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Propagating subduction initiation of the Caroline Plate at the Mussau Trench

Science China Earth Sciences 2025年第1期68卷 253-269页

作者： Chun-Feng LI Yongkang DENG Tao WU Dongdong DONG Guoliang ZHANG Jiazheng ZHANG Yutao LIU Yaqing LI Fugui TANG Lulu ZHANG Department of Marine Sciences Zhejiang University laboratory of Marine Mineral Resources Laoshan Laboratory Key lab of Marine Geology and Environment Institute of Oceanology Chinese Academy of Sciences Key lab of Ocean and Marginal Sea Geology South China Sea Institute of Oceanology Chinese Academy of Sciences Science & Technology on Integrated Information System lab Institute of Software Chinese Academy of Sciences

New seismic imaging from the Mussau Trench confirms that, under horizontal compression on an oceanic plate boundary, it is within the young and weak oceanic plate that a trench will develop, and it is the young plate, not so long after its birth, that starts to subduct toward the old plate. The Mesozoic Pacific Plate cannot easily bend, under the joint horizontal compression and vertical gravity pull, even with a pre-existing weak zone in place and with a large age offset across the plate boundary. The bending of the Caroline Plate near the northern end of the Mussau Trench started only after a long-time span of trench migration, and/or after a prolonged transition from initial compression to trench development. Highly contrasting trench deformation and sedimentary accretion between two parallel seismic sections support an evident migration of underthrusting or significant longitudinal heterogeneity along the trench. The swift transition from underthrusting to non-thrusting can be explained by a counter-clockwise rotation of the Caroline Plate. That miniature accretionary wedges develop even before the subduction initiates imply strong compression and uplifting before the underthrusting can kick-start. Rotation of the Caroline Plate have been halted as the Mussau Trench is seismically rather quiet and the Lyra Trough to the east shows relaxational extension.

关键词： Mussau Trench Subduction initiation Seismic reflection Caroline Plate

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Combinatorial therapies for spinal cord injury repair

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Neural Regeneration Research 2025年第5期20卷 1293-1308页

作者： Carla S.Sousa Andreia Monteiro António J.Salgado Nuno A.Silva Life and Health Sciences Research Institute(ICVS) School of MedicineUniversity of MinhoCampus de Gualtar-gualtarBragaPortugal ICVS/3B’s Associate lab PT Government Associated LabCampus de Gualtar-gualtarBragaPortugal

Spinal cord injuries have profound detrimental effects on individuals, regardless of whether they are caused by trauma or non-traumatic events. The compromised regeneration of the spinal cord is primarily attributed to damaged neurons, inhibitory molecules, dysfunctional immune response, and glial scarring. Unfortunately, currently, there are no effective treatments available that can fully repair the spinal cord and improve functional outcomes. Nevertheless, numerous pre-clinical approaches have been studied for spinal cord injury recovery, including using biomaterials, cells, drugs, or technological-based strategies. Combinatorial treatments, which target various aspects of spinal cord injury pathophysiology, have been extensively tested in the last decade. These approaches aim to synergistically enhance repair processes by addressing various obstacles faced during spinal cord regeneration. Thus, this review intends to provide scientists and clinicians with an overview of pre-clinical combinatorial approaches that have been developed toward the solution of spinal cord regeneration as well as update the current knowledge about spinal cord injury pathophysiology with an emphasis on the current clinical management.

关键词： electric stimulation neural tissue regeneration neuroprotection polytherapy spinal cord injury

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Optimizing battery deployment: Aging trajectory prediction enabling homogenous performance grouping

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Journal of Energy Chemistry 2025年第1期100卷 565-577页

作者： Shuquan Wang Feng Gao Zhan Ma Hao Tian Yusen Zhang School of Control Science and Engineering Shandong University Key lab of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University

As battery deployments in electric vehicles and energy storage systems grow, ensuring homogeneous performance across units is crucial. We propose a multi-derivative imaging fusion(MDIF) model, employing advanced imaging and machine learning to predict battery aging trajectories from minimal initial data, thus facilitating effective performance grouping before deployment. Utilizing a derivative strategy and Gramian Angular Difference Field for dimensional enhancement, the MDIF model uncovers subtle predictive features from discharge curve data after only ten cycles. The architecture includes a parallel convolutional neural network with lateral connections to enhance feature integration and *** on a self-developed dataset, the model achieves an average root-mean-square error of 0.047 Ah and an average mean absolute percentage error of 1.60%, demonstrating high precision and *** robustness is further validated through transfer learning on two publicly available datasets, adapting with minimal retraining. This approach significantly reduces the testing cycles required, lowering both time and costs associated with battery testing. By enabling precise battery behavior predictions with limited data, the MDIF model optimizes battery utilization and deployment strategies, enhancing system efficiency and sustainability.

关键词： Lithium-ion battery Battery prognostics Fusion model Convolutional neural network Transfer 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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Electrochemical sensor for dopamine detection based on multiwalled carbon nanotube/molybdenum disulfide quantum dots with machine learning integration and anti-interference capability

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Tungsten 2025年第1期7卷 137-151页

作者： Geethukrishnan Onkar Apte Kiran Kumar Tadi Energy Electrodics and Electrocatalysis (EEE) Lab Department of Chemistry School of Advanced Sciences Vellore Institute of Technology School of Computer Science and Engineering Vellore Institute of Technology

The precise detection and measurement of dopamine（DA）,a crucial neurotransmitter in the human body,plays a significant role in diagnosing,preventing,and treating neurological diseases associated with its levels.A highly sensitive DA electrochemical sensor was constructed by combining molybdenum disulfide quantum dots（MSQDs） with multiwalled carbon nanotubes（MWCNTs）.The MSQDs were synthesized using the shear exfoliation *** sensors consist of MSQDs with Mo-S edge catalytic centers for the DA redox reaction,and MWCNTs amplify the sensor *** linearity of the sensor for the detection of DA was tested in the presence of ascorbic acid(AA,50 μmol·L-1) and uric acid(UA,200 μmol·L-1),and exhibited linearity from 2 to 966 μmol·L-1of DA with 0.097 μA(mol·L-1)-1sensitivity and a low limit of detection of0.6 μmol·L-1（the ratio between signal and noise,S/N=3）.Moreover,the sensitivity and selectivity of the sensor were also studied using *** is no increase in amperometric current after adding the most potentially interfering *** sensor was successfully applied to recover DA in human blood sera ***,machine learning algorithms were operated to aid in the near-precise detection of DA in the heterogeneous mixture containing AA and *** algorithms facilitate the identification and quantification of DA amidst coexisting interferents,including AA,that are commonly present in biological matrices.

关键词： Biosensor Complex mixture Dopamine Machine learning MoS2 Multiwalled carbon nanotubes

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RE-SEGNN:recurrent semantic evidence-aware graph neural network for temporal knowledge graph forecasting

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Science China(Information Sciences) 2025年

作者： Wenyu CAI Mengfan LI Xuanhua SHI Yuanxin FAN Quntao ZHU Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology

Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.

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Investigating Müller glia reprogramming in mice: a retrospective of the last decade, and a look to the future

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Neural Regeneration Research 2025年第4期20卷 946-959页

作者： Zhiyuan Yin Jiahui Kang Xuan Cheng Hui Gao Shujia Huo Haiwei Xu Key lab of Visual Damage and Regeneration&Restoration of Chongqing Southwest Eye HospitalSouthwest HospitalThird Military Medical University(Army Medical University)ChongqingChina

Müller glia,as prominent glial cells within the retina,plays a significant role in maintaining retinal homeostasis in both healthy and diseased *** lower vertebrates like zebrafish,these cells assume responsibility for spontaneous retinal regeneration,wherein endogenous Müller glia undergo proliferation,transform into Müller glia-derived progenitor cells,and subsequently regenerate the entire retina with restored ***,Müller glia in the mouse and human retina exhibit limited neural reprogramming.Müller glia reprogramming is thus a promising strategy for treating neurodegenerative ocular disorders.Müller glia reprogramming in mice has been accomplished with remarkable success,through various *** in molecular,genetic,epigenetic,morphological,and physiological evaluations have made it easier to document and investigate the Müller glia programming process in ***,there remain issues that hinder improving reprogramming efficiency and ***,understanding the reprogramming mechanism is crucial toward exploring factors that will improve Müller glia reprogramming efficiency,and for developing novel Müller glia reprogramming *** review describes recent progress in relatively successful Müller glia reprogramming *** also provides a basis for developing new Müller glia reprogramming strategies in mice,including epigenetic remodeling,metabolic modulation,immune regulation,chemical small-molecules regulation,extracellular matrix remodeling,and cell-cell fusion,to achieve Müller glia reprogramming in mice.

关键词： cell fusion chemical small-molecules epigenetic extracellular matrix immune metabolic mice Müller glia neurodegenerative diseases reprogramming retina regeneration

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Secretome of polarized macrophages:potential for targeting inflammatory dynamics in spinal cord injury

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Neural Regeneration Research 2025年第11期20卷 3231-3232页

作者： Andreia Monteiro Susana Monteiro Nuno A.Silva Life and Health Sciences Research Institute(ICVS) School of MedicineUniversity of MinhoBragaPortugal ICVS/3B’s Associate lab PT Government Associated LabBraga/GuimaraesPortugal

Spinal cord injury(SCI)involves an initial traumatic phase,followed by secondary events such as ischemia,increased blood-spinal cord barrier permeability,ionic disruption,glutamate excitotoxicity,and metabolic alterations.A pe rsistent and exagge rated inflammato ry response within the spinal cord accompanies these events(Lima et al.,2022).The complexity and interplay of these mechanisms exacerbate the initial injury,leading to a degenerative process at the injury *** the initial trauma is unavoidable,the secondary injury begins within minutes and can last for months,creating an optimal window for therapeutic intervention.

关键词： alterations injury companies

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AN INVESTIGATION OF RETINEX ALGORITHMS FOR IMAGE ENHANCEMENT

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Journal of Electronics(China) 2007年第5期24卷 696-700页

作者： Lei Ling Zhou Yinqing Li Jingwen 201 lab Beijing University of Aeronautics and Astronautics Beijing 100083 China

This letter introduces color constancy and Retinex theory for image *** clas- sifies Retinex algorithms into four categories and provides their principles and implementations in *** experimental results of Frankle-McCann,MSR (Multi-Scale Retinex) and PNSD (Pro- jected Normalized Steepest Descent) Retinex algorithms are presented and ***, variance and average gradient are proposed to evaluate the performance of the different algorithms.

关键词： Color constancy Retinex Frankle-McCann MSR (Multi-Scale Retinex) PNSD (Pro-jected Normalized Steepest Descent)

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Anti-amyloid antibodies in Alzheimer’s disease: what did clinical trials teach us?

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Neural Regeneration Research 2025年第4期20卷 1092-1093页

作者： Danko Jeremic Lydia Jiménez-Díaz Juan D.Navarro-López NeuroPhysiology&Behavior lab University of Castilla-La ManchaBiomedical Research Center(CRIB)School of Medicine of Ciudad RealCiudad RealSpain

Although many causes of Alzheimer’s disease(AD)may exist,both the original amyloid cascade and tau hypotheses posit that abnormal misfolding and accumulation of amyloid-β(Aβ)and tau protein is the central event causing the ***,that conclusion could be only partly true,and there is conflicting evidence about the role of both proteins in AD,being able to precede and influence one *** researchers argue that these proteins are mere executors rather than primary causes of ***,there have been continuing refinements of both hypotheses,with alternative explanations proposed.Aβand tau proteins may be independently involved in specific neurotoxic pathways;yet there may be other crucial processes going on in early ***,accumulating evidence suggests that Aβand tau act synergistically,rather than additively in disease onset(Jeremic et al.,2021,2023a).

关键词： amyloid Alzheimer additive

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