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Background: To evaluate the outcomes of arthroscopic treatment of rotator cuff tear (RCT) in individuals under 45 years, focusing on their ability to return to sports (RTS) and work, along with different patient-reported outcomes (PROMs). Methods: Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, this systematic review encompassed articles that studied the outcomes of arthroscopic treatment of RCT in the young population (those under 45 years old). The literature search was conducted in PubMed/Medline and EMBASE until 21 May 2024. The primary outcome was the RTS, with secondary outcomes including the return to work and various PROMs. These PROMs included the American Shoulder and Elbow Surgeons (ASES) score and 10 other PROMs. Results: Out of 6267 articles, 15 met the inclusion criteria, involving 659 patients, predominantly male athletes with a weighted mean age of 28.3 years. The RCT etiology (14 studies) was primarily traumatic (72.3%), followed by chronic microtrauma in overhead athletes (16.8%) and non-traumatic (10.9%). The RTS rate (12 studies) varied between 47% and 100%, with a cumulative rate of 75.2%. The cumulative rate of return to the same or higher RTS level (11 studies) was 56.1%. Excluding non-athletes and patients treated with debridement, the RTS rates increased to 79.8% (143/179) overall, with a 61% (108/177) rate of returning to the same or higher level. The return to work (3 studies) was successful in 90.6% of cases. Postoperative ASES scores (5 studies) improved markedly to a weighted post-operative mean of 75.6, with similar positive trends across other PROMs. Conclusions: Young adults undergoing arthroscopic RCT repair typically experience a 75% RTS rate at any level, and 56.1% RTS at the same level. Excluding non-athletes and debridement patients, RTS rates rise to 79.8% (143/179), with 61% (108/177) achieving the same or higher level. Level of evidence: IV, systematic review including case series.

Several metabolites of the essential amino acid tryptophan have emerged as key players in gut homeostasis through different cellular pathways, particularly through metabolites which can activate the aryl hydrocarbon receptor (AHR). This study aimed to map the metabolism of tryptophan in early life and investigate the effects of specific metabolites on epithelial cells and barrier integrity. Twenty-one tryptophan metabolites were measured in the feces of full-term and preterm neonates as well as in human milk and formula. The ability of specific AHR metabolites to regulate cytokine-induced IL8 expression and maintain barrier integrity was assessed in Caco2 cells and human fetal organoids (HFOs). Overall, higher concentrations of tryptophan metabolites were measured in the feces of full-term neonates compared to those of preterm ones. Within AHR metabolites, indole-3-lactic acid (ILA) was significantly higher in the feces of full-term neonates. Human milk contained different levels of several tryptophan metabolites compared to formula. Particularly, within the AHR metabolites, indole-3-sulfate (I3S) and indole-3-acetic acid (IAA) were significantly higher compared to formula. Fecal-derived ILA and milk-derived IAA were capable of reducing TNFα-induced IL8 expression in Caco2 cells and HFOs in an AHR-dependent manner. Furthermore, fecal-derived ILA and milk-derived IAA significantly reduced TNFα-induced barrier disruption in HFOs.

In this research, we consider cyber risk in insurance using a quantum approach, with a focus on the differences between reported cyber claims and the number of cyber attacks that caused them. Unlike the traditional probabilistic approach, quantum modeling makes it possible to deal with non-commutative event paths. We investigate the classification of cyber claims according to different cyber risk behaviors to enable more precise analysis and management of cyber risks. Additionally, we examine how historical cyber claims can be utilized through the application of copula functions for dependent insurance claims. We also discuss classification, likelihood estimation, and risk-loss calculation within the context of dependent insurance claim data.

Most stained-glass windows installed during the Middle Ages have deteriorated over time due to climate and pollution. To reconstruct their alteration history over the centuries, evaluate the current environmental risk, and predict their alteration in the future, two modelling methodologies have been used. First, based on the short-term exposure of medieval-type glass in different sites, dose–response functions (DRFs) were established. These DRFs correlate relevant environmental factors (temperature, rain quantity, rain pH, relative humidity, and SO2 concentration) with the response of the material in terms of alteration layer thickness. The second methodology consists of laboratory experiments that aim at parametrising kinetic laws as a function of specific parameters (temperature, rain pH, and relative humidity). These kinetic laws can be extrapolated over long periods, contrary to DRFs. In this study, we compared both methodologies to simulate the alteration of a model stained glass at different European sites or over different time periods. The results highlighted that the kinetic laws were able to closely represent the data, except for the polluted sites where the alteration was underestimated. This indicated that the dependence of the alteration rate on the pollutant concentrations should be included to improve the model.

Monitoring atmospheric concentrations of greenhouse gases (GHGs) like carbon dioxide and methane in near real time and with good spatial resolution is crucial for enhancing our understanding of the sources and sinks of these gases. A novel approach can be proposed using a constellation of small satellites equipped with miniaturized spectrometers having a spectral resolution of a few nanometers. The objective of this study is to describe expected results that can be obtained with a single satellite named Uvsq-Sat NG. The SolAtmos end-to-end simulator and its three tools (IRIS, OptiSpectra, and GHGRetrieval) were developed to evaluate the performance of the spectrometer of the Uvsq-Sat NG mission, which focuses on measuring the main GHGs. The IRIS tool was implemented to provide Top-Of-Atmosphere (TOA) spectral radiances. Four scenes were analyzed (pine forest, deciduous forest, ocean, snow) combined with different aerosol types (continental, desert, maritime, urban). Simulated radiance spectra were calculated based on the wavelength ranges of the Uvsq-Sat NG, which spans from 1200 to 2000 nm. The OptiSpectra tool was used to determine optimal observational settings for the spectrometer, including Signal-to-Noise Ratio (SNR) and integration time. Data derived from IRIS and OptiSpectra served as input for our GHGRetrieval simulation tool, developed to provide greenhouse gas concentrations. The Levenberg–Marquardt algorithm was applied iteratively to fine-tune gas concentrations and model inputs, aligning observed transmittance functions with simulated ones under given environmental conditions. To estimate gas concentrations (CO2, CH4, O2, H2O) and their uncertainties, the Monte Carlo method was used. Based on this analysis, this study demonstrates that a miniaturized spectrometer onboard Uvsq-Sat NG is capable of observing different scenes by adjusting its integration time according to the wavelength. The expected precision for each measurement is of the order of a few ppm for carbon dioxide and less than 25 ppb for methane.

Text line segmentation is a necessary preliminary step before most text transcription algorithms are applied. The leading deep learning networks used in this context (ARU-Net, dhSegment, and Doc-UFCN) are based on the U-Net architecture. They are efficient, but fall under the same concept, requiring a post-processing step to perform instance (e.g., text line) segmentation. In the present work, we test the advantages of Mask-RCNN, which is designed to perform instance segmentation directly. This work is the first to directly compare Mask-RCNN- and U-Net-based networks on text segmentation of historical documents, showing the superiority of the former over the latter. Three studies were conducted, one comparing these networks on different historical databases, another comparing Mask-RCNN with Doc-UFCN on a private historical database, and a third comparing the handwritten text recognition (HTR) performance of the tested networks. The results showed that Mask-RCNN outperformed ARU-Net, dhSegment, and Doc-UFCN using relevant line segmentation metrics, that performance evaluation should not focus on the raw masks generated by the networks, that a light mask processing is an efficient and simple solution to improve evaluation, and that Mask-RCNN leads to better HTR performance.

Alzheimer’s disease (AD) is characterized by amyloid beta (Aβ) plaques and hyperphosphorylated tau in the brain. Aβ plaques precede cognitive impairments and can be detected through amyloid-positron emission tomography (PET) or in cerebrospinal fluid (CSF). Assessing the plasma Aβ42/Aβ40 ratio seems promising for non-invasive and cost-effective detection of brain Aβ accumulation. This approach involves some challenges, including the accuracy of blood-based biomarker measurements and the establishment of clear, standardized thresholds to categorize the risk of developing brain amyloid pathology. Plasma Aβ42/Aβ40 ratio was measured in 277 volunteers without dementia, 70 AD patients and 18 non-AD patients using single-molecule array. Patients (n = 88) and some volunteers (n = 66) were subject to evaluation of amyloid status by CSF Aβ quantification or PET analysis. Thresholds of plasma Aβ42/Aβ40 ratio were determined based on a Gaussian mixture model, a decision tree, and the Youden’s index. The 0.0472 threshold, the one with the highest sensitivity, was retained for general population without dementia screening, and the 0.0450 threshold was retained for research and clinical trials recruitment, aiming to minimize the need for CSF or PET analyses to identify amyloid-positive individuals. These findings offer a promising step towards a cost-effective method for identifying individuals at risk of developing AD.

Gem-quality rubies and sapphires are often commercially heat treated at about 800 °C or higher to enhance their color and clarity, and hence quality. For this study, selected corundum samples containing diaspore and goethite inclusions were heated step-by-step to a maximum of 1000 °C with the aim of monitoring the dehydration and phase transformation of these oxyhydroxides to corundum and hematite during heating. Based on our experiments and in agreement with the literature, the dehydration of diaspore in corundum occurs between 525 and 550 °C, whereas goethite transforms to hematite between 300 and 325 °C. As both diaspore and goethite may be present as inclusions in rubies, sapphires, and other corundum varieties (e.g., pink sapphires, padparadscha), these dehydration reactions and phase transformations can be considered important criteria to separate unheated from heated stones, specifically in cases in which other methods (e.g., microscopy, FTIR) are unsuccessful.

Diagnoses in forensic science cover many disciplinary and technical fields, including thanatology and clinical forensic medicine, as well as all the disciplines mobilized by these two major poles: criminalistics, ballistics, anthropology, entomology, genetics, etc. A diagnosis covers three major interrelated concepts: a categorization of pathologies (the diagnosis); a space of signs or symptoms; and the operation that makes it possible to match a set of signs to a category (the diagnostic approach). The generalization of digitization in all sectors of activity—including forensic science, the acculturation of our societies to data and digital devices, and the development of computing, storage, and data analysis capacities—constitutes a favorable context for the increasing adoption of artificial intelligence (AI). AI can intervene in the three terms of diagnosis: in the space of pathological categories, in the space of signs, and finally in the operation of matching between the two spaces. Its intervention can take several forms: it can improve the performance (accuracy, reliability, robustness, speed, etc.) of the diagnostic approach, better define or separate known diagnostic categories, or better associate known signs. But it can also bring new elements, beyond the mere improvement of performance: AI takes advantage of any data (data here extending the concept of symptoms and classic signs, coming either from the five senses of the human observer, amplified or not by technical means, or from complementary examination tools, such as imaging). Through its ability to associate varied and large-volume data sources, but also its ability to uncover unsuspected associations, AI may redefine diagnostic categories, use new signs, and implement new diagnostic approaches. We present in this article how AI is already mobilized in forensic science, according to an approach that focuses primarily on improving current techniques. We also look at the issues related to its generalization, the obstacles to its development and adoption, and the risks related to the use of AI in forensic diagnostics.

The limited availability of antiviral therapy for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spurred the search for novel antiviral drugs. Here, we investigated the potential antiviral properties of plants adapted to high-salt environments collected in the north of France. Twenty-five crude methanolic extracts obtained from twenty-two plant species were evaluated for their cytotoxicity and antiviral effectiveness against coronaviruses HCoV-229E and SARS-CoV-2. Then, a bioguided fractionation approach was employed. The most active crude methanolic extracts were partitioned into three different sub-extracts. Notably, the dichloromethane sub-extract of the whole plant Hippophae rhamnoides L. demonstrated the highest antiviral activity against both viruses. Its chemical composition was evaluated by ultra-high performance liquid chromatography (UHPLC) coupled with mass spectrometry (MS) and then it was fractionated by centrifugal partition chromatography (CPC). Six cinnamoyl triterpenoid compounds were isolated from the three most active fractions by preparative high-performance liquid chromatography (HPLC) and identified by high resolution MS (HR-MS) and mono- and bi-dimensional nuclear magnetic resonance (NMR). Specifically, these compounds were identified as 2-O-trans-p-coumaroyl-maslinic acid, 3β-hydroxy-2α-trans-p-coumaryloxy-urs-12-en-28-oic acid, 3β-hydroxy-2α-cis-p-coumaryloxy-urs-12-en-28-oic acid, 3-O-trans-caffeoyl oleanolic acid, a mixture of 3-O-trans-caffeoyl oleanolic acid/3-O-cis-caffeoyl oleanolic acid (70/30), and 3-O-trans-p-coumaroyl oleanolic acid. Infection tests demonstrated a dose-dependent inhibition of these triterpenes against HCoV-229E and SARS-CoV-2. Notably, cinnamoyl oleanolic acids displayed activity against both SARS-CoV-2 and HCoV-229E. Our findings suggest that Hippophae rhamnoides could represent a source of potential antiviral agents against coronaviruses.

The oceanic waters of the Southwest Tropical Pacific occupy a vast region including multiple Pacific Island Countries. The state of these waters is determinant for fisheries and the blue economy. Ocean color remote sensing is the main tool to survey the variability and long-term evolution of these large areas that are important for economic development but are affected by climate change. Unlike vast oligotrophic gyres, tropical waters are characterized by numerous archipelagos and islands, with deep and shallow lagoons subjected to the large impacts of the land. Strikingly large dendritic phytoplankton (Trichodesmium) blooms with high levels of chlorophyll, developing within archipelagos, as well as coastal enrichments from various origins may be observed. Algorithms to detect the presence of Trichodesmium have been developed or adapted, as well as algorithms to estimate the chlorophyll concentration ([Chl-a)]. Adapting existing [Chl-a] algorithms does not always yield high, i.e., sufficient, accuracy. A review of published regional bio-optical algorithms developed taking into account the specific phytoplankton composition and minimizing the adverse impacts of particles and the seabed bottom on [Chl-a] determination is presented, as well the bio-optical database that allowed their development. The interest of such algorithms for a variety of applications and scientific accomplishments is highlighted, with a view to further addressing the main biology and biogeochemistry questions, e.g., to determine the true impact of diazotrophs and assess lagoon [Chl-a] variability with the highest confidence. This work anticipates the use of future coarse and high-spatial-resolution and multi- and hyper-spectral satellite imagery in the Pacific.

Climate change is undeniably one of the most pressing and critical challenges facing humanity in the 21st century. In this context, monitoring the Earth’s Energy Imbalance (EEI) is fundamental in conjunction with greenhouse gases (GHGs) in order to comprehensively understand and address climate change. The French Uvsq-Sat NG pathfinder mission addresses this issue through the implementation of a Six-Unit CubeSat, which has dimensions of 111.3 × 36.6 × 38.8 cm in its unstowed configuration. Uvsq-Sat NG is a satellite mission spearheaded by the Laboratoire Atmosphères, Observations Spatiales (LATMOS), and supported by the International Satellite Program in Research and Education (INSPIRE). The launch of this mission is planned for 2025. One of the Uvsq-Sat NG objectives is to ensure the smooth continuity of the Earth Radiation Budget (ERB) initiated via the Uvsq-Sat and Inspire-Sat satellites. Uvsq-Sat NG seeks to achieve broadband ERB measurements using state-of-the-art yet straightforward technologies. Another goal of the Uvsq-Sat NG mission is to conduct precise and comprehensive monitoring of atmospheric gas concentrations (CO2 and CH4) on a global scale and to investigate its correlation with Earth’s Outgoing Longwave Radiation (OLR). Uvsq-Sat NG carries several payloads, including Earth Radiative Sensors (ERSs) for monitoring incoming solar radiation and outgoing terrestrial radiation. A Near-Infrared (NIR) Spectrometer is onboard to assess GHGs’ atmospheric concentrations through observations in the wavelength range of 1200 to 2000 nm. Uvsq-Sat NG also includes a high-definition camera (NanoCam) designed to capture images of the Earth in the visible range. The NanoCam will facilitate data post-processing acquired via the spectrometer by ensuring accurate geolocation of the observed scenes. It will also offer the capability of observing the Earth’s limb, thus providing the opportunity to roughly estimate the vertical temperature profile of the atmosphere. We present here the scientific objectives of the Uvsq-Sat NG mission, along with a comprehensive overview of the CubeSat platform’s concepts and payload properties as well as the mission’s current status. Furthermore, we also describe a method for the retrieval of atmospheric gas columns (CO2, CH4, O2, H2O) from the Uvsq-Sat NG NIR Spectrometer data. The retrieval is based on spectra simulated for a range of environmental conditions (surface pressure, surface reflectance, vertical temperature profile, mixing ratios of primary gases, water vapor, other trace gases, cloud and aerosol optical depth distributions) as well as spectrometer characteristics (Signal-to-Noise Ratio (SNR) and spectral resolution from 1 to 6 nm).

Introduction: Rare disease referral centres are entrusted with missions of clinical expertise and research, two activities that have to contend with numerous obstacles. Providing specialist opinions is time-consuming, uncompensated and limited by difficulties in exchanging medical data. Clinical research is constrained by the need for frequent research protocol visits. Our objective was to determine whether telemedicine (TLM) can overcome these difficulties. Methods: To better characterise the activity of clinical expertise provided by our French centre, each opinion delivered by our team was reported on a standardised form. To investigate our clinical research activity, investigators and patients were asked to complete a questionnaire on the acceptability of research protocol teleconsultations. Results: Regarding clinical expertise, our team delivered 120 opinions per week (representing a total of 21 h), of which 29% were delivered to patients and 69% to medical practitioners. If these were delivered using TLM, it would represent a potential weekly income of EUR 500 (tele-expertise) and EUR 775 (teleconsultations). Regarding the research activity, 70% of investigators considered the frequency of visits to be a limiting factor for patient inclusions; nearly half of the patients surveyed would be in favour of having teleconsultations in place of (40%) or in addition to (56%) in-person visits. Conclusion: Whereas TLM has become widely used as a back-up procedure to in-person consultations during the COVID-19 pandemic, the solutions it provides to the problems encountered in performing expertise and research activities have made it a new conventional follow-up modality for patients with rare diseases.

The enteric nervous system (ENS), known as the intrinsic nervous system of the gastrointestinal tract, is composed of a diverse array of neuronal and glial cell subtypes. Fascinating questions surrounding the generation of cellular diversity in the ENS have captivated ENS biologists for a considerable time, particularly with recent advancements in cell type-specific transcriptomics at both population and single-cell levels. However, the current focus of research in this field is predominantly restricted to the study of enteric neuron subtypes, while the investigation of enteric glia subtypes significantly lags behind. Despite this, enteric glial cells (EGCs) are increasingly recognized as equally important regulators of numerous bowel functions. Moreover, a subset of postnatal EGCs exhibits remarkable plasticity and multipotency, distinguishing them as critical entities in the context of advancing regenerative medicine. In this review, we aim to provide an updated overview of the current knowledge on this subject, while also identifying key questions that necessitate future exploration.

The use of sensitive animals in toxicological studies tends to be limited. Even though cell culture is an attractive alternative, it has some limitations. Therefore, we investigated the potential of the metabolomic profiling of the allantoic fluid (AF) from ex ovo chick embryos to predict the hepatotoxicity of valproate (VPA). To this end, the metabolic changes occurring during embryo development and following exposure to VPA were assessed using 1H-NMR spectroscopy. During embryonic development, our findings indicated a metabolism progressively moving from anaerobic to aerobic, mainly based on lipids as the energy source. Next, liver histopathology of VPA-exposed embryos revealed abundant microvesicles indicative of steatosis and was metabolically confirmed via the determination of lipid accumulation in AF. VPA-induced hepatotoxicity was further demonstrated by (i) lower glutamine levels, precursors of glutathione, and decreased β-hydroxybutyrate, an endogenous antioxidant; (ii) changes in lysine levels, a precursor of carnitine, which is essential in the transport of fatty acids to the mitochondria and whose synthesis is known to be reduced by VPA; and (iii) choline accumulation that promotes the export of hepatic triglycerides. In conclusion, our results support the use of the ex ovo chick embryo model combined with the metabolomic assessment of AF to rapidly predict drug-induced hepatotoxicity.

Cefiderocol (FDC) is a siderophore cephalosporin now recognized as a new weapon in the treatment of difficult-to-treat-resistant (DTR) Gram-negative pathogens, including carbapenemase-producing enterobacterales and non-fermentative Gram-negative bacilli (GNB). This article reports our experience with an FDC-based regimen in the treatment of 16 extremely severe patients (invasive mechanical ventilation, 15/16; extracorporeal membrane oxygenation, 9/16; and renal replacement therapy, 8/16) infected with DTR GNB. Our case series provides detailed insight into the pharmacokinetic profile and the microbiological data in real-life conditions. In the narrative review, we discuss the interest of FDC in the treatment of non-fermentative GNB in critically ill patients. We reviewed the microbiological spectrum, resistance mechanisms, pharmacokinetics/pharmacodynamics, efficacy and safety profiles, and real-world evidence for FDC. On the basis of our experience and the available literature, we discuss the optimal FDC-based regimen, FDC dosage, and duration of therapy in critically ill patients with DTR non-fermentative GNB infections.

The COVID-19 pandemic, caused by SARS-CoV-2, addressed the lack of specific antiviral drugs against coronaviruses. In this study, bioguided fractionation performed on both ethyl acetate and aqueous sub-extracts of Juncus acutus stems led to identifying luteolin as a highly active antiviral molecule against human coronavirus HCoV-229E. The apolar sub-extract (CH2Cl2) containing phenanthrene derivatives did not show antiviral activity against this coronavirus. Infection tests on Huh-7 cells, expressing or not the cellular protease TMPRSS2, using luciferase reporter virus HCoV-229E-Luc showed that luteolin exhibited a dose-dependent inhibition of infection. Respective IC50 values of 1.77 µM and 1.95 µM were determined. Under its glycosylated form (luteolin-7-O-glucoside), luteolin was inactive against HCoV-229E. Time of addition assay showed that utmost anti-HCoV-229E activity of luteolin was achieved when added at the post-inoculation step, indicating that luteolin acts as an inhibitor of the replication step of HCoV-229E. Unfortunately, no obvious antiviral activity for luteolin was found against SARS-CoV-2 and MERS-CoV in this study. In conclusion, luteolin isolated from Juncus acutus is a new inhibitor of alphacoronavirus HCoV-229E.

Structural health monitoring (SHM) strategies based on ultrasonic guided waves are very promising regarding thin-walled lightweight structures. To study the performance of such systems, validated numerical analysis tools have to be used. For that procedure, a benchmark between two numerical methods will be presented. The first promising approach is the elastodynamic finite integration technique (EFIT). Miscellaneous research shows that its capability of capturing wave characteristics and interactions is advanced in various media and structures, including thin-walled composites. The second approach employs conventional shell-type finite elements following the Reissner–Mindlin theory for modelling layered composite structures. The advantage of using such finite element methods (FEM) is their high availability in general purpose simulation tools. As a reference model, the measurement data coming from the Open Guided Waves Project (OGW) was taken into account. The OGW dataset provides the experimental data of ultrasonic guided wave propagation in carbon fiber composite plates with an additional omega stringer. By using this contribution, this experiment was reproduced by simulation. The paper presents the results of a validation and motivates further research, such as in research related to the probability of detection analysis and numerical performance.

The carbon system in the eastern tropical Atlantic remains poorly known. The variability and drivers of the carbon system are assessed using surface dissolved inorganic carbon (DIC), alkalinity (TA) and fugacity of CO2 (fCO2) measured in the 12° N–12° S, 12° W–12° E region from 2005 to 2019. A relationship linking DIC to temperature, salinity and year has been determined, with salinity being the strongest predictor. The seasonal variations of DIC, ranging from 80 to 120 μμmol kg−1, are more important than the year-to-year variability that is less than 50 μμmol kg−1 over the 2010–2019 period. DIC and TA concentrations are lower in the northern part of the basin where surface waters are fresher and warmer. Carbon supply dominates over biological carbon uptake during the productive upwelling period from July to September. The lowest DIC and TA are located in the Congo plume. The influence of the Congo is still observed at the mooring at 6° S, 8° E as shown by large salinity and chlorophyll variations. Nevertheless, this site is a source of CO2 emissions into the atmosphere.

Primary Carnitine Deficiency (PCD) is a fatty acid oxidation disorder that will be included in the expansion of the French newborn screening (NBS) program at the beginning of 2023. This disease is of high complexity to screen, due to its pathophysiology and wide clinical spectrum. To date, few countries screen newborns for PCD and struggle with high false positive rates. Some have even removed PCD from their screening programs. To understand the risks and pitfalls of implementing PCD to the newborn screening program, we reviewed and analyzed the literature to identify hurdles and benefits from the experiences of countries already screening this inborn error of metabolism. In this study, we therefore, present the main pitfalls encountered and a worldwide overview of current practices in PCD newborn screening. In addition, we address the optimized screening algorithm that has been determined in France for the implementation of this new condition.

Differences in disability perception between patients and care providers may impact outcomes. We aimed to explore differences in disability perception between patients and care providers in systemic sclerosis (SSc). We conducted a cross-sectional internet-based mirror survey. SSc patients participating in the online SPIN Cohort and care providers affiliated with 15 scientific societies were surveyed using the Cochin Scleroderma International Classification of Functioning, Disability and Health (ICF)-65 questionnaire, including 65 items (from 0 to 10), representing 9 domains of disability. Mean differences between patients and care providers were calculated. Care providers’ characteristics associated with a mean difference ≥ 2 of 10 points were assessed in multivariate analysis. Answers were analyzed for 109 patients and 105 care providers. The mean age of patients was 55.9 (14.7) years and the disease duration was 10.1 (7.5) years. For all domains of the ICF-65, care providers’ rates were higher than those of patients. The mean difference was 2.4 (1.0) of 10 points. Care providers’ characteristics associated with this difference were organ-based specialty (OR = 7.0 [2.3–21.2]), younger age (OR = 2.7 [1.0–7.1]) and following patients with disease duration ≥5 years (OR = 3.0 [1.1–8.7]). We found systematic differences in disability perception between patients and care providers in SSc.

Chronic low back pain (cLBP) is a public and occupational health problem that is a major professional, economic and social burden. We aimed to provide a critical overview of current international recommendations regarding the management of non-specific cLBP. We conducted a narrative review of international guidelines for the diagnosis and conservative treatment of people with non-specific cLBP. Our literature search yielded five reviews of guidelines published between 2018 and 2021. In these five reviews, we identified eight international guidelines that fulfilled our selection criteria. We added the 2021 French guidelines into our analysis. Regarding diagnosis, most international guidelines recommend searching for so-called yellow, blue and black flags, in order to stratify the risk of chronicity and/or persistent disability. The relevance of clinical examination and imaging are under debate. Regarding management, most international guidelines recommend non-pharmacological treatments, including exercise therapy, physical activity, physiotherapy and education; however, multidisciplinary rehabilitation, in selected cases, is the core treatment recommended for people with non-specific cLBP. Oral, topical or injected pharmacological treatments are under debate, and may be offered to selected and well-phenotyped patients. The diagnosis of people with cLBP may lack precision. All guidelines recommend multimodal management. In clinical practice, the management of individuals with non-specific cLBP should combine non-pharmacological and pharmacological treatments. Future research should focus on improving tailorization.

Sonoporation using microbubble-assisted ultrasound increases the permeability of a biological barrier to therapeutic molecules. Application of this method to the round window membrane could improve the delivery of therapeutics to the inner ear. The aim of this study was to assess the safety of sonoporation of the round window membrane in a sheep model. To achieve this objective, we assessed auditory function and cochlear heating, and analysed the metabolomics profiles of perilymph collected after sonoporation, comparing them with those of the control ear in the same animal. Six normal-hearing ewes were studied, with one sonoporation ear and one control ear for each. A mastoidectomy was performed on both ears. On the sonoporation side, Vevo MicroMarker® microbubbles (MBs; VisualSonics—Fujifilm, Amsterdam, The Netherlands) at a concentration of 2 × 108 MB/mL were locally injected into the middle ear and exposed to 1.1 MHz sinusoidal ultrasonic waves at 0.3 MPa negative peak pressure with 40% duty cycle and 100 μs interpulse period for 1 min; this was repeated three times with 1 min between applications. The sonoporation protocol did not induce any hearing impairment or toxic overheating compared with the control condition. The metabolomic analysis did not reveal any significant metabolic difference between perilymph samples from the sonoporation and control ears. The results suggest that sonoporation of the round window membrane does not cause damage to the inner ear in a sheep model.

Amino-terminal CuII and NiII (ATCUN) binding sequences are widespread in the biological world. Here, we report on the study of eight ATCUN peptides aimed at targeting copper ions and stopping the associated formation of reactive oxygen species (ROS). This study was actually more focused on Cu(Aβ)-induced ROS production in which the Aβ peptide is the “villain” linked to Alzheimer’s disease. The full characterization of CuII binding to the ATCUN peptides, the CuII extraction from CuII(Aβ), and the ability of the peptides to prevent and/or stop ROS formation are described in the relevant biological conditions. We highlighted in this research that all the ATCUN motifs studied formed the same thermodynamic complex but that the addition of a second histidine in position 1 or 2 allowed for an improvement in the CuII uptake kinetics. This kinetic rate was directly related to the ability of the peptide to stop the CuII(Aβ)-induced production of ROS, with the most efficient motifs being HWHG and HGHW.

The gut microbiome is involved in nutrient metabolism and produces metabolites that, via the gut–brain axis, signal to the brain and influence cognition. Human studies have so far had limited success in identifying early metabolic alterations linked to cognitive aging, likely due to limitations in metabolite coverage or follow-ups. Older persons from the Three-City population-based cohort who had not been diagnosed with dementia at the time of blood sampling were included, and repeated measures of cognition over 12 subsequent years were collected. Using a targeted metabolomics platform, we identified 72 circulating gut-derived metabolites in a case–control study on cognitive decline, nested within the cohort (discovery n = 418; validation n = 420). Higher serum levels of propionic acid, a short-chain fatty acid, were associated with increased odds of cognitive decline (OR for 1 SD = 1.40 (95% CI 1.11, 1.75) for discovery and 1.26 (1.02, 1.55) for validation). Additional analyses suggested mediation by hypercholesterolemia and diabetes. Propionic acid strongly correlated with blood glucose (r = 0.79) and with intakes of meat and cheese (r > 0.15), but not fiber (r = 0.04), suggesting a minor role of prebiotic foods per se, but a possible link to processed foods, in which propionic acid is a common preservative. The adverse impact of propionic acid on metabolism and cognition deserves further investigation.

Background: Acute kidney injury (AKI) is the second most frequent condition after acute respiratory distress syndrome (ARDS) in critically ill patients with severe COVID-19 and is strongly associated with mortality. The aim of this multicentric study was to assess the impact of the specific treatments of COVID-19 and ARDS on the risk of severe AKI in critically ill COVID-19 patients. Methods: In this cohort study, data from consecutive patients older than 18 years admitted to 6 ICUs for COVID-19-related ARDS requiring invasive mechanical ventilation were included. The incidence and severity of AKI, defined according to the 2012 KDIGO definition, were monitored during the entire ICU stay until day 90. Patients older than 18 years admitted to the ICU for COVID-19-related ARDS requiring invasive mechanical ventilation were included. Results: 164 patients were included in the final analysis; 97 (59.1%) displayed AKI, of which 39 (23.8%) had severe stage 3 AKI, and 21 (12.8%) required renal replacement therapy (RRT). In univariate analysis, severe AKI was associated with angiotensin-converting enzyme inhibitors (ACEI) exposure (p = 0.016), arterial hypertension (p = 0.029), APACHE-II score (p = 0.004) and mortality at D28 (p = 0.008), D60 (p < 0.001) and D90 (p < 0.001). In multivariate analysis, the factors associated with the onset of stage 3 AKI were: exposure to ACEI (OR: 4.238 (1.307–13.736), p = 0.016), APACHE II score (without age) (OR: 1.138 (1.044–1.241), p = 0.003) and iNO (OR: 5.694 (1.953–16.606), p = 0.001). Prone positioning (OR: 0.234 (0.057–0.967), p = 0.045) and dexamethasone (OR: 0.194 (0.053–0.713), p = 0.014) were associated with a decreased risk of severe AKI. Conclusions: Dexamethasone was associated with the prevention of the risk of severe AKI and RRT, and iNO was associated with severe AKI and RRT in critically ill patients with COVID-19. iNO should be used with caution in COVID-19-related ARDS.

Approximately 10–20% of patients who have sustained a mild Traumatic Brain Injury (mTBI) show persistent post-concussion symptoms (PCS). This review aims to summarize the level of evidence concerning interventions for PCS. Following the PRISMA guidelines, we conducted a systematic review regarding interventions for PCS post-mTBI until August 2021 using the Medline, Cochrane, and Embase databases. Inclusion criteria were the following: (1) intervention focusing on PCS after mTBI, (2) presence of a control group, and (3) adult patients (≥18 y.o). Quality assessment was determined using the Incog recommendation level, and the risk of bias was assessed using the revised Cochrane risk-of-bias tool. We first selected 104 full-text articles. Finally, 55 studies were retained, including 35 that obtained the highest level of evidence. The risk of bias was high in 22 out of 55 studies. Cognitive training, psycho-education, cognitive behavioral therapy, and graded return to physical activity demonstrated some effectiveness on persistent PCS. However, there is limited evidence of the beneficial effect of Methylphenidate. Oculomotor rehabilitation, light therapy, and headache management using repetitive transcranial magnetic stimulation seem effective regarding somatic complaints and sleep disorders. The preventive effect of early (

Metabolic syndrome (MetS) is a complex condition encompassing a constellation of cardiometabolic abnormalities. Oxylipins are a superfamily of lipid mediators regulating many cardiometabolic functions. Plasma oxylipin signature could provide a new clinical tool to enhance the phenotyping of MetS pathophysiology. A high-throughput validated mass spectrometry method, allowing for the quantitative profiling of over 130 oxylipins, was applied to identify and validate the oxylipin signature of MetS in two independent nested case/control studies involving 476 participants. We identified an oxylipin signature of MetS (coined OxyScore), including 23 oxylipins and having high performances in classification and replicability (cross-validated AUCROC of 89%, 95% CI: 85–93% and 78%, 95% CI: 72–85% in the Discovery and Replication studies, respectively). Correlation analysis and comparison with a classification model incorporating the MetS criteria showed that the oxylipin signature brings consistent and complementary information to the clinical criteria. Being linked with the regulation of various biological processes, the candidate oxylipins provide an integrative phenotyping of MetS regarding the activation and/or negative feedback regulation of crucial molecular pathways. This may help identify patients at higher risk of cardiometabolic diseases. The oxylipin signature of patients with metabolic syndrome enhances MetS phenotyping and may ultimately help to better stratify the risk of cardiometabolic diseases.

The transcription factor Krüppel-like factor 10 (Klf10), also known as Tieg1 for TGFβ (Inducible Early Gene-1) is known to control numerous genes in many cell types that are involved in various key biological processes (differentiation, proliferation, apoptosis, inflammation), including cell metabolism and human disease. In skeletal muscle, particularly in the soleus, deletion of the Klf10 gene (Klf10 KO) resulted in ultrastructure fiber disorganization and mitochondrial metabolism deficiencies, characterized by muscular hypertrophy. To determine the metabolic profile related to loss of Klf10 expression, we analyzed blood and soleus tissue using UHPLC-Mass Spectrometry. Metabolomics analyses on both serum and soleus revealed profound differences between wild-type (WT) and KO animals. Klf10 deficient mice exhibited alterations in metabolites associated with energetic metabolism. Additionally, chemical classes of aromatic and amino-acid compounds were disrupted, together with Krebs cycle intermediates, lipids and phospholipids. From variable importance in projection (VIP) analyses, the Warburg effect, citric acid cycle, gluconeogenesis and transfer of acetyl groups into mitochondria appeared to be possible pathways involved in the metabolic alterations observed in Klf10 KO mice. These studies have revealed essential roles for Klf10 in regulating multiple metabolic pathways whose alterations may underlie the observed skeletal muscle defects as well as other diseases.

Diverse biomarkers and pathological alterations have been found in muscle of patients with Amyotrophic lateral sclerosis (ALS), but the relation between such alterations and dysfunction in energetic metabolism remains to be investigated. We established the metabolome of muscle and serum of ALS patients and correlated these findings with the clinical status and pathological alterations observed in the muscle. We obtained data from 20 controls and 17 ALS patients (disease duration: 9.4 ± 6.8 months). Multivariate metabolomics analysis identified a distinct serum metabolome for ALS compared to controls (p-CV-ANOVA < 0.035) and revealed an excellent discriminant profile for muscle metabolome (p-CV-ANOVA < 0.0012). Citramalate was discriminant for both muscle and serum. High lauroylcarnitine levels in muscle were associated with low Forced Vital Capacity. Transcriptomics analysis of key antioxidant enzymes showed an upregulation of SOD3 (p = 0.0017) and GLRX2(1) (p = 0.0022) in ALS muscle. Analysis of mitochondrial enzymatic activity in muscle revealed higher complex II/CS (p = 0.04) and lower LDH (p = 0.03) activity in ALS than in controls. Our study showed, for the first time, a global dysfunction in the muscle of early-stage ALS patients. Furthermore, we identified novel metabolites to be employed as biomarkers for diagnosis and prognosis of ALS patients.

Human activities in the sea, such as intensive fishing and exploitation of offshore wind farms, may impact negatively on the marine mega fauna. As an attempt to control such impacts, surveying, and tracking of marine animals are often performed on the sites where those activities take place. Nowadays, thank to high resolution cameras and to the development of machine learning techniques, tracking of wild animals can be performed remotely and the analysis of the acquired images can be automatized using state-of-the-art object detection models. However, most state-of-the-art detection methods require lots of annotated data to provide satisfactory results. Since analyzing thousands of images acquired during a flight survey can be a cumbersome and time consuming task, we focus in this article on the weakly supervised detection of marine animals. We propose a modification of the patch distribution modeling method (PaDiM), which is currently one of the state-of-the-art approaches for anomaly detection and localization for visual industrial inspection. In order to show its effectiveness and suitability for marine animal detection, we conduct a comparative evaluation of the proposed method against the original version, as well as other state-of-the-art approaches on two high-resolution marine animal image datasets. On both tested datasets, the proposed method yielded better F1 and recall scores (75% recall/41% precision, and 57% recall/60% precision, respectively) when trained on images known to contain no object of interest. This shows a great potential of the proposed approach to speed up the marine animal discovery in new flight surveys. Additionally, such a method could be adopted for bounding box proposals to perform faster and cheaper annotation within a fully-supervised detection framework.

The French tokamak WEST supports the ITER design and operation. IRFM is designing a new Thomson scattering diagnostic to measure plasma density and temperature profiles. The diagnostic system consists of an endoscope inside a vacuum vessel, composed of actively cooled optical components. In order to validate and guarantee the diagnostic performances during normal operations, mechanical, thermal, hydraulic and vibratory behavior must be checked. Moreover, perpendicular displacement of the optical surface shall not be higher than 40 µm. Since this diagnostic operates in near infrared light, the temperature of all components must stay lower than 200 °C as not to bias the measurements. The differences in water temperature and pressure between the inlet and outlet of the diagnostic must be lower than 50 °C and 5.6 bar, respectively. The natural frequencies of the structure must be higher than 20 Hz and far enough from the frequency of external components. In this study, the worst radiative plasma scenario was chosen. The results of this study validate the accuracy of the measurements. Before manufacturing, electromagnetic disruption events must also be considered.

The search of substrates for solute carriers (SLCs) constitutes a major issue, owing notably to the role played by some SLCs, such as the renal electrogenic organic cation transporter (OCT) 2 (SLC22A2), in pharmacokinetics, drug–drug interactions and drug toxicity. For this purpose, substrates have been proposed to be identified by their cis-inhibition and trans-stimulation properties towards transporter activity. To get insights on the sensitivity of this approach for identifying SLC substrates, 15 various exogenous and endogenous OCT2 substrates were analysed in the present study, using 4-(4-(dimethylamino)styryl)-N-methylpyridinium iodide (DiASP) as a fluorescent OCT2 tracer substrate. All OCT2 substrates cis-inhibited DiASP uptake in OCT2-overexpressing HEK293 cells, with IC50 values ranging from 0.24 µM (for ipratropium) to 2.39 mM (for dopamine). By contrast, only 4/15 substrates, i.e., acetylcholine, agmatine, choline and metformin, trans-stimulated DiASP uptake, with a full suppression of the trans-stimulating effect of metformin by the reference OCT2 inhibitor amitriptyline. An analysis of molecular descriptors next indicated that trans-stimulating OCT2 substrates exhibit lower molecular weight, volume, polarizability and lipophilicity than non-trans-stimulating counterparts. Overall, these data indicated a rather low sensitivity (26.7%) of the trans-stimulation assay for identifying OCT2 substrates, and caution with respect to the use of such assay may therefore be considered.

Metabolomics has been increasingly used in animal and food sciences. Animal health is one of the most important factor that can also alter animal integrity and welfare. Some studies have already investigated the link between health and metabolic profile of dairy animals. These studies in metabolomics often consider a single type of sample using a single analytical platform (nuclear magnetic resonance or mass spectrometry). Only few studies with multi-platform approaches are also used with a single or a multi type of sample, but they mainly consider dairy cows’ metabolome although dairy goats present similar diseases, that it could be interesting to detect early to preserve animal health and milk production. This study aims to create a metabolic atlas of goat plasma, milk and feces, based on healthy animals. Our study describes a standard operating procedure for three goat matrices: blood plasma, milk, and feces using multiple platforms (NMR (1H), UHPLC (RP)-MS and UHPLC (HILIC)-MS) that follows a unique sample preparation procedure for each sample type to be analyzed on multi-platforms basis. Our method was evaluated for its robustness and allowed a better characterization of goat metabolic profile in healthy conditions.

The epidermis is a living, multilayered barrier with five functional levels, including a physical, a chemical, a microbial, a neuronal, and an immune level. Altogether, this complex organ contributes to protect the host from external aggression and to preserve its integrity. In this review, we focused on the different functional aspects.

In recent years, numerous deep learning techniques have been proposed to tackle the semantic segmentation of aerial and satellite images, increase trust in the leaderboards of main scientific contests and represent the current state-of-the-art. Nevertheless, despite their promising results, these state-of-the-art techniques are still unable to provide results with the level of accuracy sought in real applications, i.e., in operational settings. Thus, it is mandatory to qualify these segmentation results and estimate the uncertainty brought about by a deep network. In this work, we address uncertainty estimations in semantic segmentation. To do this, we relied on a Bayesian deep learning method, based on Monte Carlo Dropout, which allows us to derive uncertainty metrics along with the semantic segmentation. Built on the most widespread U-Net architecture, our model achieves semantic segmentation with high accuracy on several state-of-the-art datasets. More importantly, uncertainty maps are also derived from our model. While they allow for the performance of a sounder qualitative evaluation of the segmentation results, they also include valuable information to improve the reference databases.

Currently, most clinical studies in metabolomics only consider a single type of sample such as urine, plasma, or feces and use a single analytical platform, either NMR or MS. Although some studies have already investigated metabolomics data from multiple fluids, the information is limited to a unique analytical platform. On the other hand, clinical studies investigating the human metabolome that combine multi-analytical platforms have focused on a single biofluid. Combining data from multiple sample types for one patient using a multimodal analytical approach (NMR and MS) should extend the metabolome coverage. Pre-analytical and analytical phases are time consuming. These steps need to be improved in order to move into clinical studies that deal with a large number of patient samples. Our study describes a standard operating procedure for biological specimens (urine, blood, saliva, and feces) using multiple platforms (1H-NMR, RP-UHPLC-MS, and HILIC-UHPLC-MS). Each sample type follows a unique sample preparation procedure for analysis on a multi-platform basis. Our method was evaluated for its robustness and was able to generate a representative metabolic map.

In the context of rapid urbanization, monitoring the evolution of cities is crucial. To do so, 3D change detection and characterization is of capital importance since, unlike 2D images, 3D data contain vertical information of utmost importance to monitoring city evolution (that occurs along both horizontal and vertical axes). Urban 3D change detection has thus received growing attention, and various methods have been published on the topic. Nevertheless, no quantitative comparison on a public dataset has been reported yet. This study presents an experimental comparison of six methods: three traditional (difference of DSMs, C2C and M3C2), one machine learning with hand-crafted features (a random forest model with a stability feature) and two deep learning (feed-forward and Siamese architectures). In order to compare these methods, we prepared five sub-datasets containing simulated pairs of 3D annotated point clouds with different characteristics: from high to low resolution, with various levels of noise. The methods have been tested on each sub-dataset for binary and multi-class segmentation. For supervised methods, we also assessed the transfer learning capacity and the influence of the training set size. The methods we used provide various kinds of results (2D pixels, 2D patches or 3D points), and each of them is impacted by the resolution of the PCs. However, while the performances of deep learning methods highly depend on the size of the training set, they seem to be less impacted by training on datasets with different characteristics. Oppositely, conventional machine learning methods exhibit stable results, even with smaller training sets, but embed low transfer learning capacities. While the main changes in our datasets were usually identified, there were still numerous instances of false detection, especially in dense urban areas, thereby calling for further development in this field. To assist such developments, we provide a public dataset composed of pairs of point clouds with different qualities together with their change-related annotations. This dataset was built with an original simulation tool which allows one to generate bi-temporal urban point clouds under various conditions.

The aim of this study was to evaluate the kynurenine pathway (KP) and amino acids profile, using mass spectrometry, in the cerebrospinal fluid (CSF) of 42 amyotrophic lateral sclerosis (ALS) patients at the diagnosis and 40 controls to detect early disorders of these pathways. Diagnostic and predictive ability (based on weight loss, forced vital capacity, ALS Functional Rating Scale—Revised evolution over 12 months, and survival time) of these metabolites were evaluated using univariate followed by supervised multivariate analysis. The multivariate model between ALS and controls was not significant but highlighted some KP metabolites (kynurenine (KYN), kynurenic acid (KYNA), 3-Hydroxynurenine (3-HK)/KYNA ratio), and amino acids (Lysine, asparagine) as involved in the discrimination between groups (accuracy 62%). It revealed a probable KP impairment toward neurotoxicity in ALS patients and in bulbar forms. Regarding the prognostic effect of metabolites, 12 were commonly discriminant for at least 3 of 4 disease evolution criteria. This investigation was crucial as it did not show significant changes in CSF concentrations of amino acids and KP intermediates in early ALS evolution. However, trends of KP modifications suggest further exploration. The unclear kinetics of neuroinflammation linked to KP support the interest in exploring these pathways during disease evolution through a longitudinal strategy.

Background and purpose: Circulating tumor DNA (ctDNA) is investigated in various cancers. In squamous cell carcinoma of the anus (SCCA) infection with human papilloma virus (HPV) is found in around 90% of cases and here, plasma HPV (pHPV) can be used as ctDNA. Preliminary data have proved the ability to detect pHPV16 and -18 in SCCA. We have developed a highly sensitive method for measurement of six relevant pHPV subtypes, to investigate the elimination pattern of pHPV during chemo-radiotherapy (CRT) for SCCA and its clinical value. Material and methods: Patients treated at Aarhus University Hospital from 2016–2020 were included. P16 status in the primary biopsy was measured and 82% of patients had P16 positive tumor. Blood samples were collected prior to treatment (PT), mid treatment (MT), end of therapy (EOT), and during follow-up (FU). An in-house multiplex digital droplet PCR method measured pHPV subtypes 16, 18, 31, 33, 51, 58. Results: Samples from 88 patients were drawn PT (n = 73), MT (n = 72), EOT (n = 64) and during FU (n = 41). Plasma HPV was detectable in 52 patients and PT pHPV levels correlated to tumor stages. Three elimination patterns were observed during CRT with correlation to outcome: fast responders with no local or distant failures (0/12); slow responders with high risk of local failures (4/20), no distant failures; persistent molecular responders with high risk of distant failures (4/13), but no local failures, p < 0.01. Conclusion: During CRT, pHPV can divide patients with SCCA into three groups with significantly different risk of failure. The use of pHPV can potentially assist in clinical treatment decision.

Despite increasingly detailed knowledge of the biochemical processes involved in the determination of meat quality traits, robust models, using biochemical characteristics of the muscle to predict future meat quality, lack. The neglecting of various aspects of the model paradigm may explain this. First, preslaughter stress has a major impact on meat quality and varies according to slaughter context and individuals. Yet, it is rarely taken into account in meat quality models. Second, phenotypic similarity does not imply similarity in the underlying biological causes, and several models may be needed to explain a given phenotype. Finally, the implications of the complexity of biological systems are discussed: a homeostatic equilibrium can be reached in countless ways, involving thousands of interacting processes and molecules at different levels of the organism, changing over time and differing between animals. Consequently, even a robust model may explain a significant part, but not all of the variability between individuals.

SARS-CoV-2 infection is the cause of COVID-19 in humans. In April 2020, SARS-CoV-2 infection in farmed mink (Neovision vision) occurred in the Netherlands. The first outbreaks in Denmark were detected in June 2020 in three farms. A steep increase in the number of infected farms occurred from September and onwards. Here, we describe prevalence data collected from 215 infected mink farms to characterize spread and impact of disease in infected farms. In one third of the farms, no clinical signs were observed. In farms with clinical signs, decreased feed intake, increased mortality and respiratory symptoms were most frequently observed, during a limited time period (median of 11 days). In 65% and 69% of farms, virus and sero-conversion, respectively, were detected in 100% of sampled animals at the first sampling. SARS-CoV-2 was detected, at low levels, in air samples collected close to the mink, on mink fur, on flies, on the foot of a seagull, and in gutter water, but not in feed. Some dogs and cats from infected farms tested positive for the virus. Chickens, rabbits, and horses sampled on a few farms, and wildlife sampled in the vicinity of the infected farms did not test positive for SARS-CoV-2. Thus, mink are highly susceptible to infection by SARS-CoV-2, but routes of transmission between farms, other than by direct human contact, are unclear.

An underwater volcanic eruption off the Vava’u island group in Tonga on 7 August 2019 resulted in the creation of floating pumice on the ocean’s surface extending over an area of 150 km2. The pumice’s far-reaching effects from its origin in the Tonga region to Fiji and the methods of automatic detection using satellite imagery are described, making it possible to track the westward drift of the pumice raft over 43 days. Level 2 Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Sentinel-3 Ocean and Land Color Instrument (OLCI), and Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) imagery of sea surface temperature, chlorophyll-a concentration, quasi-surface (i.e., Rayleigh-corrected) reflectance, and remote sensing reflectance were used to distinguish consolidated and fragmented rafts as well as discolored and mesotrophic waters. The rafts were detected by a 1 to 3.5 °C enhancement in the MODIS-derived “sea surface temperature” due to the emissivity difference of the raft material. Large plumes of discolored waters, characterized by higher satellite reflectance/backscattering of particles in the blue than surrounding waters (and corresponding to either submersed pumice or associated white minerals), were associated with the rafts. The discolored waters had relatively lower chlorophyll-a concentration, but this was artificial, resulting from the higher blue/red reflectance ratio caused by the reflective pumice particles. Mesotrophic waters were scarce in the region of the pumice rafts, presumably due to the absence of phytoplanktonic response to a silicium-rich pumice environment in these tropical oligotrophic environments. As beach accumulations around Pacific islands surrounded by coral shoals are a recurrent phenomenon that finds its origin far east in the ocean along the Tongan trench, monitoring the events from space, as demonstrated for the 7 August 2019 eruption, might help mitigate their potential economic impacts.

This article tackles the problem of detecting small objects in satellite or aerial remote sensing images by relying on super-resolution to increase image spatial resolution, thus the size and details of objects to be detected. We show how to improve the super-resolution framework starting from the learning of a generative adversarial network (GAN) based on residual blocks and then its integration into a cycle model. Furthermore, by adding to the framework an auxiliary network tailored for object detection, we considerably improve the learning and the quality of our final super-resolution architecture, and more importantly increase the object detection performance. Besides the improvement dedicated to the network architecture, we also focus on the training of super-resolution on target objects, leading to an object-focused approach. Furthermore, the proposed strategies do not depend on the choice of a baseline super-resolution framework, hence could be adopted for current and future state-of-the-art models. Our experimental study on small vehicle detection in remote sensing data conducted on both aerial and satellite images (i.e., ISPRS Potsdam and xView datasets) confirms the effectiveness of the improved super-resolution methods to assist with the small object detection tasks.

Object detection from aerial and satellite remote sensing images has been an active research topic over the past decade. Thanks to the increase in computational resources and data availability, deep learning-based object detection methods have achieved numerous successes in computer vision, and more recently in remote sensing. However, the ability of current detectors to deal with (very) small objects still remains limited. In particular, the fast detection of small objects from a large observed scene is still an open question. In this work, we address this challenge and introduce an enhanced one-stage deep learning-based detection model, called You Only Look Once (YOLO)-fine, which is based on the structure of YOLOv3. Our detector is designed to be capable of detecting small objects with high accuracy and high speed, allowing further real-time applications within operational contexts. We also investigate its robustness to the appearance of new backgrounds in the validation set, thus tackling the issue of domain adaptation that is critical in remote sensing. Experimental studies that were conducted on both aerial and satellite benchmark datasets show some significant improvement of YOLO-fine as compared to other state-of-the art object detectors.

The 6th biennial conference on object-based image analysis—GEOBIA 2016—took place in September 2016 at the University of Twente in Enschede, The Netherlands (see www [...]

Land cover mapping has benefited a lot from the introduction of the Geographic Object-Based Image Analysis (GEOBIA) paradigm, that allowed to move from a pixelwise analysis to a processing of elements with richer semantic content, namely objects or regions. However, this paradigm requires to define an appropriate scale, that can be challenging in a large-area study where a wide range of landscapes can be observed. We propose here to conduct the multiscale analysis based on hierarchical representations, from which features known as differential attribute profiles are derived over each single pixel. Efficient and scalable algorithms for construction and analysis of such representations, together with an optimized usage of the random forest classifier, provide us with a semi-supervised framework in which a user can drive mapping of elements such as Small Woody Features at a very large area. Indeed, the proposed open-source methodology has been successfully used to derive a part of the High Resolution Layers (HRL) product of the Copernicus Land Monitoring service, thus showing how the GEOBIA framework can be used in a big data scenario made of more than 38,000 Very High Resolution (VHR) satellite images representing more than 120 TB of data.

This paper is concerned with an insurance risk model whose claim process is described by a Lévy subordinator process. Lévy-type risk models have been the object of much research in recent years. Our purpose is to present, in the case of a subordinator, a simple and direct method for determining the finite time (and ultimate) ruin probabilities, the distribution of the ruin severity, the reserves prior to ruin, and the Laplace transform of the ruin time. Interestingly, the usual net profit condition will be essentially relaxed. Most results generalize those known for the compound Poisson claim process.

Meteosat satellite images are processed to yield values of the incoming surface solar irradiance (SSI), one of the Essential Climate Variables. Two HelioClim databases, HC-1 and HC-3, were constructed covering Europe, Africa and the Atlantic Ocean, and contain daily and monthly means of SSI. The HC-1 database spans from 1985 to 2005; HC‑3 began in 2004 and is updated daily. Their quality and limitations in retrieving monthly means of SSI have been studied by a comparison between eleven stations offering long time-series of measurements. A good agreement was observed for each site: bias was less than 10 W/m² in absolute value (5% in relative value) for HC-3. HC-1 offers a similar quality, though it underestimates the SSI for latitudes greater than 45° and less than −45°. Time-series running from 1985 to date can be created by concatenating the HC-1 and HC-3 values and could help in assessing SSI and its changes.

The contribution of cows’ milk containing beta-casein protein A1 variant to the development of type 1 diabetes (T1D) has been controversial for decades. Despite epidemiological data demonstrating a relationship between A1 beta-casein consumption and T1D incidence, direct evidence is limited. We demonstrate that early life exposure to A1 beta-casein through the diet can modify progression to diabetes in non-obese diabetic (NOD) mice, with the effect apparent in later generations. Adult NOD mice from the F0 generation and all subsequent generations (F1 to F4) were fed either A1 or A2 beta-casein supplemented diets. Diabetes incidence in F0–F2 generations was similar in both cohorts of mice. However, diabetes incidence doubled in the F3 generation NOD mice fed an A1 beta-casein supplemented diet. In F4 NOD mice, subclinical insulitis and altered glucose handling was evident as early as 10 weeks of age in A1 fed mice only. A significant decrease in the proportion of non-conventional regulatory T cell subset defined as CD4+CD25−FoxP3+ was evident in the F4 generation of A1 fed mice. This feeding intervention study demonstrates that dietary A1 beta-casein may affect glucose homeostasis and T1D progression, although this effect takes generations to manifest.