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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Okafor, Paul Siddique, Zahed Razzaghi, Talayeh University of Oklahoma Data Science and Analytics Institute Norman United States University of Oklahoma School of Aerospace and Mechanical Engineering Norman United States University of Oklahoma School of Industrial and Systems Engineering Norman United States

ISBN: (纸本)9798350362480

Recombinant protein production in Escherichia coli (E. coli) is crucial for biotechnological applications, but optimizing protein titer is challenging and often requires iterative experimentation and expert guidance. This study utilizes advanced filtering techniques - Kalman, particle, bilateral, and extended Kalman filters - to enhance titer estimation accuracy using fermentation data provided by Cytovance Biologics. The proposed techniques effectively impute missing titer values and are validated on a secondary dataset from Chinese Hamster Ovary (CHO) cell lines, demonstrating robustness and accuracy. By reducing the need for extensive sampling and iterative experimentation, these filtering techniques enable predictive control over fermentation conditions. One significant cost-saving advantage is identifying the protein titer plateau, allowing lab scientists to conclude the fermentation process at the optimal time. This approach ensures maximum protein production while preventing unnecessary resource consumption. As a result, this method significantly lowers the overall cost of recombinant protein production in E. coli while enhancing process efficiency. © 2024 IEEE.

关键词： Wiener filtering

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Optimizing Protein Titer Production Using Animal Cells: Predictive Modeling and Recommendations for Enhanced Yield

Optimizing Protein Titer Production Using Animal Cells: Pred...

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IISE Annual Conference and Expo 2024

作者： Polk, Jackson E. Derakhshi, Mohammad Le, Huynh Wickham, Stephanie Razzaghi, Talayeh School of Industrial and Systems Engineering University of Oklahoma NormanOK United States Data Science and Analytics Institute University of Oklahoma NormanOK United States Cytovance Biologics Oklahoma CityOK United States

ISBN: (纸本)9781713877851

Current biomanufacturing processes rely heavily on human expertise, struggling to adapt to the growing complexity of bioprocessing. Decision support tools based on machine learning models play a vital role in optimizing the production and timely detection of anomalies in the production line and can save thousands of dollars and reduce quality control costs. We have performed a thorough analysis on a pharmaceutical dataset to identify significant variables that can affect protein titer production. Based on this analysis, we develop machine learning-based models for production of the protein titer in the biopharmaceutical/biotechnology-based manufacturing industry. We envision the practical applications of our models in other biopharmaceutical and biotechnology industries, leading to increased productivity and cost-effectiveness in protein production within animal cells. © IISE Annual Conference and Expo *** rights reserved.

关键词： Adversarial machine learning

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BERD+: A Generic Sequential Recommendation Framework by Eliminating Unreliable data with Item- and Attribute-level Signals

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ACM Transactions on Information systems 2023年第2期42卷 1-33页

作者： Sun, Yatong Yang, Xiaochun Sun, Zhu Wang, Bin School of Computer Science and Engineering Northeastern University School of Computing Macquarie University China Australia Institute of High Performance Computing Centre for Frontier AI Research A*STAR Singapore School of Computer Science and Engineering Northeastern University China National Frontiers Science Center for Industrial Intelligence and Systems Optimization China Key Laboratory of Data Analytics and Optimization for Smart Industry [Northeastern University Ministry of Education China

A preliminary work of our study was published at IJCAI’21 [47], which is substantially extended in the following aspects: (1) In Section 1, we analyze the necessity of introducing item attributes for detecting unreliable instances, together with the problems and challenges that attributes may bring in. (2) In Section 2, we add discussions about the limitations of existing attribute-aware recommender systems (Section 2.2) and denoising methods (Section 2.3) in the context of detecting unreliable instances. (3) In Section 4.2, we further conduct an in-depth analysis at the attribute level to demonstrate the capability of attributes for rectifying instance loss and uncertainty, as well as the disturbance caused by attributes. (4) We generalize BERD to a generic framework BERD+ in Section 5.1, equipped with novel modules, i.e., HU-GCN (Section 5.2) and EPE (Section 5.4), which properly incorporate item attributes while reducing their disturbance for rectifying instance uncer tainty (Section 5.5) and loss (Section 5.6). The generic BERD+ can be flexibly plugged into existing SRSs for performance enhanced recommendation via eliminating unreliable data. (5) In Section 6.2, we apply our BERD+ framework to seven state-of-the-art SRSs on five real-world datasets to illustrate its superiority. (6) To avoid unfair comparison caused by item attributes, we build and compare with the baseline that combines the original BERD and an advanced attribute-aware recommender system, KSR [19]. (7) For more comprehensive comparison, in Section 6.2.2, we compare BRED+ with two state-of-the-art denoising approaches;in Section 6.2.3, to examine the efficacy of HU-GCN and EPE, we compare HU-GCN with various attribute embedding techniques, i.e., variants of graph neural networks, and compare EPE with different attribute fusing methods, i.e., adding, concatenation, and weighted sum. (8) In Section 6.2.4, a detailed ablation study is conducted to verify the effectiveness of each module of BERD+. (

关键词： Convolution

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TRDM: A Two-stage Real-time Discrimination Method for Spiral Weld Defects under Dynamic Distorted Imaging

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IEEE Transactions on Automation science and engineering 2025年 22卷 15420-15434页

作者： Cui, Wenqi Song, Kechen Zhang, Yu Jia, Xiujian Liu, Xiaozheng Yan, Yunhui Northeastern University School of Mechanical Engineering and Automation Liaoning Shenyang110819 China Northeastern University National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China Northeastern University Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry China

Non-destructive testing of weld based on radio-graphic images is crucial for quality control of spiral steel pipes. However, accurately discriminating defect images from high-resolution and low-contrast weld images remains a challenging task. Moreover, the real-time imaging of radiography in industrial settings introduces dynamic distorted interferences to weld images, which has received limited attention in existing research. To address these issues, we propose a two-stage real-time discrimination method (TRDM) consisting of two carefully designed networks: an extremely lightweight weld region segmentation network and a semantic reconstruction-based discrimination network. Our method aims to adaptively reduce interference from invalid regions in the weld images to better exploit defect features from a semantic perspective and reconstruct them as defect-free regions as much as possible. To overcome the scarcity of weld defect image data, we construct two publicly available industrial weld datasets, including an image dataset and a video dataset. TRDM achieves competitive performance with state-of-the-art techniques, demonstrating its effectiveness in discriminating weld images. Furthermore, we integrate the TRDM into developed detection software for defect assessment, validating its efficacy in real welding scenarios. © 2004-2012 IEEE.

关键词： Nondestructive examination

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Research on Tumor Images Segmentation based on Improved Convolutional Neural Network 24

Research on Tumor Images Segmentation based on Improved Conv...

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5th International Symposium on Artificial Intelligence for Medicine science, ISAIMS 2024

作者： Wan, Yajie Diao, Su Jiang, Gaozhe Song, Chaoda Department of Computer Science Brown University ProvidenceRI United States Department of Industrial & Systems Engineering Auburn University AuburnAL United States Institute of Operations Research and Analytics National University of Singapore Singapore Singapore School of Engineering Department of Computer and Data Sciences ClevelandOH United States

ISBN: (纸本)9798400717826

Tumors are one of the most common tumors in humans, and the main treatment at present is surgical removal. Preoperative planning of tumors usually requires a professional doctor to manually segment the images, but this process is limited by the doctor’s technical experience, which is time-consuming and labor-intensive. In addition, segmentation is extremely difficult due to the complexity and morphological diversity of tumor lesions. We propose a tumor image segmentation method based on improved Convolutional Neural Network (CNN). The model enhances the feature extraction ability by introducing a deep separable convolutional layer and batch normalization, while using dilated convolution to increase the receptive field, thereby improving the accuracy of segmentation. We also incorporated attention mechanisms into the model to focus more accurately on the tumor area and further optimize the segmentation effect. In order to verify the effectiveness of the model, we conducted experimental analysis on multiple publicly available medical imaging datasets. Experimental results show that the improved convolutional neural network significantly improves the accuracy and efficiency of segmentation compared with traditional methods in tumor image segmentation tasks. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Hierarchical Time Series Forecasting of COVID-19 Cases Using County-Level Clustering data

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Operations Research Forum 2025年第1期6卷 1-28页

作者： Mohanty, Sonaxy Shimamura, Airi Nicholson, Charles D. González, Andrés D. Razzaghi, Talayeh School of Industrial and Systems Engineering University of Oklahoma 202 W. Boyd Norman 73019-1022 OK United States Data Science and Analytics Institute University of Oklahoma 865 Asp Ave. Norman 73019-1022 OK United States Faculty Fellow Data Institute for Societal Challenges (DISC) Five Partners Place 201 Stephenson Pkwy Ste 4600 Norman 73019 OK United States

In light of the far-reaching impact of the COVID-19 pandemic, the accurate estimation of infected cases, fatalities, and recoveries has become crucial. While much research has focused on national-level predictions, it has become evident that an exclusive focus on broader statistics may lead to inadequate preparedness in managing hospital resources in rural and smaller regions. Given the critical role local areas play in the spread of COVID-19 and the hierarchical structure of available data, this study proposes a novel modeling framework using hierarchical time series forecasting (HTSF) tailored to county-level clusters within the USA. This approach aims to provide short-term daily forecasts for every county in the USA, employing bottom-up, top-down, and minimum trace optimal reconciliation techniques. A major barrier to accurate short-term forecasting is the scarcity of COVID-19 hospitalization data in both rural and urban regions across the USA. To address this limitation, the study employs county-level clustering, a method that enables the generation of forecasts even for areas with limited or no available data. The primary aim is to improve the accuracy of COVID-19 case forecasts by combining autoregressive integrated moving average (ARIMA) models as base forecasts with the HTSF approach. The findings reveal that the bottom-up HTSF method offers comparable performance at the county level but significantly outperforms other approaches at the cluster and national levels for 3-week-ahead forecasting. This highlights the vital role of local regions in achieving more precise and effective pandemic prediction strategies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Clustering COVID-19 Hierarchical time series Time series forecasting

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Monitoring Recombinant Protein Titer in Escherichia Coli Fermentations Using Filtering Techniques

Monitoring Recombinant Protein Titer in Escherichia Coli Fer...

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IEEE International Conference on Big data

作者： Paul Okafor Zahed Siddique Talayeh Razzaghi Data Science and Analytics Institute University of Oklahoma Norman USA School of Aerospace and Mechanical Engineering University of Oklahoma Norman USA School of Industrial and Systems Engineering University of Oklahoma Norman USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Recombinant protein production in Escherichia coli (E. coli) is crucial for biotechnological applications, but optimizing protein titer is challenging and often requires iterative experimentation and expert guidance. This study utilizes advanced filtering techniques—Kalman, particle, bilateral, and extended Kalman filters—to enhance titer estimation accuracy using fermentation data provided by Cytovance Biologics. The proposed techniques effectively impute missing titer values and are validated on a secondary dataset from Chinese Hamster Ovary (CHO) cell lines, demonstrating robustness and accuracy. By reducing the need for extensive sampling and iterative experimentation, these filtering techniques enable predictive control over fermentation conditions. One significant cost-saving advantage is identifying the protein titer plateau, allowing lab scientists to conclude the fermentation process at the optimal time. This approach ensures maximum protein production while preventing unnecessary resource consumption. As a result, this method significantly lowers the overall cost of recombinant protein production in E. coli while enhancing process efficiency.

关键词： Proteins Accuracy Costs Filtering Escherichia coli Estimation Production Fermentation Kalman filters Iterative methods

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A quinolinyl analog of resveratrol improves neuronal damage after ischemic stroke by promoting Parkin-mediated mitophagy

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Chinese Journal of Natural Medicines 2025年第2期23卷 214-224页

作者： Qingqi Meng Yan Mi Libin Xu Yeshu Liu Dong Liang Yongping Wang Yan Wang Yueyang Liu Guoliang Chen Yue Hou Key Laboratory of Bioresource Research and Development of Liaoning Province College of Life and Health SciencesNational Frontiers Science Center for Industrial Intelligence and Systems OptimizationKey Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationNortheastern UniversityShenyang 110167China State Key Laboratory for Chemistry and Molecular Engineering of Medicinal Resources School of Chemistry and Pharmaceutical SciencesGuangxi Normal UniversityGuilin 541004China Shenyang Key Laboratory of Vascular Biology Science and Research CenterDepartment of PharmacologyShenyang Medical CollegeShenyang 110034China Key Laboratory of Structure-Based Drug Design&Discovery of Ministry of Education School of Pharmaceutical EngineeringShenyang Pharmaceutical UniversityShenyang 110016China

Ischemic stroke(IS)is a prevalent neurological disorder often resulting in significant disability or ***,extracted from Polygonum cuspidatum *** Zucc.(commonly known as Japanese knotweed),has been recognized for its potent neuroprotective ***,the neuroprotective efficacy of its derivative,(E)-4-(3,5-dimethoxystyryl)quinoline(RV02),against ischemic stroke remains inadequately *** study aimed to evaluate the protective effects of RV02 on neuronal ischemia-reperfusion injury both in vitro and in *** research utilized an animal model of middle cerebral artery occlusion/reperfusion and SH-SY5Y cells subjected to oxygen-glucose deprivation and reperfusion to simulate ischemic *** findings demonstrate that RV02 attenuates neuronal mitochondrial damage and scavenges reactive oxygen species(ROS)through mitophagy ***,Parkin knockdown was found to abolish RV02's ability to activate mitophagy and neuroprotection in *** results suggest that RV02 shows promise as a neuroprotective agent,with the activation of Parkin-mediated mitophagy potentially serving as the primary mechanism underlying its neuroprotective effects.

关键词： (E)-4-(3 5-dimethoxystyryl)quinoline Resveratrol Ischemic stroke Mitophagy Parkin

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A New Tree Structure for Local Diagnosis

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Journal of Information science and engineering 2024年第2期40卷 231-244页

作者： Chen, Meirun Li, Xiao-Yan Lin, Cheng-Kuan Pai, Kung-Jui School of Mathematics and Statistics Xiamen University of Technology Xiamen361024 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Computer Science National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Computer Science and Information Engineering Chung Cheng Institute of Technology National Defense University Taoyuan30010 Taiwan Department of Industrial Engineering and Management Ming Chi University of Technology New Taipei City243303 Taiwan

Diagnosability is an important parameter to measure the fault tolerance of a multiprocessor system. If we only care about the state of a node, instead of doing the global diagnosis, Hsu and Tan proposed the idea of local diagnosis. Chiang and Tan provided an extended star structure to diagnose a node under comparison model. In this work, we evaluate the local diagnosability better by proposing a tree structure around this node. We provide the corresponding algorithm to diagnose the node. Simulation results are presented for different failure probability of a node in the tree and different percentage of faulty nodes in the tree, showing the performance of our algorithm. © 2024 Institute of Information science. All rights reserved.

关键词： comparison model diagnosis algorithm fault diagnosis local diagnosability tree

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Secure decentralized decisions to enhance coordination in consolidated hospital systems

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IISE Transactions on Healthcare systems engineering 2020年第2期10卷 99-112页

作者： Badré, Adrien Mohebbi, Shima Soltanisehat, Leili Data Science and Analytics Program University of Oklahoma Norman OK United States School of Industrial and Systems Engineering and Data Science and Analytics Program University of Oklahoma Norman OK United States School of Industrial and Systems Engineering University of Oklahoma Norman OK United States

Shared decision making has become a crucial solution to build a consolidated healthcare system. While there is some research in the healthcare literature discussing the advantages and disadvantages of shared decision making, its efficiency has not been addressed quantitatively. In this paper, we propose a Decentralized Patients Assignment System (DPAS) as a universal decentralized decision making architecture. It utilizes the blockchain technology, machine learning, and integer programing to enhance coordination among healthcare providers and patients in consolidated hospital systems. To test the efficiency of the proposed DPAS, a prototype system is developed using an Agent-based model and Ethereum and is compared to the current practice of central referral systems in consolidated hospital systems. The agent-based model consists of four agents including patients, physicians, hospitals, and miners interacting within a decentralized system. The proposed system highlights the importance of interoperability and consensus among healthcare agents in the decision making process. The results demonstrate the DPAS efficiency in decreasing computational time and rejection rates for patients transfer. © 2019, © 2019 “IISE”.

关键词： Blockchain architecture intelligent agents optimization patients referral predictive analytics smart contracts

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