Pacific Lutheran University

The substantively biased learning hypothesis predicts that phonological patterns that are phonetically motivated should be easier to learn than patterns that show little or no phonetic motivation (Wilson 2006). One pattern that has received much attention is the distinction between vowel harmony (a phonetically grounded pattern) and vowel disharmony (a phonetically ungrounded pattern), as several studies comparing learnability of vowel harmony over disharmony have shown mixed or nuanced results (Zheng and Do 2025). One possibility is that metalinguistic judgments may not be sensitive enough to detect biases for categorical vowel harmony. The present study tests this idea using a modified phoneme monitoring task developed in Finley (2025), where participants were asked to listen for the final vowel of a word that either followed vowel harmony in 80% of trials, or vowel disharmony in 80% of trials (and then switched). Results showed evidence of a bias for vowel harmony over disharmony. Participants were generally faster and more accurate at identifying words that obeyed vowel harmony, particularly when the majority of items followed vowel harmony. Implications for learnability of categorical and variable patterns are discussed.

Despite recent progress, the Estimation of Model Accuracy (EMA) for protein complexes remains less advanced compared to that for protein monomers. A key challenge lies in effectively integrating both interface-specific and global structural information to accurately assess the quality of protein complexes. Here, we introduce ContrastQA, the first EMA framework for protein complexes that incorporates the proposed label-guided graph contrastive learning based on interface quality. By integrating a geometric graph neural network to model global structural features, ContrastQA effectively captures both local (interface-level) and global (structure-level) information for accurate model quality estimation. ContrastQA achieved ranking losses of 0.123 and 0.116 on the TMscore and GDT-TS metrics on the CASP16 dataset, which are 0.015 (10.9%) and 0.012 (8.7%) lower than the second-best EMA method with ranking losses of 0.138 and 0.128. Furthermore, for the more challenging CASP16 hard targets, our method achieved a TMscore ranking loss of 0.131, which is 0.023 (14.9%) lower than that of the second-best EMA method with a ranking loss of 0.154. Our study demonstrates the strong effectiveness of the label-guided graph contrastive learning module. These findings suggest that our graph contrastive learning framework serves as a valuable pre-training strategy for learning protein structure representations. The source code is available at https://github.com/Cao-Labs/ContrastQA . Significance Statement High-quality proteins generated by high-precision structural prediction methods for protein complexes helping biologists prioritize models for drug discovery and understanding disease mechanisms. Recent advances in predicting protein complex structures (like AlphaFold3) still struggle to match experimental precision. A critical bottleneck lies in accurately estimating the quality of predicted models-known as Estimation of Model Accuracy (EMA). Here, we introduce ContrastQA, implementing label-guided graph contrastive learning framework to explore the generalization of high-quality proteins. Our method demonstrates outstanding performance, provides an effective solution for assessing the structural quality of protein complexes.

Background: Access to manual uterine aspiration (MUA) training in the United States is precarious and fraught. WhatsApp, the instant messaging platform, is used in medical education and peer collaboration. Advanced registered nurse practitioners (ARNPs) lack sufficient access to mentorship for learning MUA. Objective: This pilot study examines the effect of participation in a WhatsApp Mentorship Group on perceived mentorship for ARNPs learning MUA. Methods: Study design was quasi-experimental. ARNPs attending a reproductive health conference completed a survey measuring mentorship. Participants were invited to join the WhatsApp Mentorship Group. Within the group, mentors posted predetermined questions biweekly to stimulate conversation, answer clinical practice questions, and provide access to educational media files. Results: Following participation in the WhatsApp Mentorship Group, mean informational support levels increased by a statistically significant 5% (p =03). Conclusion: WhatsApp may be a viable option for expanding mentorship among ARNPs. Implications for Nursing: ARNPs may benefit from further inquiry into the use of WhatsApp for mentorship, including fellowships. Engagement data from this study suggest a bimodal level of mentorship perception.

Behavioral intervention technologies (BITs) offer scalable, cost-effective healthcare solutions but often show reduced impacts in community settings and are rarely sustained. Human-centered design (HCD) enhances usability by tailoring BITs to stakeholder needs, while implementation science (IS) identifies contextual barriers and strategies to promote uptake and sustainability. Both HCD and IS aim to improve BIT usability and implementation through iterative, user-focused processes but are rarely integrated. We introduced the user-centered and sustainable implementation science (USIS) model, a novel and systematic framework that combines HCD and IS principles to enhance BIT effectiveness and sustainability. We aimed to (1) synthesize the HCD and IS outcomes and integrate them into a USIS framework; (2) apply USIS to a case study: the sleep shared-management intervention for children with juvenile idiopathic arthritis and their parents (SLEEPSMART). We conducted a narrative literature review on HCD and IS outcomes for BITs from the PubMed, CINAHL, and Web of Science databases. Articles were selected based on their focus on usability, implementation outcomes, and evidence-based healthcare practices. This synthesis informed the development of the USIS model. The USIS model incorporates five domains: (1) user-centeredness (empathy, engagement, and equity), (2) efficiency (cost, timeliness, and rapidity), (3) feasibility (learnability, memorability, error reduction, and low cognitive load), (4) satisfaction (acceptability and appropriateness), and (5) fidelity (adoption, penetration, and sustainability). We applied the USIS model to evaluate the SLEEPSMART project to demonstrate the application of the USIS model in guiding BIT design and refinement. Insights from the review informed the design principles applied during the development and testing stages of SLEEPSMART. The USIS model serves as a practical and theoretical guide to improve BIT design and evaluation. By emphasizing implementation considerations early and centering user needs, USIS provides a pathway to bridge HCD and IS approaches to enhance the real-world impact and sustainability of digital health innovations.

ABSTRACTWithin the area of Lutheran theology and ethics, recent scholarship has shed light on important horizons in gender, sexuality, politics and church. Five articles in this edition of Dialog (Lowe, Latour, Streufert, Micale and Lahtinen) address the ethics of consent, LGBTQ+ religious trauma, reproductive justice and the intersections of abortion politics with work and disability studies. They connect interdisciplinary methods to Lutheran theology, history, ethics and ecclesiology in order to develop relational and embodied understandings of justice. In particular, they ask how the Lutheran tradition addresses consent, moral agency, abortion access, disability and ecclesial or ecological justice in distinctive ways. They address questions such as: How does Lutheran theological language engage themes of gender and sexuality justice in diverse contexts? How might Lutheran themes contribute to, be challenged by, or transformed with perspectives from diverse and intersectional politics?

Dynamic community detection, which is capable of revealing changes in community structure over time, has garnered increasing attention in research. While evolutionary clustering methods have proven to be effective in tackling this issue, they often have a tendency to favor what are referred to as elite solutions, inadvertently neglecting the potential value of non-elite alternatives. Although elite solutions can ensure population convergence, they may result in negative population migration due to the lack of diversity when the network changes. In contrast, when the network undergoes changes, non-elite solutions could better adapt to the changed network, thereby can help the algorithm find accurate community structures in the new environment. To this end, we propose a diversified population migration strategy that consists of two-stages, i.e., solution selection and solution migration. In the first stage, we use elite solutions not only to ensure convergence but also non-elite solutions to maintain diversity and cope with network changes. In the second stage, the migration solutions are refined by using incremental changes between the two consecutive snapshots of networks. Based on the proposed strategy, we suggest a diversified population migration-based multiobjective evolutionary algorithm named DPMOEA. In DPMOEA, we design new genetic operators that utilize incremental changes between networks to make the population evolve in the right direction. Our experimental results demonstrate that the proposed method outperforms state-of-the-art baseline algorithms and can effectively solve the dynamic community detection problem.

Summary We give yet another visual proof for the sum of cubes formula using two stacks of the sum of the first n cubes.

Composting is an aerobic process that transforms organic wastes into valuable agricultural product, but the composting process frequently results in losses of nitrogen (N) through NH3 volatilization. We evaluated the potential for high-temperature, wood-based biochar to reduce N loss when included as a compost feedstock. Biochar was composted with wood shavings and poultry manure (4:1 v/v) at rates of 0, 20 and 40% (v/v) in replicated, actively aerated 1.43 m3 in-vessel compost piles. Biochar addition caused more rapid temperature increase and truncated thermophilic phase. N loss from the entire compost mass was reduced with 40% and 20% biochar addition compared to the control (29.9, 32.3, and 35.6% N loss, respectively; P value = 0.027). However, assuming no N was lost from the added biochar, difference in N loss was less significant (P value = 0.100). With 40% biochar addition, finished compost had greater NO3 --N concentration, lower electrical conductivity, greater carbon to nitrogen ratio, and greater total carbon content. Compost products were amended to soil at rates of 7 or 15 Mg C ha−1 in potato production. Relative to an unamended control, the high application rates tended to decrease bulk density and increase total N in soil, but no differences were detected between the compost or composted biochar treatments. Tuber weights were increased 40% with application of composted biochar (biochar 40% v/v). Biochar is a promising bulking agent for composting which may reduce N loss and increase nitrification. Future studies should examine how specific biochar products affect the compost process, nutrient retention, and crop production.