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Spironolactone-Induced Gynecomastia
How obesity drugs quiet ‘food noise’ in the brain
Organic parallel grouping crystals without grain boundary
Enhanced nonlinear optical properties of GO and Fe3O4-Modified CuMn2O4 nanocomposites
A highly decoupled and compact co-circularly polarized MIMO filtering antenna array system for vehicular communications
Abstract This paper presents a compact, single-layer, and highly decoupled four-element multiple-input-multiple-output (MIMO) filtering antenna array system designed for vehicular communications. A novel sequential phase feed network is developed by replacing the λ /4 transformers transmission lines with three-pole hairpin resonator-based band pass filters. The resonators provide filtering characteristics for a 2 × 2 configurations of sequentially rotated corner truncated rectangular patch antennas. The elements of the MIMO filtering antenna array system achieve out-of-band frequency suppression levels exceeding 24 dB in the lower stop band and 36 dB in the upper stop band, demonstrating a sharp filtering response. The MIMO filtering array elements exhibit excellent inter-port isolation, exceeding 34 dB across the entire operating bandwidth of 5.4 GHz to 6.3 GHz. High level of isolation is achieved through two main strategies: first, by rotating the antenna arrays by 120° along the central axis to minimize coupling, and second, by designing a cage to confine the electromagnetic fields. The MIMO filtering array elements have a -10 dB impedance bandwidth of 20%, covering the range from 5.3 GHz to 6.5 GHz, and a 3-dB axial ratio bandwidth of 15.4%, from 5.4 GHz to 6.3 GHz. The elements of the fabricated prototype exhibit a peak realized gain of 11.3 dBic, with an average realized gain of 10.5 dBic. The proposed MIMO filtering antenna array system has the potential to provide the scalability and adaptability needed to support emerging technologies and standards in vehicular communication.This work aligns with the UN Sustainable Development Goals, particularly SDG 9, SDG 11, and SDG 13, by enabling sustainable and intelligent vehicular communications.
Untangling the Risks of Antidepressants in Pregnancy
Targeting hepatocytic TβRI ameliorates liver metastatic outcomes by revitalizing stem-like CD8+ Tex subsets
Experimental and modeling studies for the simultaneous removal anionic dyes in single and binary systems using activated clay
Lipid nanocapsule-chitosan and iota-carrageenan hydrogel composite for sustained hydrophobic drug delivery
Engasertib versus Placebo for Bleeding in Hereditary Hemorrhagic Telangiectasia
Non-loss engraved circuit patterning method of semi-liquid metal for precision recyclable multi-substrate circuits
Abstract Room-temperature liquid metal alloys have emerged as promising materials for flexible electronics due to their unique fluidity, conductivity, and biocompatibility. However, traditional patterning techniques for liquid metal circuits, including additive and subtractive manufacturing, face challenges such as high costs, complex processes, and environmental issues, limiting their large-scale application. This study presents a non-loss method for fabricating high-precision semi-liquid metal circuits by leveraging ethanol to modulate interfacial adhesion between liquid metal and substrates. By precisely controlling adhesion through a custom-designed displacement apparatus, the approach enables seamless patterning from 5 μm to centimeter scales across diverse substrates with features like stretchability (1000% strain), reusability, and recyclability. The technique overcomes limitations of conventional methods, offering advantages in cost-effectiveness, operational simplicity, and substrate compatibility. Demonstrations include multifunctional flexible circuits for wearable electronics, aerospace, and smart home applications, highlighting its potential to advance sustainable, scalable liquid metal electronics manufacturing.
Assessing the environmental impacts of upcycling recycled aggregates into concrete with consideration of resource flows and substitution effects
Relationship between intramuscular fat content in longissimus thoracis and hair fatty acids in finishing crossbred bulls
Abstract Intramuscular fat (IMF) content below 6% in live cattle is difficult to estimate accurately. Therefore, this study tested whether fatty acids in the IMF of LT and hair correlate to each other, and whether fatty acids can be used to predict IMF. Forty-four finishing crossbred bulls from two farms were examined. Each bull had a hair sample collected before slaughter, and two days post-slaughter, LT muscle samples were collected to assess IMF (range: 2.3–6.6%) and fatty acids. Predictions of IMF from hair fatty acids were developed using linear, non-linear and non-parametric approaches. Nineteen congruent fatty acids could be detected in hair and LT. Although proportions varied, lauric and oleic acids showed positive correlations between LT and hair ( r = 0.48–0.49; P ≤ 0.001). Lauric acid in hair showed also a consistently prediction of IMF and moderate rank-based association between predicted and observed IMF across all tested prediction models. For the first time, the study presents evidence that hair fatty acids taken shortly before slaughter provide significant information about the IMF in bulls. To use this information for breeding or feeding strategies, it would be necessary to extend hair sampling by testing earlier stages in life.
Early Withdrawal of Aspirin after PCI in Acute Coronary Syndromes
Site-specific synergy by heteronuclear microenvironment atomic editing for oxygen reduction reaction
Feature fusion context attention gate UNet for detection of polycystic ovary syndrome
Abstract Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder affecting women of reproductive age, characterized by hormonal imbalance, irregular menstrual cycles, and ovarian cysts. Traditional diagnostic approaches, which include clinical evaluations, radiological studies, and surgical interventions, are often time-consuming, costly, and not always reliable. To improve the accuracy and efficiency of PCOS diagnosis, this research introduces the Feature Fusion Context Attention U-Net (FCAU-Net) model, leveraging deep learning (DL) techniques. This study makes two key contributions. First, it enhances dataset preparation through Fuzzy Contrast Enhanced (FCE) imaging. Second, it integrates a Feature Fusion Context (FFC) module into the Attention U-Net model, optimizing the extraction of context and position weights from feature maps for better classification performance. An openly available PCOS Ultrasound Image Dataset with 3,800 images was partitioned with 80: 20 to ensure that only original images were used for testing, while augmented samples were exclusively utilized for training to enhance model generalization and robustness. The remaining 3040 images was augmented to form 45,600 images and split into training and validation sets in an 80:20 ratio. The augmented images were processed and tested with several DL models, including DenseNet, AlexNet, VGG19, ResNet, U-Net, and Attention U-Net. Among these, the Attention U-Net initially achieved over 80% accuracy in detecting PCOS. The proposed FCAU-Net, which incorporates the FFC module, demonstrated superior performance, achieving a detection accuracy of 99.89%, significantly outperforming existing DL models. This research highlights the potential of FCAU-Net in providing a more accurate and efficient tool for the diagnosis of PCOS.
Deep learning-based AI model for predicting academic success and engagement among physical higher education students
Cardiac Rehabilitation for Older Patients
Smart 3D super-resolution microscopy reveals the architecture of the RNA scaffold in a nuclear body
Abstract Small subcellular organelles orchestrate key cellular functions. How biomolecules are spatially organized within these assemblies is poorly understood. Here, we report an automated super-resolution imaging and analysis workflow that integrates confocal microscopy, morphological object screening, targeted 3D super-resolution STED microscopy and quantitative image analysis. Using this smart microscopy workflow, we target the 3D organization of NEAT1 , an architectural RNA that constitutes the structural backbone of paraspeckles, a membraneless nuclear organelle. Using site-specific labeling, morphological sorting and particle averaging, we reconstruct the morphological space of paraspeckles along their development cycle from over 10,000 individual particles. Applying spherical harmonics analysis, we report so-far unknown heterotypes of NEAT1 RNA organization. By integrating multi-positional labeling, we determine the coarse conformation of NEAT1 within the organelle and show that the 3’ end forms a loop-like structure at the surface of the paraspeckle. Our study reveals key structural features of paraspeckle structure and growth, as well as the molecular organization of its scaffolding RNA.