QBIoT:A Quantum Blockchain Framework for IoT with an Improved Proof-of-Authority Consensus Algorithm and a Public-Key Quantum Signature

The Internet of Things(IoT)is a network system that connects physical devices through the Internet,allowing them to interact.Nowadays,IoT has become an integral part of our lives,offering convenience and smart functionality.However,the growing number of IoT devices has brought about a corresponding increase in cybersecurity threats,such as device vulnerabilities,data privacy concerns,and network susceptibilities.Integrating blockchain technology with IoT has proven to be a promising approach to enhance IoT security.Nevertheless,the emergence of quantum computing poses a significant challenge to the security of traditional classical cryptography used in blockchain,potentially exposing it to quantum cyber-attacks.To support the growth of the IoT industry,mitigate quantum threats,and safeguard IoT data,this study proposes a robust blockchain solution for IoT that incorporates both classical and post-quantum security measures.Firstly,we present the Quantum-Enhanced Blockchain Architecture for IoT(QBIoT)to ensure secure data sharing and integrity protection.Secondly,we propose an improved Proof of Authority consensus algorithm called“Proof of Authority with Random Election”(PoARE),implemented within QBIoT for leader selection and new block creation.Thirdly,we develop a publickey quantum signature protocol for transaction verification in the blockchain.Finally,a comprehensive security analysis of QBIoT demonstrates its resilience against cyber threats from both classical and quantum adversaries.In summary,this research introduces an innovative quantum-enhanced blockchain solution to address quantum security concernswithin the realmof IoT.The proposedQBIoT framework contributes to the ongoing development of quantum blockchain technology and offers valuable insights for future research on IoT security.

Blockchain-Based Certificateless Cross-Domain Authentication Scheme in the Industrial Internet of Things

The Industrial Internet of Things(IIoT)consists of massive devices in different management domains,and the lack of trust among cross-domain entities leads to risks of data security and privacy leakage during information exchange.To address the above challenges,a viable solution that combines Certificateless Public Key Cryptography(CL-PKC)with blockchain technology can be utilized.However,as many existing schemes rely on a single Key Generation Center(KGC),they are prone to problems such as single points of failure and high computational overhead.In this case,this paper proposes a novel blockchain-based certificateless cross-domain authentication scheme,that integrates the threshold secret sharing mechanism without a trusted center,meanwhile,adopts blockchain technology to enable cross-domain entities to authenticate with each other and to negotiate session keys securely.This scheme also supports the dynamic joining and removing of multiple KGCs,ensuring secure and efficient cross-domain authentication and key negotiation.Comparative analysiswith other protocols demonstrates that the proposed cross-domain authentication protocol can achieve high security with relatively lowcomputational overhead.Moreover,this paper evaluates the scheme based on Hyperledger Fabric blockchain environment and simulates the performance of the certificateless scheme under different threshold parameters,and the simulation results show that the scheme has high performance.

From 0D to 3D:Hierarchical structured high-performance free-standing silicon anodes based on binder-induced topological network architecture

Free-standing silicon anodes with high proportion of active materials have aroused great attention;however,the mechanical stability and electrochemical performance are severely suppressed.Herein,to resolve the appeal issues,a free-standing anode with a"corrugated paper"shape on micro-scale and a topological crosslinking network on the submicron and nano-scale is designed.Essentially,an integrated three-dimensional electrode structure is constructed based on robust carbon nanotubes network with firmly anchored SiNPs via forming interlocking junctions.In which,the hierarchical interlocking structure is achieved by directional induction of the binder,which ensures well integration during cycling so that significantly enhances mechanical stability as well as electronic and ionic conductivity of electrodes.Benefiting from it,this anode exhibits outsta nding performance under harsh service conditions including high Si loading,ultrahigh areal capacity(33.2 mA h cm^(-2)),and high/low temperatures(-15-60℃),which significantly extends its practical prospect.Furthermore,the optimization mechanism of this electrode is explored to verify the crack-healing and structure-integration maintaining along cycling via a unique self-stabilization process.Thus,from both the fundamental and engineering views,this strategy offers a promising path to produce high-performance free-standing electrodes for flexible device applications especially facing volume effect challenges.

XMAM:X-raying models with a matrix to reveal backdoor attacks for federated learning

Federated Learning(FL),a burgeoning technology,has received increasing attention due to its privacy protection capability.However,the base algorithm FedAvg is vulnerable when it suffers from so-called backdoor attacks.Former researchers proposed several robust aggregation methods.Unfortunately,due to the hidden characteristic of backdoor attacks,many of these aggregation methods are unable to defend against backdoor attacks.What's more,the attackers recently have proposed some hiding methods that further improve backdoor attacks'stealthiness,making all the existing robust aggregation methods fail.To tackle the threat of backdoor attacks,we propose a new aggregation method,X-raying Models with A Matrix(XMAM),to reveal the malicious local model updates submitted by the backdoor attackers.Since we observe that the output of the Softmax layer exhibits distinguishable patterns between malicious and benign updates,unlike the existing aggregation algorithms,we focus on the Softmax layer's output in which the backdoor attackers are difficult to hide their malicious behavior.Specifically,like medical X-ray examinations,we investigate the collected local model updates by using a matrix as an input to get their Softmax layer's outputs.Then,we preclude updates whose outputs are abnormal by clustering.Without any training dataset in the server,the extensive evaluations show that our XMAM can effectively distinguish malicious local model updates from benign ones.For instance,when other methods fail to defend against the backdoor attacks at no more than 20%malicious clients,our method can tolerate 45%malicious clients in the black-box mode and about 30%in Projected Gradient Descent(PGD)mode.Besides,under adaptive attacks,the results demonstrate that XMAM can still complete the global model training task even when there are 40%malicious clients.Finally,we analyze our method's screening complexity and compare the real screening time with other methods.The results show that XMAM is about 10–10000 times faster than the existing methods.

A Trust Evaluation Mechanism Based on Autoencoder Clustering Algorithm for Edge Device Access of IoT

First,we propose a cross-domain authentication architecture based on trust evaluation mechanism,including registration,certificate issuance,and cross-domain authentication processes.A direct trust evaluation mechanism based on the time decay factor is proposed,taking into account the influence of historical interaction records.We weight the time attenuation factor to each historical interaction record for updating and got the new historical record data.We refer to the beta distribution to enhance the flexibility and adaptability of the direct trust assessment model to better capture time trends in the historical record.Then we propose an autoencoder-based trust clustering algorithm.We perform feature extraction based on autoencoders.Kullback leibler(KL)divergence is used to calculate the reconstruction error.When constructing a convolutional autoencoder,we introduce convolutional neural networks to improve training efficiency and introduce sparse constraints into the hidden layer of the autoencoder.The sparse penalty term in the loss function measures the difference through the KL divergence.Trust clustering is performed based on the density based spatial clustering of applications with noise(DBSCAN)clustering algorithm.During the clustering process,edge nodes have a variety of trustworthy attribute characteristics.We assign different attribute weights according to the relative importance of each attribute in the clustering process,and a larger weight means that the attribute occupies a greater weight in the calculation of distance.Finally,we introduced adaptive weights to calculate comprehensive trust evaluation.Simulation experiments prove that our trust evaluation mechanism has excellent reliability and accuracy.

Design and Implementation of USB Key System Based on Dual-Factor Identity Authentication Protocol

With the increasing demand for information security,traditional single-factor authentication technology can no longer meet security requirements.To this end,this paper proposes a Universal Serial Bus(USB)Key hardware and software system based on a two-factor authentication protocol,aiming to improve the security and reliability of authentication.This paper first analyzes the current status and technical principles of USB Key-related research domestically and internationally and designs a two-factor authentication protocol that combines impact/response authentication and static password authentication.The system consists of a host computer and a USB Key device.The host computer interacts with the USB Key through a graphical user interface.The Secure Hash Algorithm 1(SHA-1)and MySQL database are used to implement the authentication function.Experimental results show that the designed two-factor authentication protocol can effectively prevent replay attacks and information tampering,and improve the security of authentication.If the corresponding USB Key is not inserted,the system will prompt that the device is not found.Once the USB Key is inserted,user identity is confirmed through two-factor verification,which includes impact/response authentication and static password authentication.

Research on Association Analysis Technology of Network User Accounts

As large numbers of user information on various network services are becoming valuable resources for social computing,how to associate user information scattered in a variety of services turns to one of the key issues of user identity mining.First this paper outlines the difficulties of dealing with account association,then describes the basis of account association method.After that,this paper sums up account association methods mainly from five aspects which are user naming conventions,user profiles,user writing styles,user online behavior,and user community relationship.Finally,this paper points out the development direction and prospects of account association.

An Erebus Attack Detection Method Oriented to Blockchain Network Layer

Recently,the Erebus attack has proved to be a security threat to the blockchain network layer,and the existing research has faced challenges in detecting the Erebus attack on the blockchain network layer.The cloud-based active defense and one-sidedness detection strategies are the hindrances in detecting Erebus attacks.This study designs a detection approach by establishing a ReliefF_WMRmR-based two-stage feature selection algorithm and a deep learning-based multimodal classification detection model for Erebus attacks and responding to security threats to the blockchain network layer.The goal is to improve the performance of Erebus attack detection methods,by combining the traffic behavior with the routing status based on multimodal deep feature learning.The traffic behavior and routing status were first defined and used to describe the attack characteristics at diverse stages of s leak monitoring,hidden traffic overlay,and transaction identity forgery.The goal is to clarify how an Erebus attack affects the routing transfer and traffic state on the blockchain network layer.Consequently,detecting objects is expected to become more relevant and sensitive.A two-stage feature selection algorithm was designed based on ReliefF and weighted maximum relevance minimum redundancy(ReliefF_WMRmR)to alleviate the overfitting of the training model caused by redundant information and noise in multiple source features of the routing status and traffic behavior.The ReliefF algorithm was introduced to select strong correlations and highly informative features of the labeled data.According to WMRmR,a feature selection framework was defined to eliminate weakly correlated features,eliminate redundant information,and reduce the detection overhead of the model.A multimodal deep learning model was constructed based on the multilayer perceptron(MLP)to settle the high false alarm rates incurred by multisource data.Using this model,isolated inputs and deep learning were conducted on the selected routing status and traffic behavior.Redundant intermodal information was removed because of the complementarity of the multimodal network,which was followed by feature fusion and output feature representation to boost classification detection precision.The experimental results demonstrate that the proposed method can detect features,such as traffic data,at key link nodes and route messages in a real blockchain network environment.Additionally,the model can detect Erebus attacks effectively.This study provides novelty to the existing Erebus attack detection by increasing the accuracy detection by 1.05%,the recall rate by 2.01%,and the F1-score by 2.43%.

Exposure to Electromagnetic Fields from Mobile Phones and Fructose consumption Coalesce to Perturb Metabolic Regulators AMPK/SIRT1-UCP2/FOXO1 in Growing Rats

Objective In this study,the combined effect of two stressors,namely,electromagnetic fields(EMFs)from mobile phones and fructose consumption,on hypothalamic and hepatic master metabolic regulators of the AMPK/SIRT1-UCP2/FOXO1 pathway were elucidated to delineate the underlying molecular mechanisms of insulin resistance.Methods Weaned Wistar rats(28 days old)were divided into 4 groups:Normal,Exposure Only(ExpO),Fructose Only(FruO),and Exposure and Fructose(EF).Each group was provided standard laboratory chow ad libitum for 8 weeks.Additionally,the control groups,namely,the Normal and FruO groups,had unrestricted access to drinking water and fructose solution(15%),respectively.Furthermore,the respective treatment groups,namely,the ExpO and EF groups,received EMF exposure(1,760 MHz,2 h/day x 8 weeks).In early adulthood,mitochondrial function,insulin receptor signaling,and oxidative stress signals in hypothalamic and hepatic tissues were assessed using western blotting and biochemical analysis.Result In the hypothalamic tissue of EF,SIRT1,FOXO 1,p-PI3K,p-AKT,ComplexⅢ,UCP2,MnSOD,and catalase expressions and OXPHOS and GSH activities were significantly decreased(P<0.05)compared to the Normal,ExpO,and FruO groups.In hepatic tissue of EF,the p-AMPKα,SIRT1,FOXO1,IRS1,p-PI3K,ComplexⅠ,Ⅱ,Ⅲ,Ⅳ,Ⅴ,UCP2,and MnSOD expressions and the activity of OXPHOS,SOD,catalase,and GSH were significantly reduced compared to the Normal group(P<0.05).Conclusion The findings suggest that the combination of EMF exposure and fructose consumption during childhood and adolescence in Wistar rats disrupts the closely interlinked and multi-regulated crosstalk of insulin receptor signals,mitochondrial OXPHOS,and the antioxidant defense system in the hypothalamus and liver.

Quantum-Enhanced Blockchain: A Secure and Practical Blockchain Scheme

The rapid advancement of quantum technology poses significant security risks to blockchain systems.However,quantum technology can also provide solutions for enhancing blockchain security.In this paper,we propose a quantum-enhanced blockchain scheme to achieve a high level of security against quantum computing attacks.We first discuss quantum computing attacks on classic blockchains,including attacks on hash functions,digital signatures,and consensus mechanisms.We then introduce quantum technologies,such as a quantum hash function(QHF),a quantum digital signature(QDS),and proof of authority(PoA)consensus mechanism,into our scheme to improve the security of the blockchain system.Our security analysis demonstrates that our scheme offers superior security against quantum and classic attacks.Finally,we compare our scheme with previous works,showing that our scheme has achieved a perfect balance in terms of practicality,reliability,scalability,and efficiency.Overall,this work contributes to the ongoing research on quantum blockchain in the quantum era.

Anode Interfacial Issues in Solid-State Li Batteries:Mechanistic Understanding and Mitigating Strategies

All-solid-state Li metal batteries(ASSLBs)using inorganic solid electrolyte(SE)are considered promising alternatives to conventional Li-ion batteries,offering improved safety and boosted energy density.While significant progress has been made on improving the ionic conductivity of SEs,the degradation and instability of Li metal/inorganic SE interfaces have become the critical challenges that limit the coulombic efficiency,power performance,and cycling stability of ASSLBs.Understanding the mechanisms of complex/dynamic interfacial phenomena is of great importance in addressing these issues.Herein,recent studies on identifying,understanding,and solving interfacial issues on anode side in ASSLBs are comprehensively reviewed.Typical issues at Li metal/SE interface include Li dendrite growth/propagation,SE cracking,physical contact loss,and electrochemical reactions,which lead to high interfacial resistance and cell failure.The causes of these issues relating to the chemical,physical,and mechanical properties of Li metal and SEs are systematically discussed.Furthermore,effective mitigating strategies are summarized and their effects on suppressing interfacial reactions,improving interfacial Li-ion transport,maintaining interfacial contact,and stabilizing Li plating/stripping are highlighted.The in-depth mechanistic understanding of interfacial issues and complete investigations on current solutions provide foundations and guidance for future research and development to realize practical application of high-performance ASSLB.

EduASAC:A Blockchain-Based Education Archive Sharing and Access Control System

In the education archive sharing system,when performing homomorphic ciphertext retrieval on the storage server,there are problems such as low security of shared data,confusing parameter management,and weak access control.This paper proposes an Education Archives Sharing and Access Control(EduASAC)system to solve these problems.The system research goal is to realize the sharing of security parameters,the execution of access control,and the recording of system behaviors based on the blockchain network,ensuring the legitimacy of shared membership and the security of education archives.At the same time,the system can be combined with most homomorphic ciphertext retrieval schemes running on the storage server,making the homomorphic ciphertext retrieval mechanism controllable.This paper focuses on the blockchain access control framework and specifically designs smart contracts that conform to the business logic of the EduASAC system.The former adopts a dual-mode access control mechanism combining Discretionary Access Control(DAC)and Mandatory Access Control(MAC)and improves the tagging mode after user permission verification based on the Authentication and Authorization for Constrained Environments(ACE)authorization framework of Open Authorization(OAuth)2.0;the latter is used in the system to vote on nodes to join requests,define access control policies,execute permission verification processes,store,and share system parameters,and standardize the behavior of member nodes.Finally,the EduASAC system realizes the encryption,storage,retrieval,sharing,and access control processes of education archives.To verify the performance of the system,simulation experiments were conducted.The results show that the EduASAC system can meet the high security needs of education archive sharing and ensure the system’s high throughput,low latency,fast decision-making,and fine-grained access control ability.

Research and Implementation of Credit Investigation Sharing Platform Based on Double Blockchain

As the development of the modern economy is increasingly insep-arable from credit support,the traditional credit investigation mode has yet to meet this demand.Because of the difficulties in conventional credit data sharing among credit investigation agencies,poor data portability,and centralized supervision,this paper proposes a data-sharing scheme for credit investigation agencies based on a double blockchain.Given the problems such as difficult data sharing,difficult recovery of damaged data,and accessible data leakage between institutions and users with non-traditional credit inves-tigation data other than credit,this paper proposes a data-sharing scheme for credit investigation subjects based on the digital envelope.Based on the above two solutions,this paper designs a double blockchain credit data-sharing plat-form based on the“public chain+alliance chain”from credit investigation agencies’and visiting subjects’perspectives.The sharing platform uses the alliance chain as the management chain to solve the problem of complex data sharing between credit bureaus and centralized supervision,uses the public chain as the use chain to solve the problem of complex data sharing between the access subject and the credit bureaus,uses the interplanetary file system and digital envelope and other technologies to solve the problem of difficult recovery of damaged data,data leakage,and other issues.After the upload test,the average upload speed reaches 80.6 M/s.The average download speed of the system is 88.7 M/s after the download test.The multi-thread stress test tests the linkage port on the system package,and the average response time for the hypertext transfer protocol(HTTP)is 0.6 ms.The system performance and security analysis show that the sharing platform can provide safe and reliable credit-sharing services for organizations and users and high working efficiency.

Magnetic-field-controlled spin valve and spin memory based on single-molecule magnets

A single-molecule magnet is a long-sought-after nanoscale component because it can enable us to miniaturize nonvolatile memory storage devices.The signature of a single-molecule magnet is switching between two bistable magnetic ground states under an external magnetic field.Based on this feature,we theoretically investigate a magnetic-fieldcontrolled reversible resistance change active at low temperatures in a molecular magnetic tunnel junction,which consists of a single-molecule magnet sandwiched between a ferromagnetic electrode and a normal metal electrode.Our numerical results demonstrate that the molecular magnetism orientation can be manipulated by magnetic fields to be parallel/antiparallel to the ferromagnetic electrode magnetization.Moreover,different magnetic configurations can be“read out”based on different resistance states or different spin polarization parameters in the current spectrum,even in the absence of a magnetic field.Such an external magnetic field-controlled resistance state switching effect is similar to that in traditional spin valve devices.The difference between the two systems is that one of the ferromagnetic layers in the original device has been replaced by a magnetic molecule.This proposed scheme provides the possibility of better control of the spin freedom of electrons in molecular electrical devices,with potential applications in future high-density nonvolatile memory devices.

Effects of Different Herbicides on Weed Control in Alfalfa Field

[Objectives]The paper was to systematically study the technology of weed control in alfalfa field.[Methods]Reviving alfalfa field and newly sown alfalfa field after emergence were selected,and the effects of different herbicides on weed control and alfalfa yield were discussed.[Results]The optimal herbicides after alfalfa reviving were 5%imazethapyr and 10%imazethapyr,and the optimal dosages were 1.5 and 1.05 L/hm 2,respectively.The optimal herbicides after emergence of newly born alfalfa were 5%imazethapyr and 10%imazethapyr,and the optimal dosages were 1.5 and 0.75 L/hm 2,respectively.[Conclusions]This study will provide a technical support for high quality production of alfalfa.

Image Steganalysis Based on Deep Content Features Clustering

The training images with obviously different contents to the detected images will make the steganalysis model perform poorly in deep steganalysis.The existing methods try to reduce this effect by discarding some features related to image contents.Inevitably,this should lose much helpful information and cause low detection accuracy.This paper proposes an image steganalysis method based on deep content features clustering to solve this problem.Firstly,the wavelet transform is used to remove the high-frequency noise of the image,and the deep convolutional neural network is used to extract the content features of the low-frequency information of the image.Then,the extracted features are clustered to obtain the corresponding class labels to achieve sample pre-classification.Finally,the steganalysis network is trained separately using samples in each subclass to achieve more reliable steganalysis.We experimented on publicly available combined datasets of Bossbase1.01,Bows2,and ALASKA#2 with a quality factor of 75.The accuracy of our proposed pre-classification scheme can improve the detection accuracy by 4.84%for Joint Photographic Experts Group UNIversal WAvelet Relative Distortion(J-UNIWARD)at the payload of 0.4 bits per non-zero alternating current discrete cosine transform coefficient(bpnzAC).Furthermore,at the payload of 0.2 bpnzAC,the improvement effect is minimal but also reaches 1.39%.Compared with the previous steganalysis based on deep learning,this method considers the differences between the training contents.It selects the proper detector for the image to be detected.Experimental results show that the pre-classification scheme can effectively obtain image subclasses with certain similarities and better ensure the consistency of training and testing images.The above measures reduce the impact of sample content inconsistency on the steganalysis network and improve the accuracy of steganalysis.

Facile modification of aluminum hypophosphate and its flame retardancy for polystyrene

A phosphorus-containing flame retardant, aluminum hypophosphite(AHPi), has been modified by(3-aminopropyl) triethoxysilane(KH550) to prepare flame-retardant polystyrene(PS). The influence of modified AHPi on the morphology and characterization was investigated, and differences in flame retardant properties of the PS/AHPi and PS/modified AHPi were compared. The PS composite can pass the vertical burning tests(UL-94 standard) with a V-0 rating when the mass content of modified AHPi reaches20%, compared with the mass content of 25% AHPi. The element mapping of the PS composite shows that modified AHPi has better dispersion in PS than AHPi. Thermogravimetric analysis results indicated that adding modified AHPi can advance the initial decomposition temperature of the composite material.With the addition of modified AHPi, the decrease in peak heat release rate(p HRR) is more evident than AHPi, and the char yield of the resultant PS composites gradually increased. With the addition of 25%modified AHPi, the p HRR and total heat release of PS composites decreased by 81.4% and 37.6%. The modification of AHPi promoted its dispersion in the PS matrix and improved the char formation of PS composites. The results of real-time infrared spectrometry of PS composites, Fourier transform infrared spectra and X-ray photoelectron analysis of the char layer indicated that modified AHPi has flame retardancy in condensed and gas phases.

A Leaking-Proof Theranostic Nanoplatform for Tumor-Targeted and Dual-Modality Imaging-Guided Photodynamic Therapy

Objective:A protein-based leaking-proof theranostic nanoplatform for dual-modality imaging-guided tumor photodynamic therapy(PDT)has been designed.Impact Statement:A site-specific conjugation of chlorin e6(Ce6)to ferrimagnetic ferritin(MFtn-Ce6)has been constructed to address the challenge of unexpected leakage that often occurs during small-molecule drug delivery.Introduction:PDT is one of the most promising approaches for tumor treatment,while a delivery system is typically required for hydrophobic photosensitizers.However,the nonspecific distribution and leakage of photosensitizers could lead to insufficient drug accumulation in tumor sites.Methods:An engineered ferritin was generated for site-specific conjugation of Ce6 to obtain a leaking-proof delivery system,and a ferrimagnetic core was biomineralized in the cavity of ferritin,resulting in a fluorescent ferrimagnetic ferritin nanoplatform(MFtn-Ce6).The distribution and tumor targeting of MFtn-Ce6 can be detected by magnetic resonance imaging(MRI)and fluorescence imaging(FLI).Results:MFtn-Ce6 showed effective dual-modality MRI and FLI.A prolonged in vivo circulation and increased tumor accumulation and retention of photosensitizer was observed.The time-dependent distribution of MFtn-Ce6 can be precisely tracked in real time to find the optimal time window for PDT treatment.The colocalization of ferritin and the iron oxide core confirms the high stability of the nanoplatform in vivo.The results showed that mice treated with MFtn-Ce6 exhibited marked tumor-suppressive activity after laser irradiation.Conclusion:The ferritin-based leaking-proof nanoplatform can be used for the efficient delivery of the photosensitizer to achieve an enhanced therapeutic effect.This method established a general approach for the dual-modality imagingguided tumor delivery of PDT agents.

Cryopreserved ovine spermatogonial stem cells maintain stemness and colony forming ability in vitro

Objective:To assess the effect of cryopreservation on stemness and proliferation potential of sheep spermatogonial stem cells(SSCs)in vitro.Methods:Sheep testicular cells were isolated and putative SSCs were enriched by the laminin-based differential plating method.Putative SSCs were co-cultured with the Sertoli cell feeder prepared by the Datura Stramonium Agglutinin(DSA-lectin)-based method.The cultured putative SSCs were cryopreserved in Dulbecco's Modified Eagle Medium-10%fetal bovine serum mixture(DMEM-10%FBS)media containing 10%dimethyl sulfoxide(DMSO)alone or 10%DMSO plus 200 mM trehalose.Cryopreserved putative SSCs were evaluated for their proliferation potential using in vitro culture and stemness by immunocytochemistry.Finally,the transfection ability of cryopreserved putative SSCs was analyzed.Results:We isolated 91%viable testicular cells from sheep testes.The majority of the laminin enriched cells expressed the SSC related marker,ITGA6.Co-culture of sheep putative SSCs with Sertoli cell feeder resulted in the generation of stable colonies,and the expression of SSC marker was maintained after several passages.A significantly higher number of viable putative SSCs was recovered from SSCs cryopreserved in media containing 10%DMSO and 200 mM trehalose compared to 10%DMSO alone(P<0.01).Cryopreserved putative SSCs formed colonies and showed SSC marker expression similar to the non-cryopreserved putative SSCs.The appearance of green fluorescent colonies over the Sertoli cell feeder indicated that cryopreserved sheep SSCs were successfully transfected.Conclusions:Cryopreserved putative SSCs can retain their stemness,colony forming ability,and transfection efficiency in vitro.Our research may help in the effective preservation of germplasm and the generation of transgenic ovine species.

Multiple-statistical genome-wide association analysis and genomic prediction of fruit aroma and agronomic traits in peaches

‘Chinese Cling’is an important founder in peach breeding history due to the pleasant flavor.Genome-wide association studies(GWAS)combined with genomic selection are promising tools in fruit tree breeding,as there is a considerable time lapse between crossing and release of a cultivar.In this study,242 peaches from Shanghai germplasm were genotyped with 145456 single-nucleotide polymorphisms(SNPs).The six agronomic traits of fruit flesh color,fruit shape,fruit hairiness,flower type,pollen sterility,and soluble solids content,along with 14 key volatile odor compounds(VOCs),were recorded for multiple-statistical GWAS.Except the reported candidate genes,six novel genes were identified as associated with these traits.Thirty-nine significant SNPs were associated with eight VOCs.The putative candidate genes were confirmed for VOCs by RNA-seq,including three genes in the biosynthesis pathway found to be associated with linalool,soluble solids content,and cis-3-hexenyl acetate.Multiple-trait genomic prediction enhanced the predictive ability forγ-decalactone to 0.7415 compared with the single-trait model value of 0.1017.One PTS1-SSR marker was designed to predict the linalool content,and the favorable genotype 187/187 was confirmed,mainly existing in the‘Shanghai Shuimi’landrace.Overall,our findings will be helpful in determining peach accessions with the ideal phenotype and show the potential of multiple-trait genomic prediction to improve accuracy for highly correlated genetic traits.The diagnostic marker will be valuable for the breeder to bridge the gap between quantitative trait loci and marker-assisted selection for developing strong-aroma cultivars.