University of Wisconsin–Madison

To introduce and evaluate the feasibility of a novel RF-phase modulated gradient echo (GRE) method for quantitative diffusion MRI, aimed at mitigating geometric distortion and enabling high-resolution 3D quantitative diffusion/T2 mapping as a complementary alternative to conventional DWI. The proposed phase-based diffusion (PBD) method employs RF phase modulation to encode both diffusion and T2 information into the GRE signal phase. A closed-form analytical model enables joint apparent diffusion coefficient (ADC) and T2 mapping via iterative reconstruction. The method's feasibility was evaluated via Bloch equation simulations, phantom experiments, and preliminary in vivo imaging studies. Monte Carlo simulations revealed that PBD provides more accurate median ADC estimates at low signal-to-noise ratios (SNRs) compared to conventional single-shot echo-planar imaging (SS-EPI), although PBD exhibited greater variability. Phantom studies demonstrated good agreement for PBD-derived ADC values (e.g., R2 = 0.99) with reference methods and strong correlation for PBD-derived T2 values (e.g., R2 = 0.89), though the latter showed some systematic bias in phantoms. In vivo results from patients with benign or malignant prostate disease demonstrated the feasibility of the PBD method to provide high-resolution ADC and T2 maps with minimal geometric distortions relative to conventional SS-EPI. PBD provides ADC and T2 maps with improved geometric fidelity in phantoms and in vivo, and offers robust median ADC estimates from noisy data based on simulations. This combination of spatial precision and noise characteristics makes PBD promising for applications such as high-resolution DWI for prostate MRI.

The unsatisfactory percentage for ThinPrep Pap Tests (TPPT) at our institution reached the 75th percentile (2.7%) benchmark established by the College of American Pathologists (CAP). Our aim was to identify outliers by analysis of data, provide education and feedback, and establish a system to monitor outcomes. The laboratory information system (LIS) was queried for TPPT cases diagnosed as 'Non-diagnostic' between April and June 2022. Two hundred and thirty-two cases were identified and reviewed by 2 cytopathologists and 1 cytotechnologist to identify limiting factors, including cellularity, blood, lubricant, inflammation, and mucus. The percentage of unsatisfactory TPPT was evaluated for both clinicians and clinics and stratified into peer groups based on total TPPT collected. The two clinics with the highest unsatisfactory percentage in a peer group of > 500 TPPT collected were identified. A LIS report was created to improve efficiency in future data collection. Low cellularity was a limiting factor in 100% of cases, blood in 51.7% (n = 120), lubricant in 18.5% (n = 43), inflammation in 13.8% (n = 32), and mucus in 3.9% (n = 9), with multiple factors possible per case. Twenty clinics collected > 500 TPPT for FY2022. Two clinics (1 resident-led clinic) had an unsatisfactory percentage > 3%, for which to target interventions. Clinic 1 had an unsatisfactory percentage of 6.1% (62/1018), and clinic 2 was 3.5% (21/608). To address limiting factors, a laboratory bulletin with best practices for TPPT collection and a list of Hologic TPPT approved lubricants was sent to each clinic. Statistics of unsatisfactory TPPT were provided. The highest unsatisfactory TPPT clinics received Hologic educational materials and an educational session. The resolution of laboratory quality issues requires investigations, often laborious in nature. In an era of staffing shortages, an effective LIS is essential to efficiently investigate and resolve quality variances. TPPT data via LIS reports were reviewed at 3 months and 1-year post-intervention implementation to evaluate outcomes.

ABSTRACT Since the Supreme Court’s 2023 ruling against race-conscious admissions, anti-diversity, equity, and inclusion efforts have grown, creating challenges for leaders committed to advancing racial equity on their campuses. Using racialized change work as a lens, we analyze faculty cluster hiring as a potential strategy toward racial equity that could hold up in the current political climate. This study uses data from four historically white research universities, focusing on how leaders used faculty cluster hiring as a strategy to promote racial equity. Our findings reveal that cluster hire leaders leveraged racialized expertise as hiring capital, creating a “strategic side door” to diversify faculty demographics while sidestepping risks associated with affirmative-action-like approaches. Cluster hire leaders also leveraged faculty lines to challenge — or attempt to challenge — practices that sustain a racist status quo. However, this approach could also create additional racialized labor expectations on newly hired Faculty of Color. We offer recommendations for leaders to consider when designing and implementing cluster hiring initiatives to support racial equity effectively.

Multivariate statistical process control techniques have been widely used for online monitoring in various applications. However, when monitoring high-dimensional processes, practical resource constraints such as limited data transmission bandwidth, number of sensors, and sensor battery life often restrict our ability to collect observations from all data streams. In this paper, we propose a neural network-based adaptive sampling strategy for monitoring high-dimensional processes. To adaptively select data streams for observation, it is essential to dynamically assess the importance of each stream. Accordingly, we propose learning an importance function through a neural network. By incorporating domain knowledge of process monitoring, a monotonic neural network is constructed. A key challenge in training this network is the lack of ground truth for importance assessment. To address this challenge, we simulate training data and design a novel loss function. Compared to existing adaptive sampling strategies, our method does not rely on heuristic techniques and offers enhanced scalability. With the proposed adaptive sampling strategy, a generic monitoring scheme is then developed for the monitoring of high-dimensional processes. We have conducted thorough numerical simulations and a case study, which demonstrate that our method significantly reduces detection delays and increases the likelihood of observing shifted data streams.

Developing gene therapies involving gene editing is a rapidly evolving field with large potential implications for improving health for both rare and common diseases. Ensuring that these technologies are developed safely, efficiently, and fairly is essential. To better understand the ethical considerations and regulatory requirements and challenges with gene therapies involving gene editing that may advance precision health. Through a multistakeholder workshop and subsequent engagements, multiple ethical and regulatory barriers to developing and implementing gene therapies involving gene editing were identified. Eight major themes emerged that warrant careful consideration, including (1) objectives (treatment, risk reduction, and enhancement) for the intervention; (2) competing interests of safety, equity, and desire for research efficiencies; (3) unique aspects of gene editing related to rare and ultrarare genetic conditions; (4) considerations in the pediatric population; (5) regulatory requirements and ethics oversight; (6) challenges with long-term follow-up and data sharing; and (7) communication. To promote the safe, efficient development of gene therapies involving gene editing that will reach their full potential, all stakeholders will have to undertake an unprecedented degree of collaboration. However, this will be essential to ensure that these interventions are effective, ethically sound, and patient-centered.

This study investigated the associations between cigarette dependence (CD) and memory performance among young adults while also examining behavioral, psychological, and social factors as potential mediators. Memory outcomes were compared across four groups: individuals who never smoked daily, individuals who smoked daily without CD, and individuals with lifetime or current CD. Using data from 15,041 participants in Waves I and IV of the National Longitudinal Study of Adolescent to Adult Health, we conducted ordinary least squares regression models with school fixed effects and nested models to assess mediation. The mean age at Wave IV was 28.50 years (SD = 1.78); 53% were female. Memory was assessed using the Rey Auditory Verbal Learning Test and the Digit-Span Backward Task. Mediators included physical activity, sleep problems, partnership quality, number of close friends, perceived stress, and anxiety. Both lifetime and current CD were significantly associated with poorer memory performance. Lifetime CD was linked to deficits in short-term and long-term memory but not working memory. Current CD was associated with deficits across all three domains. Daily smoking without CD was not significantly associated with memory outcomes. These patterns were consistent across sex. Perceived stress and the number of close friends partially mediated the associations between CD and memory performance, while behavioral factors explained little of the associations. CD is associated with significant memory impairments in young adulthood. Psychological and social mechanisms partially account for these associations, highlighting the importance of addressing stress and social isolation to mitigate cognitive consequences. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Individual animal identification is crucial for dairy cattle management, regulatory compliance, and enhancing food security. Although computer vision systems (CVS) have been proposed for noninvasive animal recognition, limited research has explored their capability to identify the same individual across various life phases and in open-set scenarios. This study evaluated the efficacy of Siamese neural networks (SNN) for identifying individual dairy cattle throughout extensive periods of growth and physiological change. The primary objectives were to (1) leverage a semi-supervised approach to compile and augment datasets, (2) investigate the effects of temporal growth on SNN-based identification model performance, and (3) evaluate open-set inference capabilities in real-world applications. We collected and processed 2,397,338 infrared and depth images from 106 Holstein heifers spanning from birth to first lactation (average 803 d of age). Images underwent comprehensive preprocessing including semantic segmentation via U-Net architecture, binary classification of mask quality, and image augmentation. The SNN architecture utilized a ResNet-50 backbone with L2-normalized 128-dimensional embedding vectors and was trained using categorical cross-entropy and triplet loss functions. Our best-performing model, integrating data from preweaning through 12-mo phases, achieved an accuracy of 0.946, mean average precision of 0.964, and an F1-score of 0.939 when identifying lactating animals in closed-set evaluation using 15 support images. In open-set inference under real-world farm conditions with unknown animals present, the model maintained F1-scores exceeding 0.80 across all growth phases. Infrared imaging consistently outperformed depth imaging for longitudinal identification. These findings demonstrate the feasibility of CVS for noninvasive cattle traceability across different growth phases and environmental conditions, with further research needed to validate performance in diverse commercial settings and across multiple herds.