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Multi task learning based early prediction model for antibiotic resistance using multi institutional cohort data
Optimization and experimental demonstration of mesh-patterned 4H-SiC betavoltaic cells for enhanced power density
The first high-throughput sequencing of bacterioplankton sheds light on bacterial and cyanobacterial diversity in high-altitude Lake Sevan, Armenia
Stability analysis of discrete delta fractional models under summation multipoint constraints for robust engineering systems
A novel stacking ensemble model for predicting discharge coefficient of submerged multi parallel radial gates
Abstract Enhancing the precision of discharge coefficient (C d ) prediction holds paramount importance for effective Water distribution control. Calculating the C d for radial gates is often complex, with existing methods frequently depending on intricate procedures and underlying assumptions. This study introduces a deep learning-based stacking ensemble model for C d prediction. The proposed model comprises a dual-layer structure. Four machine learning algorithms are exploited as baseline models. The Meta model employed long short-term memory (LSTM) with attention mechanism to amalgamate the outputs from the base models and assign sufficient weight to each base model. The spatial attention mechanism effectively highlighted relevant patterns within the data. The proposed model achieved an impressive root mean square error of 0.0175. The ensemble model outperformed existing longstanding models. The proposed system holds substantial strategic importance, enabling optimal water resource management.
Müller glia derived EVs promote neurite recovery of an enriched population of retinal ganglion like cells derived from hESC retinal organoids after damage
Abstract Membrane-bound extracellular vesicles (EVs) released by Müller glia contain microRNA (miRNA) and proteins with the potential to be beneficial in providing neuroprotection in retinal degenerative conditions. The aim of this study was to examine the neuroprotective effect of Müller glia derived EVs in human ESC-derived neuronal cells containing RGCs. Cells were isolated from 40–50-day-old retinal organoids and cytotoxicity was induced by addition of NMDA (1mM) for 24 h, followed by treatment with a population of Müller cell derived EVs for a further 24 h. Isolated RGC-enriched cultures expressed characteristic RGC markers such as βIII tubulin, Brn3b, RBPMS, γ-synuclein, Thy1 and NMDAR1. Exposure of this cell population to NMDA lead to a significant reduction in the average neurite length which then recovered with exposure to Müller cell-derived EVs. Investigation of kinase activity revealed increased apoptotic signalling via activation of P38 and p53 after NMDA addition to RGC-like cultures and pro-survival signalling by activation of RSK1/2/3 after addition of EV to the NMDA-damaged cells, suggesting EVs derived from Müller glia support the survival of the RGC-enriched cultures. It is hoped that these observations aid future investigations to assess new EV-related therapies to treat neuronal injury occurring in retinal degenerative conditions such as glaucoma.
Assessment of toothbrushing, bleaching pen and bleaching mouthwash in removing stains from tooth structure and single-shade resin composite
Abstract The purpose of this study was to evaluate the ability of bleaching pen and bleaching mouthwash to remove coffee stains from teeth surfaces and single-shade resin composite restorations by assessing their Vita Classic shades, changes in the Shade Guide Units (ΔSGU) and color differences (∆E00). In addition, a control group subjected to simulated toothbrushing with non-whitening toothpaste was included for comparison. Class V cavities were prepared in the labial surfaces of 30 extracted sound anterior teeth and restored with single-shade resin composite. The restored teeth were immersed in coffee at 37 °C for 12 days. The stained restored teeth were randomly distributed into three groups (n = 10): Group 1: teeth subjected to simulated toothbrushing with non-whitening toothpaste for one week (control), while groups 2 and 3 were divided according to the bleaching system applied for one week as follows: Group 2: bleaching pen and Group 3: bleaching mouthwash. Using a spectrophotometer (Vita Easy Shade V, Germany), the color was measured before and after coffee staining as well as after toothbrushing or bleaching in each tooth at the middle 1/3 (tooth surface) and cervical 1/3 (restoration). The assessment was performed in two approaches: (a) Vita Classic shades which were used to calculate ΔSGU (b) Color parameters which were utilized to assess the color difference (ΔE00). After coffee staining, the color changes were higher in the teeth surfaces (ΔSGU = 7 and ΔE00=23.8) than restorations (ΔSGU = 4 and ΔE00=8.3). These color changes were unacceptable in both the teeth and resin composite restorations (exceeding the acceptability threshold; ΔE00=1.8). The effect of toothbrushing in removing coffee stains was significantly lower than the bleaching pen and the bleaching mouthwash in both resin composite restorations and teeth (least ΔE00, P = 0.0001). After toothbrushing, the shade of the stained restorations did not change than that after coffee (ΔSGU = 0), while the shade of the stained teeth became slight lighter but still darker than the baseline. After applying the bleaching pen or the bleaching mouthwash, the color returned to the baseline in both the restorations and teeth (ΔSGU = 0). Using the ΔE00 to compare the stain removal potentials of both bleaching pen and bleaching mouthwash, there were no significant differences between them in both resin composite restorations and teeth at cervical and middle one-thirds (P = 0.1 and 0.2, respectively). Toothbrushing with non-whitening toothpaste partially reduced coffee stains, but not completely. In contrast, bleaching pens and bleaching mouthwashes effectively removed coffee discoloration from both teeth and single-shade resin composite. Color changes could be assessed using the Vita Classic shade guide unit differences (ΔSGU) and color difference (ΔE₀₀).
Gene driven analytical learning model for accurate breast cancer diagnosis
Abstract Patients diagnosed with breast cancer exhibit a diverse range of prognostic outcomes due to the varied nature of the disease across different patient groups. To address this complexity and enhance prognostic predictions based on gene expression data from breast cancer samples, this study has developed an integrated deep learning method that combines Convolutional Neural Networks (CNN) with Bidirectional Long Short-Term Memory (BiLSTM) networks. This automated pipeline conducts a correlation analysis using Pearson correlation to derive a reliable 236-gene set, ensuring no data contamination from patient samples.Furthermore, patterns of gene–gene interactions based on correlations were examined to provide further evidence of the biological relevance of the gene set that was selected. The training and validation of the proposed model utilized data from The Cancer Genome Atlas-Breast Cancer (TCGA-BRCA) and was assessed using the METABRIC dataset to enhance generalization capabilities. Experimental results indicate that the Full Hybrid (CNN BiLSTM) model significantly outperforms other machine learning and deep learning approaches. Notably, while the BiLSTM-only model achieved an optimal Recall of 0.9319, the hybrid model demonstrated a substantially higher Recall of 0.9943, accompanied by an impressive ROC AUC of 0.9955 and an F1 score of 0.9962. Furthermore, the proposed framework has been statistically validated, achieving a minimal variance of 0.000083 even under conditions of up to 20% noise perturbation. Optimization of this framework was conducted using the Optuna Bayesian Optimization methodology on a dual NVIDIA Tesla T4 array configuration. Overall, this article presents a universal computational tool for precision medicine in breast cancer, designed to yield consistent results across diverse patient scenarios.
Resilient and verifiable outsourced attribute-based non-interactive oblivious transfer protocol for tactical edge networks
Novel iterative method for the approximation of fixed point of a class of generalized ($$\alpha ,\beta$$)-nonexpansive mapping with applications to seir epidemic model
Sex-specific effects of acetylation on tauopathy in aging htau mice
Operando high speed near infrared imaging during laser sintering of nanoparticles for time and space resolved temperature measurements
Abstract A high-speed camera equipped with a near-infrared (NIR) microscope optic is employed for spatially resolved NIR thermal imaging during laser sintering. The device enables operando temperature measurements with a temporal resolution less than 1 ms and a spatial resolution better than 10 μm. The feasibility for in situ temperature measurements is demonstrated for resonant laser sintering of TiO 2 nanoparticles. The temperature range is calibrated at a framerate of 1,069 frames per second (fps) using a TiO 2 reference sample. The laser sintering process is observed at framerates of up to 15,969 fps. The resulting microstructure of the TiO 2 sample is analyzed by scanning electron microscopy (SEM) and interpreted in view of the recorded sintering temperature profiles.
Nano-enabled enhancement of salt stress tolerance in barley using chitosan-selenium nanoparticles: physiological and molecular insights
Abstract Salinity stress severely limits barley ( Hordeum vulgare L.) growth and productivity. This study examined the effects of chitosan (Cs), selenium (Se), and chitosan-selenium nanoparticles (Cs-Se NPs) on salt tolerance of two barley cultivars, Mv Initium and Tectus, exposed to 0, 100, and 200 mM NaCl. Salinity reduced plant height, biomass, and chlorophyll content. Foliar application of Cs and especially Cs-Se NPs significantly improved these traits. Cs-Se NPs enhanced proline (PRO) accumulation and activities of ascorbate peroxidase (APX) and catalase (CAT) under salt stress in both cultivars, which supports improved ROS scavenging capacity. The significant upregulation of antioxidant enzyme genes ( HvAPX , HvSOD , HvCAT ) following Cs-Se NPs treatment under salinity strongly indicates enhanced reactive oxygen species (ROS) detoxification. Key ion homeostasis genes ( HvSOS1 , HvSOS3 , HvNHX1 and HvHKT2 ) were also upregulated, supporting improved salt stress tolerance. Strong correlations were found between antioxidant activity, chlorophyll content, and growth. These findings suggest that Cs-Se NPs effectively boost barley’s physiological and molecular defenses against salinity.
Bi-stable dipole polarity in spherical shell dynamo with quadruple convection
Integration of circulating tumor DNA data enhances lung cancer prediction in patients with COPD
Phenothiazine‐Phenoxazine Hybrid Cross Hole‐Transporting Material for High Performance Perovskite Solar Cell
Abstract Hole transport materials (HTMs) critically influence perovskite solar cell (PSC) performance through three key mechanisms: facilitating hole transport, blocking electron recombination, and protecting the perovskite layer. Herein, we present novel HTMs featuring diphenothiazine (D‐PTZ) and diphenoxazine (D‐POZ) core architectures. Comprehensive characterization reveals that the hybrid cross phenothiazine‐phenoxazine (PTZ‐POZ) synergistically integrates the complementary advantages of D‐PTZ and D‐POZ demonstrating good solubility, improved film uniformity, and superior hole mobility. When implemented in small‐area PSC (0.0625 cm 2 ), PTZ‐POZ achieves record power conversion efficiency (PCE) of 25.85% with negligible hysteresis, outperforming D‐PTZ (23.09%) and D‐POZ (14.33%) based reference devices and representing the highest PCE reported to date for phenothiazine/phenoxazine‐based HTM devices. Additionally, the large‐area PSC (1.0 cm 2 ) based on PTZ‐POZ also achieved a high PCE of 23.23%. Moreover, the engineered HTM also exhibits exceptional stability, maintaining 90.4% of initial PCE after 1080 h at 40%s–50% humidity and ambient temperature. This study establishes a novel molecular design paradigm for developing low‐cost high‐efficiency PSCs.