Cognitive resources moderate the adverse impact of poor perceived neighborhood conditions on self-reported physical activity of older adults

Rebar, A ORCiD: 0000-0003-3164-993XPoor neighborhood conditions are associated with lower levels of physical activity for older adults but socio-ecological models posit that physical activity depends on both environmental and individual factors. Older adults' ability to overcome environmental barriers to physical activity may partially rely on cognitive resources. However, evidence on the moderating role of these cognitive resources in the associations between environmental barriers and physical activity is still lacking. We analyzed cross-national and longitudinal data on 28,393 adults aged 50 to 96 years as part of the SHARE. Lack of access to services and neighborhood nuisances were used as indicators of poor neighborhood conditions. Delayed recall and verbal fluency were used as indicators of cognitive resources. Confounder-adjusted generalized estimation equations were conducted to test associations between neighborhood conditions and self-reported moderate physical activity, as well as the moderating role of cognitive resources. Results showed that poor neighborhood conditions reduced the odds of engagement in physical activity. Cognitive resources robustly reduced the adverse influence of poor neighborhood conditions on physical activity. Participants with lower cognitive resource scores showed lower odds of engaging in physical activity when neighborhood conditions were poorer, whereas these conditions were not related to this engagement for participants with higher cognitive resource scores. These findings suggest that cognitive resources can temper the detrimental effect of poor neighborhood conditions on physical activity. Public policies should target both individual and environmental factors to tackle the current pandemic of physical inactivity more comprehensively. © 2019 Elsevier Inc

Expression Profile of Nuclear Receptors along Male Mouse Nephron Segments Reveals a Link between ERRβ and Thick Ascending Limb Function

The nuclear receptor family orchestrates many functions related to reproduction, development, metabolism, and adaptation to the circadian cycle. The majority of these receptors are expressed in the kidney, but their exact quantitative localization in this ultrastructured organ remains poorly described, making it difficult to elucidate the renal function of these receptors. In this report, using quantitative PCR on microdissected mouse renal tubules, we established a detailed quantitative expression map of nuclear receptors along the nephron. This map can serve to identify nuclear receptors with specific localization. Thus, we unexpectedly found that the estrogen-related receptor β (ERRβ) is expressed predominantly in the thick ascending limb (TAL) and, to a much lesser extent, in the distal convoluted tubules. In vivo treatment with an ERR inverse agonist (diethylstilbestrol) showed a link between this receptor family and the expression of the Na+,K+-2Cl− cotransporter type 2 (NKCC2), and resulted in phenotype presenting some similarities with the Bartter syndrom (hypokalemia, urinary Na+ loss and volume contraction). Conversely, stimulation of ERRβ with a selective agonist (GSK4716) in a TAL cell line stimulated NKCC2 expression. All together, these results provide broad information regarding the renal expression of all members of the nuclear receptor family and have allowed us to identify a new regulator of ion transport in the TAL segments

Human Skin Microbiota: High Diversity of DNA Viruses Identified on the Human Skin by High Throughput Sequencing

The human skin is a complex ecosystem that hosts a heterogeneous flora. Until recently, the diversity of the cutaneous microbiota was mainly investigated for bacteria through culture based assays subsequently confirmed by molecular techniques. There are now many evidences that viruses represent a significant part of the cutaneous flora as demonstrated by the asymptomatic carriage of beta and gamma-human papillomaviruses on the healthy skin. Furthermore, it has been recently suggested that some representatives of the Polyomavirus genus might share a similar feature. In the present study, the cutaneous virome of the surface of the normal-appearing skin from five healthy individuals and one patient with Merkel cell carcinoma was investigated through a high throughput metagenomic sequencing approach in an attempt to provide a thorough description of the cutaneous flora, with a particular focus on its viral component. The results emphasize the high diversity of the viral cutaneous flora with multiple polyomaviruses, papillomaviruses and circoviruses being detected on normal-appearing skin. Moreover, this approach resulted in the identification of new Papillomavirus and Circovirus genomes and confirmed a very low level of genetic diversity within human polyomavirus species. Although viruses are generally considered as pathogen agents, our findings support the existence of a complex viral flora present at the surface of healthy-appearing human skin in various individuals. The dynamics and anatomical variations of this skin virome and its variations according to pathological conditions remain to be further studied. The potential involvement of these viruses, alone or in combination, in skin proliferative disorders and oncogenesis is another crucial issue to be elucidated

Tissue Compartment Analysis for Biomarker Discovery by Gene Expression Profiling

BACKGROUND:Although high throughput technologies for gene profiling are reliable tools, sample/tissue heterogeneity limits their outcomes when applied to identify molecular markers. Indeed, inter-sample differences in cell composition contribute to scatter the data, preventing detection of small but relevant changes in gene expression level. To date, attempts to circumvent this difficulty were based on isolation of the different cell structures constituting biological samples. As an alternate approach, we developed a tissue compartment analysis (TCA) method to assess the cell composition of tissue samples, and applied it to standardize data and to identify biomarkers. METHODOLOGY/PRINCIPAL FINDINGS:TCA is based on the comparison of mRNA expression levels of specific markers of the different constitutive structures in pure isolated structures, on the one hand, and in the whole sample on the other. TCA method was here developed with human kidney samples, as an example of highly heterogeneous organ. It was validated by comparison of the data with those obtained by histo-morphometry. TCA demonstrated the extreme variety of composition of kidney samples, with abundance of specific structures varying from 5 to 95% of the whole sample. TCA permitted to accurately standardize gene expression level amongst >100 kidney biopsies, and to identify otherwise imperceptible molecular disease markers. CONCLUSIONS/SIGNIFICANCE:Because TCA does not require specific preparation of sample, it can be applied to all existing tissue or cDNA libraries or to published data sets, inasmuch specific operational compartments markers are available. In human, where the small size of tissue samples collected in clinical practice accounts for high structural diversity, TCA is well suited for the identification of molecular markers of diseases, and the follow up of identified markers in single patients for diagnosis/prognosis and evaluation of therapy efficiency. In laboratory animals, TCA will interestingly be applied to central nervous system where tissue heterogeneity is a limiting factor

Benchmarking homogenization algorithms for monthly data

The COST (European Cooperation in Science and Technology) Action ES0601: Advances in homogenization methods of climate series: an integrated approach (HOME) has executed a blind intercomparison and validation study for monthly homogenization algorithms. Time series of monthly temperature and precipitation were evaluated because of their importance for climate studies. The algorithms were validated against a realistic benchmark dataset. Participants provided 25 separate homogenized contributions as part of the blind study as well as 22 additional solutions submitted after the details of the imposed inhomogeneities were revealed. These homogenized datasets were assessed by a number of performance metrics including i) the centered root mean square error relative to the true homogeneous values at various averaging scales, ii) the error in linear trend estimates and iii) traditional contingency skill scores. The metrics were computed both using the individual station series as well as the network average regional series. The performance of the contributions depends significantly on the error metric considered. Although relative homogenization algorithms typically improve the homogeneity of temperature data, only the best ones improve precipitation data. Moreover, state-of-the-art relative homogenization algorithms developed to work with an inhomogeneous reference are shown to perform best. The study showed that currently automatic algorithms can perform as well as manual ones