Search Results (86,185)

Somatic cell nuclear transfer (SCNT) holds promise for animal cloning but remains limited by low efficiency and phenotypic abnormalities, often attributed to incomplete nuclear reprogramming. This study presents an integrative genomic and epigenomic analysis of cloned buffaloes and their respective donors using long-read Oxford Nanopore sequencing. Our results showed a high degree of genomic similarity between clones and donors, with most variations located in non-coding regions and structural variants (SV) distributions highly correlated at the chromosomal level. Gene and protein level overlap of SV-affected loci revealed 70.9–73.3% gene-level and 69.7–72.5% protein-level similarity. Despite this genetic similarity, DNA methylation analysis identified differentially methylated regions (DMRs), particularly in intergenic and promoter regions. Clones exhibited slightly lower CpG methylation than the donors. The DMRs in donor vs. clone comparisons indicated higher hypomethylated regions than hypermethylated regions. Functional enrichment of DMR-associated genes highlighted pathways linked to mitochondrial function, oxidative phosphorylation, and reproductive processes. Although clones showed moderate genome-wide methylation correlation with donors, key differences in methylation suggest incomplete epigenetic reprogramming. Despite these epigenetic differences, all clones were phenotypically normal and healthy into adulthood. This study offers the first comprehensive SV and methylome profile of SCNT-derived buffaloes and emphasizes the role of epigenetic mechanisms in clone development and health, providing valuable insights to enhance cloning efficiency. Full article

This study introduced a new low-temperature storage method that applies an additional lower strength static electric field (SEF) under the condition of a static magnetic field (SMF) to investigate the impact of magneto–electric coupling on the supercooling degree and quality of beef. The results showed that 7 mT-1 kV performs the best (−5.8 °C); the ability of SMF to maintain supercooling is less affected by SEF. Moreover, on the 15th day, magneto–electric coupling (7 mT-1 kV) outperformed SMF (7 mT) alone by reducing beef pH by 0.27, decreasing total viable counts (TVC) by 0.87 log CFU/g, maintaining TVB-N at only 12.5 mg/100 g, and limiting oxidative change, calpain activity, and shear force variation. Magnetic resonance imaging revealed that magneto–electric coupling treatment stabilized the T₂ relaxation time in meat samples, effectively inhibiting immobilized water migration and promoting more uniform moisture distribution, highlighting its application potential as a low temperature preservation method. Full article

Loading of the Earth’s crust due to variations in global atmospheric pressure can displace the position of geodetic stations. However, the station displacements induced by the diurnal and semidiurnal atmospheric tides (S₁-S₂) are often neglected during Global Positioning System (GPS) processing. We first studied the magnitudes of S₁-S₂ deformation in the Earth’s center of mass (CM) frame and compared the global S₁-S₂ grid models provided by the Global Geophysical Fluid Center (GGFC) and the Vienna Mapping Function (VMF) data server. The magnitude of S₁-S₂ tidal displacement can reach 1.5 mm in the Up component at low latitudes, approximately three times that of the horizontal components. The most significant difference between the GGFC and VMF grid models lies in the phase of S₂ in the horizontal components, with phase discrepancies of up to 180° observed at some stations. To investigate the effects of S₁-S₂ corrections on GPS coordinates, we then processed GPS data from 108 International GNSS Service (IGS) stations using the precise point positioning (PPP) method in two processing strategies, with and without the S₁-S₂ correction. We observed that the effects of S₁-S₂ on daily GPS coordinates are generally at the sub-millimeter level, with maximum root mean square (RMS) coordinate differences of 0.18, 0.08, and 0.51 mm in the East, North, and Up components, respectively. We confirmed that part of the GPS draconitic periodic signals was induced by unmodeled S₁-S₂ loading deformation, with the amplitudes of the first two draconitic harmonics induced by atmospheric tide loading reaching 0.2 mm in the Up component. Moreover, we recommend using the GGFC grid model for S₁-S₂ corrections in GPS data processing, as it reduced the weighted RMS of coordinate residuals for 45.37%, 46.30%, and 53.70% of stations in the East, North, and Up components, respectively, compared with 39.81%, 44.44%, and 50.00% for the VMF grid model. The effects of S₁-S₂ on linear velocities are very limited and remain within the Global Geodetic Observing System (GGOS) requirements for the future terrestrial reference frame at millimeter level. Full article

High genetic variation in African indigenous chicken populations provides opportunities for long-term genetic improvement. This study estimated genetic parameters for economic traits based on data derived from a nucleus flock comprising two generations, derived from 40 sires and 200 dams in a line breeding program through mass selection. Body weight (BW) at different weeks was analyzed for 1370 chickens. Similarly, egg performance was evaluated on 473 hens kept for 44 weeks. Genetic parameters were estimated using a multi-trait animal model based on an average information-restricted maximum likelihood (AI-REML) algorithm in WOMBAT software. Body weight showed significant heritability (p < 0.001), ranging from 0.251 for body weight at 8 weeks of age (BW8) to 0.34 for body weight at 16 weeks of age (BW16), indicating a good genetic improvement potential. Egg production traits had low to moderate heritability (0.08–0.37). Positive genetic correlations among growth traits, particularly BW8 and body weight at 12 weeks of age, BW12 (rG = 0.94), suggest shared genetic influences and the possibility of improving multiple traits simultaneously. The genetic correlation between BW16 and the cumulative egg number varied from low and negative (−0.02) in the first two months to high (0.51) in the cumulative egg number over six months, suggesting that heavier birds lay more eggs over time. Our limited dataset based on two generations and pedigree demonstrates that BW16 with egg production has moderate heritability and strong genetic correlations that can lead to genetic progress toward the development of a dual-purpose breed, and this offers a scientific basis for breeders to develop selection indices to develop a dual-purpose breed for smallholder production systems. Full article

Superselective intra-arterial chemotherapy (SSIAC) presents a promising approach for treating oral cancer by delivering high concentrations of anticancer agents directly to the tumor-feeding arteries. However, drug distribution can be unpredictable, particularly in patients with vascular variations, such as the linguofacial trunk. In this study, we conducted a patient-specific computational fluid dynamics (CFD) analysis using contrast-enhanced computed tomography data obtained from two patients with oral cancer. We created 40 catheter placement models to simulate both the conventional and SSIAC techniques. We analyzed the blood and agent flows using a zero-dimensional resistance boundary model validated in a previous study. The agent distribution ratios to the lingual artery and facial artery varied significantly, whereas the blood flow distribution remained consistent across all the models. High anticancer agent concentration gradients were observed within 2 mm of the catheter tip, indicating that local flow dynamics governed the drug delivery process. No significant correlation was observed between the bifurcation flow angles and agent distribution. This study demonstrates that agent delivery in SSIAC is highly sensitive to the catheter tip location and local blood flow, independent of the blood flow bifurcation angles. Patient-specific CFD may assist clinicians in preoperatively determining the optimal catheter positioning to improve the treatment efficacy. Full article

Ovarian cancer remains a significant global health concern, and its diagnosis heavily relies on whole-slide images (WSIs). Due to their gigapixel spatial resolution, WSIs must be split into patches and are usually modeled via multi-instance learning (MIL). Although previous studies have achieved remarkable performance comparable to that of humans, in clinical practice WSIs are distributed across multiple hospitals with strict privacy restrictions, necessitating secure, efficient, and effective federated MIL. Moreover, heterogeneous data distributions across hospitals lead to model heterogeneity, requiring a framework flexible to both data and model variations. This paper introduces HFed-MIL, a heterogeneous federated MIL framework that leverages gradient-based attention distillation to tackle these challenges. Specifically, we extend the intuition of Grad-CAM to the patch level and propose Patch-CAM,which computes gradient-based attention scores for each patch embedding, enabling structural knowledge distillation without explicit attention modules while minimizing privacy leakage. Beyond conventional logit distillation, we designed a dual-level objective that enforces both class-level and structural-level consistency, preventing the vanishing effect of naive averaging and enhancing the discriminative power and interpretability of the global model. Importantly, Patch-CAM scores provide a balanced solution between privacy, efficiency, and heterogeneity: they contain sufficient information for effective distillation (with minimal membership inference risk, MIA AUC ≈ 0.6) while significantly reducing communication cost (0.32 MB per round), making HFed-MIL practical for real-world federated pathology. Extensive experiments on multiple cancer subtypes and cross-domain datasets (Camelyon16, BreakHis) demonstrate that HFed-MIL achieves state-of-the-art performance with enhanced robustness under heterogeneity conditions. Moreover, the global attention visualizations yield sharper and clinically meaningful heatmaps, offering pathologists transparent insights into model decisions. By jointly balancing privacy, efficiency, and interpretability, HFed-MIL improves the practicality and trustworthiness of deep learning for ovarian cancer WSI analysis, thereby increasing its clinical significance. Full article

This scoping review assessed the current use of wearable sensors in monitoring recovery following reverse shoulder arthroplasty (RSA). A systematic search of electronic databases was undertaken (MEDLINE, EMBASE, CINAHL, and Web of Science) between 2005 and 2024 following the PRISMA-ScR protocol. Studies were eligible if they were peer reviewed, available in full text, and reported the use of wearable sensors to evaluate shoulder motion or activity in postoperative RSA patients. Fifty-seven studies were identified, of which six met the inclusion criteria. Studies were either focused on assessing shoulder motion (n = 3) or on measuring upper limb activity counts or activity intensities (n = 3); however the calculation of output variables were different across most studies. Sensors were positioned on the operated upper arm in all studies, though sensor placement on the sternum and the wrist varied. Session durations ranged from 24 h to continuous monitoring beyond seven days. Daily wear times were most commonly during full waking hours. The large variation in wearable sensor configuration, testing protocols, and the calculation of output variables limited the comparability across studies. Standardization in sensor protocols and outcomes is required to enable the reliable wearable assessment of postoperative recovery after RSA. Full article

Honeybee pollen is widely recognized as a functional apicultural product due to its rich nutritional profile, but its composition is strongly influenced by seasonality and floral availability. Understanding these seasonal dynamics is critical for optimizing the nutritional and bioactive quality of bee-collected pollen. This study investigated the seasonal variation in the physicochemical and mineral composition of honeybee pollen collected monthly from April to September 2024 from an apiary in the Tulkibas district, Turkistan region, Kazakhstan. Pollen samples were analyzed for key quality parameters, including moisture, protein, fat, fiber, carbohydrates, starch, ash, and minerals (Ca, P, K, Mg, Na, Cu, Fe, Zn). Moisture, protein, fat, fiber, starch, and ash were determined using standard AOAC methods, while minerals were quantified by flame atomic absorption spectrophotometry (Ca, Cu, Fe, Mg, Zn; Analytik Jena novAA 350), flame emission spectrophotometry (Na, K), and the molybdenum blue colorimetric method (P). The moisture content decreased significantly from 10.34 ± 1.74% in April to 5.23 ± 0.86% in June (p = 0.0030), while protein increased from 20.28 ± 2.13% to a peak of 23.66 ± 1.70% in June (p = 0.0268). The fat content reached its maximum in July at 8.67 ± 0.11% (p = 0.0446), and carbohydrates peaked at 14.41 ± 0.11% in the same month. Among minerals, Fe and Zn showed substantial increases, with iron rising from 47.51 ± 5.69 mg/kg in April to 143.39 ± 6.58 mg/kg in July (p = 0.0388), and Zn from 38.56 ± 2.36 mg/kg to 57.14 ± 8.54 mg/kg (p = 0.0302). Principal Component Analysis (PCA) and Pearson correlation confirmed strong seasonal clustering and nutrient interrelationships. These findings highlight the superior nutritional value of mid- to late-season pollen and underscore the importance of the harvest timing in optimizing the bioactive profile of bee-collected pollen for apicultural and functional food applications. Full article

The accurate extraction of water bodies from remote sensing imagery is crucial for water resource monitoring and flood disaster warning. However, this task faces significant challenges due to complex land cover, large variations in water body morphology and spatial scales, and spectral similarities between water and non-water features, leading to misclassification and low accuracy. While deep learning-based methods have become a research hotspot, traditional convolutional neural networks (CNNs) struggle to represent multi-scale features and capture global water body information effectively. To enhance water feature recognition and precisely delineate water boundaries, we propose the AMU-Net model. Initially, an improved residual connection module was embedded into the U-Net backbone to enhance complex feature learning. Subsequently, a multi-scale attention mechanism was introduced, combining grouped channel attention with multi-scale convolutional strategies for lightweight yet precise segmentation. Thereafter, a dual-attention gated modulation module dynamically fusing channel and spatial attention was employed to strengthen boundary localization. Furthermore, a cross-layer geometric attention fusion module, incorporating grouped projection convolution and a triple-level geometric attention mechanism, optimizes segmentation accuracy and boundary quality. Finally, a triple-constraint loss framework synergistically optimized global classification, regional overlap, and background specificity to boost segmentation performance. Evaluated on the GID and WHDLD datasets, AMU-Net achieved remarkable IoU scores of 93.6% and 95.02%, respectively, providing an effective new solution for remote sensing water body extraction. Full article

Optical brain monitoring techniques, including near-infrared spectroscopy (NIRS), diffuse correlation spectroscopy (DCS), and photoplethysmography (PPG) have gained attention for their non-invasive, affordable, and portable nature. These methods offer real-time insights into cerebral parameters like cerebral blood flow (CBF), intracranial pressure (ICP), and oxygenation. However, confounding factors like extracerebral layers, skin pigmentation, skull thickness, and brain-related pathologies may affect measurement accuracy. This review examines the potential impact of confounders, focusing on in vitro studies that use phantoms to simulate human head properties under controlled conditions. A systematic search identified six studies on extracerebral layers, two on skin pigmentation, two on skull thickness, and four on brain pathologies. While variation in phantom designs and optical devices limits comparability, findings suggest that the extracerebral layer and skull thickness influence measurement accuracy, and skin pigmentation introduces bias. Pathologies like oedema and haematomas affect the optical signal, though their influence on parameter estimation remains inconclusive. This review highlights limitations in current research and identifies areas for future investigation, including the need for improved brain phantoms capable of simulating pulsatile signals to assess the impact of confounders on PPG systems, given the growing interest in PPG-based cerebral monitoring. Addressing these challenges will improve the reliability of optical monitoring technologies. Full article

Air pollution has emerged as one of the most critical public health challenges globally, with an astonishing 96% of the world’s population breathing air below the health standards. This study investigates the amount and distribution of six major air pollutants, PM₁₀, PM_2.5, O₃, SO₂, NO₂, and CO, at numerous air monitoring stations across Iran from 2016 to 2021. The primary objectives were to identify the cities with the highest pollution levels, and to assess the spatiotemporal evolution of air pollution across the country, aiming to provide a comprehensive overview and climatology of air quality. The results indicate that cities such as Zabol and Ahvaz consistently rank among the most polluted, with annual average PM₁₀ concentrations exceeding 190 µg m⁻³ and PM_2.5 reaching alarming levels up to 116.7 µg m⁻³. Furthermore, O₃ and SO₂ amounts were high in Zabol too, classifying it as the most polluted city in Iran. In addition, Tehran exhibits high NO₂, SO₂, and CO concentrations due to high industrial activity and vehicular emissions. Seasonal analysis reveals significant variations in pollutant levels, with PM concentrations peaking during specific months over various parts of the country, particularly driven by local and distant dust events. By integrating MERRA-2 reanalysis pollution data and ground measurements, this research provides a robust framework for understanding pollution dynamics, thereby facilitating more effective policy-making and public health interventions. The results underscore the necessity for immediate action to mitigate the adverse effects of air pollution on public health, particularly in areas prone to industrial activities (i.e., Tehran, Isfahan) and dust events (Zabol, Ahvaz). Full article

This paper presents a fully passive and wireless glucose concentration sensor that integrates a capacitively coupled complementary split-ring resonator (CSRR) with an H-slot UHF RFID antenna. The CSRR serves as the primary sensing element, where changes in glucose concentration alter the effective permittivity of the surrounding solution, thereby modifying the resonator capacitance and shifting its resonance behavior. Through near-field capacitive coupling, these dielectric variations affect the antenna input impedance and backscatter response, enabling wireless sensing by modulating the maximum read range. The proposed sensor operates within the 902–928 MHz UHF RFID band and is interrogated using commercial RFID readers, eliminating the need for specialized laboratory equipment such as vector network analyzers. Full-wave electromagnetic simulations and experimental measurements validate the sensor performance, demonstrating a variation in the read range from 6.23 m to 4.67 m as glucose concentration increases from 50 to 200 mg/dL. Moreover, the sensor exhibits excellent linearity, with a high coefficient of determination ($R^2=0.986$) based on the curve-fitted data. These results underscore the feasibility of the proposed sensor as a low-cost and fully portable platform for concentration monitoring, with potential applications in liquid characterization and chemical sensing. Full article

In recent years, tail-sitter unmanned aerial vehicles (UAVs), capable of vertical take-off and landing (VTOL) and long-range flight, have garnered extensive attention. However, the challenge of yaw control, particularly for large-scale UAVs, has become a significant obstacle. It is challenging to generate sufficient yaw moments by motor differential thrust without compromising control authority in other channels or increasing mechanical complexity. Therefore, this paper proposes the concept of blown yaw, which utilizes the high-velocity airflow over rudders, induced by the propellers slipstream, to enhance the yaw control torque actively. An over-actuated, hundred-kilogram-class, tail-sitter UAV is designed to validate the effectiveness of the proposed method. To address the control allocation problem introduced by the implementation of blown yaw, an optimization-based control allocation module is developed, capable of precisely mapping the required forces and torques to all actuators. The proposed method, combined with computational fluid dynamics (CFD) simulations, accounts for the propeller model and the significant differences in actuator effectiveness across various flight conditions. Simulation results demonstrate that the proposed blown-yaw method significantly enhances the yaw control performance, achieving an overall energy savings of approximately 8.0% and a 60% reduction in the mean-squared error. Furthermore, the method exhibits robust performance against variations in control parameters and external disturbances. Full article

Urban environments encounter urgent challenges in transitioning to net-zero emissions, particularly with respect to the adoption and large-scale incorporation of renewable energy solutions such as photovoltaic (PV) technologies. This study explores the interrelation of digitized energy systems, digital twins, and open-access platforms in accelerating effective PV deployment in cities moving toward carbon neutrality. We examine how digital tools can enhance PV performance, demand-side management, and grid integration, while open-access platforms contribute to data sharing, raising awareness, public engagement, and stakeholder collaboration. We also present BIPV-city—a novel, open-access, digital, and climate-aware platform developed to support and optimize PV integration in building and urban areas. Validations of the solar irradiance calculations against PVGIS for several European cities exhibit a strong agreement, with a root mean square error (RMSE) extending from 3.3 to 7.6. The validation of the standardized BESTEST Case 600 against TRNSYS simulations for three representative climates—Athens, Prague, and Dubai—with tilt variations confirmed substantial alignment for plane-of-array (POA) radiation (within ±2% and ±6% for the global and direct/diffuse components, respectively) and annual PV yield estimations (within ±10%). The findings highlight that the BIPV-city platform is a reliable, user-friendly tool that can harness climate-responsible and scalable BIPV deployment in the built environment through digital innovation. Full article

As robotic systems advance in autonomy and sophistication while being used in uncertain environments, the challenge of building reliable and robust electric motors that are embedded into robotic systems has never been a more important engineering problem. Thermal distress caused by extended operation or excessive loading can negatively affect a motor’s performance and efficiency and lead to catastrophic hardware failure. This paper proposes a novel intelligent control framework that includes real-time thermal feedback for hybrid electric motors that are embedded into robotic systems. The framework relies on adaptive control techniques and lightweight machine learning techniques to estimate internal motor temperatures and dynamically change operational parameters. Unlike traditional reactive methods, this framework provides a spacious active/predictive method of heat management, while preserving efficiency and allowing for responsive control. Simulations, experimental validations, and preliminary trials that deployed real robotic systems demonstrated that our framework allows for reductions in peak temperatures by up to 18% and extends motor lifetime by 22%, while retaining control stability and a range of variations in PWM adjustments of ±12% across disparate workloads. These results demonstrate the efficacy of intelligent and thermally aware motor control architectures and processes to improve the reliability of autonomous robotic systems and open the door for next-generation embedded controllers that will allow robotic platforms to self-manage thermal effects in resilient, adaptable robots. Full article