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 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.

A new two-step variational model for multiplicative noise removal with applications to texture images

Multiplicative noise removal problems have attracted much attention in recent years.Unlike additive noise,multiplicative noise destroys almost all information of the original image,especially for texture images.Motivated by the TV-Stokes model,we propose a new two-step variational model to denoise the texture images corrupted by multiplicative noise with a good geometry explanation in this paper.In the first step,we convert the multiplicative denoising problem into an additive one by the logarithm transform and propagate the isophote directions in the tangential field smoothing.Once the isophote directions are constructed,an image is restored to fit the constructed directions in the second step.The existence and uniqueness of the solution to the variational problems are proved.In these two steps,we use the gradient descent method and construct finite difference schemes to solve the problems.Especially,the augmented Lagrangian method and the fast Fourier transform are adopted to accelerate the calculation.Experimental results show that the proposed model can remove the multiplicative noise efficiently and protect the texture well.

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.

Variational data encoding and correlations in quantum-enhanced machine learning

Leveraging the extraordinary phenomena of quantum superposition and quantum correlation,quantum computing offers unprecedented potential for addressing challenges beyond the reach of classical computers.This paper tackles two pivotal challenges in the realm of quantum computing:firstly,the development of an effective encoding protocol for translating classical data into quantum states,a critical step for any quantum computation.Different encoding strategies can significantly influence quantum computer performance.Secondly,we address the need to counteract the inevitable noise that can hinder quantum acceleration.Our primary contribution is the introduction of a novel variational data encoding method,grounded in quantum regression algorithm models.By adapting the learning concept from machine learning,we render data encoding a learnable process.This allowed us to study the role of quantum correlation in data encoding.Through numerical simulations of various regression tasks,we demonstrate the efficacy of our variational data encoding,particularly post-learning from instructional data.Moreover,we delve into the role of quantum correlation in enhancing task performance,especially in noisy environments.Our findings underscore the critical role of quantum correlation in not only bolstering performance but also in mitigating noise interference,thus advancing the frontier of quantum computing.

A Study to Observe Pulse Pressure Variation after Induction with Propofol for General Anesthesia

Background and Aims: Pulse pressure variation (PPV) is a reliable and predictive dynamic parameter presently being utilized for fluid responsiveness. In the operating room, fluid administration based on PPV monitoring helps the physician in deciding whether to volume resuscitate or use interventions in patients undergoing surgery. Propofol is an intravenous induction agent which lowers blood pressure. There are multiple causes such as depression in cardiac output, and peripheral vasodilatation for hypotension. We undertook this study to observe the utility of PPV as a guide to fluid therapy after propofol induction. Primary outcome of our study was to monitor PPV as a marker of fluid responsiveness for the hypotension caused by propofol induction. Secondary outcome included the correlation of PPV with other hemodynamic parameters like heart rate (HR), systolic blood pressure (SBP), and diastolic blood pressure (DBP);after induction with propofol at regular interval of time. Methods: A total number of 90 patients were recruited. Either of the radial artery was then cannulated under local anaesthesia with 20G VygonLeadercath arterial cannula and invasive monitoring transduced. A baseline recording of heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP) and PPV was then recorded. Patients were then induced with predetermined doses of propofol (2 mg/kg) and recordings of HR, SBP, DBP, and PPV were taken at 5, 10 and 15 minutes. Results: Intraoperatively, PPV was significantly higher at 5 minutes and significantly lower at 15 minutes after induction. It was observed that there were no statistically significant correlations between PPV and SBP or DBP. PPV was strongly and directly associated with HR. Conclusion: We were able to establish that PPV predicts fluid responsiveness in hypotension caused by propofol induction;and can be used to administer fluid therapy in managing such hypotension. However, PPV was not directly correlated with hypotension subsequent to propofol administration.

Small temporal variation in abundance of virioplankton compared to bacterioplankton and phytoplankton in two bays in the northern South China Sea

Variations of picoplankton groups were investigated over a one-month period in Daya Bay and Sanya Bay,in the northern South China Sea.The two coastal regions exhibited different variation patterns in physicochemical parameters.Moreover,the diel variations of picoplankton groups were different between the two bays.The abundance of the picoplankton in Sanya Bay displayed a pronounced diel variation,while it was not significant in Daya Bay.In addition,some similar patterns of picoplankton abundance were discovered.In the two bays,virioplankton exhibited the smallest fluctuation range,whereas picocyanobacteria fluctuated most markedly.The fluctuation range of picoplankton groups was larger in spring tide than in neap tide,especially in Sanya Bay.Random forest model analysis demonstrated that the variation of picoplankton groups was attributed to physical and chemical factors in Sanya Bay and Daya Bay,respectively.Therefore,our findings suggest that virioplankton abundance can persist more stably in response to changing environmental conditions compared to bacterioplankton and picophytoplankton.

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.

Temperature is a cryptic factor shaping the geographical pattern of genetic variation in Ceratophyllum demersum across a subtropical freshwater lake

Macrophyte habitats exhibit remarkable heterogeneity,encompassing the spatial variation of abiotic and biotic components such as changes in water conditions and weather as well as anthropogenic stressors.Environmental factors are thought to be important drivers shaping the genetic and epigenetic variation of aquatic plants.However,the links among genetic diversity,epigenetic variation,and environmental variables remain largely unclear,especially for clonal aquatic plants.Here,we performed population genetic and epigenetic analyses in conjunction with habitat discrimination to elucidate the environmental factors driving intraspecies genetic and epigenetic variation in hornwort(Ceratophyllum demersum)in a subtropical lake.Environmental factors were highly correlated with the genetic and epigenetic variation of C.demersum,with temperature being a key driver of the genetic variation.Lower temperature was detected to be correlated with greater genetic and epigenetic variation.Genetic and epigenetic variation were positively driven by water temperature,but were negatively affected by ambient air temperature.These findings indicate that the genetic and epigenetic variation of this clonal aquatic herb is not related to the geographic feature but is instead driven by environmental conditions,and demonstrate the effects of temperature on local genetic and epigenetic variation in aquatic systems.

The Variation of Plankton Community Structure in Artificial Reef Area and Adjacent Waters in Haizhou Bay

Plankton are an important component of marine protected areas(MPAs),and its communities would require much smaller interpatch distances to ensure connection among MPAs.According to the survey from MPAs dominated by artificial reefs and adjacent waters(estuary area(EA),aquaculture area(AA),artificial reef area(ARA),natural area(NA)and comprehensive effect area(CEA))in Haizhou Bay in spring and autumn,we analyzed phyto-zooplankton composition,abundance and biomass,and correlation with hydrologic variables to gain information about the forces that structure the plankton.The results showed that the dominant zooplankton were copepods(spring,98.9%;autumn,94.2%),while the phytoplankton were mainly composed of Bacillariophyta(spring,61.8%;autumn,95.6%).The RDA results showed that temperature,salinity and depth highly associated with the distribution and composition of plankton species among the habitats than other factors in spring;temperature,Chla and DO had the strongest influence in autumn.The zooplankton in the ARA and AA ecosystems basically contained the same species as those in other habitats,and each habitat also exhibited a relatively unique combination of plankton species.The structures of the EA zooplankton in spring and the EA phytoplankton in both seasons were much different than other habitats,which may have been caused by factors such as currents and tides.We concluded that there exists similarity of the plankton community between artificial reef area and adjacent waters,whereas the EAs may be relatively independent systems.Therefore,these interaction between plankton community should be considered when designing MPA networks,and ocean circulations should be considered more than the environmental factors.

Seasonal Variation of Hearing Sensitivity in the Red-eared Slider(Trachemys scripta elegans)

Seasonal variation of hearing sensitivity has been observed in many vertebrate groups with obvious vocal behaviors.Circulating hormones,conspecific calling signals,and temperature are potential factors that drive these plasticity patterns.Turtles have a hearing range that appears to be limited to under 1.5 kHz and are often thought to be non-vocal;thus,they are commonly neglected in vocal communication research.In this study,we aimed to determine whether the auditory phenotype exhibits seasonal variation in sensitivity and to analyze the potential factors driving such variation patterns in turtles.We measured hearing sensitivity and sex hormone levels in female(estradiol)and male(testosterone and dihydrotestosterone)Red-eared sliders(Trachemys scripta elegans)during spring and winter.The results showed that auditory brainstem response(ABR)thresholds were significantly lower in spring than in winter at a frequency range of 0.5-0.9 kHz.The hearing-sensitivity bandwidth was wider,and the ABR latency was significantly shorter in spring than in winter.No significant differences were found in estradiol,testosterone,and dihydrotestosterone levels in T.scripta elegans between spring and winter.This study is the first to reveal the seasonal variation of peripheral hearing sensitivity in turtles,a special animal group with limited hearing range and less vocalization.Temperature variations may be used to explain these seasonal effects,but further research is required to confirm our findings.

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.

STRONGLY CONVERGENT INERTIAL FORWARD-BACKWARD-FORWARD ALGORITHM WITHOUT ON-LINE RULE FOR VARIATIONAL INEQUALITIES

This paper studies a strongly convergent inertial forward-backward-forward algorithm for the variational inequality problem in Hilbert spaces.In our convergence analysis,we do not assume the on-line rule of the inertial parameters and the iterates,which have been assumed by several authors whenever a strongly convergent algorithm with an inertial extrapolation step is proposed for a variational inequality problem.Consequently,our proof arguments are different from what is obtainable in the relevant literature.Finally,we give numerical tests to confirm the theoretical analysis and show that our proposed algorithm is superior to related ones in the literature.

A novel diurnal and seasonal variation analysis of BTEX in Bengaluru urban area

In the urban atmosphere of Bengaluru, various volatile organic compounds(VOCs), particularly Benzene,Toluene, Ethylbenzene, and Xylene(BTEX), have shown an increasing trend in concentration. The present research was conducted during summer and monsoon seasons, focusing on Kadubeesanahalli, a high-traffic area within the Bengaluru Metropolitan City. Hourly sample data was collected using a BTEX analyzer(Model GC955-600) and subsequently transformed into daily, monthly, and seasonal values. The study revealed distinct patterns in benzene concentrations. Benzene levels were lowest during the early morning hours, specifically from 1:00 a.m.to 7:00 a.m.. Concentrations then increased from 7:00 a.m. to 9:00 a.m. and again from 4:00 p.m. to 11:00 p.m.,corresponding to the morning and evening peak traffic hours. However, between 10:00 a.m. and 4:00 p.m., the concentration decreased due to reduced traffic levels. These diurnal variations in benzene concentration are influenced by meteorological parameters. Comparing the two seasons, higher concentrations of Benzene, EthylBenzene, and MP-xylene were observed during the summer season. This increase is attributed to the elevated temperatures during summer, which promote the vaporization of BTEX compounds. Conversely, lower BTEX concentrations were recorded during the monsoon season due to the wet deposition process. The observed positive correlation(r > 0.5) among BTEX parameters strongly suggests a common source, most likely originating from vehicular emissions.

Natural variation in the cytochrome c oxidase subunit 5B OsCOX5B regulates seed vigor by altering energy production in rice

Seed vigor is a crucial trait for the direct seeding of rice.Here we examined the genetic regulation of seed vigor traits in rice,including germination index(GI)and germination potential(GP),using a genome-wide association study approach.One major quantitative trait locus,qGI6/qGP6,was identified simultaneously for both GI and GP.The candidate gene encoding the cytochrome c oxidase subunit 5B(OsCOX5B)was validated for qGI6/qGP6.The disruption of OsCOX5B caused the vigor traits to be significantly lower in Oscox5b mutants than in the japonica Nipponbare wild type(WT).Gene co-expression analysis revealed that OsCOX5B influences seed vigor mainly by modulating the tricarboxylic acid cycle process.The glucose levels were significantly higher while the pyruvic acid and adenosine triphosphate levels were significantly lower in Oscox5b mutants than in WT during seed germination.The elite haplotype of OsCOX5B facilitates seed vigor by increasing its expression during seed germination.Thus,we propose that OsCOX5B is a potential target for the breeding of rice varieties with enhanced seed vigor for direct seeding.

An efficient parallel algorithm of variational nodal method for heterogeneous neutron transport problems

The heterogeneous variational nodal method(HVNM)has emerged as a potential approach for solving high-fidelity neutron transport problems.However,achieving accurate results with HVNM in large-scale problems using high-fidelity models has been challenging due to the prohibitive computational costs.This paper presents an efficient parallel algorithm tailored for HVNM based on the Message Passing Interface standard.The algorithm evenly distributes the response matrix sets among processors during the matrix formation process,thus enabling independent construction without communication.Once the formation tasks are completed,a collective operation merges and shares the matrix sets among the processors.For the solution process,the problem domain is decomposed into subdomains assigned to specific processors,and the red-black Gauss-Seidel iteration is employed within each subdomain to solve the response matrix equation.Point-to-point communication is conducted between adjacent subdomains to exchange data along the boundaries.The accuracy and efficiency of the parallel algorithm are verified using the KAIST and JRR-3 test cases.Numerical results obtained with multiple processors agree well with those obtained from Monte Carlo calculations.The parallelization of HVNM results in eigenvalue errors of 31 pcm/-90 pcm and fission rate RMS errors of 1.22%/0.66%,respectively,for the 3D KAIST problem and the 3D JRR-3 problem.In addition,the parallel algorithm significantly reduces computation time,with an efficiency of 68.51% using 36 processors in the KAIST problem and 77.14% using 144 processors in the JRR-3 problem.

Annual variation of adult survival of a south-temperate House Wren population in Argentina

Identifying factors affecting the survival of individuals is essential for understanding the evolution of life-history traits and population dynamics.Despite numerous studies on this subject in north-temperate environments,there is a lack of equivalent studies at similar latitudes in the south.Here,we used a 14-year dataset of capture,banding,and resighting to estimate the annual variation in the apparent adult survival probability of a south-temperate population of House Wrens(Troglodytes aedon bonariae).We evaluated temporal variation in sur-vival and the effect of environmental(climatic)and demographic variables(adult abundance,total number of fledglings produced during each breeding season)on survival estimators.We found that the probability of adult survival decreased as the abundance of breeding adults increased.This density-dependent effect could be related to the resident lifestyle of southern House Wrens,which could determine an intense competition for territories and resources that ultimately would affect their survival.

An allelic variation in the promoter of the LRR-RLK gene,qSS6.1,is associated with melon seed size

Seed size is an important agronomic trait in melons that directly affects seed germination and subsequent seedling growth.However,the genetic mechanism underlying seed size in melon remains unclear.In the present study,we employed Bulked-Segregant Analysis sequencing(BSA-seq)to identify a candidate region(~1.35 Mb)on chromosome 6 that corresponds to seed size.This interval was confirmed by QTL mapping of three seed size-related traits from an F2 population across three environments.This mapping region represented nine QTLs that shared an overlapping region on chromosome 6,collectively referred to as qSS6.1.New InDel markers were developed in the qSS6.1 region,narrowing it down to a 68.35 kb interval that contains eight annotated genes.Sequence variation analysis of the eight genes identified a SNP with a C to T transition mutation in the promoter region of MELO3C014002,a leucine-rich repeat receptor-like kinase(LRR-RLK)gene.This mutation affected the promoter activity of the MELO3C014002 gene and was successfully used to differentiate the large-seeded accessions(C-allele)from the small-seeded accessions(T-allele).qRT-PCR revealed differential expression of MELO3C014002 between the two parental lines.Its predicted protein has typical LRR-RLK family domains,and phylogenetic analyses reveled its similarity with the homologs in several plant species.Altogether,these findings suggest MELO3C014002 as the most likely candidate gene involved in melon seed size regulation.Our results will be helpful for better understanding the genetic mechanism regulating seed size in melons and for genetically improving this important trait through molecular breeding pathways.