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Reducing urban carbon emissions (UCEs) holds paramount importance for global sustainable development. However, the complexity of interactions among urban spatial units has impeded further research on UCEs. This study investigates synergistic emission reduction between cities by analyzing the spatial complexity within the UCEs network. The future potential for synergistic carbon emissions reduction is predicted by the link prediction algorithm. A case study conducted in the Pearl River Basin of China demonstrates that the UCEs network has a complex spatial structure, and the synergistic capacity of emission reduction among cities is enhanced. The core cities in the UCEs network, including Dongguan, Shenzhen, and Guangzhou, have spillover effects that contribute to synergistic emission reduction. Community detection reveals that the common characteristics associated with UCEs become concentrated, thereby enhancing the synergy of joint efforts between cities. The link prediction algorithm indicates a high probability of strengthened carbon emission connections in the Pearl River Delta, alongside those between upstream cities, which shows potential in forecasting synergistic emission reductions. Our research framework offers a comprehensive analysis for synergistic emission reduction from the spatial complexity of UCEs network and link prediction. It acts as a worthwhile reference for developing differentiated policies on synergistic emission reduction.

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BACKGROUND: Examining support for substance use policies, including those for harm reduction, among the general public and policy influencers is a fundamental step to map the current policy landscape and leverage policy opportunities. Yet, this is a knowledge gap in Canada. Our paper identifies the level of support for substance use policies in two provinces in Canada and describes how the level of support is associated with intrusiveness and sociodemographic variables. METHODS: Data came from the 2019 Chronic Disease Prevention Survey. The representative sample included members of the general public (Alberta n = 1648, Manitoba n = 1770) as well as policy influencers (Alberta n = 204, Manitoba n = 98). We measured the level of support for 22 public policies concerning substance use through a 4-point Likert-scale. The Nuffield Council on Bioethics Intervention Ladder framework was applied to assess intrusiveness. We used cumulative link models to run ordinal regressions for identification of explanatory sociodemographic variables. RESULTS: Overall, there was generally strong support for the policies assessed. The general public in Manitoba was significantly more supportive of policies than its Alberta counterpart. Some differences were found between provinces and samples. For certain substance use policies, there was stronger support among women than men and among those with higher education than those with less education. CONCLUSIONS: The results highlight areas where efforts are needed to increase support from both policy influencers and general public for adoption, implementation, and scaling of substance use policies. Socio-demographic variables related to support for substance use policies may be useful in informing strategies such as knowledge mobilization to advance the policy landscape in Western Canada.

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One of the most recognizable cases of preimplantation genetic diagnosis (PGD) is X-linked diseases. Diagnosis of fetal sex is essential for couples who are known to be at risk of some X-linked disorders. The objective of this study was to discriminate between female (XX) and male (XY) embryos by detecting sex chromosomes-specific sequences in spent culture medium and comparing these results to PGD/CGH array results. It may open new window for the development of a non-invasive PGD method. 120 Embryo's spent media from Day 3 and Day 5 embryos were collected. Modified phenol-chloroform solution was used for DNA extraction from spent media. Sex determination was performed using SRY, TSPY and AMELOGENIN evaluation through quantitative polymerase chain reaction (q-PCR) method. IBM SPSS and MedCalc were used for statistical analyses to compare sex determination of embryos by spent medium with PGD/CGH array results. Culture time was demonstrated to increase the DNA amount among day 5 embryos culture medium samples. Non-invasive PGD by means of spent culture medium gave a sensitivity, specificity, positive predictive value and negative predictive value of 100% for sex determination. Results of sex determination using spent medium by q-PCR were consistent with the results of PGD/CGH array. Improvements in cell-free DNA extraction and PCR amplification procedures provide us an effective method to perform a PGD test without biopsy in the future, especially about X-linked diseases.

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Polypharmacy, the use of drug combinations, is an effective approach for treating complex diseases, but it increases the risk of adverse effects. To predict novel polypharmacy side effects based on known ones, many computational methods have been proposed. However, most of them generate deterministic low-dimensional embeddings when modeling the latent space of drugs, which cannot effectively capture potential side effect associations between drugs. In this study, we present SIPSE, a novel approach for predicting polypharmacy side effects. SIPSE integrates single-drug side effect information and drug-target protein data to construct novel drug feature vectors. Leveraging a semi-implicit graph variational auto-encoder, SIPSE models known polypharmacy side effects and generates flexible latent distributions for drug nodes. SIPSE infers the current node distribution by combining the distributions of neighboring nodes with embedding noise. By sampling node embeddings from these distributions, SIPSE effectively predicts polypharmacy side effects between drugs. One key innovation of SIPSE is its incorporation of uncertainty propagation through noise embedding and neighborhood sharing, enhancing its graph analysis capabilities. Extensive experiments on a benchmark dataset of polypharmacy side effects demonstrated that SIPSE significantly outperformed five state-of-the-art methods in predicting polypharmacy side effects.

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OBJECTIVE: As new knowledge is produced at a rapid pace in the biomedical field, existing biomedical Knowledge Graphs (KGs) cannot be manually updated in a timely manner. Previous work in Natural Language Processing (NLP) has leveraged link prediction to infer the missing knowledge in general-purpose KGs. Inspired by this, we propose to apply link prediction to existing biomedical KGs to infer missing knowledge. Although Knowledge Graph Embedding (KGE) methods are effective in link prediction tasks, they are less capable of capturing relations between communities of entities with specific attributes (Fanourakis et al., 2023). METHODS: To address this challenge, we proposed an entity distance-based method for abstracting a Community Knowledge Graph (CKG) from a simplified version of the pre-existing PubMed Knowledge Graph (PKG) (Xu et al., 2020). For link prediction on the abstracted CKG, we proposed an extension approach for the existing KGE models by linking the information in the PKG to the abstracted CKG. The applicability of this extension was proved by employing six well-known KGE models: TransE, TransH, DistMult, ComplEx, SimplE, and RotatE. Evaluation metrics including Mean Rank (MR), Mean Reciprocal Rank (MRR), and Hits@k were used to assess the link prediction performance. In addition, we presented a backtracking process that traces the results of CKG link prediction back to the PKG scale for further comparison. RESULTS: Six different CKGs were abstracted from the PKG by using embeddings of the six KGE methods. The results of link prediction in these abstracted CKGs indicate that our proposed extension can improve the existing KGE methods, achieving a top-10 accuracy of 0.69 compared to 0.5 for TransE, 0.7 compared to 0.54 for TransH, 0.67 compared to 0.6 for DistMult, 0.73 compared to 0.57 for ComplEx, 0.73 compared to 0.63 for SimplE, and 0.85 compared to 0.76 for RotatE on their CKGs, respectively. These improved performances also highlight the wide applicability of the extension approach. CONCLUSION: This study proposed novel insights into abstracting CKGs from the PKG. The extension approach indicated enhanced performance of the existing KGE methods and has applicability. As an interesting future extension, we plan to conduct link prediction for entities that are newly introduced to the PKG.

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Inferring gene regulatory networks through deep learning and causal inference methods is a crucial task in the field of computational biology and bioinformatics. This study presents a novel approach that uses a Graph Convolutional Network (GCN) guided by causal information to infer Gene Regulatory Networks (GRN). The transfer entropy and reconstruction layer are utilized to achieve causal feature reconstruction, mitigating the information loss problem caused by multiple rounds of neighbor aggregation in GCN, resulting in a causal and integrated representation of node features. Separable features are extracted from gene expression data by the Gaussian-kernel Autoencoder to improve computational efficiency. Experimental results on the DREAM5 and the mDC dataset demonstrate that our method exhibits superior performance compared to existing algorithms, as indicated by the higher values of the AUPRC metrics. Furthermore, the incorporation of causal feature reconstruction enhances the inferred GRN, rendering them more reasonable, accurate, and reliable.

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(1) Background. A definition of healthcare-associated infections is essential also for the attribution of the restorative burden to healthcare facilities in case of harm and for clinical risk management strategies. Regarding M. chimaera infections, there remains several issues on the ecosystem and pathogenesis. We aim to review the scientific evidence on M. chimaera beyond cardiac surgery, and thus discuss its relationship with healthcare facilities. (2) Methods. A systematic review was conducted on PubMed and Web of Science on 7 May 2024 according to PRISMA 2020 guidelines for reporting systematic reviews, including databases searches with the keyword "Mycobacterium chimaera". Article screening was conducted by tree authors independently. The criterion for inclusion was cases that were not, or were improperly, consistent with the in-situ deposition of aerosolised M. chimaera. (3) Results. The search yielded 290 eligible articles. After screening, 34 articles (377 patients) were included. In five articles, patients had undergone cardiac surgery and showed musculoskeletal involvement or disseminated infection without cardiac manifestations. In 11 articles, respiratory specimen reanalyses showed M. chimaera. Moreover, 10 articles reported lung involvement, 1 reported meninges involvement, 1 reported skin involvement, 1 reported kidney involvement after transplantation, 1 reported tendon involvement, and 1 reported the involvement of a central venous catheter; 3 articles reported disseminated cases with one concomitant spinal osteomyelitis. (4) Conclusions. The scarce data on environmental prevalence, the recent studies on M. chimaera ecology, and the medicalised sample selection bias, as well as the infrequent use of robust ascertainment of sub-species, need to be weighed up. The in-house aerosolization, inhalation, and haematogenous spread deserve experimental study, as M. chimaera cardiac localisation could depend to transient bacteraemia. Each case deserves specific ascertainment before tracing back to the facility, even if M. chimaera represents a core area for healthcare facilities within a framework of infection prevention and control policies.

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Postoperative stoma education is an essential aspect of care for all types of stoma formation because having a stoma impacts on every aspect of a person's life. This critical review of the literature explores stoma patients' needs and wants; postoperative education care guidelines; the role of ward link nurses; and care pathways. The findings from this review demonstrate that there is no national standard postoperative stoma care pathway, yet such pathways are a cost-effective means to improve patient outcomes and care. The review also identified that structured care pathways are not a new concept, but there is a lack of formal research to determine best practice in postoperative stoma education. In the UK, there is wide variation in practice and outcomes, which means that effectiveness cannot be accurately measured. The author has developed and implemented a multidisciplinary postoperative education pathway in line with a national need to further refine postoperative stoma care services to meet stoma patients' needs.

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Link prediction (LP) is a task for the identification of potential, missing and spurious links in complex networks. Protein-protein interaction (PPI) networks are important for understanding the underlying biological mechanisms of diseases. Many complex networks have been constructed using LP methods; however, there are a limited number of studies that focus on disease-related gene predictions and evaluate these genes using various evaluation criteria. The main objective of the study is to investigate the effect of a simple ensemble method in disease related gene predictions. Local similarity indices (LSIs) based disease related gene predictions were integrated by a simple ensemble decision method, simple majority voting (SMV), on the PPI network to detect accurate disease related genes. Human PPI network was utilized to discover potential disease related genes using four LSIs for the gene prediction. LSIs discovered potential links between disease related genes, which were obtained from OMIM database for gastric, colorectal, breast, prostate and lung cancers. LSIs based disease related genes were ranked due to their LSI scores in descending order for retrieving the top 10, 50 and 100 disease related genes. SMV integrated four LSIs based predictions to obtain SMV based the top 10, 50 and 100 disease related genes. The performance of LSIs based and SMV based genes were evaluated separately by employing overlap analyses, which were performed with GeneCard disease-gene relation dataset and Gene Ontology (GO) terms. The GO-terms were used for biological assessment for the inferred gene lists by LSIs and SMV on all cancer types. Adamic-Adar (AA), Resource Allocation Index (RAI), and SMV based gene lists are generally achieved good performance results on all cancers in both overlap analyses. SMV also outperformed on breast cancer data. The increment in the selection of the number of the top ranked disease related genes also enhanced the performance results of SMV.

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Background: Social prescribing addresses non-medical factors affecting health and well-being. Link workers are key to its delivery by connecting people to relevant support, often in the voluntary, community and social enterprise sector. Funding from the National Health Service means that link workers are becoming a common part of primary care in England. Objective: To explore and understand the implementation of link workers in primary care in England. Design: A realist evaluation addressed the question - When implementing link workers in primary care to sustain outcomes - what works, for whom, why and in what circumstances? Setting: Link workers and staff associated with seven primary care sites across England. Methods: Researchers spent 3 weeks with each link worker, going to meetings with them, watching them interact with patients, with healthcare staff and with voluntary, community and social enterprise organisations. In addition, interviews were conducted with 61 patients and 93 professionals (voluntary, community and social enterprise representatives and healthcare staff, including link workers). Follow-up interviews were conducted with 41 patients and with link workers 9-12 months later. Data were coded and developed into statements to identify how context around the link worker triggers mechanisms that lead to intended and unintended outcomes. Results: We found that link workers exercise micro-discretions in their role - actions and advice-giving based on personal judgement of a situation, which may not always reflect explicit guidance or protocols. Our analysis highlighted that micro-discretions engender positive connections (with patients, healthcare staff, the voluntary, community and social enterprise sector) and promote buy-in to the link worker role in primary care. Micro-discretions supported delivery of person-centred care and enhanced job satisfaction. Data also highlighted that lack of boundaries could place link workers at risk of overstepping their remit. Limitations: Our research focused on link workers attached to primary care; findings may not be applicable to those working in other settings. Data were collected around seven link worker cases, who were selected purposively for variation in terms of geographical spread and how/by whom link workers were employed. However, these link workers were predominately white females. Conclusions: Enabling link workers to exercise micro-discretions allows for responsiveness to individual patient needs but can result in uncertainty and to link workers feeling overstretched. Future work: Poor link worker retention may, in part, be associated with a lack of clarity around their role. Research to explore how this shapes intention to leave their job is being conducted by authors of this paper. Funding: This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme as award number NIHR130247.

Problems in life affecting people's health and well-being cannot always be fixed with medication. For example, loneliness can lower people's mood, or worries about money can cause them to feel anxious. Social prescribing link workers are employed to support individuals with these 'non-medical' issues. They listen to people to find out about them and their circumstances. They may connect them to community groups, organisations or services, or help them get advice about things like benefits or housing. Our study explored how link workers are being implemented in primary care in England. We studied seven link workers based in different parts of England. We spent 3 weeks with each link worker, observing them at their workplace. We also interviewed these link workers and people they worked with; this included 61 patients, 61 primary care staff, 5 other link workers and 20 individuals from the voluntary or community sector. We found big differences in what link workers did in their roles; in how long they saw patients for and how often, how many patients they were supporting at one time, their professional and personal backgrounds, whether they worked in a practice alone or were part of a bigger team of people delivering social prescribing. Link workers had varying levels of flexibility (or discretion) in their jobs; this allowed them to support patients' individual needs. Such flexibility gave them job satisfaction as they were able to use their judgement about how to work with patients to provide person-centred support. However, if this went too far ­ and link workers had too few boundaries and not enough guidance ­ they ended up feeling overwhelmed by their job.

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Simulation and implementation of a single DC-link-based three-phase inverter are investigated in this article. The primary focus is on designing a single DC-link three-phase inverter for high power applications. Unlike conventional inverters that require 600 V to generate 400 V (RMS) at the output, the proposed system achieves this with only 330 V, facilitated by a 12-terminal 1:1 transformer. The system employs Proportional Integral (PI) and Neural Network (NN) controllers to optimize performance. Various Carrier-Based Pulse Width Modulation (CB-PWM) techniques, including Phase Disposition (PD), Phase Opposition Disposition (POD), and Alternative Phase Opposition Disposition (APOD), are implemented and evaluated based on Total Harmonics Distortion (THD) concerning the Modulation Index (MI) of the inverter. The proposed inverter achieves a THD reduction to 4.8%, demonstrating superior performance compared to recent studies. The system's performance is validated through extensive MATLAB/Simulink simulations and practical implementation using XILINX FPGA software, confirming the efficacy of the proposed design.

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This paper introduces a novel approach to learn multi-task regression models with constrained architecture complexity. The proposed model, named RFF-BLR, consists of a randomised feedforward neural network with two fundamental characteristics: a single hidden layer whose units implement the random Fourier features that approximate an RBF kernel, and a Bayesian formulation that optimises the weights connecting the hidden and output layers. The RFF-based hidden layer inherits the robustness of kernel methods. The Bayesian formulation enables promoting multioutput sparsity: all tasks interplay during the optimisation to select a compact subset of the hidden layer units that serve as common non-linear mapping for every tasks. The experimental results show that the RFF-BLR framework can lead to significant performance improvements compared to the state-of-the-art methods in multitask nonlinear regression, especially in small-sized training dataset scenarios.

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This study comprehensively explored the helix-stabilizing effects of amine-bearing hydrocarbon cross-links (ABXs), revealing their context-dependent nature influenced by various structural parameters. Notably, we identified a 9-atom ABX as a robust helix stabilizer, showcasing versatile synthetic adaptability while preserving peptide water solubility. Future investigations are imperative to fully exploit this system's potential and enrich our chemical toolkit for designing innovative peptide-based biomolecules.

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In contemporary scenario, electric power companies have observed upsurge in penetration level of tidal power plants (TPPs) in the traditional electric power system framework. However, the tidal turbines offer less frequency assistance due to their lesser rotor mass. Hence, TPPs may be collaborated with conventional units like diesel engine generator (DEG) to confirm system frequency stability in multi-area micro-grid system. The DEG comprises of primary and proportional integral derivative (PID) secondary frequency controls. However, in TPPs, to advance the system frequency regulation, deloading control approach is suggested and a cascade fuzzy fractional order PID-ID with derivative filter (CFFOPID-IDF) droop controller is suggested in place of the conventional non-cascade controller droop in the deloaded region. The suggested controller gains are fetched exploiting Salps swarm algorithm. For further enhancement of the dynamic responses, a precise high voltage direct current (AHVDC) link with the inertia emulation-based control (INEC) scheme is adopted, which allows the utilization of the gathered energy from the capacitance of the HVDC interface for frequency regulation. It provides better results compared to conventional AC tie line interface having less undershoot (34 %/20.63 %/43.75 %) and settling time (20.45 %/59.09 %/16.83 %) for variation in area-1 frequency/area-2 frequency/tie line power, respectively. The recommended control scheme is evidenced superior over numerous existing control techniques and provides least cost function in contrast to other control techniques. Additionally, it offers a highly stable performance under variable load conditions.

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Understanding complex interactions in biomedical networks is crucial for advancements in biomedicine, but traditional link prediction (LP) methods are limited in capturing this complexity. Representation-based learning techniques improve prediction accuracy by mapping nodes to low-dimensional embeddings, yet they often struggle with interpretability and scalability. We present BioPathNet, a novel graph neural network framework based on the Neural Bellman-Ford Network (NBFNet), addressing these limitations through path-based reasoning for LP in biomedical knowledge graphs. Unlike node-embedding frameworks, BioPathNet learns representations between node pairs by considering all relations along paths, enhancing prediction accuracy and interpretability. This allows visualization of influential paths and facilitates biological validation. BioPathNet leverages a background regulatory graph (BRG) for enhanced message passing and uses stringent negative sampling to improve precision. In evaluations across various LP tasks, such as gene function annotation, drug-disease indication, synthetic lethality, and lncRNA-mRNA interaction prediction, BioPathNet consistently outperformed shallow node embedding methods, relational graph neural networks and task-specific state-of-the-art methods, demonstrating robust performance and versatility. Our study predicts novel drug indications for diseases like acute lymphoblastic leukemia (ALL) and Alzheimer's, validated by medical experts and clinical trials. We also identified new synthetic lethality gene pairs and regulatory interactions involving lncRNAs and target genes, confirmed through literature reviews. BioPathNet's interpretability will enable researchers to trace prediction paths and gain molecular insights, making it a valuable tool for drug discovery, personalized medicine and biology in general.

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The rapid progress of intelligent transportation systems (ITS) has enabled the development of a highly spatiotemporally resolved vehicular VOC emission inventory. However, up to this point, the emission factors applied in vehicular VOC emission inventories worldwide are either independent of driving conditions or estimated by emission models, resulting in significant bias. In this study, by using the speed-dependent VOC emission factor measured online from a typical fleet in Guangzhou and collecting multiple sources of ITS data, we developed, for the first time, a link-level dynamic vehicular VOC emission inventory. The results reveal that the emission factors for vehicles at speeds higher than 50 km/h are only around 30 % of those at 5-20 km/h. Consequently, the total vehicular VOC emission in Guangzhou is estimated to be 16.19 kt in 2019, around 40 % lower than the estimates by the static emission inventory using the average emission factor during a short transient driving (STD) cycle. This discrepancy is mainly due to the much lower average speed of the STD cycle (20 km/h) compared to the average traffic speed on the road network (36 km/h). The discrepancy in VOC emissions was even higher for highways, with the static emission factors being 75-93 % higher than the speed-dependent ones. Such a large discrepancy underscores the necessity of applying localised speed-dependent emission factors to improve the estimation accuracy of vehicular VOC emissions. This study provides more accurate insights for policymakers in formulating targeted strategies to reduce vehicular VOC emissions and mitigate their contributions to ozone and PM2.5 pollution in urban areas.

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Piper colubrinum Link. is an underexplored crop regarding its metabolites and therapeutic attributes. Current study aimed to identify the possible volatile and non-volatile metabolites of P. colubrinum fruit and studied its metabolite diversity with medicinally valued Piper species viz. P. nigrum L., P. longum L. and P. chaba Hunter. The volatile constituents of P. colubrinum essential oil by GC-MS revealed the presence of sesquiterpenes as the major contribution. The sesquiterpenes α-muurolol (12.5 %) and ß-caryophyllene (11.3 %) were the predominant volatile components. Few aliphatic compounds like n-heptadecane and trace amounts of monoterpenes (α- and ß-pinene and α-terpineol) were also identified from this crop. The fatty acid profiling by GC-MS revealed mainly oleic acid (41.3 %) followed by palmitic and linoleic acids. HPLC analysis demonstrated that the major pungent alkaloid piperine was found to be trace (0.04 %) in P. colubrinum. The LC-QTOF-MS/MS profiling of the chloroform extract of the P. colubrinum revealed the presence of non-volatile constituents including phenolic and alkaloid compounds. Ferulic acid, rosmarinic acid, salicylic acid, kaempferol-5-glucoside, 5-methoxysalicylic acid, apigenin-7-galactoside, kaempferide-3-glucoside, luteolin, kaempferol, apigenin and scutellarein-4'-methyl ether were the phenolic compounds whereas piperlonguminine was the alkaloid compound identified. Finally, the biochemical parameters of this crop were compared with that of P. nigrum, P. longum and P. chaba and average linkage cluster dendrogram revealed that P. colubrinum was biochemically distinct from other three Piper species.

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Automated generation of knowledge graphs that accurately capture published information can help with knowledge organization and access, which have the potential to accelerate discovery and innovation. Here, we present an integrated pipeline to construct a large-scale knowledge graph using large language models in an active learning setting. We apply our pipeline to the association of raw food, ingredients, and chemicals, a domain that lacks such knowledge resources. By using an iterative active learning approach of 4120 manually curated premise-hypothesis pairs as training data for ten consecutive cycles, the entailment model extracted 230,848 food-chemical composition relationships from 155,260 scientific papers, with 106,082 (46.0 %) of them never been reported in any published database. To augment the knowledge incorporated in the knowledge graph, we further incorporated information from 5 external databases and ontology sources. We then applied a link prediction model to identify putative food-chemical relationships that were not part of the constructed knowledge graph. Validation of the 443 hypotheses generated by the link prediction model resulted in 355 new food-chemical relationships, while results show that the model score correlates well (R2 = 0.70) with the probability of a novel finding. This work demonstrates how automated learning from literature at scale can accelerate discovery and support practical applications through reproducible, evidence-based capture of latent interactions of diverse entities, such as food and chemicals.

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Background: Intermediaries are health-related workers who facilitate connections to local physical activities. Intermediaries deliver interventions by receiving referrals, conducting assessments, connecting referred individuals to activities and/or services in the community, and following up with them over time. However, it is unclear whether individuals who are referred to physical activities by an intermediary improve their physical activity levels, and what their perspectives and experiences are of participating in this intervention. To date there has been a lack of studies investigating the effect of this intervention on physical activity using appropriate outcome measures. Methods: This will be a mixed methods pilot feasibility study. Participants will be individuals referred or self-referred to an intermediary and connected to local physical activities. Participants will be recruited through two types of intermediary services in Ireland; social prescribing and local sports partnerships. A total of 30 participants will be recruited (15 per service). Baseline demographic information will be taken upon enrolment to the study and three questionnaires will be completed: the International Physical Activity Questionnaire - Short Form, Self-Efficacy for Exercise Scale and Short Warwick Edinburgh Mental Well-being Scale. The questionnaires will be repeated after 12 weeks and in addition semi-structured interviews will be carried out to explore intervention content and delivery, as well as acceptability of the intervention and evaluation design. Discussion: This overall aim of this proposed study is to investigate the feasibility of an intervention delivered by an intermediary to improve physical activity and health-related outcomes of community-dwelling individuals. Registration: ClinicalTrials.gov (NCT06260995).