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Hydrogen-bonded cytosine-endowed supramolecular polymeric nanogels: Highly efficient cancer cell targeting and enhanced therapeutic efficacy

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Journal of Colloid and Interface Science 2024年 665卷 329-344页

作者： Fan, Wen-Lu Huang, Shan-You Yang, Xiu-Jing Bintang Ilhami, Fasih Chen, Jem-Kun Cheng, Chih-Chia Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei10607 Taiwan Advanced Membrane Materials Research Center National Taiwan University of Science and Technology Taipei10607 Taiwan Department of Natural Science Faculty of Mathematics and Natural Science Universitas Negeri Surabaya Surabaya60231 Indonesia Department of Materials Science and Engineering National Taiwan University of Science and Technology Taipei10607 Taiwan

We demonstrate that cytosine moieties within physically cross-linked supramolecular polymers not only manipulate drug delivery and release, but also confer specific targeting of cancer cells to effectively enhance the safety and efficacy of chemotherapy—and thus hold significant potential as a new perspective for development of drug delivery systems. Herein, we successfully developed physically cross-linked supramolecular polymers (PECH-PEG-Cy) comprised of hydrogen-bonding cytosine pendant groups, hydrophilic poly(ethylene glycol) side chains, and a hydrophobic poly(epichlorohydrin) main chain. The polymers spontaneously self-assemble into a reversibly hydrogen-bonded network structure induced by cytosine and directly form spherical nanogels in aqueous solution. Nanogels with a high hydrogen-bond network density (i.e., a higher content of cytosine moieties) exhibit outstanding long-term structural stability in cell culture substrates containing serum, whereas nanogels with a relatively low hydrogen-bond network density cannot preserve their structural integrity. The nanogels also exhibit numerous unique physicochemical characteristics in aqueous solution, such as a desirable spherical size, high biocompatibility with normal and cancer cells, excellent drug encapsulation capacity, and controlled pH-responsive drug release properties. More importantly, in vitro experiments conclusively indicate the drug-loaded PECH-PEG-Cy nanogels can selectively induce cancer cell-specific apoptosis and cell death via cytosine receptor-mediated endocytosis, without significantly harming normal cells. In contrast, control drug-loaded PECH-PEG nanogels, which lack cytosine moieties in their structure, can only induce cell death in cancer cells through non-specific pathways, which significantly inhibits the induction of apoptosis. This work clearly demonstrates that the cytosine moieties in PECH-PEG-Cy nanogels confer selective affinity for the surface of cancer cells, which enhances t

关键词： Nanogels

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Hybrid Deep Learning Approaches for Data Security in Cloud Environments 2

Hybrid Deep Learning Approaches for Data Security in Cloud E...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Rekha, S. Sharma, Manish Kannan, Sathish Upadhye, Vijay Jagdish Patil, Harshal Rajkumar, S. PSG College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore641 014 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of VLSI Microelectronics Tamil Nadu Chennai602105 India Parul Institute of Applied Sciences Parul University Research and Development Cell Department of Microbiology Gujarat Waghodia India Computer Science and Engineering Maharashtra Pune412115 India Institute of Technology Hawassa University Faculty of Manufacturing Department of Mechanical Engineering Ethiopia

ISBN: (纸本)9798350327533

More recent studies have concentrated on methods to improve the security and privacy of cloud operations for the benefit of user data. For example, in order to safeguard sensitive data kept by a cloud-deployed procedure, it is important to guarantee secure transmission of sensitive data across multiple stakeholders. researchers have investigated deeply into the subject of cloud security concerns, which include data breaches, denial of service, data loss, service rejection, and threats from malicious users. These issues typically stem from problems like multi-tenancy, lack of control over data, and trust issues. Cloud providers are primarily responsible for ensuring the security of the cloud platform. This study proposes a CNN-BiLSTM-AM hybrid model that combines Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and Attention Mechanism (AM). In order to determine how well our proposed hybrid model worked, this study tested it extensively against the traditional CNN and Long Short-Term Memory (LSTM) techniques. An established standard for evaluating intrusion detection systems, the CICIDS 2017 dataset was used in the study. The experimental results demonstrated that the proposed hybrid model outperformed the competition. The proposed hybrid model delivers the highest performance measure of accuracy (99.2%), detection rate (98.807%), and precision (99.59%). © 2024 IEEE.

关键词： Cardiovascular Disease Deep Learning Electrocardiogram Heartbeat Normalization

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Understanding the learning mechanism of convolutional neural network applied to fluorescence spectra 4

Understanding the learning mechanism of convolutional neural...

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AI and Optical Data Sciences IV 2023

作者： Venturini, Francesca Michelucci, Umberto Sperti, Michela Gucciardi, Arnaud Deriu, Marco A. Institute of Applied Mathematics and Physics Zurich University of Applied Sciences Technikumstrasse 9 Winterthur8401 Switzerland TOELT llc Machine Learning Research and Development Birchlenstr. 25 Dübendorf8600 Switzerland Lucerne University of Applied Sciences and Arts Computer Science Department Lucerne Switzerland PolitoBIOMed Lab Department of Mechanical and Aerospace Engineering Politecnico di Torino Turin Italy Artificial Intelligence Laboratory University of Ljubljana Ljubljana Slovenia

ISBN: (纸本)9781510659810

The power of artificial neural networks to determine the quality and properties of olive oil was proven by several studies in the last years. Less clear is, however, how the neural network is able to extract useful information from the input data. This work investigates the learning mechanism of one-dimensional convolutional neural networks (1D-CNNs) trained to predict the physicochemical properties of olive oil from single fluorescence spectra. Such a 1D-CNN can successfully predict the parameters relevant to the quality assessment: acidity, peroxide value, and UV absorbance. To go beyond a simple quality assessment algorithm, it is important to identify which spectral features in the measured spectra are correlated with each chemical parameter and therefore with the quality of olive oil. To obtain this information, explainability techniques can be used by studying the latent feature space generated by the intermediate layers of the one-dimensional trained convolutional neural network. This work analyses in detail the common features that are used by the 1D-CNN to predict the two physicochemical parameters: acidity and K232 © 2023 SPIE.

关键词： Olive oil

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Refined LSTM Network for Sensor-based Human Activity Recognition in Real World Scenario 13

Refined LSTM Network for Sensor-based Human Activity Recogni...

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13th IEEE International Conference on Software engineering and Service Science, ICSESS 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand Faculty of Engineering Mahidol University Image Information and Intelligence Laboratory Department of Computer Engineering Nakhon Pathom Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (数字)9781665410328

ISBN: (纸本)9781665410311

Sensor-based identification of human actions is an essential field of study in ubiquitous computing. This aims to facilitate the assessment or understanding of current occurrences and their context based on sensor signals. Activity recognition is employed in surveillance systems, patient health monitoring, and many other systems involving the interaction between human and intelligent wearable devices, including smartphones and smartwatches. The primary objective of this study work is to identify human behavior in the actual world. We proposed an improved long short-term memory network called RLSTM that uses a squeeze-and-excitation module to efficiently identify human actions and enhance action identification systems' interpretation. A publicly available real-world dataset known as REALWORLD16 was used to train and validate the model five times to analyze the proposed network. The proposed RLSTM achieved the highest accuracy of 98.04% and F1-score of 97.76%, as determined by several investigations. © 2022 IEEE.

关键词： Wearable sensors

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Pre- and Post-renovation Monitoring with Data Analysis as a Means to Determine Reasons for Performance Gaps 15

Pre- and Post-renovation Monitoring with Data Analysis as a ...

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15th International Conference on Thermal Performance of the Exterior Envelopes of Whole Buildings 2022

作者： Rode, Carsten Rasmussen, Christoffer Madsen, Henrik The Department of Environmental and Resource Engineering Technical University of Denmark Kgs. Lyngby Denmark The Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark

ISBN: (纸本)9781955516280

A significant fraction of current building activity in Europe is with renovation of the existing building stock. It is a well-known challenge that the realized energy performance and indoor climate of renovated buildings deviate from what was expected from the design of the renovation. Reasons may lie in poor technical performance of the renovated building envelope parts, such as failures in thermal insulation, leaks, unforeseen thermal bridges, poor workmanship, or insufficient performance of the delivered products. Deviations may also occur with the performance of the building services systems, and most notably with its operation and control. Finally, the influence from occupants of the buildings can be at least as decisive as the technical reasons related to the building products. The paper will outline a methodology whereby it is possible by use of frequently monitored data of energy use for heating, along with measurements of the indoor and outdoor environments to determine the main reasons for possible performance gaps. Key elements to the methodology are frequent measurements, few data points, time series analysis and mathematical modelling. So-called white-, black- or grey-box modelling techniques can be employed to distill the desired performance variables and explain the realized performance. This may in turn lead to guidelines on which technical issues should be rectified, if needed, or as input to feedback to occupants and caretakers that may guide them. The paper will outline the methodology and show examples of use. The method was developed as part of the project REBUS, "Renovating buildings sustainably" where social housing was used as case buildings for demonstration. © 2022 ASHRAE.

关键词： Thermal insulation

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How to beat a Bayesian adversary

arXiv

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arXiv 2024年

作者： Ding, Zihan Jin, Kexin Latz, Jonas Liu, Chenguang Department of Electrical and Computer Engineering Princeton University United States Department of Mathematics Princeton University United States Department of Mathematics University of Manchester United Kingdom Delft Institute of Applied Mathematics Technische Universiteit Delft Netherlands

Deep neural networks and other modern machine learning models are often susceptible to adversarial attacks. Indeed, an adversary may often be able to change a model’s prediction through a small, directed perturbation of the model’s input – an issue in safety-critical applications. Adversarially robust machine learning is usually based on a minmax optimisation problem that minimises the machine learning loss under maximisation-based adversarial attacks. In this work, we study adversaries that determine their attack using a Bayesian statistical approach rather than maximisation. The resulting Bayesian adversarial robustness problem is a relaxation of the usual minmax problem. To solve this problem, we propose Abram – a continuous-time particle system that shall approximate the gradient flow corresponding to the underlying learning problem. We show that Abram approximates a McKean-Vlasov process and justify the use of Abram by giving assumptions under which the McKean-Vlasov process finds the minimiser of the Bayesian adversarial robustness problem. We discuss two ways to discretise Abram and show its suitability in benchmark adversarial deep learning *** Codes 90C15, 65C35, 68T07 © 2024, CC BY.

关键词： Deep neural networks

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A Well-Structured Data Sharing System Implementation using EC Technology and Higher end Communication Network 4

A Well-Structured Data Sharing System Implementation using E...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Kumar, Ashok Jasim, Laith Pramanik, Atreyi Maheswari, G. Sangeetha, S. Angadi, Sanjeevkumar Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India The Islamic University Najaf Iraq Uttaranchal University School of Applied and Life Sciences Division of Research and Innovation Uttarakhand Dehradun India Hindustan Institute of Technology and Science Department of Information Technology Chennai603103 India Cmr College of Engineering & Technology Department of Eee India Nutan College of Engineering and Research Computer Science and Engineering Pune Maharashtra Talegaon Dabhade India

ISBN: (纸本)9798350360165

Vehicular traffic networks (VANETs) are an important part of modern transportation systems, focusing on critical applications such as collision avoidance, lane change assistance, and traffic recognition. However, data distribution in VANETs is very challenging, especially due to the unique characteristics of wireless communications and the dynamic traffic environment. In this context, packet delivery control is very important for proper communication. In this paper, research proposes a new computer defined Vehicle Network (SDVN) Controlled Routing and Scheduling (CDRS) method to solve the complex data distribution problem of VANET unlike existing approaches, CDRS uses SDVN management functions to integrate a routing strategy and a scheduling strategy that optimizes multihop data transmission. Priorities in CDRS are based on routing and scheduling constraints. Current protocols focus on finding the shortest path connecting source and destination pairs, ignoring factors such as packet proximity and link length, but performance is limited in dynamic VANET environments. Similarly, current scheduling approaches focus on single-hop data distribution and ignore the © 2024 IEEE.

关键词： Vehicular ad hoc networks

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GOAL PROGRAMMING APPROACH TO ACHIEVE SUSTAINABLE FOREST MANAGEMENT IN MONGOLIA 44

GOAL PROGRAMMING APPROACH TO ACHIEVE SUSTAINABLE FOREST MANA...

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44th Asian Conference on Remote Sensing, ACRS 2023

作者： Enkhjargal, Usukhbayar Gompil, Battur Tseveen, Batchuluun Sukhbaatar, Gerelbaatar Norovsuren, Bayanmunkh Rentsendagva, Dagva Department of Applied Mathematics National University of Mongolia Ulaanbaatar Mongolia Department of System Dynamics Modelling Centre for Policy Research and Analysis Ulaanbaatar Mongolia Department of Environment and Forest Engineering National University of Mongolia Ulaanbaatar Mongolia Department of Spatial Data Analysis Centre for Policy Research and Analysis Ulaanbaatar Mongolia Centre for Policy Research and Analysis Ulaanbaatar Mongolia

ISBN: (纸本)9781713893646

Forest resources are the most important natural resources on this earth, but they are continuously depleting due to the overgrowth of human population, needs, and industrialization, other development activities. Therefore, the conservation of forest resources has been the main problem for sustainable development and several mathematical and remote sensing methods were used to find the optimal way to achieve it. In this research, Forest Index (FI) was applied to determine forest coverage in the study area. The study area (49°15' to 49°10'N and 104°05' to 104°15'E) is located in the northern region of Mongolia and consists of mixed forest. Larch Forest dominates 86.12% of forests in the study area. FI methodology was applied for the Sentinel data in order to estimate larch forest coverage. The output map of forest coverage was compared with ground truth measurements and forest cover maps. The agreement between the forest cover map and ground measurement was 85%. Additionally, we used goal programming method to find the optimal harvest volume for Mongolian forest resources based on data containing volume and growth. © 2023 ACRS. All Rights Reserved.

关键词： FI forest resource goal programming sentinel

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Double-Quantitative Feature Selection Approach for Multigranularity Ordered Decision Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2385-2396页

作者： Li, Wentao Deng, Chaojun Pedrycz, Witold Castillo, Oscar Zhang, Chao Zhan, Tao Southwest University College of Artificial Intelligence Chongqing400715 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Tijuana Institute of Technology Division of Graduate Studies and Research Tijuana22379 Mexico Shanxi University School of Computer and Information Technology Taiyuan030006 China Southwest University School of Mathematics and Statistics Chongqing400715 China

Double-quantitative-based granular computing implies the systematic perspective, completeness, and accuracy of rough approximation. However, most of the existing research works only focus on the case of single quantification, and there are few research study on the simultaneous computing method of double quantification. In this article, we explore feature selection with double quantification in multigranularity ordered decision systems (MG-ODSs). First, the related concepts of quantitative functions are interpreted from different viewpoints of relative and absolute quantification. Then, the multigranularity double-quantitative rough sets in an ordered decision system (ODS) from optimistic and pessimistic cases, the related properties, and three-way decisions based on the presented quantitative levels are discussed. Furthermore, the greedy algorithm for feature selection is derived. By using 12 datasets from a public repository, evaluations and comparisons are made on the parameter setting and classification accuracy. From these comparative experiments, the advantages and effectiveness of the proposed feature selection algorithm could be demonstrated over the existing approaches. © 2020 IEEE.

关键词： Data mining

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Network on Chip(NoC) Mesh Topology FPGA Verification: Real Time Operating System Emulation Framework 4

Network on Chip(NoC) Mesh Topology FPGA Verification: Real T...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Rane, Udaysing V. Panem, Charanarur Abhyankar, Girish Gad, R.S. Dnyanprassarak Mandal's College and Research Centre Department of Computer Science Goa Bardez Assagao India Indian Institute of Technology Department of Electronics and Communication Engineering Uttarakhand Roorkee India Dnyanprassarak Mandal's College and Research Centre Department of Electronics Goa Bardez Assagao India Goa University School of Physical and Applied Sciences Department of Electronics India

ISBN: (纸本)9798350343670

Network on Chip (NoC) is an upcoming paradigm that copes with the increasing complexity and communication requirement of future System on Chip (SoC). In this paper, we have modeled the NoC traffic characteristics under queuing theory, demonstrating clustered phase over synthetic traffic specially under varying packets and queue/buffer *** have also demonstrated IP core design cycle verification scalable framework in a SoC design paradigm, over a 3x2 mesh NoC framework on Intel Field Programmable Gate Array Logic (FPGA) Cyclone-II FPGA (EP2C70F896), 95nm Very large Scale Integration (VLSI) technology chipset for functional and cycle behaviour characteristics through State-of-the-art software Quartus Prime computer Aided Design(CAD) system. The task management and synchronization of the traffic generation and reception is performed with the help of real-time operating system (RTOS) so as to demonstrate the Device Under Test (DUT). Such frameworks can helps in generating scalable NoCs and their cycle behaviours in re-configurable environments and applications.. © 2024 IEEE.

关键词： Mesh Topology Network on Chip Real Time Operating System System on Chip Validation Verification

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