Knowledge Graph Question Answering for Materials Science (KGQA4MAT): Developing Natural Language Interface for Metal-Organic Frameworks Knowledge Graph (MOF-KG)

We present a comprehensive benchmark dataset for Knowledge Graph Question Answering in Materials Science (KGQA4MAT), with a focus on metal-organic frameworks (MOFs). A knowledge graph for metal-organic frameworks (MOF-KG) has been constructed by integrating structured databases and knowledge extracted from the literature. To enhance MOF-KG accessibility for domain experts, we aim to develop a natural language interface for querying the knowledge graph. We have developed a benchmark comprised of 161 complex questions involving comparison, aggregation, and complicated graph structures. Each question is rephrased in three additional variations, resulting in 644 questions and 161 KG queries. To evaluate the benchmark, we have developed a systematic approach for utilizing ChatGPT to translate natural language questions into formal KG queries. We also apply the approach to the well-known QALD-9 dataset, demonstrating ChatGPT's potential in addressing KGQA issues for different platforms and query languages. The benchmark and the proposed approach aim to stimulate further research and development of user-friendly and efficient interfaces for querying domain-specific materials science knowledge graphs, thereby accelerating the discovery of novel materials.Comment: In 17th International Conference on Metadata and Semantics Research, October 202

Metadata for Scientific Experiment Reporting: A Case Study in Metal-Organic Frameworks

Research methods and procedures are core aspects of the research process. Metadata focused on these components is critical to supporting the FAIR principles, particularly reproducibility. The research reported on in this paper presents a methodological framework for metadata documentation supporting the reproducibility of research producing Metal Organic Frameworks (MOFs). The MOF case study involved natural language processing to extract key synthesis experiment information from a corpus of research literature. Following, a classification activity was performed by domain experts to identify entity-relation pairs. Results include: 1) a research framework for metadata design, 2) a metadata schema that includes nine entities and two relationships for reporting MOF synthesis experiments, and 3) a growing database of MOF synthesis reports structured by our metadata scheme. The metadata schema is intended to support discovery and reproducibility of metal-organic framework research and the FAIR principles. The paper provides background information, identifies the research goals and objectives, research design, results, a discussion, and the conclusion.Comment: Accepted by the 17th International Conference on Metadata and Semantics Researc

Building Open Knowledge Graph for Metal-Organic Frameworks (MOF-KG): Challenges and Case Studies

Metal-Organic Frameworks (MOFs) are a class of modular, porous crystalline materials that have great potential to revolutionize applications such as gas storage, molecular separations, chemical sensing, catalysis, and drug delivery. The Cambridge Structural Database (CSD) reports 10,636 synthesized MOF crystals which in addition contains ca. 114,373 MOF-like structures. The sheer number of synthesized (plus potentially synthesizable) MOF structures requires researchers pursue computational techniques to screen and isolate MOF candidates. In this demo paper, we describe our effort on leveraging knowledge graph methods to facilitate MOF prediction, discovery, and synthesis. We present challenges and case studies about (1) construction of a MOF knowledge graph (MOF-KG) from structured and unstructured sources and (2) leveraging the MOF-KG for discovery of new or missing knowledge.Comment: Accepted by the International Workshop on Knowledge Graphs and Open Knowledge Network (OKN'22) Co-located with the 28th ACM SIGKDD Conferenc

Prior mucosal exposure to heterologous cells alters the pathogenesis of cell-associated mucosal feline immunodeficiency virus challenge

<p>Abstract</p> <p>Background</p> <p>Several lines of research suggest that exposure to cellular material can alter the susceptibility to infection by HIV-1. Because sexual contact often includes exposure to cellular material, we hypothesized that repeated mucosal exposure to heterologous cells would induce an immune response that would alter the susceptibility to mucosal infection. Using the feline immunodeficiency virus (FIV) model of HIV-1 mucosal transmission, the cervicovaginal mucosa was exposed once weekly for 12 weeks to 5,000 heterologous cells or media (control) and then cats were vaginally challenged with cell-associated or cell-free FIV.</p> <p>Results</p> <p>Exposure to heterologous cells decreased the percentage of lymphocytes in the mucosal and systemic lymph nodes (LN) expressing L-selectin as well as the percentage of CD4+ CD25+ T cells. These shifts were associated with enhanced ex-vivo proliferative responses to heterologous cells. Following mucosal challenge with cell-associated, but not cell-free, FIV, proviral burden was reduced by 64% in cats previously exposed to heterologous cells as compared to media exposed controls.</p> <p>Conclusions</p> <p>The pathogenesis and/or the threshold for mucosal infection by infected cells (but not cell-free virus) can be modulated by mucosal exposure to uninfected heterologous cells.</p

Minimal information for studies of extracellular vesicles (MISEV2023): From basic to advanced approaches

Extracellular vesicles (EVs), through their complex cargo, can reflect the state of their cell of origin and change the functions and phenotypes of other cells. These features indicate strong biomarker and therapeutic potential and have generated broad interest, as evidenced by the steady year-on-year increase in the numbers of scientific publications about EVs. Important advances have been made in EV metrology and in understanding and applying EV biology. However, hurdles remain to realising the potential of EVs in domains ranging from basic biology to clinical applications due to challenges in EV nomenclature, separation from non-vesicular extracellular particles, characterisation and functional studies. To address the challenges and opportunities in this rapidly evolving field, the International Society for Extracellular Vesicles (ISEV) updates its 'Minimal Information for Studies of Extracellular Vesicles', which was first published in 2014 and then in 2018 as MISEV2014 and MISEV2018, respectively. The goal of the current document, MISEV2023, is to provide researchers with an updated snapshot of available approaches and their advantages and limitations for production, separation and characterisation of EVs from multiple sources, including cell culture, body fluids and solid tissues. In addition to presenting the latest state of the art in basic principles of EV research, this document also covers advanced techniques and approaches that are currently expanding the boundaries of the field. MISEV2023 also includes new sections on EV release and uptake and a brief discussion of in vivo approaches to study EVs. Compiling feedback from ISEV expert task forces and more than 1000 researchers, this document conveys the current state of EV research to facilitate robust scientific discoveries and move the field forward even more rapidly

The Overlooked Photochemistry of Iodine in Aqueous Suspensions of Fullerene Derivatives

Fullerene’s low water solubility was a serious challenge to researchers aiming to harness their excellent photochemical properties for aqueous applications. Cationic functionalization of the fullerene cage provided the most effective approach to increase water solubility, but common synthesis practices inadvertently complicated the photochemistry of these systems by introducing iodide as a counterion. This problem was overlooked until recent work noted a potentiation effect which occurred when photosensitizers were used to inactivate microorganisms with added potassium iodide. In this work, several photochemical pathways were explored to determine the extent and underlying mechanisms of iodide’s interference in the photosensitization of singlet oxygen by cationic fulleropyrrolidinium ions and rose bengal. Triplet excited state sensitizer lifetimes were measured via laser flash photolysis to probe the role of I– in triplet sensitizer quenching. Singlet oxygen production rates were compared across sensitizers in the presence or absence of I–, SO42–, and other anions. 3,5-Dimethyl-1H-pyrazole was employed as a chemical probe for iodine radical species, such as I·, but none were observed in the photochemical systems. Molecular iodine and triiodide, however, were found in significant quantities when photosensitizers were irradiated in the presence of I– and O2. The formation of I2 in these photochemical systems calls into question the interpretations of prior studies that used I– as a counterion for photosensitizer materials. As an example, MS2 bacteriophages were inactivated here by cationic fullerenes with and without I– present, showing that I– moderately accelerated the MS2 deactivation, likely by producing I2. Production of I2 did not appear to be directly correlated with estimates of 1O2 concentration, suggesting that the relevant photochemical pathways are more complex than direct reactions between 1O2 and I– in the bulk solution. On the basis of the results here, iodine photochemistry may be underappreciated and misunderstood in other environmental systems.ISSN:1936-0851ISSN:1936-086

Research evolution of metal organic frameworks: A scientometric approach with human-in-the-loop

This paper reports on a scientometric analysis bolstered by human-in-the-loop, domain experts, to examine the field of metal-organic frameworks (MOFs) research. Scientometric analyses reveal the intellectual landscape of a field. The study engaged MOF scientists in the design and review of our research workflow. MOF materials are an essential component in next-generation renewable energy storage and biomedical technologies. The research approach demonstrates how engaging experts, via human-in-the-loop processes, can help develop a comprehensive view of a field’s research trends, influential works, and specialized topics

A Comprehensive Review of Artificial Intelligence Used to Combat COVID-19

Color poster with text, images, charts, and graphs.Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. Over 422 million people have been infected globally and 5.8 million have died because of the disease. Artificial Intelligence (AI) solutions have played a major part in this pandemic for diagnosing and treating patients with COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. Research goals include analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease; Exploring prognostic models that were developed to predict health outcomes; Focusing on contact tracing to analyze geographical and managerial efforts taken to combat this pandemic. This comprehensive review of the different AI methods and modeling efforts will shed light on the role of AI and what path it intends to take in the fight against COVID-19.University of Wisconsin--Eau Claire Office of Research and Sponsored Program