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Stereoselective recognition of morphine enantiomers by μ-opioid receptor

National Science Review 2024年第3期11卷 183-191页

作者： Yibo Wang Van A.Ngo Xiaohui Wang Laboratory of Chemical Biology Changchun Institute of Applied ChemistryChinese Academy of Sciences Advanced Computing for Life Sciences and Engineering Group Science Engagement Section National Center for Computational Sciences Oak Ridge National Lab School of Applied Chemistry and Engineering University of Science and Technology of China Beijing National Laboratory for Molecular Sciences

Stereospecific recognition of chiral molecules plays a crucial role in biological systems. The μ-opioid receptor（MOR） exhibits binding affinity towards（-）-morphine, a well-established gold standard in pain management, while it shows minimal binding affinity for the（+）-morphine enantiomer, resulting in a lack of analgesic activity. Understanding how MOR stereoselectively recognizes morphine enantiomers has remained a puzzle in neuroscience and pharmacology for over half-a-century due to the lack of direct observation techniques. To unravel this mystery, we constructed the binding and unbinding processes of morphine enantiomers with MOR via molecular dynamics simulations to investigate the thermodynamics and kinetics governing MOR's stereoselective recognition of morphine enantiomers. Our findings reveal that the binding of（-）-morphine stabilizes MOR in its activated state, exhibiting a deep energy well and a prolonged residence time. In contrast,（+）-morphine fails to sustain the activation state of ***, the results suggest that specific residues, namely D1142.50and D1473.32, are deprotonated in the active state of MOR bound to（-）-morphine. This work highlights that the selectivity in molecular recognition goes beyond binding affinities, extending into the realm of residence time.

关键词： stereoselective recognition thermodynamics and kinetics residence time morphine enantiomers μ-opioid receptor

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An Interpretable Machine Learning Method to Inform Urban Adaptation to Heat Waves

An Interpretable Machine Learning Method to Inform Urban Ada...

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ASCE International Conference on computing in Civil engineering 2023: Resilience, Safety, and Sustainability, i3CE 2023

作者： Liu, Tong Fan, Chao School of Civil and Environmental Engineering and Earth Sciences College of Engineering Computing and Applied Sciences Clemson Univ. ClemsonSC United States

ISBN: (数字)9780784485248

ISBN: (纸本)9780784485248

As the deadliest climate event over the past 30 years in the US, heat waves are projected to be more intense and dangerous under global warming. Cities, composed of unevenly distributed populations and diverse landscape, suffer from significant adverse impacts of heat waves with varying land surface temperatures across different areas. In addition to the climate, effective adaptation to heat waves also requires an understanding and evidence regarding how urban natural, physical, and social processes influence urban thermal variations. Prior studies that mostly rely on traditional regression methods fall short in interpreting the complex role of these factors in influencing fine-scale thermal variations within and even beyond the physical boundaries of these factors. To address this research gap, we propose an interpretable machine learning method based on graph neural network to disentangle the significant factors that influence local thermal conditions. Our model can explain 35.3% extra variations compared to the baseline ridge regression model. The results of the model can inform urban adaptation strategies, including nature-based solutions, such as evaluating the cooling efficiency of spatially variant greening interventions at fine-scale levels. © ASCE *** rights reserved.

关键词： Global warming

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A roulette wheel-based pruning method to simplify cumbersome deep neural networks

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Neural computing and Applications 2024年第22期36卷 13915-13933页

作者： Chan, Kit Yan Yiu, Ka Fai Cedric Guo, Shan Jiang, Huimin School of Electrical Engineering Computing and Mathematics Sciences Curtin University Bentley Australia Department of Applied Maths The Hong Kong Polytechnic University Hong Kong School of Business Macau University of Science and Technology China

Deep neural networks (DNNs) have been applied in many pattern recognition or object detection applications. DNNs generally consist of millions or even billions of parameters. These demanding computational storage and requirements impede deployments of DNNs in resource-limited devices, such as mobile devices, micro-controllers. Simplification techniques such as pruning have commonly been used to slim DNN sizes. Pruning approaches generally quantify the importance of each component such as network weight. Weight values or weight gradients in training are commonly used as the importance metric. Small weights are pruned and large weights are kept. However, small weights are possible to be connected with significant weights which have impact to DNN outputs. DNN accuracy can be degraded significantly after the pruning process. This paper proposes a roulette wheel-like pruning algorithm, in order to simplify a trained DNN while keeping the DNN accuracy. The proposed algorithm generates a branch of pruned DNNs which are generated by a roulette wheel operator. Similar to the roulette wheel selection in genetic algorithms, small weights are more likely to be pruned but they can be kept;large weights are more likely to be kept but they can be pruned. The slimmest DNN with the best accuracy is selected from the branch. The performance of the proposed pruning algorithm is evaluated by two deterministic datasets and four non-deterministic datasets. Experimental results show that the proposed pruning algorithm generates simpler DNNs while DNN accuracy can be kept, compared to several existing pruning approaches. © The Author(s) 2024.

关键词： Deep neural networks

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Improving Existing VANETs by Incorporating UAVS Using Ant Colony and PSO Algorithms 4

Improving Existing VANETs by Incorporating UAVS Using Ant Co...

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4th IEEE International Conference on Data engineering and Communication Systems, ICDECS 2024

作者： Thangam, S. Dixit, Aniket Karn, Alok Kumar Thakur, Arnav Arunbalaji, Cg Jesy Janet Kumari, J. Saleem Raja, A. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India University of Technology and Applied Sciences-Shinas College of Computing and Information Sciences It Department Oman

ISBN: (纸本)9798350393354

Vehicular Ad Hoc Networks (VANETs) are a type of wireless communication network that enable vehicles to communicate with each other and with roadside infrastructure in a peer-to-peer fashion;despite their vast applications, they come with several shortcomings. The key challenges in their real-world implementation include scalability, connectivity, and coverage issues. Unmanned Aerial Vehicles (UAVs), also known as drones, can complement existing VANETs in several ways to enhance their functionality and address some of their limitations. This paper studies the incorporation of UAVs in VANETs to overcome the challenges faced by the present networks. The approach is based on the dynamic deployment of UAVs in the most optimal positions, found by utilizing Particle Swarm Optimization (PSO) and Ant Colony algorithms which analyze the vehicle density, and previous coverage information in the network. The deployment of UAVs is intended to provide a seamless network coverage for ground vehicles. The impact of dynamic UAV mobility in communicating VANETs is comparatively studied. The simulation is done using Network Simulator-3 (NS3) simulator to evaluate the performance of 4 VANET protocols, AODV, DSR, OLSR, and DSDV, in terms of packet delivery ratio (PDR), Average End-to-End Delay, Throughput, Average Throughput per Packet, Packet Drop Rate, and Normalized Routing Load after incorporating the proposed modification designs. The paper concludes that the incorporation of Ant Colony is better suited to enhance VANETs than PSO. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Chest X-Ray-Based Covid-19 Detection Using Preprocessing Techniques and Deep Learning Algorithms 7th

Chest X-Ray-Based Covid-19 Detection Using Preprocessing Tec...

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7th International Conference on Soft computing: Theories and Applications, SoCTA 2023

作者： Kurian, Thanu Thangam, S. Raja, A. Saleem Reddy, K. Lokeswar Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India IT Department College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas Shinas Oman

ISBN: (纸本)9789819720880

Covid-19 is a transmissible infection identified in Wuhan, China, in December 2019 where more than million people were killed. When the contamination enters body, it contacts the mucous film that lies in your mouth, eyes, and nose. The virus entering a cell makes new disease parts making use of the cell and spreads infections by attacking nearest cells. Signs could begin one to fourteen days subsequent to presenting to the infection. Majority of them suffer with pneumonia, while a few suffer with extreme side effects like shortness of breath, headache, and fast heartbeat. Medical practitioners can see indications of respiratory infections on a chest X-ray. So, to detect if a person is suffering with Covid-19, the chest images help in the early analysis and the treatment of the patient. This study utilizes multiple datasets containing images of chest X-ray and makes use of preprocessing techniques like image augmentation, resizing, Contrast-Limited Adaptive Histogram Equalization (CLAHE) and trained using Deep learning algorithms like CNN, VGG-16, VGG-19, and AlexNet. CNN model with a maximum pooling layer and a flatten layer outperformed other algorithms when trained on image augmentation and normalization images and achieved accuracy of 0.98. Finally, integrating the best suitable model with web applications like Flask helps to reduce the burden on doctors and minimize the human error in Covid-19 detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： COVID-19

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Adaptive Strategies for Scalable and Heterogeneous VANET Network 5

Adaptive Strategies for Scalable and Heterogeneous VANET Net...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Thangam, S. Jha, Priyanshu Hawari, Jawed Sah, Nabin Kumar Jain, Nikunj Jesy Janet Kumari, J.J. Saleem Raja, A. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India College of Computing and Information Sciences University of Technology and Applied Sciences It Department Shinas Oman

ISBN: (纸本)9798350385649

Despite technological advancements, Vehicular Ad-hoc Networks (VANETs) continue to face challenges related to reliability and high-speed mobility. This study focuses on enhancing scalability, improving mobility, and addressing network heterogeneity in the context of data dissemination. Data dissemination involves the distribution or transmission of statistical data to users. Tools like NS3, SUMO utilizing OSM for realistic traffic simulations, MATLAB for Data Analysis and evaluation of various protocols like ADHOC, Dynamic Source Routing (DSR), Optimized Link State Routing Protocol (OLSR), Destination Sequenced Distance Vector Routing (DSDV). The research aimed to refine VANET simulations for effective decision making, as its primary objective data dissemination i.e. distribution or transmission of statistical data to users, forming the central focus of this investigation. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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QoS CBSC: An Enhanced Metaheuristic Strategy on QoS-Cloud-based Service in Cloud 5

QoS CBSC: An Enhanced Metaheuristic Strategy on QoS-Cloud-ba...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Jesy Janet Kumari, J. Thangam, S. Saleem Raja, A. Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India College of Computing and Information Sciences University of Technology and Applied Sciences-Shinas It Department Oman

ISBN: (纸本)9798350385649

Due to the growing number of cloud users working on various cloud apps on specific infrastructure, resource allocation in cloud computing is implicitly difficult. Most resource allocation strategies currently in use focused on delivering efficiency determined by the workload of applications across many domains, such as business and scientific. Cloud service brokers compete strongly with one another to provide quality of service improvements based on the need, demand and the rapid growth of the services that are offered. Such conflict makes it difficult and complex to provide simple service selection and composition services in the cloud, this needs to be addressed to lighten the load on local resources. Because cloud-based services are too sophisticated and scalable, it is preferable to use an optimization strategy to choose the services that will meet the needs of the clients. To do this, a hybrid algorithm Modified Ant Colony System Cloud Services Composition (MACSCSC) is proposed to employ Ant Colony Optimization (ACO) incorporated with Genetic Algorithm (GA) to smoothly increase cloud-based services. When allocating cloud resources these meta-heuristic algorithms can achieve significantly better performance, lower costs and instances, better utilization of resources, and increased energy efficiency. The empirical findings on different real datasets have been demonstrated to show the improvements of the suggested method to overcome the drawbacks such as allocation of resources and high power consumption. © 2024 IEEE.

关键词： Quality of service

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Smart Traffic Management in Green VANETs: Ant-Inspired Routing Strategies with Local Search Optimization

Smart Traffic Management in Green VANETs: Ant-Inspired Routi...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Thangam, S. Jahnavi, M. Chandana, K. Dheeraj, K. Janet Kumari, J Jesy Saleem Raja, A. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India University of Technology and Applied Sciences-Shinas College of Computing and Information Sciences It Department Oman

ISBN: (纸本)9798350309140

Green Vehicular Ad-hoc Networks (VANETs) are gaining significance in smart mobility because of growing concerns about the environment. The research introduces a novel method for managing traffic in such networks by combining ant-inspired routing methods with local search optimization approaches. The solution, which draws inspiration from the efficient path-finding abilities of ants, facilitates decentralized communication and sharing of traffic data among cars. It allows rapid adaptation to changing traffic circumstances and environmental elements. Local search optimization enhances routing decisions by considering the near vicinity of each vehicle. Extensive simulations demonstrate notable enhancements in traffic efficiency, decreased congestion, and improved energy conservation as compared to conventional routing methods. The suggested solutions also enhance the development of environmentally friendly and enduring transportation networks. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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OLBR: Optimization of Location-Based Routing Protocols in Vehicular Ad Hoc Networks

OLBR: Optimization of Location-Based Routing Protocols in Ve...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Thangam, S. Srikar Reddy, M. Vivek Ullas, Karthik Khadar Jilani, N. Abdul Venkatesh, K. Janet Kumari, J. Jesy Saleem Raja, A. Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India University of Technology and Applied Sciences-Shinas College of Computing and Information Sciences It Department Oman

ISBN: (纸本)9798350309140

The proposed work employs ns-3, SUMO, and NetAnim to create geographical routing in vehicular ad hoc networks (VANETs). The research attempts to assess the performance of three well-known protocols AODV, DSDV, and OLSR in VANET settings. Utilizing SUMO's realistic mobility models and NS3's simulation capabilities, the project simulates node communication, protocol behaviour, and vehicle movement. The main purpose is to know how well various routing techniques work in dynamic vehicular situations. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Sentinel Species: Towards a Co-Evolutionary Relationship for Raising Awareness about the State of the Air 23

Sentinel Species: Towards a Co-Evolutionary Relationship for...

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17th International Conference on Tangible, Embedded, and Embodied Interaction, TEI 2023

作者： Broscheit, Jessica Draheim, Susanne Von Luck, Kai Wang, Qi Hamburg University of Applied Sciences Germany School of Engineering and Computing University of the West of Scotland United Kingdom

ISBN: (纸本)9781450399777

Interactive technologies are increasingly being used as discursive objects for raising awareness about the environment in the cultural sector, but little is known about the user's lived experience during an interaction. In this study, we present the development and evaluation of an interface designed to raise awareness about the environment within a speculative art installation. For this purpose, we drew on the concept of sentinel species, specifically the miner's canary, to enable a multisensory experience with the state of the air. We then evaluated the interface with 14 participants while interacting in a prototypical arrangement in the laboratory. Overall, the findings indicate promising directions towards a sentinel-species-mimicking interface that communicates the state of the air through its physiological behavior and thus also engages with the user's empathy on a cognitive and emotional level. Based on the findings, we highlight the implications of this study and point to further directions for human-atmosphere interactions. © 2023 ACM.

关键词： Air quality

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