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jsPCA: fast, scalable, and interpretable identification of spatial domains and variable genes across multi-slice and multi-sample spatial transcriptomics data
No full textSpatially structured cell heterogeneity within tissues is essential for healthy organ function. This heterogeneity is reflected by differential gene expression activity at various spatial location. Spatial transcriptomics technologies record genome-wide measurements of gene expression at the scale of entire tissues with high spatial resolution. While they have revolutionized our quantitative understanding of tissue architecture, these technologies generate large and high dimensional datasets encompassing tens of thousands of genes recorded at tens of thousands of spatial locations, requiring efficient automated methods for their analysis. In this study we introduce joint spatial PCA (jsPCA), a novel, fast, scalable and interpretable method for the automatic identification of spatial domains and variable genes in multi-slice and multi-sample spatial transcriptomics data. jsPCA relies on a simple mathematical formulation of a spatial covariance defined as the product of the gene expression covariance with the spatial autocorrelation.The principal components of this spatial covariance yield a biologically meaningful low-dimensional representation. From this representation, we can derive spatial domains by simple clustering. In addition, spatially variable genes can be identified directly from the principal components coefficients. Moreover, this approach enables the joint representation of multiple slices and samples, a frequent experimental setting. This joint representation is obtained without spatial alignment by computing common principal components via joint diagonalization of the set of spatial covariance matrices obtained for each slice. By leveraging sparsity and non-convex optimization on manifold, jsPCA leads to computing time in the order of seconds to minutes, substantially outperforming state-of-the-art approaches. We benchmarked jsPCA on the Visium 10x dataset of human dorsolateral prefrontal cortex and the Stereo-seq MOSTA dataset of mouse embryonic development against 10 state-of-the-art methods. Our approach demonstrated excellent performances, comparable or better than state-of-the-art methods, such as SpatialPCA, BASS, GraphPCA or Stagate, while being much faster, interpretable, and scalable to very large datasets
Exploring Extracellular Vesicle–Associated Plasma Cell-Free DNA Structure for Agnostic Biomarker Identification
No full textNational audienceCell-free DNA (cfDNA) is a promising biomarker, offering both disease-specific insights (e.g., mutations) and systemic, agnostic information (e.g., fragmentomics)1. Yet, despite its expanding clinical use, the physicochemical properties and in vivo kinetics of cfDNA remain poorly defined within the nuclease-rich plasma environment. A central and debated hypothesis is that extracellular vesicles (EVs) transport and protect cfDNA, but the evidence has been conflicting, dominated by conditioned-media experiments, and constrained by the inherently low abundance of cfDNA in plasma2,3. Resolving the nature of cfDNA carriers is therefore critical to understanding the biology of circulating nucleic acids and unlocking their full diagnostic potential.In this study, we present an analytical workflow for plasma fractionation based on size, density, or combined size–density parameters to dissect the molecular interactions of cfDNA. Applying this approach to healthy individuals, we find that cfDNA is not associated with EVs, but instead is found within small, non-vesicular particles whose density scales with cfDNA fragment size. This observation challenges the prevailing EV-mediated transport model and provides a new lens through which to examine cfDNA stability and turnover in vivo, for example through dynamic interactions with plasma proteins and lipoproteins.Extending this workflow to samples from cancer patients, we uncover evidence of altered cfDNA structural organization, suggesting that disease states reshape the molecular packaging of circulating DNA. These findings not only redefine the physical milieu of cfDNA in plasma but also highlight new avenues for biomarker discovery, with the potential to refine early detection and patient monitoring strategies.(1)Thierry, Cancer Metastasis Rev, 2016(2)Jeppesen, Cell, 2019(3)Fernando, PLOS ONE, 201
Optimizing ZIF-8 membrane growth on top of semiconductive Ga-doped ZnO sensitive layers
No full textInternational audienceFunctionalizing ZnO-based chemiresistive sensor surfaces with a ZIF-8 layer, which acts as a permselective membrane, is a well-established strategy to enhance sensor selectivity. This study examines the key factors influencing the conversion process, including the physicochemical properties of solvents or solvent mixtures and the thermal pretreatment, of Ga-doped ZnO (ZnO : Ga). We have evidenced that the polarity, viscosity, and interfacial tension of the solvent significantly affect the dissolution of ZnO : Ga and the crystallization of ZIF-8. Methanol–water mixtures were found to effectively control the conversion process, with a 3 : 1 MeOH/H2O ratio being optimal for producing continuous ZIF-8 membranes. Additionally, it has been demonstrated that annealing can greatly enhance the reactivity of the oxide. However, while it enhances the dissolution of ZnO : Ga, excessively high temperatures can lead to over-dissolution, which hinders ZIF-8 formation. These insights are crucial for optimizing ZIF-8 layers on ZnO : Ga, paving the way for the development of highly selective chemiresistive sensors
Optical refractive index measurements of AlGaAs at high temperature for fully automated molecular beam epitaxy growth of Bragg mirrors
No full textInternational audienceAbstract In-situ measurement is a key feature to better understand and precisely control the growth of complex structures, such as vertical-cavity surface-emitting lasers. In this work, we are showing the precise measurement of optical indices of AlGaAs at 600 °C over a wide spectral range (450–1400 nm). To do so, in-situ spectral reflectance measurement is used, combined with ex-situ layer thickness and composition measurement by x-ray diffraction enabling for precise determination of the optical indices with an accuracy better than 1%. To validate our measurements, we realized the complete automation of the growth of a GaAs/AlAs 940 nm-DBR by molecular beam epitaxy, without the need to pre-calibrate cells fluxes. The fabricated DBR shows a deviation of 0.2 nm of the stop-band central-wavelength compared to the targeted one. This approach holds significant interest for the III–V semiconductor community and epitaxial growth techniques
Structural mutations set an equilibrium non-coding genome fraction
No full textNon-coding genome size evolution is poorly understood. While some fraction of non-coding DNA has arguably a regulatory function, a large part does not seem to have a detectable impact on any phenotypic trait. The abundance of non-functional DNA in genomes, observed across the Tree of Life, challenges a purely adaptationist explanation. Several non-adaptive theories have been proposed to explain its presence and identify its determinants, emphasizing either the mutational processes or the mutational hazard entailed by non-coding and non-functional DNA. However, those theories have not yet been integrated into a single framework, and the exact nature of the mutational hazard is not yet fully understood. In this work, we propose a simple mathematical model of genome size evolution. The model shows how the non-coding fraction of the genome is shaped by two factors: unavoidable biases in the neutrality of the different mutation types (adding base pairs is more likely to be neutral than removing some), and the robustness selection imposed by the mere existence of structural mutations (larger genomes are more prone to double-strand breaks that can initiate structural mutations, imposing a second-order selection on robustness). Together, these two factors ensure the existence of an equilibrium non-coding fraction. We show that this equilibrium depends solely on mutation biases and the product of population size and mutation rate
Exploiting Term Sparsity in Symmetry-Adapted Basis for Polynomial Optimization
No full textInternational audiencePolynomial optimization problems are infinite-dimensional, nonconvex, NP-hard, and are often handled in practice with the moment-sums of squares hierarchy of semidefinite programming bounds. We consider problems where the objective function and constraint polynomials are invariant under the action of a finite group. The present paper simultaneously exploits group symmetry and term sparsity in order to reduce the computational cost of the hierarchy. We first exploit symmetry by writing the semidefinite matrices in a symmetry-adapted basis according to an isotypic decomposition. The matrices in such a basis are block diagonal. Secondly, we exploit term sparsity on each block to further reduce the optimization matrix variables. This is a non-trivial extension of the term sparsity-based hierarchy related to sign symmetry that was introduced by two of the authors. Our method is compared with existing techniques via benchmarks on quartics with dihedral, cyclic and symmetric group symmetry
Passivity Preservation in Interconnections of Linear Cone Complementarity Systems with State Jumps
No full textInternational audienceThis article is largely concerned with generic interconnections of a class of passive nonsmooth nonlinear dynamical systems, namely linear cone complementarity systems (LCCS). We stipulate that each subsystem admits a positive definite storage function that characterizes the passivity of an underlying nonsmooth mapping. We provide algebraic criteria in terms of these individual storage functions to find the storage function which guarantees passivity of the overall interconnected system. State jumps in the interconnections are studied in detail. Examples from dynamic feedback control, switching DAEs and nonsmooth circuits are included as an illustration of the theoretical developments
DeepInverse: A Python package for solving imaging inverse problems with deep learning
No full textInternational audienceDeepInverse is an open-source PyTorch-based library for imaging inverse problems. DeepInverse implements all steps for image reconstruction, including efficient forward operators, defining and solving variational problems and designing and training advanced neural networks, for a wide set of domains (medical imaging, astronomical imaging, remote sensing, computational photography, compressed sensing and more)
SIROCCO: A dry wind to warn you from Bluetooth attacks
No full textInternational audienceBluetooth Low Energy (BLE) has become ubiquitous in Internet of Things (IoT) devices, yet it remains vulnerable to sophisticated over-the-air attacks that can compromise data security. This paper introduces Sirocco, a novel host-based intrusion detection system that leverages the open source Zephyr RTOS to address critical security challenges in BLE communications. Sirocco provides a flexible and lightweight detection framework that integrates with the BLE protocol stack. It targets sophisticated and low-level attacks such as spoofing, signal injection, and key sharing vulnerabilities. By inserting lightweight metric collection functions directly into the BLE stack's interrupt service routines and employing a dedicated kernel thread for analysis, Sirocco minimizes performance overhead while maintaining realtime attack detection capabilities across different device roles. Experimental evaluation demonstrates the system's effectiveness, with the framework introducing minimal performance and power consumption overhead