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At least 91 records · Page 5

Safety Culture at the World’s Premier Multi-User Spaceport

NASA’s Agency-wide Safety Culture is implemented at the Kennedy Space Center (KSC) using a unique strategy due an unparalleled approach in making human spaceflight history. Kennedy Space Center, the world’s premier multi-user spaceport, enables U.S. government and commercial space access, while providing the world a resource to allow the exploration of and the ability to work in space. A consistently healthy safety culture at KSC is imperative for mission success: desired achievements, protection of space flight hardware, and ultimately, the preservation of human life requires the support of a healthy safety culture. Emphasis on the NASA Agency-wide development of Safety Culture began with the conception of the NASA Agency Safety Culture Working Group. After the devastating loss of life and mission of the Space Shuttle Colombia, a broken safety culture was identified as an organizational cause by the Columbia Accident Investigation Board Report. Thus, the Agency Safety Culture Working Group was developed in 2009 to assess the status of the Agency’s Safety Culture, while addressing safety culture concerns at the NASA Center-level. A Five-factor model was developed to serve as the guiding principles for Safety Culture: 1) Reporting Culture, 2) Just Culture, 3) Flexible Culture, 4) Learning Culture, and 5) Engaged Culture. These five factors are included in the NASA Safety Culture logo, which was intentionally designed as a DNA double helix to prompt the permeation of safety into day-to-day work. KSC specifically implements the NASA Agency-wide Safety Culture principles in a tailored approach that is relevant to the diversity of work being performed. An emphasis is placed on the implementation to include safety at home, not exclusively at work. This emphasis is a KSC-specific element that has been intentionally added to promote a closed loop Safety Culture. To advertise the safety culture, various safety and health events are held throughout the calendar year, providing innovative speakers and engaging activities, while also promoting a wide range of curated safety initiatives. Development and continuous improvement of the KSC safety tracking database, allows for advanced tracking-to-closure, along with providing data sets used to identify areas of emphasis. Other safety initiatives rely solely on employee participation, such as the photo challenges; participants are encouraged to identify and capture themselves, coworkers, or family members participating in safe or healthy activities to share with others within the Center and at Agency levels. Fabrication of exclusive videos and graphics are utilized to advertise and inform employee of safety initiatives, upcoming safety events, and general dispersion of safety information. In addition, an anonymous Agency-wide Safety Culture Survey is advertised, administered, and analyzed at Kennedy Space Center, with the purpose of receiving basic feedback on Safety Culture perceptions to help prevent future incidents from occurring. Through these briefly identified means, and many other forms of employee engagement, Kennedy Space Center aims to maintain safety in the forefront, while creating an environment where everyone trusts that safety is a priority.

Larrin E. Moody↗

Livewire: A Model Platform for Data Quality Assessment and AI Readiness Across DOE Missions

High-quality, well-governed data is essential for accelerating discovery and achieving operational excellence across DOE and national laboratory missions. The Livewire Data Platform is a DOE-supported platform that offers automated assessments of data quality, standardization, provenance, and Artificial Intelligence (AI) readiness. It allows researchers and data practitioners to systematically and easily evaluate datasets against established governance criteria and prepare them for advanced analytics. Livewire addresses critical challenges in DOE's data ecosystem with integrated capabilities for metadata validation, provenance tracking, and schema alignment. This platform's automated workflows assist users in identifying data quality gaps, enhancing interoperability between datasets collected from various stakeholders, and ensuring compliance with DOE data standards, all while reducing manual curation efforts. Additionally, we will discuss its AI readiness framework, which is being developed to prepare datasets for training models, developing advanced analytic tools, and machine learning applications. Using some of the more than one hundred tabular datasets on Livewire, processed with this open-source methodology, we will demonstrate how Livewire can serve as a model for scalable, standards-driven data management. This approach provides a pathway to leverage existing and future datasets within the DOE, boosting innovation and efficiency across national laboratories.

33 - ADVANCED PROPULSION SYSTEMS↗

The Artificial Intelligence Ontology: LLM-Assisted Construction of AI Concept Hierarchies

The Artificial Intelligence Ontology (AIO) is a systematization of artificial intelligence (AI) concepts, methodologies, and their interrelations. Developed via manual curation, with the additional assistance of large language models (LLMs), AIO aims to address the rapidly evolving landscape of AI by providing a comprehensive framework that encompasses both technical and ethical aspects of AI technologies. The primary audience for AIO includes AI researchers, developers, and educators seeking standardized terminology and concepts within the AI domain. We use the term “branches” for classes, and their subclasses, in our ontology that are subclasses of owl:Thing. AIO contains eight branches: Bias, Layer, Machine Learning Task, Mathematical Function, Model, Network, Preprocessing, and Training Strategy, each designed to support the modular composition of AI methods and facilitate a deeper understanding of deep learning architectures and ethical considerations in AI. AIO uses the Ontology Development Kit (ODK) for its creation and maintenance, with its content being more easily updated through AI-driven curation support. This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies. The ontology's utility is demonstrated through the annotation of AI methods data in a catalog of AI research publications and the integration into the BioPortal ontology resource, highlighting its potential for cross-disciplinary research. The AIO ontology is open source and is available on GitHub ( https://w3id.org/aio/ ) and BioPortal ( https://bioportal.bioontology.org/ontologies/AIO ).

Joachimiak, Marcin P. [Biosystems Data Science Dep↗

Mobility Data and Models Informing Smart Cities

Using emerging data platforms, new mobility technologies, and travel demand models (TDMs), researchers, industry, and communities seek to improve the quality of transportation while maximizing the energy efficiency, equity, and safety of transportation services. As transportation may soon reach over 30% of U.S. energy consumption and with urban areas representing an increasing proportion of the U.S. population (>80% since 2010), a critical need exists to engage in urban data science-informed approaches to enhancing mobility. The objective of this study is to explore and document how aspiring Smart Cities are using data and models to inform mobility and energy initiatives within Smart City programs and in so doing identify gaps in knowledge and processes guiding Smart City mobility investment strategies, programs, projects, and pilots. A primary focus of the Smart Cities studied was the creation of an integrated data sharing environment approach. Most of these systems are being developed in parallel with multiple new data analysis tools, while regional metropolitan planning organizations continue to slowly evolve TDMs to take into account impacts of long-term strategies for emerging mobility technologies and services. Smart City initiatives in the United States have keen interests in leveraging knowledge and research on the mobility benefits and risks of automated, connected, efficient/electric, and shared on-demand mobility services; and understanding the related energy, environmental, economic, and societal impacts of these shifts. The results serve to identify key gaps in data, knowledge, and methods required to advance energy efficient urban mobility innovation, and to enable research and analysis collaboration between Smart Cities and the U.S. Department of Energy's efforts enabling new Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility.

33 ADVANCED PROPULSION SYSTEMS↗

Educational Consortium for Energy-related Data Science & Computation in Building Engineering Programs

The project spearheaded by Pennsylvania State University aims to address the growing need for integrating energy-focused computation and data science into building engineering education. As the demand for energy-efficient building designs and operations increases, the educational sector must adapt to equip future engineers with the necessary skills. This initiative responds to this need by developing a consortium that unites multiple institutions to enhance curriculum development, dataset curation, and resource sharing, thereby ensuring students are well-prepared for the evolving energy sector. The primary goal of the project is to establish a consortium that will develop and disseminate educational materials and training programs focused on energy-related data science and computation. Key accomplishments include the creation of a beta website for resource sharing, the development of training programs and standalone modules, and the curation of datasets accessible to the public. This effort will culminate in a curriculum that incorporates advanced modeling technologies and data science skills into building engineering programs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA-ESA Mars Sample Return Program

NASA's Perseverance mission arrived at Jezero Crater on Mars in February 2021 and began scientific studies and acquisition of Martian samples for return to Earth by future missions, consistent with the recommendations of the U.S. science community in the previous Planetary Science Decadal Survey. NASA and ESA have established a joint Mars Sample Return (MSR) program to safely deliver these samples back to Earth, allowing researchers to use advanced scientific instrumentation that cannot be transported on robotic spacecraft and enable future studies of carefully curated samples using capabilities that have not yet been developed. The MSR architecture consists of two flight elements to follow Perseverance, the NASA-led Sample Retrieval Lander (SRL) and the ESA-led Earth Return Orbiter (ERO). The ERO is designed to orbit Mars and provide relay services for the SRL, including its ESA Sample Fetch Rover (SFR) and the NASA Mars Ascent Vehicle (MAV). The SRL deploys the SFR to retrieve Martian samples cached by the Perseverance rover and then returns the samples to the Orbiting Sample container (OS) on board the MAV using the ESA Sample Transfer Arm (STA). Independently, Perseverance could also deliver samples retained onboard to the OS. The MAV would launch and release the OS into low Mars orbit for rendezvous with the ERO. Upon successful capture of the OS in the ERO’s primary payload, the NASA Capture/Containment Return System (CCRS), the OS would be safely contained and loaded into the Earth Entry System (EES). The ERO will leave Mars orbit and release the EES on Earth approach on a ballistic reentry trajectory through the Earth's atmosphere for landing in the United States. Following return of the samples to Earth, the samples would be protected, preserved, assessed, curated, and made available to the international science community for scientific research and analysis. The NASA SRL and ESA ERO missions are expected to launch as early as 2026, with the return of Martian samples to Earth as early as 2031. MSR’s primary objective is the return of scientifically selected Mars samples for detailed investigation in terrestrial laboratories. The mission would also further inform the design of future human missions. The Mars Sample Return campaign is underway with the successful collection of several scientifically selected samples in Jezero Crater. The MSR Program is working towards a confirmation review in 2023 for the remaining flight elements.

Mars↗

The Trillion Pixel GeoAI Challenge Workshop

Rapid innovations in satellite and airborne remote sensing capabilities holds the promise of collecting high-resolution imagery with daily, even hourly cadence across the entire planet. Availability of such earth observation data streams at varying spatial and temporal scales, coupled with astonishing progress in AI and transformational advances in high performance computing bring into view the possibility of mapping and interpreting the surface of our planet at unprecedented detail. The implications of such capability for science, technology, policy, and security are far reaching. Moreover, even at a modest 5m resolution, 100 trillion pixels will describe the surface of the Earth every day. Consequently, monitoring the pulse of our planet will entail collecting, refining, analyzing, and curating those 100 trillion pixels within a 24-hour period. That is a challenge with unknown solutions as of today’s advances and therefore serves as a key motivation for this workshop. The GEOINT and AI communities have a unique opportunity to enable detailed monitoring the pulse of our planet and obtain new insights into how humans occupy, interact with, and alter the surface of the Earth over time. From a science and technology standpoint, this is a modern-day moonshot that presents numerous challenges particularly related to scaling the analysis to every pixel covering the planet. The challenge of making sense of all these pixels in a timely fashion include image processing at scale, selective analysis of only the pixels that matter, generalizing AI models across heterogeneous locations, computing on hardware that is memory constrained, and deploying automated feature extraction on edge devices. The designing of capable GeoAI workflows and data pipelines will require interdisciplinary efforts and partnerships. The Trillion Pixel Challenge for GeoAI workshop was is promoted as a first community effort to discuss priority research and development directions and to establish the partnerships and collaborations needed to make progress on critical scientific and operational applications from local to regional to planet scale.

97 MATHEMATICS AND COMPUTING↗

Curation of Ground-Truth Validated Benchmarking Datasets for Fault Detection & Diagnostics Tools

Fault detection and diagnostics (FDD) analytical tools for heating, ventilation and air conditioning (HVAC) systems represent one of the most active areas of smart building technology development. A diversity of techniques is used for FDD analytics, spanning physical models, black box, and rule-based approaches, and researchers continuously strive to develop improved algorithms. With FDD algorithm numbers now in the hundreds, there is a need for performance evaluation of these algorithms in order to assess improvements, improve costeffectiveness, and to prioritize investment in the further development of these technologies. A persistent challenge of FDD advance has been the lack of common datasets to benchmark the performance accuracy of FDD algorithms. This paper summarizes the successful curation of HVAC operational data, paired with validated ground-truth information regarding the presence and absence of faults. The current dataset, consisting of both simulation and experimental data, will evolve to include a larger set of HVAC systems with the objective of creating the largest publicly available dataset to be used by FDD developers, users, and researchers to compare and contrast performance accuracy across FDD algorithms, helping to drive improvements that will spur greater market adoption of FDD tools. Furthermore, in order to avoid previously observed issues with contributed datasets and ensure high quality and consistency of future submissions, the development of data validation and ground-truth assessment protocol is detailed in this study.

Casillas, Armando↗

Blast from the Past: ASDC Curation for NASA Suborbital Legacy Missions to Promote Data Discovery and Accessibility

NASA has an extensive history of conducting suborbital field campaigns to further advances in atmospheric sciences. Beginning with the Chemical Instrument Test and Evaluation (CITE) conducted in 1983-1984, NASA has completed many suborbital campaigns over the past three decades. Since the early 2010s, suborbital missions are typically assigned to a NASA Distributed Active Archive Center (DAAC) prior to the mission for long-term archival and distribution. Efforts are being made by NASA’s Earth Science Data and Information System (ESDIS) Project and the Airborne Data Management Group (ADMG) to assign legacy missions to DAACs for permanent archival and distribution, so that these valuable datasets remain to be available to the scientific community. NASA’s Atmospheric Science Data Center (ASDC) has been named the assigned DAAC for nearly 20 atmospheric composition legacy missions, including missions conducted as part of the Global Tropospheric Experiment (GTE) and expects to be named the assigned DAAC for more of these missions over the next few years. The primary goal of the ASDC is to provide access to the datasets as they are currently formatted to the broad user community and enhance their findability and accessibility. However, data reporting standards have evolved significantly since 1983 and the datasets span a wide variety of file formats, including text, Ames, GTE, and ICARTT (International Consortium for Atmospheric Research on Transport and Transformation), and the amount of metadata and relevant information included in the files also varies greatly and can not be readily extracted without subject matter knowledge. This has caused challenges for the ASDC’s suborbital metadata extraction pipeline in ensuring that accurate and necessary metadata is being provided for the missions by all the ASDC’s existing search mechanisms. To make the data more findable and accessible, the ASDC has begun researching ways to further enhance the datasets, including distributing value-added products (i.e. consistent file format such as ICARTT or netCDF), adding standard names from the ESDIS Standards Coordination Office (ESCO)-approved Atmospheric Composition Variable Standard Names Convention (ACVSNC), and creating outreach materials such as ArcGIS StoryMaps, User Guides, and Micro Articles, providing overviews of the missions and what type of data was collected during the missions. These efforts also help support NASA’s Open-Source Science by enhancing the FAIRness of the legacy data products. This presentation will review the ASDC’s ongoing efforts, progress made, and future plans for legacy missions.

Megan Buzanowicz↗

PDB‐101: Molecular Explorations through Biology and Medicine

PDB‐101 is an online portal for teachers, students, and the general public to promote exploration of the structural biology of proteins and nucleic acids ( pdb101.rcsb.org ). Learning about the diverse shapes and functions of these biological macromolecules helps to understand all aspects of biomedicine and agriculture, from protein synthesis to health and disease to biological energy. Why PDB‐101? Researchers around the world are studying these molecules at the atomic level. These 3D structures are freely available at the Protein Data Bank (PDB), the central storehouse of biomolecular structures. This website builds introductory materials to help beginners get started in the basics of biomolecular structure and function (“101”, as in an entry level course) as well as resources for extended learning. Since 2011, PDB‐101 has been developed by the RCSB PDB , a global resource for the advancement of research and education in biology and medicine. Along with our Worldwide PDB collaborators, RCSB PDB curates, annotates, and makes publicly available the PDB data deposited by scientists around the globe. The RCSB PDB then provides a window to these data through a rich online resource with powerful searching, reporting, and visualization tools for researchers. This information is then streamlined for students and teachers at PDB‐101. Features include the ongoing Molecule of the Month series, educational materials such as paper models, posters, molecular animations, educational curricula and more. The section “Guide to Understanding PDB Data” is a primer for detailed PDB‐specific information: PDB Data, Visualizing Structures, Reading Coordinate Files, scientific methods for structure determination, and more. PDB‐101 also runs annual Video Challenges for high school students. Participants create short videos that tell molecular stories that connect structural biology and medicine. Previous topics have included HIV/AIDS, diabetes, and antimicrobial resistance. The 2022 challenge will focus on Molecular Mechanisms of Cancer. PDB‐101 activities are evaluated using user surveys, feedback from in‐person activities, and website analytics. In 2020, PDB‐101 hosted >850,000 users and >2.6 million page views.

Zardecki, Christine↗

Deep Point Cloud Building Envelope Segmentation (DeeP-CuBES) using Deep Learning

Building Information Modeling (BIM) plays an important role in building design and construction, particularly for achieving energy-efficient retrofits. Building envelope retrofits using panelized prefabricated system, such as those popularized by the Energiesprong program, need accurate as-built dimensions of facade features (windows, doors, etc.) to achieve the desired thermal and air tightness. Traditionally, building surveying is done manually, resulting in a time-consuming and labor-intensive process. Recently, 3D point clouds from terrestrial LiDAR have been used to automate the generation of as-built dimensions of existing buildings. However, automated BIM using LiDAR relies on solving the point cloud semantic segmentation (PCSS) problem. In this work, we propose a robust pipeline for solving the PCSS problem using deep neural networks, focusing on overcoming challenges posed by imbalanced datasets and complex architectural features. We introduce the first high-density, labeled, and validated building envelope point cloud dataset derived from multiple building scans, specifically curated to tackle challenges in facade-level segmentation. Results from the trained neural networks show that advanced attention-based architectures and incorporating radiometry (light intensity and RGB) features significantly boost segmentation accuracy for windows and doors.

Selvakumar, Balaji [ORNL]↗

A prospective feasibility study evaluating the role of multimodality imaging and liquid biopsy for response assessment in locally advanced rectal carcinoma

Purpose: Colorectal cancer is a commonly encountered disease that poses several diagnostic and therapeutic challenges. The inherent heterogeneity of tumor biology and propensity to relapse despite “curative” resection pose significant challenges with regard to response assessment. Although MR imaging already plays a key role in primary staging of patients with rectal carcinoma, its reliability in restaging after neoadjuvant therapy is debatable (Van der broek et al. in Dis Colon Rectum 60(3):274–283, 2017). Therefore, there is significant interest in developing additional methods which may improve diagnostic accuracy. This study aims to evaluate the role of multimodality imaging and liquid biopsy in therapeutic response assessment. Methods: Seventeen patients were enrolled into the study over a span of 24 months. All underwent hybrid PET-MRI and CT-perfusion (CT-P), prior to and following neoadjuvant therapy for locally advanced rectal carcinoma. Twelve of the 17 patients also underwent liquid biopsy, which consisted of blood sampling and analysis of circulating tumor cells (CTCs) and extracellular vesicles (EVs), including cell fragments and microparticles (MPs), using the Cell Search System (Menarini Silicon Biosystems). SUV, DWI, and ADC were calculated during PET-MRI, and several parameters were evaluated during CT-perfusion, including average perfusion, blood flow (BF), blood volume (BV), mean transit time (MTT), permeability-surface area product (PS), contrast extraction efficiency (E), and K-trans (K). Changes observed pre- and post-neoadjuvant therapy in each modality were compared to tumor response at histopathology using a modified Ryan tumor regression grading system. Results: Of the 17 patients included in the study, 14 were classified as non-responders, and 3 were classified as responders as determined by the modified Ryan Tumor Regression Grade (TRG) scoring system (Van der broek et al. in Dis Colon Rectum 60(3):274–283, 2017). When combined, blood markers and CT-P parameters (mean transit time (MTT), K-trans, and permeability-surface area product (PS)) produced the strongest models (p < 0.01). PET (SUV measurement) combined with CT-P-derived K-trans produced a marginally significant (p = 0.057) model for predicting response. MRI-derived ADC value did not provide a significant model for response prediction. Conclusion: A model of CT-P parameters plus liquid biopsy more accurately predicts tumor response than PET-MRI, CT-P alone, or liquid biopsy alone. These results suggest that in the evaluation of treatment response, liquid biopsy could provide additional information to functional imaging modalities such as CT-P and should therefore be explored further in a trial with larger sample size.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Agnostic capture of pathogens for the detection and diagnostics of emerging threats

The continued emergence of pathogens, whether novel, re-emerging, or engineered, poses a persistent global biosecurity and public health challenge. Recent outbreaks, including COVID-19, Lassa fever, Marburg virus, mpox, and avian influenza, underscore the urgent need for robust systems that enable rapid surveillance, early diagnosis, and timely countermeasures before widespread human transmission occurs. In this article, we focus on early detection technologies and systematically evaluate current diagnostic and sensing modalities. We highlight sequencing and spectroscopy as two complementary approaches capable of providing broad, agnostic detection and rich biological insight. Our analysis emphasizes that scientific innovation alone is insufficient: effective preparedness also requires improved data curation, integration, and sharing to build AI-ready resources that accelerate future responses. We argue for coordinated advances in both technological capabilities and supporting infrastructure to enable the rapid identification and characterization of emerging pathogens and to fully leverage modern science against evolving infectious threats.

Environmental health↗

Cric searchable image database as a public platform for conventional pap smear cytology data

Amidst the current health crisis and social distancing, telemedicine has become an important part of mainstream of healthcare, and building and deploying computational tools to support screening more efficiently is an increasing medical priority. The early identification of cervical cancer precursor lesions by Pap smear test can identify candidates for subsequent treatment. However, one of the main challenges is the accuracy of the conventional method, often subject to high rates of false negative. While machine learning has been highlighted to reduce the limitations of the test, the absence of high-quality curated datasets has prevented strategies development to improve cervical cancer screening. The Center for Recognition and Inspection of Cells (CRIC) platform enables the creation of CRIC Cervix collection, currently with 400 images (1,376 × 1,020 pixels) curated from conventional Pap smears, with manual classification of 11,534 cells. This collection has the potential to advance current efforts in training and testing machine learning algorithms for the automation of tasks as part of the cytopathological analysis in the routine work of laboratories.

59 BASIC BIOLOGICAL SCIENCES↗

Enhancing Operational Safety via Agentic Dialogue Hazard Identification Analysis

Operational safety in high-stakes domains such as industrial process control, autonomous, and safety-critical systems demand reliable hazard identification. While large language models (LLMs) have shown promise in automating safety analysis tasks, single-turn, monolithic inference is brittle: it lacks the self-correction, deliberation, and contextual refinement that safety engineers apply iteratively. In this paper, we introduce HAZDIAL, a framework that investigates whether structured agentic dialogue (multi-agent, multi-turn interactions) improves the quality of NLP-based hazard identification over single-pass baselines. We systematically compare two dialogue modalities: adversarial debate and constructive discussion, and propose an genetic algorithm-based agentic interaction optimization. We evaluate all configurations against a curated golden dataset using standard classification metrics (accuracy, precision, recall, F1) and a novel dialogue metrics. This work advances the intersection of dialogue systems, multi-agent reasoning, and AI safety, providing empirical evidence for dialogue-driven hazard analysis.

Das, Sanjay [ORNL] (ORCID:0009000542591915)↗

LUCID Thrust 1 - Dataset Identification and Biodata Catalog Creation

The LUCID DOE consortium, part of the Department of Energy’s Biological and Environmental Research (BER) program, advances Low Dose Radiation (LDR) research through multidisciplinary efforts across seven key thrusts. This document focuses on Thrust 1, which centers on the creation of curated multimodal population health datasets and supports broader efforts within the LUCID program, including AI-based hypothesis generation, experimental design, and the study of LDR-induced health risks. Specifically, it describes the identification and cataloging of Thrust 1’s curated LDR datasets and biodata, emphasizing their critical role in supporting various research thrusts within the consortium, with potential applications in healthcare and public policy. In addition, the document includes an evaluation of three Large Language Models (LLMs)—GPT-4, SOLAR-10B, and Mixtral-8x7B—based on their ability to extract features from 25 LDR studies. The results indicate that GPT-4 performed the best, while Mixtral-8x7B demonstrated limited knowledge. Overall, this work advances understanding in radiation protection, risk assessment, and medical treatments, while providing valuable resources for researchers, educators, and policymakers.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

Discovery of hybrid chemical synthesis pathways with DORAnet

Developing efficient tools for discovering novel synthesis pathways is essential to advance chemical production methods that maximize the use of resources and energy. We introduce DORAnet (Designing Optimal Reaction Avenues Network Enumeration Tool), an open-source computational framework that addresses key limitations in current computer-aided synthesis planning (CASP) tools. DORAnet integrates both chemical/chemocatalytic (i.e., non-enzymatic) and enzymatic transformations, enabling the discovery of hybrid synthesis pathways. With 390 expert-curated chemical/chemocatalytic reaction rules and 3606 enzymatic rules derived from MetaCyc, it provides extensive flexibility for synthetic chemists and biotechnologists. The framework features customizable network expansion strategies, advanced filtering, and pathway search, ranking, and visualization tools. Validated against known reaction data, DORAnet successfully identified both established and novel synthesis routes for key industrial chemicals. In a case study involving 51 high-volume targets, DORAnet frequently ranked known commercial pathways among the top three results, demonstrating its practical relevance and ranking accuracy, while also uncovering numerous alternative (hybrid) synthesis pathways that were highly ranked.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions

The development of effective vaccines is crucial for combating current and emerging pathogens. Despite significant advances in the field of vaccine development there remain numerous challenges including the lack of standardized data reporting and curation practices, making it difficult to determine correlates of protection from experimental and clinical studies. Significant gaps in data and knowledge integration can hinder vaccine development which relies on a comprehensive understanding of the interplay between pathogens and the host immune system. In this review, we explore the current landscape of vaccine development, highlighting the computational challenges, limitations, and opportunities associated with integrating diverse data types for leveraging artificial intelligence (AI) and machine learning (ML) techniques in vaccine design. We discuss the role of natural language processing, semantic integration, and causal inference in extracting valuable insights from published literature and unstructured data sources, as well as the computational modeling of immune responses. Furthermore, we highlight specific challenges associated with uncertainty quantification in vaccine development and emphasize the importance of establishing standardized data formats and ontologies to facilitate the integration and analysis of heterogeneous data. Through data harmonization and integration, the development of safe and effective vaccines can be accelerated to improve public health outcomes. Looking to the future, we highlight the need for collaborative efforts among researchers, data scientists, and public health experts to realize the full potential of AI-assisted vaccine design and streamline the vaccine development process.

60 APPLIED LIFE SCIENCES↗