Paris Sciences et Lettres University (PSL University or simply PSL) is a French public university, established in 2010, and formally created as a University in 2019. It is a collegiate university with 11 constituent schools. PSL is located in central Paris, with its main sites in the Latin Quarter, at the Jourdan campus, at Porte Dauphine, in northern Paris, and at Carré Richelieu. PSL awards Bachelor's, Master's, and PhD diplomas for its constituent schools and institutes. It offers an education based on research and interdisciplinary instruction, and its 17,000 students have access to a broad range of disciplines in science, engineering, humanities and social sciences, and performing arts. In 2020, PSL University is ranked, globally, 36th by the Academic Ranking of World Universities, 45th by the Times Higher Education World University Rankings and 52nd in 2021 by the QS World University Rankings. PSL's alumni include 28 Nobel laureats, 10 Fields Medalists, 3 Abel laureats, 48 CNRS Gold Medalists, 50 César and 79 Molière winners.
Video-based analysis of infant movement has emerged as a promising approach for the early detection of neurodevelopmental disorders. The field has evolved from subjective clinical assessment to integrated, quantitative, and automated systems. This comprehensive survey charts the technological evolution of the field from 2014 to 2024, focusing on three core areas: pose estimation methods adapted for infant subjects, direct video analysis approaches, and dataset collection methodologies. The survey systematically analyzes the progression from basic motion tracking to advanced deep learning solutions, with particular attention to key developmental periods, including writhing movements, fidgety movements, and voluntary movements. The review highlights both significant advances and persistent challenges. Successful adaptations of pose estimation techniques to infant characteristics contrast with unresolved issues in data acquisition, privacy preservation, and clinical integration. A growing emphasis on explainable artificial intelligence is essential for fostering clinical trust and adoption. A comparative analysis of datasets and monitoring systems clarifies their respective strengths and limitations. Key challenges include the scarcity of large and diverse datasets, the lack of standardization across platforms, and the need for robust clinical validation studies. The findings suggest that while technical capabilities have advanced considerably, successful clinical implementation requires careful consideration of practical constraints and ethical issues. This survey serves as a comprehensive reference for researchers and clinicians working at the intersection of computer vision, machine learning, and pediatric healthcare.
The question of which environmental processes operate in filaments still remains unresolved. Given the ubiquity of filaments and their importance in feeding clusters, a proper understanding of these mechanisms is crucial to building a more complete picture of galaxy evolution. Carrying out such an investigation requires access to large galaxy samples with spatially resolved information. As part of this effort, we analysed resolved H alpha maps of 685 galaxies inside and outside the filaments around the Virgo cluster, in addition to extensive measurements of the integrated physical properties. We created a pipeline to decompose the H alpha images into individual clumps that trace star-forming regions. We find that the number and average size of clumps in a galaxy are well-defined functions of distance and angular resolution. In particular, the power-law relation between the number of clumps and the distance of a galaxy is consistent with a fractal structure of star forming regions. We formulated an algorithm to compare filament and non-filament galaxies after removing observational differences. Although we do not have any conclusive evidence of a difference in clump size distributions between filament and non-filament galaxies, we do find that filament galaxies have slightly more peripheral clumps than their non-filament counterparts.
Geometric morphometrics has become a widely used toolkit for quantitative morphology across diverse biological fields. It relies on the precise placement of anatomical landmarks in 2D or 3D. However, manual landmarking, especially in 3D, is labour-intensive, time-consuming, prone to intra- and inter-observer variation and requires significant expertise. These limitations are increasingly incompatible with the demands of high-throughput imaging workflows. This bottleneck has spurred efforts to automate landmarking, often through deformable registration of 3D images, surfaces, or point clouds. While these methods can effectively capture overall shape, they may introduce biases in landmark localization and distort patterns of variances and covariances. Previous attempts to mitigate these issues with deep learning (DL) have generally focused on specific anatomical parts of certain species, most notably human faces and craniofacial anatomy in model-organisms. In this study, we introduce a DL-based pipeline for refining landmark placement. The pipeline begins with approximate landmark predictions derived from a simple rigid registration. These initial predictions define local 3D surface regions, which are then parametrized into 2D using least-squares conformal maps and enhanced with colorization informed by diverse geometry and ambient lighting. The resulting standardized representations are then used to train Transformer and Convolutional Neural Network (CNN) architectures. The approach was evaluated using an open-access dataset of 3D mouse skull models with manually digitized landmarks. Focusing on ten distinct landmarks, we compared our rigid + DL predictions with those from a mainstream global registration approach, ALPACA. We assessed both distance accuracy to the manual groundtruth and preservation of the shape variation patterns. We also examined the effects of various hyperparameter settings strategies, offering practical guidelines for future implementations. Our results demonstrate the feasibility of using 2D colored-enhanced local geometric representations for accurate landmark position refinement with DL. The proposed approach mitigates biases associated with the considered deformable registration method and establishes a methodological foundation for more generalizable, high-throughput morphometric analyses using DL.
Coral reefs depend on the assimilation of off-reef nutrients, particularly from zooplankton, to fuel on-reef productivity. Planktivorous reef fishes thus play a critical role in sustaining a key ecosystem function as the major consumers of plankton. The importance of gelatinous zooplankton to the nutritional ecology of larger-bodied planktivorous fishes is becoming increasingly apparent. Yet, the extent to which feeding on gelatinous plankton within these taxa is a product of ontogeny has not yet been addressed. It has been hypothesised that Caesio cuning, a key planktivorous species that contributes substantially to reef fish biomass on Australia’s Great Barrier Reef, undergoes an ontogenetic diet shift from predominantly non-gelatinous prey as juveniles to a diet composed mainly of gelatinous zooplankton as adults. We examine high-taxonomic resolution DNA metabarcoding of gut contents, with morphometric analyses, to investigate whether changing body size and habitat are linked with a purported dietary transition in C. cuning. We reveal clear evidence of a shift towards increased feeding on gelatinous plankton with increasing body size in C. cuning. Across the sampled habitats, small- and large-sized individuals occupied distinct dietary spaces. The increase in size and ontogenetic dietary shift in C. cuning was also correlated with a transition towards morphological traits associated with feeding and movement in the water column. These results emphasise the importance of gelatinous zooplankton in shaping the distribution, feeding ecology, and productivity of C. cuning on coral reefs and underscore the close connection between large body size and exploitation of gelatinous plankton by reef fishes.
The fossil record is subject to multiple biases that can distort macroevolutionary and paleoecological inferences. Although temporal and spatial sampling biases have received substantial attention, other sources of fossil sampling heterogeneity remain less well quantified. Using the Triton database of planktonic foraminifera, we assess the influence of geographic, ecological, morphological, and methodological factors on fossil recovery rates. We first apply a temporal subsampling method to standardize fossil occurrences over geologic time, validating this approach against an expert-curated lineage-through-time trajectory. After subsampling, the occurrences remain unevenly distributed throughout species' lifetimes and inhomogeneously distributed across species, reflecting biological signal and/or persistent sampling biases.We then investigate this residual sampling heterogeneity with a generalized additive model incorporating relevant predictors from Triton. Our results reveal that, after correcting for temporal biases, geographic predictors (paleolatitude, paleolongitude, longitudinal spread) explain nearly a third of sampling variation. Species-specific ecological and morphological attributes contribute an additional fraction, among which mean relative abundance emerges as the main factor. Additional predictors of fossil sampling rates include age-calculation methods and biostratigraphic sampling biases. Despite accounting for multiple sources of variation, 37% of the deviance remains unexplained, suggesting unmodeled biological, stratigraphic, diagenetic, or taxonomic drivers of sampling heterogeneity.Overall, observed recovery rates question the validity of the homogeneous-sampling assumption used in most diversification models, and this heterogeneity cannot be reduced to a single dominant factor. This conclusion reinforces the need for integrated subsampling approaches and process-based models that explicitly account for heterogeneous fossilization rates to improve the reliability of macroevolutionary analyses.
