Time-Resolved X-ray Emission Spectroscopy and Resonant Inelastic X-ray Scattering Spectroscopy of Laser Irradiated Carbon.

The existence of liquid carbon as an intermediate phase preceding the formation of novel carbon materials has been a point of contention for several decades. Experimental observation of such a liquid state requires nonthermal melting of solid carbon materials at various laser fluences and pulse properties. Reflectivity experiments performed in the mid-1980s reached opposing conclusions regarding the metallic or insulating properties of the purported liquid state. Time-resolved X-ray absorption studies showed shortening of C-C bonds and increasing diffraction densities, thought to evidence a liquid or glassy carbon state, respectively. Nevertheless, none of these experiments provided information on the electronic structure of the proposed liquid state. Herein, we report the results of time-resolved resonant inelastic X-ray scattering (RIXS) and time-resolved X-ray emission spectroscopy (XES) studies on amorphous carbon (a-C) and ultrananocrystalline diamond (UNCD) as a function of delay time between the irradiating pulse and X-ray probe. For both a-C and UNCD, we attribute decreases in RIXS or XES signals to transition blocking, relaxation, and finally, ablation. Increased signal at 20 ps following the irradiation of the UNCD is attributed to the probable formation of nanoscale structures in the ablation plume. Differences in the amount of signal observed between a-C and UNCD are explained by the difference in sample thickness and, specifically, incomplete melting of the UNCD film. Comparisons to spectral simulations based on MD trajectories at extreme conditions indicate that the carbon state in our experiments is crystalline. Normal mode analysis confirmed that symmetrical bending or stretching of the C-C bonds in the diamond lattice results in XES spectra with small intensity differences. Overall, we observed no evidence of melting to a liquid state, as determined by the lack of changes in the spectral properties for up to 100 ps delays following the melting pulses.

A reactive molecular dynamics model for uranium/hydrogen containing systems.

Uranium-based materials are valuable assets in the energy, medical, and military industries. However, understanding their sensitivity to hydrogen embrittlement is particularly challenging due to the toxicity of uranium and the computationally expensive nature of quantum-based methods generally required to study such processes. In this regard, we have developed a Chebyshev Interaction Model for Efficient Simulation (ChIMES) that can be employed to compute energies and forces of U and UH3 bulk structures with vacancies and hydrogen interstitials with accuracy similar to that of Density Functional Theory (DFT) while yielding linear scaling and orders of magnitude improvement in computational efficiency. We show that the bulk structural parameters, uranium and hydrogen vacancy formation energies, and diffusion barriers predicted by the ChIMES potential are in strong agreement with the reference DFT data. We then use ChIMES to conduct molecular dynamics simulations of the temperature-dependent diffusion of a hydrogen interstitial and determine the corresponding diffusion activation energy. Our model has particular significance in studies of actinides and other high-Z materials, where there is a strong need for computationally efficient methods to bridge length and time scales between experiments and quantum theory.

Rapid identification of enteric bacteria from whole genome sequences using average nucleotide identity metrics.

Identification of enteric bacteria species by whole genome sequence (WGS) analysis requires a rapid and an easily standardized approach. We leveraged the principles of average nucleotide identity using MUMmer (ANIm) software, which calculates the percent bases aligned between two bacterial genomes and their corresponding ANI values, to set threshold values for determining species consistent with the conventional identification methods of known species. The performance of species identification was evaluated using two datasets: the Reference Genome Dataset v2 (RGDv2), consisting of 43 enteric genome assemblies representing 32 species, and the Test Genome Dataset (TGDv1), comprising 454 genome assemblies which is designed to represent all species needed to query for identification, as well as rare and closely related species. The RGDv2 contains six Campylobacter spp., three Escherichia/Shigella spp., one Grimontia hollisae, six Listeria spp., one Photobacterium damselae, two Salmonella spp., and thirteen Vibrio spp., while the TGDv1 contains 454 enteric bacterial genomes representing 42 different species. The analysis showed that, when a standard minimum of 70% genome bases alignment existed, the ANI threshold values determined for these species were ≥95 for Escherichia/Shigella and Vibrio species, ≥93% for Salmonella species, and ≥92% for Campylobacter and Listeria species. Using these metrics, the RGDv2 accurately classified all validation strains in TGDv1 at the species level, which is consistent with the classification based on previous gold standard methods.

Genomic and Phenotypic Characterization of Shiga Toxin-Producing Escherichia albertii Strains Isolated from Wild Birds in a Major Agricultural Region in California.

Escherichia albertii is an emerging foodborne pathogen. To better understand the pathogenesis and health risk of this pathogen, comparative genomics and phenotypic characterization were applied to assess the pathogenicity potential of E. albertii strains isolated from wild birds in a major agricultural region in California. Shiga toxin genes stx2f were present in all avian strains. Pangenome analyses of 20 complete genomes revealed a total of 11,249 genes, of which nearly 80% were accessory genes. Both core gene-based phylogenetic and accessory gene-based relatedness analyses consistently grouped the three stx2f-positive clinical strains with the five avian strains carrying ST7971. Among the three Stx2f-converting prophage integration sites identified, ssrA was the most common one. Besides the locus of enterocyte effacement and type three secretion system, the high pathogenicity island, OI-122, and type six secretion systems were identified. Substantial strain variation in virulence gene repertoire, Shiga toxin production, and cytotoxicity were revealed. Six avian strains exhibited significantly higher cytotoxicity than that of stx2f-positive E. coli, and three of them exhibited a comparable level of cytotoxicity with that of enterohemorrhagic E. coli outbreak strains, suggesting that wild birds could serve as a reservoir of E. albertii strains with great potential to cause severe diseases in humans.

Identification and Characterization of ten Escherichia coli Strains Encoding Novel Shiga Toxin 2 Subtypes, Stx2n as Well as Stx2j, Stx2m, and Stx2o, in the United States.

The sharing of genome sequences in online data repositories allows for large scale analyses of specific genes or gene families. This can result in the detection of novel gene subtypes as well as the development of improved detection methods. Here, we used publicly available WGS data to detect a novel Stx subtype, Stx2n in two clinical E. coli strains isolated in the USA. During this process, additional Stx2 subtypes were detected; six Stx2j, one Stx2m strain, and one Stx2o, were all analyzed for variability from the originally described subtypes. Complete genome sequences were assembled from short- or long-read sequencing and analyzed for serotype, and ST types. The WGS data from Stx2n- and Stx2o-producing STEC strains were further analyzed for virulence genes pro-phage analysis and phage insertion sites. Nucleotide and amino acid maximum parsimony trees showed expected clustering of the previously described subtypes and a clear separation of the novel Stx2n subtype. WGS data were used to design OMNI PCR primers for the detection of all known stx1 (283 bp amplicon), stx2 (400 bp amplicon), intimin encoded by eae (221 bp amplicon), and stx2f (438 bp amplicon) subtypes. These primers were tested in three different laboratories, using standard reference strains. An analysis of the complete genome sequence showed variability in serogroup, virulence genes, and ST type, and Stx2 pro-phages showed variability in size, gene composition, and phage insertion sites. The strains with Stx2j, Stx2m, Stx2n, and Stx2o showed toxicity to Vero cells. Stx2j carrying strain, 2012C-4221, was induced when grown with sub-inhibitory concentrations of ciprofloxacin, and toxicity was detected. Taken together, these data highlight the need to reinforce genomic surveillance to identify the emergence of potential new Stx2 or Stx1 variants. The importance of this surveillance has a paramount impact on public health. Per our description in this study, we suggest that 2017C-4317 be designated as the Stx2n type-strain.

Chemical evolution in nitrogen shocked beyond the molecular stability limit.

Evolution of nitrogen under shock compression up to 100 GPa is revisited via molecular dynamics simulations using a machine-learned interatomic potential. The model is shown to be capable of recovering the structure, dynamics, speciation, and kinetics in hot compressed liquid nitrogen predicted by first-principles molecular dynamics, as well as the measured principal shock Hugoniot and double shock experimental data, albeit without shock cooling. Our results indicate that a purely molecular dissociation description of nitrogen chemistry under shock compression provides an incomplete picture and that short oligomers form in non-negligible quantities. This suggests that classical models representing the shock dissociation of nitrogen as a transition to an atomic fluid need to be revised to include reversible polymerization effects.

Reoccurring Escherichia coli O157:H7 Strain Linked to Leafy Greens-Associated Outbreaks, 2016-2019.

Genomic characterization of an Escherichia coli O157:H7 strain linked to leafy greens-associated outbreaks dates its emergence to late 2015. One clade has notable accessory genomic content and a previously described mutation putatively associated with increased arsenic tolerance. This strain is a reoccurring, emerging, or persistent strain causing illness over an extended period.

Evaluation of the Illumina iSeq whole genome sequencing system for enteric disease surveillance and outbreak detection.

The Illumina iSeq low-capacity sequencing platform was evaluated for use in foodborne disease surveillance and outbreak detection. The platform produced high quality sequence data comparable to that of the Illumina MiSeq and was cost-effective with fast turn-around time in low sample volume environments.

Enhancing the accuracy of density functional tight binding models through ChIMES many-body interaction potentials.

Semi-empirical quantum models such as Density Functional Tight Binding (DFTB) are attractive methods for obtaining quantum simulation data at longer time and length scales than possible with standard approaches. However, application of these models can require lengthy effort due to the lack of a systematic approach for their development. In this work, we discuss the use of the Chebyshev Interaction Model for Efficient Simulation (ChIMES) to create rapidly parameterized DFTB models, which exhibit strong transferability due to the inclusion of many-body interactions that might otherwise be inaccurate. We apply our modeling approach to silicon polymorphs and review previous work on titanium hydride. We also review the creation of a general purpose DFTB/ChIMES model for organic molecules and compounds that approaches hybrid functional and coupled cluster accuracy with two orders of magnitude fewer parameters than similar neural network approaches. In all cases, DFTB/ChIMES yields similar accuracy to the underlying quantum method with orders of magnitude improvement in computational cost. Our developments provide a way to create computationally efficient and highly accurate simulations over varying extreme thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly, and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.

United atom and coarse grained models for crosslinked polydimethylsiloxane with applications to the rheology of silicone fluids.

Siloxane systems consisting primarily of polydimethylsiloxane (PDMS) are versatile, multifaceted materials that play a key role in diverse applications. However, open questions exist regarding the correlation between their varied atomic-level properties and observed macroscale features. To this effect, we have created a systematic workflow to determine coarse-grained simulation models for crosslinked PDMS in order to further elucidate the effects of network changes on the system's rheological properties below the gel point. Our approach leverages a fine-grained united atom model for linear PDMS, which we extend to include crosslinking terms, and applies iterative Boltzmann inversion to obtain a coarse-grain "bead-spring-type" model. We then perform extensive molecular dynamics simulations to explore the effect of crosslinking on the rheology of silicone fluids, where we compute systematic increases in both density and shear viscosity that compare favorably to experiments that we conduct here. The kinematic viscosity of partially crosslinked fluids follows an empirical linear relationship that is surprisingly consistent with Rouse theory, which was originally derived for systems comprised of a uniform distribution of linear chains. The models developed here serve to enable quantitative bottom-up predictions for curing- and age-induced effects on macroscale rheological properties, allowing for accurate prediction of material properties based on fundamental chemical data.

Effects of COVID-19 shelter-in-place confinement on diabetes prevention health behaviors among US adults with prediabetes: A cross-sectional survey.

The coronavirus disease 2019 (COVID-19) pandemic has resulted in significant lifestyle changes due to shelter-in-place confinement orders. The study's purpose was to assess if the COVID-19 pandemic affected self-reported diabetes prevention behaviors among American adults with prediabetes. As part of a randomized clinical trial among adults with prediabetes and overweight/obesity, questions were added to existing study surveys to assess the effect of the COVID-19 pandemic on diabetes prevention behaviors and stress. Survey responses were summarized using frequencies. 259 study participants completed seven COVID-19 survey questions from June 2020 to June 2021. Participants were 62.9% female, 42.5% White, 31.3% Black, 11.6% Asian, 8.1% Hispanic, and 6.6% Other. Over 75% of participants reported that the COVID-19 pandemic affected physical activity levels, with 82.1% of those affected reporting decreased physical activity; 70.3% reported that the pandemic affected their eating habits, with 61.7% of those affected reporting their eating habits became less healthy; 73.7% reported that the pandemic affected their level of stress, with 97.4% of those affected reporting that their level of stress had increased; 60% reported that the pandemic affected their motivation to adopt/maintain healthy habits, with 72.9% of those affected reporting their motivation decreased. A high percentage of study participants with prediabetes reported decreases in health promotion behaviors and increases in stress due to the COVID-19 pandemic. Consequently, the pandemic could lead to increased diabetes incidence. Strategies to improve diabetes prevention behaviors and address mental health concerns among those at-risk for diabetes are critical during and after the COVID-19 pandemic.

The Relation of Parental Expressed Emotion, Parental Affiliate Stigma, and Typically-developing Sibling Internalizing Behavior in Families with a Child with ASD.

Caregivers of children with autism spectrum disorder (ASD) experience greater stress, expressed emotion (EE), and affiliate stigma than caregivers of children without ASD. Siblings of children with ASD often experience greater negative functioning than siblings of individuals without ASD. The current study found significant interrelations among symptom severity and externalizing behavior in children with ASD; parental stress, affiliate stigma, and EE; and TD sibling internalizing behavior. In addition, certain subcomponents of affiliate stigma predicted unique variance in EE and TD sibling internalizing behavior. Findings may increase understanding of psychosocial functioning in families with children with ASD and allow clinicians to improve outcomes for all family members. Limitations of the study included self-report data, limited sample diversity, and a cross-sectional design.

Use of Large-Scale Genomics to Identify the Role of Animals and Foods as Potential Sources of Extraintestinal Pathogenic Escherichia coli That Cause Human Illness

Extraintestinal pathogenic Escherichia coli (ExPEC) cause urinary tract and potentially life-threatening invasive infections. Unfortunately, the origins of ExPEC are not always clear. We used genomic data of E. coli isolates from five U.S. government organizations to evaluate potential sources of ExPEC infections. Virulence gene analysis of 38,032 isolates from human, food animal, retail meat, and companion animals classified the subset of 8142 non-diarrheagenic isolates into 40 virulence groups. Groups were identified as low, medium, and high relative risk of containing ExPEC strains, based on the proportion of isolates recovered from humans. Medium and high relative risk groups showed a greater representation of sequence types associated with human disease, including ST-131. Over 90% of food source isolates belonged to low relative risk groups, while >60% of companion animal isolates belonged to medium or high relative risk groups. Additionally, 18 of the 26 most prevalent antimicrobial resistance determinants were more common in high relative risk groups. The associations between antimicrobial resistance and virulence potentially limit treatment options for human ExPEC infections. This study demonstrates the power of large-scale genomics to assess potential sources of ExPEC strains and highlights the importance of a One Health approach to identify and manage these human pathogens.

Longitudinal surveillance and comparative characterization of Escherichia albertii in wild raccoons in the United States.

Escherichia albertii is an emerging enteric bacterial pathogen causing watery diarrhea, abdominal distension, vomiting and fever in humans. E. albertii has caused many foodborne outbreaks in Japan and was also reported in other countries worldwide. However, the important animal reservoirs of this pathogen are still largely unknown, impeding us to combat this emerging pathogen. Recently, we reported that wild raccoons (Procyon lotor) and broiler chickens are significant reservoirs of E. albertii in Japan and the U.S., respectively. Here, we performed a longitudinal surveillance to monitor prevalence of E. albertii in wild raccoons in the U.S. and conducted comprehensive comparative analyses of the E. albertii of different origins. A total of 289 fecal swab samples were collected from wild raccoons in Tennessee and Kentucky in the U.S. (2018-2020). Approximately 26% (74/289) of the raccoons examined were PCR-positive for E. albertii and eventually 22 E. albertii isolates were obtained. PFGE analysis showed the U.S. raccoon E. albertii were phylogenetically distant even though the corresponding raccoons were captured from a small area. Unlike the high prevalence of multidrug resistance (83%) observed in previous chicken E. albertii survey, antibiotic resistance was rarely observed in all the U.S. raccoon and 22 Japan raccoon strains with only one Japan strain displaying multidrug resistance (2%). Whole genome sequencing of 54 diverse E. albertii strains and subsequent comparative genomics analysis revealed unique clusters that displayed close evolutionary relationships and similar virulence gene profiles among the strains of different origins in terms of geographical locations (e.g., U.S. and Japan) and hosts (raccoon, chicken, swine, and human). Challenge experiment demonstrated raccoon E. albertii strains could successfully colonize in the chicken intestine at 3 and 8 days postinfection. A pilot environmental survey further showed all the four tested water samples from Tennessee river were E. albertii-positive; two different E. albertii strains, isolated from a single water sample, showed close relationships to those of human origin. Together, the findings from this study provide new insights into the ecology, evolution, and pathobiology of E. albertii, and underscore the need to control the emerging E. albertii in a complex ecosystem using One Health approach.

Sequencing of Enteric Bacteria: Library Preparation Procedure Matters for Accurate Identification and Characterization.

Enzymatic library preparation kits are increasingly used for bacterial whole genome sequencing. While they offer a rapid workflow, the transposases used in the kits are recognized to be somewhat biased. The aim of this study was to optimize and validate a protocol for the Illumina DNA Prep kit (formerly Nextera DNA Flex) for sequencing enteric pathogens and compare its performance against the Nextera XT kit. One hundred forty-three strains of Campylobacter, Escherichia, Listeria, Salmonella, Shigella, and Vibrio were prepared with both methods and sequenced on the Illumina MiSeq using 300 and/or 500 cycle chemistries. Sequences were compared using core genome multilocus sequence typing (cgMLST), 7-gene multilocus sequence typing (MLST), and detection of markers encoding serotype, virulence, and antimicrobial resistance. Sequences for one Escherichia strain were downsampled to determine the minimum coverage required for the analyses. While organism-specific differences were observed, the Prep libraries generated longer average read lengths and less fragmented assemblies compared to the XT libraries. In downstream analysis, the most notable difference between the kits was observed for Escherichia, particularly for the 300 cycle sequences. The O group was not predicted in 32% and 4% of XT sequences when using blast and kmer algorithms, respectively, while the O group was predicted from all Prep sequences regardless of the algorithm. In addition, the ehxA gene was not detected in 6% of XT sequences and 34% were missing one or more of the type III secretion systems and/or plasmid-associated genes, which were detected in the Prep sequences. The coverage downsampling revealed that acceptable assembly quality and allele detection was achieved at 30 × coverage with the Prep libraries, whereas 40-50 × coverage was required for the XT libraries. The better performance of the Prep libraries was attributed to more even coverage, particularly in genome regions low in GC content.

Nonane and Hexanol Adsorption in the Lamellar Phase of a Nonionic Surfactant: Molecular Simulations and Comparison to Ideal Adsorbed Solution Theory.

Adsorption of n-nonane/1-hexanol (C9/C6OH) mixtures into the lamellar phase formed by a 50/50 w/w triethylene glycol mono-n-decyl ether (C10E3)/water system was studied using configurational-bias Monte Carlo simulations in the osmotic Gibbs ensemble. The interactions were described by the Shinoda-Devane-Klein coarse-grained force field. Prior simulations probing single-component adsorption indicated that C9 molecules preferentially load near the center of the bilayer, increasing the bilayer thickness, whereas C6OH molecules are more likely to be found near the interface of the polar and nonpolar moieties, swelling the bilayer in the lateral dimension. Here, we extend this work to binary C9/C6OH adsorption to probe whether the difference in the spatial preferences may lead to a synergistic effect and enhanced loadings for the mixture. Comparing loading trends and the thermodynamics of binary adsorption to unary adsorption reveals that C9-C9 interactions lead to the largest enhancement, whereas C9-C6OH and C6OH-C6OH interactions are less favorable for this bilayer system. Ideal adsorbed solution theory yields satisfactory predictions of the binary loading.

High-Accuracy Semiempirical Quantum Models Based on a Minimal Training Set.

A great need exists for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories at a fraction of the computational cost. In this regard, we have leveraged a machine-learned interaction potential based on Chebyshev polynomials to improve density functional tight binding (DFTB) models for organic materials. The benefit of our approach is two-fold: (1) many-body interactions can be corrected for in a systematic and rapidly tunable process, and (2) high-level quantum accuracy for a broad range of compounds can be achieved with ∼0.3% of data required for one advanced deep learning potential. Our model exhibits both transferability and extensibility through comparison to quantum chemical results for organic clusters, solid carbon phases, and molecular crystal phase stability rankings. Our efforts thus allow for high-throughput physical and chemical predictions with up to coupled-cluster accuracy for systems that are computationally intractable with standard approaches.

Use of Whole Genome Sequencing by the Federal Interagency Collaboration for Genomics for Food and Feed Safety in the United States.

ABSTRACT: This multiagency report developed by the Interagency Collaboration for Genomics for Food and Feed Safety provides an overview of the use of and transition to whole genome sequencing (WGS) technology for detection and characterization of pathogens transmitted commonly by food and for identification of their sources. We describe foodborne pathogen analysis, investigation, and harmonization efforts among the following federal agencies: National Institutes of Health; Department of Health and Human Services, Centers for Disease Control and Prevention (CDC) and U.S. Food and Drug Administration (FDA); and the U.S. Department of Agriculture, Food Safety and Inspection Service, Agricultural Research Service, and Animal and Plant Health Inspection Service. We describe single nucleotide polymorphism, core-genome, and whole genome multilocus sequence typing data analysis methods as used in the PulseNet (CDC) and GenomeTrakr (FDA) networks, underscoring the complementary nature of the results for linking genetically related foodborne pathogens during outbreak investigations while allowing flexibility to meet the specific needs of Interagency Collaboration partners. We highlight how we apply WGS to pathogen characterization (virulence and antimicrobial resistance profiles) and source attribution efforts and increase transparency by making the sequences and other data publicly available through the National Center for Biotechnology Information. We also highlight the impact of current trends in the use of culture-independent diagnostic tests for human diagnostic testing on analytical approaches related to food safety and what is next for the use of WGS in the area of food safety.

Chemistry-mediated Ostwald ripening in carbon-rich C/O systems at extreme conditions.

There is significant interest in establishing a capability for tailored synthesis of next-generation carbon-based nanomaterials due to their broad range of applications and high degree of tunability. High pressure (e.g., shockwave-driven) synthesis holds promise as an effective discovery method, but experimental challenges preclude elucidating the processes governing nanocarbon production from carbon-rich precursors that could otherwise guide efforts through the prohibitively expansive design space. Here we report findings from large scale atomistically-resolved simulations of carbon condensation from C/O mixtures subjected to extreme pressures and temperatures, made possible by machine-learned reactive interatomic potentials. We find that liquid nanocarbon formation follows classical growth kinetics driven by Ostwald ripening (i.e., growth of large clusters at the expense of shrinking small ones) and obeys dynamical scaling in a process mediated by carbon chemistry in the surrounding reactive fluid. The results provide direct insight into carbon condensation in a representative system and pave the way for its exploration in higher complexity organic materials. They also suggest that simulations using machine-learned interatomic potentials could eventually be employed as in-silico design tools for new nanomaterials.

Assessing Interdisciplinary Trainees' Objective and Self-Reported Knowledge of Autism Spectrum Disorder and Confidence in Providing Services.

The importance of accurate identification and high-quality intervention for individuals with autism spectrum disorder (ASD) is indisputable. Clinicians from multiple professions need adequate knowledge of ASD to make appropriate referrals to specialists, conduct thorough evaluations, and provide effective interventions. ASD knowledge development for many professionals may start at the pre-service training level. An interdisciplinary sample consisting of trainees from Leadership Education in Neurodevelopmental and Related Disabilities (LEND) programs and University Centers for Excellence in Developmental Disabilities (UCEDDs) across the country (N = 155) was evaluated on their objectively measured ASD knowledge using the ASKSP-R. Self-reported knowledge of ASD and self-reported confidence in providing services to individuals who have ASD was evaluated using a 0-100 scale. Results from an ANOVA demonstrated a significant difference in objectively measured knowledge across disciplines, F(7, 146) = 4.68, p < .001. Specifically, trainees in psychology had significantly higher levels of objectively measured ASD knowledge than trainees in physical/occupational therapy, social work, and non-clinical disciplines. Pre-service and professional development experiences predicted trainees' objectively measured ASD knowledge, self-reported ASD knowledge, and self-reported confidence. Implications and recommendations regarding interdisciplinary training to improve outcomes for individuals with ASD are discussed.