There is increasing recognition that the welfare needs of cephalopod molluscs and decapod crustaceans are important. Current commercial practices involving these animals include a range of potential threats to their welfare, such as conditions of farming, capture, transport, and slaughter. This article draws from and updates our 2021 review for the UK Government, recommending a range of relatively simple and impactful changes that could benefit welfare while highlighting important research gaps that should be prioritised to facilitate the drafting of guidelines for best-practice.

This paper presents a model specification for group comparisons regarding a functional trend over time within a trial and learning across a series of trials in intensive binary longitudinal eye-tracking data. The functional trend and learning effects are modeled using by-variable smooth functions. This model specification is formulated as a generalized additive mixed model, which allowed for the use of the freely available mgcv package (Wood in Package ‘mgcv.’ https://cran.r-project.org/web/packages/mgcv/mgcv.pdf, 2023) in R. The model specification was applied to intensive binary longitudinal eye-tracking data, where the questions of interest concern differences between individuals with and without brain injury in their real-time language comprehension and how this affects their learning over time. The results of the simulation study show that the model parameters are recovered well and the by-variable smooth functions are adequately predicted in the same condition as those found in the application.

Objectives/Goals: Obtaining reliable clinical research professional (CRP) employment data within and across Clinical and Translational Science Awards (CTSA) institutions is an ongoing challenge. We describe an intra-institutional approach implemented to generate routine and accurate CRP data reports to monitor and evaluate CRP career progression and assist in formation of an institutional CRP network. Methods/Study Population: A research job family with 47 job series including human, animal, and laboratory research positions was implemented at Virginia Commonwealth University (VCU). However, CRP job satisfaction surveys and evaluations could not be confidently interpreted due to the confounding animal and laboratory research positions. Led by VCU Clinical and Translational Science Awards Workforce Development a cross-functional team was formed to isolate specific CRP positions. The team included CRP front-line staff and managers partnering with VCU Human Resource Information Systems. Identified were 39 unique CRP positions across 13 distinct job series. This identification provides CRP new hire and job specific data for evaluation and tracking as well as the ability for CRP directed communications. Results/Anticipated Results: Initial and monthly HR data reports were used to develop an institutional CRP list-serv for 325–350 allowing for targeted CRP communications within a decentralized environment. Bimonthly HR data reports identify university new hires and internal transfers into any of the 39 unique jobs within 0 – 12 days of hire. Twelve unique data points are provided (name, email, current position hire date, job code, job title, working title, department, division, supervisor’s name, job title, email, and job code) allowing for tracking and analysis of retention rates, career progression, and lateral movement among other outcomes. Collaboration led by VCU Clinical and Translational Science Awards Workforce Development team provides the representative CRP staff, managers, and institutional leadership with a renewed confidence interpreting CRP employment data. Discussion/Significance of Impact: The team science approach to identify and develop routine and real-time reporting of CRP job specific data provides a rich source of information. The information is used to evaluate CRP job satisfaction and factors contributing to CRP retention, engage in future mixed-methods research, and support the formation of an institutional CRP network.

Objectives/Goals: The University of Utah (U of U) CTSI has partnered with the Salt Lake Center for Science Education (SLCSE), a Title I school serving grades 7–12. Goals of this partnership are to 1) bridge the gap between K12 classroom learning and real-world applications and 2) better prepare students from underrepresented populations to enter the STEM workforce. Methods/Study Population: To cultivate science self-efficacy in grade 7–12 students, experiences included interviewing a scientist for 7th graders, model organism lab visits for 11th graders, and summer research internships for rising seniors. Additional engagements on the SLCSE campus included U of U guest speakers, U of U faculty and student participation in afterschool STEM clubs, U of U graduate students’ mentorship of high school science fair projects, and U of U faculty support in establishing a zebrafish lab for biology students. All students were surveyed at the start and end of the academic year using DEVISE evaluation tools developed by the Cornell Lab of Ornithology. Students participating in the summer internship program also completed the mentoring competency assessment before and after their ten-week internship experience. Results/Anticipated Results: During the first year of a seven-year longitudinal study, 380 SLCSE students engaged in at least one science experience through the Utah CTSI-SLCSE partnership named BEES (Boosting Engagement through Experiences in Science). Pearson product-moment correlations were used in preliminary studies to examine relationships between experience type and student motivation and interest in STEM. Field trips to U of U STEM labs and of U graduate students’ mentorship of high school science fair projects were significantly correlated with student motivation and interest, while the interview-a-scientist experience was significantly correlated with motivation only. The Utah CTSI-SLCSE BEES Program’s impact on student STEM success continues to be assessed using surveys and student reflections. Discussion/Significance of Impact: Access to science for underserved K-12 students is a critical issue in addressing educational equity and improving pathways into STEM fields. Many students attending SLCSE are low-income minority students with limited access to role models in STEM. The BEES partnership provides impactful opportunities for students to gain access to STEM.

Objectives/Goals: The NIH Common Fund launched Community Partnerships to Advance Science for Society (ComPASS) to study ways to reduce health disparities by addressing underlying structural factors within communities. ComPASS was designed for community organizations to lead research that addresses community needs. Methods/Study Population: ComPASS awarded five health equity research hubs (Hubs) to provide specialized technical support to ComPASS research projects led by community organizations. Expertise provided by the Hubs to the community-led projects will focus on specific facets of community health, including nutrition access, health care access, and built environment. The Hubs support community-led piloting and testing of structural interventions within community settings by providing subject matter expertise in areas including structural and multilevel intervention study design and methods, implementation science, and community engagement. Results/Anticipated Results: The Hubs will provide expertise and support to the community-led research projects around one or more social determinants of health domains: health care access and quality, education access and quality, economic stability, social and community context, and neighborhood and built environment. The Hubs will help identify strategies for measuring health outcomes and assessing the effects of structural and contextual factors on intervention outcomes. We anticipate the ComPASS program will lead to a better understanding of how structural interventions that leverage multi-sectoral partnerships can advance health equity. Discussion/Significance of Impact: Through community-led research, ComPASS projects are implementing structural interventions to address social determinants and advance health equity. The technical scientific support rooted in health equity provided by the Hubs is essential to the success of these research projects.

Objectives/Goals: Given the challenges that early career research scientists face, especially preparing for promotion and tenure, the decision on whether to join a research team can be fraught. We developed a novel training to support informed decision-making regarding new scientific teaming opportunities. Methods/Study Population: A team science workshop entitled “Should I join this research team” was designed for early career investigators from varied disciplinary backgrounds. Learning objectives for attendees included 1) describing the role of team science in translational research, 2) determining if teaming opportunities are a good fit, and 3) crafting thoughtful responses to requests. The training was initially delivered to 38 attendees (11 K scholars) during a virtual national meeting. We adapted this training for in-person delivery to K and T scholars at our CTSA regional partners. Instructional methods shared across virtual and in-person modalities included self-reflection, think and share activities, and scenario application. In-person delivery also included short video clips and small group discussions. Results/Anticipated Results: Multiple Likert-scale items were completed by workshop participants before and after completing the workshop to evaluate attendees’ confidence in their perceived abilities to explain strengths and limitations of team science, identify characteristics of effective science teams, evaluate a team invitation, assess costs and benefits, negotiate collaborative team invitations, etc. Preliminary data from the virtual workshop suggests that 54.6% of scholars were either not at all or only slightly confident in evaluating a teaming invitation. After the workshop, 45.5% reported being very confident, and 9.1% reported extreme confidence in evaluating a team invitation. Evaluation of the in-person training, along with a comparison of virtual and in-person learning outcomes will also be presented. Discussion/Significance of Impact: Our multimodal training is designed to equip early career investigators with the tools needed to evaluate and respond effectively to research team invitations. We believe this novel training will result in informed teaming decisions for early career research scientists.

Objectives/Goals: Aspiration causes or aggravates lung diseases. While bedside swallow evaluations are not sensitive/specific, gold standard tests for aspiration are invasive, uncomfortable, expose patients to radiation, and are resource intensive. We propose the development and validation of an AI model that analyzes voice to noninvasively predict aspiration. Methods/Study Population: Retrospectively recorded [i] phonations from 163 unique ENT patients were analyzed for acoustic features including jitter, shimmer, harmonic to noise ratio (HNR), etc. Patients were classified into three groups: aspirators (Penetration-Aspiration Scale, PAS 6–8), probable (PAS 3–5), and non-aspirators (PAS 1–2) based on video fluoroscopic swallow (VFSS) findings. Multivariate analysis evaluated patient demographics, history of head and neck surgery, radiation, neurological illness, obstructive sleep apnea, esophageal disease, body mass index, and vocal cord dysfunction. Supervised machine learning using five folds cross-validated neural additive network modelling (NAM) was performed on the phonations of aspirator versus non-aspirators. The model was then validated using an independent, external database. Results/Anticipated Results: Aspirators were found to have quantifiably worse quality of sound with higher jitter and shimmer but lower harmonics noise ratio. NAM modeling classified aspirators and non-aspirators as distinct groups (aspirator NAM risk score 0.528+0.2478 (mean + std) vs. non-aspirator (control) risk score of 0.252+0.241 (mean + std); p Discussion/Significance of Impact: We report the use of voice as a novel, noninvasive biomarker to detect aspiration risk using machine learning techniques. This tool has the potential to be used for the safe and early detection of aspiration in a variety of clinical settings including intensive care units, wards, outpatient clinics, and remote monitoring.