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Thyroid function is closely linked to circadian rhythms, but the relationship between the frequency of night eating and thyroid function remains unclear. Our study aimed to investigate the association between night eating frequency and its impact on thyroid function and sensitivity. This study included 6093 participants from the U.S. National Health and Nutrition Examination Survey (2007–2012). Night eating behavior was assessed through 24-hour dietary recall, with night eating frequency calculated on the basis of food intake between 10:00 PM and 4:00 AM. The thyroid hormone indices examined included T3, T4, FT3, FT4, TSH, TGA, Tg, and TPOAb, whereas thyroid hormone sensitivity was assessed via indices such as the FT3/FT4, TSHI, TT4RI, and TFQI. The associations between night eating frequency and thyroid function were analyzed via weighted univariate and multivariate linear regression analyses. Subgroup analyses and interaction test analyses were also employed to test this correlation. Compared with individuals who did not eat at night, those who ate more frequently at night had higher levels of Tg (OR 1.223 [95% CI 1.048, 1.429], p trend=0.015) but lower levels of T3 (OR 0.728 [95% CI 0.611, 0.868], p trend=0.235) and TPOAb (OR 0.728 [95% CI 0.611, 0.868], p trend=0.235). Subgroup analysis indicated that this association between Tg and night eating was stronger in the DM group (Tg: OR 1.49 [95% CI 1.15, 1.93]), p interaction=0.022) and that the association between TPOAb and night eating was stronger in the group without DM (TPOAb: OR 0.9 [95% CI 0.82, 0.97]), p interaction=0.003). Our findings suggest a significant association between night eating frequency and thyroid function. However, no statistically significant differences were found in thyroid sensitivity based on night eating frequency. Despite these findings, the hormone fluctuations observed were within normal clinical ranges. Further rigorously designed studies are needed to establish a causal relationship between night eating frequency and thyroid function.

Background: Psoriatic patients tend to develop metabolic syndrome (MS). MS accelerates psoriasis, but the exact molecular mechanisms are poorly understood.Objectives: We aim to investigate the impact of leptin on keratinocyte insulin sensitivity and explore its underlying molecular mechanism, which might play a role in the pathogenesis of this disease.Methods: ELISA and immunohistochemistry were applied respectively to detect the level of leptin in serum and in lesion of psoriatic patients with and without MS. The HaCaT cell line was cultured and western-blot assay was performed to assess the change of insulin sensibility. q-PCR and western-blot assay were applied to detect the SOCS3 expressions. Knockdown of SOCS3 were generated in HaCaT cell line by siRNA. Leptin and insulin were treated for 6 days and K10 expression was evaluated by western-blot assay.Results: Patients with MS had higher level of leptin in serum and lesions than their counterparts without MS. Serum levels of leptin was negatively correlated to PASI decline index in psoriatic patients. Long-term treatment of leptin induced insulin resistance in HaCaT cell line, as indicated by elevated expression of p-IRS-1 (ser636) and lower p-PKB (ser473). Leptin treatment up-regulated the mRNA and protein expression of SOCS3. Knockdown of SOCS3 blocked the effect of leptin-induced insulin resistance. Leptin treatment attenuated insulin-elicited K10 expression.Conclusions: Leptin induces insulin resistance by upregulating SOCS3 and give rise to differentiation disorder of keratinocyte. Insulin resistance may serve as a target for anti-psoriatic therapies.

Landslide are one of the most common types of geological disasters in China, posing a serious threat to the safety of people's lives and properties due to their propensity and frequency. The stability assessment of landslides is crucial in landslide disaster prevention and mitigation works. Considering the fuzzy nature of landslide inducing factors, a certain accumulation layer landslide in Xinpu Town, Mianxian County is taken as an example in this article. Based on the analysis of geological environmental conditions, a fuzzy mathematical method is employed for landslide stability assessment. The concept of "entropy" from thermodynamics is introduced, and the entropy weight method is used to determine the weights of landslide inducing factors. Consequently, a new method for landslide stability assessment based on the improved entropy weighting method in fuzzy mathematics is proposed. The results show that the proposed method overcomes the subjectivity in determining the weights of landslide influencing factors in traditional fuzzy comprehensive evaluation methods. Validation through quantitative analysis of landslide stability using numerical simulation method found that the evaluation conclusions of the two methods were in good consistent, indicating the reliability of the proposed new method. The improved fuzzy mathematical landslide stability assessment based on entropy weighting method is relatively fast and simple, making it suitable for landslide disaster prevention and mitigation works in emergency situations such as rapid disaster warning of impending disasters and emergency rescue.

The Eastern Tianshan region, influenced by the far-field effect of northward compression and expansion of the Qinghai-Xizang block, features highly developed Late Quaternary active faults that exhibit significant neotectonic activity. Historically, the Barkol-Yiwu Basin, located to the north of the Eastern Tianshan, experienced two major earthquakes in 1842 and 1914, each with a magnitude of M71/2. In contrast, the Hami Basin on the southern margin of the Eastern Tianshan has no historical records of any major earthquakes, and its seismic potential, mechanisms, and future earthquake hazards remain unclear. Based on satellite image interpretation and field surveys, this study identified a relatively recent and well-preserved seismic surface rupture zone with good continuity in the Liushugou area of the western segment of the Northern Margin Fault of the Hami Basin (HMNF), which is the seismogenic structure responsible for the rupture. The surface rupture zone originates at Kekejin in the east, extends intermittently westward through Daipuseke Bulake and Liushugou, and terminates at Wuzun Bulake, with a total length of approximately 21 km. The rupture zone traverses the youngest geomorphic surface units, such as river beds or floodplains and first-order terraces (platforms), and is characterized by a series of single or multiple reverse fault scarps. The morphology of fault scarps is clear, presenting a light soil color with heights ranging from 0.15 m to 2.13 m and an average displacement of 0.56 m, suggesting that this surface rupture zone likely represents the most recent seismic event. Comparison with historical earthquake records in the Eastern Tianshan region suggests that the rupture zone may have been formed simultaneously with the Xiongkuer rupture zone by the 1842 M71/2 earthquake along the boundary faults on both sides of the Barkol Mountains, exhibiting a flower-like structural pattern. Alternatively, it might represent a separate, unrecorded seismic event occurring shortly after the 1842 earthquake. The estimated magnitude of the associated earthquake is about 6.6~6.9. Given that surface-rupturing earthquakes have already occurred in the western segment, the study indicates that the Erdaogou–Nanshankou section of the HMNF has surpassed the average recurrence interval for major earthquakes, indicating a potential future earthquake hazard.

Background Psoriasis is a chronic immune-mediated inflammatory disease. N6-methyladenosine (m6A) is involved in numerous biological processes in both normal and diseased states. Herein, we aimed to explore the potential role of m6A regulators in the diagnosis of psoriasis and predict molecular mechanisms by which m6A regulators impact psoriasis. Methods GSE30999 (170 human skin tissue samples) and GSE13355 (180 human skin tissue samples) were downloaded as the training analysis dataset and validation dataset respectively. M6A-related genes were obtained from the literature and their expression levels in GSE30999 samples were measured to identify M6A-related DEGs between psoriasis lesions (LS) and non-lesional lesions (NL). We identified m6A-related DEGs using differential expression analysis and assessed their interactions through correlation analysis and network construction. A logistic regression analysis followed by LASSO optimization was employed to select m6A-related DEGs for the construction of a diagnostic model. The performance of the model was validated using support vector machine (SVM) methodology with sigmoid kernel function and extensive cross-validation. Additionally, the correlation between m6A-related DEGs and immune cell infiltration was analyzed, as well as the association of these DEGs with psoriasis subtypes. Functional analysis of the m6A-related DEGs included the construction of regulatory networks involving miRNAs, transcription factors (TFs), and small-molecule drugs. The m6A modification patterns were also explored by examining the gene expression differences between psoriasis subtypes and their enriched biological pathways. Finally, the expression of significant m6A regulators involved in the diagnostic model was examined by RT-qPCR. Results In this study, ten optimal m6A-related DEGs were identified, including FTO, IGF2BP2, METTL3, YTHDC1, ZC3H13, HNRNPC, IGF2BP3, LRPPRC, YTHDC2, and HNRNPA2B1. A diagnostic model based on these m6A-related DEGs was constructed, demonstrating high diagnostic accuracy with an area under the curve (AUC) in GSE30999 and GSE13355 of 0.974 and 0.730, respectively. Meanwhile, the expression level of m6A regulators verified by RT-qPCR was consistent with the results in GSE30999. The infiltration of activated mast cells and NK cells was significantly associated with all ten m6A-related DEGs in psoriasis. Among them, YTHDC1, HNRNPC, and FTO were targeted by most miRNAs and were regulated by nine related TFs. Therefore, patients may benefit from dorsomorphin and cyclosporine therapy. Between the two subgroups, 1,592 DEGs were identified, including LRPPRC and METTL3. These DEGs were predicted to be involved in neutrophil activation, cytokine-cytokine receptor interactions, and chemokine signaling pathways. Conclusions A diagnostic model based on ten m6A-related DEGs in patients with psoriasis was constructed, which may provide early diagnostic biomarkers and therapeutic targets for psoriasis.

Loess slopes are often prone to creeping after being subjected to rainfall, artificial irrigation, river water infiltration, etc. Eventually a landslide occurred, even causing a catastrophic accident. There are few studies on the creep properties of loess landslide soil under different water content in the existing literature, and there is a lack of quantitative analysis of the law. A series of triaxial creep tests of sliding zone soil of loess landslide in the Secaowan Village, Yulin City have been carried. The stress-strain-time curves and isochronous stress-strain curves with different water contents (w=10%，12%，14%，16%，18%, w represents the water content) are obtained and the influence of water content on the creep characteristics of loess is analyzed as well. The long-term strength of loess is obtained by using the isochronous curve method. The research results show that (1) When the water content of the sample is larger, the deviator stress required for the creep failure of the sample is smaller, and the axial strain is larger. This phenomenon can be explained by the electric double layer theory as follows: The thickness of free water increases, the degree of cementation among soil particles decreases, and the water film will lubricate the soil particles and make them easy to slip. (2) When the water content of the sample is larger, the time required for the creep strain to reach a stable state after each level of load is applied is longer. This phenomenon can be explained as follows: The increase of loess moisture reduces the structural integrity of the soil, and the adjustment of the internal structure of the soil is relatively slow under the action of stress. Therefore, it takes a longer time for the pore water pressure to dissipate during consolidation and creep. (3) By analyzing the surface morphology of the creep failure of the loess sample, it is found that when the water content is small, the sample after the creep failure has an obvious shear failure surface. When the water content of the sample is larger, the lateral swelling is more likely to occur, showing the characteristics of plastic failure, and indicating that when the moisture content is larger, the softening effect of water is greater than the effect of water on the crack expansion of the soil. (4) The long-term strength loss rate of loess is introduced. It reveals the law of long-term strength and long-term strength loss rate of loess landslide soil under different water content. The research results can provide a reference for the long-term stability analysis of loess landslides.

Tumor Necrosis Factor-alpha (TNF-α) is ubiquitous in the human body and plays a significant role in various physiological and pathological processes. However, TNF-α-induced diseases remain poorly understood with limited efficacy due to the intricate nature of their mechanisms. N6-methyladenosine (m6A) methylation, a prevalent type of epigenetic modification of mRNA, primarily occurs at the post-transcriptional level and is involved in intranuclear and extranuclear mRNA metabolism. Evidence suggests that m6A methylation participates in TNF-α-induced diseases and signaling pathways associated with TNF-α. This review summarizes the involvement of TNF-α and m6A methylation regulators in various diseases, investigates the impact of m6A methylation on TNF-α-induced diseases, and puts forth potential therapeutic targets for treating TNF-α-induced diseases.

The Yellow River Basin in China has the world's most serious soil erosion problem. The Yellow River Basin in Sichuan Province (YRS), as the upper reaches of the Yellow River, and its water conservation (WC) capacity greatly affects the ecological environment of the downstream basin. In recent years, YRS has received more and more attention, and numerous policies have been developed to improve local WC. However, there is a vacancy in the long-term research of WC in the YRS due to the lack of in-situ data. This study quantitatively evaluated the WC of YRS from 2001 to 2020 through Google Earth Engine (GEE) and analyzed the spatio-temporal variations of WC and land cover (LC). CA-Markov predicted the LC and WC in 2025 under three scenarios to assess the contribution of different scenarios to WC. The WC in YRS fluctuated from 1.93 to 6.77 billion m3. The climate is the dominant factor of WC change, but the effect of LC on WC is also evident. The WC capacity increases with vegetation coverage and height. The WC capacity of forests per km2 exceeds 600 mm, while that of grasslands is about 250 mm, and barren can cause around 300 mm of WC loss. In 2025, the WC in YRS may exceed 7.5 billion m3, but the past ecological management mode should be transformed. Improving the quality of land use and converting grasslands to forests is better than reducing cropland to improve WC.

The structural, elastic, piezoelectric, and electronic properties of K1−xMxNbO3 (M = Li, Na) were investigated by first-principles calculations. The results show that the modifications have effects on the elastic properties of KNbO3 and strengthen the anisotropy. Na and Li doping can slightly improve the piezoelectric stress tensor (e33) and piezoelectric strain tensor (d33) of KNbO3. With the increase in the x value from 0 to 0.5 in K1−xMxNbO3, the d33 value is improved from 24.4 to 36.3 pC/N (M = Na) and 41.5 pC/N (M = Li), and the piezoelectric property is improved by 1.49 and 1.70 times with the modifications of Na and Li, respectively. This conclusion is consistent with the result of Born effective charge and band structure results. The Born effective charge average values (Z*̄) for K and Nb atoms in K0.5M0.5NbO3 (M = Li, Na) are slightly increased compared to those in KNbO3. KNbO3 has an indirect bandgap, while Na and Li doping makes K0.5Na0.5NbO3 or K0.5Li0.5NbO3 a direct bandgap semiconductor. This study may provide a theoretical insight into Na and Li modified KNbO3 for their piezoelectricity.

With the drastic change in global climate, the wide distribution of natural lakes over the Qinghai-Tibet Plateau (TP) has attracted extensive attention due to their high climate sensitivity. In this paper, we investigated the dynamics of Paiku Co, the largest inland lake in the Qomolangma Natural Reserve, with the associated response to climate change in the past three decades. The methods used contain the water index method, the spatial and temporal fusion model, the statistical mono-window algorithm, and multi-variable linear regression. Lake area fluctuated greatly in 1990–2000, followed by a continuous shrinkage in 2000–2010, and it stabilized after that (2010–2020). We forecasted that Paiku Co would enter a slow expansion period. Conjoint analysis with climate factors showed that the area variation of Paiku Co was not significantly related to precipitation change, but negatively related to the change of air temperature and lake temperature. We found that the lake change was not dominated by a single factor but showed different climate sensitivity in each period. Especially, there was a common inflection point around 2013 that might herald the occurrence of a new trend of climate change. This article provides new ideas and solutions for the research of lakes in the Qinghai-Tibet Plateau and offers a reference for water resource management. HIGHLIGHTS The lake area has experienced a process of sharp shrink – recovery – continuous retreat – stabilization.; Paiku Co is mainly affected by temperature and exhibits variable climate sensitivity.; The area of Paiku Co stabilized in 2013 and may have a slow upward trend in the future.;

Improvements in hyperspectral image technology, diversification methods, and cost reductions have increased the convenience of hyperspectral data acquisitions. However, because of their multiband and multiredundant characteristics, hyperspectral data processing is still complex. Two feature extraction algorithms, the autoencoder (AE) and restricted Boltzmann machine (RBM), were used to optimize the classification model parameters. The optimal classification model was obtained by comparing a stacked autoencoder (SAE) and a deep belief network (DBN). Finally, the SAE was further optimized by adding sparse representation constraints and GPU parallel computation to improve classification accuracy and speed. The research results show that the SAE enhanced by deep learning is superior to the traditional feature extraction algorithm. The optimal classification model based on deep learning, namely, the stacked sparse autoencoder, achieved 93.41% and 94.92% classification accuracy using two experimental datasets. The use of parallel computing increased the model’s training speed by more than seven times, solving the model’s lengthy training time limitation.

The ecological environments of urban rivers are greatly affected by human activities, leading to serious water quality deterioration. We thus investigated urban aquatic environment and water pollution factors by collecting water samples over one year upstream, midstream and downstream of the Funan River in Chengdu, Sichuan, China. Measured water quality indicators included total N/P ratio, ammonia N, chemical oxygen demand, chlorophyll a, biochemical oxygen demand, transparency, turbidity, dissolved oxygen, conductivity, and pH, as well as the heavy metal concentrations and algal communities in the river sediments. Additionally, a water pollution index (WPI) and trophic level index (TLI) were calculated and a principal component analysis (PCA) was performed. The WPI analysis indicated serious pollution in this river during the sampling year. Moreover, moderate eutrophication with high pollution was found in summer (TLI = 64.7), while mild eutrophication occurred in the other seasons. The number of Cyanobacteria species sharply increased from 6 to 23 in summer, indicating a high risk of blooms. Furthermore, the PCA also revealed that two metals, chromium and cadmium, affected the water quality, which may be because of the interaction of nutrients and metals due to the land use types, industrial activities and hydrodynamic conditions. In general, we have systematically discussed the influences of various factors on the water environment and the mechanisms of their interactions in urbanized rivers, which is very important for the protection and restoration of river ecology as China and the world become more urbanized.

Abstract Phospholipase C (PLC) can participate in cell proliferation, differentiation and aging. However, whether it has a function in apoptosis in porcine primary granulosa cells is largely uncertain. The objective of this study was to examine the effects of PLC on apoptosis of porcine primary granulosa cells cultured in vitro. The mRNA expression of BAK, BAX and CASP3, were upregulated in the cells treated with U73122 (the PLC inhibitor). The abundance of BCL2 mRNA, was upregulated, while BAX and CASP3 mRNA expression was decreased after treatment with m-3M3FBS (the PLC activator). Both the early and late apoptosis rate were maximized with 0.5 μM U73122 for 4 h. The rate of early apoptosis was the highest at 4 h and the rate of late apoptosis was the highest at 12 h in the m-3M3FBS group. The protein abundance of PLCβ1, protein kinase C β (PKCβ), calmodulin-dependent protein kinaseII α (CAMKIIα) and calcineurinA (CalnA) were decreased by U73122, and CAMKIIα protein abundance was increased by m-3M3FBS. The mRNA expression of several downstream genes (CDC42, NFATc1, and NFκB) was upregulated by PLC. Our results demonstrated that apoptosis can be inhibited by altering PLC signaling in porcine primary granulosa cells cultured in vitro, and several calcium−sensitive targets and several downstream genes might take part in the processes.

With the social and economic development in recent years, human activities have been more extensive and intensified. As a result, ecosystems are damaged to varying degrees, and regional ecological environments tend to be weaker. The socio-ecological system in Aba Prefecture, Western Sichuan Plateau, China, the researched area, also faces increasingly serious problems. To advance ecological civilization development in a coordinated way across the country, the national government and the competent authorities have launched a series of new strategies. Research on socio-ecological vulnerability, a major part of the ecosystem protection and restoration program, is provided with powerful spatial data observation and analysis tools thanks to the invention and development of remote sensing and geographic information system technologies. This study was based on the vulnerability scoping diagram (VSD) framework. Multi-source data such as digital elevation model (DEM), geographical data such as land use types, soil and geological disasters, remote sensing image data, meteorological data and social statistics data from 2005 to 2019 were used to construct the temporal social-ecosystem vulnerability evaluation index database of Aba Prefecture, Western Sichuan Plateau. The spatial principal component analysis (SPCA) is applied to evaluating the socio-ecological vulnerability and analyzing its spatial-temporal variation in Aba Prefecture, Western Sichuan Plateau. To probe into the driving effects of various impact factors on the socio-ecological vulnerability, the Geodetector is used to analyze the driving factors. The ordered weighted average (OWA) method is applied to the multi-scenario analysis of socio-ecological vulnerability in the researched area. The conclusions of this study are as follows: (1) from 2005 to 2019, the spatial distribution characteristics of exposure and sensitivity in Aba Prefecture were higher in the southeast and lower in the northwest, and the overall spatial distribution characteristics of socio-ecological system vulnerability showed that the degree of vulnerability increased from the north to the southeast. (2) Extreme natural climate conditions play a leading role in the driving of socio-ecosystem vulnerability, followed by human production activities and geological hazards. (3) The degree of social-ecosystem vulnerability in Aba Prefecture will increase with the increase of decision risk coefficient. The results of social-ecosystem vulnerability under the status quo scenario are similar to those in 2010 and 2019, indicating that the selected evaluation factors can reflect the actual social-ecosystem vulnerability. In the sustainable guided scenario and the unsustainable guided scenario, the proportion of the area of the social-ecosystem severe vulnerability level was at the minimum value and the maximum value, respectively.

Following significant urban expansion, the ecological problems of resource-based cities are gradually exposed. It is of great significance to study the ecosystem services of resource-based cities to achieve their sustainable development goals and to alleviate the conflicts between environmental protection and the utilization of the surrounding resources. However, in the current research on resource-based cities, few scholars have combined multiple minerals and multiple ecosystem services to explore the impact of mineral resources on the ecosystem. In this study, based on the historical data spanning from 2002 to 2018, we used the CA–Markov model to project the land use of Panzhihua City to 2030. Based on future land use projection, we quantified four ecosystem services (ESs) variables, including water yield, carbon storage, habitat quality, and soil conservation, using the InVEST model from the perspective of land use evolution in Panzhihua City. In addition, we explored the trade-offs and synergies of different ecosystem services and the correlations between different mineral species and ecosystem services using Spearman’s correlation coefficient. Results showed the following: (1) During 2002–2018, water yield service, habitat quality service, and carbon storage service of Panzhihua City decreased year by year, and soil conservation service showed significant fluctuations; most of the low ESs areas were distributed in the central region of Panzhihua. On the contrary, most high ESs areas were located in the forest region. (2) The trade-offs and synergistic relationships among different ecosystem services showed significant spatial variations. There were synergistic relationships among ESs and weak trade-offs between water yield services, soil conservation, and habitat quality services. There was also significant spatial variability in the trade-offs and synergies among ecosystem services, with water production services showing “east trade-offs and west synergies” with soil conservation and habitat quality services, and most of the rest showing trade-offs in urban areas. (3) ESs in mining areas showed trade-offs in general, mainly between water production services and carbon storage services, with clay as the major negative factor of mineral species, and iron ore mines that have undergone ecological protection construction showed the lowest negative impact on ecology.

Glacial lakes are important freshwater resources in southern Tibet. However, glacial lake outburst floods have significantly jeopardized the safety of local residents. To better understand the changes in glacial lakes in response to climate change, it is necessary to conduct a long-term evaluation on the areal dynamics of glacial lakes, assisted with local observations. Here, we propose an innovative method of classification and stacking extraction to accurately delineate glacial lakes in southwestern Tibet from 1990 to 2020. Based on Landsat images and meteorological data, we used geographic detectors to detect correlation factors. Multiple regression models were used to analyze the driving factors of the changes in glacier lake area. We combined bathymetric data of the glacial lakes with the changes in climatic variables and utilized HEC-RAS to determine critical circumstances for glacial lake outbursts. The results show that the area of glacial lakes in Nyalam County increased from 27.95 km2 in 1990 to 52.85 km2 in 2020, and eight more glacial lakes were observed in the study area. The glacial lake area expanded by 89.09%, where we found significant growth from 2015 to 2020. The correlation analysis between the glacial lake area and climate change throughout the period shows that temperature and precipitation dominate the expansion of these lakes from 1990 to 2020. We also discover that the progressive increase in water volume of glacial lakes can be attributed to the constant rise in temperature and freeze–thaw of surrounding glaciers. Finally, the critical conditions for the glacial lake’s outburst were predicted by using HEC-RAS combined with the changes in the water volume and climatic factors. It is concluded that GangxiCo endures a maximum water flow of 4.3 × 108 m3, and the glacial lake is in a stable changing stage. This conclusion is consistent with the field investigation and can inform the prediction of glacial lake outbursts in southwestern Tibet in the future.

Panzhihua City is a typical agricultural-forestry-pastoral and ecologically sensitive city in China. It is also an important ecological defense in the upper Yangtze River. It has abundant mineral resources, including vanadium, titanium, and water supplies. However, ecological and environmental problems emerge due to the excessive development of mining, agriculture, animal husbandry, and other non-natural urban economies. Therefore, a scientific understanding of the spatio-temporal changes of the eco-environment of Panzhihua is critical for environmental protection, city planning, and construction. To objectively evaluate the eco-environmental status of Panzhihua, the remote sensing-based ecological index (RSEI) was first applied to Panzhihua, a typical resource-based city, and its ecological environmental quality (EEQ) was quantitatively assessed from 1990 to 2020. This study explored the effects of mining activities and policies on EEQ and used change detection to reveal the spatial-temporal changes of EEQ in Panzhihua City over the past three decades. In addition, this study also verified the suitability of RSEI for evaluating EEQ in resource-based city using spatial autocorrelation, revealed the spatial heterogeneity of EEQ in Panzhihua City using optimized hot spot analysis, and showed different ecological clustering by hot spot analysis at two scales of urban and mining areas. According to the results: (1) From 1990 to 2020, the general eco-environmental condition of Panzhihua is improving, but there are still regional differences. (2) The Moran’s I value ranges from 0.436 (1990) to 0.700 (2020), indicating that there is autocorrelation in the distribution of eco-environmental quality. (3) At the mine, the mean value of RSEI dropped by 20–40%, and the EEQ decreased significantly due to mining activities. (4) A series of ecological restoration policies can buffer the negative impact of mining activities on the ecosystem, resulting in a slight improvement in the quality of the ecological environment. This study evaluates the EEQ of resource-based city and its spatial-temporal changes using RSEI constructed by the Google Earth Engine (GEE) platform, which can provide theoretical support for ecological and environmental conditions monitoring, development planning, and environmental protection policy-making of a resource-based city.

Global climate changes have a great impact on terrestrial ecosystems. Vegetation is an important component of ecosystems, and the impact of climate changes on ecosystems can be determined by studying vegetation phenology. Vegetation phenology refers to the phenomenon of periodic changes in plants, such as germination, flowering and defoliation, with the seasonal change of climate during the annual growth cycle, and it is considered to be one of the most efficient indicators to monitor climate changes. This study collected the global land surface satellite leaf area index (GLASS LAI) products, meteorological data sets and other auxiliary data in the Three-River headwaters region from 2001 to 2018; rebuilt the vegetation LAI annual growth curve by using the asymmetric Gaussian (A-G) fitting method and extracted the three vegetation phenological data (including Start of Growing Season (SOS), End of Growing Season (EOS) and Length of Growing Season (LOS)) by the maximum slope method. In addition, it also integrated Sen’s trend analysis method and the Mann-Kendall test method to explore the temporal and spatial variation trends of vegetation phenology and explored the relationship between vegetation phenology and meteorological factors through a partial correlation analysis and multiple linear regression models. The results of this study showed that: (1) the SOS of vegetation in the Three-River headwaters region is concentrated between the beginning and the end of May, with an interannual change rate of −0.14 d/a. The EOS of vegetation is concentrated between the beginning and the middle of October, with an interannual change rate of 0.02 d/a. The LOS of vegetation is concentrated between 4 and 5 months, with an interannual change rate of 0.21 d/a. (2) Through the comparison and verification with the vegetation phenological data observed at the stations, it was found that the precision of the vegetation phonology extracted by the A-G method and the maximum slope method based on GLASS LAI data is higher (MAE is 7.6 d, RMSE is 8.4 d) and slightly better than the vegetation phenological data (MAE is 9.9 d, RMSE is 10.9 d) extracted based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS NDVI) product. (3) The correlation between the SOS of vegetation and the average temperature in March–May is the strongest. The SOS of vegetation is advanced by 1.97 days for every 1 °C increase in the average temperature in March–May; the correlation between the EOS of vegetation and the cumulative sunshine duration in August–October is the strongest. The EOS of vegetation is advanced by 0.07 days for every 10-h increase in the cumulative sunshine duration in August–October.

The Three Gorges Reservoir region in China is the Yangtze River Economic Zone’s natural treasure trove. Its natural environment has an important role in development. The unique and fragile ecosystem in the Yangtze River’s Three Gorges Reservoir region is prone to natural disasters, including soil erosion, landslides, debris flows, landslides, and earthquakes. Therefore, to better alleviate these threats, an accurate and comprehensive assessment of the susceptibility of this area is required. In this study, based on the collection of relevant data and existing research results, we applied machine learning models, including logistic regression (LR), the random forest model (RF), and the support vector machine (SVM) model, to analyze landslide susceptibility in the Yangtze River’s Three Gorges Reservoir region to analyze landslide events in the whole study region. The models identified five categories (i.e., topographic, geological, ecological, meteorological, and human engineering activities), with nine independent variables, influencing landslide susceptibility. The accuracy of landslide susceptibility derived from different models and raster cells was then verified by the accuracy, recall, F1-score, ROC curve, and AUC of each model. The results illustrate that the accuracy of different machine learning algorithms is ranked as SVM > RF > LR. The LR model has the lowest generalization ability. The SVM model performs well in all regions of the study area, with an AUC value of 0.9708 for the entire Three Gorges Reservoir area, indicating that the SVM model possesses a strong spatial generalization ability as well as the highest robustness and can be adapted as a real-time model for assessing regional landslide susceptibility.

Considering the geological and regional feature of Ankang region, combining target analysis, feature, the assessment system of geological environment bearing capacity was established, and the weights assessment indexes were presented by Analytic Hierarchy Process (AHP). And weighted composite index method combined GIS spatial analysis with data attributes database management method were adopted to assess the geo-environment carrying capacity of Ankang region. Based on the evaluation results and analysis of each rate area, this paper provides the scientific basis for ecological civilization construction and sustainable development.

Coenzyme A (CoA) thioesters (acyl-CoAs) are essential intermediates of metabolism. Inborn errors of acyl-CoA metabolism include a large fraction of the classical organic acidemias. These conditions can involve liver, muscle, heart and brain, and can be fatal. These conditions are increasingly detected by newborn screening. There is a renewed interest in CoA metabolism and in developing effective new treatments. Here, we review theories of the pathophysiology in relation to mitochondrial CoA sequestration, toxicity and redistribution (CASTOR). [ABSTRACT FROM AUTHOR]