Search Results (86,266)

Background: Helicobacter pylori infection, overweight, and obesity are global health concerns. This bacterium is involved in the pathophysiology of chronic gastritis and gastric cancer. Additionally, overweight and obesity, associated with unhealthy eating habits and sedentary lifestyles, cause alterations in the gut microbiota that facilitate gastric colonization by Helicobacter pylori. Moreover, individuals with obesity tend to consume low-quality foods due to episodes of anxiety and exhibit elevated insulin levels, which may promote the development of gastric neoplasms. Studies conducted in Latin America have found that over 50% of participants are infected with Helicobacter pylori, a situation similar to that reported in Ecuador, where the prevalence of overweight and obesity in individuals aged 19 to 59 years reached 64.58% in 2018. Both health issues are influenced by the high consumption of processed foods or those prepared under inadequate hygiene conditions. Methods: In this context, this research aimed to correlate the body composition of university students with the prevalence of Helicobacter pylori. An observational, cross-sectional, and descriptive study was conducted with 57 Nursing, Medicine, and Psychology students from Universidad Indoamérica, Ambato campus, during 2024. Fecal samples were analyzed to detect the presence of the bacterium, and anthropometric measurements were taken to establish a possible relationship between these parameters. Results: Of the 57 students who participated, 54.39% tested positive for Helicobacter pylori. However, the presence of the bacteria did not show any relationship with body composition parameters such as fat mass, lean mass, BMI, weight, height, or age. Conclusions: The study found no evidence of a connection between Helicobacter pylori infection and anthropometric parameters in this university population. However, the high incidence of infections highlights the importance of promoting the consumption of safe food and ensuring timely diagnosis and treatment. Full article

Biogenic volatile organic compounds (BVOCs) significantly impact air quality and climate. Mechanical injury is a common stressor affecting plants in both natural and urban environments, and it has potentially large influences on BVOC emissions. However, the interspecific variability in wounding-induced BVOC emissions remains poorly understood, particularly for subtropical trees and shrubs. In this study, we investigated the effects of controlled mechanical injury on isoprenoid and aromatic compound emissions in a taxonomically diverse set of 45 subtropical broad-leaved woody species, 26 species without and in 19 species with BVOC storage structures (oil glands, resin ducts and glandular trichomes for volatile compound storage). Emissions of light-weight non-stored isoprene and monoterpenes and aromatic compounds in non-storage species showed moderate and variable emission increases after mechanical injury, likely reflecting the wounding impacts on leaf physiology. In storage species, mechanical injury triggered a substantial release of monoterpenes and aromatic compounds due to the rupture of storage structures. Across species, the proportion of monoterpenes in total emissions increased from 40.9% to 85.4% after mechanical injury, with 32.2% of this increase attributed to newly released compounds not detected in emissions from intact leaves. Sesquiterpene emissions, in contrast, were generally low and decreased after mechanical injury. Furthermore, wounding responses varied among plant functional groups, with evergreen species and those adapted to high temperatures and shade exhibiting stronger damage-induced BVOC emissions than deciduous species and those adapted to dry or cold environments. These findings suggest that mechanical disturbances such as pruning can significantly enhance BVOC emissions in subtropical urban forests and should be considered when modeling BVOC fluxes in both natural and managed ecosystems. Further research is needed to elucidate the relationship between storage structure characteristics and BVOC emissions, as well as their broader ecological and atmospheric implications. Full article

Trust and reputation relationships among objects represent key aspects of smart IoT object communities with social characteristics. In this context, several trustworthiness models have been presented in the literature that could be applied to IoT scenarios; however, most of these approaches use scalar measures to represent different dimensions of trust, which are then integrated into a single global trustworthiness value. Nevertheless, this scalar approach within the IoT context holds a few limitations that emphasize the need for models that can capture complex trust relationships beyond vector-based representations. To overcome these limitations, we already proposed a novel trust model where the trust perceived by one object with respect to another is represented by a directed, weighted graph. In this model, called T-pattern, the vertices represent individual trust dimensions, and the arcs capture the relationships between these dimensions. This model allows the IoT community to represent scenarios where an object may lack direct knowledge of a particular trust dimension, such as reliability, but can infer it from another dimension, like honesty. The proposed model can represent trust structures of the type described, where multiple trust dimensions are interdependent. This work represents a further contribution by presenting the first real implementation of the T-pattern model, where a neural-symbolic approach has been adopted as inference engine. We performed experiments that demonstrate the capability in inferring trust of both the T-pattern and this specific implementation. Full article

Population aging is an inevitable trend in contemporary society, and the application of technologies such as human–machine interaction, assistive healthcare, and robotics in daily service sectors continues to increase. The lower limb exoskeleton rehabilitation robot has great potential in areas such as enhancing human physical functions, rehabilitation training, and assisting the elderly and disabled. This paper integrates the structural characteristics of the human lower limb, motion mechanics, and gait features to design a biomimetic exoskeleton structure and proposes a human–machine integrated lower limb exoskeleton rehabilitation robot. Human gait data are collected using the Optitrack optical 3D motion capture system. SolidWorks 3D modeling software Version 2021 is used to create a virtual prototype of the exoskeleton, and kinematic analysis is performed using the standard Denavit–Hartenberg (D-H) parameter method. Kinematic simulations are carried out using the Matlab Robotic Toolbox Version R2018a with the derived D-H parameters. A physical prototype was fabricated and tested to verify the validity of the structural design and gait parameters. A controller based on BP fuzzy neural network PID control is designed to ensure the stability of human walking. By comparing two sets of simulation results, it is shown that the BP fuzzy neural network PID control outperforms the other two control methods in terms of overshoot and settling time. The specific conclusions are as follows: after multiple walking gait tests, the robot’s walking process proved to be relatively safe and stable; when using BP fuzzy neural network PID control, there is no significant oscillation, with an overshoot of 5.5% and a settling time of 0.49 s, but the speed was slow, with a walking speed of approximately 0.18 m/s, a stride length of about 32 cm, and a gait cycle duration of approximately 1.8 s. The model proposed in this paper can effectively assist patients in recovering their ability to walk. However, the lower limb exoskeleton rehabilitation robot still faces challenges, such as a slow speed, large size, and heavy weight, which need to be optimized and improved in future research. Full article

Neural networks (NNs) are essential in advancing modern safety-critical systems. Lightweight NN architectures are deployed on resource-constrained devices using hardware accelerators like Graphics Processing Units (GPUs) for fast responses. However, the latest semiconductor technologies may be affected by physical faults that can jeopardize the NN computations, making fault mitigation crucial for safety-critical domains. The recent studies propose software-based Hardening Techniques (HTs) to address these faults. However, the proposed fault countermeasures are evaluated through different hardware-agnostic error models neglecting the effort required for their implementation and different test benches. Comparing application-level HTs across different studies is challenging, leaving it unclear (i) their effectiveness against hardware-aware error models on any NN and (ii) which HTs provide the best trade-off between reliability enhancement and implementation cost. In this study, application-level HTs are evaluated homogeneously and independently by performing a study on the feasibility of implementation and a reliability assessment under two hardware-aware error models: (i) weight single bit-flips and (ii) neuron bit error rate. Our results indicate that not all HTs suit every NN architecture, and their effectiveness varies depending on the evaluated error model. Techniques based on the range restriction of activation function consistently outperform others, achieving up to 58.23% greater mitigation effectiveness while keeping the introduced overhead at inference time low while requiring a contained effort in their implementation. Full article

The computational investigation of nonlinear mathematical models presents significant challenges due to their complex dynamics. This paper presents a computational study of a nonlinear hepatitis C virus model that accounts for the influence of alcohol consumption on disease progression. We employ periodic neural networks, optimized using a hybrid genetic algorithm and the interior-point algorithm, to solve a system of six coupled nonlinear differential equations representing hepatitis C virus dynamics. This model has not previously been solved using the proposed technique, marking a novel approach. The proposed method’s performance is evaluated by comparing the numerical solutions with those obtained from traditional numerical methods. Statistical measures such as mean absolute error, root mean square error, and Theil’s inequality coefficient are used to assess the accuracy and reliability of the proposed approach. The weight vector distributions illustrate how the network adapts to capture the complex nonlinear behavior of the disease. A comparative analysis with established numerical methods is provided, where performance metrics are illustrated using a range of graphical tools, including box plots, histograms, and loss curves. The absolute error values, ranging approximately from ${10}^{-6}$ to ${10}^{-10}$, demonstrate the precision, convergence, and robustness of the proposed approach, highlighting its potential applicability to other nonlinear epidemiological models. Full article

The sea surface displays dynamic characteristics, such as waves and various formations. As a result, images of the sea surface usually have few stable feature points, with a background that is often complex and variable. Moreover, the sea surface undergoes significant changes due to variations in wind speed, lighting conditions, weather, and other environmental factors, resulting in considerable discrepancies between images. These variations present challenges for identification using traditional methods. This paper introduces an algorithm based on the phase-consistency model. We utilize image data collected from a specific maritime area with a high-frame-rate surface array infrared camera. By accurately detecting images with identical names, we focus on the subtle texture information of the sea surface and its rotational invariance, enhancing the accuracy and robustness of the matching algorithm. We begin by constructing a nonlinear scale space using a nonlinear diffusion method. Maximum and minimum moments are generated using an odd symmetric Log–Gabor filter within the two-dimensional phase-consistency model. Next, we identify extremum points in the anisotropic weighted moment space. We use the phase-consistency feature values as image gradient features and develop feature descriptors based on the Log–Gabor filter that are insensitive to scale and rotation. Finally, we employ Euclidean distance as the similarity measure for initial matching, align the feature descriptors, and remove false matches using the fast sample consensus (FSC) algorithm. Our findings indicate that the proposed algorithm significantly improves upon traditional feature-matching methods in overall efficacy. Specifically, the average number of matching points for long-wave infrared images is 1147, while for mid-wave infrared images, it increases to 8241. Additionally, the root mean square error (RMSE) fluctuations for both image types remain stable, averaging 1.5. The proposed algorithm also enhances the rotation invariance of image matching, achieving satisfactory results even at significant rotation angles. Full article

The aim of this study is to conduct a comparative assessment of the effectiveness of neural network models—U-Net, DeepLabV3+, SegNet and Mask R-CNN—for the semantic segmentation of micrographs of human mesenchymal stem cells (MSCs). A dataset of 320 cell micrographs annotated by cell biology experts was created. The models were trained using a transfer learning method based on ImageNet pre-trained weights. As a result, the U-Net model demonstrated the best segmentation accuracy according to the metrics of the Dice coefficient (0.876) and the Jaccard index (0.781). The DeepLabV3+ and Mask R-CNN models also showed high performance, although slightly lower than U-Net, while SegNet exhibited the least accurate results. The obtained data indicate that the U-Net model is the most suitable for automating the segmentation of MSC micrographs and can be recommended for use in biomedical laboratories to streamline the routine analysis of cell cultures. Full article

Background: Blood transfusion is a highly critical life-saving factor in neonates, especially in extremely low birth weight infants. There is a significant lack of consensus on optimal blood transfusion methods for neonates. Aim: To investigate and analyze blood transfusion practice in neonates among neonatologists and neonatal nurses in a multi-country pattern. Methods: From September 2023 to June 2024, a cross-sectional questionnaire-based study was conducted to collect data on global blood transfusion practices in neonates. A questionnaire, developed through an extensive literature review, was distributed to neonatologists and neonatal nurses primarily via e-mail, with additional distribution via social media platforms. Results: This study included a total of 180 neonatologists and neonatal nurses from 27 different countries. Almost 37.7% were working in a level 3 NICU. Approximately 37.7% of the participants stated that they transfuse blood within three hours, and approximately 45.5% stated they usually use 15 mL/kg of blood. After receiving a transfusion, 99.4% of the participants mentioned that they continue to check the vital signs. More than half (72.2%) of NICU practitioners use filters when giving blood. Regarding written instructions and guidelines in the unit for blood transfusion, the majority (84.4%) stated having them in their units, of which, 86.8% mentioned that blood transfusion threshold stated in the guidelines either using hemoglobin or hematocrit. Conclusions: This study found variability in blood transfusion practices around the world. While most have developed neonatal blood transfusion guidelines, certain countries still lack national protocols. Establishing comprehensive guidelines is essential to standardizing procedures, thereby minimizing the risk of inappropriate or unsafe blood transfusions in this neonatology practice. Full article

Sainfoin (Onobrychis viciifolia) is a type of leguminous plant with high feeding value. It contains a high concentration of tannins at all growth stages, which can precipitate soluble proteins and form a large number of persistent foams in the rumen, so that ruminant livestock will not develop dilatation disease during green feeding and grazing. The germination rate of O. viciifolia seeds is very low under natural conditions. The preliminary experiment showed that 600 mg/L GA₃ treatment significantly improved the germination rate and seed vitality of sainfoin seeds. In comparison to CK, GA₃ significantly decreased the relative content of endogenous inhibitors, with the most notable reduction observed in 4-nitroso-N-phenyl-benzenamine. Therefore, we selected the dry seed stage (GZ), imbibition stage (XZ), split stage (LK), and radicle emergence stage (MF) of four different germination stages treated with GA₃ for transcriptome analysis. RNA-seq identified 1392, 2534 and 4284 differentially expressed genes (DEGs) in GZ vs. XZ, XZ vs. LK, and LK vs. MF, respectively. During seed germination, DEGs are mainly enriched in hormone signaling and phenylalanine biosynthesis pathways, and up-down-regulation of these DEGs may alter hormone and secondary metabolite levels to promote germination. The results of weighted gene co-expression network construction (WGCNA) also indicate that plant hormone signal transduction and phenylpropanoid biosynthesis play a dominant role in GA₃-induced seed germination. In conclusion, the combined analysis of transcriptomic and physiological indicators provided new insights into seed germination and a theoretical basis for further study of candidate genes. Full article

Inverse Distance Weighting (IDW) is a common method for spatial interpolation. Still, its accuracy decreases when there is a cluster of measurement stations or when some measuring stations are hidden behind others. This paper introduces Clusters Unifying Through Hiding Interpolation (CUTHI), a simple approach to enhance IDW accuracy. CUTHI calculates a weight for each station that considers its visibility from the interpolation point, reducing the influence of clustered or hidden stations. The method is tested in three cases: elevation data, rainfall measurements, and a mathematical function. Results demonstrate that CUTHI consistently outperforms traditional IDW, especially in areas with clustered measurement stations. CUTHI also treats the bull’s eye problem. This improved accuracy makes CUTHI a valuable tool for various applications requiring precise spatial interpolation. Full article

Background and objectives: Type 2 diabetes mellitus (T2DM) affects brain white matter microstructure. While diffusion tensor imaging (DTI) has been used to study white matter abnormalities in T2DM, it lacks specificity for complex white matter tracts. Neurite orientation dispersion and density imaging (NODDI) offers a more specific approach to characterising white matter microstructures. This study aims to explore white matter alterations in T2DM using both DTI and NODDI and assess their association with disease duration and glycaemic control, as indicated by HbA1c levels. Methods and Materials: We analysed white matter microstructure in 48 tracts using data from the UK Biobank, involving 1023 T2DM participants (39% women, mean age 66) and 30,744 non-T2DM controls (53% women, mean age 64). Participants underwent 3.0T multiparametric brain imaging, including T1-weighted and diffusion imaging for DTI and NODDI. We performed region-of-interest analyses on fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), orientation dispersion index (ODI), intracellular volume fraction (ICVF), and isotropic water fraction (IsoVF) to assess white matter abnormalities. Results: We observed reduced FA and ICVF, and increased MD, AD, RD, ODI, and IsoVF in T2DM participants compared to controls (p < 0.05). These changes were associated with longer disease duration and higher HbA1c levels (0 < r ≤ 0.2, p < 0.05). NODDI identified microstructural changes in white matter that were proxies for reduced neurite density and disrupted fibre orientation, correlating with disease progression and poor glucose control. In conclusion, NODDI contributed to DTI in capturing white matter differences in participants with type 2 diabetes, suggesting the feasibility of NODDI in detecting white matter alterations in type 2 diabetes. Type 2 diabetes can cause white matter microstructural abnormalities that have associations with glucose control. Conclusions: The NODDI diffusion model allows the characterisation of white matter neuroaxonal pathology in type 2 diabetes, giving biophysical information for understanding the impact of type 2 diabetes on brain microstructure. Future research should focus on the longitudinal tracking of these microstructural changes to better understand their potential as early biomarkers for cognitive decline in T2DM. Full article

(1) Background: To explore the anti-obesity effects and mechanisms of sika deer velvet antler peptides (sVAP) on 3T3-L1 preadipocytes and in high-fat diet (HFD)-induced obese mice. (2) Methods: sVAP fractions of different molecular weights were obtained via enzymatic hydrolysis and ultrafiltration. Their anti-lipid effects on 3T3-L1 cells were assessed with Oil Red O staining. The optimal fraction was tested in HFD-induced obese C57BL/6 mice to explore anti-obesity mechanisms. Peptide purification used LC-MS/MS, followed by sequence analysis and molecular docking for activity prediction. (3) Results: The peptide with the best anti-obesity activity was identified as sVAP-3K (≤3 kDa). sVAP-3K reduced lipid content and proliferation in 3T3-L1 cells, improved lipid profiles and ameliorated adipocyte degeneration in HFD mice, promoted the growth of beneficial gut microbiota, and maintained lipid metabolism. Additionally, sVAP-3K activated the AMP-activated protein kinase (AMPK) signaling pathway, regulating adipogenic transcription factors. sVAP-3K exhibited ten major components (peak area ≥ 1.03 × 10⁸), with four of the most active components being newly discovered natural oligopeptides: RVDPVNFKL (m/z 363.21371), GGEFTPVLQ (m/z 474.24643), VDPENFRL (m/z 495.25735), and VDPVNFK (m/z 818.44043). (4) Conclusion: This study identifies four novel oligopeptides in sVAP-3K as key components for anti-obesity effects, offering new evidence for developing natural weight-loss drugs from sika deer velvet. Full article

Assessing the geological suitability of urban underground space development is crucial for mitigating geological risks. Traditional 2D evaluation methods fail to capture complex vertical variations in underground space, hindering precise planning. This paper presents an innovative 3D-CWC framework, combining a weighted cloud model with three-dimensional geological modeling, to address vertical complexity and uncertainty in geological assessments. The study area, located in the northern part of Kunming’s Second Ring Road, is divided into 22 million 25 m × 25 m × 1 m 3D units for evaluation. The framework uses the improved AHP and CRITIC methods to assign weights to key geological indicators, addressing both subjective and objective uncertainty, and employs a cloud model to determine geological suitability levels. The results are visualized using 3D geological modeling. The key findings include the following: (1) approximately 71% of the area within a −50 m depth range is suitable or more suitable for underground space development; (2) active fractures and groundwater are the main unfavorable factors; and (3) the geological suitability varies significantly with depth, with shallow areas being less suitable due to soft soil and complex hydrogeological conditions. The framework is further applied to assess the geological suitability of Kunming Metro Line 10, providing valuable decision support for infrastructure development. Compared to existing methods, this framework integrates cloud modeling and 3D geological modeling, offering a more comprehensive approach to handling underground space complexity. It is adaptable and holds potential for global applications, supporting urban underground space development in diverse geological conditions. Full article

Epilepsy is a multifaceted neurological disorder characterized by recurrent seizures and associated with molecular and immune alterations in key brain regions. The GASH/Sal (Genetic Audiogenic Seizure Hamster, Salamanca), a genetic model for audiogenic epilepsy, provides a powerful tool to study seizure mechanisms and resistance in predisposed individuals. This study investigates the proteomic and immune responses triggered by audiogenic kindling in the inferior colliculus, comparing non-responder animals exhibiting reduced seizure severity following repeated stimulation versus GASH/Sal naïve hamsters. To assess auditory pathway functionality, Auditory Brainstem Responses (ABRs) were recorded, revealing reduced neuronal activity in the auditory nerve of non-responders, while central auditory processing remained unaffected. Cytokine profiling demonstrated increased levels of proinflammatory markers, including IL-1 alpha (Interleukin-1 alpha), IL-10 (Interleukin-10), and TGF-beta (Transforming Growth Factor beta), alongside decreased IGF-1 (Insulin-like Growth Factor 1) levels, highlighting systemic inflammation and its interplay with neuroprotection. Building on these findings, a proteomic analysis identified 159 differentially expressed proteins (DEPs). Additionally, bioinformatic approaches, including Gene Set Enrichment Analysis (GSEA) and Weighted Gene Co-expression Network Analysis (WGCNA), revealed disrupted pathways related to metabolic and inflammatory epileptic processes and a module potentially linked to a rise in the threshold of seizures, respectively. Differentially expressed genes, identified through bioinformatic and statistical analyses, were validated by RT-qPCR. This confirmed the upregulation of six genes (Gpc1—Glypican-1; Sdc3—Syndecan-3; Vgf—Nerve Growth Factor Inducible; Cpne5—Copine 5; Agap2—Arf-GAP with GTPase domain, ANK repeat, and PH domain-containing protein 2; and Dpp8—Dipeptidyl Peptidase 8) and the downregulation of two (Ralb—RAS-like proto-oncogene B—and S100b—S100 calcium-binding protein B), aligning with reduced seizure severity. This study may uncover key proteomic and immune mechanisms underlying seizure susceptibility, providing possible novel therapeutic targets for refractory epilepsy. Full article