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Dataset 1. Gene co-expression network data. It contains network nodes, weighted network and list of hubs. Dataset 2. qPCR data from independent validation, including MIQE and additional information. Dataset 3. Supplementary Figures. Legends are included under each figure. Dataset 4. Full qPCR data (including raw Cq values).

This exploratory research aims to characterize, on the one hand, the concept of environmental management control (EMC) through the practices that underpin it, and, on the other hand, the tensions associated with it and the implementation of these practices in public organizations. Although EMC is attracting growing interest within the public sector, it seems to be facing technical and governance difficulties. A grid for analyzing management tools and the theory of organizational paradoxes is used to address the issue of characterizing EMC practices and the associated tensions in the local public sector. Various municipalities were mobilized to explore and characterize their first experiences of implementing EMC. Our results enable us to characterize both the tools used and the tensions gen

This is a scientific proposal for HST mission. For specific information please visit https://archive.stsci.edu/proposal_search.php?id=17753 and mission=hst [truncated! Please see actual data for full text]

Nothings

This is a scientific proposal for HST mission. For specific information please visit https://archive.stsci.edu/proposal_search.php?id=17823 and mission=hst [truncated! Please see actual data for full text]

This is a scientific proposal for HST mission. For specific information please visit https://archive.stsci.edu/proposal_search.php?id=17482 and mission=hst [truncated! Please see actual data for full text]

This study investigates the concept of “collective bricolage” within the framework of creating a universe science observatory in France. In the face of significant transformations in higher education and research, this article examines how university stakeholders can employ collective bricolage to adapt to institutional mandates for restructuring. Drawing on Lévi-Strauss’s notion of bricolage and the theoretical contributions of Duymedjian and Rüling (2010), this research investigates how this bricolage can be implemented in the absence of conventional or normative space to support the process, and particularly within a context characterized by conflictual relationships. The case study analyzes the collective bricolage process and its components, demonstrating that the dialectical nature o

This is a scientific proposal for HST mission. For specific information please visit https://archive.stsci.edu/proposal_search.php?id=17716 and mission=hst [truncated! Please see actual data for full text]

This is a scientific proposal for HST mission. For specific information please visit https://archive.stsci.edu/proposal_search.php?id=18087 and mission=hst [truncated! Please see actual data for full text]

New Public Management (NPM) has been the subject of academic study in many sectors, but much less so in the cultural sector. This article focuses on poetry, the actions of public authorities in a largely publicly funded sector, and the consequences of these actions on the status and social life of poets. Using a qualitative methodology based on interviews, this article shows that NPM has influenced cultural policy in poetry by creating a quasi-market of literary activities and sources of income for poets (readings, performances, workshops, scholarships and grants, and residencies) where demand from public authorities (state, local authorities, multiple autonomous actors financed by public funds) for “artistic services” provides work opportunities to poets in the form of short, non-renewabl

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Reconstructing articulated 3D objects is important for animation, gaming, and robotic simulations. Recent neural networks can estimate the articulated structure of 3D objects, but their generalization remains limited by the scarcity of annotated data for this task. To address this gap, we introduce Instruct-Particulate, a model that takes a 3D mesh together with a target kinematic specification, including part descriptions, connectivity, joint types, and optional point prompts, and predicts the corresponding kinematic part segmentation and joint motion parameters. The kinematic specification disambiguates the task and allows the model to target annotations of different granularity, thereby making it possible to use more abundant heterogeneous training data. At test time, the kinematic spec

AI systems coupled to proof assistants now generate formal mathematics at scale, and the gap between what a checker can verify and what a mathematician would value has become the binding constraint. We model the generation of valuable mathematics as nested language generation in the limit: a verifiable formal language $F$, accessed through a membership oracle (the proof checker), contains an unknown valuable language $H \in \mathcal{H}$ revealed only through an adversarial enumeration of a core $C \subseteq H$ of exact density $α$ (the literature). Every output is valuable ($\in H$), trivial ($\in F \setminus H$), or a hallucination ($\notin F$). We settle four questions. First, the verifier is not taste: the collections admitting generation with breadth are exactly those of the oracle-fre

This chapter presents a framework for designing therapeutic sonic interaction technologies, with a focus on movement sonification: the real-time conversion of bodily motion into sound that serves as feedback during motor rehabilitation. Despite growing evidence for their effectiveness, technologies implementing movement sonification are yet to be systematically adopted as part of clinical practice, potentially due to a lack of standardized development methodologies as well as inadequate integration of clinical stakeholder perspectives into interaction design. The framework addresses these barriers through three interconnected contributions. The first is a conceptual reframing of the design task as the calibration of sonic variability to the perceptual affordances of the listener and the de

Artificial Intelligence (AI) is increasingly used to automate a variety of real-world computer vision (CV) applications, such as autonomous vehicle control, facial recognition, and security cameras. Recent research has shown that acoustic vibration can induce real physical motion in cameras, interfering with their internal stabilization mechanisms. Because the motion falls outside the conditions the stabilization system was designed to handle, the system introduces artifacts into the frame, causing AI-based CV models to misclassify, miss targets, or hallucinate objects. Previous work used ultrasonic frequencies (>20 kHz) to perform short-range attacks, which limits them to short distances due to the attenuation exhibited by high frequencies. In this work, we investigate acoustic attacks us

Sequential or time-stamped interaction logs provide objective records of digital application usage, yet their granularity and noise often obscure meaningful insights into people's work. Such insights are essential for improving digital products in ways grounded in real-world user interactions. Prior research has applied deep learning models to cluster user actions into high-level activities, but these approaches are highly sensitive to noise and struggle to generalize across applications. To address this limitation, we introduce WorkflowView, a framework that uses large language models (LLMs) to abstract low-level action sequences into high-level activities. We establish the effectiveness and generality of our approach across three distinct, challenging sequential tasks and diverse domains

Transformer-based automatic speech recognition (ASR) models such as Whisper are highly accurate, but their predictions remain difficult to interpret. Existing explainable AI (XAI) methods often lack faithfulness and precise temporal grounding. We propose Listening with Entropy-guided Attention for Faithful explainability (LEAF-X), a model-intrinsic XAI framework for transformer-based ASR. LEAF-X combines entropy-guided attention weighting, multi-layer attention rollout, and optional causal ablations to identify low-entropy, high-impact heads and layers, producing sparse token-to-frame attributions. Unlike perturbation-based explainers or raw attention maps, LEAF-X exploits the internal structure of encoder-decoder and speech-augmented decoder-only models to generate explanations that bette