz for a resolution of 1,920 × 1,080 pixels on a single consumer-grade graphics processing unit. Leveraging low-power on-device artificial intelligence acceleration chips, our CNN also runs interactively on mobile (iPhone 11 Pro at 1.1 hertz) and edge (Google Edge TPU at 2.0 hertz) devices, promising real-time performance in future-generation virtual and augmented-reality mobile headsets. We enable this pipeline by introducing a large-scale CGH dataset (MIT-CGH-4K) with 4,000 pairs of RGB-depth images and corresponding 3D holograms. Our CNN is trained with differentiable wave-based loss functions5 and physically approximates Fresnel diffraction. With an anti-aliasing phase-only encoding method, we experimentally demonstrate speckle-free, natural-looking, high-resolution 3D holograms. Our learning-based approach and the Fresnel hologram dataset will help to unlock the full potential of holography and enable applications in metasurface design6,7, optical and acoustic tweezer-based microscopic manipulation8-10, holographic microscopy11 and single-exposure volumetric 3D printing12,13.


Assuntos
Gráficos por Computador , Sistemas Computacionais , Holografia/métodos , Holografia/normas , Redes Neurais de Computação , Animais , Realidade Aumentada , Cor , Conjuntos de Dados como Assunto , Aprendizado Profundo , Microscopia , Pinças Ópticas , Impressão Tridimensional , Fatores de Tempo , Realidade Virtual
18.
J Chem Inf Model ; 61(3): 1481-1492, 2021 03 22.
Artigo em Inglês | MEDLINE | ID: mdl-33683902

RESUMO

One of the grand challenges of this century is modeling and simulating a whole cell. Extreme regulation of an extensive quantity of model and simulation data during whole-cell modeling and simulation renders it a computationally expensive research problem in systems biology. In this article, we present a high-performance whole-cell simulation exploiting modular cell biology principles. We prepare the simulation by dividing the unicellular bacterium, Escherichia coli (E. coli), into subcells utilizing the spatially localized densely connected protein clusters/modules. We set up a Brownian dynamics-based parallel whole-cell simulation framework by utilizing the Hamiltonian mechanics-based equations of motion. Though the velocity Verlet integration algorithm possesses the capability of solving the equations of motion, it lacks the ability to capture and deal with particle-collision scenarios. Hence, we propose an algorithm for detecting and resolving both elastic and inelastic collisions and subsequently modify the velocity Verlet integrator by incorporating our algorithm into it. Also, we address the boundary conditions to arrest the molecules' motion outside the subcell. For efficiency, we define one hashing-based data structure called the cellular dictionary to store all of the subcell-related information. A benchmark analysis of our CUDA C/C++ simulation code when tested on E. coli using the CPU-GPU cluster indicates that the computational time requirement decreases with the increase in the number of computing cores and becomes stable at around 128 cores. Additional testing on higher organisms such as rats and humans informs us that our proposed work can be extended to any organism and is scalable for high-end CPU-GPU clusters.


Assuntos
Gráficos por Computador , Escherichia coli , Algoritmos , Animais , Simulação por Computador , Proteínas , Ratos
19.
Infect Dis Poverty ; 10(1): 21, 2021 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-33648606

RESUMO

BACKGROUND: Considering the widespread of coronavirus disease 2019 (COVID-19) pandemic in the world, it is important to understand the spatiotemporal development of the pandemic. In this study, we aimed to visualize time-associated alterations of COVID-19 in the context of continents and countries. METHODS: Using COVID-19 case and death data from February to December 2020 offered by Johns Hopkins University, we generated time-associated balloon charts with multiple epidemiological indicators including crude case fatality rate (CFR), morbidity, mortality and the total number of cases, to compare the progression of the pandemic within a specific period across regions and countries, integrating seven related dimensions together. The area chart is used to supplement the display of the balloon chart in daily new COVID-19 case changes in UN geographic regions over time. Javascript and Vega-Lite were chosen for programming and mapping COVID-19 data in browsers for visualization. RESULTS: From February 1st to December 20th 2020, the COVID-19 pandemic spread across UN subregions in the chronological order. It was first reported in East Asia, and then became noticeable in Europe (South, West and North), North America, East Europe and West Asia, Central and South America, Southern Africa, Caribbean, South Asia, North Africa, Southeast Asia and Oceania, causing several waves of epidemics in different regions. Since October, the balloons of Europe, North America and West Asia have been rising rapidly, reaching a dramatically high morbidity level ranging from 200 to 500/10 000 by December, suggesting an emerging winter wave of COVID-19 which was much bigger than the previous ones. By late December 2020, some European and American countries displayed a leading mortality as high as or over 100/100 000, represented by Belgium, Czechia, Spain, France, Italy, UK, Hungary, Bulgaria, Peru, USA, Argentina, Brazil, Chile and Mexico. The mortality of Iran was the highest in Asia (over 60/100 000), and that of South Africa topped in Africa (40/100 000). In the last 15 days, the CFRs of most countries were at low levels of less than 5%, while Mexico had exceptional high CFR close to 10%. CONCLUSIONS: We creatively used visualization integrating 7-dimensional epidemiologic and spatiotemporal indicators to assess the progression of COVID-19 pandemic in terms of transmissibility and severity. Such methodology allows public health workers and policy makers to understand the epidemics comparatively and flexibly.


Assuntos
COVID-19/epidemiologia , Vigilância em Saúde Pública/métodos , Gráficos por Computador , Saúde Global/estatística & dados numéricos , Humanos , Pandemias/estatística & dados numéricos , Análise Espaço-Temporal
20.
Neural Netw ; 140: 13-26, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-33743320

RESUMO

With the increasing demand of mining rich knowledge in graph structured data, graph embedding has become one of the most popular research topics in both academic and industrial communities due to its powerful capability in learning effective representations. The majority of existing work overwhelmingly learn node embeddings in the context of static, plain or attributed, homogeneous graphs. However, many real-world applications frequently involve bipartite graphs with temporal and attributed interaction edges, named temporal interaction graphs. The temporal interactions usually imply different facets of interest and might even evolve over the time, thus putting forward huge challenges in learning effective node representations. Furthermore, most existing graph embedding models try to embed all the information of each node into a single vector representation, which is insufficient to characterize the node's multifaceted properties. In this paper, we propose a novel framework named TigeCMN to learn node representations from a sequence of temporal interactions. Specifically, we devise two coupled memory networks to store and update node embeddings in the external matrices explicitly and dynamically, which forms deep matrix representations and thus could enhance the expressiveness of the node embeddings. Then, we generate node embedding from two parts: a static embedding that encodes its stationary properties and a dynamic embedding induced from memory matrix that models its temporal interaction patterns. We conduct extensive experiments on various real-world datasets covering the tasks of node classification, recommendation and visualization. The experimental results empirically demonstrate that TigeCMN can achieve significant gains compared with recent state-of-the-art baselines.


Assuntos
Gráficos por Computador , Redes Neurais de Computação
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