Search Results (485)

The gut microbiota plays a crucial role in maintaining swine health and understanding its stage-specific variations provides a scientific basis for health assessment. This study investigated the structural changes in intestinal microbiota during the development of Sichuan–Tibetan black pigs (n = 15) by collecting fecal samples at three growth stages: the nursery period (1 month), growing period (3 months), and finishing period (10 months). Microbial profiling was performed using 16S rRNA sequencing. Results showed no significant difference in the Shannon index between the nursery and growing periods, while the finishing period exhibited distinct ACE and Chao 1 indices compared to other stages. PCoA and NMDS analyses revealed significant structural divergence in the finishing period microbiota, with greater intra-group variability observed in the nursery and growing periods. At the phylum level, Firmicutes abundance increased progressively with growth, becoming the absolute dominant phylum, whereas Bacteroidota showed a declining trend. These characteristics are particularly prominent during the finishing period. At the family level, Lactobacillaceae abundance increased continuously. Oscillospiraceae remained stable during the early stages but decreased significantly in the finishing period. Genus-level analysis shows that Lactobacillus, especially L. amylovorus and L. reuteri, become dominant bacterial species during the finishing period. A total of 84 differentially abundant core microbiota were identified, with the finishing period containing the highest number. Functional annotation revealed 19 significantly different metabolic pathways across the three stages. The most significant is the enhanced activity of microorganisms during the finishing period in pathogen-related metabolism and exogenous degradation, reflecting their adaptability to complex feed. These findings demonstrate stage-dependent variations in the gut microbiota of Sichuan–Tibetan black pigs, providing valuable references for nutritional regulation and feeding management practices. Full article

Against the backdrop of global climate change and resource-environmental constraints, land ecological security is paramount to regional sustainable development. This study innovatively integrates the DPSIRM system framework with a CNN-LSTM hybrid neural network model to establish a land ecological security early warning system for China’s top 100 counties, enabling scientific diagnosis and dynamic early warning of security incidents. Findings indicate: (1) From 2010 to 2023, land ecological security conditions across counties showed continuous improvement, with the proportion of counties classified as ‘relatively safe’ or higher rising from 2% in 2010 to 68% in 2023. (2) The comprehensive early warning index exhibited a ‘stepwise leap’ trend, progressing through four stages from ‘relatively unsafe’ to ‘relatively safe’. (3) The six subsystems exhibited markedly divergent evolutionary trajectories, characterised by dual-core leadership from ‘driving-management’, fluctuating improvements in ‘pressure-impact’, and low-amplitude oscillations in ‘state-response’. (4) Over the next five years, the comprehensive early warning index will exhibit a ‘gradual stabilisation and upward trend’, yet subsystems will display a polarised pattern of ‘three rising, two stagnant, and one declining’. The early warning system developed in this study provides local decision-makers with critical leading indicators, supporting differentiated management and source-level interventions. These findings hold significant implications for refining county-level ecological governance and optimising territorial spatial patterns. Full article

Non-photosynthetic vegetation fractional cover (f_NPV) is a key indicator of vegetation decline and ecological health. Traditional inversion models assume identical spectral signatures for the same vegetation cover class across entire study areas. Spectral variations occur among regions due to divergent soil properties and vegetation types. To address this limitation, extensive ground sampling was conducted; ground observation data from multiple regions were utilized to establish localized spectral libraries, thereby enhancing spectral variability representation within the study area while concurrently optimizing vegetation indices across different sensor systems. The results reveal that, within the optimized spectral mixture analysis model, the coefficient of determination (R²) for f_NPV using the NPV soil separation index (NSSI) for Sentinel sensor is 0.6258, and that of f_PV using the modified soil adjusted vegetation index (MSAVI) is 0.8055. The MSAVI-NSSI achieved an R² of 0.7825 for f_NPV and 0.8725 for photosynthetic vegetation fractional cover (f_PV). Optimized vegetation indices also yielded favorable validation results. Landsat’s theoretical predictions improved by 0.1725, with validated results up by 0.1635. MODIS showed improvements of 0.1365 and 0.1923, respectively. This enhancement significantly improves the accuracy of NPV fractional cover identification, providing critical insights for vegetation ecological health assessment in arid and semi-arid regions under global warming. Furthermore, by optimizing the spectral constraint weights in remote sensing images, a solution is provided for the long-term monitoring of vegetation health status. Full article

The new type of gold cone filter cartridge has dual functions of increasing filter area and enhancing pulsed-jet cleaning, but the issue of patchy cleaning remains to be addressed. This study further enhances the pulsed-jet cleaning performance of cone filter cartridges by employing a porous diffusion nozzle. The temporal and spatial distributions of pulse jet velocity and pressure under the condition of porous nozzles were investigated through numerical modeling. The variation law of pressure on the side wall of the filter cartridge was analyzed. The influence of jet distance of porous nozzles on pulsed-jet pressure and pulsed-jet uniformity was experimentally investigated. Dust filtration and cleaning experiments were conducted, and the filtration pressure drop, dust emission concentration, and comprehensive filtration performance were compared. It was found that the airflow jetted by the porous diffusion nozzle is more divergent than that of the common round nozzle. This results in a larger entrainment of the jet stream, a milder collision of the jet stream with the cartridge cone, and a slower overall velocity reduction. More airflow is generated into the filter cartridge and accumulated; the accumulated static pressure covers a larger range of the upper section of the filter cartridge, with a longer duration of static pressure. In the online dust filtration and cleaning experiment, compared with the condition of the common round nozzle, the porous nozzle can reduce the residual pressure drop by 27.0%, increase the filtration cleaning interval by a factor of 3.80, reduce the average dust emission concentration by 45.2%, and increase the comprehensive performance index Q_F by 5.2%. The research conclusions can provide references for the design and optimization of industrial filter cartridge dust collectors. Full article

Background/Objectives: Ovarian adnexal masses present diagnostic challenges due to their heterogeneous etiologies. Accurately differentiating these conditions is critical for timely and effective clinical intervention. This study evaluated circulating molecules and serum-induced apoptosis as complementary tools to conventional diagnostic methods (CA125, HE4, and the ROMA index) for distinguishing benign masses from malignant masses. Methods: A cohort of 136 participants (9 healthy controls, 87 women with benign ovarian adnexal masses and 40 with malignant ovarian adnexal masses) was analyzed. The induction of apoptosis in Jurkat cells by patient serum was assessed using flow cytometry. Serum concentrations of sFas/CD95, HE4, CA125, and additional molecules were measured by ELISA and LEGENDplex™. Clinical, ultrasonographic, and histopathological data were correlated with tumor malignancy. To improve diagnostic performance beyond individual biomarkers, we developed two multiparametric classifiers that integrate the dominant parameters identified through group divergence analysis and ROC evaluation across multiple clinical comparisons. Results: Malignant tumors were associated with older age (51.45 ± 8.35 years, p = 0.0002), postmenopausal status (61.1%, p = 0.0013), and larger tumor size (>10 cm). Ultrasonographic features of complexity were observed exclusively in malignant masses. Functional assays revealed reduced apoptosis in Jurkat cells exposed to malignant sera, suggesting tumor-mediated immune evasion. Although higher sFas levels were observed in tumors, no significant differences were identified between the groups. Among the circulating biomarkers, CA125, HE4, MRP8/14, OPN, and SAA levels were significantly higher in malignant tumors than in benign tumors and controls. Conclusions: The evaluation of CA125, HE4, MRP8/14, and apoptosis (Classifier 1) and, more prominently, the measurement of additional molecules: OPN, SAA, IL-6, IL-8, and IGFBP-4 (Classifier 2), systematically outperformed the ROMA. Both achieved superior specificity and balanced accuracy (Youden’s J index) across all clinical comparisons by capturing the biological diversity of malignancies. Full article

Autism spectrum disorder (ASD) and Alzheimer’s disease (AD) are neurodevelopmental and neurodegenerative disorders, respectively. While exome sequencing is routinely employed during the early stages of ASD diagnosis, it rarely influences therapeutic strategies. To address this gap, we have reconstructed and analyzed the gene networks linking autism spectrum disorders, Alzheimer’s disease, and mTOR signaling. In addition, we have performed a phylostratigraphic analysis that reveals similarities and differences in the evolution of both ASD and Alzheimer’s disease predisposition genes. We have shown that almost half of the genes predisposing to autism and two-fifths of the genes predisposing to Alzheimer’s disease are directly related to the mTOR signaling pathway. Analysis of Phylostratigraphic Age Index (PAI) value distributions revealed a significant enrichment of evolutionarily ancient genes in both ASD- and AD-related gene sets. When studying the distribution of ASD predisposition genes by Divergence Index (DI) values, a significant enrichment with genes having extremely low DI = 0 has been found. Such low DI values indicate that most likely these genes are under stabilizing selection. Using the ANDVisio tool, both pharmacological and natural mTOR regulators with potential for ASD treatment were selected, such as propofol, dexamethasone, celecoxib, statins, berberine, resveratrol, quercetin, myricetin, mio-inositol, and several amino acids. Full article

Hymenaea martiana is a species native to Brazil. It has ecological value, contributes to forest restoration, and is economically important because of the use of its wood and fruits. However, it is frequently exploited. Therefore, understanding genetic diversity becomes essential for guiding conservation strategies as well as ecological restoration actions in the face of climate change and anthropogenic pressures. Thus, this study aimed to evaluate the intraspecific diversity of 160 H. martiana mother plants on the basis of morphological descriptors of fruits and seeds and physiological indicators of seed quality, identifying the most discriminating characters. Eighteen traits were analyzed and subjected to analysis of variance and the Scott–Knott test (p < 0.05), with estimates of heritability and the ratio between genetic and environmental coefficients of variation. Phenotypic divergence was obtained via the Mahalanobis distance (D²) and grouped via UPGMA, whereas the relative contribution of the traits was estimated via the Singh method. The results revealed that seed length and weight, emergence speed index, and shoot dry mass were the most effective descriptors for discriminating parent plants. Multivariate analysis revealed the formation of eleven phenotypically distinct groups, demonstrating high variability. These findings support the selection of superior genotypes and representative seed collection, as well as practical initiatives such as the formation of germplasm banks, the selection of breeding stock for forest nurseries, and reintroduction programs. Thus, the data obtained offer technical and scientific support for biodiversity conservation and ecosystem recovery in the semiarid region of Brazil. Full article

This study investigates the environmental dimension of sustainable development across fifteen post-Soviet republics in 2022. While sustainability is generally understood as a triadic construct—economic, social, and environmental—this paper isolates the ecological pillar to highlight cross-country differences shaped by industrial legacies, institutional capacity, and governance models. A composite Environmental Performance Index (EPI) is developed using the Mazziotta–Pareto Index (MPI), which captures both average performance and internal consistency across three SDG-related domains: SDG 6 (Clean Water and Sanitation), SDG 13 (Climate Action), and SDG 15 (Life on Land). The study adds to existing literature as it includes a non-compensatory composite index and cluster analysis, and in policy terms, it provides a benchmarking system for facilitating ecological transition in the post-Soviet context. The results reveal strong divergence across the region: Baltic countries and Moldova achieve higher scores, reflecting policy convergence with the European Union and stronger environmental institutions, while Central Asian republics lag due to resource dependence, water scarcity, and weaker governance. Geographic cluster analysis corroborates these differences, showing clear spatial patterns of environmental convergence and divergence. Correlation analysis further demonstrates that environmental sustainability is positively associated with GDP per capita, HDI, and life expectancy, while negatively linked with inequality and fertility rates. These findings stress the need for context-sensitive and evidence-based policies, intra-regional cooperation, and integrated governance mechanisms to advance ecological transition in line with the 2030 Agenda for Sustainable Development. Full article

Vegetation phenology serves as a crucial indicator reflecting vegetation responses to the growth environment and climate change. Existing studies have demonstrated that in permafrost regions, the impact of frozen soil changes on vegetation phenology is more direct and pronounced compared to climate factors. Amid the slowdown of global warming in the 21st century, permafrost dynamics continued to drive uncertain variations in vegetation phenological stages across the Qinghai–Tibet Plateau (QTP). Using MODIS Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data during 2001–2024, this study derived vegetation phenological parameters and analyzed their spatiotemporal patterns on the QTP. The results indicate that overall, the start of growing season (SOS) was advanced, the end of growing season (EOS) was delayed, and the length of growing season (LOG) was extended throughout the study period. Additionally, divergent phenological trends were observed across three distinct phases, and regarding frozen soil types, vegetation phenology in permafrost and seasonally frozen ground regions exhibited distinct characteristics. From 2001 to 2024, both permafrost and seasonally frozen ground regions showed an advanced SOS and prolonged LOG, but significant differences were observed in EOS dynamics. For vegetation types, alpine meadow displayed advanced SOS and EOS, alongside an extended LOG. The alpine steppe exhibited advanced SOS and delayed EOS with an extended LOG. Alpine desert displayed SOS advancement and EOS delay, alongside LOG extension. These findings revealed variations in vegetation phenological changes under different frozen soil types and highlighted divergent responses of distinct frozen soil types to climate change. They suggested that the influence of frozen soil types should be considered when investigating vegetation phenological dynamics at the regional scale. Full article

Artificial intelligence is rapidly reshaping skill expectations across media, marketing, and journalism, however, university curricula are not evolving at a comparable speed. To quantify the resulting curriculum–skill gap in communication-related programs, two synchronous corpora were assembled for the period July 2024–June 2025: 66 course descriptions from six leading UK universities and 107 graduate-to-mid-level job advertisements in communications, digital media, advertising, and public relations. Alignment around AI, datafication, and platform governance was assessed through a three-stage natural-language-processing workflow: a dual-tier AI-keyword index, comparative TF–IDF salience, and latent Dirichlet allocation topic modeling with bootstrap uncertainty. Curricula devoted 6.0% of their vocabulary to AI plus data/platform terms, whereas job ads allocated only 2.3% (χ² = 314.4, p < 0.001), indicating a conceptual-critical emphasis on ethics, power, and societal impact in the academy versus an operational focus on SEO, multichannel analytics, and campaign performance in recruitment discourse. Topic modeling corroborated this divergence: universities foregrounded themes labelled “Politics, Power & Governance”, while advertisers concentrated on “Campaign Execution & Performance”. Environmental and social externalities of AI—central to the Special Issue theme—were foregrounded in curricula but remained virtually absent from job advertisements. The findings are interpreted as an extension of technology-biased-skill-change theory to communication disciplines, and it is suggested that studio-based micro-credentials in automation workflows, dashboard visualization, and sustainable AI practice be embedded without relinquishing critical reflexivity, thereby narrowing the curriculum–skill gap and fostering environmentally, socially, and economically responsible media innovation. With respect to the novelty of this research, it constitutes the first large-scale, data-driven corpus analysis that empirically assessed the AI-related curriculum–skill gap in communication disciplines, thereby extending technology-biased-skill-change theory into this field. Full article

Mixed pixels remain a central obstacle to reliable endmember extraction from hyperspectral imagery. We present AMEE–PPI, a hybrid method that embeds the Pure Pixel Index (PPI) within morphological structuring elements and propagates spectral purity via dilation/erosion, thereby coupling spatial context with spectral cues while avoiding a user-fixed number of projections. On GaoFen-5 (GF-5) AHSI data from a geologically complex outcrop region, we benchmark AMEE–PPI against four widely used algorithms—PPI, OSP, VCA, and AMEE. The pipeline uses HySime for noise estimation and signal-subspace inference to set the endmember count prior to extraction and applies morphological elements spanning 3 × 3 to 15 × 15 to balance spatial support with local heterogeneity. Quantitatively, AMEE–PPI achieves the lowest spectral angle distance (SAD) for all outcrop types—purple–red: 0.135; yellow–brown: 0.316; gray: 0.191—surpassing the competing methods. It also attains the lowest spectral information divergence (SID)—purple–red: 0.028; yellow–brown: 0.184; gray: 0.055—confirming superior similarity to field reference spectra across materials. Visually, AMEE–PPI avoids the vegetation endmember leakage observed with several baselines on purple–red and gray outcrops, yielding cleaner, more representative endmembers. These results indicate that integrating spatial morphology with spectral purity improves robustness to illumination, mixing, and local variability in GF-5 imagery, with direct benefits for downstream unmixing, classification, and geological interpretation. Full article

Background/Objectives: Preclinical chest computed tomography (CT) imaging in small animals is often limited by low resolution due to scan time and dose constraints, which hinders accurate detection of subtle lesions. Traditional super-resolution (SR) metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), may not adequately reflect clinical interpretability. We aimed to evaluate whether deep learning-based SR models could enhance image quality and lesion detectability in rat chest CT, balancing quantitative metrics with radiologist assessment. Methods: We retrospectively analyzed 222 chest CT scans acquired from polyhexamethylene guanidine phosphate (PHMG-p) exposure studies in Sprague Dawley rats. Three SR models were implemented and compared: single-image SR (SinSR), segmentation-guided SinSR with lung cropping (SinSR3), and omni-super-resolution (OmniSR). Models were trained on rat CT data and evaluated using PSNR and SSIM. Two board-certified thoracic radiologists independently performed blinded evaluations of lesion margin clarity, nodule detectability, image noise, artifacts, and overall image quality. Results: SinSR1 achieved the highest PSNR (33.64 ± 1.30 dB), while SinSR3 showed the highest SSIM (0.72 ± 0.08). Despite lower PSNR (29.21 ± 1.46 dB), OmniSR received the highest radiologist ratings for lesion margin clarity, nodule detectability, and overall image quality (mean score 4.32 ± 0.41, κ = 0.74). Reader assessments diverged from PSNR and SSIM, highlighting the limited correlation between conventional metrics and clinical interpretability. Conclusions: Deep learning-based SR improved visualization of rat chest CT images, with OmniSR providing the most clinically interpretable results despite modest numerical scores. These findings underscore the need for reader-centered evaluation when applying SR techniques to preclinical imaging. Full article

This study investigates the transverse loads acting on high-speed train bogie frames under actual service conditions. To enable direct identification, the locating arms were instrumented as bending sensors and calibrated under realistic lateral-stop constraints, ensuring robustness of the measurement channels. Field tests were conducted on a CR400BF high-speed EMU over a 226 km route at six speed levels (260–390 km/h), with gyroscope and GPS signals employed to recognize typical operating conditions, including straights, curves, and switches (straight movement and diverging movements). The results show that the proposed recognition method achieves high accuracy, enabling rapid and effective identification and localization of typical operating conditions. Under switch conditions, the bogie frame transverse loads are characterized by low-frequency, large-amplitude fluctuations, with overall RMS levels being higher in diverging switches and straight-through depot switches. Curve parameters and speed levels exert significant influence on the amplitude of the transverse-load trend component. On curves with identical parameters, the trend-component amplitude exhibits a quadratic nonlinear relationship with train speed, decreasing first and then increasing in the opposite direction as speed rises. In mainline curves and straight sections, the RMS values of transverse loads on Axles 1 and 2 scale proportionally with speed level, with the leading axle in the direction of travel consistently producing higher transverse loads than the trailing axle. When load samples are balanced across both running directions, the transverse load spectra of Axles 1 and 2 at the same speed level show negligible differences, while the spectrum shape index increases proportionally with speed level. Full article

Eyespots are functionally complex and highly variable elements of butterfly wing patterns. The Meadow Brown, Maniola jurtina, is a classic model species studied for variation in eyespots as an index of evolutionary divergence and adaptation. However, the role of fine-scale ecogeographic conditions on eyespot variation remains poorly understood. In this study, we examined hindwing eyespot number, distribution, and combination patterns in male M. jurtina across climatically and topographically diverse north-western Balkans. Compared to the species average, males in this region displayed greater spottiness and phenotypic diversity. While the typical two-spot phenotype was dominant and stable, in some populations, three-spotted and even four-spotted males occurred at similar frequencies. Rare six-spotted individuals were recorded only at mountain localities above 1200 m. Geographic and climatic factors together influenced this variation: higher altitudes and cooler, thermally stable environments promoted increased eyespot number and greater phenotypic plasticity than warmer, more variable environments. This pattern contrasts with large-scale latitudinal trends previously described for the species, emphasizing the importance of local climatic heterogeneity. Our findings suggest the north-western Balkans as a possible transitional zone where environmental complexity promotes elevated eyespot variability, contributing to the understanding of adaptive morphological plasticity in M. jurtina. Full article

The ability to detect population structure and determine the extent of genetic variation among populations is critical for understanding genetic background and effective fishery management. Fifty-eight individuals of S. sinica were resequenced with an average depth of 24× based on the Illumina sequencing platform. A total of 7,409,691 high-quality single nucleotide polymorphisms (SNPs) and 327,698 linkage disequilibrium-pruned SNPs were detected by comparing with the reference genome, and the average nucleotide diversity (π) and polymorphism information content (PIC) for all SNPs were 0.0036 ± 0.0023 and 0.2358 ± 0.1013, respectively, indicating the relatively low level of genetic diversity caused by limited gene flow and small effective population size (N_e). Integrated analyses of principal component analysis (PCA), ADMIXTURE, fixation index (F_st), and cladogram showed a significant genetic divergence between the north group (Dongying and Rushan populations) and the south group (Wenzhou and Zhoushan populations), which might be related to the differences in natural and geographical environments. The comprehensive results confirmed the genetic heterogeneity of S. sinica populations from the northern and southern sea areas of China, and suggested that regionalization fishery management should be adopted for further resource protection and utilization of S. sinica. Full article