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Landriel F, Franchi BC, Mosquera C, Lichtenberger FP, Benitez S, Aineseder M, Guiroy A, Hem S. Artificial Intelligence Assistance for the Measurement of Full Alignment Parameters in Whole-Spine Lateral Radiographs. World Neurosurg 2024; 187:e363-e382. [PMID: 38649028 DOI: 10.1016/j.wneu.2024.04.091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Accepted: 04/15/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND Measuring spinal alignment with radiological parameters is essential in patients with spinal conditions likely to be treated surgically. These evaluations are not usually included in the radiological report. As a result, spinal surgeons commonly perform the measurement, which is time-consuming and subject to errors. We aim to develop a fully automated artificial intelligence (AI) tool to assist in measuring alignment parameters in whole-spine lateral radiograph (WSL X-rays). METHODS We developed a tool called Vertebrai that automatically calculates the global spinal parameters (GSPs): Pelvic incidence, sacral slope, pelvic tilt, L1-L4 angle, L4-S1 lumbo-pelvic angle, T1 pelvic angle, sagittal vertical axis, cervical lordosis, C1-C2 lordosis, lumbar lordosis, mid-thoracic kyphosis, proximal thoracic kyphosis, global thoracic kyphosis, T1 slope, C2-C7 plummet, spino-sacral angle, C7 tilt, global tilt, spinopelvic tilt, and hip odontoid axis. We assessed human-AI interaction instead of AI performance alone. We compared the time to measure GSP and inter-rater agreement with and without AI assistance. Two institutional datasets were created with 2267 multilabel images for classification and 784 WSL X-rays with reference standard landmark labeled by spinal surgeons. RESULTS Vertebrai significantly reduced the measurement time comparing spine surgeons with AI assistance and the AI algorithm alone, without human intervention (3 minutes vs. 0.26 minutes; P < 0.05). Vertebrai achieved an average accuracy of 83% in detecting abnormal alignment values, with the sacral slope parameter exhibiting the lowest accuracy at 61.5% and spinopelvic tilt demonstrating the highest accuracy at 100%. Intraclass correlation analysis revealed a high level of correlation and consistency in the global alignment parameters. CONCLUSIONS Vertebrai's measurements can accurately detect alignment parameters, making it a promising tool for measuring GSP automatically.
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Affiliation(s)
- Federico Landriel
- Neurosurgical Department, Spine Unit, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
| | - Bruno Cruz Franchi
- Health Informatic Department, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | - Candelaria Mosquera
- Health Informatic Department, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | | | - Sonia Benitez
- Health Informatic Department, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | - Martina Aineseder
- Radiology Department, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | | | - Santiago Hem
- Neurosurgical Department, Spine Unit, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
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152
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Sun F, Hao W, Zou A, Cheng K. TVGCN: Time-varying graph convolutional networks for multivariate and multifeature spatiotemporal series prediction. Sci Prog 2024; 107:368504241283315. [PMID: 39275849 PMCID: PMC11402102 DOI: 10.1177/00368504241283315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/16/2024]
Abstract
Spatiotemporal (ST) graph modeling has garnered increasing attention recently. Most existing methods rely on a predefined graph structure or construct a single learnable graph throughout training. However, it is challenging to use a predefined graph structure to capture dynamic ST changes effectively due to evolving node relationships over time. Furthermore, these methods typically utilize only the original data, neglecting external temporal factors. Therefore, we put forward a novel time-varying graph convolutional network model that integrates external factors for multifeature ST series prediction. Firstly, we construct a time-varying adjacency matrix using attention to capture dynamic spatial relationships among nodes. The graph structure adapts over time during training, validation, and testing phases. Then, we model temporal dependence by dilated causal convolution, leveraging gated activation unit and residual connection. Notably, the prediction accuracy is enhanced through the incorporation of embedding absolute time and the fusion of multifeature. This model has been applied to three real-world multifeature datasets, achieving state-of-the-art performance in all cases. Experiments show that the method has high accuracy and robustness when applied to multifeature and multivariate ST series problems.
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Affiliation(s)
- Feiyan Sun
- Command and Control Engineering College, Army Engineering University of PLA, Nanjing, China
- Software Engineering College, Jinling Institute of Technology, Nanjing, China
| | - Wenning Hao
- Command and Control Engineering College, Army Engineering University of PLA, Nanjing, China
| | - Ao Zou
- Command and Control Engineering College, Army Engineering University of PLA, Nanjing, China
| | - Kai Cheng
- Command and Control Engineering College, Army Engineering University of PLA, Nanjing, China
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153
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Shi J, Zhang K, Guo C, Yang Y, Xu Y, Wu J. A survey of label-noise deep learning for medical image analysis. Med Image Anal 2024; 95:103166. [PMID: 38613918 DOI: 10.1016/j.media.2024.103166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 04/15/2024]
Abstract
Several factors are associated with the success of deep learning. One of the most important reasons is the availability of large-scale datasets with clean annotations. However, obtaining datasets with accurate labels in the medical imaging domain is challenging. The reliability and consistency of medical labeling are some of these issues, and low-quality annotations with label noise usually exist. Because noisy labels reduce the generalization performance of deep neural networks, learning with noisy labels is becoming an essential task in medical image analysis. Literature on this topic has expanded in terms of volume and scope. However, no recent surveys have collected and organized this knowledge, impeding the ability of researchers and practitioners to utilize it. In this work, we presented an up-to-date survey of label-noise learning for medical image domain. We reviewed extensive literature, illustrated some typical methods, and showed unified taxonomies in terms of methodological differences. Subsequently, we conducted the methodological comparison and demonstrated the corresponding advantages and disadvantages. Finally, we discussed new research directions based on the characteristics of medical images. Our survey aims to provide researchers and practitioners with a solid understanding of existing medical label-noise learning, such as the main algorithms developed over the past few years, which could help them investigate new methods to combat with the negative effects of label noise.
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Affiliation(s)
- Jialin Shi
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China.
| | - Kailai Zhang
- Department of Networks, China Mobile Communications Group Co., Ltd., Beijing, China
| | - Chenyi Guo
- Department of Electronic Engineering, Tsinghua University, Beijing, China
| | | | - Yali Xu
- Department of Breast Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Ji Wu
- Department of Electronic Engineering, Tsinghua University, Beijing, China
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154
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Ma Y, Ma Y. Kernel Bayesian logistic tensor decomposition with automatic rank determination for predicting multiple types of miRNA-disease associations. PLoS Comput Biol 2024; 20:e1012287. [PMID: 38976761 PMCID: PMC11257412 DOI: 10.1371/journal.pcbi.1012287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 07/18/2024] [Accepted: 06/27/2024] [Indexed: 07/10/2024] Open
Abstract
Identifying the association and corresponding types of miRNAs and diseases is crucial for studying the molecular mechanisms of disease-related miRNAs. Compared to traditional biological experiments, computational models can not only save time and reduce costs, but also discover potential associations on a large scale. Although some computational models based on tensor decomposition have been proposed, these models usually require manual specification of numerous hyperparameters, leading to a decrease in computational efficiency and generalization ability. Additionally, these linear models struggle to analyze complex, higher-order nonlinear relationships. Based on this, we propose a novel framework, KBLTDARD, to identify potential multiple types of miRNA-disease associations. Firstly, KBLTDARD extracts information from biological networks and high-order association network, and then fuses them to obtain more precise similarities of miRNAs (diseases). Secondly, we combine logistic tensor decomposition and Bayesian methods to achieve automatic hyperparameter search by introducing sparse-induced priors of multiple latent variables, and incorporate auxiliary information to improve prediction capabilities. Finally, an efficient deterministic Bayesian inference algorithm is developed to ensure computational efficiency. Experimental results on two benchmark datasets show that KBLTDARD has better Top-1 precision, Top-1 recall, and Top-1 F1 for new type predictions, and higher AUPR, AUC, and F1 values for new triplet predictions, compared to other state-of-the-art methods. Furthermore, case studies demonstrate the efficiency of KBLTDARD in predicting multiple types of miRNA-disease associations.
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Affiliation(s)
- Yingjun Ma
- School of Mathematics and Statistics, Xiamen University of Technology, Xiamen, China
| | - Yuanyuan Ma
- School of Computer Engineering, Hubei University of Arts and Science, Xiangyang, China
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155
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Wossnig L, Furtmann N, Buchanan A, Kumar S, Greiff V. Best practices for machine learning in antibody discovery and development. Drug Discov Today 2024; 29:104025. [PMID: 38762089 DOI: 10.1016/j.drudis.2024.104025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 04/25/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024]
Abstract
In the past 40 years, therapeutic antibody discovery and development have advanced considerably, with machine learning (ML) offering a promising way to speed up the process by reducing costs and the number of experiments required. Recent progress in ML-guided antibody design and development (D&D) has been hindered by the diversity of data sets and evaluation methods, which makes it difficult to conduct comparisons and assess utility. Establishing standards and guidelines will be crucial for the wider adoption of ML and the advancement of the field. This perspective critically reviews current practices, highlights common pitfalls and proposes method development and evaluation guidelines for various ML-based techniques in therapeutic antibody D&D. Addressing challenges across the ML process, best practices are recommended for each stage to enhance reproducibility and progress.
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Affiliation(s)
- Leonard Wossnig
- LabGenius Ltd, The Biscuit Factory, 100 Drummond Road, London SE16 4DG, UK; Department of Computer Science, University College London, 66-72 Gower St, London WC1E 6EA, UK.
| | - Norbert Furtmann
- R&D Large Molecules Research Platform, Sanofi Deutschland GmbH, Industriepark Höchst, Frankfurt Am Main, Germany
| | - Andrew Buchanan
- Biologics Engineering, R&D, AstraZeneca, Cambridge CB2 0AA, UK
| | - Sandeep Kumar
- Computational Protein Design and Modeling Group, Computational Science, Moderna Therapeutics, 200 Technology Square, Cambridge, MA 02139, USA
| | - Victor Greiff
- Department of Immunology and Oslo University Hospital, University of Oslo, Oslo, Norway
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156
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Cang XL, Guerra RR, Guta B, Bucci P, Rodgers L, Mah H, Feng Q, Agrawal A, MacLean KE. FEELing (key)Pressed: Implicit Touch Pressure Bests Brain Activity for Modeling Emotion Dynamics in the Space Between Stressed & Relaxed. IEEE TRANSACTIONS ON HAPTICS 2024; 17:310-318. [PMID: 37665695 DOI: 10.1109/toh.2023.3308059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
Abstract
In-body lived emotional experiences can be complex, with time-varying and dissonant emotions evolving simultaneously; devices responding in real-time to estimate personal human emotion should evolve accordingly. Models assuming generalized emotions exist as discrete states fail to operationalize valuable information inherent in the dynamic and individualistic nature of human emotions. Our multi-resolution emotion self-reporting procedure allows the construction of emotion labels along the Stressed-Relaxed scale, differentiating not only what the emotions are, but how they are transitioning - e.g., "hopeful but getting stressed" vs. "hopeful and starting to relax". We trained participant-dependent hierarchical models of contextualized individual experience to compare emotion classification by modality (brain activity and keypress force from a physical keyboard), then benchmarked classification performance at F1-scores = [0.44, 0.82] (chance F1=0.22, σ = 0.01) and examined high-performing features. Notably, when classifying emotion evolution in the context of an experience that realistically varies in stress, pressure-based features from keypress force proved to be the more informative modality, and more convenient when considering intrusiveness and ease of collection and processing. Finally, we present our FEEL (Force, EEG and Emotion-Labelled) dataset, a collection of brain activity and keypress force data, labelled with self-reported emotion collected during tense videogame play (N = 16) and open-sourced for community exploration.
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157
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Jainonthee C, Chaisowwong W, Ngamsanga P, Meeyam T, Sampedro F, Wells SJ, Pichpol D. Exploring the influence of slaughterhouse type and slaughtering steps on Campylobacter jejuni contamination in chicken meat: A cluster analysis approach. Heliyon 2024; 10:e32345. [PMID: 38975070 PMCID: PMC11225752 DOI: 10.1016/j.heliyon.2024.e32345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 06/02/2024] [Accepted: 06/03/2024] [Indexed: 07/09/2024] Open
Abstract
Campylobacter jejuni (C. jejuni), a foodborne pathogen, poses notable hazards to human health and has significant economic implications for poultry production. This study aimed to assess C. jejuni contamination levels in chicken carcasses from both backyard and commercial slaughterhouses in Chiang Mai province, Thailand. It also sought to examine the effects of different slaughtering practices on contamination levels and to offer evidence-based recommendations for reducing C. jejuni contamination. Through the sampling of 105 chicken carcasses and subsequent enumeration of C. jejuni, the study captured the impact of various slaughtering practices. Utilizing k-modes clustering on the observational and bacterial count data, the research identified distinct patterns of contamination, revealing higher levels in backyard operations compared to commercial ones. The application of k-modes clustering highlighted the impact of critical slaughtering practices, particularly chilling, on contamination levels. Notably, samples with the lowest bacterial counts were typically from the chilling step, a practice predominantly found in commercial facilities. This observation underpins the recommendation for backyard slaughterhouses to incorporate ice in their post-evisceration soaking process. Mimicking commercial practices, this chilling method aims to inhibit C. jejuni growth by reducing carcass temperature, thereby enhancing food safety. Furthermore, the study suggests backyard operations adopt additional measures observed in commercial settings, like segregating equipment for each slaughtering step and implementing regular cleaning protocols. These strategic interventions are pivotal in reducing contamination risks, advancing microbiological safety in poultry processing, and aligning with global food safety enhancement efforts.
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Affiliation(s)
- Chalita Jainonthee
- Veterinary Public Health and Food Safety Centre for Asia Pacific (VPHCAP), Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, 50100, Thailand
- Research Center for Veterinary Biosciences and Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, 50100, Thailand
| | - Warangkhana Chaisowwong
- Veterinary Public Health and Food Safety Centre for Asia Pacific (VPHCAP), Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, 50100, Thailand
- Research Center for Veterinary Biosciences and Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, 50100, Thailand
- Department of Veterinary Biosciences and Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, 50100, Thailand
| | - Phakamas Ngamsanga
- Veterinary Public Health and Food Safety Centre for Asia Pacific (VPHCAP), Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, 50100, Thailand
| | - Tongkorn Meeyam
- Research Center for Veterinary Biosciences and Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, 50100, Thailand
- Department of Veterinary Biosciences and Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, 50100, Thailand
| | - Fernando Sampedro
- Environmental Health Sciences Division, School of Public Health, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Scott J. Wells
- Department of Veterinary Population Medicine, College of Veterinary Medicine, University of Minnesota, St. Paul, MN, 55108, USA
| | - Duangporn Pichpol
- Research Center for Veterinary Biosciences and Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, 50100, Thailand
- Department of Veterinary Biosciences and Veterinary Public Health, Faculty of Veterinary Medicine, Chiang Mai University, Chiang Mai, 50100, Thailand
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158
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Stauffer PE, Brinkley J, Jacobson DA, Quaranta V, Tyson DR. Purinergic Ca 2+ Signaling as a Novel Mechanism of Drug Tolerance in BRAF-Mutant Melanoma. Cancers (Basel) 2024; 16:2426. [PMID: 39001489 PMCID: PMC11240618 DOI: 10.3390/cancers16132426] [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: 05/30/2024] [Revised: 06/27/2024] [Accepted: 06/27/2024] [Indexed: 07/16/2024] Open
Abstract
Drug tolerance is a major cause of relapse after cancer treatment. Despite intensive efforts, its molecular basis remains poorly understood, hampering actionable intervention. We report a previously unrecognized signaling mechanism supporting drug tolerance in BRAF-mutant melanoma treated with BRAF inhibitors that could be of general relevance to other cancers. Its key features are cell-intrinsic intracellular Ca2+ signaling initiated by P2X7 receptors (purinergic ligand-gated cation channels) and an enhanced ability for these Ca2+ signals to reactivate ERK1/2 in the drug-tolerant state. Extracellular ATP, virtually ubiquitous in living systems, is the ligand that can initiate Ca2+ spikes via P2X7 channels. ATP is abundant in the tumor microenvironment and is released by dying cells, ironically implicating treatment-initiated cancer cell death as a source of trophic stimuli that leads to ERK reactivation and drug tolerance. Such a mechanism immediately offers an explanation of the inevitable relapse after BRAFi treatment in BRAF-mutant melanoma and points to actionable strategies to overcome it.
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Affiliation(s)
- Philip E. Stauffer
- Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Jordon Brinkley
- Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - David A. Jacobson
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA;
| | - Vito Quaranta
- Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Darren R. Tyson
- Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
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159
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Adamer MF, Brüningk SC, Chen D, Borgwardt K. Biomarker identification by interpretable maximum mean discrepancy. Bioinformatics 2024; 40:i501-i510. [PMID: 38940158 PMCID: PMC11211810 DOI: 10.1093/bioinformatics/btae251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION In many biomedical applications, we are confronted with paired groups of samples, such as treated versus control. The aim is to detect discriminating features, i.e. biomarkers, based on high-dimensional (omics-) data. This problem can be phrased more generally as a two-sample problem requiring statistical significance testing to establish differences, and interpretations to identify distinguishing features. The multivariate maximum mean discrepancy (MMD) test quantifies group-level differences, whereas statistically significantly associated features are usually found by univariate feature selection. Currently, few general-purpose methods simultaneously perform multivariate feature selection and two-sample testing. RESULTS We introduce a sparse, interpretable, and optimized MMD test (SpInOpt-MMD) that enables two-sample testing and feature selection in the same experiment. SpInOpt-MMD is a versatile method and we demonstrate its application to a variety of synthetic and real-world data types including images, gene expression measurements, and text data. SpInOpt-MMD is effective in identifying relevant features in small sample sizes and outperforms other feature selection methods such as SHapley Additive exPlanations and univariate association analysis in several experiments. AVAILABILITY AND IMPLEMENTATION The code and links to our public data are available at https://github.com/BorgwardtLab/spinoptmmd.
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Affiliation(s)
- Michael F Adamer
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4056, Switzerland
- Swiss Institute for Bioinformatics (SIB), Amphipôle, Quartier UNIL-Sorge, Lausanne 1015, Switzerland
| | - Sarah C Brüningk
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4056, Switzerland
- Swiss Institute for Bioinformatics (SIB), Amphipôle, Quartier UNIL-Sorge, Lausanne 1015, Switzerland
- Department of Health Sciences and Technology, ETH Zurich, Zurich 8008, Switzerland
| | - Dexiong Chen
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4056, Switzerland
- Swiss Institute for Bioinformatics (SIB), Amphipôle, Quartier UNIL-Sorge, Lausanne 1015, Switzerland
- Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried 82152, Germany
| | - Karsten Borgwardt
- Department of Biosystems Science and Engineering, ETH Zurich, Basel 4056, Switzerland
- Swiss Institute for Bioinformatics (SIB), Amphipôle, Quartier UNIL-Sorge, Lausanne 1015, Switzerland
- Department of Machine Learning and Systems Biology, Max Planck Institute of Biochemistry, Martinsried 82152, Germany
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160
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Chattaraj A, Selvam TP. Radiation-induced DNA damage by proton, helium and carbon ions in human fibroblast cell: Geant4-DNA and MCDS-based study. Biomed Phys Eng Express 2024; 10:045059. [PMID: 38870909 DOI: 10.1088/2057-1976/ad57ce] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 06/13/2024] [Indexed: 06/15/2024]
Abstract
Background. Radiation-induced DNA damages such as Single Strand Break (SSB), Double Strand Break (DSB) and Complex DSB (cDSB) are critical aspects of radiobiology with implications in radiotherapy and radiation protection applications.Materials and Methods. This study presents a thorough investigation into the effects of protons (0.1-100 MeV/u), helium ions (0.13-100 MeV/u) and carbon ions (0.5-480 MeV/u) on DNA of human fibroblast cells using Geant4-DNA track structure code coupled with DBSCAN algorithm and Monte Carlo Damage Simulations (MCDS) code. Geant4-DNA-based simulations consider 1μm × 1μm × 0.5μm water box as the target to calculate energy deposition on event-by-event basis and the three-dimensional coordinates of the interaction location, and then DBSCAN algorithm is used to calculate yields of SSB, DSB and cDSB in human fibroblast cell. The study investigated the influence of Linear Energy Transfer (LET) of protons, helium ions and carbon ions on the yields of DNA damages. Influence of cellular oxygenation on DNA damage patterns is investigated using MCDS code.Results. The study shows that DSB and SSB yields are influenced by the LET of the particles, with distinct trends observed for different particles. The cellular oxygenation is a key factor, with anoxic cells exhibiting reduced SSB and DSB yields, underscoring the intricate relationship between cellular oxygen levels and DNA damage. The study introduced DSB/SSB ratio as an informative metric for evaluating the severity of radiation-induced DNA damage, particularly in higher LET regions.Conclusions. The study highlights the importance of considering particle type, LET, and cellular oxygenation in assessing the biological effects of ionizing radiation.
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Affiliation(s)
- Arghya Chattaraj
- Radiological Physics and Advisory Division, Health, Safety and Environment Group, Bhabha Atomic Research Centre, Mumbai, 400085, India
- Homi Bhabha National Institute, Anushaktinagar, Mumbai, 400094, India
| | - T Palani Selvam
- Radiological Physics and Advisory Division, Health, Safety and Environment Group, Bhabha Atomic Research Centre, Mumbai, 400085, India
- Homi Bhabha National Institute, Anushaktinagar, Mumbai, 400094, India
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161
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Sawczuk N, Rubinstein DY, Sperling MR, Wendel-Mitoraj K, Djuric P, Slezak DF, Kamienkowski J, Weiss SA. High-gamma and beta bursts in the Left Supramarginal Gyrus can accurately differentiate verbal memory states and performance. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.29.24308117. [PMID: 38853875 PMCID: PMC11160814 DOI: 10.1101/2024.05.29.24308117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
The left supramarginal gyrus (LSMG) may mediate attention to memory, and gauge memory state and performance. We performed a secondary analysis of 142 verbal delayed free recall experiments, in patients with medically-refractory epilepsy with electrode contacts implanted in the LSMG. In 14 of 142 experiments (in 14 of 113 patients), the cross-validated convolutional neural networks (CNNs) that used 1-dimensional(1-D) pairs of convolved high-gamma and beta tensors, derived from the LSMG recordings, could label recalled words with an area under the receiver operating curve (AUROC) of greater than 60% [range: 60-90%]. These 14 patients were distinguished by: 1) higher amplitudes of high-gamma bursts; 2) distinct electrode placement within the LSMG; and 3) superior performance compared with a CNN that used a 1-D tensor of the broadband recordings in the LSMG. In a pilot study of 7 of these patients, we also cross-validated CNNs using paired 1-D convolved high-gamma and beta tensors, from the LSMG, to: a) distinguish word encoding epochs from free recall epochs [AUC 0.6-1]; and distinguish better performance from poor performance during delayed free recall [AUC 0.5-0.86]. These experiments show that bursts of high-gamma and beta generated in the LSMG are biomarkers of verbal memory state and performance.
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Affiliation(s)
- Nicolás Sawczuk
- Department of Computer Science, University of Buenos Aires, Buenos Aires, Argentina
| | - Daniel Y. Rubinstein
- Department of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Michael R. Sperling
- Department of Neurology and Neuroscience, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | | | - Petar Djuric
- Dept. of Electrical & Computer Engineering, Stony Brook, New York, 11794 USA
| | - Diego F. Slezak
- Department of Computer Science, University of Buenos Aires, Buenos Aires, Argentina
| | - Juan Kamienkowski
- Department of Computer Science, University of Buenos Aires, Buenos Aires, Argentina
| | - Shennan A. Weiss
- Department of Neurology, Stony Brook University, Stony Brook, New York, 11790 USA
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162
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Kim H, Hillis AE, Themistocleous C. Machine Learning Classification of Patients with Amnestic Mild Cognitive Impairment and Non-Amnestic Mild Cognitive Impairment from Written Picture Description Tasks. Brain Sci 2024; 14:652. [PMID: 39061392 PMCID: PMC11274603 DOI: 10.3390/brainsci14070652] [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: 06/10/2024] [Revised: 06/24/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024] Open
Abstract
Individuals with Mild Cognitive Impairment (MCI), a transitional stage between cognitively healthy aging and dementia, are characterized by subtle neurocognitive changes. Clinically, they can be grouped into two main variants, namely patients with amnestic MCI (aMCI) and non-amnestic MCI (naMCI). The distinction of the two variants is known to be clinically significant as they exhibit different progression rates to dementia. However, it has been particularly challenging to classify the two variants robustly. Recent research indicates that linguistic changes may manifest as one of the early indicators of pathology. Therefore, we focused on MCI's discourse-level writing samples in this study. We hypothesized that a written picture description task can provide information that can be used as an ecological, cost-effective classification system between the two variants. We included one hundred sixty-nine individuals diagnosed with either aMCI or naMCI who received neurophysiological evaluations in addition to a short, written picture description task. Natural Language Processing (NLP) and a BERT pre-trained language model were utilized to analyze the writing samples. We showed that the written picture description task provided 90% overall classification accuracy for the best classification models, which performed better than cognitive measures. Written discourses analyzed by AI models can automatically assess individuals with aMCI and naMCI and facilitate diagnosis, prognosis, therapy planning, and evaluation.
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Affiliation(s)
- Hana Kim
- Department of Communication Sciences and Disorders, University of South Florida, Tampa, FL 33620, USA;
| | - Argye E. Hillis
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA;
- Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD 21287, USA
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163
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Thindwa D, Li K, Cooper-Wootton D, Zheng Z, Pitzer VE, Weinberger DM. Global patterns of rebound to normal RSV dynamics following COVID-19 suppression. BMC Infect Dis 2024; 24:635. [PMID: 38918718 PMCID: PMC11201371 DOI: 10.1186/s12879-024-09509-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Accepted: 06/13/2024] [Indexed: 06/27/2024] Open
Abstract
BACKGROUND Annual epidemics of respiratory syncytial virus (RSV) had consistent timing and intensity between seasons prior to the SARS-CoV-2 pandemic (COVID-19). However, starting in April 2020, RSV seasonal activity declined due to COVID-19 non-pharmaceutical interventions (NPIs) before re-emerging after relaxation of NPIs. We described the unusual patterns of RSV epidemics that occurred in multiple subsequent waves following COVID-19 in different countries and explored factors associated with these patterns. METHODS Weekly cases of RSV from twenty-eight countries were obtained from the World Health Organisation and combined with data on country-level characteristics and the stringency of the COVID-19 response. Dynamic time warping and regression were used to cluster time series patterns and describe epidemic characteristics before and after COVID-19 pandemic, and identify related factors. RESULTS While the first wave of RSV epidemics following pandemic suppression exhibited unusual patterns, the second and third waves more closely resembled typical RSV patterns in many countries. Post-pandemic RSV patterns differed in their intensity and/or timing, with several broad patterns across the countries. The onset and peak timings of the first and second waves of RSV epidemics following COVID-19 suppression were earlier in the Southern than Northern Hemisphere. The second wave of RSV epidemics was also earlier with higher population density, and delayed if the intensity of the first wave was higher. More stringent NPIs were associated with lower RSV growth rate and intensity and a shorter gap between the first and second waves. CONCLUSION Patterns of RSV activity have largely returned to normal following successive waves in the post-pandemic era. Onset and peak timings of future epidemics following disruption of normal RSV dynamics need close monitoring to inform the delivery of preventive and control measures.
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Affiliation(s)
- Deus Thindwa
- Department of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA.
| | - Ke Li
- Department of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA
| | - Dominic Cooper-Wootton
- Department of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA
| | - Zhe Zheng
- Department of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA
| | - Virginia E Pitzer
- Department of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA
| | - Daniel M Weinberger
- Department of Epidemiology of Microbial Diseases and the Public Health Modeling Unit, Yale School of Public Health, New Haven, CT, USA.
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164
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Bilancia M, Nigri A, Cafarelli B, Di Bona D. An interpretable cluster-based logistic regression model, with application to the characterization of response to therapy in severe eosinophilic asthma. Int J Biostat 2024; 0:ijb-2023-0061. [PMID: 38910330 DOI: 10.1515/ijb-2023-0061] [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: 06/04/2023] [Accepted: 05/27/2024] [Indexed: 06/25/2024]
Abstract
Asthma is a disease characterized by chronic airway hyperresponsiveness and inflammation, with signs of variable airflow limitation and impaired lung function leading to respiratory symptoms such as shortness of breath, chest tightness and cough. Eosinophilic asthma is a distinct phenotype that affects more than half of patients diagnosed with severe asthma. It can be effectively treated with monoclonal antibodies targeting specific immunological signaling pathways that fuel the inflammation underlying the disease, particularly Interleukin-5 (IL-5), a cytokine that plays a crucial role in asthma. In this study, we propose a data analysis pipeline aimed at identifying subphenotypes of severe eosinophilic asthma in relation to response to therapy at follow-up, which could have great potential for use in routine clinical practice. Once an optimal partition of patients into subphenotypes has been determined, the labels indicating the group to which each patient has been assigned are used in a novel way. For each input variable in a specialized logistic regression model, a clusterwise effect on response to therapy is determined by an appropriate interaction term between the input variable under consideration and the cluster label. We show that the clusterwise odds ratios can be meaningfully interpreted conditional on the cluster label. In this way, we can define an effect measure for the response variable for each input variable in each of the groups identified by the clustering algorithm, which is not possible in standard logistic regression because the effect of the reference class is aliased with the overall intercept. The interpretability of the model is enforced by promoting sparsity, a goal achieved by learning interactions in a hierarchical manner using a special group-Lasso technique. In addition, valid expressions are provided for computing odds ratios in the unusual parameterization used by the sparsity-promoting algorithm. We show how to apply the proposed data analysis pipeline to the problem of sub-phenotyping asthma patients also in terms of quality of response to therapy with monoclonal antibodies.
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Affiliation(s)
- Massimo Bilancia
- Department of Precision and Regenerative Medicine and Jonian Area (DiMePRe-J), 9295 University of Bari Aldo Moro , Bari, Italy
| | - Andrea Nigri
- Department of Economics, Management and Territory (DEMeT), 18972 University of Foggia , Foggia, Italy
| | - Barbara Cafarelli
- Department of Economics, Management and Territory (DEMeT), 18972 University of Foggia , Foggia, Italy
| | - Danilo Di Bona
- Department of Medical and Surgical Sciences (DSMC), 18972 University of Foggia , Foggia, Italy
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165
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Jiang M, Li N, Li M, Wang Z, Tian Y, Peng K, Sheng H, Li H, Li Q. E-Nose: Time-Frequency Attention Convolutional Neural Network for Gas Classification and Concentration Prediction. SENSORS (BASEL, SWITZERLAND) 2024; 24:4126. [PMID: 39000905 PMCID: PMC11243837 DOI: 10.3390/s24134126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Revised: 06/14/2024] [Accepted: 06/20/2024] [Indexed: 07/16/2024]
Abstract
In the electronic nose (E-nose) systems, gas type recognition and accurate concentration prediction are some of the most challenging issues. This study introduced an innovative pattern recognition method of time-frequency attention convolutional neural network (TFA-CNN). A time-frequency attention block was designed in the network, aiming to excavate and effectively integrate the temporal and frequency domain information in the E-nose signals to enhance the performance of gas classification and concentration prediction tasks. Additionally, a novel data augmentation strategy was developed, manipulating the feature channels and time dimensions to reduce the interference of sensor drift and redundant information, thereby enhancing the model's robustness and adaptability. Utilizing two types of metal-oxide-semiconductor gas sensors, this research conducted qualitative and quantitative analysis on five target gases. The evaluation results showed that the classification accuracy could reach 100%, and the coefficient of the determination (R2) score of the regression task was up to 0.99. The Pearson correlation coefficient (r) was 0.99, and the mean absolute error (MAE) was 1.54 ppm. The experimental test results were almost consistent with the system predictions, and the MAE was 1.39 ppm. This study provides a method of network learning that combines time-frequency domain information, exhibiting high performance in gas classification and concentration prediction within the E-nose system.
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Affiliation(s)
- Minglv Jiang
- Key Laboratory of Physical Electronics and Devices for Ministry of Education and Shaanxi Provincial Key Laboratory of Photonics & Information Technology, Xi’an Jiaotong University, Xi’an 710049, China;
- School of Electronic Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (K.P.); (H.S.); (H.L.)
| | - Na Li
- Northwest Survey & Planning Institute of National Forestry and Grassland Administration, Xi’an 710048, China; (N.L.); (Z.W.)
- Key Laboratory of National Forestry and Grassland Administration on Ecological Hydrology and Disaster Prevention in Arid Regions, Xi’an 710048, China
| | - Mingyong Li
- CSSC AlphaPec Instrument (Hubei) Co., Ltd., Yichang 443005, China;
| | - Zhou Wang
- Northwest Survey & Planning Institute of National Forestry and Grassland Administration, Xi’an 710048, China; (N.L.); (Z.W.)
- Key Laboratory of National Forestry and Grassland Administration on Ecological Hydrology and Disaster Prevention in Arid Regions, Xi’an 710048, China
| | - Yuan Tian
- China National Engineering Laboratory for Coal Mining Machinery, CCTEG Taiyuan Research Institute Co., Ltd., Taiyuan 030032, China;
| | - Kaiyan Peng
- School of Electronic Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (K.P.); (H.S.); (H.L.)
| | - Haoran Sheng
- School of Electronic Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (K.P.); (H.S.); (H.L.)
| | - Haoyu Li
- School of Electronic Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (K.P.); (H.S.); (H.L.)
| | - Qiang Li
- Key Laboratory of Physical Electronics and Devices for Ministry of Education and Shaanxi Provincial Key Laboratory of Photonics & Information Technology, Xi’an Jiaotong University, Xi’an 710049, China;
- School of Electronic Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China; (K.P.); (H.S.); (H.L.)
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166
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Gadár L, Abonyi J. Finding multifaceted communities in multiplex networks. Sci Rep 2024; 14:14521. [PMID: 38914589 PMCID: PMC11196740 DOI: 10.1038/s41598-024-65049-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 06/17/2024] [Indexed: 06/26/2024] Open
Abstract
Identifying communities in multilayer networks is crucial for understanding the structural dynamics of complex systems. Traditional community detection algorithms often overlook the presence of overlapping edges within communities, despite the potential significance of such relationships. In this work, we introduce a novel modularity measure designed to uncover communities where nodes share specific multiple facets of connectivity. Our approach leverages a null network, an empirical layer of the multiplex network, not a random network, that can be one of the network layers or a complement graph of that, depending on the objective. By analyzing real-world social networks, we validate the effectiveness of our method in identifying meaningful communities with overlapping edges. The proposed approach offers valuable insights into the structural dynamics of multiplex systems, shedding light on nodes that share similar multifaceted connections.
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Affiliation(s)
- László Gadár
- HUN-REN-PE Complex Systems Monitoring Research Group, University of Pannonia, Veszprém, Hungary.
| | - János Abonyi
- HUN-REN-PE Complex Systems Monitoring Research Group, University of Pannonia, Veszprém, Hungary
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167
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Hinder F, Vaquet V, Hammer B. One or two things we know about concept drift-a survey on monitoring in evolving environments. Part A: detecting concept drift. Front Artif Intell 2024; 7:1330257. [PMID: 38962502 PMCID: PMC11220237 DOI: 10.3389/frai.2024.1330257] [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: 10/30/2023] [Accepted: 04/02/2024] [Indexed: 07/05/2024] Open
Abstract
The world surrounding us is subject to constant change. These changes, frequently described as concept drift, influence many industrial and technical processes. As they can lead to malfunctions and other anomalous behavior, which may be safety-critical in many scenarios, detecting and analyzing concept drift is crucial. In this study, we provide a literature review focusing on concept drift in unsupervised data streams. While many surveys focus on supervised data streams, so far, there is no work reviewing the unsupervised setting. However, this setting is of particular relevance for monitoring and anomaly detection which are directly applicable to many tasks and challenges in engineering. This survey provides a taxonomy of existing work on unsupervised drift detection. In addition to providing a comprehensive literature review, it offers precise mathematical definitions of the considered problems and contains standardized experiments on parametric artificial datasets allowing for a direct comparison of different detection strategies. Thus, the suitability of different schemes can be analyzed systematically, and guidelines for their usage in real-world scenarios can be provided.
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168
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Voisard C, de l'Escalopier N, Ricard D, Oudre L. Automatic gait events detection with inertial measurement units: healthy subjects and moderate to severe impaired patients. J Neuroeng Rehabil 2024; 21:104. [PMID: 38890696 PMCID: PMC11184826 DOI: 10.1186/s12984-024-01405-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 06/11/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Recently, the use of inertial measurement units (IMUs) in quantitative gait analysis has been widely developed in clinical practice. Numerous methods have been developed for the automatic detection of gait events (GEs). While many of them have achieved high levels of efficiency in healthy subjects, detecting GEs in highly degraded gait from moderate to severely impaired patients remains a challenge. In this paper, we aim to present a method for improving GE detection from IMU recordings in such cases. METHODS We recorded 10-meter gait IMU signals from 13 healthy subjects, 29 patients with multiple sclerosis, and 21 patients with post-stroke equino varus foot. An instrumented mat was used as the gold standard. Our method detects GEs from filtered acceleration free from gravity and gyration signals. Firstly, we use autocorrelation and pattern detection techniques to identify a reference stride pattern. Next, we apply multiparametric Dynamic Time Warping to annotate this pattern from a model stride, in order to detect all GEs in the signal. RESULTS We analyzed 16,819 GEs recorded from healthy subjects and achieved an F1-score of 100%, with a median absolute error of 8 ms (IQR [3-13] ms). In multiple sclerosis and equino varus foot cohorts, we analyzed 6067 and 8951 GEs, respectively, with F1-scores of 99.4% and 96.3%, and median absolute errors of 18 ms (IQR [8-39] ms) and 26 ms (IQR [12-50] ms). CONCLUSIONS Our results are consistent with the state of the art for healthy subjects and demonstrate a good accuracy in GEs detection for pathological patients. Therefore, our proposed method provides an efficient way to detect GEs from IMU signals, even in degraded gaits. However, it should be evaluated in each cohort before being used to ensure its reliability.
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Affiliation(s)
- Cyril Voisard
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Gif-sur-Yvette, France.
- Service de Neurologie, Service de Santé des Armées, HIA Percy, Clamart, France.
| | - Nicolas de l'Escalopier
- Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Paris, France
- Service de Chirurgie Orthopédique, Traumatologique et Réparatrice des Membres, Service de Santé des Armées, HIA Percy, Clamart, France
| | - Damien Ricard
- Université Paris Cité, Université Paris Saclay, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Paris, France
- Service de Neurologie, Service de Santé des Armées, HIA Percy, Clamart, France
- Ecole du Val-de-Grâce, Service de Santé des Armées, Paris, France
| | - Laurent Oudre
- Université Paris Saclay, Université Paris Cité, ENS Paris Saclay, CNRS, SSA, INSERM, Centre Borelli, Gif-sur-Yvette, France
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169
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Zeiser J. Owning Decisions: AI Decision-Support and the Attributability-Gap. SCIENCE AND ENGINEERING ETHICS 2024; 30:27. [PMID: 38888795 PMCID: PMC11189344 DOI: 10.1007/s11948-024-00485-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 04/30/2024] [Indexed: 06/20/2024]
Abstract
Artificial intelligence (AI) has long been recognised as a challenge to responsibility. Much of this discourse has been framed around robots, such as autonomous weapons or self-driving cars, where we arguably lack control over a machine's behaviour and therefore struggle to identify an agent that can be held accountable. However, most of today's AI is based on machine-learning technology that does not act on its own, but rather serves as a decision-support tool, automatically analysing data to help human agents make better decisions. I argue that decision-support tools pose a challenge to responsibility that goes beyond the familiar problem of finding someone to blame or punish for the behaviour of agent-like systems. Namely, they pose a problem for what we might call "decision ownership": they make it difficult to identify human agents to whom we can attribute value-judgements that are reflected in decisions. Drawing on recent philosophical literature on responsibility and its various facets, I argue that this is primarily a problem of attributability rather than of accountability. This particular responsibility problem comes in different forms and degrees, most obviously when an AI provides direct recommendations for actions, but also, less obviously, when it provides mere descriptive information on the basis of which a decision is made.
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Affiliation(s)
- Jannik Zeiser
- Leibniz Universität Hannover, Institut für Philosophie, Im Moore 21, 30167, Hannover, Germany.
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170
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Muñoz-Mata BG, Dorantes-Méndez G, Piña-Ramírez O. Classification of Parkinson's disease severity using gait stance signals in a spatiotemporal deep learning classifier. Med Biol Eng Comput 2024:10.1007/s11517-024-03148-2. [PMID: 38884852 DOI: 10.1007/s11517-024-03148-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 06/03/2024] [Indexed: 06/18/2024]
Abstract
Parkinson's disease (PD) is a degenerative nervous system disorder involving motor disturbances. Motor alterations affect the gait according to the progression of PD and can be used by experts in movement disorders to rate the severity of the disease. However, this rating depends on the expertise of the clinical specialist. Therefore, the diagnosis may be inaccurate, particularly in the early stages of PD where abnormal gait patterns can result from normal aging or other medical conditions. Consequently, several classification systems have been developed to enhance PD diagnosis. In this paper, a PD gait severity classification algorithm was developed using vertical ground reaction force (VGRF) signals. The VGRF records used are from a public database that includes 93 PD patients and 72 healthy controls adults. The work presented here focuses on modeling each foot's gait stance phase signals using a modified convolutional long deep neural network (CLDNN) architecture. Subsequently, the results of each model are combined to predict PD severity. The classifier performance was evaluated using ten-fold cross-validation. The best-weighted accuracies obtained were 99.296(0.128)% and 99.343(0.182)%, with the Hoehn-Yahr and UPDRS scales, respectively, outperforming previous results presented in the literature. The classifier proposed here can effectively differentiate gait patterns of different PD severity levels based on gait signals of the stance phase.
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Affiliation(s)
- Brenda G Muñoz-Mata
- Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, Av. Parque Chapultepec 1570, San Luis Potosí, 78295, San Luis Potosí, México
| | - Guadalupe Dorantes-Méndez
- Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, Av. Parque Chapultepec 1570, San Luis Potosí, 78295, San Luis Potosí, México.
| | - Omar Piña-Ramírez
- Departamento de Bioinformática y Análisis Estadísticos, Instituto Nacional de Perinatología "Isidro Espinosa de los Reyes", Montes Urales 800, Ciudad de México, 11000, Ciudad de México, México
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171
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Czekaj Ł, Kowalewski M, Domaszewicz J, Kitłowski R, Szwoch M, Duch W. Real-Time Sensor-Based Human Activity Recognition for eFitness and eHealth Platforms. SENSORS (BASEL, SWITZERLAND) 2024; 24:3891. [PMID: 38931675 PMCID: PMC11207732 DOI: 10.3390/s24123891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2024] [Revised: 05/30/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024]
Abstract
Human Activity Recognition (HAR) plays an important role in the automation of various tasks related to activity tracking in such areas as healthcare and eldercare (telerehabilitation, telemonitoring), security, ergonomics, entertainment (fitness, sports promotion, human-computer interaction, video games), and intelligent environments. This paper tackles the problem of real-time recognition and repetition counting of 12 types of exercises performed during athletic workouts. Our approach is based on the deep neural network model fed by the signal from a 9-axis motion sensor (IMU) placed on the chest. The model can be run on mobile platforms (iOS, Android). We discuss design requirements for the system and their impact on data collection protocols. We present architecture based on an encoder pretrained with contrastive learning. Compared to end-to-end training, the presented approach significantly improves the developed model's quality in terms of accuracy (F1 score, MAPE) and robustness (false-positive rate) during background activity. We make the AIDLAB-HAR dataset publicly available to encourage further research.
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Affiliation(s)
- Łukasz Czekaj
- Aidmed, 80-254 Gdańsk, Poland; (M.K.); (J.D.); (R.K.)
| | | | | | | | - Mariusz Szwoch
- Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, 80-233 Gdańsk, Poland;
| | - Włodzisław Duch
- Department of Informatics, Institute of Engineering and Technology, Faculty of Physics, Astronomy & Informatics, Nicolaus Copernicus University, 87-100 Torun, Poland;
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172
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Zhang Y, Wang Z, Hu J, Pu C. Intelligent management of carbon emissions of urban domestic sewage based on the Internet of Things. ENVIRONMENTAL RESEARCH 2024; 251:118594. [PMID: 38442818 DOI: 10.1016/j.envres.2024.118594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 02/05/2024] [Accepted: 02/28/2024] [Indexed: 03/07/2024]
Abstract
Domestic wastewater is one of the major carbon sources that cannot be ignored by human society. Against the background of carbon peaking & carbon neutrality (Double Carbon) goals, the continuous urbanization has put heavy pressure on urban drainage systems. Nevertheless, the complex subjective and objective conditions of drainage systems restrict the field monitoring, measurement, and analysis of drainage systems, which has become a great obstacle to the study of carbon emissions from drainage system. In this paper, 3389 sensor terminals of Internet of Things (IoT) are used to build a field monitoring IoT for urban domestic wastewater methane (CH4) carbon emission, with 21 main districts of Chongqing Municipality in China as the study area. Incorporating Fick's law of diffusion, this field monitoring IoT derives a measurement model for methane carbon emissions based on measured concentrations, and solves the problems of long-term and stable monitoring and measurement of methane gas in complex underground environment. With GIS spatio-temporal analysis used to analyze the spatial and temporal evolution patterns of carbon emissions from septic tanks in drainage systems, it successfully reveals the spatial and temporal distribution of methane carbon emissions from drainage systems in different seasons, as well as the relationship between methane carbon emissions from drainage systems and the latitude of direct sunlight. Applying the DTW method, it quantifies the stability of methane monitoring in drainage systems and evaluates the effects of Sampling Frequency (SF) and Number of Devices Terminal (NDT) on the stability of methane monitoring. Consequently, an intelligent management system for carbon emissions from urban domestic wastewater is constructed on the base of IoT, which integrates methane monitoring, measurement and analysis in septic tanks of drainage systems.
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Affiliation(s)
- Yanjing Zhang
- School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
| | - Zhoufeng Wang
- School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China; Chongqing Rongguan Science Technologies Co.Ltd., Chongqing 400039, China; State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China.
| | - Jiaxing Hu
- School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
| | - Chaodong Pu
- Chongqing Rongguan Science Technologies Co.Ltd., Chongqing 400039, China
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173
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Ziraldo E, Govers ME, Oliver M. Enhancing Autonomous Vehicle Decision-Making at Intersections in Mixed-Autonomy Traffic: A Comparative Study Using an Explainable Classifier. SENSORS (BASEL, SWITZERLAND) 2024; 24:3859. [PMID: 38931644 PMCID: PMC11207970 DOI: 10.3390/s24123859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 06/07/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024]
Abstract
The transition to fully autonomous roadways will include a long period of mixed-autonomy traffic. Mixed-autonomy roadways pose a challenge for autonomous vehicles (AVs) which use conservative driving behaviours to safely negotiate complex scenarios. This can lead to congestion and collisions with human drivers who are accustomed to more confident driving styles. In this work, an explainable multi-variate time series classifier, Time Series Forest (TSF), is compared to two state-of-the-art models in a priority-taking classification task. Responses to left-turning hazards at signalized and stop-sign-controlled intersections were collected using a full-vehicle driving simulator. The dataset was comprised of a combination of AV sensor-collected and V2V (vehicle-to-vehicle) transmitted features. Each scenario forced participants to either take ("go") or yield ("no go") priority at the intersection. TSF performed comparably for both the signalized and sign-controlled datasets, although all classifiers performed better on the signalized dataset. The inclusion of V2V data led to a slight increase in accuracy for all models and a substantial increase in the true positive rate of the stop-sign-controlled models. Additionally, incorporating the V2V data resulted in fewer chosen features, thereby decreasing the model complexity while maintaining accuracy. Including the selected features in an AV planning model is hypothesized to reduce the need for conservative AV driving behaviour without increasing the risk of collision.
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Affiliation(s)
| | | | - Michele Oliver
- School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada; (E.Z.); (M.E.G.)
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174
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Cai M, Zheng Y, Peng Z, Huang C, Jiang H. Research on load clustering algorithm based on variational autoencoder and hierarchical clustering. PLoS One 2024; 19:e0303977. [PMID: 38870191 PMCID: PMC11175499 DOI: 10.1371/journal.pone.0303977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 05/03/2024] [Indexed: 06/15/2024] Open
Abstract
Time series data complexity presents new challenges in clustering analysis across fields such as electricity, energy, industry, and finance. Despite advances in representation learning and clustering with Variational Autoencoders (VAE) based deep learning techniques, issues like the absence of discriminative power in feature representation, the disconnect between instance reconstruction and clustering objectives, and scalability challenges with large datasets persist. This paper introduces a novel deep time series clustering approach integrating VAE with metric learning. It leverages a VAE based on Gated Recurrent Units for temporal feature extraction, incorporates metric learning for joint optimization of latent space representation, and employs the sum of log likelihoods as the clustering merging criterion, markedly improving clustering accuracy and interpretability. Experimental findings demonstrate a 27.16% improvement in average clustering accuracy and a 47.15% increase in speed on industrial load data. This study offers novel insights and tools for the thorough analysis and application of time series data, with further exploration of VAE's potential in time series clustering anticipated in future research.
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Affiliation(s)
- Miaozhuang Cai
- Guangzhou Power Supply Bureau, Guangdong Power Grid Company, Guangzhou, China
| | - Yin Zheng
- Guangzhou Power Supply Bureau, Guangdong Power Grid Company, Guangzhou, China
| | - Zhengyang Peng
- Guangzhou Power Supply Bureau, Guangdong Power Grid Company, Guangzhou, China
| | - Chunyan Huang
- Guangzhou Benliu Power Technology Company, Guangzhou, China
| | - Haoxia Jiang
- Guangzhou Benliu Power Technology Company, Guangzhou, China
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175
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Zhao P, Lian C, Xu B, Su Y, Zeng Z. Driving Cognitive Alertness Detecting Using Evoked Multimodal Physiological Signals Based on Uncertain Self-Supervised Learning. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2165-2176. [PMID: 38848231 DOI: 10.1109/tnsre.2024.3410990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2024]
Abstract
Multimodal physiological signals play a pivotal role in drivers' perception of work stress. However, the scarcity of labels and the multitude of modalities render the utilization of physiological signals for driving cognitive alertness detection challenging. We thus propose a multimodal physiological signal detection model based on self-supervised learning. First, in order to mine the intrinsic information of data and enable data to highlight effective information, we introduce a multiscale entropy (MSE) evoked attention mechanism. Secondly, the multimodal patches undergo processing through a novel cascaded attention mechanism. This attention mechanism is rooted in patch-level interactions within each modality, progressively integrating and interacting with other modalities in a cascading manner, thereby mitigating computational complexity. Moreover, a multimodal uncertainty-aware module is devised to effectively cope with intricate variations in the data. This module enhances its generalization ability through the incorporation of uncertain resampling. Experiments were conducted on the DriveDB dataset and the CogPilot dataset with both the linear probing and the fine-tuning evaluation protocols. Experimental results in subject-dependent setting show that our model significantly outperforms previous competitive baselines. In the linear probing evaluation, our model achieves on average 6.26%, 6.64%, and 7.75% improvements in Accuracy (Acc), Recall (Rec), and F1 Score. It also outperforms other models by 7.96% in Acc, 9.13% in Rec, and 9.2% in F1 using the fine-tuning evaluation. Furthermore, our model also demonstrates robust performance in subject-independent setting.
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176
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Xu N, Li Z, Fu X, Hu X, Xu W, Han ZK. Unveiling the physical mechanisms driving delafossite crystal (ABX 2) formation through interpretable machine learning. Chem Commun (Camb) 2024; 60:6324-6327. [PMID: 38826149 DOI: 10.1039/d4cc01490a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
A method integrating machine learning with first-principles calculations is employed to forecast the formation energy of delafossite crystals, facilitating the rapid identification of stable crystals. This approach identifies several stable candidates and highlights the importance of atomic ionization energy and electron affinity in the formation of delafossite crystals.
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Affiliation(s)
- Ning Xu
- Department of Physics, School of Physical Science and Technology, Ningbo University, Ningbo, 315211, China.
| | - Zheng Li
- Department of Physics, School of Physical Science and Technology, Ningbo University, Ningbo, 315211, China.
| | - Xiaolan Fu
- Department of Physics, School of Physical Science and Technology, Ningbo University, Ningbo, 315211, China.
| | - Xiaojuan Hu
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Faradayweg 4-6, 14195 Berlin, Germany.
- School of Materials Science and Engineering, Zhejiang University, Hangzhou, 310027, China.
| | - Wenwu Xu
- Department of Physics, School of Physical Science and Technology, Ningbo University, Ningbo, 315211, China.
| | - Zhong-Kang Han
- School of Materials Science and Engineering, Zhejiang University, Hangzhou, 310027, China.
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177
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Casanova IJ, Campos M, Juarez JM, Gomariz A, Canovas-Segura B, Lorente-Ros M, Lorente JA. Surprising and novel multivariate sequential patterns using odds ratio for temporal evolution in healthcare. BMC Med Inform Decis Mak 2024; 24:165. [PMID: 38872146 PMCID: PMC11170878 DOI: 10.1186/s12911-024-02566-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 06/05/2024] [Indexed: 06/15/2024] Open
Abstract
BACKGROUND Pattern mining techniques are helpful tools when extracting new knowledge in real practice, but the overwhelming number of patterns is still a limiting factor in the health-care domain. Current efforts concerning the definition of measures of interest for patterns are focused on reducing the number of patterns and quantifying their relevance (utility/usefulness). However, although the temporal dimension plays a key role in medical records, few efforts have been made to extract temporal knowledge about the patient's evolution from multivariate sequential patterns. METHODS In this paper, we propose a method to extract a new type of patterns in the clinical domain called Jumping Diagnostic Odds Ratio Sequential Patterns (JDORSP). The aim of this method is to employ the odds ratio to identify a concise set of sequential patterns that represent a patient's state with a statistically significant protection factor (i.e., a pattern associated with patients that survive) and those extensions whose evolution suddenly changes the patient's clinical state, thus making the sequential patterns a statistically significant risk factor (i.e., a pattern associated with patients that do not survive), or vice versa. RESULTS The results of our experiments highlight that our method reduces the number of sequential patterns obtained with state-of-the-art pattern reduction methods by over 95%. Only by achieving this drastic reduction can medical experts carry out a comprehensive clinical evaluation of the patterns that might be considered medical knowledge regarding the temporal evolution of the patients. We have evaluated the surprisingness and relevance of the sequential patterns with clinicians, and the most interesting fact is the high surprisingness of the extensions of the patterns that become a protection factor, that is, the patients that recover after several days of being at high risk of dying. CONCLUSIONS Our proposed method with which to extract JDORSP generates a set of interpretable multivariate sequential patterns with new knowledge regarding the temporal evolution of the patients. The number of patterns is greatly reduced when compared to those generated by other methods and measures of interest. An additional advantage of this method is that it does not require any parameters or thresholds, and that the reduced number of patterns allows a manual evaluation.
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Affiliation(s)
- Isidoro J Casanova
- AIKE research team (INTICO), Facultad de Informatica, University of Murcia, Campus de Espinardo, Murcia, 30100, Spain.
| | - Manuel Campos
- AIKE research team (INTICO), Facultad de Informatica, University of Murcia, Campus de Espinardo, Murcia, 30100, Spain
- Murcian Bio-Health Institute (IMIB-Arrixaca), Murcia, Spain
| | - Jose M Juarez
- AIKE research team (INTICO), Facultad de Informatica, University of Murcia, Campus de Espinardo, Murcia, 30100, Spain
| | - Antonio Gomariz
- AIKE research team (INTICO), Facultad de Informatica, University of Murcia, Campus de Espinardo, Murcia, 30100, Spain
| | - Bernardo Canovas-Segura
- AIKE research team (INTICO), Facultad de Informatica, University of Murcia, Campus de Espinardo, Murcia, 30100, Spain
| | - Marta Lorente-Ros
- Department of Cardiology, Washington Hospital Center, Georgetown University, Washington, DC, USA
| | - Jose A Lorente
- CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain
- University Hospital of Getafe, Getafe, Spain
- European University of Madrid, Madrid, Spain
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178
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Zhao W, Fan L. Time-series representation learning via Time-Frequency Fusion Contrasting. Front Artif Intell 2024; 7:1414352. [PMID: 38933470 PMCID: PMC11199859 DOI: 10.3389/frai.2024.1414352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 05/27/2024] [Indexed: 06/28/2024] Open
Abstract
Time series is a typical data type in numerous domains; however, labeling large amounts of time series data can be costly and time-consuming. Learning effective representation from unlabeled time series data is a challenging task. Contrastive learning stands out as a promising method to acquire representations of unlabeled time series data. Therefore, we propose a self-supervised time-series representation learning framework via Time-Frequency Fusion Contrasting (TF-FC) to learn time-series representation from unlabeled data. Specifically, TF-FC combines time-domain augmentation with frequency-domain augmentation to generate the diverse samples. For time-domain augmentation, the raw time series data pass through the time-domain augmentation bank (such as jitter, scaling, permutation, and masking) and get time-domain augmentation data. For frequency-domain augmentation, first, the raw time series undergoes conversion into frequency domain data following Fast Fourier Transform (FFT) analysis. Then, the frequency data passes through the frequency-domain augmentation bank (such as low pass filter, remove frequency, add frequency, and phase shift) and gets frequency-domain augmentation data. The fusion method of time-domain augmentation data and frequency-domain augmentation data is kernel PCA, which is useful for extracting nonlinear features in high-dimensional spaces. By capturing both the time and frequency domains of the time series, the proposed approach is able to extract more informative features from the data, enhancing the model's capacity to distinguish between different time series. To verify the effectiveness of the TF-FC method, we conducted experiments on four time series domain datasets (i.e., SleepEEG, HAR, Gesture, and Epilepsy). Experimental results show that TF-FC significantly improves in recognition accuracy compared with other SOTA methods.
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Affiliation(s)
- Wenbo Zhao
- International School, Beijing University of Posts and Telecommunications, Beijing, China
| | - Ling Fan
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing, China
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179
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Saini R, Tiwari AK, Nath A, Singh P, Maurya SP, Shah MA. Covering assisted intuitionistic fuzzy bi-selection technique for data reduction and its applications. Sci Rep 2024; 14:13568. [PMID: 38866851 DOI: 10.1038/s41598-024-62099-8] [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: 12/07/2023] [Accepted: 05/13/2024] [Indexed: 06/14/2024] Open
Abstract
The dimension and size of data is growing rapidly with the extensive applications of computer science and lab based engineering in daily life. Due to availability of vagueness, later uncertainty, redundancy, irrelevancy, and noise, which imposes concerns in building effective learning models. Fuzzy rough set and its extensions have been applied to deal with these issues by various data reduction approaches. However, construction of a model that can cope with all these issues simultaneously is always a challenging task. None of the studies till date has addressed all these issues simultaneously. This paper investigates a method based on the notions of intuitionistic fuzzy (IF) and rough sets to avoid these obstacles simultaneously by putting forward an interesting data reduction technique. To accomplish this task, firstly, a novel IF similarity relation is addressed. Secondly, we establish an IF rough set model on the basis of this similarity relation. Thirdly, an IF granular structure is presented by using the established similarity relation and the lower approximation. Next, the mathematical theorems are used to validate the proposed notions. Then, the importance-degree of the IF granules is employed for redundant size elimination. Further, significance-degree-preserved dimensionality reduction is discussed. Hence, simultaneous instance and feature selection for large volume of high-dimensional datasets can be performed to eliminate redundancy and irrelevancy in both dimension and size, where vagueness and later uncertainty are handled with rough and IF sets respectively, whilst noise is tackled with IF granular structure. Thereafter, a comprehensive experiment is carried out over the benchmark datasets to demonstrate the effectiveness of simultaneous feature and data point selection methods. Finally, our proposed methodology aided framework is discussed to enhance the regression performance for IC50 of Antiviral Peptides.
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Affiliation(s)
- Rajat Saini
- Department of Mathematics, School of Basic Sciences, Central University of Haryana, Mahendergarh, 123031, India
| | - Anoop Kumar Tiwari
- Department of Computer Science and Information Technology, Central University of Haryana, Mahendergarh, 123031, India.
| | - Abhigyan Nath
- Department of Biochemistry, Pt. Jawahar Lal Nehru Memorial Medical College, Raipur, 492001, India
| | - Phool Singh
- Department of Mathematics (SoET), Central University of Haryana, Mahendergarh, 123031, India
| | - S P Maurya
- Department of Geophysics, Institute of Science, Banaras Hindu University, Varanasi, 221005, India
| | - Mohd Asif Shah
- Department of Economics, Kebri Dehar University, 250, Kebri Dehar, Somali, Ethiopia.
- Division of Research and Development, Lovely Professional University, Phagwara, Punjab, 144001, India.
- Department of Economics, Kardan University, Parwan e Du, Kabul, 1001, Afghanistan.
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180
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Rao L, Lu J, Wu HR, Zhao S, Lu BC, Li H. Automatic classification of fetal heart rate based on a multi-scale LSTM network. Front Physiol 2024; 15:1398735. [PMID: 38933361 PMCID: PMC11202091 DOI: 10.3389/fphys.2024.1398735] [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: 03/10/2024] [Accepted: 05/02/2024] [Indexed: 06/28/2024] Open
Abstract
Introduction Fetal heart rate monitoring during labor can aid healthcare professionals in identifying alterations in the heart rate pattern. However, discrepancies in guidelines and obstetrician expertise present challenges in interpreting fetal heart rate, including failure to acknowledge findings or misinterpretation. Artificial intelligence has the potential to support obstetricians in diagnosing abnormal fetal heart rates. Methods Employ preprocessing techniques to mitigate the effects of missing signals and artifacts on the model, utilize data augmentation methods to address data imbalance. Introduce a multi-scale long short-term memory neural network trained with a variety of time-scale data for automatically classifying fetal heart rate. Carried out experimental on both single and multi-scale models. Results The results indicate that multi-scale LSTM models outperform regular LSTM models in various performance metrics. Specifically, in the single models tested, the model with a sampling rate of 10 exhibited the highest classification accuracy. The model achieves an accuracy of 85.73%, a specificity of 85.32%, and a precision of 85.53% on CTU-UHB dataset. Furthermore, the area under the receiver operating curve of 0.918 suggests that our model demonstrates a high level of credibility. Discussion Compared to previous research, our methodology exhibits superior performance across various evaluation metrics. By incorporating alternative sampling rates into the model, we observed improvements in all performance indicators, including ACC (85.73% vs. 83.28%), SP (85.32% vs. 82.47%), PR (85.53% vs. 82.84%), recall (86.13% vs. 84.09%), F1-score (85.79% vs. 83.42%), and AUC(0.9180 vs. 0.8667). The limitations of this research include the limited consideration of pregnant women's clinical characteristics and disregard the potential impact of varying gestational weeks.
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Affiliation(s)
- Lin Rao
- International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
| | - Jia Lu
- International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
| | - Hai-Rong Wu
- Key Laboratory of System Control and Information Processing, Ministry of Education of Shanghai Jiao Tong University, Shanghai, China
| | - Shu Zhao
- International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
| | - Bang-Chun Lu
- International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
| | - Hong Li
- International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
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181
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Wang Y, Jiang Y, Zhou Y, He H, Tang J, Luo A, Liu Z, Ma C, Xiao Q, Guan T, Dai C. Cocrystal Prediction of Nifedipine Based on the Graph Neural Network and Molecular Electrostatic Potential Surface. AAPS PharmSciTech 2024; 25:133. [PMID: 38862767 DOI: 10.1208/s12249-024-02846-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 05/20/2024] [Indexed: 06/13/2024] Open
Abstract
Nifedipine (NIF) is a dihydropyridine calcium channel blocker primarily used to treat conditions such as hypertension and angina. However, its low solubility and low bioavailability limit its effectiveness in clinical practice. Here, we developed a cocrystal prediction model based on Graph Neural Networks (CocrystalGNN) for the screening of cocrystals with NIF. And scoring 50 coformers using CocrystalGNN. To validate the reliability of the model, we used another prediction method, Molecular Electrostatic Potential Surface (MEPS), to verify the prediction results. Subsequently, we performed a second validation using experiments. The results indicate that our model achieved high performance. Ultimately, cocrystals of NIF were successfully obtained and all cocrystals exhibited better solubility and dissolution characteristics compared to the parent drug. This study lays a solid foundation for combining virtual prediction with experimental screening to discover novel water-insoluble drug cocrystals.
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Affiliation(s)
- Yuting Wang
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Yanling Jiang
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Yu Zhou
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Huai He
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Jincao Tang
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Anqing Luo
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Zeng Liu
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Chi Ma
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Qin Xiao
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Tianbing Guan
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China
| | - Chuanyun Dai
- Chongqing Key Laboratory of Digitalization of Pharmaceutical Processes and Equipment, College of Chemistry and Chemical Engineering, Chongqing University of Science and Technology, No. 20, University City East Road, Chongqing, 401331, China.
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182
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Bryant AG, Aquino K, Parkes L, Fornito A, Fulcher BD. Extracting interpretable signatures of whole-brain dynamics through systematic comparison. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.10.573372. [PMID: 38915560 PMCID: PMC11195072 DOI: 10.1101/2024.01.10.573372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.
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Affiliation(s)
- Annie G. Bryant
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
| | - Kevin Aquino
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
- Brain Key Incorporated, San Francisco, CA, USA
| | - Linden Parkes
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ, USA
- Turner Institute for Brain & Mental Health, Monash University, VIC, Australia
| | - Alex Fornito
- Turner Institute for Brain & Mental Health, Monash University, VIC, Australia
| | - Ben D. Fulcher
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
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183
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Jiao B. Sampling unknown large networks restricted by low sampling rates. Sci Rep 2024; 14:13340. [PMID: 38858487 PMCID: PMC11164934 DOI: 10.1038/s41598-024-64018-3] [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: 02/25/2024] [Accepted: 06/04/2024] [Indexed: 06/12/2024] Open
Abstract
Graph sampling plays an important role in data mining for large networks. Specifically, larger networks often correspond to lower sampling rates. Under the situation, traditional traversal-based samplings for large networks usually have an excessive preference for densely-connected network core nodes. Aim at this issue, this paper proposes a sampling method for unknown networks at low sampling rates, called SLSR, which first adopts a random node sampling to evaluate a degree threshold, utilized to distinguish the core from periphery, and the average degree in unknown networks, and then runs a double-layer sampling strategy on the core and periphery. SLSR is simple that results in a high time efficiency, but experiments verify that the proposed method can accurately preserve many critical structures of unknown large scale-free networks with low sampling rates and low variances.
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Affiliation(s)
- Bo Jiao
- School of Information Science and Technology, Xiamen University Tan Kah Kee College, Zhangzhou, 363123, Fujian, China.
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184
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Li Z, Lei H, Ma Z, Zhang F. Code Similarity Prediction Model for Industrial Management Features Based on Graph Neural Networks. ENTROPY (BASEL, SWITZERLAND) 2024; 26:505. [PMID: 38920514 PMCID: PMC11203227 DOI: 10.3390/e26060505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 06/04/2024] [Accepted: 06/05/2024] [Indexed: 06/27/2024]
Abstract
The code of industrial management software typically features few system API calls and a high number of customized variables and structures. This makes the similarity of such codes difficult to compute using text features or traditional neural network methods. In this paper, we propose an FSPS-GNN model, which is based on graph neural networks (GNNs), to address this problem. The model categorizes code features into two types, outer graph and inner graph, and conducts training and prediction with four stages-feature embedding, feature enhancement, feature fusion, and similarity prediction. Moreover, differently structured GNNs were used in the embedding and enhancement stages, respectively, to increase the interaction of code features. Experiments with code from three open-source projects demonstrate that the model achieves an average precision of 87.57% and an F0.5 Score of 89.12%. Compared to existing similarity-computation models based on GNNs, this model exhibits a Mean Squared Error (MSE) that is approximately 0.0041 to 0.0266 lower and an F0.5 Score that is 3.3259% to 6.4392% higher. It broadens the application scope of GNNs and offers additional insights for the study of code-similarity issues.
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Affiliation(s)
- Zhenhao Li
- School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China; (H.L.)
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185
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Tibrewala R, Brantner D, Brown R, Pancoast L, Keerthivasan M, Bruno M, Block KT, Madore B, Sodickson DK, Collins CM. Preliminary Experience with Three Alternative Motion Sensors for 0.55 Tesla MR Imaging. SENSORS (BASEL, SWITZERLAND) 2024; 24:3710. [PMID: 38931494 PMCID: PMC11207459 DOI: 10.3390/s24123710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/27/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024]
Abstract
Due to limitations in current motion tracking technologies and increasing interest in alternative sensors for motion tracking both inside and outside the MRI system, in this study we share our preliminary experience with three alternative sensors utilizing diverse technologies and interactions with tissue to monitor motion of the body surface, respiratory-related motion of major organs, and non-respiratory motion of deep-seated organs. These consist of (1) a Pilot-Tone RF transmitter combined with deep learning algorithms for tracking liver motion, (2) a single-channel ultrasound transducer with deep learning for monitoring bladder motion, and (3) a 3D Time-of-Flight camera for observing the motion of the anterior torso surface. Additionally, we demonstrate the capability of these sensors to simultaneously capture motion data outside the MRI environment, which is particularly relevant for procedures like radiation therapy, where motion status could be related to previously characterized cyclical anatomical data. Our findings indicate that the ultrasound sensor can track motion in deep-seated organs (bladder) as well as respiratory-related motion. The Time-of-Flight camera offers ease of interpretation and performs well in detecting surface motion (respiration). The Pilot-Tone demonstrates efficacy in tracking bulk respiratory motion and motion of major organs (liver). Simultaneous use of all three sensors could provide complementary motion information outside the MRI bore, providing potential value for motion tracking during position-sensitive treatments such as radiation therapy.
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Affiliation(s)
- Radhika Tibrewala
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Douglas Brantner
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Ryan Brown
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Leanna Pancoast
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | | | - Mary Bruno
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Kai Tobias Block
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Bruno Madore
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Daniel K. Sodickson
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA
| | - Christopher M. Collins
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA
- Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, NY 10016, USA
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186
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Yan J, Toyoura M, Wu X. Identification of a Person in a Trajectory Based on Wearable Sensor Data Analysis. SENSORS (BASEL, SWITZERLAND) 2024; 24:3680. [PMID: 38894472 PMCID: PMC11175260 DOI: 10.3390/s24113680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/03/2024] [Accepted: 06/04/2024] [Indexed: 06/21/2024]
Abstract
Human trajectories can be tracked by the internal processing of a camera as an edge device. This work aims to match peoples' trajectories obtained from cameras to sensor data such as acceleration and angular velocity, obtained from wearable devices. Since human trajectory and sensor data differ in modality, the matching method is not straightforward. Furthermore, complete trajectory information is unavailable; it is difficult to determine which fragments belong to whom. To solve this problem, we newly proposed the SyncScore model to find the similarity between a unit period trajectory and the corresponding sensor data. We also propose a Likelihood Fusion algorithm that systematically updates the similarity data and integrates it over time while keeping other trajectories in mind. We confirmed that the proposed method can match human trajectories and sensor data with an accuracy, a sensitivity, and an F1 of 0.725. Our models achieved decent results on the UEA dataset.
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Affiliation(s)
- Jinzhe Yan
- Department of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
- Department of Computer Science and Engineering, University of Yamanashi, 4-3-11 Takeda, Kofu 400-8511, Japan
| | - Masahiro Toyoura
- Department of Computer Science and Engineering, University of Yamanashi, 4-3-11 Takeda, Kofu 400-8511, Japan
| | - Xiangyang Wu
- Department of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
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187
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Shagurin A, Miannay FA, Kiselev MG, Jedlovszky P, Affouard F, Idrissi A. Widom Line in Supercritical Water in Terms of Changes in Local Structure: Theoretical Perspective. J Phys Chem Lett 2024; 15:5831-5837. [PMID: 38787641 DOI: 10.1021/acs.jpclett.4c01142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Performing molecular dynamics simulations with the TIP4P/2005 water model along 9 isobars (from 175 to 375 bar) in the temperature range between 300 and 1100 K, we have found that the loci of the extrema in the rate of change of specific structural properties can be used to define purely structure-based Widom lines. We have examined several parameters that describe the local structure of water, such as the tetrahedral arrangement, nearest neighbor distance, local density around the molecules, and the size of the largest dense domain. The last two parameters were determined using the Voronoi polyhedral and density-based spatial clustering of applications with noise methods, respectively. By analyzing the moments of the associated distributions, we show that along a given isobar, the temperature at which we observe a maximum in the fluctuation, the rate of change of the average values, or in the skewness values unambiguously determines the Widom line that is in agreement with the experimentally detected, thermodynamic response function-based ones.
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Affiliation(s)
- Artem Shagurin
- University of Lille, CNRS UMR 8516 -LASIRe - Laboratoire Avancé de Spectroscopie pour les Interactions la Réactivité et l'environnement, 59000 Lille, France
- Univ. Lille, CNRS, INRAE, Centrale Lille, UMR 8207 - UMET - Unité Matériaux et Transformations, F-59000 Lille, France
- Krestov Institute of Solution Chemistry, Russian Academy of Sciences, Ivanovo, 153045 Russia
| | - Francois A Miannay
- University of Lille, CNRS UMR 8516 -LASIRe - Laboratoire Avancé de Spectroscopie pour les Interactions la Réactivité et l'environnement, 59000 Lille, France
| | - Michael G Kiselev
- Krestov Institute of Solution Chemistry, Russian Academy of Sciences, Ivanovo, 153045 Russia
| | - Pal Jedlovszky
- Department of Chemistry, Eszterházy Károly Catholic University, Leányka u. 6, 3300 Eger, Hungary
| | - Frederic Affouard
- Univ. Lille, CNRS, INRAE, Centrale Lille, UMR 8207 - UMET - Unité Matériaux et Transformations, F-59000 Lille, France
| | - Abdenacer Idrissi
- University of Lille, CNRS UMR 8516 -LASIRe - Laboratoire Avancé de Spectroscopie pour les Interactions la Réactivité et l'environnement, 59000 Lille, France
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188
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Valdés JJ, Tchagang AB. Novel machine learning insights into the QM7b and QM9 quantum mechanics datasets. J Comput Chem 2024; 45:1193-1214. [PMID: 38329198 DOI: 10.1002/jcc.27295] [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: 10/27/2023] [Revised: 12/06/2023] [Accepted: 12/12/2023] [Indexed: 02/09/2024]
Abstract
This paper (i) explores the internal structure of two quantum mechanics datasets (QM7b, QM9), composed of several thousands of organic molecules and described in terms of electronic properties, and (ii) further explores an inverse design approach to molecular design consisting of using machine learning methods to approximate the atomic composition of molecules, using QM9 data. Understanding the structure and characteristics of this kind of data is important when predicting the atomic composition from physical-chemical properties in inverse molecular designs. Intrinsic dimension analysis, clustering, and outlier detection methods were used in the study. They revealed that for both datasets the intrinsic dimensionality is several times smaller than the descriptive dimensions. The QM7b data is composed of well-defined clusters related to atomic composition. The QM9 data consists of an outer region predominantly composed of outliers, and an inner, core region that concentrates clustered inliner objects. A significant relationship exists between the number of atoms in the molecule and its outlier/inliner nature. The spatial structure exhibits a relationship with molecular weight. Despite the structural differences between the two datasets, the predictability of variables of interest for inverse molecular design is high. This is exemplified by models estimating the number of atoms of the molecule from both the original properties and from lower dimensional embedding spaces. In the generative approach the input is given by a set of desired properties of the molecule and the output is an approximation of the atomic composition in terms of its constituent chemical elements. This could serve as the starting region for further search in the huge space determined by the set of possible chemical compounds. The quantum mechanic's dataset QM9 is used in the study, composed of 133,885 small organic molecules and 19 electronic properties. Different multi-target regression approaches were considered for predicting the atomic composition from the properties, including feature engineering techniques in an auto-machine learning framework. High-quality models were found that predict the atomic composition of the molecules from their electronic properties, as well as from a subset of only 52.6% size. Feature selection worked better than feature generation. The results validate the generative approach to inverse molecular design.
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Affiliation(s)
- Julio J Valdés
- National Research Council Canada, Digital Technologies Research Centre, Ottawa, Canada
| | - Alain B Tchagang
- National Research Council Canada, Digital Technologies Research Centre, Ottawa, Canada
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189
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Mohamed Selim A, Barz M, Bhatti OS, Alam HMT, Sonntag D. A review of machine learning in scanpath analysis for passive gaze-based interaction. Front Artif Intell 2024; 7:1391745. [PMID: 38903158 PMCID: PMC11188426 DOI: 10.3389/frai.2024.1391745] [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: 02/26/2024] [Accepted: 05/15/2024] [Indexed: 06/22/2024] Open
Abstract
The scanpath is an important concept in eye tracking. It refers to a person's eye movements over a period of time, commonly represented as a series of alternating fixations and saccades. Machine learning has been increasingly used for the automatic interpretation of scanpaths over the past few years, particularly in research on passive gaze-based interaction, i.e., interfaces that implicitly observe and interpret human eye movements, with the goal of improving the interaction. This literature review investigates research on machine learning applications in scanpath analysis for passive gaze-based interaction between 2012 and 2022, starting from 2,425 publications and focussing on 77 publications. We provide insights on research domains and common learning tasks in passive gaze-based interaction and present common machine learning practices from data collection and preparation to model selection and evaluation. We discuss commonly followed practices and identify gaps and challenges, especially concerning emerging machine learning topics, to guide future research in the field.
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Affiliation(s)
- Abdulrahman Mohamed Selim
- German Research Center for Artificial Intelligence (DFKI), Interactive Machine Learning Department, Saarbrücken, Germany
| | - Michael Barz
- German Research Center for Artificial Intelligence (DFKI), Interactive Machine Learning Department, Saarbrücken, Germany
- Applied Artificial Intelligence, University of Oldenburg, Oldenburg, Germany
| | - Omair Shahzad Bhatti
- German Research Center for Artificial Intelligence (DFKI), Interactive Machine Learning Department, Saarbrücken, Germany
| | - Hasan Md Tusfiqur Alam
- German Research Center for Artificial Intelligence (DFKI), Interactive Machine Learning Department, Saarbrücken, Germany
| | - Daniel Sonntag
- German Research Center for Artificial Intelligence (DFKI), Interactive Machine Learning Department, Saarbrücken, Germany
- Applied Artificial Intelligence, University of Oldenburg, Oldenburg, Germany
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190
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Gligorić K, Zbinden R, Chiolero A, Kıcıman E, White RW, Horvitz E, West R. Measuring and shaping the nutritional environment via food sales logs: case studies of campus-wide food choice and a call to action. Front Nutr 2024; 11:1231070. [PMID: 38899323 PMCID: PMC11186467 DOI: 10.3389/fnut.2024.1231070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 05/14/2024] [Indexed: 06/21/2024] Open
Abstract
Although diets influence health and the environment, measuring and changing nutrition is challenging. Traditional measurement methods face challenges, and designing and conducting behavior-changing interventions is conceptually and logistically complicated. Situated local communities such as university campuses offer unique opportunities to shape the nutritional environment and promote health and sustainability. The present study investigates how passively sensed food purchase logs typically collected as part of regular business operations can be used to monitor and measure on-campus food consumption and understand food choice determinants. First, based on 38 million sales logs collected on a large university campus over eight years, we perform statistical analyses to quantify spatio-temporal determinants of food choice and characterize harmful patterns in dietary behaviors, in a case study of food purchasing at EPFL campus. We identify spatial proximity, food item pairing, and academic schedules (yearly and daily) as important determinants driving the on-campus food choice. The case studies demonstrate the potential of food sales logs for measuring nutrition and highlight the breadth and depth of future possibilities to study individual food-choice determinants. We describe how these insights provide an opportunity for stakeholders, such as campus offices responsible for managing food services, to shape the nutritional environment and improve health and sustainability by designing policies and behavioral interventions. Finally, based on the insights derived through the case study of food purchases at EPFL campus, we identify five future opportunities and offer a call to action for the nutrition research community to contribute to ensuring the health and sustainability of on-campus populations-the very communities to which many researchers belong.
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Affiliation(s)
| | | | - Arnaud Chiolero
- Population Health Laboratory (#PopHealthLab), University of Fribourg, Fribourg, Switzerland
- School of Population and Global Health, McGill University, Montreal, QC, Canada
- Swiss School of Public Health (SSPH+), Zurich, Switzerland
| | | | | | - Eric Horvitz
- Office of the Chief Scientific Officer, Microsoft, Redmond, WA, United States
| | - Robert West
- Data Science Lab, EPFL, Lausanne, Switzerland
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191
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Astuti PK, Hegedűs B, Oleksa A, Bagi Z, Kusza S. Buzzing with Intelligence: Current Issues in Apiculture and the Role of Artificial Intelligence (AI) to Tackle It. INSECTS 2024; 15:418. [PMID: 38921133 PMCID: PMC11203513 DOI: 10.3390/insects15060418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 06/02/2024] [Accepted: 06/03/2024] [Indexed: 06/27/2024]
Abstract
Honeybees (Apis mellifera L.) are important for agriculture and ecosystems; however, they are threatened by the changing climate. In order to adapt and respond to emerging difficulties, beekeepers require the ability to continuously monitor their beehives. To carry out this, the utilization of advanced machine learning techniques proves to be an exceptional tool. This review provides a comprehensive analysis of the available research on the different applications of artificial intelligence (AI) in beekeeping that are relevant to climate change. Presented studies have shown that AI can be used in various scientific aspects of beekeeping and can work with several data types (e.g., sound, sensor readings, images) to investigate, model, predict, and help make decisions in apiaries. Research articles related to various aspects of apiculture, e.g., managing hives, maintaining their health, detecting pests and diseases, and climate and habitat management, were analyzed. It was found that several environmental, behavioral, and physical attributes needed to be monitored in real-time to be able to understand and fully predict the state of the hives. Finally, it could be concluded that even if there is not yet a full-scale monitoring method for apiculture, the already available approaches (even with their identified shortcomings) can help maintain sustainability in the changing apiculture.
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Affiliation(s)
- Putri Kusuma Astuti
- Centre for Agricultural Genomics and Biotechnology, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, 4032 Debrecen, Hungary; (P.K.A.); (B.H.); (Z.B.)
- Doctoral School of Animal Science, University of Debrecen, 4032 Debrecen, Hungary
- Department of Animal Breeding and Reproduction, Faculty of Animal Science, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
| | - Bettina Hegedűs
- Centre for Agricultural Genomics and Biotechnology, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, 4032 Debrecen, Hungary; (P.K.A.); (B.H.); (Z.B.)
- Doctoral School of Animal Science, University of Debrecen, 4032 Debrecen, Hungary
| | - Andrzej Oleksa
- Department of Genetics, Faculty of Biological Sciences, Kazimierz Wielki University, 85-090 Bydgoszcz, Poland;
| | - Zoltán Bagi
- Centre for Agricultural Genomics and Biotechnology, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, 4032 Debrecen, Hungary; (P.K.A.); (B.H.); (Z.B.)
| | - Szilvia Kusza
- Centre for Agricultural Genomics and Biotechnology, Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, 4032 Debrecen, Hungary; (P.K.A.); (B.H.); (Z.B.)
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192
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Bennett JJR, Stern AD, Zhang X, Birtwistle MR, Pandey G. Low-frequency ERK and Akt activity dynamics are predictive of stochastic cell division events. NPJ Syst Biol Appl 2024; 10:65. [PMID: 38834572 PMCID: PMC11150372 DOI: 10.1038/s41540-024-00389-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/20/2024] [Indexed: 06/06/2024] Open
Abstract
Understanding the dynamics of intracellular signaling pathways, such as ERK1/2 (ERK) and Akt1/2 (Akt), in the context of cell fate decisions is important for advancing our knowledge of cellular processes and diseases, particularly cancer. While previous studies have established associations between ERK and Akt activities and proliferative cell fate, the heterogeneity of single-cell responses adds complexity to this understanding. This study employed a data-driven approach to address this challenge, developing machine learning models trained on a dataset of growth factor-induced ERK and Akt activity time courses in single cells, to predict cell division events. The most predictive models were developed by applying discrete wavelet transforms (DWTs) to extract low-frequency features from the time courses, followed by using Ensemble Integration, a data integration and predictive modeling framework. The results demonstrated that these models effectively predicted cell division events in MCF10A cells (F-measure=0.524, AUC=0.726). ERK dynamics were found to be more predictive than Akt, but the combination of both measurements further enhanced predictive performance. The ERK model`s performance also generalized to predicting division events in RPE cells, indicating the potential applicability of these models and our data-driven methodology for predicting cell division across different biological contexts. Interpretation of these models suggested that ERK dynamics throughout the cell cycle, rather than immediately after growth factor stimulation, were associated with the likelihood of cell division. Overall, this work contributes insights into the predictive power of intra-cellular signaling dynamics for cell fate decisions, and highlights the potential of machine learning approaches in unraveling complex cellular behaviors.
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Affiliation(s)
- Jamie J R Bennett
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alan D Stern
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xiang Zhang
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA
| | - Marc R Birtwistle
- Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA.
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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193
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Yang H, Yang X, Zhang Q, Lu D, Wang W, Zhang H, Yu Y, Liu X, Zhang A, Liu Q, Jiang G. Precisely Identifying the Sources of Magnetic Particles by Hierarchical Classification-Aided Isotopic Fingerprinting. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:9770-9781. [PMID: 38781163 DOI: 10.1021/acs.est.4c02702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Magnetic particles (MPs), with magnetite (Fe3O4) and maghemite (γ-Fe2O3) as the most abundant species, are ubiquitously present in the natural environment. MPs are among the most applied engineered particles and can be produced incidentally by various human activities. Identification of the sources of MPs is crucial for their risk assessment and regulation, which, however, is still an unsolved problem. Here, we report a novel approach, hierarchical classification-aided stable isotopic fingerprinting, to address this problem. We found that naturally occurring, incidental, and engineered MPs have distinct Fe and O isotopic fingerprints due to significant Fe/O isotope fractionation during their generation processes, which enables the establishment of an Fe-O isotopic library covering complex sources. Furthermore, we developed a three-level machine learning model that not only can distinguish the sources of MPs with a high precision (94.3%) but also can identify the multiple species (Fe3O4 or γ-Fe2O3) and synthetic routes of engineered MPs with a precision of 81.6%. This work represents the first reliable strategy for the precise source tracing of particles with multiple species and complex sources.
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Affiliation(s)
- Hang Yang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xuezhi Yang
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310000, China
| | - Qinghua Zhang
- College of Geography and Environmental Science, Henan University, Kaifeng 475004, China
| | - Dawei Lu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weichao Wang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310000, China
| | - Huazhou Zhang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yunbo Yu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xian Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Aiqian Zhang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qian Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
- Institute of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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194
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Weng WH, Baur S, Daswani M, Chen C, Harrell L, Kakarmath S, Jabara M, Behsaz B, McLean CY, Matias Y, Corrado GS, Shetty S, Prabhakara S, Liu Y, Danaei G, Ardila D. Predicting cardiovascular disease risk using photoplethysmography and deep learning. PLOS GLOBAL PUBLIC HEALTH 2024; 4:e0003204. [PMID: 38833495 PMCID: PMC11149850 DOI: 10.1371/journal.pgph.0003204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 04/12/2024] [Indexed: 06/06/2024]
Abstract
Cardiovascular diseases (CVDs) are responsible for a large proportion of premature deaths in low- and middle-income countries. Early CVD detection and intervention is critical in these populations, yet many existing CVD risk scores require a physical examination or lab measurements, which can be challenging in such health systems due to limited accessibility. We investigated the potential to use photoplethysmography (PPG), a sensing technology available on most smartphones that can potentially enable large-scale screening at low cost, for CVD risk prediction. We developed a deep learning PPG-based CVD risk score (DLS) to predict the probability of having major adverse cardiovascular events (MACE: non-fatal myocardial infarction, stroke, and cardiovascular death) within ten years, given only age, sex, smoking status and PPG as predictors. We compare the DLS with the office-based refit-WHO score, which adopts the shared predictors from WHO and Globorisk scores (age, sex, smoking status, height, weight and systolic blood pressure) but refitted on the UK Biobank (UKB) cohort. All models were trained on a development dataset (141,509 participants) and evaluated on a geographically separate test (54,856 participants) dataset, both from UKB. DLS's C-statistic (71.1%, 95% CI 69.9-72.4) is non-inferior to office-based refit-WHO score (70.9%, 95% CI 69.7-72.2; non-inferiority margin of 2.5%, p<0.01) in the test dataset. The calibration of the DLS is satisfactory, with a 1.8% mean absolute calibration error. Adding DLS features to the office-based score increases the C-statistic by 1.0% (95% CI 0.6-1.4). DLS predicts ten-year MACE risk comparable with the office-based refit-WHO score. Interpretability analyses suggest that the DLS-extracted features are related to PPG waveform morphology and are independent of heart rate. Our study provides a proof-of-concept and suggests the potential of a PPG-based approach strategies for community-based primary prevention in resource-limited regions.
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Affiliation(s)
- Wei-Hung Weng
- Google LLC, Mountain View, California, United States of America
| | - Sebastien Baur
- Google LLC, Mountain View, California, United States of America
| | - Mayank Daswani
- Google LLC, Mountain View, California, United States of America
| | - Christina Chen
- Google LLC, Mountain View, California, United States of America
| | - Lauren Harrell
- Google LLC, Mountain View, California, United States of America
| | - Sujay Kakarmath
- Google LLC, Mountain View, California, United States of America
| | - Mariam Jabara
- Google LLC, Mountain View, California, United States of America
| | - Babak Behsaz
- Google LLC, Mountain View, California, United States of America
| | - Cory Y. McLean
- Google LLC, Mountain View, California, United States of America
| | - Yossi Matias
- Google LLC, Mountain View, California, United States of America
| | - Greg S. Corrado
- Google LLC, Mountain View, California, United States of America
| | - Shravya Shetty
- Google LLC, Mountain View, California, United States of America
| | | | - Yun Liu
- Google LLC, Mountain View, California, United States of America
| | - Goodarz Danaei
- Department of Global Health and Population, Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, United States of America
| | - Diego Ardila
- Google LLC, Mountain View, California, United States of America
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195
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Yang S, Abdel-Aty M, Islam Z, Wang D. Real-time crash prediction on express managed lanes of Interstate highway with anomaly detection learning. ACCIDENT; ANALYSIS AND PREVENTION 2024; 201:107568. [PMID: 38581772 DOI: 10.1016/j.aap.2024.107568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 04/01/2024] [Accepted: 04/02/2024] [Indexed: 04/08/2024]
Abstract
To facilitate efficient transportation, I-4 Express is constructed separately from general use lanes in metropolitan area to improve mobility and reduce congestion. As this new infrastructure would undoubtedly change the traffic network, there is a need for more understanding of its potential safety impact. Unfortunately, many advanced real-time crash prediction models encounter an important challenge in their applicability due to their demand for a substantial volume of data for direct modeling. To tackle this challenge, we proposed a simple yet effective approach - anomaly detection learning, which formulates model as an anomaly detection problem, solves it through normality feature recognition, and predicts crashes by identifying deviations from the normal state. The proposed approach demonstrates significant improvement in the Area Under the Curve (AUC), sensitivity, and False Alarm Rate (FAR). When juxtaposed with the prevalent direct classification paradigm, our proposed anomaly detection learning (ADL) consistently outperforms in AUC (with an increase of up to 45%), sensitivity (experiencing up to a 45% increase), and FAR (reducing by up to 0.53). The most performance gain is attained through the combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) in an ensemble, resulting in a 0.78 AUC, 0.79 sensitivity, and a 0.22 false alarm rate. Furthermore, we analyzed model features with a game-theoretic approach illustrating the most correlated features for accurate prediction, revealing the attention of advanced convolution neural networks to occupancy features. This provided crucial insights into improving crash precaution, the findings from which not only benefit private stakeholders but also extend a promising opportunity for governmental intervention on the express lane. This work could promote express lane with more efficient resource allocation, real-time traffic management optimization, and high-risk area prioritization.
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Affiliation(s)
- Samgyu Yang
- Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA.
| | - Mohamed Abdel-Aty
- Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA.
| | - Zubayer Islam
- Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA.
| | - Dongdong Wang
- Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816, USA.
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196
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Liang S, Xu J, Liu H, Liang R, Guo Z, Lu M, Liu S, Gao J, Ye Z, Yi H. Automatic Recognition of Auditory Brainstem Response Waveforms Using a Deep Learning-Based Framework. Otolaryngol Head Neck Surg 2024. [PMID: 38822760 DOI: 10.1002/ohn.840] [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: 12/04/2023] [Revised: 04/24/2024] [Accepted: 05/09/2024] [Indexed: 06/03/2024]
Abstract
OBJECTIVE Recognition of auditory brainstem response (ABR) waveforms may be challenging, particularly for older individuals or those with hearing loss. This study aimed to investigate deep learning frameworks to improve the automatic recognition of ABR waveforms in participants with varying ages and hearing levels. STUDY DESIGN The research used a descriptive study design to collect and analyze pure tone audiometry and ABR data from 100 participants. SETTING The research was conducted at a tertiary academic medical center, specifically at the Clinical Audiology Center of Tsinghua Chang Gung Hospital (Beijing, China). METHODS Data from 100 participants were collected and categorized into four groups based on age and hearing level. Features from both time-domain and frequency-domain ABR signals were extracted and combined with demographic factors, such as age, sex, pure-tone thresholds, stimulus intensity, and original signal sequences to generate feature vectors. An enhanced Wide&Deep model was utilized, incorporating the Light-multi-layer perceptron (MLP) model to train the recognition of ABR waveforms. The recognition accuracy (ACC) of each model was calculated for the overall data set and each group. RESULTS The ACC rates of the Light-MLP model were 97.8%, 97.2%, 93.8%, and 92.0% for Groups 1 to 4, respectively, with a weighted average ACC rate of 95.4%. For the Wide&Deep model, the ACC rates were 93.4%, 90.8%, 92.0%, and 88.3% for Groups 1 to 4, respectively, with a weighted average ACC rate of 91.0%. CONCLUSION Both the Light-MLP model and the Wide&Deep model demonstrated excellent ACC in automatic recognition of ABR waveforms across participants with diverse ages and hearing levels. While the Wide&Deep model's performance was slightly poorer than that of the Light-MLP model, particularly due to the limited sample size, it is anticipated that with an expanded data set, the performance of Wide&Deep model may be further improved.
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Affiliation(s)
- Sichao Liang
- Department of Otolaryngology, Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Jia Xu
- Department of Otolaryngology, Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Haixu Liu
- Institute of Integrated Circuit, Tsinghua University, Beijing, China
| | - Renhe Liang
- Beijing Jingyi Tianhe Intelligent Equipment Co, Ltd, Beijing, China
| | - Zhenping Guo
- Department of Otolaryngology, Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Manlin Lu
- Department of Otolaryngology, Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Sisi Liu
- Department of Otolaryngology, Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Juanjuan Gao
- Department of Otolaryngology, Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Zuochang Ye
- Institute of Integrated Circuit, Tsinghua University, Beijing, China
| | - Haijin Yi
- Department of Otolaryngology, Head and Neck Surgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
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197
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Chen Z, Wu Z, Zhong L, Plant C, Wang S, Guo W. Attributed Multi-Order Graph Convolutional Network for Heterogeneous Graphs. Neural Netw 2024; 174:106225. [PMID: 38471260 DOI: 10.1016/j.neunet.2024.106225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 01/17/2024] [Accepted: 03/02/2024] [Indexed: 03/14/2024]
Abstract
Heterogeneous graph neural networks play a crucial role in discovering discriminative node embeddings and relations from multi-relational networks. One of the key challenges in heterogeneous graph learning lies in designing learnable meta-paths, which significantly impact the quality of learned embeddings. In this paper, we propose an Attributed Multi-Order Graph Convolutional Network (AMOGCN), which automatically explores meta-paths that involve multi-hop neighbors by aggregating multi-order adjacency matrices. The proposed model first constructs different orders of adjacency matrices from manually designed node connections. Next, AMOGCN fuses these various orders of adjacency matrices to create an intact multi-order adjacency matrix. This process is supervised by the node semantic information, which is extracted from the node homophily evaluated by attributes. Eventually, we employ a one-layer simplifying graph convolutional network with the learned multi-order adjacency matrix, which is equivalent to the cross-hop node information propagation with multi-layer graph neural networks. Substantial experiments reveal that AMOGCN achieves superior semi-supervised classification performance compared with state-of-the-art competitors.
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Affiliation(s)
- Zhaoliang Chen
- College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116, China
| | - Zhihao Wu
- College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116, China
| | - Luying Zhong
- College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116, China
| | - Claudia Plant
- Faculty of Computer Science, University of Vienna, Vienna 1090, Austria; ds:UniVie, Vienna 1090, Austria
| | - Shiping Wang
- College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116, China
| | - Wenzhong Guo
- College of Computer and Data Science, Fuzhou University, Fuzhou 350116, China; Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou 350116, China.
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198
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Bai L, Wang D, Wang H, Barnett M, Cabezas M, Cai W, Calamante F, Kyle K, Liu D, Ly L, Nguyen A, Shieh CC, Sullivan R, Zhan G, Ouyang W, Wang C. Improving multiple sclerosis lesion segmentation across clinical sites: A federated learning approach with noise-resilient training. Artif Intell Med 2024; 152:102872. [PMID: 38701636 DOI: 10.1016/j.artmed.2024.102872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 03/28/2024] [Accepted: 04/15/2024] [Indexed: 05/05/2024]
Abstract
Accurately measuring the evolution of Multiple Sclerosis (MS) with magnetic resonance imaging (MRI) critically informs understanding of disease progression and helps to direct therapeutic strategy. Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. Obtaining sufficient data from a single clinical site is challenging and does not address the heterogeneous need for model robustness. Conversely, the collection of data from multiple sites introduces data privacy concerns and potential label noise due to varying annotation standards. To address this dilemma, we explore the use of the federated learning framework while considering label noise. Our approach enables collaboration among multiple clinical sites without compromising data privacy under a federated learning paradigm that incorporates a noise-robust training strategy based on label correction. Specifically, we introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions, enabling the correction of false annotations based on prediction confidence. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites, enhancing the reliability of the correction process. Extensive experiments conducted on two multi-site datasets demonstrate the effectiveness and robustness of our proposed methods, indicating their potential for clinical applications in multi-site collaborations to train better deep learning models with lower cost in data collection and annotation.
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Affiliation(s)
- Lei Bai
- Brain and Mind Centre, The University of Sydney, NSW 2050, Australia; School of Electrical and Information Engineering, The University of Sydney, NSW 2006, Australia
| | - Dongang Wang
- Brain and Mind Centre, The University of Sydney, NSW 2050, Australia; Sydney Neuroimaging Analysis Centre, 94 Mallett Street, NSW 2050, Australia.
| | - Hengrui Wang
- Sydney Neuroimaging Analysis Centre, 94 Mallett Street, NSW 2050, Australia
| | - Michael Barnett
- Brain and Mind Centre, The University of Sydney, NSW 2050, Australia; Sydney Neuroimaging Analysis Centre, 94 Mallett Street, NSW 2050, Australia; Royal Prince Alfred Hospital, NSW, 2050, Australia
| | - Mariano Cabezas
- Brain and Mind Centre, The University of Sydney, NSW 2050, Australia
| | - Weidong Cai
- Brain and Mind Centre, The University of Sydney, NSW 2050, Australia; School of Computer Science, The University of Sydney, NSW 2006, Australia
| | - Fernando Calamante
- Brain and Mind Centre, The University of Sydney, NSW 2050, Australia; School of Biomedical Engineering, The University of Sydney, NSW 2006, Australia; Sydney Imaging, The University of Sydney, NSW 2006, Australia
| | - Kain Kyle
- Brain and Mind Centre, The University of Sydney, NSW 2050, Australia; Sydney Neuroimaging Analysis Centre, 94 Mallett Street, NSW 2050, Australia
| | - Dongnan Liu
- Brain and Mind Centre, The University of Sydney, NSW 2050, Australia; School of Computer Science, The University of Sydney, NSW 2006, Australia
| | - Linda Ly
- Sydney Neuroimaging Analysis Centre, 94 Mallett Street, NSW 2050, Australia
| | - Aria Nguyen
- Sydney Neuroimaging Analysis Centre, 94 Mallett Street, NSW 2050, Australia
| | - Chun-Chien Shieh
- Sydney Neuroimaging Analysis Centre, 94 Mallett Street, NSW 2050, Australia
| | - Ryan Sullivan
- School of Biomedical Engineering, The University of Sydney, NSW 2006, Australia; Australian Imaging Service, NSW 2006, Australia
| | - Geng Zhan
- Brain and Mind Centre, The University of Sydney, NSW 2050, Australia; Sydney Neuroimaging Analysis Centre, 94 Mallett Street, NSW 2050, Australia
| | - Wanli Ouyang
- School of Electrical and Information Engineering, The University of Sydney, NSW 2006, Australia
| | - Chenyu Wang
- Brain and Mind Centre, The University of Sydney, NSW 2050, Australia; Sydney Neuroimaging Analysis Centre, 94 Mallett Street, NSW 2050, Australia.
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199
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Baruah B, Dutta MP, Banerjee S, Bhattacharyya DK. EnsemBic: An effective ensemble of biclustering to identify potential biomarkers of esophageal squamous cell carcinoma. Comput Biol Chem 2024; 110:108090. [PMID: 38759483 DOI: 10.1016/j.compbiolchem.2024.108090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 03/28/2024] [Accepted: 04/29/2024] [Indexed: 05/19/2024]
Abstract
The development of functionally enriched and biologically competent biclustering algorithm is essential for extracting hidden information from massive biological datasets. This paper presents a novel biclustering ensemble called EnsemBic based on p-value, which calculates the functional similarity of genetic associations. To validate the effectiveness and robustness of EnsemBic, we apply three well-known biclustering techniques, viz. Laplace Prior, iBBiG, and xMotif to implement EnsemBic and have been compared using different leading parameters. It is observed that the EnsemBic outperforms its competing algorithms in several prominent functional and biological measures. Next, the biclusters obtained from EnsemBic are used to identify potential biomarkers of Esophageal Squamous Cell Carcinoma (ESCC) by exploring topological and biological relevance with reference to the elite genes, attained from genecards. Finally, we discover that the genes F2RL3, APPL1, CALM1, IFNGR1, LPAR1, ANGPT2, ARPC2, CGN, CLDN7, ATP6V1C2, CEACAM1, FTL, PLAU,PSMB4, and EPHB2 carry both the topological and biological significance of previously established ESCC elite genes. Therefore, we declare the aforementioned genes as potential biomarkers of ESCC.
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Affiliation(s)
- Bikash Baruah
- Dept. of Computer Science and Engineering, NIT Arunachal Pradesh, India
| | - Manash P Dutta
- Dept. of Computer Science & Information Technology, Cotton University, Guwahati, Assam, India.
| | | | - Dhruba K Bhattacharyya
- Dept. of Computer Science and Engineering, Tezpur University, School of Engineering, Tezpur, India
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200
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Khalis M, Elbadisy I, Bouaddi O, Luo A, Bendriouich A, Addahri B, Charaka H, Chahboune M, Foucaud J, Badou A, Belyamani L, Huybrechts I. Cluster analysis of cancer knowledge, attitudes and behaviors in the Moroccan population. BMC Cancer 2024; 24:669. [PMID: 38824496 PMCID: PMC11143602 DOI: 10.1186/s12885-024-12226-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 04/03/2024] [Indexed: 06/03/2024] Open
Abstract
BACKGROUND Cancer has become a major health concern due to the increasing morbidity and mortality rates, and its negative social, economic consequences and the heavy financial burden incurred by cancer patients. About 40% of cancers are preventable. The aim of this study was to assess the knowledge, attitudes, and practices regarding cancer prevention, and associated characteristics to inform the development of targeted cancer prevention campaigns and policies. METHODS We conducted a cross-sectional survey of adult patients at Mohamed Sekkat and Sidi Othmane Hospitals in Casablanca, Morocco. Data collection was conducted by two trained interviewers who administered the questionnaire in-person in the local language. An unsupervised clustering approach included 17 candidate variables for the cluster analysis. The variables covered a wide range of characteristics, including demographics, health perceptions and attitudes. Survey answers were calculated to compose qualitative ordinal categories, including a cancer attitude score and knowledge score. RESULTS The cluster-based analysis showed that participants in cluster 1 had the highest mean attitude score (13.9 ± 2.15) and percentage of individuals with a high level of knowledge (50.8%) whereas the lowest mean attitude score (9.48 ± 2.02) and knowledge level (7.5%.) were found in cluster 3. The participants with the lowest cancer attitude scores and knowledge levels were aged 34 to 47 years old (middle age group), predominantly females, living in rural settings, and were least likely to report health professionals as a source of health information. CONCLUSIONS The findings showed that female individuals living in rural settings, belonging to an older age group, who were least likely to use health professionals as an information source had the lowest levels of knowledge and attitudes. These groups are amenable to targeted and tailored interventions aiming to modify their understanding of cancer in order to enhance the outcomes of Morocco's on-going efforts in cancer prevention and control strategies.
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Affiliation(s)
- Mohamed Khalis
- Department of Public Health, Mohammed VI Center for Research and Innovation, Rabat, Morocco.
- Mohammed VI International School of Public Health, Mohammed VI University of Sciences and Health, Casablanca, Morocco.
- Higher Institute of Nursing Professions and Health Techniques, Rabat, Morocco.
- Laboratory of Biostatistics, Clinical, and Epidemiological Research, & Laboratory of Community Health (Public Health, Preventive Medicine and Hygiene), Department of Public Health, Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Rabat, Morocco.
| | - Imad Elbadisy
- Department of Public Health, Mohammed VI Center for Research and Innovation, Rabat, Morocco
- Mohammed VI International School of Public Health, Mohammed VI University of Sciences and Health, Casablanca, Morocco
| | - Oumnia Bouaddi
- Department of Public Health, Mohammed VI Center for Research and Innovation, Rabat, Morocco
- Mohammed VI International School of Public Health, Mohammed VI University of Sciences and Health, Casablanca, Morocco
| | - Amy Luo
- Department of Population, Family and Reproductive Health, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Amina Bendriouich
- Mohammed VI Faculty of Medicine, Mohammed VI University of Sciences and Health, Casablanca, Morocco
| | - Badr Addahri
- Mohammed VI International School of Public Health, Mohammed VI University of Sciences and Health, Casablanca, Morocco
| | - Hafida Charaka
- Higher Institute of Nursing Professions and Health Techniques, Rabat, Morocco
| | - Mohamed Chahboune
- Higher Institute of Health Sciences, Laboratory of Sciences and Health Technologies, Hassan First University of Settat, Settat, Morocco
| | - Jérôme Foucaud
- Institut National du Cancer, Boulogne Billancourt, France
- Laboratory of Education and Health Practice, Sorbonne Paris Nord University, Paris, France
| | - Abdallah Badou
- Faculty of Medicine and Pharmacy, Hassan II University, Casablanca, Morocco
| | - Lahcen Belyamani
- Mohammed VI Faculty of Medicine, Mohammed VI University of Sciences and Health, Casablanca, Morocco
- Faculty of Medicine and Pharmacy, Mohammed V University in Rabat, Rabat, Morocco
| | - Inge Huybrechts
- Nutrition and Metabolism Branch, International Agency for Research On Cancer, Lyon, France
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