Professional practices of dentists and unscheduled dental care in Nouvelle-Aquitaine (France) / Pratiques professionnelles et soins dentaires non programmés en Nouvelle-Aquitaine.

In a context of saturation of private dental practices and medical demography issues, responses to requests for emergency dental care are a poorly documented problem. In partnership with the Observatoire Regional de la Santé, the URPS Chirurgiens-Dentistes Nouvelle-Aquitaine, a union, conducted a survey of private dentists in May and June 2022. The objective was to estimate the volume of requests for unscheduled dental care and to describe the responses provided by professionals. More than eight out of ten professionals said they were often called upon for unscheduled care and more than four out of ten set aside specific time slots to provide it. More than a quarter of them said they provided care in 90 percent of cases, in response to requests of this type, and 40 percent provided care in at least half of the cases. For most professionals, the average waiting time for patients requesting unscheduled care was less than 24 hours. Respondents cited patient education as a general avenue for improvement, in addition to the creation of a specific pricing structure for unscheduled care. This survey provides a better understanding of the difficulties faced by professionals on a subject not yet investigated by the dental profession. It documents the acceptability of possible responses in terms of improving professional practices and institutional organizations.

Dans un contexte de saturation des cabinets dentaires libéraux et de démographie médicale tendue, l'apport de réponses aux demandes de soins dentaires non programmés constitue une réelle problématique assez peu documentée. En partenariat avec l'Observatoire régional de la santé, l'URPS Chirurgiens-dentistes Nouvelle-Aquitaine a mené en mai-juin 2022 une enquête auprès de chirurgiens-dentistes libéraux. L'objectif était d'estimer le volume des demandes de soins non programmés en soins dentaires et de décrire les réponses apportées par les professionnels. Plus de huit professionnels sur dix ont déclaré être souvent sollicités pour des soins non programmés, et plus de quatre sur dix prévoyaient des créneaux spécifiques pour les assurer. Plus d'un quart d'entre eux ont déclaré répondre à 90 % des sollicitations pour ce type de soins et 40 % répondre à moins de la moitié des demandes. Les soins non programmés étaient pris en charge dans les 24 heures en moyenne pour la majorité des professionnels. L'éducation des patients a été citée comme une piste d'amélioration générale ou institutionnelle, devant la création d'une cotation spécifique pour les soins non programmés. Cette enquête permet de mieux connaître les difficultés des professionnels sur un sujet non encore investigué auprès de la profession dentaire. Elle documente l'acceptabilité de pistes de réponses pouvant être apportées pour améliorer les pratiques professionnelles et les organisations institutionnelles.

Microbial biomarkers of tree water status for next-generation biomonitoring of forest ecosystems.

Next-generation biomonitoring proposes to combine machine-learning algorithms with environmental DNA data to automate the monitoring of the Earth's major ecosystems. In the present study, we searched for molecular biomarkers of tree water status to develop next-generation biomonitoring of forest ecosystems. Because phyllosphere microbial communities respond to both tree physiology and climate change, we investigated whether environmental DNA data from tree phyllosphere could be used as molecular biomarkers of tree water status in forest ecosystems. Using an amplicon sequencing approach, we analysed phyllosphere microbial communities of four tree species (Quercus ilex, Quercus robur, Pinus pinaster and Betula pendula) in a forest experiment composed of irrigated and non-irrigated plots. We used these microbial community data to train a machine-learning algorithm (Random Forest) to classify irrigated and non-irrigated trees. The Random Forest algorithm detected tree water status from phyllosphere microbial community composition with more than 90% accuracy for oak species, and more than 75% for pine and birch. Phyllosphere fungal communities were more informative than phyllosphere bacterial communities in all tree species. Seven fungal amplicon sequence variants were identified as candidates for the development of molecular biomarkers of water status in oak trees. Altogether, our results show that microbial community data from tree phyllosphere provides information on tree water status in forest ecosystems and could be included in next-generation biomonitoring programmes that would use in situ, real-time sequencing of environmental DNA to help monitor the health of European temperate forest ecosystems.

[Psychological state of Covid-19 patients after resuscitation]. / État psychologique des patients Covid-19 après réanimation.

The epidemic of Covid-19 was characterized, from its beginning, by "emergency". A state of emergency enacted by the state authorities to fight, on one hand, against the pandemic as such and, on the other hand, to manage the influx of patients admitted in intensive care. In this unprecedented context, the suffering of the people goes beyond the emergency situation and persists in forms ranging from a pseudo-banality to the complexity of an insidious evolution.

Studying speciation and extinction dynamics from phylogenies: addressing identifiability issues.

A lot of what we know about past speciation and extinction dynamics is based on statistically fitting birth-death processes to phylogenies of extant species. Despite their wide use, the reliability of these tools is regularly questioned. It was recently demonstrated that vast 'congruent' sets of alternative diversification histories cannot be distinguished (i.e., are not identifiable) using extant phylogenies alone, reanimating the debate about the limits of phylogenetic diversification analysis. Here, we summarize what we know about the identifiability of the birth-death process and how identifiability issues can be addressed. We conclude that extant phylogenies, when combined with appropriate prior hypotheses and regularization techniques, can still tell us a lot about past diversification dynamics.

Querying multiple sets of P-values through composed hypothesis testing.

MOTIVATION: Combining the results of different experiments to exhibit complex patterns or to improve statistical power is a typical aim of data integration. The starting point of the statistical analysis often comes as a set of P-values resulting from previous analyses, that need to be combined flexibly to explore complex hypotheses, while guaranteeing a low proportion of false discoveries. RESULTS: We introduce the generic concept of composed hypothesis, which corresponds to an arbitrary complex combination of simple hypotheses. We rephrase the problem of testing a composed hypothesis as a classification task and show that finding items for which the composed null hypothesis is rejected boils down to fitting a mixture model and classifying the items according to their posterior probabilities. We show that inference can be efficiently performed and provide a thorough classification rule to control for type I error. The performance and the usefulness of the approach are illustrated in simulations and on two different applications. The method is scalable, does not require any parameter tuning, and provided valuable biological insight on the considered application cases. AVAILABILITY AND IMPLEMENTATION: The QCH methodology is available in the qch package hosted on CRAN. Additionally, R codes to reproduce the Einkorn example are available on the personal webpage of the first author: https://www6.inrae.fr/mia-paris/Equipes/Membres/Tristan-Mary-Huard. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Joint species distributions reveal the combined effects of host plants, abiotic factors and species competition as drivers of species abundances in fruit flies.

The relative importance of ecological factors and species interactions for shaping species distributions is still debated. The realised niches of eight sympatric tephritid fruit flies were inferred from field abundance data using joint species distribution modelling and network inference, on the whole community and separately on three host plant groups. These estimates were then confronted the fundamental niches of seven fly species estimated through laboratory-measured fitnesses on host plants. Species abundances depended on host plants, followed by climatic factors, with a dose of competition between species sharing host plants. The relative importance of these factors mildly changed among the three host plant groups. Despite overlapping fundamental niches, specialists and generalists had almost distinct realised niches, with possible competitive exclusion of generalists by specialists on Cucurbitaceae. They had different assembly rules: Specialists were mainly influenced by their adaptation to host plants, while generalist abundances varied regardless of their fundamental host use.

SimkaMin: fast and resource frugal de novo comparative metagenomics.

MOTIVATION: De novo comparative metagenomics is one of the most straightforward ways to analyze large sets of metagenomic data. Latest methods use the fraction of shared k-mers to estimate genomic similarity between read sets. However, those methods, while extremely efficient, are still limited by computational needs for practical usage outside of large computing facilities. RESULTS: We present SimkaMin, a quick comparative metagenomics tool with low disk and memory footprints, thanks to an efficient data subsampling scheme used to estimate Bray-Curtis and Jaccard dissimilarities. One billion metagenomic reads can be analyzed in <3 min, with tiny memory (1.09 GB) and disk (≈0.3 GB) requirements and without altering the quality of the downstream comparative analyses, making of SimkaMin a tool perfectly tailored for very large-scale metagenomic projects. AVAILABILITY AND IMPLEMENTATION: https://github.com/GATB/simka. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Variational Inference for Coupled Hidden Markov Models Applied to the Joint Detection of Copy Number Variations.

Hidden Markov models provide a natural statistical framework for the detection of the copy number variations (CNV) in genomics. In this context, we define a hidden Markov process that underlies all individuals jointly in order to detect and to classify genomics regions in different states (typically, deletion, normal or amplification). Structural variations from different individuals may be dependent. It is the case in agronomy where varietal selection program exists and species share a common phylogenetic past. We propose to take into account these dependencies inthe HMM model. When dealing with a large number of series, maximum likelihood inference (performed classically using the EM algorithm) becomes intractable. We thus propose an approximate inference algorithm based on a variational approach (VEM), implemented in the CHMM R package. A simulation study is performed to assess the performance of the proposed method and an application to the detection of structural variations in plant genomes is presented.

A variable selection approach in the multivariate linear model: an application to LC-MS metabolomics data.

Omic data are characterized by the presence of strong dependence structures that result either from data acquisition or from some underlying biological processes. Applying statistical procedures that do not adjust the variable selection step to the dependence pattern may result in a loss of power and the selection of spurious variables. The goal of this paper is to propose a variable selection procedure within the multivariate linear model framework that accounts for the dependence between the multiple responses. We shall focus on a specific type of dependence which consists in assuming that the responses of a given individual can be modelled as a time series. We propose a novel Lasso-based approach within the framework of the multivariate linear model taking into account the dependence structure by using different types of stationary processes covariance structures for the random error matrix. Our numerical experiments show that including the estimation of the covariance matrix of the random error matrix in the Lasso criterion dramatically improves the variable selection performance. Our approach is successfully applied to an untargeted LC-MS (Liquid Chromatography-Mass Spectrometry) data set made of African copals samples. Our methodology is implemented in the R package MultiVarSel which is available from the Comprehensive R Archive Network (CRAN).

Inference of Adaptive Shifts for Multivariate Correlated Traits.

To study the evolution of several quantitative traits, the classical phylogenetic comparative framework consists of a multivariate random process running along the branches of a phylogenetic tree. The Ornstein-Uhlenbeck (OU) process is sometimes preferred to the simple Brownian motion (BM) as it models stabilizing selection toward an optimum. The optimum for each trait is likely to be changing over the long periods of time spanned by large modern phylogenies. Our goal is to automatically detect the position of these shifts on a phylogenetic tree, while accounting for correlations between traits, which might exist because of structural or evolutionary constraints. We show that, in the presence of shifts, phylogenetic Principal Component Analysis fails to decorrelate traits efficiently, so that any method aiming at finding shifts needs to deal with correlation simultaneously. We introduce here a simplification of the full multivariate OU model, named scalar OU, which allows for noncausal correlations and is still computationally tractable. We extend the equivalence between the OU and a BM on a rescaled tree to our multivariate framework. We describe an Expectation-Maximization (EM) algorithm that allows for a maximum likelihood estimation of the shift positions, associated with a new model selection criterion, accounting for the identifiability issues for the shift localization on the tree. The method, freely available as an R-package (PhylogeneticEM) is fast, and can deal with missing values. We demonstrate its efficiency and accuracy compared to another state-of-the-art method ($\ell$1ou) on a wide range of simulated scenarios and use this new framework to reanalyze recently gathered data sets on New World Monkeys and Anolis lizards.

Screening for adolescent suicidality in primary care: the bullying-insomnia-tobacco-stress test. A population-based pilot study.

AIM: Adolescents at risk for suicide often see their general practitioner solely for somatic or administrative reasons. A simple screening test given during a conversation would be of substantial help to send a signal and tackle the problem. We propose to update a screening test previously validated in France - the TSTS-Cafard - because of significant changes in the lives of adolescents with the growth of the cyber world since 2000. METHODS: The design and setting was a cross-sectional study involving 912 15-year-old adolescents in 90 French schools. They completed a questionnaire that included the TSTS-Cafard and risk factors extracted from the Health Behaviour in School-Aged Children survey. To improve the test, we selected questions drawn from the recent literature. Answers were analysed according to 'suicidality' = at least one suicide attempt in life or suicidal ideation often over the past 12 months. RESULTS: Suicidality rates were 9.6% for boys and 23.1% for girls. Although the TSTS-Cafard test was generally effective, one question was no longer discriminating. A new test, entitled 'BITS', included only four questions on bullying, insomnia, tobacco and stress, with three levels of response and scores ranging from 0 to 8. Improvement was achieved without loss of performance. Using a cut-off score of 3, we achieved 78% accuracy (area under the curve), 75% sensitivity and 70% specificity. CONCLUSION: The BITS test could allow the question of suicide risk to be addressed during a routine check-up in primary care but the results need to be validated with 13 to 18-year olds.

SegCorr a statistical procedure for the detection of genomic regions of correlated expression.

BACKGROUND: Detecting local correlations in expression between neighboring genes along the genome has proved to be an effective strategy to identify possible causes of transcriptional deregulation in cancer. It has been successfully used to illustrate the role of mechanisms such as copy number variation (CNV) or epigenetic alterations as factors that may significantly alter expression in large chromosomal regions (gene silencing or gene activation). RESULTS: The identification of correlated regions requires segmenting the gene expression correlation matrix into regions of homogeneously correlated genes and assessing whether the observed local correlation is significantly higher than the background chromosomal correlation. A unified statistical framework is proposed to achieve these two tasks, where optimal segmentation is efficiently performed using dynamic programming algorithm, and detection of highly correlated regions is then achieved using an exact test procedure. We also propose a simple and efficient procedure to correct the expression signal for mechanisms already known to impact expression correlation. The performance and robustness of the proposed procedure, called SegCorr, are evaluated on simulated data. The procedure is illustrated on cancer data, where the signal is corrected for correlations caused by copy number variation. It permitted the detection of regions with high correlations linked to epigenetic marks like DNA methylation. CONCLUSIONS: SegCorr is a novel method that performs correlation matrix segmentation and applies a test procedure in order to detect highly correlated regions in gene expression.

Modelling the influence of dimerisation sequence dissimilarities on the auxin signalling network.

BACKGROUND: Auxin is a major phytohormone involved in many developmental processes by controlling gene expression through a network of transcriptional regulators. In Arabidopsis thaliana, the auxin signalling network is made of 52 potentially interacting transcriptional regulators, activating or repressing gene expression. All the possible interactions were tested in two-way yeast-2-hybrid experiments. Our objective was to characterise this auxin signalling network and to quantify the influence of the dimerisation sequence dissimilarities on the interaction between transcriptional regulators. RESULTS: We applied model-based graph clustering methods relying on connectivity profiles between transcriptional regulators. Incorporating dimerisation sequence dissimilarities as explanatory variables, we modelled their influence on the auxin network topology using mixture of linear models for random graphs. Our results provide evidence that the network can be simplified into four groups, three of them being closely related to biological groups. We found that these groups behave differently, depending on their dimerisation sequence dissimilarities, and that the two dimerisation sub-domains might play different roles. CONCLUSIONS: We propose here the first pipeline of statistical methods combining yeast-2-hybrid data and protein sequence dissimilarities for analysing protein-protein interactions. We unveil using this pipeline of analysis the transcriptional regulator interaction modes.

Modeling overdispersion heterogeneity in differential expression analysis using mixtures.

Next-generation sequencing technologies now constitute a method of choice to measure gene expression. Data to analyze are read counts, commonly modeled using negative binomial distributions. A relevant issue associated with this probabilistic framework is the reliable estimation of the overdispersion parameter, reinforced by the limited number of replicates generally observable for each gene. Many strategies have been proposed to estimate this parameter, but when differential analysis is the purpose, they often result in procedures based on plug-in estimates, and we show here that this discrepancy between the estimation framework and the testing framework can lead to uncontrolled type-I errors. Instead, we propose a mixture model that allows each gene to share information with other genes that exhibit similar variability. Three consistent statistical tests are developed for differential expression analysis. We show through a wide simulation study that the proposed method improves the sensitivity of detecting differentially expressed genes with respect to the common procedures, since it reaches the nominal value for the type-I error, while keeping elevate discriminative power between differentially and not differentially expressed genes. The method is finally illustrated on prostate cancer RNA-Seq data.

Network impact on persistence in a finite population dynamic diffusion model: application to an emergent seed exchange network.

Dynamic extinction colonisation models (also called contact processes) are widely studied in epidemiology and in metapopulation theory. Contacts are usually assumed to be possible only through a network of connected patches. This network accounts for a spatial landscape or a social organization of interactions. Thanks to social network literature, heterogeneous networks of contacts can be considered. A major issue is to assess the influence of the network in the dynamic model. Most work with this common purpose uses deterministic models or an approximation of a stochastic Extinction-Colonisation model (sEC) which are relevant only for large networks. When working with a limited size network, the induced stochasticity is essential and has to be taken into account in the conclusions. Here, a rigorous framework is proposed for limited size networks and the limitations of the deterministic approximation are exhibited. This framework allows exact computations when the number of patches is small. Otherwise, simulations are used and enhanced by adapted simulation techniques when necessary. A sensitivity analysis was conducted to compare four main topologies of networks in contrasting settings to determine the role of the network. A challenging case was studied in this context: seed exchange of crop species in the Réseau Semences Paysannes (RSP), an emergent French farmers׳ organisation. A stochastic Extinction-Colonisation model was used to characterize the consequences of substantial changes in terms of RSP׳s social organization on the ability of the system to maintain crop varieties.

Segmentor3IsBack: an R package for the fast and exact segmentation of Seq-data.

BACKGROUND: Change point problems arise in many genomic analyses such as the detection of copy number variations or the detection of transcribed regions. The expanding Next Generation Sequencing technologies now allow to locate change points at the nucleotide resolution. RESULTS: Because of its complexity which is almost linear in the sequence length when the maximal number of segments is constant, and as its performance had been acknowledged for microarrays, we propose to use the Pruned Dynamic Programming algorithm for Seq-experiment outputs. This requires the adaptation of the algorithm to the negative binomial distribution with which we model the data. We show that if the dispersion in the signal is known, the PDP algorithm can be used, and we provide an estimator for this dispersion. We describe a compression framework which reduces the time complexity without modifying the accuracy of the segmentation. We propose to estimate the number of segments via a penalized likelihood criterion. We illustrate the performance of the proposed methodology on RNA-Seq data. CONCLUSIONS: We illustrate the results of our approach on a real dataset and show its good performance. Our algorithm is available as an R package on the CRAN repository.

Genomic instability: a stronger prognostic marker than proliferation for early stage luminal breast carcinomas.

BACKGROUND: The accurate prognosis definition to tailor treatment for early luminal invasive breast carcinoma patients remains challenging. MATERIALS AND METHODS: Two hundred fourteen early luminal breast carcinomas were genotyped with single nucleotide polymorphisms (SNPs) array to determine the number of chromosomal breakpoints as a marker of genomic instability. Proliferation was assessed by KI67 (immunohistochemistry) and genomic grade index (transcriptomic analysis). IHC3 (IHC4 score for HER2 negative tumors) was also determined. RESULTS: In the training set (109 cases), the optimal cut-off was 34 breakpoints with a specificity of 0.94 and a sensitivity of 0.57 (Area under the curve (AUC): 0.81[0.71; 0.91]). In the validation set (105 cases), the outcome of patients with > 34 breakpoints (11 events / 22 patients) was poorer (logrank test p < 0.001; Relative Risk (RR): 3.7 [1.73; 7.92]), than that of patients with < 34 breakpoints (19 events / 83 patients).Whereas genomic grade and KI67 had a significant prognostic value in univariate analysis in contrast to IHC3 that failed to have a statistical significant prognostic value in this series, the number of breakpoints remained the only significant parameter predictive of outcome (RR: 3.47, Confidence Interval (CI [1.29; 9.31], p = 0.014)) in multivariate analysis . CONCLUSION: Genomic instability, defined herein as a high number of chromosomal breakpoints, in early stage luminal breast carcinoma is a stronger prognostic marker than proliferation.

ExactDAS: an exact test procedure for the detection of differential alternative splicing in microarray experiments.

The aim of this paper is to propose a test procedure for the detection of differential alternative splicing across conditions for tiling array or exon chip data. While developed in a mixed model framework, the test procedure is exact (avoiding computational burden) and applicable to a large variety of contrasts, including several previously published ones. A simulation study is presented to evaluate the robustness and performance of the method. It is found to have a good detection power of genes under differential alternative splicing, even for five biological replicates and four probes per exon. The methodology also enables the comparison of various experimental designs through exact power curves. This is illustrated with the comparison of paired and unpaired experiments. The test procedure was applied to two publicly available cancer data sets based on exon arrays, and showed promising results.

Integrative epigenomic mapping defines four main chromatin states in Arabidopsis.

Post-translational modification of histones and DNA methylation are important components of chromatin-level control of genome activity in eukaryotes. However, principles governing the combinatorial association of chromatin marks along the genome remain poorly understood. Here, we have generated epigenomic maps for eight histone modifications (H3K4me2 and 3, H3K27me1 and 2, H3K36me3, H3K56ac, H4K20me1 and H2Bub) in the model plant Arabidopsis and we have combined these maps with others, produced under identical conditions, for H3K9me2, H3K9me3, H3K27me3 and DNA methylation. Integrative analysis indicates that these 12 chromatin marks, which collectively cover ∼90% of the genome, are present at any given position in a very limited number of combinations. Moreover, we show that the distribution of the 12 marks along the genomic sequence defines four main chromatin states, which preferentially index active genes, repressed genes, silent repeat elements and intergenic regions. Given the compact nature of the Arabidopsis genome, these four indexing states typically translate into short chromatin domains interspersed with each other. This first combinatorial view of the Arabidopsis epigenome points to simple principles of organization as in metazoans and provides a framework for further studies of chromatin-based regulatory mechanisms in plants.