A phenology-based vegetation index for improving ratoon rice mapping using harmonized Landsat and Sentinel-2 data

Ratoon rice,which refers to a second harvest of rice obtained from the regenerated tillers originating from the stubble of the first harvested crop,plays an important role in both food security and agroecology while requiring minimal agricultural inputs.However,accurately identifying ratoon rice crops is challenging due to the similarity of its spectral features with other rice cropping systems(e.g.,double rice).Moreover,images with a high spatiotemporal resolution are essential since ratoon rice is generally cultivated in fragmented croplands within regions that frequently exhibit cloudy and rainy weather.In this study,taking Qichun County in Hubei Province,China as an example,we developed a new phenology-based ratoon rice vegetation index(PRVI)for the purpose of ratoon rice mapping at a 30 m spatial resolution using a robust time series generated from Harmonized Landsat and Sentinel-2(HLS)images.The PRVI that incorporated the red,near-infrared,and shortwave infrared 1 bands was developed based on the analysis of spectro-phenological separability and feature selection.Based on actual field samples,the performance of the PRVI for ratoon rice mapping was carefully evaluated by comparing it to several vegetation indices,including normalized difference vegetation index(NDVI),enhanced vegetation index(EVI)and land surface water index(LSWI).The results suggested that the PRVI could sufficiently capture the specific characteristics of ratoon rice,leading to a favorable separability between ratoon rice and other land cover types.Furthermore,the PRVI showed the best performance for identifying ratoon rice in the phenological phases characterized by grain filling and harvesting to tillering of the ratoon crop(GHS-TS2),indicating that only several images are required to obtain an accurate ratoon rice map.Finally,the PRVI performed better than NDVI,EVI,LSWI and their combination at the GHS-TS2 stages,with producer's accuracy and user's accuracy of 92.22 and 89.30%,respectively.These results demonstrate that the proposed PRVI based on HLS data can effectively identify ratoon rice in fragmented croplands at crucial phenological stages,which is promising for identifying the earliest timing of ratoon rice planting and can provide a fundamental dataset for crop management activities.

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.

Rice Heat Tolerance Breeding: A Comprehensive Review and Forward Gaze

The yield potential of rice is seriously affected by heat stress due to climate change. Since rice is a staple food globally, it is imperative to develop heat-resistant rice varieties. Thus, a thorough understanding of the complex molecular mechanisms underlying heat tolerance and the impact of high temperatures on various critical stages of the crop is needed. Adoption of both conventional and innovative breeding strategies offers a long-term advantage over other methods, such as agronomic practices, to counter heat stress. In this review, we summarize the effects of heat stress, regulatory pathways for heat tolerance, phenotyping strategies, and various breeding methods available for developing heat-tolerant rice. We offer perspectives and knowledge to guide future research endeavors aimed at enhancing the ability of rice to withstand heat stress and ultimately benefit humanity.

Artificial intelligence-driven radiomics study in cancer:the role of feature engineering and modeling

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.

Step‑by‑Step Modulation of Crystalline Features and Exciton Kinetics for 19.2%Efficiency Ortho‑Xylene Processed Organic Solar Cells

With plenty of popular and effective ternary organic solar cells(OSCs)construction strategies proposed and applied,its power conversion efficiencies(PCEs)have come to a new level of over 19%in single-junction devices.However,previous studies are heavily based in chloroform(CF)leaving behind substantial knowledge deficiencies in understanding the influence of solvent choice when introducing a third component.Herein,we present a case where a newly designed asymmetric small molecular acceptor using fluoro-methoxylated end-group modification strategy,named BTP-BO-3FO with enlarged bandgap,brings different morphological evolution and performance improvement effect on host system PM6:BTP-eC9,processed by CF and ortho-xylene(o-XY).With detailed analyses supported by a series of experiments,the best PCE of 19.24%for green solvent-processed OSCs is found to be a fruit of finely tuned crystalline ordering and general aggregation motif,which furthermore nourishes a favorable charge generation and recombination behavior.Likewise,over 19%PCE can be achieved by replacing spin-coating with blade coating for active layer deposition.This work focuses on understanding the commonly met yet frequently ignored issues when building ternary blends to demonstrate cutting-edge device performance,hence,will be instructive to other ternary OSC works in the future.

Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection

In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA.

Liver transplantation as an alternative for the treatment of neuroendocrine liver metastasis: Appraisal of the current evidence

Background:Liver transplantation(LT)for neuroendocrine liver metastases(NELM)is still in debate.Studies comparing LT with liver resection(LR)for NELM are scarce,as patient selection is heterogeneous and experience is limited.The goal of this review was to provide a critical analysis of the evidence on LT versus LR in the treatment of NELM.Data sources:A scoping literature search on LT and LR for NELM was performed with PubMed,including English articles up to March 2023.Results:International guidelines recommend LR for NELM in resectable,well-differentiated tumors in the absence of extrahepatic metastatic disease with superior results of LR compared to systemic or liver-directed therapies.Advanced liver surgery has extended resectability criteria whilst entailing increased perioperative risk and short disease-free survival.In highly selected patients(based on the Milan criteria)with unresectable NELM,oncologic results of LT are promising.Prognostic factors include tumor biology(G1/G2)and burden,waiting time for LT,patient age and extrahepatic spread.Based on low-level evi-dence,LT for low-grade NELM within the Milan criteria resulted in improved disease-free survival and overall survival compared to LR.The benefits of LT were lost in patients beyond the Milan NELM-criteria.Conclusions:With adherence to strict selection criteria especially tumor biology,LT for NELM is becoming a valuable option providing oncologic benefits compared to LR.Recent evidence suggests even stricter selection criteria with regard to tumor biology.

Anti-Byzantine Attacks Enabled Vehicle Selection for Asynchronous Federated Learning in Vehicular Edge Computing

In vehicle edge computing(VEC),asynchronous federated learning(AFL)is used,where the edge receives a local model and updates the global model,effectively reducing the global aggregation latency.Due to different amounts of local data,computing capabilities and locations of the vehicles,renewing the global model with same weight is inappropriate.The above factors will affect the local calculation time and upload time of the local model,and the vehicle may also be affected by Byzantine attacks,leading to the deterioration of the vehicle data.However,based on deep reinforcement learning(DRL),we can consider these factors comprehensively to eliminate vehicles with poor performance as much as possible and exclude vehicles that have suffered Byzantine attacks before AFL.At the same time,when aggregating AFL,we can focus on those vehicles with better performance to improve the accuracy and safety of the system.In this paper,we proposed a vehicle selection scheme based on DRL in VEC.In this scheme,vehicle’s mobility,channel conditions with temporal variations,computational resources with temporal variations,different data amount,transmission channel status of vehicles as well as Byzantine attacks were taken into account.Simulation results show that the proposed scheme effectively improves the safety and accuracy of the global model.

Applying an Improved Dung Beetle Optimizer Algorithm to Network Traffic Identification

Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification.

Cooperative Anti-Jamming and Interference Mitigation for UAV Networks: A Local Altruistic Game Approach

To improve the anti-jamming and interference mitigation ability of the UAV-aided communication systems, this paper investigates the channel selection optimization problem in face of both internal mutual interference and external malicious jamming. A cooperative anti-jamming and interference mitigation method based on local altruistic is proposed to optimize UAVs’ channel selection. Specifically, a Stackelberg game is modeled to formulate the confrontation relationship between UAVs and the jammer. A local altruistic game is modeled with each UAV considering the utilities of both itself and other UAVs. A distributed cooperative anti-jamming and interference mitigation algorithm is proposed to obtain the Stackelberg equilibrium. Finally, the convergence of the proposed algorithm and the impact of the transmission power on the system loss value are analyzed, and the anti-jamming performance of the proposed algorithm can be improved by around 64% compared with the existing algorithms.

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

Evaluating reservoir suitability for large-scale hydrogen storage:A preliminary assessment considering reservoir properties

With rising demand for clean energy,global focus turns to finding ideal sites for large-scale underground hydrogen storage(UHS)in depleted petroleum reservoirs.A thorough preliminary reservoir evaluation before hydrogen(H_(2))injection is crucial for UHS success and safety.Recent criteria for UHS often emphasize economics and chemistry,neglecting key reservoir attributes.This study introduces a comprehensive framework for the reservoir-scale preliminary assessment,specifically tailored for long-term H_(2) storage within depleted gas reservoirs.The evaluation criteria encompass critical components,including reservoir geometry,petrophysical properties,tectonics,and formation fluids.To illustrate the practical application of this approach,we assess the Barnett shale play reservoir parameters.The assessment unfolds through three key stages:(1)A systematic evaluation of the reservoir's properties against our comprehensive screening criteria determines its suitability for H_(2) storage.(2)Using both homogeneous and multilayered gas reservoir models,we explore the feasibility and efficiency of H_(2) storage.This phase involves an in-depth examination of reservoir behavior during the injection stage.(3)To enhance understanding of UHS performance,sensitivity analyses investigate the impact of varying reservoir dimensions and injection/production pressures.The findings reveal the following:(a)Despite potential challenges associated with reservoir compaction and aquifer support,the reservoir exhibits substantial promise as an H_(2) storage site.(b)Notably,a pronounced increase in reservoir pressure manifests during the injection stage,particularly in homogeneous reservoirs.(c)Furthermore,optimizing injection-extraction cycle efficiency can be achieved by augmenting reservoir dimensions while maintaining a consistent thickness.To ensure a smooth transition to implementation,further comprehensive investigations are advised,including experimental and numerical studies to address injectivity concerns and explore storage site development.This evaluation framework is a valuable tool for assessing the potential of depleted gas reservoirs for large-scale hydrogen storage,advancing global eco-friendly energy systems.

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Monitoring Surface Deformation Using Distributed Scatterers InSAR

In the past two decades,extensive and in-depth research has been conducted on Time Series InSAR technology with the advancement of high-performance SAR satellites and the accumulation of big SAR data.The introduction of distributed scatterers in Distributed Scatterers InSAR(DS-InSAR)has significantly expanded the application scenarios of InSAR geodetic measurement by increasing the number of measurement points.This study traces the history of DS-InSAR,presents the definition and characteristics of distributed scatterers,and focuses on exploring the relationships and distinctions among proposed algorithms in two crucial steps:statistically homogeneous pixel selection and phase optimization.Additionally,the latest research progress in this field is tracked and the possible development direction in the future is discussed.Through simulation experiments and two real InSAR case studies,the proposed algorithms are compared and verified,and the advantages of DS-InSAR in deformation measurement practice are demonstrated.This work not only offers insights into current trends and focal points for theoretical research on DS-InSAR but also provides practical cases and guidance for applied research.

Epistasis-aware genome-wide association studies provide insights into the efficient breeding of high-yield and high-quality rice

Marker-assisted selection(MAS)and genomic selection(GS)breeding have greatly improved the efficiency of rice breeding.Due to the influences of epistasis and gene pleiotropy,ensuring the actual breeding effect of MAS and GS is still a difficult challenge to overcome.In this study,113 indica rice varieties(V)and their 565 testcross hybrids(TC)were used as the materials to investigate the genetic basis of 12 quality traits and nine agronomic traits.The original traits and general combining ability of the parents,as well as the original traits and midparent heterosis of TC,were subjected to genome-wide association analysis.In total,381 primary significantly associated loci(SAL)and 1,759 secondary SALs that had epistatic interactions with these primary SALs were detected.Among these loci,322 candidate genes located within or nearby the SALs were screened,204 of which were cloned genes.A total of 39 MAS molecular modules that are beneficial for trait improvement were identified by pyramiding the superior haplotypes of candidate genes and desirable epistatic alleles of the secondary SALs.All the SALs were used to construct genetic networks,in which 91 pleiotropic loci were investigated.Additionally,we estimated the accuracy of genomic prediction in the parent V and TC by incorporating either no SALs,primary SALs,secondary SALs or epistatic effect SALs as covariates.Although the prediction accuracies of the four models were generally not significantly different in the TC dataset,the incorporation of primary SALs,secondary SALs,and epistatic effect SALs significantly improved the prediction accuracies of 5(26%),3(16%),and 11(58%)traits in the V dataset,respectively.These results suggested that SALs and epistatic effect SALs identified based on an additive genotype can provide considerable predictive power for the parental lines.They also provide insights into the genetic basis of complex traits and valuable information for molecular breeding in rice.

Bird species present in urban parks are more colorful than urban avoiders:A test in the Argentinian Pampas

Bird plumage color has been assessed as a possible trait driving the presence of bird species in urban areas.Although some species can see the ultraviolet(UV) spectrum,the mentioned studies did not take into account UV reflectance when characterizing bird plumage.This study aimed to use a recent database of the colorfulness in passerines that incorporated the UV spectrum to compare bird colorfulness and other traits between urban parks and rural areas in Central-East Argentina.Birds in urban parks were surveyed in 51 parks in 6 cities during breeding and non-breeding seasons.A list of Passeriformes species from parks was created,and a list of urban avoider species was created from the bibliography.Species traits were body mass,clutch size,migratory status,nesting site,diet and habitat breadth,and plumage colorfulness.A total of 85 species were detected in the regional pool,of which 30 species were detected in urban parks.Bird species present in urban parks were more colorful than bird species only present in rural areas.In addition,bird presence in urban parks was positively related to their regional frequency and diet breadth.Moreover,urban presence was related to nesting on trees and buildings,whereas species not present in urban parks nested on the ground.The results obtained showed that bird color is significantly associated with presence of bird species in urban parks.

Evaluating the performance of genomic selection on purebred population by incorporating crossbred data in pigs

Genomic selection(GS)has been widely used in livestock,which greatly accelerated the genetic progress of complex traits.The population size was one of the significant factors affecting the prediction accuracy,while it was limited by the purebred population.Compared to directly combining two uncorrelated purebred populations to extend the reference population size,it might be more meaningful to incorporate the correlated crossbreds into reference population for genomic prediction.In this study,we simulated purebred offspring(PAS and PBS)and crossbred offspring(CAB)base on real genotype data of two base purebred populations(PA and PB),to evaluate the performance of genomic selection on purebred while incorporating crossbred information.The results showed that selecting key crossbred individuals via maximizing the expected genetic relationship(REL)was better than the other methods(individuals closet or farthest to the purebred population,CP/FP)in term of the prediction accuracy.Furthermore,the prediction accuracy of reference populations combining PA and CAB was significantly better only based on PA,which was similar to combine PA and PAS.Moreover,the rank correlation between the multiple of the increased relationship(MIR)and reliability improvement was 0.60-0.70.But for individuals with low correlation(Cor(Pi,PA or B),the reliability improvement was significantly lower than other individuals.Our findings suggested that incorporating crossbred into purebred population could improve the performance of genetic prediction compared with using the purebred population only.The genetic relationship between purebred and crossbred population is a key factor determining the increased reliability while incorporating crossbred population in the genomic prediction on pure bred individuals.

Inbreeding and genetic load in a pair of sibling grouse species:Tetrastes sewersowi and T.bonasia

Genetic load and inbreeding are recognized as important factors to be considered in conservation programs.Elevated levels of both can increase the risk of population extinction by negatively impacting fitness-related characters in many species of plants and animals,including humans(inbreeding depression).Genomic tech-niques are increasingly used in measuring and understanding genetic load and inbreeding and their importance in evolution and conservation.We used whole genome resequencing data from two sibling grouse species in subarctic Eurasia to quantify both.We found a large range of inbreeding measured as FROH(fraction of runs of homozygosity)in individuals from different populations of Chinese Grouse(Tetrastes sewerzowi)and Hazel Grouse(T.bonasia).FROH estimated from genome-wide runs of homozygosity(ROH)ranged from 0.02 to 0.24 among Chinese Grouse populations and from 0.01 to 0.44 in Hazel Grouse.Individuals from a population of Chinese Grouse residing in the Qilian mountains and from the European populations of Hazel Grouse(including samples from Sweden,Germany and Northeast Poland)were the most inbred(FROH ranged from 0.10 to 0.23 and 0.11 to 0.44,respectively).These levels are comparable to other highly inbred populations of birds.Hazel Grouse from northern China and Chinese Grouse residing in the Qinghai-Tibetan Plateau showed relatively lower inbreeding levels.Comparisons of the ratio between deleterious missense mutations and synonymous mutations revealed higher levels in Chinese Grouse as compared to Hazel Grouse.These results are possibly explained by higher fixation rates,mutational melt down,in the range-restricted Chinese Grouse compared to the wide-ranging Hazel Grouse.However,when we compared the relatively more severe class of loss-of-function muta-tions,Hazel Grouse had slightly higher levels than Chinese Grouse,a result which may indicate that purifying selection(purging)has been more efficient in Chinese Grouse on this class of mutations.

Experimental clearance rate and intraguild predation of jellyfish Cyanea nozakii

Cyanea nozakii,a common jellyfish distributed in offshore China,has a complex trophic relationship with other zooplankton groups.However,few studies have reported the predation rates and prey selection patterns of C.nozakii medusae on different prey items.Research is also lacking on the intraguild predation of Aurelia coerulea(another common bloom jellyfish in offshore China)by C.nozakii.To address the knowledge gaps,the clearance rates of C.nozakii for different prey items,including copepods(small<1000μm and large>1000μm),fish larvae,and gelatinous prey(hydromedusae,A.coerulea ephyrae,and chaetognaths),were measured.The influence of predator size on the clearance rate was also determined.Additionally,we examined the intraguild predation of C.nozakii on A.coerulea medusae.The clearance rates of C.nozakii varied widely with prey organisms,being independent of prey concentrations.Gelatinous organisms,except for chaetognaths,were captured with considerably high efficiency,followed by fish larvae and copepods,indicating the preferential prey selection of gelatinous organisms by C.nozakii.The clearance rate increased linearly with the cross-sectional area of C.nozakii.Body size in medusae may,to some extents,underpin their capacity to capture more prey by increasing the encounter rate and capture success through ontogeny.C.nozakii preyed voraciously on A.coerulea in high feeding efficiency,but the clearance rate decreased with increasing A.coerulea(as prey)size.This phenomenon of intraguild predation suggests a speculative hypothesis of potential population regulation of A.coerulea by C.nozakii.The information regarding the feeding ecology of C.nozakii reported in this study is important for understanding plankton dynamics in marine ecosystems with extensive occurrences of this jellyfish.