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IntroductionHearing parents of deaf or hard of hearing (DHH) children may experience parenting stress and social support could be a buffer to this stress. Differences in levels of these two indicators may exist between mothers and fathers. This study focuses on the parenting stress and social support needs of mothers and fathers of DHH children.MethodsTwenty-seven French parental couples of DHH children completed the Parenting Stress Index and the Family Needs Survey, a questionnaire on social support needs.ResultsTheir overall stress scores showed no difference, but subdomain scores show that mothers and fathers are more stressed by the child’s hyperactivity, and fathers by the child’s adaptability, than parents of children with normal hearing. Mothers are more stressed than fathers by role restriction; they feel less free because of their parenting role. Fathers have a lower quality of attachment to their child than mothers. Parents have a high social support need, especially for obtaining information about their child’s individual characteristics and health situation. The ranking of mothers and fathers in the top 10 needs reveals different needs profiles. Parenting stress profiles show that mothers and fathers with higher-than-normal stress levels have a greater overall need for social support than mothers and fathers with lower than normal stress levels.DiscussionThis study highlights the value of assessing parenting stress and social support needs in parents of DHH children for a better understanding of their situation in research and its clinical implications, as well as the importance of differentiating outcomes for mothers and fathers.

Satellite observations provide critical data for a myriad of applications, but automated information extraction from such vast datasets remains challenging. While artificial intelligence (AI), particularly deep learning methods, offers promising solutions for land cover classification, it often requires massive amounts of accurate, error-free annotations. This paper introduces a novel approach to generate a segmentation task dataset with minimal human intervention, thus significantly reducing annotation time and potential human errors. ‘Samples’ extracted from actual imagery were utilized to construct synthetic composite images, representing 10 segmentation classes. A DeepResUNet was solely trained on this synthesized dataset, eliminating the need for further fine-tuning. Preliminary findings demonstrate impressive generalization abilities on real data across various regions of Quebec. We endeavored to conduct a quantitative assessment without reliance on manually annotated data, and the results appear to be comparable, if not superior, to models trained on genuine datasets.

Digital twins are increasingly gaining popularity as a method for simulating intricate natural and urban environments, with the precise segmentation of 3D objects playing an important role. This study focuses on developing a methodology for extracting buildings from textured 3D meshes, employing the PicassoNet-II semantic segmentation architecture. Additionally, we integrate Markov field-based contextual analysis for post-segmentation assessment and cluster analysis algorithms for building instantiation. Training a model to adapt to diverse datasets necessitates a substantial volume of annotated data, encompassing both real data from Quebec City, Canada, and simulated data from Evermotion and Unreal Engine. The experimental results indicate that incorporating simulated data improves segmentation accuracy, especially for under-represented features, and the DBSCAN algorithm proves effective in extracting isolated buildings. We further show that the model is highly sensible for the method of creating 3D meshes.

The kinetic stability of any material in water relies on the presence of surface weak spots responsible for chemical weathering by hydrolysis. Being able to identify the atomistic nature of these sites and the first steps of transformation is therefore critical to master the decomposition processes. This is the challenge that we tackle here: combining experimental and modeling studies we investigate the stability of alumina in water. Exploring the reactivity of shape-controlled crystals, we identify experimentally a specific facet as the location of the weak spots. Using biased ab initio molecular dynamics, we recognize this weak spot as a surface exposed tetra-coordinated Al atom and further provide a detailed mechanism of the first steps of hydrolysis. This understanding is of great importance to heterogeneous catalysis where alumina is a major support. Furthermore, it paves the way to atomistic understanding of interfacial reactions, at the crossroad of a variety of fields of research.

Abstract We previously showed that the physiological concentration of 17β-estradiol in the vaginal environment is sufficient to affect the membrane dynamics and adhesion phenotype of the Lactobacillus crispatus strain CIP104459. However, L. crispatus is a heterogeneous species. Here, we investigated the effect of 17β-estradiol on the recently isolated L. crispatus vaginal strain V4, related to a cluster distant from CIP104459 and at the limit of being a different subspecies. Grown in the same medium, the two strains expressed a highly similar pool of proteins. However, in contrast to CIP104459, L. crispatus V4 showed high aggregation potential and 17β-estradiol promoted this phenotype. This effect was associated with large changes in cell-surface polarity and Lewis acid/base properties. In addition, we observed no effect on the membrane dynamics, contrary to CIP104459. These results can be explained by differences in the properties and organization of the S layer between the two strains. However, as for CIP104459, 17β-estradiol increased biosurfactant production of L. crispatus V4 and their adhesion to vaginal cells. This suggests that 17β-estradiol agonists would be valuable tools to favor a stable re-implantation of L. crispatus in the vaginal mucosa.

BackgroundSystemic inflammation is the main factor underlying secondary osteoporosis in patients with rheumatoid arthritis (RA). Janus kinase inhibitors (JAKi), such as tofacitinib (Tofa), can control systemic inflammation and may have beneficial effects on bone in various models. This might be due to direct effects on the bone microenvironment and not exclusively based on their anti-inflammatory function. Bone marrow adipocytes (BMAds) are abundant in the bone microenvironment. The effect of JAKi on BMAds is unknown, but evidence suggests that there is competition between human bone marrow-derived stromal cell (hBMSC) differentiation routes towards BMAds and osteoblasts (Ob) in osteoporosis.ObjectivesThe aims of the study are to determine whether Tofa influences BMAds and Ob derived from hBMSCs and to investigate the potential effects of Tofa on bone marrow adiposity in RA patients.MethodsTo determine the effect of Tofa on cellular commitment, hBMSCs were differentiated to BMAds or OBs for 3 days together with Tofa at 200, 400, or 800 nM and TNFα. This study was also conducted using differentiated BMAds. The impact of Tofa was determined by gene and protein expression analysis and cell density monitoring. In parallel, in a pilot study of 9 RA patients treated with Tofa 5 mg twice a day (NCT04175886), the proton density fat fraction (PDFF) was measured using MRI at the lumbar spine at baseline and at 6 months.ResultsIn non-inflammatory conditions, the gene expression of Runx2 and Dlx5 decreased in Ob treated with Tofa (p

Abstract Lactobacilli and estrogens play essential roles in vaginal homeostasis. We investigated the potential direct effect of 17β-estradiol on a vaginal strain of Lactobacillus crispatus, the major bacterial species of the vaginal microbiota. 17β-estradiol (10–6 to 10–10 M) had no effect on L. crispatus growth, but markedly affected the membrane dynamics of this bacterium. This effect appeared consistent with a signal transduction process. The surface polarity and aggregation potential of the bacterium were unaffected by exposure to 17β-estradiol, but its mean size was significantly reduced. 17β-estradiol also promoted biosurfactant production by L. crispatus and adhesion to vaginal VK2/E6E7 cells, but had little effect on bacterial biofilm formation activity. Bioinformatic analysis of L. crispatus identified a membrane lipid raft–associated stomatin/prohibitin/flotillin/HflK domain containing protein as a potential 17β-estradiol binding site. Overall, our results reveal direct effects of 17β-estradiol on L. crispatus. These effects are of potential importance in the physiology of the vaginal environment, through the promotion of lactobacillus adhesion to the mucosa and protection against pathogens.

Sclerostin is a Wnt signaling pathway inhibitor that negatively regulates bone formation. Bone-marrow-derived stromal cell (BMSC) differentiation is influenced by the Wnt pathway, leading to the hypothesis that higher levels of sclerostin might be associated with an increase in bone marrow adiposity (BMA). The main purpose of this study was to determine whether a relationship exists between circulating sclerostin and BMA in post-menopausal women with and without fragility fractures. The relationships between circulating sclerostin and body composition parameters were then examined. The outcomes measures included vertebral and hip proton density fat fraction (PDFF) using the water fat imaging (WFI) MRI method; DXA scans; and laboratory measurements, including serum sclerostin. In 199 participants, no significant correlations were found between serum sclerostin and PDFF. In both groups, serum sclerostin was correlated positively with bone mineral density (R = 0.27 to 0.56) and negatively with renal function (R = −0.22 to −0.29). Serum sclerostin correlated negatively with visceral adiposity in both groups (R = −0.24 to −0.32). Serum sclerostin correlated negatively with total body fat (R = −0.47) and appendicular lean mass (R = −0.26) in the fracture group, but not in the controls. No evidence of a relationship between serum sclerostin and BMA was found. However, serum sclerostin was negatively correlated with body composition components, such as visceral adiposity, total body fat and appendicular lean mass.

Phthalates are used in a variety of applications—for example, as plasticizers in polyvinylchloride products to improve their flexibility—and can be easily released into the environment. In addition to being major persistent organic environmental pollutants, some phthalates are responsible for the carcinogenicity, teratogenicity, and endocrine disruption that are notably affecting steroidogenesis in mammals. Numerous studies have thus focused on deciphering their effects on mammals and eukaryotic cells. While multicellular organisms such as humans are known to display various microbiota, including all of the microorganisms that may be commensal, symbiotic, or pathogenic, few studies have aimed at investigating the relationships between phthalates and bacteria, notably regarding their effects on opportunistic pathogens and the severity of the associated pathologies. Herein, the effects of phthalates and their substitutes were investigated on the human pathogen, Pseudomonas aeruginosa, in terms of physiology, virulence, susceptibility to antibiotics, and ability to form biofilms. We show in particular that most of these compounds increased biofilm formation, while some of them enhanced the bacterial membrane fluidity and altered the bacterial morphology.

Solvent effects are notoriously difficult to describe for metallic nanoparticles (NPs). Here, we introduce GAL21 which is the first pairwise additive force field that is specifically designed to modulate the near chemisorption energy of water as a function of the coordination numbers of the metallic atoms. We find a quadratic dependence to be most suitable for capturing the dependence of the adsorption energy of water on the generalized coordination number (GCN) of the metal atoms. GAL21 has been fitted against DFT adsorption energies for Cu, Ag, Au, Ni, Pd, Pt, and Co on 500 configurations and validated on about 3000 configurations for each metal, constructed on five surfaces with GCNs varying from 2.5 to 11.25. Depending on the metals, the root mean square deviation is found between 0.7 kcal mol−1 (Au) to 1.6 kcal mol−1 (Ni). Using GAL21, as implemented in the open-source code CP2K, we then evaluate the solvation energy of Au55 and Pt55 NPs in water using thermodynamic integration. The solvation free energy is found to be larger for Pt than for Au and systematically larger than 200 kcal mol−1, demonstrating the large impact of solvent on the surface energetics of NPs. Still, given that the amorphous NPs are both, the most stable and the most solvated ones, we do not predict a change in the preferred morphology between the gas-phase and in water. Finally, based on a linear regression on three sizes of NPs (from 38 to 147), the solvation energy for Au and Pt surface atoms is found to be −5.2 and −9.9 kcal mol−1, respectively. [ABSTRACT FROM AUTHOR]

Gaining atomistic level understanding of alumina/water interfaces is key to unraveling alumina decomposition processes. Here the authors combine the experimental synthesis of shape-controlled γ-Al2O3 samples with metadynamics simulations to identify the surface weak spots responsible for alumina decomposition.

Our preliminary findings have lead us to propose bone marrow adipocyte secretions as new contributors to bone loss. Indeed, using a coculture model based on human bone marrow stromal cells, we previously showed that soluble factors secreted by adipocytes induced the conversion of osteoblasts towards an adipocyte-like phenotype. In this study, microarray gene expression profiling showed profound transcriptomic changes in osteoblasts following coculture and confirmed the enrichment of the adipocyte gene signature. Double immunofluorescence microscopic analyses demonstrated the coexpression of adipogenic and osteoblastic specific markers in individual cells, providing evidence for a transdifferentiation event. At the molecular level, this conversion was associated with upregulated expression levels of reprogramming genes and a decrease in the DNA methylation level. In line with these in vitro results, preliminary immunohistochemical analysis of bone sections revealed adipogenic marker expression in osteoblasts from elderly subjects. Altogether, these data suggest that osteoblast transdifferentiation could contribute to decreased bone mass upon ageing.

Abstract Background A classical example of repeated speciation coupled with ecological diversification is the evolution of 14 closely related species of Darwin’s (Galápagos) finches (Thraupidae, Passeriformes). Their adaptive radiation in the Galápagos archipelago took place in the last 2–3 million years and some of the molecular mechanisms that led to their diversification are now being elucidated. Here we report evolutionary analyses of genome of the large ground finch, Geospiza magnirostris. Results 13,291 protein-coding genes were predicted from a 991.0 Mb G. magnirostris genome assembly. We then defined gene orthology relationships and constructed whole genome alignments between the G. magnirostris and other vertebrate genomes. We estimate that 15% of genomic sequence is functionally constrained between G. magnirostris and zebra finch. Genic evolutionary rate comparisons indicate that similar selective pressures acted along the G. magnirostris and zebra finch lineages suggesting that historical effective population size values have been similar in both lineages. 21 otherwise highly conserved genes were identified that each show evidence for positive selection on amino acid changes in the Darwin's finch lineage. Two of these genes (Igf2r and Pou1f1) have been implicated in beak morphology changes in Darwin’s finches. Five of 47 genes showing evidence of positive selection in early passerine evolution have cilia related functions, and may be examples of adaptively evolving reproductive proteins. Conclusions These results provide insights into past evolutionary processes that have shaped G. magnirostris genes and its genome, and provide the necessary foundation upon which to build population genomics resources that will shed light on more contemporaneous adaptive and non-adaptive processes that have contributed to the evolution of the Darwin’s finches.

Training a deep learning model requires highly variable data to permit reasonable generalization. If the variability in the data about to be processed is low, the interest in obtaining this generalization seems limited. Yet, it could prove interesting to specialize the model with respect to a particular theme. The use of enhanced super-resolution generative adversarial networks (ERSGAN), a specific type of deep learning architecture, allows the spatial resolution of remote sensing images to be increased by “hallucinating” non-existent details. In this study, we show that ESRGAN create better quality images when trained on thematically classified images than when trained on a wide variety of examples. All things being equal, we further show that the algorithm performs better on some themes than it does on others. Texture analysis shows that these performances are correlated with the inverse difference moment and entropy of the images.

Objectives: The aim of this study is to carry out performance qualifications of 2 processor-controlled dosing pumps for the production of mixed infusion solutions in a closed system.

Gossans are surficial deposits that form in host bedrock by the alteration of sulphides by acidic and oxidizing fluids. These deposits are typically a few meters to kilometers in size and they constitute important vectors to buried ore deposits. Hundreds of gossans have been mapped by field geologists in sparsely vegetated areas of the Canadian Arctic. However, due to Canada’s vast northern landmass, it is highly probable that many existing occurrences have been missed. In contrast, a variety of remote sensing data has been acquired in recent years, allowing for a broader survey of gossans from orbit. These include band ratioing or methods based on principal component analysis. Spectrally, the 809 gossans used in this study show no significant difference from randomly placed points on the Landsat 8 imageries. To overcome this major issue, we propose a deep learning method based on convolutional neural networks and relying on geo big data (Landsat-8, Arctic digital elevation model lithological maps) that can be used for the detection of gossans. Its application in different regions in the Canadian Arctic shows great promise, with precisions reaching 77%. This first order approach could provide a useful precursor tool to identify gossans prior to more detailed surveys using hyperspectral imaging.

Water molecules adsorbed on noble metal surfaces are of fundamental interest in surface science, in heterogeneous catalysis, and as a model for the metal/water interface. Herein, we analyze 28 water structures adsorbed on five noble metal surfaces (Cu, Ag, Au, Pd, and Pt) via density functional theory and energy decomposition analysis based on the block localized wave function technique. Structures, ranging from monomers to ice adlayers, reveal that the charge transfer from water to the surface is nearly independent from the charge transfer between the water molecules, while the polarization energies are cooperative. Dense water–water networks with small surface dipoles, such as the 39 × 39 unit cell [experimentally observed on Pt(111)], are favored compared to the highly ordered and popular Hup and Hdown phases. The second main result of our study is that the many-body interactions, which stabilize the water assemblies on the metal surfaces, are dominated by the polarization energies, with the charge transfer scaling with the polarization energies. Hence, if an empirical model could be found that reproduces the polarization energies, the charge transfer could be predicted as well, opening exciting perspectives for force field development. [ABSTRACT FROM AUTHOR]