Search Results (128,250)

This study explores the neuroprotective effects of neuropeptide FF (NPFF, FLFQPQRFamide) in the context of ischemic injury. Based on transcriptomic analysis in stroke models treated with 5-Aza-dC and task-specific training, we identified significant gene expression changes, particularly involving NPFF. To further explore NPFF’s role in promoting neuronal recovery, recombinant NPFF protein (rNPFF) was used in primary mixed cortical cultures subjected to oxygen-glucose deprivation and reoxygenation. Our results demonstrated that rNPFF significantly reduced lactate dehydrogenase release, indicating decreased cellular damage. It also significantly increased the expression of TUJ1 and MAP2, markers of neuronal survival and dendritic integrity. Additionally, rNPFF significantly upregulated key synaptic proteins, including GAP43, PSD95, and synaptophysin, which are essential for synaptic repair and plasticity. Post-injury rNPFF treatment led to a significant upregulation of pro-brain-derived neurotrophic factor (BDNF) and mature BDNF, which play critical roles in neuronal survival, growth, and synaptic plasticity. Moreover, rNPFF activated the protein kinase Cε isoform, Sirtuin 1, and peroxisome proliferator-activated receptor gamma pathways, which are crucial for regulating cellular stress responses, synaptic plasticity, and energy homeostasis, further promoting neuronal survival and recovery. These findings suggest that rNPFF may play a pivotal role in enhancing neuronal survival and synaptic plasticity after ischemic injury, highlighting its potential as a therapeutic target for stroke recovery. Full article

With the development of remote sensing technology, the estimation of the chlorophyll content (CHLC) of vegetation via satellite data has become an important means of monitoring vegetation health, and high-precision estimation has been the focus of research in this field. In this study, we used larch affected by Yarl’s larch looper (Erannis jacobsoni Djak) in the boundary region of Mongolia as the research object, simulated the multispectral reflectance, downscaled Sentinel-2A satellite data, performed mixed-pixel decomposition, analyzed the potential of Sentinel-2A satellite data for estimating the chlorophyll content by calculating the spectral indices (SIs) and spectral derivatives (SDFs) of images, and then extracted sensitive spectral features as the model training set. Spectral features sensitive to the chlorophyll content were extracted to establish the training set, and, finally, the chlorophyll content estimation model for larch was constructed on the basis of the partial least squares algorithm (PLSR). The results revealed that SI and SDF based on simulated remote sensing data were highly sensitive to the chlorophyll content under the influence of pests, with the SAVI and EVI2 spectral indices as well as the D_B2 and D_B5 spectral derivatives being the most sensitive to the chlorophyll content. The estimation models based on simulated data performed significantly better than models without simulated data in terms of accuracy, especially those based on SDF-PLSR. The simulated spectral reflectance well reflected the spectral characteristics of the larch canopy and was sensitive to damaged larch, especially in the green light, red edge, and near-infrared bands. The proposed approach improves the accuracy of chlorophyll content estimation via Sentinel-2A data and enhances the ability to monitor changes in the chlorophyll content under complex forest conditions through simulations, providing new technical means and a theoretical basis for forestry pest monitoring and vegetation health management. Full article

Video surveillance is an effective tool for traffic management and safety, but it may face challenges in extreme weather, low visibility, areas outside the monitoring field of view, or during nighttime conditions. Therefore, abnormal sound detection is used in traffic management and safety as an auxiliary tool to complement video surveillance. In this paper, a novel lightweight method for abnormal sound detection based on 1D CNN and Multi-Head Self-Attention Mechanism on the embedded system is proposed, which is named HADNet. First, 1D CNN is employed for local feature extraction, which minimizes information loss from the audio signal during time-frequency conversion and reduces computational complexity. Second, the proposed block based on Multi-Head Self-Attention Mechanism not only effectively mitigates the issue of disappearing gradients, but also enhances detection accuracy. Finally, the joint loss function is employed to detect abnormal audio. This choice helps address issues related to unbalanced training data and class overlap, thereby improving model performance on imbalanced datasets. The proposed HADNet method was evaluated on the MIVIA Road Events and UrbanSound8K datasets. The results demonstrate that the proposed method for abnormal audio detection on embedded systems achieves high accuracy of 99.6% and an efficient detection time of 0.06 s. This approach proves to be robust and suitable for practical applications in traffic management and safety. By addressing the challenges posed by traditional video surveillance methods, HADNet offers a valuable and complementary solution for enhancing safety measures in diverse traffic conditions. Full article

Efficiently reconstructing complex and intricate surfaces at scale remains a significant challenge in 3D surface reconstruction. Recently, implicit neural representations have become a popular topic in 3D surface reconstruction. However, how to handle loop closure and bundle adjustment is a tricky problem for neural methods, because they learn the neural parameters globally. We present an algorithm that leverages the concept of surfels and expands relevant definitions to address such challenges. By integrating neural descriptors with surfels and framing surfel association as a deformation graph optimization problem, our method is able to effectively perform loop closure detection and loop correction in challenging scenarios. Furthermore, the surfel-level representation simplifies the complexity of 3D neural reconstruction. Meanwhile, the binding of neural descriptors to corresponding surfels produces a dense volumetric signed distance function (SDF), enabling the mesh reconstruction. Our approach demonstrates a significant improvement in reconstruction accuracy, reducing the average error by 16.9% compared to previous methods, while also generating modeling files that are up to 90% smaller than those produced by traditional methods. Full article

Ordinary 3D Baryon Acoustic Oscillations (BAO) data are model-dependent, requiring the assumption of a cosmological model to calculate comoving distances during data reduction. Throughout the present-day literature, the assumed model is $\Lambda$CDM. However, it has been pointed out in several recent works that this assumption can be inadequate when analyzing alternative cosmologies, potentially biasing the Hubble constant ($H_0$) low, thus contributing to the Hubble tension. To address this issue, 3D BAO data can be replaced with 2D BAO data, which are only weakly model-dependent. The impact of using 2D BAO data, in combination with alternative cosmological models beyond $\Lambda$CDM, has been explored for several phenomenological models, showing a promising reduction in the Hubble tension. In this work, we accommodate these models in the theoretically robust framework of bimetric gravity. This is a modified theory of gravity that exhibits a transition from a (possibly) negative cosmological constant in the early universe to a positive one in the late universe. By combining 2D BAO data with cosmic microwave background and type Ia supernovae data, we find that the inverse distance ladder in this theory yields a Hubble constant of $H_0=(71.0\pm 0.9)\mathrm{km}/\mathrm{s}/\mathrm{Mpc}$, consistent with the SH0ES local distance ladder measurement of $H_0=(73.0\pm 1.0)\mathrm{km}/\mathrm{s}/\mathrm{Mpc}$. Replacing 2D BAO with 3D BAO results in $H_0=(68.6\pm 0.5)\mathrm{km}/\mathrm{s}/\mathrm{Mpc}$ from the inverse distance ladder. We conclude that the choice of BAO data significantly impacts the Hubble tension, with ordinary 3D BAO data exacerbating the tension, while 2D BAO data provide results consistent with the local distance ladder. Full article

Background/Objectives: Cancer cachexia is a multifactorial syndrome characterized by the progressive loss of skeletal muscle mass and adipose tissue. Dalbergia odorifer is widely used in traditional medicine in Korea and China to treat various diseases. However, its exact role and underlying mechanism in regulating cancer cachexia have not been elucidated yet. This research was conducted to investigate the effect of D. odorifer extract (DOE) in preventing the development of cancer-induced cachexia symptoms and figure out the relevant mechanisms. Methods: A cancer cachexia model was established in Balb/c mice using the CT26 colon carcinoma cell line. To evaluate the anti-cachexia effect of Dalbergia odorifer extract (DOE), CT26-bearing mice were orally administered with DOE at concentrations of 50 and 100 mg/kg BW for 14 days. C2C12 myotubes and 3T3L1 adipocytes were treated with 80% CT26 conditioned medium, DOE, and wortmannin, a particular AKT inhibitor to determine the influence of DOE in the AKT signaling pathway. Mice body weight, food intake, myofiber cross-sectional area, adipocyte size, myotube diameter, lipid accumulation, and relevant gene expression were analyzed. Results: The oral administration of DOE at doses of 50 and 100 mg/kg body weight to CT26 tumor-bearing mice resulted in a significant reduction in body weight loss, an increase in food intake, and a decrease in serum glycerol levels. Furthermore, DOE treatment led to an increase in muscle mass, larger muscle fiber diameter, and elevated expression levels of MyH2 and Igf1, while simultaneously reducing the expression of Atrogin1 and MuRF1. DOE also attenuated adipose tissue wasting, as evidenced by increased epididymal fat mass, enlarged adipocyte size, and upregulated Pparγ expression, alongside a reduction in Ucp1 and IL6 levels. In cachectic C2C12 myotubes and 3T3-L1 adipocytes induced by the CT26 conditioned medium, DOE significantly inhibited muscle wasting and lipolysis by activating the AKT signaling pathway. The treatment of wortmannin, a specific AKT inhibitor, effectively neutralized DOE’s impact on the AKT pathway, myotube diameter, and lipid accumulation. Conclusions: DOE ameliorates cancer cachexia through the expression of genes involved in protein synthesis and lipogenesis, while suppressing those related to protein degradation, suggesting its potential as a plant-derived therapeutic agent in combating cancer cachexia. Full article

The addition of physical field enhancement measures to improve the purification effect of vertical flow artificial wetlands has gradually become popular. In this study, a vertical flow artificial wetland system reinforced by electric and magnetic fields was constructed. These fields were first optimized using finite element 3D simulation software to obtain the optimal electric and magnetic field parameters. Then, the pollutant removal effects and changes in microbial community structure were comparatively analyzed. The optimal electromagnetic field parameters (applied voltage of 15 V and applied magnetic field of 20 mT) resulted in significantly enhanced removal rates of chemical oxygen demand (COD), nitrate nitrogen (NH₄⁺-N), total phosphorus (TP), and orthophosphorus (PO₄³⁻-P) in wastewater, with rates of 74.47%, 45.44%, 89.85%, and 90.04%, respectively. These rates were notably higher than those observed in the vertical flow artificial wetland system. The microbial community structure analysis revealed that the vertical flow constructed wetland with enhanced electric and magnetic fields exhibited (EM-VFCW) a more diverse and complex microbial community structure. Notably, the abundance of bacteria capable of removing NH₄⁺-N and COD, including Aspergillus, Fusarium, and Actinobacteria, was significantly elevated. Full article

Currently, research on employing finite difference method and discrete element method (FDM-DEM) coupling to assess the stability of tunnel lining structures is limited. This study utilized the FDM-DEM coupling approach, with the F2 fault of the East Tianshan Tunnel as a case study, to develop a numerical model in conjunction with PFC3D 6.0 and FLAC3D 6.0 software. We conducted a comprehensive analysis of the displacement deformation and crack progression of the tunnel lining structure under varying dislocation momentum conditions, unveiling the underlying mechanisms. The findings indicated that as the dislocation increased, the extent of damage to the vault intensified, and the particle contact force within the tunnel lining shifted from compression to tension, significantly contributing to the crack formation. Fault dislocation influenced the gradual expansion of cracks from the vault to the spandrel and arch waist, with the crack width increasing alongside the rising dislocation momentum. In particular, under substantial dislocation momentum, the overall stability of the tunnel lining was markedly diminished. The safety factor at the tunnel section declined progressively as the dislocation momentum escalated, with values of 2.53, 2.49, 2.43, 2.39, and 2.32 corresponding to dislocation momenta of 0.01 m, 0.05 m, 0.1 m, 0.15 m, and 0.2 m, respectively. This research offers valuable insights and a reference framework for investigating the stability of tunnel lining structures in proximity to fault dislocations, pinpointing potential failure points, and bolstering the structural integrity of tunnels. Full article

With the recent prominence of artificial intelligence (AI) technology, various research outcomes and applications in the field of image recognition and processing utilizing AI have been continuously emerging. In particular, the domain of object recognition using 3D time-of-flight (ToF) sensors has been actively researched, often in conjunction with augmented reality (AR) and virtual reality (VR). However, for more precise analysis, high-quality images are required, necessitating significantly larger parameters and computations. These requirements can pose challenges, especially in developing AR and VR technologies for low-power portable devices. Therefore, we propose a dynamic feedback configuration image signal processor (ISP) for 3D ToF sensors. The ISP achieves both accuracy and energy efficiency through dynamic feedback. The proposed ISP employs dynamic area extraction to perform computations and post-processing only for pixels within the valid area used by the application in each frame. Additionally, it uses dynamic resolution to determine and apply the appropriate resolution for each frame. This approach enhances energy efficiency by avoiding the processing of all sensor data while maintaining or surpassing accuracy levels. Furthermore, These functionalities are designed for hardware-efficient implementation, improving processing speed and minimizing power consumption. The results show a maximum performance of 178 fps and a high energy efficiency of up to 123.15 fps/W. When connected to the hand pose estimation (HPE) accelerator, it demonstrates an average mean squared error (MSE) of 10.03 mm, surpassing the baseline ISP value of 20.25 mm. Therefore, the proposed ISP can be effectively utilized in low-power, small form-factor devices. Full article

Many designs for natural-convection heat sinks and semi-empirical correlations have been proposed in the recent years, but they are only valid in a limited range of Elenbaas numbers $El$ and were mostly tested for laminar flows. To alleviate those limits, parametric studies with 2D and quasi-3D models were carried out, in ranges of Grashof numbers up to $1.55\times {10}^{11}$ and Elenbaas numbers up to $3.42\times {10}^7$. Ansys Fluent’s laminar, transition-SST, SST k-$\omega$ and k-$ϵ$ models were applied. In addition, when used in this valid range, i.e., mean Elenbaas numbers, with the simplified quasi-3D model, the transition-SST model could predict better results, overestimating the heat flux by 10 to $15\%$ compared to semi-empirical correlations. The 2D model was not deemed satisfying, regarding turbulence models. Consequently, a quasi-3D model was developed: it appeared to be an efficient trade-off between computational time and prediction accuracy, in particular for turbulence models. New grouping factors were also found, to ensure proper dimensioning of natural-convection heat sinks. They corresponded to non-dimensional parameters that dictated the physical behaviour of the heat sink with respect to the semi-empirical correlations. Typically, the ratio of the spacing to the optimal spacing predicted by Bar-Cohen’s correlation turned out to be an appropriate grouping factor with a threshold of 1, above which the fins could safely be considered as isolated, thus greatly simplifying all further calculations. Full article

Background/Objectives: Studies have shown a high prevalence of anemia and vitamin D insufficiency in older adults, and the literature suggests a relationship between these two conditions, as vitamin D insufficiency may impair erythrocyte synthesis. Food insecurity refers to the lack of regular access to sufficient and nutritious food, which can directly affect health by worsening conditions such as anemia and vitamin D insufficiency. This study evaluated the association between vitamin D insufficiency and anemia in older adults. Methods: We conducted a cross-sectional study with 430 individuals aged 60 and older, using personal interviews and blood tests for data collection. Anemia was identified with serum hemoglobin levels of <12 g/dL for women and <13 g/dL for men, while vitamin D insufficiency was defined as serum levels <30 ng/mL. We used multiple logistic regression to analyze associations through Stata version 17.0 software. Results: The prevalence of anemia was identified in 14.7% of the sample, and vitamin D insufficiency was observed in 63.5%. We found an association between vitamin D insufficiency and anemia (OR = 2.4; 95% CI = 1.2–4.7). In the final model, factors such as male sex (OR = 2.7; 95% CI = 1.5–4.9) and polypharmacy use (OR = 2.0; 95% CI = 1.0–3.9) were also associated, regardless of age group, food insecurity, and multimorbidity. Conclusions: Vitamin D insufficiency increased the likelihood of anemia among the older adults evaluated, suggesting that prevention and treatment strategies for anemia should consider vitamin D serum levels. Full article

Audio-driven facial animation is a rapidly evolving field that aims to generate realistic facial expressions and lip movements synchronized with a given audio input. This survey provides a comprehensive review of deep learning techniques applied to audio-driven facial animation, with a focus on both audio-driven facial image animation and audio-driven facial mesh animation. These approaches employ deep learning to map audio inputs directly onto 3D facial meshes or 2D images, enabling the creation of highly realistic and synchronized animations. This survey also explores evaluation metrics, available datasets, and the challenges that remain, such as disentangling lip synchronization and emotions, generalization across speakers, and dataset limitations. Lastly, we discuss future directions, including multi-modal integration, personalized models, and facial attribute modification in animations, all of which are critical for the continued development and application of this technology. Full article

Convolutional neural networks (CNNs) have been widely applied to parse indoor scenes and segment objects represented by color images. Nonetheless, the lack of geometric and context information is a problem for most RGB-based methods, with which depth features are only used as an auxiliary module in RGB-D semantic segmentation. In this study, a novel depth dominant network (DDNet) is proposed to fully utilize the rich context information in the depth map. The critical insight is that obvious geometric information from the depth image is more conducive to segmentation than RGB data. Compared with other methods, DDNet is a depth-based network with two branches of CNNs to extract color and depth features. As the core of the encoder network, the depth branch is given a larger fusion weight to extract geometric information, while semantic information and complementary geometric information are provided by the color branch for the depth feature maps. The effectiveness of our proposed depth-based architecture has been demonstrated by comprehensive experimental evaluations and ablation studies on challenging RGB-D semantic segmentation benchmarks, including NYUv2 and a subset of ScanNetv2. Full article

The main objective of this experiment was to investigate the technological potential of upcycling unsparged non-conventional brewers’ spent grains (BSGs) in bread-making and assess the comparative quality of bread enriched with non-fermented and lactic acid-fermented BSGs obtained from mashes brewed with starch adjuncts of buckwheat and oats. After the runoff of the first wort, unsparged non-conventional BSGs with approximately 75% moisture, acidic pH, and yield in the soluble extract above 56.6% (w/w d.m.) were used in substituting wheat flour with 5 and 15% (w/w d.m.) in bread-making recipes. The highest loaf volume value (318.68 cm³/100 g) was observed for 5% fermented buckwheat-BSG addition. Except for the samples with 5% fermented BSGs, specific volumes decreased. Crumb moisture was reduced by up to 22% for all samples, with this parameter related to bread weight. Bread porosity, elasticity, acidity, and overall sensory acceptability were better for fermented than non-fermented BSGs. The results proved that non-conventional BSGs with buckwheat and oats addition have the potential to be valorized in new bread assortments, and lactic acid fermentation applied to the BSGs is beneficial, even for overall sensory acceptability and quality of baked end-products. Technological, buckwheat-BSG was more convenient than oats-BSG. Further research continues to optimize and upscale Technology Readiness Levels. Full article

The importance of information gathering is emphasized to minimize casualties and economic losses in warfare. Through electronic warfare, which utilizes electromagnetic waves, it is possible to discern the enemy’s intentions and respond accordingly, thereby leading the battle advantageously. Consequently, related research is actively underway. The development of various radar signal modulation techniques has revealed limitations in the existing modulation recognition methods, necessitating the development of distinguishing features to overcome these limitations. This paper proposes and analyzes distinguishing features that can differentiate various modulation schemes. Eleven distinguishing features were employed, and twenty-two types of modulated signals, including analog, digital, and composite modulation, were classified using hierarchical classification approach and maximum likelihood estimation (MLE). The proposed method achieves a recognition performance of 99.76% at an SNR of 20 dB and 98.45% at an SNR of 8 dB. Full article