Search Results (319)

Sexual dimorphism (SD) is a widespread phenomenon among animals and has attracted considerable interest in evolutionary biology. Most studies on SD have been limited to a single population, and few have focused on multiple populations. In this study, size and shape SD were evaluated in three populations of Batrachuperus karlschmidti, a hynobiid species endemic to China. SD was not found in body size, but was observed in body shape. Males had larger relative dimensions in head length, head width, forelimb length, forelimb width, hindlimb length, hindlimb width, and tail length. Conversely, females were larger in the relative dimension of interlimb distance. Sexual selection can account for SD in head and limbs, thereby enhancing male reproductive success. Conversely, fecundity selection drives SD in limbs, tail length, and interlimb distance, ultimately improving the reproductive ability of both sexes. Differences in sexual shape dimorphism (SShD) traits were also found among populations, which may be caused by dissimilar levels of selection forces in the environment. This study provides insight into identifying the causes that promote sexual dimorphism, as well as the degree of difference in SShD traits among populations. Full article

This study examined the effects of pollution from the Shituru hydrometallurgic complex on the Upper Lufira Basin, Democratic Republic of the Congo, between September 2015 and September 2017. Physico-chemical water variables and trace metal elements in water and sediment, as well as diversity and infection parameters of monopisthocotylan parasites infesting Clarias ngamensis, were assessed at three sites: the Lufira River, Panda River, and Lake Tshangalele. We hypothesised that low pollution would correlate with greater ectoparasite species richness and higher infection parameters. Results indicated severe ecological degradation in the highly polluted Panda River (with high concentrations of TMEs; e.g., 510.830 ± 0.86; 82.470 ± 0.200 µg/L for Co²⁺ and Cu²⁺ in water; 15,771 ± 7068 and 1585 ± 1450 µg/g for Cu²⁺ and Zn²⁺ in the sediment), where neither fish nor parasites were present. Across the other sites, eight parasite species were identified. Seven species occurred on fish from the slightly polluted Lufira River (mean intensity (MI) of 31.28 ± 28.95 parasites per infested fish), while five were found in Lake Tshangalele (MI: 3.23 ± 2.89 parasites per infested fish), confirming the hypothesis. Three species, Quadriacanthus halajiani, Q. domatanai, and Macrogyrodactylus clarii, demonstrated potential as sensitive bioindicators of aquatic pollution in the region. Full article

The persistent expansion in the intensity and scope of human disturbance has become a key driver of global biodiversity loss, affecting wildlife behavior and population stability across multiple dimensions. As a characteristic symbiotic assemblage in the subalpine forest ecosystems of Sichuan, the giant panda (Ailuropoda melanoleuca), red panda (Ailurus fulgens), and forest musk deer (Moschus berezovskii) exhibit significant research value in their responses to human disturbance. However, existing studies lack systematic analysis of multiple disturbances within the same protected area. This study was conducted in the Sichuan Liziping National Nature Reserve, where infrared camera traps were deployed using a kilometer-grid layout. By integrating spatiotemporal pattern analysis and Generalized Additive Models (GAM), we investigated the characteristics of human disturbance and the response strategies of the three species within their habitats. The results show that: (1) A total of seven types of human disturbance were identified in the reserve, with the top three by frequency being cattle disturbance, goat disturbance, and walking disturbance; (2) Temporally, summer and winter were high-occurrence seasons for disturbance, with peaks around 12:00–14:00, while the giant panda exhibited a bimodal diurnal activity pattern (10:00–12:00, 14:00–16:00), the red panda peaked mainly at 8:00–10:00, and the forest musk deer preferred crepuscular and nocturnal activity—all three species displayed activity rhythms that temporally avoided peak disturbance periods; (3) Spatially, giant pandas were sparsely distributed, red pandas showed aggregated distribution, and forest musk deer exhibited a multi-core distribution, with the core distribution areas of each species spatially segregated from high-disturbance zones; (4) GAM analysis revealed that the red panda responded most significantly to disturbance, the giant panda showed marginal significance, and the forest musk deer showed no significant response. This study systematically elucidates the spatiotemporal differences in responses to multiple human disturbances among three sympatric species within the same landscape, providing a scientific basis for the management of human activities, habitat optimization, and synergistic biodiversity conservation in protected areas. It holds practical significance for promoting harmonious coexistence between human and wildlife. Full article

The viscoelastic properties of ultraviolet radiation-curable polymer coatings of optical fibers were studied experimentally and numerically. The test setup was completed, and a series of natural experiments were conducted for an extended temperature range from −110 °C to +120 °C using a dynamic mechanical analyzer (DMA). Discrete dependencies of the complex modulus on temperature and frequency of kinematic loading were obtained. The problem of multiparametric optimization was solved. Defining relations were obtained for protective coating polymers, making it possible to describe the thermomechanical behavior of the glass-forming materials under consideration in a wide temperature range, including relaxation transition. The optimal solution was found for 18 series terms at the selected reference temperature T_r = −70 °C, C₁ = 20.036, and C₂ = 32.666 for the DeSolite 3471-1-152A material. The optimal solution was found for 60 series terms at the selected reference temperature T_r = 0 °C, C₁ = 40,242.2827, and C₂ = 267,448.888 for the DeSolite DS-2015 material. The models were verified according to the data of creep experiments. The capabilities of the viscoelastic model were demonstrated by the example of a numerical experiment on free thermal heating/cooling of a Panda-type optical fiber. Full article

To investigate the influence of host ecological niche on fecal microbial community composition, this investigation employed high-throughput sequencing to characterize the microbiota composition in fecal samples. Giant pandas (GP), red pandas (RP), sympatric wildlife (SA), and domesticated animals (HA) in the Meigu Dafengding National Nature Reserve were used in the research. The research has found that GP bacteria are mainly composed of Proteobacteria and Pseudomonas, RP is enriched in Proteobacteria and Arthrobacter, SA is characterized by Firmicutes and Bacillus, and HA is dominated by Firmicutes and UCG-005 (uncultured Lachnospiraceae). In terms of fungi, GP and RP are mainly dominated by Ascomycota, enriched in Mrakia and Thelebolus, respectively, while SA is dominated by Ascomycota and Thelebolus, and HA is dominated by Chytridiomycota and Geotrichum. The assessment of alpha diversity demonstrated that HA had the highest microbial diversity and GP had the lowest; evaluation of beta diversity established that the community structures of each group were significantly separated. The study revealed a significant ecological divergence between the bacterial and fungal communities in sympatric wildlife, while giant pandas exhibit synergistic variation in their microbiota. This study provides new scientific basis for wildlife conservation from the perspective of focusing on microbial interactions. Full article

Habitat degradation and fragmentation pose severe threats to biodiversity in protected areas, including the Giant Panda National Park (GPNP). Effective restoration strategies are urgently needed to enhance habitat connectivity and support the recovery of giant panda (Ailuropoda melanoleuca David, 1869) populations. This study aimed to evaluate the impact of targeted artificial restoration measures on plant diversity and community structure in four typical degraded habitats within the Niba Mountain Corridor of the GPNP. Over a three-year monitoring period, vegetation surveys and infrared camera trapping were conducted across pure plantations and secondary forests, with/without bamboo, using suitable habitats as controls. The results showed that: (1) Artificial restoration significantly increased shrub layer species richness and Shannon–Wiener index in most degraded habitats, approaching control levels after two years, while herb layer diversity initially increased then declined due to shrub competition. (2) Sorensen’s similarity between degraded and suitable habitats increased over time, rising from 0.08–0.42 to 0.46–0.67 for the shrub layer and from 0.09–0.22 to 0.30–0.40 for the herb layer. (3) Key species showing high variability during restoration included Litsea pungens Hemsl., Actinidia spp., Salix spp., Rubus spp., Hydrangea macrophylla (Thunb.) Ser, Carex spp., and Elatostema involucratum Franch. et Savat. (4) Bamboo regeneration was enhanced with peak live shoots in 2024. (5) Increased activity of medium-to-large mammals, notably the tufted deer (Elaphodus cephalophus Milne-Edwards, 1872), may indicate initial stages of functional recovery for resources in the restored habitats. The results confirmed that differentiated artificial restoration can effectively promote species diversity recovery and habitat convergence, providing a scientific basis for optimizing GPNP corridor management and improving population connectivity for giant pandas. Full article

Giant pandas feed preferentially on bamboo but choose different species and organs depending on factors such as the altitude and season, suggesting preferential selection according to their nutritional requirements. However, the mechanism of selection is unclear. Pandas cannot directly sense the nutritional quality of bamboo shoots but tend to sniff their food before consumption, inferring that odors inform their choice, which is then reinforced by the selection of positive and negative taste cues. To investigate the basis of selection, we observed the effects of 10 different bamboo species on feeding behavior, including food item selection, feeding frequency, portion size, food processing time per bite, and amount consumed per meal. Three of the bamboos were preferred, another four were consumed when the preferable bamboos were unavailable, and the remaining three were always rejected. We then characterized the volatile components of the bamboo leaves as well as the primary and secondary metabolites, allowing us, for the first time, to correlate feeding behavior with metabolomics. The three groups shared some volatile compounds but 21 volatiles were unique to the preferred leaves and appeared to confer sweet and fresh aromas, whereas the inedible leaves produced 20 unique volatiles that were pungent or floral, which appeared to discourage feeding. The three groups also shared many common nonvolatile metabolites, but pairwise comparisons revealed both qualitative and quantitative differences in metabolite abundance that resulted in the preferred leaves accumulating compounds associated with a sweet taste in humans (e.g., sugars), while the inedible leaves contained metabolites often associated with sour and bitter tastes (e.g., certain flavonoids and acids). Following attraction by certain volatiles, giant pandas may therefore consolidate their selection of leaves that are potentially more nutritious by consuming those with sweeter, less bitter and less sour tastes. Full article

National parks are the core carriers for implementing the concept of “ecological protection first, public welfare for all”. This study used the fuzzy comprehensive evaluation method to evaluate the ecological experience resource quality of the Laohegou area in Giant Panda National Park of China. Based on the standardized psychological assessment scale and the Kessler Psychological Distress Scale, we have established an evaluation model for the psychological health benefits, quantifying the psychological health benefits of tourists in the ecological experience of national parks. At the same time, the relationship between the demographic characteristics, ecological experience characteristics, psychological health level, and psychological health benefits of visitors was analyzed. The ecological environment in Laohegou area is excellent (evaluated as “excellent” in multiple key indicators), with outstanding resource and value characteristics (evaluated as “good” in multiple indicators), and has the ability to provide high-quality public ecological experiences, which has significant benefits in promoting tourists’ mental health. The ecological experience within national parks could bring high psychological health benefits to visitors. The educational background, age, and income of visitors had a significant impact on the psychological health benefits of their ecological experience. Therefore, ecological experience products within national parks should be designed in layers based on age, income, and other factors, and spaces should be controlled at different levels. Full article

Nature reserves serve as core spatial units for maintaining regional ecological security and biodiversity. Owing to their high ecosystem integrity, extensive vegetation cover, and low levels of disturbance, they play a crucial role in sustaining ecological processes and ensuring functional stability. Taking the Giant Panda National Park (GPNP), which spans the provinces of Gansu, Sichuan, and Shaanxi in China, as the study region, the vegetation net primary productivity (NPP) during 2001–2023 was simulated using the Carnegie–Ames–Stanford Approach (CASA) model. Spatial and temporal variations in NPP were examined using Moran’s I, Getis-Ord Gi* hotspot analysis, Theil–Sen trend estimation, and the Mann–Kendall test. In addition, the Optimal Parameters-based Geographical Detector (OPGD) model was applied to quantitatively assess the relative contributions of natural and anthropogenic factors to NPP dynamics. The results demonstrated that: (1) The mean annual NPP within the GPNP reached 646.90 gC·m⁻²·yr⁻¹, exhibiting a fluctuating yet generally upward trajectory, with an average growth rate of approximately 0.65 gC·m⁻²·yr⁻¹, reflecting the positive ecological outcomes of national park establishment and ecological restoration projects. (2) NPP exhibits significant spatial heterogeneity, with higher NPP values in the northern, while the central and western regions and some high-altitude areas remain at relatively low levels. Across the four major subregions of the GPNP, the Qinling has the highest mean annual NPP at 758.89 gC·m⁻²·yr⁻¹, whereas the Qionglai–Daxiaoxiangling subregion shows the lowest value at 616.27 gC·m⁻²·yr⁻¹. (3) Optimal NPP occurred under favorable temperature and precipitation conditions combined with relatively high solar radiation. Low elevations, gentle slopes, south facing aspects, and leached soils facilitated productivity accumulation, whereas areas with high elevation and steep slopes exhibited markedly lower productivity. Moderate human disturbance contributed to sustaining and enhancing NPP. (4) Factor detection results indicated that elevation, mean annual temperature, and land use were the dominant drivers of spatial heterogeneity when considering all natural and anthropogenic variables. Their interactions further enhanced explanatory power, particularly the interaction between elevation and climatic factors. Overall, these findings reveal the complex spatiotemporal characteristics and multi-factorial controls of vegetation productivity in the GPNP and provide scientific guidance for strengthening habitat conservation, improving ecological restoration planning, and supporting adaptive vegetation management within the national park systems. Full article

Wildlife viewing has long been a central feature of tourism. However, growing concerns over the welfare of animals involved have raised critical questions about human management and the use of wildlife. In response, scholars have developed frameworks to assess and guide the operations of animal-based attractions. This paper applies such a framework to the Chengdu Research Base of Giant Panda Breeding in China, empirically examining visitor perceptions of animal welfare management practices. Drawing on survey data from over 1000 visitors, the study finds that, overall, tourists hold positive evaluations of the Base’s governance, welfare, and conservation practices. However, these perceptions are influenced by factors such as prior exposure to panda-related social media and individual knowledge of animal welfare, highlighting the role of mediated experiences in shaping visitor judgments. The findings underscore the need for further research on ethical wildlife tourism practices and suggest that engaging and empowering tourists—through education, participatory evaluation, and digital platforms—can enhance both public awareness and the effectiveness of conservation and welfare initiatives. Full article

Understanding how individual personality traits relate to stress and abnormal behaviors is essential for improving animal welfare and management. However, few studies have quantitatively examined these relationships in giant pandas. This study aimed to investigate how personality traits predict stereotypic behavior and physiological stress responses in captive giant pandas under a simulated transport context. Stereotypies are abnormal repetitive behaviors thought to reflect stress coping, and personality is increasingly recognized as a factor shaping animal welfare. We compared non-stereotypic (n = 16) and stereotypic (n = 16) giant pandas using keeper-based ratings, crate-entry training, and urinary cortisol. Intraclass correlation and principal component analyses identified two personality components, activity and timidity. Non-stereotypic pandas were significantly less timid (p = 0.005), whereas activity did not differ (p = 0.355). In crate-entry training, non-stereotypic pandas tended to enter faster (48.5 vs. 139.5 s; p = 0.074), and timidity was strongly correlated with latency (ρ = 0.74, p < 0.01). Censoring-aware analyses treating 300 s as right-censoring further supported this pattern (stratified Cox (Hazard Ratio) HR = 3.09, 95% CI 0.93–10.25; likelihood-ratio p = 0.052; log-normal accelerated failure-time model time ratio = 0.36, p = 0.054). Urinary cortisol showed no between-group difference after adjustment (geometric mean ratio = 0.93, 95% CI 0.57–1.49; p = 0.75), with consistent sensitivity analyses. These findings indicate that stereotypic pandas are more timid, and higher timidity predicts slower adjustment to a stress context, highlighting the value of personality assessment in management to mitigate stereotypies and improve welfare, while inconsistent cortisol results warrant further study. Full article

Gas-insulated switchgear (GIS) serves as the core equipment in substations. Its partial discharge detection requires ultrasonic sensors to be precisely aligned with millimeter-level measurement points. However, existing technologies face three major bottlenecks: the lack of surface texture on GIS makes visual feature extraction difficult; strong electromagnetic interference in substations causes image noise and loss of feature point tracking; and fixed gain control easily leads to end-effector jitter, reducing positioning accuracy. To address these challenges, this paper first employs AprilTag visual markers to define GIS measurement point features, establishing an image-based visual servo model that integrates GIS surface curvature constraints. Second, it proposes an adaptive gain algorithm based on model predictive control, dynamically adjusting gain in real-time according to visual error, electromagnetic interference intensity, and contact force feedback, balancing convergence speed and motion stability. Finally, experiments conducted on a GIS inspection platform built using a Franka Panda robotic arm demonstrate that the proposed algorithm reduces positioning errors, increases positioning speed, and improves positioning accuracy compared to fixed-gain algorithms, providing technical support for the engineering application of GIS partial discharge detection robots. Full article

Metals are widely used in animal feed for their growth-stimulating and antimicrobial effects; yet, there is potential concern that their use can promote antimicrobial resistance through co-selection. However, the prevalence of these metal resistance genes in Salmonella and their impact on the induction of antimicrobial resistance remain unclear. To aid in this understanding, this study investigated of the prevalence of heavy metal resistance genes (HMRGs) and their comparison with antimicrobial resistance genes (ARGs) in Salmonella enterica strains isolated from various sources, across different locations and time periods. Data on stress and AMR genotypes, serovar, source, location, and collection date were retrieved from the NCBI Pathogen Detection Isolate Browser. Isolates from the United States with complete metadata were analyzed using Microsoft Excel and PANDAS (Python Data Analysis Library). Chi-square tests were conducted to assess differences in ARG presence between HMRG-positive and HMRG-negative isolates. Additionally, the co-localization of HMRGs and ARGs on plasmids was examined, and plasmid incompatibility types were assessed. The results show that HMRG prevalence varied significantly across serovars and sources. Certain ARGs occurred at significantly higher frequencies in isolates harboring HMRGs. Co-localization of HMRGs and ARGs on plasmids was frequently observed, although no specific plasmid incompatibility group was uniquely associated with this co-localization. These findings highlight a potential link between metal resistance and antibiotic resistance in S. enterica, reinforcing concerns about the use of heavy metals in agriculture. The results provide important insights for risk assessment and inform strategies aimed at mitigating AMR as a global public health threat. Full article

Early-life infections can produce durable changes in immune function and behavior. We propose a mechanistic hypothesis positioning the mechanistic target of rapamycin (mTOR) as the link between peripheral inflammation and central nervous system dysfunction in pediatric post-infectious syndromes. Based on clinical, translational, and experimental literature, we outline a stepwise pathway. First, sustained mTOR activation skews T-cell and macrophage differentiation toward pro-inflammatory and autoimmune states. Second, endothelial mTOR signaling weakens tight junctions and increases vesicular transport, compromising blood–brain barrier integrity. Third, cytokines and sometimes autoreactive cells enter the brain and engage mTOR in microglia and neurons, driving neuroinflammation, impaired synaptic maintenance and plasticity, and neurotransmitter disruption. This framework accounts for features observed in Long COVID, myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), and pediatric acute neuropsychiatry syndrome (PANS/PANDAS) and yields testable predictions on pathway activity and barrier permeability. It also motivates targeted interventions that modulate mTOR-related processes in immune and endothelial compartments and within neural circuits in children. So, this article aims to outline a mechanistic framework linking infection-driven mTOR activation to post-infectious neuropsychiatric syndromes. Full article

Accurate prediction of building energy performance is critical for achieving sustainability goals and reducing operational costs. This study presents a novel and automated simulation framework that integrates EnergyPlus 25.1 with modular JSON configurations and Python 3.11 scripting to streamline the modelling and analysis process. Using the Hilton Watford Hotel in the UK as a case study, the framework generates detailed Input Data Files (IDFs) based on architectural and operational data, enabling efficient exploration of various usage scenarios through batch simulations. Automation is achieved using custom Python scripts built on the Eppy library, allowing scalable modification and generation of simulation inputs. Post-processing and visualisation are performed using Pandas 2.0.3, NumPy 1.25.2, and Matplotlib 3.7.2, while model outputs are calibrated against measured performance data in accordance with ASHRAE guidelines. To enhance predictive capabilities, machine learning algorithms—Random Forest and XGBoost—are applied to estimate annual energy consumption under different operating conditions. This integrated approach not only reduces manual modelling effort but also narrows the gap between predicted and actual performance, offering a replicable pathway for retrofitting analysis and energy policy support in similar commercial buildings. Full article