The European Space Analogue Rock Collection (ESAR) at the OSUC-Orleans for in situ planetary missions

International audienceThe ESAR is a collection of well-characterised planetary analogue rocks and minerals that can be used for testing in situ instrumentation for planetary exploration. An online database of all relevant structural, compositional and geotechnics information is also available to the instrument teams and to aid data interpretation during missions

Author Correction: Modelling and predicting the spatio‑temporal spread of COVID‑19, associated deaths and impact of key risk factors in England

The original version of this Article contained an error in the Introduction where, “We utilise weekly MSOA level population mobility data against observed confirmed COVID-19 case data to assess the impact mobility reduction at small-area scale has had on case transmission, and counterfactually what the magnitude may have been under the scenario of no mobility loss.” now reads: “We utilise weekly Clinical Commission Group (CCG) level population mobility data against observed confirmed COVID-19 case data to assess the impact mobility reduction at small-area scale has had on case transmission, and counterfactually what the magnitude may have been under the scenario of no mobility loss.” In addition, in Table 2, the Spatial resolution was incorrectly given in the column “Model component”, rows “Covariate (daily varying)” and “Covariates (annual estimates, weekly fixed), Elderly population proportion living in deprivation”. The correct and incorrect values appear below. Incorrect: (Table presented.) Correct: (Table presented.) Finally, in the Methods section, under the subheadings ‘Data’, and ‘Data analysis”, “Daily population movement data by MSOA was extracted from the COVID-19 Impact monitor (https:// www. oxford- covid- 19. com/).” now reads: “Daily population movement data by Clinical Commission Group (CCG) was extracted from the COVID-19 Impact monitor (https:// www. oxford- covid- 19. com/). Weekly mobility in a given MSOA where assumed to be the same as the weekly mobility in the higher level CCG containing most of that MSOA.” “Here b = (b0, b1, b2) is the vector of regression coefficients for the intercept (representing the log-transformed baseline transmission rate across all locations), mobility represents the observed weekly mobility by MSOA;” now reads: “Here b = (b0, b1, b2) is the vector of regression coefficients for the intercept (representing the log-transformed baseline transmission rate across all locations), mobility represents the observed weekly mobility in a given MSOA based on the mobility in the CCG containing most of that MSOA;” The original Article has been corrected

Modelling and predicting the spatio-temporal spread of COVID-19, associated deaths and impact of key risk factors in England.

COVID-19 caseloads in England have passed through a first peak, and at the time of this analysis appeared to be gradually increasing, potentially signalling the emergence of a second wave. To ensure continued response to the epidemic is most effective, it is imperative to better understand both retrospectively and prospectively the geographical evolution of COVID-19 caseloads and deaths at small-area resolution, identify localised areas in space-time at significantly higher risk, quantify the impact of changes in localised population mobility (or movement) on caseloads, identify localised risk factors for increased mortality and project the likely course of the epidemic at high spatial resolution in coming weeks. We applied a Bayesian hierarchical space-time SEIR model to assess the spatiotemporal variability of COVID-19 caseloads (transmission) and deaths at small-area scale in England [Middle Layer Super Output Area (MSOA), 6791 units] and by week (using observed data from week 5 to 34 of 2020), including key determinants, the modelled transmission dynamics and spatial-temporal random effects. We also estimate the number of cases and deaths at small-area resolution with uncertainty projected forward in time by MSOA (up to week 51 of 2020), the impact mobility reductions (and subsequent easing) have had on COVID-19 caseloads and quantify the impact of key socio-demographic risk factors on COVID-19 related mortality risk by MSOA. Reductions in population mobility during the course of the first lockdown had a significant impact on the reduction of COVID-19 caseloads across England, however local authorities have had a varied rate of reduction in population movement which our model suggest has substantially impacted the geographic heterogeneity in caseloads at small-area scale. The steady gain in population mobility, observed from late April, appears to have contributed to a slowdown in caseload reductions towards late June and subsequent start of the second wave. MSOA with higher proportions of elderly (70+ years of age) and elderly living in deprivation, both with very distinct geographic distributions, have a significantly elevated COVID-19 mortality rates. While non-pharmaceutical interventions (that is, reductions in population mobility and social distancing) had a profound impact on the trajectory of the first wave of the COVID-19 outbreak in England, increased population mobility appears to have significantly contributed to the second wave. A number of contiguous small-areas appear to be at a significant elevated risk of high COVID-19 transmission, many of which are also at increased risk for higher mortality rates. A geographically staggered re-introduction of intensified social distancing measures is advised and limited cross MSOA movement if the magnitude and geographic extent of the second wave is to be reduced

A physiologically based, multi-scale model of skeletal muscle structure and function

Models of skeletal muscle can be classified as phenomenological or biophysical. Phenomenological models predict the muscle’s response to a specified input based on experimental measurements. Prominent phenomenological models are the Hill-type muscle models, which have been incorporated into rigid-body modeling frameworks, and three-dimensional continuum-mechanical models. Biophysically based models attempt to predict the muscle’s response as emerging from the underlying physiology of the system. In this contribution, the conventional biophysically based modeling methodology is extended to include several structural and functional characteristics of skeletal muscle. The result is a physiologically based, multi-scale skeletal muscle finite element model that is capable of representing detailed, geometrical descriptions of skeletal muscle fibers and their grouping. Together with a well-established model of motor-unit recruitment, the electro-physiological behavior of single muscle fibers within motor units is computed and linked to a continuummechanical constitutive law. The bridging between the cellular level and the organ level has been achieved via a multi-scale constitutive law and homogenization. The effect of homogenization has been investigated by varying the number of embedded skeletal muscle fibers and/or motor units and computing the resulting exerted muscle forces while applying the same excitatory input. All simulations were conducted using an anatomically realistic finite element model of the tibialis anterior muscle. Given the fact that the underlying electro-physiological cellular muscle model is capable of modeling metabolic fatigue effects such as potassium accumulation in the T-tubular space and inorganic phosphate build-up, the proposed framework provides a novel simulation-based way to investigate muscle behavior ranging from motor-unit recruitment to force generation and fatigue

Transcriptional landscape of Aspergillus niger at breaking of conidial dormancy revealed by RNA-sequencing

Background Genome-wide analysis was performed to assess the transcriptional landscape of germinating A. niger conidia using both next generation RNA-sequencing and GeneChips. The metabolism of storage compounds during conidial germination was also examined and compared to the transcript levels from associated genes. Results The transcriptome of dormant conidia was shown to be highly differentiated from that of germinating conidia and major changes in response to environmental shift occurred within the first hour of germination. The breaking of dormancy was associated with increased transcript levels of genes involved in the biosynthesis of proteins, RNA turnover and respiratory metabolism. Increased transcript levels of genes involved in metabolism of nitrate at the onset of germination implies its use as a source of nitrogen. The transcriptome of dormant conidia contained a significant component of antisense transcripts that changed during germination. Conclusion Dormant conidia contained transcripts of genes involved in fermentation, gluconeogenesis and the glyoxylate cycle. The presence of such transcripts in dormant conidia may indicate the generation of energy from non-carbohydrate substrates during starvation-induced conidiation or for maintenance purposes during dormancy. The immediate onset of metabolism of internal storage compounds after the onset of germination, and the presence of transcripts of relevant genes, suggest that conidia are primed for the onset of germination. For some genes, antisense transcription is regulated in the transition from resting conidia to fully active germinants