Amyloid-beta and tau protein beyond Alzheimer's disease

The aggregation of amyloid-beta peptide and tau protein dysregulation are implicated to play key roles in Alzheimer's disease pathogenesis and are considered the main pathological hallmarks of this devastating disease.Physiologically,these two proteins are produced and expressed within the normal human body.However,under pathological conditions,abnormal expression,posttranslational modifications,conformational changes,and truncation can make these proteins prone to aggregation,triggering specific disease-related cascades.Recent studies have indicated associations between aberrant behavior of amyloid-beta and tau proteins and various neurological diseases,such as Alzheimer's disease,Parkinson's disease,and amyotrophic lateral sclerosis,as well as retinal neurodegenerative diseases like Glaucoma and age-related macular degeneration.Additionally,these proteins have been linked to cardiovascular disease,cancer,traumatic brain injury,and diabetes,which are all leading causes of morbidity and mortality.In this comprehensive review,we provide an overview of the connections between amyloid-beta and tau proteins and a spectrum of disorders.

蚕丝基生物材料精准和功能性组装的3D打印策略

In recent years,significant progress has been made in both three-dimensional(3D)printing technologies and the exploration of silk as an ink to produce biocompatible constructs.Combined with the unlimited design potential of 3D printing,silk can be processed into a broad range of functional materials and devices for various biomedical applications.The ability of silk to be processed into various materials,including solutions,hydrogels,particles,microspheres,and fibers,makes it an excellent candidate for adaptation to different 3D printing techniques.This review presents a didactic overview of the 3D printing of silk-based materials,major categories of printing techniques,and their prototyping mechanisms and structural features.In addition,we provide a roadmap for researchers aiming to incorporate silk printing into their own work by summarizing promising strategies from both technical and material aspects,to relate state-of-the-art silk-based material processing with fast-developing 3D printing technologies.Thus,our focus is on elucidating the techniques and strategies that advance the development of precise assembly strategies for silk-based materials.Precise printing(including high printing resolution,complex structure realization,and printing fidelity)is a prerequisite for the digital design capability of 3D printing technology and would definitely broaden the application era of silk,such as complex biomimetic tissue structures,vasculatures,and transdermal microneedles.

Anomalous{1012}tensile twinning and subsequent detwinning in a friction stir processed carbon fiber-reinforced Mg composite

1.Main text Owing to their low density and high specific strength,magnesium alloys and magnesium-based composites have great potential as structure metal materials in applications where lightweight matters[1–4].Deformation twins[5],especially the{1012}tension twins(also called tensile or extension twins)with a low critical resolved shear stress(CRSS)[6],are commonly observed in Mg alloys.They can provide the much-needed deformation along the c-axis in their hcp structure resulting from the very few easily activated slip systems in this crystal structure[7].The tensile twinning activation usually follows the macroscopic Schmid factor law[2],i.e.,the twin variant with the highest Schmid factor occurs,and it only appears when its Schmid factor is positive.

Machine learning for predicting the outcome of terminal ballistics events

Machine learning(ML) is well suited for the prediction of high-complexity,high-dimensional problems such as those encountered in terminal ballistics.We evaluate the performance of four popular ML-based regression models,extreme gradient boosting(XGBoost),artificial neural network(ANN),support vector regression(SVR),and Gaussian process regression(GP),on two common terminal ballistics’ problems:(a)predicting the V50ballistic limit of monolithic metallic armour impacted by small and medium calibre projectiles and fragments,and(b) predicting the depth to which a projectile will penetrate a target of semi-infinite thickness.To achieve this we utilise two datasets,each consisting of approximately 1000samples,collated from public release sources.We demonstrate that all four model types provide similarly excellent agreement when interpolating within the training data and diverge when extrapolating outside this range.Although extrapolation is not advisable for ML-based regression models,for applications such as lethality/survivability analysis,such capability is required.To circumvent this,we implement expert knowledge and physics-based models via enforced monotonicity,as a Gaussian prior mean,and through a modified loss function.The physics-informed models demonstrate improved performance over both classical physics-based models and the basic ML regression models,providing an ability to accurately fit experimental data when it is available and then revert to the physics-based model when not.The resulting models demonstrate high levels of predictive accuracy over a very wide range of projectile types,target materials and thicknesses,and impact conditions significantly more diverse than that achievable from any existing analytical approach.Compared with numerical analysis tools such as finite element solvers the ML models run orders of magnitude faster.We provide some general guidelines throughout for the development,application,and reporting of ML models in terminal ballistics problems.

Enabling Efficient Data Transmission in Wireless Sensor Networks-Based IoT Application

The use of the Internet of Things(IoT)is expanding at an unprecedented scale in many critical applications due to the ability to interconnect and utilize a plethora of wide range of devices.In critical infrastructure domains like oil and gas supply,intelligent transportation,power grids,and autonomous agriculture,it is essential to guarantee the confidentiality,integrity,and authenticity of data collected and exchanged.However,the limited resources coupled with the heterogeneity of IoT devices make it inefficient or sometimes infeasible to achieve secure data transmission using traditional cryptographic techniques.Consequently,designing a lightweight secure data transmission scheme is becoming essential.In this article,we propose lightweight secure data transmission(LSDT)scheme for IoT environments.LSDT consists of three phases and utilizes an effective combination of symmetric keys and the Elliptic Curve Menezes-Qu-Vanstone asymmetric key agreement protocol.We design the simulation environment and experiments to evaluate the performance of the LSDT scheme in terms of communication and computation costs.Security and performance analysis indicates that the LSDT scheme is secure,suitable for IoT applications,and performs better in comparison to other related security schemes.

Comprehensive insights into recent innovations:Magnesium-inclusive high-entropy alloys

This review focuses on thermodynamic and physical parameters,synthesis methods,and reported phases of Magnesium(Mg)containing high-entropy alloys(HEAs).Statistical data of publications concerning Mg-containing HEAs were collected and analyzed.Data on the chemical elements included in Mg-containing HEAs,their theoretical end experimental densities,thermodynamic parameters,physical parameters,fabricated techniques and reported phases were also collected and discussed.On the basis of this information,a new classification for HEAs was proposed.It is also shown that the existing thermodynamic parameters cannot accurately predict the formation of a single phase solid solution for Mg-containing HEAs.The physical parameters of Mg-containing HEAs are within a wide range,and most of the synthesized Mg-containing HEAs have a complex multiphase structure.

Prediction of corrosion rate for friction stir processed WE43 alloy by combining PSO-based virtual sample generation and machine learning

The corrosion rate is a crucial factor that impacts the longevity of materials in different applications.After undergoing friction stir processing(FSP),the refined grain structure leads to a notable decrease in corrosion rate.However,a better understanding of the correlation between the FSP process parameters and the corrosion rate is still lacking.The current study used machine learning to establish the relationship between the corrosion rate and FSP process parameters(rotational speed,traverse speed,and shoulder diameter)for WE43 alloy.The Taguchi L27 design of experiments was used for the experimental analysis.In addition,synthetic data was generated using particle swarm optimization for virtual sample generation(VSG).The application of VSG has led to an increase in the prediction accuracy of machine learning models.A sensitivity analysis was performed using Shapley Additive Explanations to determine the key factors affecting the corrosion rate.The shoulder diameter had a significant impact in comparison to the traverse speed.A graphical user interface(GUI)has been created to predict the corrosion rate using the identified factors.This study focuses on the WE43 alloy,but its findings can also be used to predict the corrosion rate of other magnesium alloys.

Guest Editorial:Special issue on trustworthy machine learning for behavioural and social computing

Machine learning has been extensively applied in behavioural and social computing,encompassing a spectrum of applications such as social network analysis,click stream analysis,recommendation of points of interest,and sentiment analysis.The datasets pertinent to these applications are inherently linked to human behaviour and societal dynamics,posing a risk of disclosing personal or sensitive information if mishandled or subjected to attacks.

Recent Advances in Nanoengineering of Electrode-Electrolyte Interfaces to Realize High-Performance Li-Ion Batteries

A suitable interface between the electrode and electrolyte is crucial in achieving highly stable electrochemical performance for Li-ion batteries,as facile ionic transport is required.Intriguing research and development have recently been conducted to form a stable interface between the electrode and electrolyte.Therefore,it is essential to investigate emerging knowledge and contextualize it.The nanoengineering of the electrode-electrolyte interface has been actively researched at the electrode/electrolyte and interphase levels.This review presents and summarizes some recent advances aimed at nanoengineering approaches to build a more stable electrode-electrolyte interface and assess the impact of each approach adopted.Furthermore,future perspectives on the feasibility and practicality of each approach will also be reviewed in detail.Finally,this review aids in projecting a more sustainable research pathway for a nanoengineered interphase design between electrode and electrolyte,which is pivotal for high-performance,thermally stable Li-ion batteries.

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.

Adaptive optimisation of explosive reactive armour for protection against kinetic energy and shaped charge threats

We evaluate an adaptive optimisation methodology,Bayesian optimisation(BO),for designing a minimum weight explosive reactive armour(ERA)for protection against a surrogate medium calibre kinetic energy(KE)long rod projectile and surrogate shaped charge(SC)warhead.We perform the optimisation using a conventional BO methodology and compare it with a conventional trial-and-error approach from a human expert.A third approach,utilising a novel human-machine teaming framework for BO is also evaluated.Data for the optimisation is generated using numerical simulations that are demonstrated to provide reasonable qualitative agreement with reference experiments.The human-machine teaming methodology is shown to identify the optimum ERA design in the fewest number of evaluations,outperforming both the stand-alone human and stand-alone BO methodologies.From a design space of almost 1800 configurations the human-machine teaming approach identifies the minimum weight ERA design in 10 samples.

GAN‐MD:A myocarditis detection using multi‐channel convolutional neural networks and generative adversarial network‐based data augmentation

Myocarditis is a significant public health concern because of its potential to cause heart failure and sudden death.The standard invasive diagnostic method,endomyocardial bi-opsy,is typically reserved for cases with severe complications,limiting its widespread use.Conversely,non‐invasive cardiac magnetic resonance(CMR)imaging presents a promising alternative for detecting and monitoring myocarditis,because of its high signal contrast that reveals myocardial involvement.To assist medical professionals via artificial intelligence,the authors introduce generative adversarial networks‐multi discriminator(GAN‐MD),a deep learning model that uses binary classification to diagnose myocarditis from CMR images.Their approach employs a series of convolutional neural networks(CNNs)that extract and combine feature vectors for accurate diagnosis.The authors suggest a novel technique for improving the classification precision of CNNs.Using generative adversarial networks(GANs)to create synthetic images for data augmentation,the authors address challenges such as mode collapse and unstable training.Incorporating a reconstruction loss into the GAN loss function requires the generator to produce images reflecting the discriminator features,thus enhancing the generated images'quality to more accurately replicate authentic data patterns.Moreover,combining this loss function with other reg-ularisation methods,such as gradient penalty,has proven to further improve the perfor-mance of diverse GAN models.A significant challenge in myocarditis diagnosis is the imbalance of classification,where one class dominates over the other.To mitigate this,the authors introduce a focal loss‐based training method that effectively trains the model on the minority class samples.The GAN‐MD approach,evaluated on the Z‐Alizadeh Sani myocarditis dataset,achieves superior results(F‐measure 86.2%;geometric mean 91.0%)compared with other deep learning models and traditional machine learning methods.

Preparation of single atom catalysts for high sensitive gas sensing

Single atom catalysts(SACs)have garnered significant attention in the field of catalysis over the past decade due to their exceptional atom utilization efficiency and distinct physical and chemical properties.For the semiconductor-based electrical gas sensor,the core is the catalysis process of target gas molecules on the sensitive materials.In this context,the SACs offer great potential for highly sensitive and selective gas sensing,however,only some of the bubbles come to the surface.To facilitate practical applications,we present a comprehensive review of the preparation strategies for SACs,with a focus on overcoming the challenges of aggregation and low loading.Extensive research efforts have been devoted to investigating the gas sensing mechanism,exploring sensitive materials,optimizing device structures,and refining signal post-processing techniques.Finally,the challenges and future perspectives on the SACs based gas sensing are presented.

Understanding rhabdomyolysis induced acute kidney injury in patients with COVID-19

This work comments on an article published in the recent issue of the World Journal of Virology.Rhabdomyolysis is a complex condition with symptoms such as myalgia,changes to urination,and weakness.With the potential for substantial kidney impairment,it has also been shown to be a severe complication of coronavirus disease 2019(COVID-19).To date,various theoretical explanations exist for the development of rhabdomyolysis induced acute kidney injury(RIAKI)in COVID-19 infection,including the accumulation of released striated muscle myoglobin in the urine(myoglobinuria).In their article,they(2024)demonstrate in a retrospective study that RIAKI in COVID-19 patients tended to have elevated levels of C-reactive protein,ferritin,and procalcitonin.These patients also had poorer overall prognoses when compared to COVID-19 patients who have acute kidney injury(AKI)due to other causes.It is clear from these findings that clinicians must closely monitor and assess for the presence of rhabdomyolysis in COVID-19 patients who have developed AKIs.Moreover,additional research is required to further understand the mechanisms behind the development of RIAKI in COVID-19 patients in order to better inform treatment guidelines and protocols.

Finding biomarkers of experience in animals

At a time when there is a growing public interest in animal welfare,it is critical to have objective means to assess the way that an animal experiences a situation.Objectivity is critical to ensure appropriate animal welfare outcomes.Existing behavioural,physiological,and neurobiological indicators that are used to assess animal welfare can verify the absence of extremely negative outcomes.But welfare is more than an absence of negative outcomes and an appropriate indicator should reflect the full spectrum of experience of an animal,from negative to positive.In this review,we draw from the knowledge of human biomedical science to propose a list of candidate biological markers(biomarkers)that should reflect the experiential state of non-human animals.The proposed biomarkers can be classified on their main function as endocrine,oxidative stress,non-coding molecular,and thermobiological markers.We also discuss practical challenges that must be addressed before any of these biomarkers can become useful to assess the experience of an animal in real-life.

Epidemiology and Clonal Spread Evidence of Carbapenem-Resistant Organisms in the Center of Care and Protection of Orphaned Children, Vietnam

Objective: To determine the prevalence of colonization and transmission of carbapenem-resistant Gram-negative organisms in order to develop of an effective infection prevention program. Design: Cross-sectional study with carbapenem-resistant organisms (CRO) colonization detection from the fecal specimens of 20 Health Care Workers (HCWs) and 67 residents and 175 random environment specimens from September 2022 to September 2023. Setting: A Care and Protection Centre of Orphaned Children in South of HCM City. Participants: It included 20 HCWs, 67 residents, and 175 randomly collected environmental specimens. Method: Rectal and environmental swabs were collected from 20 HCWs, 67 residents (most of them were children), and 175 environmental specimens. MELAB Chromogenic CARBA agar plates, Card NID, and NMIC-500 CPO of the BD Phoenix TM Automated Microbiology System and whole genome sequencing (WGS) were the tests to screen, confirm CROs, respectively and determine CRO colonization and transmission between HCWs, residents, and the environment. Result: We detected 36 CRO isolates, including 6, 11 and 19 CROs found in 6 HCWs, 10 residents and 19 environments. The prevalence of detectable CRO was 30% (6/20) in HCWs, 14.92% (10/67) in residents, and 10.86% (19/175) in environmental swabs in our study. WGS demonstrated CRO colonization and transmission with the clonal spread of E. coli and A. nosocomialis, among HCWs and residents (children). Conclusion: Significant CRO colonization and transmission was evident in HCWs, residents, and the center environment. Cleaning and disinfection of the environment and performing regular hand hygiene are priorities to reduce the risk of CRO colonization and transmission.

知识图谱赋能的知识工程:理论、技术与系统专题序言

知识图谱是人工智能在知识工程理论和技术发展中的前沿。知识图谱方法、技术与应用在新一代人工智能由“感知智能”迈向“认知智能”的过程中扮演重要角色。近年来,随着大规模知识图谱的发布和知识图谱赋能系统的应用,国内外学术界和产业界均在多个维度对知识图谱赋能的知识工程进行了研究与开发。虽然国内外学者在知识图谱及相关方向上已取得若干研究成果,但知识图谱赋能的知识工程尚未形成成熟的理论体系、技术方法、应用与系统实践,仍有众多有待解决的具有挑战性的难题。本专题旨在促进知识图谱赋能的知识工程研究、开发与应用,及时、集中、全面地报道知识图谱赋能的知识工程在理论、方法、技术、系统与应用实践等方面的最新成果和进展。

Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes

The fast proliferation of edge devices for the Internet of Things(IoT)has led to massive volumes of data explosion.The generated data is collected and shared using edge-based IoT structures at a considerably high frequency.Thus,the data-sharing privacy exposure issue is increasingly intimidating when IoT devices make malicious requests for filching sensitive information from a cloud storage system through edge nodes.To address the identified issue,we present evolutionary privacy preservation learning strategies for an edge computing-based IoT data sharing scheme.In particular,we introduce evolutionary game theory and construct a payoff matrix to symbolize intercommunication between IoT devices and edge nodes,where IoT devices and edge nodes are two parties of the game.IoT devices may make malicious requests to achieve their goals of stealing privacy.Accordingly,edge nodes should deny malicious IoT device requests to prevent IoT data from being disclosed.They dynamically adjust their own strategies according to the opponent's strategy and finally maximize the payoffs.Built upon a developed application framework to illustrate the concrete data sharing architecture,a novel algorithm is proposed that can derive the optimal evolutionary learning strategy.Furthermore,we numerically simulate evolutionarily stable strategies,and the final results experimentally verify the correctness of the IoT data sharing privacy preservation scheme.Therefore,the proposed model can effectively defeat malicious invasion and protect sensitive information from leaking when IoT data is shared.

Microstructures, mechanical properties, corrosion, and biocompatibility of extruded Mg-Zr-Sr-Ho alloys for biodegradable implant applications

In this study,the microstructures,mechanical properties,corrosion behaviors,and biocompatibility of extruded magnesium-zirconiumstrontium-holmium(Mg-Zr-Sr-Ho)alloys were comprehensively investigated.The effect of different concentrations of Ho on the microstructural characteristics,tensile and compressive properties,corrosion resistance,and biocompatibility were investigated.The microstructures of the extruded Mg-1Zr-0.5Sr-xHo(x=0.5,1.5,and 4 wt.%)alloys consisted ofα-Mg matrix,fineα-Zr particles,and intermetallic phase particles of Mg_(17)Sr_(2) and Ho_(2)Mg mainly distributed at the grain boundaries.Extensive{1012}tensile twins were observed in the partially recrystallized samples of Mg-1Zr-0.5Sr-0.5Ho and Mg-1Zr-0.5Sr-1.5Ho.Further addition of Ho to 4 wt.%resulted in a complete recrystallization due to activation of the particle stimulated nucleation around the Mg_(17)Sr_(2) particles.The evolution of a rare earth(RE)texture was observed with the Ho addition,which resulted in the weakened basal and prismatic textures.Furthermore,a drastic increase of 200%in tensile elongation and 89%in compressive strain was observed with Ho addition increased from 0.5 to 4 wt%,respectively.The tension-compression yield asymmetry was significantly decreased from 0.62 for Mg-1Zr-0.5Sr-0.5Ho to 0.98 for Mg-1Zr-0.5Sr-4Ho due to the weakening of textures.Corrosion analysis of the extruded Mg-Zr-Sr-Ho alloys revealed the presence of pitting corrosion.A minimum corrosion rate of 4.98 mm y^(−1) was observed in Mg-1Zr-0.5Sr-0.5Ho alloy.The enhanced corrosion resistance is observed due to the presence of Ho_(2)O_(3) in the surface film which reduced galvanic effect.The formation of a stabilized surface film due to the Ho_(2)O_(3) was confirmed through the electrical impedance spectroscopy and XPS analysis.An in vitro cytotoxicity assessment revealed good biocompatibility and cell adhesion in relation to SaOS2 cells.

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.