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Systems-level transcriptomic analysis reveals synapse-related gene dysregulation in peripheral leukocytes of MDD patients
A Hooker Oxygenase Archetype in Polyketide Biosynthesis Challenging the Baeyer–Villiger Monooxygenase Paradigm
Comparison of low temperature thermoplastic and 3D printed (TPU and PLA) CMC joint stabilization orthoses in healthy participants
Abstract Carpometacarpal (CMC) osteoarthritis (OA) significantly impacts hand function and quality of life, particularly in older adults, and highlights the importance of effective conservative management strategies. Orthoses are a preferred conservative treatment, with recent advancements in 3D printing offering personalized alternatives. However, data comparing the outcomes of 3D-printed orthoses and traditional thermoplastic options are limited. Evaluating orthosis materials under controlled conditions may provide preliminary insights relevant for future clinical research. This study aims to evaluate and compare user satisfaction and functional outcomes of CMC orthoses made from thermoplastic polyurethane (TPU), polylactic acid (PLA), and low temperature thermoplastic (LTT) materials in healthy individuals. A comparative analysis conducted on healthy volunteers to assess satisfaction and functionality of different orthosis materials. Thirty healthy participants (15 males, 15 females, mean age 21 years) were recruited. Each participant used three types of CMC orthoses (TPU, PLA, and LTT) for two hours. User satisfaction was assessed using the Quebec Assistive Technology User Satisfaction Evaluation (QUEST 2.0), and hand function was measured using the Jebsen Taylor Hand Function Test (JTT). Statistical analyses appropriate for repeated-measures designs were used for group comparisons. TPU and LTT orthoses achieved statistically significant higher total scores on the QUEST 2.0 survey compared to PLA orthoses ( p < 0.05). However, no statistically significant differences were observed between TPU and LTT orthoses. In the JTT total score, TPU orthoses performed statistically significantly better than both PLA and LTT orthoses ( p < 0.05). No statistically significant differences were found between PLA and LTT orthoses. TPU orthoses demonstrated higher satisfaction scores compared to PLA and similar outcomes to LTT under short-term experimental conditions in healthy participants. These findings provide preliminary, experimental data obtained in healthy participants and may inform future clinical studies evaluating orthosis materials in symptomatic CMC osteoarthritis. Further studies in symptomatic populations with longer follow-up periods are needed to determine clinical relevance.
Transient Compartmentalization in Coacervates Driven by Chemically Fueled Autocatalysis and Interfacial Remodeling
Explainable analysis of the complex maze magnetic domain structure through extension of the Landau free energy model by adding an entropy feature
Acetylation-Tagging-Assisted Cocrystallization of Stubborn Molecules with Trinuclear Complexes
Sustainable urban farming using a smart hydroponic approach using IoT and real time monitoring
Development of Multiple Local Computational Models in Retrosynthetic Analysis: Total Synthesis of (−)-Deoxylimonin
Tunable multi-band terahertz sensor based on graphene plasmonic metasurfaces
Abstract This study introduces a highly sensitive and tunable plasmonic refractive index sensor based on a novel metal-dielectric-dielectric-metal (MDDM) metasurface architecture operating in the Terahertz (THz) region. The proposed structure consists of a graphene-based fractal pattern integrated with a dielectric layer, a silicon substrate, and a bottom aluminum layer, leveraging commercially available and easy-to-fabricate materials. This multilayer configuration supports strong plasmonic resonances and enhanced absorption, enabling triple-band refractive index sensing with high sensitivities of 10 μm/RIU, 3 μm/RIU, and 2.75 μm/RIU across three distinct modes, surpassing previously reported single- and dual-band plasmonic sensors. Unlike conventional metal-dielectric-metal (MDM) absorbers, the dual-dielectric configuration enhances field localization and supports three distinct resonance modes: a dipolar mode at approximately 7.69 THz, a quadrupolar mode near 25.4 THz, and a hybridized higher-order mode around 30.2 THz. These hybridized resonances arise from the coupled interactions between the central hexagonal graphene core and the concentric double nanorings, producing intense localized electromagnetic “hot spots” that significantly amplify the sensor’s spectral response. The multiple resonances offer improved flexibility for detecting various analytes or extending the sensing range. The proposed MDDM sensor maintains stable and tunable performance under environmental variations, demonstrating significant potential for practical applications in biomedical diagnostics, gas sensing, and glucose monitoring.
Instability of cooperation based on fictitious belief: an experiment with artificial supernatural punishment
AI-driven diagnosis of acute aortic syndrome based on multi-modal information fusion
Automated cone photoreceptor detection using synthetic data and deep learning in confocal adaptive optics scanning laser ophthalmoscope images
Abstract Adaptive optics scanning laser ophthalmoscope (AOSLO) imaging enables the cone photoreceptor mosaic to be visualised in the living human eye. Performing quantitative analysis of these images requires identification of individual photoreceptors. This is typically performed by manual labelling, which is subjective, time consuming and not feasible on a large scale. Automated algorithms to replace manual labelling are required and deep learning-based methods provide an effective way of achieving this. However, this approach requires large volumes of annotated training data that are difficult to acquire. Synthetic data may help to bridge this lack of annotated training data. A U-Net configuration was trained using a large synthetic dataset of confocal AOSLO images generated using ERICA alongside a smaller dataset of real confocal AOSLO images (Milwaukee dataset). Model performance was assessed by calculating the Dice coefficient, a metric quantifying segmentation overlap, on both a real held-out test set and an independent real dataset (Oxford dataset). Results from this evaluation were benchmarked against expert labelling and two automated cone detection methods: a confocal convolutional neural network (CNN) (1), and a combined graph-theory and dynamic programming approach (2)). The mean Dice coefficient compared to manual labelling was 0.989 (U-Net), 0.989 (confocal CNN), and 0.985 (graph-theory and dynamic programming) on the held-out test set. On the independent Oxford dataset, the U-Net achieved a mean Dice coefficient of 0.962 compared to manual labelling. Results show performance that is comparable to the gold standard of manual labelling and two automated cone detection methods. Furthermore, we demonstrate generalisability of this approach on an independent real dataset with images from higher retinal eccentricities. This approach may be useful for quantitative analysis of the photoreceptor mosaic in patients with retinal disease to provide cell-specific imaging biomarkers from AOSLO images.
An end-to-end convolutional neural network for secure image transmission via joint encryption and steganography
Bioinspired 8‑hydroxyquinoline-Fe3O4 nanostructures from Citrullus colocynthis exhibit strong antibacterial, antifungal, and anticancer effects
ACFM: adaptive channel weighted fusion algorithm for improving small object detection performance in UAV traffic
Abstract In terms of small objects in drone traffic monitoring, problems like insufficient feature representation, serious background interference, and poor multi-scale adaptability are often encountered. Especially when dealing with complex traffic situations, poor context linking between objects as well as poor detection in congested regions are more noticeable. In order to solve the above problems, we put forward an adaptive channel weighted fusion module, which is ACFM. First, we build a multi-scale refinement module, which can do cross-scale feature interaction via a downsampling-upsampling path. It is combined with a residual calibration mechanism to greatly improve both the localization consistency and the detail preservation of small objects over different resolutions of feature maps. And then, a grouped sparse mask attention module is created to reduce background noise through channel grouping and sparse gating techniques in order to enhance the local saliency features of sparse small targets. Finally, we add a channel-wise adaptive weighting by using the global context. Using an α weight generator which can change the contribution of features based on scene complexity, it get rid of traditional fixed combination plans. From the experimental results, we can see that adopting GFL as the detector, ACFM achieves better performance improvements on the widely used VisDrone2019, UAVDT dataset, and the maximum gain in mAP is more than 0.8% and 1.3% higher than the comparison methods. On the AU - AIR dataset, ACFM is still a little bit better than the other 0. 5% mAP, it is still robust in the complicated situation.
Manual pointing bias reflects spatial organization of number knowledge
Abstract Number concepts are thought to be spatially organized along a mental number line increasing left-to-right (in Westerners) and bottom-to-top. However, the evidence for the emergence of horizontal and/or vertical spatial-numerical associations (SNAs) in manual pointing is mixed, and tasks implying magnitude-dependent number arrangements in physical space prevent conclusions about the emergence and extent of SNAs. Addressing these issues, we investigated SNAs with a two-step pointing task where the movement’s first step (provided after listening to a spoken number) always ended on the same central target displayed on a touchscreen. We analyzed spatial bias of this space-invariant, magnitude-independent first step. In the second step, participants localized the spoken number’s position in a clock-face arrangement, decoupling physical and numerical distance. In Experiment 1 (N = 20, spoken numbers from 1 to 12), pairwise numerical and Euclidean distances between (space-invariant) pointing locations were positively associated. Changes in magnitude of spoken numbers across successive trials yielded trends suggesting systematic pointing-location shifts. Experiment 2 (N = 20) presented 24 spoken targets (1–12.5, e.g., “three point five”) to increase clock-face salience. Both clock-face distance and numerical difference predicted Euclidean distances between pointing locations. In successive trials, positive magnitude changes resulted in leftward (clock-face congruent) and upward (MNL-congruent) shifts of pointing locations. The results suggest that numerical differences may be represented as 2D physical distances and support the emergence of vertical SNAs in manual pointing while avoiding previous experimental confounds. The findings align with the view that conceptual knowledge is represented in low-dimensional models that are spatially organized.
Optimized paraquat removal using Bi₄O₅Br₂: synthesis, performance evaluation, and mechanistic insights
Urinary CD4+ T helper cells are a potential biomarker for tubulointerstitial nephritis in Sjögren’s disease
Abstract Sjögren’s Disease (SjD) is the most common connective tissue disease. An estimated 5–27% of patients have kidney involvement, usually in the form of tubulointerstitial nephritis (TIN). To date, there are no validated biomarkers for either diagnosis or assessment of therapeutic response. Since one of the hallmarks of TIN is leukocyturia, we aimed at investigating the potential role of urinary leukocytes as a diagnostic tool for SjD-TIN. We prospectively recruited 13 patients with SjD, that underwent kidney biopsy for suspected TIN. A multicolor flow cytometry panel was established to quantify 8 different urinary leukocyte populations. Our analyses showed, that urinary CD4 + T H cells are increased in patients with SjD-TIN and can differentiate precisely between SjD-TIN and SjD-patients with other non-inflammatory kidney pathologies (SjD-CKD). In contrast, no other urinary cell population or clinical marker was able to differentiate between SjD-TIN and SjD-CKD. Furthermore, we found a very strong correlation between urinary CD4 + T H cells and histological severity of TIN. Finally, during longitudinal follow up of 8 SjD-TIN patients undergoing immunosuppressive therapy, we saw a large and significant drop in urinary CD4 + T H cells in parallel with clinical and histological improvement. Taken together our data suggest, that urinary CD4 + T H cells might be a novel biomarker for diagnosis and follow up of SjD-TIN and might help to guide treatment decisions.