Spatial-temporal variations and driving factors of soil organic carbon in forest ecosystems of Northeast China

Forest soil carbon is a major carbon pool of terrestrial ecosystems,and accurate estimation of soil organic carbon(SOC)stocks in forest ecosystems is rather challenging.This study compared the prediction performance of three empirical model approaches namely,regression kriging(RK),multiple stepwise regression(MSR),random forest(RF),and boosted regression trees(BRT)to predict SOC stocks in Northeast China for 1990 and 2015.Furthermore,the spatial variation of SOC stocks and the main controlling environmental factors during the past 25 years were identified.A total of 82(in 1990)and 157(in 2015)topsoil(0–20 cm)samples with 12 environmental factors(soil property,climate,topography and biology)were selected for model construction.Randomly selected80%of the soil sample data were used to train the models and the other 20%data for model verification using mean absolute error,root mean square error,coefficient of determination and Lin's consistency correlation coefficient indices.We found BRT model as the best prediction model and it could explain 67%and 60%spatial variation of SOC stocks,in 1990,and 2015,respectively.Predicted maps of all models in both periods showed similar spatial distribution characteristics,with the lower SOC in northeast and higher SOC in southwest.Mean annual temperature and elevation were the key environmental factors influencing the spatial variation of SOC stock in both periods.SOC stocks were mainly stored under Cambosols,Gleyosols and Isohumosols,accounting for 95.6%(1990)and 95.9%(2015).Overall,SOC stocks increased by 471 Tg C during the past 25 years.Our study found that the BRT model employing common environmental factors was the most robust method for forest topsoil SOC stocks inventories.The spatial resolution of BRT model enabled us to pinpoint in which areas of Northeast China that new forest tree planting would be most effective for enhancing forest C stocks.Overall,our approach is likely to be useful in forestry management and ecological restoration at and beyond the regional scale.

Spatial-Temporal Variations in January Precipitation over the Period of 1950-2000 in Pakistan and Possible Links with Global Teleconnections: Geographical Perspective

Empirical Orthogonal Function (EOF) was performed to investigate spatial variation in January precipitation over Pakistan using ground observed mean monthly precipitation data from 1950-2000 with a combination of gridded reanalysis data of sea level pressure (SLP) and 500 hPa geopotential height. The leading EOF mode captures 37.51% of the total variance and the spatial-temporal variability of January precipitation was consistent in the study area. The temporal changes explicate non-periodic interannual variability and some tacit interdecadal variation. The anomalous condition is more prominent along the western bordering mountains and northern high mountainous region than any other region of Pakistan. Based on results the study reveals spatial-temporal variation in January precipitation and possible links with global teleconnections located both in the proximity as well as in the remote areas from the study locus.

Spatial-Temporal Variations in January Temperature in Pakistan and Their Possible Links with SLP and 500-hPa Levels over the Period of 1950-2000: A Geographical Approach

By making use of Empirical Orthogonal Function (EOF) analysis the spatial and temporal variability was investigated in January over the period of 1950 to 2000 in Pakistan. The analysis is based on the combination of ground observed mean monthly temperature data and National Centre for Environmental Prediction (NCEP) reanalysis data of sea level pressure (SLP) and 500-hPa fields. The results reasonably reveal that the variation in January temperature have links with global teleconnections at SLP and 500-hPa pressure heights. The analysis shows variability at interannual to interdecadal time scale. The interannual variation is more prominent than the interdecadal signal of temperature anomaly. The simulated coefficient patterns show reasonable variation with regional detail from south (north) to north (south) in the study area. The study could be useful as baseline information for climate change studies in Pakistan.

Spatial-temporal Variation Characteristics of Water Quality in the Lower Reaches of the Nenjiang River

As an important river in the western part of Jilin Province,the lower reach of the Nenjiang River is an important wetland water source conservation area in Jilin Province.Within the watershed,it governs the Momoge Wetland,the Xianghai Wetland,and the Danjiang Wetland in Jilin Province.The main problem in the lower reaches of the Nenjiang River is the uneven distribution of water resources in time and space,and the intensification of land salinization.Zhenlai County and Da an City in the Nenjiang River Basin have sufficient surface water resources,with surface water as the drinking water source.Baicheng City and Tongyu County have scarce surface water resources,and both use groundwater as their domestic water source.The main polluted section in the basin is the Xianghai Reservoir,and the annual water quality evaluation is Class V.However,the water quality of the Tao er River,the main stream of the Nenjiang River,is significantly better than that of the Xianghai Reservoir.In order to better study the water environmental pollution situation in the Nenjiang River basin,monitoring data from five sections of non seasonal rivers in the basin from 2012 to 2021 were selected for studying water quality.This in-depth exploration of the water pollution status and river water quality change trends in the Nenjiang River basin is of great significance for future rural development,agricultural pattern transformation,and the promotion of water ecological civilization construction.

Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things

In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.

AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

Spatial-temporal dynamics of desert vegetation and its responses to climatic variations over the last three decades:a case study of Hexi region in Northwest China

Analysis of spatial-temporal variations of desert vegetation under the background of climate changes can provide references for ecological restoration in arid and semi-arid areas. In this study, we used the Global Inventory Modeling and Mapping Studies （GIMMS） NDVI data from 1982 to 2006 and Moderate Resolution Imaging Spectroradiometer （MODIS） NDVI data from 2000 to 2013 to reveal the dynamics of desert vegetation in Hexi region of Northwest China over the past three decades. We also used the annual temperature and precipitation data acquired from the Chinese meteorological stations to analyze the response of desert vegetation to climatic variations. The average value of NDVImax （the maximum NDVI during the growing season） for desert vegetation in Hexi region increased at the rate of 0.65x10-3/a （P〈0.05） from 1982 to 2013, and the significant increases of NDVImax mainly appeared in the typical desert vegetation areas. Vegetation was significantly improved in the lower reaches of Shule and Shiyang river basins, and the weighted mean center of desert vegetation mainly shifted toward the lower reaches of the two basins. Almost 95.32% of the total desert vegetation area showed positive correlation between NDVImax and annual precipitation, indicating that precipitation is the key factor for desert vegetation growth in the entire study area. Moreover, the areas with non-significant positive correlation between NDVImax and annual precipitation mainly located in the lower reaches of Shiyang and Shule river basins, this may be due to human activities. Only 7.64% of the desert vegetation showed significant positive correlation between NDVImax and annual precipitation in the Shule River Basin （an extremely arid area）, indicating that precipitation is not the most important factor for vegetation growth in this basin, and further studies are needed to investigate the mechanism for this phenomenon.

Comprehensive evaluation and spatial-temporal evolution characteristics of urban resilience in Chengdu-Chongqing Economic Circle

To clarify the connotations and extensions of urban resilience,this study focuses on the Chengdu-Chongqing Economic Circle with 16 cities as research subjects.A comprehensive evaluation index system was constructed to measure the resilience of each city from 2003 to 2020.The spatial-temporal evolution characteristics were analyzed using Kernel density estimation,standard deviation ellipse,and spatial Markov chain analysis,and the spatial Tobit model was introduced to discover the influencing factors.The results indicate the following:①Urban resilience in the Chengdu-Chongqing Economic Circle displays an upward trend,with the center of gravity moving to the southwest,and the polarization phenomenon intensifying.②The urban resilience level in a region has certain spatial and geographical dependence,while the probability of urban resilience transfer differs in adjacent cities with different resilience levels.③Urban centrality,economic scale,openness level,and financial development promote urban resilience,whereas government scale significantly inhibits it.Finally,this paper proposes countermeasures and suggestions to improve the urban resilience of the Chengdu-Chongqing Economic Circle.

Associations of genome-wide structural variations with phenotypic differences in cross-bred Eurasian pigs

Background During approximately 10,000 years of domestication and selection,a large number of structural variations(SVs)have emerged in the genome of pig breeds,profoundly influencing their phenotypes and the ability to adapt to the local environment.SVs(≥50 bp)are widely distributed in the genome,mainly in the form of insertion(INS),mobile element insertion(MEI),deletion(DEL),duplication(DUP),inversion(INV),and translocation(TRA).While studies have investigated the SVs in pig genomes,genome-wide association studies(GWAS)-based on SVs have been rarely conducted.Results Here,we obtained a high-quality SV map containing 123,151 SVs from 15 Large White and 15 Min pigs through integrating the power of several SV tools,with 53.95%of the SVs being reported for the first time.These high-quality SVs were used to recover the population genetic structure,confirming the accuracy of genotyping.Potential functional SV loci were then identified based on positional effects and breed stratification.Finally,GWAS were performed for 36 traits by genotyping the screened potential causal loci in the F2 population according to their corresponding genomic positions.We identified a large number of loci involved in 8 carcass traits and 6 skeletal traits on chromosome 7,with FKBP5 containing the most significant SV locus for almost all traits.In addition,we found several significant loci in intramuscular fat,abdominal circumference,heart weight,and liver weight,etc.Conclusions We constructed a high-quality SV map using high-coverage sequencing data and then analyzed them by performing GWAS for 25 carcass traits,7 skeletal traits,and 4 meat quality traits to determine that SVs may affect body size between European and Chinese pig breeds.

Insights into genetic diversity and phenotypic variations in domestic geese through comprehensive population and pan-genome analysis

Background Domestic goose breeds are descended from either the Swan goose(Anser cygnoides)or the Greylag goose(Anser anser),exhibiting variations in body size,reproductive performance,egg production,feather color,and other phenotypic traits.Constructing a pan-genome facilitates a thorough identification of genetic variations,thereby deepening our comprehension of the molecular mechanisms underlying genetic diversity and phenotypic variability.Results To comprehensively facilitate population genomic and pan-genomic analyses in geese,we embarked on the task of 659 geese whole genome resequencing data and compiling a database of 155 RNA-seq samples.By constructing the pan-genome for geese,we generated non-reference contigs totaling 612 Mb,unveiling a collection of 2,813 novel genes and pinpointing 15,567 core genes,1,324 softcore genes,2,734 shell genes,and 878 cloud genes in goose genomes.Furthermore,we detected an 81.97 Mb genomic region showing signs of genome selection,encompassing the TGFBR2 gene correlated with variations in body weight among geese.Genome-wide association studies utilizing single nucleotide polymorphisms(SNPs)and presence-absence variation revealed significant genomic associations with various goose meat quality,reproductive,and body composition traits.For instance,a gene encoding the SVEP1 protein was linked to carcass oblique length,and a distinct gene-CDS haplotype of the SVEP1 gene exhibited an association with carcass oblique length.Notably,the pan-genome analysis revealed enrichment of variable genes in the“hair follicle maturation”Gene Ontology term,potentially linked to the selection of feather-related traits in geese.A gene presence-absence variation analysis suggested a reduced frequency of genes associated with“regulation of heart contraction”in domesticated geese compared to their wild counterparts.Our study provided novel insights into gene expression features and functions by integrating gene expression patterns across multiple organs and tissues in geese and analyzing population variation.Conclusion This accomplishment originates from the discernment of a multitude of selection signals and candidate genes associated with a wide array of traits,thereby markedly enhancing our understanding of the processes underlying domestication and breeding in geese.Moreover,assembling the pan-genome for geese has yielded a comprehensive apprehension of the goose genome,establishing it as an indispensable asset poised to offer innovative viewpoints and make substantial contributions to future geese breeding initiatives.

Inter-annual variations of dissolved oxygen and hypoxia off the northern Changjiang River(Yangtze River) Estuary in summer from 1997 to 2014

Hypoxia off the Changjiang River Estuary has been the subject of much attention,yet systematic observations have been lacking,resulting in a lack of knowledge regarding its long-term change and drivers.By revisiting the repeated surveys of dissolved oxygen(DO) and other relevant hydrographic parameters along the section from the Changjiang River Estuary to the Jeju-do in the summer from 1997 to 2014,rather different trends were revealed for the dual low-DO cores.The nearshore low-DO core,located close to the river mouth and relatively stable,shows that hypoxia has become more severe with the lowest DO descen ding at a rate of -0.07 mg/(L·a) and the thickness of low-DO zone rising at a rate of 0.43 m/a.The offshore core,centered around 40-m isobath but moving back and forth between 123.5°-125°E,shows large fluctuations in the minimum DO concentration,with the thickness of low-DO zone falling at a rate of -1.55 m/a.The probable factors affecting the minimum DO concentration in the two regions also vary.In the nearshore region,the decreasing minimum DO is driven by the increase in both stratification and primary productivity,with the enhanced extension of the Changjiang River Diluted Water(CDW) strengthening stratification.In the offshore region,the fluctuating trend of the minimum DO concentration indicates that both DO loss and DO supplement are distinct.The DO loss is primarily attributed to bottom apparent oxygen utilization caused by the organic matter decay and is also relevant to the advection of low-DO water from the nearshore region.The DO supplement is primarily due to weakened stratification.Our analysis also shows that the minimum DO concentration in the nearshore region was extremely low in 1998,2003,2007 and 2010,related to El Ni?o signal in these summers.

Spatiotemporal variations,influencing factors,and configurational promotion paths of energy efficiency in China’s cities

The promotion of energy efficiency(EE)helps address energy constraints and promote environmental sustainability.This study comprehensively explores the spatiotemporal variations,influencing factors,and configuration promotion paths of EE in 284 Chinese cities during 2003‒2019 using the global super-efficiency minimum distance to strong efficient frontier(G-S-MinDS),exploratory spatial data analysis(ESDA),multiscale geographically weighted regression(MGWR),and fuzzy set qualitative comparative analysis(fsQCA)methods.The findings are:①China’s cities have an annual average EE of 0.658 with a growth rate of 0.53%,showing considerable promotion potential.②Industrial structure optimization,population agglomeration,economic development,and increased green coverage contribute positively,while government intervention and openness hinder China’s urban EE.③Four configurational promotion paths for enhancing China’s urban EE are identified,where among those paths population density is a core condition,while government intervention is not.This study provides valuable insights into substantially improving urban EE,emphasizing the need for targeted policies to address energy and environmental crises in China.

Spatial-temporal distribution and geochemistry of highly evolved Mesozoic granites in Great Xing’an Range,NE China:Discriminant criteria and geological significance

Highly evolved granite is an important sign of the mature continent crust and closely associated with deposits of rare metals.In this work,the authors undertake systematically zircon U-Pb ages and whole rock elemental data for highly evolved granitic intrusions from the Great Xing’an Range(GXR),NE China,to elucidate their discriminant criteria,spatial-temporal distribution,differentiation and geodynamic mecha-nism.Geochemical data of these highly evolved granites suggest that high w(SiO_(2))(>70%)and differentiation index(DI>88)could be quantified indicators,while strong Eu depletion,high TE_(1,3),lowΣREE and low Zr/Hf,Nb/Ta,K/Rb could only be qualitative indicators.Zircon U-Pb ages suggest that the highly evolved gran-ites in the GXR were mainly formed in Late Mesozoic,which can be divided into two major stages:Late Ju-rassic-early Early Cretaceous(162-136 Ma,peak at 138 Ma),and late Early Cretaceous(136-106 Ma,peak at 126 Ma).The highly evolved granites are mainly distributed in the central-southern GXR,and display a weakly trend of getting younger from northwest to southeast,meanwhile indicating the metallogenic potential of rare metals within the central GXR.The spatial-temporal distribution,combined with regional geological data,indicates the highly evolved Mesozoic granites in the GXR were emplaced in an extensional environ-ment,of which the Late Jurassic-early Early Cretaceous extension was related to the closure of the Mongol-Okhotsk Ocean and roll-back of the Paleo-Pacific Plate,while the late Early Cretaceous extension was mainly related to the roll-back of the Paleo-Pacific Plate.

High Prevalence of Anatomical Variations and Anomalies of the Coronary Arteries Detected by CT Angiography in Symptomatic Patients

Objective:Coronary artery anatomical variations and anomalies are an important topic due to their potential clinical manifestations.This study aims to investigate the prevalence of coronary artery anatomical variations and anomalies in symptomatic patients with coronary computed tomography angiography(CCTA).Methods:This is a retrospective study that included all symptomatic patients who had CCTA in a tertiary care hospital in Saudi Arabia during a period of seven years.Results:The total number of included patients was 507(60%males)with a mean age of 57.4 years.Approximately 41%had luminal stenoses,averaging 49.7%.The total num-ber of patients with coronary anatomical variations(CAV)and coronary artery anomalies(CAA)was 217(43%).CAV prevalence was 26%,which included 14%non-right coronary dominance,5%short left main coronary artery(LMCA),and 7%division variations(trifurcation and quadrifurcarion)of the LMCA.The prevalence of CAA was 29%,which included 5%origin anomalies,22%myocardial bridge,and 2%course anomalies.Conclusions:A high prevalence of coronary artery anatomic variations and anomalies in symptomatic patients is reported in this study.Systematic reviews,meta-analyses,reporting guidelines,and unified definitions and classifications of cor-onary variations and anomalies are lacking in the literature,presenting potential opportunities for future research and publications.

Adaptive spatial-temporal graph attention network for traffic speed prediction

Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.

Temporal and spatial variations hydrochemical components and driving factors in Baiyangdian Lake in the Northern Plain of China

Understanding the temporal and spatial variation of hydrochemical components in large freshwater lakes is crucial for effective management and conversation.In this study,we identify the temporalspatial characteristics and driving factors of the hydrochemical components in Baiyangdian Lake using geochemical methods(Gibbs diagram,Piper diagram and End-element diagram of ion ratio)and multivariate statistical techniques(Principal component analysis and Correlation analysis).16 sets of samples were collected from Baiyangdian Lake in May(normal season),July(flood season),and December(dry season)of 2022.Results indicate significant spatial variation in Nat,ci,SO and NO,,suggesting a strong influence of human activities.Cation concentrations exhibit greater seasonal variation in the dry season compared to the flood season,while the concentrations of the four anions show inconsistent seasonal changes due to the combined effects of river water chemical composition and human activities.The hydrochemical type of Baiyangdian Lake is primarily HCO,Cl-Na.Ca,Mg*and HCO,originate mainly from silicate and carbonate rock dissolution,while Kt,Nat and CI originate mainly from sewage and salt dissolution in sediments.SO42 may mainly stem from industrial wastewater,while NO,primarily originates from animal feces and domestic sewage.Through the use of Principal Component Analysis,it is identified that water-rock interaction(silicate and carbonate rocks dissolution,and dissolution of salt in sediments),carbonate sedimentation,sewage,agricultural fertilizer and manure,and nitrification are the main driving factors of the variation of hydrochemical components of Baiyangdian Lake across three hydrological seasons.These findings suggest the need for effective control of substandard domestic sewage discharge,optimization of agricultural fertilization strategies,and proper management of animal manure to comprehensively improve the water environment in Baiyangdian Lake.

Variations in evaporation from water surfaces along the margins of the Badain Jaran Desert over nearly 60 years and influencing factors

Based on meteorological data collected over nearly 60 years(1960-2017)from four national meteorological stations along the margins of the Badain Jaran Desert,this study analyzed the spatiotemporal variations in evaporation from water surfaces and identified the dominant controlling factors.Methods used included linear trend analysis,linear tendency estimation,the departure method,the rank correlation coefficient-based method,and Multiple Linear Regression(MLR).Results indicate notable spatiotemporal differences in evaporation distribution and evolution.Spatially,average annual evaporation exhibited a pronounced altitude effect,decreasing at a rate of about 8.23 mm/m from east to west with increasing altitude.Temporally,annual evaporation showed significant upward trends after 1996 at the northeastern(Guaizi Lake)and western(Dingxin)margins,with rates of 132 mm/10a and 105 mm/10a,respectively.Conversely,along the northwestern(Ejina Banner)and southern(Alxa Right Banner)margins of the desert,an evaporation paradox was observed,with annual evaporation trending downward at rates of 162 mm/10a and 187 mm/10a,respectively,especially after 1987.The dominant factors controlling evaporation varied spatially:Average annual temperature and relative humidity influended the western margin(Dingxin),average annual temperature was the key factor for the northeastern margin(Guaizi Lake),and average wind speed was crucial for the northern(Ejina Banner)and southern(Alxa Right Banner)margins.

Spatiotemporal variations of parameters of internal solitary waves in the northern South China Sea

The dynamic parameters for internal solitary waves(ISWs)derived from the extended Korteweg-de Vries(eKdV)equation play an important role in the understanding and prediction of ISWs.The spatiotemporal variations of the dynamic parameters of the ISWs in the northern South China Sea(SCS)were studied based on the reanalysis of long-term temperature and salinity datasets.The results for spectrum analysis show that there are definite geographical differences for the periodic variation of the parameters:in shallow water,all parameters vary with a wave period of one year,while in deep water wave components of the parameters at other frequencies exist.Using wavelet analysis,the wavelet power spectral densities in deep water exhibited an inter-annual variation pattern.For example,the wave component of the dispersion coefficient with a wave period of about half a year reached its power peak once every two years.Based on previous work,this inter-annual variation pattern was deduced to be caused by dynamic processes.In further work on the regulatory mechanisms,empirical orthogonal function(EOF)decomposition was performed.It was found that the modes of the dispersion coefficient have different geographical distributions,explaining the reason why the wave components in different frequencies appeared in different locations.The numerical simulation results confirm that the variations in the parameters of the ISWs derived from the eKdV equation could affect the waveforms significantly because of changes in the polarity of the ISWs.Therefore,the periodic variations of the dynamic parameters are related to the geographical location because of dynamic processes operating.

Relationship Between Gene-Phenotype and Clinical Manifestations of Chromosomal Copy Number Variations Indicated by Non-Invasive Prenatal Testing

Objective:To analyze the clinical value of non-invasive prenatal testing(NIPT)in detecting chromosomal copy number variations(CNVs)and to explore the relationship between gene expression and clinical manifestations of chromosomal copy number variations.Methods:3551 naturally conceived singleton pregnant women who underwent NIPT were included in this study.The NIPT revealed abnormalities other than sex chromosome abnormalities and trisomy 13,18,and 21.Pregnant women with chromosome copy number variations underwent genetic counseling and prenatal ultrasound examination.Interventional prenatal diagnosis and chromosome microarray analysis(CMA)were performed.The clinical phenotypes and pregnancy outcomes of different prenatal diagnoses were analyzed.Additionally,a follow-up was conducted by telephone to track fetal development after birth,at six months,and one year post-birth.Results:A total of 53 cases among 3551 cases showed chromosomal copy number variation.Interventional prenatal diagnosis was performed in 36 cases:27 cases were negative and 8 were consistent with the NIPT test results.This indicates that NIPT’s positive predictive value(PPV)in CNVs is 22.22%.Conclusion:NIPT has certain clinical significance in screening chromosome copy number variations and is expected to become a routine screening for chromosomal microdeletions and microduplications.However,further interventional prenatal diagnosis is still needed to identify fetal CNVs.

Seasonal variations in glacier velocity in the High Mountain Asia region during 2015–2020

Velocity is an important component of glacier dynamics and directly reflects the response of glaciers to climate change.As a result,an accurate determination of seasonal variation in glacier velocity is very important in understanding the annual variation in glacier dynamics.However,few studies of glacier velocity in the High Mountain Asia(HMA)region were done.Along these lines,in this work,based on Sentinel-1 glacier velocity data,the distribution of glacier velocity in the HMA region was plotted and their seasonal variations during 2015-2020 were systematically analysed.The average glacier velocity in the HMA region was 0.053 m/d,and was positively correlated with the glacier area and slope.Glaciers in the Karakoram Mountains had the fastest average flow velocity(0.060 m/d),where the glaciers exhibited the largest average area and average slope.Moreover,glaciers in the GangdisêMountains had the slowest velocity(0.022 m/d)and the smallest average glacier area.The glacier flows were the fastest in spring(0.058 m/d),followed by summer(0.050 m/d),autumn(0.041 m/d),and winter(0.040 m/d).In addition,the glacier flows were the maximum in May,being 1.4 times of the annual average velocity.In some areas,such as the Qilian,Altun,Tibetan Interior,Eastern Kunlun,and Western Kunlun mountains,the peak glacier velocities appeared in June and July.The glacier velocity in the HMA region decreased in midsummer and reached the minimum in December when it was 75%of the annual average.These results highlight the role of meltwater in the seasonal variation in glacier flows in late spring and early summer.The seasonal velocity variation of lake-terminating glaciers was similar to that of land-terminating ones,but the former flowed faster.The velocity difference close to the mass balance line between the lake-and land-terminating glaciers was obviously greater in spring than in other seasons.In summer,the difference between the lake-and land-terminating glaciers at a normalized distance of 0.05-0.40 from the terminus was significantly greater than those of other seasons.The velocity difference between the lake-and land-terminating glaciers is closely related to the variable of ice thickness,and also to the frictional force of the terminal base reduced by proglacial lakes.Thus,it can be concluded that in addition to the variation of the glacier thickness and viscosity,the variation of glacier water input also plays a key role in the seasonal variation of glacier velocity.