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Acceleration of 60 MeV proton beams in the commissioning experiment of the SULF-10 PW laser

High Power Laser Science and Engineering 2022年第4期10卷 36-44页

作者： A.x.Li C.Y.Qin H.Zhang S.Li L.L.Fan Q.S.wang T.J.xu N.W.wang L.H.Yu Y.xu Y.Q.Liu C.wang x.L.wang Z.x.Zhang x.Y.Liu P.L.bai Z.b.Gan x.b.Zhang x.b.wang C.Fan Y.J.Sun Y.H.Tang b.Yao x.Y.Liang Y.x.Leng b.F.Shen L.L.Ji R.x.Li Z.Z.xu State Key Laboratory of High Field Laser Physics Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China Shanghai Tech University Shanghai 201210China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBejing 100049China CAS Centerfor Excellence inUltra-intense Laser Science Shanghai 201800China College of Science University of Shanghai for Science and TechnologyShanghai 200093China Department of Physics Shanghai Normal UniversityShanghai 200234China

We report the experimental results of the commissioning phase in the 10 PW laser beamline of the Shanghai Superintense Ultrafast Laser Facility(SULF).The peak power reaches 2.4 PW on target without the last amplifying during the *** laser energy of 72±9 J is directed to a focal spot of approximately 6μm diameter(full width at half maximum)in 30 fs pulse duration,yielding a focused peak intensity around 2.0×10^(21)W/cm^(2).The first laser-proton acceleration experiment is performed using plain copper and plastic ***-energy proton beams with maximum cut-off energy up to 62.5 MeV are achieved using copper foils at the optimum target thickness of 4μm via target normal sheath *** plastic targets of tens of nanometers thick,the proton cut-off energy is approximately 20 MeV,showing ring-like or flamented density *** experimental results reflect the capabilities of the SULF-10 PW beamline,for example,both ultrahigh intensity and relatively good beam *** optimization for these key parameters is underway,where peak laser intensities of 10^(22)-10^(23)w/cm^(2)are anticipated to support various experiments on extreme field physics.

关键词： high-energy proton source laser-plasma interaction ultraintense lasers

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Antithetical impacts of deleterious LRP1b mutations in non-squamous and squamous NSCLCs on predicting benefits from immune checkpoint inhibitor alone or with chemotherapy over chemotherapy alone: retrospective analyses of the POPLAR/OAK and CHOICE-01 trial

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Science China Life Sciences 2025年第1期 249-262页

作者： Jinliang wang Wenyong Zhou Yu xu Jianchun Duan Qiaoxia Zhou Guoqiang wang Leo Li Chunwei xu Wenxian wang Shangli Cai Zhijie wang Jie wang Department of Oncology The Fifth Medical Center of Chinese PLA General Hospital Department of Thoracic Surgery Shanghai Chest HospitalSchool of MedicineShanghai Jiao Tong University burning Rock biotech CAMS Key Laboratory of Translational Research on Lung Cancer State Key Laboratory of Molecular OncologyDepartment of Medical OncologyNational Cancer Center/National Clinical Research Center for ancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical College Institute of basic Medicine and Cancer (IbMC) Chinese Academy of Sciences Department of Clinical Trial The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)

In non-small cell lung cancers,the non-squamous and squamous subtypes（nsqNSCLC and sqNSCLC） exhibit disparities in pathophysiology,tumor immunology,and potential genomic correlates affecting responses to immune checkpoint inhibitor（ICI）-based *** our inhouse training cohort（n=85）,the presence of the LRP1b deleterious mutation（LRP1b-del） was associated with longer and shorter progression-free survival（PFS） on ICIs alone in nsqNSCLCs and sqNSCLCs,respectively(Pinteraction=0.008).These results were validated using a larger public ICI cohort(n=208,Pinteractionx immunofluorescence staining revealed an association between LRP1b-del and increased and decreased numbers of tumor-infiltrating CD8+T cells in nsqNSCLCs（P=0.040） and sqNSCLCs（P=0.014）,*** the POPLAR/OAK cohort,nsqNSCLCs with LRP1b-del demonstrated improved PFS benefits from atezolizumab over docetaxel(hazard ratio（HR）=0.70,P=0.046),whereas this benefit was negligible in those without LRP1b-del（HR=1.05,P=0.64）.Conversely,sqNSCLCs without LRP1b-del benefited more from atezolizumab（HR=0.60,P=0.002） than those with LRP1b-del（HR=1.30,P=0.31）.Consistent results were observed in the in-house CHOICE-01 cohort,in which nsqNSCLCs with LRP1b-del and sqNSCLCs without LRP1b-del benefited more from toripalimab plus chemotherapy than from chemotherapy alone(Pinteraction=0.008).This multi-cohort study delineates the antithetical impacts of LRP1b-del in nsqNSCLCs and sqNSCLCs on predicting the benefits from ICI alone or with chemotherapy over chemotherapy *** findings highlight the distinct clinical utility of LRP1b-del in guiding treatment choices for nsqNSCLCs and sqNSCLCs,emphasizing the necessity for a detailed analysis based on pathological subtypes when investigating biomarkers for cancer therapeutics.

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Constraints on holographic QCD phase transitions from PTA observations

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 71-78页

作者： Song He Li Li Sai wang Shao-Jiang wang Center for Theoretical Physics and College of Physics Jilin University School of Physical Science and Technology Ningbo University Max Planck Institute for Gravitational Physics (Albert Einstein Institute) CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of Sciences (UCAS) School of Physical Sciences University of Chinese Academy of Sciences (UCAS) Theoretical Physics Division Institute of High Energy Physics Chinese Academy of Sciences

The underlying physics of QCD phase transition in the early Universe remains largely unknown due to its strong-coupling nature during the quark-gluon plasma/hadron gas transition, yet a holographic model has been proposed to quantitatively fit the lattice QCD data while with its duration of the first-order phase transition(FoPT) left undetermined. At specific baryon chemical potential, the first-order QCD phase transition agrees with the observational constraint of baryon asymmetry. It, therefore, provides a scenario for phase transition gravitational waves(GWs) within the standard model of particle physics. If these background GWs could contribute dominantly to the recently claimed common-spectrum red noise from pulsar timing array(PTA) observations, the duration of this FoPT can be well constrained, and the associated primordial black holes are still allowed by current observations.

关键词： pulsar timing array QCD phase transition gravitational waves primordial black holes

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Deep learning identification of novel autophagic protein-protein interactions and experimental validation of beclin 2-Ubiquilin 1 axis in triple-negative breast cancer

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Oncology research 2025年第1期33卷 67-81页

作者： xiang Li Wenke Jin Lifeng Wu Huan wang xin xie Wei Huang b O Liu State Key Laboratory of Southwestern Chinese Medicine Resources School of Pharmacy and College of Medical Technology Chengdu University of Traditional Chinese Medicine Chengdu 611137 China. Department of biotherapy Cancer Center and State Key Laboratory of Biotherapy West China Hospital Sichuan University Chengdu 610041 China.

background:Triple-negative breast cancer (TNbC), characterized by its lack of traditional hormone receptors and HER2, presents a significant challenge in oncology due to its poor response to conventional therapies. Autophagy is an important process for maintaining cellular homeostasis, and there are currently autophagy biomarkers that play an effective role in the clinical treatment of tumors. In contrast to targeting protein activity, intervention with protein-protein interaction (PPI) can avoid unrelated crosstalk and regulate the autophagy process with minimal interference pathways.Methods:Here, we employed Naive bayes, Decision Tree, and k-Nearest Neighbors to elucidate the complex PPI network associated with autophagy in TNbC, aiming to uncover novel therapeutic targets. Meanwhile, the candidate proteins interacting with beclin 2 were initially screened in MDA-Mb-231 cells using beclin 2 as bait protein by immunoprecipitation-mass spectrometry assay, and the interaction relationship was verified by molecular docking and CO-IP experiments after intersection. Colony formation, cellular immunofluorescence, cell scratch and 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) tests were used to predict the clinical therapeutic effects of manipulating candidate PPI.Results:by developing three PPI classification models and analyzing over 13,000 datasets, we identified 3733 previously unknown autophagy-related PPIs. Our network analysis revealed the central role of beclin 2 in autophagy regulation, uncovering its interactions with 39 newly identified proteins. Notably, the CO-IP studies identified the substantial interaction between beclin 2 and Ubiquilin 1, which was anticipated by our model and discovered in immunoprecipitation-mass spectrometry assay results. Subsequently, investigations showed that overexpressing beclin 2 increased Ubiquilin 1, promoted autophagy-dependent cell death, and inhibited proliferation and metastasis in MDA-Mb-231 cells.

关键词： Artificial intelligence (AI) Autophagy beclin 2 Protein-protein interactions (PPI) Triple-negative breast cancer (TNbC) Ubiquilin 1

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RF Optimizer Model for Predicting Compressive Strength of Recycled Concrete

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Journal of Wuhan University of Technology(Materials Science) 2025年第1期40卷 215-223页

作者： LIU Lin wang Liuyan wang Hui SUN Huayue School of Civil Engineering and Architecture Shenyang UniversityShenyang 110000China

Traditional machine learning(ML)encounters the challenge of parameter adjustment when predicting the compressive strength of reclaimed *** address this issue,we introduce two optimized hybrid models:the bayesian optimization model(b-RF)and the optimal model(Stacking model).These models are applied to a data set comprising 438 observations with five input variables,with the aim of predicting the compressive strength of reclaimed ***,we evaluate the performance of the optimized models in comparison to traditional machine learning models,such as support vector regression(SVR),decision tree(DT),and random forest(RF).The results reveal that the Stacking model exhibits superior predictive performance,with evaluation indices including R2=0.825,MAE=2.818 and MSE=14.265,surpassing the traditional ***,we also performed a characteristic importance analysis on the input variables,and we concluded that cement had the greatest influence on the compressive strength of reclaimed concrete,followed by ***,the Stacking model can be recommended as a compressive strength prediction tool to partially replace laboratory compressive strength testing,resulting in time and cost savings.

关键词： machine learning recycled concrete compressive strength

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Structure modification and recovery of amorphous Fe_(73.5)Si_(13.5)b_9Nb_3Cu_1 magnetic ribbons after autoclave treatment:SAxS and thermodynamic analysis

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Journal of Materials Science & Technology 2019年第1期35卷 118-126页

作者： L.Y.Guo x.wang K.C.Shen K.b.Kim S.Lan x.L.wang W.M.wang Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials Ministry of Education Shandong University Department of Nanotechnology and Advanced Materials Engineering Sejong University Herbert Gleiter Institute of Nanoscience School of Materials Science and Engineering Nanjing University of Science and Technology Department of Physics City University of Hong Kong

The structure, crystallization kinetics and magnetic property of as-quenched Fe_(73.5)Si_(13.5)b_9Nb_3Cu_1 amorphous ribbon(R0) as well as ribbons after autoclave treatment at 100°C and 150°C(R1 and R2) have been systematically studied by various techniques. With increasing autoclave treatment temperature,the measured structural, kinetic and magnetic parameters of samples increase firstly, i.e. R0 by transforming from fcc to bcc structure during the autoclave treatment and that the autoclave treatment can decrease the large radius(r_M) MRO(medium range order),but increase the small rMMRO. The measured structural, thermal and magnetic parameters of R2 sample have a tendency to recover toward as-quenched R0 sample. The thermal and magnetic parameters of samples after solely annealing treatment at higher temperature have no obvious recover *** uneven actions of pressure and temperature in autoclave treatment may be helpful for us to search a new method to improve the magnetic properties of Fe-based glasses.

关键词： SAxS (small-angle x-ray scattering) Fe-based glass Thermodynamic Magnetic ribbon DSC (differential scanning calorimetry)

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Silk damping in scalar-induced gravitational waves: a novel probe for new physics

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 79-85页

作者： Yan-Heng Yu Sai wang Theoretical Physics Division Institute of High Energy Physics Chinese Academy of Sciences School of Physics University of Chinese Academy of Sciences

Silk damping is well known in the study of cosmic microwave background（CMb） and accounts for suppression of the angular power spectrum of CMb on large angular multipoles. In this article, we study the effect of Silk damping on the scalar-induced gravitational waves（SIGWs）. Resulting from the dissipation of cosmic fluid, the Silk damping notably suppresses the energydensity spectrum of SIGWs on scales comparable to a diffusion scale at the decoupling time of weakly-interacting particles. The effect offers a novel observable for probing the underlying particle interaction, especially for those mediated by heavy gauge bosons beyond the standard model of particles. We anticipate that pulsar timing arrays are sensitive to gauge bosons with mass ～10～3-10～4 GeV, while space-and ground-based interferometers to those with mass ～10～7-1012GeV, leading to essential complements to on-going and future experiments of high-energy physics.

关键词： scalar-induced gravitational waves Silk damping new physics

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Dative b–N bonds weaven toward two-dimensional purely organic materials for single crystals

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Science China Chemistry 2025年第01期 12-14页

作者： Wei-Lei Zhou Siwei wang Yong Chen College of Chemistry and Materials Science Inner Mongolia Key Laboratory of Chemistry for Nature Products and Synthesis for Functional Molecules Innovation Team of Optical Functional Molecular Devices Inner Mongolia Minzu University College of Chemistry Nankai University

Molecular topology is one of the important topics to study and understand the relationship between molecular structure and properties [1]. Particularly, two-dimensional (2D) materials with extremely high surface area and ultra-thin morphology have attracted considerable attention due to their unexpected properties in materials and chemistry [2,3].

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Near-perfect replication on amorphous alloys through active force modulation based on machine learning/neural network parameter prediction

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 181-191页

作者： Senkuan Meng Zheng wang Ruisong Zhu Ruijie Liu Jiang Ma Lina Hu Weihua wang Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials (Ministry of Education) School of Materials Science and Engineering Shandong University College of Mechatronics and Control Engineering Shenzhen University Songshan Lake Materials Laboratory Institute of Physics Chinese Academy of Sciences

As a microforming technique, micro/nano-structural replication possesses advantages of high precision and efficiency. With the remarkable superplasticity in the supercooled liquid region, amorphous alloys or metallic glasses(MGs) are regarded as ideal materials for miniature fabrication. However, due to the intrinsic metastable nature of supercooled liquids, the design of imprinting processes for MGs poses a challenge. In the past, process parameters have largely relied on trial-and-error *** this work, a low-frequency active force modulation system is employed to apply a stable, precise, and real-time feedback stress field for imprinting of MG samples. Low-frequency vibrations can facilitate the filling of microstructures on the template surface by reducing the effective viscosity of the supercooled liquid. With the dataset composed of orthogonal experiments, a machine learning strategy based on back-propagation(bP) neural networks was utilized to construct a 3D parameter space for temperature, stress, and time, and to predict the corresponding filling ratio. Furthermore, the optimal combination of imprinting process parameters was identified, and its filling ratio was experimentally validated to reach as high as 0.94. The near-perfect replication of microstructures confirms the superiority of the active force modulation system and the data-driven strategy of machine learning-assisted parameter design. At the same time, this one-step microforming process provides a new approach to addressing the accuracy-cost trade-off dilemma in precision manufacturing.

关键词： metallic glass amorphous alloy microforming imprinting machine learning

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Local-contrastive-learning machine with both generalization and adversarial robustness: A statistical physics analysis

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 109-123页

作者： Mingshan xie Yuchen wang Haiping Huang PMI Lab School of Physics Sun Yat-sen University Guangdong Provincial Key Laboratory of Magnetoelectric Physics and Devices Sun Yat-sen University

Distinct from human cognitive processing, deep neural networks trained by backpropagation can be easily fooled by adversarial examples. To design a semantically meaningful representation learning, we discard backpropagation, and instead, propose a local contrastive learning, where the representations for the inputs bearing the same label shrink(akin to boson) in hidden layers,while those of different labels repel(akin to fermion). This layer-wise learning is local in nature, being biologically plausible.A statistical mechanics analysis shows that the target fermion-pair-distance is a key parameter. Moreover, the application of this local contrastive learning to MNIST benchmark dataset demonstrates that the adversarial vulnerability of standard perceptron can be greatly mitigated by tuning the target distance, i.e., controlling the geometric separation of prototype manifolds.

关键词： machine learning statistical mechanics adversarial robustness neural networks

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