The Wasserstein Believer: Learning Belief Updates for Partially Observable Environments through Reliable Latent Space Models

Partially Observable Markov Decision Processes (POMDPs) are used to model environments where the full state cannot be perceived by an agent. As such the agent needs to reason taking into account the past observations and actions. However, simply remembering the full history is generally intractable due to the exponential growth in the history space. Maintaining a probability distribution that models the belief over what the true state is can be used as a sufficient statistic of the history, but its computation requires access to the model of the environment and is often intractable. While SOTA algorithms use Recurrent Neural Networks to compress the observation-action history aiming to learn a sufficient statistic, they lack guarantees of success and can lead to sub-optimal policies. To overcome this, we propose the Wasserstein Belief Updater, an RL algorithm that learns a latent model of the POMDP and an approximation of the belief update. Our approach comes with theoretical guarantees on the quality of our approximation ensuring that our outputted beliefs allow for learning the optimal value function

The respiratory microbiota alpha-diversity in chronic lung diseases: a systematic review and meta-analysis

International audienceImbalance in microbial composition (i.e. dysbiosis) in the gut microbiome is consensually considered an indicator of deteriorated health and has been associated to different chronic health conditions. However, there is no clear evidence how this generalizes to the other human microbiomes. Especially, researches on the relationship between respiratory microbiota imbalance and chronic lung diseases are recent whereas microbial colonization of the airways respiratory tract have characterized chronic lung diseases. Imbalance is mainly measured through the relative abundance of microbial species in space and time within a given community (i.e. alpha-diversity). Identifying a range of values in alpha-diversity when comparing exacerbated, stable patients and healthy subjects may lead to identify new biomarker in chronic respiratory diseases. In the present work, we propose a systematic review of studies investigating the lung microbiota alpha-diversity in patients with chronic respiratory diseases in which a control group based on disease status or healthy subjects is provided for comparison. We focused on the most common measures of alpha-diversity (Chao1, Shannon, and Simpson) indexes and the most common chronic diseases (asthma, chronic obstructive pulmonary disease –COPD-, cystic fibrosis –CF-, bronchiectasis, and pulmonary hypertension). Subsequently, we conducted a meta-analysis based on random-effects models using the R package metafor to characterize the difference in alpha-diversity indexes when comparing cases to controls. We also explored heterogeneity of sources and risk of bias though Factor Analysis of Mixed Data (FAMD) using the FactoMineR R package.After removing duplicate records, we screened 351 articles on title and abstract, of which 27 met our inclusion criteria for the systematic review. Finally, data from 25 studies were used in the meta-analysis. Eight studies deal with CF, 8 with COPD, 10 with asthma and 1 with bronchiectasis. All of the studies dedicated to the respiratory tract microbiota, mainly based on sputum samples analysis and, the majority of the studies used metataxonomy approaches. As highlighted by the meta-analysis, these metataxonomy methods exhibited numerous heterogeneities. Differences in alpha-diversity indexes between healthy and diseased people were observed only in some of the diseases studied. However, prudence is required in its interpretation because of substantial heterogeneity

The Nature of Barrenness in the Hebrew Bible

Explores some of the nuances of barrenness as disability in the Hebrew Bible, with the fundamental question in mind: what can we know from the biblical material about the reality of barrenness, and the treatment of barren women, in ancient Israel

The respiratory microbiome alpha-diversity and chronic respiratory diseases in children: A systematic review

Background and Aims: While there seems to be a consensus that a decrease in gut microbiome diversity is related to poorer health status, the associations between respiratory microbiome diversity and chronic respiratory disease remain a matter of debate, as highlighted by a recent review of the literature in adults. In industrialized countries, respiratory diseases and allergies are the most common chronic diseases in children. These diseases vary considerably between childhood and adulthood. We performed a systematic review of the alpha diversity of the respiratory microbiome in children with chronic respiratory diseases. We focused on studies in which a control allowed comparison.Methods: We searched PubMed and Scopus. Four items were required: respiratory microbiome, diversity, chronic respiratory disease, and children younger than 18 years. Any articles that did not address these items, were not research articles, or did not have a case-control design were excluded.Results: We reviewed 486 articles on the basis of title and abstract, of which 25 met our inclusion criteria. The diseases studied were mainly asthma, cystic fibrosis, wheezing, and respiratory allergies. The main sampling technique was the nasopharyngeal swab. All studies focused solely on bacteriome and measured the Shannon index. In more than half of the studies, the control group consisted of healthy subjects.Conclusions: The majority of diseases were significantly associated with a decrease in diversity, with the exception of asthma, which, on the contrary, led to an increase. Only the studies on bronchiectasis, pulmonary hypertension and bronchitis showed no change or not concluding difference in the respiratory microbiome diversity

New Insights in Microbial Species Predicting Lung Function Decline in CF: Lessons from the MucoFong Project

International audienceSeveral predictive models have been proposed to understand the microbial risk factors associated with cystic fibrosis (CF) progression. Very few have integrated fungal airways colonisation, which is increasingly recognized as a key player regarding CF progression. To assess the association between the percent predicted forced expiratory volume in 1 s (ppFEV1) change and the fungi or bacteria identified in the sputum, 299 CF patients from the “MucoFong” project were included and followed-up with over two years. The relationship between the microorganisms identified in the sputum and ppFEV1 course of patients was longitudinally analysed. An adjusted linear mixed model analysis was performed to evaluate the effect of a transient or chronic bacterial and/or fungal colonisation at inclusion on the ppFEV1 change over a two-year period. Pseudomonas aeruginosa, Achromobacter xylosoxidans, Stenotrophomonas maltophilia, and Candida albicans were associated with a significant ppFEV1 decrease. No significant association was found with other fungal colonisations. In addition, the ppFEV1 outcome in our model was 11.26% lower in patients presenting with a transient colonisation with non-pneumoniae Streptococcus species compared to other patients. These results confirm recently published data and provide new insights into bacterial and fungal colonisation as key factors for the assessment of lung function decline in CF patients

Sci Rep

Lung infections play a critical role in cystic fibrosis (CF) pathogenesis. CF respiratory tract is now considered to be a polymicrobial niche and advances in high-throughput sequencing allowed to analyze its microbiota and mycobiota. However, no NGS studies until now have characterized both communities during CF pulmonary exacerbation (CFPE). Thirty-three sputa isolated from patients with and without CFPE were used for metagenomic high-throughput sequencing targeting 16S and ITS2 regions of bacterial and fungal rRNA. We built inter-kingdom network and adapted Phy-Lasso method to highlight correlations in compositional data. The decline in respiratory function was associated with a decrease in bacterial diversity. The inter-kingdom network revealed three main clusters organized around Aspergillus, Candida, and Scedosporium genera. Using Phy-Lasso method, we identified Aspergillus and Malassezia as relevantly associated with CFPE, and Scedosporium plus Pseudomonas with a decline in lung function. We corroborated in vitro the cross-domain interactions between Aspergillus and Streptococcus predicted by the correlation network. For the first time, we included documented mycobiome data into a version of the ecological Climax/Attack model that opens new lines of thoughts about the physiopathology of CF lung disease and future perspectives to improve its therapeutic management