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At least 127 records · Page 7

MIKA: Manager for Intelligent Knowledge Access Toolkit for Engineering Knowledge Discovery and Information Retrieval

Repositories of safety reports are often underutilized and only analyzed manually by trained experts, despite safety management systems requiring reports. These collections of documents contain a wealth of information from past projects and operations that could improve system safety and design. Advances in natural language processing techniques have improved information extraction and retrieval in consumer technology, biomedicine, and finance, for instance, but have not been applied to engineering documents on the same scale. To this end, the Manager for Intelligent Knowledge Access (MIKA) open-source toolkit has been developed for rapid knowledge discovery and information retrieval in safety engineering applications. The MIKA toolkit uses state-of-the-art natural language processing algorithms and allows a user to apply these methods to their own dataset. This paper describes the MIKA toolkit and its two primary capabilities, knowledge discovery and information retrieval, and demonstrates the toolkit via a case study on National Transportation Safety Board (NTSB) reports.

Machine Learning↗

MIKA: Manager for Intelligent Knowledge Access Toolkit for Engineering Knowledge Discovery and Information Retrieval

Repositories of safety reports are often underutilized and only analyzed manually by trained experts, despite safety management systems requiring reports. These collections of documents contain a wealth of information from past projects and operations that could improve system safety and design. Advances in natural language processing techniques have improved information extraction and retrieval in consumer technology, biomedicine, and finance, for instance, but have not been applied to engineering documents on the same scale. To this end, the Manager for Intelligent Knowledge Access (MIKA) open-source toolkit has been developed for rapid knowledge discovery and information retrieval in safety engineering applications. The MIKA toolkit uses state-of-the-art natural language processing algorithms and allows a user to apply these methods to their own dataset. This paper describes the MIKA toolkit and its two primary capabilities, knowledge discovery and information retrieval, and demonstrates the toolkit via a case study on National Transportation Safety Board (NTSB) reports.

Systems Engineering↗

Preface

Crystallization via nonclassical pathways is important during the formation of minerals in nature and has become a popular method to synthesize advanced materials on both laboratory and industrial scales. Unlike classical crystal growth pathways via monomer-by-monomer addition, crystallization via nonclassical pathways such as particle-by-particle attachment can form bigger crystals more quickly. Many materials prepared via non-classical crystallization have been applied in various fields such as energy, catalysis, environment, biomedicine, optics, electrics, and magnetics. Understanding nonclassical crystallization pathways could provide new insights on design and synthesize novel materials. This ACS Book series aims to provide updated information on fundamental nonclassical crystallization research, including theory and experiments. It’s also designed for experienced researchers to reinforce their knowledge on the scopes of development of new techniques, especially state-of-the-art in situ characterization tools, to understand mechanisms of crystallization via nonclassical pathways. Topics that would be covered in this book series include: (1) nonclassical nucleation; (2) cluster assembly; (3) particlebased crystallization; (4) crystal formation from amorphous intermedium phases; (5) biomineralization via nonclassical pathways; (6) theoretical developments to simulate nonclassical crystallization; (7) observation of the nonclassical crystallization pathways via various characterization techniques; and (8) the application of nonclassical crystallization. Volume I of the Nonclassical Crystallization Book Series will cover a broad range of topics about nucleation, assembly, observation, and applications of nonclassical crystallization.

Zhang, Xin↗

Preface

Crystallization via nonclassical pathways is important during the formation of minerals in nature and has become a popular method to synthesize advanced materials on both laboratory and industrial scales. Unlike classical crystal growth pathways via monomer-by-monomer addition, crystallization via nonclassical pathways, such as particle-by-particle attachment, can form larger crystals more quickly. Materials prepared via nonclassical crystallization have been applied in various fields such as energy, catalysis, environment, biomedicine, optics, electrics, and magnetics. Understanding nonclassical crystallization pathways could provide new insights into synthesizing novel materials. This ACS Book series aims to provide updated information on fundamental nonclassical crystallization research, including theory and experiments. It is also intended for experienced researchers, to reinforce their knowledge of the developmental scope of new techniques–especially state-of-the-art in situ characterization tools–to understand mechanisms of crystallization via nonclassical pathways. Topics covered in this book series include: (1) nonclassical nucleation; (2) cluster assembly; (3) particle-based crystallization; (4) crystal formation from amorphous intermedium phases; (5) biomineralization via nonclassical pathways; (6) theoretical developments to simulate nonclassical crystallization; (7) observation of the nonclassical crystallization pathways via various characterization techniques; and (8) the application of nonclassical crystallization. Volume II of the Nonclassical Crystallization Book Series will cover a broad range of topics including particle-based crystallization, biomolecular self-assembly, biomineralization, nanoscale imaging, and applications of nonclassical crystallization.

Zhang, Xin↗

IMPECCABLE: Integrated Modeling Pipeline for COVID Cure by Assessing Better Leads

ABSTRACT The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silico methodologies need to be improved both to select better lead compounds, so as to improve the efficiency of later stages in the drug discovery protocol, and to identify those lead compounds more quickly. No known methodological approach can deliver this combination of higher quality and speed. Here, we describe an Integrated Modeling PipEline for COVID Cure by Assessing Better LEads (IMPECCABLE) that employs multiple methodological innovations to overcome this fundamental limitation. We also describe the computational framework that we have developed to support these innovations at scale, and characterize the performance of this framework in terms of throughput, peak performance, and scientific results. We show that individual workflow components deliver 100× to 1000× improvement over traditional methods, and that the integration of methods, supported by scalable infrastructure, speeds up drug discovery by orders of magnitudes. IMPECCABLE has screened ~10 11 ligands and has been used to discover a promising drug candidate. These capabilities have been used by the US DOE National Virtual Biotechnology Laboratory and the EU Centre of Excellence in Computational Biomedicine.

97 MATHEMATICS AND COMPUTING↗

Biomineralization

The hierarchically structured biominerals with excellent functions are produced by the regulation of organisms via biomineralization. The organic matrix and molecules regulate the inorganic mineralization to fabricate the delicate structural materials with the optimized properties. Inspired by natural biomineralization process, may biomimetic tactics have been developed for applications for materials and biomedicines, such as collagen re-mineralization, tooth and bone repairs. Biomineralization always highlights the control of inorganic materials by using organisms; reversely, the mineralized materials can also regulate or improve the living organisms. By conferring materials on to organism, the rationally designed organism-material hybrids can be created artificially, which are featured by their improved or new functions such as cell protection, bioenergy production, vaccine modification and cell treatment. Such a combination follows a materials-based biological modification, which would contribute a more comprehensive view of biomineralization as well as a new window for biological inorganic chemistry.

Biomineralization↗

Electrospinner Upgrades for Nanofiber Production

Electrospinning is an inexpensive and flexible method for producing nanofibers. Nanofibers are highly adaptable with potential applications in accelerator target systems, air and water filtration, and biomedicine. This project aims to upgrade and test an existing roll-to-roll electrospinner that is more economical for industrial nanofiber production. Varying the diameter of nanofiber can change its functional properties. The current unit cannot adjust spinneret-collector separation, which determines nanofiber diameter. The electro-spinneret channel also does not have lateral adjustment capabilities for precise alignment. Lastly, the viscosities of our polymer solutions have not been quantified. Solution injection into the channel is presently inconsistent because of imperfect nozzle sizing, resulting in waste and decreased efficiency. Modeling was done with Siemens NX CAD software, and viscosity was measured using a Brookfield DVE-LV viscometer. A dual scissor lift design for channel displacement, powered by a dual-shaft DC motor coupled to precision lead screws, was approved and construction was started. Preliminary channel modifications also were initiated. Going forward, the scissor lift system will be integrated and evaluated with our electrospinner, along with further channel modifications. Viscosity measurements of polyvinyldimethylformamide (PVDF) were recorded with inconsistent results due to inadequate testing conditions. Future viscosity trials must be completed in accordance with testing requirements. The optimization and commercialization of roll-to-roll electrospinner units can make nanofiber production more feasible for many new industries and consumer products.

Black, Niko↗

Investigating the impact of donor atoms of single-source precursors on f-element nanomaterial formation and mechanisms of initial bond cleavage

Nanotechnology is advancing exponentially, with novel discoveries and applications emerging every day across various fields, including environmental remediation, biomedicine, electronics, and textiles. Nanoparticles (NPs) are defined as particles with sizes ranging from 1 to 100 nm, and their properties are influenced by factors such as composition, phase, size, and shape. Single-source precursors (SSPs) offer a promising method for controlling the synthesis of phase-pure nanoparticles with the desired stoichiometry and have been successfully applied to prepare transition metal and lanthanide NPs. SSPs are molecular compounds that contain all the necessary components to form the final nanomaterial and decompose under mild conditions, such as heating, to create the solid-state product. The objective of this work was to garner a mechanistic understanding of cerium oxide NP formation from cerium SSPs. To achieve these goals, first, ligands with oxygen and sulfur donor atoms were synthesized; among the array of ligands synthesized, some were novel. These ligands were then chelated to cerium ions to synthesize cerium precursors. Initially, known cerium alkoxide precursors were synthesized. Subsequently, these basic cerium complexes were further manipulated through acid-base protonolysis reactions to install a diverse set of ligands. Within this body of work, 10 novel cerium precursors were successfully synthesized. The ligands and the SSPs were analyzed using nuclear magnetic resonance (NMR) and thermogravimetric analysis coupled with differential scanning calorimetry (TGA-DSC). These SSPs were then introduced to a collision-induced dissociation mass spectrometer (CID-MS), and their dissociation pathways were studied.

37 - INORGANIC, ORGANIC, PHYSICAL AND ANALYTICAL C↗

RCSB protein data Bank: Next‐generation advanced search for exploration of experimental structures and computed structure models

Abstract The Protein Data Bank (PDB), established in 1971, is the primary global, open‐access archive for experimentally determined 3D macromolecular structures (proteins, RNA, DNA). The research‐focused RCSB.org web‐portal provides access to these data alongside more than one million machine‐learning‐predicted structure models, greatly expanding the available structural landscape. Rapid growth of both experimental and computational structures has increased the need for powerful yet accessible search tools that serve a broad and diverse scientific community. Herein, we describe a redesigned RCSB Protein Data Bank RCSB.org Advanced Search capability that supports intuitive discovery of 3D structures through a unified interface. This interface integrates annotation‐, sequence‐, and 3D structure‐based searches, embeds an interactive 3D viewer, and incorporates curated biological knowledge, such as catalytic site definitions from Mechanism and Catalytic Site Atlas and ligand‐guided structural motifs, for constructing geometry‐driven queries. A new Chemical Search tool allows definition of chemical queries via an integrated drawing tool or standard identifiers, seamlessly combining them with annotation filters. By allowing query definition directly within spatial and chemical contexts, these search interfaces reduce the need for detailed knowledge of residue numbering, chain identifiers, or external cheminformatics software. This capability enables efficient exploration of structures, chemical diversity, and structure–function relationships across all life domains. The redesigned interfaces can be accessed directly at rcsb.org/search/advanced for Advanced Search and rcsb.org/search/chemical for Chemical Search.

Rose, Yana [Research Collaboratory for Structural ↗

MIBiG 4.0: advancing biosynthetic gene cluster curation through global collaboration

Specialized or secondary metabolites are small molecules of biological origin, often showing potent biological activities with applications in agriculture, engineering and medicine. Usually, the biosynthesis of these natural products is governed by sets of co-regulated and physically clustered genes known as biosynthetic gene clusters (BGCs). To share information about BGCs in a standardized and machine-readable way, the Minimum Information about a Biosynthetic Gene cluster (MIBiG) data standard and repository was initiated in 2015. Since its conception, MIBiG has been regularly updated to expand data coverage and remain up to date with innovations in natural product research. Here, we describe MIBiG version 4.0, an extensive update to the data repository and the underlying data standard. In a massive community annotation effort, 267 contributors performed 8304 edits, creating 557 new entries and modifying 590 existing entries, resulting in a new total of 3059 curated entries in MIBiG. Particular attention was paid to ensuring high data quality, with automated data validation using a newly developed custom submission portal prototype, paired with a novel peer-reviewing model. MIBiG 4.0 also takes steps towards a rolling release model and a broader involvement of the scientific community. MIBiG 4.0 is accessible online at https://mibig.secondarymetabolites.org/.

59 BASIC BIOLOGICAL SCIENCES↗

Comparison and multi-model inference of excess risks models for radiation-related solid cancer

In assessments of detrimental health risks from exposures to ionising radiation, many forms of risk to dose–response models are available in the literature. The usual practice is to base risk assessment on one specific model and ignore model uncertainty. The analysis illustrated here considers model uncertainty for the outcome all solid cancer incidence, when modelled as a function of colon organ dose, using the most recent publicly available data from the Life Span Study on atomic bomb survivors of Japan. Seven recent publications reporting all solid cancer risk models currently deemed plausible by the scientific community have been included in a model averaging procedure so that the main conclusions do not depend on just one type of model. The models have been estimated with different baselines and presented for males and females at various attained ages and ages at exposure, to obtain specially computed model-averaged Excess Relative Risks (ERR) and Excess Absolute Risks (EAR). Monte Carlo simulated estimation of uncertainty on excess risks was accounted for by applying realisations including correlations in the risk model parameters. Three models were found to weight the model-averaged risks most strongly depending on the baseline and information criteria used for the weighting. Fitting all excess risk models with the same baseline, one model dominates for both information criteria considered in this study. Based on the analysis presented here, it is generally recommended to take model uncertainty into account in future risk analyses.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗