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Physics of North Indian Ocean tropical cyclones
A deep learning-enriched framework for analyzing brain functional connectivity
Age-related differences in activities of daily living among older Chinese adults
Abstract The decline in activities of daily living (ADL) significantly impacts independence and quality of life among older adults. This study used data from the China Health and Retirement Longitudinal Study (CHARLS) to conduct a cross-sectional analysis of 4,751 functionally impaired older Chinese adults aged ≥ 60 years with Modified Barthel Index (MBI) scores < 100. Participants were stratified into seven age groups (60–64, 65–69, 70–74, 75–79, 80–84, 85–89, and ≥ 90 years), and simple linear models were used to analyze age-related differences across 12 ADL domains. Results revealed three patterns of functional decline: (1) basic self-care activities (eating, getting up) remained relatively stable across age groups; (2) mobility functions (walking, stair climbing) showed progressive decline, with walking demonstrating continuous deterioration across multiple age transitions; and (3) personal care activities (bathing, grooming, toileting, dressing) showed significant decline primarily between ages 75–84. Gender-stratified analysis identified female advantages in self-care abilities and age-related changes in walking performance. The 75–84 years period emerged as a critical transition, with six functional domains showing significant deterioration. These findings suggest that interventions targeting mobility preservation and targeted interventions during the late 70s to early 80s may help delay functional decline and maintain independence in older adults.
Estimation of sexual dimorphism of adult human mandibles of South Indian origin using non-metric parameters and machine learning classification algorithms
Abstract The mandible is one of the most reliable in sex determination in forensic anthropology. The shape of the mandible provides valuable information regarding the male and female distinctions. Machine learning algorithms are widely used for various applications due to their accuracy and reliability, extending their application in biological profiling. This study aims to estimate sexual dimorphism using various machine-learning algorithms based on non-metric features of the mandible. This study uses four machine-learning algorithms—k-nearest neighbors, decision tree, support vector machines, and random forest to determine sex based on 12 mandibular non-metric parameters. The data was collected from three medical institutes in Karnataka, India, involving a sample of 156 individuals. Random Forest consistently achieved the highest Jaccard Index (0.86), F1 score (0.92), and accuracy (0.92) across both SMOTE and Random Over-Sampling (ROS) methods, showing stable and robust performance. ROS improved balanced accuracy for KNN, Decision Tree, and SVM by up to 9.7%. Feature importance analysis highlighted N6 Gonial angle and N12 Flexure ramal post border as key predictors. Statistical tests found no significant accuracy differences among models. Female specificity remained lower across all models. This study offers insights into employing machine learning algorithms for sex identification using non-metric observations of the mandible.
Enhancing sustainability in RC beams with magnetically treated mixing water for improved flexural performance
Genome-wide identification and expression analysis of HD-ZIP gene family in sunflower (Helianthus annuus L.) under water deficit stress
Impact of the October 7 Gaza war on post-traumatic stress symptoms and quality of life in Palestinian nursing
Fall injuries among patients in selected hospitals of nepal: A cross-sectional study
Abstract Fall injury statistics are scarce in low- and middle-income countries like Nepal. This study examines fall-related injuries and associated risk factors to address critical gaps in injury epidemiology and prevention. Data was collected from 18 August to 17 September 2023 from the emergency and inpatient departments of seven hospitals in Nepal, one from each province. A structured questionnaire, based on the WHO Guidelines, was used for data collection from patients or caregivers. Among 1,175 injury patients, 38.6% sustained nonfatal fall injuries. Males comprised 58.9% of cases. Most falls (93.6%) were unintentional, and 64.5% occurred at home. 32% of all fall injury patients were children under 15 years, and 17.7% were those aged 65 years or older. Farmers and students accounted for 58.5% of all falls. Injured limbs were most common, followed by head injuries (21.7%). Regression analysis revealed disparities in fall injuries associated with province, geography, place of residence, occupation groups, castes, and gender of the patients. Marital status and household income were not found to be significantly associated with falls. Males were more likely than females to receive immediate care after fall injuries. Falls occurring at home disproportionately affect children, the elderly, and those involved in agricultural occupations. The findings highlight the urgent need for implementing targeted interventions, particularly in domestic and rural settings, and addressing inequalities in injury treatment.
Multi-agent collaborative pathways for Chinese traditional architectural image generation
Validity and reliability testing of the Pelvic Pain Impact Questionnaire
A deep learning pipeline for age prediction from vocalisations of the domestic feline
Abstract Accurate age estimation is essential for advancing interspecies communication but remains a challenge across non-human species. This study presents the first dataset of domestic feline vocalisations specifically designed for age prediction and introduces a novel deep learning pipeline for this purpose. By applying transfer learning with models like VGGish, YAMNet, and Perch, we demonstrate the potential for automated age classification, with VGGish achieving the best results. Our findings hold significant potential for applications in veterinary care and wildlife conservation, building on existing research and pushing forward the boundaries of automated age classification within digital bioacoustics. Future work could explore improving model generalisability and robustness, potentially expanding its application across species.
Investigation of seasonal soil moisture and temperature variations underneath a waffle raft foundation built on reactive soil
A novel role for colistin as an efflux pump inhibitor in multidrug-resistant Klebsiella pneumoniae
Reliable on-treatment prognostication and target identification with a customized assay for circulating tumor DNA in patients with newly diagnosed pancreatic cancer
Abstract The vast majority of patients with pancreatic cancer present with unresectable disease and precision medicine is lagging behind. Circulating tumor DNA (ctDNA) has emerged as a promising tool, both as a proxy for tumor burden and for capturing tumor heterogeneity, but optimal gene panels and prognostic cutoffs remain to be determined. Herein, we applied ultra-deep ctDNA sequencing using a customized panel targeting 23 genes and six frequently altered chromosomes on plasma samples obtained before, during and after chemotherapy from 60 patients enrolled in a prospective clinical study. At baseline, positive versus negative ctDNA was not prognostic, neither in the adjuvant nor in the palliative setting, but in palliative patients, an independent prognostic cutoff could be calculated from the absolute number of mutated DNA molecules. Median overall survival was 3.7 months in the ctDNAhigh compared to 11.9 months in the ctDNAlow group (p < 0.0001), and the cutoff remained prognostic at one and three months. Moreover, relevant genetic alterations were highly concordant in ctDNA and paired tumor tissue. These findings demonstrate the potential clinical utility of a customized and focused gene panel for prognostication and target identification over time in patients with newly diagnosed pancreatic cancer, in particular in the palliative setting. ClinicalTrials.gov number: NCT03724994.
Sutureless versus transcatheter valves for the treatment of aortic valve stenosis: a systematic review and meta-analysis
Abstract An important limitation of the pivotal randomized controlled trials that compared transcatheter aortic valve replacement (TAVR) to surgical aortic valve replacement (SAVR) is that the SAVR arm scarcely included sutureless bioprosthetic valves. We identified 13 retrospective propensity-matched studies in low (n = 2), intermediate (n = 8), and high-risk (n = 3) patients, using EuroSCORE and STS score to assess perioperative risk. One large registry drove the outcome in low-risk patients, showing better early survival with TAVR, lower rates of stroke and acute kidney injury. Intermediate-risk patients showed improved early and medium-term survival with SU-AVR, whilst in high-risk patients, no significant differences were seen between treatment options. Overall, across all risk categories, the rates of moderate and severe aortic regurgitation and permanent pacemaker implantation were significantly lower with SU-AVR, while transprosthetic gradients and duration of hospital stay were higher compared to TAVR. The differences in survival in the intermediate risk group are not in line with conclusions of pivotal randomized trials comparing TAVR with SAVR. Specific features of SU-AVR may account for these survival differences, positioning SU-AVR as a valid and safe alternative for patients at intermediate risk. Awaiting confirmation in randomized trials, careful patient selection and consideration of either of the AVR options in the heart team remain crucial.