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Dimeric natural product panepocyclinol A inhibits STAT3 via di-covalent modification

Acta Pharmaceutica Sinica B 2025年第1期15卷 409-423页

作者： Li Li Yuezhou Wang Yiqiu Wang Xiaoyang Li Qihong Deng Fei Gao Wenhua Lian Yunzhan Li Fu Gui Yanling Wei Su-Jie Zhu Cai-Hong Yun Lei Zhang Zhiyu Hu Qingyan Xu Xiaobing Wu Lanfen Chen Dawang Zhou Jianming Zhang Fei Xia Xianming Deng State Key Laboratory of Cellular Stress Biology School of Life SciencesFaculty of Medicine and Life SciencesXiamen UniversityXiamen 361102China State-province Joint Engineering Laboratory of Targeted Drugs from Natural Products Xiamen UniversityXiamen 361102China Cancer Research Center of Xiamen University Xiamen 361102China School of Chemistry and Molecular Engineering NYU-ECNU Center for Computational Chemistry at NYU ShanghaiEast China Normal UniversityShanghai 200062China Department of Biochemistry and Biophysics Institute of Systems BiomedicineSchool of Basic Medical SciencesPeking University Health Science CenterBeijing 100191China Institute of Translational Medicine&Zhangjiang Institute for Advanced Study Shanghai Jiao Tong UniversityShanghai 201203China Department of Hematology the First Affiliated Hospital of Xiamen UniversityXiamen UniversityXiamen 361003China

Homo-or heterodimeric compounds that affect dimeric protein function through interaction between monomeric moieties and protein subunits can serve as valuable sources of potent and selective drug ***,we screened an in-house dimeric natural product collection,and panepocyclinol A(PecA)emerged as a selective and potent STAT3 inhibitor with profound anti-tumor *** cross-linking C712/C718 residues in separate STAT3 monomers with two distinct Michael receptors,PecA inhibits STAT3 DNA binding affinity and transcription *** dynamics simulation reveals the key conformation changes of STAT3 dimers upon the di-covalent binding with PecA that abolishes its DNA ***,PecA exhibits high efficacy against anaplastic large T cell lymphoma in vitro and in vivo,especially those with constitutively activated STAT3 or STAT3^(Y640F).In summary,our study describes a distinct and effective di-covalent modification for the dimeric compound PecA to disrupt STAT3 function.

关键词： Signal transducer and activator of transcription 3 STAT3 inhibitor Anaplastic large T cell

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Sewer Network Model of a City with a Medium-Sized Population

Sewer Network Model of a City with a Medium-Sized Population

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International Carpathian control Conference (ICCC)

作者： Iulian Vasiliev Laurentiu Luca Marian Barbu Ramon Vilanova Sergiu Caraman Department of Automation and Electrical Engineering Faculty of Automation Computers Electrical and Electronic Engineering Galati Romania Department of Automation Systems and Advanced Control Research Autonomous University of Barcelona Barcelona Spain

A model of a sewer network for a medium city located in Easter Romania with 250,000 inhabitants is presented in the paper. The model was developed using BSM Sewer software package. An influent was also determined considering the 5 collecting areas and defined on a time- horizon of 28 days. The influent has the following components: a domestic one calculated using the average wastewater production of each inhabitant with specific loads and considering daily, weekly and yearly variations, a pluviometric one proportional to the collecting area surface that includes a rain event and a storm one and an industrial influent (considered only for the 5 th collecting area). The model was tested in different operating conditions of the sewer network considering three operating points given by the values of the valve opening and pumps flows at the tank outlets. The results were analyzed based on 10 performance indicators implemented by researchers from Lund University for BSM Sewer. They showed the need for an optimization of the operating regime of the sewer network in terms of volume and quality of discharged water.

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Development of Soft Magnetic Composites for Improving the Efficiency of Electric Vehicle's Wireless Power Transfer

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SSRN 2023年

作者： Li, Yanjie Li, Feng Zhou, Siqi Ma, Xiaolei Hou, Yue Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang University Beijing100191 China Department of Civil Engineering Faculty of Science and Engineering Swansea University SwanseaSA2 8PP United Kingdom

Integrating the wireless power transfer (WPT) system into pavement is an effective way to solve the inconvenience of electric vehicles charging, accelerating the growth of ownership of EVs to achieve net carbon zero in transportation. However, the existence of a cement paving layer between coils was a negative impact factor. Here, two types of ferrite powders were separately mixed into the cement to prepare soft magnetic composite (SMC) material to enhance the coupling degree of coils. The magnetic and mechanical performance of the composite were tested by vibrating sample magnetometer and compressive and flexural strength. Furthermore, to explore the optimal layout of the magnetized pavement structure, nine pavement layouts were designed based on the distribution of magnetic flux. Finally, the performance of the WPT system was validated. Results showed the higher the ferrite powder content, the stronger its magnetic properties but the weaker its mechanical performance. It was recommended to place SMC in the area of pavement inside the empty core and outside the coil. By incorporating N/C-10 material, the efficiency achieved the highest, 95.44%. However, considering the mechanical performance, it was recommended to use the SMC prepared with a 0.6 mass ratio of Mn-Zn ferrite powder to cement. © 2023, The Authors. All rights reserved.

关键词： Magnetic properties

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The Development of Deep Learning Methods to Select Passion Fruit for the Ageing Society

The Development of Deep Learning Methods to Select Passion F...

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2021 Research, Invention, and Innovation Congress: Innovation Electricals and Electronics, RI2C 2021

作者： Duangsuphasin, Akksatcha Rungsaksangmanee, Preecha Kengpol, Athakorn Elfvengren, Kalle King Mongkut's University of Technology North Bangkok Department of Industrial Engineering Faculty of Engineering Bangkok Thailand Infirmary of Kasetsart University Bangkok Thailand Mongkut's University of Technology North Bangkok Advanced Industrial Engineering Management Systems Research Center Department of Industrial Engineering Faculty of Engineering King Bangkok Thailand Lut University School of Engineering Science Department of Industrial Engineering and Management Lappeenranta Finland

ISBN: (纸本)9781665403009

The objective of this research is to develop a decision support framework using deep learning methods to select passion fruit for the ageing society. Many substances present in passion fruit such as vitamin A, vitamin C, and vitamin E can contribute to beneficial effects in our body. Especially, it could help to reduce the risk of cardiovascular diseases suitable for the ageing society. It is expected that 4-age levels of passion fruit for three groups of ageing society can be classified and suggested by using multi-layer perceptron neural network (MLPNN) architectures in Python program. The implications of the study are that selecting age levels of passion fruit is appropriate for three ageing society groups. The deep neural network model can generate the accuracy of a trained dataset is 73.9 % and a tested dataset is 72.5%. This model is used to create the computer software which is convenient for the selection of passion fruit or other fruits for the fruit juice industry. © 2021 IEEE.

关键词： Neural network models

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Energy harvesting from living plant: A review on past research and way forward

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Energy Reports 2025年 14卷 268-281页

作者： Sk Rakibur Rahman Ngu Eng Eng Muhammad Aqeel Ashraf Wai-Leong Pang Kar Ban Tan Ajay Kumar Singh Kah-Yoong Chan Faculty of Artificial Intelligence and Engineering Multimedia University Persiaran Multimedia Cyberjaya Selangor 63100 Malaysia Centre for Electric Energy and High Voltage Engineering COE for Robotics and Sensing Technologies Multimedia University Persiaran Multimedia Cyberjaya Selangor 63100 Malaysia School of Engineering Faculty of Innovation and Technology Taylor’s University Subang Jaya Selangor 47500 Malaysia Department of Chemistry Faculty of Science Universiti Putra Malaysia Serdang Selangor 43400 Malaysia Faculty of Electronics and Communications Engineering NIIT University Rajasthan India Centre for Advanced Devices and Systems COE for Robotics and Sensing Technologies Multimedia University Persiaran Multimedia Cyberjaya Selangor 63100 Malaysia

In the face of the global warming crisis, reliance on non-renewable energy sources poses significant ecological threats, prompting the international community to explore renewable alternatives. Numerous alternative energy sources are currently being studied, including solar, wind, hydropower, geothermal, plant-based, and biomass. This review highlights recent advancements in extracting energy from living plants by looking at 70 peer-reviewed research studies published from 2010 to 2025, including 15 that focused on Plant-Based Cells (PBCs), 24 on Plant Microbial Fuel Cells (PMFCs), and 21 that examined the physical traits of plants that influence how efficiently energy can be harvested. PMFC harnesses electron generation through the interaction of plant roots and soil bacteria during organic matter decomposition, while PBC captures electrical energy directly from the plant’s metabolic processes via embedded electrodes. PMFCs typically generate lower voltage and power, whereas PBCs achieve significantly higher outputs, up to 3.5 V and 58.8 mW. The analysis examines key factors influencing energy efficiency, including plant species, age, soil composition, electrode materials, environmental conditions, and circuit design, and addresses scalability and integration challenges. The findings underscore the necessity for further research on optimal sizing, aging effects, and practical applications. Overall, this review aims to provide a solid foundation for advancing green energy technologies and their deployment in low-energy devices, such as IoT sensors, in smart farming and remote areas.

关键词： PBC PMFC Plant-Based Energy Living Plant Electrode Potential Energy Harvesting Low-Energy

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Predicting of Uncertainty Facial Expressions in Children with Autism Spectrum Disorder Based on an Improved Fuzzy Convolutional Neural Network Model

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SSRN 2024年

作者： Abd-Alhalem, Samia M. Ali, Hesham A. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Takieldeen, Ali E. Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa35712 Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Zagazig University Egypt Menoufia University Egypt

Facial expression recognition plays a pivotal role in comprehending emotions and social interactions, particularly in individuals grappling with autism spectrum disorder (ASD), who often encounter challenges in non-verbal communication. For some with ASD, even subtle shifts in their facial expressions can prove elusive, rendering it arduous to categorize their emotional states accurately. Consequently, the analysis and processing of facial expression data hold the potential to facilitate early autism diagnosis, thereby enabling timely therapeutic interventions and alleviating the economic and psychological burdens experienced by patients. In this study, we introduce an enhanced approach that leverages a fuzzy convolutional neural network (CNN) and an Improved Binary Genetic Algorithm (IBGA) for autism diagnosis based on facial expression data. Our model confronts the intricate task of recognizing facial images by selecting salient features employing the IBGA method. Moreover, it employs fuzzy weights to ascertain the probability of similarity within the classifier layer. Our proposed model has yielded impressive outcomes, boasting an average accuracy of 0.9405 for ASD detection and 0.9365 for the recognition of six distinct facial expressions. These results hold profound implications for the enhancement of emotion recognition systems and the refinement of interventions aimed at bolstering social interactions for individuals grappling with ASD. © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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Interaction-Aware Cut-In Trajectory Prediction and Risk Assessment in Mixed Traffic

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1752-1762页

作者： Xianglei Zhu Wen Hu Zejian Deng Jinwei Zhang Fengqing Hu Rui Zhou Keqiu Li Fei-Yue Wang the College of Intelligence and Computing Tianjin UniversityTianjin 300350 the China Automotive Technology and Research Center Co.Ltd. Tianjin 300300China the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle EngineeringHunan UniversityChangsha 410082China the Department of Mechanical and Mechatronics Engineering University of WaterlooWaterlooON N2L3G1Canada the School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China the Macao University of Science and Technology MacaoChina the Waytous Inc. Qingdao 266000China IEEE the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Accurately predicting the trajectories of surrounding vehicles and assessing the collision risks are essential to avoid side and rear-end collisions caused by *** improve the safety of autonomous vehicles in the mixed traffic,this study proposes a cut-in prediction and risk assessment method with considering the interactions of multiple traffic *** integration of the support vector machine and Gaussian mixture model(SVM-GMM)is developed to simultaneously predict cut-in behavior and *** dimension of the input features is reduced through Chebyshev fitting to improve the training efficiency as well as the online inference *** on the predicted trajectory of the cut-in vehicle and the responsive actions of the autonomous vehicles,two risk measurements are introduced to formulate the comprehensive interaction risk through the combination of Sigmoid function and Softmax ***,the comparative analysis is performed to validate the proposed method using the naturalistic driving *** results show that the proposed method can predict the trajectory with higher precision and effectively evaluate the risk level of a cut-in maneuver compared to the methods without considering interaction.

关键词： Cut-in behavior interaction-aware mixed traffic risk assessment trajectory prediction

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Assessing the relative activity of faulting along both flanks of the Ou Backbone Range, Tohoku Region, Japan, from fluvial geomorphic analyses

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Progress in Earth and Planetary science 2024年第1期11卷 1-15页

作者： Shyu, J. Bruce H. Liao, Jhih-Hao Chen, Chia-Yu Tsutsumi, Hiroyuki Iryu, Yasufumi Department of Geosciences National Taiwan University Taipei Taiwan Department of Earth and Environmental Sciences National Chung Cheng University Chiayi Taiwan Department of Environmental Systems Science Faculty of Science and Engineering Doshisha University Kyotanabe Japan Institute of Geology and Paleontology Graduate School of Science Tohoku University Sendai Japan Advanced Institute for Marine Ecosystem Change (WPI-AIMEC) Tohoku University Sendai Japan

The Ou Backbone Range in the Tohoku Region of Japan is bounded on its both sides by two major active fault systems: the Western Fault Zone of Kitakami Lowland in the east and the Eastern Fault Zone of Yokote Basin in the west. Although these two systems are primary active fault systems in the region, information on their long-term activity characteristics is still quite limited. Therefore, we analyzed the normalized channel steepness indexes of river valleys trending perpendicular to the range front along both flanks of the Ou Backbone Range. Our results show that the eastern flank has gentler river valleys, whereas rivers along the northwestern flank are steeper. Our field investigation shows that knickpoints in this area are mostly related to local lithologic boundaries or are check dams along the valleys, thus the river systems are likely under steady-state conditions. Hence, the steeper river valleys in northwestern Ou Backbone Range indicate a higher uplift rate of the area. Because both fault systems are primarily dip-slip reverse faults and do not have significant variations in their subsurface geometry, the faster uplift suggests that the northern segment of the Eastern Fault Zone of Yokote Basin has a higher slip rate. This is consistent with results of previous studies, and the fact that the rupture of the 1896 Rikuu earthquake, the only historical surface-rupturing event in this region, was only limited along the northern segment of this fault system.

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Distributed No-Regret Learning for Stochastic Aggregative Games over Networks

Distributed No-Regret Learning for Stochastic Aggregative Ga...

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第41届中国控制会议

作者： Jinlong Lei Peng Yi Li Li Department of Control Science and Engineering Tongji University Institute for Advanced Study Tongji University Shanghai Research Institute for Intelligent Autonomous Systems

In this work,we propose a distributed no-regret learning for stochastic aggregative games over time-varying *** consider a finite set of players repeatedly playing the network aggregative game in stochastic regimes,where each player has an expectation-valued objective function depending on its own strategy and the aggregate of all player strategies,and the players can estimate gradients of their objective functions up to a zero-mean error with bounded *** consider the scenario in which players cannot directly obtain the aggregate value,but they are able to share their estimates of the aggregate with their neighbors without disclosing their own *** design a distributed learning algorithm based on the mirror descent and dynamical averaging *** then provide analysis for both the variational regret and the cost regret for aggregative games with player-specific problem being convex,and show that the expected regret bounds can be O(K) for specific *** analyses indicate the key correlations between the regret bounds,the network connectivity,and game structures,*** addition,we validate the almost sure convergence to the Nash equilibrium for the class of strictly monotone ***,we present preliminary numerics by applying the proposed scheme to the Nash-Cournot competition problem.

关键词： Distributed No-Regret Learning for Stochastic Aggregative Games over Networks

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Predicting of Uncertainty Facial Expressions in Children with Autism Spectrum Disorder Based on an Improved Fuzzy Convolutional Neural Network Model

SSRN

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SSRN 2024年

作者： Abd-Alhalem, Samia M. Ali, Hesham A. El-Shafai, Walid Algarni, Abeer D. Soliman, Naglaa F. El-Samie, Fathi E. Faculty of Artificial Intelligence Delta University for Science and Technology Gamasa35712 Egypt Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Zagazig University Egypt Menoufia University Egypt

关键词： Emotion Recognition

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