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Annotating opportunistic camera-trap images with conditions of recording, for the disease surveillance of Eurasian lynx (Lynx lynx)
No full textInternational audienceThe French population of the Eurasian lynx (Lynx lynx) is small and fragmented. Any emerging disease would endanger it even further, so disease surveillance is crucial. Currently, it relies on lynx carcass surveillance. In parallel, the Eurasian lynx population is being monitored since 1997 by a large network of observers in different regions and this trove of camera-trap images could allow for the opportunistic detection of clinical signs. Camera traps have been used for a very long time in ecology and, more recently, in epidemiology to study e.g. sarcoptic mange. However, depending on image quality, the level of detail visible in the animal's body varies. This work examines how the quality of the images relates to the ability to detect cutaneous changes and abnormal body conditions. Different factors affect image quality and the detection of changes. These include intrinsic camera parameters like the type and settings of the camera trap, extrinsic factors like the external lighting conditions or the position of the animal in relation to the camera. In our data set, clearly visible cutaneous changes were associated with a different set of factors than visible abnormal body conditions. The camera-trap conditionscurrently used for ecological monitoring of the lynx are very diverse and may allow for the general surveillance of abnormal health signs. However, for monitoring specific health signs, the camera settings as well as the shooting context should be optimized to ensure the best possible sensitivity and specificity of the detection
Pest damage in sugarcane is shaped by temperature, farming practices and landscape context
No full textSource Agritrop Cirad (https://agritrop.cirad.fr/616997/) * Autres projets (id;sigle;titre): ;TERRITOIRES DURABLES;(FRA) Faciliter la transition agroécologie de trois territoires : Guadeloupe, Martinique et Réunion//International audienceChilo sacchariphagus is a major sugarcane pest, primarily affecting producing regions in Asia and around the Indian Ocean, yet little is known about the biotic and abiotic factors driving its population dynamics and associated crop damage. This study assesses, for the first time, the relative influence of meteorological conditions, farming practices, landscape context, and natural enemy communities on herbivory damage caused by this pest on Reunion Island. 2. In-field surveys were conducted at 60 sampling points during 2022 and 2023, recording crop damage and multiple explanatory variables across spatial scales, from the field to the landscape. Data were analyzed using generalized additive models and a multi-model inference framework. 3. Crop damage was primarily explained by meteorological conditions (36 % of deviance), farming practices (36 %), and landscape context within a 250 m buffer (27 %), while natural enemy abundance had no significant effect. Five key predictors shaped damage: average temperature during the wettest season, agrochemical inputs (fertilizer and herbicides), landscape edge density, and sugarcane proportion within the 250 m buffer. Lower fertilizer and herbicide use was associated with crop damage, while edge density and sugarcane proportion showed non-linear relationships with damage. 4. This study underscores the value of an integrative, cross-scale approach to identify drivers of crop damage by C. sacchariphagus. Findings suggest that reduced agrochemical inputs, coupled with increased landscape fragmentation and lower host crop dominance, may mitigate pest damage. Future work should further examine the role of natural enemies and explore the ecological mechanisms behind the non-linear impacts of landscape structure on pest dynamics
Posttraumatic stress disorder after second trimester medical termination of pregnancy
No full textInternational audienceObjectives: To evaluate the risk of posttraumatic stress disorder (PTSD) after indicated second-trimester termination of pregnancy (TOP) and to identify factors associated with a probable diagnosis of severe PTSD.Study design: Secondary analysis of a multicenter randomized controlled trial comparing the efficacy of cervical dilators inserted concurrently with misoprostol with that of misoprostol alone for women undergoing TOP between 150/7 and 276/7 weeks of gestation. PTSD was evaluated by the Impact of Event Scale-Revised (IES-R) questionnaire, self-administered 1-4 months after TOP. This 22-item scale is designed to assess subjective distress caused by traumatic events and has been validated in perinatal care. The literature suggests that a score ≥33 indicates a probable diagnosis of PTSD and a score ≥37 a probable diagnosis of severe PTSD. Maternal and obstetric characteristics associated with a score ≥37 were studied with mixed models. We present results after multiple imputation to take selective dropouts and missing information at follow-up into account and for complete cases.Results: Among the 347 women enrolled, 247 (71.2%) IES-R questionnaires were available. Median time between TOP and completion of the questionnaire was 7 weeks (IQR, 4.9-13.3). The mean IES-R score was 32.1 (SD 15.4) The IES-R score was ≥33 for 44.9% (95%CI, 38.4-51.4) of women and ≥37 for 35.8% (95%CI, 29.7-41.8). After multivariate analysis, obstetric or labor-related characteristics such as parity, gestational age over 22 weeks, use of cervical dilators, labor > 12 h, and pain or complications during delivery or postpartum were not associated with an IES-R ≥37. The results were similar in complete cases.Conclusion: Nearly half of women undergoing medically indicated second-trimester TOP were at risk of PTSD and more than one-third of severe PTSD. The absence of risk factors underlines the potential benefits of systematic psychological evaluation after TOP for all women
Mechanistic insights into enterocin C targeting the undecaprenyl phosphate recycling protein BacA
No full textInternational audienceEnterococcus faecalis is an important opportunistic pathogen responsible for healthcare-associated infections. It is intrinsically resistant to various antibiotics, particularly to cephalosporins and vancomycin, creating an urgent need for alternative therapeutics. In this context, bacteriocins warrant investigations as a potential source of medically useful antibiotics. Herein, we demonstrate that Enterocin C, a class IIb two-peptide bacteriocin, specifically targets the membrane-embedded undecaprenyl phosphate recycling protein BacA from enterococci as a cell surface receptor. Using biochemical and biophysical methods, supported by computer modeling and mutagenesis, we deciphered the EntC's molecular interaction pattern with its target, marking the first mechanistic insight of a two-peptide bacteriocin. The two peptides act cooperatively at nanomolar concentrations to interact with the outward-open catalytic pocket of BacA: the peptide EntC1 docks deeply into the catalytic site, inhibits BacA's enzymatic activity, and enables the binding of peptide EntC2, eliciting membrane permeabilization, eventually leading to cell death. This work paves the way for the bioengineering of BacAtargeting bacteriocins to develop tailored antimicrobial strategies
Benchmarking Permeability Predictions for an Engineered Reference Medium: Toward Calibration of Permeability Rigs
No full textInternational audienceAccurate permeability prediction in fibrous media is essential for modeling liquid composite manufacturing processes, yet numerical predictions are often inconsistent owing to modeling assumptions and numerical implementation choices. This work introduces a 3D‐printable anisotropic reference porous medium designed to reproduce the main flow pathways and anisotropy trends observed in textile reinforcements, while avoiding the inherent variability of real fabrics. The idealized geometry provides a well‐controlled, reproducible benchmark for permeability prediction, acknowledging that it does not capture fine‐scale features such as intra‐tow pores or fiber surface curvature, and may have higher absolute permeability values than real textiles. Fourteen research groups independently simulated fully saturated, incompressible, laminar, and steady‐state flow through a given CAD‐based medium using their own numerical setups. While simple analytical flows (e.g., laminar flow in a pipe or slit) can validate individual codes, they are insufficient to capture the complexity and anisotropy of textile‐like porous media. Benchmarking is therefore necessary to reveal real‐world variability in permeability predictions. Results show good consistency for in‐plane permeability ( K xx : mean 2.75 × 10 −9 m 2 , CoV = 0.105; K zz : mean 6.8 × 10 −10 m 2 , CoV = 0.205), while out‐of‐plane permeability ( K yy ) shows much higher variability (unit‐cell mean 1.40 × 10 −10 m 2 , CoV = 0.790; periodic‐assembly mean 1.27 × 10 −10 m 2 , CoV = 0.470). These findings (i) demonstrate the feasibility of using idealized additive‐manufactured porous media as reproducible calibration benchmarks and (ii) highlight the value of cross‐validation across multiple numerical platforms to bolster confidence in permeability prediction for composite manufacturing
Paying attention to other animal detections improves camera trap classification models
No full textInternational audience1. In ecological studies, automated species classification models are increasingly used to process large volumes of camera trap images. Most current classification models rely on a two-step pipeline: a detector first locates and crops animals, followed by a classifier that predicts species independently for each crop. While effective, these models still struggle under challenging conditions and ignore temporal context or information from nearby animals available in sequences of camera trap images, whereas human annotators often use it in difficult cases.2. We propose to leverage self-attention, a core mechanism of Large Language Models and Vision Transformers, to enable the model to learn relationships between crops occurring in similar contexts. Our self-attention module operates directly on the set of crop embeddings, producing new representations enriched with information from other crops, improving species classification. The module fits into the two-step pipeline without requiring structural change and adds only minimal computational overhead. To address the lack of annotated multi-species sequences, we develop a training strategy that synthetically generates multispecies sequences from mono-specific ones.3. Compared with an independent classifier baseline, our multi-crop model achieves higher accuracy on mono-specific sequences, both real and synthetic. In multispecies settings, evaluated using synthetic test sets, we also observe a substantial improvement in accuracy. Using real but weakly annotated multi-species sequences, we reformulate the task as multi-label set classification and conduct a visual analysis to highlight the benefits of our approach.4. By leveraging the information brought by all detections of animals in the image and others of the same sequence, our approach reduces species misclassifications and enables more accurate estimates for downstream ecological analyses focusing on, for instance, species richness, occupancy and species interaction
Influence of added calcium on the rehydration behavior and processability of high-protein casein-based solutions reconstituted from micellar casein powder (MCP) and demineralized MCP
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Approche territoriale pour la prévention des risques en montagne, Proposition de cadre technique pour l’analyse et la gestion intégrée des risques naturels en montagne, Module 8 : Méthodes d’aide à la décision
No full textLa gestion intégrée des risques naturels en montagne est un concept visant à coconstruire des stratégies de réduction des risques dans le cadre d’approches territoriales multi-phénomènes. Pour accompagner ce type d’approche, des documents techniques ont été imaginés pour aider les opérateurs à réaliser ces étapes principales. Le module Méthodes d’aide à la décision décrit les méthodologies d’aide à la décision mises en œuvre dans les autres modules. Il concerne d’une part l’aide multicritères (hiérarchique) à la décision et les approches économiques de type Coût/Bénéfices. Les principes des méthodes sont explicités et les exemples parfois mentionnés dans d’autres modules comme la note technique DGPR relative à l’analyse socio-économique sont détaillés. Sur cette base, le lecteur peut suivre pas à pas le déroulement de la mise en œuvre. D’autres méthodes intéressantes comme la méthode SPOTIS sont proposées et discutées dans ce module
Approche territoriale pour la prévention des risques en montagne, Proposition de cadre technique pour l’analyse et la gestion intégrée des risques naturels en montagne, Module 2 : Analyse des risques indirects et distants
No full textLa gestion intégrée des risques naturels en montagne est un concept visant à coconstruire des stratégies de réduction des risques dans le cadre d’approches territoriales multi-phénomènes. Pour accompagner ce type d’approche, des documents techniques ont été imaginés pour aider les opérateurs à réaliser ces étapes principales. L’analyse des risques indirects et distants s’intéresse aux conséquences économiques distantes correspondant par exemple à l’allongement des temps de transport suite à des évènements naturels, à des ruptures d’approvisionnement, de communication entre différentes zones du territoire. Ce module aborde tout d’abord les méthodologies permettant d’identifier l’existence et la nature de dommages indirects potentiels sur la base d’une analyse socio-économique. Il propose ensuite des approches simplifiées pour quantifier les dommages distants liés aux coupures d’infrastructures de transport
