A Wearable AI‐Driven Mask with Humidity‐Sensing Respiratory Microphone for Non‐Vocal Communication

International audienceHoarseness and dysphonia caused by vocal cord conditions or laryngeal surgeries significantly hinder communication and quality of life. This study presents a plug‐and‐play humidity‐sensing respiratory microphone (HSRM) with generalized features for individual users. Leveraging gold nanoparticle‐based humidity sensors integrated into commercially available wearable face masks, the system enables patients to produce verbal communication without relying on vocal cord activity. By integrating nanoparticle‐enhanced humidity sensors with advanced convolutional neural networks, the HSRM system accurately decodes respiratory patterns into intelligible speech, achieving a recognition accuracy of 85.61%. Leveraging nanoparticle‐polymer interfaces that effectively convert atmospheric humidity fluctuations into precise electrical signals, the system pioneers a contactless and non‐invasive paradigm in assistive speech technology. This innovation addresses limitations of existing devices, such as reliance on residual vocal fold vibrations or skin‐contact sensors, offering a practical generalized solution for individuals with aphonia. With its potential to facilitate naturalistic communication and transform healthcare applications, the HSRM system sets a new benchmark in wearable assistive technologies for voice rehabilitation and human‐machine interaction

Unveiling complex lithiation/delithiation mechanism in AgNbO3 model perovskite using operando X-ray absorption spectroscopy

International audienceIn AgNbO3 perovskite structure, electrochemical activation is speculated during the first lithiation cycle enabling the material to reversibly store Li+ by the contributions of both Ag and Nb cation. However, the origin of electrochemically induced structural activation and understanding of cations involvement in complex Li+ storage mechanism is still elusive. Herein, operando synchrotron X-ray absorption spectroscopy (XAS) was applied to clarify this mechanism under different cycling conditions. Ag K-edge XAS measurements during first lithiation revealed a gradual Ag+ to Ag0 reduction starting at a relatively high potential of 1.0 V vs Li+/Li, thus creating vacancies in the lattice for Li+ insertion and inducing a crystalline-to-amorphous structural transition. Below 0.3 V vs Li+/Li, metallic Ag forms multiple intermetallic Li-Ag alloys, resulting in lithium-rich Li9Ag at the end of lithiation. Simultaneously, Nb K-edge XAS measurements indicate an irreversible Nb5+ to Nb3+ reduction with formation of metastable phases during first lithiation. Upon extended cycling at high current densities, intermediate phases sustain reversible Li+ storage through Nb-redox activity and Li-Ag (de)alloying reactions, facilitating fast charging capability. This study will help in designing new conversion-alloying type negative electrodes for fast-charging batteries

New insights into the molecular phylogenetic relationships of lizards in the Neotropical genus Arthrosaura (reptilia: gymnophthalmidae) reveal rampant ‘cryptic’ speciation in the Guiana shield

International audienceAbstract The gymnophthalmid lizard genus Arthrosaura currently contains seven species distributed in the Amazonian lowlands and in the Pantepui region. The phylogenetic position and taxonomic status of most species in the genus are surrounded by considerable uncertainty. The type locality of the widespread Ar. reticulata (type species for the genus) is Canelos in Ecuador, but no specimen from Ecuador has ever been included in molecular phylogenies. Here we reassessed the molecular systematics and species’ diversity of Arthrosaura based on a multilocus analysis of a six-gene region matrix of an extensive dataset, including several species/populations that had never been sequenced previously, most from close to the type localities. Our results reveal a non-monophyletic Arthrosaura, with Ar. testigensis nested within Yanomamia, and Arthrosaura kockii recovered sister to all other Ecpleopodinae. Rampant ‘cryptic’ speciation is recovered in the Amazonian lowlands, with at least four undescribed species. The genus is particularly diverse in the Guiana Shield, which harbours nine of the 10 species recovered in our analyses (Ar. kockii excluded). Eight of these species are endemic to the Guiana Shield, four in the western part (west of the Essequibo River), four in the eastern part (east of the Essequibo River)

Quantitative genetics of lifetime growth curves in a lizard

Abstract While body size is an iconic trait in quantitative genetics, it bears little meaning without age information for species with indefinite growth. Instead, lifetime growth curves capture the same central aspect of individuals life-history, while accounting for its dynamics through time. Growth curve is a complex, function-valued traits, which requires a specialised framework to study its quantitative genetics. Here, we study growth in a squamate, the common lizard ( Zootoca vivipara ). We use two large datasets with available pedigree information: one from a wild population in a colder, montane environment; the other from an experimental population in a warmer, lowland environment. We found slower growth and larger asymptotic sizes in the colder population. We also found faster growth to lower asymptotic sizes for males. Inferences using a non-linear animal model show very low to moderate heritabilities in the parameters of individual growth curves, varying across populations and sexes. The decomposition of the additive genetic variance show a small to moderate heritability of the overall growth curves, with a stronger importance of the heritability in the shape of growth curves (variation in the dynamics of growth) than on their offset (variation between smaller and larger individuals). Genetic variation in growth curves differed between the two populations, with higher heritabilities in the experimental population. It also differed between sexes: there was more genetic variation in the growth rate at earlier ages for males, while genetic variation was larger for asymptotic size and at later ages for females. Our results tend to show that there is more adaptive potential in the earlier ages for males and in the later ages for females, which seems to be linked to their different life-history strategies. They also suggest that climate might have a positive impact on the adaptive potential of growth, which remains to be confirmed

Học máy dự báo ung thư tuyến tiền liệt: Kết quả ban đầu từ sinh thiết hợp nhất đàn hồi

International audienceBackgrounds: Prostate cancer (PCa) is a common malignancy among men, requiring accurate risk stratification to optimize diagnostic pathways and reduce unnecessary biopsy. This study aimed to develop a machine learning model to predict the risk of PCa using data from elastography-guided fusion biopsies at IUCT-Oncopole.Methods: A retrospective study was conducted on 1,550 patients with suspected PCa between January 2018 and March 2023. The dataset was split into a training set (80%) and a test set (20%). The implemented models included logistic regression, Random Forest (RF), XGBoost, and Support Vector Machine (SVM), using clinical variables and prostate MRI data (PI-RADS v2.1 scores).Results: All models demonstrated good classification performance with area-under-curve (AUCs) values ranging from 0.80 to 0.84. Logistic regression achieved the highest AUC (0.84) but had the largest number of false negatives (FN = 68). RF and XGBoost had the lowest FN (28), with a lower proportion of Gleason ≥ 7 cancers in this group. SVM yielded the highest accuracy and Cohen’s Kappa score. Logistic regression with LASSO regularization offered reasonable performance, was simple to interpret, and easy to implement in clinical settings.Conclusions: Machine learning models show great potential in guiding prostate biopsy decisions. Model selection should be context-dependent, balancing accuracy, interpretability, and ease of implementation.Đặt vấn đề: Ung thư tuyến tiền liệt (UTTTL) là bệnh lý ác tính phổ biến ở nam giới, đòi hỏi phân tầng nguy cơ để tối ưu hóa chẩn đoán và giảm sinh thiết không cần thiết. Nghiên cứu này nhằm xây dựng mô hình học máy dự báo nguy cơ UTTTL dựa trên dữ liệu sinh thiết hợp nhất đàn hồi tại IUCT-Oncopole. Đối tượng và phương pháp nghiên cứu: Nghiên cứu hồi cứu trên 1550 trường hợp nghi ngờ UTTTL từ 1/2018 đến 3/2023. Dữ liệu được chia thành tập huấn luyện (80%) và kiểm tra (20%). Các mô hình được triển khai gồm hồi quy logistic, RF, XGBoost và SVM, sử dụng các biến lâm sàng và dữ liệu cộng hưởng từ (điểm PI-RADS 2.1). Kết quả: Tất cả mô hình cho kết quả phân loại tốt với AUC từ 0,80 đến 0,84. Mô hình logistic đạt AUC cao nhất (0,84) nhưng có số âm tính giả (FN) lớn (68). RF và XGBoost có FN thấp nhất (28), với tỷ lệ ung thư Gleason ≥ 7 thấp hơn. SVM đạt độ chính xác và hệ số Kappa cao nhất. Hồi quy logistic với chính quy hoá LASSO đơn giản, dễ giải thích, với hiệu suất khá và dễ triển khai. Kết luận: Các mô hình học máy có tiềm năng hỗ trợ quyết định sinh thiết. Lựa chọn mô hình cần dựa trên bối cảnh, cân bằng giữa độ chính xác và mức độ dễ triển khai

The PeatPic project: predicting plot-scale green leaf phenology across peatlands

International audienceAbstract Peatlands store approximately one-third of the world’s soil carbon (C), but their functioning is highly variable at fine spatial scales due to differences in vegetation cover and environmental conditions such as water table depth. This fine-scale heterogeneity plays a key role in carbon dynamics yet capturing it—particularly in relation to green leaf phenology (GLP)—is challenging with traditional remote sensing methods. To address this, we developed a smartphone-based methodology and community-science project called the PeatPic Project. We gathered over 3700 photographs from 27 sites across 10 countries in 2021 and 2022, representing different peatland types (bog, fen, and swamp), at 1–2 week intervals. We calculated GLP metrics, such as the data of the start of the season and end of the season, based on the red-blue-green bands from these photographs. We found that GLP metrics varied significantly across peatland types and dominant vegetation communities. Notably, peak greenness at bog sites occurring approximately 10 days later in the year compared to fen sites. Furthermore, variables relation to peatland/vegetation type and energy balance were key predictors of peatland GLP. The PeatPic Project’s readily available methodology offers low-cost opportunities for further research into peatland phenology, enabling the calculation of additional phenological indices and integration with other data types. By refining our understanding of peatland GLP, we can improve predictive C modelling and better assess the impacts of future changes on these important ecosystems

Utilisation des séries temporelles NDVI de PlanetScope pour détecter la phénologie des arbres individuels au Sahel

New advancements in satellite technology enable more accurate observation of woody population dynamics, providing greater insights into the underlying processes that influence their change. In this study, we evaluate the use of PlanetScope NDVI time series to track the phenology of individual trees in the Sahel, where ground-based environmental surveys are scarce. Five-year NDVI time series were produced for 398 trees with known species recorded in Mali, Senegal, and Niger. Clouds and high aerosol contamination were filtered using MODIS products and focused on the dry season to minimize the influence of background NDVI directly (through-crown influence) or indirectly (through adjacency effects). Each NDVI time series profile was fitted with a spline model to obtain the minimum NDVI day of year during the dry season. PlanetScope NDVI time series accurately captured the photosynthetic phenology of individual tree crowns in the Sahel, with discernable differences between individuals and species. When species were grouped based on four phenology types, deciduous and inverse deciduous species exhibited a relatively consistent phenological pattern across all sites. The phenology of evergreen species, which include species with few leaves, and to a lesser extent semi-evergreen species, were more heterogeneous. Intra-species variation was relatively modest between sites, and most species maintained a similar NDVI profile, with shifts in leaf phenology events correlating with the timing of the wet season in each site. Overlap between the different phenology groups indicates that transitions between phenology types and species is not clear-cut, and even individuals of the same species can demonstrate plasticity. Furthermore, NDVI profiles were extracted for 500 randomly selected tree samples within eight 10 km² clip boxes distributed along the West African rainfall gradient from 9.9 to 16.6 latitude at -1.6 longitude. This analysis showed a strong relationship between the phenology of woody plants and the timing and distribution of rainfall at each latitude. Green-up of woody vegetation before herbaceous vegetation was marked in the more southern Sahelo-Sudanian latitudes. Additionally, despite the prolonged dry season in the more northern semi-arid latitudes, trees retained their greenness remarkably late into the dry season. Increased air temperature and dryness as a result of climate change could impact tree function in this region and needs individual-based monitoring.Les avancées récentes des technologies satellitaires permettent une observation plus précise de la dynamique des populations ligneuses, offrant ainsi de meilleures perspectives sur les processus sous-jacents qui influencent leur fonctionnement. Dans cette étude, nous évaluons l’utilisation des séries temporelles d'indice de végétation NDVI issues de la constellation de satellites PlanetScope pour suivre la phénologie des arbres dans le Sahel, où les enquêtes environnementales au sol sont rares. Des séries temporelles de NDVI sur cinq ans ont été produites pour 398 arbres dont les espèces sont connues, sur des sites au Mali, au Sénégal et au Niger. Les effets des nuages et la contamination élevée par les aérosols ont été filtrés à l'aide des produits MODIS. La phénologie a été étudiée en se concentrant sur la saison sèche pour minimiser l'influence directe du NDVI de fond (influence à travers la canopée) ou indirecte (par effets de l’adjacence). Chaque profil temporel de NDVI a été ajusté avec un modèle spline pour dériver des métriques comme le jour de l'année où le NDVI est minimal pendant la saison sèche. Les séries temporelles de NDVI de PlanetScope témoignent avec précision de la phénologie du feuillage des arbres 'individuels' (arbre par arbre) au Sahel, avec des différences perceptibles entre les individus et les espèces. Lorsque les espèces sont regroupées en fonction de quatre types phénologiques, les espèces décidues et et phénologie 'inversée' ont montré un schéma phénologique relativement cohérent sur tous les sites. La phénologie des espèces persistantes, qui incluent des espèces à feuillage peu abondant, et dans une moindre mesure les espèces semi-persistantes, était plus hétérogène. La variation intra-espèce était relativement modeste sur chaque site, et la plupart des espèces montrent un profil de NDVI similaire, avec des décalages des événements phénologiques en lien avec la période des pluies de chaque site. Le chevauchement entre les différents groupes phénologiques indique que les transitions entre types phénologiques et espèces ne sont pas tranchées et des individus de la même espèce peuvent montrer une certaine plasticité. En outre, des profils de NDVI ont été extraits pour 500 arbres sélectionnés aléatoirement au sein de huit zones de 10 km² réparties le long du gradient pluviométrique d'Afrique de l'Ouest, de 9.9° à 16.6° de latitude à -1,6° de longitude. Cette analyse a révélé une forte relation entre la phénologie des plantes ligneuses et le calendrier ainsi que la distribution des précipitations à chaque latitude. La mise du feuillage de la végétation ligneuse avant la végétation herbacée est observée surtout aux latitudes les plus méridionales de la zone sahélo-soudanienne. De plus, malgré une saison sèche prolongée dans les latitudes semi-arides plus au nord, les arbres conservent leurs feuilles remarquablement tard dans la saison sèche. L'augmentation de la température et de la sécheresse de l'air due au changement climatique pourrait affecter le fonctionnement des arbres dans cette région et plaide pour une surveillance à l'échelle de chaque individu

Optimisation des systèmes de contrôle complexes avec des simulateurs différentiables : une approche hybride de l'apprentissage par renforcement et de la planification de trajectoire

International audienceDeep reinforcement learning (RL) often relies on simulators as abstract oracles to model interactions within complex environments. While differentiable simulators have recently emerged for multi-body robotic systems, they remain underutilized, despite their potential to provide richer information. This underutilization, coupled with the high computational cost of exploration-exploitation in high-dimensional state spaces, limits the practical application of RL in the real-world. We propose a method that integrates learning with differentiable simulators to enhance the efficiency of exploration-exploitation. Our approach learns value functions, state trajectories, and control policies from locally optimal runs of a model-based trajectory optimizer. The learned value function acts as a proxy to shorten the preview horizon, while approximated state and control policies guide the trajectory optimization. We benchmark our algorithm on three classical control problems and a torque-controlled 7 degree-of-freedom robot manipulator arm, demonstrating faster convergence and a more efficient symbiotic relationship between learning and simulation for end-to-end training of complex, poly-articulated systems.L'apprentissage par renforcement profond (RL) s'appuie souvent sur des simulateurs comme oracles abstraits pour modéliser les interactions au sein d'environnements complexes. Bien que des simulateurs différentiables aient récemment émergé pour les systèmes robotiques multi-corps, ils restent sous-utilisés, malgré leur potentiel à fournir des informations plus riches. Cette sous-utilisation, conjuguée au coût de calcul élevé de l'exploration-exploitation dans des espaces d'état de grande dimension, limite l'application pratique de l'RL en situation réelle. Nous proposons une méthode intégrant l'apprentissage à des simulateurs différentiables afin d'améliorer l'efficacité de l'exploration-exploitation. Notre approche apprend des fonctions de valeur, des trajectoires d'état et des politiques de contrôle à partir d'exécutions localement optimales d'un optimiseur de trajectoire basé sur un modèle. La fonction de valeur apprise agit comme un proxy pour raccourcir l'horizon de prévisualisation, tandis que les politiques d'état et de contrôle approximatives guident l'optimisation de la trajectoire. Nous comparons notre algorithme à trois problèmes de contrôle classiques et à un bras manipulateur robotique à 7 degrés de liberté contrôlé par couple, démontrant une convergence plus rapide et une relation symbiotique plus efficace entre apprentissage et simulation pour l'apprentissage complet de systèmes complexes et polyarticulés

A new Constraint Programming model for the Multiple Constant Multiplication

International audienceThe Multiple Constant Multiplication (MCM) problem arises in many applications such as, for example, digital signal processing. Given a set T of target constants, the goal of MCM is to find the most efficient way for multiplying an input number with each constant in T , where multiplications are realized through bit-shifts and additions, and where intermediate results may be shared to produce different target constants. Different metrics may be considered for evaluating the cost of a solution, and a classical objective function is to minimize the number of adders. State-of-the-art methods, based on Integer Linear Programming (ILP), suffer from numerous performance and scalability bottlenecks. In this work, we propose for the first time a Constraint Programming (CP) model for minimizing the number of adders for the MCM. Compared to the state-of-the-art ILP approach, CP does not suffer from the curse of linearization, hence permits significantly simpler formulations of the mathematical model. In order to evaluate our CP model, we focus on a widely used benchmark extracted from a collection of digital filter designs and compare ourselves with state-of-the-art ILP and SAT models. We show that our CP approach is less efficient on some easy instances, but more efficient on hard instances. We also introduce a pseudo-polynomial time algorithm which is able to solve some instances, and show that using this algorithm during a preprocessing step improves the solution process

Discernibility in explanations: Designing more acceptable and meaningful machine learning models for medicine

International audienc