The First Large Absorption Survey in H i (FLASH) is a large-area radio survey for neutral hydrogen in and around galaxies in the intermediate redshift range $0.4\lt z\lt1.0$, using the 21-cm H i absorption line as a probe of cold neutral gas. The survey uses the ASKAP radio telescope and will cover 24,000 deg$^2$ of sky over the next five years. FLASH breaks new ground in two ways – it is the first large H i absorption survey to be carried out without any optical preselection of targets, and we use an automated Bayesian line-finding tool to search through large datasets and assign a statistical significance to potential line detections. Two Pilot Surveys, covering around 3000 deg$^2$ of sky, were carried out in 2019-22 to test and verify the strategy for the full FLASH survey. The processed data products from these Pilot Surveys (spectral-line cubes, continuum images, and catalogues) are public and available online. In this paper, we describe the FLASH spectral-line and continuum data products and discuss the quality of the H i spectra and the completeness of our automated line search. Finally, we present a set of 30 new H i absorption lines that were robustly detected in the Pilot Surveys, almost doubling the number of known H i absorption systems at $0.4\lt z\lt1$. The detected lines span a wide range in H i optical depth, including three lines with a peak optical depth $\tau\gt1$, and appear to be a mixture of intervening and associated systems. Interestingly, around two-thirds of the lines found in this untargeted sample are detected against sources with a peaked-spectrum radio continuum, which are only a minor (5–20%) fraction of the overall radio-source population. The detection rate for H i absorption lines in the Pilot Surveys (0.3 to 0.5 lines per 40 deg$^2$ ASKAP field) is a factor of two below the expected value. One possible reason for this is the presence of a range of spectral-line artefacts in the Pilot Survey data that have now been mitigated and are not expected to recur in the full FLASH survey. A future paper in this series will discuss the host galaxies of the H i absorption systems identified here.

Ultra-processed foods (UPFs) have negative health consequences. Food insecurity and Supplemental Nutrition Assistance Program (SNAP) are associated with higher UPF intake in U.S. adults, but this has not been examined in U.S. adolescents. This study assesses associations between food security status and SNAP participation with UPF intake in 3,067 adolescents aged 12–19 years with household incomes at or below 300% of the federal poverty line from the 2007–2016 National Health and Nutrition Examination Survey. UPF is defined using the Nova classification and measured as a percentage of daily total energy intake (TEI). High food security, marginal food security, or food insecurity status was determined through the U.S. Department of Agriculture’s eighteen-item Household Food Security Survey. SNAP participation was deemed affirmative if the household reported receiving SNAP benefits in the last year. Multivariable linear regressions that controlled for TEI and sociodemographic covariates and accounted for the complex survey design examined associations between food insecurity and SNAP participation with UPF intake. In the sample, the prevalence of marginal food security was 15.9%, the prevalence of food insecurity was 33.8%, and the prevalence of SNAP participation was 36.5%. After multivariate adjustment, there were no significant differences in UPF intake by food security status. Adolescents participating in SNAP consumed 2.7% higher UPF intake (95% CI: 0.1%, 5.2%, p = 0.04) compared to adolescents not participating in SNAP. Among lower-income U.S. adolescents, SNAP participation but not food security status was associated with higher UPF intake. Programs and policies promoting the intake of more healthful, minimally processed foods should be strengthened.

The scatter in global atomic hydrogen (Hi) scaling relations is partly attributed to differences in how Hi and stellar properties are measured, with Hi reservoirs typically extending beyond the inner regions of galaxies where star formation occurs. Using pilot observations from the Widefield ASKAP L-band Legacy All-sky Blind Survey (WALLABY), we present the first measurements of Hi mass enclosed within the stellar-dominated regions of galaxies for a statistical sample of 995 local gas-rich systems, investigating the factors driving its variation. We examine how global Hi scaling relations change when measurements are restricted to $R_{\text{25}}$ and $R_{\text{24}}$ – the isophotal radii at 25 and 24 mag arcsec$^{-2}$ in the i-band – and explore how the fraction of Hi mass and Hi surface density within these radii correlate with other galaxy properties. On average, 68% of the total Hi mass is enclosed within $R_{\text{25}}$ and 54% within $R_{\text{24}}$, though significant variation exists between galaxies, ranging from $\sim$20% to 100%. The fraction of Hi mass within $R_{\text{25}}$ shows a mild correlation with stellar properties, with galaxies of higher stellar mass, greater stellar surface density, or redder colours enclosing a larger fraction of their Hi reservoirs. These correlations do not significantly strengthen when considering $R_{\text{24}}$. Conversely, global Hi surface densities show no significant correlation with stellar mass or stellar surface density, but trends start emerging when these are measured within the inner regions of galaxies. The strongest correlation is observed with optical colour, with bluer galaxies having higher average Hi surface densities within $R_{\text{25}}$. This trend of the average Hi surface density with optical colour strengthens when we restrict from $R_{\text{25}}$ to $R_{\text{24}}$, suggesting a closer connection between inner Hi reservoirs and star formation. This study underscores the value of (at least marginally) resolved Hi surveys of statistical samples for advancing our understanding of the gas-star formation cycle in galaxies.

Objectives/Goals: The objective of this study is to explore strategies for AI-physician collaboration in diagnosing acute respiratory distress syndrome (ARDS) using chest X-rays. By comparing the diagnostic accuracy of different AI deployment methods, the study aims to identify optimal strategies that leverage both AI and physician expertise to improve outcomes. Methods/Study Population: The study analyzed 414 frontal chest X-rays from 115 patients hospitalized between August 15 and October 2, 2017, at the University of Michigan. Each X-ray was reviewed by six physicians for ARDS presence and diagnostic confidence. We developed a deep learning AI model for detecting ARDS and explored the strengths, weaknesses, and blind spots of both physicians and AI systems to inform optimal system deployment. We then investigated several AI-physician collaboration strategies, including: 1) AI-aided physician: physicians interpret chest X-rays first and defer to the AI model if uncertain, 2) physician-aided AI: the AI model interprets chest X-rays first and defers to a physician if uncertain, and 3) AI model and physician interpreting chest X-rays separately and then averaging their interpretations. Results/Anticipated Results: While the AI model (84.7% accuracy) had higher accuracy than physicians (80.8%), we found evidence that AI and physician expertise are complementary. When physicians lacked confidence in a chest X-ray’s interpretation, the AI model had higher accuracy. Conversely, in cases of AI uncertainty, physicians were more accurate. The AI excelled with easier cases, while physicians were better with difficult cases, defined as those where at least two physicians disagreed with the majority label. Collaboration strategies tested include AI-aided physician (82.4%), physician-aided AI (86.9%), and averaging interpretations (86%). The physician-aided AI approach had the highest accuracy, could off-load the human expert workload on the reading of up to 79% chest X-rays, allowing physicians to focus on challenging cases. Discussion/Significance of Impact: This study shows AI and physicians complement each other in ARDS diagnosis, improving accuracy when combined. A physician-aided AI strategy, where the AI defers to physicians when uncertain, proved most effective. Implementing AI-physician collaborations in clinical settings could enhance ARDS care, especially in low-resource environments.

Objectives/Goals: Manual skin assessment in chronic graft-versus-host disease (cGVHD) can be time consuming and inconsistent (>20% affected area) even for experts. Building on previous work we explore methods to use unmarked photos to train artificial intelligence (AI) models, aiming to improve performance by expanding and diversifying the training data without additional burden on experts. Methods/Study Population: Common to many medical imaging projects, we have a small number of expert-marked patient photos (N = 36, n = 360), and many unmarked photos (N = 337, n = 25,842). Dark skin (Fitzpatrick type 4+) is underrepresented in both sets; 11% of patients in the marked set and 9% in the unmarked set. In addition, a set of 20 expert-marked photos from 20 patients were withheld from training to assess model performance, with 20% dark skin type. Our gold standard markings were manual contours around affected skin by a trained expert. Three AI training methods were tested. Our established baseline uses only the small number of marked photos (supervised method). The semi-supervised method uses a mix of marked and unmarked photos with human feedback. The self-supervised method uses only unmarked photos without any human feedback. Results/Anticipated Results: We evaluated performance by comparing predicted skin areas with expert markings. The error was given by the absolute difference between the percentage areas marked by the AI model and expert, where lower is better. Across all test patients, the median error was 19% (interquartile range 6 – 34) for the supervised method and 10% (5 – 23) for the semi-supervised method, which incorporated unmarked photos from 83 patients. On dark skin types, the median error was 36% (18 – 62) for supervised and 28% (14 – 52) for semi-supervised, compared to a median error on light skin of 18% (5 – 26) for supervised and 7% (4 – 17) for semi-supervised. Self-supervised, using all 337 unmarked patients, is expected to further improve performance and consistency due to increased data diversity. Full results will be presented at the meeting. Discussion/Significance of Impact: By automating skin assessment for cGVHD, AI could improve accuracy and consistency compared to manual methods. If translated to clinical use, this would ease clinical burden and scale to large patient cohorts. Future work will focus on ensuring equitable performance across all skin types, providing fair and accurate assessments for every patient.

We present the first results from a new backend on the Australian Square Kilometre Array Pathfinder, the Commensal Realtime ASKAP Fast Transient COherent (CRACO) upgrade. CRACO records millisecond time resolution visibility data, and searches for dispersed fast transient signals including fast radio bursts (FRB), pulsars, and ultra-long period objects (ULPO). With the visibility data, CRACO can localise the transient events to arcsecond-level precision after the detection. Here, we describe the CRACO system and report the result from a sky survey carried out by CRACO at 110-ms resolution during its commissioning phase. During the survey, CRACO detected two FRBs (including one discovered solely with CRACO, FRB 20231027A), reported more precise localisations for four pulsars, discovered two new RRATs, and detected one known ULPO, GPM J1839 $-$10, through its sub-pulse structure. We present a sensitivity calibration of CRACO, finding that it achieves the expected sensitivity of 11.6 Jy ms to bursts of 110 ms duration or less. CRACO is currently running at a 13.8 ms time resolution and aims at a 1.7 ms time resolution before the end of 2024. The planned CRACO has an expected sensitivity of 1.5 Jy ms to bursts of 1.7 ms duration or less and can detect $10\times$ more FRBs than the current CRAFT incoherent sum system (i.e. 0.5 $-$2 localised FRBs per day), enabling us to better constrain the models for FRBs and use them as cosmological probes.

North Carolina growers have long struggled to control Italian ryegrass, and recent research has confirmed that some Italian ryegrass biotypes have become resistant to nicosulfuron, glyphosate, clethodim, and paraquat. Integrating alternative management strategies is crucial to effectively control such biotypes. The objectives of this study were to evaluate Italian ryegrass control with cover crops and fall-applied residual herbicides and investigate cover crop injury from residual herbicides. This study was conducted during the fall/winter of 2021–22 in Salisbury, NC, and fall/winter of 2021–22 and 2022–23 in Clayton, NC. The study was designed as a 3 × 5 split-plot in which the main plot consisted of three cover crop treatments (no-cover, cereal rye at 80 kg ha−1, and crimson clover at 18 kg ha−1), and the subplots consisted of five residual herbicide treatments (S-metolachlor, flumioxazin, metribuzin, pyroxasulfone, and nontreated). In the 2021–22 season at Clayton, metribuzin injured cereal rye and crimson clover 65% and 55%, respectively. However, metribuzin injured both cover crops ≤6% in 2022–23. Flumioxazin resulted in unacceptable crimson clover injury of 50% and 38% in 2021–22 and 2022–23 in Clayton and 40% in Salisbury, respectively. Without preemergence herbicides, cereal rye controlled Italian ryegrass by 85% and 61% at 24 wk after planting in 2021–22 and 2022–23 in Clayton and 82% in Salisbury, respectively. In 2021–22, Italian ryegrass seed production was lowest in cereal rye plots at both locations, except when it was treated with metribuzin. For example, in Salisbury, cereal rye plus metribuzin resulted in 39,324 seeds m–2, compared to ≤4,386 seeds m–2 from all other cereal rye treatments. In 2022–23, Italian ryegrass seed production in cereal rye was lower when either metribuzin or pyroxasulfone were used preemergence (2,670 and 1,299 seeds m–2, respectively) compared with cereal rye that did not receive an herbicide treatment (5,600 seeds m–2). cereal rye (Secale cereale L.) and crimson clover (Trifolium incarnatum L.)

Two studies were conducted in 2022 and 2023 near Rocky Mount and Clayton, NC, to determine the optimal granular ammonium sulfate (AMS) rate and application timing for pyroxasulfone-coated AMS. In the rate study, AMS rates included 161, 214, 267, 321, 374, 428, and 481 kg ha−1, equivalent to 34, 45, 56, 67, 79, 90, and 101 kg N ha−1, respectively. All rates were coated with pyroxasulfone at 118 g ai ha−1 and topdressed onto 5- to 7-leaf cotton. In the timing study, pyroxasulfone (118 g ai ha−1) was coated on AMS and topdressed at 321 kg ha−1 (67 kg N ha−1) onto 5- to 7-leaf, 9- to 11-leaf, and first bloom cotton. In both studies, weed control and cotton tolerance to pyroxasulfone-coated AMS were compared to pyroxasulfone applied POST and POST-directed. The check in both studies received non-herbicide-treated AMS (321 kg ha−1). Before treatment applications, all plots (including the check) were maintained weed-free with glyphosate and glufosinate. In both studies, pyroxasulfone applied POST was most injurious (8% to 16%), while pyroxasulfone-coated AMS resulted in ≤4% injury. Additionally, no differences in cotton lint yield were observed in either study. With the exception of the lowest rate of AMS (161 kg ha−1; 79%), all AMS rates coated with pyroxasulfone controlled Palmer amaranth ≥83%, comparably to pyroxasulfone applied POST (92%) and POST-directed (89%). In the timing study, the application method did not affect Palmer amaranth control; however, applications made at the mid- and late timings outperformed early applications. These results indicate that pyroxasulfone-coated AMS can control Palmer amaranth comparably to pyroxasulfone applied POST and POST-directed, with minimal risk of cotton injury. However, the application timing could warrant additional treatment to achieve adequate late-season weed control.

We describe a case of novel use of trametinib in treating arrythmia without concomitant cardiomyopathy. Our patient is a two-year-old female born with Costello syndrome due to heterozygous mutations in the HRAS gene c34 G > T p (G12C). Shortly after birth, she was diagnosed with multifocal atrial tachyarrhythmia. Her imaging studies have shown no hypertrophy or CHD. There was poor arrhythmia control despite triple antiarrhythmic therapy. Trametinib, a MEK1 and MEK2 inhibitor, was used in treating her isolated atrial arrythmia, allowing her to wean off other antiarrhythmics. Other case reports have shown trametinib to benefit certain RASopathy patients with lymphatic abnormalities, hypertrophic cardiomyopathy, and concurrent arrhythmias. This case demonstrates effective treatment of isolated arrhythmia without cardiomyopathy, broadening the potential indications for use of trametinib in certain RASopathy patients.

In intertemporal and risky choice decisions, parametric utility models are widely used for predicting choice and measuring individuals’ impulsivity and risk aversion. However, parametric utility models cannot describe data deviating from their assumed functional form. We propose a novel method using cubic Bezier splines (CBS) to flexibly model smooth and monotonic utility functions that can be fit to any dataset. CBS shows higher descriptive and predictive accuracy over extant parametric models and can identify common yet novel patterns of behavior that are inconsistent with extant parametric models. Furthermore, CBS provides measures of impulsivity and risk aversion that do not depend on parametric model assumptions.

Non-spurious methods are needed for estimating the coefficient of equivalence for speeded tests from single-trial data. Spuriousness in a split-half estimate depends on three conditions; the split-half method may be used if any of these is demonstrated to be absent. A lower-bounds formula, rc, is developed. An empirical trial of this coefficient and other bounds proposed by Gulliksen demonstrates that, for moderately speeded tests, the coefficient of equivalence can be determined approximately from single-trial data. It is proposed that the degree to which tests are speeded be investigated explicitly, and an index τ is advanced to define this concept.

Evidence-based insertion and maintenance bundles are effective in reducing the incidence of central line-associated bloodstream infections (CLABSI) in intensive care unit (ICU) settings. We studied the adoption and compliance of CLABSI prevention bundle programs and CLABSI rates in ICUs in a large network of acute care hospitals across Canada.

The incidence of Kawasaki Disease has a peak in the winter months with a trough in late summer/early fall. Environmental/exposure factors have been associated with a time-varying incidence. These factors were altered during the COVID-19 pandemic. The study was performed through the International Kawasaki Disease Registry. Data from patients diagnosed with acute Kawasaki Disease and Multiple Inflammatory Syndrome-Children were obtained. Guideline case definitions were used to confirm site diagnosis. Enrollment was from 1/2020 to 7/2023. The number of patients was plotted over time. The patients/month were tabulated for the anticipated peak Kawasaki Disease season (December–April) and non-peak season (May–November). Data were available for 1975 patients from 11 large North American sites with verified complete data and uninterrupted site reporting. The diagnosis criteria were met for 531 Kawasaki Disease and 907 Multiple Inflammatory Syndrome-Children patients. For Multiple Inflammatory Syndrome-Children there were peaks in January of 2021 and 2022. For Kawasaki Disease, 2020 began (January–March) with a seasonal peak (peak 26, mean 21) with a subsequent fall in the number of cases/month (mean 11). After the onset of the pandemic (April 2020), there was no clear seasonal Kawasaki Disease variation (December–April mean 12 cases/month and May–November mean 10 cases/month). During the pandemic, the prevalence of Kawasaki Disease decreased and the usual seasonality was abolished. This may represent the impact of pandemic public health measures in altering environmental/exposure aetiologic factors contributing to the incidence of Kawasaki Disease.

An experiment was conducted in 2022 and 2023 near Rocky Mount and Clayton, NC, to evaluate residual herbicide-coated fertilizer for cotton tolerance and Palmer amaranth control. Treatments included acetochlor, atrazine, dimethenamid-P, diuron, flumioxazin, fluometuron, fluridone, fomesafen, linuron, metribuzin, pendimethalin, pyroxasulfone, pyroxasulfone + carfentrazone, S-metolachlor, and sulfentrazone. Each herbicide was individually coated on granular ammonium sulfate (AMS) and top-dressed at 321 kg ha−1 (67 kg N ha−1) onto 5- to 7-leaf cotton. The check plots received the equivalent rate of nonherbicide-treated AMS. Before top-dress, all plots (including the check) were treated with glyphosate and glufosinate to control previously emerged weeds. All herbicides except metribuzin resulted in transient cotton injury. Cotton response to metribuzin varied by year and location. In 2022, metribuzin caused 11% to 39% and 8% to 17% injury at the Clayton and Rocky Mount locations, respectively. In 2023, metribuzin caused 13% to 32% injury at Clayton and 73% to 84% injury at Rocky Mount. Pyroxasulfone (91%), pyroxasulfone + carfentrazone (89%), fomesafen (87%), fluridone (86%), flumioxazin (86%), and atrazine (85%) controlled Palmer amaranth ≥85%. Pendimethalin and fluometuron were the least effective treatments, resulting in 58% and 62% control, respectively. As anticipated, early season metribuzin injury translated into yield loss; plots treated with metribuzin yielded 640 kg ha−1 and were comparable to yields after linuron (790 kg ha−1) was used. These findings suggest that with the exception of metribuzin, residual herbicides coated onto AMS may be suitable and effective in cotton production, providing growers with additional modes of action for late-season control of multiple herbicide–resistant Palmer amaranth.