Objectives/Goals: Substantial evidence supports the use of community engagement in CTS. Yet, there is a lack of empirical basis for recommending a particular level of community engagement over others. We aimed to identify associations between level of community involvement and study process outcomes, focusing on procedures to promote enrollment and inclusion. Methods/Study Population: Using manifest content analysis, we analyzed community engagement (CEn) strategies of studies indexed in ClinicalTrials.gov, focusing on studies 1) associated with 20 medical schools located in 8 southern states in the Black Belt, 2) conducted in 2015–2019, and 3) on 7 topics: cancer, depression, anxiety, hypertension, substance use disorder, cardiovascular disease, and HIV/AIDS. Data source was the ClinicalTrials.gov entry and publication for each study. We categorized each study on level of community involvement as described by the study protocol CTSA Consortium Community Engagement Key Function Committee Task Force on the Principles of Community Engagement continuum. Outcomes included recruitment and representativeness. Other codes included funder type, study phase, study status, and time to enrollment. Results/Anticipated Results: Of 890 studies that met inclusion criteria, only 493 had published findings. 286 studies (58%) met enrollment targets. Only 9 studies described any level of CEn (1 outreach, 3 consult, 1 involvement, 3 collaboration, and 1 shared leadership). Time to enrollment for these 9 studies (mean 28.78 mos.) was shorter than for studies without CEn (mean 37.43 months) (n.s.). CEn studies reached significantly higher enrollment (CEn mean  = 2395.11, non-CEn mean  = 463.93), p Discussion/Significance of Impact: Results demonstrate the substantial effect of CEn on enrollment and inclusion in clinical studies. However, the infinitesimal number of studies that reported CEn did not allow comparisons of level of engagement on the outcomes. Findings highlight ethical questions surrounding the lack of publishing incomplete studies.

The role of diet in the prevention and management of menopausal symptoms is poorly understood1,2. Digital health platforms have the potential to optimise collection of population-level menopause symptom data particularly in a rapidly changing environment2,3. This study explored the prevalence of menopause symptoms utilising data collection capabilities of a mobile app in a large population of peri- and postmenopausal women and to investigate novel associations between diet quality and symptoms.

Baseline data on diet, 20 menopause symptoms (i.e. 3 vasomotor, 3 sexual, 4 psychological and 10 somatic) and other characteristics of 70,412 participants of the ZOE PREDICT 3 app-based study in UK (n = 27,932 peri- and n = 42,480 postmenopausal) were analysed (NCT04735835). Dietary assessment was performed through an app-based food frequency questionnaire (PREDICTFFQ)4 and diet quality assessed using the Healthy Eating Index-2020 (HEI, score 0-100)5.

The burden of menopausal symptomatology was high; with 99.8% and 92.7% of peri- and postmenopausal participants reporting 1 or more symptoms respectively and 66.0% and 41.2% of peri- and postmenopausal participants experiencing >12 symptoms, respectively (P<0.001). Mean ( ±SD) symptom number was greater in the peri- versus postmenopausal group; 13.5±3.8, vs. 10.5±5.5 (P<0.001). HEI (mean±SD) was slightly lower in the peri- versus postmenopausal group; 73.9±9.7 vs. 76.6±8.9 respectively (P<0.001). Logistic regression analyses showed that a 20 unit increase in HEI was associated with reduction of 6%-37% and 12%-43% in likelihood of symptom presence (dependent on the symptom) in peri- and postmenopausal participants, respectively (P<0.05). ORs remained significant in adjusted models (for age, BMI, use of hormone replacement therapy, total energy intake and home environment (urban, rural) showing reductions of 7%-19% and 6-21%, respectively (P<0.05). Logistic regression analyses revealed that a similar increase in HEI (20 units) was associated with 17% and 19% reductions in the number of symptoms reported by peri- and postmenopausal women after multiple adjustments (P<0.05).

This large observational study confirms the high prevalence of menopausal symptoms among peri- and postmenopausal women and provides new evidence between adherence to healthy eating and reduced likelihood of menopausal symptoms, increasing the potential for symptom reduction by specific dietary approaches. Further studies should examine this relationship in longitudinal and dietary intervention settings.

Digital health technologies delivering dietary and lifestyle advice1,2 may offer a contemporary approach to menopause symptom mitigation in a large population base3. This study aimed to evaluate alterations in both diet quality and menopause symptom burden following the implementation of an app-based, personalised dietary intervention in peri- and postmenopausal participants.

Baseline and follow-up data (mean follow-up time, 207 ± 72 days) on diet, menopause symptom number (from 20 vasomotor, sexual, psychological and somatic symptoms), symptom severity (0-not at all, 1-a little, 3-quite a bit and 5-extremely) and other characteristics of 4,287 UK based participants of the ZOE PREDICT 3 study (1,000 perimenopausal and 3,287 postmenopausal) were analysed (NCT04735835). Dietary assessment was performed through an app-based food frequency questionnaire (PREDICT-FFQ)2 and diet quality assessed using the Healthy Eating Index-2020 (HEI, score 0-100)4. A novel Menopause Symptom Tracker Score (MSTS 0-100, Copyright © 2024 Zoe Limited) was used to capture symptom number and severity.

There was a modest increase in diet quality after the intervention in both peri- and postmenopausal participants; mean HEI from 76.4 ± 8.4 to 79.7 ± 6.8 (P<0.001) and from 78.7 ± 7.8 to 81.1 ± 6.4 (P<0.001), respectively. Baseline MSTS was higher in peri- versus postmenopausal participants 28.9 ± 15.1 vs. 18.1 ± 15.0 (P<0.001). However, both groups showed a significant decrease in mean MSTS after the intervention compared to baseline; mean change (95%CI) in perimenopausal was -8.7 (-9.5, -7.9; 30.1% reduction) and in postmenopausal was -6.6 (-6.9, -6.2; 36.5% reduction) (P for both <0.001).

The reduction in the overall MSTS was greater in perimenopausal compared to postmenopausal (P<0.001). Reductions in domain-specific MSTS for peri- and postmenopausal groups respectively, were as follows: vasomotor −0.56 (−0.71, −0.40; 24.9% reduction) and −0.47 (−0.54, −0.41; 31.8% reduction), sexual −0.72 (−0.87, −0.57; 18.6%) and -1.08 (-1.16, −0.99; 28.7%), psychological -2.3 (-2.5, 2.0; 35.0%) and -1.2 (-1.3, -1.1; 44.2%), somatic -5.1 (-5.6, -4.7; 31.5%) and -3.8 (-4.0, -3.6; 37.5%) (P for all <0.001). The reduction in the psychological and somatic domain-specific MSTS was greater in peri-compared to postmenopausal group (P for both <0.001).

This study supports the potential of personalised app-based dietary interventions in the mitigation of menopausal symptoms during menopause transition and its potential to affect the health and quality of life of millions. Further studies should investigate their efficacy in a randomised, controlled dietary intervention setting.

The emergence of innovative neuroimaging technologies, particularly highly portable magnetic resonance imaging (pMRI), has the potential to spawn a transformative era in neuroscience research. Resourced academic institutional review boards (IRBs) with experience overseeing traditional MRI have a special role to play in ethical governance of pMRI research and should facilitate the collaborative development of nuanced and culturally sensitive guidelines and educational resources for pMRI protocols. This paper explores the ethical challenges of pMRI in neuroscience research and the dynamic leadership role that IRBs should play to promote ethical oversight of emerging pMRI research.

Highly portable and accessible MRI technology will allow researchers to conduct field-based MRI research in community settings. Previous guidance for researchers working with fixed MRI does not address the novel ethical, legal, and societal issues (ELSI) of portable MRI (pMRI). Our interdisciplinary Working Group (WG) previously identified 15 core ELSI challenges associated with pMRI research and recommended solutions. In this article, we distill those detailed recommendations into a Portable MRI Research ELSI Checklist that offers practical operational guidance for researchers contemplating using this technology.

We present the fourth data release (DR4) of the SkyMapper Southern Survey (SMSS), the last major step in our hemispheric survey with six optical filters: u, v, g, r, i, z. SMSS DR4 covers 26 000 deg$^{2}$ from over 400 000 images acquired by the 1.3 m SkyMapper telescope between 2014-03 and 2021-09. The 6-band sky coverage extends from the South Celestial Pole to $\delta=+16^{\circ}$, with some images reaching $\delta\sim +28^{\circ}$. In contrast to previous DRs, we include all good-quality images from the facility taken during that time span, not only those explicitly taken for the public Survey. From the image dataset, we produce a catalogue of over 15 billion detections made from $\sim$700 million unique astrophysical objects. The typical 10$\sigma$ depths for each field range between 18.5 and 20.5 mag, depending on the filter, but certain sky regions include longer exposures that reach as deep as 22 mag in some filters. As with previous SMSS catalogues, we have cross-matched with a host of other imaging and spectroscopic datasets to facilitate additional science outcomes. SMSS DR4 is now available to the worldwide astronomical community.

The U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) has been a leader in weed science research covering topics ranging from the development and use of integrated weed management (IWM) tactics to basic mechanistic studies, including biotic resistance of desirable plant communities and herbicide resistance. ARS weed scientists have worked in agricultural and natural ecosystems, including agronomic and horticultural crops, pastures, forests, wild lands, aquatic habitats, wetlands, and riparian areas. Through strong partnerships with academia, state agencies, private industry, and numerous federal programs, ARS weed scientists have made contributions to discoveries in the newest fields of robotics and genetics, as well as the traditional and fundamental subjects of weed–crop competition and physiology and integration of weed control tactics and practices. Weed science at ARS is often overshadowed by other research topics; thus, few are aware of the long history of ARS weed science and its important contributions. This review is the result of a symposium held at the Weed Science Society of America’s 62nd Annual Meeting in 2022 that included 10 separate presentations in a virtual Weed Science Webinar Series. The overarching themes of management tactics (IWM, biological control, and automation), basic mechanisms (competition, invasive plant genetics, and herbicide resistance), and ecosystem impacts (invasive plant spread, climate change, conservation, and restoration) represent core ARS weed science research that is dynamic and efficacious and has been a significant component of the agency’s national and international efforts. This review highlights current studies and future directions that exemplify the science and collaborative relationships both within and outside ARS. Given the constraints of weeds and invasive plants on all aspects of food, feed, and fiber systems, there is an acknowledged need to face new challenges, including agriculture and natural resources sustainability, economic resilience and reliability, and societal health and well-being.

Back overuse injuries are a significant problem in the U.S. Army, responsible for nearly a quarter of musculoskeletal injuries. Back exosuits are wearable devices that relieve musculoskeletal strain, make lifting easier, and could potentially reduce Soldier overuse injuries. But published studies have not evaluated exosuits during realistic field operations to assess acceptability to Soldiers. We tested a back exosuit on field artillery Soldiers during a field training exercise. Afterward, Soldiers completed a survey to quantify their satisfaction, intent to use, and performance impact of the exosuit. Feedback was overwhelmingly positive: Approximately 90% of Soldiers reported that exosuits increased their ability to perform their duties, and 100% said that if the exosuit were further developed and made available to them, they would be likely to wear it. These numerical survey results indicated that exosuits can provide a practical and acceptable way to assist lifting and augment physical performance during realistic Army operations without interfering with other duties.

Background: Since its approval by Health Canada in 2020, several neurosurgical centres across Canada have used 5-ALA, an oral drug that assists with surgical resection of malignant gliomas by causing tumour cells to fluoresce under the microscope. The study’s objective is to prospectively evaluate the extent of resection (EOR) and clinical outcomes in 5-ALA-guided surgery at HSN compared to historical controls. Methods: A retrospective analysis was performed of patients with malignant gliomas having undergone surgery at HSN from 2011 to December 2020, assessing the EOR (contrast-enhanced tumour on post-operative CT/MRI), progression-free survival (PFS), overall survival (OS). Results: 235 patients underwent surgery for malignant glioma including 51 newly-diagnosed patients felt to be surgically resectable and with post-operative imaging. 25/51 (49%) had no residual tumour. The median PFS and OS were 7.1 and 11.5 months respectively. To date, 3 patients have successfully undergone 5-ALA-guided surgery with complete resection of contrast-enhancing tumour and no new focal neurological deficit post-operatively. Conclusions: We continue to recruit and follow prospectively patients having undergone 5-ALA-guided resection of malignant gliomas at HSN. Patients living in Northern Ontario may derive significant benefits from the use of 5-ALA-guided surgery, particularly since other technologies, such as intraoperative MRI and ultrasound, are costly and not available.

A linear stability analysis is employed to investigate thermal effects in shear flows. The cases analysed consist of unstably stratified horizontal boundary layers under a mixed convection regime, where forced and free convection mechanisms compete. Governing equations are given by the incompressible Navier–Stokes equations with the Oberbeck–Boussinesq approximation, where the base flow comes from their boundary layer approximation. Modal and non-modal analyses are used to investigate the behaviour of small-amplitude disturbances superposed to this base flow. An evaluation of the inertial, shearing and buoyancy mechanisms in the mixed convection boundary layer stability is performed through variations in the Reynolds, Prandtl and Richardson numbers. On the one hand, the spectra lead to the parametric conditions for the time-asymptotic onset of instability, which is still caused by Tollmien–Schlichting (TS) waves as in the traditional Blasius case. However, thermal effects have a destabilizing effect on them, more so for liquids than gases. On the other hand, the pseudospectra obtained from a resolvent analysis indicate the existence of transient growth at this same onset. However, contrary to the traditional Blasius case, thermal effects cause it to be dominated by the continuous frequency spectrum instead of the discrete TS modes. In order to elucidate this qualitative change, a componentwise input–output analysis is employed to quantify the receptivity to specific external disturbances. It shows that thermal effects directly impact the conversion of thermal to kinetic linear disturbance energy, causing a strong amplification of the flow response due to the non-normality of the linear operator. Results reveal that heating from below causes the forcing and response modes of the input–output analysis to have a free-stream spatial support due to non-modal excitation of the continuous spectrum. Such a behaviour suggests that the unstably stratified boundary layer is susceptible to free-stream thermal disturbances, which can potentially impact bypass transition.

Plasma jets are widely investigated both in the laboratory and in nature. Astrophysical objects such as black holes, active galactic nuclei and young stellar objects commonly emit plasma jets in various forms. With the availability of data from plasma jet experiments resembling astrophysical plasma jets, classification of such data would potentially aid in not only investigating the underlying physics of the experiments but also the study of astrophysical jets. In this work we use deep learning to process all of the laboratory plasma images from the Caltech Spheromak Experiment spanning two decades. We found that cosine similarity can aid in feature selection, classify images through comparison of feature vector direction and be used as a loss function for the training of AlexNet for plasma image classification. We also develop a simple vector direction comparison algorithm for binary and multi-class classification. Using our algorithm we demonstrate 93 % accurate binary classification to distinguish unstable columns from stable columns and 92 % accurate five-way classification of a small, labelled data set which includes three classes corresponding to varying levels of kink instability.

Scrub typhus is a common bacterial infection in Asia caused by Orientia tsutsugamushi. This serological cohort study estimated the incidence of infection in a rural population in South India. Participants were enrolled through systematic sampling in 46 villages at baseline, and revisited the following year. Blood samples were tested for IgG antibodies using ELISA, followed by indirect immunofluorescence assays (IFA) in those positive for ELISA at both rounds. A case was defined as sero-conversion (ELISA), or at least a 4-fold titre increase (IFA), between the two time points. In addition to crude incidence rate estimates, we used piecewise linear rates across calendar months, with rates proportional to the monthly incidence of local hospital cases to address seasonality and unequal follow-up times. Of 402 participants, 61.7% were female. The mean age was 46.7 years, (range 13–88). 21 participants showed evidence for serological infection. The estimated incidence was 4.4 per 100 person-years (95% CI 2.8–6.7). The piecewise linear rates approach resulted in a similar estimate of 4.6 per 100 person years (95% CI 2.9–6.9). Considering previous estimates of symptomatic scrub typhus incidence in the same study population, only about 2–5% of infections may result in clinically relevant disease.

In user-centred design, digital human models hold the potential for proactive evaluations of ergonomics or discomfort in terms of a computer aided ergonomics tool. Therefore, models predicting human interaction behaviour, are necessary. In this contribution we present such a model as well as its initial evaluation. The evaluation is performed by applying the interaction model to a specific use case, conducting a comparison with an empirical subject study. The evaluation shows that similar and realistic human behaviour was predicted, which was consistent in terms of whole-body strain.