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Abstract Over the past several decades, strategic planning has become ubiquitous in higher education. Yet, there are varying perceptions of its overall value. Perceptions of value are critically important as they can influence the effectiveness and breadth of participation in the strategic planning process, and such participation is widely held to be critical for success. We investigated these issues using an on-line survey distributed to faculty, chairs, and deans at public colleges and universities in the United States. We found that, compared to both faculty and chairs, deans had a more positive assessment of whether the benefits of the process were greater than the costs and were significantly more familiar with strategic plans. On the other hand, faculty indicated strategic plans had less effect on how they do their jobs, while chairs held a negative view as to whether the process led to meaningful evaluation and change. Interestingly, there were no significant differences between responses from baccalaureate, master’s, and doctoral institutions. Although shared governance in the strategic planning process is widely endorsed, our work suggests that new approaches may be necessary to achieve the goal of an effective strategic planning process.

Objective: To investigate a deep learning model for predicting hepatic decompensation using computed tomography (CT) imaging in patients with primary sclerosing cholangitis (PSC). Patients and Methods: Retrospective cohort study involving 277 adult patients with large-duct PSC who underwent an abdominal CT scan. The portal venous phase CT images were used as input to a 3D-DenseNet121 model, which was trained using 5-fold crossvalidation to classify hepatic decompensation. To further investigate the role of each anatomic region in the model’s decision-making process, we trained the model on different sections of 3-dimensional CT images. This included training on the right, left, anterior, posterior, inferior, and superior halves of the image data set. For each half, as well as for the entire scan, we performed area under the receiving operating curve (AUROC) analysis. Results: Hepatic decompensation occurred in 128 individuals after a median (interquartile range) of 1.5 years (142-1318 days) after the CT scan. The deep learning model exhibited promising results, with a mean ± SD AUROC of 0.89±0.04 for the baseline model. The mean ± SD AUROC for left, right, anterior, posterior, superior, and inferior halves were 0.83±0.03, 0.83±0.03, 0.82±0.09, 0.79±0.02, 0.78±0.02, and 0.76±0.04, respectively. Conclusion: The study illustrates the potential of examining CT imaging using 3D-DenseNet121 deep learning model to predict hepatic decompensation in patients with PSC.

Abstract The continental United States is well instrumented with facilities for mid‐latitude upper atmosphere research that operate on a continuous basis. In addition, citizen scientists provide a wealth of information when unusual events occur. We combine ionospheric total electron content (TEC) data from distributed arrays of GNSS receivers, magnetometer chains, and auroral observations obtained by citizen scientists, to provide a detailed view of the intense auroral breakup and westward surge occurring at the peak of the 10–11 May 2024 extreme geomagnetic storm. Over a 20‐min interval, vertical TEC (vTEC) increased at unusually low latitude (∼45°) and rapidly expanded azimuthally across the continent. Individual receiver/satellite data sets indicate sharp bursts of greatly elevated of vTEC (∼50 TECu). Intense red aurora was co‐located with the leading edge of the equatorward and westward TEC enhancements, indicating that the large TEC enhancement was created by extremely intense low‐energy precipitation during the rapid substorm breakup.

Background We developed a simplified ABC/2‐derived method to estimate total subarachnoid hemorrhage volume (SAHV) on noncontrast computed tomography in patients with aneurysmal SAH and compared the clinical and radiographic outcomes. Methods and Results In this retrospective observational cohort study, we analyzed 277 patients with SAH admitted to our Comprehensive Stroke Center between 2012 and 2022. We derived a mathematical model (model 1) by measuring SAH basal cisternal blood volume using an ABC/2‐derived ellipsoid formula (A=width/thickness, B=length, C=vertical extension) on head noncontrast computed tomography in 5 major SAH cisternal compartments. We compared model 1 against a manual segmentation method (model 2) on noncontrast computed tomography. Data were analyzed using logistic regression analysis, t test, receiver operator characteristic curves, and area under the curve analysis. There was no significant difference in cisternal SAHV analysis between the 2 models (P=0.14). Mean SAHV by the simplified method was 7.0 mL (95% CI, 5.89–8.09) for good outcome and 16.6 mL (95% CI, 13.49–19.77) for poor outcome. Patients with delayed cerebral ischemia had higher SAHV, with a cutoff value of 10 mL. Conclusions Our simplified ABC/2‐derived method to estimate SAHV is comparable to manual segmentation and can be performed in low‐resource settings. Higher total SAHV was associated with worse outcomes and higher risk of delayed cerebral ischemia. A potential dose–response relationship was observed, with SAHV >10 mL predicting worse outcomes and higher risk of DCI.

Background: Discrepancies in medical data sets can perpetuate bias, especially when training deep learning models, potentially leading to biased outcomes in clinical applications. Understanding these biases is crucial for the development of equitable healthcare technologies. This study employs generative deep learning technology to explore and understand radiographic differences based on race among patients undergoing total hip arthroplasty. Methods: Utilizing a large institutional registry, we retrospectively analyzed pelvic radiographs from total hip arthroplasty patients, characterized by demographics and image features. Denoising diffusion probabilistic models generated radiographs conditioned on demographic and imaging characteristics. Fréchet Inception Distance assessed the generated image quality, showing the diversity and realism of the generated images. Sixty transition videos were generated that showed transforming White pelvises to their closest African American counterparts and vice versa while controlling for patients’ sex, age, and body mass index. Two expert surgeons and 2 radiologists carefully studied these videos to understand the systematic differences that are present in the 2 races’ radiographs. Results: Our data set included 480,407 pelvic radiographs, with a predominance of White patients over African Americans. The generative denoising diffusion probabilistic model created high-quality images and reached an Fréchet Inception Distance of 6.8. Experts identified 6 characteristics differentiating races, including interacetabular distance, osteoarthritis degree, obturator foramina shape, femoral neck-shaft angle, pelvic ring shape, and femoral cortical thickness. Conclusions: This study demonstrates the potential of generative models for understanding disparities in medical imaging data sets. By visualizing race-based differences, this method aids in identifying bias in downstream tasks, fostering the development of fairer healthcare practices.

Objective: To conduct a study comparing the performance of automated segmentation techniques using 2 different contrast-enhanced T1-weighted (CET1) magnetic resonance imaging (MRI) acquisition protocol. Patients and Methods: We collected 100 preoperative glioblastoma (GBM) MRIs consisting of 50 IR-GRE and 50 3-dimensional fast spin echo (3D-FSE) image sets. Their gold-standard tumor segmentation mask was created based on the expert opinion of a neuroradiologist. Cases were randomly divided into training and test sets. We used the no new UNet (nnUNet) architecture pretrained on the 501-image public data set containing IR-GRE sequence image sets, followed by 2 training rounds with the IR-GRE and 3D-FSE images, respectively. For each patient, in the IR-GRE and 3D-FSE test sets, we had 2 prediction masks, one from the model fine-tuned with the IR-GRE training set and one with 3D-FSE. The dice similarity coefficients (DSCs) of the 2 sets of results for each case in the test sets were compared using the Wilcoxon tests. Results: Models trained on 3D-FSE images outperformed IR-GRE models in lesion segmentation, with mean DSC differences of 0.057 and 0.022 in the respective test sets. For the 3D-FSE and IR-GRE test sets, the calculated P values comparing DSCs from 2 models were .02 and .61, respectively. Conclusion: Including 3D-FSE MRI in the training data set improves segmentation performance when segmenting 3D-FSE images.

Hypothesis and/or Background: The incidence of elbow medial ulnar collateral ligament (MUCL) injuries has been increasing, leading to advances in surgical treatments. However, it is not clear that there is consensus among surgeons regarding diagnostic imaging, the indications for acute surgery and postoperative rehabilitation. The purpose of this study is evaluate surgeon variability in the presurgical, surgical, and postsurgical treatment of MUCL injuries regarding the imaging modalities used for diagnosis, indications for acute surgical treatment, and postoperative treatment recommendations for rehabilitation and return to play (RTP). Our hypothesis is that indications for acute surgical treatment will be highly variable based on MUCL tear patterns and that agreement on the time to RTP will be consistent for throwing athletes and inconsistent for nonthrowing athletes. Methods: A survey developed by 6 orthopedic surgeons with expertise in throwing athlete elbow injuries was distributed to 31 orthopedic surgeons who routinely treat MUCL injuries. The survey evaluated diagnostic and treatment topics related to MUCL injuries, and responses reaching 75% agreement were considered as high-level agreement. Results: Twenty-four surgeons responded to the survey, resulting in a 77% response rate. There is 75% or better agreement among surveyed surgeons regarding acute surgical treatment for distal full thickness tears, ulnar nerve transposition in symptomatic patients or with ulnar nerve subluxation, postoperative splinting for 1-2 weeks with initiation of rehabilitation within 2 weeks, the use of bracing after surgery and the initiation of a throwing program at 3 months after MUCL repair with internal brace by surgeons performing 20 or more MUCL surgeries per year. There were a considerable number of survey topics without high-level agreement, particularly regarding the indications for acute surgical treatment, the time to return to throwing and time RTP in both throwing and nonthrowing athletes. Discussion and/or Conclusion: The study reveals that there is agreement for the indication of acute surgical treatment of distal MUCL tears, duration of bracing after surgery, and the time to initiate physical therapy after surgery. There is not clear agreement on indications for surgical treatment for every MUCL tear pattern, RTP time for throwing, hitting and participation in nonthrowing sports.

Abstract The health of honey bee queens is crucial for colony success, particularly during stressful periods like overwintering. To accompany a previous longitudinal study of colony and worker health, we explored niche-specific gut microbiota, host gene expression, and pathogen prevalence in honey bee queens overwintering in a warm southern climate. We found differential gene expression and bacterial abundance with respect to various pathogens throughout the season. Biologically older queens had larger microbiotas, particularly enriched in Bombella and Bifidobacterium. Both Deformed Wing Virus A and B subtypes were highest in the fat body tissue in January, correlating with colony Varroa levels, and Deformed Wing Virus titers in workers. High viral titers in queens were associated with decreased vitellogenin expression, suggesting a potential trade-off between immune function and reproductive capacity. Additionally, we found a complex and dynamic relationship between these viral loads and immune gene expression, indicating a possible breakdown in the coordinated immune response as the season progressed. Our study also revealed a potential link between Nosema and Melissococcus plutonius infections in queens, demonstrating that seasonal opportunism is not confined to just workers. Overall, our findings highlight the intricate interplay between pathogens, metabolic state, and immune response in honey bee queens. Combined with worker and colony-level metrics from the same colonies, our findings illustrate the social aspect of queen health and resilience over the winter dearth.

Introduction: Articular cartilage injuries of the knee are a complex and challenging clinical pathology. Objectives: The purpose of this study was to establish consensus statements via a Delphi process on bone marrow stimulation (BMS) for knee cartilage injuries. Methods: A consensus process on knee cartilage injuries utilizing a modified Delphi technique was conducted. Seventy-nine surgeons across 17 countries participated in these consensus statements. Fourteen questions were generated on BMS, with 3 rounds of questionnaires and final voting occurring. Consensus was defined as achieving 80% to 89% agreement, whereas strong consensus was defined as 90% to 99% agreement, and unanimous consensus was defined as 100% agreement with a proposed statement. Results: Of the 14 total questions and consensus statements on BMS developed from 3 rounds of voting, 0 achieved unanimous consensus, 3 achieved strong consensus, 4 achieved consensus, and 7 did not achieve consensus. Conclusions: The statements that achieved strong consensus related to lesion site preparation, ability to differentiate healthy/damaged cartilage, and distance between BMS holes. The statements that did not achieve consensus were primarily related to the indications for BMS, as well as the instrumentation and whether orthobiologics/scaffolds should be utilized.

Background The goal was to compare patterns of physical activity (PA) behaviors (sedentary behavior [SB], light PA, moderate‐to‐vigorous PA [MVPA], and sleep) measured via accelerometers for 7 days between patients with incident cerebrovascular disease (CeVD) (n=2141) and controls (n=73 938). Methods and Results In multivariate models, cases spent 3.7% less time in MVPA (incidence rate ratio [IRR], 0.963 [95% CI, 0.929–0.998]) and 1.0% more time in SB (IRR, 1.010 [95% CI, 1.001–1.018]). Between 12 and 24 months before diagnosis, cases spent more time in SB (IRR, 1.028 [95% CI, 1.001–1.057]). Within the year before diagnosis, cases spent less time in MVPA (IRR, 0.861 [95% CI, 0.771–0.964]). Although SB time was not associated with CeVD risk, MVPA time, both total min/d (hazard ratio [HR], 0.998 [95% CI, 0.997–0.999]) and guideline threshold adherence (≥150 min/wk) (HR, 0.909 [95% CI, 0.827–0.998]), was associated with decreased CeVD risk. Comorbid burden had a significant partial mediation effect on the relationship between MVPA and CeVD. Cases slept more during 12:00 to 17:59 hours (IRR, 1.091 [95% CI, 1.002–1.191]) but less during 0:00 to 5:59 hours (IRR, 0.984 [95% CI, 0.977–0.992]). No between‐group differences were significant at subgroup analysis. Conclusions Daily behavior patterns were significantly different in patients before CeVD. Although SB was not associated with CeVD risk, the association between MVPA and CeVD risk is partially mediated by comorbid burden. This study has implications for understanding observable behavior patterns in cerebrovascular dysfunction and may help in developing remote monitoring strategies to prevent or reduce cerebrovascular decline.

Summary: Background: Chest X-rays (CXR) are essential for diagnosing a variety of conditions, but when used on new populations, model generalizability issues limit their efficacy. Generative AI, particularly denoising diffusion probabilistic models (DDPMs), offers a promising approach to generating synthetic images, enhancing dataset diversity. This study investigates the impact of synthetic data supplementation on the performance and generalizability of medical imaging research. Methods: The study employed DDPMs to create synthetic CXRs conditioned on demographic and pathological characteristics from the CheXpert dataset. These synthetic images were used to supplement training datasets for pathology classifiers, with the aim of improving their performance. The evaluation involved three datasets (CheXpert, MIMIC-CXR, and Emory Chest X-ray) and various experiments, including supplementing real data with synthetic data, training with purely synthetic data, and mixing synthetic data with external datasets. Performance was assessed using the area under the receiver operating curve (AUROC). Findings: Adding synthetic data to real datasets resulted in a notable increase in AUROC values (up to 0.02 in internal and external test sets with 1000% supplementation, p-value

Purpose: To examine the relationship between tibial tubercle–trochlear groove (TT-TG) distance and patellar tendon length. Methods: All healthy athletes who underwent anterior cruciate ligament reconstruction who had a magnetic resonance imaging (MRI) study of the knee on file between July 2018 and June 2019 at a single institution were retrospectively reviewed. Exclusion criteria included patients without an MRI study of the knee on file or with an MRI of insufficient quality precluding reliable calculation of TT-TG and patellar tendon length. MRIs were reviewed to calculate TT-TG, patellar tendon length, and Caton–Deschamps Index (CDI). Patient charts were reviewed to obtain anthropometric characteristics including sex, concomitant injuries, and previous knee procedures as well as age at time of MRI. Spearman correlations were used to assess the relationship between TT-TG, patellar tendon length, and CDI, with regression analysis performed to assess for relationships between TT-TG, patellar tendon length, and patient-specific factors. Results: Overall, 235 patients (99 female [42.1%], 136 male [57.9%]; mean age: 30.0 years [23.0; 40.0]) were included. Inter-rater reliability between the 2 reviewers was 0.888 for TT-TG, 0.804 for patellar tendon length, and 0.748 for CDI, indicating strong agreement. The correlation between TT-TG and patellar tendon length was 0.021, indicating no true relationship. The correlation between TT-TG and CDI was –0.048 and that of patellar tendon length and CDI was 0.411, indicating a weak positive relationship. Regression analysis found that male sex is strongly correlated with a longer patellar tendon length (odds ratio 2.65, 95% confidence interval 1.33-3.97, P < .001). Conclusions: In this study, no correlation was found between TT-TG and patellar tendon length or CDI. Male sex was correlated with a longer patellar length. Clinical Relevance: TT-TG does not appear to be a useful preoperative indicator of patellar tendon length and likely does not hold substantial value in preoperative templating of BTB graft sizes.

Background: The impact of single-sport specialization on performance and injury risk in youth and adolescent athletes remains debated. The purpose of this study was to determine the incidence of single-sport specialization and its association with pitch velocity, pitcher utilization, range of motion, strength, and injury history. Methods: High school pitchers in the Northeastern, Southeastern, Midwestern, and Western Regions of the United States were prospectively recruited. Prior to the season, pitchers were surveyed about their basic demographics, sport participation history, and injury history. At this timepoint, a standardized physical examination was performed and max pitch velocity was measured. They were then prospectively followed over the ensuing high school season, where pitch utilization and counts were followed and injuries were tracked. Results: Across the study, 115 pitchers were recruited, of whom 53% played baseball as their only sport and 47% played other sports. Single- position specialization within baseball was uncommon as 90% of athletes played positions other than pitcher. There were no differences in range of motion, strength, or pitch velocity between multisport athletes and single-sport specialists. Within the multisport group, 26% reported a history of injury, while 38% of the single-sport specialists reported a history of injury (P = .179). There were no significant differences in pitcher utilization between multisport athletes and single-sport specialists. Only a single prospective injury occurred in the 115 players. Conclusion: Although single-sport specialization is common, nearly half of all high school pitchers in this study were multisport athletes. While injury history was generally similar between the two groups, single-sport baseball players did not have the following: improved motion, increased strength, higher ball velocity, or greater pitching volumes during the high school baseball season compared to multisport athletes. Accordingly, single-sport specialization did not have a competitive advantage for these high school pitchers based on the performance factors studied. Further prospective studies are necessary to understand the association between single-sport specialization and injury risk in high school baseball players.

Objective: To efficiently store, transfer, and analyze whole slide imaging (WSI), we developed an image-processing algorithm to remove the unneeded background in a WSI and assemble tissue-containing parts into smaller WSIs without any change in tissue area image resolution. Patients and Methods: We used histology slides of nondysplastic Barrett esophagus, low-grade dysplasia, and high-grade dysplasia, which were digitized using Aperio AT2 Scanner from January 1992 to September 2020. The algorithm involved converting color images to grayscale images, binarizing images by assigning zero to the background and 1 to the foreground, filling the holes and dilating the foreground masks, and extracting connected components. Using the coordinates of each component, the vertices of the smallest surrounding bounding box were calculated, and tissue-containing parts were cropped from the original slide. The smallest possible rectangle that encloses all bounding boxes containing tissue was found using the rectangle-packer package. Results: The algorithm resulted in a mean reduction of 7.11×. The performance of a previously developed deep learning model for the detection of Barrett esophagus dysplasia grade on the size-reduced WSIs was comparable with that on the original WSIs. Conclusion: Our algorithm for WSI size reduction can assist researchers in storing, transferring, and analyzing WSIs while optimally using them in their workflow.

Abstract Background Lower respiratory infections are a leading cause of severe morbidity and mortality among older adults. Despite ubiquitous exposure to common respiratory pathogens throughout life and near universal seropositivity, antibodies fail to effectively protect the elderly. Therefore, we hypothesized that severe respiratory illness in the elderly is due to deficient CD8+ T cell responses. Results Here, we establish an aged mouse model of human metapneumovirus infection (HMPV) wherein aged C57BL/6 mice exhibit worsened weight loss, clinical disease, lung pathology and delayed viral clearance compared to young adult mice. Aged mice generate fewer lung-infiltrating HMPV epitope-specific CD8+ T cells. Those that do expand demonstrate higher expression of PD-1 and other inhibitory receptors and are functionally impaired. Transplant of aged T cells into young mice and vice versa, as well as adoptive transfer of young versus aged CD8+ T cells into Rag1 −/− recipients, recapitulates the HMPV aged phenotype, suggesting a cell-intrinsic age-associated defect. HMPV-specific aged CD8+ T cells exhibit a terminally exhausted TCF1/7− TOX+ EOMES+ phenotype. We confirmed similar terminal exhaustion of aged CD8+ T cells during influenza viral infection. Conclusions This study identifies terminal CD8+ T cell exhaustion as a mechanism of severe disease from respiratory viral infections in the elderly.

Background: Dual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Most software labels MSU as green and calcium as blue. There are limitations in the current image processing methods of segmenting green-encoded pixels. Additionally, identifying green foci is tedious, and automated detection would improve workflow. This study aimed to determine the optimal deep learning (DL) algorithm for segmenting green-encoded pixels of MSU crystals on DECTs. Methods: DECT images of positive and negative gout cases were retrospectively collected. The dataset was split into train (N = 28) and held-out test (N = 30) sets. To perform cross-validation, the train set was split into seven folds. The images were presented to two musculoskeletal radiologists, who independently identified green-encoded voxels. Two 3D Unet-based DL models, Segresnet and SwinUNETR, were trained, and the Dice similarity coefficient (DSC), sensitivity, and specificity were reported as the segmentation metrics. Results: Segresnet showed superior performance, achieving a DSC of 0.9999 for the background pixels, 0.7868 for the green pixels, and an average DSC of 0.8934 for both types of pixels, respectively. According to the post-processed results, the Segresnet reached voxel-level sensitivity and specificity of 98.72 % and 99.98 %, respectively. Conclusion: In this study, we compared two DL-based segmentation approaches for detecting MSU deposits in a DECT dataset. The Segresnet resulted in superior performance metrics. The developed algorithm provides a potential fast, consistent, highly sensitive and specific computer-aided diagnosis tool. Ultimately, such an algorithm could be used by radiologists to streamline DECT workflow and improve accuracy in the detection of gout.

An understanding of the development of strong very low frequency chorus elements is important in the study of the rapid MeV electron acceleration observed during radiation belt recovery events. During such events, chorus elements with long-duration (20–40 ms), strong (|Bw| 0.5–2.0 nT) subpackets with smoothly varying frequency and phase capable of producing nonlinear energy gain of 1%–2% for multi-MeV seed electrons. For such strong chorus elements, we examine the consequences of an upstream motion of the chorus wave generation region using Van Allen Probes observations and nonlinear theory. For a given upstream velocity, vs, resonant electron energy (50–350 keV) and pitch angle (105–115 deg) are uniquely determined for each wave frequency. We examine the effect of an upstream vs on the inhomogeneity factor that controls wave growth. For steadily increasing upstream motion as the chorus element evolves, vs/c ranging over [-0.001, −0.065], nonlinear wave growth takes place at ≥ 50% of the theoretical maximal value during the development of the observed strong subpackets. For the cases examined, resonant electron energies and pitch angles closely match those of the observed injected electron flux enhancements responsible for chorus development and the nonlinear acceleration of MeV radiation belt electrons.

IntroductionDual-energy CT (DECT) is a non-invasive way to determine the presence of monosodium urate (MSU) crystals in the workup of gout. Color-coding distinguishes MSU from calcium following material decomposition and post-processing. Manually identifying these foci (most commonly labeled green) is tedious, and an automated detection system could streamline the process. This study aims to evaluate the impact of a deep-learning (DL) algorithm developed for detecting green pixelations on DECT on reader time, accuracy, and confidence.MethodsWe collected a sample of positive and negative DECTs, reviewed twice—once with and once without the DL tool—with a 2-week washout period. An attending musculoskeletal radiologist and a fellow separately reviewed the cases, simulating clinical workflow. Metrics such as time taken, confidence in diagnosis, and the tool's helpfulness were recorded and statistically analyzed.ResultsWe included thirty DECTs from different patients. The DL tool significantly reduced the reading time for the trainee radiologist (p = 0.02), but not for the attending radiologist (p = 0.15). Diagnostic confidence remained unchanged for both (p = 0.45). However, the DL model identified tiny MSU deposits that led to a change in diagnosis in two cases for the in-training radiologist and one case for the attending radiologist. In 3/3 of these cases, the diagnosis was correct when using DL.ConclusionsThe implementation of the developed DL model slightly reduced reading time for our less experienced reader and led to improved diagnostic accuracy. There was no statistically significant difference in diagnostic confidence when studies were interpreted without and with the DL model.

Cerebrovascular disease (CeVD) is a leading cause of death and disability worldwide. Early detection of behavioral and physiologic changes associated with CeVD may be critical to improving patient outcomes. The growing prevalence of remote monitoring tools, from wearable devices to smartphone applications, which facilitate in situ observation of patients, holds promise for more timely recognition and possible prevention of stroke. The goal of this review was to examine and establish categories of innovation with digital sensors that monitor physiologic and behavioral variables in situ to augment the current CeVD screening and diagnostic processes. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist, a search strategy spanning multiple databases from January 2012 to September 30, 2022, was implemented, aggregating 729 articles, of which 51 (7.0%) met the inclusion criteria. The articles were divided into 2 categories on the basis of their focus: physiologic and behavioral. Physiologic articles were sorted into 1 of the following 6 subcategories according to the signal(s) monitored: motor function, heart rhythm, heart rate, kinematic analysis, physical activity, and blood pressure. Behavioral articles were sorted into the following 3 subcategories: mood, cognitive function, and fatigue. Most studies used a wearable accelerometer, photoplethysmography-enabled smartwatch, or smartphone-based sensors. This scoping review identified disparate methods and conclusions associated with the use of digital sensors for in situ physiologic and behavioral monitoring of patients with CeVD. Although most articles evaluated pilot validation and feasibility trials, the lack of randomized controlled trials was identified as a critical gap specific to this evolving research area.

Background: Weighted-ball velocity training programs are popular but may be associated with increased injury risk. The purpose of this study was to determine the current frequency of these programs, as well as their association with range of motion, strength, velocity, injury, and utilization in high school baseball pitchers. Methods: This was a multicenter, prospective, community-based study including high schools from the Northeastern, Southeastern, Midwestern, and Western regions of the United States. Preseason pitchers were surveyed about their use of weighted ball velocity programs and history of injury. Preseason pitchers underwent standardized physical examinations and pitch velocity was measured. During a single high school season, pitch counts and injury incidence were prospectively collected. Results: In total, 115 pitchers were recruited, of whom 63% had participated in a weighted ball program. There were no differences in range of motion or strength between groups who used weighted balls and those who did not. Pitchers that utilized weighted ball programs demonstrated a higher average fastball velocity (114.26 vs. 106.22 km/h [71 vs. 66 mph]; P < .001) as well as peak fastball velocity (115.87 vs. 109.44 km/h [72 vs. 68 mph]; P = .001) compared to those that did not use weighted ball programs. Pitchers that utilized weighted ball programs were no more likely to be single-sport athletes (44%) than those who were multisport athletes (49%, P = .667). Within the group who had used a weighted ball program 29% (13/45) reported a history of injury and within the group who had not used a weighted ball program 25% (10/40) reported a history of injury (P = .687). As only a single injury occurred in the entire cohort, comparison of injury rates could not be completed. Conclusion: In this prospective study, the majority of high school pitchers reported using weighted ball programs to increase velocity, and these programs were associated with increased average and peak velocity compared to pitchers who did not use weighted ball programs. There were no significant differences in injury history between the 2 groups. Further study is necessary to understand the injury risk associated with weighted ball programs.

Key points 1. Topological data analysis (TDA) provides information on the shape of data. 2. In radiology, the shape of 2D and 3D images contains additional information. 3. TDA can be combined with other applications, such as textural analysis. 4. Persistent homology can provide a visual representation of extracted TDA data.

Thyroid Ultrasound (US) is the primary method to evaluate thyroid nodules. Deep learning (DL) has been playing a significant role in evaluating thyroid cancer. We propose a DL-based pipeline to detect and classify thyroid nodules into benign or malignant groups relying on two views of US imaging. Transverse and longitudinal US images of thyroid nodules from 983 patients were collected retrospectively. Eighty-one cases were held out as a testing set, and the rest of the data were used in five-fold cross-validation (CV). Two You Look Only Once (YOLO) v5 models were trained to detect nodules and classify them. For each view, five models were developed during the CV, which was ensembled by using non-max suppression (NMS) to boost their collective generalizability. An extreme gradient boosting (XGBoost) model was trained on the outputs of the ensembled models for both views to yield a final prediction of malignancy for each nodule. The test set was evaluated by an expert radiologist using the American College of Radiology Thyroid Imaging Reporting and Data System (ACR-TIRADS). The ensemble models for each view achieved a mAP0.5 of 0.797 (transverse) and 0.716 (longitudinal). The whole pipeline reached an AUROC of 0.84 (CI 95%: 0.75–0.91) with sensitivity and specificity of 84% and 63%, respectively, while the ACR-TIRADS evaluation of the same set had a sensitivity of 76% and specificity of 34% (p-value = 0.003). Our proposed work demonstrated the potential possibility of a deep learning model to achieve diagnostic performance for thyroid nodule evaluation.

Accelerated by the adoption of remote monitoring during the COVID-19 pandemic, interest in using digitally captured behavioral data to predict patient outcomes has grown; however, it is unclear how feasible digital phenotyping studies may be in patients with recent ischemic stroke or transient ischemic attack. In this perspective, we present participant feedback and relevant smartphone data metrics suggesting that digital phenotyping of post-stroke depression is feasible. Additionally, we proffer thoughtful considerations for designing feasible real-world study protocols tracking cerebrovascular dysfunction with smartphone sensors.

Summary: Intestinal colonization by antigenically foreign microbes necessitates expanded peripheral immune tolerance. Here we show commensal microbiota prime expansion of CD4 T cells unified by the Kruppel-like factor 2 (KLF2) transcriptional regulator and an essential role for KLF2+ CD4 cells in averting microbiota-driven intestinal inflammation. CD4 cells with commensal specificity in secondary lymphoid organs and intestinal tissues are enriched for KLF2 expression, and distinct from FOXP3+ regulatory T cells or other differentiation lineages. Mice with conditional KLF2 deficiency in T cells develop spontaneous rectal prolapse and intestinal inflammation, phenotypes overturned by eliminating microbiota or reconstituting with donor KLF2+ cells. Activated KLF2+ cells selectively produce IL-10, and eliminating IL-10 overrides their suppressive function in vitro and protection against intestinal inflammation in vivo. Together with reduced KLF2+ CD4 cell accumulation in Crohn’s disease, a necessity for the KLF2+ subpopulation of T regulatory type 1 (Tr1) cells in sustaining commensal tolerance is demonstrated.

Abstract A new observational phenomenon, named Simultaneous Global Ionospheric Density Disturbance (SGD), is identified in GNSS total electron content (TEC) data during periods of three typical geospace disturbances: a Coronal Mass Ejection‐driven severe disturbance event, a high‐speed stream event, and a minor disturbance day with a maximum Kp of 4. SGDs occur frequently on dayside and dawn sectors, with a ∼1% TEC increase. Notably, SGDs can occur under minor solar‐geomagnetic disturbances. SGDs are likely caused by penetration electric fields (PEFs) of solar‐geomagnetic origin, as they are associated with Bz southward, increased auroral AL/AU, and solar wind pressure enhancements. These findings offer new insights into the nature of PEFs and their ionospheric impact while confirming some key earlier results obtained through alternative methods. Importantly, the accessibility of extensive GNSS networks, with at least 6,000 globally distributed receivers for ionospheric research, means that rich PEF information can be acquired, offering researchers numerous opportunities to investigate geospace electrodynamics.

Background and Hypothesis: Glenohumeral internal rotation deficit has been identified as a significant risk factor for upper-extremity injuries in pitchers across all ages. Humeral retroversion (HR), posterior capsule thickness (PCT), and posterior rotator cuff muscle pennation angle (PA) have been independently associated with internal rotation range of motion (IR ROM); however, these anatomic structures have not been collectively measured in baseball pitchers to determine the underlying mechanisms responsible for IR ROM. Therefore, the purpose of this study was to determine the contributions of HR, PCT, and posterior rotator cuff PA on IR ROM during a preseason evaluation in healthy professional baseball pitchers. The authors hypothesized that HR, PCT, and posterior rotator cuff PA would have a significant contribution to IR ROM. Methods: This is a cross-sectional study. Healthy professional pitchers from a single organization were recruited at the beginning of the 2021 Major League Baseball Spring Training. Participants received bilateral IR ROM assessment while laying supine with the shoulder at 90 degrees of abduction and the scapula stabilized. Ultrasound imaging was also performed bilaterally to assess HR, PCT, infraspinatus (superficial + deep) PA, and teres minor (superficial + deep) PA. All ultrasound imaging processes were performed utilizing previously published, highly reliable techniques. A stepwise regression was performed, which included both arms to determine the mechanisms of IR ROM. Results: Overall, 49 pitchers (88 shoulders) with an average age of 22.5 ± 2.2 years were included in the final data analysis. Stepwise linear regression found that only HR and PCT were associated with the preseason IR ROM. There was a moderate relationship between HR and PCT relative to IR ROM (R = 0.535, P

Abstract As essential pollinators of ecosystems and agriculture, honey bees (Apis mellifera) are host to a variety of pathogens that result in colony loss. Two highly prevalent larval diseases are European foulbrood (EFB) attributed to the bacterium Melissococcus plutonius, and Varroosis wherein larvae can be afflicted by one or more paralytic viruses. Here we used high-throughput sequencing and qPCR to detail microbial succession of larval development from six diseased, and one disease-free apiary. The disease-free larval microbiome revealed a variety of disease-associated bacteria in early larval instars, but later developmental stages were dominated by beneficial symbionts. Microbial succession associated with EFB pathology differed by apiary, characterized by associations with various gram-positive bacteria. At one apiary, diseased larvae were uniquely described as “melting and deflated”, symptoms associated with Varroosis. We found that Acute Bee Paralysis Virus (ABPV) levels were significantly associated with these symptoms, and various gram-negative bacteria became opportunistic in the guts of ABPV afflicted larvae. Perhaps contributing to disease progression, the ABPV associated microbiome was significantly depleted of gram-positive bacteria, a likely result of recent antibiotic application. Our results contribute to the understanding of brood disease diagnosis and treatment, a growing problem for beekeeping and agriculture worldwide.

Abstract Background Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease that can lead to cirrhosis and hepatic decompensation. However, predicting future outcomes in patients with PSC is challenging. Our aim was to extract magnetic resonance imaging (MRI) features that predict the development of hepatic decompensation by applying algebraic topology-based machine learning (ML). Methods We conducted a retrospective multicenter study among adults with large duct PSC who underwent MRI. A topological data analysis-inspired nonlinear framework was used to predict the risk of hepatic decompensation, which was motivated by algebraic topology theory-based ML. The topological representations (persistence images) were employed as input for classification to predict who developed early hepatic decompensation within one year after their baseline MRI. Results We reviewed 590 patients; 298 were excluded due to poor image quality or inadequate liver coverage, leaving 292 potentially eligible subjects, of which 169 subjects were included in the study. We trained our model using contrast-enhanced delayed phase T1-weighted images on a single center derivation cohort consisting of 54 patients (hepatic decompensation, n = 21; no hepatic decompensation, n = 33) and a multicenter independent validation cohort of 115 individuals (hepatic decompensation, n = 31; no hepatic decompensation, n = 84). When our model was applied in the independent validation cohort, it remained predictive of early hepatic decompensation (area under the receiver operating characteristic curve = 0.84). Conclusions Algebraic topology-based ML is a methodological approach that can predict outcomes in patients with PSC and has the potential for application in other chronic liver diseases.

Background: The ulnar collateral ligament (UCL) of the elbow is subject to repetitive stress in overhead throwing athletes. This can lead to morphologic changes at the bony attachments of the UCL and hypertrophy of the sublime tubercle. The purpose of this case series is to describe the surgical details and clinical outcomes of a series of competitive baseball pitchers with hypertrophic sublime tubercles who underwent UCL reconstruction (UCLR). Methods: All baseball pitchers who were treated for UCL injuries with significant hypertrophy of the sublime tubercle on preoperative imaging were included in the series. Clinical history, preoperative imaging, intraoperative findings during UCLR, and postoperative outcomes measured with the Conway scale were described. Results: Ten players (average age of 22.9 years [range 13-39]) were included (average follow-up 20.4 months [range 3-38 months]). Five patients also had symptoms of ulnar nerve compression, with 4 requiring transposition at the time of ULCR and 1 at 3 months postoperatively. Bony hypertrophy of the sublime tubercle was confirmed intraoperatively in all cases and excised before UCLR with the docking technique. Of the 7 patients with at least 12-month follow-up postoperatively, 6 had excellent outcomes, and 1 had a fair outcome. Conclusion: Although UCLR in the setting of hypertrophic sublime tubercle can be more complex than typical UCLR, excellent outcomes are achievable with preoperative recognition and surgical planning.

Background: Humeral stem length in reverse total shoulder arthroplasty (RTSA) has decreased in recent years in an attempt to preserve more bone and facilitate stem removal in the revision setting. The purpose of this study was to compare the clinical and radiographic outcomes of a short- to standard-length stem RTSA. The authors hypothesized that there would be no difference in radiographic or clinical outcomes at short-term follow-up. Methods: Patients who underwent RTSA using a press-fit standard- or short-length humeral component with a consistent geometry (Univers Revers, or Revers Apex; Arthrex, Inc., Naples, FL, USA) were evaluated in a multicenter retrospective review. The minimum clinical follow-up was 2 years. Immediate postoperative radiographs were used to assess initial alignment and filling ratios. In addition, radiographs at 2 years were evaluated for signs of stress shielding and/or loosening. Clinical outcome scores and range of motion were evaluated at the final follow-up and compared between groups. Results: A total of 220 patients with short-stem RTSA and 357 patients with standard-length stem RTSA were analyzed. There was no difference in baseline function between short- and standard-length stem patients. Patients in the short stem group had higher postoperative American Shoulder and Elbow Surgeons (84.6 vs. 80.8; P = .014) and Western Ontario Osteoarthritis of the Shoulder (86.5 vs. 82.7; P = .025). Patients in the short stem group also had greater postoperative active forward flexion (139° vs. 132°; P = .003) and internal rotation with the arm at 90° of abduction (43° vs. 32°; P .05). Conclusion: A short inlay stem leads to comparable radiographic findings and revision-free survival compared with a standard-length stem when placed with a press-fit technique for RTSA. Clinical outcomes are also equivalent or slightly improved with a short stem compared with a standard-length stem.

Abstract Thermal equilibrium in planetary atmospheres occurs at altitudes where the ion, electron, and neutral temperatures are equal. Thermal equilibrium is postulated to occur in the collision‐dominated ionosphere. This postulated altitude is above the lower boundary of all empirical models of planetary ionospheres. Physics‐based model predictions of the altitude cannot be validated due to a lack of adequate simultaneous observations of temperature profiles. This study presents temperature profiles from simultaneous observations on Atmosphere Explorer–C below 140 km and quiet‐time neutral observations from Thermosphere Ionosphere Mesosphere Energy and Dynamics/Global UltraViolet Imager over Millstone Hill. These are compared with profiles from physics‐based models with a discussion of their respective limitations. We conclude that there does not yet exist a quantitative understanding of the ion, electron, and neutral thermalization processes in low‐altitude planetary ionospheres. Progress on this topic requires an adequate database of simultaneous ion, electron, and neutral temperature profiles in the 110–140 km altitude range.

Abstract This study develops a new Bubble Index to quantify the intensity of 2‐D postsunset equatorial plasma bubbles (EPBs) in the American/Atlantic sector, using Global‐scale Observations of the Limb and Disk (GOLD) nighttime data. A climatology and day‐to‐day variability analysis of EPBs is conducted based on the newly‐derived Bubble Index with the following results: (a) EPBs show considerable seasonal and solar activity dependence, with stronger (weaker) intensity around December (June) solstice and high (low) solar activity years. (b) EPBs exhibit opposite geomagnetic activity dependencies during different storm phases: EPBs are intensified concurrently with an increasing Kp, but are suppressed with high Kp occurring 3–6 hr earlier. (c) For the first time, we found that EPBs' day‐to‐day variation exhibited quasi‐3‐day and quasi‐6‐day periods. A coordinated analysis of Ionospheric Connection Explorer (ICON) winds and ionosonde data suggests that this multi‐day periodicity was related to the planetary wave modulation through the wind‐driven dynamo.

Lesbian, gay, bisexual, queer, and questioning (LGBQ) adolescents experience large disparities in an array of health problems and bullying experiences. Supportive LGBTQ community and school climates may protect LGBQ youth from general and bias-based bullying victimization. We combined data from LGBQ respondents on the 2013 Minnesota Student Survey (N = 2404) with information on the presence of a GSA (N = 79 schools; 2014 School Health Profiles), and new data collection on a range of LGBTQ-supportive community indicators within a 15-min drive time around the school. Hierarchical logistic regressions assessed the role of community supportiveness, GSA presence, and prevalence of LGBQ peers on general and bias-based bullying victimization, adjusted for sociodemographic covariates. Results indicated that for birth-assigned females, attending a school with a GSA was related to lower odds of sexual orientation--based bullying and attending school in an area with more LGBTQ-supportive community resources predicted lower odds of sexual orientation- and weight/appearance-based bullying. Having more LGBQ peers was related to lower levels of race- and weight/appearance-based bullying for all LGBQ youth. These findings provide support for the assumption that more LGBTQ-supportive communities and schools protect against bias-based bullying, particularly for girls, but they are not related to general bullying. Expanding and strengthening supportive resources for LGBTQ youth in schools and communities and identifying protective factors for GBQ boys are recommended.