Aminoglycosides are a widely used class of antibacterials renowned for their effectiveness and broad antimicrobial spectrum.However,their use leads to irreversible hearing damage by causing apoptosis of hair cells as ...Aminoglycosides are a widely used class of antibacterials renowned for their effectiveness and broad antimicrobial spectrum.However,their use leads to irreversible hearing damage by causing apoptosis of hair cells as their direct target.In addition,the hearing damage caused by aminoglycosides involves damage of spiral ganglion neurons upon exposure.To investigate the mechanisms underlying spiral ganglion neuron degeneration induced by aminoglycosides,we used a C57BL/6J mouse model treated with kanamycin.We found that the mice exhibited auditory deficits following the acute loss of outer hair cells.Spiral ganglion neurons displayed hallmarks of pyroptosis and exhibited progressive degeneration over time.Transcriptomic profiling of these neurons showed significant upregulation of genes associated with inflammation and immune response,particularly those related to the NLRP3 inflammasome.Activation of the canonical pyroptotic pathway in spiral ganglion neurons was observed,accompanied by infiltration of macrophages and the release of proinflammatory cytokines.Pharmacological intervention targeting NLRP3 using Mcc950 and genetic intervention using NLRP3 knockout ameliorated spiral ganglion neuron degeneration in the injury model.These findings suggest that NLRP3 inflammasome-mediated pyroptosis plays a role in aminoglycoside-induced spiral ganglion neuron degeneration.Inhibition of this pathway may offer a potential therapeutic strategy for treating sensorineural hearing loss by reducing spiral ganglion neuron degeneration.展开更多
Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications.In this work,an optoelectronic memristor with Au/a-C:Te/Pt structure is developed.Synaptic ...Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications.In this work,an optoelectronic memristor with Au/a-C:Te/Pt structure is developed.Synaptic functions,i.e.,excita-tory post-synaptic current and pair-pulse facilitation are successfully mimicked with the memristor under electrical and optical stimulations.More importantly,the device exhibited distinguishable response currents by adjusting 4-bit input electrical/opti-cal signals.A multi-mode reservoir computing(RC)system is constructed with the optoelectronic memristors to emulate human tactile-visual fusion recognition and an accuracy of 98.7%is achieved.The optoelectronic memristor provides potential for developing multi-mode RC system.展开更多
Dear Editor,Growing clinical evidence shows that brain disorders are heterogeneous in phenotype,genetics,and neuropathology[1].Diagnosis and treatment tend to be affected by symptom presentation and the heterogeneity ...Dear Editor,Growing clinical evidence shows that brain disorders are heterogeneous in phenotype,genetics,and neuropathology[1].Diagnosis and treatment tend to be affected by symptom presentation and the heterogeneity of pathology,potentially hindering clinical trials in the development of medical treatment.Brain-based subtyping studies utilize magnetic resonance imaging(MRI)and data-driven methods to discover the subtypes of diseases,providing a new perspective on disease heterogeneity.展开更多
Ni-based superalloys play a critical role in the aerospace industry due to their exceptional mechanical properties and oxidation resistance.However,the conventional development of new superalloys is often constrained ...Ni-based superalloys play a critical role in the aerospace industry due to their exceptional mechanical properties and oxidation resistance.However,the conventional development of new superalloys is often constrained by lengthy experimental cycles and high costs.To address these challenges,machine learning has emerged as an effective strategy for accelerating alloy design by efficiently exploring composition-property relationship,optimizing processing parameters,and enhancing predictive accuracy.This review summarizes recent progress in applying machine learning to composition optimization and mechanical property prediction of Ni-based superalloys,emphasizing the integration of theoretical modeling and experimental validation.The importance of feature engineering,including data collection,preprocessing,feature construction,and dimensionality reduction,was first highlighted.Subsequently,the machine learning approaches for novel alloy design and prediction of key properties including fatigue resistance,creep resistance,and oxidation resistance were discussed.Through data-driven approaches,machine learning not only enhances predictive capabilities but also uncovers complex composition-property relationship,which accelerates the development of next-generation Ni-based superalloys.We anticipate that the continued advancements in this field will drive more efficient and cost-effective alloy design,ultimately accelerating the transition from computational predictions to experimental realizations.展开更多
An effective and time-saving early life prediction model is crucial for rapid battery assessment.However,existing models face a dilemma:they either rely heavily on extensive historical data or provide limited predicti...An effective and time-saving early life prediction model is crucial for rapid battery assessment.However,existing models face a dilemma:they either rely heavily on extensive historical data or provide limited predictive insights into battery degradation.To address this,this study proposes a cycle-efficient battery life assessment framework integrating data-driven and empirical models.The framework consists of two components:degradation stage detection relying solely on data from one cycle and early life prediction using five-cycle data.The early life prediction model is capable of achieving joint prediction of the battery's remaining useful life and the cycle to knee point.Experimental results demonstrate that the degradation staging model achieves an accuracy of 0.977,6 for lithium iron phosphate batteries.Meanwhile,the early life prediction model yields mean absolute percentage errors of 10.5%for remaining useful life and 12.8%for the cycle to knee predictions.The model's accuracy and generalizability have been validated across diverse battery types,health states,and operating conditions.This proposed framework exhibits excellent generalizability capability under all evaluated scenarios,establishing a robust foundation for rapid battery design assessment and retirement decisions.展开更多
Three-dimensional printed Ti-6Al-4V hemipelvic prosthesis has become a current popular method for pelvic defect reconstruction.This paper presents a novel biomimetic hemipelvic prosthesis design that utilises patient-...Three-dimensional printed Ti-6Al-4V hemipelvic prosthesis has become a current popular method for pelvic defect reconstruction.This paper presents a novel biomimetic hemipelvic prosthesis design that utilises patient-specific anatomical data in conjunction with the Voronoi diagram algorithm.Unlike traditional design methods that rely on fixed,homogeneous unit cell,the Voronoi diagram enables to create imitation of trabecular structure(ITS).The proposed approach was conducted for six patients.The entire contour of the customised prosthesis matched well with the residual bone.The porosity and pore size of the ITS were evaluated.The distribution of the pore size ranged from 500 to 1400μm.Porosity calculations indicated the average porosity was 63.13±0.30%.Cubic ITS samples were fabricated for micrograph and mechanical analysis.Scanning electron microscopy images of the ITS samples exhibited rough surface morphology without obvious defects.The Young’s modulus and compressive strength were 1.68±0.05 GPa and 174±8 MPa,respectively.Post-operative X-rays confirmed proper matching of the customised prostheses with the bone defect.Tomosynthesis-Shimadzu metal artifact reduction technology images indicated close contact between the implant and host bone,alongside favourable bone density and absence of resorption or osteolysis around the implant.At the last follow-up,the average Musculoskeletal Tumour Society score was 23.2(range,21-26).By leveraging additive manufacturing and Voronoi diagram algorithm,customised implants tailored to individual patient anatomy can be fabricated,offering wide distribution of the pore size,reasonable mechanical properties,favourable osseointegration,and satisfactory function.展开更多
In the case of asymmetric loads of power grid,load currents are composed of four components:positive sequence active and reactive components and negative sequence active and reactive components that can pollute the po...In the case of asymmetric loads of power grid,load currents are composed of four components:positive sequence active and reactive components and negative sequence active and reactive components that can pollute the power grid with harmonics and reactive power and interrupt the normal operation of power grid.Therefore,increasing numbers of static var generators(SVGs)are used to stabilize the power grid.In this study,a new type of current compensation control strategy of a three-phase three-wire SVG under an asymmetric load is proposed.According to the symmetric component method and dual dq synchronous transformation theory,the load currents are transformed into four components under dual dq coordinates.Each component is compensated separately by the SVG.Simultaneously,the proportional-limiter method is adopted to minimize the harmonics from the SVG by optimizing the waveform of the modulation wave and limiting its amplitude.Finally,the validity and feasibility of the control strategy are verified by simulation and experimental results.展开更多
Background:Alzheimer’s disease(AD)is one of the most common neurodegenerative disorders in the elderly.Although numerous structural magnetic resonance imaging(sMRI)studies have reported diagnostic models that could d...Background:Alzheimer’s disease(AD)is one of the most common neurodegenerative disorders in the elderly.Although numerous structural magnetic resonance imaging(sMRI)studies have reported diagnostic models that could distinguish AD from normal controls(NCs)with 80–95%accuracy,limited efforts have been made regarding the clinically practical computer-aided diagnosis(CAD)system for AD.Objective:To explore the potential factors that hinder the clinical translation of the AD-related diagnostic mod-els based on sMRI.Methods:To systematically review the diagnostic models for AD based on sMRI,we identified relevant studies published in the past 15 years on PubMed,Web of Science,Scopus,and Ovid.To evaluate the heterogeneity and publication bias among those studies,we performed subgroup analysis,meta-regression,Begg’s test,and Egger’s test.Results:According to our screening criterion,101 studies were included.Our results demonstrated that high diagnostic accuracy for distinguishing AD from NC was obtained in recently published studies,accompanied by significant heterogeneity.Meta-analysis showed that many factors contributed to the heterogeneity of high diagnostic accuracy of AD using sMRI,which included but was not limited to the following aspects:(i)different datasets;(ii)different machine learning models,e.g.traditional machine learning or deep learning model;(iii)different cross-validation methods,e.g.k-fold cross-validation leads to higher accuracies than leave-one-out cross-validation,but both overestimate the accuracy when compared to validation in independent samples;(iv)different sample sizes;and(v)the publication times.We speculate that these complicated variables might be the adverse factor for developing a clinically applicable system for the early diagnosis of AD.Conclusions:Our findings proved that previous studies reported promising results for classifying AD from NC with different models using sMRI.However,considering the many factors hindering clinical radiology practice,there would still be a long way to go to improve.展开更多
基金supported by the National Natural Science Foundation of China,Nos.81800919(to YX),82171140(to PW)the International Cooperation and Exchange of the National Natural Science Foundation of China,Nos.82020108008(to HS),81720108010(to SY).
文摘Aminoglycosides are a widely used class of antibacterials renowned for their effectiveness and broad antimicrobial spectrum.However,their use leads to irreversible hearing damage by causing apoptosis of hair cells as their direct target.In addition,the hearing damage caused by aminoglycosides involves damage of spiral ganglion neurons upon exposure.To investigate the mechanisms underlying spiral ganglion neuron degeneration induced by aminoglycosides,we used a C57BL/6J mouse model treated with kanamycin.We found that the mice exhibited auditory deficits following the acute loss of outer hair cells.Spiral ganglion neurons displayed hallmarks of pyroptosis and exhibited progressive degeneration over time.Transcriptomic profiling of these neurons showed significant upregulation of genes associated with inflammation and immune response,particularly those related to the NLRP3 inflammasome.Activation of the canonical pyroptotic pathway in spiral ganglion neurons was observed,accompanied by infiltration of macrophages and the release of proinflammatory cytokines.Pharmacological intervention targeting NLRP3 using Mcc950 and genetic intervention using NLRP3 knockout ameliorated spiral ganglion neuron degeneration in the injury model.These findings suggest that NLRP3 inflammasome-mediated pyroptosis plays a role in aminoglycoside-induced spiral ganglion neuron degeneration.Inhibition of this pathway may offer a potential therapeutic strategy for treating sensorineural hearing loss by reducing spiral ganglion neuron degeneration.
基金supported by the"Science and Technology Development Plan Project of Jilin Province,China"(Grant No.20240101018JJ)the Fundamental Research Funds for the Central Universities(Grant No.2412023YQ004)the National Natural Science Foundation of China(Grant Nos.52072065,52272140,52372137,and U23A20568).
文摘Optoelectronic memristor is generating growing research interest for high efficient computing and sensing-memory applications.In this work,an optoelectronic memristor with Au/a-C:Te/Pt structure is developed.Synaptic functions,i.e.,excita-tory post-synaptic current and pair-pulse facilitation are successfully mimicked with the memristor under electrical and optical stimulations.More importantly,the device exhibited distinguishable response currents by adjusting 4-bit input electrical/opti-cal signals.A multi-mode reservoir computing(RC)system is constructed with the optoelectronic memristors to emulate human tactile-visual fusion recognition and an accuracy of 98.7%is achieved.The optoelectronic memristor provides potential for developing multi-mode RC system.
基金supported by the National Natural Science Foundation of China(82102018,62333002,T2425027,and 82327809)Data collection and sharing for this project were supported by the National Natural Science Foundation of China(61633018,81571062,81471120,and 81901101)+30 种基金Data collection and sharing for this project were funded by the ADNI(National Institutes of Health Grant U01 AG024904)the Department of Defense ADNI(award number W81XWH-12-2-0012).The ADNI is funded by the National Institute on Aging,the National Institute of Biomedical Imaging and Bioengineering,and through generous contributions from the following:AbbVie,Alzheimer’s AssociationAlzheimer’s Drug Discovery FoundationAraclon BiotechBioClinica,Inc.BiogenBristol-Myers Squibb Co.CereSpir,Inc.CogstateEisai Inc.Elan Pharmaceuticals,Inc.Eli Lilly and Co.EuroImmunF.Hoffmann-La Roche Ltd and its affiliated company Genentech,Inc.FujirebioG.E.HealthcareIXICO Ltd.Janssen Alzheimer Immunotherapy Research&Development,LLC.Johnson&Johnson Pharmaceutical Research&Development LLC.LumosityLundbeckMerck&Co.,Inc.Meso Scale Diagnostics,LLC.NeuroRx ResearchNeurotrack TechnologiesNovartis Pharmaceuticals Corp.Pfizer Inc.Piramal ImagingServierTakeda Pharmaceutical Co.and Transition Therapeutics.The Canadian Institutes of Health Research provides funds to support ADNI clinical sites in Canada.Private sector contributions are facilitated by the Foundation for the National Institutes of Health(www.fnih.org).The grantee organization was the Northern California Institute for Research and Education,and the study was coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California.ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.
文摘Dear Editor,Growing clinical evidence shows that brain disorders are heterogeneous in phenotype,genetics,and neuropathology[1].Diagnosis and treatment tend to be affected by symptom presentation and the heterogeneity of pathology,potentially hindering clinical trials in the development of medical treatment.Brain-based subtyping studies utilize magnetic resonance imaging(MRI)and data-driven methods to discover the subtypes of diseases,providing a new perspective on disease heterogeneity.
基金financially supported by the National Natural Science Foundation of China(Nos.52201203 and 52471004)the Fundamental Research Funds for the Central Universities(No.N2423030)the Science and Technology Project of Hebei Education Department(No.QN2023155).
文摘Ni-based superalloys play a critical role in the aerospace industry due to their exceptional mechanical properties and oxidation resistance.However,the conventional development of new superalloys is often constrained by lengthy experimental cycles and high costs.To address these challenges,machine learning has emerged as an effective strategy for accelerating alloy design by efficiently exploring composition-property relationship,optimizing processing parameters,and enhancing predictive accuracy.This review summarizes recent progress in applying machine learning to composition optimization and mechanical property prediction of Ni-based superalloys,emphasizing the integration of theoretical modeling and experimental validation.The importance of feature engineering,including data collection,preprocessing,feature construction,and dimensionality reduction,was first highlighted.Subsequently,the machine learning approaches for novel alloy design and prediction of key properties including fatigue resistance,creep resistance,and oxidation resistance were discussed.Through data-driven approaches,machine learning not only enhances predictive capabilities but also uncovers complex composition-property relationship,which accelerates the development of next-generation Ni-based superalloys.We anticipate that the continued advancements in this field will drive more efficient and cost-effective alloy design,ultimately accelerating the transition from computational predictions to experimental realizations.
基金the National Natural Science Foundation of China(Grant No.52177210 and U24A20158).
文摘An effective and time-saving early life prediction model is crucial for rapid battery assessment.However,existing models face a dilemma:they either rely heavily on extensive historical data or provide limited predictive insights into battery degradation.To address this,this study proposes a cycle-efficient battery life assessment framework integrating data-driven and empirical models.The framework consists of two components:degradation stage detection relying solely on data from one cycle and early life prediction using five-cycle data.The early life prediction model is capable of achieving joint prediction of the battery's remaining useful life and the cycle to knee point.Experimental results demonstrate that the degradation staging model achieves an accuracy of 0.977,6 for lithium iron phosphate batteries.Meanwhile,the early life prediction model yields mean absolute percentage errors of 10.5%for remaining useful life and 12.8%for the cycle to knee predictions.The model's accuracy and generalizability have been validated across diverse battery types,health states,and operating conditions.This proposed framework exhibits excellent generalizability capability under all evaluated scenarios,establishing a robust foundation for rapid battery design assessment and retirement decisions.
基金supported by Qingdao Research Institutes of Sichuan University,Research of Biomedical Materials and 3D Printing Related Products(No.20GZ30301).
文摘Three-dimensional printed Ti-6Al-4V hemipelvic prosthesis has become a current popular method for pelvic defect reconstruction.This paper presents a novel biomimetic hemipelvic prosthesis design that utilises patient-specific anatomical data in conjunction with the Voronoi diagram algorithm.Unlike traditional design methods that rely on fixed,homogeneous unit cell,the Voronoi diagram enables to create imitation of trabecular structure(ITS).The proposed approach was conducted for six patients.The entire contour of the customised prosthesis matched well with the residual bone.The porosity and pore size of the ITS were evaluated.The distribution of the pore size ranged from 500 to 1400μm.Porosity calculations indicated the average porosity was 63.13±0.30%.Cubic ITS samples were fabricated for micrograph and mechanical analysis.Scanning electron microscopy images of the ITS samples exhibited rough surface morphology without obvious defects.The Young’s modulus and compressive strength were 1.68±0.05 GPa and 174±8 MPa,respectively.Post-operative X-rays confirmed proper matching of the customised prostheses with the bone defect.Tomosynthesis-Shimadzu metal artifact reduction technology images indicated close contact between the implant and host bone,alongside favourable bone density and absence of resorption or osteolysis around the implant.At the last follow-up,the average Musculoskeletal Tumour Society score was 23.2(range,21-26).By leveraging additive manufacturing and Voronoi diagram algorithm,customised implants tailored to individual patient anatomy can be fabricated,offering wide distribution of the pore size,reasonable mechanical properties,favourable osseointegration,and satisfactory function.
文摘In the case of asymmetric loads of power grid,load currents are composed of four components:positive sequence active and reactive components and negative sequence active and reactive components that can pollute the power grid with harmonics and reactive power and interrupt the normal operation of power grid.Therefore,increasing numbers of static var generators(SVGs)are used to stabilize the power grid.In this study,a new type of current compensation control strategy of a three-phase three-wire SVG under an asymmetric load is proposed.According to the symmetric component method and dual dq synchronous transformation theory,the load currents are transformed into four components under dual dq coordinates.Each component is compensated separately by the SVG.Simultaneously,the proportional-limiter method is adopted to minimize the harmonics from the SVG by optimizing the waveform of the modulation wave and limiting its amplitude.Finally,the validity and feasibility of the control strategy are verified by simulation and experimental results.
基金supported by the Beijing Natural Science Funds for Distinguished Young Scholars(No.JQ20036)the Fundamental Research Funds for the Central Universities(No.2021XD-A03-1)the National Natural Science Foundation of China(Nos.81871438 and 82172018).
文摘Background:Alzheimer’s disease(AD)is one of the most common neurodegenerative disorders in the elderly.Although numerous structural magnetic resonance imaging(sMRI)studies have reported diagnostic models that could distinguish AD from normal controls(NCs)with 80–95%accuracy,limited efforts have been made regarding the clinically practical computer-aided diagnosis(CAD)system for AD.Objective:To explore the potential factors that hinder the clinical translation of the AD-related diagnostic mod-els based on sMRI.Methods:To systematically review the diagnostic models for AD based on sMRI,we identified relevant studies published in the past 15 years on PubMed,Web of Science,Scopus,and Ovid.To evaluate the heterogeneity and publication bias among those studies,we performed subgroup analysis,meta-regression,Begg’s test,and Egger’s test.Results:According to our screening criterion,101 studies were included.Our results demonstrated that high diagnostic accuracy for distinguishing AD from NC was obtained in recently published studies,accompanied by significant heterogeneity.Meta-analysis showed that many factors contributed to the heterogeneity of high diagnostic accuracy of AD using sMRI,which included but was not limited to the following aspects:(i)different datasets;(ii)different machine learning models,e.g.traditional machine learning or deep learning model;(iii)different cross-validation methods,e.g.k-fold cross-validation leads to higher accuracies than leave-one-out cross-validation,but both overestimate the accuracy when compared to validation in independent samples;(iv)different sample sizes;and(v)the publication times.We speculate that these complicated variables might be the adverse factor for developing a clinically applicable system for the early diagnosis of AD.Conclusions:Our findings proved that previous studies reported promising results for classifying AD from NC with different models using sMRI.However,considering the many factors hindering clinical radiology practice,there would still be a long way to go to improve.
