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Integrated proteomic profiling identifies amino acids selectively cytotoxic to pancreatic cancer cells

The Innovation 2024年第3期5卷 138-145页

作者： Alfred Akinlalu Zachariah Flaten Komila Rasuleva Md Saimon Mia Aaron Bauer Santhalingam Elamurugan Nega Ejjigu Sudipa Maity Amara Arshad Min Wu Wenjie Xia Jia Fan Ang Guo Sijo Mathew Dali Sun Department of Electrical and Computer Engineering University of Denver2155 E Wesley AvenueDenverCO 80210USA Biomedical Engineering Program North Dakota State University1401 Centennial BoulevardEngineering AdministrationRoom 203FargoND 58102USA Department of Pharmaceutical Sciences School of PharmacyNorth Dakota State University1001 S.1401 Albrecht Boulevard Sudro HallFargoND 58102USA Materials and Nanotechnology Program North Dakota State University1410 North 14th AvenueCIE 201FargoND 58102USA Wenzhou Institute University of Chinese Academy of SciencesWenzhouZhejiang 325000China Department of Aerospace Engineering Iowa State UniversityAmesIA 50011USA Center for Cellular and Molecular Diagnostics Tulane University School of Medicine1430 Tulane AvenueNew OrleansLA 70112USA Department of Biochemistry and Molecular Biology Tulane University School of Medicine1430 Tulane AvenueNew OrleansLA 70112USA Knoebel Institute for Healthy Aging University of Denver2155 E Wesley AvenueDenverCO 80210USA

Pancreatic adenocarcinoma(PDAC)is one of the most deadly cancers,characterized by extremely limited therapeutic options and a poor prognosis,as it is often diagnosed during late disease *** and selective treatments are urgently needed,since current therapies have limited efficacy and significant side *** proteomics analysis of extracellular vesicles,we discovered an imbalanced distribution of amino acids secreted by PDAC tumor *** findings revealed that PDAC cells preferentially excrete proteins with certain preferential amino acids,including isoleucine and histidine,via extracellular *** amino acids are associated with disease progression and can be targeted to elicit selective toxicity to PDAC tumor *** in vitro and in vivo experiments demonstrated that supplementation with these specific amino acids effectively eradicated PDAC ***,we also identified XRN1 as a potential target for these amino *** high selectivity of this treatment method allows for specific targeting of tumor metabolism with very low toxicity to normal ***,we found this treatment approach is easy-to-administer and with sustained tumor-killing ***,our findings reveal that exocytosed amino acids may serve as therapeutic targets for designing treatments of intractable PDAC and potentially offer alternative treatments for other types of cancers.

关键词： finding identif selectivity

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Near-linear time approximation algorithms for k-means with outliers 24

Near-linear time approximation algorithms for k-means with o...

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Proceedings of the 41st International Conference on Machine Learning

作者： Junyu Huang Qilong Feng Ziyun Huang Jinhui Xu Jianxin Wang School of Computer Science and Engineering Central South University Changsha China and Xiangjiang Laboratory Changsha China Department of Computer Science and Software Engineering Penn State Erie The Behrend College Department of Computer Science and Engineering State University of New York at Buffalo NY School of Computer Science and Engineering Central South University Changsha China and Xiangjiang Laboratory Changsha China and The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha China

The k-means with outliers problem is one of the most extensively studied clustering problems in the field of machine learning, where the goal is to discard up to z outliers and identify a minimum k-means clustering on the remaining data points. Most previous results for this problem have running time dependent on the aspect ratio Δ(the ratio between the maximum and the minimum pairwise distances) to achieve fast approximations. To address the issue of aspect ratio dependency on the running time, we propose sampling-based algorithms with almost linear running time in the data size, where a crucial component of our approach is an algorithm called Fast-Sampling. Fast-Sampling algorithm can find inliers that well approximate the optimal clustering centers without relying on a guess for the optimal clustering costs, where a 4-approximate solution can be obtained in time $O(\frac{ndk\log\log n}{\epsilon^2})$ with O(k/ε) centers opened and (1 + ε)z outliers discarded. To reduce the number of centers opened, we propose a center reduction algorithm, where an O(1/ε)-approximate solution can be obtained in time $O(\frac{ndk\log \log n}{\epsilon^2} + dpoly(k, \frac{1}{\epsilon})\log(n\Delta))$ with (1 + ε)z outliers discarded and exactly k centers opened. Empirical experiments suggest that our proposed sampling-based algorithms outperform state-of-the-art algorithms for the k-means with outliers problem.

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Drowsiness Detection Using CNN Architecture

Drowsiness Detection Using CNN Architecture

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International Conference on Futuristic Technologies (INCOFT)

作者： N.T. Renukadevi K. Kishore K. Saraswathi T. Roshan Nithish Department of Computer Technology-UG Kongu Engineering college Erode India B.Sc. (Software Systems) Kongu Engineering College Erode India B.Sc.(Computer Systems and Design) Kongu Engineering College Erode India

Nowadays, road accidents occur owing to the distractions of people who drives the vehicles by Internal or External factors. Using of Mobile phones and driving continuously for several hours are some of the reasons of driver distraction. Among these, the main distractions faced by drivers are drowsiness. Specifically, a driver turns to sleep while driving causes an accident. Latest studies reveal that approximately 20% of vehicle hits have caused by drowsy drivers. And now, these types of accidents can be detected by using modern software technologies. In that, Deep learning is a category of Artificial Intelligence (AI) concepts, which imitate the way humans, gain certain types of knowledge. Deep Learning plays a vital role in health care industry also. The main characteristic of deep learning is, it can built a model automatically by self-learning. In deep learning, Convolutional Neural Network (CNN) is a type of deep neural networks, which is applied to investigate visual imagery. In this research work, a CNN model will be built to identify the drowsiness of the driver by observing the driver's face reactions. The driver drowsiness dataset is downloaded from data-flair website and trained with CNN architecture. In this work, an image of driver's face will be captured through web camera and compared with trained images .If matches occur, an alarm will be given to make awareness to the driver and to avoid accidents.

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SSRN

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SSRN 2022年

作者： Khalid, Rida Majid, Muhammad Anwar, Syed Muhammad University of Engineering And Technology Taxila Department of Software Engineering Punjab Taxila Pakistan University of Engineering And Technology Taxila Department of Computer Engineering Punjab Taxila Pakistan

Streaming content can engage people from all walks of life and in recent years people tend to spend a significant amount of time interacting with videos, television shows, and podcasts. User engagement plays a vital role when dealing with such content on online streaming platforms. However, quantifying user engagement with the streaming content is a challenging task. Towards this, there are different parameters through which user engagement can be represented, such as the number of views, likes, dislikes, and the number of comments. In general, the popularity or user engagement of a video can not be directly established using simple indices such as view count. Herein, we have combined implicit and explicit feedback (and call this metric ranking score) from viewers to deduce the popularity of the video content. Further, a new metric: comparative watch percentage, is proposed which is computed by using views and watch duration during a specified period. Particularly, we propose a method for ranking score calculation, where we combine the results of implicit (watch duration and watch percentage, and relative watch percentage) and explicit (number of likes and dislikes) feedback. By using multiple linear regression we predict the ranking score for a video with a root mean squared error of 0.446. We further propose a search mechanism in which we identify similar videos based on title similarity with the user query. Our predicted ranking score is used for sorting the search results for video recommendation systems. Hence, through this search mechanism we attempt to solve the cold start problem in video recommendation systems. attempt to solve the cold start problem in video recommendation systems. attempt to solve the cold start problem in video recommendation systems. to solve the cold start problem in video recommendation systems. © 2022, The Authors. All rights reserved.

关键词： Recommender systems

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Face Inverse Rendering from Single Images in the Wild

Face Inverse Rendering from Single Images in the Wild

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Wang, Meng Dai, Wenjing Guo, Xiaojie Zhang, Jiawan School of Computer Software Tianjin University Tianjin China Technical University of Denmark Department of Management Engineering Denmark Key Research Center for Surface Monitoring and Analysis of Cultural Relics Sach China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Face inverse rendering, an important and challenging task in computer vision and computer graphics, attempts to decompose face image into shape, reflectance, and illuminance. This problem becomes fundamentally difficult under non-laboratory conditions without controlled illumination. Though recent works have produced compelling results, most of these techniques rely on multiple lighting images captured under contronlled lighting by complex equipment, such as Light Stage, which is not flexible and applicable to common users. In this paper, we propose a novel face inverse rendering framework, which neither relies on complex devices nor labeled training data. Instead, it learns reflectance, shape, and illuminance from its physical constraints. Extensive experiments on both synthetic and real image datasets demonstrate consistently superior performance of the proposed method. Our code will be made publicly available. © 2022 IEEE.

关键词： Reflection

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Predicting the insulating paper state of the power transformer based on XGBoost/LightGBM models

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Scientific Reports 2025年第1期15卷 1-16页

作者： Ghoneim, Sherif S. M. Baz, Mohammed Alzaed, Ali Zewdie, Yohannes Tesfaye Department of Electrical Engineering College of Engineering Taif University Taif 21944 Saudi Arabia Department of Computer Engineering College of Computers and Information Technology Taif University Taif 21944 Saudi Arabia Architectural Engineering Program - Civil Engineering Department Faculty of Engineering Taif University Taif 21944 Saudi Arabia Department of Electrical and Computer Engineering Faculty of Technology Debre Markos University P. BOX 269 Debre Markos Ethiopia

Power transformer plays a crucial role in the power networks. Most of the transformer malfunctions was due to failure in the insulating systems. The utilities keen on the contentious operation of the power network, so early detection of the transformer faults avoiding the undesirable outage of the transformer from the service. The insulating paper state is an indication to the transformer health and aging of it may lead to failure of the transformer, so some periodic and routine test must be performed on the insulting oil to get information about the insulating paper condition. The value of the degree of polymerization (DP) is a key of the insulating paper state. Various recommended tests such as the dissolved gases (DGA), breakdown voltage (BDV), oil interfacial tension (IF), oil acidity (ACI), moisture content (MC), oil color (OC), dielectric loss (Tan δ), and furans concentration specifically (2-furfuraldehyde (2-FAL)) were carried out to correlate between these variables and the DP and then to the insulating oil state. The collected data from these tests were used to supply XGBoost/LightGBM to build artificial intelligence model to predict the insulating paper state. The results indicated that the great ability of the proposed model to predict the insulating state with high accuracy. Of these various configurations for these two classification models, one achieved perfect prediction accuracy (100%) of the insulating paper state prediction accuracy. The other configurations showed accuracy values from 1.0 down to 0.955. © The Author(s) 2025.

关键词： Dissolved gas analysis Furnic compounds Insulating oil Insulating paper Power transformer XGBoost/LightGBM

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Exploring the Potential of 3d Printing Technology for Sustainable Plastic Roads: A Preliminary Investigation

SSRN

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SSRN 2023年

作者： Al-Atroush, Mohamed Ezzat Almushcab, Jumana Alhudaif, Duha Meskinyar, Yosra Civil and Environmental Engineering Program College of Engineering Prince Sultan University Riyadh11543 Saudi Arabia Software Engineering College of Computer and Information Systems Prince Sultan University Riyadh11543 Saudi Arabia Architectural Engineering Department College of Architecture and Design Prince Sultan University Riyadh11543 Saudi Arabia

The urgency of climate change has highlighted the need for sustainable road construction materials, replacing the conventional asphalt that significantly contributes to global warming and the urban heat island effect. With that in mind, the construction of the world's first 30-meter plastic road in Zwolle, Netherlands, in 2018 opened the door for novel plastic applications as paving materials. However, the current application is still limited to sidewalks and light-loaded cycling lanes. This preliminary study investigated the feasibility of using 3D printing technology to provide the required structural design flexibility for manufacturing plastic pavement modules able to sustain heavy traffic and extreme weather conditions. The suitability of six plastic materials for 3D printing was evaluated. Polylactic Acid (PLA), and Polyethylene Terephthalate Glycol (PETG) were identified as the most suitable materials for this study. Three small-scale structural systems, namely hollow modular with plastic columns, hollow modular with solid plastic cones, and hollow modular with X-bracing, were designed and successfully printed using the adopted materials and a 3D printer. The developed systems were then tested under compression to evaluate their structural performance under heavy traffic loads and prove the concept. The results showed that the PLA conic structural system was effective and exhibited the highest compression strength, while PETG conic system exhibited ductile behavior with superior thermal stability. The study suggested that the hybrid system of PLA and PETG materials may improve overall performance, combine flexibility and strength, and potentially improve resistance to extreme weather and heavy traffic. These findings proved that 3D printing technology has the potential to revolutionize the road construction industry and provide more sustainable solutions for infrastructure development. © 2023, The Authors. All rights reserved.

关键词： Plastic bottles

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Overlapped Pedestrian Detection Based on YOLOv5 in Crowded Scenes 3

Overlapped Pedestrian Detection Based on YOLOv5 in Crowded S...

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3rd International Conference on computer Vision, Image and Deep Learning and International Conference on computer engineering and Applications, CVIDL and ICCEA 2022

作者： Guo, Weijie Shen, Nanbo Zhang, Tianze Hunan University of Technology Department of Computer Science and Technology Zhuzhou China China University of Geosciences School of Information Engineering Beijing China Beijing University of Technology School of Software Beijing China

ISBN: (纸本)9781665459112

Pedestrian detection in a crowded environment is challenging for vehicle intelligent driving systems. At present, pedestrian detection algorithms have achieved great performance in detecting well-separated figures. However, pedestrians are generally overlapped in crowded scenes, resulting in slow detection speed, low detection accuracy, and poor robustness in pedestrian detection technology. In this paper, the YOLOv5 algorithm is used for pedestrian detection. In the aspect of data pretreatment, Mosaic data enhancement, unified image size, adaptive anchor frame calculation, and other processing are carried out for ***5 can detect targets at multiple scales, and CIOU_Loss and DIOU_nms are applied to the YOLOv5 algorithm. It can improve the recognition ability of the occlusion target and has a good detection effect on the detection of the occlusion pedestrian target through the training network of amplified data set. The verification experiment shows that the pedestrian detection model based on YOLOv5 has great detection accuracy and recall rate in detecting covered pedestrians. © 2022 IEEE.

关键词： Feature extraction

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Absolute Pose from One or Two Scaled and Oriented Features

Absolute Pose from One or Two Scaled and Oriented Features

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jonathan Ventura Zuzana Kukelova Torsten Sattler Dániel Baráth Department of Computer Science & Software Engineering Cal Poly San Luis Obispo Faculty of Electrical Engineering Visual Recognition Group Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Department of Computer Science Computer Vision and Geometry Group ETH Zürich

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Keypoints used for image matching often include an estimate of the feature scale and orientation. While recent work has demonstrated the advantages of using feature scales and orientations for relative pose estimation, relatively little work has considered their use for absolute pose estimation. We introduce minimal solutions for absolute pose from two oriented feature correspondences in the general case, or one scaled and oriented correspondence given a known vertical direction. Nowadays, assuming a known direction is not particularly restrictive as modern consumer devices, such as smartphones or drones, are equipped with Inertial Measurement Units (IMU) that provide the gravity direction by default. Compared to traditional absolute pose methods requiring three point correspondences, our solvers need a smaller minimal sample, reducing the cost and complexity of robust estimation. Evaluations on large-scale and public real datasets demonstrate the advantage of our methods for fast and accurate localization in challenging conditions. Code is available at https://***/danini/absolute-pose-from-oriented-and-sealed-features.

关键词： Location awareness Accuracy Simultaneous localization and mapping Measurement units Pose estimation Noise Inertial navigation

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Impact of Digital Twin Technology Utilization in Manufacturing on Sustainability: An Industrial Case Study

Impact of Digital Twin Technology Utilization in Manufacturi...

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Portland International Conference on Management of engineering and Technology (PICMET)

作者： A. Fırat Ünal Özlem Albayrak Perin Ünal Electrical and Computer Engineering University of California Los Angeles USA Research and Development TEKNOPAR Ankara Türkiye Software Engineering Department TEDU Ankara Türkiye

Parallel to the advancements in technologies that form the core of Industry 4.0, there is an accelerated use of digital twins in many industries. Digital twin utilization aims at various objectives including but not limited to increasing overall equipment efficiency, minimizing energy consumption, enabling predictive maintenance, process optimization, condition monitoring, and waste minimization. Being one of the essential components in contemporary technology management, digital twins positively impact economic and environmental sustainability. This study briefs the management and development phases of a cognitive digital twin implemented as an H2020 Research and Development project for a steel pipe manufacturing factory. The study also presents literature review results related to the impact of digital twins on sustainability, with a special emphasis on energy consumption. The study explains the experiences and the lessons learned, during the digital twin project. Based on the observed experiences, the authors state that in the use of the digital twin technology, the evidenced benefit of technology is a major reason for the technology utilization. The developed digital twin enabled a 10% decrease in energy consumption of the production line and resulted in a reduction in carbon footprint.

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