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Enhancing spatial omics resolution by pseudo-interstitial pixels inference
No full textMotivation Spatially resolved omics technologies are enhancing our understanding of tissues architecture. Despite major technological improvements, gaining in spatial resolution becomes experimentally expensive, while generating spatial landscapes at moderate resolution combined with computational methods for depixelating data represent a cost-effective strategy allowing to enlarge the number of experiments to be performed. Results We have developed a computational strategy able to gain several-folds of resolution by inferring pseudo-interstitial pixels from their closest neighbors. This strategy has been validated in the context of public spatial transcriptomics data issued from melanoma, and human brain cortex tissue sections, by improving the identification of distinct tissue substructures. Furthermore, this methodology has been used for enhancing the resolution of consecutive sections collected from human brain organoids, as a way to demonstrate that a moderate resolution technology, combined with spatial depixelation processing allows to properly discern molecular tissue structures even in small tissues
Small firms’ open innovation: leveraging business model Innovation through dynamic capabilities
No full textInternational audienceOpen innovation practices require firms to transform certain aspects of their organisational structures and processes to accomplish success. The level of openness determines how firms should adapt their business models to align with specific open innovation practices. Although open innovation and business model innovation are closely interconnected, the extant literature provides limited evidence on how firms embark on these two processes together, particularly in the context of smaller firms. To address this gap, we adopt dynamic capabilities (sensing, seizing, and reconfiguring capabilities) as a theoretical lens to explore this phenomenon in SMEs and startups. Using a multiple case study approach, we draw insights and identify key concepts from biotech SMEs and startups. This methodology enables us to uncover emerging theories related to the interplay between open innovation and business model innovation, as well as the critical role of dynamic capabilities. Our research advances the literature on open innovation, business model innovation, and dynamic capabilities by providing empirical evidence and developing theoretical frameworks
New groups of highly divergent proteins in families as old as cellular life with important biological functions in the ocean
No full textInternational audienceMetagenomics has considerably broadened our knowledge of microbial diversity, unravelling fascinating adaptations and characterising multiple novel major taxonomic groups, e.g. CPR bacteria, DPANN and Asgard archaea, and novel viruses. Such findings profoundly reshaped the structure of the known Tree of Life and emphasised the central role of investigating uncultured organisms. However, despite significant progresses, a large portion of proteins predicted from metagenomes remain today unannotated, both taxonomically and functionally, across many biomes and in particular in oceanic waters.Results Here, we used an iterative, network-based approach for remote homology detection, to probe a dataset of 40 million ORFs predicted in marine environments. We assessed the environmental diversity of 53 core gene families broadly distributed across the Tree of Life, with essential functions including translational, replication and trafficking processes. For nearly half of them, we identified clusters of remote environmental homologues that showed divergence from the known genetic diversity comparable to the divergence between Archaea and Bacteria, with representatives distributed across all the oceans. In particular, we report the detection of environmental clades with new structural variants of essential SMC (Structural Maintenance of Chromosomes) genes, divergent polymerase subunits forming deep-branching clades in the polymerase tree, and variant DNA recombinases in Bacteria as well as viruses. Conclusions These results indicate that significant environmental diversity may yet be unravelled even in strongly conserved gene families. Protein sequence similarity network approaches, in particular, appear well-suited to highlight potential sources of biological novelty and make better sense of microbial dark matter across taxonomical scales
Surface charge pattern: Impact on vibrational spectroscopy and physics of charged interfaces
No full textInternational audienceSurface-specific vibrational spectroscopies revolutionized the study of charged interfaces, by sensitively probing water's response in the electric double layer (EDL) and correlating it with surface charge via models like Gouy-Chapman-Stern. The assumed one-to-one relationship between water's spectroscopic response and surface charge has been widely accepted without question. We hereby propose a theoretical experiment to evaluate this assumption. Interestingly, our findings reveal a non-one-to-one relationship between surface charge and spectroscopic response, exhibiting a fascinating dependence on surface topology
Surface Speciation of α-Al 2 O 3 (11̅02) in Contact with Liquid Water
No full textInternational audienc
Gait Phase Recognition Based on A Multimodal Sensing-Driven Smart Shoe System
No full textInternational audienc
Next-generation Disaster Management using Drones, AI, and Generative Models
No full textInternational audienceEffective disaster management requires rapid, accurate decision-making, which is often hindered by traditionalresponse methods that lack scalability, real-time adaptability, and situational awareness. This paper presents an integratedframe-work combining drone-based surveillance, artificial intelligence (AI), and generative AI for enhanced disastermanagement. The framework leverages drones equipped with advanced sensors (e.g., GPS, LiDAR, and environmentalmonitors) to collect high-resolution data and optimize coverage of disaster areas. Real-time processing onboard the dronesenables immediate anomaly detection and event identification, while multimodal data analysis using edge computing providescomprehensive situational awareness. Generative AI models are employed for further analysis, automatically captioningimages and generating actionable insights for disaster response teams. This framework optimizes the detection-to-actiontimeline, improving response efficiency, and ensuring faster, more informed decision-making
A synchronous control strategy of robot social behavior driven by scenario information and neural modulation mechanism
No full textInternational audienceRobots have been widely employed in scenarios that involve various environmental factors and social individuals. As one kind of social companion, robot is supposed to obey human social protocol and display anthropomorphic behaviors. In this paper, we focus on the problem of robot behavior control in multi-individuals scenarios, and build a coordinated robot behavior model containing body movement/orientation, head rotation and eyeball movement. Within the proposed model, a synchronous control strategy based on social space theory and neural modulation mechanism is proposed. This strategy collects RGB-D camera stream and acoustic field data perceived from multi-individuals scenario, and controls the robot to complete movement and social gaze behaviors. As for the eye-head coordinated gaze behavior, it is modulated by a novel optimal control algorithm based on the minimum neural transmission noise. Above works are validated on the Xiaopang robot platform, the experimental observations indicate that the robot can achieve anthropomorphic response in dynamic multi-individuals scenario. Within above promising results, the effectiveness of this strategies could be proven
Early Successful Recanalization After Intravenous Thrombolysis With Tenecteplase Versus Alteplase In Distal Vessel Occlusion Strokes
No full textInternational audienceBackground and Aims: Intravenous thrombolysis (IVT) remains the standard treatment for distal and medium vessel occlusion strokes (DMVO-S). However, unlike proximal occlusions, the superiority of tenecteplase over alteplase in achieving early successful recanalization (ESR) remains uncertain. This study aimed to compare ESR in DMVO-S between the two thrombolytics, based on a retrospective analysis of magnetic resonance imaging (MRI) conducted 1–2 hours after IVT.Methods: This monocentric study included consecutive patients with DMVO-S identified on baseline MRI and eligible for IVT but not mechanical thrombectomy treated with alteplase (0.9 mg/kg) from 2016 to 2018 or tenecteplase (0.25 mg/kg) from 2018 to December 2023, depending on availability. MRI follow-up was performed 1–2 hours after IVT. ESR was assessed in a blinded manner using a modified Arterial Occlusion Lesion scale designed for MRI and DMVO. Infarct size evolution was also analyzed using a semi-automatic segmentation technique.Results: Of the 319 patients, 158 were treated with tenecteplase and 161 with alteplase. In propensity score-weighted analyses, ESR rates, at a median post-IVT time of 80 minutes (70–94), were 47.8% (tenecteplase) versus 36.0% (alteplase) (wOR 1.48 [95% CI, 1.07–2.03], P = 0.02). Infarct volume extension on diffusion-weighted imaging was lower with tenecteplase (26.8%) compared to alteplase (39.8%) (wOR 0.49 [95% CI, 0.35–0.69], P<0.0001).Conclusion: In this observational study, tenecteplase-treated DMVO-S patients showed higher rates of early recanalization and lower infarct growth than alteplase at 1–2 hrs after IVT, suggesting that tenecteplase may be preferred for this situation.Disclosure of interest: Nothing to disclos
The exploitation of data to support decision-making in healthcare: a systematic literature review and future research directions
No full textABS 1International audienceThe development of new technologies and their continued adoption allow data to be collected, analysed and exploited for decision-making. Data can play an important role in the healthcare industry since it is a complex system where every decision is strongly affected by risk and uncertainty. Although the proliferation of data and the awareness of the importance of new technologies to support decision-making in presence of risk and uncertainty, there is a lack of understanding of the interrelations between data, decision-making process and risk management in healthcare organizations and their role to deliver healthcare services. Pursued by this research gap, the objective of this study is to understand how data can optimize decisions confronted with risk and uncertainty in the main domains (structure, process, outcome) of healthcare organizations. Thus, we conducted a systematic literature review based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, by selecting and analysing peer-reviewed journal articles from three databases: Scopus, Web of Science and PubMed. The paper’s findings suggest that although data are widely used to optimize the decisions in the healthcare organization domains in presence of risk and uncertainty, there are still many scientific and practice gaps that lead to the definition of a future research agenda