Plant-based vaccines against viral hepatitis: A panoptic review

The traditional vaccines against hepatitis have been instrumental in reducing the incidence of some types of viral hepatitis;however,the need for cost-effective,easily distributable,and needle-free vaccine alternatives has led to the exploration of plant-based vaccines.Plant-based techniques offer a promising avenue for producing viral hepatitis vaccines due to their low-cost cultivation,scalability,and the potential for oral administration.This review highlights the successful expression of hepatitis B surface antigens in plants and the subsequent formation of virus-like particles,which have shown immunogenicity in preclinical and clinical trials.The challenges such as achieving sufficient antigen expression levels,ensuring consistent dosing,and navigating regulatory frameworks,are addressed.The review considers the potential of plant-based vaccines to meet the demands of rapid vaccine deployment in response to outbreaks and their role in global immunization strategies,particularly in resource-limited settings.This review underscores the significant strides made in plant molecular farming and the potential of plant-based vaccines to complement existing immunization methods against viral hepatitis.

Gut microbiota axis:potential target of phytochemicals from plant-based foods

Food-microbiota-host interactions provide an overarching framework for understanding the function of the gut microbiota axis.Diet is a major modulator of gut microbiota.Plant-based foods are rich in phytochemicals;therefore,it is essential to assess such foods and elucidate the mechanisms underlying their action.In this review,we summarize the role of gut microbiota in the communication between the gut and the brain,liver,lung,kidney,and joints,as well as the role of the gut microbiota axis in diseases involving these organs.In addition,we assess the effects of phytochemicals from plant-based foods on the gut microbiota axis via different pathways.

Consumers’experiences and preferences for plant-based meat food:Evidence from a choice experiment in four cities of China

This paper examined consumers’experiences in and preferences for plant-based meat(PBM)food and their respective correlates,based on data from an online survey of 579 consumers in four major cities in China in early 2021.We first described consumers’experiences in consuming and purchasing PBM food and their correlates,and then analyzed consumer preferences using hypothetical choice experiment.The experiment offered consumers various options to purchase burgers made from PBM or animal-based meat(ABM),combined with different countries of origin(COO),taste labels,and prices.Our data showed that respondents hold overall positive attitudes toward PBM food;85 and 82%of respondents reported experience in eating and purchasing PBM food,respectively.More than half of them ate PBM food because they wanted to try new food(58%),or were interested in healthy food(56%).Income,religion,and dietary restrictions were significantly correlated with consumers’experiences in PBM food consumption.Results from the Random Parameter Logit Model based on the hypothetical choice experiment data showed that 79%of respondents chose PBM burgers and were willing to pay an average of 88 CNY for a PBM burger.We also found that 99.8 and 83%of respondents are willing to buy burgers made in China and those with a taste label,with a willingness to pay(WTP)of 208 and 120 CNY,respectively.The heterogeneity test revealed that females and those with at least a bachelor’s degree,higher income,religious beliefs,and dietary restrictions are more likely to buy PBM burgers than their counterparts.

Plant-based diet and its effect on coronary artery disease:A narrative review

Coronary artery disease(CAD),a primary component of cardiovascular diseases,is one of the top contributors to mortality rates worldwide.In 2021,dietary risk was estimated to be attributed to 6.58 million cardiovascular deaths.Plant-based diets(PBDs),which encourage higher consumption of plant foods and lower intake of animal-based foods,have been shown to reduce the risk of CAD by up to 29% when compared to non-vegetarian diets in a meta-analysis.This article aims to summarize the array of PBDs and compare them with conventional Western diets that include meat.We review the various proposed mechanisms for how the bioactive nutrients of PBDs aid in preventing atherosclerosis and CAD events,as well as other cardiac diseases.We conducted a detailed search of PubMed using our exclusive search strategy using the keywords plant-based diet,vegan diet,phytosterols,CAD,myocardial ischemia,and atherosclerosis.A total of 162 pertinent articles published within the past decade were identified for qualitative synthesis.To ensure the accuracy and reliability of our review,we included a total of 55 full-text,peer-reviewed articles that demonstrated the effects of plant-based diets on CAD and were written in English.We excluded animal studies,in vitro or molecular studies,and non-original data like editorials,letters,protocols,and conference abstracts.In this article,we emphasize the importance of dietary interventions,such as PBDs,to prevent CAD and their benefits on environmental sustainability.Integrating plant foods and whole grains into one's daily eating habits leads to an increase in the intake of nutrient-rich foods while reducing the consumption of processed food could not only prevent millions of premature deaths but also provide prevention against many chronic gastrointestinal and metabolic diseases.

Integrated Use of Alternative Methods for Evaluating the Skin Sensitization Potencies of Five Frequently Used Preservatives in Cosmetics

In vitro skin sensitization testing methods based on the adverse outcome pathway(AOP)were used to evaluate the skin sensitization potencies of 5 commonly used preservatives.According to the“2 out of 3”principle of the integrated approaches to testing and assessment(IATA)the direct peptide reactivity assay(DPRA)and the human cell line activation test(h-CLAT)were used to detect the preservatives commonly used in cosmetics,including phenoxyethanol.methyl paraben,propyl paraben,imidazolidinyl urea and DMDM hydantoin.The DPRA and the h-CLA were carried out according to the OEC442C and 442E guidelines,respectively.The results show that.phenoxyethanol and methyl paraben are both negative in DPRA and h-CLAT while imidazolidinyl urea and DMDM hydantoin are both positive in these two tests.Propyl paraben has negative result in DPRA but positive result in h-CLAT.Therefore,imidazolidiny urea and DMDM hydantoin are sensitizers,while phenoxyethanol and methylparaben are non-sensitizers.Taken animal and human data into consideration,it is predicted that propyl paraben should be a non-sensitizer.The combination of DPRA and h-CLAT can make up for the limitations of using a single method,and it is suitable for the preliminary screening of cosmetic raw materials according to skin sensitization.

SDGNN: Symmetry-Preserving Dual-Stream Graph Neural Networks

Dear Editor,This letter proposes a symmetry-preserving dual-stream graph neural network(SDGNN) for precise representation learning to an undirected weighted graph(UWG). Although existing graph neural networks(GNNs) are influential instruments for representation learning to a UWG, they invariably adopt a unique node feature matrix for illustrating the sole node set of a UWG.

Privacy-Preserving Consensus-Based Distributed Economic Dispatch of Smart Grids via State Decomposition

This paper studies the privacy-preserving distributed economic dispatch(DED)problem of smart grids.An autonomous consensus-based algorithm is developed via local data exchange with neighboring nodes,which covers both the islanded mode and the grid-connected mode of smart grids.To prevent power-sensitive information from being disclosed,a privacy-preserving mechanism is integrated into the proposed DED algorithm by randomly decomposing the state into two parts,where only partial data is transmitted.Our objective is to develop a privacy-preserving DED algorithm to achieve optimal power dispatch with the lowest generation cost under physical constraints while preventing sensitive information from being eavesdropped.To this end,a comprehensive analysis framework is established to ensure that the proposed algorithm can converge to the optimal solution of the concerned optimization problem by means of the consensus theory and the eigenvalue perturbation approach.In particular,the proposed autonomous algorithm can achieve a smooth transition between the islanded mode and the grid-connected mode.Furthermore,rigorous analysis is given to show privacy-preserving performance against internal and external eavesdroppers.Finally,case studies illustrate the feasibility and validity of the developed algorithm.

Association of plant-based diets with the risk of upper gastrointestinal tract cancers:A systematic review and meta-analysis

Background:Diets rich in red or processed meat have been linked to an increased risk of cancers within the digestive system.It has been suggested that a plant-based diet may have protective effects against digestive system cancers.This study aimed to determine the association between plant-based diets and upper gastrointestinal tract cancers(UGTC).Methods:We conducted a systematic review and meta-analysis of observational studies.We searched the PubMed,Medline,Embase,and Web of Science databases for articles published up to September 30,2023.We pooled the risk ratios(RR)with the corresponding 95%confi-dence intervals(CI)using fixed or random-effects models.Results:Our meta-analysis included 16 studies(30 results).The data revealed a strong inverse association between a high intake of plant-based diets and UGTC(RR=0.60,95%CI=0.49-0.72),specifically gastric cancer(GC,RR=0.53,95%CI=0.42-0.67)and esophageal can-cer(EC,RR=0.63,95%CI=0.42-0.96).This relationship was not significant for gastric cardia cancer(GCA)or esophagogastric junctional cancer(EGJC,RR=0.76,95%CI=0.47-1.22).A subgroup analysis showed the association was significant in studies from Asia and Europe,as well as in studies utilizing indices such as a vegetarian diet,Mediterranean diet,the plant-based diet index,and principal component analy-sis(PCA)dietary patterns.There was no indication of publication bias among the analyzed studies.Conclusions:This meta-analysis highlights the potential health benefits of plant-based diets in preventing UGTC,particularly regarding esophageal squamous cell carcinoma(ESCC)and GC.Nevertheless,additional research is required to validate these results and explore the un-derlying mechanisms.

Implementation of a New Solution for the Preservation of Anatomical Specimens Made of Non-Toxic Substances

The preservation of anatomical pieces in Veterinary Anatomy is essential since it is not possible to dissect all domestic species. Most techniques use reagents with high levels of toxicity such as formaldehyde. The objective of this work was to develop a new preservation technique that uses reagents with zero toxicity and that allows obtaining preserved pieces suitable for anatomical studies. The alcohol propylene glycol technique was developed, the method of which uses a fixation step with alcohol, sodium chloride, commercial vinegar and subsequently the impregnation of the preservation solution made from propylene glycol and commercial vinegar, which are non-toxic. As a result of this work, adequately preserved sheep hearts were obtained that preserved their morphology with slight changes in size and weight, which did not affect their external and internal anatomical structure. Its coloration was not substantially affected, remaining a little lighter. The pieces obtained showed flexibility which allowed dissections to be carried out. The time to develop the technique was 20 days. A comparative study was carried out with the phenolated glycerin technique that uses toxic reagents (formaldehyde and phenol) and the pieces obtained with the alcohol propylene glycol technique were of better quality, observing that the pieces with phenolated glycerin tend to darken and are more rigid. And the time to develop the technique is 24 days. In conclusion, a preservation technique for anatomical pieces was developed that allowed the preservation of the organs under study, which allow their use for anatomical studies, and which have been preserved without changes until the time of this publication (8 months) and there are pieces preserved with this technique for 2 years.

Extracellular Vesicles from Mesenchymal Stromal Cells (imEVs) Improve Cold Preservation of Isolated Mitochondria

Mitochondrial organelle transplantation (MOT) is an innovative strategy for the treatment of mitochondrial dysfunction such as cardiac ischemic reperfusion injuries, Parkinson’s diseases, brain and spinal cord injuries, and amyotrophic lateral sclerosis (ALS). However, one of the major challenges for widespread usage is a methodology for preservation of isolated mitochondria. Extracellular vesicles (EVs) are phospholipid bilayer-enclosed vesicles released from cells. EVs carry a cargo of proteins, nucleic acids, lipids, metabolites, and even organelles such as mitochondria. Purpose: To test if EVs enhance the stability of isolated mitochondria. Methods: We mixed isolated mitochondria of fibroblasts with EVs of mesenchymal stromal cells (imEVs) (9:1 in volume) and stored the mixture at 2°C - 6°C for different time periods. We measured morphology, mitochondrial membrane potential (MMP) and mitochondrial ATP content at 0, 2, 5 days. Key findings: After 2 days of storage, the mito-chondria without imEVs lost approximate 70% MMP (RFU: 1822 ± 68), compared to the fresh mitochondria (RFU: 5458 ± 52) (p 0.05). In agreement with MMP, mitochondria without imEVs lost significant mitochondrial ATP content (p 0.05), after 2 days of cold storage, compared to fresh mitochondria. Microscopy showed that imEVs promoted aggregation of isolated mitochondria. Summary: The preliminary data showed that imEVs enhanced the stability of isolated mitochondria in cold storage.

2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

A Cloud-Fog Enabled and Privacy-Preserving IoT Data Market Platform Based on Blockchain

The dynamic landscape of the Internet of Things(IoT)is set to revolutionize the pace of interaction among entities,ushering in a proliferation of applications characterized by heightened quality and diversity.Among the pivotal applications within the realm of IoT,as a significant example,the Smart Grid(SG)evolves into intricate networks of energy deployment marked by data integration.This evolution concurrently entails data interchange with other IoT entities.However,there are also several challenges including data-sharing overheads and the intricate establishment of trusted centers in the IoT ecosystem.In this paper,we introduce a hierarchical secure data-sharing platform empowered by cloud-fog integration.Furthermore,we propose a novel non-interactive zero-knowledge proof-based group authentication and key agreement protocol that supports one-to-many sharing sets of IoT data,especially SG data.The security formal verification tool shows that the proposed scheme can achieve mutual authentication and secure data sharing while protecting the privacy of data providers.Compared with previous IoT data sharing schemes,the proposed scheme has advantages in both computational and transmission efficiency,and has more superiority with the increasing volume of shared data or increasing number of participants.

Privacy-preserved learning from non-i.i.d data in fog-assisted IoT:A federated learning approach

With the prevalence of the Internet of Things(IoT)systems,smart cities comprise complex networks,including sensors,actuators,appliances,and cyber services.The complexity and heterogeneity of smart cities have become vulnerable to sophisticated cyber-attacks,especially privacy-related attacks such as inference and data poisoning ones.Federated Learning(FL)has been regarded as a hopeful method to enable distributed learning with privacypreserved intelligence in IoT applications.Even though the significance of developing privacy-preserving FL has drawn as a great research interest,the current research only concentrates on FL with independent identically distributed(i.i.d)data and few studies have addressed the non-i.i.d setting.FL is known to be vulnerable to Generative Adversarial Network(GAN)attacks,where an adversary can presume to act as a contributor participating in the training process to acquire the private data of other contributors.This paper proposes an innovative Privacy Protection-based Federated Deep Learning(PP-FDL)framework,which accomplishes data protection against privacy-related GAN attacks,along with high classification rates from non-i.i.d data.PP-FDL is designed to enable fog nodes to cooperate to train the FDL model in a way that ensures contributors have no access to the data of each other,where class probabilities are protected utilizing a private identifier generated for each class.The PP-FDL framework is evaluated for image classification using simple convolutional networks which are trained using MNIST and CIFAR-10 datasets.The empirical results have revealed that PF-DFL can achieve data protection and the framework outperforms the other three state-of-the-art models with 3%–8%as accuracy improvements.

Apatite Fission Track Thermochronology of Granite from the Xiazhuang Uranium Ore Field,South China:Implications for Exhumation History and Ore Preservation

Xiazhuang uranium ore field,located in the southern part of the Nanling Metallogenic Belt,is considered one of the largest granite-related U regions in South China.In this paper,we contribute new apatite fission track data and thermal history modeling to constrain the exhumation history and evaluate preservation potential of the Xiazhuang Uranium ore field.Nine Triassic outcrop granite samples collected from different locations of Xiazhuang Uranium ore field yield AFT ages ranging from 43 to 24 Ma with similar mean confined fission track lengths ranging from 11.8±2.0 to 12.9±1.9μm and Dpar values between 1.01 and 1.51μm.The robustness time-temperature reconstructions of samples from the hanging wall of Huangpi fault show that the Xiazhuang Uranium ore field experienced a time of monotonous and slow cooling starting from middle Paleocene to middle Miocene(~60-10 Ma),followed by relatively rapid exhumation in the late Miocene(~10-5 Ma)and nearly thermal stability in the Pliocene-Quaternary(~5-0 Ma).The amount of exhumation after U mineralization since the Middle Paleogene was estimated as~4.3±1.8 km according to the integrated thermal history model.Previous studies indicate that the ore-forming ages of U deposits in the Xiazhuang ore field are mainly before Middle Paleocene and the mineralization depths are more than 4.4±1.2 km.Therefore,the exhumation history since middle Paleocene plays important roles in the preservation of the Xiazhuang Uranium ore field.

Exploring the combination of biochar‐amended soil and automated irrigation technology for water regulation and preservation in green infrastructure

Biochar is a carbon sink material with the potential to improve water retention in various soils.However,for the long‐term maintenance of green infrastructure,there is an additional need to regulate the water contents in the covers to maintain vegetation growth in semiarid conditions.In this study,biochar‐amended soil was combined with subsurface drip irrigation,and the water preservation characteristics of this treatment were investigated through a series of one‐dimensional soil column tests.To ascertain the best treatment method specific to semiarid climatic conditions,the test soil was amended with 0%,1%,3%,and 5%biochar.Automatic irrigation devices equipped with soil moisture sensors were used to control the subsurface water content with the aim of enhancing vegetation growth.Each soil column test lasted 150 h,during which the volumetric water contents and soil suction data were recorded.The experimental results reveal that the soil specimen amended with 3%biochar is the most water‐saving regardless of the time cost.Soil with a higher biochar content(e.g.,5%)consumes a more significant amount of water due to the enhancement of the water‐holding capacity.Based on the experimental results,it can be concluded that the appropriate ratio can be determined within 1%–3%,which can reduce not only the amount of irrigated/used water but also the time cost.Such technology can be explored for water content regulation in green infrastructure and the development of barriers for protecting the environment around deep underground waste containment.

Privacy-Preserving Healthcare and Medical Data Collaboration Service System Based on Blockchain and Federated Learning

As the volume of healthcare and medical data increases from diverse sources,real-world scenarios involving data sharing and collaboration have certain challenges,including the risk of privacy leakage,difficulty in data fusion,low reliability of data storage,low effectiveness of data sharing,etc.To guarantee the service quality of data collaboration,this paper presents a privacy-preserving Healthcare and Medical Data Collaboration Service System combining Blockchain with Federated Learning,termed FL-HMChain.This system is composed of three layers:Data extraction and storage,data management,and data application.Focusing on healthcare and medical data,a healthcare and medical blockchain is constructed to realize data storage,transfer,processing,and access with security,real-time,reliability,and integrity.An improved master node selection consensus mechanism is presented to detect and prevent dishonest behavior,ensuring the overall reliability and trustworthiness of the collaborative model training process.Furthermore,healthcare and medical data collaboration services in real-world scenarios have been discussed and developed.To further validate the performance of FL-HMChain,a Convolutional Neural Network-based Federated Learning(FL-CNN-HMChain)model is investigated for medical image identification.This model achieves better performance compared to the baseline Convolutional Neural Network(CNN),having an average improvement of 4.7%on Area Under Curve(AUC)and 7%on Accuracy(ACC),respectively.Furthermore,the probability of privacy leakage can be effectively reduced by the blockchain-based parameter transfer mechanism in federated learning between local and global models.

Trusted Certified Auditor Using Cryptography for Secure Data Outsourcing and Privacy Preservation in Fog-Enabled VANETs

With the recent technological developments,massive vehicular ad hoc networks(VANETs)have been established,enabling numerous vehicles and their respective Road Side Unit(RSU)components to communicate with oneanother.The best way to enhance traffic flow for vehicles and traffic management departments is to share thedata they receive.There needs to be more protection for the VANET systems.An effective and safe methodof outsourcing is suggested,which reduces computation costs by achieving data security using a homomorphicmapping based on the conjugate operation of matrices.This research proposes a VANET-based data outsourcingsystem to fix the issues.To keep data outsourcing secure,the suggested model takes cryptography models intoaccount.Fog will keep the generated keys for the purpose of vehicle authentication.For controlling and overseeingthe outsourced data while preserving privacy,the suggested approach considers the Trusted Certified Auditor(TCA).Using the secret key,TCA can identify the genuine identity of VANETs when harmful messages aredetected.The proposed model develops a TCA-based unique static vehicle labeling system using cryptography(TCA-USVLC)for secure data outsourcing and privacy preservation in VANETs.The proposed model calculatesthe trust of vehicles in 16 ms for an average of 180 vehicles and achieves 98.6%accuracy for data encryption toprovide security.The proposedmodel achieved 98.5%accuracy in data outsourcing and 98.6%accuracy in privacypreservation in fog-enabled VANETs.Elliptical curve cryptography models can be applied in the future for betterencryption and decryption rates with lightweight cryptography operations.

Research on the Method of Heat Preservation and Heating for the Drilling System of Polar Offshore Drilling Platform

This study investigates the heat dissipation mechanism of the insulation layer and other plane insulation layers in the polar drilling rig system.Combining the basic theory of heat transfer with the environmental requirements of polar drilling operations and the characteristics of polar drilling processes,we analyze the factors that affect the insulation effect of the drilling rig system.These factors include the thermal conductivity of the insulation material,the thickness of the insulation layer,ambient temperature,and wind speed.We optimize the thermal insulation material of the polar drilling rig system using a steady-state method to measure solid thermal conductivity.By analyzing the distribution of temperature in space after heating,we optimize the distribution and air outlet angle of the heater using Fluent hydrodynamics software.The results demonstrate that under polar conditions,polyisocyanurate with stable thermodynamic properties is selected as the thermal insulation material.The selection of thermal insulation material and thickness significantly affects the thermal insulation effect of the system but has little effect on its heating effect.Moreover,when the air outlet angle of the heater is set to 32.5°,the heating efficiency of the system can be effectively improved.According to heat transfer equations and heat balance theory,we determine that the heating power required for the system to reach 5°C is close to numerical simulation.

Insights into upper blepharoplasty: Conservative volume-preserving techniques

This editorial commentary critically examines the systematic review by Miotti et al,which discusses the evolving trends in upper lid blepharoplasty towards a conservative,volume-preserving approach.The review emphasizes the shift from traditional tissue resection to techniques that maintain anatomical integrity,paralleling broader trends in panfacial rejuvenation.Miotti et al delve into the nuances of fat pad management,advocating for conservation over reduction to sustain natural contours and improve long-term aesthetic outcomes.This perspective is supported by comparative studies and empirical data,such as those from Massry and Alghoul et al,highlighting the benefits of conservative approaches in terms of patient satisfaction and aesthetic longevity.The review also stresses the importance of surgeon discretion in adapting procedures to diverse patient demographics,particularly in addressing distinct features such as the Asian upper eyelid.However,it identifies a significant gap in long-term comparative research,underscoring the need for future studies to substantiate the safety and efficacy of these minimalist techniques.Overall,Miotti et al.'s work contributes profoundly to the discourse on personalized,conservative cosmetic surgery,urging ongoing research to refine and validate surgical best practices in upper eyelid blepharoplasty.

An Arbitrarily High Order and Asymptotic Preserving Kinetic Scheme in Compressible Fluid Dynamic

We present a class of arbitrarily high order fully explicit kinetic numerical methods in compressible fluid dynamics,both in time and space,which include the relaxation schemes by Jin and Xin.These methods can use the CFL number larger or equal to unity on regular Cartesian meshes for the multi-dimensional case.These kinetic models depend on a small parameter that can be seen as a"Knudsen"number.The method is asymptotic preserving in this Knudsen number.Also,the computational costs of the method are of the same order of a fully explicit scheme.This work is the extension of Abgrall et al.(2022)[3]to multidimensional systems.We have assessed our method on several problems for two-dimensional scalar problems and Euler equations and the scheme has proven to be robust and to achieve the theoretically predicted high order of accuracy on smooth solutions.