INTRODUCTION:Clinicopathological studies offer crucial interpretations of 18F-flortaucipir (FTP) tau positron emission tomography (PET) signal but are limited by methods. We leveraged whole-brain, quantitative immunohistochemical (IHC) Alzheimer's Disease (AD) tau density maps to comprehensively evaluate the FTP tracer. METHODS:We generated IHC maps for three AD-tau antibodies-AT8, AT100, and MC1-using two human brains histologically staged at Braak IV and VI. FTP-PET scans were acquired 6 and 10 weeks prior to death. Using region-wise and voxelwise methods, we correlated FTP-PET with IHC signals. RESULTS:Only AT8 (p-tau Ser202/Thr205; neuronal and neuritic tau pathology) showed a notable correlation with FTP standardized uptake value ratios (SUVRs) in the Braak VI case (Spearman's rank correlation coefficient [rs] = 0.461, p < 0.001). FTP SUVRs failed to capture medial temporal lobe (MTL) tau burden, whereas neocortical regions showed lower IHC burden but more variability in FTP uptake. DISCUSSION:Although FTP signals correlate well with AT8-positive tau in the more advanced case, they underestimate the severity of MTL burden, potentially confounding assessments of tau-targeted therapies.

Flortaucipir PET imaging has significantly advanced our ability to visualize tau pathology in vivo. However, off-target Flortaucipir signal remains a considerable challenge for interpreting of imaging results, particularly in non-Alzheimer's tauopathies and non-tau pathologies. To better understand this off-target signal, we used an innovative voxel-to-voxel correlation approach, analyzing thousands of histology-Flortaucipir pairs from individual cases. This allowed us to quantitatively assess the relationship between Flortaucipir PET signal and three key biological factors: histological tau burden (CP-13 phospho-tau), ferric iron (Perls’ Prussian blue), and monoamine oxidase B (MAO-B). Our study included individuals with Alzheimer's disease (AD), various non-AD tauopathies, and a case of FTLD-TDP-43 type A. In AD, Flortaucipir signal showed a significant but moderate correlation with histological tau pathology, suggesting that while tau is a major contributor, other biological factors also influence Flortaucipir binding in AD. Conversely, in non-AD tauopathies and FTLD-TDP-43, correlations between Flortaucipir signal and tau pathology were weak or negligible. Instead, Flortaucipir signal correlated more strongly with ferric iron and MAO-B. However, these factors did not fully explain all the off-target signals, implying other unknown contributors are likely involved. These findings underscore the complexity of interpreting Flortaucipir PET scans. A thorough understanding of off-target binding mechanisms is crucial for improving the diagnostic accuracy of Flortaucipir PET and its specificity.

Scientific visualization is changing from passive observation to active, AI-assisted collaboration. While Extended Reality (XR) has proven valuable for comprehending dense 3D arrays, traditional VR applications are typically deployed in rigid, single-purpose, and monolithic architectures. In this paper, we present the evolution of ASCRIBE-XR: a virtual reality platform backed by remote computation that has been re-engineered into a dynamic, service-oriented ecosystem. We introduce three core innovations that make immersive data analysis easier, faster, and more flexible when using multimodal scientific imaging. First, a lightweight Python REST interface decouples XR logic from the rendering engine, enabling real-time, programmable scene customization and on-demand data generation. Second, we present a Specimen Catalog architecture that lets the platform pivot between radically different disciplines, ranging from archaeological heterogeneous concrete and fuel-cell membranes to the root system of a bioenergy grass, by describing each dataset through portable metadata rather than hard-coded application logic. Finally, we introduce a prompt-driven layer powered by the Claude Agent SDK, allowing researchers to generate, segment, and manipulate volumetric and mesh data through natural language dialogue within the virtual space. For example, applying foundation models such as the Segment Anything Model (SAM) to perform zero-shot segmentation on demand. By bridging human intent with remote computation, ASCRIBE-XR relaxes the constraints of conventional visualization tools, offering a highly adaptable, conversational platform for scientific discovery with human auditing.

The increasing complexity of modern computational environments often burdens researchers with infrastructure management, authentication protocols, and container deployments. We present Sci-Orchestra, a layered orchestration framework designed to fully automate experimental workflows, allowing scientists to prioritize scientific discovery over backend operations. By abstracting execution through an API-driven interface, the system assumes responsibility for secure authentication, resource management, and scalable deployment across diverse high-performance computing environments using Kubernetes architectures. A key innovation of Sci-Orchestra is its autonomous marketplace, which serves as a catalyst for cross-institutional collaboration. Through an intuitive user interface, researchers can rapidly deploy and share specialized services via simple selections, eliminating the need for complex installations and technical setups. This modular infrastructure is specifically designed to facilitate industry partnerships as it provides a secure execution environment and allows external collaborators to test and validate proprietary tools without the need for source-code exchange. This “black-box” interoperability protects intellectual property while enabling seamless integration into broader scientific pipelines, ultimately accelerating the transition from laboratory prototypes to industrial-scale applications.

OBJECTIVE:Though it is widely known that tau deposition affects brain structure, the precise localization of these effects is poorly understood, especially in relation to gyral and sulcal anatomy. We investigated whether tau pathology in Alzheimer's disease (AD) preferentially affects sulci, and particularly sulcal depths. METHODS:We analyzed 675 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) with magnetic resonance imaging (MRI) and positron emission tomography (PET) data to investigate relationships between neocortical tau PET signal and cortical thickness. We then examined an advanced AD case with postmortem MRI and coregistered whole-brain phospho-tau staining for evidence of sulcal tau distribution in AD. Finally, in a sample of 187 cognitively unimpaired young and older adults with resting-state functional MRI, we examined connectivity strength between tau-vulnerable regions and the hippocampus across adulthood, prior to disease-related cognitive decline. RESULTS:Our findings revealed that tau-related cortical thinning predominantly occurs in sulcal regions, especially the deepest parts. Postmortem histology confirmed preferential tau accumulation in sulcal depths. Additionally, connectivity analyses revealed that, across adulthood, these primarily sulcal regions most susceptible to tau-related thinning also have stronger connectivity to the hippocampus, suggesting a role for network connectivity in the vulnerability of sulci to the effects of tau pathology later in life. INTERPRETATION:These findings support the hypothesis that sulci, and particularly their depths, represent structurally and functionally vulnerable regions for tau deposition in AD. Understanding the mechanisms underlying this sulcal vulnerability provides insight into general principles driving regional susceptibility to pathology, and sheds light on the detrimental functional and cognitive effects of tau pathology. ANN NEUROL 2026;99:1343-1353.

Zero-shot and prompt-based technologies capitalized on using frequently occurring images to transform visual reasoning tasks, which explains why such technologies struggle with valuable yet scarce scientific image sets. In this work, we propose Zenesis, a comprehensive no-code interactive platform designed to minimize barriers posed by data readiness for scientific images. We develop lightweight multi-modal adaptation techniques that enable zero-shot operation on raw scientific data, along with human-in-the-loop refinement and heuristic-based temporal enhancement options. We demonstrate the performance of our approach through comprehensive comparison and validation on challenging Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) data of catalyst-loaded membranes. Zenesis significantly outperforms baseline methods, achieving an average accuracy of 0.947, an Intersection over Union (IOU) of 0.858, and a Dice score of 0.923 for amorphous catalyst samples and accuracy of 0.987, an IOU of 0.857, and a Dice score of 0.923 for crystalline samples. These results mark a substantial improvement over traditional methods like Otsu thresholding and even advanced models like Segment Anything Model (SAM) when used in isolation. Our results demonstrate that Zenesis is a powerful tool for scientific applications, particularly in fields where high-quality annotated datasets are unavailable, accelerating accurate analysis of experimental imaging.

IntroductionAdvances in automation and AI/ML offer new opportunities for plant science, including design, modeling, and analysis. This study aimed to develop an automated platform for researching small model plants under axenic conditions and integrate it with AI/ML tools.MethodsThe EcoBOT platform was developed, which consists of sterile containers (EcoFABs) for growing plants and imaging for monitoring plant growth and health. Brachypodium distachyon was grown on the EcoBOT, and its response to nutrient limitation and copper stress was evaluated.ResultsThe results showed that Brachypodium distachyon grown in the EcoBOT maintained sterility and responded to nutrient limitation and copper stress. Analysis of over 6,500 root and shoot images revealed varying sensitivity and response rates to copper. Bayesian Optimization was used to improve model accuracies relating copper concentrations to plant biomass via sequential experiments, resulting in a >30% improvement.DiscussionThe findings of this study demonstrate the potential of the EcoBOT platform for researching plant responses to environmental factors. Future experiments could focus on relating other chemical stresses and microbial interactions to create generalized models of plant responses.

We introduce ASCRIBE-XR, an immersive software application designed to accelerate the visualization and exploration of 3D dense arrays and mesh files from scientific experiments. Based on Godot and PC-VR technologies, the platform enables users to dynamically load and manipulate scientific records to dive into the structure of data. The novelty lies in the unique integration at the system level, combining disparate technologies, such as VR, HPC, and AI-driven object modeling for scientific visualization. Its integration with HPC resources grants remote processing of large-scale data with results streamed directly into the VR environment. The program's multi-user capabilities, enabled throughWebRTC and MQTT, allow multiple users to share data and visualize together in real-time, promoting a more interactive and engaging research experience. We describe the design and implementation of ASCRIBE-XR, highlighting its key features and capabilities. We also include examples of its application and discuss the potential benefits to the scientific community.

This study introduces a novel labeling pipeline to accelerate the labeling process of scientific data sets by using artificial intelligence (AI)-guided tagging techniques. This pipeline includes a set of interconnected web-based graphical user interfaces (GUIs), where Data Clinic and MLCoach enable the preparation of machine learning (ML) models for data reduction and classification, respectively, while Label Maker is used for label assignment. Throughout this pipeline, data can be accessed through a direct connection to a file system or through Tiled for access through Hypertext Transfer Protocol (HTTP). Our experimental results present three use cases where this labeling pipeline has been instrumental for the study of large X-ray scattering data sets in the area of pattern recognition, the remote analysis of resonant soft X-ray scattering data and the fine-tuning process of foundation models. These use cases highlight the labeling capabilities of this pipeline, including the ability to label large data sets in a short period of time, to perform remote data analysis while minimizing data movement and to enhance the fine-tuning process of complex ML models with human involvement.

Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive comparative analysis of leading generative architectures, ranging from Variational Autoencoders (VAEs) to Generative Adversarial Networks (GANs) on through to Diffusion Models, in the context of scientific image synthesis. We examine each model’s foundational principles, recent architectural advancements, and practical trade-offs. Our evaluation, conducted on domain-specific datasets including microCT scans of rocks and composite fibers, as well as high-resolution images of plant roots, integrates both quantitative metrics (SSIM, LPIPS, FID, CLIPScore) and expert-driven qualitative assessments. Results show that GANs, particularly StyleGAN, produce images with high perceptual quality and structural coherence. Diffusion-based models for inpainting and image variation, such as DALL-E 2, delivered high realism and semantic alignment but generally struggled in balancing visual fidelity with scientific accuracy. Importantly, our findings reveal limitations of standard quantitative metrics in capturing scientific relevance, underscoring the need for domain-expert validation. We conclude by discussing key challenges such as model interpretability, computational cost, and verification protocols, and discuss future directions where generative AI can drive innovation in data augmentation, simulation, and hypothesis generation in scientific research.

For over half a century, the computer mouse has been the primary tool for interacting with digital data, yet it remains a limiting factor in exploring complex, multi-scale scientific images. Traditional 2D visualization methods hinder intuitive analysis of inherently 3D structures. Virtual Reality (VR) offers a transformative alternative, providing immersive, interactive environments that enhance data comprehension. This article introduces ASCRIBE-VR, a VR platform of Autonomous Solutions for Computational Research with Immersive Browsing & Exploration, which integrates AI-driven algorithms with scientific images. ASCRIBE-VR enables multimodal analysis, structural assessments, and immersive visualization, supporting scientific visualization of advanced datasets such as X-ray CT, Magnetic Resonance, and synthetic 3D imaging. Our VR tools, compatible with Meta Quest, can consume the output of our AI-based segmentation and iterative feedback processes to enable seamless exploration of large-scale 3D images. By merging AI-generated results with VR visualization, ASCRIBE-VR enhances scientific discovery, bridging the gap between computational analysis and human intuition in materials research, connecting human-in-the-loop with digital twins.

ASCRIBE-XR, a novel computational platform designed to facilitate the visualization and exploration of 3D volumetric data and mesh data in the context of synchrotron experiments, is described. Using Godot and PC-VR technologies, the platform enables users to dynamically load and manipulate 3D data sets to gain deeper insights into their research. The program's multi-user capabilities, enabled through WebRTC, and MQTT, allow multiple users to share data and visualize together in real-time, promoting a more interactive and engaging research experience. We describe the design and implementation of ASCRIBE-XR, highlighting its key features and capabilities. We will also discuss its utility in the context of synchrotron research, including examples of its application and potential benefits for the scientific community.

This review surveys the state-of-the-art in text-to-image and image-to-image generation within the scope of generative AI. We provide a comparative analysis of three prominent architectures: Variational Autoencoders, Generative Adversarial Networks and Diffusion Models. For each, we elucidate core concepts, architectural innovations, and practical strengths and limitations, particularly for scientific image understanding. Finally, we discuss critical open challenges and potential future research directions in this rapidly evolving field.

Inter-laboratory replicability is crucial yet challenging in microbiome research. Leveraging microbiomes to promote soil health and plant growth requires understanding underlying molecular mechanisms using reproducible experimental systems. In a global collaborative effort involving five laboratories, we aimed to help advance reproducibility in microbiome studies by testing our ability to replicate synthetic community assembly experiments. Our study compared fabricated ecosystems constructed using two different synthetic bacterial communities, the model grass Brachypodium distachyon , and sterile EcoFAB 2.0 devices. All participating laboratories observed consistent inoculum-dependent changes in plant phenotype, root exudate composition, and final bacterial community structure where Paraburkholderia sp. OAS925 could dramatically shift microbiome composition. Comparative genomics and exudate utilization linked the pH-dependent colonization ability of Paraburkholderia , which was further confirmed with motility assays. The study provides detailed protocols, benchmarking datasets, and best practices to help advance replicable science and inform future multi-laboratory reproducibility studies. ### Competing Interest Statement P.F.A. and T.R.N. are inventors of patent US11510376B2, held by the University of California, covering an Ecosystem device for determining plant-microbe interactions. In addition, T.R.N. is an advisor to Brightseed Bio. All other authors declare no competing interest.

The introduction of BERTopic marked a crucial advancement in topic modeling and presented a topic model that outperformed both traditional and modern topic models in terms of topic modeling metrics on a variety of corpora. However, unique issues arise when topic modeling is performed on scientific articles. This paper introduces BERTeley, an innovative tool built upon BERTopic, designed to alleviate these shortcomings and improve the usability of BERTopic when conducting topic modeling on a corpus consisting of scientific articles. This is accomplished through BERTeley’s three main features: scientific article preprocessing, topic modeling using pre-trained scientific language models, and topic model metric calculation. Furthermore, an experiment was conducted comparing topic models using four different language models in three corpora consisting of scientific articles.

The aim of this study is to enable the hydrogen economy and decarbonize various sectors in our environment that requires less expensive and more durable water electrolyzers, which can meet the Hydrogen-Shot target. The key is to improve the ionomer interfaces in low-temperature water electrolyzers as rapidly as possible, but to do so, it requires a systematic and holistic campaign combining both experiments and theory. In this perspective, we discuss the issues of electrolyzers and needs for translational science. We then present the approach that the Energy EarthShot Research Center: Center for Ionomer-based Water Electrolysis is taking in hopes of inspiring the community with this approach that can be leveraged to multiple problems and technologies. One way to achieve the Hydrogen-Shot goal of low-cost, clean hydrogen, is advancing research and development on the interfaces of water electrolyzers for both performance and lifetime. The Center for Ionomer-based Water Electrolysis is exploring new techniques and strategies to not only interrogate interfacial phenomena in water electrolyzers to increase efficiency and durability, but also a new paradigm related to synergistic, cojoined experimental and theoretical research.