-
[hal-05652708] Text-to-MDX: LLM-assisted generation of MDX queries from user questions
MDX (MultiDimensional Expressions) is the standard language for querying multidimensional data in OLAP systems, but its complex syntax poses challenges for non-expert users. While a lot of research has focused on natural language interfaces for SQL, little attention has been given to MDX. This paper explores the potential of Large Language Models (LLMs), specifically GPT-4o, in translating natural language questions into MDX statements. We investigate whether LLMs can act as full MDX query generators or assistants, and study how the writing style of questions affects output correctness. Through four research questions, we evaluate ChatGPT’s basic capabilities and the effectiveness of prompt engineering in improving text-to-MDX performance. Our evaluation confirms that, with ad-hoc prompt engineering, GPT-4o is indeed able to generate complex MDX queries —particularly when the natural language question is given a structured formulation.
ano.nymous@ccsd.cnrs.fr.invalid (Sandro Bimonte) 10 Jun 2026
https://hal.inrae.fr/hal-05652708v1
-
[hal-05230510] Seed Inference in Interacting Microbial Communities Using Combinatorial Optimization
The behaviour of microorganisms and microbial communities can be abstracted by models combining a description of their metabolic capabilities as metabolic networks, and suitable computational or mathematical paradigms that further integrate simulation conditions. A major component of the latter is the composition of the environment or growth medium that can be referred to as seeds. Predicting the seeds from the metabolic network and an expected behaviour is an inverse problem that can be addressed with linear programming or logic paradigms such as Answer Set Programming (ASP). Here, we formalise seed prediction for microbial communities, taking into account that their members may interact positively through metabolite transfers, which may reduce the need for external seed metabolites. We address the problem with ASP and add a hybrid component ensuring the satisfiability of linear constraints. We explore the subset-minimality solving heuristic of the Clingo solver and develop two heuristics supporting priority of seeds over transfers. We present a proof of concept of seed inference in small-scale communities, and assess the scalability of the three heuristics at genome-scale. Overall, our work introduces a hybrid logic-linear model for seed inference in interacting microbial communities, and new heuristics for the exploration of the solution space with subset minimality optimisations.
ano.nymous@ccsd.cnrs.fr.invalid (Chabname Ghassemi Nedjad) 29 Aug 2025
https://inria.hal.science/hal-05230510v1
-
[hal-05708804] comFI: Inferring metabolic fluxes in microbial communities from time-resolved multi-omics data
Microbial communities are complex ecosystems that are actively studied for the multiple beneficial services they provide, spanning from food fermentation and wastewater treatment, to biotechnology, medicine, crop protection and animal health. Understanding the functioning of microbial communities remains challenging due to the complexity of metabolic interactions and community assembly mechanisms. Recent advances in high-throughput sequencing and analytical technologies enable the collection of longitudinal multi-omics data at the community scale, including population counts, metabolomics, and metatranscriptomics. However, integrative modeling frameworks capable of exploiting such heterogeneous data to infer community-level metabolic activity remain limited. Here, we introduce the community Metabolic Flux Inference (comFI) method, a biology-informed inference framework that enables the integration of multi-omics time series to infer metabolic fluxes at both the community and species levels. The method aims to (i) quantify the contribution of each community member to targeted extracellular metabolite consumption and production, and (ii) infer intracellular flux distributions consistent with transcriptomics and observed community-scale metabolite dynamics. We extensively benchmarked comFI robustness on synthetic datasets for varying ecological interactions, community richnesses, observation noises or transcriptomic depths. The method provides accurate reconstructions of metabolic fluxes across benchmarks, with an average goodness-of-fit R2 of 94.16% for observed metabolic exchanges and 94,83 % for unobserved intracellular metabolic flux reconstruction, achieving very low false positive and negative rates of predicted activated reactions compared to metatranscriptomics (2 % and 5 % respectively). We then applied comFI on two experimental datasets illustrative of different ecosystems and applications: (i) a denitrifying synthetic community, representative of natural soil microbiota and of direct relevance for global N- cycle ; and (ii) a controlled cheese fermentation community involving three species engaged in cooperative and competitive metabolic interactions during lactose-to-lactate conversion and subsequent ripening stages. The comFI method recovered known interactions, while providing quantitative insights on metabolic exchanges and microbial physiology, consistent with metabolomics and metatranscriptomics data.
ano.nymous@ccsd.cnrs.fr.invalid (Sthyve Junior Tatho Djeanou) 07 Aug 2026
https://hal.science/hal-05708804v2
-
[hal-05709904] Leveraging hologenomic data for phenotypic prediction: potential and pitfalls
The microbiota is increasingly recognized as an active component of host biology, influencing various host phenotypes. Advances in high-throughput sequencing and the emergence of the holobiont perspective have raised expectations regarding hologenomic-informed prediction. Yet, whether and under which conditions integrating microbiota and genomic data meaningfully improves phenotypic prediction remains unclear. The biological characteristics of the microbiota, including but not limited to transmission mechanisms, environmental effects and interactions with host genetics, complicate their integration into classical evaluation frameworks. In addition, microbiota datasets are high-dimensional, highly dispersed, sparse and compositional. Finally, analytical choices such as the taxonomic granularity considered for aggregation or the similarity matrix used in prediction models may impact downstream inference and prediction accuracy. Here we explore these challenges using a comprehensive set of transgenerational hologenomic simulations. By generating controlled and contrasted biological scenarios across a broad parameter space, we examine how microbiota granularity, variance structure and host modulation influence (i) the estimation of variance components and (ii) the accuracy of phenotypic prediction. We show that the added value of hologenomic, compared to genomic prediction, is highly context dependent. Our results provide a structured framework to interrogate when and how integrating microbiota may enhance phenotypic prediction in breeding applications.
ano.nymous@ccsd.cnrs.fr.invalid (Solène Pety) 03 Aug 2026
https://hal.science/hal-05709904v1
-
[hal-05657073] A free time machine? Milking robots and transformations in the temporal regime of dairy farmers in France
This article examines the effects of the large-scale diffusion of milking robots on dairy farmers’ lifestyle, with a special attention to the temporal patterns of their daily life. The study is based on quantitative data from a questionnaire completed by 831 respondents and qualitative data from 43 interviews with dairy farmers. It critically questions prevailing narratives depicting milking robots as technologies that liberate farmers' time and modernize their lifestyles. Our findings show instead that the robot reconfigures labour along principles of fluidity, and further blurs the already fuzzy boundaries between work and the domestic sphere. Farmers' daily lives remain tied to a largely unchanged temporal regime defined by long working hours. The reduction of total work hour is on average of 6% and mostly concerns evening hours. The morphology of work time, as well as the patterns of articulation of social times within farming households, are only marginally altered by the adoption of milking robots.
ano.nymous@ccsd.cnrs.fr.invalid (Nicolas Deffontaines) 15 Jun 2026
https://u-picardie.hal.science/hal-05657073v1
-
[hal-05656546] A soil-type-specific stratified approach to bare soil mosaicking and SOC prediction from Sentinel-2 time series
Accurate, high-resolution mapping of soil organic carbon (SOC) is essential for environmental modelling and sustainable land management, yet its prediction based on satellite imagery is often affected by vegetation and moisture, possibly causing generalised models to fail in landscapes with heterogeneous soils. To address this, we developed a stratified framework that tailors bare soil thresholding and SOC modelling to specific soil types. Using a 7-year Sentinel-2 time series and 414 soil samples over the Centre-Val de Loire region in France, we first identified the Visible and Shortwave Infrared Drought Index (VSDI) as an effective moisture proxy, avoiding the need for availability-limited external moisture data. We then optimised NDVI, NBR2, and VSDI thresholds individually for each major soil type to filter bare soil observations. Finally, soil-type-specific partial least squares regression (PLSR) models were built and compared against a single generalised model.</p><p>Our results showed that our soil-type-specific strategy substantially outperformed the generalised model (e.g., RPIQ increased from 0.68 to 2.38 for Brunisols eutriques). The primary spectral predictors for SOC were highly variable, varying from visible to SWIR bands according to the soil type. The optimised VSDI filter was also critical for this improvement, reducing prediction RMSE by nearly 50% for loamy-texture soils like Luvisols. This study demonstrates that, for accurate SOC prediction at a very large regional scale, context (soil-type)-specific stratification of bare soil thresholding and SOC modelling is critical, serving as a framework for integrating pedological knowledge into SOC prediction and subsequent digital soil mapping workflow.
ano.nymous@ccsd.cnrs.fr.invalid (Qianqian Chen) 14 Jun 2026
https://hal.inrae.fr/hal-05656546v1
-
[hal-05704872] SelNeTime
[...]
ano.nymous@ccsd.cnrs.fr.invalid (Mathieu Uhl) 27 Jul 2026
https://hal.inrae.fr/hal-05704872v1
-
[hal-05708825] Metabolic Flux Inference in a Cheese Microbial Community via comFI: a Biology-informed Approach for Time-resolved Multi-omics Integration
Microbial communities play a central role in many bioprocesses with key applications in food fermentation, waste treatment, human and animal well-being, plant protection or metabolite transformation in industrial bioprocesses. However, the metabolic microbial interactions driving the community dynamics remain difficult to characterize because of their complexity and their temporal variability. Recent advances in sequencing and analytical technologies now provide time-resolved multi-omics data at the community scale providing key insights into the mechanisms shaping the community dynamics. However, integrating these heterogeneous data in an interpretable way to decipher species-specific metabolic activity and microbial interactions remains a major challenge in the study of microbial communities. We introduce the community metabolic flux inference (comFI) method, a mathematical framework for inferring the metabolic fluxes of individual microorganisms from community-level longitudinal data. The method formulates flux estimation as a biology-informed constrained inference problem that combines observed microbial abundances and extracellular metabolite exchange data, with metabolic constraints encoded in a metabolic model and transcriptomic-based lasso regularization terms. We evaluated comFI on synthetic datasets generated from dynamic models of microbial communities involving three Escherichia coli mutant strains. The comFI method showed a very good reconstruction accuracy for exchange fluxes, intracellular metabolic fluxes distribution, metabolic pathway activation patterns and strain contribution. We also applied the method to experimental cheese fermentation data involving three bacteria (Lactococcus lactis, Lactobacillus plantarum, and Propionibacterium freudenreichii), combining abundance measurements, targeted metabolomics and metatranscriptomics data. The comFI framework enabled to recover previously identified interaction patterns, and to reconstruct latent intracellular flux states for individual microorganisms alongside with their respective metabolic contributions within the community, consistently with the omics data and known physiology. All together, we demonstrate that comFI provides a practical framework for recovering the metabolic activity of individual microorganisms from community-scale multi-omics time-resolved data.
ano.nymous@ccsd.cnrs.fr.invalid (Sthyve Junior Tatho Djeanou) 31 Jul 2026
https://hal.science/hal-05708825v1
-
[hal-05701255] Information criteria exploiting latent structure for model selection in Structural Equation Models
Structural equation models (SEM) are widely used to describe dependency structures between latent variables, making model selection a key issue in many applications. Existing information criteria are generally based on the integrated observed-data likelihood and therefore do not explicitly account for the latent structure of the model. In this paper, we propose two new information criteria derived from the integrated complete-data likelihood. The first adapts the Integrated Completed Likelihood criterion to Gaussian SEM, while the second proposes an alternative approach to approximating the integrated observed-data log-likelihood by incorporating latent structural information and using an importance sampling strategy. Their performance is assessed through an extensive simulation study covering null, direct, indirect and complete latent structures under different sample sizes and signal strengths. The results show that the proposed importance sampling strategy provides robust and competitive model selection across a wide range of scenarios, whereas the proposed ICL criterion is particularly effective for recovering latent dependency structures when the latent variables are accurately estimated. These findings demonstrate the potential benefits of explicitly exploiting the latent structure when developing information criteria for structural equation models.
ano.nymous@ccsd.cnrs.fr.invalid (Marion Naveau) 22 Jul 2026
https://hal.science/hal-05701255v1
-
[hal-05692432] Text guidance is powerful but prompt-sensitive for weakly-supervised leaf symptom segmentation
Accurate segmentation of plant disease symptoms is essential for crop monitoring and phenotyping, yet it typically requires costly pixel-level annotations. Weakly supervised semantic segmentation (WSSS) alleviates this burden using image-level labels, but its performance depends on the quality of spatial priors such as class activation maps (CAMs). We investigate whether text-guided segmentation with the Segment Anything Model 3 (SAM3) can serve as an alternative weak supervision signal. Three pseudo-mask generation strategies are compared: (i) CAMs refined with SAM or SAM3, (ii) zero-shot text-guided SAM3, and (iii) a hybrid approach combining weak spatial cues with text prompts. The resulting pseudo-masks are used to train a DeepLabV3 model. Text guidance alone matches or outperforms conventional WSSS, achieving up to 0.46 IoU without spatial supervision and 0.61 IoU on a public dataset, although performance is sensitive to text prompt formulation. The hybrid strategy improves robustness, reaching 0.50 IoU on the primary dataset and 0.58 IoU on the additional dataset while reducing prompt sensitivity. Overall, text guidance is a promising alternative to conventional weak supervision, while hybrid approaches provide a more robust solution for plant disease segmentation.
ano.nymous@ccsd.cnrs.fr.invalid (Romane Dubois) 15 Jul 2026
https://hal.science/hal-05692432v1
-
[hal-05688477] Ancient wheat DNA: How and what for?
In complement to classical approaches currently used in agricultural science in characterizing and exploiting in breeding traits- or phenotype-driving genes and genetic variants from modern genetic diversity, knowledge of past evolution and adaptation processes from ancient plant DNA can be mobilized to develop varieties adapted to current environmental challenges. The article reviews the current scientific and technical insights from the investigation of ancient DNA from plant remains with a focus in wheat.
ano.nymous@ccsd.cnrs.fr.invalid (Caroline Pont) 10 Jul 2026
https://hal.science/hal-05688477v1
-
[hal-05710282] Using Energy Flow Analyses (EFA) in a local participatory process for energy policy-making: lessons learned
Over the past few decades, conflicts have emerged at the local scale over the prioritisation of sustainability issues. In the Grand Briançonnais, a region of the French Alps with approximately 34,500 inhabitants across 36 municipalities, these conflicts focus on the construction of new micro-hydroelectric power plants. Although these facilities produce low-carbon energy, they also affect ecosystems and water sports through the artificialisation of river sections. These conflicts have led various local stakeholders to take legal action against such projects. The strong opposition between proponents and opponents of hydropower is partly rooted in significant differences in values, perceptions and understandings of sustainability in the region. In response, the local agency coordinating several inter-municipal authorities—the PETR du Grand Briançonnais —announced in 2022 its intention to develop a coherent local energy policy co-produced with citizens and local energy stakeholders. The objective was to reopen dialogue by debating new micro-hydroelectric projects while broadening the discussion to the entire regional energy policy and opening participation to all interested inhabitants. To achieve this objective, the president of the PETR contacted scientists who decided to help to create the required participatory process and to launch an action research project aimed at evaluating the use of energy flow analyses as a basis for systemic representations of energy issues to support local participatory processes. This research objective has been further detailed into three research intentions : (1) to test the feasibility of local-scale energy flow modelling, detailed enough to be relevant to stakeholders; (2) to assess citizens’ understanding of energy flow analyses and the learning outcomes associated with different formats, namely facilitated workshops and a serious game; and (3) to assess the perceived relevance of energy flow analyses as scientific support for the different phases of the participatory process. The evaluation followed a twofold approach. First, the impact of the use of energy flow analyses was assessed through observations and participant questionnaires. Second, a global evaluation of their mobilisation throughout the participatory process was conducted using participant questionnaires and an ex-post self-reflexive approach. Depending on the criteria considered, between approximately 20 and 50 participants were engaged in the evaluation, within a process involving 330 inhabitants. The results show that local-scale modelling of energy flows is feasible, although it presents several challenges. Data availability, dispersion and relevance, as well as the articulation between scientific modelling and territory-specific perceptions, are key factors in producing meaningful support for local policy-making. Energy flow analyses were generally understood by participants not necessarily familiar with energy issues, provided that appropriate support was offered. Learning outcomes differed depending on the format. Facilitated workshops mainly improved cognitive knowledge of the region’s physical dynamics and helped prioritise actions for energy policy formulation. In contrast, the serious game fostered a deeper understanding of socio-ecological complexity and highlighted the role of governance and relational capacities in achieving sustainability. Overall, energy flow analyses were considered relevant and useful in the consultation process, particularly during the diagnostic phase and issue prioritisation. However, identifying and operationalising effective levers for action remained a major challenge.
ano.nymous@ccsd.cnrs.fr.invalid (Emmanuel Krieger) 03 Aug 2026
https://hal.science/hal-05710282v1
-
[hal-05710002] On Computing and Exploring Multiple Consistent Metabolisms in Material Flow Analysis
This communication focuses on how to compute and explore bio-physically consistent metabolisms for foresight scenarios using Material Flow Analysis (MFA). MFA tools usually output a single solution or a posterior distribution. We consider another option, particularly for the iterative construction of foresight scenarios, by proposing to compute and then explore several close-to-optimal but widely different solutions. This work is embedded in the development of the STAX software.
ano.nymous@ccsd.cnrs.fr.invalid (Thibaut Coudroy) 03 Aug 2026
https://inria.hal.science/hal-05710002v1
-
[hal-05712379] Simulating population pangenomes under coalescent demographic models with MSpangenome
Abstract Motivation Pangenome variation graphs (PVGs) are increasingly used to represent genomic diversity, yet there is currently no general framework for generating population pangenomes directly from explicit evolutionary histories. Existing simulators typically focus on individual classes of variation and do not integrate these variations within a genealogy-aware framework driven by explicit demographic histories. As a result, evaluating pangenome methods in realistic population-genetic settings remains challenging, and benchmark datasets with known evolutionary ground truth are scarce. Results We present MSpangenome , a genealogy-aware framework that bridges coalescent population genetic simulations and pangenome graph analyses. The pipeline combines ancestry simulation with msprime and a de novo graph construction algorithm to generate PVGs directly from simulated genealogies. By explicitly modeling recombination, demographic history and incomplete lineage sorting, MSpangenome produces structurally complex pangenomes in which nested and overlapping structural variants emerge naturally from the underlying genealogies, while their evolutionary history and graph topology remain known by construction. This provides a general framework for generating realistic population pangenomes and establishing ground-truth datasets for methodological evaluation. We demonstrate its utility by generating population-scale pangenomes and using them as controlled references to benchmark the widely used graph construction tools, PGGB and Minigraph-Cactus . Our analyses reveal contrasting performance regimes across levels of sequence diversity, sample sizes and classes of structural variation, highlighting the value of simulation-based benchmarking for identifying reconstruction errors that are hard to detect using empirical datasets alone. Availability and implementation MSpangenome is implemented in Python, fully containerized, freely available at https://forge.inrae.fr/pangepop/MSpangepop and mirrored at https://github.com/inrae/MSpangepop .
ano.nymous@ccsd.cnrs.fr.invalid (Lucien Piat) 06 Aug 2026
https://hal.inrae.fr/hal-05712379v1
-
[hal-05682039] Reconstruction of ancestral plant genomes for inter-crop translational research
We present an initial exploratory framework, Ancestral Genome Reconstruction (AGR), to automatically infer ‘paleogenomes’ from large comparative datasets. By analyzing 84 extant angiosperm species, we reconstructed 10 key ancestral plant genomes of millions of years old. These reconstructed ancestors were instrumental in (i) estimating when emerged the angiosperms as well as major botanical families as well as ancestral shared whole genome duplication events, (ii) tracing the evolutionary trajectories of ancestral chromosomes as well as genes, especially those that may have driven the emergence of key life history traits (exemplifies with woody vs. herbaceous, aquatic vs. terrestrial, C3 vs. C4 and symbiotic root nodulators vs. non-nodulators species). We demonstrate that these paleogenomes serve as tractable backbones for inter-crop translational research, in delivering though an open access web tool (OrthoViewer) genes that have conserved the same ancestral genomic context favoring the identification of ‘phenologs’ -genes underlying similar phenotypes, traits, or processes across species- exemplified with FUWA for yield components, FLC for flowering time, and DDM1 for DNA methylation. Taken together, this study provides a testable paleogenomics workflow, opening novel avenues to integrate evolutionary genomics data into modern climate-smart breeding and support the agroecological transition.
ano.nymous@ccsd.cnrs.fr.invalid (Cléa Siguret) 10 Jul 2026
https://hal.science/hal-05682039v1
-
[hal-05694907] How Metagenomic Analysis Strategy Shapes Functional Inference? Metabolic Landscapes from Le French Gut Cohort
Background The human gut microbiome plays a key role in host health by contributing to digestion, immune regulation, and metabolic processes. Alterations in microbial community composition have been linked to numerous diseases, motivating large-scale metagenomic studies to better characterise microbial diversity and function. Projects such as Le French Gut aim to describe gut microbiota structure at the population level (currently n = 10,000 individuals) using shotgun metagenomic sequencing. However, different analytical strategies may strongly influence genome reconstruction, genome-scale metabolic network (GSMN) inference, and downstream analyses. Results We analyzed 105 fecal metagenomes from the Le French Gut cohort using both a reference-based profiling strategy and a de novo assembly-based protocol to retrieve gene catalogues and metagenome-assembled genomes (MAGs). Each sample was characterised as a collection of reference genomes, de novo MAGs, and as a gene catalogue. We first assessed the impact of sequencing depth on GSMN reconstruction quality, then compared three reconstruction tools — CarveMe, gapseq and Pathway Tools — and finally evaluated different metagenomic units, including reference genomes, gene catalogues and (MAGs). Higher sequencing depth consistently improved GSMN completeness by increasing the number of reconstructed reactions and accessible metabolites. Although no reconstruction tool performs best in all contexts, gapseq provided the best balance between metabolic coverage and genetic support for the reconstructed GSMNs. Finally, the gene catalogue approach offered a good balance between functional completeness and biological realism, whereas compartmentalised approaches, particularly MAG-based reconstructions, remain essential to preserve species-level organization and investigate potential metabolic interactions within microbial communities. Conclusions Our results demonstrate that the choice of metagenomic analysis strategy significantly impacts functional inference. De novo and reference-based approaches provide complementary views of the gut microbiota. We further analysed reconstructed metabolic profiles in the context of the extensive health, lifestyle, and dietary metadata associated with the dataset. Overall, our findings highlight the importance of methodological choices in metagenomic studies and provide a framework for integrating microbial genomic data with clinical, dietary, and lifestyle information in large population cohorts.
ano.nymous@ccsd.cnrs.fr.invalid (Sarah Toubal) 16 Jul 2026
https://hal.science/hal-05694907v1
-
[hal-05697497] Assessing Dorado pseudourydilation RNA modification prediction on Arabidopsis thaliana ribosomal RNA
[...]
ano.nymous@ccsd.cnrs.fr.invalid (Emma Rodriguez) 19 Jul 2026
https://hal.science/hal-05697497v1
-
[hal-05683977] Assessing the structure of DNA representation spaces using graph-based comparisons
Many models have been proposed to create embeddings of DNA sequences. While these models are typically evaluated using downstream tasks such as species prediction, such evaluations offer limited insight into the intrinsic differences between their embedding spaces. To address this gap, we focus on direct comparison of the geometric and topological properties of embeddings generated by those models. We consider five models (TNF, dna2vec, DNABERT-S, DNABERT-2 and HyenaDNA) chosen to represent a broad range of embedding techniques: from simple k-mer counts to state-of-the-art transformer architectures. Our comparison centers on two key questions. First, how do these models organize sequences from the same species in their latent spaces ? Second, how do they differ in terms of local and global topological properties ? We first evaluated whether sequences from the same species cluster together in each model’s embedding space. PCA projections revealed that, while all models grouped sequences by species, the degree of separation varied. Strikingly, the order of species along the first principal component was consistent across models, as were the relative distances between clusters. This suggests that, despite wide architectural differences, the models capture a shared global biological signal in their embeddings. Three models (TNF, dna2vec, and HyenaDNA) produced tightly clustered species groups, yielding similar silhouette scores. In contrast, DNABERT-S and DNABERT-2 exhibited more dispersed clusters: DNABERT-S achieved a higher silhouette score due to better separation, while DNABERT-2’s lower score reflected greater intra-cluster variance. This divergence may stem from the transformer architectures’ ability to capture more nuanced sequence features, albeit at the cost of cluster compactness. To further probe the embedding spaces using quantitative metrics, we constructed k-nearest neighbors (K-NN) graphs and applied two complementary analyses: (i) Jaccard distance to quantify local neighborhood similarities and (ii) a permutation test based on Random Dot Product Graphs (RDPG) to compare global topological properties. These methods enabled us to assess both fine-grained and large-scale differences between embedding spaces. Our analysis revealed that both Jaccard and RDPG comparisons are sensitive to hyperparameter choices. For K-NN graphs, the distance metric and number of neighbors (k) significantly impacted results, as did the size and composition of the input dataset. The RDPG framework introduced an additional hyperparameter: the latent dimensionality (d) for spectral embedding. Surprisingly, it also resulted in strong asymmetry in model comparisons (A vs. B ≠ B vs. A). This asymmetry poses a challenge for aggregating results and drawing robust conclusions. In summary, our study establishes Jaccard distance and RDPG on k-NN graphs as a unified framework for comparing DNA sequence embeddings, offering insights into both local and global properties of latent spaces. While methodological challenges, in particular hyperparameter sensitivity and comparison asymmetry, remain, addressing them could provide a way toward more biologically interpretable embeddings and deepen our understanding of what genomic models actually learn.
ano.nymous@ccsd.cnrs.fr.invalid (Juliette Francis) 07 Jul 2026
https://hal.science/hal-05683977v1
-
[hal-05686148] PanGen1C: a Snakemake Workflow for k-mers based genotyping of large cohorts on pangenome graph
Advances in long read sequencing have shown previously unstudied structural and sequence variation, often poorly represented by a single linear reference genome. Pangenome graphs address this limitation by integrating multiple haplotypes into a reference structure, more suited to represent the genomic diversity of a given species. Graph-based genotyping uses these references to improve read mapping and variant detection, particularly in highly variable regions, but are intensive both computationally and on storage. K-mer based approaches allow the use of short-read data sequencing data without graph alignment, enabling efficient and scalable detection of both small and structural variants in population-scale datasets. We present PanGen1C, a Pangenie-based workflow to facilitate genotyping large-scale short read sequencing data.
ano.nymous@ccsd.cnrs.fr.invalid (Martin Racoupeau) 08 Jul 2026
https://hal.science/hal-05686148v1
-
[hal-05673352] EweAcT: Ewe behaviour aligned to accelerometer data for activity monitoring in extensive grazing systems.
Monitoring livestock behaviour under extensive conditions would provide valuable insights to assess animal adaption to environmental perturbations in agroecological systems (e.g., heat waves, parasitism, predator attacks). Animal behaviour can be monitored using accelerometer data collected from neck-collars combined with artificial intelligence models. However, large amounts of accelerometer data aligned with annotated behaviours are necessary to develop accurate models of behaviour prediction. In particular, developing reliable models for extensive systems requires data collected across a wide range of representative conditions. The dataset includes 79 hours of tri-axial accelerometer data aligned with behaviours manually annotated from video recordings for 120 Romane ewes born between 2021 and 2024. The ewes were derived from two divergent genetic lines after three and four generations of selection started 10 years ago: low and high social attractiveness, noted S-and S+, and low and high tolerance towards humans, noted H-and H+. They were reared under the extensive system applied to the Experimental Unit of La Fage (UEF, INRAE, Saint-Jean-et-saint Paul, Aveyron) where 250 sheep were reared exclusively outdoors on 280 hectares of rangeland in southern France. First batch of data was collected on March, June and July 2024 at the UEF under a range of extensive conditions, including sloping pastures and heat-wave periods. Ewes were equipped with accelerometer neck-collars specifically designed for young sheep on pasture. They were grouped on experimental paddocks for 4 to 8 hours and provided with fresh grass and ad libitum access to water. The animals were simultaneously video-recorded using an elevated CCTV camera. Behaviour annotation was carried out using Behavioral Observation Research Interactive Software focusing on the main behaviours on pasture: Grazing, Ruminating, Resting, Moving, and "Other", grouping all remaining activities. Annotations and corresponding accelerometer sequences were aligned using Python language, based on a time synchronization procedure. A second batch of data was acquired on November 2025 to supplement the dataset with the moving activity. For that purpose, ewes were equipped with the accelerometer collars and moved on tracks from the housing area to the pastures, corresponding to an approximately 10 minute-walk. The start and end times of the moves for each ewe were used to align the corresponding accelerometer data with the moving activity. These data were then merged with the dataset from the first batch. The resulting dataset is ready to use for applying artificial intelligence models to classify the 5 main behaviours of sheep under extensive grazing systems from accelerometer data.
ano.nymous@ccsd.cnrs.fr.invalid (Lucile Riaboff) 01 Jul 2026
https://hal.inrae.fr/hal-05673352v1
-
[hal-05688139] Joint inference of selection and demography from genomic time series
[...]
ano.nymous@ccsd.cnrs.fr.invalid (Paul Bunel) 14 Jul 2026
https://hal.science/hal-05688139v1
-
[hal-05686843] Lossless compression of k-mer matrices enabling random row access
<div><p>Genomic search engines such as Logan-Search index petabytes of sequencing data as large binary matrices, called k-mer matrices, where each row encodes the presence of a k-mer across thousands to millions of genomic samples. Logan-Search contains a petabyte of binary matrices, and storing them is expensive, yet compression must not prevent fast random access to any matrix row at query time. We present kmcomp, a lossless compression method for k-mer matrices that satisfies these competing requirements. Block compression partitions the matrix into fixed-size row blocks, each compressed independently; block start positions are stored in an Elias-Fano encoded array, enabling O(1) random access to any block. To improve compressibility without introducing additional decompression steps, we introduce the π-compression: a column reordering that groups similar samples together by solving the Traveling Salesman Problem via a nearest-neighbor heuristic. We accelerate this heuristic with a novel variant of the vantage-point tree, the masked vp-tree, which dynamically prunes nearest-neighbor search space. On three (meta)genomic datasets, kmcomp achieves compression ratios of 1.3 to 5.4; π-compression further improves these to 1.5 to 51.3. Applied to the Logan-Search petabyte-scale index, compression reduces storage by approximately half, and π-compression adds a further 13% gain. Query overhead remains modest: queries of hundreds of nucleotides incur an absolute latency increase of ≈ 100 ms, and highly compressed indexes can match uncompressed query times thanks to reduced disk reads.</p></div>
ano.nymous@ccsd.cnrs.fr.invalid (Alix Regnier) 09 Jul 2026
https://hal.science/hal-05686843v1
-
[hal-05665434] Horloges épigénétiques pour la longévité fonctionnelle des vaches laitières
Chez les bovins lait, la longévité fonctionnelle est un caractère d’intérêt majeur sur les plans économique, environnemental et du bien-être animal mais elle n’est mesurable que tardivement dans la vie des individus. L’objectif de ce stage était d’évaluer si l’âge épigénétique, prédit à partir de la méthylation de l’ADN pouvait constituer un biomarqueur précoce du vieillissement et de la longévité fonctionnelle. Le méthylome sanguin de 4 751 vaches Holstein a été analysé à l’aide de la puce RUMIGEN EpiChip, ciblant 44 053 sites CpGs. Plusieurs modèles de prédiction ont été comparés afin de construire une horloge épigénétique. Le meilleur modèle a prédit l’âge épigénétique des animaux avec une précision de 129 jours. L’écart entre l’âge épigénétique et l’âge réel a été calculé pour chaque animal. Dans les modèles analysés, cet écart présentait une héritabilité modérée et était associé à plusieurs régions génomiques où plusieurs gènes candidats ont été identifiés (dont DNMT3B et RCAN2). De plus, cet écart a permis d’observer que l’accélération du vieillissement épigénétique était associée à une réforme précoce des animaux. Ces résultats montrent qu’une horloge épigénétique peut être construite chez la vache laitière et que l’âge épigénétique pourrait constituer un éventuel biomarqueur précoce de la longévité fonctionnelle. Ces approches sont très prometteuses pour être appliquées à d’autres contextes et à différentes échelles d’étude.
ano.nymous@ccsd.cnrs.fr.invalid (Margaux Gaury) 22 Jun 2026
https://hal.inrae.fr/hal-05665434v1
-
[hal-05643765] DRL-Based Pose Control for Double-Ackermann Robots Under Actuation Uncertainties
Robust deployment of deep reinforcement learning (DRL) policies on real robots remains challenging due to discrepancies between simulation and real-world dynamics. We address this issue in the context of maneuvering with double-Ackermann-steering mobile robots, which introduce additional constraints due to their non-holonomic nature. Building upon the DRL framework ManeuverNet, we extend its objective from position control to full pose control, resulting in a more challenging task. We further investigate the impact of actuationrelated uncertainties on policy transfer. The use of simplified actuation models during training of the extended policy can lead to poor generalization, shown by a success rate drop from 100% in PyBullet to 25% in Gazebo under stricter evaluation conditions. To address this limitation, we adopt a sim-to-simto-real approach, where actuation effects observed in Gazebo are incorporated into the PyBullet training environment. Using multi-environment DRL with SAC and CrossQ, we learn policies that remain robust despite modeling inaccuracies. This approach can significantly reduce the performance gap across simulators, achieving up to 92% success rate in Gazebo and maintaining 69% under stricter thresholds, with successful transfer to a real robot without additional tuning.
ano.nymous@ccsd.cnrs.fr.invalid (Oussama Zaim) 04 Jun 2026
https://hal.science/hal-05643765v1
-
[hal-05578828] Use of Surface Water and Ocean Topography (SWOT) observations to support Land Use/Land Cover (LULC) change products: the case of the pacific coast of Ecuador
<div><p>Radar altimetry has been used to characterize land surfaces. However, the nadir configuration of the radar altimeter sensor and its coarse spatial resolution were limiting factor. The Surface Water and Ocean Topography (SWOT) mission overcomes these limitations through its Ka-band Radar Interferometer (KaRIn), a synthetic aperture radar (SAR) system, providing high spatial resolution and accurate surface height measurements. Initially used for hydrology and oceanography, this study explores an innovative use of SWOT to analyse changes in Land Use/Land Cover (LULC). To do this, three study areas located on the Pacific Coast of Ecuador were considered. The area in the south (A) is characteristic of cultivated areas, while the area in the center (B) presents a landscape mosaic and the area in the north (C) hosts tropical rainforests. For each study area, the SWOT backscatter coefficient (sig0) was analysed for the year 2024 from the raster product at 100m spatial resolution. We calculated the number of occurrences and the sig0 average from the raster product over each pixel. The spatial patterns obtained from these two variables enabled us to assign a LULC class (city, water, road, crop, or no forest, depending on the study area) to each pixel, using a Support Vector Machine (SVM). The assigned LULC classes depend on the partial spatial coverage of the SWOT data, which does not allow representing all the LULC classes. The classification results were compared with the LULC map provided by the Ministry of the Environment using a confusion matrix and obtained an accuracy greater than 0.87 and an F1 score greater than 0.89 for the three study areas. In the forest area (C), the SWOT observations were also compared to two change detection products: RAdar for Detecting Deforestation (RADD) alerts and detections by the Cumulative Sum (CuSum) method. 39% of the SWOT observations were in areas identified as forest by these products but classified as no forest or water in our SWOT classification. By detecting small streams (areas A, B and C), roads (area A), the boundaries of agricultural plots and the state of cultivated land (area A) as well as recent forms of deforestation (zone C), SWOT was found to be a complementary source of information for LULC change products.</p></div>
ano.nymous@ccsd.cnrs.fr.invalid (Valentine Sollier) 03 Apr 2026
https://hal.inrae.fr/hal-05578828v1
-
[hal-05643469] OrthoViewer
OrthoViewer (interactive exploration of orthologous gene families). OrthoViewer is a publicly accessible web platform that enables real-time exploration of orthologous gene families across 84 angiosperm species. It integrates phylogenetic trees, eggNOG-mapper functional annotations (GO, SO, CoG namespaces) and a manually curated layer of reference genes with associated literature, into a single interactive query environment.
ano.nymous@ccsd.cnrs.fr.invalid (Raphaël Flores) 17 Jun 2026
https://hal.inrae.fr/hal-05643469v1
-
[hal-05665454] Epigenetics clocks for functional longevity of dairy cows
Chez les bovins laitiers la longévité fonctionnelle se définit comme la capacité d’un individu à rester productif dans le troupeau tout en maintenant un bon état de santé et des performances satisfaisantes au cours de sa vie productive (Schuster et al, 2020). L’augmentation de la durée de vie fonctionnelle des individus permettrait de diminuer les besoins de renouvellement du troupeau et de réduire les impacts environnementaux et économiques associés. La longévité fonctionnelle n’est mesurable que tard dans la vie d’un animal et disposer de biomarqueurs précoces permettrait d’accélérer le progrès génétique sur ce caratère. On appelle âge épigénétique l’âge d’un animal prédit via une horloge épigénétique, un algorithme utilisant des données de méthylations de l’ADN comme variables prédictives (Horvath, 2013). L’âge épigénétique pourrait-il constituer un biomarqueur précoce de la longévité fonctionnelle chez les bovins laitiers ? Pour construire l’horloge épigénétique, nous avons analysé le méthylome sanguin de 4 751 vaches Holstein, à l’aide d’une puce de méthylation bovine (RUMIGEN EpiChip), mesurant la méthylation de 44 053 sites CpG. Nous avons comparé plusieurs méthodes d’apprentissage en variant : (1) la nature des variables prédictives (matrices de méthylation de l’ADN ou valeurs propres issues d’une analyse en composante principale, données corrigées de co-variables ou non), et (2) la méthode de prédiction (régressions linéaires pénalisées ou forêts aléatoires). Les modèles les plus performant prédisent un âge épigénétique fortement corrélé à l’âge réel des animaux lors du prélèvement (RMSE = 128.9 jours, R² = 0.95). Nous avons prédit un âge épigénétique pour l’ensemble des échantillons du dispositif, en utilisant les paramètres des meilleurs modèles et en utilisant différentes répartitions des animaux dans les jeux de données d’apprentissage et de test. Cela nous a permis de calculer des écarts d’âges entre l’âge prédit et l’âge réel pour chaque animal de la cohorte. L’héritabilité des écarts d’âges est modérée (h² > 0,2) et plusieurs régions du génome (QTLs) associées à ce phénotype ont été détectés. L’identification des gènes candidats dans chacun de ces QTLs est en cours. L’étape suivante consistera à estimer la corrélation entre cet écart d’âge et différents phénotypes de production, de santé, et de longévité mesurés sur les mêmes animaux. Ces travaux confirment qu’il est possible d’entraîner des horloges épigénétiques chez la vache laitière avec une bonne précision, et nous espérons qu’ils nous apportent des connaissances nouvelles sur l’efficacité de ce biomarqueur pour prédire la longévité fonctionnelle des animaux.
ano.nymous@ccsd.cnrs.fr.invalid (Margaux Gaury) 22 Jun 2026
https://hal.inrae.fr/hal-05665454v1
-
[hal-05651322] Spécificité des associations génétiques en contexte multi-populations : une étude par simulation
Les études d'association à l'échelle du génome (GWAS) sont cruciales pour comprendre les liens entre variations génétiques et phénotypes complexes d'intérêt. En agriculture, la sélection a induit la structuration des espèces agricoles en sous-populations (races ou écotypes) hautement spécialisées. À ce jour, peu d'études ont évalué l'impact conjoint de cette structuration et du choix du modèle sur les résultats d'un GWAS. Dans cette étude, nous proposons un cadre de simulation réaliste dans le but d'évaluer la performance des méthodes existantes pour détecter des associations spécifiques à une population ou capturer des effets partagés ou non entre sous-populations. Les résultats obtenus dans cette étude mettent en évidence les limites des approches classiques fondées sur des modèles linéaires mixtes, notamment pour la détection de variants présentant des effets hétérogènes ou opposés entre populations.
ano.nymous@ccsd.cnrs.fr.invalid (Kossi Julien Kowou) 10 Jun 2026
https://hal.science/hal-05651322v1
-
[hal-05682255] Towards lucerne varieties used as living mulch for cereal crops in agroecological systems
Lucerne, a perennial legume known for its nitrogen fixation, persistence, and soil-covering capacity, shows strong potential as a living mulch for cereal cropping. However, its vigorous growth often results in excessive competition with cash crops. The selection of lucerne varieties adapted to living mulch could be a solution to reduce this competition. We synthetize the state of the art on this subject. Wheat–lucerne interactions occur from the earliest stages of wheat cycle until its harvest and are mainly driven by lucerne morphological and phenological traits. Autumn dormancy, growth habit, height, and cover state of lucerne determine the trade-off between reducing competition with wheat and maintaining the ecosystem services provided by lucerne. An intermediate dormancy, combined with moderate height and upright cover, appears to provide the most favourable balance. Genetic correlations between traits measured in spaced plants and living mulch conditions reveal that some traits, such as height, remain stable across designs, whereas others are highly design-dependent. This supports a two-step breeding strategy combining early indirect selection in nursery of spaced plants with an indirect selection under living mulch conditions. Finally, molecular markers used for genomic prediction could accelerate the identification of genotypes suited for living mulch systems. This knowledge can be used to create dedicated varieties.
ano.nymous@ccsd.cnrs.fr.invalid (Zineb El Ghazzal) 06 Jul 2026
https://hal.inrae.fr/hal-05682255v1
-
[hal-05639710] Modelling soil microbial functions at large spatial scale based on metagenomic dimensionality reduction
[...]
ano.nymous@ccsd.cnrs.fr.invalid (Emna Stambouli) 01 Jun 2026
https://inria.hal.science/hal-05639710v1
-
[hal-05105798] Metabolic Flux Inference in a Cheese Microbial Community via comFI: a Biology-informed Approach for Time-resolved Multi-omics Integration
Microbial communities play a central role in many bioprocesses with key applications in food fermentation, waste treatment, human and animal well-being, plant protection or metabolite transformation in industrial bioprocesses. However, the metabolic microbial interactions driving the community dynamics remain difficult to characterize because of their complexity and their temporal variability. Recent advances in sequencing and analytical technologies now provide time-resolved multi-omics data at the community scale providing key insights into the mechanisms shaping the community dynamics. However, integrating these heterogeneous data in an interpretable way to decipher species-specific metabolic activity and microbial interactions remains a major challenge in the study of microbial communities. We introduce the community metabolic flux inference (comFI) method, a mathematical framework for inferring the metabolic fluxes of individual microorganisms from community-level longitudinal data. The method formulates flux estimation as a biology-informed constrained inference problem that combines observed microbial abundances and extracellular metabolite exchange data, with metabolic constraints, encoded in a metabolic model, and transcriptomic-based lasso regularization terms. We evaluated comFI on synthetic datasets generated from dynamic models of microbial communities involving three Escherichia coli mutant strains. The comFI method showed a very good reconstruction accuracy for exchange fluxes, intracellular metabolic fluxes distribution, metabolic pathway activation patterns and strain contribution. We also applied the method to experimental cheese fermentation data involving three bacteria (Lactococcus lactis, Lactobacillus plantarum, and Propionibacterium freudenreichii ), combining abundance measurements, targeted metabolomics and transcriptomics data. The comFI framework enabled to recover previously identified interaction patterns, and to reconstruct latent intracellular flux states for individual microorganisms alongside with their respective metabolic contributions within the community, consistently with the omics data. All together, we demonstrate that comFI provides a practical framework for recovering the metabolic activity of individual microorganisms from community-scale multi-omics time-resolved data.
ano.nymous@ccsd.cnrs.fr.invalid (Sthyve Junior Tatho Djeanou) 24 May 2026
https://hal.science/hal-05105798v2
-
[hal-05628728] How is decision-making about grazing management influenced by reliance on rangelands at farm level? Insights from small ruminant farms in French Mediterranean areas
Shrubby and wooded rangelands are highly heterogeneous environments that require adaptive grazing management. In the Mediterranean basin, their use is increasingly challenged by climate change, land abandonment, and the erosion of intergenerational knowledge. This study examined how small-ruminant farmers structure their decision-making about rangeland use, depending on their reliance on them to feed the flock. We conducted 18 semi-structured interviews in Ardèche and Hérault (France), covering diverse farm types and levels of reliance on pastoral resources. Discourse analysis identified four objects shaping decision-making for all farmers: landscape units, vegetation, pastoral resources, and animal behaviour. The farms were categorized in three groups (P−, P, P+) on the basis of the integration of rangelands into the feeding system, ranging from input-dependent feeding systems aimed at the stability of intake and animal performance, to systems relying heavily on rangelands and aimed at self-sufficiency, accepting variations in animal performance. The three types of farms were associated with contrasted decision-making systems, ranging from simplified and reactive approaches based on a limited set of measurable indicators (e.g., resource availability, animal performance), to more integrative and anticipatory strategies combining multiple indicators such as vegetation dynamics, resource diversity and animal behaviour. This study provides a structured way to analyse how farmers mobilise knowledge and indicators in their decision-making, and helps identify how additional information may complement farmers’ decision-making in a context-specific manner.
ano.nymous@ccsd.cnrs.fr.invalid (Elisa Deschamps) 21 May 2026
https://hal.science/hal-05628728v1
-
[hal-05630049] Automated mapping of metabolomic compounds onto metabolic networks using MetaNetMap
Understanding biological systems requires integrative and multi-level approaches. Genome Scale Metabolic Networks (GSMNs), that are derived from genome annotation, capture the metabolic capabilities of an organism. In contrast, metabolomics gives an insight into what is really happening in an organism under specific conditions. Mapping molecules identified from metabolomic experiments onto GSMNs offers several advantages: mapped compounds can be used for visualisation or quality assessment of the GSMN; and conversly, unidentified metabolites highlight gaps in the network and create model curation opportunities. This is especially important for specialised metabolism that is currently largely overlooked in GSMNs. Such mapping is thus attractive but it remains cumbersome due to several challenges such as harmonisation and matching of identifiers between metabolomic annotation profiles and GSMNs, and dispersion of information across various knowledge bases and input files. Currently, mapping requires manual or semi-manual mapping, but it is quite fastidious and prone to errors. To overcome these challenges, we developed MetaNetMap, a Python package that automatically matches metabolite information between metabolomic annotations and GSMNs. It improves mapping rates through direct mapping taking into account metadata of input files, indirect matching by relying on conversion data tables built from third-party knowledge bases, and partial matching techniques. It offers an automatic solution for ambiguous mapping, providing relevant information for manual curation. By automating and harmonising metabolite mapping, MetaNetMap aims to overcome a major barrier in multi-omic integration, enabling more efficient and reproducible integration of metabolomic data onto GSMNs.
ano.nymous@ccsd.cnrs.fr.invalid (Coralie Muller) 22 May 2026
https://inria.hal.science/hal-05630049v1
-
[hal-05630041] Automated mapping of metabolomic compounds onto metabolic networks using MetaNetMap
Understanding biological systems requires integrative and multi-level approaches. Genome Scale Metabolic Networks (GSMNs), that are derived from genome annotation, capture the metabolic capabilities of an organism. In contrast, metabolomics gives an insight into what is really happening in an organism under specific conditions. Mapping molecules identified from metabolomic experiments onto GSMNs offers several advantages: mapped compounds can be used for visualisation or quality assessment of the GSMN; and conversly, unidentified metabolites highlight gaps in the network and create model curation opportunities. This is especially important for specialised metabolism that is currently largely overlooked in GSMNs. Such mapping is thus attractive but it remains cumbersome due to several challenges such as harmonisation and matching of identifiers between metabolomic annotation profiles and GSMNs, and dispersion of information across various knowledge bases and input files. Currently, mapping requires manual or semi-manual mapping, but it is quite fastidious and prone to errors. To overcome these challenges, we developed MetaNetMap, a Python package that automatically matches metabolite information between metabolomic annotations and GSMNs. It improves mapping rates through direct mapping taking into account metadata of input files, indirect matching by relying on conversion data tables built from third-party knowledge bases, and partial matching techniques. It offers an automatic solution for ambiguous mapping, providing relevant information for manual curation. By automating and harmonising metabolite mapping, MetaNetMap aims to overcome a major barrier in multi-omic integration, enabling more efficient and reproducible integration of metabolomic data onto GSMNs.
ano.nymous@ccsd.cnrs.fr.invalid (Coralie Muller) 22 May 2026
https://inria.hal.science/hal-05630041v1
-
[hal-05613501] From Concept to Perspective: Digital Twins of Microbial Systems
Digital twins (DTs) are increasingly recognized across diverse sectors for their capacity to enhance the control, efficiency, and comprehension of the physical or biological systems they represent. For microbial systems, DTs could allow model-guided improvements of the services provided by the microbial communities in the agrifood chain. While DTs definitions are generally built on the same core idea of bi-directional exchanges between digital and physical counterparts, where realtime data feeds digital models and model-driven insights guide the real system, a wide variety of definitions of what is a DT still co-exist across domains. This variability underscores the need for a clear, system-specific definition of DTs for microbial ecosystems. In this perspective paper, we propose a conceptual framework for microbial system digital twins (MSDTs), defined as a collection of models dynamically linked to the microbiological system through in-line, at-line or off-line data and control flows. We illustrate this framework with examples spanning environmental, bioprocess, plant, animal, food, and human microbial systems, in a One Health perspective. For each ecosystem, we explore the potential applications of MSDTs. We also identify the scientific challenges that remain in experiments, bioinformatics, data science, modeling, control and microbial ecosystem engineering to build accurate MSDTs. We advocate for the development of MSDT in laboratory settings, as a catalyst for interdisciplinary sciences, and we stress practical and ethical issues preventing the generalization of MSDT for large-scale applications. However, high-tech MSDTs in laboratory environments may pave the way for low-tech, generalizable microbial solutions for improved ecosystemic microbial services.
ano.nymous@ccsd.cnrs.fr.invalid (Simon Labarthe) 06 May 2026
https://hal.inrae.fr/hal-05613501v1
-
[hal-05610014] Romane sheep divergently selected on residual feed intake: consequences on production and feeding behaviour traits
Breeding companies are highly interested in including feed efficiency into their breeding programmes because of the economic and environmental benefits this would bring. We developed divergent lines on residual feed intake (RFI) in the Romane breed in order to estimate the consequences of RFI selection on other traits. In this experiment, RFI was phenotyped in growing lambs under an ad libitum low-energy concentrate diet, distributed through automated concentrate feeders, during 6-week test periods. The divergent selection experiment encompasses five generations of selection, and a total of 821 male lambs were phenotyped for production, feed efficiency and feeding behaviour traits. After five generations of selection, the difference between the efficient and less efficient lines reached 2.6 genetic SD, and efficient lambs ate on average 9.36% less concentrate than less efficient lambs. No significant differences in production traits were observed between both lines, except for on-farm breeding values, with daughters from efficient rams having higher estimated breeding values (EBVs) for prolificacy but lower EBVs for maternal traits. Significant differences between both lines were observed for feeding behaviour traits. The efficient lambs visited the feeders less and tend to spend less time in feeding activity. Less efficient lambs had a marked hourly feed intake pattern compared to efficient lambs. Nevertheless, the genetic correlations between RFI and production or behaviour traits were of low magnitude (from-0.33 to 0.24 with conformation score and BW at the beginning of the test, respectively), with most of the estimates being close to zero. This divergent experiment demonstrated that selecting RFI is feasible with low impacts on production traits, but particular caution must be paid to prolificacy and maternal traits to avoid any undesirable changes in these important traits. This also illustrated that feeding behaviour is different in efficient and less efficient animals, which provides clues to further investigate the mechanisms underlying feed efficiency in meat sheep. (c) 2026 The Author(s). Published by Elsevier B.V. on behalf of The animal Consortium.
ano.nymous@ccsd.cnrs.fr.invalid (F. Tortereau) 03 May 2026
https://hal.inrae.fr/hal-05610014v1
-
[hal-05604339] WP 2.3: Réduction de dimension et analyse de série temporelles multiomiques
In order to elucidate bioprotection against mildiou on tomato leaves, I explore way to integrate time series and dimension reduction on top of a metabarcoding dataset.
ano.nymous@ccsd.cnrs.fr.invalid (Sébastien Raguideau) 28 Apr 2026
https://hal.science/hal-05604339v1
-
[hal-05610911] Metagenome assembly and evaluation in taxonomically rich ecosystems
Presentation for the M36 meeting of the MICTIC project. Showcases advancements on the Mapler pipeline (https://hal.science/hal-05288241), cluster-based assemblies (https://hal.science/hal-05444605) as well as an assortment of quickly explored or unexplored ideas, including assembly consensus, analysis of assembly graphs, exploitation of reference genome and realignment of reads on the assembly.
ano.nymous@ccsd.cnrs.fr.invalid (Nicolas Maurice) 04 May 2026
https://hal.science/hal-05610911v1
-
[hal-05604272] Generation of metabolomic-informed models of metabolism in complex microbial communities
Presentation for the M36 meeting of the MICTIC project Resume of the presentation: The generation of genome-wide metabolic networks has become a routine analysis for individual organisms or communities communities. However, these automatically generated metabolic networks are incomplete because they are constructed by based on the combination of gene annotation and reactions available in generic available in generic databases (Metacyc, BIGG, ModelSEED...). These are oriented towards well-known organisms or organisms or model organisms and miss out on important functions secondary metabolism. We propose to combine metabolomic data analysis, metabolic modelling and annotation metabolic modelling and annotation mining to build high-quality models of high quality models of microbial metabolism with the long-term aim of better understanding of microbial communities. In terms of application of the methods to plant microbial communities, we hope that the plant microbial communities, we hope that the newly developed models will provide a better understanding of the process of microbial recruitment by the plant: metabolic functions involved, micro-organisms associated with these functions.
ano.nymous@ccsd.cnrs.fr.invalid (Coralie Muller) 28 Apr 2026
https://hal.science/hal-05604272v1
-
[hal-05602030] Do farmers trust digital decision support tools on pesticide use?
Les outils d'aide à la décision (OAD) sont de plus en plus mobilisés en agriculture pour raisonner l'usage des pesticides en fournissant des recommandations de traitement, souvent au jour près. Malgré les performances reconnues de ces outils, on constate que les agriculteurs suivent relativement peu les recommandations qu'ils fournissent. Cet article cherche à expliquer ces décisions à travers le prisme de la confiance en l'outil et des biais de perception du risque par les agriculteurs. Nous étudions le cas d'un OAD français destiné à la gestion du mildiou de la pomme de terre. À partir d'une base de données originale contenant les recommandations émises par l'OAD et les décisions de traitement des agriculteurs abonnés, nous analysons le suivi des recommandations de l'OAD par les agriculteurs ainsi que l'évolution de ce suivi à la suite d'épisodes de contamination par le mildiou. Nos résultats montrent que les agriculteurs suivent moins les recommandations de non traitement que celles qui recommandent de traiter. Ce résultat s'accentue pour les agriculteurs ayant subi une contamination dans le passé. On montre qu'ils choisissent de moins suivre les recommandations que l'OAD ait fait une erreur de prédiction dans le passé, ou non. Ces résultats suggèrent que le suivi ou non des recommandations est moins lié au niveau de confiance des agriculteurs dans l'OAD, que dans l'évolution de la perception du risque de contamination future, plus forte chez l'agriculteur ayant déjà subi une contamination dans le passé.
ano.nymous@ccsd.cnrs.fr.invalid (Alban Cornier) 24 Apr 2026
https://hal.inrae.fr/hal-05602030v1
-
[hal-05601510] Public and private support of the AgriTech : what rationale for what innovation pathway ? A multiple case study in France and Chile
Both public and private actors are increasingly using a diversity of support instruments to foster AgriTech innovation, including a growing interest in the agrifood sector from venture capital and private equity (Lajoie-O’Malley et al., 2020; Sippel and Dolinga, 2023). This support is not only legitimized by the classic rationale of the effect of innovation on economic efficiency and the need for support due to uncertain returns on investment - but also by the socio-ecological effects that these technological developments would enable (Martin and Schnebelin, 2024). Despite this evolution and the growing influence of private funding (Glenna et al., 2015), the AgriTech ecosystems, and their impact on the agricultural innovation system, remain poorly studied (Klerkx and Villalobos, 2024). Who supports AgTech development, what rationales underpin support instruments and how hybrid public and private arrangements configure who and what is supported ? At the crossroad of literature on the agricultural innovation system and entrepreneurial and innovation ecosystems, our research aims to decipher how the development of private venture capital, alongside new innovation support rationales, shape innovation pathways in the agrifood sector. To address these aims, we present a comparative study of AgriTech innovation ecosystems in France and Chile. While these two countries offer different institutional context, they are both very dynamic in terms of AgriTech development. Through an analysis of the CrunchBase database and interviews with key public and private stakeholders of AgriTech innovation ecosystems, we describe the landscape of AgriTech support services. We characterize these instruments according to their providers, beneficiaries and means (Audretsch et al., 2020), as well as their objectives and rationales (Kerr et al., 2017; Laranja et al., 2008). The results highlight the multi-level, multi-actor and multi-instrument ecosystem of innovation support instruments for AgTech in France and Chile. We observe a strong entanglement of public and private support. Most instruments provide a diversity of resources such as funds, knowledge, network, legal expertise…. While they incorporate “mission-oriented” dimensions, they have few processes to evaluate, support and foster these missions. Despite similarities, the instruments refer to different rationales and objectives. These results will enable us to discuss the evolution of agricultural innovation policies within a neoliberal context and a regime of entrepreneurial innovation (« the Silicon Valley model of innovation »).
ano.nymous@ccsd.cnrs.fr.invalid (Éléonore Schnebelin) 24 Apr 2026
https://hal.inrae.fr/hal-05601510v1
-
[hal-05661789] Automated monitoring of the activity of lactating sows in conventional and organic systems from the use of AI with video images
In the last decade, we developed experimental research projects to determine the impact of the transition to looser housing on sow health, activity and welfare around farrowing and during lactation. We use high-throughput phenotyping based on the use of AI with video images to address these questions. We developed an automated device for the recording and monitoring of sow behaviour. The system consists of a Raspberry Pi protected in a waterproof box, connected to 3 closed-circuit television cameras to monitor up to 3 sows simultaneously. The system can be controlled using a smartphone with a direct wireless connection which does not require Wi-Fi. We developed an ethogram for the analysis of sow postures. The software differentiates eight postures : sitting, standing, kneeling, lying on the belly, right/left side with teats not visible, and lying on the right/left side with teats visible. The system uses a convolutional neural network (CNN Yolo-v11) to estimate sow postures from the images at a rate of one estimate every second. The CNN was set up with more than ten thousand labelled images from different sows collected in our 2 experimental farms. It allows the identification of sow body with corresponding posture, sow head and piglets. The microcomputer manages the data flow and estimates sow posture in real-time. The CNN was set up on many and diverses images with the objective of guaranteeing the quality of the prediction in our experimental farms. We also developed a web application to supervise and pre-analyse sow activity (e.g. time budget). Studies of temporal changes in activity will be possible, as well as the search for sows with greater capacity of adaptation (Canario et al., EAAP 2023 ; Girardie et al., 2024). In addition, we created an ethogram for the analysis of sow facial expressions (Barry et al., EAAP 2025). Having developed a database of ten thousand labelled images from different sows, we are preparing a proof of concept for the automated monitoring of changes in facial expressions. The device can be equipped with several algorithm(s) trained and adapted to the trait(s) and condition of interest. Developments received financial support from the animal genetic division of INRAE and the PEPR WAIT4 ANR-22-PEAE-0008 and H2020 PPILOW projects.
ano.nymous@ccsd.cnrs.fr.invalid (Téo Cochou) 18 Jun 2026
https://hal.science/hal-05661789v1
-
[hal-05686261] Mapping the agricultural digital ecosystem: An analysis of the French AgTech market actors and dynamics
This communication proposes a systematic and dynamic understanding of the firms that commercialise new equipment and digital technologies (EDiTs) to farmers. Agriculture's digitalisation is mostly analysed through the lens of equipment (hardware and software), their adoption paths and impacts. However, research on the ecosystem supporting these innovations remains scarce, particularly outside North America (Klerkx &amp; Villalobos, 2024). Several questions emerge: what kind of technologies are produced? What kind of organisations develop and finance them? What is the sector's dynamic, especially regarding the viability of organisations and technologies? This work addresses these questions by characterising the actors of the French « AgTech » market. We aim to describe their diversity, trajectories, and funding dynamics using a quantitative approach. We built an original database of organisations based on former initiatives that propose inventories of digital tools, apps, and robots sold to farmers, including Wiki AgriTech and outputs from various projects. The database was then coupled with other sources providing economic information about the firm, including CrunchBase or the French business database Pappers. We collected information on the firm's characteristics (size, localisation, age, funders, funding sources, turnovers), and on the EDiTs sold to farmers: nature (robots, apps, software), functions (supporting decision-making, field observations, etc.), and targeted sector. In total, more than 600 organisations were identified. The database shows that the technologies are primarily developed by private actors (almost 90% of organisations). Half of them were established after 2014 and are specialised in both the agricultural and digital sectors. Analyses show that company characteristics, particularly size and location, vary depending on the type of technology developed and the targeted agricultural sector. The collected data indicate that the sector is still consolidating, as more than a quarter of the firms have gone bankrupt and/or been acquired in recent years. An econometric analysis of this data enables us to understand the factors contributing to the success or failure of firms commercialising EDiTs, including the level of specialisation, the nature of the technologies sold, the integration in policy frameworks, and the sources of funding. By shedding light on the construction and dynamics of the emerging French AgTech market, this work challenges the idea of a homogeneous economic sector. It raises questions about the roles of both public and private ecosystems in its emergence. Finally, this work opens up avenues for reflection to better understand the contribution of different innovation systems to sustainability challenges.
ano.nymous@ccsd.cnrs.fr.invalid (Romane Guillot-Pelliet) 08 Jul 2026
https://hal.science/hal-05686261v1
-
[hal-05667139] Reconstruction of Root System Architecture in 2D+t: benchmarking loss functions in deep learning reconstruction pipelines
Context: Automatic Root System Architecture (RSA) reconstruction methods generally use a two-step process: 1) segmentation, and 2) tree-graph extraction. Given the hierarchical structure of this pipeline, the alignment of segmentation objectives and graph extraction targets is investigated. Goal: 1) Investigate the downstream impact of the segmentation model’s architecture and training loss on the traits predicted on the graphs. 2) Determine which metrics computed in the early segmentation step are useful for predicting overall pipeline performance. Method: Graph extraction step was fixed using RootSystemTracker (Fernandez et al., 2022). Six variations of the segmentation setup (2 architectures × 3 loss functions) were evaluated through training epochs. A step-by-step evaluation was conducted for each setup to measure the accuracy of the segmented masks and the trait estimation error (SMAPE) derived from the resulting RSML tree-graph files (e.g., lengths, counts).
ano.nymous@ccsd.cnrs.fr.invalid (Loaï Gandeel) 23 Jun 2026
https://hal.science/hal-05667139v1
-
[hal-05601671] Counting large flea beetle larvae using computer vision
In rapeseed, the Berlese method is currently the only reliable method to assess the pressure of large flea beetles (Psylliodes chrysocephala) on a field and determine whether or not it is relevant to apply insecticide (also based on other indicators like the number of larvae per plant and the biomass of the rapeseed). This method involves repeated, long and tedious countings. To avoid this and facilitate the deployment of the Berlese method, Terres Inovia has developed an application capable of determining the number of larvae using computer vision from smartphone images. To date, more than 16,000 sub-images (640x640 pixels) have been annotated to create an algorithm. This tool works using computer vision and processes images using the YOLOv8 model. Currently, the model has a performance rate of 88%, with an average error of 12%. The higher the quality of the photos, the better the tool will be able to detect larvae, even when they are clustered together. The resolution, framing and brightness of the photos remain essential conditions for successful measurement. This method, which is not yet official, does not replace Berlèse tests or field observation, but it does facilitate their interpretation and monitoring, which is thus faster and more accurate. It should be noted that this tool is expected to evolve to detect not only the number of larvae but also their stage of development (L1, L2 or L3, the most harmful). This image processing will require higher resolution. However, it will enable to measure the degree of harmfulness of the larvae within the plot to be determined more accurately.
ano.nymous@ccsd.cnrs.fr.invalid (Jean-Eudes Hollebecq) 24 Apr 2026
https://hal.science/hal-05601671v1
-
[hal-05567423] Video of the presentation at LREC conference: EPOP: A benchmark corpus for Assessing NLP Models on Structured Information Extraction in Plant Health
This video presents the work published in LREC conference proceedings in 2026. In this presentation, we introduce the EPOP (Epidemiomonitoring of Plants) corpus, a new annotated resource for structured information extraction in the domain of plant health epidemiology. The corpus consists of translated news reports that reflect real-world phytosanitary monitoring scenarios. It includes annotations for named entities (e.g. Plant, Pest, Vector, Disease, Dissemination Pathway), identity coreferences, and both binary and complex n-ary relations that represent key events such as Transmits or Causes, along with their modalities. A distinctive feature of EPOP is its normalization layer where mentions of species and geographical locations are linked to canonical identifiers in the NCBI Taxonomy and GeoNames, enabling semantic disambiguation and integration with external knowledge bases. As the first publicly available corpus of its kind, EPOP presents a realistic and challenging benchmark, with high linguistic variability, entity role ambiguity, and long-distance relations. We report baseline results on core tasks (named entity recognition, normalization (entity-linking), and relation extraction) using both fine-tuned BERT-based models and hard-prompted large language models. These experiments demonstrate the utility of EPOP while also identifying areas for improvement, particularly in the extraction of complex relations. The corpus is released under an open license, to support research in environmental NLP, crop protection, and knowledge graph enrichment.
ano.nymous@ccsd.cnrs.fr.invalid (Claire Nédellec) 25 Mar 2026
https://hal.inrae.fr/hal-05567423v1
-
[hal-05591524] LifeCLEF 2026 Teaser: AI Challenges for Biodiversity Understanding and Ecosystem Management
AI is increasingly central to understanding and managing biodiversity and ecosystems. Since 2011, the LifeCLEF lab has provided large-scale benchmarks that stimulate progress in multimodal species recognition, ecological prediction, and knowledge extraction. The 2026 edition expands this scope with five complementary challenges spanning visual, acoustic, and textual data: (i) AnimalCLEF: discovery and re-identification of individual animals, (ii) BirdCLEF+: multi-taxonomic species recognition in complex soundscapes, (iii) MarineCLEF: detection of marine species in underwater imagery under positive-unlabeled constraints, (iv) PestCLEF: extraction of information on plant pests from heterogeneous textual sources, (v) PlantCLEF: multi-species plant identification in quadrat images. Together, these challenges address critical dimensions of biodiversity science and ecosystem management, while fostering collaboration between AI researchers, ecologists, and practitioners. This paper provides an overview of the LifeCLEF 2026 lab and its tasks, outlining their motivation, data, and evaluation methodology to guide participants and inform the wider research community.
ano.nymous@ccsd.cnrs.fr.invalid (Alexis Joly) 14 Apr 2026
https://hal.inrae.fr/hal-05591524v1
-
[hal-05593509] Integrating metagenome-scale metabolic modelling and metabolomics to identify biochemical interactions in Microcystis phycospheres
Favoured by global changes, freshwater cyanobacterial harmful blooms generate major ecological, economical and public health challenges. Microcystis , one of the most widespread cyanobacterial genera, grows within a phycosphere where specialised interactions with its microbiome occur, and are suspected to influence bloom appearance and its potential toxicity. Using a combination of metagenomic, metabolomic and metabolic modelling, we characterised the phycospheres of twelve Microcystis strains isolated from a French pond. The distribution of metabolic reactions within Microcystis was consistent with their genospecies, whereas the metabolic landscape at the community level diverged from cyanobacterial phylogeny indicating functional decoupling between cyanobacteria and their associated microbiomes. Phycosphere-associated bacteria substantially expand the metabolic repertoire of the system, while maintaining functional redundancy within and across communities. On the other hand, metabolomic profiles were largely driven by cyanobacterial metabolic outputs. Metabolic modelling, together with the identification of toxic specialised metabolites produced by specific biosynthetic gene clusters, further highlighted differences in metabolic potential among phycospheres. Together, these findings deepen the understanding of Microcystis ’ phycosphere functioning, demonstrate the value of multi-omics systems biology approaches, and underscore the ecological relevance of interspecies and inter-phycosphere metabolic interactions as a structuring process in bloom-associated microbiomes.
ano.nymous@ccsd.cnrs.fr.invalid (Juliette Audemard) 16 Apr 2026
https://inria.hal.science/hal-05593509v1
-
[hal-05584190] Diversité génétique mondiale du complexe Medicago sativa : implications pour l’amélioration variétale de la luzerne.
La luzerne cultivée appartient au complexe Medicago sativa, un ensemble de quatre sous-espèces (sativa, falcata, caerulea, ×varia) dont la délimitation taxonomique demeure incertaine, ce qui limite l'exploitation rationnelle de la diversité. Afin d'évaluer la structure génétique et de clarifier les relations entre sous-espèces, formes sauvages et cultivars, nous avons génotypé environ 1 500 accessions à l'aide de 9 761 SNP. Une analyse discriminante des composantes principales (DAPC) a confirmé la différenciation génétique entre les quatre sous-espèces. Au sein de chaque sous-espèce, une structure géographique marquée a été mise en évidence ; toutefois, pour les cultivars — tous appartenant à la ssp. sativa — les groupes régionaux se recouvraient largement. Au sein de chaque groupe régional, le nombre d'allèles privés le plus élevé a été observé dans les groupes de la ssp. falcata et de la ssp. ×varia. Les groupes de la ssp. caerulea présentent également un nombre d'allèles privés modéré. En revanche, les groupes de la ssp. sativa, qu'ils soient cultivés ou sauvages, en contiennent très peu, voire aucun, à l'exception des accessions cultivées de Chine et de Scandinavie. Afin d'identifier les origines géographiques de la domestication, nous avons projeté les groupes cultivés dans l'espace génétique des pools sauvages. Les accessions chinoises, indo-moyen-orientales et afghano-persanes se projettent presque exclusivement sur le groupe sauvage d'Asie centrale, tandis que les cultivars occidentaux se superposent au groupe sauvage méditerranéen. Ces trajectoires suggèrent l'existence d'au moins deux centres de domestication distincts. Ces résultats ouvrent la voie à l'identification de pools géniques régionaux spécifiques encore sous-exploités, et offrent des opportunités concrètes pour une meilleure valorisation de la diversité génétique dans les schémas d'amélioration de la luzerne.
ano.nymous@ccsd.cnrs.fr.invalid (Irving Arcia Ruiz) 08 Apr 2026
https://hal.science/hal-05584190v1
-
[hal-05613681] DNA methylation around transcription start sites is not globally associated with transcription in the grain of natural and synthetic hexaploid wheat
Epigenetic mechanisms including DNA methylation are assumed to play crucial roles in the maintenance of genome integrity, regulation of gene expression and development, and their increasing exploitation in breeding applications is anticipated. However, the relationship between DNA methylation and gene expression remains ambiguous and difficult to generalize. Here we explored the hypothesized causality between the level of transcription and cytosine methylation at the 5' end of genes (around transcription start sites and start codons) in relation to whole-genome duplication in natural and synthetic allohexaploid wheat (Triticum/Aegilops complex). Using transcriptomes and a sequence capture protocol coupled with bisulfite sequencing, we observed sometimes significant, but overall very weak associations between gene expression and 5' end methylation on a genome-wide scale. In synthetic wheat allohexaploids, global methylation differences between subgenomes are not triggered by the polyploidization, as the subgenome patterns are rather faithfully inherited from parents. A small number of genes differentially methylated between the parents and synthetics was consistently recovered in reciprocal synthetics and subsequent generations. Differences in transcription between homeologs are not clearly associated with 5' end methylation in either natural or synthetic wheat. Overall, allopolyploidization triggers only minor methylation changes around transcription start sites and start codons of nascent wheat allopolyploids, and these are not statistically associated with differential expression. Although there is a measurable methylation difference between silent and expressed genes in the developing grain, our results do not support the hypothesis that 5' end DNA methylation is engaged in the regulation of gene expression in natural and synthetic wheat. While a 'genome shock' hypothesis predicts extensive transcriptomic and epigenetic reorganization after polyploidization, DNA methylation patterns around transcription start sites are generally undisturbed in nascent wheat allohexaploids. Although this stability might indicate importance for gene regulation, a clear relationship between DNA methylation and transcription was not observed either on a genome-wide scale, or among triads of homeologous genes.
ano.nymous@ccsd.cnrs.fr.invalid (Meriem Banouh) 06 May 2026
https://hal.inrae.fr/hal-05613681v1
