The Application of Nicotiana benthamiana as a Transient Expression Host to Clone the Coding Sequences of Plant Genes

Coding sequences (CDS) are commonly used for transient gene expression, in yeast two-hybrid screening, to verify protein interactions and in prokaryotic gene expression studies. CDS are most commonly obtained using complementary DNA (cDNA) derived from messenger RNA (mRNA) extracted from plant tissues and generated by reverse transcription. However, some CDS are difficult to acquire through this process as they are expressed at extremely low levels or have specific spatial and/or temporal expression patterns in vivo. These challenges require the development of alternative CDS cloning technologies. In this study, we found that the genomic intron-containing gene coding sequences (gDNA) from Arabidopsis thaliana, Oryza sativa, Brassica napus, and Glycine max can be correctly transcribed and spliced into mRNA in Nicotiana benthamiana. In contrast, gDNAs from Triticum aestivum and Sorghum bicolor did not function correctly. In transient expression experiments, the target DNA sequence is driven by a constitutive promoter. Theoretically, a sufficient amount of mRNA can be extracted from the N. benthamiana leaves, making it conducive to the cloning of CDS target genes. Our data demonstrate that N. benthamiana can be used as an effective host for the cloning CDS of plant genes.

Mitochondrial targeting sequence of magnetoreceptor MagR:More than just targeting

Iron-sulfur clusters(ISC)are essential cofactors for proteins involved in various biological processes,such as electron transport,biosynthetic reactions,DNA repair,and gene expression regulation.ISC assembly protein IscA1(or MagR)is found within the mitochondria of most eukaryotes.Magnetoreceptor(MagR)is a highly conserved A-type iron and iron-sulfur cluster-binding protein,characterized by two distinct types of iron-sulfur clusters,[2Fe-2S]and[3Fe-4S],each conferring unique magnetic properties.MagR forms a rod-like polymer structure in complex with photoreceptive cryptochrome(Cry)and serves as a putative magnetoreceptor for retrieving geomagnetic information in animal navigation.Although the N-terminal sequences of MagR vary among species,their specific function remains unknown.In the present study,we found that the N-terminal sequences of pigeon MagR,previously thought to serve as a mitochondrial targeting signal(MTS),were not cleaved following mitochondrial entry but instead modulated the efficiency with which iron-sulfur clusters and irons are bound.Moreover,the N-terminal region of MagR was required for the formation of a stable MagR/Cry complex.Thus,the N-terminal sequences in pigeon MagR fulfil more important functional roles than just mitochondrial targeting.These results further extend our understanding of the function of MagR and provide new insights into the origin of magnetoreception from an evolutionary perspective.

Pig pangenome graph reveals functional features of non‑reference sequences

Background The reliance on a solitary linear reference genome has imposed a significant constraint on our compre-hensive understanding of genetic variation in animals.This constraint is particularly pronounced for non-reference sequences(NRSs),which have not been extensively studied.Results In this study,we constructed a pig pangenome graph using 21 pig assemblies and identified 23,831 NRSs with a total length of 105 Mb.Our findings revealed that NRSs were more prevalent in breeds exhibiting greater genetic divergence from the reference genome.Furthermore,we observed that NRSs were rarely found within coding sequences,while NRS insertions were enriched in immune-related Gene Ontology terms.Notably,our investigation also unveiled a close association between novel genes and the immune capacity of pigs.We observed substantial differences in terms of frequencies of NRSs between Eastern and Western pigs,and the heat-resistant pigs exhibited a substantial number of NRS insertions in an 11.6 Mb interval on chromosome X.Additionally,we discovered a 665 bp insertion in the fourth intron of the TNFRSF19 gene that may be associated with the ability of heat tolerance in South-ern Chinese pigs.Conclusions Our findings demonstrate the potential of a graph genome approach to reveal important functional features of NRSs in pig populations.

Phylogenetic, phylogeographic and divergence time analysis of Anopheles subpictus species complex using ITS2 and COI sequences

Objective:To address the phylogenetic and phylogeographic relationship between different lineages of Anopheles(An.)subpictus species complex in most parts of the Asian continent by maximum utilization of Internal Transcriber Spacer 2(ITS2)and cytochrome C oxidase I(COI)sequences deposited at the GenBank.Methods:Seventy-five ITS2,210 COI and 26 concatenated sequences available in the NCBI database were used.Phylogenetic analysis was performed using Bayesian likelihood trees,whereas median-joining haplotype networks and time-scale divergence trees were generated for phylogeographic analysis.Genetic diversity indices and genetic differentiation were also calculated.Results:Two genetically divergent molecular forms of An.subpictus species complex corresponding to sibling species A and B are established.Species A evolved around 37-82 million years ago in Sri Lanka,India,and the Netherlands,and species B evolved around 22-79 million years ago in Sri Lanka,India,and Myanmar.Vietnam,Thailand,and Cambodia have two molecular forms:one is phylogenetically similar to species B.Other forms differ from species A and B and evolved recently in the above mentioned countries,Indonesia and the Philippines.Genetic subdivision among Sri Lanka,India,and the Netherlands is almost absent.A substantial genetic differentiation was obtained for some populations due to isolation by large geographical distances.Genetic diversity indices reveal the presence of a long-established stable mosquito population,at mutation-drift equilibrium,regardless of population fluctuations.Conclusions:An.subpictus species complex consists of more than two genetically divergent molecular forms.Species A is highly divergent from the rest.Sri Lanka and India contain only species A and B.

On power series statistical convergence and new uniform integrability of double sequences

In the present paper,we mostly focus on P_(p)^(2)-statistical convergence.We will look into the uniform integrability via the power series method and its characterizations for double sequences.Also,the notions of P_(p)^(2)-statistically Cauchy sequence,P_(p)^(2)-statistical boundedness and core for double sequences will be described in addition to these findings.

A cloud model target damage effectiveness assessment algorithm based on spatio-temporal sequence finite multilayer fragments dispersion

To solve the problem of target damage assessment when fragments attack target under uncertain projectile and target intersection in an air defense intercept,this paper proposes a method for calculating target damage probability leveraging spatio-temporal finite multilayer fragments distribution and the target damage assessment algorithm based on cloud model theory.Drawing on the spatial dispersion characteristics of fragments of projectile proximity explosion,we divide into a finite number of fragments distribution planes based on the time series in space,set up a fragment layer dispersion model grounded in the time series and intersection criterion for determining the effective penetration of each layer of fragments into the target.Building on the precondition that the multilayer fragments of the time series effectively assail the target,we also establish the damage criterion of the perforation and penetration damage and deduce the damage probability calculation model.Taking the damage probability of the fragment layer in the spatio-temporal sequence to the target as the input state variable,we introduce cloud model theory to research the target damage assessment method.Combining the equivalent simulation experiment,the scientific and rational nature of the proposed method were validated through quantitative calculations and comparative analysis.

Landscape of Sequence Variations in Homologous Copies of FAD2 and FAD3 in Rapeseed(Brassica napus L.)Germplasm with High/Low Linolenic Acid Trait

Genetic manipulation(either restraint or enhancement)of the biosynthesis pathway ofα-linolenic acid(ALA)in seed oil is an important goal in Brassica napus breeding.B.napus is a tetraploid plant whose genome often har-bors four and six homologous copies,respectively,of the two fatty acid desaturases FAD2 and FAD3,which con-trol the last two steps of ALA biosynthesis during seed oil accumulation.In this study,we compared their promoters,coding sequences,and expression levels in three high-ALA inbred lines 2006L,R8Q10,and YH25005,a low-ALA line A28,a low-ALA/high-oleic-acid accession SW,and the wildtype ZS11.The expression levels of most FAD2 and FAD3 homologs in the three high-ALA accessions were higher than those in ZS11 and much higher than those in A28 and SW.The three high-ALA accessions shared similar sequences with the pro-moters and CDSs of BnFAD3.C4 and BnFAD3.A3.In A28 and SW,substitution of three amino acid residues in BnFAD2.A5 and BnFAD2.C5,an absence of BnFAD2.C1 locus,and a 549 bp long deletion on the BnFAD3.A3 promoter were detected.The profile of BnFAD2 mutation in the two low-ALA accessions A28 and SW is different from that reported in previous studies.The mutations in BnFAD3 in the high-ALA accessions are reported for thefirst time.In identifying the sites of these mutations,we provide detailed information to aid the design of mole-cular markers for accelerated breeding schemes.

Adaptive Successive POI Recommendation via Trajectory Sequences Processing and Long Short-Term Preference Learning

Point-of-interest(POI)recommendations in location-based social networks(LBSNs)have developed rapidly by incorporating feature information and deep learning methods.However,most studies have failed to accurately reflect different users’preferences,in particular,the short-term preferences of inactive users.To better learn user preferences,in this study,we propose a long-short-term-preference-based adaptive successive POI recommendation(LSTP-ASR)method by combining trajectory sequence processing,long short-term preference learning,and spatiotemporal context.First,the check-in trajectory sequences are adaptively divided into recent and historical sequences according to a dynamic time window.Subsequently,an adaptive filling strategy is used to expand the recent check-in sequences of users with inactive check-in behavior using those of similar active users.We further propose an adaptive learning model to accurately extract long short-term preferences of users to establish an efficient successive POI recommendation system.A spatiotemporal-context-based recurrent neural network and temporal-context-based long short-term memory network are used to model the users’recent and historical checkin trajectory sequences,respectively.Extensive experiments on the Foursquare and Gowalla datasets reveal that the proposed method outperforms several other baseline methods in terms of three evaluation metrics.More specifically,LSTP-ASR outperforms the previously best baseline method(RTPM)with a 17.15%and 20.62%average improvement on the Foursquare and Gowalla datasets in terms of the Fβmetric,respectively.

Secure Transmission of Compressed Medical Image Sequences on Communication Networks Using Motion Vector Watermarking

Medical imaging plays a key role within modern hospital management systems for diagnostic purposes.Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all while upholding image quality.Moreover,an increasing number of hospitals are embracing cloud computing for patient data storage,necessitating meticulous scrutiny of server security and privacy protocols.Nevertheless,considering the widespread availability of multimedia tools,the preservation of digital data integrity surpasses the significance of compression alone.In response to this concern,we propose a secure storage and transmission solution for compressed medical image sequences,such as ultrasound images,utilizing a motion vector watermarking scheme.The watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem(BCH)and is subsequently embedded into the compressed sequence via block-based motion vectors.In the process of watermark embedding,motion vectors are selected based on their magnitude and phase angle.When embedding watermarks,no specific spatial area,such as a region of interest(ROI),is used in the images.The embedding of watermark bits is dependent on motion vectors.Although reversible watermarking allows the restoration of the original image sequences,we use the irreversible watermarking method.The reason for this is that the use of reversible watermarks may impede the claims of ownership and legal rights.The restoration of original data or images may call into question ownership or other legal claims.The peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)serve as metrics for evaluating the watermarked image quality.Across all images,the PSNR value exceeds 46 dB,and the SSIM value exceeds 0.92.Experimental results substantiate the efficacy of the proposed technique in preserving data integrity.

Optimal mining sequence for coal faces under a bedding slope:insight from landslide prevention

Repetitive mining beneath bedding slopes is identified as a critical factor in geomorphic disturbances, especially landslides and surface subsidence. Prior research has largely concentrated on surface deformation in plains due to multi-seam coal mining and the instability of natural bedding slopes, yet the cumulative impact of different mining sequences on bedding slopes has been less explored. This study combines drone surveys and geological data to construct a comprehensive three-dimensional model of bedding slopes. Utilizing FLAC3D and PFC2D models, derived from laboratory experiments, it simulates stress, deformation, and failure dynamics of slopes under various mining sequences. Incorporating fractal dimension analysis, the research evaluates the stability of slopes in relation to different mining sequences. The findings reveal that mining in an upslope direction minimizes disruption to overlying strata. Initiating extraction from lower segments increases tensile-shear stress in coal pillar overburdens, resulting in greater creep deformation towards the downslope than when starting from upper segments, potentially leading to localized landslides and widespread creep deformation in mined-out areas. The downslope upward mining sequence exhibits the least fractal dimensions, indicating minimal disturbance to both strata and surface. While all five mining scenarios maintain good slope stability under normal conditions, recalibrated stability assessments based on fractal dimensions suggest that downslope upward mining offers the highest stability under rainfall, contrasting with the lower stability and potential instability risks of upslope downward mining. These insights are pivotal for mining operations and geological hazard mitigation in multi-seam coal exploitation on bedding slopes.

Intelligent extraction of road cracks based on vehicle laser point cloud and panoramic sequence images

In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs,this study presents an intelligent strategy for extracting road cracks.This methodology involves the integration of laser point cloud data obtained from a vehicle-mounted system and a panoramic sequence of images.The study employs a vehicle-mounted LiDAR measurement system to acquire laser point cloud and panoramic sequence image data simultaneously.A convolutional neural network is utilized to extract cracks from the panoramic sequence image.The extracted sequence image is then aligned with the laser point cloud,enabling the assignment of RGB information to the vehicle-mounted three dimensional(3D)point cloud and location information to the two dimensional(2D)panoramic image.Additionally,a threshold value is set based on the crack elevation change to extract the aligned roadway point cloud.The three-dimensional data pertaining to the cracks can be acquired.The experimental findings demonstrate that the use of convolutional neural networks has yielded noteworthy outcomes in the extraction of road cracks.The utilization of point cloud and image alignment techniques enables the extraction of precise location data pertaining to road cracks.This approach exhibits superior accuracy when compared to conventional methods.Moreover,it facilitates rapid and accurate identification and localization of road cracks,thereby playing a crucial role in ensuring road maintenance and traffic safety.Consequently,this technique finds extensive application in the domains of intelligent transportation and urbanization development.The technology exhibits significant promise for use in the domains of intelligent transportation and city development.

Influence of Production Sequence of Aluminum Alloy Hot Rolling on Strip Surface Quality

With the intensification of market competition in the aluminum alloy strip processing industry,it is dif-ficult to control the mass production of the same specifications,which is bound to affect the hot rolling production.This paper studied the effect of the hot rolling order of aluminum alloy on the surface quality of strip,such as roll printing,color difference,anodic oxidation,etc.,reasonable discharge sequence and corresponding optimization measures were formulated.

The Upper Jurassic sediments,Marib-Shabwa Basin,Yemen:Lithofacies aspects and sequence stratigraphic analysis

The present study is devoted to understanding the evolution of the Upper Jurassic Sab'atayn Formation in the Marib-Shabwa Basin,Yemen,through a sequence stratigraphic analysis based on integrating datasets of sedimentology,seismic sections,and well logs.The Sab'atayn Formation(Tithonian age)is represented by a series of clastic and evaporites that were deposited under fluvio-deltaic to prodeltaic settings.It is divided into four members including Yah(at the base),upwards to Seen,Alif,and Safir at the top.Two third-order depositional sequences were determined for the Tithonian succession which were separated by three sequence boundaries.These sequences were classified into their systems tracts signifying several sedimentation patterns of progradational,aggradational,and retrogradational parasequence sets.The first depositional sequence corresponds to the early-middle Tithonian Yah and Seen units that can be classified into lowstand,transgressive,and highstand systems tracts.The second sequence comprises the late Tithonian Alif unit that can be subdivided into transgressive and highstand systems tracts.The sandy deposits of the Alif Member(highstand deposits)represent the most productive hydrocarbon reservoir in the basin.The Upper Jurassic sediments in the study area were resulted from a combination of eustatic and tectonic effects.

Highlighting relationships between sand thicknesses,reservoir-seal pairs and paleobathymetry from a sequence stratigraphic perspective:An example from Tortonian Serravallian deposits,onshore Niger Delta Basin

The utilization of sequence stratigraphic concepts in identifying sands and their spatial continuity in distinct gross depositional settings is key,especially in frontier settings where data paucity is a common challenge.In the Baka field,onshore Niger Delta,detailed reservoir correlation guided by sequence stratigraphic framework analysis showed the distribution of sand and shale units constituting reservoirseal pairs(RSP)correlatable across the field.Within the 3rd-order packages,it is observed that the lowstand systems tract(LST)and highstand systems tract(HST)contain more RSPs and thicker 4th-and 5th-order sands than the transgressive systems tract(TST).In terms of bathymetry,it is noted that irrespective of systems tracts,the RSP Index(RI)decreases from the proximal shallow/inner shelf settings to the more distal outer shelf areas.Amongst all three systems tracts,intervals interpreted as lowstand prograding complexes contain the best developed sands and highest RSP.Sand development within the LSTs has been controlled by a pronounced growth fault regime accompanied by high subsidence and sedimentation rates.This is linked to the basinward migration of the sands during prolonged sea-level fall,creating significant accommodation space for sand deposition.On the other hand,the TSTs known to mark periods of progressive sea-level rise and landward migration of sandy facies,show thinner sands enclosed in much thicker,laterally extensive,and better-preserved deeper marine shales.Interpreted seismic sections indicate intense growth faulting and channelization that influenced the syn-and postdepositional development of the sand packages across the field.The initial timing of deformation of subregional faults in this area coincides with periods of abrupt falls in sea level.This approach could be useful for predicting sand-prone areas in frontier fields as well as possible reservoir-seal parameters required for some aspects of petroleum system analysis and quick-look volume estimation.

Method and Description of Sedimentary System Characterization Based on Sequence Stratigraphy

A Oilfield is the most representative mid to deep oil field in the Bohai Sea, with the main oil bearing intervals being the upper and middle Shahejie-3 sections of the Paleogene Shahejie Formation. By combining well seismic analysis, the middle section of Shahejie-3 is divided into high-level system tract and forced lake retreat system tract, corresponding to the II oil formation and I oil formation, respectively. Using sequence stratigraphy methods, based on seismic profiles and drilling lithological cycles, the high stand system tract is divided into 5 stages of delta progradation. The first and second stages are high angle S-type progradation with large sedimentary thickness, the third stage is oblique progradation, and the fourth and fifth stages are S-oblique composite progradation;By combining seismic data, we characterized the large-scale (8 small-scale) progradation bodies of 5 periods, clarified the distribution characteristics of reservoir planes, and laid the foundation for the later exploration of oilfield potential.

Coseismic Coulomb stress changes induced by a 2020-2021 M_(W)>7.0 Alaska earthquake sequence in and around the Shumagin gap and its influence on the Alaska-Aleutian subduction interface

Three M_(W)>7.0 earthquakes in 2020-2021 occurred in the Shumagin seismic gap and its adjacent area of the Alaska-Aleutian subduction zone,including the Mw7.8 Simeonof thrust earthquake on July 22,2020,the M_(W)7.6 Sand Point strike-slip earthquake on October 19,2020,and the M_(W)8.2 Chignik thrust earthquake on July 29,2021.The spatial and temporal proximity of these three earthquakes prompts us to probe stress-triggering effects among them.Here we examine the coseismic Coulomb stress change imparted by the three earthquakes and their influence on the subduction interface.Our results show that:(1)The Simeonof earthquake has strong loading effects on the subsequent Sand Point and Chignik earthquakes,with the Coulomb stress changes of 3.95 bars and 2.89 bars,respectively.The Coulomb stress change caused by the Sand Point earthquake at the hypocenter of the Chignik earthquake is merely around 0.01 bars,suggesting the negligible triggering effect on the latter earthquake;(2)The triggering effects of the Simeonof,Sand Point,and Chignik earthquakes on aftershocks within three months are not well pronounced because of the triggering rates of 38%,14%,and 43%respectively.Other factors may have played an important role in promoting the occurrence of these aftershocks,such as the roughness of the subduction interface,the complicated velocity structure of the lithosphere,and the heterogeneous prestress therein;(3)The three earthquakes caused remarkable coseismic Coulomb stress changes at the subduction interface nearby these mainshocks,with an average Coulomb stress change of 3.2 bars in the shallow region directly inwards the trench.

A Novel Method of Deinterleaving Radar Pulse Sequences Based on a Modified DBSCAN Algorithm

A modified DBSCAN algorithm is presented for deinterleaving of radar pulses in modern EW environments.A main characteristic of the proposed method is that using only time of arrival of pulses,the method can sort the pulses efficiently.Other PDW information such as rise time,carrier frequency,pulse width,modulation on pulse,fall time and direction of arrival are not required.To identify the valid PRIs in a set of interleaved pulses,an innovative modification of the DBSCAN algorithm is introduced which is accurate and easy to implement.The proposed method determines valid PRIs more accurately and neglects the spurious ones more efficiently as compared to the classical histogram based algorithms such as SDIF.Furthermore,without specifying any input parameter,the proposed method can deinterleave radar pulses while up to 30%jitter is present in the associated PRI.The accuracy and efficiency of the proposed method are verified by computer simulations and real data results.Experimental simulations are based on different real and operational scenarios where the presence of missing and spurious pulses are also considered.So,the simulation results can be of practical significance.

Bi-LSTM-Based Deep Stacked Sequence-to-Sequence Autoencoder for Forecasting Solar Irradiation and Wind Speed

Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050.However,they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions.In microgrids,smart energy management systems,such as integrated demand response programs,are permanently established on a step-ahead basis,which means that accu-rate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids.With this in mind,a novel“bidirectional long short-term memory network”(Bi-LSTM)-based,deep stacked,sequence-to-sequence autoencoder(S2SAE)forecasting model for predicting short-term solar irradiation and wind speed was developed and evaluated in MATLAB.To create a deep stacked S2SAE prediction model,a deep Bi-LSTM-based encoder and decoder are stacked on top of one another to reduce the dimension of the input sequence,extract its features,and then reconstruct it to produce the forecasts.Hyperparameters of the proposed deep stacked S2SAE forecasting model were optimized using the Bayesian optimization algorithm.Moreover,the forecasting performance of the proposed Bi-LSTM-based deep stacked S2SAE model was compared to three other deep,and shallow stacked S2SAEs,i.e.,the LSTM-based deep stacked S2SAE model,gated recurrent unit-based deep stacked S2SAE model,and Bi-LSTM-based shallow stacked S2SAE model.All these models were also optimized and modeled in MATLAB.The results simulated based on actual data confirmed that the proposed model outperformed the alternatives by achieving an accuracy of up to 99.7%,which evidenced the high reliability of the proposed forecasting.

Breed identification using breed‑informative SNPs and machine learning based on whole genome sequence data and SNP chip data

Background Breed identification is useful in a variety of biological contexts.Breed identification usually involves two stages,i.e.,detection of breed-informative SNPs and breed assignment.For both stages,there are several methods proposed.However,what is the optimal combination of these methods remain unclear.In this study,using the whole genome sequence data available for 13 cattle breeds from Run 8 of the 1,000 Bull Genomes Project,we compared the combinations of three methods(Delta,FST,and In)for breed-informative SNP detection and five machine learning methods(KNN,SVM,RF,NB,and ANN)for breed assignment with respect to different reference population sizes and difference numbers of most breed-informative SNPs.In addition,we evaluated the accuracy of breed identification using SNP chip data of different densities.Results We found that all combinations performed quite well with identification accuracies over 95%in all scenarios.However,there was no combination which performed the best and robust across all scenarios.We proposed to inte-grate the three breed-informative detection methods,named DFI,and integrate the three machine learning methods,KNN,SVM,and RF,named KSR.We found that the combination of these two integrated methods outperformed the other combinations with accuracies over 99%in most cases and was very robust in all scenarios.The accuracies from using SNP chip data were only slightly lower than that from using sequence data in most cases.Conclusions The current study showed that the combination of DFI and KSR was the optimal strategy.Using sequence data resulted in higher accuracies than using chip data in most cases.However,the differences were gener-ally small.In view of the cost of genotyping,using chip data is also a good option for breed identification.

Improving the accuracy of genomic prediction for meat quality traits using whole genome sequence data in pigs

Background Pork quality can directly affect customer purchase tendency and meat quality traits have become valu-able in modern pork production.However,genetic improvement has been slow due to high phenotyping costs.In this study,whole genome sequence(WGS)data was used to evaluate the prediction accuracy of genomic best linear unbiased prediction(GBLUP)for meat quality in large-scale crossbred commercial pigs.Results We produced WGS data(18,695,907 SNPs and 2,106,902 INDELs exceed quality control)from 1,469 sequenced Duroc×(Landrace×Yorkshire)pigs and developed a reference panel for meat quality including meat color score,marbling score,L*(lightness),a*(redness),and b*(yellowness)of genomic prediction.The prediction accuracy was defined as the Pearson correlation coefficient between adjusted phenotypes and genomic estimated breeding values in the validation population.Using different marker density panels derived from WGS data,accuracy differed substantially among meat quality traits,varied from 0.08 to 0.47.Results showed that MultiBLUP outperform GBLUP and yielded accuracy increases ranging from 17.39%to 75%.We optimized the marker density and found medium-and high-density marker panels are beneficial for the estimation of heritability for meat quality.Moreover,we conducted genotype imputation from 50K chip to WGS level in the same population and found average concord-ance rate to exceed 95%and r^(2)=0.81.Conclusions Overall,estimation of heritability for meat quality traits can benefit from the use of WGS data.This study showed the superiority of using WGS data to genetically improve pork quality in genomic prediction.