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1.
BMC Bioinformatics ; 21(Suppl 14): 368, 2020 Sep 30.
Artículo en Inglés | MEDLINE | ID: mdl-32998690

RESUMEN

BACKGROUND: Lung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma is the most common form of lung cancer. In order to understand the molecular basis of lung adenocarcinoma, integrative analysis have been performed by using genomics, transcriptomics, epigenomics, proteomics and clinical data. Besides, molecular prognostic signatures have been generated for lung adenocarcinoma by using gene expression levels in tumor samples. However, we need signatures including different types of molecular data, even cohort or patient-based biomarkers which are the candidates of molecular targeting. RESULTS: We built an R pipeline to carry out an integrated meta-analysis of the genomic alterations including single-nucleotide variations and the copy number variations, transcriptomics variations through RNA-seq and clinical data of patients with lung adenocarcinoma in The Cancer Genome Atlas project. We integrated significant genes including single-nucleotide variations or the copy number variations, differentially expressed genes and those in active subnetworks to construct a prognosis signature. Cox proportional hazards model with Lasso penalty and LOOCV was used to identify best gene signature among different gene categories. We determined a 12-gene signature (BCHE, CCNA1, CYP24A1, DEPTOR, MASP2, MGLL, MYO1A, PODXL2, RAPGEF3, SGK2, TNNI2, ZBTB16) for prognostic risk prediction based on overall survival time of the patients with lung adenocarcinoma. The patients in both training and test data were clustered into high-risk and low-risk groups by using risk scores of the patients calculated based on selected gene signature. The overall survival probability of these risk groups was highly significantly different for both training and test datasets. CONCLUSIONS: This 12-gene signature could predict the prognostic risk of the patients with lung adenocarcinoma in TCGA and they are potential predictors for the survival-based risk clustering of the patients with lung adenocarcinoma. These genes can be used to cluster patients based on molecular nature and the best candidates of drugs for the patient clusters can be proposed. These genes also have a high potential for targeted cancer therapy of patients with lung adenocarcinoma.


Asunto(s)
Adenocarcinoma del Pulmón/patología , Genómica/métodos , Neoplasias Pulmonares/patología , Transcriptoma , Adenocarcinoma del Pulmón/genética , Adenocarcinoma del Pulmón/mortalidad , Área Bajo la Curva , Análisis por Conglomerados , Variaciones en el Número de Copia de ADN , Bases de Datos Genéticas , Regulación Neoplásica de la Expresión Génica , Humanos , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/mortalidad , Estadificación de Neoplasias , Pronóstico , Modelos de Riesgos Proporcionales , Mapas de Interacción de Proteínas/genética , Curva ROC , Factores de Riesgo , Tasa de Supervivencia
2.
BMC Bioinformatics ; 21(Suppl 14): 367, 2020 Sep 30.
Artículo en Inglés | MEDLINE | ID: mdl-32998698

RESUMEN

BACKGROUND: Essential genes are those genes that are critical for the survival of an organism. The prediction of essential genes in bacteria can provide targets for the design of novel antibiotic compounds or antimicrobial strategies. RESULTS: We propose a deep neural network for predicting essential genes in microbes. Our architecture called DEEPLYESSENTIAL makes minimal assumptions about the input data (i.e., it only uses gene primary sequence and the corresponding protein sequence) to carry out the prediction thus maximizing its practical application compared to existing predictors that require structural or topological features which might not be readily available. We also expose and study a hidden performance bias that effected previous classifiers. Extensive results show that DEEPLYESSENTIAL outperform existing classifiers that either employ down-sampling to balance the training set or use clustering to exclude multiple copies of orthologous genes. CONCLUSION: Deep neural network architectures can efficiently predict whether a microbial gene is essential (or not) using only its sequence information.


Asunto(s)
Bacterias/genética , Genes Esenciales , Redes Neurales de la Computación , Área Bajo la Curva , Análisis por Conglomerados , Codón , Bacterias Gramnegativas/genética , Bacterias Grampositivas/genética , Curva ROC
3.
BMC Bioinformatics ; 21(Suppl 14): 364, 2020 Sep 30.
Artículo en Inglés | MEDLINE | ID: mdl-32998700

RESUMEN

BACKGROUND: Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients' primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine. RESULTS: We focused on 5-Fluorouracil and Gemcitabine because based on our exclusion criteria, they provide the largest numbers of patients within TCGA. Normalized gene expression data were clustered and used as the input features for the study. We used matching clinical trial data to ascertain the response of these patients via multiple classification methods. Multiple clustering and classification methods were compared for prediction accuracy of drug response. Clara and random forest were found to be the best clustering and classification methods, respectively. The results show our models predict with up to 86% accuracy; despite the study's limitation of sample size. We also found the genes most informative for predicting drug response were enriched in well-known cancer signaling pathways and highlighted their potential significance in chemotherapy prognosis. CONCLUSIONS: Primary tumor gene expression is a good predictor of cancer drug response. Investment in larger datasets containing both patient gene expression and drug response is needed to support future work of machine learning models. Ultimately, such predictive models may aid oncologists with making critical treatment decisions.


Asunto(s)
Antineoplásicos/farmacología , Regulación Neoplásica de la Expresión Génica/efectos de los fármacos , Aprendizaje Automático , Antineoplásicos/uso terapéutico , Área Bajo la Curva , Análisis por Conglomerados , Bases de Datos Genéticas , Desoxicitidina/análogos & derivados , Desoxicitidina/farmacología , Desoxicitidina/uso terapéutico , Fluorouracilo/uso terapéutico , Humanos , Neoplasias/tratamiento farmacológico , Curva ROC
4.
BMC Public Health ; 20(1): 1525, 2020 Oct 08.
Artículo en Inglés | MEDLINE | ID: mdl-33032575

RESUMEN

BACKGROUND: This study was intended to investigate the epidemiological characteristics of COVID-19 clusters and the severity distribution of clinical symptoms of involved cases in Sichuan Province, so as to provide information support for the development and adjustment of strategies for the prevention and control of local clusters. METHODS: The epidemiological characteristics of 67 local clusters of COVID-19 cases in Sichuan Province reported as of March 17, 2020 were described and analyzed. Information about all COVID-19 clusters and involved cases was acquired from the China Information System for Disease Control and Prevention and analyzed with the epidemiological investigation results taken into account. RESULTS: The clusters were temporally and regionally concentrated. Clusters caused by imported cases from other provinces accounted for 73.13%; familial clusters accounted for 68.66%; the average attack rate was 8.54%, and the average secondary attack rate was 6.11%; the median incubation period was 8.5 d; a total of 28 cases met the criteria for incubation period determination, and in the 28 cases, the incubation period was > 14 d in 21.43% (6/28). a total of 226 confirmed cases were reported in the 67 clusters. Ten cases were exposed before the confirmed cases they contacted with developed clinical symptoms, and the possibility of exposure to other infection sources was ruled out; two clusters were caused by asymptomatic carriers; confirmed cases mainly presented with fever, respiratory and systemic symptoms; a gradual decline in the severity of clinical symptoms was noted with the increase of the case generation. CONCLUSIONS: Population movement and gathering restrictions and strict close contact management measures will significantly contribute to the identification and control of cases. Transmission during the incubation period and asymptomatic infections have been noted. Studies on the pathogenicity and transmissibility in these populations and on COVID-19 antibody levels and protective effects in healthy people and cases are required.


Asunto(s)
Infecciones por Coronavirus/epidemiología , Neumonía Viral/epidemiología , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Niño , Preescolar , China/epidemiología , Análisis por Conglomerados , Femenino , Humanos , Lactante , Masculino , Persona de Mediana Edad , Pandemias , Adulto Joven
5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1568-1571, 2020 07.
Artículo en Inglés | MEDLINE | ID: mdl-33018292

RESUMEN

There is growing evidence that the use of stringent and dichotomic diagnostic categories in many medical disciplines (particularly 'brain sciences' as neurology and psychiatry) is an oversimplification. Although clear diagnostic boundaries remain useful for patients, families, and their access to dedicated NHS and health care services, the traditional dichotomic categories are not helpful to describe the complexity and large heterogeneity of symptoms across many and overlapping clinical phenotypes. With the advent of 'big' multimodal neuroimaging databases, data-driven stratification of the wide spectrum of healthy human physiology or disease based on neuroimages is theoretically become possible. However, this conceptual framework is hampered by severe computational constraints. In this paper we present a novel, deep learning based encode-decode architecture which leverages several parameter efficiency techniques generate latent deep embedding which compress the information contained in a full 3D neuroimaging volume by a factor 1000 while still retaining anatomical detail and hence rendering the subsequent stratification problem tractable. We train our architecture on 1003 brain scan derived from the human connectome project and demonstrate the faithfulness of the obtained reconstructions. Further, we employ a data driven clustering technique driven by a grid search in hyperparameter space to identify six different strata within the 1003 healthy community dwelling individuals which turn out to correspond to highly significant group differences in both physiological and cognitive data. Indicating that the well-known relationships between such variables and brain structure can be probed in an unsupervised manner through our novel architecture and pipeline. This opens the door to a variety of previously inaccessible applications in the realm of data driven stratification of large cohorts based on neuroimaging data.Clinical Relevance -With our approach, each person can be described and classified within a multi-dimensional space of data, where they are uniquely classified according to their individual anatomy, physiology and disease-related anatomical and physiological alterations.


Asunto(s)
Conectoma , Aprendizaje Profundo , Neuroimagen , Encéfalo , Análisis por Conglomerados , Bases de Datos Factuales , Humanos
6.
Zhongguo Zhong Yao Za Zhi ; 45(15): 3659-3665, 2020 Aug.
Artículo en Chino | MEDLINE | ID: mdl-32893555

RESUMEN

As a traditional Chinese medicinal material, Chrysosplenium is urgently needed for genetic resource investigation and protection research due to the decrease of its wild resources in recent years. After investigating the wild resources, we conducted genetic polymorphism and clustering studies of 24 species(a total of 36 samples) of Chrysosplenium using SRAP technique. The results showed that a total of 374 polymorphic bands were obtained using 18 pairs of SRAP primers to amplify these samples, on average of 20.7 bands for each primer pair. We used the biological software to analyze the population's genetic parameter and got the N_a value as 2.000 0, N_(e )value as 1.408 4, the average Nei's index as 0.263 5, and the average Shannon information index as 0.419 1. UPGMA cluster analysis showed that all the samples can be divided into three major groups at the genetic similarity coefficient of 0.70: there are 18 species(24 samples) gathered for the Ⅰ groups, 3 species or variation(7 samples) for Ⅱ groups, and 3 species(5 samples) for Ⅲ groups. The differences of these Chrysosplenium species at the molecular level are consistent with that of their geographical and ecological distribution. At the same time, we used SRAP technology to construct 36 DNA digital fingerprints of Chrysosplenium and obtained the unique molecular identification band type of each material. These results will provide effective methods and reliable basis for the identification, protection and genetic diversity analysis of the germplasm resources of Chrysosplenium, and lay a foundation for the further development and utilization of them.


Asunto(s)
Dermatoglifia del ADN , Variación Genética , Análisis por Conglomerados , Marcadores Genéticos , Filogenia , Polimorfismo Genético
7.
PLoS One ; 15(8): e0236186, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32866164

RESUMEN

AIM: To establish a gene co-expression network for identifying principal modules and hub genes that are associated with drought resistance mechanisms, analyzing their mechanisms, and exploring candidate genes. METHODS AND FINDINGS: 42 data sets including PRJNA380841 and PRJNA369686 were used to construct the co-expression network through weighted gene co-expression network analysis (WGCNA). A total of 1,896,897,901 (284.30 Gb) clean reads and 35,021 differentially expressed genes (DEGs) were obtained from 42 samples. Functional enrichment analysis indicated that photosynthesis, DNA replication, glycolysis/gluconeogenesis, starch and sucrose metabolism, arginine and proline metabolism, and cell cycle were significantly influenced by drought stress. Furthermore, the DEGs with similar expression patterns, detected by K-means clustering, were grouped into 29 clusters. Genes involved in the modules, such as dark turquoise, yellow, and brown, were found to be appreciably linked with drought resistance. Twelve central, greatly correlated genes in stage-specific modules were subsequently confirmed and validated at the transcription levels, including TraesCS7D01G417600.1 (PP2C), TraesCS5B01G565300.1 (ERF), TraesCS4A01G068200.1 (HSP), TraesCS2D01G033200.1 (HSP90), TraesCS6B01G425300.1 (RBD), TraesCS7A01G499200.1 (P450), TraesCS4A01G118400.1 (MYB), TraesCS2B01G415500.1 (STK), TraesCS1A01G129300.1 (MYB), TraesCS2D01G326900.1 (ALDH), TraesCS3D01G227400.1 (WRKY), and TraesCS3B01G144800.1 (GT). CONCLUSIONS: Analyzing the response of wheat to drought stress during different growth stages, we have detected three modules and 12 hub genes that are associated with drought resistance mechanisms, and five of those genes are newly identified for drought resistance. The references provided by these modules will promote the understanding of the drought-resistance mechanism. In addition, the candidate genes can be used as a basis of transgenic or molecular marker-assisted selection for improving the drought resistance and increasing the yields of wheat.


Asunto(s)
Aclimatación/genética , Regulación de la Expresión Génica de las Plantas , Redes Reguladoras de Genes , Estrés Fisiológico/genética , Triticum/genética , China , Análisis por Conglomerados , Conjuntos de Datos como Asunto , Sequías , Perfilación de la Expresión Génica , Genes de Plantas , Fitomejoramiento/métodos , RNA-Seq , Selección Genética
8.
Nat Commun ; 11(1): 4669, 2020 09 16.
Artículo en Inglés | MEDLINE | ID: mdl-32938940

RESUMEN

The prefrontal cortex and striatum form a recurrent network whose spiking activity encodes multiple types of learning-relevant information. This spike-encoded information is evident in average firing rates, but finer temporal coding might allow multiplexing and enhanced readout across the connected network. We tested this hypothesis in the fronto-striatal network of nonhuman primates during reversal learning of feature values. We found that populations of neurons encoding choice outcomes, outcome prediction errors, and outcome history in their firing rates also carry significant information in their phase-of-firing at a 10-25 Hz band-limited beta frequency at which they synchronize across lateral prefrontal cortex, anterior cingulate cortex and anterior striatum when outcomes were processed. The phase-of-firing code exceeds information that can be obtained from firing rates alone and is evident for inter-areal connections between anterior cingulate cortex, lateral prefrontal cortex and anterior striatum. For the majority of connections, the phase-of-firing information gain is maximal at phases of the beta cycle that were offset from the preferred spiking phase of neurons. Taken together, these findings document enhanced information of three important learning variables at specific phases of firing in the beta cycle at an inter-areally shared beta oscillation frequency during goal-directed behavior.


Asunto(s)
Cuerpo Estriado/fisiología , Giro del Cíngulo/fisiología , Aprendizaje/fisiología , Neuronas/fisiología , Corteza Prefrontal/fisiología , Animales , Análisis por Conglomerados , Cuerpo Estriado/citología , Sincronización de Fase en Electroencefalografía , Electrofisiología/métodos , Electrofisiología/estadística & datos numéricos , Giro del Cíngulo/citología , Macaca mulatta , Masculino , Red Nerviosa , Corteza Prefrontal/citología , Recompensa
9.
BMC Bioinformatics ; 21(1): 399, 2020 Sep 09.
Artículo en Inglés | MEDLINE | ID: mdl-32907544

RESUMEN

BACKGROUND: Cryo-electron tomography is an important and powerful technique to explore the structure, abundance, and location of ultrastructure in a near-native state. It contains detailed information of all macromolecular complexes in a sample cell. However, due to the compact and crowded status, the missing edge effect, and low signal to noise ratio (SNR), it is extremely challenging to recover such information with existing image processing methods. Cryo-electron tomogram simulation is an effective solution to test and optimize the performance of the above image processing methods. The simulated images could be regarded as the labeled data which covers a wide range of macromolecular complexes and ultrastructure. To approximate the crowded cellular environment, it is very important to pack these heterogeneous structures as tightly as possible. Besides, simulating non-deformable and deformable components under a unified framework also need to be achieved. RESULT: In this paper, we proposed a unified framework for simulating crowded cryo-electron tomogram images including non-deformable macromolecular complexes and deformable ultrastructures. A macromolecule was approximated using multiple balls with fixed relative positions to reduce the vacuum volume. A ultrastructure, such as membrane and filament, was approximated using multiple balls with flexible relative positions so that this structure could deform under force field. In the experiment, 400 macromolecules of 20 representative types were packed into simulated cytoplasm by our framework, and numerical verification proved that our method has a smaller volume and higher compression ratio than the baseline single-ball model. We also packed filaments, membranes and macromolecules together, to obtain a simulated cryo-electron tomogram image with deformable structures. The simulated results are closer to the real Cryo-ET, making the analysis more difficult. The DOG particle picking method and the image segmentation method are tested on our simulation data, and the experimental results show that these methods still have much room for improvement. CONCLUSION: The proposed multi-ball model can achieve more crowded packaging results and contains richer elements with different properties to obtain more realistic cryo-electron tomogram simulation. This enables users to simulate cryo-electron tomogram images with non-deformable macromolecular complexes and deformable ultrastructures under a unified framework. To illustrate the advantages of our framework in improving the compression ratio, we calculated the volume of simulated macromolecular under our multi-ball method and traditional single-ball method. We also performed the packing experiment of filaments and membranes to demonstrate the simulation ability of deformable structures. Our method can be used to do a benchmark by generating large labeled cryo-ET dataset and evaluating existing image processing methods. Since the content of the simulated cryo-ET is more complex and crowded compared with previous ones, it will pose a greater challenge to existing image processing methods.


Asunto(s)
Citoplasma/ultraestructura , Tomografía con Microscopio Electrónico/métodos , Simulación de Dinámica Molecular , Algoritmos , Análisis por Conglomerados , Microscopía por Crioelectrón , Citoplasma/metabolismo , Procesamiento de Imagen Asistido por Computador , Sustancias Macromoleculares/química , Sustancias Macromoleculares/metabolismo , Relación Señal-Ruido
10.
Zhonghua Liu Xing Bing Xue Za Zhi ; 41(8): 1319-1323, 2020 Aug 10.
Artículo en Chino | MEDLINE | ID: mdl-32867443

RESUMEN

Objective: To understand the characteristics of spatiotemporal clustering on hepatitis A in Gansu province and to provide evidence for hepatitis A prevention and control. Methods: Data related to hepatitis A were retrieved from National Notifiable Disease Report System, ArcGIS 10.3 and SaTScan 9.1 in Gansu province from 2004 to 2018. Results: The annual average report incidence rate of hepatitis A was 10.91/100 000, showing a descending trend with no periodic or seasonal features. After the implementation of national expanded immunization program, high annual incidence rates had been seen in Linxia Hui autonomous prefecture and Gannan Tibetan autonomous prefecture. From 2004 to 2012, the lowest RR value appeared in the 0-9 age group (P=0.000) while the highest RR value was in the over 60 age group during 2013-2018 except for the age 0-9 group in 2015. The annual average incidence rate was increasing from south to north and west to east, across the territory. Results from the temporal scanning program revealed that the incidence of hepatitis A was temporally aggregated from 2004 to 2018. For spatio-temporal scanning of 2004-2008, data showed one most likely cluster area (radius: 91.95 km, Time frame: 2004-2005), apparel mainly in Linxia and Longnan cities. Results from the spatio-temporal scanning program of 2009-2018 also showed that the most likely cluster areas (radius: 183.26 km, Time frame: 2009-2012) were in Gannan, Linxia, Dingxi and Longnan areas. Conclusions: The reported incidence rates of hepatitis A were declining, without significant periodic or seasonal pattern in Gansu province from 2004 to 2018. In the 0-9 years-old group, the incidence rate showed the lowest, while the highest was in the 60 year-olds group. Spatio-temporal clustering of hepatitis A was observed in Gansu province from 2004 to 2018. Strategies on prevention and control of the disease should be targeted in the southwest regions of the province.


Asunto(s)
Hepatitis A/epidemiología , Niño , Preescolar , China/epidemiología , Ciudades/epidemiología , Análisis por Conglomerados , Humanos , Incidencia , Lactante , Recién Nacido , Persona de Mediana Edad , Análisis Espacio-Temporal
11.
BMJ Open ; 10(9): e039886, 2020 Sep 01.
Artículo en Inglés | MEDLINE | ID: mdl-32873684

RESUMEN

OBJECTIVES: To illustrate the intersections of, and intercounty variation in, individual, household and community factors that influence the impact of COVID-19 on US counties and their ability to respond. DESIGN: We identified key individual, household and community characteristics influencing COVID-19 risks of infection and survival, guided by international experiences and consideration of epidemiological parameters of importance. Using publicly available data, we developed an open-access online tool that allows county-specific querying and mapping of risk factors. As an illustrative example, we assess the pairwise intersections of age (individual level), poverty (household level) and prevalence of group homes (community-level) in US counties. We also examine how these factors intersect with the proportion of the population that is people of colour (ie, not non-Hispanic white), a metric that reflects histories of US race relations. We defined 'high' risk counties as those above the 75th percentile. This threshold can be changed using the online tool. SETTING: US counties. PARTICIPANTS: Analyses are based on publicly available county-level data from the Area Health Resources Files, American Community Survey, Centers for Disease Control and Prevention Atlas file, National Center for Health Statistic and RWJF Community Health Rankings. RESULTS: Our findings demonstrate significant intercounty variation in the distribution of individual, household and community characteristics that affect risks of infection, severe disease or mortality from COVID-19. About 9% of counties, affecting 10 million residents, are in higher risk categories for both age and group quarters. About 14% of counties, affecting 31 million residents, have both high levels of poverty and a high proportion of people of colour. CONCLUSION: Federal and state governments will benefit from recognising high intrastate, intercounty variation in population risks and response capacity. Equitable responses to the pandemic require strategies to protect those in counties at highest risk of adverse COVID-19 outcomes and their social and economic impacts.


Asunto(s)
Factores de Edad , Infecciones por Coronavirus , Grupos Étnicos/estadística & datos numéricos , Composición Familiar , Pandemias , Neumonía Viral , Pobreza/estadística & datos numéricos , Salud Pública , Análisis de Supervivencia , Adulto , Anciano , Betacoronavirus , Análisis por Conglomerados , Infecciones por Coronavirus/diagnóstico , Infecciones por Coronavirus/epidemiología , Estudios Transversales , Femenino , Humanos , Masculino , Neumonía Viral/diagnóstico , Neumonía Viral/epidemiología , Prevalencia , Salud Pública/métodos , Salud Pública/estadística & datos numéricos , Medición de Riesgo/métodos , Medición de Riesgo/estadística & datos numéricos , Factores de Riesgo , Índice de Severidad de la Enfermedad , Estados Unidos
12.
Artículo en Inglés | MEDLINE | ID: mdl-32907528

RESUMEN

Cumulatively to 30 August there have been 25,686 case notifications and 577 deaths. The number of new cases reported nationally this fortnight was 1,751, a 61% decrease from the previous fortnight (4,501). On average this represented 125 cases diagnosed each day over the reporting period, a decrease from 322 cases per day over the previous reporting period. 94% (1,640) of all cases were reported in Victoria, with a smaller number of cases reported from New South Wales (86), Queensland (19), Western Australia (5) and South Australia (1). In Victoria, the majority of cases (1,528; 93%) were locally acquired, with a further 112 (7%) under investigation at the time of analysis, but likely also to be locally acquired. Of the remaining 111 cases reported, 22 (20%) were overseas acquired; 82 (74%) were locally acquired, predominantly in NSW, and 7 (6%) were reported as under investigation. The continued decrease in new cases observed this fortnight in Victoria is likely associated with the enhanced public health measures that are currently in place in Victoria. Locally acquired cases which were predominantly associated with several interconnected clusters continued to be reported in NSW. In Qld a cluster of cases associated with a youth detention centre was identified. A total of 26 deaths were reported from cases diagnosed in this reporting period, all from Victoria and aged 75 years or older. Testing rates remain high across all jurisdictions, with an overall positivity rate for the reporting period of 0.27%. Victoria reported a positivity rate of 0.90% for this reporting period; in all other jurisdictions the positivity rate was 0.03% or lower.


Asunto(s)
Betacoronavirus/aislamiento & purificación , Infecciones por Coronavirus/diagnóstico , Infecciones por Coronavirus/epidemiología , Neumonía Viral/diagnóstico , Neumonía Viral/epidemiología , Adolescente , Adulto , Anciano , Anciano de 80 o más Años , Australia/epidemiología , Betacoronavirus/genética , Niño , Preescolar , Análisis por Conglomerados , Infecciones por Coronavirus/mortalidad , Salud Global , Humanos , Lactante , Persona de Mediana Edad , Grupo de Ascendencia Oceánica , Pandemias , Neumonía Viral/mortalidad , Administración en Salud Pública , Adulto Joven
13.
J Chem Theory Comput ; 16(10): 6383-6396, 2020 Oct 13.
Artículo en Inglés | MEDLINE | ID: mdl-32905698

RESUMEN

Molecular dynamics simulations are a popular means to study biomolecules, but it is often difficult to gain insights from the trajectories due to their large size, in both time and number of features. The Sapphire (States And Pathways Projected with HIgh REsolution) plot allows a direct visual inference of the dominant states visited by high-dimensional systems and how they are interconnected in time. Here, we extend this visual inference into a clustering algorithm. Specifically, the automatic procedure derives from the Sapphire plot states that are kinetically homogeneous, structurally annotated, and of tunable granularity. We provide a relative assessment of the kinetic fidelity of the Sapphire-based partitioning in comparison to popular clustering methods. This assessment is carried out on trajectories of n-butane, a ß-sheet peptide, and the small protein BPTI. We conclude with an application of our approach to a recent 100 µs trajectory of the main protease of SARS-CoV-2.


Asunto(s)
Butanos/química , Simulación de Dinámica Molecular , Péptidos/química , Proteínas/química , Algoritmos , Betacoronavirus/química , Análisis por Conglomerados , Infecciones por Coronavirus/virología , Cisteína Endopeptidasas/química , Humanos , Cinética , Pandemias , Neumonía Viral/virología , Conformación Proteica , Proteínas no Estructurales Virales/química
14.
PLoS One ; 15(9): e0239572, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32960932

RESUMEN

Social distancing, a non-pharmaceutical tactic aimed at reducing the spread of COVID-19, can arise because individuals voluntarily distance from others to avoid contracting the disease. Alternatively, it can arise because of jurisdictional restrictions imposed by local authorities. We run reduced form models of social distancing as a function of county-level exogenous demographic variables and jurisdictional fixed effects for 49 states to assess the relative contributions of demographic and jurisdictional effects in explaining social distancing behavior. To allow for possible spatial aspects of a contagious disease, we also model the spillovers associated with demographic variables in surrounding counties as well as allow for disturbances that depend upon those in surrounding counties. We run our models weekly and examine the evolution of the estimated coefficients over time since the onset of the COVID-19 pandemic in the United States. These estimated coefficients express the revealed preferences of individuals who were able to and chose to stay at home to avoid the disease. Stay-at-home behavior measured using cell phone tracking data exhibits considerable cross-sectional variation, increasing over nine-fold from the end of January 2020 to the end of March 2020, and then decreasing by about 50% through mid-June 2020. Our estimation results show that demographic exogenous variables explain substantially more of this variation than predictions from jurisdictional fixed effects. Moreover, the explanations from demographic exogenous variables and jurisdictional fixed effects show an evolving correlation over the sample period, initially partially offsetting, and eventually reinforcing each other. Furthermore, the predicted social distance from demographic exogenous variables shows substantial spatial autoregressive dependence, indicating clustering in social distancing behavior. The increased variance of stay-at-home behavior coupled with the high level of spatial dependence can result in relatively intense hotspots and coldspots of social distance, which has implications for disease spread and mitigation.


Asunto(s)
Betacoronavirus , Infecciones por Coronavirus/epidemiología , Modelos Teóricos , Neumonía Viral/epidemiología , Distancia Social , Análisis por Conglomerados , Infecciones por Coronavirus/prevención & control , Estudios Transversales , Demografía , Humanos , Gobierno Local , Pandemias/prevención & control , Neumonía Viral/prevención & control , Aislamiento Social , Estados Unidos
15.
Environ Monit Assess ; 192(10): 638, 2020 Sep 14.
Artículo en Inglés | MEDLINE | ID: mdl-32924079

RESUMEN

Surface water is one of the primary sources for drinking, irrigation, and industrial activities in Ergene River, Turkey. However, its quality has deteriorated due to the point and non-point pollution sources. Therefore, an appropriate assessment of surface water quality is very important. Water quality classification is calculated separately for each quality parameter in Turkey. An overall assessment of surface water quality is essential for water management. In this study, self-organizing maps (SOMs) and fuzzy C-means clustering (FCM) methods have been used for assessing surface water quality in the Ergene River. Seven water quality parameters have been considered as important indicators to evaluate water quality status in 7 observation points located in the river, covering the period from 1985 to 2013.


Asunto(s)
Ríos , Calidad del Agua , Análisis por Conglomerados , Monitoreo del Ambiente , Turquia
16.
Nat Commun ; 11(1): 4556, 2020 09 11.
Artículo en Inglés | MEDLINE | ID: mdl-32917883

RESUMEN

Previous genetic studies have identified local population structure within the Netherlands; however their resolution is limited by use of unlinked markers and absence of external reference data. Here we apply advanced haplotype sharing methods (ChromoPainter/fineSTRUCTURE) to study fine-grained population genetic structure and demographic change across the Netherlands using genome-wide single nucleotide polymorphism data (1,626 individuals) with associated geography (1,422 individuals). We identify 40 haplotypic clusters exhibiting strong north/south variation and fine-scale differentiation within provinces. Clustering is tied to country-wide ancestry gradients from neighbouring lands and to locally restricted gene flow across major Dutch rivers. North-south structure is temporally stable, with west-east differentiation more transient, potentially influenced by migrations during the middle ages. Despite superexponential population growth, regional demographic estimates reveal population crashes contemporaneous with the Black Death. Within Dutch and international data, GWAS incorporating fine-grained haplotypic covariates are less confounded than standard methods.


Asunto(s)
Grupos Étnicos/genética , Genética de Población , Estudio de Asociación del Genoma Completo , Estudios de Casos y Controles , Análisis por Conglomerados , Emigración e Inmigración , Grupo de Ascendencia Continental Europea/genética , Flujo Génico , Variación Genética/genética , Genoma , Geografía , Haplotipos , Humanos , Persona de Mediana Edad , Modelos Genéticos , Países Bajos , Polimorfismo de Nucleótido Simple
17.
Virol J ; 17(1): 138, 2020 09 14.
Artículo en Inglés | MEDLINE | ID: mdl-32928234

RESUMEN

The outbreak of coronavirus disease 2019 (COVID-19) due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has posed significant threats to international health. The genetic traits as well as evolutionary processes in this novel coronavirus are not fully characterized, and their roles in viral pathogenesis are yet largely unknown. To get a better picture of the codon architecture of this newly emerging coronavirus, in this study we perform bioinformatic analysis, based on publicly available nucleotide sequences of SARS-CoV-2 along with those of other members of human coronaviruses as well as non-human coronaviruses in different hosts, to take a snapshot of the genome-wide codon usage pattern of SARS-CoV-2 and uncover that all over-represented codons end with A/U and this newly emerging coronavirus has a relatively low codon usage bias, which is shaped by both mutation pressure and natural selection. Additionally, there is slight variation in the codon usage pattern among the SARS-CoV-2 isolates from different geo-locations. Furthermore, the overall codon usage pattern of SARS-CoV-2 is generally similar to that of its phylogenetic relatives among non-human betacoronaviruses such as RaTG13. Taken together, we comprehensively analyze the characteristics of codon usage pattern in SARS-CoV-2 via bioinformatic approaches. The information from this research may not only be helpful to get new insights into the evolution of SARS-CoV-2, but also have potential value for developing coronavirus vaccines.


Asunto(s)
Betacoronavirus/genética , Uso de Codones , Infecciones por Coronavirus/virología , Genoma Viral , Neumonía Viral/virología , Animales , Secuencia de Bases , Análisis por Conglomerados , Codón , Biología Computacional , Evolución Molecular , Humanos , Mutación , Pandemias , Filogenia , Selección Genética , Proteínas Virales/genética , Secuenciación Completa del Genoma
18.
BMJ ; 370: m3208, 2020 09 16.
Artículo en Inglés | MEDLINE | ID: mdl-32938633

RESUMEN

OBJECTIVE: To investigate whether opportunistic screening in primary care increases the detection of atrial fibrillation compared with usual care. DESIGN: Cluster randomised controlled trial. SETTING: 47 intention-to-screen and 49 usual care primary care practices in the Netherlands, not blinded for allocation; the study was carried out from September 2015 to August 2018. PARTICIPANTS: In each practice, a fixed sample of 200 eligible patients, aged 65 or older, with no known history of atrial fibrillation in the electronic medical record system, were randomly selected. In the intention-to-screen group, 9218 patients eligible for screening were included, 55.0% women, mean age 75.2 years. In the usual care group, 9526 patients were eligible for screening, 54.3% women, mean age 75.0 years. INTERVENTIONS: Opportunistic screening (that is, screening in patients visiting their general practice) consisted of three index tests: pulse palpation, electronic blood pressure measurement with an atrial fibrillation algorithm, and electrocardiography (ECG) with a handheld single lead electrocardiographic device. The reference standard was 12 lead ECG, performed in patients with at least one positive index test and in a sample of patients (10%) with three negative tests. If 12 lead ECG showed no atrial fibrillation, patients were invited for more screening by continuous monitoring with a Holter electrocardiograph for two weeks. MAIN OUTCOME MEASURES: Difference in the detection rate of newly diagnosed atrial fibrillation over one year in intention-to-screen versus usual care practices. RESULTS: Follow-up was complete for 8874 patients in the intention-to-screen practices and for 9102 patients in the usual care practices. 144 (1.62%) new diagnoses of atrial fibrillation in the intention-to-screen group versus 139 (1.53%) in the usual care group were found (adjusted odds ratio 1.06 (95% confidence interval 0.84 to 1.35)). Of 9218 eligible patients in the intention-to-screen group, 4106 (44.5%) participated in the screening protocol. In these patients, 12 lead ECG detected newly diagnosed atrial fibrillation in 26 patients (0.63%). In the 266 patients who continued with Holter monitoring, four more diagnoses of atrial fibrillation were found. CONCLUSIONS: Opportunistic screening for atrial fibrillation in primary care patients, aged 65 and over, did not increase the detection rate of atrial fibrillation, which implies that opportunistic screening for atrial fibrillation is not useful in this setting. TRIAL REGISTRATION: Netherlands Trial Register No NL4776 (old NTR4914).


Asunto(s)
Fibrilación Atrial/diagnóstico , Selección de Paciente , Atención Primaria de Salud , Anciano , Anciano de 80 o más Años , Algoritmos , Análisis por Conglomerados , Electrocardiografía , Femenino , Humanos , Análisis de Intención de Tratar , Masculino , Tamizaje Masivo , Factores de Riesgo
19.
PLoS One ; 15(9): e0238214, 2020.
Artículo en Inglés | MEDLINE | ID: mdl-32946442

RESUMEN

Brazil detected community transmission of COVID-19 on March 13, 2020. In this study we identified which areas in the country were the most vulnerable for COVID-19, both in terms of the risk of arrival of cases, the risk of sustained transmission and their social vulnerability. Probabilistic models were used to calculate the probability of COVID-19 spread from São Paulo and Rio de Janeiro, the initial hotspots, using mobility data from the pre-epidemic period, while multivariate cluster analysis of socio-economic indices was done to identify areas with similar social vulnerability. The results consist of a series of maps of effective distance, outbreak probability, hospital capacity and social vulnerability. They show areas in the North and Northeast with high risk of COVID-19 outbreak that are also highly socially vulnerable. Later, these areas would be found the most severely affected. The maps produced were sent to health authorities to aid in their efforts to prioritize actions such as resource allocation to mitigate the effects of the pandemic. In the discussion, we address how predictions compared to the observed dynamics of the disease.


Asunto(s)
Betacoronavirus , Infecciones por Coronavirus/transmisión , Modelos Teóricos , Morbilidad/tendencias , Neumonía Viral/transmisión , Brasil/epidemiología , Análisis por Conglomerados , Infecciones por Coronavirus/epidemiología , Brotes de Enfermedades/estadística & datos numéricos , Predicción/métodos , Humanos , Pandemias , Neumonía Viral/epidemiología , Factores Socioeconómicos
20.
Science ; 369(6509): 1305-1306, 2020 09 11.
Artículo en Inglés | MEDLINE | ID: mdl-32913091
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