Daycare use of disinfectants and cleaning products and wheeze among children: Cross-sectional analyses in the French CRESPI cohort.

Background: Evidence is mounting that domestic use of disinfectants and cleaning products (DCP), particularly in spray form, is associated with wheezing in children. Beyond the home environment, many children are also exposed to DCP in daycare. The links between daycare exposures to DCP and child respiratory health have never before been studied.

Biocide and other semi-volatile organic compound concentrations in settled indoor dust of CRESPI daycare centers and implication for public health.

This study investigates the presence of biocides and other semi-volatile organic compounds (SVOCs) in cleaning products used in daycare centers and health impact through ingestion of settled dust by young children. In Paris metropolitan area, 106 daycares area were investigated between 2019-2022. Fifteen substances were analyzed in settled indoor dust by gas chromatography-tandem mass spectrometry.

Insertion of fluorine into a LiFePO electrode material by gas-solid fluorination.

In this study, the insertion of fluorine into LiFePO was carried out under molecular fluorine F at different temperatures. The reactivity strongly depends on the applied fluorination temperature, leading to very different products: core delithiation of the material is observed at low temperatures with the formation of a LiF shell around particles, while the material decomposes to gradually form a mixture of α-FeF and α-LiFeF iron fluorides at higher temperatures. A second thermal treatment under N leads to the formation of LiFePOF in a new way that has not been reported before.

What is a Rhythm for the Brain? The Impact of Contextual Temporal Variability on Auditory Perception.

Temporal predictions can be formed and impact perception when sensory timing is fully predictable: for instance, the discrimination of a target sound is enhanced if it is presented on the beat of an isochronous rhythm. However, natural sensory stimuli, like speech or music, are not entirely predictable, but still possess statistical temporal regularities. We investigated whether temporal expectations can be formed in non-fully predictable contexts, and how the temporal variability of sensory contexts affects auditory perception.

Envisaging a global infrastructure to exploit the potential of digitised collections.

Tens of millions of images from biological collections have become available online over the last two decades. In parallel, there has been a dramatic increase in the capabilities of image analysis technologies, especially those involving machine learning and computer vision. While image analysis has become mainstream in consumer applications, it is still used only on an artisanal basis in the biological collections community, largely because the image corpora are dispersed.

Estimating Compositions and Nutritional Values of Seed Mixes Based on Vision Transformers.

The cultivation of seed mixtures for local pastures is a traditional mixed cropping technique of cereals and legumes for producing, at a low production cost, a balanced animal feed in energy and protein in livestock systems. By considerably improving the autonomy and safety of agricultural systems, as well as reducing their impact on the environment, it is a type of crop that responds favorably to both the evolution of the European regulations on the use of phytosanitary products and the expectations of consumers who wish to increase their consumption of organic products. However, farmers find it difficult to adopt it because cereals and legumes do not ripen synchronously and the harvested seeds are heterogeneous, making it more difficult to assess their nutritional value.

Validating instructional design and predicting student performance in histology education: Using machine learning via virtual microscopy.

As a part of modern technological environments, virtual microscopy enriches histological learning, with support from large institutional investments. However, existing literature does not supply empirical evidence of its role in improving pedagogy. Virtual microscopy provides fresh opportunities for investigating user behavior during the histology learning process, through digitized histological slides.

Atovaquone exposure and Pneumocystis jirovecii cytochrome b mutations: French data and review of the literature.

Pneumocystis jirovecii is a transmissible fungus responsible for severe pneumonia (Pneumocystis pneumonia [PCP]) in immunocompromised patients. Missense mutations due to atovaquone selective pressure have been identified on cytochrome b (CYB) gene of P. jirovecii.

Changes in Gene Expression After Exposing Arabidopsis thaliana Plants to Nanosecond High Amplitude Electromagnetic Field Pulses.

The biological effects of exposure to electromagnetic fields due to wireless technologies and connected devices are a subject of particular research interest. Ultrashort high-amplitude electromagnetic field pulses delivered to biological samples using immersed electrodes in a dedicated cuvette have widely demonstrated their effectiveness in triggering several cell responses including increased cytosolic calcium concentration and reactive oxygen species (ROS) production. In contrast, the effects of these pulses are poorly documented when electromagnetic pulses are delivered through an antenna.

Exposure to Disinfectants and Cleaning Products and Respiratory Health of Workers and Children in Daycares: The CRESPI Cohort Protocol.

Although cleaning tasks are frequently performed in daycare, no study has focused on exposures in daycares in relation to respiratory health. The CRESPI cohort is an epidemiological study among workers (n~320) and children (n~540) attending daycares. The purpose is to examine the impact of daycare exposures to disinfectants and cleaning products (DCP) on the respiratory health of workers and children.

Non thermal 2.45 GHz electromagnetic exposure causes rapid changes in Arabidopsis thaliana metabolism.

Numerous studies report different types of responses following exposure of plants to high frequency electromagnetic fields (HF-EMF). While this phenomenon is related to tissue heating in animals, the situation is much less straightforward in plants where metabolic changes seem to occur without tissue temperature increase. We have set up an exposure system allowing reliable measurements of tissue heating (using a reflectometric probe and thermal imaging) after a long exposure (30 min) to an electromagnetic field of 2.

Reactive sputtering onto an ionic liquid, a new synthesis route for bismuth-based nanoparticles.

Metallic bismuth and Bi-oxyfluoride nanoparticles (NPs) are successfully synthesized by non-reactive and reactive sputtering of a Bi target onto 1-butyl-3-methylimidazolium bis(trifluoro-methylsulfonyl)imide ([BMIM][TFSI]) ionic liquid (IL). Non-reactive sputtering is realized in pure Ar plasma, where isotropic, well crystallized and dispersed Bi NPs of 3-7 nm are obtained. The diameter and the size distribution of these NPs do not significantly vary with the power, gas pressure, and sputtering time; but these sputtering parameters seem to influence the NP concentration.

Domain Decomposition Spectral Method Applied to Modal Method: Direct and Inverse Spectral Transforms.

We introduce a Domain Decomposition Spectral Method (DDSM) as a solution for Maxwell's equations in the frequency domain. It will be illustrated in the framework of the Aperiodic Fourier Modal Method (AFMM). This method may be applied to compute the electromagnetic field diffracted by a large-scale surface under any kind of incident excitation.

Apparent Absence of Selective Pressure on Pneumocystis jirovecii Organisms in Patients with Prior Methotrexate Exposure.

Pneumocystis jirovecii infections occur in patients treated with methotrexate (MTX) because of immunosuppressive effects of this highly potent dihydrofolate reductase (DHFR) inhibitor. Conversely, MTX may act as an anti drug and consequently may exert a selective pressure on this fungus. In this context, we compared the sequences of the gene of isolates obtained from two groups of patients with infections.

Dynamic Species Distribution Modeling Reveals the Pivotal Role of Human-Mediated Long-Distance Dispersal in Plant Invasion.

Plant invasions generate massive ecological and economic costs worldwide. Predicting their spatial dynamics is crucial to the design of effective management strategies and the prevention of invasions. Earlier studies highlighted the crucial role of long-distance dispersal in explaining the speed of many invasions.

Diagnosis and treatment of pudendal and inferior cluneal nerve entrapment syndrome: a narrative review.

Aim: Pudendal and inferior cluneal nerve entrapment can cause a neuropathic pain syndrome in the sensitive areas innervated by these nerves. Diagnosis is challenging and patients often suffer several years before diagnosis is made. The purpose of the review was to inform healthcare workers about this disease and to provide a basis of anatomy and physiopathology, to inform about diagnostic tools and invasive or non-invasive treatment modalities and outcome.

The dural vascular plexus in subdural hematoma: Illustration through a case of dural arteriovenous fistula.

Background: The initiation of chronic subdural hematoma (cSDH) is traditionally explained by rupture of bridging veins. Recent descriptions of the embryology and anatomy of the meninges and their vascularization, however, point to the dural vascular plexus (DVP) as a plausible origin of cSDH. This dural plexus is supplied by meningeal arteries.

Very High Resolution Species Distribution Modeling Based on Remote Sensing Imagery: How to Capture Fine-Grained and Large-Scale Vegetation Ecology With Convolutional Neural Networks?

Species Distribution Models (SDMs) are fundamental tools in ecology for predicting the geographic distribution of species based on environmental data. They are also very useful from an application point of view, whether for the implementation of conservation plans for threatened species or for monitoring invasive species. The generalizability and spatial accuracy of an SDM depend very strongly on the type of model used and the environmental data used as explanatory variables.

Deep Species Distribution Modeling From Sentinel-2 Image Time-Series: A Global Scale Analysis on the Orchid Family.

Species distribution models (SDMs) are widely used numerical tools that rely on correlations between geolocated presences (and possibly absences) and environmental predictors to model the ecological preferences of species. Recently, SDMs exploiting deep learning and remote sensing images have emerged and have demonstrated high predictive performance. In particular, it has been shown that one of the key advantages of these models (called deep-SDMs) is their ability to capture the spatial structure of the landscape, unlike prior models.

Can Artificial Intelligence Help in the Study of Vegetative Growth Patterns from Herbarium Collections? An Evaluation of the Tropical Flora of the French Guiana Forest.

A better knowledge of tree vegetative growth phenology and its relationship to environmental variables is crucial to understanding forest growth dynamics and how climate change may affect it. Less studied than reproductive structures, vegetative growth phenology focuses primarily on the analysis of growing shoots, from buds to leaf fall. In temperate regions, low winter temperatures impose a cessation of vegetative growth shoots and lead to a well-known annual growth cycle pattern for most species.

Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of .

Machine learning (ML) can accelerate the extraction of phenological data from herbarium specimens; however, no studies have assessed whether ML-derived phenological data can be used reliably to evaluate ecological patterns. In this study, 709 herbarium specimens representing a widespread annual herb, were scored both manually by human observers and by a mask R-CNN object detection model to (1) evaluate the concordance between ML and manually-derived phenological data and (2) determine whether ML-derived data can be used to reliably assess phenological patterns. The ML model generally underestimated the number of reproductive structures present on each specimen; however, when these counts were used to provide a quantitative estimate of the phenological stage of plants on a given sheet (i.