Luther College

Abstract Ticks and tick-borne diseases are increasingly threatening public health in the United States, emphasizing the importance of accurately predicting their distribution to develop effective management strategies. However, modeling tick distributions can be challenging due to their three-host life cycle, clustered dispersion, and dependence on specific microhabitats. In this study, we compared three modeling methods for predicting the distribution of blacklegged ticks ( Ixodes scapularis ) across three urban parks in Maryland: presence-only Maximum Entropy (MaxEnt), presence-only Log Gaussian Cox Processes (LGCP) utilizing a latent stochastic partial differential equation (SPDE), and a presence-absence GLMM with an SPDE, based on site-specific, field-collected non-detections. We aimed to assess whether a spatially continuous presence-absence GLMM-SPDE could serve as an alternative or complement to the popular MaxEnt model, potentially offering better computational efficiency and predictive accuracy. Results indicated that both MaxEnt and LGCP models predicted tick distributions moderately well, although the MaxEnt model tended to overpredict presence in fragmented urban environments. The presence-absence model achieved the highest accuracy (mean AUC = 0.854 ± 0.04; CBI = 0.985), effectively identifying occupied sites while maintaining reasonable specificity, primarily when park-specific thresholds were used. These findings demonstrate that integrating a continuous spatial autocorrelation structure enables presence-only GLMMs to perform adequately. However, the most precise predictions in diverse urban areas come from field-collected presence-absence data. Therefore, we recommend using spatially explicit binomial SPDE-based GLMMs that require field ecologists to check drags or flags during tick sampling systematically and to record both absences and presences.

Looking for a text to help familiarize students, from a variety of backgrounds, with the tools and theories of physical chemistry and help introduce them to the extensive range of applications in biochemistry and cell biology? Biophysical chemistry is an interdisciplinary field which applies the principles of physical chemistry to biochemical systems. Students entering the field of biochemistry generally have diverse backgrounds, either coming from predominantly chemical training with extensive exposure to quantitative descriptions of chemical structure and reactivity, but with limited application to biological systems, or they come from predominantly biological training where their exposure to biological systems is more complete, but they are often intimidated by the mathematical descriptions of these systems. This book works for both audiences by showing how quantitative descriptions can enhance the understanding of biological systems. This is done with examples from current applications to ongoing biochemical problems such as protein mis-folding, diseased states and their possible remedies. Written by an author with extensive experience in teaching biophysical chemistry and first-hand knowledge of how students approach their course work, this text provides extensive supplementary material, interactive activities and thought-provoking questions to encourage discussion. This exposure provides the student with opportunities to envision how they might contribute to research in these areas, and even launch a career in the field of biophysical chemistry.

ABSTRACT Objective This study evaluated the psychometric properties of the Scales for Outcomes in Parkinson’s Disease – Sleep Scale (SCOPA-Sleep) in a sample of patients with fibromyalgia syndrome (FMS), and examined the unique and interactive associations between sleep problems, daytime fatigue, and quality of life. Method A total of 79 adults diagnosed with FMS according to the 2010 American College of Rheumatology criteria completed measures assessing sleep problems, daytime fatigue (SCOPA-Sleep), and health-related quality of life (EuroQOL-5D). Exploratory factor analysis (EFA) and internal consistency analyses assessed the dimensionality and reliability of the SCOPA-Sleep. Multiple linear regression examined confounding and moderation effects of sleep problems and fatigue on quality of life, controlling for sociodemographic variables. Results EFA supported a two-factor structure of the SCOPA-Sleep reflecting distinct subscales for nighttime sleep problems and daytime fatigue. The subscales were uncorrelated, and both demonstrated strong internal consistency (α = .87 and .84, respectively). Regression analyses revealed that daytime fatigue was not significantly associated with quality of life after adjusting for covariates. However, sleep problems were a significant predictor of reduced quality of life (β = – .33, p < .01). No significant interaction between sleep problems and fatigue was observed. Conclusions The SCOPA-Sleep may be a useful brief screening tool for sleep-related impairments in clinical FMS assessment. However, given the relatively small and homogeneous sample (predominantly middle-aged women not currently employed), the generalizability of these findings is limited. Future research should replicate these results in larger and more diverse FMS populations.

IntroductionSleep disturbances are prevalent among primary care patients, and psychological dysfunction, including stress, anxiety, and depression, are robust contributors to poor sleep health. Yet, the presence of potential protective characteristics, such as health self-efficacy and engaging in adaptive health behaviors, may mitigate such outcomes. Gratitude (i.e., recognition and appreciation of experiences, relationships, and surroundings), a positive psychological cognitive-emotional characteristic, may serve as a catalyst of these beneficial downstream effects, given its association with improved health functioning and sleep.MethodsIn a sample of primary care patients (N = 869, M age = 53; 60.7% female) from 50 urban and 30 rural practices in Germany, health self-efficacy (i.e., belief in ability to perform necessary actions to manage health) and constructive health behaviors (i.e., actions taken to modify health positively), separately and together as parallel first-order mediators, and stress, anxiety, and depression, as parallel second-order mediators, were investigated as potential serial mediators of the association between gratitude and sleep disturbances. Participants completed self-report measures in person and online.ResultsSignificant serial mediation was observed across models, although effects varied. In the first model, gratitude was associated with greater health self-efficacy and, in turn, to less stress (a1d21b4 = −0.019, 95% CI [−0.039, −0.002], SE = 0.010), anxiety (a1d31b5 = −0.026, 95% CI [−0.045, −0.008], SE = 0.009), and depression (a1d41b6 = −0.020, 95% CI [−0.040, −0.003], SE = 0.009), and fewer consequent sleep disturbances. In the second model, health behaviors, and anxiety (a1d31b5 = −0.009, 95% CI [−0.019, −0.002], SE = 0.004) and depression (a1d41b6 = −0.007, 95% CI [−0.016, −0.001], SE = 0.004), were serial mediators, but health behaviors and stress were not. In a final combined model, serial mediation occurred on two pathways, health self-efficacy and anxiety (a1d41b6 = −0.026, 95% CI [−0.046, −0.009], SE = 0.009), and health self-efficacy and depression (a1d51b7 = −0.019, 95% CI [−0.037, −0.003], SE = 0.009), and a specific indirect effect was found for health behaviors (a2b4= −0.086, 95% CI [−0.140, −0.030], SE = 0.026), but not self-efficacy.DiscussionOverall, gratitude was associated with reduced sleep disturbances through positive health behavior engagement, and via the serial mediation effects of greater health self-efficacy and lower psychological distress. Clinical interventions that enhance gratitude (e.g., gratitude listing or diaries), self-efficacy (e.g., disease self-management programs), or health behavior engagement (e.g., weight management programs) may promote favorable downstream effects on psychological distress and sleep disturbances among primary care patients.

The rapid advancement of artificial intelligence techniques, particularly deep learning, has transformed medical imaging. This paper presents a comprehensive review of recent research that leverage vision transformer (ViT) models for medical image classification across various disciplines. The medical fields of focus include breast cancer, skin lesions, magnetic resonance imaging brain tumors, lung diseases, retinal and eye analysis, COVID-19, heart diseases, colon cancer, brain disorders, diabetic retinopathy, skin diseases, kidney diseases, lymph node diseases, and bone analysis. Each work is critically analyzed and interpreted with respect to its performance, data preprocessing methodologies, model architecture, transfer learning techniques, model interpretability, and identified challenges. Our findings suggest that ViT shows promising results in the medical imaging domain, often outperforming traditional convolutional neural networks (CNN). A comprehensive overview is presented in the form of figures and tables summarizing the key findings from each field. This paper provides critical insights into the current state of medical image classification using ViT and highlights potential future directions for this rapidly evolving research area.

It is widely known that losing a baby is a stressful experience in a woman's life. However, the mechanisms through which loss and grief affect mental health are complex and multifaceted. Based on the framework of stress-and-coping theory, this cross-sectional study aimed to evaluate the potential moderating effects of social support, resilience, and spirituality on the relationship between complicated grief and quality of life among women who have experienced pregnancy loss. Data from paper-pencil surveys were obtained from 333 Bavarian females aged 18-67 years who had suffered the loss of a pregnancy. Moderated regression modelling using 5,000 bootstrap confidence intervals was conducted to test the hypotheses. The results confirmed the moderating role of spirituality and social support. More specifically, the relationship between complicated grief and quality of life was significant when social support or spirituality was low and non-significant when levels of each moderator were high. While resilience was a positive predictor of quality of life, it did not prove to be a significant moderator in the impact of complicated grief. Our findings suggest that healthcare providers and support systems should consider tailoring interventions that focus on spirituality and social support to break the negative impact of experiences related to pregnancy loss on overall mental health. Nevertheless, it is important to note that the data were drawn from support groups organized by Caritas Germany, underscoring the need for more diverse demographic representation to enhance generaliz-ability.

Abstract The early detection and classification of skin cancer are pivotal in improving patient outcomes and reducing healthcare burdens. However, traditional deep learning models in dermatological diagnostics often struggle with the nuanced differentiation of skin lesions. This paper introduces a novel approach, integrating an Advanced Heat Flow Layer into deep learning architectures for skin cancer classification, this method is centered on the principles of anisotropic diffusion, distinguishing itself from conventional image processing techniques by selectively smoothing image areas while preserving critical edge details, essential for accurate lesion identification. In our research, we utilized the Ham10000 dataset, enriched with data augmentation to simulate real-world variability, we conducted a comprehensive comparison of our model, featuring the Advanced Heat Flow Layer, against several benchmark deep learning models, including Sobel Edge Detection Layer. Our model, integrated with various layers of DenseNet121, consistently outperformed these benchmarks across key metrics such as accuracy, precision, recall, F1 score, and AUC, particularly with augmented data, this indicates a significant enhancement in the model's ability to generalize and maintain critical diagnostic features under diverse conditions. Our code is available at, https://github.com/sanadv/SkinCancerClassificationModels/blob/main/Models.ipynb