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Validation of simulated training sets using a convolutional neural network for isotope identification in urban environments
Real-time isotope identification in urban environments can aid law enforcement by providing additional information about the nature of a potential threat. Neural networks have shown promise in isotope identification but the large range of potential isotopes, activities and shielding in uncontrolled urban environments makes creating a representative training set challenging. In this work, a method of generating gamma spectra datasets without requiring radioactive sources is validated with representative data. Simulated spectra are added to background radiation taken from a large dataset of unlabelled gamma spectra (the SIGMA dataset) collected in London by AWE Nuclear Security Technologies. A testing set of 12748 spectra was extracted from the SIGMA dataset by applying k-means clustering to the 10% of spectra with the highest gross counts. Manual inspection and labelling of a subset of each cluster showed that five clusters contained single isotopes and two contained multiple isotopes which were discarded. A convolutional neural network classifier was trained and tested using these two datasets. The model was able to identify isotopes from real SIGMA dataset spectra. The lowest prediction accuracy for a given class was 96% when presented with simulated data, and 89.8% on SIGMA dataset spectra. The high prediction accuracy validates the method for generating spectra and facilitates future work increasing the range of isotopes present in the training set and developing more complex models.
SCR-Net: A novel lightweight aquatic biological detection network
Marine biological detection is critical to environmental conservation and the use of marine resources. In actual applications, detecting aquatic species quickly and accurately while using few resources remains a difficulty. To address this problem, this research proposes a novel fast and efficient lightweight target detection network (SCR-Net). First, a fast and lightweight Spatial Pyramid Pool ELAN (SPPE) module is proposed and implemented, which enhances the model’s performance by leveraging ELAN’s effective feature aggregation ability and SPPF’s spatial pyramid pooling capacity. Second, a cross-scale feature fusion pyramid (CFFP) structure is introduced, which significantly reduces the number of parameters and computational cost during feature fusion. Third, a lightweight feature extraction module named RGE is designed, utilizing low-cost processes to create duplicate feature maps and reparameterization to drastically accelerate model inference. Compared to the baseline model, SCR-Net has 57.4% fewer parameters, 37% less computation, and an mAP@0.5 of 83.2% on the DUO dataset. Ablation experiments validate the effectiveness of the proposed modules, and comparative experiments on DUO and UDD datasets demonstrate that SCR-Net achieves superior overall performance compared to existing lightweight state-of-the-art underwater target detection models.
Enhanced pedestrian trajectory prediction via overlapping field-of-view domains and integrated Kolmogorov-Arnold networks
Accurate pedestrian trajectory prediction is crucial for applications such as autonomous driving and crowd surveillance. This paper proposes the OV-SKTGCNN model, an enhancement to the Social-STGCNN model, aimed at addressing its low prediction accuracy and limitations in dealing with forces between pedestrians. By rigorously dividing monocular and binocular overlapping visual regions and utilizing different influence factors, the model pedestrian interactions more realistically. The Kolmogorov-Arnold Networks (KANs) combined with Temporal Convolutional Networks (TCNs) greatly improve the ability to extract temporal features. Experimental results on the ETH and UCY datasets demonstrate that the model reduces the Final Displacement Error (FDE) by an average of 23% and the Average Displacement Error (ADE) by 18% compared to Social-STGCNN. The proposed OV-SKTGCNN model demonstrates improved prediction accuracy and better captures the subtleties of pedestrian movements.
Comparative evaluation of the utility of two oral examination tools in assessing oral health in stroke patients with indwelling gastric tubes
Purpose To identify the scale that is more suitable for oral health assessment in stroke patient population with indwelling gastric tubes. Methods A total of 198 patients with indent gastric tubes were selected from 1250 stroke patients to evaluate their oral health using both the BOAS and the OHAT scales. The scores obtained from both scales were then compared to evaluate the feasibility, reliability, and validity of each scale in assessing oral health among stroke patients with indwelling gastric tubes. Results The results showed that both the BOAS and OHAT scales exhibited good reliability and validity in stroke patients with indwelling gastric tubes. The Cronbach’s alpha coefficients of BOAS and OHAT in stroke patients with indwelling gastric tubes were 0.89 and 0.91, respectively. In the exploratory factor analysis, one and two common factors were extracted from the two scales, with cumulative variance contributions of 65.89% and 71.85%, respectively. In addition, potential influencing factor correlation analysis found that gender and marital status had a significant correlation with the BOAS score(P < 0.05), the Activities of daily living (ADL) score was found to be significantly correlated with the OHAT score (P < 0.05). Drinking, smoking, income, consciousness, and the result of the water swallow test were all correlated with BOAS and OHAT scores(P < 0.01). Conclusions The BOAS and OHAT have demonstrated good reliability and validity and in their ability to assess the oral health of stroke patients with indwelling gastric tubes. Therefore, it is recommended that the selection of oral assessment scales should be further refined in different disease stages of stroke patients to assess the oral health status of patients more accurately and personalized.
SCARS-LOGISTIC: A novel variable selection approach for binary classification model to identify the significant determinants of sexually transmitted infections
Variable selection methods are very popular, especially in the field of big data with large predictors. These procedures improve the accuracy and performance of the model by eliminating irrelevant and redundant variables. The main contribution of this study is to couple a logit model with a novel variable selection approach, "Stability Competitive Adaptive Re-weighted Sampling" to address binary response. The efficiency of the proposed method is compared with the traditional logistic regression model based on eight model assessment criteria over real data from sexually transmitted infections in Indian men. Due to higher stability, the proposed method outperformed having a lower Akaike information criterion, and the Bayesian information criterion, as well as higher R-squared measures. The finally selected proposed model identified essential information regarding sexually transmitted infections in India for policymakers.
Genetic analysis and functional assessment of a TGFBR2 variant in micrognathia and cleft palate
Cleft lip and cleft palate are among the most common congenital anomalies and are the result of incomplete fusion of embryonic craniofacial processes or palatal shelves, respectively. We know that genetics play a large role in these anomalies but the list of known causal genes is far from complete. As part of a larger sequencing effort of patients with congenital craniofacial anomalies, we identified a rare candidate variant in transforming growth factor beta receptor 2 (TGFBR2). This variant alters a highly conserved amino acid and is predicted to be pathogenic by a number of metrics. The family history and population genetics suggest that this specific variant would be incompletely penetrant, but this gene has been convincingly implicated in craniofacial development. In order to test the hypothesis this might be a causal variant, we used genome editing to create the orthologous variant in a new mouse model. Surprisingly, Tgfbr2V387M mice did not exhibit craniofacial anomalies or have reduced survival, suggesting Tgfbr2V387M is not a causal variant for cleft palate/ micrognathia. The discrepancy between in silico predictions and mouse phenotypes highlights the complexity of translating human genetic findings to mouse models. We expect these findings will aid in interpretation of future variants seen in TGFBR2 from ongoing sequencing of patients with congenital craniofacial anomalies.
Characteristics of SLS-made 3D gyroid cubic lattice nanoporous polyamide membrane
A novel membrane can enhance the efficiency of various industrial processes and help address critical issues. Membranes made of polyamide are widely used and successful in membrane separation processes. This paper outlines a viable method for creating a three-dimensional gyroid nanostructured polyamide membrane through selective laser sintering. This method has easy setup, fast membrane preparation, no pollution, and low preparation cost. It is better than old-style solvent casting methods, which have inadequate management over the membrane structure. The prepared membrane was characterised using various essential techniques, and its properties were examined. The size of the membrane was 3 × 3 cm. A negative skewness value of −0.107 and a surface roughness of 22.4 nm indicate the presence of small peaks and high roughness. The CHN analysis shows the presence of 8.310% nitrogen, 42.100% carbon, 4.327% hydrogen, and 19.076% sulfur in the prepared membrane. The compressive strength of the membrane was calculated to be approximately 30 Mpa. A preliminary experiment on oil-water separation was conducted to address the growing issue of industrial oily wastewater. This study highlights the critical impact of surface properties on enhancing membrane performance, further solidifying their significance in membrane technology. This study provides insights for optimising membrane performance in future research.
Agent-based modeling for personalized prediction of an experimental immune response to immunotherapeutic antibodies
Targeting immune checkpoint pathways to evoke an immune response against tumors has revolutionized clinical oncology over the last decade. Antibodies that block the PD-1/PD-L1 pathway have demonstrated effective antitumor activity in cancer patients and are approved for treatment of several different types of cancer. However, many patients do not experience durable beneficial clinical responses. The ability to predict response to immunotherapy is a clinical need with immediate implications on the optimization of oncologic treatments. In this work we developed and tested the ability of an Agent-Based Model (ABM) to predict the ex vivo immune response of memory T cells to anti-PD-L1 blocking antibody, based on personalized immune-phenotypes. We performed mixed lymphocyte reaction (MLR) experiments on blood samples of healthy volunteers to model the dose-response kinetics of the immune response to anti-PD-L1 antibody. Additionally, immunophenotype of peripheral lymphocyte and monocyte populations was used for modeling and prediction. In silico MLR experiments were conducted using the ABM-based Cell Studio Platform, and the results of ex vivo vs. in silico experiments were compared. Our ABM accurately recapitulates MLR-derived immune responses, achieving >80% predictive accuracy. Notably, given the relatively small cohort tested, such results are typically impossible to model with methods based solely on statistical or data-driven approaches. Importantly, the use of this modeling strategy not only predicts the outcome of the immune response, but also provides insights into the exact biological parameters and related cellular mechanisms that lead to differential immune response.
Association between smoking status and non-alcoholic fatty liver disease
Background The relationship between cigarette smoking and nonalcoholic fatty liver disease (NAFLD) remains controversial. Recent studies have demonstrated that cigarette smoking is a significant risk factor for the development of NAFLD. This study aimed to examine the association between smoking and NAFLD according to smoking status among Korean males, and to examine the relationship between smoking cessation and NAFLD. Methods This cross-sectional study included data from 12,241 adult males who underwent health checkups at a university hospital health promotion center between January 2018 and December 2019. Fatty liver was diagnosed using abdominal ultrasonography. The participants were categorized according to self-reported smoking status, pack-years, and period of smoking cessation. Odds ratio (OR) and corresponding 95% confidence interval (CI) for NAFLD were calculated using logistic regression analysis. Results After adjusting for confounding factors, the OR for NAFLD was 1.190 (95% CI 1.071–1.322, P = .001) among ex-smokers. Among current smokers, the risk for NAFLD increased with an increase in the amount of cigarette smoking (10–20 and ≥20 pack-years versus [vs.] never smoker, adjusted OR [aOR] 1.289 [95% CI 1.107–1.500]; P = .001 and 1.235 [95% CI 1.043–1.461]). The prevalence of NAFLD was inversely associated with the duration of smoking cessation (< 10 years vs. 10–20 years and ≥ 20 years; aOR 0.748 [95% CI 0.638–0.876], P < .001 and 0.750 [95% CI 0.592–0.950], P = .017, respectively). Conclusion Cigarette smoking was significantly associated with increased odds of NAFLD, whereas smoking cessation for more than 10 years was associated with decreased odds.
WLreg: A new re-parametrization of the Weighted Lindley distribution and its regression model
A novel re-parametrization of the weighted Lindley distribution is introduced to develop a regression model suitable for skewed dependent variables defined on ℝ+. This new model is called the WL2 regression model. It is shown to outperform existing models such as the gamma, extended gamma, and Maxwell-Boltzmann-exponential regression models. Parameter estimation is performed using the maximum likelihood estimation technique, and the efficiency of these estimates is assessed through a simulation study. An application to a house price data set is presented to highlight the importance of the WL2 regression model. In addition, we propose the WLreg software, accessible via https://bartinuni.shinyapps.io/WLreg, to facilitate the application of the new regression model for practitioners in the field.
Automated VMAT planning for short-course radiotherapy in locally advanced rectal cancer
Purpose This study aims to develop a fully automated VMAT planning program for short-course radiotherapy (SCRT) in Locally Advanced Rectal Cancer (LARC) and assess its plan quality, feasibility, and efficiency. Materials and methods Thirty LARC patients who underwent short-course VMAT treatment were retrospectively selected from our institution for this study. An auto-planning program for neoadjuvant short-course radiotherapy (SCRT) in LARC was developed using the RayStation scripting platform integrated with the Python environment. The patients were re-planned using this auto-planning program. Subsequently, the differences between the automatic plans (APs) and existing manual plans (MPs) were compared in terms of plan quality, monitor units (MU), plan complexity, and other dosimetric parameters. Plan quality assurance (QA) was performed using the ArcCHECK dosimetric verification system. Results Compared to MPs, the APs achieved similar target coverage and conformity, while providing more rapid dose fall-off. Except for the V5Gy dose level, other dosimetric metrics (V25 Gy, V23 Gy, V15 Gy, Dmean, etc.) for the small bowel were significantly lower in the AP compared to the MP (p < 0.001). Additionally, the dosimetric parameters for the bladder, pelvic marrow, and femoral head were also lower in the AP, except for the V25Gy for the bladder. The MUs of the AP were approximately 4% lower than those of the MP. The AP showed high consistency in dosimetric parameters across five organs at risk (OARs). Conclusion We developed a fully automated, feasible SCRT VMAT planning program for LARC. This program significantly enhanced plan quality and efficiency while substantially reducing the dose to OARs.
An exploration of patients’ perceptions and coping strategies for LBP
Background Evidence-based guidelines for managing LBP exist but their recommendations are often not used by health professionals in primary care. A key challenge to address this issue is understanding how people understand LBP, how they feel about it, and cope with it – particularly with regard to why they visit their doctors and their treatment expectations. This is important to understand, particularly since physician barriers to following LBP treatment guidelines have centered on patient issues (such as patient demand for imaging). Methods This was a qualitative, exploratory study using semi-structured interviews to explore patient perceptions of LBP and their coping strategies, paying particular attention to why patients with LBP in Newfoundland and Labrador (NL) seek care from family physicians and their treatment expectations, especially with regard to imaging. Eligible patients included adults aged 18 + years or older, living in both rural and urban settings in NL, Canada, who had visited their family physician about low back pain within the year prior to the interview. Researchers experienced in applying the Common-sense Model of Self-regulation (CSM), used the model to inform the development of our question guide and as a framework for the data analysis. Principal findings We found that new onset, severity, or persistent pain prompted patients to visit their family doctor, primarily to seek advice and/or a diagnosis, or for a referral to imaging or other providers. While patients believed that imaging was essential to understanding the underlying cause of their symptoms or informing their treatment, they were divided about its effectiveness – some felt it was beneficial to their treatment while others reported that it had no effect. We found that patients were unified in their largely negative views regarding prognosis and all experienced a range of negative emotions surrounding their LBP such as fear, stress, frustration, and guilt. We also found wide variation in understanding of cause and use of coping strategies. Patients posited several causes for the pain including injury, overexertion, comorbid conditions, and issues related to posture and sitting, and were split on their thoughts regarding prevention – about half thought it could be prevented, half did not. We found that patients coped with their LBP using a variety of strategies but were often disappointed in the results. Most reported no benefit to visiting their family doctors for their LBP. Some were pleased with their experiences with allied HCPs, noting small, but steady, improvements using recommended exercises but others were generally dissatisfied. Conclusion Our exploration of patient views and expectations for low back pain care indicates a mismatch between the care they are looking for and the care they receive. It also suggested a general lack of knowledge about the cause of LBP, the value and usefulness of imaging for its diagnosis and treatment, and poor physician-patient communication.
Vaccine effectiveness of inactivated and mRNA COVID-19 vaccine platform during Delta and Omicron wave in Jakarta, Indonesia: A test-negative case-control study
Background Vaccination was included in the Indonesian government policy to address Delta and Omicron waves of SAR-CoV-2 infections. This study assesses the effectiveness of inactivated (CoronaVac, BBIBP-Cor) and mRNA vaccines (mRNA-1273, BNT162b2) against COVID-19 regardless of symptoms and fatal COVID-19 (mortality within 30 days after confirmed RT-PCR) during Delta and Omicron period in Jakarta, Indonesia. Methods This study case-control, test-negative study included all individuals aged over 18 years in Jakarta with complete and consistent SARS-CoV-2 RT-PCR results from 1 June to 31 August 2021 (Delta period) and 1 January to 2 April 2022 (Omicron period), as well as complete vaccination status. This study integrates several public health data from the Jakarta provincial government. From the odds ratio, vaccine effectiveness (VE) was analyzed as the primary outcome using unmatched analysis, matched analysis, and adjustments for other factors. Results This study includes 982,885 eligible subjects recorded from March 2021 to April 2022. All subjects generally underwent testing 4–9 weeks after their last vaccine dose. The VE of 2-dose inactivated vaccine against SARS-CoV-2 infection during Delta wave was 22.06% (95% CI 20.63–24.54) and the VE against fatal COVID-19 was 78.55% (95% CI 72.91–83.00). A complete primary dose of mRNA vaccine showed VE of 24.81% (95% CI 16.81–32.09) against infection during Omicron wave. Furthermore an additional mRNA booster dose showed VE of 68.82% (95% CI 54.11–78.82) based on unmatched analysis. Conclusion A complete primary dose of inactivated vaccine provided mild protection against COVID-19 and essential protection against fatal cases during the Delta wave, but offered little to no protection during the Omicron wave. In contrast, the mRNA vaccine, either as primary vaccination, homologous, or heterologous booster regimen, conferred acceptable protection against Omicron. This study recommends real-world vaccination strategies for LMICs with typical vaccine supply constraints.
Day-ahead optimal dispatch considering demand response compensation and carbon trading under uncertain environment
To fully explore the regulation resources on both sides of the source and load under uncertain environment and collaboratively achieve the energy saving and emission reduction goals, a low-carbon economic optimization dispatch model combining demand response and carbon trading mechanism is proposed in this paper. Firstly, the economic principle of demand response (DR) is analyzed, as well as the demand response compensation model is constructed for shiftable loads and curtailable loads respectively. Second, we describe the source-load synergistic low-carbon effect. The source side further reduces carbon emissions by establishing a reward-punishment laddered carbon trading model. Accordingly, the optimization model is constructed with the objective of minimizing the sum of DR compensation cost, carbon trading cost and system operation cost. The triangular fuzzy method is used to deal with the uncertainty problem of new energy and load forecasting. Finally, the economic and low-carbon nature of this proposed model is verified by simulation and example analysis.
Improving electron mobility in InAs quantum wells on GaAs by removing bunched surface steps generated in strain relaxation
The effect of surface smoothing in the heteroepitaxy of InAs quantum wells on GaAs(001) is investigated using transmission electron microscopy. While the stress fields cause the relaxation of the lattice in the growth of the (Al,In)As buffer layer, step bunching occurs with creating wide flat terraces in the process of forming the cross-hatch pattern. The bunched steps lower the electron mobility in the quantum wells considerably when the GaAs substrates are used instead of InP substrates due to their pronounced presence resulting from the larger lattice mismatch. The enhancement of the surface migration of adatoms in growth interruptions is shown to annihilate the steps, giving rise to an improvement of the mobility due to the suppression of the interface roughness scattering. With the introduction of the surface smoothing, GaAs substrates thus replace InP substrates as a less-expensive and superior alternative.
A multi-assay assessment of insecticide resistance in Culex pipiens (Diptera: Culicidae) informs a decision-making framework
Insecticide resistance (IR) is an increasing problem globally, making control of vector-borne diseases more difficult. Reduced susceptibility to permethrin in Culex pipiens, an important vector for West Nile virus, has been reported across the US based on a standardized laboratory method: the CDC bottle bioassay. This bioassay uses a rapid phenotypic outcome to reveal evidence for IR, but how this translates to the effectiveness of formulated products used in an operational setting is unclear. Therefore, other methods for IR monitoring are recommended to quantify IR or evaluate formulated products against field populations in real-world conditions. To compare some of the available methods, we collected populations of Cx. pipiens from six sites in the Northwest Mosquito Abatement District (Cook Co., Illinois), and used a susceptible laboratory strain of Cx. pipiens as a control, to test for IR to pyrethroids using CDC bottle bioassays, caged field trials, and topical applications. CDC bottle bioassays suggested that Cx. pipiens from this area exhibit IR to both etofenprox and Sumithrin®. Caged field trials with ultra-low volume Anvil® 10 + 10 (Sumithrin®) demonstrated resistance to the product and underscored the need for inclusion of a susceptible control to differentiate IR from inadequate product distribution. Topical applications revealed low to high levels of resistance to synergized and unsynergized pyrethroids (etofenprox, Sumithrin®, and deltamethrin) in all field populations. Based on these data, we provide a new decision-making tree for mosquito control professionals which will guide selection of the most optimal assay for IR surveillance based on their goals, needs, and resources.
Electron transport in N-polar AlGaN:Si films grown by MOVPE on annealed and sputtered AlN templates
N-polar AlGaN films have the potential to improve the performance of optoelectronic and electronic devices, owing to their reversed polarization electric field orientation when compared with their metal-polar counterparts. However, the inferior surface morphology and electrical properties of these films have hindered the performance of N-polar devices, falling behind their metal-polar counterparts. This study develops an epitaxial strategy for fabricating AlGaN:Si films with a step-and-terrace surface morphology, achieved through the utilization of high growth temperature in conjunction with a substantial off-cut angle. The N-polar Al0.36Ga0.64N:Si film exhibits an electron mobility of 74.8 cm2/Vs at a free electron concentration of 3.7 × 1019 cm−3, surpassing previously reported values in the literature. Temperature-dependent mobility analysis confirms that ionized impurity scattering is the dominant factor influencing electron mobility in the degenerate N-polar AlGaN:Si films. Enhanced threading dislocation scattering is observed to cause an anomalous reduction in mobility at lower temperatures. A comparison of experimental and theoretical mobilities across the full range of Al content highlights that the decline in mobility for high Al-content AlGaN:Si is due to compensation defects. This study offers an epitaxial approach for N-polar AlGaN:Si films and delves into the underlying electron transport mechanisms, driving advances in N-polar light-emitting diodes and high electron mobility transistors.
Prediction of air temperature and humidity in greenhouses via artificial neural network
Accurate prediction of greenhouse temperature and relative humidity is critical for developing environmental control systems. Effective regulation strategies can help improve crop yields while reducing energy consumption. In this study, Multilayer Perceptron (MLP) and Radial Basis Function (RBF) networks were used for short-term prediction of temperature and relative humidity in a double-film greenhouse. The prediction models used indoor soil temperature, light intensity, and historical measurements of temperature and humidity from the previous 10 minutes as inputs. Results show that the MLP model with Levenberg-Marquardt optimization performs best in predicting the current temperature and humidity, with an RMSE of 0.439°C and R2 of 0.997 for temperature prediction and an RMSE of 1.141% and R2 of 0.996 for relative humidity prediction. For 30-minute short-term prediction, the Bayesian optimized RBF model showed better temperature prediction with an RMSE of 1.579°C and an R2 of 0.958, while the MLP model performed better in relative humidity prediction with an RMSE of 4.299% and an R2 of 0.948. This study provides theoretical support for advancing the intelligent regulation of greenhouse environmental factors in cold and arid regions, and the application of predictive models to intelligent environmental management systems could help optimize cultivation practices and energy efficiency.
Controlling thermoelectric properties of epitaxial GeSn film/Si by tuning strain and composition
We develop compressively-strained epitaxial Ge1−xSnx films with large x (&gt;0.1) on Si substrates. Therein, large x and compressive biaxial strain cause a band convergence by movement of Γ valley down to L valleys near the Fermi level, which leads to high Seebeck coefficient while increasing alloy phonon scattering rate related to a low thermal conductivity. Furthermore, a higher electron mobility is also realized due to the reduction of the conductivity effective mass coming from the large contribution of small effective mass of Γ valley. Further increase in x of Ge1−xSnx films decreases thermal conductivities. As a result, the doped compressively-strained epitaxial Ge0.88Sn0.12 films on Si substrates exhibit two times higher thermoelectric power factor (∼15 μW cm−1 K−2) at room temperature than the previously-reported Ge1−xSnx films at almost the same thermal conductivity. This highlights that the application of compressive in-plane strain in Ge1−xSnx films with large x is a promising approach for enhancing a thermoelectric performance.
Developing a stakeholder-informed social responsibility model for translational science
Innovation in biomedical research has increased markedly over the last few decades. However, clinical, therapeutic, and public health advances have often not yielded expected improvements in health outcomes nor reduced disparities. Translational science was developed to improve social benefits related to research and development. We propose a practical model for socially responsible translational science that aims to better align research with its expected social benefits. Scientists and community members from the Houston-Galveston region participated in 12 focus groups and a one-day Deliberative Dialogue Summit to examine the expected social benefits of science, establish the factors and practices of social responsibility, and design an empirical model for socially responsible translational science. Researchers and community members discussed three distinct fields of research – HIV, maternal health, and mental health and substance use disorders. We conducted deductive qualitative data analysis based on theoretical social responsibility criteria of translational science, namely: relevance, usability, and sustainability. We then developed inductive codes to capture the factors and practices identified during discussions as necessary for the translation of research to increase social benefit. First, participants explored ways to broaden the scope of biomedical research beyond a narrow emphasis on scientific impact to also consider social impacts and determinants of health; this heightens the relevance of research and underscores its responsibility to address social needs and reduce inequities. Second, to improve usability of translational research, participants suggested increasing access to research products, processes, and participation. They also recommended modifying the research infrastructure to incorporate other systems that can assist with translation including the system of care and the broader community-based systems. Third and finally, for the long-term sustainability of research practices, co-development and co-funding of research was promoted to include local community needs, cultures, knowledges and preferences from project commencement to completion.