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Role of copper chelating agents: between old applications and new perspectives in neuroscience

Neural Regeneration Research 2025年第3期20卷 751-762页

作者： Rosalba Leuci Leonardo Brunetti Vincenzo Tufarelli Marco Cerini Marco Paparella Nikola Puvača Luca Piemontese Department of Pharmacy-Pharmaceutical Science University of Bari Aldo MoroBariItaly Department of Precision and Regenerative Medicine and Jonian Area(DiMePRe-J) Section of Veterinary Science and Animal ProductionUniversity of Bari Aldo MoroBariItaly Department of Engineering Management in Biotechnology Faculty of Economics and Engineering Management in Novi SadUniversity Business Academy in Novi SadNovi SadSerbia

The role of copper element has been an increasingly relevant topic in recent years in the fields of human and animal health, for both the study of new drugs and innovative food and feed supplements. This metal plays an important role in the central nervous system, where it is associated with glutamatergic signaling, and it is widely involved in inflammatory processes. Thus, diseases involving copper(Ⅱ) dyshomeostasis often have neurological symptoms, as exemplified by Alzheimer's and other diseases(such as Parkinson's and Wilson's diseases). Moreover, imbalanced copper ion concentrations have also been associated with diabetes and certain types of cancer, including glioma. In this paper, we propose a comprehensive overview of recent results that show the importance of these metal ions in several pathologies, mainly Alzheimer's disease, through the lens of the development and use of copper chelators as research compounds and potential therapeutics if included in multi-target hybrid drugs. Seeing how copper homeostasis is important for the well-being of animals as well as humans, we shortly describe the state of the art regarding the effects of copper and its chelators in agriculture, livestock rearing, and aquaculture, as ingredients for the formulation of feed supplements as well as to prevent the effects of pollution on animal productions.

关键词： agriculture Alzheimer's disease chelators copper feed supplements multi-target

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multi-target combinatory strategy to overcome tumor immune escape

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Frontiers of Medicine 2022年第2期16卷 208-215页

作者： Yingyan Yu Department of General Surgery of Ruijin Hospital Shanghai Institute of Digestive Surgeryand Shanghai Key Laboratory for Gastric NeoplasmsShanghai Jiao Tong University School of MedicineShanghai 200025China

Immune therapy has become the fourth approach after surgery,chemotherapy,and radiotherapy in cancer treatment.Many immune checkpoints were identified in the last decade since ipilimumab,which is the first immune checkpoint inhibitor to cytotoxic T-lymphocyte associated protein 4,had been approved by the US Food and Drug Administration(FDA)for the treatment of unresectable or metastatic melanoma in 2011.The use of several antibody drugs that target PD1/PD-L1 for various cancer treatments has been approved by the FDA.However,fewer people are benefitting from immune checkpoint inhibitor treatment in solid cancers.Approximately 80%of patients do not respond appropriately because of primary or acquired therapeutic resistance.Along with the characterization of more immune checkpoints,the combinatory treatment of multi-immune checkpoint inhibitors becomes a new option when monotherapy could not receive a good response.In this work,the author focuses on the combination therapy of multiple immune checkpoints(does not include targeted therapy of oncogenes or chemotherapy),introduces the current progression of multiple immune checkpoints and their related inhibitors,and discusses the advantages of combination therapy,as well as the risk of immune-related adverse events.

关键词： immune checkpoints multi-target immune escape immune-related adverse events combination therapy

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Identification of multi-target anti-cancer agents from TCM formula by in silico prediction and in vitro validation

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Chinese Journal of Natural Medicines 2022年第5期20卷 332-351页

作者： ZHANG Bao-Yue ZHENG Yi-Fu ZHAO Jun KANG De WANG Zhe XU Lv-Jie LIU Ai-Lin DU Guan-Hua State Key Laboratory of Bioactive Substance and Function of Natural Medicines Institute of Materia MedicaChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing 100050China School of Life Sciences Tsinghua UniversityBeijing 100084China

Cancer is a complex disease associated with multiple gene mutations and malignant phenotypes,and multi-target drugs provide a promising therapy idea for the treatment of cancer.Natural products with abundant chemical structure types and rich pharmacological characteristics could be ideal sources for screening multi-target antineoplastic drugs.In this paper,50 tumor-related targets were collected by searching the Therapeutic target Database and Thomson Reuters Integrity database,and a multi-target anti-cancer prediction system based on mt-QSAR models was constructed by using naïve Bayesian and recursive partitioning algorithm for the first time.Through the multi-target anti-cancer prediction system,some dominant fragments that act on multiple tumor-related targets were analyzed,which could be helpful in designing multi-target anti-cancer drugs.Anti-cancer traditional Chinese medicine(TCM)and its natural products were collected to form a TCM formula-based natural products library,and the potential targets of the natural products in the library were predicted by multi-target anti-cancer prediction system.As a result,alkaloids,flavonoids and terpenoids were predicted to act on multiple tumor-related targets.The predicted targets of some representative compounds were verified according to literature review and most of the selected natural compounds were found to exert certain anti-cancer activity in vitro biological experiments.In conclusion,the multi-target anti-cancer prediction system is very effective and reliable,and it could be further used for elucidating the functional mechanism of anti-cancer TCM formula and screening for multi-target anti-cancer drugs.The anti-cancer natural compounds found in this paper will lay important information for further study.

关键词： Cancer multi-target mt-QSAR model Nave Bayesian Recursive partitioning TCM formulae

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multi-target Track Initiation in Heavy Clutter

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Computers, Materials & Continua 2022年第9期72卷 4489-4507页

作者： Li Xu Ruzhen Lou Chuanbin Zhang Bo Lang Weiyue Ding College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China Norinco Group Air Ammunition Research Institute Co.Ltd. Harbin150036China Harvard Medical School Boston02115USA

In the heavy clutter environment, the information capacity is large,the relationships among information are complicated, and track initiationoften has a high false alarm rate or missing alarm rate. Obviously, it is adifficult task to get a high-quality track initiation in the limited measurementcycles. This paper studies the multi-target track initiation in heavy clutter.At first, a relaxed logic-based clutter filter algorithm is presented. In thealgorithm, the raw measurement is filtered by using the relaxed logic method.We not only design a kind of incremental and adaptive filtering gate, but alsoadd the angle extrapolation based on polynomial extrapolation. The algorithm eliminates most of the clutter and obtains the environment with highdetection rate and less clutter. Then, we propose a fuzzy sequential Houghtransform-based track initiation algorithm. The algorithm establishes a newmeshing rule according to system noise to balance the relationship between thegrid granularity and the track initiation quality. And a flexible superpositionmatrix based on fuzzy clustering is constructed, which avoids the transformation error caused by 0–1 voting method in traditional Hough transform.In addition, the algorithm allows the superposition matrixes of nonadjacentcycles to be associated to overcome the shortcoming that the track can’t beinitiated in time when the measurements appear in an intermittent way. Anda slope verification method is introduced to detect formation-intensive serialtracks. Last, the sliding window method is employed to feedback the trackinitiation results timely and confirm the track. Simulation results verify thatthe proposed algorithms can initiate the tracks accurately in heavy clutter.

关键词： Track initiation heavy clutter multi-target Hough transform

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Few-shot object detection via class encoding and multi-target decoding

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IET Cyber-Systems and Robotics 2023年第2期5卷 1-14页

作者： Xueqiang Guo Hanqing Yang Mohan Wei Xiaotong Ye Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhouChina Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province HangzhouChina

The task of few‐shot object detection is to classify and locate objects through a few annotated samples.Although many studies have tried to solve this problem,the results are still not satisfactory.Recent studies have found that the class margin significantly impacts the classification and representation of the targets to be detected.Most methods use the loss function to balance the class margin,but the results show that the loss‐based methods only have a tiny improvement on the few‐shot object detection problem.In this study,the authors propose a class encoding method based on the transformer to balance the class margin,which can make the model pay more attention to the essential information of the features,thus increasing the recognition ability of the sample.Besides,the authors propose a multi‐target decoding method to aggregate RoI vectors generated from multi‐target images with multiple support vectors,which can significantly improve the detection ability of the detector for multi‐target images.Experiments on Pascal visual object classes(VOC)and Microsoft Common Objects in Context datasets show that our proposed Few‐Shot Object Detection via Class Encoding and multi‐target Decoding significantly improves upon baseline detectors(average accuracy improvement is up to 10.8%on VOC and 2.1%on COCO),achieving competitive performance.In general,we propose a new way to regulate the class margin between support set vectors and a way of feature aggregation for images containing multiple objects and achieve remarkable results.Our method is implemented on mmfewshot,and the code will be available later.

关键词： Class Margin Few-Shot Object Detection multi-target Transformer

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Cardinality compensation method based on information-weighted consensus filter using data clustering for multi-target tracking

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Chinese Journal of Aeronautics 2019年第9期32卷 2164-2173页

作者： Sunyoung KIM Changho KANG Changook PARK Department of Mechanical and Aerospace Engineering Seoul National UniversitySeoul 08826Republic of Korea BK21+Transformative Training Program for Creative Mechanical and Aerospace Engineers Seoul National UniversitySeoul 08826Republic of Korea Automation and Systems Research Institute Seoul 08826Republic of Korea Institute of Advanced Aerospace Technology Seoul 08826Republic of Korea

In this paper, a cardinality compensation method based on Information-weighted Consensus Filter(ICF) using data clustering is proposed in order to accurately estimate the cardinality of the Cardinalized Probability Hypothesis Density(CPHD) filter. Although the joint propagation of the intensity and the cardinality distribution in the CPHD filter process allows for more reliable estimation of the cardinality(target number) than the PHD filter, tracking loss may occur when noise and clutter are high in the measurements in a practical situation. For that reason, the cardinality compensation process is included in the CPHD filter, which is based on information fusion step using estimated cardinality obtained from the CPHD filter and measured cardinality obtained through data clustering. Here, the ICF is used for information fusion. To verify the performance of the proposed method, simulations were carried out and it was confirmed that the tracking performance of the multi-target was improved because the cardinality was estimated more accurately as compared to the existing techniques.

关键词： Cardinality compensation Cardinalized probability hypothesis density filter Data clustering Information-weighted consensus filter multi-target tracking

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multi-target Collaborative Combat Decision-Making by Improved Particle Swarm Optimizer

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Transactions of Nanjing University of Aeronautics and Astronautics 2018年第1期35卷 181-187页

作者： Ding Yongfei Yang Liuqing Hou Jianyong Jin Guting Zhen Ziyang Science and Technology on Avionics Integration Laboratory Shanghai 200233P.R.China College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016P.R.China China National Aeronautical Radio Electronics Research Institute Shanghai 200233P.R.China

A decision-making problem of missile-target assignment with a novel particle swarm optimization algorithm is proposed when it comes to a multiple target collaborative combat situation.The threat function is established to describe air combat situation.Optimization function is used to find an optimal missile-target assignment.An improved particle swarm optimization algorithm is utilized to figure out the optimization function with less parameters,which is based on the adaptive random learning approach.According to the coordinated attack tactics,there are some adjustments to the assignment.Simulation example results show that it is an effective algorithm to handle with the decision-making problem of the missile-target assignment(MTA)in air combat.

关键词： collaborative combat multi-target decision-making improved particle swarm optimization

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Classification on Grade, Price, and Region with multi-Label and multi-target Methods in Wineinformatics

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Big Data Mining and Analytics 2020年第1期3卷 1-12页

作者： James Palmer Victor S.Sheng Travis Atkison Bernard Chen the Department of Computer Science University of Central ArkansasConwayAR 72034USA the Department of Computer Science University of AlabamaTuscaloosaAL 35487USA

Classifying wine according to their grade,price,and region of origin is a multi-label and multi-target problem in wineinformatics.Using wine reviews as the attributes,we compare several different multi-label/multitarget methods to the single-label method where each label is treated independently.We explore both single-label and multi-label approaches for a two-class problem for each of the labels and we explore both single-label and multi-target approaches for a four-class problem on two of the three labels,with the third label remaining a twoclass problem.In terms of per-label accuracy,the single-label method has the best performance,although some multi-label methods approach the performance of single-label.However,multi-label/multi-target metrics approaches do exceed the performance of the single-label method.

关键词： classification informatics machine learning multi-label multi-target support vector machines wine wineinformatics

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A multi-target stance detection based on Bi-LSTM network with position-weight

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High Technology Letters 2020年第4期26卷 442-447页

作者：徐翼龙 Li Wenfa Wang Gongming Huang Lingyun Smart City College Beijing Union UniversityBeijing 100101P.R.China College of Robotics Beijing Union UniversityBeijing 100101P.R.China Tianyuan Network Co. Ltd.Beijing 100193P.R.China Beijing Tianyuan Network Co. Ltd.Beijing 100193P.R.China Chinatelecom Information Development Co. Ltd.Beijing 100093P.R.China

In the task of multi-target stance detection,there are problems the mutual influence of content describing different targets,resulting in reduction in accuracy.To solve this problem,a multi-target stance detection algorithm based on a bidirectional long short-term memory(Bi-LSTM)network with position-weight is proposed.First,the corresponding position of the target in the input text is calculated with the ultimate position-weight vector.Next,the position information and output from the Bi-LSTM layer are fused by the position-weight fusion layer.Finally,the stances of different targets are predicted using the LSTM network and softmax classification.The multi-target stance detection corpus of the American election in 2016 is used to validate the proposed method.The results demonstrate that the Bi-LSTM network with position-weight achieves an advantage of 1.4%in macro average F1 value in the comparison of recent algorithms.

关键词： long short-term memory(LSTM) multi-target natural language processing stance detection

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Novel real-time safety algorithm for predicting multi-targets in the farmland road

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International Journal of Agricultural and Biological Engineering 2023年第5期16卷 198-203页

作者： Xiaoming Liang Fu’en Chen Longhan Chen Deyue Li Bin Guo Yubo Liang Yubin Lan China North Vehicle Research Institute Beijing 100072China School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China Department of Mechanical Engineering Oakland UniversityRochesterMI 48309USA School of Electronics and Electrical Engineering Beijing Jiaotong University Haibin CollegeHuanghua 061199HebeiChina The First Research Institute of the Ministry of Public Security Beijing 100048China College of Engineering South China Agricultural UniversityGuangzhou510642China

The more information obtained about the driving environment,the more ensures driving safety.Due to the complex driving environment of farmland roads,targets beside the road sometimes have an important impact on driving safety.To achieve this goal,a novel real-time detection and prediction algorithm of targets was proposed.The whole image was divided into four parts by RCM:driving region,crossroad region,roadside region,and the other region.In addition,a safety policy for every part was enforced by the algorithm,which was based mainly on the combination of the YOLACT and GPM.On this basis,a self-collected data set of 5000 test samples is used for testing.The detection accuracy of the algorithm in the data set could reach up to 90%,and the processing speed to 30.4 fps.In addition,experiments were carried out on actual farmland roads,and the results showed that the proposed algorithm was able to detect,track,and predict targets on the farmland road,and alarm to driver in time before the targets rush into the road.This study provides an important reference for the safe driving of agricultural vehicles.

关键词： real-time safety algorithm for predicting multi-target farmland road computer vision

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