Search Results (23,483)

Iron is an important trace element that affects the growth and development of animals and regulates oxygen transport, hematopoiesis, and hypoxia adaptations. Wujin pig has unique hypoxic adaptability and iron homeostasis; however, the specific regulatory mechanisms have rarely been reported. This study randomly divided 18 healthy Wujin piglets into three groups: the control group, supplemented with 100 mg/kg iron (as iron glycinate); the low-iron group, no iron supplementation; and the high-iron group, supplemented with 200 mg/kg iron (as iron glycinate). The pre-feeding period was 5 days, and the formal period was 30 days. Serum was collected from empty stomachs before slaughter and at slaughter to detect changes in the serum iron metabolism parameters. Gene expression in the liver was analyzed via transcriptome analysis to determine the effects of low- and high-iron diets on transcriptome levels. Correlation analysis was performed for apparent serum parameters, and transcriptome sequencing was performed using weighted gene co-expression network analysis to reveal the key pathways underlying hypoxia regulation and iron metabolism. The main results are as follows. (1) Except for the hypoxia-inducible factor 1 (HIF-1) content (between the low- and high-iron groups), significant differences were not observed among the serum iron metabolic parameters. The serum HIF-1 content of the low-iron group was significantly higher than that of the high-iron group (p < 0.05). (2) Sequencing analysis of the liver transcriptome revealed 155 differentially expressed genes (DEGs) between the low-iron and control groups, 229 DEGs between the high-iron and control groups, and 279 DEGs between the low- and high-iron groups. Bioinformatics analysis showed that the HIF-1 and transforming growth factor-beta (TGF-β) signaling pathways were the key pathways for hypoxia regulation and iron metabolism. Four genes were selected for qPCR validation, and the results were consistent with the transcriptome sequencing data. In summary, the serum iron metabolism parameter results showed that under the influence of low- and high-iron diets, Wujin piglets maintain a steady state of physiological and biochemical indices via complex metabolic regulation of the body, which reflects their stress resistance and adaptability. The transcriptome results revealed the effects of low-iron and high-iron diets on the gene expression level in the liver and showed that the HIF-1 and TGF-β signaling pathways were key for regulating hypoxia adaptability and iron metabolism homeostasis under low-iron and high-iron diets. Moreover, HIF-1α and HEPC were the key genes. The findings provide a theoretical foundation for exploring the regulatory pathways and characteristics of iron metabolism in Wujin pigs. Full article

With the development of satellite technology, the importance of multi-temporal remote sensing (RS) image change detection (CD) in urban planning, environmental monitoring, and other fields is increasingly prominent. Deep learning techniques enable a profound exploration of the intrinsic features within hyperspectral (HS) data, leading to substantial enhancements in CD accuracy while addressing several challenges posed by traditional methodologies. However, existing convolutional neural network (CNN)-based CD approaches frequently encounter issues during the feature extraction process, such as the loss of detailed information due to downsampling, which hampers a model’s ability to accurately capture complex spectral features. Additionally, these methods often neglect the integration of multi-scale information, resulting in suboptimal local feature extraction and, consequently, diminished model performance. To address these limitations, we propose a multi-scale fusion network (MsFNet) which leverages dynamic spectral features for effective multi-temporal HS-CD. Our approach incorporates a dynamic convolution module with spectral attention, which adaptively modulates the receptive field size according to the spectral characteristics of different bands. This flexibility enhances the model’s capacity to focus on critical bands, thereby improving its ability to identify and differentiate changes across spectral dimensions. Furthermore, we develop a multi-scale feature fusion module which extracts and integrates features from deep feature maps, enriching local information and augmenting the model’s sensitivity to local variations. Experimental evaluations conducted on three real-world HS-CD datasets demonstrate that the proposed MsFNet significantly outperforms contemporary advanced CD methods in terms of both efficacy and performance. Full article

Ophiocordyceps sinensis, a medicinal fungus utilized in traditional Chinese medicine, exhibits a range of biological activities and pharmacological functions. In this study, we determined the amino acid composition of 94 amino acids in Ophiocordyceps sinensis using liquid chromatography–tandem mass spectrometry (LC-MS/MS). Fresh samples of Ophiocordyceps sinensis were analyzed under three different drying methods: vacuum freeze drying (DG), oven drying (HG), and air drying (YG). This investigation aims to assess the effects of these drying methods on the content and quality of amino acid metabolites in Ophiocordyceps sinensis. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed for sample classification and the identification of differentially accumulated metabolites (DAMs). The results revealed the detection of 79 amino acid metabolites, which included elevated levels of oxidized L-glutamic acid, L-glutamic acid, and glutathione. Differential amino acid metabolites that met the criteria of fold change (|FC|) ≥ 2, p-value (p) ≤ 0.5, and variable importance in projection (VIP) ≥ 1 were analyzed. Significant differences in 48 amino acid metabolites between the groups were primarily related to protein synthesis. According to the KEGG analysis, all three comparison samples exhibited significant enrichment in several pathways. These pathways included the interaction of neuroactive ligands with receptors, the metabolism of cysteine and methionine, and the biosynthesis of plant hormones. The variations in amino acid metabolite levels observed across the three drying methods may be attributed to the degradation of proteins or amino acid metabolites, influenced by several factors, including temperature, enzyme activity, and moisture content. Additionally, Maillard and oxidative reactions involving substances such as amino acids, sugars, and oxygen may also play a significant role. This study demonstrates that various drying methods significantly influence the amino acid metabolite content of Ophiocordyceps sinensis. Therefore, the selection of drying methods should be tailored to meet specific requirements. This research provides important insights into the metabolite composition of Ophiocordyceps sinensis under different drying techniques, thereby contributing to a more comprehensive understanding of its nutritional and therapeutic properties. Full article

Persistent droughts pose a threat to agricultural production, and the changing environment worsens the risk of drought exposure. Understanding the propagation of drought in changing environments and assessing possible impact factors can help in the early detection of drought, guiding agricultural production practices. The current study cannot reflect the propagation status of drought to the total terrestrial hydrological drought, so this work creatively investigated the atmospheric to hydrological drought propagation time in the Yangtze River Basin under the dynamic and static perspectives based on the Standardized Precipitation Evapotranspiration Index and the Terrestrial Water Storage Anomalous Drought Index, fine-tuned the time scale to the seasonal scale, and explored the contributing capacity of the variable interactions. The results show that: (1) under the dynamic perspective, while the propagation time is decreasing in the annual scale, the spring season shows the opposite trend; and (2) large variability exists in the timing of drought propagation at spatial scales, with elevation playing the most important influential role, and bivariate interactions contributing stronger explanations compared to single variables. This study highlights the importance of considering the impact of variable interactions and contributes to our understanding of the response of secondary droughts to upper-level droughts, providing valuable insights into the propagation of droughts to total terrestrial hydrologic drought. Full article

To investigate the effects of early administration of ciprofloxacin (CIP) on Xueshan chickens, in this study Xueshan chickens were measured for growth performance, tested for drug residues, evaluated for meat quality, and muscle metabolism changes were explored using a non-target metabolomics approach. Experimental findings revealed that early CIP use did not significantly impact the overall growth rate of Xueshan chickens (p > 0.05). However, notable alterations in meat quality were observed: the CIP-treated group exhibited a significant decrease in muscle pH (pH₁ and pH₂₄) and a marked increase in drip loss and moisture content (p > 0.05). No CIP residues were detected in muscle tissue. Untargeted metabolomics analyses unveiled significant alterations in the metabolic profile of market-age chickens following CIP treatment. Both functional enrichment and metabolic network analyses indicated significant effects on the ko01120 (microbial metabolism in diverse environments) and ko00350 (tyrosine metabolism) pathways, implying that CIP treatment may influence chicken meat quality by modulating microbial communities and amino acid metabolism. This study provides a crucial foundation for understanding the impact of antibiotics on meat quality and metabolism in poultry production, offering scientific insights for optimizing antibiotic-use strategies and safeguarding poultry product quality. Full article

Understanding the dietary preferences and ecological niche characteristics of mammals not only reveals their adaptive strategies under environmental changes but also reveals the interspecific relationships and coexistence mechanisms among sympatric species. Nevertheless, such data are scarce for rodents inhabiting areas spanning a wide altitude range. This study employed DNA metabarcoding technology to analyze the stomach contents of Apodemus ilex, Apodemus chevrieri, and Niviventer confucianus, aiming to investigate their dietary compositions and diversity in the Meili Snow Mountain in Yunnan Province, China. Levins’s and Pianka’s indices were used to compare the interspecific niche breadth and niche overlaps. The results revealed the following: (1) Insecta (relative abundance: 59.4–78.4%) and Clitellata (relative abundance: 5.2–25.5%) were the primary animal food sources for the three species, while Magnoliopsida (relative abundance: 90.3–99.9%) constitutes their main plant food source. Considerable interspecific differences were detected in the relative abundance of primary animal and plant foods among the three species; (2) There was partial overlap in the genus-level animal food between A. ilex and N. confucianus (O_jk = 0.4648), and partial overlap in plant food between A. ilex and A. chevrieri (O_jk = 0.3418). However, no overlap exists between A. chevrieri and N. confucianus, either in animal or plant food; (3) There were no significant interspecific differences in the α-diversity of animal and plant foods among the three species. The feeding strategies and ecological niche variations of these rodents support the niche differentiation hypothesis, indicating that they have diversified in their primary food sources. This diversification may be a strategy to reduce competition and achieve long-term coexistence by adjusting the types and proportions of primary foods consumed. Full article

Renewable energy accommodation in power grids leads to frequent load changes in power plants. Sensitive turbine fault monitoring technology is critical to ensure the stable operation of the power system. Existing techniques do not use information sufficiently and are not sensitive to early fault signs. To solve this problem, an unsupervised fault warning method based on hybrid information gain and a convolutional autoencoder (CAE) for turbine intermediate flux is proposed. A high-precision intermediate-stage flux prediction model is established using the CAE. The hybrid information gain calculation method is proposed to filter the features of multi-dimensional sensors. The Hampel filter for time series outlier detection is introduced to deal with factors such as sensor faults and noise. The proposed method achieves the highest fault diagnosis accuracy through experiments on real data compared to traditional methods. Real data experiments show that the proposed method relatively improves the diagnostic accuracy by an average of 2.12% compared to the gate recurrent unit networks, long short-term memory networks, and other traditional models. Meanwhile, the proposed hybrid information gain can effectively improve the detection accuracy of the traditional models, with a maximum of 1.89% relative accuracy improvement. The proposed method is noteworthy for its superiority and applicability. Full article

Annual urban forest expansion dynamics are crucial for assessing the benefits and potential issues associated with vegetation accumulation over time. LandTrendr (Landsat-Based Detection of Trends in Disturbance and Recovery) can efficiently detect the dynamics of interannual land cover change, but it has difficulty distinguishing urban forest expansion from urban surface rapid conversions, as changes are usually filtered by magnitude-of-change thresholds. To accurately detect annual urban forest expansion dynamics, we developed an improved method using random forest-supervised classification to filter urban forests. We further enhanced the performance of the improved method by incorporating trend features between segments. Additionally, we tested two threshold-based filtering baseline methods. These methods were tested with various spectral and parameter combinations in Beijing’s Central District and the 1st Greenbelt from 1994 to 2022. The improved method with trend features achieved the highest average accuracy of 89.35%, representing a 25% improvement over baseline methods. Post-change trend features aided in accurate identification, while quantitative features rather than extremum features were more important in filtering. The improved method with trend features tested in Beijing’s 2nd Greenbelt also showed an accuracy of 88.27%, confirming its stability. SWIR2 and a higher maximum segment number are efficient for filtering by providing the most detailed dynamics. Accurate annual expansion dynamic mapping offers insights into change rates and precise expansion years, providing a new perspective for urban forest research and management. Full article

Understanding population genetic structure and its possible causal factors is critical for utilizing genetic resources and genetic breeding of economically important plants. Although Torreya grandis is an important conifer producing nuts in China, little is known about its population structure, let alone the causal factors that shaped its genetic variation pattern and population structure. In this work, we intended to characterize the genetic variation pattern and population structure of the nut-yielding conifer T. grandis throughout its whole geographical distribution and further explore the potentially causal factors for the population structure using multiple approaches. A moderate level of genetic diversity and a novel population structure were revealed in T. grandis based on eleven robust EST-SSR loci and three chloroplast fragments. Alien genetic composition derived from the closely related species T. nucifera endemic to Japan was detected in the Kuaiji Mountain area, where the seed quality of T. grandis is considered the best in China. Demography history and niche modeling were inferred and performed, and the contribution of geographic isolation to its population structure was compared with that of environmental isolation. Significant demographic changes occurred, including a dramatic population contraction during the Quaternary, and population divergence was significantly correlated with geographic distance. These results suggested that early breeding activities and demographic changes significantly contributed to the population structure of T. grandis. In turn, the population structure was potentially associated with the excellent variants and adaptation of cultivars of T. grandis. The findings provide important information for utilizing genetic resources and genetic breeding of T. grandis in the future. Full article

The dynamic structure of the ionosphere and its changes play an important role in comprehending the natural cycle by linking earth sciences and space sciences. Ionosphere research includes a variety of fields like meteorology, radio wave reflection from the atmosphere, atmospheric anomaly detection, the impact on GNSS (Global Navigation Satellite Systems) signals, the exploration of earthquake precursors, and the formation of the northern lights. To gain further insight into this layer and to monitor variations in the total electron content (TEC), ionospheric maps are created using a variety of data sources, including satellite sensors, GNSS data, and ionosonde data. In these maps, data deficiencies are addressed by using interpolation methods. The objective of this study was to obtain high-accuracy VTEC (Vertical Total Electron Content) information to analyze TEC anomalies as precursors to earthquakes. We propose an innovative approach: employing alternative mathematical surfaces for VTEC calculations, leading to enhanced change analytical interpretation for anomaly detections. Within the scope of the application, the second-degree polynomial method, kriging (point and block model), the radial basis multiquadric, and the thin plate spline (TPS) methods were implemented as interpolation methods. During a 49-day period, the TEC values were computed at three different IGS stations, generating 1176 hourly grids for each interpolation model. As reference data, the ionospheric maps produced by the CODE (Center for Orbit Determination in Europe) Analysis Center were used. This study’s findings showed that, based on statistical values, the TPS model offered more accurate results than other methods. Additionally, it has been observed that the peak values in TEC calculations based on polynomial surfaces are eliminated in TPSs. Full article

The main concern of remote control systems for autonomous ground vehicles (AGVs) is to perform the given mission according to the purpose of the operator. Although most remote systems are composed of a screen-based architecture, they are insufficient to transfer sufficient information to the remote operator. Therefore, in this paper, we present and experimentally validate an abnormal driving area detection system using an interacting multiple model (IMM) filter for the remote control system. In the proposed IMM filter, the unknown dynamic behavior of the vehicle, which changes according to changes in the driving environment, was lumped into a parameter change of the system model. As a result, we can obtain the probability of each model representing the reliability of each model, but an index can be used to infer the current status of the AGV and the driving environment. The index can help us detect both unusual behavior of the AGV such as skidding or sliding, and areas with low-friction road conditions that are not confirmed by images from the camera sensor. Thus, the remote operator can directly decide whether to continue operating or not. The proposed method is simple but useful and meaningful for the remote operator compared to the image-only method. The overall procedure of the proposed method was experimentally validated via a multi-purpose AGV on rough unpaved proving ground. Nine abnormal driving areas were discovered on the ground. In five of these areas, vehicles consistently exhibited abnormal driving behavior. The remaining four areas were confirmed to be affected by variables such as weather conditions and vehicle tire wear. Full article

Analyzing electrocardiographic (ECG) signals is crucial for evaluating heart function and diagnosing cardiac pathology. Traditional methods for detecting ECG changes often rely on offline analysis or subjective visual inspection, which may overlook subtle variations, particularly in the case of artifacts. In this theoretical, proof-of-concept study, we investigated the potential of five different machine learning algorithms [random forests (RFs), gradient boosting methods (GBMs), deep neural networks (DNNs), an ensemble learning technique, as well as logistic regression] to detect subtle changes in the morphology of synthetically generated ECG beats despite artifacts. Following the generation of a synthetic ECG beat using the standardized McSharry algorithm, the baseline ECG signal was modified by changing the amplitude of different ECG components by 0.01–0.06 mV. In addition, a Gaussian jitter of 0.1–0.3 mV was overlaid to simulate artifacts. Five different machine learning algorithms were then applied to detect differences between the modified ECG beats. The highest discriminatory potency, as assessed by the discriminatory accuracy, was achieved by RFs and GBMs (accuracy of up to 1.0), whereas the least accurate results were obtained by logistic regression (accuracy approximately 10% less). In a second step, a feature importance algorithm (Boruta) was used to discriminate which signal parts were responsible for difference detection. For all comparisons, only signal components that had been modified in advance were used for discretion, demonstrating that the RF model focused on the appropriate signal elements. Our findings highlight the potential of RFs and GBMs as valuable tools for detecting subtle ECG changes despite artifacts, with implications for enhancing clinical diagnosis and monitoring. Further studies are needed to validate our findings with clinical data. Full article

The intracavitary coronary arteries (ICCA) course is a rare phenomenon, where the segments of the coronary artery go through the atria or ventricles of the heart. In the past, these changes were incidentally detected during invasive diagnostic procedures for other reasons, as well as during postmortem examinations. As the use of multidetector computed tomography angiography (CTA) becomes more widespread, it has emerged that the incidence of ICCA has been underestimated. We present images from two coronary computed tomography angiography cases, which document the existence of ICCA in patients with non-specific chest pain. In the first case, in a 66-year-old woman, in addition to confirming coronary artery disease without significant stenosis (CAD-RADS 2-category 2 in the coronary-artery-disease-reporting and data system), the course of the middle section of the right coronary artery (RCA) in the lumen of the right atrium was demonstrated. In the second case, in a 47-year-old man in whom the presence of atherosclerotic lesions in the coronary arteries was excluded (CAD-RADS 0), the course of the distal segment of the left anterior descending (LAD) was found in the lumen of the apical layers of the right ventricle. To sum up, it should be stated that coronary CTA is a non-invasive diagnostic method that allows for visualization of the ICCA. In coronary CTA performed for indications consistent with the guidelines of scientific societies, attention should also be paid to the possible intracavitary course of the coronary arteries. The identification of such a course of the coronary arteries may be useful when preparing the patient for potential future invasive procedures involving the cardiac cavities. Full article

Biosensors are a new type of sensor that utilize biologically sensitive materials and microbially active analytes to measure a variety of biological signals. The purpose of monitoring is achieved by combining these sensitive materials with analytes such as proteins, cells, viruses, and bacteria, inducing changes in their physical or chemical properties. The use of nanomaterials in fabricating surface acoustic wave (SAW) biosensors is particularly noteworthy for the label-free detection of organisms due to their compact size, portability, and high sensitivity. Recent advancements in the manufacturing techniques of SAW biosensors have significantly enhanced sensor performance and reliability. These techniques not only ensure precise control over sensor dimensions and material properties but also facilitate scalable and cost-effective production processes. As a result, SAW biosensors are poised to become powerful tools for various clinical and rapid detection applications. Full article

Climate change causes frequent and severe disasters. A comprehensive assessment of disaster hazards is thus crucial to understanding variations in disaster patterns and planning mitigation and adaptation strategies. This study obtained information from a crop disaster dataset of Taiwan covering the period from 2003 to 2022. Additionally, principal component analysis and K-means clustering were used to create climate clusters to evaluate the effect of climate patterns on disaster hazards. The results revealed that tropical storm frequency substantially decreased, whereas rain disasters exhibited an increasing trend. The four regions of Taiwan exhibited variations in terms of hazards of various disasters. The cold wave hazard showed a significant upward trend in the central region. An upward trend of rain disaster hazards was only detected in the southern region. However, a downward trend in tropical storm hazards was detected across all regions. A distinct climate pattern was identified over the study period. After 2012, high temperature and dry climate were the primary climate patterns. These patterns exhibited a high hazard value for cold waves, droughts, and rain disasters. Hence, the present study’s findings indicate that managing cold waves and rain disasters is crucial to protecting crop production in Taiwan. Full article