1
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Callegari F, Brofiga M, Tedesco M, Massobrio P. Electrophysiological features of cortical 3D networks are deeply modulated by scaffold properties. APL Bioeng 2024; 8:036112. [PMID: 39193551 PMCID: PMC11348497 DOI: 10.1063/5.0214745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Accepted: 08/05/2024] [Indexed: 08/29/2024] Open
Abstract
Three-dimensionality (3D) was proven essential for developing reliable models for different anatomical compartments and many diseases. However, the neuronal compartment still poses a great challenge as we still do not understand precisely how the brain computes information and how the complex chain of neuronal events can generate conscious behavior. Therefore, a comprehensive model of neuronal tissue has not yet been found. The present work was conceived in this framework: we aimed to contribute to what must be a collective effort by filling in some information on possible 3D strategies to pursue. We compared directly different kinds of scaffolds (i.e., PDMS sponges, thermally crosslinked hydrogels, and glass microbeads) in their effect on neuronal network activity recorded using micro-electrode arrays. While the overall rate of spiking activity remained consistent, the type of scaffold had a notable impact on bursting dynamics. The frequency, density of bursts, and occurrence of random spikes were all affected. The examination of inter-burst intervals revealed distinct burst generation patterns unique to different scaffold types. Network burst propagation unveiled divergent trends among configurations. Notably, it showed the most differences, underlying that functional variations may arise from a different 3D spatial organization. This evidence suggests that not all 3D neuronal constructs can sustain the same level of richness of activity. Furthermore, we commented on the reproducibility, efficacy, and scalability of the methods, where the beads still offer superior performances. By comparing different 3D scaffolds, our results move toward understanding the best strategies to develop functional 3D neuronal units for reliable pre-clinical studies.
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Affiliation(s)
- Francesca Callegari
- Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genova, Genova, Italy
| | | | - Mariateresa Tedesco
- Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genova, Genova, Italy
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2
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Connor R, Dearle A, Claydon B, Vadicamo L. Correlations of Cross-Entropy Loss in Machine Learning. ENTROPY (BASEL, SWITZERLAND) 2024; 26:491. [PMID: 38920500 PMCID: PMC11203011 DOI: 10.3390/e26060491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 05/23/2024] [Accepted: 05/30/2024] [Indexed: 06/27/2024]
Abstract
Cross-entropy loss is crucial in training many deep neural networks. In this context, we show a number of novel and strong correlations among various related divergence functions. In particular, we demonstrate that, in some circumstances, (a) cross-entropy is almost perfectly correlated with the little-known triangular divergence, and (b) cross-entropy is strongly correlated with the Euclidean distance over the logits from which the softmax is derived. The consequences of these observations are as follows. First, triangular divergence may be used as a cheaper alternative to cross-entropy. Second, logits can be used as features in a Euclidean space which is strongly synergistic with the classification process. This justifies the use of Euclidean distance over logits as a measure of similarity, in cases where the network is trained using softmax and cross-entropy. We establish these correlations via empirical observation, supported by a mathematical explanation encompassing a number of strongly related divergence functions.
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Affiliation(s)
- Richard Connor
- School of Computer Science, University of St Andrews, St Andrews KY16 9SS, UK; (A.D.); (B.C.)
| | - Alan Dearle
- School of Computer Science, University of St Andrews, St Andrews KY16 9SS, UK; (A.D.); (B.C.)
| | - Ben Claydon
- School of Computer Science, University of St Andrews, St Andrews KY16 9SS, UK; (A.D.); (B.C.)
| | - Lucia Vadicamo
- Institute of Information Science and Technologies, Italian National Research Council (CNR), 56124 Pisa, Italy;
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3
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Wang H, He J, Cui H, Yuan B, Xia Y. Robust Stochastic Neural Ensemble Learning With Noisy Labels for Thoracic Disease Classification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2180-2190. [PMID: 38265913 DOI: 10.1109/tmi.2024.3357986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
Chest radiography is the most common radiology examination for thoracic disease diagnosis, such as pneumonia. A tremendous number of chest X-rays prompt data-driven deep learning models in constructing computer-aided diagnosis systems for thoracic diseases. However, in realistic radiology practice, a deep learning-based model often suffers from performance degradation when trained on data with noisy labels possibly caused by different types of annotation biases. To this end, we present a novel stochastic neural ensemble learning (SNEL) framework for robust thoracic disease diagnosis using chest X-rays. The core idea of our method is to learn from noisy labels by constructing model ensembles and designing noise-robust loss functions. Specifically, we propose a fast neural ensemble method that collects parameters simultaneously across model instances and along optimization trajectories. Moreover, we propose a loss function that both optimizes a robust measure and characterizes a diversity measure of ensembles. We evaluated our proposed SNEL method on three publicly available hospital-scale chest X-ray datasets. The experimental results indicate that our method outperforms competing methods and demonstrate the effectiveness and robustness of our method in learning from noisy labels. Our code is available at https://github.com/hywang01/SNEL.
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4
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Kirkley A. Inference of dynamic hypergraph representations in temporal interaction data. Phys Rev E 2024; 109:054306. [PMID: 38907453 DOI: 10.1103/physreve.109.054306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 04/08/2024] [Indexed: 06/24/2024]
Abstract
A range of systems across the social and natural sciences generate data sets consisting of interactions between two distinct categories of items at various instances in time. Online shopping, for example, generates purchasing events of the form (user, product, time of purchase), and mutualistic interactions in plant-pollinator systems generate pollination events of the form (insect, plant, time of pollination). These data sets can be meaningfully modeled as temporal hypergraph snapshots in which multiple items within one category (i.e., online shoppers) share a hyperedge if they interacted with a common item in the other category (i.e., purchased the same product) within a given time window, allowing for the application of hypergraph analysis techniques. However, it is often unclear how to choose the number and duration of these temporal snapshots, which have a strong influence on the final hypergraph representations. Here we propose a principled nonparametric solution to this problem by extracting temporal hypergraph snapshots that optimally capture structural regularities in temporal event data according to the minimum description length principle. We demonstrate our methods on real and synthetic data sets, finding that they can recover planted artificial hypergraph structure in the presence of considerable noise and reveal meaningful activity fluctuations in human mobility data.
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Affiliation(s)
- Alec Kirkley
- Institute of Data Science, University of Hong Kong, Hong Kong; Department of Urban Planning and Design, University of Hong Kong, Hong Kong; and Urban Systems Institute, University of Hong Kong, Hong Kong
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5
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Veiner J, Alajaji F, Gharesifard B. A Unifying Generator Loss Function for Generative Adversarial Networks. ENTROPY (BASEL, SWITZERLAND) 2024; 26:290. [PMID: 38667843 PMCID: PMC11049335 DOI: 10.3390/e26040290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Revised: 03/18/2024] [Accepted: 03/22/2024] [Indexed: 04/28/2024]
Abstract
A unifying α-parametrized generator loss function is introduced for a dual-objective generative adversarial network (GAN) that uses a canonical (or classical) discriminator loss function such as the one in the original GAN (VanillaGAN) system. The generator loss function is based on a symmetric class probability estimation type function, Lα, and the resulting GAN system is termed Lα-GAN. Under an optimal discriminator, it is shown that the generator's optimization problem consists of minimizing a Jensen-fα-divergence, a natural generalization of the Jensen-Shannon divergence, where fα is a convex function expressed in terms of the loss function Lα. It is also demonstrated that this Lα-GAN problem recovers as special cases a number of GAN problems in the literature, including VanillaGAN, least squares GAN (LSGAN), least kth-order GAN (LkGAN), and the recently introduced (αD,αG)-GAN with αD=1. Finally, experimental results are provided for three datasets-MNIST, CIFAR-10, and Stacked MNIST-to illustrate the performance of various examples of the Lα-GAN system.
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Affiliation(s)
- Justin Veiner
- Department of Mathematics and Statistics, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Fady Alajaji
- Department of Mathematics and Statistics, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Bahman Gharesifard
- Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 90095, USA;
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6
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Diggans CT, AlMomani AAR. Boltzmann-Shannon interaction entropy: A normalized measure for continuous variables with an application as a subsample quality metric. CHAOS (WOODBURY, N.Y.) 2023; 33:123131. [PMID: 38149993 DOI: 10.1063/5.0182349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 11/29/2023] [Indexed: 12/28/2023]
Abstract
The recent introduction of geometric partition entropy offered an alternative to differential Shannon entropy for the quantification of uncertainty as estimated from a sample drawn from a one-dimensional bounded continuous probability distribution. In addition to being a fresh perspective for the basis of continuous information theory, this new approach provided several improvements over traditional entropy estimators including its effectiveness on sparse samples and a proper incorporation of the impact from extreme outliers. However, a complimentary relationship exists between the new geometric approach and the basic form of its frequency-based predecessor that is leveraged here to define an entropy measure with no bias toward the sample size. This stable normalized measure is named the Boltzmann-Shannon interaction entropy (BSIE)) as it is defined in terms of a standard divergence between the measure-based and frequency-based distributions that can be associated with the two historical figures. This parameter-free measure can be accurately estimated in a computationally efficient manner, and we illustrate its utility as a quality metric for subsampling in the context of nonlinear polynomial regression.
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Affiliation(s)
- C Tyler Diggans
- Air Force Research Laboratory Information Directorate, 525 Brooks Rd., Rome, New York 13441, USA
| | - Abd AlRahman R AlMomani
- Department of Mathematics, Embry-Riddle Aeronautical University, 3700 Willow Creek Rd., Prescott, Arizona 86301, USA
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7
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Venkatesan VK, Kuppusamy Murugesan KR, Chandrasekaran KA, Thyluru Ramakrishna M, Khan SB, Almusharraf A, Albuali A. Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques. Diagnostics (Basel) 2023; 13:3452. [PMID: 37998588 PMCID: PMC10670706 DOI: 10.3390/diagnostics13223452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 10/30/2023] [Accepted: 11/04/2023] [Indexed: 11/25/2023] Open
Abstract
Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen-Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model's decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies.
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Affiliation(s)
- Vinoth Kumar Venkatesan
- School of Computer Science Engineering and Information Systems (SCORE), Vellore Institute of Technology, Vellore 632014, India;
| | - Karthick Raghunath Kuppusamy Murugesan
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore 562112, India; (K.R.K.M.); (M.T.R.)
| | | | - Mahesh Thyluru Ramakrishna
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore 562112, India; (K.R.K.M.); (M.T.R.)
| | - Surbhi Bhatia Khan
- Department of Data Science, School of Science Engineering and Environment, University of Salford, Manchester M5 4WT, UK
- Department of Engineering and Environment, University of Religions and Denominations, Qom 37491-13357, Iran
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon
| | - Ahlam Almusharraf
- Department of Business Administration, College of Business and Administration, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia;
| | - Abdullah Albuali
- Department of Computer Science, School of Computer Science and Information Technology, King Faisal University, Hofuf 11671, Saudi Arabia;
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8
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Ziolkowski P. Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design. MATERIALS (BASEL, SWITZERLAND) 2023; 16:5956. [PMID: 37687648 PMCID: PMC10489033 DOI: 10.3390/ma16175956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 08/24/2023] [Accepted: 08/25/2023] [Indexed: 09/10/2023]
Abstract
The design of concrete mixtures is crucial in concrete technology, aiming to produce concrete that meets specific quality and performance criteria. Modern standards require not only strength but also eco-friendliness and production efficiency. Based on the Three Equation Method, conventional mix design methods involve analytical and laboratory procedures but are insufficient for contemporary concrete technology, leading to overengineering and difficulty predicting concrete properties. Machine learning-based methods offer a solution, as they have proven effective in predicting concrete compressive strength for concrete mix design. This paper scrutinises the association between the computational complexity of machine learning models and their proficiency in predicting the compressive strength of concrete. This study evaluates five deep neural network models of varying computational complexity in three series. Each model is trained and tested in three series with a vast database of concrete mix recipes and associated destructive tests. The findings suggest a positive correlation between increased computational complexity and the model's predictive ability. This correlation is evidenced by an increment in the coefficient of determination (R2) and a decrease in error metrics (mean squared error, Minkowski error, normalized squared error, root mean squared error, and sum squared error) as the complexity of the model increases. The research findings provide valuable insights for increasing the performance of concrete technical feature prediction models while acknowledging this study's limitations and suggesting potential future research directions. This research paves the way for further refinement of AI-driven methods in concrete mix design, enhancing the efficiency and precision of the concrete mix design process.
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Affiliation(s)
- Patryk Ziolkowski
- Faculty of Civil and Environmental Engineering, Gdansk University of Technology, Gabriela Narutowicza 11/12, 80-233 Gdansk, Poland
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9
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Li W, Liu Y, Ma Y, Xu K, Qiu J, Gan Z. A self-learning Monte Carlo tree search algorithm for robot path planning. Front Neurorobot 2023; 17:1039644. [PMID: 37483541 PMCID: PMC10358331 DOI: 10.3389/fnbot.2023.1039644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 06/19/2023] [Indexed: 07/25/2023] Open
Abstract
This paper proposes a self-learning Monte Carlo tree search algorithm (SL-MCTS), which has the ability to continuously improve its problem-solving ability in single-player scenarios. SL-MCTS combines the MCTS algorithm with a two-branch neural network (PV-Network). The MCTS architecture can balance the search for exploration and exploitation. PV-Network replaces the rollout process of MCTS and predicts the promising search direction and the value of nodes, which increases the MCTS convergence speed and search efficiency. The paper proposes an effective method to assess the trajectory of the current model during the self-learning process by comparing the performance of the current model with that of its best-performing historical model. Additionally, this method can encourage SL-MCTS to generate optimal solutions during the self-learning process. We evaluate the performance of SL-MCTS on the robot path planning scenario. The experimental results show that the performance of SL-MCTS is far superior to the traditional MCTS and single-player MCTS algorithms in terms of path quality and time consumption, especially its time consumption is half less than that of the traditional MCTS algorithms. SL-MCTS also performs comparably to other iterative-based search algorithms designed specifically for path planning tasks.
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Affiliation(s)
- Wei Li
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Yi Liu
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Yan Ma
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Kang Xu
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Jiang Qiu
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Zhongxue Gan
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- Ji Hua Laboratory, Department of Engineering Research Center for Intelligent Robotics, Foshan, China
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10
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Queiroz L, Rebello CM, Costa EA, Santana V, Rodrigues BCL, Rodrigues AE, Ribeiro AM, Nogueira IBR. Generating Flavor Molecules Using Scientific Machine Learning. ACS OMEGA 2023; 8:10875-10887. [PMID: 37008127 PMCID: PMC10061502 DOI: 10.1021/acsomega.2c07176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 03/03/2023] [Indexed: 06/19/2023]
Abstract
Flavor is an essential component in the development of numerous products in the market. The increasing consumption of processed and fast food and healthy packaged food has upraised the investment in new flavoring agents and consequently in molecules with flavoring properties. In this context, this work brings up a scientific machine learning (SciML) approach to address this product engineering need. SciML in computational chemistry has opened paths in the compound's property prediction without requiring synthesis. This work proposes a novel framework of deep generative models within this context to design new flavor molecules. Through the analysis and study of the molecules obtained from the generative model training, it was possible to conclude that even though the generative model designs the molecules through random sampling of actions, it can find molecules that are already used in the food industry, not necessarily as a flavoring agent, or in other industrial sectors. Hence, this corroborates the potential of the proposed methodology for the prospecting of molecules to be applied in the flavor industry.
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Affiliation(s)
- Luana
P. Queiroz
- LSRE-LCM-Laboratory
of Separation and Reaction Engineering-Laboratory of Catalysis and
Materials, Faculty of Engineering, University
of Porto, Rua Dr. Roberto
Frias, 4200-465 Porto, Portugal
- ALiCE-Associate
Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Carine M. Rebello
- Departamento
de Engenharia Química, Escola Politécnica (Polytechnic
Institute), Universidade Federal da Bahia, 40210-630 Salvador, Brazil
| | - Erbet A. Costa
- Departamento
de Engenharia Química, Escola Politécnica (Polytechnic
Institute), Universidade Federal da Bahia, 40210-630 Salvador, Brazil
| | - Vinícius
V. Santana
- LSRE-LCM-Laboratory
of Separation and Reaction Engineering-Laboratory of Catalysis and
Materials, Faculty of Engineering, University
of Porto, Rua Dr. Roberto
Frias, 4200-465 Porto, Portugal
- ALiCE-Associate
Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Bruno C. L. Rodrigues
- LSRE-LCM-Laboratory
of Separation and Reaction Engineering-Laboratory of Catalysis and
Materials, Faculty of Engineering, University
of Porto, Rua Dr. Roberto
Frias, 4200-465 Porto, Portugal
- ALiCE-Associate
Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Alírio E. Rodrigues
- LSRE-LCM-Laboratory
of Separation and Reaction Engineering-Laboratory of Catalysis and
Materials, Faculty of Engineering, University
of Porto, Rua Dr. Roberto
Frias, 4200-465 Porto, Portugal
- ALiCE-Associate
Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Ana M. Ribeiro
- LSRE-LCM-Laboratory
of Separation and Reaction Engineering-Laboratory of Catalysis and
Materials, Faculty of Engineering, University
of Porto, Rua Dr. Roberto
Frias, 4200-465 Porto, Portugal
- ALiCE-Associate
Laboratory in Chemical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
| | - Idelfonso B. R. Nogueira
- Chemical
Engineering Department, Norwegian University
of Science and Technology, Sem Sælandsvei 4, Kjemiblokk 5, 7491 Trondheim, Norway
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11
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Sarmah P, Shang W, Origi A, Licheva M, Kraft C, Ulbrich M, Lichtenberg E, Wilde A, Koch HG. mRNA targeting eliminates the need for the signal recognition particle during membrane protein insertion in bacteria. Cell Rep 2023; 42:112140. [PMID: 36842086 PMCID: PMC10066597 DOI: 10.1016/j.celrep.2023.112140] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 01/10/2023] [Accepted: 02/02/2023] [Indexed: 02/26/2023] Open
Abstract
Signal-sequence-dependent protein targeting is essential for the spatiotemporal organization of eukaryotic and prokaryotic cells and is facilitated by dedicated protein targeting factors such as the signal recognition particle (SRP). However, targeting signals are not exclusively contained within proteins but can also be present within mRNAs. By in vivo and in vitro assays, we show that mRNA targeting is controlled by the nucleotide content and by secondary structures within mRNAs. mRNA binding to bacterial membranes occurs independently of soluble targeting factors but is dependent on the SecYEG translocon and YidC. Importantly, membrane insertion of proteins translated from membrane-bound mRNAs occurs independently of the SRP pathway, while the latter is strictly required for proteins translated from cytosolic mRNAs. In summary, our data indicate that mRNA targeting acts in parallel to the canonical SRP-dependent protein targeting and serves as an alternative strategy for safeguarding membrane protein insertion when the SRP pathway is compromised.
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Affiliation(s)
- Pinku Sarmah
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany; Faculty of Biology, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany
| | - Wenkang Shang
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany; Faculty of Biology, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany
| | - Andrea Origi
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany; Faculty of Biology, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany
| | - Mariya Licheva
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany; Faculty of Biology, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany
| | - Claudine Kraft
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany; CIBSS - Centre for Integrative Biological Signalling Studies, University Freiburg, 79104 Freiburg, Germany
| | - Maximilian Ulbrich
- Internal Medicine IV, Faculty of Medicine, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany; BIOSS Centre for Biological Signaling Studies, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany
| | | | - Annegret Wilde
- Faculty of Biology, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany
| | - Hans-Georg Koch
- Institute of Biochemistry and Molecular Biology, ZBMZ, Faculty of Medicine, Albert-Ludwigs-University Freiburg, 79104 Freiburg, Germany.
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Li YL, Zheng MX, Hua XY, Gao X, Wu JJ, Shan CL, Zhang JP, Wei D, Xu JG. Cross-modality comparison between structural and metabolic networks in individual brain based on the Jensen-Shannon divergence method: a healthy Chinese population study. Brain Struct Funct 2023; 228:761-773. [PMID: 36749387 DOI: 10.1007/s00429-023-02616-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 01/25/2023] [Indexed: 02/08/2023]
Abstract
The study aimed to investigate the consistency and diversity between metabolic and structural brain networks at individual level constructed with divergence-based method in healthy Chinese population. The 18F-FDG PET and T1-weighted images of brain were collected from 209 healthy participants. The Jensen-Shannon divergence (JSD) was used to calculate metabolic or structural connectivities between any pair of brain regions and then individual brain networks were constructed. The global and regional topological properties of both networks were analyzed with graph theoretical analysis. Regional properties including nodal efficiency, degree, and betweenness centrality were used to define hub regions of networks. Cross-modality similarity of brain connectivity was analyzed with differential power (DP) analysis. The default mode network (DMN) had the largest number of brain connectivities with high DP values. The small-worldness indexes of metabolic and structural networks in all participants were greater than 1. The structural network showed higher assortativity and local efficiency than metabolic network, while hierarchy and global efficiency were greater in the metabolic network (all P < 0.001). Most of hubs in both networks were symmetrically spatial distributed in the regions of the DMN and subcortical nuclei including thalamus and amygdala, etc. The human brain presented small-world architecture both in perspective of individual metabolic and structural networks. There was a structural substrate that supported the brain to globally and efficiently integrate and process metabolic interaction across brain regions. The cross-modality cooperation or specialization in both networks might imply mechanisms of achieving higher-order brain functions.
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Affiliation(s)
- Yu-Lin Li
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, No.1200 Cailun Road, Shanghai, China
| | - Mou-Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Gao
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chun-Lei Shan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, No.1200 Cailun Road, Shanghai, China.,Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
| | - Jun-Peng Zhang
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, No.1200 Cailun Road, Shanghai, China
| | - Dong Wei
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, No.1200 Cailun Road, Shanghai, China
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, No.1200 Cailun Road, Shanghai, China. .,Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
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13
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Huang PW, Flemisch B, Qin CZ, Saar MO, Ebigbo A. Relating Darcy-Scale Chemical Reaction Order to Pore-Scale Spatial Heterogeneity. Transp Porous Media 2022; 144:507-543. [PMID: 36051176 PMCID: PMC9420689 DOI: 10.1007/s11242-022-01817-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 06/08/2022] [Indexed: 11/16/2022]
Abstract
Due to spatial scaling effects, there is a discrepancy in mineral dissolution rates measured at different spatial scales. Many reasons for this spatial scaling effect can be given. We investigate one such reason, i.e., how pore-scale spatial heterogeneity in porous media affects overall mineral dissolution rates. Using the bundle-of-tubes model as an analogy for porous media, we show that the Darcy-scale reaction order increases as the statistical similarity between the pore sizes and the effective-surface-area ratio of the porous sample decreases. The analytical results quantify mineral spatial heterogeneity using the Darcy-scale reaction order and give a mechanistic explanation to the usage of reaction order in Darcy-scale modeling. The relation is used as a constitutive relation of reactive transport at the Darcy scale. We test the constitutive relation by simulating flow-through experiments. The proposed constitutive relation is able to model the solute breakthrough curve of the simulations. Our results imply that we can infer mineral spatial heterogeneity of a porous media using measured solute concentration over time in a flow-through dissolution experiment.
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14
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Absent Color Indexing: Histogram-Based Identification Using Major and Minor Colors. MATHEMATICS 2022. [DOI: 10.3390/math10132196] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The color histogram is a statistical behavior for robust pattern search or matching; however, difficulties have arisen in using it to discriminate among similar objects. Our method, called absent color indexing (ABC), describes how to use absent or minor colors as a feature in order to solve problems while robustly recognizing images, even those with similar color features. The proposed approach separates a source color histogram into apparent (AP) and absent (AB) color histograms in order to provide a fair way of focusing on the major and minor contributions together. A threshold for this separation is automatically obtained from the mean color histogram by considering the statistical significance of the absent colors. After these have been separated, an inversion operation is performed to reinforce the weight of AB. In order to balance the contributions of the two histograms, four similarity measures are utilized as candidates for combination with ABC. We tested the performance of ABC in terms of the F-measure using different similarity measures, and the results show that it is able to achieve values greater than 0.95. Experiments on Mondrian random patterns verify the ability of ABC to distinguish similar objects by margin. The results of extensive experiments on real-world images and open databases are presented here in order to demonstrate that the performance of our relatively simple algorithm remained robust even in difficult cases.
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15
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Wu H, Long J, Li N, Yu D, Ng MK. Adversarial Auto-encoder Domain Adaptation for Cold-start Recommendation with Positive and Negative Hypergraphs. ACM T INFORM SYST 2022. [DOI: 10.1145/3544105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
This paper presents a novel model named Adversarial Auto-encoder Domain Adaptation (AADA) to handle the recommendation problem under cold-start settings. Specifically, we divide the hypergraph into two hypergraphs, i.e., a positive hypergraph and a negative one. Below, we adopt the cold-start user recommendation for illustration. After achieving positive and negative hypergraphs, we apply hypergraph auto-encoders to them to obtain positive and negative embeddings of warm users and items. Additionally, we employ a multi-layer perceptron to get warm and cold-start user embeddings called regular embeddings. Subsequently, for warm users, we assign positive and negative pseudo-labels to their positive and negative embeddings, respectively, and treat their positive and regular embeddings as the source and target domain data, respectively. Then, we develop a matching discriminator to jointly minimize the classification loss of the positive and negative warm user embeddings and the distribution gap between the positive and regular warm user embeddings. In this way, warm users’ positive and regular embeddings are connected. Since the positive hypergraph maintains the relations between positive warm user and item embeddings, and the regular warm and cold-start user embeddings follow a similar distribution, the regular cold-start user embedding and positive item embedding are bridged to discover their relationship. The proposed model can be easily extended to handle the cold-start item recommendation by changing inputs. We perform extensive experiments on real-world datasets for both cold-start user and cold-start item recommendations. Promising results in terms of precision, recall, NDCG, and hit rate verify the effectiveness of the proposed method.
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Affiliation(s)
- Hanrui Wu
- College of Information Science and Technology, Jinan University, China
| | - Jinyi Long
- College of Information Science and Technology, Guangdong Key Lab of Traditional Chinese Medicine Information Technology, Jinan University, Pazhou Lab, China
| | - Nuosi Li
- College of Information Science and Technology, Jinan University, China
| | - Dahai Yu
- TCL Corporate Research Hong Kong, China
| | - Michael K. Ng
- Institute of Data Science and Department of Mathematics, The University of Hong Kong, China
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16
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Divergence Measures: Mathematical Foundations and Applications in Information-Theoretic and Statistical Problems. ENTROPY 2022; 24:e24050712. [PMID: 35626595 PMCID: PMC9141399 DOI: 10.3390/e24050712] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 05/13/2022] [Indexed: 12/04/2022]
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17
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Li YL, Wu JJ, Ma J, Li SS, Xue X, Wei D, Shan CL, Hua XY, Zheng MX, Xu JG. Alteration of the Individual Metabolic Network of the Brain Based on Jensen-Shannon Divergence Similarity Estimation in Elderly Patients With Type 2 Diabetes Mellitus. Diabetes 2022; 71:894-905. [PMID: 35133397 DOI: 10.2337/db21-0600] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 02/03/2022] [Indexed: 11/13/2022]
Abstract
The aim of this study was to investigate the interactive effect between aging and type 2 diabetes mellitus (T2DM) on brain glucose metabolism, individual metabolic connectivity, and network properties. Using a 2 × 2 factorial design, 83 patients with T2DM (40 elderly and 43 middle-aged) and 69 sex-matched healthy control subjects (HCs) (34 elderly and 35 middle-aged) underwent 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance scanning. Jensen-Shannon divergence was applied to construct individual metabolic connectivity and networks. The topological properties of the networks were quantified using graph theoretical analysis. The general linear model was used to mainly estimate the interaction effect between aging and T2DM on glucose metabolism, metabolic connectivity, and network. There was an interaction effect between aging and T2DM on glucose metabolism, metabolic connectivity, and regional metabolic network properties (all P < 0.05). The post hoc analyses showed that compared with elderly HCs and middle-aged patients with T2DM, elderly patients with T2DM had decreased glucose metabolism, increased metabolic connectivity, and regional metabolic network properties in cognition-related brain regions (all P < 0.05). Age and fasting plasma glucose had negative correlations with glucose metabolism and positive correlations with metabolic connectivity. Elderly patients with T2DM had glucose hypometabolism, strengthened functional integration, and increased efficiency of information communication mainly located in cognition-related brain regions. Metabolic connectivity pattern changes might be compensatory changes for glucose hypometabolism.
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Affiliation(s)
- Yu-Lin Li
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jia-Jia Wu
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jie Ma
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Si-Si Li
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Xue
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Dong Wei
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Chun-Lei Shan
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, China
| | - Xu-Yun Hua
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Mou-Xiong Zheng
- Department of Traumatology and Orthopedics, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jian-Guang Xu
- School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China
- Department of Rehabilitation Medicine, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
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18
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Kontio J, Soñora VR, Pesola V, Lamba R, Dittmann A, Navarro AD, Koivunen J, Pihlajaniemi T, Izzi V. Analysis of extracellular matrix network dynamics in cancer using the MatriNet database. Matrix Biol 2022; 110:141-150. [DOI: 10.1016/j.matbio.2022.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 04/23/2022] [Accepted: 05/10/2022] [Indexed: 10/18/2022]
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19
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Zhu W, Chen Y, Ko ST, Lu Z. Redundancy Reduction for Sensor Deployment in Prosthetic Socket: A Case Study. SENSORS (BASEL, SWITZERLAND) 2022; 22:3103. [PMID: 35590792 PMCID: PMC9105868 DOI: 10.3390/s22093103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 04/12/2022] [Accepted: 04/16/2022] [Indexed: 06/15/2023]
Abstract
The irregular pressure exerted by a prosthetic socket over the residual limb is one of the major factors that cause the discomfort of amputees using artificial limbs. By deploying the wearable sensors inside the socket, the interfacial pressure distribution can be studied to find the active regions and rectify the socket design. In this case study, a clustering-based analysis method is presented to evaluate the density and layout of these sensors, which aims to reduce the local redundancy of the sensor deployment. In particular, a Self-Organizing Map (SOM) and K-means algorithm are employed to find the clustering results of the sensor data, taking the pressure measurement of a predefined sensor placement as the input. Then, one suitable clustering result is selected to detect the layout redundancy from the input area. After that, the Pearson correlation coefficient (PCC) is used as a similarity metric to guide the removal of redundant sensors and generate a new sparser layout. The Jenson-Shannon Divergence (JSD) and the mean pressure are applied as posterior validation metrics that compare the pressure features before and after sensor removal. A case study of a clinical trial with two sensor strips is used to prove the utility of the clustering-based analysis method. The sensors on the posterior and medial regions are suggested to be reduced, and the main pressure features are kept. The proposed method can help sensor designers optimize sensor configurations for intra-socket measurements and thus assist the prosthetists in improving the socket fitting.
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Affiliation(s)
- Wenyao Zhu
- School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 10044 Stockholm, Sweden; (W.Z.); (Y.C.)
| | - Yizhi Chen
- School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 10044 Stockholm, Sweden; (W.Z.); (Y.C.)
| | - Siu-Teing Ko
- Research and Innovation, Össur, 110 Reykjavík, Iceland;
| | - Zhonghai Lu
- School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 10044 Stockholm, Sweden; (W.Z.); (Y.C.)
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20
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Zunino L, Olivares F, Ribeiro HV, Rosso OA. Permutation Jensen-Shannon distance: A versatile and fast symbolic tool for complex time-series analysis. Phys Rev E 2022; 105:045310. [PMID: 35590550 DOI: 10.1103/physreve.105.045310] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 03/21/2022] [Indexed: 06/15/2023]
Abstract
The main motivation of this paper is to introduce the permutation Jensen-Shannon distance, a symbolic tool able to quantify the degree of similarity between two arbitrary time series. This quantifier results from the fusion of two concepts, the Jensen-Shannon divergence and the encoding scheme based on the sequential ordering of the elements in the data series. The versatility and robustness of this ordinal symbolic distance for characterizing and discriminating different dynamics are illustrated through several numerical and experimental applications. Results obtained allow us to be optimistic about its usefulness in the field of complex time-series analysis. Moreover, thanks to its simplicity, low computational cost, wide applicability, and less susceptibility to outliers and artifacts, this ordinal measure can efficiently handle large amounts of data and help to tackle the current big data challenges.
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Affiliation(s)
- Luciano Zunino
- Centro de Investigaciones Ópticas (CONICET La Plata-CIC-UNLP), 1897 Gonnet, La Plata, Argentina
- Departamento de Ciencias Básicas, Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), 1900 La Plata, Argentina
| | - Felipe Olivares
- Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC (CSIC-UIB), Campus Universitat de les Illes Balears, E-07122 Palma de Mallorca, Spain
| | - Haroldo V Ribeiro
- Departamento de Física, Universidade Estadual de Maringá, Maringá, PR 87020-900, Brazil
| | - Osvaldo A Rosso
- Instituto de Física, Universidade Federal de Alagoas, Maceió, Alagoas 57072-970, Brazil
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21
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Entezami A, Mariani S, Shariatmadar H. Damage Detection in Largely Unobserved Structures under Varying Environmental Conditions: An AutoRegressive Spectrum and Multi-Level Machine Learning Methodology. SENSORS (BASEL, SWITZERLAND) 2022; 22:1400. [PMID: 35214303 PMCID: PMC8963060 DOI: 10.3390/s22041400] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/09/2022] [Accepted: 02/09/2022] [Indexed: 06/14/2023]
Abstract
Vibration-based damage detection in civil structures using data-driven methods requires sufficient vibration responses acquired with a sensor network. Due to technical and economic reasons, it is not always possible to deploy a large number of sensors. This limitation may lead to partial information being handled for damage detection purposes, under environmental variability. To address this challenge, this article proposes an innovative multi-level machine learning method by employing the autoregressive spectrum as the main damage-sensitive feature. The proposed method consists of three levels: (i) distance calculation by the log-spectral distance, to increase damage detectability and generate distance-based training and test samples; (ii) feature normalization by an improved factor analysis, to remove environmental variations; and (iii) decision-making for damage localization by means of the Jensen-Shannon divergence. The major contributions of this research are represented by the development of the aforementioned multi-level machine learning method, and by the proposal of the new factor analysis for feature normalization. Limited vibration datasets relevant to a truss structure and consisting of acceleration time histories induced by shaker excitation in a passive system, have been used to validate the proposed method and to compare it with alternate, state-of-the-art strategies.
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Affiliation(s)
- Alireza Entezami
- Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy;
- Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad 9177948944, Iran;
| | - Stefano Mariani
- Department of Civil and Environmental Engineering, Politecnico di Milano, Piazza L. da Vinci 32, 20133 Milano, Italy;
| | - Hashem Shariatmadar
- Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad 9177948944, Iran;
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22
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Andrzejewska M, Żebrowski JJ, Rams K, Ozimek M, Baranowski R. Assessment of time irreversibility in a time series using visibility graphs. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:877474. [PMID: 36926071 PMCID: PMC10013024 DOI: 10.3389/fnetp.2022.877474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 08/24/2022] [Indexed: 11/07/2022]
Abstract
In this paper, we studied the time-domain irreversibility of time series, which is a fundamental property of systems in a nonequilibrium state. We analyzed a subgroup of the databases provided by University of Rochester, namely from the THEW Project. Our data consists of LQTS (Long QT Syndrome) patients and healthy persons. LQTS may be associated with an increased risk of sudden cardiac death (SCD), which is still a big clinical problem. ECG-based artificial intelligence methods can identify sudden cardiac death with a high accuracy. It follows that heart rate variability contains information about the possibility of SCD, which may be extracted, provided that appropriate methods are developed for this purpose. Our aim was to assess the complexity of both groups using visibility graph (VG) methods. Multivariate analysis of connection patterns of graphs built from time series was performed using multiplex visibility graph methods. For univariate time series, time irreversibility of the ECG interval QT of patients with LQTS was lower than for the healthy. However, we did not observe statistically significant difference in the comparison of RR intervals time series of the two groups studied. The connection patterns retrieved from multiplex VGs have more similarity with each other in the case of LQTS patients. This observation may be used to develop better methods for SCD risk stratification.
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Affiliation(s)
- Małgorzata Andrzejewska
- Cardiovascular Physics Group, Physics of Complex Systems Division, Faculty of Physics, Warsaw University of Technology, Warszawa, Poland
| | - Jan J Żebrowski
- Cardiovascular Physics Group, Physics of Complex Systems Division, Faculty of Physics, Warsaw University of Technology, Warszawa, Poland
| | - Karolina Rams
- Cardiovascular Physics Group, Physics of Complex Systems Division, Faculty of Physics, Warsaw University of Technology, Warszawa, Poland
| | - Mateusz Ozimek
- Cardiovascular Physics Group, Physics of Complex Systems Division, Faculty of Physics, Warsaw University of Technology, Warszawa, Poland
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23
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Bedbur S, Kamps U. On Representations of Divergence Measures and Related Quantities in Exponential Families. ENTROPY (BASEL, SWITZERLAND) 2021; 23:726. [PMID: 34201023 PMCID: PMC8227757 DOI: 10.3390/e23060726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 06/03/2021] [Accepted: 06/05/2021] [Indexed: 11/19/2022]
Abstract
Within exponential families, which may consist of multi-parameter and multivariate distributions, a variety of divergence measures, such as the Kullback-Leibler divergence, the Cressie-Read divergence, the Rényi divergence, and the Hellinger metric, can be explicitly expressed in terms of the respective cumulant function and mean value function. Moreover, the same applies to related entropy and affinity measures. We compile representations scattered in the literature and present a unified approach to the derivation in exponential families. As a statistical application, we highlight their use in the construction of confidence regions in a multi-sample setup.
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Affiliation(s)
| | - Udo Kamps
- Institute of Statistics, RWTH Aachen University, 52056 Aachen, Germany;
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24
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α-Geodesical Skew Divergence. ENTROPY 2021; 23:e23050528. [PMID: 33923103 PMCID: PMC8146191 DOI: 10.3390/e23050528] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 04/22/2021] [Accepted: 04/24/2021] [Indexed: 01/25/2023]
Abstract
The asymmetric skew divergence smooths one of the distributions by mixing it, to a degree determined by the parameter λ, with the other distribution. Such divergence is an approximation of the KL divergence that does not require the target distribution to be absolutely continuous with respect to the source distribution. In this paper, an information geometric generalization of the skew divergence called the α-geodesical skew divergence is proposed, and its properties are studied.
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25
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Nielsen F. On a Variational Definition for the Jensen-Shannon Symmetrization of Distances Based on the Information Radius. ENTROPY (BASEL, SWITZERLAND) 2021; 23:464. [PMID: 33919986 PMCID: PMC8071043 DOI: 10.3390/e23040464] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 04/09/2021] [Accepted: 04/09/2021] [Indexed: 01/21/2023]
Abstract
We generalize the Jensen-Shannon divergence and the Jensen-Shannon diversity index by considering a variational definition with respect to a generic mean, thereby extending the notion of Sibson's information radius. The variational definition applies to any arbitrary distance and yields a new way to define a Jensen-Shannon symmetrization of distances. When the variational optimization is further constrained to belong to prescribed families of probability measures, we get relative Jensen-Shannon divergences and their equivalent Jensen-Shannon symmetrizations of distances that generalize the concept of information projections. Finally, we touch upon applications of these variational Jensen-Shannon divergences and diversity indices to clustering and quantization tasks of probability measures, including statistical mixtures.
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Affiliation(s)
- Frank Nielsen
- Sony Computer Science Laboratories, Tokyo 141-0022, Japan
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26
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RSSI Probability Density Functions Comparison Using Jensen-Shannon Divergence and Pearson Distribution. TECHNOLOGIES 2021. [DOI: 10.3390/technologies9020026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The performance of a free-space optical (FSO) communications link suffers from the deleterious effects of weather conditions and atmospheric turbulence. In order to better estimate the reliability and availability of an FSO link, a suitable distribution needs to be employed. The accuracy of this model depends strongly on the atmospheric turbulence strength which causes the scintillation effect. To this end, a variety of probability density functions were utilized to model the optical channel according to the strength of the refractive index structure parameter. Although many theoretical models have shown satisfactory performance, in reality they can significantly differ. This work employs an information theoretic method, namely the so-called Jensen–Shannon divergence, a symmetrization of the Kullback–Leibler divergence, to measure the similarity between different probability distributions. In doing so, a large experimental dataset of received signal strength measurements from a real FSO link is utilized. Additionally, the Pearson family of continuous probability distributions is also employed to determine the best fit according to the mean, standard deviation, skewness and kurtosis of the modeled data.
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27
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Melbourne J. Strongly Convex Divergences. ENTROPY 2020; 22:e22111327. [PMID: 33287092 PMCID: PMC7712413 DOI: 10.3390/e22111327] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 11/06/2020] [Accepted: 11/09/2020] [Indexed: 11/16/2022]
Abstract
We consider a sub-class of the f-divergences satisfying a stronger convexity property, which we refer to as strongly convex, or κ-convex divergences. We derive new and old relationships, based on convexity arguments, between popular f-divergences.
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Affiliation(s)
- James Melbourne
- Department of Electrical and Computer Engineering, University of Minnesota-Twin Cities, Minneapolis, MN 55455, USA
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28
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On Relations Between the Relative Entropy and χ2-Divergence, Generalizations and Applications. ENTROPY 2020; 22:e22050563. [PMID: 33286335 PMCID: PMC7848888 DOI: 10.3390/e22050563] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 05/12/2020] [Accepted: 05/17/2020] [Indexed: 11/29/2022]
Abstract
This paper is focused on a study of integral relations between the relative entropy and the chi-squared divergence, which are two fundamental divergence measures in information theory and statistics, a study of the implications of these relations, their information-theoretic applications, and some generalizations pertaining to the rich class of f-divergences. Applications that are studied in this paper refer to lossless compression, the method of types and large deviations, strong data–processing inequalities, bounds on contraction coefficients and maximal correlation, and the convergence rate to stationarity of a type of discrete-time Markov chains.
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