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Ultrahigh spatiotemporal resolution beam signal reconstruction with bunch phase compensation

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Nuclear Science and Techniques 2024年第5期35卷 99-108页

作者： You-Ming deng Yong-Bin Leng Xing-Yi xu Jian Chen Yi-mei Zhou Shanghai Institute of Applied Physics Chinese Academy of SciencesShanghai 201800China University of the Chinese Academy of Sciences Beijing 100049China Shanghai Synchrotron Radiation Facility Chinese Academy of SciencesShanghai 201204China University of Science and Technology of China Hefei 230026China Shanghai Advanced Research Institute Chinese Academy of SciencesShanghai 201204China

Various electromagnetic signals are excited by the beam in the acceleration and beam-diagnostic elements of a particle accelerator.It is important to obtain time-domain waveforms of these signals with high temporal resolution for research,such as the study of beam–cavity interactions and bunch-by-bunch parameter measurements.Therefore,a signal reconstruction algorithm with ultrahigh spatiotemporal resolution and bunch phase compensation based on equivalent sampling is proposed in this paper.Compared with traditional equivalent sampling,the use of phase compensation and setting the bunch signal zero-crossing point as the time reference can construct a more accurate reconstructed signal.The basic principles of the method,simulation,and experimental comparison are also introduced.Based on the beam test platform of the Shanghai Synchrotron Radiation Facility(SSRF)and the method of experimental verification,the factors that affect the reconstructed signal quality are analyzed and discussed,including the depth of the sampled data,quantization noise of analog-to-digital converter,beam transverse oscillation,and longitudinal oscillation.The results of the beam experiments show that under the user operation conditions of the SSRF,a beam excitation signal with an amplitude uncertainty of 2%can be reconstructed.

关键词： Turn-by-turn bunch phase compensation technique Equivalent sampling Signal reconstruction algorithm Ultrahigh spatiotemporal resolution SSRF

Simultaneous integration of Fe clusters and NiFe dual single atoms in nitrogen-doped carbon for oxygen reduction reaction

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Nano Research 2024年第4期17卷 2291-2297页

作者： Jirong Bai Yuebin Lian Yaoyao deng mei Xiang Peng xu Quanfa Zhou Yawen Tang Yaqiong Su Research Center of Secondary Resources and Environment School of Chemical Engineering and MaterialsChangzhou Institute of TechnologyChangzhou 213022China Jiangsu Key Laboratory of New Power Batteries School of Chemistry and Materials ScienceNanjing Normal UniversityNanjing 210023China School of Chemistry Engineering Research Center of Energy Storage Materials and Devices of Ministry of EducationNational Innovation Platform(Center)for Industry-Education Integration of Energy Storage TechnologyXi’an Jiaotong UniversityXi’an 710049China

Atomically-dispersed iron-based electrocatalysts are promising substitutes for noble metal electrocatalysts because of excellent performance in oxygen reduction reaction(ORR).Rationally modulating the local coordination environment of the Fe site and optimizing the binding energy of oxygen reduction intermediates are effective strategies to optimize ORR activity.Herein,we report a new method in which Ni is introduced to construct NiFe dual single atoms and iron nanoclusters loaded on the nitrogen-doped carbon with a highly porous structure.This design plays a synergistic role of dual single atoms and clusters,optimizes the 3d orbital and Fermi level of Fe,breaks the symmetrical structure of Fe-N_(4),and effectively improves the adsorption/desorption behavior of the oxygen-containing intermediates.Electrochemical tests show FeNCs/NiFeSAs-NC has an excellent intrinsic activity.Theoretical calculations show the oxygen-containing species on the Ni active site will move to the middle of NiFe(bridge site connection)after optimization and that the key step is OH desorption,with a reaction energy of 0.27 eV.The electron exchange between NiFe-N6 and Fe-cluster is very strong,further indicating the introduction of Ni species and Fe clusters has a regulatory effect on the electronic structure of Fe-N_(4).

关键词： NiFe dual single atoms Fe clusters OH*adsorption energy oxygen reduction reaction

A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score,revised Geneva score,and Years algorithm

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Chinese Medical Journal 2024年第6期137卷 676-682页

作者： Linfeng Xi Han Kang mei deng Wenqing xu Feiya xu Qian Gao Wanmu Xie Rongguo Zhang Min Liu Zhenguo Zhai Chen Wang Capital Medical University Beijing 100069China National Center for Respiratory Medicine State Key Laboratory of Respiratory Health and MultimorbidityNational Clinical Research Center for Respiratory DiseasesInstitute of Respiratory MedicineChinese Academy of Medical SciencesDepartment of Pulmonary and Critical Care MedicineCenter of Respiratory MedicineChina-Japan Friendship HospitalBeijing 100029China Institute of Advanced Research Infervision Medical Technology Co.Ltd.Beijing 100025China Department of Radiology China-Japan Friendship HospitalBeijing 100029China Chinese Academy of Medical Sciences and Peking Union Medical College Beijing 100730China Department of Radiology Peking University China-Japan Friendship School of Clinical MedicineBeijing 100191China

Background:Acute pulmonary embolism(APE)is a fatal cardiovascular disease,yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs.A simple,objective technique will help clinicians make a quick and precise diagnosis.In population studies,machine learning(ML)plays a critical role in characterizing cardiovascular risks,predicting outcomes,and identifying biomarkers.This work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment models.Methods:This is a single-center retrospective study.Patients with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets.A total of 8 ML models,including random forest(RF),Naïve Bayes,decision tree,K-nearest neighbors,logistic regression,multi-layer perceptron,support vector machine,and gradient boosting decision tree were developed based on the training set to diagnose APE.Thereafter,the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies,including the Wells score,revised Geneva score,and Years algorithm.Eventually,the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic(ROC)analysis.Results:The ML models were constructed using eight clinical features,including D-dimer,cardiac troponin T(cTNT),arterial oxygen saturation,heart rate,chest pain,lower limb pain,hemoptysis,and chronic heart failure.Among eight ML models,the RF model achieved the best performance with the highest area under the curve(AUC)(AUC=0.774).Compared to the current clinical assessment strategies,the RF model outperformed the Wells score(P=0.030)and was not inferior to any other clinical probability assessment strategy.The AUC of the RF model for diagnosing APE onset in internal validation set was 0.726.Conclusions:Based on RF algorithm,a novel prediction model was finally constructed for APE diagnosis.When compared to the current clinica

关键词： Acute pulmonary embolism Machine learning Wells score Revised Geneva score Years algorithm

Transition metals for stabilizing lithium metal anode:advances and perspectives

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Tungsten 2024年第1期6卷 212-229页

作者： Si-Cheng Miao Ye Jia Zhi-Wen deng Yan deng Ruo-xuan Chen xue-mei Zhang Chang-Haoyue xu Meng Yao Wen-Long Cai Department of Advanced Energy Materials College of Materials Science and EngineeringSichuan UniversityChengdu 610064China

As the capacity of lithium-ion batteries gradually reaches its limit,the high-capacity characteristics of lithium metal batteries(LMBs)make them one of the most promising electrochemical energy storage devices currently.However,uncontrolled lithium dendrite growth can cause poor cell performance and severe safety issues,seriously slowing down the commercialization of LMBs.Transition metals(TMs)are lithiophilic materials that effectively stabilize lithium metal anode(LMA)and inhibit dendrite growth.Herein,the growth of lithium dendrites and the performance of TMs in stabilizing lithium metals are comprehensively discussed in this review.First,we elucidated the formation mechanisms of lithium dendrite,and we put forward several strategies to suppress lithium dendrite formation based on the understanding of these mechanisms.Then,the application of TMs and their compounds to stabilize LMA and their corresponding mechanisms are comprehensively clarified.Last,further research perspectives of TMs in stabilizing LMA are emphasized in detail.

关键词： Transition metals Lithium metal batteries Lithium dendrite Solid electrolyte interphase Formation mechanism

Increased long-distance and homo-trans interactions related to H3K27me3 in Arabidopsis hybrids

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Journal of Integrative Plant Biology 2024年第2期66卷 208-227页

作者： Zhaoxu Gao Yanning Su Le Chang Guanzhong Jiao Yang Ou mei Yang Chao xu Pengtao Liu Zejia Wang Zewen Qi Wenwen Liu Linhua Sun Guangming He Xing Wang deng Hang He School of Advanced Agriculture Sciences and School of Life Sciences State Key Laboratory of Protein and Plant Gene ResearchPeking UniversityBeijing 100871China College of Grassland Science Beijing Forestry UniversityBeijing 100083China Peking University Institute of Advanced Agricultural Sciences Shandong Laboratory of Advanced Agricultural Sciences in WeifangWeifang 261325China

In plants,the genome structure of hybrids changes compared with their parents,but the effects of these changes in hybrids remain elusive.Comparing reciprocal crosses between Col×C24 and C24×Col in Arabidopsis using high-throughput chromosome conformation capture assay(Hi-C)analysis,we found that hybrid three-dimensional(3D)chromatin organization had more long-distance interactions relative to parents,and this was mainly located in promoter regions and enriched in genes with heterosis-related pathways.The interactions between euchromatin and heterochromatin were increased,and the compartment strength decreased in hybrids.In compartment domain(CD)boundaries,the distal interactions were more in hybrids than their parents.In the hybrids of CURLY LEAF(clf)mutants clfCol×clfC24and clfC24×clfCol,the heterosis phenotype was damaged,and the long-distance interactions in hybrids were fewer than in their parents with lower H3K27me3.ChIP-seq data revealed higher levels of H3K27me3 in the region adjacent to the CD boundary and the same interactional homo-trans sites in the wild-type(WT)hybrids,which may have led to more long-distance interactions.In addition,the differentially expressed genes(DEGs)located in the boundaries of CDs and loop regions changed obviously in WT,and the functional enrichment for DEGs was different between WT and clf in the longdistance interactions and loop regions.Our findings may therefore propose a new epigenetic explanation of heterosis in the Arabidopsis hybrids and provide new insights into crop breeding and yield increase.

关键词： 3D chromatin organization H3K27me3 homo-trans hybrids long-distanceinteractions

Maternal weight and its association with risk of overweight in offspring:a trajectory analysis from a birth cohort in China

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World Journal of Pediatrics 2024年第5期20卷 496-505页

作者： Rui deng Wei-Qin Li Xing-Xiu Li Liu-mei Wei Jie Hu Jun-Hong Leng Bin Dong Institute of Child and Adolescent Health School of Public HealthPeking University Health Science CenterNo.38 Xueyuan RdHaidian DistrictBeijing100191China Tianjin Women and Children’s Health Center No.96 Guizhou RdHeping DistrictTianjin300070China Menzies Health Institute Queensland Griffith UniversityNathanQLD4111Australia

Background Most studies on the association of maternal pregnancy weight with offspring weight trajectory have a short follow-up time.This study aimed to explore the associations of maternal prepregnancy body mass index(BMI)and gestational weight gain(GWG)with childhood weight trajectories in a 7-year birth cohort.Methods A total of 946 mother–child pairs(467 boys and 479 girls)from a longitudinal birth cohort in Tianjin City,China,were included in this study,ranging from pregnancy to offspring at 7 years.The outcome variable was defined as overweight or not overweight in offspring at the last round.A group-based trajectory model was applied to identify childhood BMI trajectory groups.Results Five discrete BMI trajectory groups were identified and characterized as constant underweight(25.2%),constant normal weight(42.8%),and high or increasing trajectory[at risk of overweight(16.9%),progressive overweight(11.0%)and progressive obesity(4.1%)].Maternal prepregnancy overweight was associated with 1.72(95%CI 1.14–2.60,P=0.01)to 4.02(95%CI 1.94–8.36,P<0.001)times the risk of all high or increasing trajectory groups,and excessive GWG was related to groups at risk of overweight[relative risk ratio(RRR)2.09,95%CI 1.27–3.46,P=0.004]and progressive obesity(RRR 3.33,95%CI 1.13–9.79,P=0.029).Children in all high or increasing trajectory groups were associated with greater overweight risk at the last round[risk ratios(RRs)ranged from 3.54(95%CI 2.53–4.95,P<0.001)to 6.18(95%CI 4.05–9.42,P<0.001)].Conclusion Maternal prepregnancy overweight and excessive gestational weight gain were associated with increasing or high-level childhood body mass index trajectories as well as a greater risk of overweight at 7 years.

关键词： Body-weight trajectory Child Gestational weight gain Overweight Pregnancy

Microstructure Regulation and Combustion Performance Optimization of PVDF/Al Composite Powder by Non-covalent Functionalized Graphenes

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Journal of Wuhan University of Technology(Materials Science) 2024年第4期39卷 904-911页

作者：易卓然 deng Haoyuan QIN mei 孙一 LUO Guoqiang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China

Graphene prepared by non-covalent modification of sulfonated poly(ether-ether-ketone)(SPG)was combined with polyvinylidene fluoride(PVDF)/Al to improve the PVDF/Al thermal conductivity while reducing the effect of the thermal resistance at the graphene-polymer interface.The regulation rule of SPG with different contents on the energy release of fluorine-containing system was studied.When the content of SPG is 4%,the peak pressure and rise rate of SPG/PVDF/Al composite powder during ignition reach the maximum of 4845.28 kPa and 8683.58 kPa/s.When the content of SPG is 5%,the PVDF/Al composite powder is completely coated by SPG,and the calorific value of the material reachs the maximum of 29.094 kJ/g.Through the design and micro-control of the composite powder,the calorific value of the material can be effectively improved,but the improvement of the mass release rate still depends on the graphene content and surface modification state.

关键词： energetic materials PVDF/Al composites graphene modification energy release combustion

Correlative factors of poor prognosis and abnormal cellular immune function in patients with Alzheimer’s disease

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World Journal of Clinical Cases 2024年第6期12卷 1063-1075页

作者： Hua Bai Hong-mei Zeng Qi-Fang Zhang Yue-Zhi Hu Fei-Fei deng Department of Neurology The Third Affiliated Hospital of Guizhou Medical University in ChinaDuyun 558099Guizhou ProvinceChina Department of Neurology Guizhou Medical UniversityDuyun 558099Guizhou ProvinceChina Key Laboratory of Medical Molecular Biology Guizhou Medical UniversityGuiyang 550004Guizhou ProvinceChina

BACKGROUND Alzheimer’s disease(AD)is a serious disease causing human dementia and social problems.The quality of life and prognosis of AD patients have attracted much attention.The role of chronic immune inflammation in the pathogenesis of AD is becoming more and more important.AIM To study the relationship among cognitive dysfunction,abnormal cellular immune function,neuroimaging results and poor prognostic factors in patients.METHODS A retrospective analysis of 62 hospitalized patients clinical diagnosed with AD who were admitted to our hospital from November 2015 to November 2020.Collect cognitive dysfunction performance characteristics,laboratory test data and neuroimaging data from medical records within 24 h of admission,including Mini Mental State Examination Scale score,drawing clock test,blood T lymphocyte subsets,and neutrophils and lymphocyte ratio(NLR),disturbance of consciousness,extrapyramidal symptoms,electroencephalogram(EEG)and head nucleus magnetic spectroscopy(MRS)and other data.Multivariate logistic regression analysis was used to determine independent prog-nostic factors.the modified Rankin scale(mRS)was used to determine whether the prognosis was good.The correlation between drug treatment and prognostic mRS score was tested by the rank sum test.RESULTS Univariate analysis showed that abnormal cellular immune function,extrapyramidal symptoms,obvious disturbance of consciousness,abnormal EEG,increased NLR,abnormal MRS,and complicated pneumonia were related to the poor prognosis of AD patients.Multivariate logistic regression analysis showed that the decrease in the proportion of T lym-phocytes in the blood after abnormal cellular immune function(odd ratio:2.078,95%confidence interval:1.156-3.986,P<0.05)was an independent risk factor for predicting the poor prognosis of AD.The number of days of donepezil treatment to improve cognitive function was negatively correlated with mRS score(r=0.578,P<0.05).CONCLUSION The decrease in the proportion of T lymphocytes may have predictive value for the po

关键词： Alzheimer’s disease Cellular immunity Prognosis T lymphocytes Magnetic resonance spectroscopy

Toward Improved Accuracy in Quasi-Static Elastography Using Deep Learning

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 911-935页

作者： Yue mei Jianwei deng Dongmei Zhao Changjiang Xiao Tianhang Wang Li Dong xuefeng Zhu Department of Engineering Mechanics State Key Laboratory of Structural Analysis for Industrial EquipmentDalian University of TechnologyDalian116023China International Research Center for Computational Mechanics Dalian University of TechnologyDalian116023China DUT-BSU Joint Institute Dalian University of TechnologyDalian116023China Department of Automotive Engineering Tongji UniversityShanghai201804China *** SeattleWA98109USA School of Automotive Engineering Dalian University of TechnologyDalian116023China

Elastography is a non-invasive medical imaging technique to map the spatial variation of elastic properties of soft tissues.The quality of reconstruction results in elastography is highly sensitive to the noise induced by imaging measurements and processing.To address this issue,we propose a deep learning(DL)model based on conditional Generative Adversarial Networks(cGANs)to improve the quality of nonhomogeneous shear modulus reconstruction.To train this model,we generated a synthetic displacement field with finite element simulation under known nonhomogeneous shear modulus distribution.Both the simulated and experimental displacement fields are used to validate the proposed method.The reconstructed results demonstrate that the DL model with synthetic training data is able to improve the quality of the reconstruction compared with the well-established optimization method.Moreover,we emphasize that our DL model is only trained on synthetic data.This might provide a way to alleviate the challenge of obtaining clinical or experimental data in elastography.Overall,this work addresses several fatal issues in applying the DL technique into elastography,and the proposed method has shown great potential in improving the accuracy of the disease diagnosis in clinical medicine.

关键词： Nonhomogeneous elastic property distribution reconstruction deep learning finite element method inverse problem elastography conditional generative adversarial network