CPR and DPANN Have an Overlooked Role in Corals' Microbial Community Structure.

Understanding how microbial communities are structured in coral holobionts is important to estimate local and global impacts and provide efficient environment management strategies. Several studies investigated the relationship between corals and their microbial communities, including the environmental drivers of shifts in this relationship, associated with diseases and coral cover loss. However, these studies are often geographically or taxonomically restricted and usually focused on the most abundant microbial groups, neglecting the rare biosphere, including archaea in the group DPANN and the recently discovered bacterial members of the candidate phyla radiation (CPR). Although it is known that rare microbes can play essential roles in several environments, we still lack understanding about which taxa comprise the rare biosphere of corals' microbiome. Here, we investigated the host-related and technical factors influencing coral microbial community structure and the importance of CPR and DPANN in this context by analyzing more than a hundred coral metagenomes from independent studies worldwide. We show that coral genera are the main biotic factor shaping coral microbial communities. We also detected several CPR and DPANN phyla comprising corals' rare biosphere for the first time and showed that they significantly contribute to shaping coral microbial communities.

Natural language processing in at-risk mental states: enhancing the assessment of thought disorders and psychotic traits with semantic dynamics and graph theory.

OBJECTIVE: Verbal communication has key information for mental health evaluation. Researchers have linked psychopathology phenomena to some of their counterparts in natural-language-processing (NLP). We study the characterization of subtle impairments presented in early stages of psychosis, developing new analysis techniques and a comprehensive map associating NLP features with the full range of clinical presentation. METHODS: We used NLP to assess elicited and free-speech of 60 individuals in at-risk-mental-states (ARMS) and 73 controls, screened from 4,500 quota-sampled Portuguese speaking citizens in Sao Paulo, Brazil. Psychotic symptoms were independently assessed with Structured-Interview-for-Psychosis-Risk-Syndromes (SIPS). Speech features (e.g.sentiments, semantic coherence), including novel ones, were correlated with psychotic traits (Spearman's-ρ) and ARMS status (general linear models and machine-learning ensembles). RESULTS: NLP features were informative inputs for classification, which presented 86% balanced accuracy. The NLP features brought forth (e.g. Semantic laminarity as 'perseveration', Semantic recurrence time as 'circumstantiality', average centrality in word repetition graphs) carried most information and also presented direct correlations with psychotic symptoms. Out of the standard measures, grammatical tagging (e.g. use of adjectives) was the most relevant. CONCLUSION: Subtle speech impairments can be grasped by sensitive methods and used for ARMS screening. We sketch a blueprint for speech-based evaluation, pairing features to standard thought disorder psychometric items.

Hyperscanning fNIRS data analysis using multiregression dynamic models: an illustration in a violin duo.

Introduction: Interpersonal neural synchronization (INS) demands a greater understanding of a brain's influence on others. Therefore, brain synchronization is an even more complex system than intrasubject brain connectivity and must be investigated. There is a need to develop novel methods for statistical inference in this context. Methods: In this study, motivated by the analysis of fNIRS hyperscanning data, which measure the activity of multiple brains simultaneously, we propose a two-step network estimation: Tabu search local method and global maximization in the selected subgroup [partial conditional directed acyclic graph (DAG) + multiregression dynamic model]. We illustrate this approach in a dataset of two individuals who are playing the violin together. Results: This study contributes new tools to the social neuroscience field, which may provide new perspectives about intersubject interactions. Our proposed approach estimates the best probabilistic network representation, in addition to providing access to the time-varying parameters, which may be helpful in understanding the brain-to-brain association of these two players. Discussion: The illustration of the violin duo highlights the time-evolving changes in the brain activation of an individual influencing the other one through a data-driven analysis. We confirmed that one player was leading the other given the ROI causal relation toward the other player.

Detecting at-risk mental states for psychosis (ARMS) using machine learning ensembles and facial features.

AIMS: Our study aimed to develop a machine learning ensemble to distinguish "at-risk mental states for psychosis" (ARMS) subjects from control individuals from the general population based on facial data extracted from video-recordings. METHODS: 58 non-help-seeking medication-naïve ARMS and 70 healthy subjects were screened from a general population sample. At-risk status was assessed with the Structured Interview for Prodromal Syndromes (SIPS), and "Subject's Overview" section was filmed (5-10 min). Several features were extracted, e.g., eye and mouth aspect ratio, Euler angles, coordinates from 51 facial landmarks. This elicited 649 facial features, which were further selected using Gradient Boosting Machines (AdaBoost combined with Random Forests). Data was split in 70/30 for training, and Monte Carlo cross validation was used. RESULTS: Final model reached 83 % of mean F1-score, and balanced accuracy of 85 %. Mean area under the curve for the receiver operator curve classifier was 93 %. Convergent validity testing showed that two features included in the model were significantly correlated with Avolition (SIPS N2 item) and expression of emotion (SIPS N3 item). CONCLUSION: Our model capitalized on short video-recordings from individuals recruited from the general population, effectively distinguishing between ARMS and controls. Results are encouraging for large-screening purposes in low-resource settings.

Gesticulation in individuals with at risk mental states for psychosis.

Nonverbal communication (NVC) is a complex behavior that involves different modalities that are impaired in the schizophrenia spectrum, including gesticulation. However, there are few studies that evaluate it in individuals with at-risk mental states (ARMS) for psychosis, mostly in developed countries. Given our prior findings of reduced movement during speech seen in Brazilian individuals with ARMS, we now aim to determine if this can be accounted for by reduced gesticulation behavior. Fifty-six medication-naïve ARMS and 64 healthy controls were filmed during speech tasks. The frequency of specifically coded gestures across four categories (and self-stimulatory behaviors) were compared between groups and tested for correlations with prodromal symptoms of the Structured Interview for Prodromal Syndromes (SIPS) and with the variables previously published. ARMS individuals showed a reduction in one gesture category, but it did not survive Bonferroni's correction. Gesture frequency was negatively correlated with prodromal symptoms and positively correlated with the variables of the amount of movement previously analyzed. The lack of significant differences between ARMS and control contradicts literature findings in other cultural context, in which a reduction is usually seen in at-risk individuals. However, gesture frequency might be a visual proxy of prodromal symptoms, and of other movement abnormalities. Results show the importance of analyzing NVC in ARMS and of considering different cultural and sociodemographic contexts in the search for markers of these states.

Ethical Implications of the Use of Language Analysis Technologies for the Diagnosis and Prediction of Psychiatric Disorders.

Recent developments in artificial intelligence technologies have come to a point where machine learning algorithms can infer mental status based on someone's photos and texts posted on social media. More than that, these algorithms are able to predict, with a reasonable degree of accuracy, future mental illness. They potentially represent an important advance in mental health care for preventive and early diagnosis initiatives, and for aiding professionals in the follow-up and prognosis of their patients. However, important issues call for major caution in the use of such technologies, namely, privacy and the stigma related to mental disorders. In this paper, we discuss the bioethical implications of using such technologies to diagnose and predict future mental illness, given the current scenario of swiftly growing technologies that analyze human language and the online availability of personal information given by social media. We also suggest future directions to be taken to minimize the misuse of such important technologies.

Use of a Bayesian Network Model to predict psychiatric illness in individuals with 'at risk mental states' from a general population cohort.

The 'at risk mental state' (ARMS) paradigm has been introduced in psychiatry to study prodromal phases of schizophrenia. With time it was seen that the ARMS state can also precede mental disorders other than schizophrenia, such as depression and anxiety. However, several problems hamper the paradigm's use in preventative medicine, such as varying transition rates across studies, the use of non-naturalistic samples, and the multifactorial nature of psychiatric disorders. To strengthen ARMS predictive power, there is a need for a holistic model incorporating-in an unbiased fashion-the small-effect factors that cause mental disorders. Bayesian networks, a probabilistic graphical model, was used in a populational cohort of 83 ARMS individuals to predict conversion to psychiatric illness. Nine predictors-including state, trait, biological and environmental factors-were inputted. Dopamine receptor 2 polymorphism, high private religiosity, and childhood trauma remained in the final model, which reached an 85.51% (SD = 0.1190) accuracy level in predicting conversion. This is the first time a robust model was produced with Bayesian networks to predict psychiatric illness among at risk individuals from the general population. This could be an important tool to strengthen predictive measures in psychiatry which should be replicated in larger samples to provide the model further learning.

Integrating Computational Methods to Investigate the Macroecology of Microbiomes.

Studies in microbiology have long been mostly restricted to small spatial scales. However, recent technological advances, such as new sequencing methodologies, have ushered an era of large-scale sequencing of environmental DNA data from multiple biomes worldwide. These global datasets can now be used to explore long standing questions of microbial ecology. New methodological approaches and concepts are being developed to study such large-scale patterns in microbial communities, resulting in new perspectives that represent a significant advances for both microbiology and macroecology. Here, we identify and review important conceptual, computational, and methodological challenges and opportunities in microbial macroecology. Specifically, we discuss the challenges of handling and analyzing large amounts of microbiome data to understand taxa distribution and co-occurrence patterns. We also discuss approaches for modeling microbial communities based on environmental data, including information on biological interactions to make full use of available Big Data. Finally, we summarize the methods presented in a general approach aimed to aid microbiologists in addressing fundamental questions in microbial macroecology, including classical propositions (such as "everything is everywhere, but the environment selects") as well as applied ecological problems, such as those posed by human induced global environmental changes.

IDS-COVID-19: índice de desigualdades sociais para a Covid-19: policy brief

O Índice de Desigualdades Sociais para a Covid-19 (IDS-COVID-19) foi construído com o intuito de medir os efeitos das desigualdades sociais no contexto da pandemia em 5.562 municípios brasileiros e em suas regiões de saúde. O objetivo foi compreender como diversos grupos populacionais no país foram atingidos de modo desigual pela Covid-19 e de quais maneiras os fatores socioeconômicos e sociodemográficos bem como a dificuldade de acesso aos serviços de saúde agravaram suas situações. Neste documento, entenda como esse Índice foi feito e como ele pode ser utilizado na construção e avaliação de políticas públicas em saúde.

Folder do IDS-COVID-19

O Índice de Desigualdades Sociais para a Covid-19 (IDS-COVID-19) foi construído para medir os efeitos das desigualdades sociais no contexto da pandemia em todos os municípios brasileiros. Seu objetivo é demonstrar como diversos grupos populacionais foram atingidos de modo desigual pela Covid-19 e de quais maneiras os fatores socioeconômicos, sociodemográficos e de dificuldade de acesso a serviços de saúde agravaram suas situações.

IDS-COVID-19: índice de desigualdades sociais para a Covid-19: guia rápido do painel de dados

Este guia tem como finalidade apresentar as informações principais sobre o painel de visualização de dados do Índice de Desigualdades Sociais para Covid-19 (IDS-COVID-19). Nesse documento será possível compreender as formas visuais que são apresentadas pelo painel e as etapas de construção dele. Além disso, o guia descreve as estratégias de disponibilização de acesso aos dados para o público interessado em fazer download.

IDS-COVID-19: índice de desigualdades sociais para a Covid-19: guia rápido dos padrões epidemiológicos de Covid-19

Este guia tem como finalidade apresentar como foram obtidos os padrões epidemiológicos por município no Índice de Desigualdades Sociais para Covid-19 (IDS-COVID-19). Para isso, apresenta o conceito de Padrão Epidemiológico, descreve a sua metodologia e explica os níveis dos padrões referentes à intensidade da pandemia nos municípios. Esse documento também cita quais resultados foram obtidos na análise da relação e os padrões epidemiológicos e justifica a necessidade de analisar padrões epidemiológicos municipais.

IDS-COVID-19: índice de desigualdades sociais para COVID-19: guia sobre o índice de desigualdades sociais para Covid-19: IDS-COVID-19

Este guia tem como finalidade apresentar o modo como foi construído o Índice de Desigualdades Sociais para Covid-19 (IDSCOVID-19), suas possíveis aplicações para analisar as desigualdades sociais em saúde durante a pandemia, seus limites e potencialidades de uso. Assim, este índice mede as desigualdades sociais em saúde associadas à Covid-19 nos municípios brasileiros, sendo uma ferramenta potencial para apoiar a tomada de decisão de gestores e grupos comunitários com relação à pandemia, bem como auxiliar em pesquisas e disseminação do conhecimento sobre Covid-19.