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Porous tantalum cage loaded with CGF promotes interbody fusion in a rat XLIF model
A novel scheme for speed variation of a robotic cane to improve step length symmetry during overground walking
Intradermal immunization with Plasmodium berghei late-arresting genetically attenuated sporozoites induces PD-L1 expression on regulatory macrophages and dendritic cells
The interaction of gait characteristics and concerns about falling in community-dwelling older adults
Abstract Concerns about falling (CaF) are common in older adults and are associated with increased falls. Although cautious gait—a gait pattern linked to CaF—has been described, the specific gait parameters most strongly associated with CaF remain unclear. This study investigates the association between gait characteristics at normal and maximal gait speed and CaF in community-dwelling older adults. This cross-sectional analysis merged data from two studies including participants aged 65 years and older: the FEARFALL-study, an intervention study to reduce CaF and improving walking stability, and from the MOGA-study, investigating the harmonization of supervised short-walk test protocols. Gait analysis was performed using an instrumented walkway and CaF was assessed by the Falls Efficacy Scale International (FES-I). Multiple stepwise regression models were used to explore the association of gait parameters with CaF and the associations of individual FES-I items with gait speed. Data from 261 participants (MOGA-study n = 150; FEARFALL-study n = 111; mean age 80.0 (± 4.6) years; 67% women) were analysed. Increasing FES-I levels were associated with decreasing walking performance across most gait parameters. Normal walking speed explained 24.9% of the variance in FES-I increasing to 29.2% by adding maximum gait speed and walk ratio. Four FES-I items dedicated to dynamic balance could be used to screen for cautious gait (adjusted R 2 = .201). Gait variables, especially gait speed, are strongly associated with CaF. FES-I items related to dynamic balance might be used to screen for cautious gait in community-dwelling older adults.
Exploring the chemical and biological landscape of a Nickel (II) schiff base complex via azomethine linkage
Optimized nanofluid coolants enhance thermal performance in ruffled fin automotive radiators
Effect of waveguide wall roughness on quantum signal transmission
Investigation of the effects and mechanisms of the wetting-Induced deformation behavior of compacted loess in Yan’an, China
A quantum machine learning-based predictive analysis of CERN collision events
Comparative analysis of novel preprocessing techniques and deep learning based multi modal feature fusion for diabetic retinopathy grading
Abstract Fundus images are crucial for the detection and monitoring of retinal diseases such as diabetic retinopathy (DR). However, issues such as uneven illumination, low contrast, and noise often degrade image quality, impacting the accuracy of automated grading systems. This study introduces three novel preprocessing techniques Adaptive Sigmoid Enhancement, LAB-ACE Image Enhancement, and Multi-channel Image Enhancement designed to address these challenges. Adaptive Sigmoid Enhancement adaptively adjusts local contrast to highlight subtle lesions, LAB-ACE operates in the LAB color space to selectively enhance the lightness channel while preserving color fidelity, and Multi-channel Image Enhancement applies targeted green-channel optimization combined with contrast stretching and channel recombination. These methods extend beyond conventional contrast enhancement and normalization by integrating multi-stage adaptive processing and color-channel-specific optimization to improve lesion visibility and vessel delineation while minimizing background noise. Following pre-processing, handcrafted features (LBP, GLCM) and deep features from a pre-trained ResNet-50 are fused in a multi-modal framework and evaluated using multiple classifiers, including SVM, KNN, Random Forest, and XGBoost. Results demonstrate that XGBoost with fused features and Adaptive Sigmoid Enhancement achieves the highest accuracy (96.39%), outperforming other combinations. The findings highlight the effectiveness of the proposed pre-processing strategies in enhancing DR grading performance, paving the way for improved computer-aided diagnosis systems.
Genes, shells, and AI: using computer vision to detect cryptic morphological divergence between genetically distinct populations of limpets
Abstract Many species are composed of two or more genetically distinct clades, indicating ongoing or past evolutionary divergence. Often however, there are no obvious morphological differences between clades, making it difficult to accurately assess specific aspects of biodiversity or to enact targeted conservation efforts. New advancements in artificial intelligence tools can be used to categorise individuals into their respective genetic clades and to highlight their distinguishing morphological characters that would otherwise be hidden from human observers. Here, we applied computer vision and explainable artificial intelligence techniques to four limpet species that display well-defined phylogeographic breaks along the Baja California and California coasts. A fine-tuned convolutional network, trained and evaluated over 100 resampling iterations, classified individuals into their genetic clades with median F1-scores of up to 0.96. F1-score performance was markedly higher for true clade groups than the controlled mixed-groups, confirming the presence of features specific to the clades. Saliency maps consistently emphasised structures such as the keyhole in Fissurella volcano and the ridge tips in Lottia conus as distinguishing features, and subsequent shape analyses confirmed significant divergence between clades. These results demonstrate the power of computer vision and explainable artificial intelligence to expose otherwise cryptic morphological diversity and provide a scalable, reproducible workflow that can broaden the biodiversity toolkit and refine eco-evolutionary research across taxa.
Dual mode fluorescence and spectrophotometric cefixime sensing using onion juice nitrogen doped carbon dots with smartphone and paper strip readouts
Abstract The widespread presence of cefixime (CFX) in the environment has raised concerns about potential risks to ecosystems and human health, including bronchitis, tonsillitis, and laryngitis. Therefore, developing an accurate and convenient on-site analysis device for CFX is crucial. Herein, a dual-mode fluorescence–spectrophotometric platform with smartphone and test-strip readouts was designed to detect CFX. A microwave method was demonstrated for synthesizing N-doped fluorescent Carbon dots (N-CDs) by using onion juice as a precursor, with optimization achieved through Box-Behnken Design (BBD). XPS, HRTEM, XRD, FTIR, TRF, UV-Vis spectrophotometry, and fluorescence were used to investigate the N-CDs structure and optical properties. N-CDs displayed satisfactory optical properties, an extraordinary quantum yield of 72.4%, an average particle size of 6.12 ± 0.5 nm, excellent water solubility, high ionic strength, and outstanding pH stability. The limits of detection (LOD) for the fluorescence, spectrophotometric, smartphone, and test strip were 5.16 nM, 138.08 nM, 0.56 µM, and 0.65 µM, respectively. Detection operates via a synergistic quenching mechanism (inner-filter effect plus dynamic quenching). In real samples (honey and tap water), recoveries of 94.21–108.83% with RSD ≤ 1.87% confirmed the accuracy of quantification. This developed platform provides a reliable, environmentally friendly, and efficient platform for on-site CFX monitoring in environmental applications.
CMR-LGE imaging features of dilated cardiomyopathy and relationship with left ventricular function
Ustekinumab may have therapeutic effects on autoimmune thyroid disease
Dynamic characteristics and constitutive model of improved coarse-grained roadbed filler under traffic cyclic loading
Virological characterization of SARS-CoV-2 BA.2.86 variants by assessing antiviral susceptibility, in vivo infectivity and replicative fitness
Real time urban traffic prediction using RFID and a hybrid LSTM random forest model
Proteasomal activity and disease outcome in phenylketonuria patients with a structural SLC7A5 variant
Abstract Treatment in phenylketonuria is based on using a diet aimed at protecting the brain from uncontrolled hyperphenylalaninemia. Although treatment methods are well established, clinical outcomes vary among patients. This may be due to alterations of phenylalanine transport mediated by the amino acid transporter LAT1, which also regulates the function of the mTORC1/proteasomal pathway. We investigated the clinical and cellular effects of a structural variant in the SLC7A5 gene, which encodes LAT1. The investigated variant was previously associated with altered phenylalanine metabolism in preliminary studies. We assessed physical and intellectual development in children with phenylketonuria. Next, we explored the cellular mechanisms underlying potential phenotypic differences between carriers and noncarriers of the rs113883650 polymorphism – a marker of the studied SLC7A5 variant. For cellular experiments we used a model of hyperphenylalaninemia based on induced pluripotent stem cells. We assessed LAT1 abundance and performed transcriptomic and proteomic analyses. We found that carriers of rs113883650 were more prone to being overweight but exhibited significantly better intellectual development. They also showed a corresponding increased LAT1 abundance and decreased expression of proteasomal genes and the FOXO signalling pathway. We propose considering of the rs113883650 status during the follow-up of children with phenylketonuria. It may also hold relevance in other LAT1-related conditions including cancers.