阿尔茨海默病居家筛查专家共识

老年人痴呆或认知障碍多由一种以上年龄相关的常见脑部疾病所致。阿尔茨海默病(Alzheimer's disease,AD)是其中最常见的神经变性疾病,且是全球前10位死因中唯一无法治愈或缺乏长期对症疗效的疾病,给个人、家庭和全球经济都带来了巨大的负担。早期及时发现和干预是对抗AD的最佳策略。在过去的30年中,许多研究都提出了降低痴呆风险的方法,2020年《柳叶刀》杂志的痴呆预防报告已阐明通过应对风险因素可以预防或延缓超过40%的痴呆。然而,目前全球医疗体系尚未具备早期或及时发现AD的足够能力。最近的一项研究发现,只有不到10%的轻度认知障碍(mild cognitive impairment,MCI)是在初级医疗机构中诊断出来的。近来,抗淀粉样蛋白β(Amyloid beta,Aβ)抗体药物lecanemab和donanemab被批准上市用于早期AD治疗,以及30年的随访研究证明改善风险因素显著减少AD痴呆的发病率并延长了寿命,使得人们对AD早期识别的关注迅速增加。阿尔茨海默病防治协会(China Association for Alzheimer's Disease,CAAD)认识到居家早期和及时发现AD的重要性,并成立了一个由协会成员、临床医生和研究人员组成的全球AD多领域专家团队,就以下目标达成共识:①为个人、家庭、社区、协会和组织提供专家指导意见;②介绍用于认知障碍和痴呆居家筛查的数字工具和可用资源,并为AD高危人群或疑似患者制定下一步应对策略;③讨论现有可用或将来可能的居家筛查适宜AD生物标志物;④为未来的改进和全球应用建立可行性框架。专家组对于当前可用的证据、工具和资源进行综述,并进一步考量其在AD居家筛查中的价值。

Fiber optic monitoring of an anti-slide pile in a retrogressive landslide

Anti-slide piles are one of the most important reinforcement structures against landslides,and evalu-ating the working conditions is of great significance for landslide mitigation.The widely adopted analytical methods of pile internal forces include cantilever beam method and elastic foundation beam method.However,due to many assumptions involved in calculation,the analytical models cannot be fully applicable to complex site situations,e.g.landslides with multi-sliding surfaces and pile-soil interface separation as discussed herein.In view of this,the combination of distributed fiber optic sensing(DFOS)and strain-internal force conversion methods was proposed to evaluate the working conditions of an anti-sliding pile in a typical retrogressive landslide in the Three Gorges reservoir area,China.Brillouin optical time domain reflectometry(BOTDR)was utilized to monitor the strain distri-bution along the pile.Next,by analyzing the relative deformation between the pile and its adjacent inclinometer,the pile-soil interface separation was profiled.Finally,the internal forces of the anti-slide pile were derived based on the strain-internal force conversion method.According to the ratio of calculated internal forces to the design values,the working conditions of the anti-slide pile could be evaluated.The results demonstrated that the proposed method could reveal the deformation pattern of the anti-slide pile system,and can quantitatively evaluate its working conditions.

Clinical associations of corneal neuromas with ocular surface diseases

Corneal neuromas,also termed microneuromas,refer to microscopic,irregula rly-shaped enlargements of terminal subbasal nerve endings at sites of nerve damage or injury.The formation of corneal neuromas results from damage to corneal nerves,such as following corneal pathology or corneal or intraocular surge ries.Initially,denervated areas of sensory nerve fibers become invaded by sprouts of intact sensory nerve fibers,and later injured axons regenerate and new sprouts called neuromas develop.In recent years,analysis of corneal nerve abnormalities including corneal neuromas which can be identified using in vivo confocal microscopy,a non-invasive imaging technique with microscopic resolution,has been used to evaluate corneal neuropathy and ocular surface dysfunction.Corneal neuromas have been shown to be associated with clinical symptoms of discomfort and dryness of eyes,and are a promising surrogate biomarker for ocular surface diseases,such as neuropathic corneal pain,dry eye disease,diabetic corneal neuropathy,neurotrophic keratopathy,Sjogren's syndrome,bullous keratopathy,post-refra ctive surgery,and others.In this review,we have summarized the current literature on the association between these ocular surface diseases and the presentation of corneal microneuromas,as well as elaborated on their pathogenesis,visualization via in vivo confocal microscopy,and utility in monitoring treatment efficacy.As current quantitative analysis on neuromas mainly relies on manual annotation and quantification,which is user-dependent and labor-intensive,future direction includes the development of artificial intelligence software to identify and quantify these potential imaging biomarkers in a more automated and sensitive manner,allowing it to be applied in clinical settings more efficiently.Combining imaging and molecular biomarkers may also help elucidate the associations between corneal neuromas and ocular surface diseases.

Autolysosomal acidification impairment as a mediator for TNFR1 induced neuronal necroptosis in Alzheimer’s disease

Neuronal necroptosis-an emerging form of regulated cell death associated with neuroinflammatory signaling:Alzheimer’s disease(AD)is characterized by the presence of extracellular amyloid-β(Aβ)plaques and intracellular tau neurofibrillary tangles as well as progressive neuronal loss.Recent evidence has suggested that prolonged neuroinflammation with increased levels of cytokines,arising from neuronal injury,innate immune responses from glial cells,and peripheral inflammation,leads to neuronal death and AD progression.

Design of Protograph LDPC-Coded MIMO-VLC Systems with Generalized Spatial Modulation

This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID scheme, the information bits conveyed by the signal-domain(SiD) symbols and the spatial-domain(SpD) light emitting diode(LED)-index patterns are coded by a protograph low-density parity-check(P-LDPC) code. Specifically, we propose a signal-domain symbol expanding and re-allocating(SSER) method for constructing a type of novel generalized spatial modulation(GSM) constellations, referred to as SSERGSM constellations, so as to boost the performance of the BICGSM-ID MIMO-VLC systems.Moreover, by applying a modified PEXIT(MPEXIT) algorithm, we further design a family of rate-compatible P-LDPC codes, referred to as enhanced accumulate-repeat-accumulate(EARA) codes,which possess both excellent decoding thresholds and linear-minimum-distance-growth property. Both analysis and simulation results illustrate that the proposed SSERGSM constellations and P-LDPC codes can remarkably improve the convergence and decoding performance of MIMO-VLC systems. Therefore, the proposed P-LDPC-coded SSERGSM-mapped BICGSMID configuration is envisioned as a promising transmission solution to satisfy the high-throughput requirement of MIMO-VLC applications.

Investigating the impact of dynamic structural changes of Au/rutile catalysts on the catalytic activity of CO oxidation

The surface properties of oxidic supports and their interaction with the supported metals play critical roles in governing the catalytic activities of oxide‐supported metal catalysts.When metals are supported on reducible oxides,dynamic surface reconstruction phenomena,including strong metal–support interaction(SMSI)and oxygen vacancy formation,complicate the determination of the structural–functional relationship at the active sites.Here,we performed a systematic investigation of the dynamic behavior of Au nanocatalysts supported on flame‐synthesized TiO_(2),which takes predominantly a rutile phase,using CO oxidation above room temperature as a probe reaction.Our analysis conclusively elucidated a negative correlation between the catalytic activity of Au/TiO_(2) and the oxygen vacancy at the Au/TiO_(2) interface.Although the reversible formation and retracting of SMSI overlayers have been ubiquitously observed on Au/TiO_(2) samples,the catalytic consequence of SMSI remains inconclusive.Density functional theory suggests that the electron transfer from TiO_(2) to Au is correlated to the presence of the interfacial oxygen vacancies,retarding the catalytic activation of CO oxidation.

Preparation of MXene-based hybrids and their application in neuromorphic devices

The traditional von Neumann computing architecture has relatively-low information processing speed and high power consumption,making it difficult to meet the computing needs of artificial intelligence(AI).Neuromorphic computing systems,with massively parallel computing capability and low power consumption,have been considered as an ideal option for data storage and AI computing in the future.Memristor,as the fourth basic electronic component besides resistance,capacitance and inductance,is one of the most competitive candidates for neuromorphic computing systems benefiting from the simple structure,continuously adjustable conductivity state,ultra-low power consumption,high switching speed and compatibility with existing CMOS technology.The memristors with applying MXene-based hybrids have attracted significant attention in recent years.Here,we introduce the latest progress in the synthesis of MXene-based hybrids and summarize their potential applications in memristor devices and neuromorphological intelligence.We explore the development trend of memristors constructed by combining MXenes with other functional materials and emphatically discuss the potential mechanism of MXenes-based memristor devices.Finally,the future prospects and directions of MXene-based memristors are briefly described.

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Direct recycling of Li-ion batteries from cell to pack level:Challenges and prospects on technology,scalability,sustainability,and economics

Direct recycling is a novel approach to overcoming the drawbacks of conventional lithium-ion battery(LIB)recycling processes and has gained considerable attention from the academic and industrial sectors in recent years.The primary objective of directly recycling LIBs is to efficiently recover and restore the active electrode materials and other components in the solid phase while retaining electrochemical performance.This technology's advantages over traditional pyrometallurgy and hydrometallurgy are costeffectiveness,energy efficiency,and sustainability,and it preserves the material structure and morphology and can shorten the overall recycling path.This review extensively discusses the advancements in the direct recycling of LIBs,including battery sorting,pretreatment processes,separation of cathode and anode materials,and regeneration and quality enhancement of electrode materials.It encompasses various approaches to successfully regenerate high-value electrode materials and streamlining the recovery process without compromising their electrochemical properties.Furthermore,we highlight key challenges in direct recycling when scaled from lab to industries in four perspectives:(1)battery design,(2)disassembling,(3)electrode delamination,and(4)commercialization and sustainability.Based on these challenges and changing market trends,a few strategies are discussed to aid direct recycling efforts,such as binders,electrolyte selection,and alternative battery designs;and recent transitions and technological advancements in the battery industry are presented.

In vitro investigations on the effects of graphene and graphene oxide on polycaprolactone bone tissue engineering scaffolds

Polycaprolactone(PCL)scaffolds that are produced through additive manufacturing are one of the most researched bone tissue engineering structures in the field.Due to the intrinsic limitations of PCL,carbon nanomaterials are often investigated to reinforce the PCL scaffolds.Despite several studies that have been conducted on carbon nanomaterials,such as graphene(G)and graphene oxide(GO),certain challenges remain in terms of the precise design of the biological and nonbiological properties of the scaffolds.This paper addresses this limitation by investigating both the nonbiological(element composition,surface,degradation,and thermal and mechanical properties)and biological characteristics of carbon nanomaterial-reinforced PCL scaffolds for bone tissue engineering applications.Results showed that the incorporation of G and GO increased surface properties(reduced modulus and wettability),material crystallinity,crystallization temperature,and degradation rate.However,the variations in compressive modulus,strength,surface hardness,and cell metabolic activity strongly depended on the type of reinforcement.Finally,a series of phenomenological models were developed based on experimental results to describe the variations of scaffold’s weight,fiber diameter,porosity,and mechanical properties as functions of degradation time and carbon nanomaterial concentrations.The results presented in this paper enable the design of three-dimensional(3D)bone scaffolds with tuned properties by adjusting the type and concentration of different functional fillers.

Jetting-based bioprinting:process,dispense physics,and applications

Jetting-based bioprinting facilitates contactless drop-on-demand deposition of subnanoliter droplets at well-defined positions to control the spatial arrangement of cells,growth factors,drugs,and biomaterials in a highly automated layer-by-layer fabrication approach.Due to its immense versatility,jetting-based bioprinting has been used for various applications,including tissue engineering and regenerative medicine,wound healing,and drug development.A lack of in-depth understanding exists in the processes that occur during jetting-based bioprinting.This review paper will comprehensively discuss the physical considerations for bioinks and printing conditions used in jetting-based bioprinting.We first present an overview of different jetting-based bioprinting techniques such as inkjet bioprinting,laser-induced forward transfer bioprinting,electrohydrodynamic jet bioprinting,acoustic bioprinting and microvalve bioprinting.Next,we provide an in-depth discussion of various considerations for bioink formulation relating to cell deposition,print chamber design,droplet formation and droplet impact.Finally,we highlight recent accomplishments in jetting-based bioprinting.We present the advantages and challenges of each method,discuss considerations relating to cell viability and protein stability,and conclude by providing insights into future directions of jetting-based bioprinting.

Automation 5.0: The Key to Systems Intelligence and Industry 5.0

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with machinery.The rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:L.Vlacic,H.Huang,M.Dotoli,Y.Wang,P.Ioanno,L.Fan,X.Wang,R.Carli,C.Lv,L.Li,X.Na,Q.-L.Han,and F.-Y.Wang,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA J.Autom.Sinica,vol.11,no.8,pp.1723-1727,Aug.2024.

Process,Material,and Regulatory Considerations for 3D Printed Medical Devices and Tissue Constructs

Three-dimensional(3D)printing is a highly automated platform that facilitates material deposition in a layer-by-layer approach to fabricate pre-defined 3D complex structures on demand.It is a highly promising technique for the fabrication of personalized medical devices or even patient-specific tissue constructs.Each type of 3D printing technique has its unique advantages and limitations,and the selection of a suitable 3D printing technique is highly dependent on its intended application.In this review paper,we present and highlight some of the critical processes(printing parameters,build orientation,build location,and support structures),material(batch-to-batch consistency,recycling,protein adsorption,biocompatibility,and degradation properties),and regulatory considerations(sterility and mechanical properties)for 3D printing of personalized medical devices.The goal of this review paper is to provide the readers with a good understanding of the various key considerations(process,material,and regulatory)in 3D printing,which are critical for the fabrication of improved patient-specific 3D printed medical devices and tissue constructs.

In-situ AFM and quasi-in-situ studies for localized corrosion in Mg-9Al-1Fe-(Gd) alloys under 3.5 wt.% NaCl environment

Revealing the localized corrosion process of Mg alloy is considered as one of the most significant ways for improving its corrosion resistance.The reliable monitor should be high distinguishability and real-time in liquid environment.Herein,Mg-9Al-1Fe and Mg-9Al-1Fe-1Gd alloys were designed to highlight the impact of intermetallic on the corrosion behaviour.In-situ AFM with a special electrolyte circulation system and quasi-in-situ SEM observation were used to monitor the corrosion process of the designed alloys.SEM-EDS and TEM-SAED were applied to identify the intermetallic in the designed alloys,and their volta potentials were measured by SKPFM.According to the real-time and real-space in-situ AFM monitor,the corrosion process consisted of dissolution of anodicα-Mg phase,accumulation of corrosion products around cathodic phase and shedding of some fine cathodic phase.Then,the localized corrosion process of Mg alloy was revealed combined with the results of the monitor of corrosion process and Volta potential difference.

Automatic Miscalibration Detection and Correction of LiDAR and Camera Using Motion Cues

This paper aims to develop an automatic miscalibration detection and correction framework to maintain accurate calibration of LiDAR and camera for autonomous vehicle after the sensor drift.First,a monitoring algorithm that can continuously detect the miscalibration in each frame is designed,leveraging the rotational motion each individual sensor observes.Then,as sensor drift occurs,the projection constraints between visual feature points and LiDAR 3-D points are used to compute the scaled camera motion,which is further utilized to align the drifted LiDAR scan with the camera image.Finally,the proposed method is sufficiently compared with two representative approaches in the online experiments with varying levels of random drift,then the method is further extended to the offline calibration experiment and is demonstrated by a comparison with two existing benchmark methods.

Effect of process parameters on microstructure and mechanical properties of a nickel-aluminum-bronze alloy fabricated by laser powder bed fusion

This work investigated the effect of process parameters on densification,microstructure,and mechanical properties of a nickel-aluminum-bronze(NAB)alloy fabricated by laser powder bed fusion(LPBF)additive manufacturing.The LPBF-printed NAB alloy samples with relative densities of over 98.5%were obtained under the volumetric energy density range of 200−250 J/mm^(3).The microstructure of the NAB alloy printed in both horizontal and vertical planes primarily consisted ofβ'martensitic phase and bandedαphase.In particular,a coarser-columnar grain structure and stronger crystallographic texture were achieved in the vertical plane,where the maximum texture intensity was 30.56 times greater than that of random textures at the(100)plane.Increasing the volumetric energy density resulted in a decrease in the columnar grain size,while increasing the amount ofαphase.Notably,β_(1)'martensitic structures with nanotwins and nanoscaleκ-phase precipitates were identified in the microstructure of LPBF-printed NAB samples with a volumetric energy density of 250 J/mm^(3).Furthermore,under optimal process parameters with a laser power of 350 W and scanning speed of 800 mm/s,significant improvements were observed in the microhardness(HV 386)and ultimate tensile strength(671 MPa),which was attributed to an increase in refined acicular martensite.

Role of metabolic dysfunction and inflammation along the liver-brain axis in animal models with obesity-induced neurodegeneration

The interaction between metabolic dysfunction and inflammation is central to the development of neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease.Obesity-related conditions like type 2 diabetes and non-alcoholic fatty liver disease exacerbate this relationship.Peripheral lipid accumulation,particularly in the liver,initiates a cascade of inflammatory processes that extend to the brain,influencing critical metabolic regulatory regions.Ceramide and palmitate,key lipid components,along with lipid transporters lipocalin-2 and apolipoprotein E,contribute to neuroinflammation by disrupting blood–brain barrier integrity and promoting gliosis.Peripheral insulin resistance further exacerbates brain insulin resistance and neuroinflammation.Preclinical interventions targeting peripheral lipid metabolism and insulin signaling pathways have shown promise in reducing neuroinflammation in animal models.However,translating these findings to clinical practice requires further investigation into human subjects.In conclusion,metabolic dysfunction,peripheral inflammation,and insulin resistance are integral to neuroinflammation and neurodegeneration.Understanding these complex mechanisms holds potential for identifying novel therapeutic targets and improving outcomes for neurodegenerative diseases.

Nonlinear Absorption and Low-threshold Two-photon Pumped Amplified Stimulated Emission from FAPbBr_(3) Nanocrystals

Formamidinium lead bromide(FAPbBr_(3))nanocrystals(NCs)have been considered to be a good optoelectronic material due to their pure green emission,excellent stability and superior carrier transport characteristics.However,two-photon pumped amplified spontaneous emission(ASE)and the corresponding nonlinear optical properties of FAPbBr_(3) NCs are scarcely revealed.Herein,we synthesized colloidal FAPbBr_(3) NCs with different sizes by changing the molar ratio of FABr/PbBr_(2) in the precursor solution,using ligand assisted precipitation(LARP)technology at room temperature.Photoluminescence(PL)and time resolved photoluminescence(TRPL)spectroscopy were measured to characterize their ASE properties.And their nonlinear optical properties were studied through the Zscan technique and the two-photon excited fluorescence method.The stimulated emission properties including oneand two-photon pumped ASE have been realized from FAPbBr_(3) NCs.With large two-photon absorption coefficient(0.27 cm/GW)and high non-linear absorption cross-section(7.52×10^(5) GM),ASE with threshold as low as 9.8μJ/cm^(2) and 487μJ/cm^(2) have been obtained from colloidal FAPbBr_(3) NCs using one-and two-photon excitations.These results indicate that as a new possible green-emitting frequency-upconversion material with low thresholds,FAPbBr_(3) NCs hold great potential in the development of high-performance two-photon pump lasers.

Advancements in machine learning for material design and process optimization in the field of additive manufacturing

Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.