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Role of cancer stem cell ecosystem on breast cancer metastasis and related mouse models

Zoological Research 2024年第3期45卷 506-517页

作者： Xilei Peng Haonan dong Lixing zhang Suling Liu Fudan University Shanghai Cancer Center&Institutes of Biomedical Sciences/State Key Laboratory of Genetic Engineering/Cancer Institutes/Department of Oncology/Key Laboratory of Breast Cancer in Shanghai/Shanghai Key Laboratory of Medical Epigenetics/Shanghai Key Laboratory of Radiation Oncology/International Co-laboratory of Medical Epigenetics and Metabolism Ministry of Science and Technology/Shanghai Medical College/Fudan UniversityShanghai 200032China

Breast cancer metastasis is responsible for most breast cancer-related deaths and is influenced by many factors within the tumor ecosystem,including tumor cells and *** cancer stem cells(BCSCs)constitute a small population of cancer cells with unique characteristics,including their capacity for self-renewal and *** have shown that BCSCs not only drive tumorigenesis but also play a crucial role in promoting metastasis in breast *** tumor microenvironment(TME),composed of stromal cells,immune cells,blood vessel cells,fibroblasts,and microbes in proximity to cancer cells,is increasingly recognized for its crosstalk with BCSCs and role in BCSC survival,growth,and dissemination,thereby influencing metastatic ***,a thorough understanding of BCSCs and the TME is critical for unraveling the mechanisms underlying breast cancer *** this review,we summarize current knowledge on the roles of BCSCs and the TME in breast cancer metastasis,as well as the underlying regulatory ***,we provide an overview of relevant mouse models used to study breast cancer metastasis,as well as treatment strategies and clinical trials addressing BCSC-TME interactions during ***,this study provides valuable insights for the development of effective therapeutic strategies to reduce breast cancer metastasis.

关键词： Breast cancer Metastasis Cancer stem cell Ecosystem Tumor microenvironment Mouse model

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Prospective life cycle assessment of CO2 conversion by photocatalytic reaction

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Green Chemical Engineering 2024年第3期5卷 383-389页

作者： Jun-Qi Wang Xiang-Jun Liu Jing-Jing Ma Shuai zhang Hao-Ran Liu yong-Li dong Qing-Yuan Yang School of Human Settlements and Civil Engineering Xi'an Jiaotong University School of Chemical Engineering and Technology Xi'an Jiaotong University

CO2conversion is gradually seen as a better way for society to effectively use carbon sources and avoid climate crisis associated with fossil CO2emissions. And the decision to deploy CO2technology scale should be relied on its environmental impact. In this work, life cycle assessment model evaluates the environmental performance of CO2conversion by photocatalytic reaction process with two different catalysts（NiAl-LDH and Co-ZIF-9）. Six impact categories considered in this analysis, including climate change, acidification potential, depletion of abiotic resources, eutrophication potential, ozone layer depletion potential, and photochemical oxidation potential. Results indicated that CO2conversion with Co-ZIF-9 photocatalyst has a better environmental impact than the NiAl-LDH photocatalyst route. Moreover, the Co-ZIF-9 catalyst scenario also has a lower total environmental burden than the conventional CO production route. Sensitivity analysis shows that recycle performance of the catalyst is highly sensitive to the production process in two scenarios. This study could provide a framework for robust decisions in CO2conversion by photocatalytic reaction, which is useful for policymakers to decide the feasibility of industrialization.

关键词： Life cycle assessment (LCA) CO 2 Photocatalytic reaction Sensitivity analysis Uncertainty analysis

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A Galaxy Image Augmentation Method Based on Few-shot Learning and Generative Adversarial Networks

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Research in Astronomy and Astrophysics 2024年第3期24卷 180-193页

作者： Yiqi Yao Jinqu zhang Ping Du Shuyu dong School of Artificial Intelligence South China Normal UniversityFoshan 528225China School of Computer Science South China Normal UniversityGuangzhou 510631China Guangdong Construction Vocational Technology Institute Guangzhou 511500China

Galaxy morphology classifications based on machine learning are a typical technique to handle enormous amounts of astronomical observation data,but the key challenge is how to provide enough training data for the machine learning *** this article proposes an image data augmentation method that combines few-shot learning and generative adversarial *** Galaxy10 DECaLs data set is selected for the experiments with consistency,variance,and augmentation effects being *** popular networks,including AlexNet,VGG,and ResNet,are used as examples to study the effectiveness of different augmentation methods on galaxy morphology *** results show that the proposed method can generate galaxy images and can be used for expanding the classification model’s training *** to comparative studies,the best enhancement effect on model performance is obtained by generating a data set that is 0.5–1 time larger than the original data ***,different augmentation strategies have considerably varied effects on different types of ***-GAN achieved the best classification performance on the ResNet network for In-between Round Smooth Galaxies and Unbarred Loose Spiral Galaxies,with F1 Scores of 89.54%and 63.18%,*** comparison reveals that various data augmentation techniques have varied effects on different categories of galaxy morphology and machine learning ***,the best augmentation strategies for each galaxy category are suggested.

关键词： techniques image processing-galaxies structure-galaxies general

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Hydrogen storage thermodynamics and kinetics of as-cast Ce-Mg-Ni-based alloy

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Journal of Iron and Steel Research International 2024年第3期31卷 752-766页

作者： Yan Qi Xin zhang Jun Li dong-liang Zhao Shi-hai Guo Yang-huan zhang Department of Functional Material Research Central Iron and Steel Research InstituteBeijing 100081China China Rare Earth New Material(Weishan)Co..Ltd. Jining 277600ShandongChina Collaborative Innovation Center of Integrated Exploitation of Bayan Obo Multi-Metal Resources Inner Mongolia University of Science and TechnologyBaotou 014010Inner MongoliaChina

The reaction kinetics of alloys based on magnesium are known to be greatly improved by the partial substitution of Mg with rare earths and transition metals,particularly *** enhanced superficial hydrogen dissociation rate,the weakened Mg-H bond and the lower activation energy following element replacement are thought to be related to the better *** experimental alloys Ce5Mg_(95-x)Ni_(x)(x=5,10,15)were smelted by the vacuum induction *** phase transformation and structural evolution of experimental alloys before and after reaction with hydrogen were char-acterized by X-ray diffraction,scanning electron microscopy and transmission electron *** cast specimens contain CeMg_(12),Mg and Mg_(2)Ni phases,and the increase in Ni content results in an obvious growth of Mg_(2)Ni *** isothermal and non-isothermal hydrogenation and dehydrogenation kinetics of the experimental specimens were investi-gated using the Sievert apparatus,differential scanning calorimetry and thermal gravimetric *** activation energy may be calculated using the Arrhenius and Kissinger *** experimental alloys have been shown to have good activation properties,with a reversible hydriding and dehydriding capacities of around 5.0 wt.%in the first *** initial dehydrogenation temperature of MgH_(2) decreases from 557.5 to 537.7 K with changing Ni content from 5 to 15 at.%.The dehydrogenation activation energy also reduces from 77.09 to 62.96 kJ/mol,which explains the improved hydrogen storage performance caused by Ni *** can be shown that the impact of Ni on the decomposition enthalpy of MgH_(2) is quite modest,with the absolute enthalpy(ΔHr)only decreasing from 78.48 to 76.15 kJ/mol.

关键词： Mg-based alloy Ni content Hydrogen storage kinetics Thermodynamic property Activation energy

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Deep learning CNN-APSO-LSSVM hybrid fusion model for feature optimization and gas-bearing prediction

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Petroleum Science 2024年第4期21卷 2329-2344页

作者： Jiu-Qiang Yang Nian-Tian Lin Kai zhang Yan Cui Chao Fu dong zhang College of Earth Sciences and Engineering Shandong University of Science and Technology Laboratory for Marine Mineral Resources Qingdao National Laboratory for Marine Science and Technology Transportation Institute of Inner Mongolia University Key Laboratory of Gas Hydrate Qingdao Institute of Marine Geology Ministry of Natural Resources

Conventional machine learning(CML) methods have been successfully applied for gas reservoir prediction. Their prediction accuracy largely depends on the quality of the sample data; therefore, feature optimization of the input samples is particularly important. Commonly used feature optimization methods increase the interpretability of gas reservoirs; however, their steps are cumbersome, and the selected features cannot sufficiently guide CML models to mine the intrinsic features of sample data efficiently. In contrast to CML methods, deep learning(DL) methods can directly extract the important features of targets from raw data. Therefore, this study proposes a feature optimization and gas-bearing prediction method based on a hybrid fusion model that combines a convolutional neural network(CNN)and an adaptive particle swarm optimization-least squares support vector machine(APSO-LSSVM). This model adopts an end-to-end algorithm structure to directly extract features from sensitive multicomponent seismic attributes, considerably simplifying the feature optimization. A CNN was used for feature optimization to highlight sensitive gas reservoir information. APSO-LSSVM was used to fully learn the relationship between the features extracted by the CNN to obtain the prediction results. The constructed hybrid fusion model improves gas-bearing prediction accuracy through two processes of feature optimization and intelligent prediction, giving full play to the advantages of DL and CML methods. The prediction results obtained are better than those of a single CNN model or APSO-LSSVM model. In the feature optimization process of multicomponent seismic attribute data, CNN has demonstrated better gas reservoir feature extraction capabilities than commonly used attribute optimization methods. In the prediction process, the APSO-LSSVM model can learn the gas reservoir characteristics better than the LSSVM model and has a higher prediction accuracy. The constructed CNN-APSO-LSSVM mode

关键词： Multicomponent seismic data Deep learning Adaptive particle swarm optimization Convolutional neural network Least squares support vector machine Feature optimization Gas-bearing distribution prediction

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Multi-objective optimization and evaluation of supercritical CO_(2) Brayton cycle for nuclear power generation

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Nuclear Science and Techniques 2024年第2期35卷 183-209页

作者： Guo-Peng Yu yong-Feng Cheng Na zhang Ping-Jian Ming Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen UniversityZhuhai 519082China School of Civil Engineering and Architecture East China Jiaotong UniversityNanchang 330013China

The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle *** models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear *** analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic *** show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic *** the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to *** performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,*** performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited *** the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully.

关键词： Supercritical CO_(2)Brayton cycle Nuclear power generation Thermo-economic analysis Multi-objective optimization Decision-making methods

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Regulating the intrinsic electronic structure of carbon nanofibers with high-spin state Ni for sodium storage with high-power density

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Journal of Materials Science & Technology 2024年第4期171卷 16-23页

作者： Zhijia zhang Gang Xie Yuefang Chen Yanhao Wei Mengmeng zhang Shulei Chou Yunxiao Wang Yifang zhang yong Jiang School of Materials Science and Engineering Institute of Quantum Materials and DevicesState Key Laboratory of Separation Membrane and Membrane ProcessesTiangong UniversityTianjin 300387China Institute for Carbon Neutralization College of Chemistry and Materials EngineeringWenzhou UniversityWenzhou 325035China Institute for Superconducting and Electronic Materials University of WollongongWollongongNSW 2522Australia

Carbon nanofibers(CNFs)with high specific surface area show great potential for sodium storage as a hard carbon ***,CNFs anchored with Ni nanoparticles(CNFs/Ni)were prepared through chemical vapor deposition and impregnation reduction methods,in situ growing on the three-dimensional porous copper current collector(3DP-Cu).The coupling effect of high-spin state Ni nanopar-ticles leads to the increase of defect density and the expansion of lattice spacing of ***,the 3DP-Cu ensures a high loading capacity of CNFs and short ion/electron transport *** an integral binder-free anode,the 3DP-Cu/CNFs/Ni exhibits excellent electrochemical performance,which demon-strates a high specific capacity with 298.5 mAh g^(-1)at 1000 mA g^(-1)after 1500 cycles,and a high power density with 200 mAh g^(-1)over 1000 cycles at 5000 mA g^(-1).Density functional theory calculation re-sults show that the high-spin state Ni regulates the electronic structure of CNFs,which significantly reduces the adsorption energy for Na^(+)(-2.7 Ev)and thus enables high-rate *** regulation of the electronic structure of carbon materials by high-spin state metal provides a new strategy for developing high-power carbonaceous anode materials for sodium-ion batteries.

关键词： Carbon nanofibers Ni nanoparticles High-spin state Sodium-ion batteries Anode materials Density functional theory calculation

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Impact wear properties of dissimilar joints between bainitic frogs and pearlite rails

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Journal of Iron and Steel Research International 2024年第1期31卷 275-287页

作者： Pin-yong Zeng Chun-guang Zuo Xiao-yu zhang Wei Li Tribology Research Institute Southwest Jiaotong UniversityChengdu610031SichuanChina

The impact wear behavior and damage mechanism of dissimilar welded joints between U26Mn frog and U75 V rail before and after normalizing treatment were studied by cyclic impact *** experiment indicated that the impact wear volume of the joints increased with the increasing number of impact *** main wear mechanisms include pitting wear,mild fatigue wear,delamination wear,and fatigue wear,and plastic deformation was the primary impact wear *** them,fatigue wear had the greatest influence on wear volume,while other wear mechanisms had limited *** impact wear resistance of the base material was better than that of the heat-affected *** treatment was beneficial to improving the impact wear resistance of welded joints owing to its effect to promote pearlite recovery,grain refinement,and uniform distribution of *** martensite generated in the rail welded joints aggravated the impact wear damage to the materials,which should be avoided.

关键词： Dissimilar welded joint Impact wear Bainitic steel Damage mechanism Wear rate

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Factors affecting the long-term renal allograft survival

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Chinese Medical Journal 2011年第8期124卷 1181-1184页

作者： WANG Wei LI Xiao-bei YIN Hang YANG Xiao-yong LIU Hang REN Liang HU Xiao-peng WANG yong zhang Xiao-dong Department of Urology Beijing Chaoyang Hospital CapitalMedical University Beijing 100020 China

Background In the past decades, the one-year graft survival of cadaveric renal allografts has been markedly improved,but their long-term survival has not kept pace. The attrition rate of renal allografts surviving after one year remains almost unchanged. The causes for late graft loss are multiple. The aim of this study was to analyze the predictive factors that impact long-term survival of grafts after kidney *** We retrospectively analyzed 524 kidney transplantation patients who were treated in our hospital between January 1991 and January 2000, including 254 patients who had lived more than 10 years with normal graft function （long survival group）, and 270 cases whose renal graft had survived less than 10 years （control group）. Specifically, we analyzed 10 factors that may potentially affect graft survival by both univariate and Logistic model multivariate analyses to pinpoint the independent risk *** Univariate analyses showed that no significant differences existed in the age or gender of recipients, dialysis time, lymphotoxin levels, or cold ischemia time between the two groups. However, the ratio of delayed graft function and acute rejection, and the uric acid levels of patients in the long survival group were significantly lower than those in the control group （P ＜0.01）. Furthermore, we found that the concentration of cyclosporin A at one year after transplantation and the histocompatibility antigen match of donor-recipients for patients within the long survival group were significantly higher than those in the control group （P ＜0.01 ）. Furthermore, multivariate analyses showed that these four factors were independent risk factors that impact patient *** The ratios of delayed graft function and acute rejection, the concentration of cyclosporin A at one year after transplantation, and serum uric acid levels are very important factors that affect the long-term survival of renal grafts.

关键词： kidney transplantation long-term survival predictive factors

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Modified NiFe-MOF with defects induced by-NH_(2)and-SH for enhanced adsorption and photocatalytic reduction of CO_(2)

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Journal of Materials Science & Technology 2024年第3期170卷 167-176页

作者： Ruting Huang Yeyin zhang Wenqiang Li Wanxia zhang yong Fang Wenrui zhang Anqi Cui Yu Ying Xianyang Shi Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Province Key Laboratory of Wetland Ecosystem Protection and RestorationSchool of Resource and Environmental EngineeringAnhui UniversityHefei230601China Key Laboratory of Nutrient Cycling Resources and Environment of Anhui Institute of Soil and FertilizerAnhui Academy of Agricultural SciencesHefei230001China

In this study,a Ni-Fe metal-organic framework modified by-NH_(2)and-SH was synthesized using a sim-ple hydrothermal process for enhancing photocatalytic CO_(2)*** expected,the N2S-NiFe(NH_(2)-BDC/H_(4)DSBDC,2:1)displayed excellent photocatalytic CO_(2)reduction activity and high CO selectivity(12,412.23μmol g^(−1)h^(−1))under visible light irradiation(λ≥420 nm),which is 5 times that of ***,the excellent photocatalytic performance of N2S-NiFe can benefit from the rich defect trap site caused by the introduction of amino and sulfhydryl groups,accelerating charge transfer and promoting space charge separation,as supported by the photo-electrochemical *** better understand the CO_(2)adsorption mechanism,density functional theory calculations were performed,which revealed that the Niov site has more negative adsorption energy compared with the Feov *** study provides a simple strategy to establish efficient photocatalysts for CO_(2)reduction through the modification of organic ligands.

关键词： NiFe-MOF CO_(2)reduction Defect structure Photocatalytic

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