From genomics to metagenomics: benchmark of variation graphs

International audienc

Social reintegration of drug-addicted individuals living in therapeutic communities

El estudio tuvo por objetivo caracterizar las acciones y actividades vueltas para la reinserción social de dependientes químicos residentes en comunidades terapéuticas. Fueron evaluadas 43 comunidades, localizadas en el estado de Goiás, con la utilización de un cuestionario semiestructurado. Los datos fueron analizados por medio de estadística descriptiva y los resultados apuntaron que esas comunidades presentan mecanismos de preparo del residente para la alta terapéutica y encaminamiento al mercado de trabajo. Mientras, desarrollan pocas actividades para la inserción en el mercado de trabajo. El envolvimiento de la familia en el tratamiento de los residentes es estimulado con visitas y actividades conjuntas. Se concluye que aunque el trabajo de las comunidades sea relevante, aún carece de ayuda de políticas públicas de asistencia social que aporten con la reinserción de eses individuos en la sociedad.The study aimed to characterize actions and activities aimed at social reintegration of drug-addicted living in therapeutic communities. Forty-three communities were evaluated in the state of Goiás, with the use of a semi-structured questionnaire. Data were analyzed using descriptive statistics and the results showed that these communities have resident preparation mechanisms for therapeutic discharge and referral to the labor market. However, few activities are developed for inclusion in the labor market. Family’s involvement in treatment of residents is stimulated with joint visits and activities. Although the relevant work of communities, help from public policies is needed for social assistance to contribute to the reintegration of these individuals in society.O estudo teve por objetivo caracterizar as ações e atividades voltadas para a reinserção social de dependentes químicos residentes em comunidades terapêuticas. Foram avaliadas 43 comunidades, localizadas no estado de Goiás, com a utilização de um questionário semiestruturado. Os dados foram analisados por meio de estatística descritiva e os resultados apontaram que essas comunidades apresentam mecanismos de preparo do residente para a alta terapêutica e encaminhamento ao mercado de trabalho. Entretanto, desenvolvem poucas atividades para a inserção no mercado de trabalho. O envolvimento da família no tratamento dos residentes é estimulado com visitas e atividades conjuntas. Conclui-se que embora o trabalho das comunidades seja relevante, ainda carece de ajuda de políticas públicas de assistência social que contribuam com a reinserção desses indivíduos na sociedade

An open vibration platform to evaluate postural control using a simple reinforcement learning agent

In this paper, our research team proposes an inexpensive open vibration platform built from easily available electronic components to be used as a tool by physiotherapists in order to help them in their evaluation of the postural control of individuals at risk of postural imbalance which could lead to falls. The platform has been thought to be easily reproducible and all the code necessary to make it work is made available on the researchers’ websites. In addition, a simple reinforcement learning agent has been developed and tested to automatically calibrate the vibration motors for optimal stimulation. Finally, we present in this paper pilot experiments done on 7 healthy participants (40.8 years old) to validate the proper functioning of the platform

Comportamento alimentar, satisfação corporal e percepção da qualidade de vida na população transgênera brasileira

Pessoas trans apresentam níveis elevados de insatisfação corporal devido à disforia de gênero e fatores sociais, podendo gerar pior percepção da qualidade de vida e risco para desenvolvimento de Transtornos Alimentares (TA). Esta pesquisa objetiva elucidar os fatores associados à qualidade de vida da população trans, entre eles a disforia de gênero, percepção da imagem corporal, nível de satisfação corporal e comportamento alimentar

Prognostic indicators and outcomes of hospitalised COVID-19 patients with neurological disease: An individual patient data meta-analysis

BACKGROUND: Neurological COVID-19 disease has been reported widely, but published studies often lack information on neurological outcomes and prognostic risk factors. We aimed to describe the spectrum of neurological disease in hospitalised COVID-19 patients; characterise clinical outcomes; and investigate factors associated with a poor outcome. METHODS: We conducted an individual patient data (IPD) meta-analysis of hospitalised patients with neurological COVID-19 disease, using standard case definitions. We invited authors of studies from the first pandemic wave, plus clinicians in the Global COVID-Neuro Network with unpublished data, to contribute. We analysed features associated with poor outcome (moderate to severe disability or death, 3 to 6 on the modified Rankin Scale) using multivariable models. RESULTS: We included 83 studies (31 unpublished) providing IPD for 1979 patients with COVID-19 and acute new-onset neurological disease. Encephalopathy (978 [49%] patients) and cerebrovascular events (506 [26%]) were the most common diagnoses. Respiratory and systemic symptoms preceded neurological features in 93% of patients; one third developed neurological disease after hospital admission. A poor outcome was more common in patients with cerebrovascular events (76% [95% CI 67-82]), than encephalopathy (54% [42-65]). Intensive care use was high (38% [35-41]) overall, and also greater in the cerebrovascular patients. In the cerebrovascular, but not encephalopathic patients, risk factors for poor outcome included breathlessness on admission and elevated D-dimer. Overall, 30-day mortality was 30% [27-32]. The hazard of death was comparatively lower for patients in the WHO European region. INTERPRETATION: Neurological COVID-19 disease poses a considerable burden in terms of disease outcomes and use of hospital resources from prolonged intensive care and inpatient admission; preliminary data suggest these may differ according to WHO regions and country income levels. The different risk factors for encephalopathy and stroke suggest different disease mechanisms which may be amenable to intervention, especially in those who develop neurological symptoms after hospital admission

Prognostic indicators and outcomes of hospitalised COVID-19 patients with neurological disease: An individual patient data meta-analysis.

BackgroundNeurological COVID-19 disease has been reported widely, but published studies often lack information on neurological outcomes and prognostic risk factors. We aimed to describe the spectrum of neurological disease in hospitalised COVID-19 patients; characterise clinical outcomes; and investigate factors associated with a poor outcome.MethodsWe conducted an individual patient data (IPD) meta-analysis of hospitalised patients with neurological COVID-19 disease, using standard case definitions. We invited authors of studies from the first pandemic wave, plus clinicians in the Global COVID-Neuro Network with unpublished data, to contribute. We analysed features associated with poor outcome (moderate to severe disability or death, 3 to 6 on the modified Rankin Scale) using multivariable models.ResultsWe included 83 studies (31 unpublished) providing IPD for 1979 patients with COVID-19 and acute new-onset neurological disease. Encephalopathy (978 [49%] patients) and cerebrovascular events (506 [26%]) were the most common diagnoses. Respiratory and systemic symptoms preceded neurological features in 93% of patients; one third developed neurological disease after hospital admission. A poor outcome was more common in patients with cerebrovascular events (76% [95% CI 67-82]), than encephalopathy (54% [42-65]). Intensive care use was high (38% [35-41]) overall, and also greater in the cerebrovascular patients. In the cerebrovascular, but not encephalopathic patients, risk factors for poor outcome included breathlessness on admission and elevated D-dimer. Overall, 30-day mortality was 30% [27-32]. The hazard of death was comparatively lower for patients in the WHO European region.InterpretationNeurological COVID-19 disease poses a considerable burden in terms of disease outcomes and use of hospital resources from prolonged intensive care and inpatient admission; preliminary data suggest these may differ according to WHO regions and country income levels. The different risk factors for encephalopathy and stroke suggest different disease mechanisms which may be amenable to intervention, especially in those who develop neurological symptoms after hospital admission

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

Identification et quantification de souches microbiennes dans des échantillons métagénomiques par utilisation de graphes de variations

Current studies are shifting from the use of single linear references to graph structures in order to represent multiple genomes. In parallel, resolving strain-level abundances within metagenomic samples is of growing interest for microbiome studies, as it would highlight new associations between strain variants and phenotypes that suggest major steps for diagnostic and therapeutic purposes. We developed StrainFLAIR that shows the use of variation graphs in this context by indexing highly similar genomic sequences as found with strains of a species, and we propose novel algorithmic solutions to identify and quantify strains in a set of sequenced genomes by querying this graph. We validated our approach first on simulated datasets which focused on a mixture of strains from a single species. The results show that StrainFLAIR was able to identify the present strains among the existing references, to detect new strains close to the existing references, and to estimate their relative abundances. We also validated StrainFLAIR on a mock composed of several species and strains. The results show again StrainFLAIR’s ability to profile correctly the sample even in this more complex configuration.Les études actuelles se tournent vers l’utilisation de graphes au lieu de références linéaires afin de représenter plusieurs génomes. En parallèle, calculer les abondances des souches dans des échantillons métagénomiques suscite un intérêt croissant. Cela permettrait de mettre en évidence de nouvelles associations entre souches et phénotypes ouvrant des avancées pour le diagnostique et thérapeutiques. Nous avons développé StrainFLAIR, démontrant l’utilisation de graphes de variations dans ce contexte en indexant des séquences génomiques similaires telles que retrouvées entre souches d’une même espèce, et nous proposons de nouvelles solutions algorithmiques afin d’identifier et quantifier les souches à partir d’un ensemble de génomes séquencés en requêtant le graphe. Nous avons validé notre approche sur des données simulées constituées d’un mélange de souches d’une seule espèce. Les résultats montrent que StrainFLAIR a pu identifier les souches présentes dans l’échantillon parmi les références utilisées, détecter la présence de nouvelles souches proches de ces références, et estimer les abondances de ces souches. Nous avons également validé notre approche sur un mock composé de plusieurs espèces et souches. Les résultats montrent à nouveau que StrainFLAIR a pu profiler correctement l’échantillon même dans une configuration plus complexe

Identification et quantification de souches microbiennes dans des échantillons métagénomiques par utilisation de graphes de variations

Les études actuelles se tournent vers l'utilisation de graphes au lieu de références linéaires afin de représenter plusieurs génomes. En parallèle, calculer les abondances des souches dans des échantillons métagénomiques suscite un intérêt croissant. Cela permettrait de mettre en évidence de nouvelles associations entre souches et phénotypes ouvrant des avancées pour le diagnostique et thérapeutiques. Nous avons développé StrainFLAIR, démontrant l'utilisation de graphes de variations dans ce contexte en indexant des séquences génomiques similaires telles que retrouvées entre souches d'une même espèce, et nous proposons de nouvelles solutions algorithmiques afin d'identifier et quantifier les souches à partir d'un ensemble de génomes séquencés en requêtant le graphe. Nous avons validé notre approche sur des données simulées constituées d'un mélange de souches d'une seule espèce. Les résultats montrent que StrainFLAIR a pu identifier les souches présentes dans l'échantillon parmi les références utilisées, détecter la présence de nouvelles souches proches de ces références, et estimer les abondances de ces souches. Nous avons également validé notre approche sur un mock composé de plusieurs espèces et souches. Les résultats montrent à nouveau que StrainFLAIR a pu profiler correctement l'échantillon même dans une configuration plus complexe.Current studies are shifting from the use of single linear references to graph structures in order to represent multiple genomes. In parallel, resolving strain-level abundances within metagenomic samples is of growing interest for microbiome studies, as it would highlight new associations between strain variants and phenotypes that suggest major steps for diagnostic and therapeutic purposes. We developed StrainFLAIR that shows the use of variation graphs in this context by indexing highly similar genomic sequences as found with strains of a species, and we propose novel algorithmic solutions to identify and quantify strains in a set of sequenced genomes by querying this graph. We validated our approach first on simulated datasets which focused on a mixture of strains from a single species. The results show that StrainFLAIR was able to identify the present strains among the existing references, to detect new strains close to the existing references, and to estimate their relative abundances. We also validated \tool on a mock composed of several species and strains. The results show again StrainFLAIR's ability to profile correctly the sample even in this more complex configuration

Identification et quantification de souches microbiennes dans des échantillons métagénomiques par utilisation de graphes de variations

Current studies are shifting from the use of single linear references to graph structures in order to represent multiple genomes. In parallel, resolving strain-level abundances within metagenomic samples is of growing interest for microbiome studies, as it would highlight new associations between strain variants and phenotypes that suggest major steps for diagnostic and therapeutic purposes. We developed StrainFLAIR that shows the use of variation graphs in this context by indexing highly similar genomic sequences as found with strains of a species, and we propose novel algorithmic solutions to identify and quantify strains in a set of sequenced genomes by querying this graph. We validated our approach first on simulated datasets which focused on a mixture of strains from a single species. The results show that StrainFLAIR was able to identify the present strains among the existing references, to detect new strains close to the existing references, and to estimate their relative abundances. We also validated StrainFLAIR on a mock composed of several species and strains. The results show again StrainFLAIR’s ability to profile correctly the sample even in this more complex configuration.Les études actuelles se tournent vers l’utilisation de graphes au lieu de références linéaires afin de représenter plusieurs génomes. En parallèle, calculer les abondances des souches dans des échantillons métagénomiques suscite un intérêt croissant. Cela permettrait de mettre en évidence de nouvelles associations entre souches et phénotypes ouvrant des avancées pour le diagnostique et thérapeutiques. Nous avons développé StrainFLAIR, démontrant l’utilisation de graphes de variations dans ce contexte en indexant des séquences génomiques similaires telles que retrouvées entre souches d’une même espèce, et nous proposons de nouvelles solutions algorithmiques afin d’identifier et quantifier les souches à partir d’un ensemble de génomes séquencés en requêtant le graphe. Nous avons validé notre approche sur des données simulées constituées d’un mélange de souches d’une seule espèce. Les résultats montrent que StrainFLAIR a pu identifier les souches présentes dans l’échantillon parmi les références utilisées, détecter la présence de nouvelles souches proches de ces références, et estimer les abondances de ces souches. Nous avons également validé notre approche sur un mock composé de plusieurs espèces et souches. Les résultats montrent à nouveau que StrainFLAIR a pu profiler correctement l’échantillon même dans une configuration plus complexe