Acute and subacute toxicity study of Aucklandia lappa Decne seed oil

Purpose: To investigate the acute and subacute toxicity of Aucklandia lappa Decne. seed oil (ALDO) in mice and rats.Methods: A single dose of 10 g ALDO/kg was administered to Kunming mice in an acute oral toxicity experiment. Their weight and feed consumption were recorded for 14 days to observe whether they had symptoms of poisoning and mortality. Sprague-Dawley (SD) rats were administered 0.89, 1.77 and 3.54 g/kg for 28 days, and symptoms of poisoning and mortality were monitored daily. Body weight, feedconsumption, hematology, serum biochemical parameters, relative organ weight, and histopathology of the experimental and control groups were compared.Results: The acute oral toxicity study revealed that there was no significant difference in the macroscopic results, including mortality, feed consumption and weight growth between the group dosed with 10 g ALDO/kg (p > 0.05) and the control group. In the subacute toxicity test, SD rats had a higher weight growth rate and feed utilization after doses of 0.89 g ALDO/kg (p < 0.01). However, compared with the control group (p > 0.05), there was also no significant difference in biochemical and hematological parameters, relative organ weight, or in macroscopic and histological features of both animal types. The electrolyte concentrations of Na and Cl increased at the doses of 1.77 and 3.54 g/kg (p < 0.01).Conclusion: These results suggest that ALDO is relatively safe when administered orally to rats and provide a theoretical basis for the development of new food resources

Assessing the implications of temperature extremes during the period 1959-2014 on the Inner Mongolia Plateau to sustainable development

The study sought to foster a better understanding of the nature of extreme temperature events and variations, and their implications to sustainable development, based on 16 indices of extreme temperature obtained from 43 meteorological stations on the Inner Mongolia Plateau (IMP). By using linear trend and Mann-Kendall abrupt change tests to investigate temporal variation trends, coupled with spatial distribution patterns and abrupt changes of extreme temperature events, the study revealed that the IMP has experienced extreme warming during 1959–2014 with warm extremes increasing significantly (p < 0.01) and cold extremes apparently decreasing (p < 0.01). The most significant increasing trends of warm extreme indices occurred in the desert steppe area (DSA) and sand desert area (SDA), suggesting that warming trends for night-time indices were larger than for daytime indices, while the most significant decreases in cold extreme indices were detected in forest area (FA) and forest steppe area (FSA). In addition, the significant cold day and cold night indices showed a decreasing trend, while warm day and warm night indices showed an increasing trend across the entire study area. Moreover, the study identified that topography has a large impact on the spatial distribution of extreme temperature indices, as does the type of grassland, and the ubiquity of the heat island effect in constructed urban regions. Finally, the study posits that to mitigate the effects of extreme temperatures, it is imperative to foster adaptive actions based on the principles of sustainable development

Exploring the relationship between local participation and perceived Co-management performance: Evidence from China’s Giant Panda National Park

Understanding local perceptions is essential to ensure the good functioning of co-management in protected areas (PAs). However, more research is required quantitatively to assess the extent locals are empowered. This paper seeks to investigate the effects of varied participation types and levels on perceived performance in a centralized co-management regime in Giant Panda National Park, China. Using 353 survey questionnaires, we identified six co-management subtypes that were classified into four empowerment levels: instruction, consultation, agreement, and cooperation. Notably, our analysis suggests that involvement at the cooperation level was not clearly linked with more favorable local perceptions of conservation. In contrast, local residents engaged in the instruction level of co-management (support, training, and employment subtypes) were more inclined to develop positive perceptions across the ecological, social, and livelihood dimensions. This study suggests the conclusion that merely empowering locals might not facilitate favorable perceptions of conservation. Instruction co-management intended to enhance social well-being if it is tailored to the needs of local residents

Effect of heat-moisture treatment on physicochemical properties of chickpea starch

Abstract Chickpea starch was modified by heat-moisture treatment (HMT). The effect of heat-moisture treatment on the amylose content of chickpea starch was studied, and the physicochemical properties, gelatinization properties, texture properties and digestion properties of modified chickpea starch were compared and analyzed by scanning electron microscopy (SEM), X-ray diffraction (XRD) and Fourier transform infrared (FT-IR). After HMT, the amylose content of chickpea starch increased. Compared with the natural starch, the morphology of starch granule was changed and destroyed under the condition of higher water content (30%). It was found that the crystalline morphology had no obvious change, and the structure was still C-type. In FT-IR spectra, the position of characteristic absorption peak had no obvious change, and the internal structure and main components of chickpea starch had no obvious change. The solubility, swelling power, transparency, freeze-thaw stability and gelation ability of chickpea starch treated with HMT decreased, while the thermal stability increased and the anti-digestibility enhanced

Suitability of Taxodium distichum for Afforesting the Littoral Zone of the Three Gorges Reservoir.

The littoral zone ecosystem of the Three Gorges Reservoir (TGR) has become significantly degraded by annual cycles of prolonged winter flooding and summer drought. For purposes of flood control and sediment management, the water level in the reservoir is lowered by 30 m during the summer monsoon season and raised again to 175 m above sea level each year at the end of the monsoon period. To explore an effective way to promote biodiversity and associated ecosystem services, we examined Taxodium distichum as a species for afforesting the littoral zone. Sapling growth variations were measured after two rounds of winter flooding. Dominant influence factors were determined by redundancy analysis. Herb community similarities between the experimental afforested areas and nearby control areas were assessed to detect the ecosystem influence of the experimental afforestation. 94.5% of saplings planted at elevations above 168 m survived. All measured growth indices (tree height, diameter at breast height, crown width and foliage density) decreased as the flood depth increased. Completely submerged saplings had a mean dieback height of -0.65 m. Greater initial foliage density led to increased tree height and stem diameter. Shannon-Wiener indices were not significantly different between plots in experimental and control areas, but the low similarity of herb communities between experimental and control areas (0.242 on average) suggested that afforestation would enrich plant community structure and improve littoral zone ecosystem stability. Because littoral zone afforestation provides several ecosystem services (habitat, carbon sink, water purification and landscaping), it is a promising revegetation model for the TGR

Improving pixel-based regional landslide susceptibility mapping

Regional landslide susceptibility mapping (LSM) is essential for risk mitigation. While deep learning algorithms are increasingly used in LSM, their extensive parameters and scarce labels (limited landslide records) pose training challenges. In contrast, classical statistical algorithms, with typically fewer parameters, are less likely to overfit, easier to train, and offer greater interpretability. Additionally, integrating physics-based and data-driven approaches can potentially improve LSM. This paper makes several contributions to enhance the practicality, interpretability, and cross-regional generalization ability of regional LSM models: (1) Two new hybrid models, composed of data-driven and physics-based modules, are proposed and compared. Hybrid Model I combines the infinite slope stability analysis (ISSA) with logistic regression, a classical statistical algorithm. Hybrid Model II integrates ISSA with a convolutional neural network, a representative of deep learning techniques. The physics-based module constructs a new explanatory factor with higher nonlinearity and reduces prediction uncertainty caused by incomplete landslide inventory by pre-selecting non-landslide samples. The data-driven module captures the relation between explanatory factors and landslide inventory. (2) A step-wise deletion process is proposed to assess the importance of explanatory factors and identify the minimum necessary factors required to maintain satisfactory model performance. (3) Single-pixel and local-area samples are compared to understand the effect of pixel spatial neighborhood. (4) The impact of nonlinearity in data-driven algorithms on hybrid model performance is explored. Typical landslide-prone regions in the Three Gorges Reservoir, China, are used as the study area. The results show that, in the testing region, by using local-area samples to account for pixel spatial neighborhoods, Hybrid Model I achieves roughly a 4.2% increase in the AUC. Furthermore, models with 30 m resolution land-cover data surpass those using 1000 m resolution data, showing a 5.5% improvement in AUC. The optimal set of explanatory factors includes elevation, land-cover type, and safety factor. These findings reveal the key elements to enhance regional LSM, offering valuable insights for LSM practices

Potential impacts of climate extremes on snow under global warming conditions in the Mongolian Plateau

Purpose: The paper aims to investigate the possible changes in mean temperature in the Mongolian Plateau associated with the 1.5 and 2°C global warming targets and how snow changes in the Mongolian Plateau when the mean global warming is well below 2°C or limited to 1.5°C. Design/methodology/approach: In total, 30 model simulations of consecutive temperature and precipitation days from Coupled Model Inter-comparison Project Phase 5 (CMIP5) are assessed in comparison with the 111 meteorological monitoring stations from 1961–2005. Multi-model ensemble and model relative error were used to evaluate the performance of CMIP5 models. Slope and the Mann–Kendall test were used to analyze the magnitude of the trends and evaluate the significance of trends of snow depth (SD) from 1981 to 2014 in the Mongolian Plateau. Findings: Some models perform well, even better than the majority (80%) of the models over the Mongolian Plateau, particularly HadGEM2-CC, CMCC-CM, BNU-ESM and GFDL-ESM2M, which simulate best in consecutive dry days (CDD), consecutive wet days (CWD), cold spell duration indicator (CSDI) and warm spell duration indicator (WSDI), respectively. Emphasis zones of WSDI on SD were deeply analysed in the 1.5 and 2 °C global warming period above pre-industrial conditions, because it alone has a significant negative relation with SD among the four indices. It is warmer than before in the Mongolian Plateau, particularly in the southern part of the Mongolian Plateau, indicating less SD. Originality/value: Providing climate extremes and SD data sets with different spatial-temporal scales over the Mongolian Plateau. Zoning SD potential risk areas and proposing adaptations to promote regional sustainable development.PeerReviewe

Variations of TH, DBH, CW and FC under partial and complete submersion conditions.

<p>Significant differences between the two conditions were analyzed using the Mann-Whitney U test. Box plots with different letters (a, b) were significantly different at the 0.05 level. "·" indicates data considered mild outliers, and "*" indicates data considered extreme outliers.</p

Growth variation-influence factor biplot diagram from the RDA.

<p>Growth variations are shown as arrows with solid arrowheads, and influence factors are shown as arrows with empty arrowheads. The approximate correlation between growth variation and influence factor is equal to the cosine of the angle between the corresponding arrows.</p