Browse Articles

Seismic response and collapse capacity assessment of dual RC buildings with vertical irregularities in shear walls

Characterizing SSTR2 expression and modulation for targeted imaging and therapy in preclinical models of triple-negative breast cancer

Cohesin and condensin regulate chromosome topology and play an essential role in maintaining pluripotency in embryonic stem cells

Nature inspired optimization of IoT network for delay resistant and energy efficient applications

Abstract LoRa being an open standard has fascinated the research community due to its promising features to support IoT applications. LoRa fulfils all the requirements of low power, delay tolerance, long transmission range and scalability of the application nodes in the IoT concept. The duty cycle limitations imposed by LoRaWAN hinder the overall performance of the network. The network performance declines due to increasing in several devices communicating through the same channel, thereby degrading the network efficiency. Certain IoT deployments such as monitoring and control applications require low latency and extended network lifetime. Aspiring to attain efficient network performance, the current work proposes a nature-inspired low duty cycle MAC algorithm using the concept of the golden ratio (GR) approach to optimize the duty cycle of the LoRa network. Further, PSO algorithms have also been utilized to validate the performance of the proposed algorithm. The simulation results unveil that the proposed method outperforms the PSO algorithm by reducing the latency and power consumption by 26% and 12% respectively and extending the network lifetime by 14% as compared to the DC constraint approach.

Choroidal blood flow changes in central serous chorioretinopathy

Effect of PHF-1 hyperphosphorylation on the seeding activity of C-terminal Tau fragments

Abstract Tau proteins as neurofibrillary tangles are one of the molecular hallmarks of Alzheimer’s disease (AD) and play a central role in tauopathies, a group of age-related neurodegenerative disorders. The filament cores from diverse tauopathies share a common region of tau consisting of the R3-R4 microtubule-binding repeats and part of the C-terminal domain, but present a structural polymorphism. Unlike the fibril structure, the PTM signature of tau found in neuronal inclusions, more particularly hyperphosphorylation, is variable between individuals with the same tauopathy, giving rise to diverse strains with different seeding properties that could modulate the aggressiveness of tau pathology. Here, we investigate the conformation, function and seeding activity of two tau fragments and their GSK3β-phosphorylated variants. The R2Ct and R3Ct fragments encompass the aggregation-prone region of tau starting at the R2 and R3 repeats, respectively, and the full C-terminal domain including the PHF-1 epitope (S396, S400, S404), which undergoes a triple phosphorylation upon GSK3β activity. We found that the R3Ct fragment shows both a greater loss of function and pathological activity in seeding of aggregation than the R2Ct fragment which imposes a cross-seeding barrier. PHF-1 hyperphosphorylation induces a local conformational change with a propensity to adopt a β-sheet conformation in the region spanning residues 392–402, and exacerbates the seeding ability of fragments to induce aggregation by overcoming a cross-seeding barrier between tau variants.

Research on the water absorption diffusion model and kinetics of pretreated straw

A novel framework for segmentation of small targets in medical images

Biodegradability and biocompatibility test of Magnesium Carbonate apatite composite implants fabricated by extrusion technique on Sprague Dawley Rats

Cutaneous wound healing functions of novel milk-derived antimicrobial peptides, hLFT-68 and hLFT-309 from human lactotransferrin, and bLGB-111 from bovine β-lactoglobulin

An innovative wearable device for sensing mechanoreceptor activation during touch

SARS-Cov-2 vaccination strategies in hospitalized recovered COVID-19 patients: a randomized clinical trial (VATICO Trial)

VRd vs. VPd as induction therapy in high risk newly diagnosed multiple myeloma

An efficient ECC and fuzzy verifier based user authentication protocol for IoT enabled WSNs

Machine learning-based prediction of vesicoureteral reflux outcomes in infants under antibiotic prophylaxis

Machine learning analysis of cardiovascular risk factors and their associations with hearing loss

Technical efficiency, energy balance, and economic analysis of honey production in Bingöl, Turkey

Short video addiction scale for middle school students: development and initial validation

Abstract The rise of short video platforms has increased concerns about addiction, especially among adolescents. This study aimed to develop a Short Video Addiction Scale for middle school students. A sample of 1492 middle school students participated in the study. Initial items were derived from qualitative interviews and refined through psychometric analyses, including Exploratory and Confirmatory Factor Analyses. The final scale, consisting of 15 items across five factors (Academic Procrastination, Interpersonal Strain, Social Communication Difficulties, Attention Concentration Difficulties, and Impaired Control over Short Video Use), demonstrated high internal consistency (Cronbach’s α = 0.900), test-retest reliability, and strong validity. The Short Video Addiction Scale, a 15-item scale, demonstrates robust psychometric properties. It is reliable and valid for assessing short video addiction among middle school students and will be a valuable tool for identifying and addressing the growing concerns of short video addiction in adolescents.

A stacking ensemble machine learning model for predicting postoperative axial pain intensity in patients with degenerative cervical myelopathy

Brain tumor segmentation using multi-scale attention U-Net with EfficientNetB4 encoder for enhanced MRI analysis

Abstract Accurate brain tumor segmentation is critical for clinical diagnosis and treatment planning. This study proposes an advanced segmentation framework that combines Multiscale Attention U-Net with the EfficientNetB4 encoder to enhance segmentation performance. Unlike conventional U-Net-based architectures, the proposed model leverages EfficientNetB4’s compound scaling to optimize feature extraction at multiple resolutions while maintaining low computational overhead. Additionally, the Multi-Scale Attention Mechanism (utilizing $$1\times 1, 3\times 3$$ , and $$5\times 5$$ kernels) enhances feature representation by capturing tumor boundaries across different scales, addressing limitations of existing CNN-based segmentation methods. Our approach effectively suppresses irrelevant regions and enhances tumor localization through attention-enhanced skip connections and residual attention blocks. Extensive experiments were conducted on the publicly available Figshare brain tumor dataset, comparing different EfficientNet variants to determine the optimal architecture. EfficientNetB4 demonstrated superior performance, achieving an Accuracy of 99.79%, MCR of 0.21%, Dice Coefficient of 0.9339, and an Intersection over Union (IoU) of 0.8795, outperforming other variants in accuracy and computational efficiency. The training process was analyzed using key metrics, including Dice Coefficient, dice loss, precision, recall, specificity, and IoU, showing stable convergence and generalization. Additionally, the proposed method was evaluated against state-of-the-art approaches, surpassing them in all critical metrics, including accuracy, IoU, Dice Coefficient, precision, recall, specificity, and mean IoU. This study demonstrates the effectiveness of the proposed method for robust and efficient segmentation of brain tumors, positioning it as a valuable tool for clinical and research applications.