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.

Epicardial adipose tissue in obesity with heart failure with preserved ejection fraction: Cardiovascular magnetic resonance biomarker study

BACKGROUND Obesity has become a serious public health issue,significantly elevating the risk of various complications.It is a well-established contributor to Heart failure with preserved ejection fraction(HFpEF).Evaluating HFpEF in obesity is crucial.Epicardial adipose tissue(EAT)has emerged as a valuable tool for validating prognostic biomarkers and guiding treatment targets.Hence,assessing EAT is of paramount importance.Cardiovascular magnetic resonance(CMR)imaging is acknowledged as the gold standard for analyzing cardiac function and mor-phology.We hope to use CMR to assess EAT as a bioimaging marker to evaluate HFpEF in obese patients.AIM To assess the diagnostic utility of CMR for evaluating heart failure with preserved ejection fraction[HFpEF;left ventricular(LV)ejection fraction≥50%]by measuring the epicardial adipose tissue(EAT)volumes and EAT mass in obese patients.METHODS Sixty-two obese patients were divided into two groups for a case-control study based on whether or not they had heart failure with HFpEF.The two groups were defined as HFpEF+and HFpEF-.LV geometry,global systolic function,EAT volumes and EAT mass of all subjects were obtained using cine magnetic resonance sequences.RESULTS Forty-five patients of HFpEF-group and seventeen patients of HFpEF+group were included.LV mass index(g/m2)of HFpEF+group was higher than HFpEF-group(P<0.05).In HFpEF+group,EAT volumes,EAT volume index,EAT mass,EAT mass index and the ratio of EAT/[left atrial(LA)left-right(LR)diameter]were higher compared to HFpEF-group(P<0.05).In multivariate analysis,Higher EAT/LA LR diameter ratio was associated with higher odds ratio of HFpEF.CONCLUSION EAT/LA LR diameter ratio is highly associated with HFpEF in obese patients.It is plausible that there may be utility in CMR for assessing obese patients for HFpEF using EAT/LA LR diameter ratio as a diagnostic biomarker.Further prospective studies,are needed to validate these proof-of-concept findings.

Heart failure with preserved ejection fraction and the first law of thermodynamics

In heart failure with preserved ejection fraction,significant left ventricular diastolic abnormalities are present,despite a normal systolic ejection fraction.This article will consider whether this is consistent with the law of conservation of energy,also know as the first law of thermodynamics.

Preserved egg white alleviates DSS-induced colitis in mice through the reduction of oxidative stress,modulation of inflammatory cytokines,NF-κB,MAPK and gut microbiota composition

Traditional Chinese preserved egg products have exhibited some anti-inflammatory effects,but their mechanisms of action remain unknown.This study aimed to investigate the anti-inflammatory effects of preserved egg white(PEW)treatment on dextran sulfate sodium(DSS)-induced colitis in mice and the underlying mechanisms.The results showed that treatment with PEW in mice with DSS-induced colitis for 14 days effectively improved the clinical signs,inhibited the secretion and gene expression of pro-inflammatory cytokines,and reduced myeloperoxidase(MPO)activity and oxidative stress levels.In addition,western blotting results showed that PEW significantly suppressed DSS-induced phosphorylation levels of nuclear factor-kappa B(NF-κB)p65 and p38 mitogen-activated protein kinase(MAPK)in colon tissues of mice with colitis.PEW also enhanced the production of short-chain fatty acids(SCFAs)and modulated gut microbiota composition in mice with DSS-induced colitis,including increasing the relative abundance of beneficial bacteria Lachnospiraceae,Ruminococcaceae and Muribaculaceae,and reducing the relative abundance of harmful bacteria Proteobacteria.Taken together,our study demonstrated that preserved egg white could alleviate DSS-induced colitis in mice through the reduction of oxidative stress,modulation of inflammatory cytokines,NF-κB,MAPK and gut microbiota composition.

Targeting epicardial adipose tissue:A potential therapeutic strategy for heart failure with preserved ejection fraction with type 2 diabetes mellitus

Heart failure with preserved ejection fraction(HFpEF)is a heterogeneous syndrome with various comorbidities,multiple cardiac and extracardiac pathophysiologic abnormalities,and diverse phenotypic presentations.Since HFpEF is a heterogeneous disease with different phenotypes,individualized treatment is required.HFpEF with type 2 diabetes mellitus(T2DM)represents a specific phenotype of HFpEF,with about 45%-50% of HFpEF patients suffering from T2DM.Systemic inflammation associated with dysregulated glucose metabolism is a critical pathological mechanism of HFpEF with T2DM,which is intimately related to the expansion and dysfunction(inflammation and hypermetabolic activity)of epicardial adipose tissue(EAT).EAT is well established as a very active endocrine organ that can regulate the pathophysiological processes of HFpEF with T2DM through the paracrine and endocrine mechanisms.Therefore,suppressing abnormal EAT expansion may be a promising therapeutic strategy for HFpEF with T2DM.Although there is no treatment specifically for EAT,lifestyle management,bariatric surgery,and some pharmaceutical interventions(anti-cytokine drugs,statins,proprotein convertase subtilisin/kexin type 9 inhibitors,metformin,glucagon-like peptide-1 receptor agonists,and especially sodium-glucose cotransporter-2 inhibitors)have been shown to attenuate the inflammatory response or expansion of EAT.Importantly,these treatments may be beneficial in improving the clinical symptoms or prognosis of patients with HFpEF.Accordingly,well-designed randomized controlled trials are needed to validate the efficacy of current therapies.In addition,more novel and effective therapies targeting EAT are needed in the future.

Computer Forensics Framework for Efficient and Lawful Privacy-Preserved Investigation

Privacy preservation(PP)in Digital forensics(DF)is a conflicted and non-trivial issue.Existing solutions use the searchable encryption concept and,as a result,are not efficient and support only a keyword search.Moreover,the collected forensic data cannot be analyzed using existing well-known digital tools.This research paper first investigates the lawful requirements for PP in DF based on the organization for economic co-operation and development OECB)privacy guidelines.To have an efficient investigation process and meet the increased volume of data,the presented framework is designed based on the selective imaging concept and advanced encryption standard(AES).The proposed framework has two main modules,namely Selective Imaging Module(SIM)and Selective Analysis Module(SAM).The SIM and SAM modules are implemented based on advanced forensic format 4(AFF4)and SleuthKit open source forensics frameworks,respectively,and,accordingly,the proposed framework is evaluated in a forensically sound manner.The evaluation result is compared with other relevant works and,as a result,the proposed solution provides a privacy-preserving,efficient forensic imaging and analysis process while having also sufficient methods.Moreover,the AFF4 forensic image,produced by the SIM module,can be analyzed not only by SAM,but also by other well-known analysis tools available on the market.

Privacy Preserved Brain Disorder Diagnosis Using Federated Learning

Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence(AI)algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy.Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s.Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients.The healthcare industry faces two significant challenges:security and privacy issues and the personalization of cloud-trained AI models.This paper proposes a Deep Neural Network(DNN)based approach embedded in a federated learning framework to detect and diagnose brain disorders.We extracted the data from the database of Kay Elemetrics voice disordered and divided the data into two windows to create training models for two clients,each with different data.To lessen the over-fitting aspect,every client reviewed the outcomes in three rounds.The proposed model identifies brain disorders without jeopardizing privacy and security.The results reveal that the global model achieves an accuracy of 82.82%for detecting brain disorders while preserving privacy.

Preserved amplitude migration based on the one way wave equation in the angle domain

Traditional pre-stack depth migration can only provide subsurface structural information. However, simple structure information is insufficient for petroleum exploration which also needs amplitude information proportional to reflection coefficients. In recent years, pre-stack depth migration algorithms which preserve amplitudes and based on the one- way wave equation have been developed. Using the method in the shot domain requires a deconvolution imaging condition which produces some instability in areas with complicated structure and dramatic lateral variation in velocity. Depth migration with preserved amplitude based on the angle domain can overcome the instability of the one-way wave migration imaging condition with preserved amplitude. It can also offer provide velocity analysis in the angle domain of common imaging point gathers. In this paper, based on the foundation of the one-way wave continuation operator with preserved amplitude, we realized the preserved amplitude prestack depth migration in the angle domain. Models and real data validate the accuracy of the method.

A game-theoretic approach for federated learning:A trade-off among privacy,accuracy and energy

Benefiting from the development of Federated Learning(FL)and distributed communication systems,large-scale intelligent applications become possible.Distributed devices not only provide adequate training data,but also cause privacy leakage and energy consumption.How to optimize the energy consumption in distributed communication systems,while ensuring the privacy of users and model accuracy,has become an urgent challenge.In this paper,we define the FL as a 3-layer architecture including users,agents and server.In order to find a balance among model training accuracy,privacy-preserving effect,and energy consumption,we design the training process of FL as game models.We use an extensive game tree to analyze the key elements that influence the players’decisions in the single game,and then find the incentive mechanism that meet the social norms through the repeated game.The experimental results show that the Nash equilibrium we obtained satisfies the laws of reality,and the proposed incentive mechanism can also promote users to submit high-quality data in FL.Following the multiple rounds of play,the incentive mechanism can help all players find the optimal strategies for energy,privacy,and accuracy of FL in distributed communication systems.

New pharmacological tools:the use of diterpenes to promote adult hippocampal neurogenesis

Tissue regeneration maintains homeostasis and preserves the functional features of each tissue.However,not all tissues show a strong repairing capacity.This is the case of the central nervous system.It is now well established that the generation of new functional neurons from stem cells in the adult brain occurs in specific regions of the brain of different species such as rodents,birds,primates,and humans(Eriksson et al.,1998).

Amplitude preserved VSP reverse time migration for angle-domain CIGs extraction

An improved method of generating angle-domain common-image gathers（ADCIGs） by VSP reverse time migration（RTM） is introduced in this paper.The formula which is used to compute the receiver wavefield for VSP RTM is modified by adding an amplitude correction term in order to conveniently output amplitude-preserved ADCIGs.Compared with the surface seismic data,VSP data contains much richer wavefields.However,the direct and downgoing waves can bring about serious imaging artifacts in ADCIGs,especially the direct wave.The feasibility and validity of this method is demonstrated by both numerical and real VSP data from western China.Thus,the ADCIGs from this method can provide reliable basic data for VSP migration velocity analysis,VSP AVO/AVA analysis,and inversion.

Abnormal uterine bleeding successfully treated via ultrasoundguided microwave ablation of uterine myoma lesions: Three case reports

BACKGROUND Microwave endometrial ablation(MEA)is a minimally invasive treatment method for heavy menstrual bleeding.However,additional treatment is often required after recurrence of uterine myomas treated with MEA.Additionally,because this treatment ablates the endometrium,it is not indicated for patients planning to become pregnant.To overcome these issues,we devised a method for ultrasound-guided microwave ablation of uterine myoma feeder vessels.We report three patients successfully treated for heavy menstrual bleeding,secondary to uterine myoma,using our novel method.CASE SUMMARY All patients had a favorable postoperative course,were discharged within 4 h,and experienced no complications.Further,no postoperative recurrence of heavy menstrual bleeding was noted.Our method also reduced the myoma’s maximum diameter.CONCLUSION This method does not ablate the endometrium,suggesting its potential appli-cation in patients planning to become pregnant.

Glucagon-like peptide-1 receptor agonists as a possible intervention to delay the onset of type 1 diabetes:A new horizon

Type 1 diabetes(T1D)is a chronic autoimmune condition that destroys insulinproducing beta cells in the pancreas,leading to insulin deficiency and hyperglycemia.The management of T1D primarily focuses on exogenous insulin replacement to control blood glucose levels.However,this approach does not address the underlying autoimmune process or prevent the progressive loss of beta cells.Recent research has explored the potential of glucagon-like peptide-1 receptor agonists(GLP-1RAs)as a novel intervention to modify the disease course and delay the onset of T1D.GLP-1RAs are medications initially developed for treating type 2 diabetes.They exert their effects by enhancing glucose-dependent insulin secretion,suppressing glucagon secretion,and slowing gastric emptying.Emerging evidence suggests that GLP-1RAs may also benefit the treatment of newly diagnosed patients with T1D.This article aims to highlight the potential of GLP-1RAs as an intervention to delay the onset of T1D,possibly through their potential immunomodulatory and anti-inflammatory effects and preservation of beta-cells.This article aims to explore the potential of shifting the paradigm of T1D management from reactive insulin replacement to proactive disease modification,which should open new avenues for preventing and treating T1D,improving the quality of life and long-term outcomes for individuals at risk of T1D.

Phosphodiesterase 9 localization in cytoplasm and nucleus:the gateway to selective targeting in neuroprotection?

The umbrella term"neurodege ne rative disorders"(NDDs) refers to several conditions characterized by a progressive loss of structure and function of cells belonging to the nervous system.Such diseases affect more than 50million people worldwide.Neurodegenerative disorders are characterized by sundry factors and pathophysiological mechanisms that a re challenging to be fully profiled.Many of these rely on cell signaling pathways to preserve homeostasis,involving second messengers such as cyclic adenosine monophosphate (cAMP)and cyclic guanosine 3',5'-monophosphate(cGMP).Their ability to control the duration and amplitude of the signaling cascade is given by the presence of several common and uncommon effectors.Protein kinases A and G (PKA and PKG),phosphodiesterases (PDEs),and scaffold proteins are among them.

A Holistic Secure Communication Mechanism Using a Multilayered Cryptographic Protocol to Enhanced Security

In an era characterized by digital pervasiveness and rapidly expanding datasets,ensuring the integrity and reliability of information is paramount.As cyber threats evolve in complexity,traditional cryptographic methods face increasingly sophisticated challenges.This article initiates an exploration into these challenges,focusing on key exchanges(encompassing their variety and subtleties),scalability,and the time metrics associated with various cryptographic processes.We propose a novel cryptographic approach underpinned by theoretical frameworks and practical engineering.Central to this approach is a thorough analysis of the interplay between Confidentiality and Integrity,foundational pillars of information security.Our method employs a phased strategy,beginning with a detailed examination of traditional cryptographic processes,including Elliptic Curve Diffie-Hellman(ECDH)key exchanges.We also delve into encrypt/decrypt paradigms,signature generation modes,and the hashes used for Message Authentication Codes(MACs).Each process is rigorously evaluated for performance and reliability.To gain a comprehensive understanding,a meticulously designed simulation was conducted,revealing the strengths and potential improvement areas of various techniques.Notably,our cryptographic protocol achieved a confidentiality metric of 9.13 in comprehensive simulation runs,marking a significant advancement over existing methods.Furthermore,with integrity metrics at 9.35,the protocol’s resilience is further affirmed.These metrics,derived from stringent testing,underscore the protocol’s efficacy in enhancing data security.

Neural membrane repair at the core of regeneration

Loss of plasma membrane integrity can compromise cell functioning and viability.To countera ct this eminent threat,euka ryotic cells have developed efficient repair mechanisms,which seem to have co-evolved with the emergence of vital membrane processes(Cooper and McNeil,2015).This relationship between basic cellular functioning and membrane repair highlights the fundamental significance of preserving membrane integrity for cellular life.

Blockchain-Enabled Privacy Protection and Access Control Scheme Towards Sensitive Digital Assets Management

With the growth of requirements for data sharing,a novel business model of digital assets trading has emerged that allows data owners to sell their data for monetary gain.In the distributed ledger of blockchain,however,the privacy of stakeholder's identity and the confidentiality of data content are threatened.Therefore,we proposed a blockchainenabled privacy-preserving and access control scheme to address the above problems.First,the multi-channel mechanism is introduced to provide the privacy protection of distributed ledger inside the channel and achieve coarse-grained access control to digital assets.Then,we use multi-authority attribute-based encryption(MAABE)algorithm to build a fine-grained access control model for data trading in a single channel and describe its instantiation in detail.Security analysis shows that the scheme has IND-CPA secure and can provide privacy protection and collusion resistance.Compared with other schemes,our solution has better performance in privacy protection and access control.The evaluation results demonstrate its effectiveness and practicability.

Establishment of microneurovascular units in alginate hydrogel microspheres to reveal the anti-hypoxic effect of salidroside

Microneurovascular units(mNVUs),comprising neurons,micro-glia,and blood-brain barrier(BBB)endothelial cells,are pivotal to the central nervous system and are associated with cerebral hypoxia and brain injuries.Cerebral hypoxia triggers microglial overactivity,causing inflammation,neuronal injury,and disruption of the BBB[1].Salidroside(Sal),a key compound in Tibetan medicine Rhodiola crenulata,mitigates hypoxia-induced metabolic disorders and neuronal damage by preserving mitochondrial function[2].

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.

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.