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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Machine learning substitutional defect formation energies in ABO 3 perovskites

Perovskite oxides are a promising material platform for use in a wide range of technological applications including electronics, sensors, fuel cells, and catalysis. This is owing to the extraordinary tunability of their physical and chemical properties via defect engineering. The feasibility and the stability of a defect, such as a substitutional dopant, in the host lattice is usually obtained via experiments and/or through detailed quantum mechanical calculations. Both of these conventional routes are expensive and time consuming. An alternative is a data-driven machine learning (ML)-based approach. In this work, we have applied ML techniques to identify the factors that influence defect formation energy, which is an important measure of the stability of the defects, in perovskite oxides. Using 13 elemental properties as features and random forest regression, we demonstrate a systematic approach to down-selecting from the larger set of features to those that are important, establishing a framework for accurate predictions of the defect formation energy. We quantitatively show that the most important factors that control the dopant stability are the dopant ionic size, heat of formation, effective tolerance factor, and oxidation state. Our work reveals previously unknown correlations, chemical trends, and the interplay between stability and underlying chemistries. Hence, these results showcase the efficacy of ML tools in identifying and quantifying different feature-dependencies and provide a promising route toward dopant selection in the perovskites. Herein, we have developed a framework that itself is general and can be potentially applied to other material classes.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Development of a Host-Based Intrusion Detection and Control Device for Industrial Field Control Devices

Industrial control systems contain a multitude of devices deployed into the field to observe and control a physical process. Currently, most of these devices lack common host-based cybersecurity features, which can lead them to be seen as vulnerable targets. The Cymbiote device was developed to address this problem by examining multiple streams of data to determine what might indicate a potential threat, and to evaluate and execute appropriate response actions. This paper discusses the evolution of the Cymbiote device in response to evaluation and testing done on real-world hardware. Experimental results are discussed, as are the new developments that have been made to the device in response to the findings. In addition, we describe future research directions for this approach along with the experimental setup changes in response to the requirements for statistical rigor.

Rice, Theora R.↗
DOE OSTI2023

Electrochemical Formation of Li-M-(M')-Si Phases Using Multivalent Electrolyte Salt Additives

Lithium-rich silicides (Li 15 Si 4 ), formed during the electrochemical lithiation of silicon, show high reactivity with electrolyte components that contribute to capacity decay, formal lithium loss, and low coulombic efficiency. Recently, the reactivity of lithium silicides was found to be suppressed by substituting a multivalent cation (i.e. Mg, Ca) for lithium that results in the room temperature formation of a ternary Li-M-Si phase. In this study, we explored a range of multivalent electrolyte salt additives (M = Ni, Cu, La, Ce, Sr, Ba, and Ca-Mg mixed salt) in a lithium-ion cell configuration and identified a room temperature electrochemical route to the formation of new ternary and quaternary lithium silicides. Using this method, both nickel and copper salts were found to plate onto the silicon electrode surface upon lithiation. Further, based on refined synchrotron XRD data, multivalent cations with an ionic radius similar to Na (~1.03 Å) or smaller can be inserted electrochemically into a formally cation-deficient Li 15 Si 4 host lattice to form new ternary (or quartenary) phases. The electrochemical synthesis of a new quaternary Li-M-M’-Si phase represents a facile route to preparing and scaling materials isostructural to the Heusler phase and electron-precise Li 14 MgSi 4 phase that results in enhanced cycling and calendar life performance.

25 ENERGY STORAGE↗
DOE OSTI2020

Structures of diverse poxin cGAMP nucleases reveal a widespread role for cGAS-STING evasion in host–pathogen conflict

DNA viruses in the family Poxviridae encode poxin enzymes that degrade the immune second messenger 2'3'-cGAMP to inhibit cGAS-STING immunity in mammalian cells. The closest homologs of poxin exist in the genomes of insect viruses suggesting a key mechanism of cGAS-STING evasion may have evolved outside of mammalian biology. Here we use a biochemical and structural approach to discover a broad family of 369 poxins encoded in diverse viral and animal genomes and define a prominent role for 2'3'-cGAMP cleavage in metazoan host-pathogen conflict. Structures of insect poxins reveal unexpected homology to flavivirus proteases and enable identification of functional self-cleaving poxins in RNA-virus polyproteins. Our data suggest widespread 2'3'-cGAMP signaling in insect antiviral immunity and explain how a family of cGAS-STING evasion enzymes evolved from viral proteases through gain of secondary nuclease activity. Poxin acquisition by poxviruses demonstrates the importance of environmental connections in shaping evolution of mammalian pathogens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Critical Minerals and Rare Earth Elements in Powder River Basin Coal and Associated Sediments, Wyoming, USA

The demand for Rare Earth Elements (REE) and other critical minerals (CM) required for consumer products, defense-related applications, and low-carbon energy technology is increasing rapidly. Unconventional sources of REE/CM, such as coal and associated sediments, could prove very important in building resilient and ethical energy supply chains. Utilization of existing infrastructure and the highly trained energy workforce in traditionally coal-producing regions in emerging REE/CM industries could provide an economic boost to coal communities. The Powder River Basin (PRB) of Wyoming and Montana, USA, is a prime candidate to investigate the feasibility of extracting REE/CM from coal and associated sediments. The PRB hosts thick (>50 ft) coal seams that are mined at the surface and more than 40% of the coal produced in the US comes from the PRB. Geochemical data from multiple locations in PRB coal systems show REE enrichments at the top and bottom margins and at internal partings in coal seams. This trend is exhibited in two cores drilled at Peabody’s North Antelope Rochelle mine in the east central PRB, the largest coal mine in the world. The two cores include the Wyodak Anderson coal zone as well as the over- and underlying shale units. The Wyodak Anderson coal zone is part of the Paleocene Tongue River Member of the Fort Union Formation in which most of the coal resources in the PRB reside. To understand REE/CM enrichment, a total of 188 samples were analyzed for their major and trace element chemistry from both cores, sampled at one foot or smaller intervals. The two cores show distinct REE enrichments in the uppermost and lowermost 3 to 12 feet of the Wyodak Anderson coal zone, as well as enrichments in bounding carbonaceous shale units. Total REE+Y (REY) concentrations as high as 2510 ppm (all concentrations reported on an ash basis), or 15 times average upper continental crust values (Taylor and McLennan, 1995), were identified in the coal. Partings within the coal zone also show relative REE enrichment, with concentrations up to 485 ppm REY. The remaining interior portions of the coal zone contain lower concentrations of REE, resulting in an average REY for all samples from both cores of 288 ppm. The proportion of high-value critical REE (Nd, Eu, Tb, Dy, Er, Y) compared to REY averages 36%, which is higher than the critical REE proportion in average upper continental crust of 33%. This finding highlights the relative enrichment of middle REE compared to light REE, in contrast to many conventional REE deposits. Other critical trace elements that show enrichments above average upper continental crust include Ga, Nb, and V. Within this sample suite, Ga concentrations range from 2.8 to 213 ppm, with an average of 29 ppm; Nb from 2.9 to 166 ppm with an average of 26 ppm; and V from 27 to 1695 ppm with an average of 188 ppm. None of these trace elements exhibit the same distinct pattern of enrichment as REE in the upper and lower bounding layers and internal partings of the coal zone. Major element chemistry indicates CaO concentrations averaging ~25% in the coal samples from both cores, which has important implications for the extractability of REE from coal. Calcium-rich PRB coal has been shown to be more amenable to REE extraction than lower calcium coals by some methods (Stuckman et al., 2019; Taggart et al., 2016), highlighting the importance of REE extractability in addition to REE concentration in assessing the value of potential feedstocks. The original coal resource in the PRB is estimated at 1.16 trillion short tons (Luppens et al., 2015). Thus, the PRB represents an important potential unconventional source for REE and other CM. Ongoing research to fully characterize the REE/CM resource, identify enrichment mechanisms, and further develop and scale extraction technologies is necessary to understand the full potential of this resource.

Phillips, Erin↗
DOE OSTI2023

Genetic determinants of switchgrass-root-associated microbiota in field sites spanning its natural range

A fundamental goal in plant microbiome research is to determine the relative impacts of host and environmental effects on root microbiota composition, particularly how host genotype impacts bacterial community composition. Most studies characterizing the effect of plant genotype on root microbiota undersample host genetic diversity and grow plants outside of their native ranges, making the associations between host and microbes difficult to interpret. Here, we characterized the root microbiota of a large diversity panel of switchgrass, a North American native C4 bioenergy crop, in three field locations spanning its native range. Our data, composed of 1,961 samples, suggest that field location is the primary determinant of microbiome composition; however, substantial heritable variation is widespread across bacterial taxa, especially those in the Sphingomonadaceae family. Despite diverse compositions, relatively few highly prevalent taxa make up the majority of the switchgrass root microbiota, a large fraction of which is shared across sites. Local genotypes preferentially recruit/filter for local microbes, supporting the idea of affinity between local plants and their microbiota. Using genome-wide association, we identified loci impacting the abundance of >400 microbial strains and found an enrichment of genes involved in immune responses, signaling pathways, and secondary metabolism. We found loci associated with over half of the core microbiota (i.e., microbes in >80% of samples), regardless of field location. Finally, we show a genetic relationship between a basal plant immunity pathway and relative abundances of root microbiota. Furthermore, this study brings us closer to harnessing and manipulating beneficial microbial associations via host genetics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

FAST: Continuing the Focus on Data Quality

This presentation provides an overview of fiscal year 2019 federal motor vehicle fleet data, collected at the individual vehicle level during the fall of 2019, how the the collecting project has reviewed that information for potential quality issues, how the quality of this year's data submission compare to the prior year, and recommendations for federal agencies in their efforts to continue to improve the quality of their submissions. This presentation will be given at the January 2020 FedFleet training event, hosted by the US General Services Administration in Washington, DC. The information is collected through the Federal Automotive Statistical Tool (FAST) project. FAST is a Web-based information system managed by the US Department of Energy, the US General Services Administration, and the Energy Information Administration. FAST is used to collect information about the fleet of motor vehicles used and managed by the Federal government. FAST is developed and maintained by DOE's Idaho National Laboratory (INL).

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

eCounter: Inline Per-IP Network Monitoring at Millisecond Resolution via eBPF

Scientific data acquisition (SciDAQ) systems are shifting from archive-based workflows to streaming paradigms, where real-time, fine-grained network monitoring becomes essential. While P4-enabled devices offer per-packet in-band observability, they require specialized switches and routers. Host-side tools like Prometheus exporters lack sufficient temporal granularity. To bridge this gap, we present eCounter, a lightweight, hardware-agnostic, inline telemetry agent built on extended Berkeley Packet Filter (eBPF). eCounter captures per-interface ingress and egress traffic, categorized by IP address and protocol, at millisecond to sub-millisecond resolution. In a 100 Gbps environment, it continuously exports up to 3,257 time-series bins per second with only 4% CPU utilization at a 35¿KiB/s data rate. We evaluate eCounter across diverse NIC MTU settings, hook types, CPU architectures and operating systems, and observed negligible impact on concurrent high-throughput streaming applications. Complexity analysis confirms that it can be readily scaled to distributed SciDAQ deployments.

Mei, Xinxin [Computational Sciences and Technology↗
DOE OSTI2024

Toward a Machine Learning Approach to Interpreting X-ray Spectra of Trace Impurities by Converting XANES to EXAFS

The fact that the photoabsorption spectrum of a material contains information about the atomic structure, commonly understood in terms of multiple scattering theory, is the basis of the popular extended X-ray absorption spectroscopy (EXAFS) technique. How much of the same structural information is present in other complementary spectroscopic signals is not obvious. Here we use a machine learning approach to demonstrate that within theoretical models that accurately predict the EXAFS signal, the extended near-edge region does indeed contain the EXAFS-accessible structural information. We do this by exhibiting deep operator neural networks (DeepONets) that have learned the relationship between the extended and near edge portions of the X-ray absorption spectrum to predict the former from the latter. We find that we can accurately predict the EXAFS spectrum between 6 and 14 Å –1 from the first 6 Å –1 (≈100 eV) of the absorption spectrum of Cu 2 + substitutional defects in the Fe 3+ mineral hematite (α-Fe 2 O 3 ). This surprising finding implies that theoretical analyses of X-ray absorption spectra could be implemented that extract the same conclusions as high-quality EXAFS studies from spectra collected over a much smaller range of photon energies. This relaxes a host of experimental limitations related to the X-ray source and measurement sample, including collection time, minimum dopant concentration, source brilliance, and energy range. We describe the theoretical data sets and DeepONet construction and show that the resulting DeepONets produce EXAFS that recovers linear combination fits to experimental data with accuracy approaching the original ab initio calculations. We discuss the implications of our findings for minor constituent characterization and for understanding the information content of spectroscopic data more broadly, including how this approach might be applied to measured experimental spectra. In conclusion, to encourage similar efforts, the simulated X-ray spectra, machine learning, and fitting code are publicly available.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences

Abstract The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), the US data center for the global PDB archive and a founding member of the Worldwide Protein Data Bank partnership, serves tens of thousands of data depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without restrictions to millions of RCSB.org users around the world, including >660 000 educators, students and members of the curious public using PDB101.RCSB.org. PDB data depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy, 3D electron microscopy and micro-electron diffraction. PDB data consumers accessing our web portals include researchers, educators and students studying fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. During the past 2 years, the research-focused RCSB PDB web portal (RCSB.org) has undergone a complete redesign, enabling improved searching with full Boolean operator logic and more facile access to PDB data integrated with >40 external biodata resources. New features and resources are described in detail using examples that showcase recently released structures of SARS-CoV-2 proteins and host cell proteins relevant to understanding and addressing the COVID-19 global pandemic.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Prevalence and diversity of TAL effector-like proteins in fungal endosymbiotic Mycetohabitans spp.

EndofungalMycetohabitans(formerlyBurkholderia) spp. rely on a type III secretion system to deliver mostly unidentified effector proteins when colonizing their host fungus,Rhizopus microsporus. The one known secreted effector family fromMycetohabitansconsists of homologues of transcription activator-like (TAL) effectors, which are used by plant pathogenicXanthomonasandRalstoniaspp. to activate host genes that promote disease. These ‘BurkholderiaTAL-like (Btl)’ proteins bind corresponding specific DNA sequences in a predictable manner, but their genomic target(s) and impact on transcription in the fungus are unknown. Recent phenotyping of Btl mutants of twoMycetohabitansstrains revealed that the single Btl in oneMycetohabitans endofungorumstrain enhances fungal membrane stress tolerance, while others in aMycetohabitans rhizoxinicastrain promote bacterial colonization of the fungus. The phenotypic diversity underscores the need to assess the sequence diversity and, given that sequence diversity translates to DNA targeting specificity, the functional diversity of Btl proteins. Using a dual approach to maximize capture of Btl protein sequences for our analysis, we sequenced and assembled nineMycetohabitansspp. genomes using long-read PacBio technology and also mined available short-read Illumina fungal–bacterial metagenomes. We show thatbtlgenes are present across diverseMycetohabitansstrains from Mucoromycota fungal hosts yet vary in sequences and predicted DNA binding specificity. Phylogenetic analysis revealed distinct clades of Btl proteins and suggested thatMycetohabitansmight contain more species than previously recognized. Within our data set, Btl proteins were more conserved acrossM. rhizoxinicastrains than acrossM. endofungorum, but there was also evidence of greater overall strain diversity within the latter clade. Overall, the results suggest that Btl proteins contribute to bacterial–fungal symbioses in myriad ways.

Genetics & Heredity↗
DOE OSTI2022

A Scalable Pipeline for Gigapixel Whole Slide Imaging Analysis on Leadership Class HPC Systems

Whole Slide Imaging (WSI) captures microscopic details of a patient's histopathological features at multiple res-olutions organized across different levels. Images produced by WSI are gigapixel-sized, and saving a single image in memory requires a few gigabytes which is scarce since a complicated model occupies tens of gigabytes. Performing a simple met-ric operation on these large images is also expensive. High-performance computing (HPC) can help us quickly analyze such large images using distributed training of complex deep learning models. One popular approach in analyzing these images is to divide a WSI image into smaller tiles (patches) and then train a simpler model with these reduced-sized but large numbers of patches. However, we need to solve three pre-processing challenges efficiently for pursuing this patch-based approach. 1) Creating small patches from a high-resolution image can result in a high number (hundreds of thousands per image) of patches. Storing and processing these images can be challenging due to a large number of I/O and arithmetic operations. To reduce I/Oand memory accesses, an optimal balance between the size and number of patches must exist to reduce I/O and memory accesses. 2) WSI images may have tiny annotated regions for cancer tissue and a significant portion with normal and fatty tissues; correct patch sampling should avoid dataset imbalance. 3) storing and retrieving many patches to and from disk storage might incur I/O latency while training a deep learning model. An efficient distributed data loader should reduce I/O latency during the training and inference steps. This paper explores these three challenges and provides empirical and algorithmic solutions deployed on the Summit supercomputer hosted at the Oak Ridge Leadership Computing Facility.

Dash, Sajal↗
DOE OSTI2026

Database of virus genomes from ultra-deep sequencing of wastewater

Researchers at University of Missouri have conducted ultra-deep RNA sequencing of viral concentrates from wastewater (1 billion Illumina reads per sample). The resulting dataset spans 321 samples collected weekly from 11 cities between 2023-2025. As part of a tri-lab collaboration, scientists at LLNL and LANL cleaned, assembled, and annotated this metagenomic data, identifying nearly 200,000 viral genomes. Careful data curation resulted in a database containing 21,015 high-quality, near-complete viral genomes from wastewater. This database contains viruses predicted to infect a range of hosts including bacteria (most common viruses), plants (most abundant viruses), and vertebrates (rarest viruses). There are also numerous novel viruses that could not be well identified and whose host(s) are unknown. Just 7% of all genomes in the wastewater virus database had genus-level matches in the public NCBI database, and 17% matched to a recently created metagenomic virus database at that level (metaVR). The database will provide baseline information about viruses in wastewater that may be used to additional identify novel viruses during ongoing monitoring

Allen, Jonathan [Lawrence Livermore National Labor↗
DOE OSTI2023

Understanding Mobility Behavior Using OpenPATH

In this presentation, we present the motivation behind the creation of NRELOpenPATH, what data the tool can collect, how the app works, and how interested parties can set up a deployment for their own project. OpenPATH is unique in it's ability to engage users in longitudinal travel diary collection with a smartphone app. The app allows projects to examine the modes of users travel, as well as the purpose of that travel. Many deployments have examined the way users travel by e-bike, and the app supports data collection across all modes to gain a holistic view of mobility behavior. The app is configurable for each deployment, and we will describe the way in which NREL hosts project deployments of our open-source project. As this presentation is a part of a panel, time for questions will be included.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2026

Surrogate model for third-integer resonance extraction at the Fermilab Delivery Ring

We present an ongoing work in which a surrogate model is being developed to reproduce the response dynamics of the third-integer resonant extraction process in the Delivery Ring (DR) at Fermilab. This is in pursuit of smoothly extracting circulating beam to the Mu2e Experiment’s production target, whereby the goal is to extract a uniform slice of the circulating 1e12 protons in the DR over 25,000 turns (43 ms). The DR contains 3 harmonic sextupoles that excite a third-integer resonance and three fast, tune-ramping quadrupole magnets that drive the horizontal tune towards the 29/3 resonance. In our initial work, the surrogate model trains on a semi-analytical simulation provided in the same format as live data. Using Reinforcement Learning (and other potential ML methods), the trained surrogate acts as the “environment” in which a simple ML control agent could learn to dynamically adjust the quadrupole ramp at 430 break points within the 43 microsecond spill window. The controller will be hosted on a dedicated Arria 10 FPGA. In this work, we report the accuracy and fidelity of the surrogate model in comparison to the response dynamics of the physics simulator.

Narayanan, Aakaash [Fermilab]↗
DOE OSTI2020

Enhanced Sulfur Dioxide Adsorption in UiO-66 Through Crystal Engineering and Chalcogen Bonding

Adsorption of corrosive SO 2 gas occurs in metal–organic frameworks (MOFs) including UiO-66. Improvements in SO 2 capacity is obtained through the incorporation of residual modulators in the UiO-66 framework by introducing new binding sites in the material, through residual modulators. Four residual modulators were investigated (acetic acid, trifluoroacetic acid, 3-DMAP acid, cyanoacetic acid), and the UiO-66 framework modulated with cyanoacetic acid exhibited nearly twice the SO 2 uptake for the 18:1 modulator/linker synthesis ratio compared with other modulated UiO-66 structures. Density functional theory investigations confirmed that targeted host–guest interactions were maintained after the modulator was incorporated into the framework. The strongest binding energy was between SO 2 and cyanoacetic acid, consistent with dynamic SO 2 adsorption data, and identified contributions from both the SO 2 reacting with the residual modulator and the coordinating linkers. The successful increase in dynamic SO 2 capacity illustrates how often-overlooked non-covalent interactions can be used in targeted adsorption applications. Further investigation into weak electrostatic interactions for adsorption properties is also needed to advance the potential selectivity and capacity in the adsorption sphere.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Observation of multiple flat bands and Van Hove singularities in the distorted kagome metal NdTi 3 Bi 4

Kagome materials have attracted enormous research interest recently owing to their diverse topological phases and manifestation of electronic correlation. Here, in this study, we present the electronic structure of a distorted ferromagnetic kagome metal, NdTi 3 ⁢Bi 4 , exhibiting a transition temperature of 9 K. Our investigation employs a combination of angle-resolved photoemission spectroscopy (ARPES) measurements and density functional theory (DFT) calculations. We discover the presence of two “flat” bands which are found to originate from the kagome structure formed by Ti atoms with a major contribution from Ti 𝑑 𝑥⁢𝑦 and Ti 𝑑 𝑥 2 −𝑦 2 orbitals. We also observed multiple Van Hove singularities (VHSs) in its electronic structure, with one VHS lying near the Fermi level. The ARPES data reveal the existence of Dirac cone at the $\overline{𝐾}$ point, a finding which is corroborated by our DFT calculations. These findings present a detailed electronic structure capable of hosting correlation-driven phenomenon in this novel ferromagnetic kagome metal.

Fermi surface↗
DOE OSTI2022

Short Chain Fatty Acids and Bacterial Taxa Associated with Reduced Salmonella enterica serovar I 4,[5],12:i:- Shedding in Swine Fed a Diet Supplemented with Resistant Potato Starch

Salmonella enterica serovar I 4,[5],12:i:- is a foodborne pathogen of concern because many isolates are multidrug-resistant (resistant to ≥3 antimicrobial classes) and metal tolerant. In this study, three in-feed additives were individually tested for their ability to reduce Salmonella I 4,[5],12:i:- shedding in swine: resistant potato starch (RPS), high amylose corn starch, and a fatty acid blend, compared with a standard control diet over 21 days. Only RPS-fed pigs exhibited a reduction in Salmonella fecal shedding, different bacterial community compositions, and different cecal short chain fatty acid (SCFA) profiles relative to control animals. Within the RPS treatment group, pigs shedding the least Salmonella tended to have greater cecal concentrations of butyrate, valerate, caproate, and succinate. Additionally, among RPS-fed pigs, several bacterial taxa (Prevotella_7, Olsenella, and Bifidobacterium, and others) exhibited negative relationships between their abundances of and the amount of Salmonella in the feces of their hosts. Many of these same taxa also had significant positive associations with cecal concentrations of butyrate, valerate, caproate, even though they are not known to produce these SCFAs. Together, these data suggest the RPS-associated reduction in Salmonella shedding may be dependent on the establishment of bacterial cross feeding interactions that result in the production of certain SCFAs. However, directly feeding a fatty acid mix did not replicate the effect. RPS supplementation could be an effective means to reduce multidrug-resistant (MDR) S. enterica serovar I 4,[5],12:i:- in swine, provided appropriate bacterial communities are present in the gut.

59 BASIC BIOLOGICAL SCIENCES↗