Search Results (24,559)

Porcine circovirus type 2 (PCV2) is the main pathogen causing post-weaning multisystemic wasting syndrome (PMWS), which mainly targets the body’s immune system and poses a serious threat to the global pig industry. 5-Azacytidine is a potent inhibitor of DNA methylation, which can participate in many important physiological and pathological processes, including virus-related processes, by inhibiting gene expression. However, the impact of 5-Aza on PCV2 replication in cells is not yet clear. We explored the impact of 5-Aza on PCV2 infection utilizing PK15 cells as a cellular model. Our objective was to gain insights that could potentially offer novel therapeutic strategies for PCV2. Our results showed that 5-Aza significantly enhanced the infectivity of PCV2 in PK15 cells. Transcriptome analysis revealed that PCV2 infection activated various immune-related signaling pathways. 5-Aza may activate the MAPK signaling pathway to exacerbate PCV2 infection and upregulate the expression of inflammatory and apoptotic factors. Full article

Effectively extracting a lithium-ion battery’s impedance is of great importance for various onboard applications, which requires consideration of both the time consumption and accuracy of the measurement process. Although the pseudorandom binary sequence (PRBS) excitation signal can inject the superposition frequencies with high time efficiency and an easily implementable device, processing the data of the battery’s impedance measurement is still a challenge at present. This study proposes a fast impedance spectrum construction method for lithium-ion batteries, where a multi-density clustering algorithm was designed to effectively extract the useful impedance after PRBS injection. According to the distribution properties of the measurement points by PRBS, a density-based spatial clustering of applications with noise (DBSCAN) was used for processing the data of the lithium-ion battery’s impedance. The two key parameters of the DBSCAN were adjusted by a delicate workflow according to the frequency range. The validation of the proposed method was proved on a 3 Ah lithium-ion battery under nine different test conditions, considering both the SOC and temperature variations. Full article

Current advancements in biosignal-based user recognition technology are paving the way for a next-generation solution that addresses the limitations of face- and fingerprint-based user recognition methods. However, existing biosignal benchmark databases (DBs) for user recognition often suffer from limitations, such as data collection from a small number of subjects in a single session, hindering comprehensive analysis of biosignal variability. This study introduces CSU_MBDB1 and CSU_MBDB2, databases containing electrocardiogram (ECG) and electromyogram (EMG) signals from diverse experimental subjects recorded across multiple sessions. These in-house DBs comprise ECG and EMG data recorded in multiple sessions from 36 and 58 subjects, respectively, with a time interval of more than one day between sessions. During the experiments, subjects performed a total of six gestures while comfortably seated at a desk. CSU_MBDB1 and CSU_MBDB2 consist of three identical gestures, providing expandable data for various applications. When the two DBs are expanded, ECGs and EMGs from 94 subjects can be used, which is the largest number among the multi-biosignal benchmark DBs built by multi-sessions. To assess the usability of the constructed DBs, a user recognition experiment was conducted, resulting in an accuracy of 66.39% for ten subjects. It is important to emphasize that we focused on demonstrating the applicability of the constructed DBs using a basic neural network without signal denoising capabilities. While this approach results in a sacrifice in accuracy, it concurrently provides substantial opportunities for performance enhancement through the implementation of optimized algorithms. Adapting signal denoising processes to the constructed DBs and designing a more sophisticated neural network would undoubtedly contribute to improving the recognition accuracy. Consequently, these constructed DBs hold promise in user recognition, offering valuable research for future investigations. Additionally, DBs can be used in research to analyze the nonlinearity characteristics of ECG and EMG. Full article

This paper presents a study of 2D roughness profiles on a flat surface generated on a steel workpiece by ball nose end milling with linear equidistant tool paths (pick-intervals). The exploration of the milled surface with a surface roughness tester (on the pick and feed directions) produces 2D roughness profiles that usually have periodic evolutions. These evolutions can be considered as time-dependent signals, which can be described as a sum of sinusoidal components (the wavelength of each component is considered as a period). In order to obtain a good approximate description of these sinusoidal components, two suitable signal processing techniques are used in this work: the first technique provides a direct mathematical (analytical) description and is based on computer-aided curve (signal) fitting (more accurate); the second technique (synthetic, less accurate, providing an indirect and incomplete description) is based on the spectrum generated by fast Fourier transform. This study can be seen as a way to better understand the interaction between the tool and the workpiece or to achieve a mathematical characterisation of the machined surface microgeometry in terms of roughness (e.g., its description as a collection of closely spaced 2D roughness profiles) and to characterise the workpiece material in terms of machinability by cutting. Full article

Non-side-looking airborne radar provides important applications on account of its all-round multi-angle airspace coverage. However, it suffers clutter range dependence that makes the samples fail to satisfy the condition of being independent and identically distributed (IID), and it severely degrades traditional approaches to clutter suppression and target detection. In this paper, a novel reweighted extreme learning machine (ELM)-based clutter suppression and range compensation algorithm is proposed for non-side-looking airborne radar. The proposed method involves first designing the pre-processing stage, the special reweighted complex-valued activation function containing an unknown range compensation matrix, and two new objective outputs for constructing an initial reweighted ELM-based network with its training. Then, two other objective outputs, a new loss function, and a reverse feedback framework driven by the specifically designed objectives are proposed for the unknown range compensation matrix. Finally, aiming to estimate and reconstruct the unknown compensation matrix, special processes of the complex-valued structures and the theoretical derivations are designed and analyzed in detail. Consequently, with the updated and compensated samples, further processing including space–time adaptive processing (STAP) can be performed for clutter suppression and target detection. Compared with the classic relevant methods, the proposed algorithm achieves significantly superior performance with reasonable computation time. It provides an obviously higher detection probability and better improvement factor (IF). The simulation results verify that the proposed algorithm is effective and has many advantages. Full article

The Chinese soft-shelled turtle (Pelodiscus sinensis) is an important freshwater aquaculture turtle due to its taste and nutritional and medicinal value. More ecological culturing modes, such as rice–turtle co-culture, should be developed to meet the ecological benefit demand. We compared growth, physiological parameters, and transcriptome data to detect the physiological responses and regulatory mechanisms of pond-cultured turtles as compared to co-cultured turtles. The co-cultured turtles grew slower than pond-cultured turtles. The gonadosomatic index of co-cultured male turtles was lower than that of pond-cultured male turtles, and both the mesenteric fat index and limb fat index were lower in co-cultured turtles than in pond-cultured turtles (p < 0.05). The blood GLU of the co-cultured turtles was significantly lower than the GLU of the pond-cultured turtles (p < 0.05), while the values of CRE, UA, BUN, AKP, ACP, GOT, and CAT were higher in the co-cultured turtles than in the pond-cultured turtles (p < 0.05). In total, 246 and 598 differentially expressed genes (DEGs) were identified in the brain and gut from turtles cultured in the two different modes, respectively. More DEGs were related to environmental information processing, metabolism, and human diseases. In the brain, the top enriched pathways of DEGs included the longevity regulating pathway, glycerolipid metabolism, cytokine–cytokine receptor interaction, Toll-like receptor signaling pathway, and PI3K-Akt signaling pathway, while in the gut, the top enriched pathways of DEGs included the cell cycle, DNA replication, cellular senescence, and p53 signaling pathway. The turtles acclimated to the different culturing conditions by adjusting their growth, physiological, and biochemical characteristics and related gene expression during a short culture period. Full article

The remote monitoring of clinical parameters plays a fundamental role in different situations, like pandemic health emergencies and post-surgery conditions. In these situations, the patients might be impeded in their movements, and it could be difficult to have specific health monitoring. In recent years, technological advances in smartphones have opened up new possibilities in this landscape. The present work aims to propose a new method for respiratory kinematics monitoring via smartphone sensors. In particular, a specific application was developed to register inertial measurement unit (IMU) sensor data from the smartphone for respiratory kinematics measurement and to guide the user through a specific acquisition session. The session was defined to allow the monitoring of the respiratory movement in five prescribed positions. The application and the sequence were successfully tested on a given population of 77 healthy volunteers. The resulting accelerometers and gyroscope signals were processed to evaluate the significance of differences according to participants’ sex, vector components, and smartphone positioning and, finally, to estimate the respiratory rate. The statistical differences that emerged revealed the significance of information in the different acquisition positions. Full article

Pedestrian crossings are an essential part of the urban landscape, providing safe passage for pedestrians to cross busy streets. While some are regulated by timed signals and are marked with signs and lights, others are simply marked on the road and do not have additional infrastructure. Nevertheless, the markings undergo wear and tear due to traffic, weather, and road maintenance activities. If pedestrian crossing markings are excessively worn, drivers may not be able to see them, which creates road safety issues. This paper presents a study of computer vision techniques that can be used to identify and classify pedestrian crossings. It first introduces the related concepts. Then, it surveys related work and categorizes existing solutions, highlighting their key features, strengths, and limitations. The most promising techniques are identified and described: Convolutional Neural Networks, Histogram of Oriented Gradients, Maximally Stable Extremal Regions, Canny Edge, and thresholding methods. Their performance is evaluated and compared on a custom dataset developed for this work. Insights on open issues and research opportunities in the field are also provided. It is shown that managers responsible for road safety, in the context of a smart city, can benefit from computer vision approaches to automate the process of determining the wear and tear of pedestrian crossings. Full article

Hyperspectral imaging (HSI) is a remote sensing technique that has been successfully applied for the task of damage detection in glass fibre-reinforced plastic (GFRP) materials. Similarly to other vision-based detection methods, one of the drawbacks of HSI is its susceptibility to the lighting conditions during the imaging, which is a serious issue for gathering hyperspectral data in real-life scenarios. In this study, a data conditioning procedure is proposed for improving the results of damage detection with various classifiers. The developed procedure is based on the concept of signal stationarity and cointegration analysis, and achieves its goal by performing the detection and removal of the non-stationary trends in hyperspectral images caused by imperfect lighting. To evaluate the effectiveness of the proposed method, two damage detection tests have been performed on a damaged GFRP specimen: one using the proposed method, and one using an established damage detection workflow, based on the works of other authors. Application of the proposed procedure in the processing of a hyperspectral image of a damaged GFRP specimen resulted in significantly improved accuracy, sensitivity, and F-score, independently of the type of classifier used. Full article

Type-2 diabetes mellitus (T2DM)-induced sarcopenia is intertwined with diminished insulin sensitivity and extracellular matrix (ECM) remodeling in skeletal muscle and other organs. Physical activities such as aerobic exercise play a crucial role in regulating blood glucose levels, insulin sensitivity, metabolic pathways, oxidative stress, fibrosis, ECM remodeling, and muscle regeneration by modulating differentially expressed protein (DEP) levels. The objectives of our research were to investigate the effect of six weeks of aerobic exercise on the gastrocnemius and soleus muscle of db/db mice’s DEP levels compared to those of sedentary db/db mice. A total of eight db/db mice were divided into two groups (n = 4 per group), namely sedentary mice (SED) and exercise-trained mice (ET), of which the latter were subjected to six weeks of a moderate-intensity aerobic exercise intervention for five days per week. After the exercise intervention, biochemical tests, including analyses of blood glucose and HbA1c levels, were performed. Histological analysis using H & E staining on tissue was performed to compare morphological characters. Gastrocnemius and soleus muscles were dissected and processed for proteomic analysis. Data were provided and analyzed based on the DEPs using the label-free quantification (LFQ) algorithm. Functional enrichment analysis and Ingenuity Pathway Analysis (IPA) were employed as bioinformatics tools to elucidate the molecular mechanisms involved in the DEPs and disease progression. Significantly reduced blood glucose and HbA1c levels and an increased cross-sectional area (CSA) of gastrocnemius muscle fibers were seen in the ET group after the exercise interventions due to upregulations of metabolic pathways. Using proteomics data analysis, we found a significant decrease in COL1A1, COL4A2, ENG, and LAMA4 protein levels in the ET gastrocnemius, showing a significant improvement in fibrosis recovery, ECM remodeling, and muscle regeneration via the downregulation of the TGF-β signaling pathway. Upregulated metabolic pathways due to ET-regulated DEPs in the gastrocnemius indicated increased glucose metabolism, lipid metabolism, muscle regeneration, and insulin sensitivity, which play a crucial role in muscle regeneration and maintaining blood glucose and lipid levels. No significant changes were observed in the soleus muscle due to the type of exercise and muscle fiber composition. Our research suggests that engaging in six weeks of aerobic exercise may have a positive impact on the recovery of T2DM-induced sarcopenia, which might be a potential candidate for mitigation, prevention, and therapeutic treatment in the future. Full article

The gated recurrent unit (GRU) network can effectively capture temporal information for 1D signals, such as electroencephalography and event-related brain potential, and it has been widely used in the field of EEG emotion recognition. However, multi-domain features, including the spatial, frequency, and temporal features of EEG signals, contribute to emotion recognition, while GRUs show some limitations in capturing frequency–spatial features. Thus, we proposed a hybrid architecture of convolutional neural networks and GRUs (CGRU) to effectively capture the complementary temporal features and spatial–frequency features hidden in signal channels. In addition, to investigate the interactions among different brain regions during emotional information processing, we considered the functional connectivity relationship of the brain by introducing a phase-locking value to calculate the phase difference between the EEG channels to gain spatial information based on functional connectivity. Then, in the classification module, we incorporated attention constraints to address the issue of the uneven recognition contribution of EEG signal features. Finally, we conducted experiments on the DEAP and DREAMER databases. The results demonstrated that our model outperforms the other models with remarkable recognition accuracy of 99.51%, 99.60%, and 99.59% (58.67%, 65.74%, and 67.05%) on DEAP and 98.63%, 98.7%, and 98.71% (75.65%, 75.89%, and 71.71%) on DREAMER in a subject-dependent experiment (subject-independent experiment) for arousal, valence, and dominance. Full article

Brushstroke segmentation algorithms are critical in computer-based analysis of fine motor control via handwriting, drawing, or tracing tasks. Current segmentation approaches typically rely only on one type of feature, either spatial, temporal, kinematic, or pressure. We introduce a segmentation algorithm that leverages both spatiotemporal and pressure features to accurately identify brushstrokes during a tracing task. The algorithm was tested on both a clinical and validation dataset. Using validation trials with incorrectly identified brushstrokes, we evaluated the impact of segmentation errors on commonly derived biomechanical features used in the literature to detect graphomotor pathologies. The algorithm exhibited robust performance on validation and clinical datasets, effectively identifying brushstrokes while simultaneously eliminating spurious, noisy data. Spatial and temporal features were most affected by incorrect segmentation, particularly those related to the distance between brushstrokes and in-air time, which experienced propagated errors of 99% and 95%, respectively. In contrast, kinematic features, such as velocity and acceleration, were minimally affected, with propagated errors between 0 to 12%. The proposed algorithm may help improve brushstroke segmentation in future studies of handwriting, drawing, or tracing tasks. Spatial and temporal features derived from tablet-acquired data should be considered with caution, given their sensitivity to segmentation errors and instrumentation characteristics. Full article

Low-frequency and 1/f noise are common measurement limitations that arise in a variety of physical processes. Mitigation methods for these noises are dependent on their source. Here, we present a method for removing $1/f$ noise of optical origin using a micro-cavity Fabry–Pérot (FP) interferometer. A mechanical modulation of the FP cavity length was applied to a previously studied opto-mechanical sensor. It effectively mimics an up-conversion of the laser frequency, shifting signals to a region where lower white-noise sources dominate and $1/f$ noise is not present. Demodulation of this signal shifts the results back to the desired frequency range of observation with the reduced noise floor of the higher frequencies. This method was found to improve sensitivities by nearly two orders of magnitude at 1 Hz and eliminated 1/f noise in the range from 1 Hz to 4 kHz. A mathematical model for low-finesse FP cavities is presented to support these results. This study suggests a relatively simple and efficient method for $1/f$ noise suppression and improving the device sensitivity of systems with an FP interferometer readout. Full article

The plant hormone ethylene is a key regulator of plant growth, development, and stress adaptation. Many ethylene-related responses, such as abscission, seed germination, or ripening, are of great importance to global agriculture. Ethylene perception and response are mediated by a family of integral membrane receptors (ETRs), which form dimers and higher-order oligomers in their functional state as determined by the binding of Cu(I), a cofactor to their transmembrane helices in the ER-Golgi endomembrane system. The molecular structure and signaling mechanism of the membrane-integral sensor domain are still unknown. In this article, we report on the crystallization of transmembrane (TM) and membrane-adjacent domains of plant ethylene receptors by Lipidic Cubic Phase (LCP) technology using vapor diffusion in meso crystallization. The TM domain of ethylene receptors ETR1 and ETR2, which is expressed in E. coli in high quantities and purity, was successfully crystallized using the LCP approach with different lipids, lipid mixtures, and additives. From our extensive screening of 9216 conditions, crystals were obtained from identical crystallization conditions for ETR1 (aa 1-316) and ETR2 (aa 1-186), diffracting at a medium–high resolution of 2–4 Å. However, data quality was poor and not sufficient for data processing or further structure determination due to rotational blur and high mosaicity. Metal ion loading and inhibitory peptides were explored to improve crystallization. The addition of Zn(II) increased the number of well-formed crystals, while the addition of ripening inhibitory peptide NIP improved crystal morphology. However, despite these improvements, further optimization of crystallization conditions is needed to obtain well-diffracting, highly-ordered crystals for high-resolution structural determination. Overcoming these challenges will represent a major breakthrough in structurally determining plant ethylene receptors and promote an understanding of the molecular mechanisms of ethylene signaling. Full article

The characterization of the PYL/RCAR ABA receptors in a great deal of plant species has dramatically advanced the study of ABA functions involved in key physiological processes. However, the genes in this family are still unclear in Lycium (Goji) plants, one of the well–known economically, medicinally, and ecologically valuable fruit crops. In the present work, 12 homologs of Arabidopsis PYL/RCAR ABA receptors were first identified and characterized from Lycium (L.) barbarum (LbPYLs). The quantitative real–time PCR (qRT–PCR) analysis showed that these genes had clear tissue–specific expression patterns, and most of them were transcribed in the root with the largest amount. Among the three subfamilies, while the Group I and Group III members were down–regulated by extraneous ABA, the Group II members were up–regulated. At 42 °C, most transcripts showed a rapid and violent up–regulation response to higher temperature, especially members of Group II. One of the genes in the Group II members, LbPYL10, was further functionally validated by virus–induced gene silencing (VIGS) technology. LbPYL10 positively regulates heat stress tolerance in L. barbarum by alleviating chlorophyll degradation, thus maintaining chlorophyll stability. Integrating the endogenous ABA level increase following heat stress, it may be concluded that LbPYL–mediated ABA signaling plays a vital role in the thermotolerance of L. barbarum plants. Our results highlight the strong potential of LbPYL genes in breeding genetically modified L. barbarum crops that acclimate to climate change. Full article