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Automated identification of fossil benthic foraminifera from the Peruvian margin using convolutional neural networks
No full textInternational audienceAbstract. Benthic foraminifera tests preserved in marine sediments are well-established proxies for bottom-water dynamics, yet their minute size and high diversity demand laborious manual identification of hundreds of individuals to reconstruct subtle faunal shifts and are prone to observer-dependent taxonomic inconsistencies. The recent advances in image acquisition hardware and image identification software have made it possible to acquire and identify large image datasets quickly. Here, we trained convolutional neural networks (CNNs) to identify benthic foraminifera morphospecies from 31 samples from two sedimentary cores from offshore Peru, spanning the past 18 000 years. Our best-performing model achieves 92 % overall classification accuracy, 93.4 % precision, and 92.4 % recall, enabling high-temporal-resolution reconstructions of benthic foraminifera assemblages along the Peruvian margin. Automated outputs closely matched manual results across 31 samples, from counts and relative abundances to diversity indices, multivariate assemblage patterns, and dissolved oxygen estimates, indicating the suitability of automated identification for paleo-ecological applications. The highest-performing CNN model (trained on a dataset of 5860 images) from this study can be adapted to analyse benthic foraminifera from equivalent depths of the Peruvian margin, providing high-resolution insights into the eastern tropical Pacific oxygen minimum zone (OMZ). In addition to offering a scalable, objective alternative for high-temporal-resolution analysis of benthic foraminifera, this study also highlights the current limitations of automated workflows
Solid-angle based nearest-neighbor algorithm adapted for systems with low coordination number
No full textInternational audienceNearest-neighbor identification is central to the analysis of local structure in condensed matter systems. The solid-angle-based nearest-neighbor (SANN) algorithm is widely used, offering a parameter-free and computationally efficient alternative to cutoff- or Voronoi-based methods. Unfortunately, however, in systems with low coordination numbers, SANN tends to identify many particles as neighbors that are outside the nearest neighbor shell. Here, we propose a solution to this problem. In particular, we propose a geometric modification, the “inscribed circle modification,” that resolves systematic overcounting in low-coordination lattices without introducing free parameters. We benchmark the modified SANN algorithm against Voronoi and the original SANN algorithm in crystalline, quasicrystalline, and heterogeneous systems and demonstrate that it provides robust and low-cost neighbor identification across both two and three dimensions
Réparation par fabrication additive des pièces endommagées d'un lanceur réutilisable
No full textThe objective of the thesis is to answer the problem encountered by CNES in the repair of reusable launch vehicle parts. The particularity comes from the fact that the chemical nature of the alloys constituting the candidate parts does not allow the use of the Additive Manufacturing processes conventionally used, which imply a passage in liquid phase of the material and induce, in addition to the problems of segregation and cracking which can be controlled, an evaporation of certain elements of alloy. This work studies, in the case of aerospace aluminium alloys, the effect on microstructures and mechanical behaviour of different alternative repair options. In particular, the use of so-called solid-phase friction-stir processes such as AFSD (Additive Friction Stir Deposition) is investigated. The analyses performed are multi-scale, examining the quality of the deposited material, the interface with the damaged part, and the effects of the repair on the part.L’objectif de la thèse est de répondre à la problématique rencontrée par le CNES de réparation de pièces de lanceurs réutilisables. La particularité vient du fait que la nature chimique des alliages constituants les pièces candidates ne permet pas l’utilisation des procédés de Fabrication Additive classiquement utilisés, qui impliquent un passage en phase liquide de la matière et induisent, outre les problématiques de ségrégation et de fissuration qui peuvent être maîtrisées, une évaporation de certains éléments d’alliage. Ce travail consiste à étudier, dans le cas d’alliages d’aluminium aérospatiaux, l’effet sur les microstructures et sur le comportement mécanique de différentes options de réparation alternatives. On s’intéresse en particulier à l’utilisation de procédés en phase solide dits de friction malaxage, tel que l’AFSD (Additive Friction Stir Deposition). Les analyses effectuées sont multi- échelles, et examinent à la fois la qualité du matériau déposé, à l’interface avec la pièce endommagée, et aux effets de la réparation sur la pièce
Understanding the chemistry of temperate exoplanet atmospheres through experimental and numerical simulations
No full textInternational audienceContext. Characterizing temperate exoplanet atmospheres remains challenging due to their small size and low temperatures. Recent JWST observations provide valuable data, but their interpretation has led to diverging conclusions, highlighting the limitations of observations alone. Complementary approaches combining laboratory experiments and photochemical modeling are essential for constraining atmospheric chemistry and interpreting observations. Aims. This study investigates out-of-equilibrium chemistry in the upper atmospheres of H 2 -dominated temperate sub-Neptunes enriched in carbon-bearing species (CH 4 , CO, or CO 2 ). We aim to identify chemical pathways governing the formation and evolution of neutral species and to assess their sensitivity to key parameters such as C/O ratio and metallicity. Methods. Our approach combines experimental and numerical simulations on H 2 -rich gas mixtures representative of sub-Neptune atmospheres and spanning a wide range of CH 4 , CO, and CO 2 mixing ratios. We used a cold plasma reactor to simulate out-ofequilibrium upper-atmospheric chemistry. Chemical evolution was tracked by mass spectrometry and infrared spectroscopy (IR). A 0D photochemical model was used to reproduce reactor conditions, guiding interpretation of the key pathways and abundance trends. Results. We observed the formation of both reduced and oxidized organic compounds. In CH 4 -rich mixtures, hydrocarbons formed efficiently through methane chemistry, correlating with CH 4 concentration and agreeing with models. In more oxidizing environments, particularly CO 2 -rich mixtures, hydrocarbon formation was inhibited by complex reaction networks and oxidative losses. We find that oxygen incorporation enhances chemical diversity and promotes the formation of oxidized organic compounds of prebiotic interest (H 2 CO, CH 3 OH, CH 3 CHO), especially in atmospheres containing both CH 4 and CO 2 . Atmospheres containing CH 4 and CO -which balance carbon and oxygen supply without excessive oxidative destruction -favor efficient production of hydrocarbons and oxidized compounds.Conclusions. Out-of-equilibrium chemistry plays a key role in the diversification and organic complexification of temperate exoplanet atmospheres. Combining laboratory experiments with photochemical modeling elucidates pathways to hydrocarbon and oxidized organic formation. Studying detectability of these photoproducts with JWST and new high-resolution ground-based instruments is an important focus for future studies
Exclusive four pion photoproduction in ultraperipheral Pb-Pb collisions at TeV
No full textInternational audienceThe intense photon fluxes from relativistic nuclei provide an opportunity to study photonuclear interactions in ultraperipheral collisions. The measurement of coherently photoproduced final states in ultraperipheral Pb-Pb collisions at TeV is presented for the first time. The cross section, d/d, times the branching ratio () is found to be mb in the rapidity interval . The invariant mass distribution is not well described with a single Breit-Wigner resonance. The production of two interfering resonances, and , provides a good description of the data. The values of the masses () and widths () of the resonances extracted from the fit are MeV/, MeV/, MeV/ and MeV/, respectively. The measured cross sections times the branching ratios are compared to recent theoretical predictions
Repeated low-intensity focused ultrasound led to microglial profile changes in TgF344-AD rats
No full textAlzheimer's disease (AD), the most common cause of dementia, represents one of the main clinical challenges of the century as the number of patients is predicted to triple by 2050. Despite the recent approval of three monoclonal antibodies targeting Amyloid β (Aβ) aggregates by the Food and Drug Administration (FDA), immunotherapies still face challenges due to the difficulty of antibodies crossing the blood-brain barrier (BBB). This necessitates administering large doses of drugs to achieve their therapeutic effects, which is associated with significant side effects. In this context, low-intensity focused ultrasound (LiFUS) appears as an innovative and non-invasive method which, in association with intravenous injection of microbubbles (MB), leads to a transient BBB opening. This innovative strategy has been extensively studied in different preclinical models and more recently in human clinical trials, particularly in the context of AD. LiFUS+MB seems to increase the inflammatory response at short term, but the time course of this response is not consistent between studies, certainly due to the discrepancy between LiFUS protocols used. Moreover, the impact at longer term is understudied and the mechanisms underlying this effect are still not well understood. In our study, we therefore used the TgF344-AD rat model of AD, to investigate the effect of a single or multiple exposures to LiFUS+MB in the entire brain, on inflammatory response and amyloid load. The ultrasound attenuation through the skull was corrected to apply a peak negative acoustic pressure of 450 kHz in all treated animals. Single LiFUS+MB exposure induces a slight astrocyte and microglial response 24 hours post-treatment whereas repeated LiFUS treatment seems to induce microglial reprogramming, leading to the adaptation of gene expression related to key functions such as inflammatory response, mitochondrial and energetic metabolism. In our rat model and LiFUS+MB protocol conditions, multiple exposures did not modulate soluble/poorly aggregated forms nor the highly aggregated forms of Aβ40 and Aβ42. For therapeutic AD management, LiFUS+MB could be combined with drugs such as immunotherapies. In a proof-ofconcept experiment, we validated that LiFUS was also efficient to improve the brain entry of the anti-Aβ antibody, Aducanumab.</div
Machine learning for ammonia volatilization prediction and slurry application management
No full textInternational audienceAnthropogenic ammonia emissions primarily originate from agriculture, especially field fertilization. These emissions represent nitrogen loss for farmers and contribute to air pollution, posing risks to human health and the environment. Estimating ammonia emissions is crucial for national inventories and policy-making. Various models exist for predicting emissions, including mechanistic, empirical, and semi-empirical approaches. While machine learning (ML) is widely used in environmental science, its application to ammonia emissions remains limited. In this study, we used 5939 ammonia emission data from 538 trials, extracted from the ALFAM2 database, to train three machine learning methods - random forest, gradient boosting, and lasso - for predicting cumulative ammonia emissions 72 hours after manure application. These methods were compared to the semi-empirical ALFAM2 model using an independent test dataset. Random forest (RMSE = 4.51, r = 0.94, MAE = 3.28, Bias = 0.92) and gradient boosting (RMSE = 6.19, r = 0.89, MAE = 4.10, Bias = 0.51) showed the best performance, while the lasso log-linear model (RMSE = 7.30, r = 0.84, MAE = 5.57, Bias = -1.38) performed worst. Both random forest and gradient boosting outperformed the semi-empirical ALFAM2 model, which showed performance comparable to the lasso model. We then used these models and the ALFAM2 model to compare five slurry management techniques, varying in application method (trailing hoses, trailing shoes, and open slot) and post-application incorporation, across 128 scenarios with different manure types and weather conditions. Compared to broadcast application, alternative techniques reduced emissions by a median of -13.6 % to -61.7 %. This study highlights the promise of ML models in assessing ammonia emission reduction methods, while emphasizing the importance of evaluating model sensitivity to algorithm choice
Euclid Quick Data Release (Q1). First detections from the galaxy cluster workflow
No full textInternational audienceThe first survey data release by the Euclid mission covers approximately in the Euclid Deep Fields to the same depth as the Euclid Wide Survey. This paper showcases, for the first time, the performance of cluster finders on Euclid data and presents examples of validated clusters in the Quick Release 1 (Q1) imaging data. We identify clusters using two algorithms (AMICO and PZWav) implemented in the Euclid cluster-detection pipeline. We explore the internal consistency of detections from the two codes, and cross-match detections with known clusters from other surveys using external multi-wavelength and spectroscopic data sets. This enables assessment of the Euclid photometric redshift accuracy and also of systematics such as mis-centring between the optical cluster centre and centres based on X-ray and/or Sunyaev--Zeldovich observations. We report 426 joint PZWav and AMICO-detected clusters with high signal-to-noise ratios over the full Q1 area in the redshift range . The chosen redshift and signal-to-noise thresholds are motivated by the photometric quality of the early Euclid data. We provide richness estimates for each of the Euclid-detected clusters and show its correlation with various external cluster mass proxies. Out of the full sample, 77 systems are potentially new to the literature. Overall, the Q1 cluster catalogue demonstrates a successful validation of the workflow ahead of the Euclid Data Release 1, based on the consistency of internal and external properties of Euclid-detected clusters
Search for displaced decays of long-lived particles in events with missing transverse momentum in TeV collisions with the ATLAS detector
No full textInternational audienceA search for long-lived particles in events with significant missing transverse momentum and at least one displaced vertex is presented. This analysis is performed using 137 of collision data collected between 2016--2018 during Run 2 of the Large Hadron Collider by the ATLAS detector. Displaced vertices are identified using two different secondary vertexing algorithms, including a novel ``fuzzy'' vertexing algorithm optimized for identifying displaced decays of heavy quarks. Separate searches are performed using each algorithm, and the expected Standard Model background is independently estimated for each search using a data-driven procedure. No significant excess is observed over the background in either case. The results are used to set 95% confidence-level limits on potential beyond-the-Standard Model physics that could produce this final state. Results are interpreted in the context of four models: long-lived gluinos that form -hadrons before decaying, neutralinos decaying via Higgs-mediated channels in the Bino-Wino coannihilation model, long-lived Higgsinos decaying to axinos, and an exotic Higgs portal model predicting displaced decays of light pseudoscalars
Preparation and Calibration of 17 O-Enriched Nitrite Isotope Standards
No full textInternational audienceThe oxygen-17 isotope anomaly (Δ17O) serves as a powerful tool to elucidate the chemical transformation mechanisms of atmospheric reactive nitrogen species such as NO2 and HONO. Current studies employ denuder collection methods to convert atmospheric NO2 and HONO into nitrite for isotopic analysis. However, accurate Δ17O measurement of atmospheric NO2 and HONO is hampered by the lack of internationally recognized nitrite isotope reference materials with applicable Δ17O signals. In this study, we prepared new nitrite isotope standards with nonzero Δ17O signals through oxygen isotope exchange between high-purity nitrite reagents and 17O-enriched water. Using a developed ozone oxidation calibration method, the Δ17O values of a newly prepared nitrite standard (i.e., N-Δ17O-1) and the international nitrite reference material RSIL-N10219 were determined as (69.7 ± 1.0) ‰ (n = 10, 1σ) and (−8.7 ± 0.3) ‰ (n = 11, 1σ), respectively. The two additional O-17-enriched nitrite standards were then measured and calibrated against RSIL-N10219 and N-Δ17O-1, yielding Δ17O values of (34.5 ± 0.3) ‰ (n = 6, 1σ) and (6.4 ± 0.1) ‰ (n = 8, 1σ), respectively. The δ15N and δ18O values of the three homemade nitrite isotope standards were also calibrated against international nitrite reference materials. This study introduces a new and reliable method to obtain the Δ17O values of nitrite, and the establishment of Δ17O values of nitrite standards provides a foundation for accurately assessing Δ17O variations of atmospheric NO2 and HONO. The latter will facilitate the application of the Δ17O tracer in investigating atmospheric cycling of reactive nitrogen and radicals