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Fluorescence microscopy increasingly produces complex volumetric datasets whose biologically meaningful differences are difficult to capture with hand-crafted measurements, especially when signal is distributed across three-dimensional space. Here, we present an interpretable 3D Bag-of-Visual-Words (BoVW) pipeline for classification and analysis of volumetric microscopy data. The framework detects multiscale local keypoints, computes rotationally robust 3D gradient-based descriptors, and aggregates them into image-level visual-word representations. These features are then used for low-dimensional visualization and logistic regression classification, while model weights are mapped back to the original volumes to generate attention maps that localize discriminative structures. We applied the pipeline to two cerebellar granule neuron datasets spanning both ideal and non-ideal imaging conditions. In a near-isotropic lattice light-sheet dataset of chromatin organization, the method separated control and NIPBL loss-of-function nuclei and supported accurate classification, with strongest performance in the facultative heterochromatin and H3.3 channels. Attention mapping and downstream connected-component and Haralick analyses revealed that loss-of-function nuclei contained more fragmented high-attention regions and smoother, more homogeneous chromatin-associated textures than controls. We then evaluated the same framework on an anisotropic confocal timelapse dataset of receptor clustering in dense neuronal cultures, where single-cell segmentation was impractical. Despite these challenges, the representation captured the expected ligand-driven clustering response and resolved subtler differences associated with a polarity protein overexpression. Together, these results establish a simple, interpretable, and broadly applicable framework for extracting biologically meaningful structure from volumetric microscopy datasets while preserving native 3D context.

Precision-driven nanomaterial approaches continue to show enormous promise and are forecast to transform contemporary medicine. The core strength lies in the ability to engineer materials at the nanoscale to achieve unique physicochemical properties. A theory that is paramount to its unlimited future promise is in creating "designer" or "intelligent" nanomaterials precisely crafted to enable specific biological interactions for applications in health, disease, and infection. Fuelled by their ease of synthesis, catalytic nature, complex surface character, and tunability, the development of next-generation reducible metal oxide nanozymes (rNZs) that mimic the complex perceptive and adaptive capabilities of natural enzymes is a topic of remarkable curiosity. In pursuit of decoding rNZ catalytic mechanisms, this review spotlights the critical contribution of atomistic features. The promise of rNZs to circumvent the therapeutic insufficiencies surrounding bacterial antimicrobial resistance is confirmed to be a viable route forward, and the first evidence of the use of ionizing radiation to augment activity is presented as a novel antibacterial strategy. In some respects, the micromechanisms remain cryptic and elusive. The data reveal the critical contribution of crystal facets and oxygen vacancies within an orchestrated, heterogeneous, tunable, but strikingly complex system. Deciphering these sophisticated behaviors is arguably the next frontier in the field.

Speech-preserving facial expression manipulation (SPFEM) aims to modify facial emotions while meticulously maintaining the mouth animation associated with spoken content. Current works depend on inaccessible paired training samples for the person, where two aligned frames exhibit the same speech content yet differ in emotional expression, limiting the SPFEM applications in real-world scenarios. In this work, we discover that speakers who convey the same content with different emotions exhibit highly correlated local facial animations in both spatial and temporal spaces, providing valuable supervision for SPFEM. To capitalize on this insight, we propose a novel spatial-temporal coherent correlation learning (STCCL) algorithm, which models the aforementioned correlations as explicit metrics and integrates the metrics to supervise manipulating facial expression and meanwhile better preserving the facial animation of spoken content. To this end, it first learns a spatial coherent correlation metric, ensuring that the visual correlations of adjacent local regions within an image linked to a specific emotion closely resemble those of corresponding regions in an image linked to a different emotion. Simultaneously, it develops a temporal coherent correlation metric, ensuring that the visual correlations of specific regions across adjacent image frames associated with one emotion are similar to those in the corresponding regions of frames associated with another emotion. Recognizing that visual correlations are not uniform across all regions, we have also crafted a correlation-aware adaptive strategy that prioritizes regions that present greater challenges. During SPFEM model training, we construct the spatial-temporal coherent correlation metric between corresponding local regions of the input and output image frames as additional loss to supervise the generation process. We conduct extensive experiments on various datasets, and the results demonstrate the effectiveness of the proposed STCCL algorithm.

High-quality radiotherapy requires accurate dose delivery to target volumes while protecting organs-at-risk (OARs). However, current clinical workflows remain constrained by labor-intensive multidisciplinary collaboration, prolonged planning cycles, and limited scalability. Intelligent automation capable of integrating clinical knowledge and real-world decision patterns is needed to enhance precision and efficiency in radiotherapy planning. This study proposes MARTP, a \textbf{M}ulti-\textbf{A}gent \textbf{R}adiation \textbf{T}herapy \textbf{P}lanning framework driven by large language models (LLMs), to emulate multidisciplinary clinical workflows and enable end-to-end intelligent radiotherapy planning and evaluation.
 Approach: MARTP coordinates five specialized agents to integrate data analysis, weight adjustment, plan optimization, plan evaluation, and report generation into a unified radiotherapy planning workflow. The framework leverages supervised fine-tuning (SFT) on expert weight adjustment demonstrations to improve adaptation to complex clinical cases, incorporates retrieval-augmented generation (RAG) to ground planning decisions in case-specific knowledge, and employs a predefined model-context protocol (MCP) to enable high-precision treatment plan generation. In addition, reinforcement learning (RL) with expert preference data is used to develop an intelligent plan evaluation mechanism.
Results: Statistical analysis reveals that the dosimetric metrics of plans generated by MARTP exhibit no statistically significant differences compared with expert-crafted clinical plans, while the planning efficiency is substantially improved. In addition, the framework demonstrates robust and safe behavior under abnormal input scenarios, maintaining clinically acceptable outputs. The SFT and RL components further enhance the consistency, semantic accuracy, and reliability of model-generated weight adjustments and plan evaluations.
Significance: MARTP demonstrates that LLMs-driven multi-agent systems can effectively replicate multidisciplinary radiotherapy workflows, generating clinically comparable plans with substantially improved efficiency. The framework provides a promising pathway for integrating intelligent automation into radiotherapy practice, supporting more consistent decision-making and scalable treatment planning.

Cryo-electron tomography (cryo-ET) enables in situ three-dimensional visualization of many protein complexes and other macromolecular assemblies such as ribosomes in cells, yet automated macromolecule particle identification in 3D cryo-ET tomograms remains a major bottleneck due to dose-limited low signal-to-noise ratios, missing-wedge artifacts, and densely crowded cellular backgrounds. We present TomoSwin3D, an end-to-end three-dimensional (3D) macromolecule particle identification and classification pipeline centered on a Swin Transformer-based U-Net that performs particle identification and classification and outputs particle centroid coordinates. TomoSwin3D leverages a multi-channel input representation that augments raw tomogram densities with complementary 3D feature maps capturing edge strength (Sobel gradients), local contrast enhancement (morphological top-hat), and multiscale blob responses (Difference-of-Gaussians), improving detectability of small and low-contrast targets. To better preserve particle geometry and avoid hand-crafted shape assumptions, it adopts occupancy-preserving supervision that directly uses available 3D instance masks rather than heuristic Gaussian/spherical labels and applies scalable patch-wise inference followed by lightweight post-processing (connected-component analysis, size filtering, centroid extraction) for robust centroid coordinate extraction. Across diverse simulated and experimental cryo-ET tomogram benchmarks including SHREC 2021 and 2020 test datasets, EMPIAR dataset, and Cryo-ET data portal dataset, TomoSwin3D achieves strong and consistent performance in detecting proteins and other particles, outperforming existing methods, with a pronounced advantage in picking hard, small protein particles. These results establish TomoSwin3D as a scalable and accurate solution for high-throughput cryo-ET macromolecule particle picking and downstream subtomogram averaging.

Humans engage daily in procedural activities such as cooking a recipe or fixing a bike, which can be described as goal-oriented sequences of key-steps following certain ordering constraints. Task graphs mined from videos or textual descriptions have recently gained popularity as a human-readable, holistic representation of procedural activities encoding a partial ordering over key-steps, and have shown promise in supporting downstream video understanding tasks. While previous works generally relied on hand-crafted procedures to extract task graphs from videos, this paper introduces an approach based on gradient-based maximum likelihood optimization of edge weights, which can be used to directly estimate an adjacency matrix and can also be naturally plugged into more complex neural network architectures. We validate the ability of the proposed approach to generate accurate task graphs on the CaptainCook4D and EgoPER datasets. Moreover, we extend our validation analysis to the EgoProceL dataset, which we manually annotate with task graphs as an additional contribution. The three datasets together constitute a new benchmark for task graph learning, where our approach obtains improvements of +14.5%, +10.2% and +13.6% in $F_{1}$ score, respectively, over previous approaches. Thanks to the differentiability of the proposed framework, we also introduce a feature-based approach for predicting task graphs from key-step textual or video embeddings, which exhibits emerging video understanding abilities. Beyond that, task graphs learned with our approach obtain top performance in the Ego-Exo4D procedure understanding benchmark including 5 different downstream tasks, with gains of up to +4.61%, +0.10%, +5.02%, +8.62%, and +15.16% in finding Previous Keysteps, Optional Keysteps, Procedural Mistakes, Missing Keysteps, and Future Keysteps, respectively. We finally show significant enhancements to the challenging task of online mistake detection in procedural egocentric videos, achieving notable gains of +19.8% and +6.4% in the Assembly101-O and EPIC-Tent-O datasets, respectively, compared to the state of the art. The code for replicating the experiments is available at https://github.com/fpv-iplab/Differentiable-Task-Graph-Learning.

Gene essentiality, the requirement of a gene for survival or proliferation, is central to understanding cellular processes and identifying drug targets. Experimental determination requires large growth screens that are time-consuming and expensive, motivating in silico approaches. Existing methods predominantly use flux balance analysis (FBA), a constraint-based optimisation framework that requires a predefined cellular objective function. This can introduce observer bias, because the objective often reflects the researcher's assumptions rather than the cell's biological goals. Here, we present FluxGAT, a graph neural network (GNN) that predicts gene essentiality from graphical representations of flux sampling data. Flux sampling removes the need for an explicit objective and instead characterises feasible steady-state fluxes. FluxGAT combines this information with metabolic network topology to learn flux-informed node representations and classify reactions as essential or non-essential. We apply FluxGAT to the iCHO2291 genome-scale model of Chinese hamster ovary cells and Mouse1, a generic mouse model with independent essentiality labels. In both systems, FluxGAT improves sensitivity over FBA while maintaining high precision and specificity, and recovers more experimentally essential genes, especially where FBA predicts very few essentials. These results show that flux-informed GNNs can provide more general gene essentiality predictions across mammalian genome-scale models without hand-crafted objective functions.

Generalist foundation models (GFMs) are renowned for their exceptional capability and flexibility in diverse tasks. In the field of medicine, while GFMs exhibit superior generalizability, specialist models excel in precision because of their domain-specific knowledge. Here we show a cooperative framework, Generalist-Specialist Collaboration (GSCo), that synergistically combines a powerful generalist model with lightweight specialists. In this framework, specialists provide expert guidance, such as diagnostic predictions and visually similar clinical cases, as contextual information to the generalist, which then makes a final diagnosis. We developed MedDr, an open-source GFM tailored for medicine, as well as a suite of lightweight specialist models crafted for specific downstream tasks. A comprehensive evaluation on 32 datasets across diverse medical modalities shows that MedDr outperforms state-of-the-art GFMs on downstream datasets. Furthermore, GSCo exceeds GFMs and specialists in medical image diagnosis and report generation. This approach offers an effective and computationally efficient paradigm for deploying GFMs in clinical settings, enhancing scalability and enabling precise analysis across a wide range of scenarios.

Artificial intelligence (AI) chatbots powered by large language models (LLMs) such as ChatGPT offer a promising approach for delivering scalable, personalized physical activity interventions. Despite growing interest in applying these tools to health behaviour change, concerns remain regarding accuracy, safety, hallucinations, privacy, and theoretical grounding. This mini-review summarizes current methods for creating customized ChatGPT-based chatbots for physical activity promotion and outlines approaches for evaluating their performance. A literature search was conducted across five databases, white papers, and OpenAI technical reports. Three primary customization strategies were identified: retrieval-augmented generation (RAG), system prompt engineering, and fine-tuning. RAG enhances accuracy by grounding responses in curated guidelines and behaviour-change frameworks. System prompts define the chatbot's role, tone, and reasoning logic. Fine-tuning adapts the model's communication style using expert-crafted prompt-response pairs. These methods can be implemented independently or in combination, depending on intervention goals. Evaluation of customized chatbots requires both intrinsic model-based testing and extrinsic human-centred assessment. Additional considerations include protecting user privacy by avoiding collecting identifiable data, implementing data-minimization safeguards, and managing token-based operational costs associated with ChatGPT systems. Customized ChatGPT chatbots offer substantial potential for advancing physical activity promotion; however, safe and effective deployment requires thoughtful design, rigorous evaluation, and careful attention to privacy and cost.

People complain that they do not know what to say to soften the blow of social rejection. The adoption of language principles (i.e., avoiding apologies while using positive regard, sincere alternatives, and more words) may mitigate the negative consequences of social rejection. Does training help people independently exhibit greater communication skill or do people fail to perform despite "knowing better?" In Study 1, drift diffusion modeling suggested that training helped people "know it when they see it" (i.e., recognize precrafted wording options which better conveyed the language principles). Study 2 found that training aided people's success in independently crafting wording to convey the language principles. The current research highlights that existing social rejector frameworks and previous research on skill acquisition do not capture the experience of nonpunitive rejectors and presents a way that psychological science can address people's concerns about how to soften the blow of social rejection.

Artificial intelligence has advanced cancer pathology, but many systems still depend on hand-crafted features, are hard to explain and rely on fragmented workflows. We introduce SPARK (System of Pathology Agents for Research and Knowledge), a foundational agentic artificial intelligence approach that uses language as a universal interface to autonomously generate biologically driven concepts for tumor analysis. SPARK turns biological ideas into analytical tools and works directly with complex pathology data without extra model training. We evaluated SPARK across 18 patient cohorts spanning five cancer types (lung adenocarcinoma, lung squamous cell carcinoma, colorectal cancer, breast cancer and oropharyngeal squamous cell carcinoma) and more than 5,400 patients with available histopathology images and clinical/follow-up information, in both prognostic and predictive settings and on a well characterized spatial biology breast cancer dataset (patient n = 625). We found that SPARK produced clinically and biologically relevant concepts correlated with prognosis, known pathological variables and predictive biomarkers, including patterns of tumor progression and temporal change inferred from static images. A dedicated module allows for human interaction with SPARK. Further prospective validation is needed to evaluate the clinical utility of the tools created by SPARK. All code, parameters and results are openly released to help researchers and clinicians improve diagnostic precision and deepen tumor biology insights.

X-ray spectroscopy provides sensitive, element-specific insight into local geometric and electronic structures, but predictive first-principles simulations can be computationally expensive for large and chemically diverse molecular systems. Recent machine-learning approaches have shown promise in accelerating structure-to-spectrum prediction; however, most directly regress discretized spectral intensities and rely on hand-crafted geometric descriptors centered on the absorbing atom. Herein, we introduce a machine learning framework that encodes a detailed, environment-aware representation of the nuclear structure beyond the absorbing site. The model combines these descriptors with a physically motivated, multiscale Gaussian spectral basis whose coefficients are obtained via ridge projection, enforcing smoothness and spectral consistency. To further enhance robustness across chemical and conformational diversity, we employ a multiscale structural similarity loss that couples geometric and spectral resolution. This integrated approach yields accurate and transferable predictions across a wide range of molecular geometries and chemical environments while maintaining physical interpretability. The proposed framework establishes a physically structured and scalable route to machine-learned X-ray spectroscopy.

"DO EVERYTHING!!" How many times have you heard that phrase from a distressed family member? We routinely receive this desperate plea, and our knee-jerk reaction is to press on, push harder, be more aggressive. We, as acute care surgeons, have spent our lives learning a craft geared towards "doing everything" to heal patients so this response comes naturally. However, how do we respond to "do everything" when the probability of a meaningful recovery is extremely low or non-existent? These situations create moral tension for surgeons who must reconcile the desire to preserve life with the obligation to avoid harm, respect patient values, and provide care that is medically appropriate. We will explore three components of this challenging topic: (1) the utility and limitations of risk calculators and prognostic tools in trauma, (2) the best case/worst case model as a structured communication strategy and (3) approaches to counseling families when expectations for recovery are not realistic. Together, these elements provide a framework for decision-making that is compassionate, ethically grounded, and anchored in clinical reality.

A squaramide-functionalized tetranuclear coordination capsule was constructed as an efficient catalyst for Friedel-Crafts alkylation. The supramolecular architecture suppresses self-association and creates a confined cavity enriched with directional hydrogen-bonding sites. This defined microenvironment enables substrate encapsulation, promotes activation, and stabilizes a preorganized conformation, thereby accelerating the nucleophilic addition step.

To describe a video-based teaching and assessment curriculum for general surgery residents focused on laparoscopic and robotic cases, integrating preoperative preparation, operative review, and postoperative reflection. This is a description of an ongoing, weekly 1-hour educational conference for surgical trainees focused on entrustable professional activities and operative video review. Eastern Colorado Healthcare System Rocky Mountain Regional Veterans Affairs Hospital, a large 1a tertiary facility teaching hospital in Aurora, CO. Surgical residents, medical students, advanced practice providers, and attending surgeons. Successful implementation of a weekly operative review conference for surgical training. A standardized, recurrent video-based conference provides an effective and scalable framework for surgical skill development. By combining objective assessment with reflective learning and team-based discussion, this model may enhance operative readiness, reinforce best practices, and serve as a platform for future integration with video analytics and console time metrics.

In the oil industry, undesirable events in oil wells can cause economic losses, environmental accidents, and human casualties. In 2019, recognizing the importance and the lack of public datasets related to undesirable events in oil wells, Petrobras developed and publicly released the first version of the 3W Dataset, which is essentially a set of Multivariate Time Series labeled by experts. Since then, the 3W Dataset has been developed collaboratively and has become a foundational reference for numerous works in the field. This data article describes the current publicly available version of the 3W Dataset, which contains additional instances, more variables, and a new label. Furthermore, a new data structure has been developed to make data access more robust and efficient. The detailed description we provide encourages and supports the 3W community and new 3W users to improve previous published results and to develop new robust methodologies, digital products and services capable of detecting undesirable events in oil wells with enough anticipation to enable corrective or mitigating actions.

Although many existing works have studied probabilistic or dynamic environments where the objects used in daily life may be moved due to human activities, the scale of datasets is usually limited due to the cost of human annotation or manual configuration. This paper introduces a framework that simulates human activities and corresponding object dynamics using Large Language Models (LLMs) and applies the simulated human residents to embodied scenes to generate dynamic scenes. Using this framework, we craft a dataset named DynamicTHOR with 50 characters and 100 dynamic scenes, which can be easily extended in scale. A user study comparing our generated scene dynamics with a baseline approach and human annotations validates that our framework successfully produces believable, diversified data of a quality comparable to human annotations. The novel framework and dataset can facilitate the study of embodied intelligence, such as the navigation task in dynamic scenarios.

School-based interventions offer a promising setting to promote healthier nutritional behaviours (NB) such as physical activity (PA), sedentary behaviour (SB), and eating behaviour, while addressing weight social inequalities. NB changes may occur before measurable effects on weight, which can take longer to emerge. This study evaluated the overall effectiveness of the school-based PRALIMAP-INÈS trial on weight and NB social inequalities reduction among adolescents with overweight or obesity. Adolescents were divided into two intervention groups according to their socio-economic status (socially advantaged, and socially less advantaged). NB were self-reported by adolescents. Outcomes were body mass index z-score (BMIz), fruit and vegetables (FV) consumption, sweetened products and beverages (SPB) consumption, vigorous/moderate PA, walking, and SB. Overall effectiveness was estimated using generalized pairwise comparisons, estimating net benefit for each outcome (δ), and overall net benefit (Δ). Of 985 adolescents (age= 15.3 ± 0.7 years; 46.7% boys), those in less advantaged group were 12.5% more likely to have a favourable change in weight status and NB than those in advantaged group (Δ= 12.5% [6.1-19.1%]). For each outcome, net benefits were as follows: BMIz (δ= 4.2% [0.0; 8.6]), vigorous PA (δ= 4.2% [0.4; 8.3]), FV (δ= 3.2% [0.9; 5.5]), SB (δ= 0.8% [-1.6; 3.2]), SPB (δ= -0.2% [-1.1; 0.6]), moderate PA (δ= 0.2%) [-0.7; 1.1], walking (δ= 0.2% [-0.2; 0.6]). Results showed an overall beneficial effect of the PRALIMAP-INÈS trial in reducing social inequalities in weight and NB among adolescents with overweight or obesity. Long-term effectiveness could be expected by reducing social inequalities in NB.