McCormick (United States)

Numerous approaches have been used to denoise spinal cord functional magnetic resonance imaging (fMRI) data. Principal component analysis (PCA)-based techniques, which derive regressors from a noise region of interest (ROI), have been used in both brain (e.g., CompCor) and spinal cord fMRI. However, spinal cord fMRI denoising methods have yet to be systematically evaluated. Here, we formalize and evaluate a PCA-based technique for deriving nuisance regressors for spinal cord fMRI analysis (SpinalCompCor). In this method, regressors are derived with PCA from a noise ROI, an area defined outside of the spinal cord and cerebrospinal fluid. A parallel analysis is used to systematically determine how many components to retain as regressors for modeling; this designated a median of 9 regressors across four fMRI datasets: motor task (n = 26), breathing task (n = 27), and resting state (n = 15 and n = 10). First-level fMRI modeling demonstrated that principal component regressors did fit noise (e.g., physiological noise from blood vessels), though the effectiveness may be dependent upon the acquisition parameters. However, group-level activation maps did not show a clear benefit from including SpinalCompCor regressors. The potential for collinearity of principal component regressors with the task may be a concern, and this should be considered in future implementations for which task-correlated noise is anticipated. In general, denoising with SpinalCompCor regressors in place of physiological recording-derived regressors is only recommended when the latter are unavailable, as SpinalCompCor may not consistently reproduce recording-based denoising across datasets or acquisitions.

Multi-echo independent component analysis (ME-ICA) has been demonstrated to improve sensitivity and reliability of task functional magnetic resonance imaging (fMRI) data and, in particular, motor-task data with inherent task-correlated head motion. However, previous work has shown that an overly aggressive ME-ICA denoising approach may unintentionally remove task-related signal, while a more conservative approach may not effectively mitigate noise. While the effects of varied implementations of ME-ICA on signal and noise characteristics have been tested thoroughly in breath-hold data, the effects of similar modeling decisions have not been studied in motor-task data, which present with a more localized neural response. Here, we tested and compared the impacts of three analysis methods using rejected ME-ICA components as regressors in subject-level modeling: Aggressive (simple inclusion of ME-ICA regressors), Moderate (excluding task-correlated ME-ICA regressors from the model), and Conservative (orthogonalization of ME-ICA regressors to the base model and accepted ME-ICA components). We applied these methods to data from healthy and multiple sclerosis populations that included performance of hand-grasp, shoulder-abduction, and ankle-flexion tasks. We found that when the amount of head motion and its correlation with the task was high and the expected task-evoked signal was relatively low, the Conservative method led to significantly higher activation, t-statistics, and test-retest reliability in motor regions compared to the Aggressive and Moderate methods. Future motor-task studies may wish to implement similar models to prevent loss of motor signal, while still mitigating the effects of task-correlated head motion.

Abstract We present panchromatic 0.4–21 μ m observations of the nearby (∼7.2 Mpc) Type II supernova (SN) 2024ggi, obtained during the plateau phase at ∼55 days past explosion. Our data set includes JWST spectra spanning 1.7–14 μ m, mid-infrared (MIR) imaging at 7.7 and 21 μ m, and near-simultaneous ground-based optical and near-infrared (NIR) spectra covering 0.32−1.8 μ m. The NIR and MIR spectral features of SN 2024ggi are dominated by H i emission. We present line IDs and a toy PHOENIX/1D model that reproduces the observations well, especially the continuum redward of 0.9 μ m. We compare SN 2024ggi to SN 2022acko and SN 2023ixf, two other Type II SNe that were also observed by JWST, and highlight key similarities and differences in their spectral features. No evidence for a MIR excess or dust is found at these epochs, with the model matching the observed flux out to 21 μ m. We discuss the model’s shortcomings, focusing on the density profile, which suppresses line blanketing and produces features in the optical that are too narrow. Our results show the power of panchromatic studies in both exploring the nature of the SN ejecta and constraining detailed models of SNe.

Developing a skilled governmental public health workforce requires intentional training opportunities that extend beyond foundational skills. Field placement programs, offered through the Public Health Training Center Network, provide students with practical experience while supporting agency capacity. This Practice Brief Report examines governmental public health field placements sponsored by the Region IV Public Health Training Center between 2019 and 2024 (n=75). Student evaluations showed frequent practice in data analytics and assessment, policy development and program planning, and communication skills, areas reflecting organizational strengths. However, the findings of Public Health Workforce Interest and Needs Survey highlighted critical workforce gaps in higher-level skills such as budget and financial management, policy engagement, and leadership and systems thinking. Field placement experiences offer an opportunity to introduce students to these complex competencies early in their careers. Intentionally integrating higher-level skills into placement design can strengthen student preparation and help ensure a future workforce ready to address evolving public health challenges.

The influence ofmaterial properties on ion-hydrated polymer thermodynamicinteractions is not fully understood. In this study, we probed howpolymer properties (i.e., the network mesh size and dielectric constant)contribute to interactions between ions, water molecules, and thesolvated polymer by synthesizing polymer networks with varied cross-linkdensity and functionality (e.g., hydroxyl, ether, or nitrile). Wecharacterized the hydration-dependent network mesh size, relativepermittivity (i.e., dielectric constant), and the sodium chloridemean ionic activity coefficients in the polymers and related theseproperties to each other using a theoretical model that describesquantitatively the influence of ion solvation and ion pairing interactionson ionic activity coefficients in neutral polymers. These resultsand analysis help to explain the relationship between the networkmesh size, polymer dielectric constant, and mean ionic activity coefficientsin solvated polymers, which may be useful to guide molecular engineeringstrategies for polymer membrane materials.

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Numerous approaches have been used to denoise spinal cord functional magnetic resonance imaging (fMRI) data. Principal component analysis (PCA)-based techniques, which derive regressors from a noise region of interest (ROI), have been used in both brain (e.g., CompCor) and spinal cord fMRI. However, spinal cord fMRI denoising methods have yet to be systematically evaluated. Here, we formalize and evaluate a PCA-based technique for deriving nuisance regressors for spinal cord fMRI analysis (SpinalCompCor). In this method, regressors are derived with PCA from a noise ROI, an area defined outside of the spinal cord and cerebrospinal fluid. A parallel analysis is used to systematically determine how many components to retain as regressors for modeling; this designated a median of 9 regressors across four fMRI datasets: motor task (n=26), breathing task (n=27), and resting state (n=15 and n=10). First-level fMRI modeling demonstrated that principal component regressors did fit noise (e.g., physiological noise from blood vessels), though the effectiveness may be dependent upon the acquisition parameters. However, group-level activation maps did not show a clear benefit from including SpinalCompCor regressors. The potential for collinearity of principal component regressors with the task may be a concern, and this should be considered in future implementations for which task-correlated noise is anticipated.

Electronic waste (E-waste) is the fastest-growing wastestreamglobally, reaching 74.7 tonnes by 2030, containing significant amountsof valuable metals such as gold, silver, platinum, and copper. Mechanical,hydrometallurgical, pyrometallurgical, electrochemical, and biotechnologicalmethods for recovering these metals from E-waste are often inefficient,costly, and environmentally harmful. This study presents the firstdemonstrations of laser ablation in recovering gold in the form ofmicro and higher-valued nanoparticles from E-waste. The ablation thresholdis identified using modeling performed using the two-temperature model(TTM). Printed Circuit Boards (PCBs) with gold-plated electrodes wereused as the target material. The laser ablation process was conductedusing a picosecond UV-355 nm laser at maximum average laser power(18 W). The analysis, using UV–visible spectroscopy, showsthe surface plasmon resonance peak at 523 nm for gold nanoparticles­(AuNPs), and SEM–EDX mapping confirmed the successful creationof high-purity (90 wt %) Au NPs with an average size of 100 nm. Laser-InducedBreakdown Spectroscopy (LIBS) was used to monitor the elemental compositionof the E-waste sample during the ablation process to demonstrate real-timeprocessing monitoring. The ability to recover gold in nanoparticleform further enhances the economic viability of this technique givinga wide range of applications for gold nanoparticles in various fields.The findings underscore the potential of laser ablation as a sustainablesolution for E-waste recycling, addressing critical global challengesrelated to the recovery of valuable materials.