Federal Impacts on Buprenorphine Prescribing in Washington State, 2012 to 2022.

Objectives. To evaluate changes in monthly buprenorphine dispensation associated with federal prescribing policies in Washington State from 2012 to 2022. Methods. We conducted an interrupted time series analysis comparing monthly buprenorphine prescriptions dispensed per 1000 population after the Comprehensive Addiction and Recovery Act (CARA), Substance Use-Disorder Prevention That Promotes Opioid Recovery and Treatment for Patients and Communities Act (SUPPORT), and new prescribing rules during the COVID-19 pandemic. Buprenorphine formulated for opioid use disorder was included from the Washington State Prescription Monitoring Program. A log-linear autoregressive model measured linear trend changes. Results. Physician prescribing increased by 1.63% (95% confidence interval [CI] = 1.41%, 1.85%) per month after CARA with sustained declines after SUPPORT. Nurse practitioner (NP) prescribing increased by 19.48% (95% CI = 18.8%, 20.16%) per month after CARA with physician assistants (PAs) showing similar trends. Following the implementation of SUPPORT, NP and PA trends continued to increase at a reduced growth rate of 3.96% (95% CI = 2.01%, 5.94%) and 1.87% (95% CI = 0.56%, 3.19%), respectively. No prescribers experienced increases during the COVID-19 pandemic. Conclusions. CARA nearly tripled the buprenorphine prescribing rate. The SUPPORT Act initiated sustained declines for physician prescribing, and the COVID-19 period reversed gains for PAs and NPs. The current opioid crisis requires expanded efforts in Washington State. (Am J Public Health. 2024;114(7):696-704. https://doi.org/10.2105/AJPH.2024.307649).

Sparse multiway canonical correlation analysis for multimodal stroke recovery data.

Conventional canonical correlation analysis (CCA) measures the association between two datasets and identifies relevant contributors. However, it encounters issues with execution and interpretation when the sample size is smaller than the number of variables or there are more than two datasets. Our motivating example is a stroke-related clinical study on pigs. The data are multimodal and consist of measurements taken at multiple time points and have many more variables than observations. This study aims to uncover important biomarkers and stroke recovery patterns based on physiological changes. To address the issues in the data, we develop two sparse CCA methods for multiple datasets. Various simulated examples are used to illustrate and contrast the performance of the proposed methods with that of the existing methods. In analyzing the pig stroke data, we apply the proposed sparse CCA methods along with dimension reduction techniques, interpret the recovery patterns, and identify influential variables in recovery.

Regularized aggregation of statistical parametric maps.

Combining statistical parametric maps (SPM) from individual subjects is the goal in some types of group-level analyses of functional magnetic resonance imaging data. Brain maps are usually combined using a simple average across subjects, making them susceptible to subjects with outlying values. Furthermore, t tests are prone to false positives and false negatives when outlying values are observed. We propose a regularized unsupervised aggregation method for SPMs to find an optimal weight for aggregation, which aids in detecting and mitigating the effect of outlying subjects. We also present a bootstrap-based weighted t test using the optimal weights to construct an activation map robust to outlying subjects. We validate the performance of the proposed aggregation method and test using simulated and real data examples. Results show that the regularized aggregation approach can effectively detect outlying subjects, lower their weights, and produce robust SPMs.

Identification of quantitative trait loci for increased α-tocopherol biosynthesis in wild soybean using a high-density genetic map.

BACKGROUND: Soybean is one of the most important crop sources of tocopherols (Toc). However, the content of α-Toc, an isoform with the highest vitamin E activity in humans, is low in most cultivars. With the aim of broadening genetic variability, we performed quantitative trait locus (QTL) analysis for a high seed α-Toc trait detected in a wild soybean and characterized the sequence polymorphisms and expression profiles of γ-tocopherol methyltransferase (γ-TMT) genes as potential candidates. RESULTS: A recombinant inbred line population was developed from a cross between the low α-Toc breeding line TK780 and the high α-Toc wild accession B04009. The α-Toc content in seeds correlated strongly with the ratio of α-Toc to γ-Toc contents. QTL analysis using a high-density map constructed with 7710 single nucleotide polymorphisms (SNPs) generated by restriction site-associated DNA sequencing detected six QTLs involved in α-Toc biosynthesis. Of these, three in chromosomes (Chr) 9, 11, and 12 produced consistent effects during a 2-year trial. B04009 allele at QTLs in Chr9 and Chr12 and TK780 allele at the QTL in Chr11 each promoted the conversion of γ-Toc to α-Toc, which elevated the seed α-Toc content. SNPs and indels were detected between the parents in three γ-TMT genes (γ-TMT1, γ-TMT2, and γ-TMT3) co-located in the QTLs in Chr9 and Chr12, of which some existed in the cis-regulatory elements associated with seed development and functions. In immature cotyledons, γ-TMT3 was expressed at higher levels in B04009 than TK780, irrespective of two thermal conditions tested, whereas the expression of γ-TMT2 was markedly upregulated under higher temperatures, particularly in B04009. CONCLUSIONS: We identified QTLs consistently controlling α-Toc biosynthesis in wild soybean seeds in 2-year trials. The QTL on Chr9 had been previously identified in soybean, whereas the QTLs on Chr11 and Chr12 were novel. Further molecular dissections and characterization of the QTLs may facilitate the use of high α-Toc alleles from wild soybean in soybean breeding and an understanding of the molecular mechanisms underlying α-Toc biosynthesis in soybean seeds.

Convex clustering analysis for histogram-valued data.

In recent years, there has been increased interest in symbolic data analysis, including for exploratory analysis, supervised and unsupervised learning, time series analysis, etc. Traditional statistical approaches that are designed to analyze single-valued data are not suitable because they cannot incorporate the additional information on data structure available in symbolic data, and thus new techniques have been proposed for symbolic data to bridge this gap. In this article, we develop a regularized convex clustering approach for grouping histogram-valued data. The convex clustering is a relaxation of hierarchical clustering methods, where prototypes are grouped by having exactly the same value in each group via penalization of parameters. We apply two different distance metrics to measure (dis)similarity between histograms. Various numerical examples confirm that the proposed method shows better performance than other competitors.

Mindfulness, awareness, partner caring, and romantic relationship quality: Structural equation modeling.

Studies investigating the potential structures underlying the connection between mindfulness and romantic relationship quality are not sufficient and require additional empirical findings. The current study aimed to examine a hypothesized model underlying the association between mindfulness and relationship quality, while investigating the roles of individuals' level of awareness and application of care in a romantic relationship among 199 adult participants in a southeastern region of the United States. Structural equation modeling (SEM) and path analysis were executed to examine the hypothesized structural model, testing a multi-step pathway as to how mindfulness influences relationship quality. The SEM and path analyses showed a good fit for the present study's hypothesized model, indicating that individuals' mindfulness had a direct influence on their self-awareness level. Individuals' self-awareness and application of care mediated the link between mindfulness and relationship quality. Implications for family life educators, dyadic couples researchers, and marriage and family practitioners are discussed.

C-terminally encoded peptide-like genes are associated with the development of primary root at qRL16.1 in soybean.

Root architecture traits are belowground traits that harness moisture and nutrients from the soil and are equally important to above-ground traits in crop improvement. In soybean, the root length locus qRL16.1 was previously mapped on chromosome 16. The qRL16.1 has been characterized by transcriptome analysis of roots in near-isogenic lines (NILs), gene expression analysis in a pair of lines contrasting with alleles of qRL16.1, and differential gene expression analysis in germplasm accessions contrasting with root length. Two candidate genes, Glyma.16g108500 and Glyma.16g108700, have shown relatively higher expression in longer root accessions than in shorter rooting accessions. The C-terminal domain of Glyma.16g108500 and Glyma.16g108700 is similar to the conserved domain of C-terminally encoded peptides (CEPs) that regulate root length and nutrient response in Arabidopsis. Two polymorphisms upstream of Glyma.16g108500 showed a significant association with primary root length and total root length traits in a germplasm set. Synthetic peptide assay with predicted CEP variants of Glyma.16g108500 and Glyma.16g108700 demonstrated their positive effect on primary root length. The two genes are root-specific in the early stage of soybean growth and showed differential expression only in the primary root. These genes will be useful for improving soybean to develop a deep and robust root system to withstand low moisture and nutrient regimes.

A Study on the Impact of Financial Literacy and Digital Capabilities on Entrepreneurial Intention: Mediating Effect of Entrepreneurship.

In the post-COVID-19 era, the content of work and the necessary skills are rapidly changing due to the digital transformation of the way people work. Entrepreneurial adaptability and digital capability are the most necessary competencies for exploring opportunities and quickly turning them into a professional career amid a crisis. Financial literacy is also essential for expanding skills in economic and social life. The purpose of this study is to verify the influence of university students' financial literacy and digital capability on entrepreneurial intention and the mediating effect of entrepreneurship. To this end, a survey was conducted on university students in Busan and Gyeongnam, and a sample of 162 respondents was verified using SPSS 28.0. As a result of the study, it was found that financial literacy had a partially positive effect on entrepreneurship and entrepreneurial intention. Digital capability was found to have a positive effect on entrepreneurship and entrepreneurial intention. It was found that entrepreneurship had a partially positive effect on entrepreneurial intention. It was found that entrepreneurship had a partially positive mediating effect between financial literacy and entrepreneurial intention. It was found that entrepreneurship had a positive mediating effect between digital capability and entrepreneurial intention. As a result of this study, it was confirmed that financial literacy, digital capability, and entrepreneurship are very important competencies for university students to adapt to new trends and promote start-ups in a rapidly changing job environment after COVID-19, suggesting the need for further education.

Time-resolved spectroscopic investigation for the practical application of a photocatalytic air purifier.

The photocatalytic efficiency for removing volatile organic compounds (VOCs) is significantly influenced by operational parameters like humidity and flow velocity, exhibiting notable and inconsistent fluctuations in both lab-scale and large-scale demonstrations. In this study, operando spectroscopy and isotope analysis were employed to investigate the correlation between humidity levels and degradation of gaseous acetaldehyde using TiO2 photocatalysts, aiming to demonstrate the scaling-up of photocatalytic air purifier. It was observed that rate constants for the mineralization of acetaldehyde rapidly decreased by 30% as relative humidity increased from 25% to 80% in the flow system (with an air velocity, v = 0.78 m/s). However, batch system showed smaller change with only a 10% reduction of the rate constant. Humidity fluctuations were more pronounced under high-speed conditions and were amplified in air purifier (v = 3.8 m/s). Time-resolved operando spectroscopy using an 13C isotope of acetaldehyde revealed that humidity's distinct role in dark adsorption and photocatalytic reactions. Water was found to inhibit the formation of crotonaldehyde during aldol condensation reaction in dark condition. Moreover, water suppressed photocatalytic mineralization by inhibiting acetate oxidation to formate. These findings provide valuable insights for improving realistic air purification processes, underscoring the importance of identifying key intermediates and controlling humidity to enhance the selectivity of gaseous pollutant oxidation reactions.

Practice-related changes in neural activation patterns investigated via wavelet-based clustering analysis.

OBJECTIVES: To evaluate brain activation using functional magnetic resonance imaging (fMRI) and specifically, activation changes across time associated with practice-related cognitive control during eye movement tasks. EXPERIMENTAL DESIGN: Participants were engaged in antisaccade performance (generating a glance away from a cue) while fMR images were acquired during two separate test sessions: (1) at pre-test before any exposure to the task and (2) at post-test, after 1 week of daily practice on antisaccades, prosaccades (glancing toward a target), or fixation (maintaining gaze on a target). PRINCIPAL OBSERVATIONS: The three practice groups were compared across the two test sessions, and analyses were conducted via the application of a model-free clustering technique based on wavelet analysis. This series of procedures was developed to avoid analysis problems inherent in fMRI data and was composed of several steps: detrending, data aggregation, wavelet transform and thresholding, no trend test, principal component analysis (PCA), and K-means clustering. The main clustering algorithm was built in the wavelet domain to account for temporal correlation. We applied a no trend test based on wavelets to significantly reduce the high dimension of the data. We clustered the thresholded wavelet coefficients of the remaining voxels using PCA K-means clustering. CONCLUSION: Over the series of analyses, we found that the antisaccade practice group was the only group to show decreased activation from pre-test to post-test in saccadic circuitry, particularly evident in supplementary eye field, frontal eye fields, superior parietal lobe, and cuneus.

Classification of histogram-valued data with support histogram machines.

The current large amounts of data and advanced technologies have produced new types of complex data, such as histogram-valued data. The paper focuses on classification problems when predictors are observed as or aggregated into histograms. Because conventional classification methods take vectors as input, a natural approach converts histograms into vector-valued data using summary values, such as the mean or median. However, this approach forgoes the distributional information available in histograms. To address this issue, we propose a margin-based classifier called support histogram machine (SHM) for histogram-valued data. We adopt the support vector machine framework and the Wasserstein-Kantorovich metric to measure distances between histograms. The proposed optimization problem is solved by a dual approach. We then test the proposed SHM via simulated and real examples and demonstrate its superior performance to summary-value-based methods.

Dimension-wise sparse low-rank approximation of a matrix with application to variable selection in high-dimensional integrative analyzes of association.

Many research proposals involve collecting multiple sources of information from a set of common samples, with the goal of performing an integrative analysis describing the associations between sources. We propose a method that characterizes the dominant modes of co-variation between the variables in two datasets while simultaneously performing variable selection. Our method relies on a sparse, low rank approximation of a matrix containing pairwise measures of association between the two sets of variables. We show that the proposed method shares a close connection with another group of methods for integrative data analysis - sparse canonical correlation analysis (CCA). Under some assumptions, the proposed method and sparse CCA aim to select the same subsets of variables. We show through simulation that the proposed method can achieve better variable selection accuracies than two state-of-the-art sparse CCA algorithms. Empirically, we demonstrate through the analysis of DNA methylation and gene expression data that the proposed method selects variables that have as high or higher canonical correlation than the variables selected by sparse CCA methods, which is a rather surprising finding given that objective function of the proposed method does not actually maximize the canonical correlation.

Blood cell assisted in vivo Particle Image Velocimetry using the confocal laser scanning microscope.

We demonstrated the feasibility of blood cell assisted in vivo Particle Image Velocimetry using confocal microscopy. Blood flow of skin vessel in a mouse was non-invasively imaged in vivo using a confocal microscopy. The video-rate confocal microscope was used to monitor the motion of the blood cells in the capillary of a live mouse ear. The home-built confocal laser scanning microscopy allowed us to take images at the acquisition rate of 30 frames per second. The individual blood cells could be distinguished from other cells and the trajectory of the each cell could be followed in the sequential images. The acquired confocal images were used to get the velocity profile of the in vivo blood flow in conjunction with the Particle Image Velocimetry (PIV), without injecting any exogenous nano/micro particles into the mouse. We were able to measure the blood velocity up to a few hundreds µm/sec for various vessels in a live mouse. Because there is no need for the injection of the exogenous tracing particles, it is expected that we could apply the current technology to the study of human capillary blood stream.

Inference from clinical and fluid dynamic studies about underlying cause of spontaneous isolated superior mesenteric artery dissection.

PURPOSE: Due to increased use and improvements in diagnostic imaging studies, spontaneous isolated superior mesenteric artery dissection (SISMAD), which is a rare vascular event, has been reported to occur on a more frequent basis. Although there have been some anecdotal case reports describing the underlying pathology of SISMAD, the etiology of the majority of SISMAD is still poorly understood. The purpose of this study was to determine the underlying cause of SISMAD. METHOD: From July 2001 to March 2010, 51 consecutive patients with SISMAD (symptomatic 39, asymptomatic 12) and 38 patients with combined aortic and superior mesenteric artery dissection (CASMAD) were identified in a single institution by retrospective investigations. Diagnosis was dependent on multi-detector helical computed tomography (CT) scan. To find clinical characteristics of SISMAD, we compared demographic, clinical, and lesion (site of entry tear, type, length) characteristics between the two groups. To find any flow dynamic abnormalities at the proximal segment of the superior mesenteric artery (SMA), we conducted flow dynamic studies using computational fluid dynamic models (V.12; ANSYS, Inc., Canonsburg, Pa). Streamline patterns and wall shear stress distributions were tested with computer simulation models using three different branching angles of SMA from the abdominal aorta. RESULTS: Compared to CASMAD, SISMAD was more common in men (90.2% vs 71.1%; P = .02), less frequently associated with hypertension (31.4% vs 65.8%; P = .001), and more frequently associated with intra-abdominal cancers (11.8% vs 0%; P = .036). In a fluid dynamic study using computational fluid dynamic models, we found abnormal mechanical stresses at the anterior wall around the convex portion of the SMA. CONCLUSION: Development of SISMAD seems to be less likely the result of hypertension or connective tissue disease but more likely due to hemodynamic force caused by convex curvature.

Development of Er3+, Yb3+ Co-Doped Y2O3 NPs According to Yb3+ Concentration by LP-PLA Method: Potential Further Biosensor.

As diagnostic biosensors for analyzing fluids from the human body, the development of inorganic NPs is of increasing concern. For one, nanoceramic phosphors have been studied to meet the increasing requirements for biological, imaging, and diagnostic applications. In this study, Y2O3 NPs co-doped with trivalent rare earths (erbium and ytterbium) were obtained using a liquid phase-pulsed laser ablation (LP-PLA) method after getting high density Er, Yb:Y2O3 ceramic targets by Spark plasma sintering (SPS). Most NPs are under 50 nm in diameter and show high crystallinity of cubic Y2O3 structure, containing (222), (440), and (332) planes via HR-TEM. Excitation under a 980 nm laser to a nanoparticle solution showed 525 and 565 nm green, and 660 nm red emissions. The green emission intensity increased and decreased with increasing Yb3+ additive concentration, when the red spectrum continuously strengthened. Utilizing this study's outcome, we suggest developing technology to mark invisible biomolecules dissolved in a solvent using UC luminescence of Er3+, Yb3+ co-doped Y2O3 NPs by LP-PLA. The LP-PLA method has a potential ability for the fabrication of UC NPs for biosensors with uniform size distribution by laser parameters.

Joint estimation and regularized aggregation of brain network in FMRI data.

BACKGROUND: In the Gaussian graphical model framework, precision matrices reveal conditional dependence structure among random variables. In functional magnetic resonance imaging (fMRI) data, estimating such precision matrices of multi-subjects and aggregating them to a group-level is an essential step for constructing a group brain network. NEW METHOD: In this article, we considered joint estimation of multiple precision matrices with regularized aggregation. Also, in the construction of a group precision matrix, we integrated robust aggregation to the estimation. In the estimation of individual precision matrices, we took a regularization approach to induce sparsity, which made brain network estimation more realistic. RESULTS: We demonstrated the effectiveness of the proposed method through simulated examples, and analyses on real fMRI data acquired during eye movement tasks assessing cognitive control. For the fMRI data, the joint estimation of multiple precision matrices (JEMP) with regularized aggregation (RA) captured more robust associations between task-relevant neural regions of interest (ROIs), compared to the analyses using JEMP alone. The JEMP with RA also was sensitive to increased neural efficiency after task practice. COMPARISON WITH EXISTING METHOD(S): The simple average of individual precision matrices may be affected by outliers and provide inconsistent outcomes between subject-level and group-level networks. In contrast, the proposed method yielded a robust group graph that could identify and ease the effect of outliers. CONCLUSIONS: The proposed method identified regions of practice-induced attenuation associated with reduced cognitive demand after repeat task exposure. Through simulated and real data, we demonstrated that this method does not require any distribution assumption, can identify outliers, and provides robust, representative group brain networks. This method can be applied to datasets that have extensive variability and/or multiple outliers, including applications to specific, and general, cognitive processes, as well as for studies that may require longitudinal data, such as pharmaceutical trials.

Malonic Acid Isolated from Pinus densiflora Inhibits UVB-Induced Oxidative Stress and Inflammation in HaCaT Keratinocytes.

Skin aging is caused by exposure to various external factors. Ultraviolet B (UVB) irradiation induces oxidative stress, photoaging, and inflammation in skin cells. Pinus densiflora Sieb. et Zucc. (red pine) has various antimicrobial and antioxidant activities. However, the anti-inflammatory effects of red pine on skin have rarely been reported. The protective effects of malonic acid (MA) isolated from Pinus densiflora were investigated against UVB-induced damage in an immortalized human keratinocyte cell line (HaCaT). MA increased levels of the antioxidant enzymes superoxide dismutase 1 (SOD-1) and heme oxygenase 1 (HO-1) via activation of nuclear factor-erythroid 2-related factor-2 (Nrf2), resulting in a reduction in UVB-induced reactive oxygen species (ROS) levels. Additionally, the inhibition of ROS increased HaCaT cell survival rate. Thus, MA downregulated the expression of ROS-induced nuclear factor-κB, as well as inflammation-related cytokines (interleukin-6, cyclooxygenase-2, and tumor necrosis factor-α). Furthermore, MA significantly suppressed the mitogen-activated protein kinase/activator protein 1 signaling pathway and reduced the expression of matrix metalloproteinases (MMPs; MMP-1, MMP-3, and MMP-9). In contrast, MA treatment increased the expression of collagen synthesis regulatory genes (COL1A1 and COL3A1) via regulation of Smad2/3 signal induction through transforming growth factor-ß. In conclusion, MA protected against UVB-induced photoaging via suppression of skin inflammation and induction of collagen biosynthesis.

Antihypertensive Effects of Dehydroabietic and 4-Epi-Trans-Communic Acid Isolated from Pinus densiflora.

Korean red pine needle (RPN) exhibits various biological and pharmacological activities. Among the various compounds of RPN, we isolated dehydroabietic and 4-epi-trans-communic acid. At first, we confirmed that two compounds inhibited angiotensin converting enzyme (ACE) and induced p-Akt in human umbilical vein endothelial cells (HUVEC). RPN extract powder significantly reduced systolic blood pressure in spontaneous hypertensive rats (SHRs) through the reduced expression of ACE and angiotensin type I receptors in the lungs of SHRs. The Lineweaver-Burk plots suggested that the two compounds were noncompetitive inhibitors of ACE. Using docking analysis, we found that two compounds showed the best returned pose at ACE active sites, and formed hydrogen and hydrophobic bonds with ACE residues. These results demonstrate that RPNs may be a source of compounds effective for preventing hypertension and may be useful in the development of antihypertensive drugs.

Unusual Hole Transfer Dynamics of the NiO Layer in Methylammonium Lead Tri-iodide Absorber Solar Cells.

Nickel oxides (NiO) as hole transport layers (HTLs) in inverted-type perovskite solar cells (PSCs) have been widely studied mainly because of their high stability under illumination. Increases in the power conversion efficiency (PCE) with NiO HTLs have been presented in numerous reports, although the photoluminescence (PL) quenching behavior does not coincide with the PCE increase. The dynamics of the charge carrier transport between the NiO HTLs and the organic-inorganic halide perovskite absorbers is not clearly understood yet and quite unusual, in contrast to organic/polymerics HTLs. We deposited NiO HTLs with precisely controlled thicknesses by atomic layer deposition (ALD) and studied their photovoltaic performances and hole transfer characteristics. Ground state bleaching (GSB) recovery was observed by ultrafast transient absorption spectroscopy (TAS), which suggested that backward hole injection occurred between the perovskites and NiO HTLs, so that the uncommon PL behaviors can be clearly explained. Backward hole injection from the NiO HTL to the perovskite absorber originated from their similar valence band (VB) energy positions. The thickness increase of the NiO HTLs induced VB sharing, which caused a red-shift of the photoinduced hole absorption spectrum in near-infrared (NIR) femtosecond TAS and a decrease in the PL intensity. Our studies on inorganic metal oxide transport layers, NiO in this work, with a thickness dependence and the comparison with organic layers provide a better understanding of the interfacial carrier dynamics in PSCs.

An integrative multivariate approach for predicting functional recovery using magnetic resonance imaging parameters in a translational pig ischemic stroke model.

Magnetic resonance imaging (MRI) is a clinically relevant, real-time imaging modality that is frequently utilized to assess stroke type and severity. However, specific MRI biomarkers that can be used to predict long-term functional recovery are still a critical need. Consequently, the present study sought to examine the prognostic value of commonly utilized MRI parameters to predict functional outcomes in a porcine model of ischemic stroke. Stroke was induced via permanent middle cerebral artery occlusion. At 24 hours post-stroke, MRI analysis revealed focal ischemic lesions, decreased diffusivity, hemispheric swelling, and white matter degradation. Functional deficits including behavioral abnormalities in open field and novel object exploration as well as spatiotemporal gait impairments were observed at 4 weeks post-stroke. Gaussian graphical models identified specific MRI outputs and functional recovery variables, including white matter integrity and gait performance, that exhibited strong conditional dependencies. Canonical correlation analysis revealed a prognostic relationship between lesion volume and white matter integrity and novel object exploration and gait performance. Consequently, these analyses may also have the potential of predicting patient recovery at chronic time points as pigs and humans share many anatomical similarities (e.g., white matter composition) that have proven to be critical in ischemic stroke pathophysiology. The study was approved by the University of Georgia (UGA) Institutional Animal Care and Use Committee (IACUC; Protocol Number: A2014-07-021-Y3-A11 and 2018-01-029-Y1-A5) on November 22, 2017.