Search Results (1,659)

Background: Population persistence for organisms to survive in a world with a rapidly changing climate will require either dispersal to suitable areas, evolutionary adaptation to altered conditions and/or sufficient phenotypic plasticity to withstand it. Given the slow growth and geographically isolated populations of many tree species, there is a high likelihood of local adaption or the acclimation of functional traits in these populations across the UK. Objectives: Given the slow growth and often isolated populations of Tilia cordata (lime tree), we hypothesised that there is a high likelihood of local adaptation or the acclimation of metabolic traits in these populations across the UK. Our aim was to test if the functional metabolomic traits of Tilia cordata (lime tree), collected in situ from natural populations, varied within and between populations and to compare this to neutral allele variation in the population. Methods: We used a metabolic fingerprinting approach to obtain a snapshot of the metabolic status of leaves collected from T. cordata from six populations across the UK. Environmental metadata, longer-term functional traits (specific leaf area) and neutral allelic variation in the population were also measured to assess the plastic capacity and local adaptation of the species. Results: The metabolic fingerprints derived from leaf material collected and fixed in situ from individuals in six populations of T. cordata across its UK range were similar, despite contrasting environmental conditions during sampling. Neutral allele frequencies showed almost no significant group structure, indicating low differentiation between populations. The specific leaf area did vary between sites. Conclusions: The low metabolic variation between UK populations of T. cordata despite contrasting environmental conditions during sampling indicates high levels of phenotypic plasticity. Full article

Miller–Dieker Syndrome (MDS) is a rare neurodevelopmental disorder caused by a heterozygous deletion of approximately 26 genes within the MDS locus of human chromosome 17. MDS, which affects 1 in 100,000 babies, can lead to a range of phenotypes, including lissencephaly, severe neurological defects, distinctive facial abnormalities, cognitive impairments, seizures, growth retardation, and congenital heart and liver abnormalities. One hallmark feature of MDS is an unusually smooth brain surface due to abnormal neuronal migration during early brain development. Several genes located within the MDS locus have been implicated in the pathogenesis of MDS, including PAFAH1B1, YWHAE, CRK, and METTL16. These genes play a role in the molecular and cellular pathways that are vital for neuronal migration, the proper development of the cerebral cortex, and protein translation in MDS. Improved model systems, such as MDS patient-derived organoids and multi-omics analyses indicate that WNT/β-catenin signaling, calcium signaling, S-adenosyl methionine (SAM) homeostasis, mammalian target of rapamycin (mTOR) signaling, Janus kinase/signal transducer and activator of transcription (JAK/STAT) signaling, and others are dysfunctional in MDS. This review of MDS integrates details at the clinical level alongside newly emerging details at the molecular and cellular levels, which may inform the development of novel therapeutic strategies for MDS. Full article

Methyl groups can be obtained either from the diet (labile methyl groups) or produced endogenously (methylneogenesis) via one-carbon (C1-) metabolism as S-adenosylmethionine (SAM). The essential nutrients folate and choline (through betaine) are metabolically entwined to feed their methyl groups into C1-metabolism. A choline-deficient diet in rats produces a 31–40% reduction in liver folate content, 50% lower hepatic SAM levels, and a doubling of plasma homocysteine. Similarly, folate deficiency results in decreased total hepatic choline. Thus, sufficient intakes of both folate and choline (or betaine) contribute to safeguarding the methyl balance in the body. A significant amount of choline (as phosphatidylcholine) is produced in the liver via the SAM-dependent phosphatidylethanolamine methyltransferase. Experimental studies using diets deficient in several methyl donors have shown that supplemental betaine was able to rescue not only plasma betaine but also plasma folate. Fasting plasma homocysteine concentrations are mainly determined by folate intake or status, while the effect of choline or betaine on fasting plasma homocysteine is minor. This appears to contradict the finding that approximately 50% of cellular SAM is provided via the betaine-homocysteine methyltransferase (BHMT) pathway, which uses dietary choline (after oxidation to betaine) or betaine to convert homocysteine to methionine and then to SAM. However, it has been shown that the relative contribution of choline and betaine to cellular methylation is better reflected by measuring plasma homocysteine after a methionine load test. Choline or betaine supplementation significantly lowers post-methionine load homocysteine, whereas folate supplementation has a minor effect on post-methionine load homocysteine concentrations. This review highlights the interactions between folate and choline and the essentiality of choline as a key player in C1-metabolism. We further address some areas of interest for future work. Full article

This study aimed to train and validate two artificial neural network (ANN) models, one with four hidden layers and the other with five hidden layers, to predict the daily photovoltaic power output of a 20 Kw photovoltaic power plant with bifacial photovoltaic modules with tilt angle variation from 0° to 90° in two Moroccan cities, Ouarzazate and Oujda. To validate the two proposed models, photovoltaic power data calculated using the System Advisor Model (SAM) software version 2023.12.17 were employed to predict the average daily power of the photovoltaic plant for December, utilizing MATLAB software Version R2020a 9.8, and for the tilt angles corresponding to the latitudes of the two cities studied. The results differ from one model to another according to their mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R²) values. The artificial neural network model with five hidden layers obtained better results with a R² value of 0.99354 for Ouarzazate and 0.99836 for Oujda. These two proposed models are trained using the Levenberg Marquardt (LM) optimizer, which is proven to be the best training procedure. Full article

Rye regeneration in anther cultures is problematic and affected by albino plants. DNA methylation changes linked to Cu²⁺ ions in the induction medium affect reprogramming microspores from gametophytic to sporophytic path. Alternations in S-adenosyl-L-methionine (SAM), glutathione (GSH), or β-glucans and changes in DNA methylation in regenerants obtained under different in vitro culture conditions suggest a crucial role of biochemical pathways. Thus, understanding epigenetic and biochemical changes arising from the action of Cu²⁺ and Zn²⁺ that participate in enzymatic complexes may stimulate progress in rye doubled haploid plant regeneration. The Methylation-Sensitive Amplified Fragment Length Polymorphism approach was implemented to identify markers related to DNA methylation and sequence changes following the quantification of variation types, including symmetric and asymmetric sequence contexts. Reverse-Phase High-Pressure Liquid Chromatography (RP-HPLC) connected with mass spectrometry was utilized to determine SAM, GSH, and glutathione disulfide, as well as phytohormones, and RP-HPLC with a fluorescence detector to study polyamines changes originating in rye regenerants due to Cu²⁺ or Zn²⁺ presence in the induction medium. Multivariate and regression analysis revealed that regenerants derived from two lines treated with Cu²⁺ and those treated with Zn²⁺ formed distinct groups based on DNA sequence and methylation markers. Zn²⁺ treated and control samples formed separate groups. Also, Cu²⁺ discriminated between controls and treated samples, but the separation was less apparent. Principal coordinate analysis explained 85% of the total variance based on sequence variation and 69% of the variance based on DNA methylation changes. Significant differences in DNA methylation characteristics were confirmed, with demethylation in the CG context explaining up to 89% of the variance across genotypes. Biochemical profiles also demonstrated differences between controls and treated samples. The changes had different effects on green and albino plant regeneration efficiency, with cadaverine (Cad) and SAM affecting regeneration parameters the most. Analyses of the enzymes depend on the Cu²⁺ or Zn²⁺ ions and are implemented in the synthesis of Cad, or SAM, which showed that some of them could be candidates for genome editing. Alternatively, manipulating SAM, GSH, and Cad may improve green plant regeneration efficiency in rye. Full article

Background and Clinical Significance: Serratus Anterior Muscle Pain Syndrome (SAMPS) is an underdiagnosed cause of anterior chest wall pain, often attributed to myofascial trigger points of the serratus anterior muscle (SAM) or dysfunction of the Long Thoracic Nerve (LTN), leading to significant disability and affecting ipsilateral upper limb movement and quality of life. Current diagnosis relies on exclusion and physical examination, with limited treatment options beyond conservative approaches. This case report presents a novel approach to chronic SAMPS, successfully diagnosed using Sonoguided Digital Palpation (SDP) and treated with ultrasound-guided hydrodissection of the LTN using 5% dextrose in water (D5W) without local anesthetic (LA), in a patient where conventional treatments had failed. Case Presentation: A 72-year-old male presented with a three-year history of persistent left chest pain radiating to the upper back, exacerbated by activity and mimicking cardiac pain. His medical history included two percutaneous coronary interventions. Physical examination revealed tenderness along the anterior axillary line and a positive hyperirritable spot at the mid axillary line at the 5th rib level. SDP was used to visualize the serratus anterior fascia (SAF) and LTN, and to reproduce the patient’s concordant pain by palpating the LTN. Ultrasound-guided hydrodissection of the LTN was then performed using 20–30cc of D5W without LA to separate the nerve from the surrounding tissues, employing a “fascial unzipping” technique. The patient reported immediate pain relief post-procedure, with the pain reducing from 9/10 to 1/10 on the Numeric Rating Scale (NRS), and sustained relief and functional improvement at the 12-month follow-up. Conclusions: Sonoguided Digital Palpation (SDP) of the LTN can serve as a valuable diagnostic adjunct for visualizing and diagnosing SAMPS. Ultrasound-guided hydrodissection of the LTN with D5W without LA may provide a promising and safe treatment option for patients with chronic SAMPS refractory to conservative management, resulting in rapid and sustained pain relief. Further research, including controlled trials, is warranted to evaluate the long-term efficacy and generalizability of these findings and to compare D5W to other injectates. Full article

The human Tau protein stands for one of the most conspicuous and crucial hallmarks of Alzheimer’s disease (AD) diagnosis, along with other tauopathies. However, the assay for direct detection of tiny Tau protein concentrations in human samples continues to pose a significant challenge for the early diagnosis of AD. Thus, an amplification-based strategy is required. In this proposed work, we established an impedimetric immunosensor to detect human Tau-441 protein in PBS buffer using a sandwich approach, wherein we employed two distinct monoclonal antibodies (HT7 and BT2) that specifically recognize the amino acids 159–198 of the target protein. Through this strategy, we were able to detect as low as 0.08 pg/mL. These findings were attributed to the use of a biotinylated antibody (BT2)-streptavidin complex, which facilitated the amplification of the normalized signal, resulting in a lower limit of detection in comparison to the directly based immunosensors. Subsequently, we investigated the designed immunosensor to assess the assay’s selectivity in the presence of different off-targets, and no cross-interaction was recorded. The outcomes of our study provide valuable new insights into the application of sandwich-based assay as a highly sensitive and selective immunosensor for the detection of small protein. Full article

In wind-assisted orchard spraying operations, the dynamic response of leaves—manifested through changes in their posture—critically influences droplet deposition on both sides of the leaf surface and the penetration depth into the canopy. These factors are pivotal in determining spray coverage and the spatial distribution of pesticide efficacy. However, current research lacks comprehensive quantification and correlation analysis of the temporal response characteristics of leaves under wind disturbances. To address this gap, a systematic analytical framework was proposed, integrating real-time leaf segmentation and tracking, geometric feature quantification, and statistical correlation modeling. High-frame-rate videos of fluttering leaves were acquired under controlled wind conditions, and background segmentation was performed using principal component analysis (PCA) followed by clustering in the reduced feature space. A fine-tuned Segment Anything Model 2 (SAM2-FT) was employed to extract dynamic leaf masks and enable frame-by-frame tracking. Based on the extracted masks, time series of leaf area and inclination angle were constructed. Subsequently, regression analysis, cross-correlation functions, and Granger causality tests were applied to investigate cooperative responses and potential driving relationships among leaves. Results showed that the SAM2-FT model significantly outperformed the YOLO series in segmentation accuracy, achieving a precision of 98.7% and recall of 97.48%. Leaf area exhibited strong linear coupling and directional causality, while angular responses showed weaker correlations but demonstrated localized synchronization. This study offers a methodological foundation for quantifying temporal dynamics in wind–leaf systems and provides theoretical insights for the adaptive control and optimization of intelligent spraying strategies. Full article

Hybridization of solar and wind energy sources is a promising solution to enhance the dispatch capability of renewables. The complementarity of wind and solar radiation, as well as the sharing of transmission lines and other infrastructures, can notably benefit the deployment of renewable power. Mapping of hybrid solar–wind potential can help identify new emplacements or existing power facilities where an extension with a hybrid system might work. This paper presents an analysis of a hybrid solar–wind potential by considering a reference power plant of 40 MW in the Iberian Peninsula and comparing the hybrid and non-hybrid energy generated. The generation of energy is estimated using SAM for a typical meteorological year, using PVGIS and ERA5 meteorological information as input. Modeling the hybrid plant in relation to individual PV and wind power plants minimizes the dependence on technical and economic input data, allowing for the expression of potential hybridization analysis in relative numbers through maps. Correlation coefficient and capacity factor maps are presented here at different time scales, showing the complementarity in most of the spatial domain. In addition, economic analysis in comparison with non-hybrid power plants shows a reduction of around 25–30% in the LCOE in many areas of interest. Finally, a sizing sensitivity analysis is also performed to select the most beneficial sharing between PV and wind. Full article

Camouflaged Object Detection (COD) aims to segment objects that are highly integrated with their background, presenting significant challenges such as low contrast, complex textures, and blurred boundaries. Existing deep learning methods often struggle to achieve robust segmentation under these conditions. To address these limitations, this paper proposes a novel COD network, SAM2-DFBCNet, built upon the SAM2 Hiera architecture. Our network incorporates three key modules: (1) the Camouflage-Aware Context Enhancement Module (CACEM), which fuses local and global features through an attention mechanism to enhance contextual awareness in low-contrast scenes; (2) the Cross-Scale Feature Interaction Bridge (CSFIB), which employs a bidirectional convolutional GRU for the dynamic fusion of multi-scale features, effectively mitigating representation inconsistencies caused by complex textures and deformations; and (3) the Dynamic Boundary Refinement Module (DBRM), which combines channel and spatial attention mechanisms to optimize boundary localization accuracy and enhance segmentation details. Extensive experiments on three public datasets—CAMO, COD10K, and NC4K—demonstrate that SAM2-DFBCNet outperforms twenty state-of-the-art methods, achieving maximum improvements of 7.4%, 5.78%, and 4.78% in key metrics such as S-measure ($S_{\alpha }$), F-measure ($F_{\beta }$), and mean E-measure ($E_{\varphi }$), respectively, while reducing the Mean Absolute Error (M) by 37.8%. These results validate the superior performance and robustness of our approach in complex camouflage scenarios. Full article

This study presented an autonomous shield-cutting end-effector for maize surrounding weeding (SEMSW), addressing the challenges of the low weed removal rate (WRR) and high seedling damage rate (SDR) in northern China’s 3–5 leaf stage maize. The SEMSW integrated seedling positioning, robotic arm control, and precision weeding functionalities: a seedling positioning sensor identified maize seedlings and weeds, guiding XYZ translational motions to align the robotic arm. The seedling-shielding anti-cutting mechanism (SAM) enclosed crop stems, while the contour-adaptive weeding mechanism (CWM) activated two-stage retractable blades (TRWBs) for inter/intra-row weeding operations. The following key design parameters were determined: 150 mm inner diameter for the seedling-shielding disc; 30 mm minimum inscribed-circle for retractable clamping units (RCUs); 40 mm ground clearance for SAM; 170 mm shielding height; and 100 mm minimum inscribed-circle diameter for the TRWB. Mathematical optimization defined the shape-following weeding cam (SWC) contour and TRWB dimensional chain. Kinematic/dynamic models were introduced alongside an adaptive sliding mode controller, ensuring lateral translation error convergence. A YOLOv8 model achieved 0.951 precision, 0.95 mAP50, and 0.819 mAP50-95, striking a balance between detection accuracy and localization precision. Field trials of the prototype showed 88.3% WRR and 2.2% SDR, meeting northern China’s agronomic standards. Full article

Background/Objective: Precise aneurysm volume measurement offers a transformative edge for risk assessment and treatment planning in clinical settings. Currently, clinical assessments rely heavily on manual review of medical imaging, a process that is time-consuming and prone to inter-observer variability. The widely accepted standard of care primarily focuses on measuring aneurysm diameter at its widest point, providing a limited perspective on aneurysm morphology and lacking efficient methods to measure aneurysm volumes. Yet, volume measurement can offer deeper insight into aneurysm progression and severity. In this study, we propose an automated approach that leverages the strengths of pre-trained neural networks and expert systems to delineate aneurysm boundaries and compute volumes on an unannotated dataset from 60 patients. The dataset includes slice-level start/end annotations for aneurysm but no pixel-wise aorta segmentations. Method: Our method utilizes a pre-trained UNet to automatically locate the aorta, employs SAM2 to track the aorta through vascular irregularities such as aneurysms down to the iliac bifurcation, and finally uses a Long Short-Term Memory (LSTM) network or expert system to identify the beginning and end points of the aneurysm within the aorta. Results: Despite no manual aorta segmentation, our approach achieves promising accuracy, predicting the aneurysm start point with an R² score of 71%, the end point with an R² score of 76%, and the volume with an R² score of 92%. Conclusions: This technique has the potential to facilitate large-scale aneurysm analysis and improve clinical decision-making by reducing dependence on annotated datasets. Full article

Acoustic metamaterials (AMs) are artificially structured materials composed of subwavelength units that enable acoustic phenomena not achievable with conventional materials and structures. This review defines and presents a distinct category referred to as soft acoustic metamaterials (SAMs), which use soft materials or reconfigurable structures to achieve enhanced acoustic functionality. These systems make use of the inherent flexibility of their materials or the deformability of their geometry to support passive, active, and adaptive functions. To capture this structural and functional diversity, we introduce a three-dimensional classification that considers geometry, acoustic control mechanisms, and functional responsiveness as interrelated aspects. The geometry is classified into two-dimensional metasurfaces and three-dimensional bulk structures. The control mechanisms include local resonance, phase modulation, attenuation, and structural reconfiguration. The response type refers to whether the system behaves passively, actively, or adaptively. Using this approach, we provide an overview of representative implementations and compare different design approaches to highlight their working principles and application areas. This review presents a structured classification for soft acoustic metamaterials and offers a foundation for future research, with broad potential in intelligent sound systems, wearable acoustics, and architectural applications. Full article

This study aimed to explore how social media usage influenced both parent and adolescent mental health and social identity during and after the COVID-19 pandemic through the theoretical foundational lens of social comparison theory. In-depth interviews with 24 mothers of adolescent children (ages 10–19) were conducted to address the research questions. Qualitative thematic analysis of the interview transcripts revealed eight emerging themes: (1) learning and entertainment, (2) maternal fears related to content binging and cyberbullying, (3) finding connection and comfort through social media during the pandemic, (4) ongoing digital care work as lasting maternal labor, (5) iterative dialogue: platform restrictions and content curation boundaries, (6) upward and downward social comparison, (7) fear of missing out (FoMO), and (8) third-person perception (TPP). The findings show that mothers perceive social media usage as either beneficial or harmful among adolescents (their children); upward and downward social comparison via social media exhibits more dynamic mechanisms. Moreover, this study enhances our theoretical understanding by linking social media usage to social identity, social comparison, and mental health during a global health crisis. Full article

Federated learning (FL) has emerged as a prominent distributed machine learning paradigm that facilitates collaborative model training across multiple clients while ensuring data privacy. Despite its growing adoption in practical applications, performance degradation caused by data heterogeneity—commonly referred to as the non-independent and identically distributed (non-IID) nature of client data—remains a fundamental challenge. To mitigate this issue, a heterogeneity-aware and robust FL framework is proposed to enhance model generalization and stability under non-IID conditions. The proposed approach introduces two key innovations. First, a heterogeneity quantification mechanism is designed based on statistical feature distributions, enabling the effective measurement of inter-client data discrepancies. This metric is further employed to guide the model aggregation process through a heterogeneity-aware weighted strategy. Second, a multi-loss optimization scheme is formulated, integrating classification loss, heterogeneity loss, feature center alignment, and L2 regularization for improved robustness against distributional shifts during local training. Comprehensive experiments are conducted on four benchmark datasets, including CIFAR-10, SVHN, MNIST, and NotMNIST under Dirichlet-based heterogeneity settings (alpha = 0.1 and alpha = 0.5). The results demonstrate that the proposed method consistently outperforms baseline approaches such as FedAvg, FedProx, FedSAM, and FedMOON. Notably, an accuracy improvement of approximately 4.19% over FedSAM is observed on CIFAR-10 (alpha = 0.5), and a 1.82% gain over FedMOON on SVHN (alpha = 0.1), along with stable enhancements on MNIST and NotMNIST. Furthermore, ablation studies confirm the contribution and necessity of each component in addressing data heterogeneity. Full article