The advent of next-generation telescope facilities brings with it an unprecedented amount of data, and the demand for effective tools to process and classify this information has become increasingly important. This work proposes a novel approach to quantify the radio galaxy morphology, through the development of a series of algorithmic metrics that can quantitatively describe the structure of radio source, and can be applied to radio images in an automatic way. These metrics are intuitive in nature and are inspired by the intrinsic structural differences observed between the existing Fanaroff-Riley (FR) morphology types. The metrics are defined in categories of asymmetry, blurriness, concentration, disorder, and elongation (ABCDE/single-lobe metrics), as well as the asymmetry and angle between lobes (source metrics). We apply these metrics to a sample of 480 sources from the Evolutionary Map of the Universe Pilot Survey (EMU-PS) and 72 well resolved extensively studied sources from An Atlas of DRAGNs, a subset of the revised Third Cambridge Catalogue of Radio Sources (3CRR). We find that these metrics are relatively robust to resolution changes, independent of each other, and measure fundamentally different structural components of radio galaxy lobes. These metrics work particularly well for sources with reasonable signal-to-noise and well separated lobes. We also find that we can recover the original FR classification using probabilistic combinations of our metrics, highlighting the usefulness of our approach for future large data sets from radio sky surveys.

Objectives/Goals: We developed an educational online module to equip researchers with knowledge, skills, and resources for conducting community-engaged research, aiming to foster meaningful collaboration between academia and communities. Methods/Study Population: A working group was formed, including three research faculty, four staff members, and four community partners who have partnered with researchers on community engaged projects. The working group first identified three objectives for the module and outlined what should be covered for each objective. The working group identified existing resources, texts, and videos that would address the objectives and worked in small groups to create additional content for the module. A smaller subgroup then took this content, organized it, and worked with the Office of Online Education to put the content into an interactive online format. Results/Anticipated Results: The three objectives identified for the online module are 1) Describe community engaged research, the purpose it serves, and why researchers do it; 2) Identify how to seek and collaboratively engage with a community partner; and 3) Identify and connect with resources for conducting community engaged research in Indiana. Each objective contains text, interactive figures and images, links to external resources or further reading, and videos of researchers and community partners talking about their own experiences and lessons learned. Each objective also includes activities and prompts for the learner to complete to apply the module content to the work they want to do. Discussion/Significance of Impact: Community engagement ensures research addresses real-world needs, builds trust, and includes diverse perspectives. Many researchers lack best practices to do this ethically. This module teaches skills needed to foster trust through transparency, respect, and by incorporating community voices.

This article addresses a critical gap in international research concerning digital literacies and empowerment among adults who are English as an additional language (EAL) learners. In the Australian context, where digital communication and services are embedded in all aspects of life and work, proficiency in digital literacies, including advanced technologies like generative artificial intelligence (AI), is vital for working and living in Australia. Despite the increasing prevalence and significance of generative AI platforms such as ChatGPT, there is a notable absence of dedicated programs to assist EAL learners in understanding and utilising generative AI, potentially impacting their employability and everyday life. This article presents findings from a larger study conducted within training providers, spanning adult educational institutions nationwide. Through analysis of data gathered from surveys and focus groups, the article investigates the knowledge and attitudes of students, educators, and leaders regarding integrating generative AI into the learning program for adult EAL learners. The results reveal a hesitance among educators, particularly concerning beginning language learners, in incorporating generative AI into educational programs. Conversely, many adult learners demonstrate enthusiasm for learning about its potential benefits despite having limited understanding. These disparities underscore the pressing need for comprehensive professional development for educators and program leaders. The findings also highlight the need to develop the AI literacy of learners to foster their understanding and digital empowerment. The article concludes by advocating for a systemic approach to include generative AI as an important part of learning programs with students often from adult migrant and refugee backgrounds.

Artificial intelligence (AI) holds immense promise for accelerating and improving all aspects of drug discovery, not least target discovery and validation. By integrating a diverse range of biological data modalities, AI enables the accurate prediction of drug target properties, ultimately illuminating biological mechanisms of disease and guiding drug discovery strategies. Despite the indisputable potential of AI in drug target discovery, there are many challenges and obstacles yet to be overcome, including dealing with data biases, model interpretability and generalisability, and the validation of predicted drug targets, to name a few. By exploring recent advancements in AI, this review showcases current applications of AI for drug target discovery and offers perspectives on the future of AI for the discovery and validation of drug targets, paving the way for the generation of novel and safer pharmaceuticals.

Background: Antibiotic use without a prescription (nonprescription use) leads to antibiotic overuse, with negative consequences for patient and public health. We studied whether screening patients for prior nonprescription antibiotic use in the past 12 months predicted their intentions to use them in the future. Methods: A survey asking respondents about prior and intended nonprescription antibiotic use was performed between January 2020 and June 2021 among patients in waiting rooms of 6 public clinics and 2 private emergency departments in economically and socially diverse urban and suburban areas. Respondents were classified as prior nonprescription users if they reported previously taking oral antibiotics without contacting a doctor, dentist, or nurse. Intended use was defined as answering “yes” or “maybe” to the question, “Would you use antibiotics without contacting a doctor, nurse, or dentist?” We calculated the sensitivity, specificity, and positive and negative predictive value (PPV and NPV) of prior nonprescription antibiotic use in the past 12 months for future intended nonprescription use. Bayes PPV and NPV were also calculated, considering the prevalence of nonprescription antibiotic use (24.8%) in our study. Results: Of the 564 patients surveyed, the median age was 51 years (SD, 19–92), with 72% of patients identifying as female. Most were from the public healthcare system (72.5%). Most respondents identified as Hispanic or Latino(a) (47%) or African American (33%), and 57% received Medicaid or the county financial assistance program. Prior nonprescription use was reported by 246 (43%) of 564 individuals, with 91 (16%) reporting nonprescription use within the previous 12 months. Intention to use nonprescription antibiotics was reported by 140 participants (25%). The sensitivity and specificity of prior nonprescription use in the past 12 months to predict the intention to use nonprescription antibiotics in the future were 75.9% (95% CI, 65.3–84.6) and 91.4% (95% CI, 87.8–94.2), respectively. After the Bayes’ adjustment, the PPV and NPV of prior use to predict future intention were 74.5% (95% CI, 66.7–80.9) and 92.0% (95% CI, 88.7–94.4) (Table 1). Conclusions: These results show that prior nonprescription antibiotic use in the past 12 months predicted the intention to use nonprescription antibiotics in the future (PPV of 75%). As a stewardship effort, we suggest clinicians use a simple question about prior nonprescription antibiotic use in primary-care settings as a screening question for patients at high risk for future nonprescription antibiotic use.

Financial support: HSQR-R 5R01HS026901-04

Disclosure: None

OBJECTIVES/GOALS: Assess the association of PK-related single nucleotide variants (SNVs) with the risk of bleeding from DOACs in non-valvular AF patients. METHODS/STUDY POPULATION: A retrospective cohort study was carried out with 2,364 Caucasians with non-valvular AF and treated with rivaroxaban or apixaban. Patients were genotyped as part of the genomic biobank at the University of Michigan Health System. The primary endpoint was a composite of major and clinically relevant non-major (CRNM) bleeding. Cox proportional hazards regression with time-varying analysis assessed the association of 8 SNVs in 5 PK genes (ABCB1, ABCG2, CYP3A4, CYP3A5, CYP2J2) with the risk of bleeding from DOACs in unadjusted and covariate-adjusted models. Six tests were performed as 3 of the SNVs are in the same haplotype. P-values below the Bonferroni-corrected level of 8.33e-3 were considered statistically significant. RESULTS/ANTICIPATED RESULTS: A total of 412 (17.4%) major and CRNM bleeding events occurred over 2.27 ± 2.03 years of follow-up. None of the PK SNVs were significantly associated with bleeding risk on DOACs in both unadjusted and covariate-adjusted Cox regression models. DISCUSSION/SIGNIFICANCE: The effects of these eight genetic variants on exposure to DOACs may not be strong enough to translate into differences in clinical outcomes. Especially if the genetic inheritance underlying the risk of bleeding from DOACs is polygenic, reinforcing the need for further genomic studies on this subject.

A new algorithm for toroidal flow shear in a linearly implicit, local $\delta f$ gyrokinetic code is described. Unlike the current approach followed by a number of codes, it treats flow shear continuously in time. In the linear gyrokinetic equation, time-dependences arising from the presence of flow shear are decomposed in such a way that they can be treated explicitly in time with no stringent constraint on the time step. Flow shear related time dependences in the nonlinear term are taken into account exactly, and time dependences in the quasineutrality equation are interpolated. Test cases validating the continuous-in-time implementation in the code GS2 are presented. Lastly, nonlinear gyrokinetic simulations of a JET discharge illustrate the differences observed in turbulent transport compared with the usual, discrete-in-time approach. The continuous-in-time approach is shown, in some cases, to produce fluxes that converge to a different value than with the discrete approach. The new approach can also lead to substantial computational savings by requiring radially narrower boxes. At fixed box size, the continuous implementation is only modestly slower than the previous, discrete approach.

Background: Rapidly identifying patients colonized with multidrug-resistant organisms (MDROs) upon ICU admission is critical to control and prevent the spread of these pathogens in healthcare facilities. Electronic health records (EHR) provide a rich source of data to predict the likelihood of MDRO colonization at admission, whereas surveillance methods are resource intensive and results are not immediately available. Our objectives were (1) to predict VRE and CRO colonization at ICU admission and (2) to identify patient subpopulations at higher risk for colonization with these MDROs. Methods: We conducted a retrospective analysis of patients aged ≥16 years admitted to any of 6 medical or surgical intensive care units (ICU) in the Johns Hopkins Hospital from July 1, 2016, through June 30, 2018. Perirectal swabs were collected at ICU unit admission and were tested for VRE and CRO. Patient demographic data, prior hospitalizations, and preadmission clinical data, including prior medication administration, prior diagnoses, and prior procedures, were extracted to develop prediction models. We employed the machine-learning algorithms logistic regression (LR), random forest (RF), and XGBoost (XG). The sum of sensitivity and specificity (ie, Youden’s index) was selected as the performance metric. Results: In total, 5,033 separate ICU visits from 3,385 patients were included, where 555 (11%) and 373 (7%) admissions tested positive for VRE and CRO, respectively. The sensitivity and specificity of our models for VRE were 78% and 80% with LR, 80% and 82% with RF, and 77% and 87% with XG. Predictions for CRO were not as precise, with LR at 73% and 53%, RF at 81% and 48%, and XG at 69% and 61%. The XG algorithm was the best-performing algorithm for both VRE and CRO. Prior VRE colonization, recent (<180 days) long-term care facility stay, and prior hospitalization >60 days were the key predictors for VRE, whereas the primary predictor for CRO colonization was prior carbapenem use. Conclusions: We demonstrated that EHR data can be used to predict >75% of VRE positive cases with a <15% false-positive rate and ~70% of CRO cases with a <40% false-positive rate. Future studies using larger sample sizes may improve the prediction accuracy and inform model generalizability across sites and thus reduce the risk of transmission of MDROs by rapidly identifying MDRO-colonized patients.

Funding: This work was funded by the Centers for Disease Control and Prevention (CDC) Epicenters Program (Grant Number 1U54CK000447) and the CDC MInD-Healthcare Program (Grant Number 1U01CK000536).

Disclosures: Aaron Milstone, BD (consulting)

Active labour market policy (ALMP) is a well-established strategy but one aspect is greatly neglected – employer participation – about which there is a lack of systematic evidence. The question of why and how employers participate in ALMP, and whether there may be some shift from employers solely being passive recipients of job-ready candidates to having a more proactive and strategic role, is addressed by drawing on new research into Talent Match, a contemporary UK employability programme which places particular emphasis on employer involvement. The research findings point to a conceptual distinction between employers’ roles as being reactive gatekeepers to jobs and/or being proactive strategic partners, with both evident. It is argued that the Talent Match programme demonstrates potential to benefit employers, jobseekers and programme providers, with devolution of policy to the local level a possible way forward. The conclusion, however, is that the barrier to wider replication is not necessarily a problem of practice but of centralised control of policy and, in particular, commitment to a supply-side approach. Empirical, conceptual and policy contributions are made to this under-researched topic.

Observational evidence in space and astrophysical plasmas with a long collisional mean free path suggests that more massive charged particles may be preferentially heated. One possible mechanism for this is the turbulent cascade of energy from injection to dissipation scales, where the energy is converted to heat. Here we consider a simple system consisting of a magnetized plasma slab of electrons and a single ion species with a cross-field density gradient. We show that such a system is subject to an electron drift wave instability, known as the universal instability, which is stabilized only when the electron and ion thermal speeds are equal. For unequal thermal speeds, we find from quasilinear analysis and nonlinear simulations that the instability gives rise to turbulent energy exchange between ions and electrons that acts to equalize the thermal speeds. Consequently, this turbulent heating tends to equalize the component temperatures of pair plasmas and to heat ions to much higher temperatures than electrons for conventional mass-ratio plasmas.

In bioassays, rice (Oryza sativa L.) recovery from metolachlor [2-chloro-N-(2-ethyl-6-methylphenyl)-N-(2-methoxy-1-methylethyl)acetamide] injury tended to be slower in flooded rice, but was not significantly different from the recovery rate in a nonflooded rice. In soils treated with 1 ppm (w/w) metolachlor and incubated in constant-temperature chambers, the half-life of metolachlor was shorter at 40 C than at 30 C. The degradation rate of metolachlor was not significantly correlated with declining moisture potentials in the range of −30 to −80 kPa. The CO2 evolution from metolachlor-treated soil was negatively correlated with incubation time and positively correlated to declining moisture levels. In a field study, metolachlor, as determined by bioassay, was mobile in a Taloka silt loam soil profile. After being incorporated to 7.5 cm, it became evenly distributed in the top 15 cm of the soil profile within 18 days. Metolachlor adsorption was positively correlated with clay and organic carbon content.

Annual bluegrass is a problematic weed in bermudagrass and other intensively maintained turfgrasses. Flumioxazin is reported to control annual bluegrass both PRE and POST; however, as a contact herbicide, flumioxazin injures actively growing bermudagrass. Research was conducted in Alabama and California to evaluate optimal flumioxazin application timing for annual bluegrass control, bermudagrass response, and overall sward quality in the field, and to assess annual bluegrass control at various growth stages in the greenhouse. November and December application timings resulted in the best balance of the three parameters. When bermudagrass was not dormant at application, treatment resulted in necrosis of green tissues and thus induced dormancy. The herbicide-induced dormancy resulted in better sward quality due to more uniform and therefore more aesthetically pleasing dormant turfgrass relative to natural dormancy. Flumioxazin at 0.43 kg ai ha−1 resulted in better annual bluegrass control and improved sward quality relative to 0.21 kg ha−1. Incomplete POST annual bluegrass control from later applications was attributed to larger weed size, limiting the effectiveness of this contact herbicide. Greenhouse data corroborated field results and indicated that flumioxazin at 0.43 kg ha−1 controlled ≥ 95% of annual bluegrass up to two tillers. Flumioxazin can be utilized for PRE and POST annual bluegrass control, but utilization of this herbicide is limited to dormant bermudagrass unless induced dormancy can be tolerated, and POST control is limited to annual bluegrass plants ≤ two tillers in size.

Mathematical modeling is a valuable methodology used to study healthcare epidemiology and antimicrobial stewardship, particularly when more traditional study approaches are infeasible, unethical, costly, or time consuming. We focus on 2 of the most common types of mathematical modeling, namely compartmental modeling and agent-based modeling, which provide important advantages—such as shorter developmental timelines and opportunities for extensive experimentation—over observational and experimental approaches. We summarize these advantages and disadvantages via specific examples and highlight recent advances in the methodology. A checklist is provided to serve as a guideline in the development of mathematical models in healthcare epidemiology and antimicrobial stewardship.

Infect Control Hosp Epidemiol 2016;1–7

A complete copy of the compressed COSMOS/UKST Southern Sky Object Catalogue is now available on-line at the Anglo-Australian Observatory and the Australia Telescope National Facility. The catalogue lists image parameters for all objects detected to a limit of BJ ≈ 21·5 in the UK Schmidt Southern Sky Survey. We have written software to access the catalogue efficiently and generate finding charts or text listings of the image parameters. In this paper we describe the software and give some examples of its use. We also discuss the astrometric precision of the catalogue.