Search Results (1,874)

Background: Liver cirrhosis is a complex and chronic pathology with the potential to impact a number of factors, including the patient’s health, nutritional status and diet. Proper nutritional intake plays an essential role alongside the necessary medical and recovery treatments. Methods: This study was conducted on a group that included patients of varying age demographics. They were required to undertake a 24 h food recall as well as two other questionnaires (CNAQ and CLDQ-NASH) that reported the level of appetite and nutrition and other aspects that focused on the patient’s general health and quality of life, respectively. Results: The results of the study indicated the presence of reduced appetite and a decrease in quality of life, as reported by questionnaire scores of less than 28 points for appetite and less than 4 points for quality of life. The 24 h dietary recalls revealed that the majority of patients exhibited a preference for meals comprising red and processed meats and traditional foods such as soups and animal foods and a low consumption of white meat, fish, legumes and fiber. Conclusions: The study’s findings reveal an imbalance in the patients’ nutritional intake and underscore the critical importance of nutritional support for patients with liver cirrhosis. However, further research is needed in this regard to determine the factors leading to nutritional deficiencies and the causes leading to refusal of nutritional intervention within the management of this disease. Full article

Iron represents an essential element required for normal physiologic processes throughout organ systems. A vast network of transporters is involved not only in uptake of this element but in processing, oxidation, and recycling to maintain it in a tight balance to avoid excess storage. This complex network of transporters, including heme and ferroportin, among many others, are responsible for facilitating inter-organ tissue iron exchange and availability, contributing to overall heme homeostasis. However, exposure to high levels of iron can overwhelm compensatory mechanisms that result in its accumulation and toxicity. This is the case of patients with genetic diseases such as hemoglobinopathies who suffer from chronic anemia and require, in most instances, a lifetime of red blood cell transfusions to overcome disease crises. Thus, in light of the extensive role of iron in the body, the aim of this review is to present important metabolic pathways involved in iron homeostasis across the cardiovascular, reproductive, hematopoietic, urinary, respiratory, endocrine, and central nervous systems while contrasting these against negative effects caused by iron excess. Full article

The red soil region in southern China is an ecologically fragile area. Although ecological engineering construction has achieved phased results, there are still obvious gaps in research on the mechanisms underlying vegetation dynamics in response to natural and anthropogenic variables. Changting County (CTC) serves as a typical case of vegetation degradation and restoration in the region. We examined the vegetation dynamics in CTC with the fraction vegetation cover (FVC) based on kernel normalized difference vegetation index-based dimidiate pixel model (kNDVI-DPM) and employed the optimal parameter-based geographical detector (OPGD), multiscale geographically weighted regression (MGWR), and partial least square structural equation modeling (PLS-SEM) to analyze interaction mechanisms between vegetation dynamics and underlying factors. The FVC showed a fluctuating upward trend at a rate of 0.0065 yr⁻¹ (p < 0.001) from 2000 to 2020. The spatial distribution pattern was high in the west and low in the east. Soil and terrain factors were the primary factors dominating the spatial heterogeneity of FVC, soil organic matter and elevation showing the most significant influence, with annual mean q-values of 0.4 and 0.3, respectively. Climate, terrain, and soil properties positively and anthropogenic activities negatively impacted vegetation. From 2000 to 2020, the path coefficient of anthropogenic activities to FVC decreases from −0.152 to −0.045, the adverse effects of human activities are diminishing with ongoing ecological construction efforts. Climate and anthropogenic activities act indirectly on vegetation through negative effects on soils and terrain. The impact of climate on soils and terrain is gradually lessening, whilst the influence of anthropogenic activities continues to grow. This study provides an analytical framework for understanding the complex interrelationships between vegetation changes and the underlying factors. Full article

Benthic habitats are highly complex and diverse ecosystems that are increasingly threatened by human-induced stressors and the impacts of climate change. Therefore, accurate classification and mapping of these marine habitats are essential for effective monitoring and management. In recent years, Unmanned Aerial Vehicles (UAVs) have been increasingly used to expand the spatial coverage of surveys and to produce high-resolution imagery. These images can be processed using photogrammetry-based techniques to generate high-resolution digital elevation models (DEMs) and orthomosaics. In this study, we demonstrate that integrating descriptors extracted from pre-trained Convolutional Neural Networks (CNNs) with geomorphometric attributes derived from DEMs significantly enhances the accuracy of automatic benthic habitat classification. To assess this integration, we analyzed orthomosaics and DEMs generated from UAV imagery across three shallow reef zones along the Red Sea coast of Saudi Arabia. Furthermore, we tested various combinations of feature layers from pre-trained CNNs—including ResNet-50, VGG16, and AlexNet—together with several geomorphometric variables to evaluate classification accuracy. The results showed that features extracted from the ResNet-50 FC1000 layer, when combined with twelve geomorphometric attributes based on curvature, slope, the Topographic Ruggedness Index (TRI), and DEM-derived heights, achieved the highest overall accuracies. Moreover, training a Support Vector Machine (SVM) classifier using both pre-trained ResNet-50 features and geomorphometric variables led to an improvement in overall accuracy of up to 5%, compared to using ResNet-50 features alone. The proposed integration effectively improves the automation and accuracy of benthic habitat mapping processes. Full article

As software systems continue to increase in complexity and scale, the number of reported bugs also grows. Bug reports are essential artifacts in software maintenance, supporting critical tasks such as detecting duplicate reports, predicting bug severity, and assigning priority levels. Although stop word removal is a common text preprocessing step in natural language processing, its effectiveness in deep learning-based bug report analysis has not been thoroughly evaluated. This study investigates the impact of stop word removal on three core bug report classification tasks. The analysis uses a dataset containing over 1.9 million bug reports from eight large-scale open-source projects, including Eclipse, FreeBSD, GCC, Gentoo, Kernel, RedHat, Sourceware, and WebKit. Five deep learning models are applied: convolutional neural networks, long short-term memory networks, gated recurrent units, Transformers, and BERT. Each model is evaluated on its performance with and without stop word removal during preprocessing. The results show that the F1 score difference was less than 0.01 in over 85% of comparisons, so stop word removal has little to no effect on predictive performance in eight open-source projects. Average F1-scores remain consistent across all tasks and models, with 0.36 for duplicate detection, 0.33 for severity prediction, and 0.33 for priority prediction. Statistical significance tests confirm that the observed differences are not meaningful across datasets or model types. The findings suggest that stop word removal is not necessary in deep learning-based bug report analysis. Removing this step may simplify preprocessing pipelines without reducing accuracy, particularly in large-scale and real-world software engineering applications. Full article

Australian elapid snakes possess potent procoagulant venoms, capable of inducing severe venom-induced consumption coagulopathy (VICC) in snakebite victims through rapid activation of the coagulation cascade by converting the FVII and prothrombin zymogens into their active forms. These venoms fall into two mechanistic categories: FXa-only venoms, which hijack host factor Va, and FXa:FVa venoms, containing a complete venom-derived prothrombinase complex. While previous studies have largely focused on human plasma, the ecological and evolutionary drivers behind prey-selective venom efficacy remain understudied. Here, thromboelastography was employed to comparatively evaluate venom coagulotoxicity across prey classes (amphibian, avian, rodent) and human plasma, using a taxonomically diverse selection of Australian snakes. The amphibian-specialist species Pseudechis porphyriacus (Red-Bellied Black Snake) exhibited significantly slower effects on rodent plasma, suggesting evolutionary refinement towards ectothermic prey. In contrast, venoms from dietary generalists retained broad efficacy across all prey types. Intriguingly, notable divergence was observed within Pseudonaja textilis (Eastern Brown Snake): Queensland populations of this species, and all other Pseudonaja (brown snake) species, formed rapid but weak clots in prey and human plasma. However, the South Australian populations of P. textilis produced strong, stable clots across prey plasmas and in human plasma. This is a trait shared with Oxyuranus species (taipans) and therefore represents an evolutionary reversion towards the prothrombinase phenotype present in the Oxyuranus and Pseudonaja last common ancestor. Clinically, this distinction has implications for the pathophysiology of human envenomation, potentially influencing clinical progression, including variations in clinical coagulopathy tests, and antivenom effectiveness. Thus, this study provides critical insight into the ecological selection pressures shaping venom function, highlights intraspecific venom variation linked to geographic and phylogenetic divergence, and underscores the importance of prey-focused research for both evolutionary toxinology and improved clinical management of snakebite. Full article

The Ili River Basin is characterized by complex topography and diverse climatic zones with limited in situ observations. This study evaluates the performance of six widely used precipitation datasets, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), ERA5_Land (European Centre for Medium-Range Weather Forecasts—ECMWF Reanalysis 5_Land), GPCC (Global Precipitation Climatology Centre), IMERG (Integrated Multi-satellite Retrievals for GPM), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and TerraClimate, against ground-based data from 2001 to 2023. The evaluation is conducted across multiple spatial scales and temporal resolutions. At the basin scale, most datasets exhibit strong correlations with in situ observations across all temporal scales (r > 0.7), except for PERSIANN, which demonstrates a relatively weaker performance during summer and winter (r < 0.6). All datasets except ERA5_ Land show low annual and monthly bias (<5%), although larger errors are observed during summer, particularly for IMERG and PERSIANN. Dataset performance generally declines with increasing elevation. Basin-wide gridded evaluations reveal distinct spatial variations across all elevation zones, with CHIRPS showing the strongest ability to capture orographic precipitation gradients throughout the basin. All datasets correctly identified 2008 as a drought year and 2016 as a wet year, even though the magnitude and spatial resolution of the anomalies varied among them. These findings highlight the importance of selecting precipitation datasets that are suited to the complex topographic and climatic characteristics of transboundary basins. Our study provides valuable insights for improving hydrological modeling and can be used for water sustainability and flood–drought mitigation support activities in the Ili River Basin. Full article

Picea neoveitchii Mast., a critically endangered spruce species endemic to China, is classified as a national second-level key protected wild plant and listed as critically endangered (CR) on the International Union for Conservation of Nature (IUCN) Red List. Its habitat features complex forest light environments, and global climate change coupled with environmental pollution has increased regional nitrogen deposition, posing significant challenges to its survival. This study explores the effects of light–nitrogen interactions on the leaf functional traits of Picea neoveitchii Mast. seedlings by simulating combinations of light intensities (100%, 70%, and 40% full sunlight) and nitrogen application levels (0, 10, and 20 g N·m ⁻²·a⁻¹, where g N·m⁻²·a⁻¹ denotes grams of nitrogen applied per square meter per year). We examined changes in morphological traits, anatomical structures, photosynthetic physiology, and stress resistance traits. Results indicate that moderate shading (70% full sunlight) significantly enhances leaf morphological traits (e.g., leaf length, leaf area, and specific leaf area) and anatomical features (e.g., mesophyll tissue area and resin duct cavity area), improving light capture and stress resistance. Medium- to high-nitrogen treatments (10 or 20 g N·m⁻²·a⁻¹) under moderate shading further increase photosynthetic efficiency, stomatal conductance, and antioxidant enzyme activity. According to the comprehensive membership function evaluation, the L2N0 (70% full sunlight, 0 g N·m⁻²·a⁻¹) treatment exhibits the most balanced performance across both growth and stress-related traits. These findings underscore the critical role of light–nitrogen interactions in the growth and adaptability of Picea neoveitchii Mast. leaves, offering a scientific foundation for the conservation and ecological restoration of endangered plant populations. Full article

The recently proposed concept of “precursory fingerprint” is a logical consequence of the commonsense statement that seismic structures are unique and that their expected preshock behaviors, including precursory phenomena, are also unique. Our new prediction-related research strategy is conceptually based on the principles of (1) the uniqueness of seismogenic structures, (2) interconnected and interacting geospheres, and (3) non-equivalence of Earth’s surface spots in terms of precursory signal receptivity. The precursory fingerprint of a given seismic structure is a unique assemblage of precursory signals of various natures (seismic, physical, chemical, and biological), detectable in principle by using a system of proper monitoring equipment that consists of a matrix of n sensors placed on the ground at “sensitive” spots identified beforehand and on orbiting satellites. In principle, it is composed of a combination of signals that are emitted by the “responsive sensors”, in addition to the “non-responsive sensors”, coming from the sensor matrix, monitoring as many virtual precursory processes as possible by continuously measuring their relevant parameters. Each measured parameter has a pre-established (by experts) threshold value and an uncertainty interval, discriminating between background and anomalous values that are visualized similarly to traffic light signals (green, yellow, and red). The precursory fingerprint can thus be viewed as a particular configuration of “precursory signals” consisting of anomalous parameter values that are unique and characteristic to the targeted seismogenic structure. Presumably, it is a complex entity that consists of pattern, space, and time components. The “pattern component” is a particular arrangement of the responsive sensors on the master board of the monitoring system yielding anomalous parameter value signals, that can be re-arranged, after a series of experiments, in a spontaneously understandable new pattern. The “space component” is a map position configuration of the signal-detecting sensors, whereas the “time component” is a characteristic time sequence of the anomalous signals including the order, occurrence time before the event, transition time between yellow and red signals, etc. Artificial intelligence using pattern-recognition algorithms can be used to follow, evaluate, and validate the precursory signal assemblage and, finally, to judge, together with an expert board of human operators, its “precursory fingerprint” relevance. Signal interpretation limitations and uncertainties related to dependencies on sensor sensibility, focal depth, and magnitude can be established by completing all three phases (i.e., experimental, validation, and implementation) of the precursory fingerprint-based earthquake prediction research strategy. Full article

Accurate detection of tomato fruits is a critical component in vision-guided robotic harvesting systems, which play an increasingly important role in automated agriculture. However, this task is challenged by variable lighting conditions and background clutter in natural environments. In addition, the arbitrary orientations of fruits reduce the effectiveness of traditional horizontal bounding boxes. To address these challenges, we propose a novel object detection framework named SN-YOLO. First, we introduce the StarNet’ backbone to enhance the extraction of fine-grained features, thereby improving the detection performance in cluttered backgrounds. Second, we design a Color-Prior Spatial-Channel Attention (CPSCA) module that incorporates red-channel priors to strengthen the model’s focus on salient fruit regions. Third, we implement a multi-level attention fusion strategy to promote effective feature integration across different layers, enhancing background suppression and object discrimination. Furthermore, oriented bounding boxes improve localization precision by better aligning with the actual fruit shapes and poses. Experiments conducted on a custom tomato dataset demonstrate that SN-YOLO outperforms the baseline YOLOv8 OBB, achieving a 1.0% improvement in precision and a 0.8% increase in mAP@0.5. These results confirm the robustness and accuracy of the proposed method under complex field conditions. Overall, SN-YOLO provides a practical and efficient solution for fruit detection in automated harvesting systems, contributing to the deployment of computer vision techniques in smart agriculture. Full article

Wound healing is a complex process in which TGFβ plays a key role. Previous studies have shown that syringin, a phenylpropanoid glycoside present in lilac bark (Syringa vulgaris L.), stimulates TGFβ expression in human monocyte-derived macrophages in addition to inhibiting the secretion of pro-inflammatory cytokines. Here, we investigated the effect of syringin on migration, invasion, and TGFβ production, as well as the effect on the release of pro-inflammatory cytokines in human dermal fibroblasts (NHDF) and keratinocytes (HaCaT) and its mechanism of action. NHDF and HaCaT cells were treated with the tested compound (12.5–100 µM), and a scratch assay was performed. The effect of migration using modified Boyden chambers was analyzed. TGFβ and IL-6 release were also assessed using ELISA kits. Cell proliferation was assessed using MTT and BrdU incorporation tests, while cytotoxicity was assessed using a neutral red uptake test. Smad2 and Smad3 phosphorylation were assessed using Western Blotting. ACTA2, COL1A1, and TIMP3 expression was analyzed using qPCR. Cells treated with syringin showed an increase in invasion potential in the scratch assay. A significant increase in skin fibroblast migration through the porous membrane was also observed. Syringin increased TGFβ release and inhibited IL-6 release by NHDF and HaCaT cells. No effect of syringin on cell proliferation or cytotoxic effects was observed. Western blot analysis showed significant activation of Smad2 and Smad3 in the presence of syringin in NHDF cells, but not in HaCaT. Quantitative PCR analysis revealed a strong increase in ACTA2 and COL1A1 gene expression in fibroblast cells treated with syringin. The present study demonstrated that syringin present in S. vulgaris stem bark increased dermal fibroblasts and keratinocytes’ wound healing function through activation of cell migration. Full article

Yerkapı, a prominent structure within Ḫattuša, the capital of the Hittite Empire (17th–12th century BC), exemplifies the sophisticated architectural and cultural practices of this ancient civilisation. The monument, encompassing a Sphinx Gate and an underground tunnel (postern) featuring 249 hieroglyphic inscriptions, is hypothesised to have served ceremonial rather than defensive purposes. This study employs a multidisciplinary approach to document, analyse, and interpret the inscriptions and their architectural context through advanced methodologies. High-resolution 3D digitisation was conducted using drones, terrestrial laser scanning, and photogrammetric techniques, enabling the creation of detailed models of the site. Specific focus was given to the postern, with comprehensive surveys delineating the geometries of the inscriptions and their spatial relationships to the Sphinx Gate. Diagnostic pigment analysis provided insights into the mineralogical and chemical composition of the red figures, further informing the interpretation of the hieroglyphs. The integration of 3D models and petrographic data allowed for the identification of previously unobservable details and facilitated a sequential reading of the inscriptions within their architectural framework. The findings emphasise Yerkapı’s function as a site of symbolic and ritual importance, thereby advancing our comprehension of Hittite ceremonial practices and establishing a methodological paradigm for the integration of digital archaeology with the study of geo-materials in the investigation of complex ancient monuments. Full article

Autism spectrum disorder (ASD) is a complex neurodevelopmental disease of multifactorial etiologies, manifesting as persistent challenges in social interactions, restrictive interests, and repetitive behaviors. Glucose-6-phosphate dehydrogenase (G6PD) deficiency is the most common human enzymopathy affecting red blood cell function. Although G6PD enzyme deficiency is known for its role in hemolytic anemia, emerging studies have suggested a potential association between G6PD deficiency and neurodegenerative and neurodevelopmental disorders, including autism. This narrative review explores the possible connection between G6PD deficiency and autism by analyzing relevant literature from the PubMed and Scopus databases. Current evidence points to plausible biological links, particularly oxidative stress and folate metabolism, warranting further investigation into G6PD deficiency as a potential risk modifier in ASD. Moreover, further research is necessary to elucidate the nature of this relationship and its implications for clinical practice. Full article

To address the limitations of the Red-billed Blue Magpie Optimization algorithm (RBMO), such as its tendency to get trapped in local optima and its slow convergence rate, an enhanced version called MRBMO was proposed. MRBMO was improved by integrating Good Nodes Set Initialization, an Enhanced Search-for-food Strategy, a newly designed Siege-style Attacking-prey Strategy, and Lens-Imaging Opposition-Based Learning (LIOBL). The experimental results showed that MRBMO demonstrated strong competitiveness on the CEC2005 benchmark. Among a series of advanced metaheuristic algorithms, MRBMO exhibited significant advantages in terms of convergence speed and solution accuracy. On benchmark functions with 30, 50, and 100 dimensions, the average Friedman values of MRBMO were 1.6029, 1.6601, and 1.8775, respectively, significantly outperforming other algorithms. The overall effectiveness of MRBMO on benchmark functions with 30, 50, and 100 dimensions was 95.65%, which confirmed the effectiveness of MRBMO in handling problems of different dimensions. This paper designed two types of simulation experiments to test the practicability of MRBMO. First, MRBMO was used along with other heuristic algorithms to solve four engineering design optimization problems, aiming to verify the applicability of MRBMO in engineering design optimization. Then, to overcome the shortcomings of metaheuristic algorithms in antenna S-parameter optimization problems—such as time-consuming verification processes, cumbersome operations, and complex modes—this paper adopted a test suite specifically designed for antenna S-parameter optimization, with the goal of efficiently validating the effectiveness of metaheuristic algorithms in this domain. The results demonstrated that MRBMO had significant advantages in both engineering design optimization and antenna S-parameter optimization. Full article

Megacrystalline uraninite within Neoproterozoic migmatites in the Haita area of the Kangdian region of China provides a unique condition for the investigation of uraninite typomorphism under high-temperature conditions. The present study represents the first systematic characterization of the typomorphic signatures and genetic significance of megacrystalline uraninite via optical microscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XRS), and electron probe microanalysis (EPMA). The results show that uranium mineralization occurs as euhedral megacrystalline uraninite (black grains ≤ 10 mm) hosted in quartz veins, exhibiting frequent rhombic dodecahedral and subordinate cubic–octahedral morphologies. The paragenetic assemblage is quartz–uraninite–titanite–apatite–molybdenite. The investigated uraninite is characterized by elevated unit-cell parameters and a reduced oxygen index, with complex chemical compositions enriched in ThO₂ and Y₂O₃. These typomorphic characteristics indicate crystallization under high-temperature reducing conditions with gradual cooling. Post-crystallization tectonic fragmentation and uplift-facilitated oxidation occur, generating secondary uranium minerals with concentric color zonation (orange–red to yellow–green halos). Mineralization was jointly controlled by migmatization and late-stage tectonism, with the breakup of the Rodinia supercontinent serving as the key driver of fluid mobilization and ore deposition. The data materialized in the present study improve our knowledge about uranium mineralization during continental breakup events. Full article