The formation of Kelvin–Helmholtz-like rollers (referred to as K–H rollers) over riblet surfaces has been linked to the drag-increasing behaviour seen in certain riblet geometries, such as sawtooth and blade riblets, when the riblet size reaches sufficiently large viscous scales (Endrikat et al. (2021a), J. Fluid Mech. 913, A37). In this study, we focus on the sawtooth geometry of fixed physical size, and experimentally examine the response of these K–H rollers to further increases in viscous scaled riblet sizes, by adopting the conventional approach of increasing freestream speeds (and consequently, the friction Reynolds number). Rather than continual strengthening, the present study shows a gradual weakening of these K–H rollers with increasing sawtooth riblet size. This is achieved by an analysis of the roller geometric characteristics using both direct numerical simulations and hot-wire anemometry databases at matched viscous scaled riblet spacings, with the former used to develop a novel methodology for detecting these rollers via streamwise velocity signatures (e.g. as acquired by hot wires). Spectral analysis of the streamwise velocity time series, acquired within riblet grooves, reveals that the frequencies (and the streamwise wavelengths) of the K–H rollers increase with increasing riblet size. Cross-correlation spectra, estimated from unique two-point hot-wire measurements in the cross-plane, show a weakening of the K–H rollers and a reduction in their wall-normal coherence with increasing riblet size. Besides contributing to our understanding of the riblet drag-increasing mechanisms, the present findings also have implications for the heat transfer enhancing capabilities of sawtooth riblets, which have been associated previously with the formation of K–H rollers. The present study also suggests conducting future investigations by decoupling the effects of viscous scaled riblet spacing and friction Reynolds numbers, to characterise their influence on the K–H rollers independently.

Florpyrauxifen-benzyl is a postemergence (POST) rice herbicide that has reduced rice yield in some situations, and producers are concerned that the impact could be even greater with low rice seeding densities. Therefore, research was conducted in Stoneville, MS, from 2019 to 2021 to evaluate the effect of florpyrauxifen-benzyl on rice yield when a hybrid was seeded at reduced densities. Hybrid rice ‘RT 7521 FP’ was seeded at 10, 17, 24, 30, and 37 kg ha-1. At the four-leaf to one-tiller growth stage, florpyrauxifen-benzyl was applied at 0 or 59 g ai ha-1. Rice injury following the application of florpyrauxifen-benzyl was ≤ 8% across all seeding rates and evaluation intervals. Application of florpyrauxifen-benzyl reduced plant heights by 14% across all seeding rates but did not result in delayed rice maturity. When florpyrauxifen-benzyl was not applied rice in the 10 and 17 kg ha-1 seeding rates, rice matured slower than when seeded at 24, 30, and 37 kg ha-1. When florpyrauxifen-benzyl was applied, rough rice grain yields were reduced by 17 and 37 kg ha-1 but not at any other seeding rate. In conclusion, the application of florpyrauxifen-benzyl at a 2x rate can cause a loss of yield resulting from variation in rice densities.

Objectives/Goals: Electronic health record (EHR)-based recruitment can facilitate participation in clinical trials, but is not a panacea to trial accrual challenges. We conducted a root cause analysis to identify EHR-based accrual barriers and facilitators in a pragmatic randomized trial of metformin for those with prostate cancer and glucose intolerance. Methods/Study Population: We quantitatively analyzed enrollment drop-offs among eligible patients who either did not complete a consent (with analysis of EHR-embedded consent process) or who completed a consent but were not enrolled (with analysis of EHR implementation of a Best Practice Alert). We summarized data from the EHR by eligibility, provider encounters, and alerts, and generated CONSORT diagrams and tables to trace the enrollment pathway. We supplemented quantitative findings with a thematic analysis of semi-structured individual interviews with eligible patients (n = 10) and study providers (n = 4) to identify systematic barriers to recruitment and enrollment of eligible patients. Results/Anticipated Results: CONSORT diagram analysis found that 24% of potentially eligible patients (268 of 1130) had an eligible study encounter but were not enrolled. Additionally, BPAs were not triggering for some eligible patients. Interviews revealed that study providers wanted more detailed information about which study arm their patient would be assigned to, and about next steps after enrollment, especially relating to additional lab tests and follow-up care needed. Patient interviews suggested that patients often did not remember completing the consent process and felt overwhelmed with appointments and information; patients expected providers to actively bring up research opportunities during appointments. Discussion/Significance of Impact: While pragmatic EHR-embedded trials are often characterized as lower-burden, these trials still require active engagement by providers, as well as ongoing attention from both research and informatics teams to ensure that EHR-embedded processes are functioning as designed, and that they are effective in recruiting study participants.

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.

Objectives/Goals: Cerebral amyloid angiopathy (CAA) characterized by the accumulation of amyloid-beta in the cerebrovasculature, affects blood vessel integrity leading to brain hemorrhages and an accelerated cognitive decline in Alzheimer’s disease patients. In this study, we are conducting a genome-wide association study to identify genetic risk factors for CAA. Methods/Study Population: We genotyped 1253 additional AD cases using and curated existing genome-wide genotype data from 110 AD and 502 non-AD donors from the Mayo Clinic Brain Bank. We performed QC and imputation of all datasets. We conducted GWAS in AD only (N =  1,363), non-AD only, as well as the combined cohort (N = 1,865) by testing imputed variant dosages for association with square root transformed CAA using linear regression, adjusting for relevant covariates. To assess associations in the context of major CAA risk factors, we performed interaction analysis with APOEe4 presence and sex; and pursued stratified analyses. We collected peripheral gene expression measures using RNA isolated from 188 PAXgene tube samples of 95 donors collected across multiple time points. More than 1/3 of these participants have MRI measures collected. Results/Anticipated Results: Variants at the APOE locus were identified as the most significant in our study. In addition, several other variants with suggestive association were found under the main model adjusting for AD neuropathology (Braak and Thal). LINC-PINT splice variant remained associated with lower CAA scores in AD cases without the APOEe4 risk allele. To enhance the robustness of our findings, we are pursuing further expansion of our study cohort. In the periphery, we expect to identify expression changes associated with neuroimaging indicators of CAA and determine if variants and genes discovered via GWAS are implicated in these changes. Discussion/Significance of Impact: We expect this study will provide further insight into the genetic architecture underlying risk for CAA both in the context of significant AD pathology and without. Characterization of genetic variants and functional outcomes in the context of neuropathology may lead to new avenues of research aimed at identifying biomarkers and therapies to treat CAA

Objectives/Goals: The timing of neurosurgery is highly variable for post-hemorrhagic hydrocephalus (PHH) of prematurity. We sought to utilize microvascular imaging (MVI) in ultrasound (US) to identify biomarkers to discern the opportune time for intervention and to analyze the cerebrospinal fluid (CSF) characteristics as they pertain to neurosurgical outcome. Methods/Study Population: The inclusion criteria for the study are admission to the neonatal intensive care unit (NICU) with a diagnosis of Papile grade III or IV. Exclusion criteria are congenital hydrocephalus and hydrocephalus secondary to myelomeningocele/brain tumor/vascular malformation. We are a level IV tertiary referral center. Our current clinical care pathway utilizes brain US at admission and at weekly intervals. Patients who meet certain clinical and radiographic parameters undergo temporary or permanent CSF diversion. Results/Anticipated Results: NEL was implemented at our institution for PHH of prematurity in fall 2022. To date, we have had 20 patients who were diagnosed with grade III or IV IVH, of which 12 qualified for NEL. Our preliminary safety and feasibility results as well as the innovative bedside technique pioneered at our institution are currently in revision stages for publication. Preliminary results of the MVI data have yielded that hyperemia may confer venous congestion in the germinal matrix, which should then alert the neurosurgeon to delay any intervention to avoid progression of intraventricular blood. With regard to CSF characteristics, we anticipate that protein, cell count, hemoglobin, iron, and ferritin will decrease with NEL. Discussion/Significance of Impact: The timing of PHH of prematurity is highly variable. We expect that MVI will offer radiographic biomarkers to guide optimal timing of neurosurgical intervention. A better understanding of CSF characteristics could potentially educate the neurosurgeon with regard to optimal timing of permanent CSF diversion based on specific CSF parameters.

Objectives/Goals: This study aims to evaluate the performance of a third-party artificial intelligence (AI) product in predicting diagnosis-related groups (DRGs) in a community healthcare system. We highlight a use case illustrating how clinical practice leverages AI-predicted information in unexpected yet advantageous ways and assess the AI predictions accuracy and practical application. Methods/Study Population: DRGs are crucial for hospital reimbursement under the prospective payment model. The Mayo Clinic Health System (MCHS), a network of clinics and hospitals serving a substantial rural population in Minnesota and Wisconsin, has recently adopted an AI algorithm developed by Xsolis (an AI-focused healthcare solution provider). This algorithm, a 1D convolutional neural network, predicts DRGs based on clinical documentation. To assess the accuracy of AI-generated DRG predictions for inpatient discharges, we analyzed data from 930 patients hospitalized at MCHS Mankato between March 2 and May 13, 2024. The Xsolis platform provided the top three DRG predictions for the first 48 hours of each inpatient stay. The accuracy of these predictions was then compared against the final billed DRG codes from the hospital’s records. Results/Anticipated Results: In our validation set, Xsolis achieved a top-3 DRG prediction accuracy of 71% at 24 hours and 81% at 48 hours, which is lower than the originally reported accuracy of 81.1% and 83.3%, respectively. Interestingly, discussions with clinical practice leaders revealed that the most valuable information derived from the AI predictions was the expected geometric mean length of stay (GMLOS), which Xsolis was perceived to predict accurately. In the Medicare system, each DRG is associated with an expected GMLOS, a critical factor for efficient hospital flow planning. A subsequent analysis comparing predicted GMLOS with the actual length of stay showed variances of -0.10 days on day 1 and 0.14 days on day 2, indicating a high degree of accuracy and aligning with clinical practice perceptions. Discussion/Significance of Impact: Our research underscores that clinical practice can leverage AI predictions in unexpected yet beneficial ways. While initially focused on DRG prediction, the associated GMLOS emerged as more significant. This suggests that AI algorithm validation should be tailored to specific clinical needs rather than relying solely on generalized benchmarks.

We provide an assessment of the Infinity Two Fusion Pilot Plant (FPP) baseline plasma physics design. Infinity Two is a four-field period, aspect ratio A = 10, quasi-isodynamic stellarator with improved confinement appealing to a max-J approach, elevated plasma density and high magnetic fields (⟨B⟩ = 9 T). At the envisioned operating point [800 MW deuterium-tritium (DT) fusion], the configuration has robust magnetic surfaces based on magnetohydrodynamic (MHD) equilibrium calculations and is stable to both local and global MHD instabilities. The configuration has excellent confinement properties with small neoclassical transport and low bootstrap current (|Ibootstrap| ∼ 2 kA). Calculations of collisional alpha particle confinement in a DT FPP scenario show small energy losses to the first wall (< 1.5%) and stable energetic particle/Alfvén eigenmodes at high ion density. Low turbulent transport is produced using a combination of density profile control consistent with pellet fueling and reduced stiffness to turbulent transport via three-dimensional shaping. Transport simulations with the T3D-GX-SFINCS code suite with self-consistent turbulent and neoclassical transport predict that the Pfus = 800 MW operating point is attainable with high fusion gain (Q = 40) at volume-averaged electron densities ne ≈ 2×1020 m−3, below the Sudo density limit. Additional transport calculations show that an ignited (Q = ∞) solution is available at slightly higher density (2.2×1020 m−3) with Pfus = 1.5 GW. The magnetic configuration is defined by a magnetic coil set with sufficient room for an island divertor, shielding and blanket solutions with tritium breeding ratios (TBR) above unity. An optimistic estimate for the gas-cooled solid breeder designed Helium Cooled Pebble Bed is TBR ∼ 1.3. Infinity Two satisfies the physics requirements of a stellarator fusion pilot plant.

Using National Healthcare Safety Network data, an interrupted time series of intravenous antimicrobial starts (IVAS) among hemodialysis patients was performed. Annual adjusted rates decreased by 6.64% (January 2012–March 2020) and then further decreased by 8.91% until December 2021. IVAS incidence trends have decreased since 2012, including during the early COVID-19 pandemic.

In this work, we present a detailed assessment of fusion-born alpha-particle confinement, their wall loads, and stability of Alfvén eigenmodes driven by these energetic particles in the Infinity Two Fusion Pilot Plant Baseline Plasma Design, a 4-field-period quasiisodynamic stellarator to operate in deuterium-tritium fusion conditions. Using the Monte-Carlo codes SIMPLE, ASCOT5, and KORC-T, we study the collisionless and collisional dynamics of guiding-center and full-orbit alpha-particles in the core plasma. We find that core energy losses to the wall are less than 4%. Our simulations shows that peak power loads on the wall of this configuration are around 2.5 MW/m2 and are spatially localized, toroidally, and poloidaly in the vicinity of x-points of the magnetic island chain n/m = 4/5 outside the plasma volume. Also, an exploratory analysis using various simplified walls shows that shaping and distance of the wall from the plasma volume can help reduce peak power loads. Our stability assessment of Alfvén eigenmodes using the STELLGAP and FAR3d codes shows the absence of unstable modes driven by alpha-particles in Infinity Two due to the relatively low alpha-particle beta at the envisioned 800 MW operating scenario.

An analysis of the divertor designs for the Infinity Two Fusion Pilot Plant (FPP) Baseline Plasma Design is presented. The divertor uses an m = 5, n = 4 magnetic island chain. Two divertor designs are presented. A classical divertor that is similar to the W7-X island divertor is analyzed using diffusive field line following and the fluid code EMC3-Lite. For a baseline 800 MW operating point in Infinity Two, the conditions where the heat flux on the divertor plate remains in the acceptable region are analyzed. In addition a related, but different and novel Large Island Backside Divertor (LIBD) design is shown. The LIBD promises improved neutral pumping by closing the divertor through the use of baffling and with a structure inside the island, thus preventing neutralized plasma particles from reentering the plasma.

Transport characteristics and predicted confinement are shown for the Infinity Two fusion pilot plant baseline plasma physics design, a high field stellarator concept developed using modern optimization techniques. Transport predictions are made using high fidelity nonlinear gyrokinetic turbulence simulations along with drift kinetic neoclassical simulations. A pellet fueled scenario is proposed that enables supporting an edge density gradient to substantially reduce ion temperature gradient turbulence. Trapped electron mode turbulence is minimized through the quasi-isodynamic configuration that has been optimized with max-J. A baseline operating point with deuterium-tritium fusion power of Pfus,DT = 800 MW with high fusion gain Qfus = 40 is demonstrated, respecting the Sudo density limit and magnetohydrodynamic stability limits. Additional higher power operating points are also predicted, including a fully ignited (Qfus = ∞) case with Pfus,DT = 1.5 GW. Pellet ablation calculations indicate it is plausible to fuel and sustain the desired density profile. Impurity transport calculations indicate turbulent fluxes dominate neoclassical fluxes deep into the core, and it is predicted that impurity peaking will be smaller than assumed in the transport simulations. A path to access large radiation fraction needed to satisfy exhaust requirements while sustaining core performance is also discussed.

The magneto-hydrodynamic equilibrium and stability properties of the Infinity Two Fusion Pilot Plant baseline plasma physics design are presented. The configuration is a four field period, aspect ratio A = 10 quasi-isodynamic stellarator optimized for excellent confinement at elevated density and high magnetic field B = 9 T. Magnetic surfaces exist in the plasma core in vacuum and retain good equilibrium surface integrity from vacuum to an operational β = 1.6%, the ratio of the volume average of the plasma and magnetic pressures, corresponding to 800 MW Deuterium-Tritium fusion operation. Neoclassical calculations show that a selfconsistent bootstrap current on the order of ∼ 1 kA slightly increases the rotational transform profile by less than 0.001. The configuration has a magnetic well across its entire radius. From vacuum to the operating point, the configuration exhibits good ballooning stability characteristics, exhibits good Mercier stability across most of its minor radius, and it is stable against global low-n MHD instabilities up to β = 3.2%.

The selection, design, and optimization of a suitable blanket configuration for an advanced high-field stellarator concept is seen as a key feasibility issue and has been incorporated as a vital and necessary part of the Infinity Two Fusion Pilot Plant (FPP) physics basis. The focus of this work was to identify a baseline blanket which can be rapidly deployed for Infinity Two while also maintaining flexibility and opportunities for higher performing concepts later in development. Results from this analysis indicate that gas-cooled solid breeder designs such as the Helium Cooled Pebble Bed (HCPB) are the most promising concepts, primarily motivated by the neutronics performance at applicable blanket build depths, and the relatively mature technology basis. The lithium lead (PbLi) family of concepts, particularly the Dual Cooled Lithium Lead (DCLL), offer a compelling alternative to solid blanket concepts as they have synergistic developmental pathways while simultaneously mitigating much of the technical risk of those designs. Homogenized 3-dimensional neutronics analysis of the Infinity Two configuration indicates that the HCPB achieves an adequate tritium breeding ratio (TBR) (1.30 which enables sufficient margin at low engineering fidelity), and near appropriate shielding of the magnets (average fast fluence of 1.3 x 1018 n/cm2 per fullpower year). The thermal analysis indicates that reasonably high thermal efficiencies (greater than 30%) are readily achievable with the HCPB paired with a simple Rankine cycle using reheat. Finally, the tritium fuel cycle analysis for Infinity Two shows viability, with anticipated operational inventories of less than one kilogram (approximately 675 grams) and a required TBR (TBRreq) of less than 1.05 to maintain fuel self-sufficiency (approximately 1.023 for a driver blanket with no inventory doubling). Although further optimization and engineering design is still required, at the physics basis stage all initial targets have been met for the Infinity Two configuration.

This work presents visual morphological and dynamical classifications for 637 spatially resolved galaxies, most of which are at intermediate redshift (z ∼ 0.3), in the Middle-Ages Galaxy Properties with Integral field spectroscopy (MAGPI) Survey. For each galaxy, we obtain a minimum of 11 independent visual classifications by knowledgeable classifiers. We use an extension of the standard Dawid-Skene Bayesian model introducing classifier-specific confidence parameters and galaxy-specific difficulty parameters to quantify classifier confidence and infer reliable statistical confidence estimates. Selecting sub-samples of 86 bright (r < 20 mag) high-confidence (> 0.98) morphological classifications at redshifts (0.2 ≤ z ≤ 0.4), we confirm the full range of morphological types is represented in MAGPI as intended in the survey design. Similarly, with a sub-sample of 82 bright high-confidence stellar kinematic classifications, we find that the rotating and non-rotating galaxies seen at low redshift are already in place at intermediate redshifts. We do not find evidence that the kinematic morphology-density relation seen at z ∼ 0 is established at z ∼ 0.3. We suggest that galaxies without obvious stellar rotation are dynamically pre-processed sometime before z ∼ 0.3 within lower mass groups before joining denser environments.

Experimental methods are currently being extensively used to elicit subjective values for commodities and projects. Three methodological problems are not systematically addressed in this emerging literature. The first is the potential for laboratory responses to be censored by field opportunities, so that lab responses can be confounded by uncontrolled knowledge of the field; the second is the potential for subjective perceptions about field opportunities, and hence valuation responses, to be affected by the institution used to elicit values; and the third is the potential for some elicitation institutions to influence subjective perceptions of characteristics of the commodity or project being valued, and hence change the very commodity being valued. All three problems result in potential loss of control over the value elicitation process. For example, we show that censoring affects conclusions drawn in a major study of beef packaging valuation. We derive implications for experimental designs that minimize the potential effect of these methodological problems.