Conservatoire national des arts et métiers

About: Conservatoire national des arts et métiers is a education organization based out in Paris, France. It is known for research contribution in the topics: Population & Context (language use). The organization has 3573 authors who have published 7127 publications receiving 141430 citations. The organization is also known as: CNAM & Conservatoire des arts et métiers.

Optimizing COVID-19 surveillance in long-term care facilities: a modelling study.

Abstract: Long-term care facilities (LTCFs) are vulnerable to outbreaks of coronavirus disease 2019 (COVID-19). Timely epidemiological surveillance is essential for outbreak response, but is complicated by a high proportion of silent (non-symptomatic) infections and limited testing resources. We used a stochastic, individual-based model to simulate transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) along detailed inter-individual contact networks describing patient-staff interactions in a real LTCF setting. We simulated distribution of nasopharyngeal swabs and reverse transcriptase polymerase chain reaction (RT-PCR) tests using clinical and demographic indications and evaluated the efficacy and resource-efficiency of a range of surveillance strategies, including group testing (sample pooling) and testing cascades, which couple (i) testing for multiple indications (symptoms, admission) with (ii) random daily testing. In the baseline scenario, randomly introducing a silent SARS-CoV-2 infection into a 170-bed LTCF led to large outbreaks, with a cumulative 86 (95% uncertainty interval 6–224) infections after 3 weeks of unmitigated transmission. Efficacy of symptom-based screening was limited by lags to symptom onset and silent asymptomatic and pre-symptomatic transmission. Across scenarios, testing upon admission detected just 34–66% of patients infected upon LTCF entry, and also missed potential introductions from staff. Random daily testing was more effective when targeting patients than staff, but was overall an inefficient use of limited resources. At high testing capacity (> 10 tests/100 beds/day), cascades were most effective, with a 19–36% probability of detecting outbreaks prior to any nosocomial transmission, and 26–46% prior to first onset of COVID-19 symptoms. Conversely, at low capacity (< 2 tests/100 beds/day), group testing strategies detected outbreaks earliest. Pooling randomly selected patients in a daily group test was most likely to detect outbreaks prior to first symptom onset (16–27%), while pooling patients and staff expressing any COVID-like symptoms was the most efficient means to improve surveillance given resource limitations, compared to the reference requiring only 6–9 additional tests and 11–28 additional swabs to detect outbreaks 1–6 days earlier, prior to an additional 11–22 infections. COVID-19 surveillance is challenged by delayed or absent clinical symptoms and imperfect diagnostic sensitivity of standard RT-PCR tests. In our analysis, group testing was the most effective and efficient COVID-19 surveillance strategy for resource-limited LTCFs. Testing cascades were even more effective given ample testing resources. Increasing testing capacity and updating surveillance protocols accordingly could facilitate earlier detection of emerging outbreaks, informing a need for urgent intervention in settings with ongoing nosocomial transmission.

Multi-Scale Partitions: Application to Spatial and Statistical Databases

Abstract: We study the impact of scale on data representation from both the modelling and querying points of view. While our starting point was geographical applications, statistical databases also address this problem of data representation at various levels of abstraction. From these requirements, we propose a model which allows: (i) database querying without exact knowledge of the data abstraction level, (ii) the computation of multiple representations of data, one per abstraction level, and (iii) its application to the computation of statistical summaries. The model has been partially implemented with the DBMS O2 by means of tree-structured domains: we give some examples which illustrate the above features.

An RNA polymerase III subunit determines sites of retrotransposon integration

Abstract: Mobile genetic elements are ubiquitous. Their integration site influences genome stability and gene expression. The Ty1 retrotransposon of the yeast Saccharomyces cerevisiae integrates upstream of RNA polymerase III (Pol III)-transcribed genes, yet the primary determinant of target specificity has remained elusive. Here we describe an interaction between Ty1 integrase and the AC40 subunit of Pol III and demonstrate that AC40 is the predominant determinant targeting Ty1 integration upstream of Pol III-transcribed genes. Lack of an integrase-AC40 interaction dramatically alters target site choice, leading to a redistribution of Ty1 insertions in the genome, mainly to chromosome ends. The mechanism of target specificity allows Ty1 to proliferate and yet minimizes genetic damage to its host.

Marine Algae: a Source of Biomass for Biotechnological Applications

Abstract: Biomass derived from marine microalgae and macroalgae is globally recognized as a source of valuable chemical constituents with applications in the agri-horticultural sector (including animal feeds and health and plant stimulants), as human food and food ingredients as well as in the nutraceutical, cosmeceutical, and pharmaceutical industries. Algal biomass supply of sufficient quality and quantity however remains a concern with increasing environmental pressures conflicting with the growing demand. Recent attempts in supplying consistent, safe and environmentally acceptable biomass through cultivation of (macro- and micro-) algal biomass have concentrated on characterizing natural variability in bioactives, and optimizing cultivated materials through strain selection and hybridization, as well as breeding and, more recently, genetic improvements of biomass. Biotechnological tools including metabolomics, transcriptomics, and genomics have recently been extended to algae but, in comparison to microbial or plant biomass, still remain underdeveloped. Current progress in algal biotechnology is driven by an increased demand for new sources of biomass due to several global challenges, new discoveries and technologies available as well as an increased global awareness of the many applications of algae. Algal diversity and complexity provides significant potential provided that shortages in suitable and safe biomass can be met, and consumer demands are matched by commercial investment in product development.