Spatiotemporal deformation characteristics of Outang landslide and identification of triggering factors using data mining

Since the impoundment of Three Gorges Reservoir(TGR)in 2003,numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall.One case is the Outang landslide,a large-scale and active landslide,on the south bank of the Yangtze River.The latest monitoring data and site investigations available are analyzed to establish spatial and temporal landslide deformation characteristics.Data mining technology,including the two-step clustering and Apriori algorithm,is then used to identify the dominant triggers of landslide movement.In the data mining process,the two-step clustering method clusters the candidate triggers and displacement rate into several groups,and the Apriori algorithm generates correlation criteria for the cause-and-effect.The analysis considers multiple locations of the landslide and incorporates two types of time scales:longterm deformation on a monthly basis and short-term deformation on a daily basis.This analysis shows that the deformations of the Outang landslide are driven by both rainfall and reservoir water while its deformation varies spatiotemporally mainly due to the difference in local responses to hydrological factors.The data mining results reveal different dominant triggering factors depending on the monitoring frequency:the monthly and bi-monthly cumulative rainfall control the monthly deformation,and the 10-d cumulative rainfall and the 5-d cumulative drop of water level in the reservoir dominate the daily deformation of the landslide.It is concluded that the spatiotemporal deformation pattern and data mining rules associated with precipitation and reservoir water level have the potential to be broadly implemented for improving landslide prevention and control in the dam reservoirs and other landslideprone areas.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Landscape ecological risk assessment and its driving factors in the Weihe River basin,China

Weihe River basin is of great significance to analyze the changes of land use pattern and landscape ecological risk and to improve the ecological basis of regional development.Based on land use data of the Weihe River basin in 2000,2010,and 2020,with the support of Aeronautical Reconnaissance Coverage Geographic Information System(ArcGIS),GeoDa,and other technologies,this study analyzed the spatial-temporal characteristics and driving factors of land use pattern and landscape ecological risk.Results showed that land use structure of the Weihe River basin has changed significantly,with the decrease of cropland and the increase of forest land and construction land.In the past 20 a,cropland has decreased by 7347.70 km2,and cropland was mainly converted into forest land,grassland,and construction land.The fragmentation and dispersion of ecological landscape pattern in the Weihe River basin were improved,and land use pattern became more concentrated.Meanwhile,landscape ecological risk of the Weihe River basin has been improved.Severe landscape ecological risk area decreased by 19,177.87 km2,high landscape ecological risk area decreased by 3904.35 km2,and moderate and low landscape ecological risk areas continued to increase.It is worth noting that landscape ecological risks in the upper reaches of the Weihe River basin are still relatively serious,especially in the contiguous areas of high ecological risk,such as Tianshui,Pingliang,Dingxi areas and some areas of Ningxia Hui Autonomous Region.Landscape ecological risk showed obvious spatial dependence,and high ecological risk area was concentrated.Among the driving factors,population density,precipitation,normalized difference vegetation index(NDVI),and their interactions are the most important factors affecting the landscape ecological risk of the Weihe River basin.The findings significantly contribute to our understanding of the ecological dynamics in the Weihe River basin,providing crucial insights for sustainable management in the region.

Factors affecting land dissection density in geomorphological regions of China

Land dissection density(LDD)provides morphological evidence regarding prior intense soil erosion and quantifies the distribution of land dissections.A comprehensive understanding of the potential factors influencing the spatial pattern and value of the LDD is vital in geological disasters,soil erosion,and other related domains.Land dissection phenomena in China affects large areas with different morphological,pedological,and climatic characteristics.Prior studies have focused on the potential factors influencing the LDD at a watershed scale.However,these results are insufficient to reflect the status quo of dissection development and its primary influencing factors on a national scale.LDD’s spatial patterns and the dominant factors at a regional scale in millions of square kilometers remain to be ascertained.This study used the geomorphon-based method and the geographical detector model to quantify the spatial pattern of LDD over China and identify the dominant factors affecting this pattern in China’s six first-order geomorphological regions(GR1~GR6).The results yield the following findings:(1)LDD in China ranges from 0~4.55 km/km^(2),which is larger in central and eastern regions than in other regions of China;(2)dominant factors and their dominant risk subcategories vary with each geomorphological region’s primary internal and external forces;(3)the influence of natural factors is more significant on the large regional scale in millions of square kilometers compared to anthropogenic factors;relief degree of land surface(RDLS)is dominant in GR1,GR2,and GR5;the slope is dominant in GR6,soil type is dominant in GR3 and GR4,and lithology plays a critical role in the dominant interactions of GR3,GR4,and GR6;(4)the interactions between factors on LDD’s spatial pattern have a more significant effect than individual factors.

Review of coastal land transformation:Factors,impacts,adaptation strategies,and future scopes

Coastal land transformation has been identified as a topic of research in many countries around the world.Several studies have been conducted to determine the causes and impacts of land transformation.However,much less is understood about coupling change detection,factors,impacts,and adaptation strategies for coastal land transformation at a global scale.This review aims to present a systematic review of global coastal land transformation and its leading research areas.From 1,741 documents of Scopus and Web of Science,60 studies have been selected using the PRISMA-2020 guideline.Results revealed that existing literature included four leading focus areas regarding coastal land transformation:change detection,driving factors,impacts,and adaptation measures.These focus areas were further analyzed,and it was found that more than 80%of studies used Landsat imagery to detect land transformation.Population growth and urbanization were among the major driving factors identified.This review further identified that about 37%of studies included impact analysis.These studies identified impacts on ecosystems,land surface temperature,migration,water quality,and occupational effects as significant impacts.However,only four studies included adaptation strategies.This review explored the scope of comprehensive research in coastal land transformation,addressing change detection,factor and impact analysis,and mitigation-adaptation strategies.The research also proposes a conceptual framework for comprehensive coastal land transformation analysis.The framework can provide potential decision-making guidance for future studies in coastal land transformation.

Land use change and its driving factors in the ecological function area:A case study in the Hedong Region of the Gansu Province,China

Land use and cover change(LUCC)is important for the provision of ecosystem services.An increasing number of recent studies link LUCC processes to ecosystem services and human well-being at different scales recently.However,the dynamic of land use and its drivers receive insufficient attention within ecological function areas,particularly in quantifying the dynamic roles of climate change and human activities on land use based on a long time series.This study utilizes geospatial analysis and geographical detectors to examine the temporal dynamics of land use patterns and their underlying drivers in the Hedong Region of the Gansu Province from 1990 to 2020.Results indicated that grassland,cropland,and forestland collectively accounted for approximately 99% of the total land area.Cropland initially increased and then decreased after 2000,while grassland decreased with fluctuations.In contrast,forestland and construction land were continuously expanded,with net growth areas of 6235.2 and 455.9 km^(2),respectively.From 1990 to 2020,cropland was converted to grassland,and both of them were converted to forestland as a whole.The expansion of construction land primarily originated from cropland.From 2000 to 2005,land use experienced intensified temporal dynamics and a shift of relatively active zones from the central to the southeastern region.Grain yield,economic factors,and precipitation were the major factors accounting for most land use changes.Climatic impacts on land use changes were stronger before 1995,succeeded by the impact of animal husbandry during 1995-2000,followed by the impacts of grain production and gross domestic product(GDP)after 2000.Moreover,agricultural and pastoral activities,coupled with climate change,exhibited stronger enhancement effects after 2000 through their interaction with population and economic factors.These patterns closely correlated with ecological restoration projects in China since 1999.This study implies the importance of synergy between human activity and climate change for optimizing land use via ecological patterns in the ecological function area.

Spatiotemporal characteristics of cultivated land use eco-efficiency and its influencing factors in China from 2000 to 2020

Improving cultivated land use eco-efficiency(CLUE)can effectively promote agricultural sustainability,particularly in developing countries where CLUE is generally low.This study used provincial-level data from China to evaluate the spatiotemporal evolution of CLUE from 2000 to 2020 and identified the influencing factors of CLUE by using a panel Tobit model.In addition,given the undesirable outputs of agricultural production,we incorporated carbon emissions and nonpoint source pollution into the global benchmark-undesirable output-super efficiency-slacks-based measure(GB-US-SBM)model,which combines global benchmark technology,undesirable output,super efficiency,and slacks-based measure.The results indicated that there was an upward trend in CLUE in China from 2000 to 2020,with an increase rate of 2.62%.The temporal evolution of CLUE in China could be classified into three distinct stages:a period of fluctuating decrease(2000-2007),a phase of gradual increase(2008-2014),and a period of rapid growth(2015-2020).The major grain-producing areas(MPAs)had a lower CLUE than their counterparts,namely,non-major grain-production areas(non-MPAs).The spatial agglomeration effect followed a northeast-southwest strip distribution;and the movement path of barycentre revealed a"P"shape,with Luoyang City,Henan Province,as the centre.In terms of influencing factors of CLUE,investment in science and technology played the most vital role in improving CLUE,while irrigation index had the most negative effect.It should be noted that these two influencing factors had different impacts on MPAs and non-MPAs.Therefore,relevant departments should formulate policies to enhance the level of science and technology,improve irrigation condition,and promote sustainable utilization of cultivated land.

Land-population-industry based village evolution and its influencing factors in the upper Tuojiang River

With economic development and urbanization in China,the rural settlements have experienced great change.To explore the evolution process of rural settlements in terms of land,population and industry can reveal the development law of rural spatial distribution,population structure and industrial economy in different stages and regions.Studying the development status and evolution characteristics of villages in the upper Tuojiang River basin in Southwest China in the past 20 years are of significant value.The upper Tuojiang River basin includes the main types of terrain found in the Southwest region:mountainous,plains,and hills,exhibiting a certain typicality of geographical characteristics.This study took towns and townships at the town-level scale as the basic unit of research,and constructed an evaluation system for village evolution based on'land,population,and industry'.It employed Criteria Importance Through Inter-Criteria Correlation(CRITIC)analysis to examine the characteristics of village evolution in the area from 2000 to 2020,and used geographic detector analysis to identify the leading factors affecting village evolution.The results show that:(1)From 2000 to 2010,villages in the upper Tuojiang River basin experienced significant changes,and the pace of these transformations slowed from 2010 to 2020.(2)From a comprehensive perspective,from 2000 to 2020,villages in hilly areas show a decline,while villages in plain areas near the city center show a positive urbanization development.(3)Road accessibility and distance from the city center are the main factors that explain the spatial differentiation of village evolution degree in the study area.This study elucidates the spatiotemporal evolution characteristics of villages in the upper Tuojiang River basin and identifies the primary factors contributing to their changes,which will provide a reference for investigating the development of rural areas in different terrains of Southwest China.

Land use and cover change and influencing factor analysis in the Shiyang River Basin,China

Land use and cover change(LUCC)is the most direct manifestation of the interaction between anthropological activities and the natural environment on Earth's surface,with significant impacts on the environment and social economy.Rapid economic development and climate change have resulted in significant changes in land use and cover.The Shiyang River Basin,located in the eastern part of the Hexi Corridor in China,has undergone significant climate change and LUCC over the past few decades.In this study,we used the random forest classification to obtain the land use and cover datasets of the Shiyang River Basin in 1991,1995,2000,2005,2010,2015,and 2020 based on Landsat images.We validated the land use and cover data in 2015 from the random forest classification results(this study),the high-resolution dataset of annual global land cover from 2000 to 2015(AGLC-2000-2015),the global 30 m land cover classification with a fine classification system(GLC_FCS30),and the first Landsat-derived annual China Land Cover Dataset(CLCD)against ground-truth classification results to evaluate the accuracy of the classification results in this study.Furthermore,we explored and compared the spatiotemporal patterns of LUCC in the upper,middle,and lower reaches of the Shiyang River Basin over the past 30 years,and employed the random forest importance ranking method to analyze the influencing factors of LUCC based on natural(evapotranspiration,precipitation,temperature,and surface soil moisture)and anthropogenic(nighttime light,gross domestic product(GDP),and population)factors.The results indicated that the random forest classification results for land use and cover in the Shiyang River Basin in 2015 outperformed the AGLC-2000-2015,GLC_FCS30,and CLCD datasets in both overall and partial validations.Moreover,the classification results in this study exhibited a high level of agreement with the ground truth features.From 1991 to 2020,the area of bare land exhibited a decreasing trend,with changes primarily occurring in the middle and lower reaches of the basin.The area of grassland initially decreased and then increased,with changes occurring mainly in the upper and middle reaches of the basin.In contrast,the area of cropland initially increased and then decreased,with changes occurring in the middle and lower reaches.The LUCC was influenced by both natural and anthropogenic factors.Climatic factors and population contributed significantly to LUCC,and the importance values of evapotranspiration,precipitation,temperature,and population were 22.12%,32.41%,21.89%,and 19.65%,respectively.Moreover,policy interventions also played an important role.Land use and cover in the Shiyang River Basin exhibited fluctuating changes over the past 30 years,with the ecological environment improving in the last 10 years.This suggests that governance efforts in the study area have had some effects,and the government can continue to move in this direction in the future.The findings can provide crucial insights for related research and regional sustainable development in the Shiyang River Basin and other similar arid and semi-arid areas.

Analysis of the Spatiotemporal Variation Characteristics and Driving Factors of Land Vegetation GPP in a Certain Region of Asia

Gross primary productivity (GPP) of vegetation is a critical indicator of ecosystem growth and carbon sequestration. The spatiotemporal variation characteristics of land vegetation GPP trends in a specific region of Asia from 2001 to 2020 were analyzed by Sen and MK trend analysis methods in this study .Moreover , a GPP change attribution model was established to explore the driving influences of factors such as Leaf Area Index (LAI), Land Surface Temperature (LST), Vapor Pressure Deficit (VPD), Soil Moisture, Solar Radiation and Wind Speed on GPP. The results indicate that summer GPP values are significantly higher than those in other months, accounting for 60.8% of the annual total GPP;spring and autumn contribute 18.91% and 13.04%, respectively. In winter, due to vegetation being nearly dormant, the contribution is minimal at 7.19%. Spatially, GPP shows a decreasing trend from southeast to northwest. LAI primarily drives the spatial and seasonal variations of regional GPP, while VPD, surface temperature, solar radiation, and soil moisture have varying impacts on GPP across different dimensions. Additionally, wind speed exhibits a minor contribution to GPP across different dimensions.

Activity Data and Emission Factor for Forestry and Other Land Use Change Subsector to Enhance Carbon Market Policy and Action in Malawi

Activity data and emission factors are critical for estimating greenhouse gas emissions and devising effective climate change mitigation strategies. This study developed the activity data and emission factor in the Forestry and Other Land Use Change (FOLU) subsector in Malawi. The results indicate that “forestland to cropland,” and “wetland to cropland,” were the major land use changes from the year 2000 to the year 2022. The forestland steadily declined at a rate of 13,591 ha (0.5%) per annum. Similarly, grassland declined at the rate of 1651 ha (0.5%) per annum. On the other hand, cropland, wetland, and settlements steadily increased at the rate of 8228 ha (0.14%);5257 ha (0.17%);and 1941 ha (8.1%) per annum, respectively. Furthermore, the results indicate that the “grassland to forestland” changes were higher than the “forestland to grassland” changes, suggesting that forest regrowth was occurring. On the emission factor, the results interestingly indicate that there was a significant increase in carbon sequestration in the FOLU subsector from the year 2011 to 2022. Carbon sequestration increased annually by 13.66 ± 0.17 tCO2 e/ha/yr (4.6%), with an uncertainty of 2.44%. Therefore, it can be concluded that there is potential for a Carbon market in Malawi.

Spatial distribution characteristics and influence factor analysis of landslides——case study of the Hanwang area in Qinba Mountains

The geological hazards of landslides in Hanwang Town, Ziyang County, Ankang City, Shaanxi Province, have emerged. Yet, the current understanding of the spatial distribution characteristics and influencing factors of landslides in this area remains unclear. Combining the results of remote sensing interpretation and field investigation, seven influencing factors, namely, elevation, slope direction, slope gradient, distance from rivers, distance from faults, engineering geologic lithology, and distance from roads, are selected for the study. The distribution characteristics of landslides in each influencing factor and the influence of the resolution of the Digital Elevation Model(DEM) on the results are statistically and analytically analyzed. Furthermore, two highrisk landslides within the study area were subjected to comprehensive analysis, integrating the findings from drilling and other field investigations in order to examine their deformation mechanisms. Based on this analysis,the following conclusions were derived:(1) 34 landslides in the study area, mainly small earth landslides, with a distribution density of 0.42/km~2, threatening 414 people and property of about 55.87 million Yuan.(2)The landslides in the study area easily occur in the <400 m elevation range;the landslides are developed in all slope directions, the gradient is mainly concentrated in the range of 10°–40°, the distribution density of the landslides is higher in the closer distance from the river and the faults(0–200 m), the landslide-prone strata are mainly the softer and weaker metamorphic rocks, and the landslides are mainly around roads.(3) The resolution of the DEM should be selected based on the specific conditions of the study area, the requirements of the investigation, and the scale of the landslide. Opting for an appropriate DEM resolution is advantageous for understanding the patterns of landslides and conducting risk assessments in the region.(4) The Zhengjiabian landslide is a traction Landslide. The landslide body is a binary structure of gravel soil and slate weathering layer, and the damage process can be divided into three stages:(1)damage to the leading edge and stress release,(2)continuous creep and cracking,(3)rainfall infiltration and damage. The predominant slope material in the Brickyard landslide comprises clay, and the landslide is triggered by a combination of the traction effect resulting from the excavation at the slope's base and the nudging effect caused by the stacking load of the brick factory. Additionally, the Brickyard landslide exhibits persistent creep deformation. The study results provide a scientific basis for disaster prevention and mitigation in the Hanwang Township area.

Prognostic factors for lacrimal gland adenoid cystic carcinoma:a retrospective study in Chinese patients

AIM:To explore the prognostic factors for lacrimal gland adenoid cystic carcinoma(LGACC)in Chinese patients.METHODS:Clinical and histopathological data were reviewed in patients with pathologically confirmed LGACC.Local recurrence,metastasis,and disease-specific death were the main outcome measures.Univariate and multivariate analyses were performed by the Kaplan-Meier method and a Cox proportional hazard model.RESULTS:This retrospective cohort study included 45 patients with pathologically confirmed LGACC between January 2008 and June 2022.Tumor(T)classification(P=0.005),nodal metastasis(N)classification(P=0.018)and positive margin(P=0.008)were independent risk factors of recurrence;T(P=0.013)and N(P=0.003)classification and the basaloid tumor type(P=0.032)were independent risk factors for metastasis;T classification(P<0.001)was an independent factor of death of disease.In the further analysis,the durations from first surgery to radiotherapy is correlated with metastatic risk in LGACC patients with basaloid component(P=0.022).CONCLUSION:Histological subtype should be emphasized when evaluating prognosis and guiding treatment.Timely radiotherapy may reduce the risk of metastasis in patients with basaloid component.

Disentangling the influencing factors of spatiotemporal evolution of sloping farmland in the Yangtze River Economic Belt, China

Sloping farmland(SpF)is not only an important space for food production and supply in China’s hilly areas,but also a major source of soil erosion.Thus,it is important to achieve a healthy balance between regional food security and environmental protection.Yangtze River Economic Belt(YREB),an important grain production base where SpF concentrated in China,is also faced with serious soil erosion.However,research at the macro scale on the spatiotemporal change of SpF and its driving forces in YREB is still lacking.To bridge the gap,we first analyzed the long-term evolution characteristics of SpF in 1069 counties in the YREB and then explored the driving mechanism of SpF changes during 1980-2020.Results showed that the SpF in the YREB continuously decreased during the study period,with a total area decreasing by 26,300 km2.SpF was primarily concentrated in the upper reaches of the YREB while SpF use dynamic degree varied significantly with the most active change in the lower reaches,reaching to a maximum of 0.324%.The spatial gravity of SpF distribution relocated 20.15 km towards the southwest.As for the driving factors,the socioeconomic factors contributed greater to SpF changes in the whole YREB and its subregions.The intensity of human activities is the most crucial,with factor contribution rate constantly above 0.76.The interactive detection revealed that the prevailing interaction format was primarily bi-enhanced,supplemented with nonlinear-enhanced,which amplified the role of different factors after interacting with them.The pair-wise interaction involving socioeconomic factors had a more potential effect on SpF changes compared to those between physical geography and locational factors.The influence of the intensity of human activities on SpF changes is greatly enhanced after interacting with any factor.It dominated SpF changes in the YREB and its interaction with GDP played an important role at all times.These findings can enlighten differential management strategies of SpF use and ecological conservation in the YREB.

Refining the Factors Affecting N_(2)O Emissions from Upland Soils with and without Nitrogen Fertilizer Application at a Global Scale

Nitrous oxide(N_(2)O)is a long-lived greenhouse gas that mainly originates from agricultural soils.More and more studies have explored the sources,influencing factors and effective mitigation measures of N_(2)O in recent decades.However,the hierarchy of factors influencing N_(2)O emissions from agricultural soils at the global scale remains unclear.In this study,we carry out correlation and structural equation modeling analysis on a global N_(2)O emission dataset to explore the hierarchy of influencing factors affecting N_(2)O emissions from the nitrogen(N)and non-N fertilized upland farming systems,in terms of climatic factors,soil properties,and agricultural practices.Our results show that the average N_(2)O emission intensity in the N fertilized soils(17.83 g N ha^(-1)d^(-1))was significantly greater than that in the non-N fertilized soils(5.34 g N ha^(−1) d^(−1))(p<0.001).Climate factors and agricultural practices are the most important influencing factors on N_(2)O emission in non-N and N fertilized upland soils,respectively.For different climatic zones,without fertilizer,the primary influence factors on soil N_(2)O emissions are soil physical properties in subtropical monsoon zone,whereas climatic factors are key in the temperate zones.With fertilizer,the primary influence factors for subtropical monsoon and temperate continental zones are soil physical properties,while agricultural measures are the main factors in the temperate monsoon zone.Deploying enhanced agricultural practices,such as reduced N fertilizer rate combined with the addition of nitrification and urease inhibitors can potentially mitigate N_(2)O emissions by more than 60%in upland farming systems.

Spatiotemporal variation of land surface temperature and its driving factors in Xinjiang,China

Land surface temperature(LST) directly affects the energy balance of terrestrial surface systems and impacts regional resources, ecosystem evolution, and ecosystem structures. Xinjiang Uygur Autonomous Region is located at the arid Northwest China and is extremely sensitive to climate change. There is an urgent need to understand the distribution patterns of LST in this area and quantitatively measure the nature and intensity of the impacts of the major driving factors from a spatial perspective, as well as elucidate the formation mechanisms. In this study, we used the MOD11C3 LST product developed on the basis of Moderate Resolution Imaging Spectroradiometer(MODIS) to conduct regression analysis and determine the spatiotemporal variation and differentiation pattern of LST in Xinjiang from 2000 to 2020. We analyzed the driving mechanisms of spatial heterogeneity of LST in Xinjiang and the six geomorphic zones(the Altay Mountains, Junggar Basin, Tianshan Mountains, Tarim Basin, Turpan-Hami(Tuha) Basin, and Pakakuna Mountain Group) using geographical detector(Geodetector) and geographically weighted regression(GWR) models. The warming rate of LST in Xinjiang during the study period was 0.24℃/10a, and the spatial distribution pattern of LST had obvious topographic imprints, with 87.20% of the warming zone located in the Gobi desert and areas with frequent human activities, and the cooling zone mainly located in the mountainous areas. The seasonal LST in Xinjiang was at a cooling rate of 0.09℃/10a in autumn, and showed a warming trend in other seasons. Digital elevation model(DEM), latitude, wind speed, precipitation, normalized difference vegetation index(NDVI), and sunshine duration in the single-factor and interactive detections were the key factors driving the LST changes. The direction and intensity of each major driving factor on the spatial variations of LST in the study area were heterogeneous. The negative feedback effect of DEM on the spatial differentiation of LST was the strongest. Lower latitudes, lower vegetation coverage, lower levels of precipitation, and longer sunshine duration increased LST. Unused land was the main heat source landscape, water body was the most important heat sink landscape, grassland and forest land were the land use and land cover(LULC) types with the most prominent heat sink effect, and there were significant differences in different geomorphic zones due to the influences of their vegetation types, climatic conditions, soil types, and human activities. The findings will help to facilitate sustainable climate change management, analyze local climate and environmental patterns, and improve land management strategies in Xinjiang and other arid areas.

Reduced mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor contributes to neurodegeneration in a model of spinal and bulbar muscular atrophy pathology

Spinal and bulbar muscular atrophy is a neurodegenerative disease caused by extended CAG trinucleotide repeats in the androgen receptor gene,which encodes a ligand-dependent transcription facto r.The mutant androgen receptor protein,characterized by polyglutamine expansion,is prone to misfolding and forms aggregates in both the nucleus and cytoplasm in the brain in spinal and bulbar muscular atrophy patients.These aggregates alter protein-protein interactions and compromise transcriptional activity.In this study,we reported that in both cultured N2a cells and mouse brain,mutant androgen receptor with polyglutamine expansion causes reduced expression of mesencephalic astrocyte-de rived neurotrophic factor.Overexpressio n of mesencephalic astrocyte-derived neurotrophic factor amelio rated the neurotoxicity of mutant androgen receptor through the inhibition of mutant androgen receptor aggregation.Conversely.knocking down endogenous mesencephalic astrocyte-derived neurotrophic factor in the mouse brain exacerbated neuronal damage and mutant androgen receptor aggregation.Our findings suggest that inhibition of mesencephalic astrocyte-derived neurotrophic factor expression by mutant androgen receptor is a potential mechanism underlying neurodegeneration in spinal and bulbar muscular atrophy.

Functions of nuclear factor Y in nervous system development,function and health

Nuclear factor Y is a ubiquitous heterotrimeric transcription factor complex conserved across eukaryotes that binds to CCAAT boxes,one of the most common motifs found in gene promoters and enhancers.Over the last 30 years,research has revealed that the nuclear factor Y complex controls many aspects of brain development,including differentiation,axon guidance,homeostasis,disease,and most recently regeneration.However,a complete understanding of transcriptional regulatory networks,including how the nuclear factor Y complex binds to specific CCAAT boxes to perform its function remains elusive.In this review,we explore the nuclear factor Y complex’s role and mode of action during brain development,as well as how genomic technologies may expand understanding of this key regulator of gene expression.

Risk factors for congenital nasolacrimal duct obstruction in children under two years of age

·AIM:To identify various risk factors that may play a significant role in the development of congenital nasolacrimal duct obstruction(CNLDO).·METHODS:This observational case-control study included a case group of 122 children less than two years of age with CNLDO who underwent probing and irrigation treatment at the ophthalmology department of Imam Khomeini Hospital in Ahvaz,Iran,from June 2022 to June2024.A control group of 122 age-matched children without CNLDO was also included for comparison.Data was collected from the children's medical records.·RESULTS:The study found a significant correlation between the occurrence of CNLDO and several maternal factors,such as preeclampsia,the use of levothyroxine,hypothyroidism,having more than three pregnancies(gravidity>3),natural pregnancy,and gestational diabetes mellitus.Additionally,in children,factors,such as oxygen therapy,anemia,reflux,jaundice,and a family history of CNLDO in first-degree relatives were associated with CNLDO,and maternal preeclampsia and hypothyroidism were found to significantly increase the risk of developing CNLDO in children.·CONCLUSION:Given that CNLDO affects both premature and full-term children,the present findings may potentially facilitate the early identification of children and infants at risk of nasolacrimal duct obstruction,thereby preventing the onset of chronic dacryocystitis.

The effects of exercise interventions on brain-derived neurotrophic factor levels in children and adolescents:a meta-analysis

Brain-derived neurotrophic factor is a crucial neurotrophic factor that plays a significant role in brain health. Although the vast majority of meta-analyses have confirmed that exercise interventions can increase brain-derived neurotrophic factor levels in children and adolescents, the effects of specific types of exercise on brain-derived neurotrophic factor levels are still controversial. To address this issue, we used meta-analytic methods to quantitatively evaluate, analyze, and integrate relevant studies. Our goals were to formulate general conclusions regarding the use of exercise interventions, explore the physiological mechanisms by which exercise improves brain health and cognitive ability in children and adolescents, and provide a reliable foundation for follow-up research. We used the Pub Med, Web of Science, Science Direct, Springer, Wiley Online Library, Weipu, Wanfang, and China National Knowledge Infrastructure databases to search for randomized controlled trials examining the influences of exercise interventions on brain-derived neurotrophic factor levels in children and adolescents. The extracted data were analyzed using Review Manager 5.3. According to the inclusion criteria, we assessed randomized controlled trials in which the samples were mainly children and adolescents, and the outcome indicators were measured before and after the intervention. We excluded animal experiments, studies that lacked a control group, and those that did not report quantitative results. The mean difference(MD;before versus after intervention) was used to evaluate the effect of exercise on brain-derived neurotrophic factor levels in children and adolescents. Overall, 531 participants(60 children and 471 adolescents, 10.9–16.1 years) were included from 13 randomized controlled trials. Heterogeneity was evaluated using the Q statistic and I^(2) test provided by Review Manager software. The meta-analysis showed that there was no heterogeneity among the studies(P = 0.67, I^(2) = 0.00%). The combined effect of the interventions was significant(MD = 2.88, 95% CI: 1.53–4.22, P < 0.0001), indicating that the brain-derived neurotrophic factor levels of the children and adolescents in the exercise group were significantly higher than those in the control group. In conclusion, different types of exercise interventions significantly increased brain-derived neurotrophic factor levels in children and adolescents. However, because of the small sample size of this meta-analysis, more high-quality research is needed to verify our conclusions. This metaanalysis was registered at PROSPERO(registration ID: CRD42023439408).