Alphaherpesvirus-vectored vaccines against animal diseases: Current progress

Recombinant virus-vectored vaccines are novel agents that can effectively activate specific and nonspecific immunity,are multivalent and multieffective,and have high safety ratings.Animal alphaherpesviruses have a large genome,contain multiple nonessential regions that do not affect viral replication and are capable of accepting the insertion of an exogenous gene and expressing the antigen protein.Furthermore,animal alphaherpesviruses have a wide host spectrum,can replicate in the host and continuously stimulate the animal to produce immunity to the corresponding pathogen,thus making them ideal carriers for recombinant virus-vectored vaccines.With the development of gene-editing technology,recombinant viruses capable of expressing foreign genes can be constructed by various methods.Currently,studies on recombinant virusvectored vaccines constructed based on animal alphaherpesviruses have involved poultry,pigs,cattle,sheep,and companion animals.Studies have shown that the construction of recombinant animal alphaherpesviruses enables the acquisition of immunity to multiple diseases.This article mainly summarizes the current progress on animal alphaherpesvirus-vectored vaccines,aiming to provide reference for the development of new animal alphaherpesvirus-vectored vaccines.

A Novel Parallel Computing Confidentiality Scheme Based on Hindmarsh-Rose Model

Due to the inherent insecure nature of the Internet,it is crucial to ensure the secure transmission of image data over this network.Additionally,given the limitations of computers,it becomes evenmore important to employ efficient and fast image encryption techniques.While 1D chaotic maps offer a practical approach to real-time image encryption,their limited flexibility and increased vulnerability restrict their practical application.In this research,we have utilized a 3DHindmarsh-Rosemodel to construct a secure cryptosystem.The randomness of the chaotic map is assessed through standard analysis.The proposed system enhances security by incorporating an increased number of system parameters and a wide range of chaotic parameters,as well as ensuring a uniformdistribution of chaotic signals across the entire value space.Additionally,a fast image encryption technique utilizing the new chaotic system is proposed.The novelty of the approach is confirmed through time complexity analysis.To further strengthen the resistance against cryptanalysis attacks and differential attacks,the SHA-256 algorithm is employed for secure key generation.Experimental results through a number of parameters demonstrate the strong cryptographic performance of the proposed image encryption approach,highlighting its exceptional suitability for secure communication.Moreover,the security of the proposed scheme has been compared with stateof-the-art image encryption schemes,and all comparison metrics indicate the superior performance of the proposed scheme.

An Immutable Framework for Smart Healthcare Using Blockchain Technology

The advancements in sensing technologies,information processing,and communication schemes have revolutionized the healthcare sector.Electronic Healthcare Records(EHR)facilitate the patients,doctors,hospitals,and other stakeholders to maintain valuable data and medical records.The traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks.A single attempt of a successful Denial of Service(DoS)attack can compromise the complete healthcare system.This article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things(IoMT)to address the stated challenges.The proposed architecture is on the idea of a lightweight private blockchain-based network that facilitates the users and hospitals to perform multiple healthcare-related operations in a secure and trustworthy manner.The efficacy of the proposed framework is evaluated in the context of service execution time and throughput.The experimental outcomes indicate that the proposed design attained lower service execution time and higher throughput under different control parameters.

THREE POINT BOUNDARY VALUE PROBLEMS FOR NONLINEAR FRACTIONAL DIFFERENTIAL EQUATIONS

In this paper, we study existence and uniqueness of solutions to nonlinear three point boundary value problems for fractional differential equation of the type c D δ 0＋ u（t） = f （t, u（t）, c D σ 0＋ u（t））, t ∈ [0, T ], u（0） = αu（η）, u（T ） = βu（η）, where 1 〈 δ 〈 2, 0 〈 σ 〈 1, α, β∈ R, η∈ （0, T ）, αη（1 -β） ＋ （1-α）（T βη） = 0 and c D δ 0＋ , c D σ 0＋ are the Caputo fractional derivatives. We use Schauder fixed point theorem and contraction mapping principle to obtain existence and uniqueness results. Examples are also included to show the applicability of our results.

ULAM-HYERS-RASSIAS STABILITY AND EXISTENCE OF SOLUTIONS TO NONLINEAR FRACTIONAL DIFFERENCE EQUATIONS WITH MULTIPOINT SUMMATION BOUNDARY CONDITION

The purpose of this study is to acquire some conditions that reveal existence and stability for solutions to a class of difference equations with non-integer orderμ∈(1,2].The required conditions are obtained by applying the technique of contraction principle for uniqueness and Schauder’s fixed point theorem for existence.Also,we establish some conditions under which the solution of the considered class of difference equations is generalized Ulam-Hyers-Rassias stable.Example for the illustration of results is given.

Non-Invasive Early Diagnosis of Obstructive Lung Diseases Leveraging Machine Learning Algorithms

Lungs are a vital human body organ,and different Obstructive Lung Diseases(OLD)such as asthma,bronchitis,or lung cancer are caused by shortcomings within the lungs.Therefore,early diagnosis of OLD is crucial for such patients suffering from OLD since,after early diagnosis,breathing exercises and medical precautions can effectively improve their health state.A secure non-invasive early diagnosis of OLD is a primordial need,and in this context,digital image processing supported by Artificial Intelligence(AI)techniques is reliable and widely used in the medical field,especially for improving early disease diagnosis.Hence,this article presents an AIbased non-invasive and secured diagnosis for OLD using physiological and iris features.This research work implements different machine-learning-based techniques which classify various subjects,which are healthy and effective patients.The iris features include gray-level run-length matrix-based features,gray-level co-occurrence matrix,and statistical features.These features are extracted from iris images.Additionally,ten different classifiers and voting techniques,including hard and soft voting,are implemented and tested,and their performances are evaluated using several parameters,which are precision,accuracy,specificity,F-score,and sensitivity.Based on the statistical analysis,it is concluded that the proposed approach offers promising techniques for the non-invasive early diagnosis of OLD with an accuracy of 97.6%.

A novel medical image data protection scheme for smart healthcare system

The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the Internet.Recently,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning systems.Smart healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical data.In smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance Imaging.The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI.Moreover,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission delays.To address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory.The results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,respectively.This validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.