Browse Articles

Alcohol use and optimal chronic diseases’ treatment outcomes among adults aged 40 years and above in rural South Africa

Longitudinal study of central sensitization and chronic low back pain in a Japanese cohort during the COVID-19 pandemic

Effect of waste water bottle and treated sisal fibers on the durability and mechanical properties of concrete

Personalizing product sets to individual health priorities increases the healthfulness of hypothetical food choices in US adults

Abstract Recently, the potential for dietary personalization based on genetic/phenotypic characteristics to improve health has been studied. While promising, inputs into this biology-focused personalization process are intensive and may not align with an individual’s own health priorities, which drive health behaviors. Here, we examine how personalizing food suggestions based on individuals’ health priorities affects the healthiness of their food choices. We conducted a pre-registered experiment examining hypothetical food choices from three food categories in six conditions: (1) control, (2) health priority prime, (3) healthy product subset, (4) health priority prime + healthy product subset, (5) health priority prime + priority subset, and (6) health priority-based personalized product suggestions. Participants in conditions 2, 4, 5, and 6 first encountered a question asking them to select their top health priority from a list of options. In conditions 5, the subset of healthy items was described as foods beneficial for the selected health priority, while in condition 6, participants immediately saw the set of foods beneficial for the selected health priority, but had the option to see all foods instead. After making food choices, participants completed a survey with questions about the choice process, health priorities, and demographic variables. We used logistic regression to analyze the impact of condition on healthiness of food choices, and ordered logistic regression to examine the impact of condition on satisfaction with choices made. The experiment and survey were completed by 4171 adults (≥ 19 years) in the US, with the sample closely matching US distribution of age, sex, education, and income characteristics. There were no significant differences in the distribution of demographic characteristics among conditions. All intervention conditions significantly increased the likelihood that an individual chose a healthy food. However, interventions that combined priming with healthy subsets were significantly more effective than single interventions. Conditions that connected the healthy subsets to individuals’ health priorities were particularly effective. The adjusted odds ratio (aOR) of selecting a healthy food was 4.77 (95% CI 4.12, 5.52) relative to the control condition when participants could view a subset described as helpful for their health priority. When people immediately viewed the personalized product set, the aOR increased to 11.67 (95% CI 0.1, 13,5). Likewise, analysis of nutrient content from food choices revealed that personalization decreased saturated fat, added sugar, and sodium and increased dietary fiber, potassium, iron, and calcium. However, product choice satisfaction was significantly lower in the personalized product set, which appears to be partially due to a tendency in this condition to forego choosing a product rather than selecting an unhealthy product. Personalization of product options based on individual health priorities should be tested in real-choice environments.

AAV-IKV mediated expression of decorin inhibits EMT and fibrosis in a murine model of Glaucoma and AAV-IKV transduction in Non-Human Primates

Diversity of intestinal microbiota and inflammatory cytokines after severe trauma

Development of a MVI associated HCC prognostic model through single cell transcriptomic analysis and 101 machine learning algorithms

A novel vascularized urethra-on-a-chip model

Study on the measurement of high-quality development efficiency and influencing factors in the Yellow River Basin, China

Identification and validation of palmitoylation-related biomarkers in gestational diabetes mellitus

Ameliorative effect of rhizobacteria Bradyrhizobium japonicum on antioxidant enzymes, cell viability and biochemistry in tomato plant under nematode stress

Modeling Intraday Aedes-human exposure dynamics enhances dengue risk prediction

Abstract Cities are the hot spots for global dengue transmission. The increasing availability of human movement data obtained from mobile devices presents a substantial opportunity to address this prevailing public health challenge. Leveraging mobile phone data to guide vector control can be relevant for numerous mosquito-borne diseases, where the influence of human commuting patterns impacts not only the dissemination of pathogens but also the daytime exposure to vectors. This study utilizes hourly mobile phone records of approximately 3 million urban residents and daily dengue case counts at the address level, spanning 8 years (2015–2022), to evaluate the importance of modeling human-mosquito interactions at an hourly resolution in elucidating sub-neighborhood dengue occurrence in the municipality of Rio de Janeiro. The findings of this urban study demonstrate that integrating knowledge of Aedes biting behavior with human movement patterns can significantly improve inferences on urban dengue occurrence. The inclusion of spatial eigenvectors and vulnerability indicators such as healthcare access, urban centrality measures, and estimates for immunity as predictors, allowed a further fine-tuning of the spatial model. The proposed concept enabled the explanation of 77% of the deviance in sub-neighborhood DENV infections. The transfer of these results to optimize vector control in urban settings bears significant epidemiological implications, presumably leading to lower infection rates of Aedes-borne diseases in the future. It highlights how increasingly collected human movement patterns can be utilized to locate zones of potential DENV transmission, identified not only by mosquito abundance but also connectivity to high incidence areas considering Aedes peak biting hours. These findings hold particular significance given the ongoing projection of global dengue incidence and urban sprawl.

Dynamic niche technology based hybrid breeding optimization algorithm for multimodal feature selection

Wideband filtering antenna loading U-shaped parasitic patches based on characteristic mode analysis

Mitigation of salt stress in Camelina sativa by epibrassinolide and salicylic acid treatments

Research on “theory of force-center” based on failure characteristics of different roadway sections and its application

Changes in feeding behavior, milk yield, serum indexes, and metabolites of dairy cows in three weeks postpartum

Exploring the prevalence and risks of eating disorders in Lebanon’s athletic community

Characterization of the ceramic coating formed on 2024 al alloy by scanning plasma electrolytic oxidation

Recent metaheuristic algorithms for solving some civil engineering optimization problems

Abstract In this study, a novel hybrid metaheuristic algorithm, termed (BES–GO), is proposed for solving benchmark structural design optimization problems, including welded beam design, three-bar truss system optimization, minimizing vertical deflection in an I-beam, optimizing the cost of tubular columns, and minimizing the weight of cantilever beams. The performance of the proposed BES–GO algorithm was compared with ten state-of-the-art metaheuristic algorithms: Bald Eagle Search (BES), Growth Optimizer (GO), Ant Lion Optimizer, Tuna Swarm Optimization, Tunicate Swarm Algorithm, Harris Hawk Optimization, Artificial Gorilla Troops Optimizer, Dingo Optimizer, Particle Swarm Optimization, and Grey Wolf Optimizer. The hybrid algorithm leverages the strengths of both BES and GO techniques to enhance search capabilities and convergence rates. The evaluation, based on the CEC’20 test suite and the selected structural design problems, shows that BES–GO consistently outperformed the other algorithms in terms of convergence speed and achieving optimal solutions, making it a robust and effective tool for structural Optimization.