Human liver microbiota modeling strategy at the early onset of fibrosis.

BACKGROUND: Gut microbiota is involved in the development of liver diseases such as fibrosis. We and others identified that selected sets of gut bacterial DNA and bacteria translocate to tissues, notably the liver, to establish a non-infectious tissue microbiota composed of microbial DNA and a low frequency live bacteria. However, the precise set of bacterial DNA, and thereby the corresponding taxa associated with the early stages of fibrosis need to be identified. Furthermore, to overcome the impact of different group size and patient origins we adapted innovative statistical approaches. Liver samples with low liver fibrosis scores (F0, F1, F2), to study the early stages of the disease, were collected from Romania(n = 36), Austria(n = 10), Italy(n = 19), and Spain(n = 17). The 16S rRNA gene was sequenced. We considered the frequency, sparsity, unbalanced sample size between cohorts to identify taxonomic profiles and statistical differences. RESULTS: Multivariate analyses, including adapted spectral clustering with L1-penalty fair-discriminant strategies, and predicted metagenomics were used to identify that 50% of liver taxa associated with the early stage fibrosis were Enterobacteriaceae, Pseudomonadaceae, Xanthobacteriaceae and Burkholderiaceae. The Flavobacteriaceae and Xanthobacteriaceae discriminated between F0 and F1. Predicted metagenomics analysis identified that the preQ0 biosynthesis and the potential pathways involving glucoryranose and glycogen degradation were negatively associated with liver fibrosis F1-F2 vs F0. CONCLUSIONS: Without demonstrating causality, our results suggest first a role of bacterial translocation to the liver in the progression of fibrosis, notably at the earliest stages. Second, our statistical approach can identify microbial signatures and overcome issues regarding sample size differences, the impact of environment, and sets of analyses. TRIAL REGISTRATION: TirguMECCH ROLIVER Prospective Cohort for the Identification of Liver Microbiota, registration 4065/2014. Registered 01 01 2014.

Influence of SARS-CoV-2 Variant B.1.1.7, Vaccination, and Public Health Measures on the Spread of SARS-CoV-2.

The spread of SARS-CoV-2 and the resulting disease COVID-19 has killed over 2.6 million people as of 18 March 2021. We have used a modified susceptible, infected, recovered (SIR) epidemiological model to predict how the spread of the virus in regions of France will vary depending on the proportions of variants and on the public health strategies adopted, including anti-COVID-19 vaccination. The proportion of SARS-CoV-2 variant B.1.1.7, which was not detected in early January, increased to become 60% of the forms of SARS-CoV-2 circulating in the Toulouse urban area at the beginning of February 2021, but there was no increase in positive nucleic acid tests. Our prediction model indicates that maintaining public health measures and accelerating vaccination are efficient strategies for the sustained control of SARS-CoV-2.

The real seroprevalence of SARS-CoV-2 in France and its consequences for virus dynamics.

The SARS-CoV-2 virus has spread world-wide since December 2019, killing more than 2.9 million of people. We have adapted a statistical model from the SIR epidemiological models to predict the spread of SARS-CoV-2 in France. Our model is based on several parameters and assumed a 4.2% seroprevalence in Occitania after the first lockdown. The recent use of serological tests to measure the effective seroprevalence of SARS-CoV-2 in the population of Occitania has led to a seroprevalence around 2.4%. This implies to review the parameters of our model to conclude at a lower than expected virus transmission rate, which may be due to infectivity varying with the patient's symptoms or to a constraint due to an uneven population geographical distribution.

Estimating the impact of public health strategies on the spread of SARS-CoV-2: Epidemiological modelling for Toulouse, France.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the resulting disease COVID-19 has killed over 2 million people as of 22 January 2021. We have used a modified susceptible, infected, recovered epidemiological model to predict how the spread of the virus in France will vary depending on the public health strategies adopted, including anti-COVID-19 vaccination. Our prediction model indicates that the French authorities' adoption of a gradual release from lockdown could lead in March 2021 to a virus prevalence similar to that before lockdown. However, a massive vaccination campaign initiated in January 2021 and the continuation of public health measures over several months could curb the spread of virus and thus relieve the load on hospitals.

Side effect of a 6 p.m curfew for preventing the spread of SARS-CoV-2: A modeling study from Toulouse, France.

The spread of SARS-CoV-2 and the resulting disease Covid-19 has killed over 2 million people as of January 22, 2021. We have designed a model and used it to quantify the effect of a 6 p.m curfew on the SARS-CoV-2 epidemic in Toulouse, France. The data show that this measure can lead to the opposite effect from that intended due to larger groups of people on the authorized hours.

Quantifying the impact of public health protection measures on the spread of SARS-CoV-2.

The new virus, SARS-CoV-2, has probably affected millions of people world-wide since December 2019 and killed thousand. We have designed a model and used it to quantify the effect of local protective measures on the SARS-CoV-2 epidemic, assess their effectiveness and adapt health service strategies in Toulouse, France.

optimalFlow: optimal transport approach to flow cytometry gating and population matching.

BACKGROUND: Data obtained from flow cytometry present pronounced variability due to biological and technical reasons. Biological variability is a well-known phenomenon produced by measurements on different individuals, with different characteristics such as illness, age, sex, etc. The use of different settings for measurement, the variation of the conditions during experiments and the different types of flow cytometers are some of the technical causes of variability. This mixture of sources of variability makes the use of supervised machine learning for identification of cell populations difficult. The present work is conceived as a combination of strategies to facilitate the task of supervised gating. RESULTS: We propose optimalFlowTemplates, based on a similarity distance and Wasserstein barycenters, which clusters cytometries and produces prototype cytometries for the different groups. We show that supervised learning, restricted to the new groups, performs better than the same techniques applied to the whole collection. We also present optimalFlowClassification, which uses a database of gated cytometries and optimalFlowTemplates to assign cell types to a new cytometry. We show that this procedure can outperform state of the art techniques in the proposed datasets. Our code is freely available as optimalFlow, a Bioconductor R package at https://bioconductor.org/packages/optimalFlow . CONCLUSIONS: optimalFlowTemplates + optimalFlowClassification addresses the problem of using supervised learning while accounting for biological and technical variability. Our methodology provides a robust automated gating workflow that handles the intrinsic variability of flow cytometry data well. Our main innovation is the methodology itself and the optimal transport techniques that we apply to flow cytometry analysis.

The SARS-CoV-2 seroprevalence is the key factor for deconfinement in France.

A new virus, SARS-CoV-2, has spread world-wide since December 2019, probably affecting millions of people and killing thousands. Failure to anticipate the spread of the virus now seriously threatens many health systems. We have designed a model for predicting the evolution of the SARS-CoV-2 epidemic in France, which is based on seroprevalence and makes it possible to anticipate the deconfinement strategy.

Oral health and microbiota status in professional rugby players: A case-control study.

OBJECTIVE: Elite athletes are prone to develop oral diseases, which could increase the risk for injuries. The aim of this study was to evaluate the oral health and the composition of oral microbiota of elite rugby players compared to the general population. METHODS: We set up a case-control study by screening 24 professional rugby players (PRG) and 22 control patients (CG) for dental and gingival examinations and performed a taxonomic analysis and a predicted functional analysis of oral microbiota. RESULTS: The Decay, Missing and Filled (DMF) teeth index (5.54 ± 6.18 versus 2.14 ± 3.01; p = 0.01) and the frequency of gingivitis (58,33% versus 13.63%) were significantly increased in PRG compared to CG. PRG were characterized by a dysbiotic oral microbiota (Shannon Index: 3.32 ± 0.62 in PRG versus 3.79 ± 0.68 in CG; p = 0.03) with an increase of Streptococcus (58.43 ± 16.84 versus 42.60 ± 17.45; p = 0.005), the main genus implicated in caries. Predicted metagenomics of oral microbiota in rugby players was suggestive of a cariogenic metagenome favourable to the development of caries. CONCLUSIONS: Our study shows that the oral health of PRG was poorer than the general population. PRG are characterized by a dysbiotic oral microbiota with an increase of the relative abundance of Streptococcus genus, positively correlated to the weight and negatively correlated to the diversity of oral microbiota. CLINICAL SIGNIFICANCE: Dental screening should be included in the medical follow-up of professional rugby players as a part of their health management. New strategies such as using probiotics like Lactobacillus could help to control the dysbiosis of oral microbiota.

A new partial temporal bone of a juvenile hominin from the site of Kromdraai B (South Africa).

The site of Kromdraai B (KB) (Gauteng, South Africa) has yielded a minimum number of nine hominins including the type specimen of Paranthropus robustus (TM 1517), the only partial skeleton of this species known to date. Four of these individuals are juveniles, one is a subadult and four are young adults. They all occur with a macrofaunal assemblage spread across the succession of at least two time periods that occurred in South Africa approximately two million years ago. Here we report on an additional, newly discovered petrous temporal bone of a juvenile hominin, KB 6067. Following the description of KB 6067, we assess its affinities with Australopithecus africanus, P. robustus and early Homo. We discuss its developmental age and consider its association with other juvenile hominin specimens found at Kromdraai B. KB 6067 probably did not reach five years of age and in bony labyrinth morphology it is close to P. robustus, but also to StW 53, a specimen with uncertain affinities. However, its cochlear and oval window size are closer to some hominin specimens from Sterkfontein Member 4 and if KB 6067 is indeed P. robustus this may represent a condition that is evolutionarily less derived than that shown by TM 1517 and other conspecifics sampled so far. The ongoing fieldwork at KB, as well as the petrography and geochemistry of its deposits, will help to determine when the various KB breccias accumulated, and how time may be an important factor underlying the variation seen among KB 6067 and the rest of the fossil hominin sample from this site.

Statistical properties of the quantile normalization method for density curve alignment.

The article investigates the large sample properties of the quantile normalization method by Bolstad et al. (2003) [4] which has become one of the most popular methods to align density curves in microarray data analysis. We prove consistency of this method which is viewed as a particular case of the structural expectation procedure for curve alignment, which corresponds to a notion of barycenter of measures in the Wasserstein space. Moreover, we show that, this method fails in some case of mixtures, and we propose a new methodology to cope with this issue.