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The purpose of this research is to develop a new method for automatically optimizing the seawall cross-section with composite slopes and a berm, considering both overtopping discharge and construction cost. Minimizing these competing multi-objectives is highly challenging due to the intricate geometry of seawalls. In this study, the surrogate model optimization algorithm DYCORS (Dynamic COordinate search using Response Surface models) is employed to search for the optimal seawall geometry, coupled with the ANN (Artificial Neural Network) model for determining the overtopping discharge. A total of 20 trials have been run to evaluate the performance of our methodology. Even the worst-performing Trial 7 among these 20 trials shows a satisfactory performance, with a reduction of 17.67% in overtopping discharge and a 12.1% decrease in cost compared to the original solution. Furthermore, compared to other optimization schemes using GAs (Genetic Algorithms) with the same decision vectors, constraints, and multi-objective functions, the methodology has been proven to be more effective and robust. Additionally, when facing different combinations of wave conditions and water levels, there was a 27.8% reduction in objective function value compared to the original solution. The optimal results indicate that this method can still be effectively applied for optimizing the seawall cross-section as it is a general method. [ABSTRACT FROM AUTHOR]

Glaucoma is a leading cause of permanent blindness, affecting 80 million people worldwide. Recent studies have emphasized the importance of neuroinflammation in the early stages of glaucoma, involving immune and glial cells. To investigate this further, we used the GSE27276 dataset from the GEO (Gene Expression Omnibus) database and neuroinflammation genes from the GeneCards database to identify differentially expressed neuroinflammation-related genes associated with primary open-angle glaucoma (POAG). Subsequently, these genes were submitted to Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes for pathway enrichment analyses. Hub genes were picked out through protein-protein interaction networks and further validated using the external datasets (GSE13534 and GSE9944) and real-time PCR analysis. The gene–miRNA regulatory network, receiver operating characteristic (ROC) curve, genome-wide association study (GWAS), and regional expression analysis were performed to further validate the involvement of hub genes in glaucoma. A total of 179 differentially expressed genes were identified, comprising 60 upregulated and 119 downregulated genes. Among them, 18 differentially expressed neuroinflammation–related genes were found to overlap between the differentially expressed genes and neuroinflammation–related genes, with six genes (SERPINA3, LCN2, MMP3, S100A9, IL1RN, and HP) identified as potential hub genes. These genes were related to the IL-17 signaling pathway and tyrosine metabolism. The gene–miRNA regulatory network showed that these hub genes were regulated by 118 miRNAs. Notably, GWAS data analysis successfully identified significant single nucleotide polymorphisms (SNPs) corresponding to these six hub genes. ROC curve analysis indicated that our genes showed significant accuracy in POAG. The expression of these genes was further confirmed in microglia, Müller cells, astrocytes, and retinal ganglion cells in the Spectacle database. Moreover, three hub genes, SERPINA3, IL1R1, and LCN2, were validated as potential diagnostic biomarkers for high-risk glaucoma patients, showing increased expression in the OGD/R-induced glaucoma model. This study suggests that the identified hub genes may influence the development of POAG by regulation of neuroinflammation, and it may offer novel insights into the management of POAG. [ABSTRACT FROM AUTHOR]

Objective: Uptake of the imaging tracers [18F]AlF-NOTA-FAPI-04 and [18F]FDG varies in some inflammatory lesions, which may result in false-positive findings for malignancy on PET/CT. Our aim was to compare the [18F]AlF-NOTA-FAPI-04 and [18F]FDG PET/CT imaging features of malignant and various inflammatory lung lesions and to analyze their value for differential diagnosis. Methods: We retrospectively analyzed [18F]AlF-NOTA-FAPI-04 PET/CT scans from 67 cancer patients taken between December 2020 and January 2022, as well as the scans of 32 patients who also underwent [18F]FDG PET/CT imaging. The maximum and mean standardized uptake values (SUVmax and SUVmean, respectively) and lesion-to-background ratio (LBR) were calculated. The predictive capabilities of semiquantitative PET/CT parameters were analyzed by receiver operating characteristic curve analysis. Results: A total of 70 inflammatory and 37 malignant lung lesions were evaluated by [18F]AlF‑NOTA‑FAPI‑04 PET/CT, and 33 inflammatory and 26 malignant lung lesions also were evaluated by [18F]FDG PET/CT. Inflammatory lesions exhibited lower [18F]AlF-NOTA-FAPI-04 and [18F]FDG uptake compared to malignant lesions, with statistically significant differences in SUVmax, SUVmean, and LBR (all p < 0.001). [18F]AlF-NOTA-FAPI-04 uptake also varied among different types of inflammatory lesions (SUVmax, p = 0.005; SUVmean, p = 0.008; LBR, p < 0.001), with the highest uptake observed in bronchiectasis with infection, followed by postobstructive pneumonia, and the lowest in pneumonia. [18F]FDG uptake was higher in postobstructive pneumonia than in pneumonia (SUVmax, p = 0.009; SUVmean, p = 0.016; LBR, p = 0.004). Conclusion: [18F]AlF-NOTA-FAPI-04/[18F]FDG PET/CT showed significantly lower uptake in inflammatory lesions than malignancies as well as variation in different types of inflammatory lesions, and thus, may be valuable for distinguishing malignant and various inflammatory findings. Clinical relevance statement: Our study confirmed that the uptake of [18F]AlF-NOTA-FAPI-04/[18F]FDG PET/CT in inflammatory and malignant lung lesions is different, which is beneficial to distinguish inflammatory and malignant lung lesions in clinic. Key Points: • Malignant and different inflammatory lung lesions showed varying degrees of uptake of [18F]AlF-NOTA-FAPI-04 and [18F]FDG. • Inflammatory lung lesions showed significantly less uptake than malignancies, and uptake varied among different types of inflammatory lesions. • Both types of PET/CT could differentiate malignant and various inflammatory lung findings. [ABSTRACT FROM AUTHOR]

Background: Financial incentives and peer networks could be delivered through eHealth technologies to encourage older adults to walk more. Methods: We conducted a 24-week randomized trial in which 92 older adults with a computer and Internet access received a pedometer, daily walking goals, and weekly feedback on goal achievement. Participants were randomized to weekly feedback only (Comparison), entry into a lottery with potential to earn up to $200 each week walking goals were met (Financial Incentive), linkage to four other participants through an online message board (Peer Network), or both interventions (Combined). Main outcomes were the proportion of days walking goals were met during the 16-week intervention and 8-week follow-up. We conducted a content analysis of messages posted by Peer Network and Combined arm participants. Results: During the 16-week intervention, there were no differences in the proportion of days walking goals were met in the Financial Incentive (39.7%; p = 0.78), Peer Network (24.9%; p = 0.08), and Combined (36.0%; p = 0.77) arms compared with the Comparison arm (36.0%). During 8 weeks of follow-up, the proportion of days walking goals were met was lower in the Peer Network arm (18.7%; p = 0.025) but not in the Financial Incentive (29.3%; p = 0.50) or Combined (24.8%; p = 0.37) arms, relative to the Comparison arm (34.5%). Messages posted by participants focused on barriers to walking and provision of social support. Conclusions: Financial incentives and peer networks delivered through eHealth technologies did not result in older adults walking more. [This article is part of an open access supplement "Fostering Engagement and Independence: Opportunities and Challenges for an Aging Society," published in SOPHE's "Health Education & Behavior." This supplement was supported by funding provided by the Centers for Disease Control and Prevention's (CDC) National Center for Chronic Disease Prevention and Health Promotion, Healthy Aging Program (Cooperative Agreement #U38HM000454) via the Association of State and Territorial Health Officials, and from a grant provided by the Retirement Research Foundation.]