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Association of maternal multi-metal exposure and dyslipidemia: a study of air pollution on pregnancy outcomes

BMC Pregnancy and Childbirth 2025年第1期25卷 1-9页

作者： Go, Yoon-Young Hur, Young Min You, Young-Ah Park, Sunwha Lee, Gain Chae, Rin Kim, Soo-Min Kim, Young Ju Department of Obstetrics and Gynecology College of Medicine and Ewha Medical Institute Ewha Womans University 1071 Anyangcheon-Ro Yangcheon-Gu Seoul 07985 South Korea Graduate Program in System Health Science and Engineering Ewha Womans University Seoul 07985 South Korea Graduate Program in Division of Artificial Intelligence and Software/Artificial Intelligence Convergence Ewha Womans University Seoul 07985 South Korea

Background: Exposure to air pollutants, including heavy metals, is a major environmental concern of public health and these environmental toxicants have been associated with pregnancy complications. Objectives: An air pollution on pregnancy outcome (APPO) study was performed to investigate the adverse effects of fine particulate matter (PM_2.5) exposure on pregnancy outcomes. This study examined the association between maternal urinary metal mixtures and pregnancy complications, including dyslipidemia and preterm birth (PTB). Methods: The concentrations of 16 metals were measured in 60 urine samples collected during the second trimester pregnancy. Logistic regression and Bayesian kernel machine regression (BKMR) models were used to analyze the single and overall effects of metal exposure on pregnancy complications, respectively. Results: Logistic regression analysis showed a significant difference in urinary Ni and Zn concentrations between those exposed to high and low concentrations of fine particulate matter with an aerodynamic diameter of less than 2.5 µm (PM_2.5) and those not exposed. Four metals (Ni, Sc, Mo, and Cs) were positively associated with total cholesterol (TC) levels, but not with triglyceride (TG) levels and PTB. The BKMR model showed that the overall mixture of 16 metals was positively correlated with high TC and TG levels during the third trimester of pregnancy, and the individual effects of Mo and Pb were the most significant. However, we were only able to identify a trend between maternal exposure to metal mixtures and PTB. Conclusions: BKMR analyses showed a positive association between exposure to multi-metal mixtures and higher maternal TC and TG levels, a factor that contributes to PTB. Therefore, this also suggests that multi-metal exposure during pregnancy may be a potential risk factor for PTB. © The Author(s) 2025.

关键词： Heavy metals Overall effect Pregnancy outcomes Total cholesterol Triglyceride

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Generalize or Specialize? The Effect of Coil Combining in Deep Learning Based MR Image Reconstruction

Generalize or Specialize? The Effect of Coil Combining in De...

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IEEE International Symposium on Biomedical Imaging

作者： Natalia Dubljevic Richard Frayne Roberto Souza Biomedical Engineering Graduate Program Hotchkiss Brain Institute University of Calgary Calgary AB Canada Seaman Family MR Research Centre Foothills Medical Centre Calgary AB Canada Biomedical Engineering Graduate Program Radiology Clinical Neurosciences Hotchkiss Brain Institute University of Calgary Calgary AB Canada Biomedical Engineering Graduate Program Electrical and Software Engineering Hotchkiss Brain Institute University of Calgary Calgary AB Canada

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

With the advent of deep-learning based MR image reconstruction, new questions are raised regarding best design choices. In particular, when using recent reconstruction models that have been developed for processing multi-channel MR data. A fundamental question is whether to first combine channels to improve model generalizability (i.e., a “coil-combined” approach) or to keep channel processing separate to fully utilize multi-channel information (i.e., an “all-coil” approach). In this work, we compare three popular architectures using coil-combined and all-coil styles on brain MR images. All-coil styles improved in-distribution performance, such as when reconstructing only presumed healthy individu-als. Coil-combined designs better generalized to unseen data from patients with pathology.

关键词： Deep learning Coils Pathology Codes Magnetic resonance Brain modeling Data models Image reconstruction Biomedical imaging

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Quantifying White Matter Hyperintensities: Predicting Periventricular Fazekas Scores with Uncertainty Estimation

Quantifying White Matter Hyperintensities: Predicting Perive...

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IEEE International Symposium on Biomedical Imaging

作者： Susanne Schmid Amirmohammed Shamaei Roberto Souza Richard Frayne Biomedical Engineering Graduate Program University of Calgary Hotchkiss Brain Institute University of Calgary Seaman Family MR Research Centre Foothills Medical Centre Calgary AB Canada Electrical and Software Engineering University of Calgary Radiology University of Calgary Clinical Neurosciences University of Calgary

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

White matter hyperintensities (WMH) are crucial markers in brain magnetic resonance (MR) images, often quantified using the Fazekas score. This study presents a deep learning model to predict Fazekas scores in the periventricular region from T1-weighted and FLAIR images. Our model achieved a Matthew correlation coefficient of 0.68 and F1 scores of 0.69, 0.88, 0.86, and 1.0 for Fazekas scores from 0 to 3, respectively, surpassing previous methods. We introduce a novel integration of t-distributed stochastic neighbor embedding (t-SNE) with uncertainty analysis using Monte Carlo dropout, offering insights into the model's decision-making. Results show effective class distinction, with increased uncertainty at class transitions indicating ambiguity, and confident misclassified cases suggesting overlapping features or label noise. These findings highlight that simple, well-tuned models, coupled with interpretability techniques, can provide robust predictions of WMH severity.

关键词： Deep learning Uncertainty Monte Carlo methods Noise Stochastic processes Magnetic resonance Estimation Predictive models Brain modeling White matter

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A lifecycle-based design methodology of lightweight ontology and its application to cultivating high quality Mandarin orange 21

A lifecycle-based design methodology of lightweight ontology...

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21st Asia-Pacific software engineering Conference, APSEC 2014

作者： Fujimoto, Reiko Aoyama, Mikio Graduate Program of Software Engineering Graduate School of Sciences and Engineering Nanzan University Seto Japan Department of Software Engineering Faculty of Science and Engineering Nanzan University Seto Japan

ISBN: (纸本)9781479974252

Information technology is introduced to agriculture in order to improve all the cultivating process and the quality of the products. In this article, we propose a method to organize the cultivated knowledge with a focus on the life cycle process, based on the concept of lightweight ontology. We also propose a method for acquiring the knowledge easily by applying the technology of software engineering. We interviewed the farmers cultivating high quality Mandarin orange and organized the ontology using the proposed method, and show the effectiveness of the proposed method. © 2014 IEEE.

关键词： Ontology

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Mining periodic patterns from floating and ambiguous time segments

Mining periodic patterns from floating and ambiguous time se...

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IEEE Systems, Man and Cybernetics Society, Proceedings - 2005 International Conference on Systems, Man and Cybernetics

作者： Lai, Chih Nguyen, Nga T. Nelson, Dwight E. Graduate Program in Software Engineering Department of Biology University of St. Thomas St. Paul MN United States

A partial periodic pattern is referred to as a set of events that exhibits cyclic behavior over some periods in a time series. Previous studies focused on mining such patterns from constant-length segments that are always stationary between fixed offsets within periods. Unfortunately, many objects under study, like mice, seldom align their fluctuating behaviors with stationary segments that are created based on artificial concepts such as hours. As the result, many patterns that occur across artificial boundaries may not be detected. In this paper, we present a more flexible model that can dynamically create floating segments from a time series such that each segment captures one type of sporadic activity, such as running, over irregular length of time. Temporal sections that are frequently overlapped by floating segments are detected as 1-section patterns. Combinations of some 1-section patterns, referred to as K-section patterns, can then be discovered by the Apriori algorithm. ©2005 IEEE.

关键词： Pattern recognition

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Towards a formal model to reason about context-aware exception handling

Towards a formal model to reason about context-aware excepti...

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2012 5th International Workshop on Exception Handling, WEH 2012

作者： Rocha, Lincoln S. Andrade, Rossana M. C. Group of Computer Networks Software Engineering and Systems Graduate Program in Computer Science Federal University of Ceará Fortaleza - CE Brazil

ISBN: (纸本)9781467317665

The context-awareness is a central aspect in the design of pervasive systems, characterizing their ability to adapt its structure and behavior. The context-aware exception handling (CAEH) is an existing approach employed to design exception handling in pervasive systems. In this approach, the context is used to define, detect, propagate, and handle exceptions. CAEH is a complex and error prone activity, needing designers' insights and domain expertise to identify and characterize contextual exceptions. However, despite the existence of formal methods to analyze the adaptive behavior of pervasive systems, such methods lack specific support to specify the CAEH behavior. In this paper, we propose a formal model to reason about the CAEH behavior. It comprises an extension of the Kripke Structure to model the context evolution of a pervasive system and a transformation function that derivates the CAEH control flow from that proposed structure. © 2012 IEEE.

关键词： Model checking

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Security Dynamics of Blockchain-Enabled SDN Systems: A Taxonomic Approach 9

Security Dynamics of Blockchain-Enabled SDN Systems: A Taxon...

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9th International Conference on Computer Science and engineering, UBMK 2024

作者： Dudukcu, Deniz Karakus, Murat Ankara University Artificial Intelligence Technologies Graduate Program Ankara Turkey Ankara University Department of Software Engineering Ankara Turkey

ISBN: (纸本)9798350365887

In the current landscape, security challenges extend to various domains, such as IoT networking, UAV communication, and VANETs, rendering underlying infrastructures increasingly susceptible to cyber-attacks. The taxonomies, targets, and purposes of these attacks are closely linked to the properties of the underlying infrastructures. With the elevated vulnerability risks and attacks targeting specific elements of software Defined Networking (SDN), integrating blockchain technology into networking has emerged as a potential solution. However, this convergence of SDN and blockchain introduces new security risks. This position paper overviews blockchain-enhanced SDN systems focusing on security issues and reviews existing research and arguments. Notably, there is currently no agreed-upon established taxonomy for the integrated security of SDN and blockchain. As a contribution to the literature, this paper proposes a taxonomy for classifying security approaches within SDN and blockchain deployment scenarios. This taxonomy categorizes solutions based on defense mechanisms, defense planes, blockchain deployment types, and SDN deployment types. Additionally, we discuss the open issues and challenges at the intersection of SDN and blockchain technologies from a security perspective. © 2024 IEEE.

关键词： Cyber attacks

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Design and Construction of a Low-Cost Combat Robot 16

Design and Construction of a Low-Cost Combat Robot

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16th Brazilian Symposium on Robotics and 15th Workshop on Robotics in Education, SBR-WRE 2024

作者： Costa, R.B. Itacaramby, G. Cabral, J. Pastrana, M.A. Peñaloza, J.M. Muñoz, Daniel M. Department of Mechanical Engineering Mechatronics Graduate Program University of Brasilia DF Brasilia Brazil Faculty of Gama Aerospace Engineering Undergraduate Program University of Brasilia DF Brasilia Brazil Faculty of Gama Software Engineering Undergraduate Program University of Brasilia DF Brasilia Brazil

ISBN: (纸本)9798350391084

This paper presents the design and construction of a low-cost combat robot named Artemis, developed for participation in the Robocore Experience 2024 competition. The project emphasizes the integration of cost-effective components and innovative methodologies to create a competitive robot. The ESP32 microcontroller was selected for its advantages in energy efficiency and connectivity, surpassing traditional platforms like Arduino. The construction process involved several phases, including planning, development, testing, correction, and performance evaluation, utilizing tools such as CAD software for design and 3D printing for fabrication. Key components included a DC motor, L298N H-bridge, and a LiPo battery, all chosen to optimize performance while adhering to budget constraints. The robot’s design evolved through iterative testing and adjustments, ultimately leading to a successful competition debut. This work highlights the importance of resourcefulness and collaboration in robotics, showcasing how educational institutions can foster innovation in engineering projects. ©2024 IEEE.

关键词： Budget control

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A Machine Learning Approach To The Prediction Of Bank Customer Churn Problem 3

A Machine Learning Approach To The Prediction Of Bank Custom...

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3rd International Informatics and software engineering Conference, IISEC 2022

作者： Huseyinov, Ilham Okocha, Omobola Istanbul Aydin University Software Engineering Department Istanbul Turkey Graduate Institute of Science Artificial Intelligence and Data Science Program Istanbul Aydin University Istanbul Turkey

ISBN: (数字)9781665459952

ISBN: (纸本)9781665459952

Customers in the banking industry nowadays have many options when deciding where to invest their money. Customer retention and churn have thus emerged as crucial challenges for the majority of banks. This research tries to solve the customer churn problem by applying machine learning algorithms. Classifiers such as Logistic Regression, Support Vector Machine, Random Forest, Gradient Boosting, eXtreme Gradient Boosting, and Light Gradient Boosting are employed. To this end, first, the irrelevant features are removed by applying a feature selection technique, and the most pertinent features are identified, second, the resulting data set is balanced using the SMOTE method. The results of experiments on classifiers with balanced data and with imbalanced data are compared in terms of accuracy, recall, precision, and performance. In the case of imbalanced data (before the SMOTE is applied) no classifier over-performed others. In the case of balanced data (after SMOTE is applied) the Random Forest classifier outrightly overperformed other classifiers. © 2022 IEEE.

关键词： Support vector machines

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A Lifecycle-Based Design Methodology of the Lightweight Ontology and Its Application to Cultivating High Quality Mandarin Orange

A Lifecycle-Based Design Methodology of the Lightweight Onto...

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Asia-Pacific Conference on software engineering

作者： Reiko Fujimoto Mikio Aoyama Graduate Program of Software Engineering Nanzan University Seto Japan

Information technology is introduced to agriculture in order to improve all the cultivating process and the quality of the products. In this article, we propose a method to organize the cultivated knowledge with a focus on the life cycle process, based on the concept of lightweight ontology. We also propose a method for acquiring the knowledge easily by applying the technology of software engineering. We interviewed the farmers cultivating high quality mandarin orange and organized the ontology using the proposed method, and show the effectiveness of the proposed method.

关键词： Ontologies Agriculture software engineering software Physiology Design methodology

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