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Shukla S, Deo BS, Vishwakarma C, Mishra S, Ahirwar S, Sah AN, Pandey K, Singh S, Prasad SN, Padhi AK, Pal M, Panigrahi PK, Pradhan A. A smartphone-based standalone fluorescence spectroscopy tool for cervical precancer diagnosis in clinical conditions. J Biophotonics 2024:e202300468. [PMID: 38494870 DOI: 10.1002/jbio.202300468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/07/2024] [Accepted: 02/07/2024] [Indexed: 03/19/2024]
Abstract
Real-time prediction about the severity of noncommunicable diseases like cancers is a boon for early diagnosis and timely cure. Optical techniques due to their minimally invasive nature provide better alternatives in this context than the conventional techniques. The present study talks about a standalone, field portable smartphone-based device which can classify different grades of cervical cancer on the basis of the spectral differences captured in their intrinsic fluorescence spectra with the help of AI/ML technique. In this study, a total number of 75 patients and volunteers, from hospitals at different geographical locations of India, have been tested and classified with this device. A classification approach employing a hybrid mutual information long short-term memory model has been applied to categorize various subject groups, resulting in an average accuracy, specificity, and sensitivity of 96.56%, 96.76%, and 94.37%, respectively using 10-fold cross-validation. This exploratory study demonstrates the potential of combining smartphone-based technology with fluorescence spectroscopy and artificial intelligence as a diagnostic screening approach which could enhance the detection and screening of cervical cancer.
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Affiliation(s)
- Shivam Shukla
- Center for Lasers and Photonics, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India
| | - Bhaswati Singha Deo
- Center for Lasers and Photonics, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India
| | - Chaitanya Vishwakarma
- Center for Lasers and Photonics, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India
| | - Subrata Mishra
- Center for Lasers and Photonics, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India
| | - Shikha Ahirwar
- PhotoSpIMeDx Pvt. Ltd., Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India
| | - Amar Nath Sah
- Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India
| | - Kiran Pandey
- Obstetrics and Gynecology Department, GSVM Medical College Kanpur, Kanpur, Uttar Pradesh, India
| | - Sweta Singh
- Department of Obstetrics and Gynecology, AIIMS Bhubaneswar, Bhubaneswar, Odisha, India
| | - S N Prasad
- Radiation Oncology Department, J.K. Cancer Institute Kanpur, Kanpur, Uttar Pradesh, India
| | - Ashok Kumar Padhi
- Gynecologic Oncology Department, Acharya Harihar Regional Cancer Research Centre, Cuttack, Odisha, India
| | - Mayukha Pal
- ABB Ability Innovation Center, Asea Brown Boveri Company, Hyderabad, India
| | - Prasanta K Panigrahi
- Department of Physical Sciences, Indian Institute of Science Education and Research Kolkata, Mohanpur, West Bengal, India
- Centre for Quantum Science and Technology, Siksha 'O' Anusandhan University, Bhubaneswar, Odisha, India
| | - Asima Pradhan
- Center for Lasers and Photonics, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India
- PhotoSpIMeDx Pvt. Ltd., Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India
- Department of Physics, Indian Institute of Technology Kanpur, Kanpur, Uttar Pradesh, India
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Corsini A, Tomassini A, Pastore A, Delis I, Fadiga L, D'Ausilio A. Speech perception difficulty modulates theta-band encoding of articulatory synergies. J Neurophysiol 2024; 131:480-491. [PMID: 38323331 DOI: 10.1152/jn.00388.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 01/04/2024] [Accepted: 01/25/2024] [Indexed: 02/08/2024] Open
Abstract
The human brain tracks available speech acoustics and extrapolates missing information such as the speaker's articulatory patterns. However, the extent to which articulatory reconstruction supports speech perception remains unclear. This study explores the relationship between articulatory reconstruction and task difficulty. Participants listened to sentences and performed a speech-rhyming task. Real kinematic data of the speaker's vocal tract were recorded via electromagnetic articulography (EMA) and aligned to corresponding acoustic outputs. We extracted articulatory synergies from the EMA data with principal component analysis (PCA) and employed partial information decomposition (PID) to separate the electroencephalographic (EEG) encoding of acoustic and articulatory features into unique, redundant, and synergistic atoms of information. We median-split sentences into easy (ES) and hard (HS) based on participants' performance and found that greater task difficulty involved greater encoding of unique articulatory information in the theta band. We conclude that fine-grained articulatory reconstruction plays a complementary role in the encoding of speech acoustics, lending further support to the claim that motor processes support speech perception.NEW & NOTEWORTHY Top-down processes originating from the motor system contribute to speech perception through the reconstruction of the speaker's articulatory movement. This study investigates the role of such articulatory simulation under variable task difficulty. We show that more challenging listening tasks lead to increased encoding of articulatory kinematics in the theta band and suggest that, in such situations, fine-grained articulatory reconstruction complements acoustic encoding.
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Affiliation(s)
- Alessandro Corsini
- Center for Translational Neurophysiology of Speech and Communication, Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Università di Ferrara, Ferrara, Italy
| | - Alice Tomassini
- Center for Translational Neurophysiology of Speech and Communication, Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Università di Ferrara, Ferrara, Italy
| | - Aldo Pastore
- Laboratorio NEST, Scuola Normale Superiore, Pisa, Italy
| | - Ioannis Delis
- School of Biomedical Sciences, University of Leeds, Leeds, United Kingdom
| | - Luciano Fadiga
- Center for Translational Neurophysiology of Speech and Communication, Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Università di Ferrara, Ferrara, Italy
| | - Alessandro D'Ausilio
- Center for Translational Neurophysiology of Speech and Communication, Istituto Italiano di Tecnologia, Ferrara, Italy
- Department of Neuroscience and Rehabilitation, Università di Ferrara, Ferrara, Italy
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Prusinowski M, Tavadze P, Andrews Z, Lang L, Pulivendhan D, Neumann C, Romero AH, Trejos T. Experimental results on data analysis algorithms for extracting and interpreting edge feature data for duct tape and textile physical fit examinations. J Forensic Sci 2024; 69:498-514. [PMID: 38111135 DOI: 10.1111/1556-4029.15449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 11/28/2023] [Accepted: 12/03/2023] [Indexed: 12/20/2023]
Abstract
A physical fit is an important observation that can result from the forensic analysis of trace evidence as it conveys a high degree of association between two items. However, physical fit examinations can be time-consuming, and potential bias from analysts may affect judgment. To overcome these shortcomings, a data analysis algorithm using mutual information and a decision tree has been developed to support practitioners in interpreting the evidence. We created these tools using data obtained from physical fit examinations of duct tape and textiles analyzed in previous studies, along with the reasoning behind the analysts' decisions. The relative feature importance is described by material type, enhancing the knowledge base in this field. Compared with the human analysis, the algorithms provided accuracies above 90%, with an improved rate of true positives for most duct tape subsets. Conversely, false positives were observed in high-quality scissor cut (HQ-HT-S) duct tape and textiles. As such, it is advised to use these algorithms in tandem with human analysis. Furthermore, the study evaluated the accuracy of physical fits when only partial sample lengths are available. The results of this investigation indicated that acceptable accuracies for correctly identifying true fits and non-fits occurred when at least 35% of a sample length was present. However, lower accuracies were observed for samples prone to stretching or distortion. Therefore, the models described here can provide a valuable supplementary tool but should not be the sole means of evaluating samples.
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Affiliation(s)
- Meghan Prusinowski
- Department of Forensic and Investigative Science, West Virginia University, Morgantown, West Virginia, USA
| | - Pedram Tavadze
- Department of Forensic and Investigative Science, West Virginia University, Morgantown, West Virginia, USA
- Department of Physics and Astronomy, West Virginia University, Morgantown, West Virginia, USA
| | - Zachary Andrews
- Department of Forensic and Investigative Science, West Virginia University, Morgantown, West Virginia, USA
| | - Logan Lang
- Department of Physics and Astronomy, West Virginia University, Morgantown, West Virginia, USA
| | - Divyanjali Pulivendhan
- Department of Forensic and Investigative Science, West Virginia University, Morgantown, West Virginia, USA
| | | | - Aldo H Romero
- Department of Physics and Astronomy, West Virginia University, Morgantown, West Virginia, USA
| | - Tatiana Trejos
- Department of Forensic and Investigative Science, West Virginia University, Morgantown, West Virginia, USA
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Machida I, Shishikura M, Yamane Y, Sakai K. Representation of Natural Contours by a Neural Population in Monkey V4. eNeuro 2024; 11:ENEURO.0445-23.2024. [PMID: 38423791 PMCID: PMC10946029 DOI: 10.1523/eneuro.0445-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 02/18/2024] [Accepted: 02/22/2024] [Indexed: 03/02/2024] Open
Abstract
The cortical visual area, V4, has been considered to code contours that contribute to the intermediate-level representation of objects. The neural responses to the complex contour features intrinsic to natural contours are expected to clarify the essence of the representation. To approach the cortical coding of natural contours, we investigated the simultaneous coding of multiple contour features in monkey (Macaca fuscata) V4 neurons and their population-level representation. A substantial number of neurons showed significant tuning for two or more features such as curvature and closure, indicating that a substantial number of V4 neurons simultaneously code multiple contour features. A large portion of the neurons responded vigorously to acutely curved contours that surrounded the center of classical receptive field, suggesting that V4 neurons tend to code prominent features of object contours. The analysis of mutual information (MI) between the neural responses and each contour feature showed that most neurons exhibited similar magnitudes for each type of MI, indicating that many neurons showing the responses depended on multiple contour features. We next examined the population-level representation by using multidimensional scaling analysis. The neural preferences to the multiple contour features and that to natural stimuli compared with silhouette stimuli increased along with the primary and secondary axes, respectively, indicating the contribution of the multiple contour features and surface textures in the population responses. Our analyses suggested that V4 neurons simultaneously code multiple contour features in natural images and represent contour and surface properties in population.
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Affiliation(s)
- Itsuki Machida
- Department of Computer Science, University of Tsukuba, Tsukuba 305-8573, Japan
| | - Motofumi Shishikura
- Department of Computer Science, University of Tsukuba, Tsukuba 305-8573, Japan
| | - Yukako Yamane
- Neural Computation Unit, Okinawa Institute of Science and Technology, Okinawa 904-0495, Japan
| | - Ko Sakai
- Department of Computer Science, University of Tsukuba, Tsukuba 305-8573, Japan
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Díaz-Campos MÁ, Vasquez-Arriaga J, Ochoa S, Hernández-Lemus E. Functional impact of multi-omic interactions in lung cancer. Front Genet 2024; 15:1282241. [PMID: 38389572 PMCID: PMC10881857 DOI: 10.3389/fgene.2024.1282241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 01/23/2024] [Indexed: 02/24/2024] Open
Abstract
Lung tumors are a leading cause of cancer-related death worldwide. Lung cancers are highly heterogeneous on their phenotypes, both at the cellular and molecular levels. Efforts to better understand the biological origins and outcomes of lung cancer in terms of this enormous variability often require of high-throughput experimental techniques paired with advanced data analytics. Anticipated advancements in multi-omic methodologies hold potential to reveal a broader molecular perspective of these tumors. This study introduces a theoretical and computational framework for generating network models depicting regulatory constraints on biological functions in a semi-automated way. The approach successfully identifies enriched functions in analyzed omics data, focusing on Adenocarcinoma (LUAD) and Squamous cell carcinoma (LUSC, a type of NSCLC) in the lung. Valuable information about novel regulatory characteristics, supported by robust biological reasoning, is illustrated, for instance by considering the role of genes, miRNAs and CpG sites associated with NSCLC, both novel and previously reported. Utilizing multi-omic regulatory networks, we constructed robust models elucidating omics data interconnectedness, enabling systematic generation of mechanistic hypotheses. These findings offer insights into complex regulatory mechanisms underlying these cancer types, paving the way for further exploring their molecular complexity.
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Affiliation(s)
| | - Jorge Vasquez-Arriaga
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
| | - Soledad Ochoa
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City, Mexico
- Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City, Mexico
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Li Y, Gao J, Yang Y, Zhuang Y, Kang Q, Li X, Tian M, Lv H, He J. Temporal and spatial variability of dynamic microstate brain network in disorders of consciousness. CNS Neurosci Ther 2024; 30:e14641. [PMID: 38385681 PMCID: PMC10883110 DOI: 10.1111/cns.14641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 01/17/2024] [Accepted: 02/01/2024] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND Accurately diagnosing patients with the vegetative state (VS) and the minimally conscious state (MCS) reached a misdiagnosis of approximately 40%. METHODS A method combined microstate and dynamic functional connectivity (dFC) to study the spatiotemporal variability of the brain in disorders of consciousness (DOC) patients was proposed. Resting-state EEG data were obtained from 16 patients with MCS and 16 patients with VS. Mutual information (MI) was used to assess the EEG connectivity in each microstate. MI-based features with statistical differences were selected as the total feature subset (TFS), then the TFS was utilized to feature selection and fed into the classifier, obtaining the optimal feature subsets (OFS) in each microstate. Subsequently, an OFS-based MI functional connectivity network (MIFCN) was constructed in the cortex. RESULTS The group-average MI connectivity matrix focused on all channels revealed that all five microstates exhibited stronger information interaction in the MCS when comparing with the VS. While OFS-based MIFCN, which only focused on a few channels, revealed greater MI flow in VS patients than in MCS patients under microstates A, B, C, and E, except for microstate D. Additionally, the average classification accuracy of OFS in the five microstates was 96.2%. CONCLUSION Constructing features based on microstates to distinguish between two categories of DOC patients had effectiveness.
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Affiliation(s)
- Yaqian Li
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Junfeng Gao
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Ying Yang
- College of Foreign LanguagesWuhan University of TechnologyWuhanChina
| | - Yvtong Zhuang
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
| | - Qianruo Kang
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Xiang Li
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Min Tian
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Haoan Lv
- Key Laboratory of Cognitive Science of State Ethnic Affairs Commission, College of Biomedical EngineeringSouth‐Central Minzu UniversityWuhanChina
| | - Jianghong He
- Department of Neurosurgery, Beijing Tiantan HospitalCapital Medical UniversityBeijingChina
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7
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Choi I, Kim WC. Enhancing Exchange-Traded Fund Price Predictions: Insights from Information-Theoretic Networks and Node Embeddings. Entropy (Basel) 2024; 26:70. [PMID: 38248195 PMCID: PMC10814172 DOI: 10.3390/e26010070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/02/2023] [Accepted: 12/22/2023] [Indexed: 01/23/2024]
Abstract
This study presents a novel approach to predicting price fluctuations for U.S. sector index ETFs. By leveraging information-theoretic measures like mutual information and transfer entropy, we constructed threshold networks highlighting nonlinear dependencies between log returns and trading volume rate changes. We derived centrality measures and node embeddings from these networks, offering unique insights into the ETFs' dynamics. By integrating these features into gradient-boosting algorithm-based models, we significantly enhanced the predictive accuracy. Our approach offers improved forecast performance for U.S. sector index futures and adds a layer of explainability to the existing literature.
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Affiliation(s)
| | - Woo Chang Kim
- Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea;
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8
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Kotamraju BP, Eggers TE, McCallum GA, Durand DM. Selective chronic recording in small nerve fascicles of sciatic nerve with carbon nanotube yarns in rats. J Neural Eng 2024; 20:066041. [PMID: 38100824 PMCID: PMC10765114 DOI: 10.1088/1741-2552/ad1611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 11/15/2023] [Accepted: 12/15/2023] [Indexed: 12/17/2023]
Abstract
Objective. The primary challenge faced in the field of neural rehabilitation engineering is the limited advancement in nerve interface technology, which currently fails to match the mechanical properties of small-diameter nerve fascicles. Novel developments are necessary to enable long-term, chronic recording from a multitude of small fascicles, allowing for the recovery of motor intent and sensory signals.Approach. In this study, we analyze the chronic recording capabilities of carbon nanotube yarn electrodes in the peripheral somatic nervous system. The electrodes were surgically implanted in the sciatic nerve's three individual fascicles in rats, enabling the recording of neural activity during gait. Signal-to-noise ratio (SNR) and information theory were employed to analyze the data, demonstrating the superior recording capabilities of the electrodes. Flat interface nerve electrode and thin-film longitudinal intrafascicular electrode electrodes were used as a references to assess the results from SNR and information theory analysis.Main results. The electrodes exhibited the ability to record chronic signals with SNRs reaching as high as 15 dB, providing 12 bits of information for the sciatic nerve, a significant improvement over previous methods. Furthermore, the study revealed that the SNR and information content of the neural signals remained consistent over a period of 12 weeks across three different fascicles, indicating the stability of the interface. The signals recorded from these electrodes were also analyzed for selectivity using information theory metrics, which showed an information sharing of approximately 1.4 bits across the fascicles.Significance. The ability to safely and reliably record from multiple fascicles of different nerves simultaneously over extended periods of time holds substantial implications for the field of neural and rehabilitation engineering. This advancement addresses the limitation of current nerve interface technologies and opens up new possibilities for enhancing neural rehabilitation and control.
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Affiliation(s)
- B P Kotamraju
- Case Western Reserve University, Neural Engineering Center, Biomedical Engineering, Cleveland, OH, United States of America
| | - Thomas E Eggers
- Department of Neurosurgery, Emory University, Atlanta, GA, United States of America
| | - Grant A McCallum
- Case Western Reserve University, Neural Engineering Center, Biomedical Engineering, Cleveland, OH, United States of America
| | - Dominique M Durand
- Case Western Reserve University, Neural Engineering Center, Biomedical Engineering, Cleveland, OH, United States of America
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Formentin M, Chignola R, Favretti M. Optimal entropic properties of SARS-CoV-2 RNA sequences. R Soc Open Sci 2024; 11:231369. [PMID: 38298394 PMCID: PMC10827432 DOI: 10.1098/rsos.231369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 01/02/2024] [Indexed: 02/02/2024]
Abstract
The reaction of the scientific community against the COVID-19 pandemic has generated a huge (approx. 106 entries) dataset of genome sequences collected worldwide and spanning a relatively short time window. These unprecedented conditions together with the certain identification of the reference viral genome sequence allow for an original statistical study of mutations in the virus genome. In this paper, we compute the Shannon entropy of every sequence in the dataset as well as the relative entropy and the mutual information between the reference sequence and the mutated ones. These functions, originally developed in information theory, measure the information content of a sequence and allows us to study the random character of mutation mechanism in terms of its entropy and information gain or loss. We show that this approach allows us to set in new format known features of the SARS-CoV-2 mutation mechanism like the CT bias, but also to discover new optimal entropic properties of the mutation process in the sense that the virus mutation mechanism track closely theoretically computable lower bounds for the entropy decrease and the information transfer.
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Affiliation(s)
- Marco Formentin
- Department of Mathematics Tullio Levi-Civita, University of Padova, via Trieste 63 35131 Padova, Italy
| | - Roberto Chignola
- Department of Biotechnology, University of Verona, Strada le Grazie 15-CV1, 37134 Verona, Italy
| | - Marco Favretti
- Department of Mathematics Tullio Levi-Civita, University of Padova, via Trieste 63 35131 Padova, Italy
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Hacisuleyman A, Erman B. Synergy and anti-cooperativity in allostery: Molecular dynamics study of WT and oncogenic KRAS-RGL1. Proteins 2023. [PMID: 38153169 DOI: 10.1002/prot.26657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/03/2023] [Accepted: 12/15/2023] [Indexed: 12/29/2023]
Abstract
This study focuses on investigating the effects of an oncogenic mutation (G12V) on the stability and interactions within the KRAS-RGL1 protein complex. The KRAS-RGL1 complex is of particular interest due to its relevance to KRAS-associated cancers and the potential for developing targeted drugs against the KRAS system. The stability of the complex and the allosteric effects of specific residues are examined to understand their roles as modulators of complex stability and function. Using molecular dynamics simulations, we calculate the mutual information, MI, between two neighboring residues at the interface of the KRAS-RGL1 complex, and employ the concept of interaction information, II, to measure the contribution of a third residue to the interaction between interface residue pairs. Negative II indicates synergy, where the presence of the third residue strengthens the interaction, while positive II suggests anti-cooperativity. Our findings reveal that MI serves as a dominant factor in determining the results, with the G12V mutation increasing the MI between interface residues, indicating enhanced correlations due to the formation of a more compact structure in the complex. Interestingly, although II plays a role in understanding three-body interactions and the impact of distant residues, it is not significant enough to outweigh the influence of MI in determining the overall stability of the complex. Nevertheless, II may nonetheless be a relevant factor to consider in future drug design efforts. This study provides valuable insights into the mechanisms of complex stability and function, highlighting the significance of three-body interactions and the impact of distant residues on the binding stability of the complex. Additionally, our findings demonstrate that constraining the fluctuations of a third residue consistently increases the stability of the G12V variant, making it challenging to weaken complex formation of the mutated species through allosteric manipulation. The novel perspective offered by this approach on protein dynamics, function, and allostery has potential implications for understanding and targeting other protein complexes involved in vital cellular processes. The results contribute to our understanding of the effects of oncogenic mutations on protein-protein interactions and provide a foundation for future therapeutic interventions in the context of KRAS-associated cancers and beyond.
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Affiliation(s)
- Aysima Hacisuleyman
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Burak Erman
- Department of Chemical and Biological Engineering Koc University, Istanbul, Turkey
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11
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Iranzo S, Belda-Lois JM, Martinez-de-Juan JL, Prats-Boluda G. Assessment of Muscle Coordination Changes Caused by the Use of an Occupational Passive Lumbar Exoskeleton in Laboratory Conditions. Sensors (Basel) 2023; 23:9631. [PMID: 38139478 PMCID: PMC10747114 DOI: 10.3390/s23249631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/29/2023] [Accepted: 12/02/2023] [Indexed: 12/24/2023]
Abstract
The introduction of exoskeletons in industry has focused on improving worker safety. Exoskeletons have the objective of decreasing the risk of injury or fatigue when performing physically demanding tasks. Exoskeletons' effect on the muscles is one of the most common focuses of their assessment. The present study aimed to analyze the muscle interactions generated during load-handling tasks in laboratory conditions with and without a passive lumbar exoskeleton. The electromyographic data of the muscles involved in the task were recorded from twelve participants performing load-handling tasks. The correlation coefficient, coherence coefficient, mutual information, and multivariate sample entropy were calculated to determine if there were significant differences in muscle interactions between the two test conditions. The results showed that muscle coordination was affected by the use of the exoskeleton. In some cases, the exoskeleton prevented changes in muscle coordination throughout the execution of the task, suggesting a more stable strategy. Additionally, according to the directed Granger causality, a trend of increasing bottom-up activation was found throughout the task when the participant was not using the exoskeleton. Among the different variables analyzed for coordination, the most sensitive to changes was the multivariate sample entropy.
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Affiliation(s)
- Sofía Iranzo
- Instituto de Biomecánica de Valencia, Universitat Politècnica de València, 46022 Valencia, Spain; (S.I.); (J.-M.B.-L.)
| | - Juan-Manuel Belda-Lois
- Instituto de Biomecánica de Valencia, Universitat Politècnica de València, 46022 Valencia, Spain; (S.I.); (J.-M.B.-L.)
| | - Jose Luis Martinez-de-Juan
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, 46022 Valencia, Spain;
| | - Gema Prats-Boluda
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, 46022 Valencia, Spain;
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12
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Zeng Q, Li R, Wang J. Nonequilibrium Effects on Information Recoverability of the Noisy Channels. Entropy (Basel) 2023; 25:1589. [PMID: 38136470 PMCID: PMC10742946 DOI: 10.3390/e25121589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 08/23/2023] [Accepted: 11/18/2023] [Indexed: 12/24/2023]
Abstract
We investigated the impact of nonequilibrium conditions on the transmission and recovery of information through noisy channels. By measuring the recoverability of messages from an information source, we demonstrate that the ability to recover information is connected to the nonequilibrium behavior of the information flow, particularly in terms of sequential information transfer. We discovered that the mathematical equivalence of information recoverability and entropy production characterizes the dissipative nature of information transfer. Our findings show that both entropy production (or recoverability) and mutual information increase monotonically with the nonequilibrium strength of information dynamics. These results suggest that the nonequilibrium dissipation cost can enhance the recoverability of noise messages and improve the quality of information transfer. Finally, we propose a simple model to test our conclusions and found that the numerical results support our findings.
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Affiliation(s)
- Qian Zeng
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Changchun 130022, China
| | - Ran Li
- Center for Theoretical Interdisciplinary Sciences, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325001, China
| | - Jin Wang
- Department of Chemistry, State University of New York, Stony Brook, NY 11794, USA
- Department of Physics and Astronomy, State University of New York, Stony Brook, NY 11794, USA
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13
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Emaminejad SA, Sparks J, Cusick RD. Integrating Bio-Electrochemical Sensors and Machine Learning to Predict the Efficacy of Biological Nutrient Removal Processes at Water Resource Recovery Facilities. Environ Sci Technol 2023; 57:18372-18381. [PMID: 37386725 DOI: 10.1021/acs.est.3c00352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Monitoring biological nutrient removal (BNR) processes at water resource recovery facilities (WRRFs) with data-driven models is currently limited by the data limitations associated with the variability of bioavailable carbon (C) in wastewater. This study focuses on leveraging the amperometric response of a bio-electrochemical sensor (BES) to wastewater C variability, to predict influent shock loading events and NO3- removal in the first-stage anoxic zone (ANX1) of a five-stage Bardenpho BNR process using machine learning (ML) methods. Shock loading prediction with BES signal processing successfully detected 86.9% of the influent industrial slug and rain events of the plant during the study period. Extreme gradient boosting (XGBoost) and artificial neural network (ANN) models developed using the BES signal and other recorded variables provided a good prediction performance for NO3- removal in the ANX1, particularly within the normal operating range of WRRFs. A sensitivity analysis of the XGBoost model using SHapley Additive exPlanations indicated that the BES signal had the strongest impact on the model output and current approaches to methanol dosing that neglect C availability can negatively impact nitrogen (N) removal due to cascading impacts of overdosing on nitrification efficacy.
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Affiliation(s)
- Seyed Aryan Emaminejad
- Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
| | - Jeff Sparks
- Hampton Roads Sanitation District Nansemond Treatment Plant, Virginia Beach, Virginia 23455, United States
| | - Roland D Cusick
- Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States
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14
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Karell-Albo JA, Legón-Pérez CM, Socorro-Llanes R, Rojas O, Sosa-Gómez G. Complexity Reduction in Analyzing Independence between Statistical Randomness Tests Using Mutual Information. Entropy (Basel) 2023; 25:1545. [PMID: 37998237 PMCID: PMC10670732 DOI: 10.3390/e25111545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 11/25/2023]
Abstract
The advantages of using mutual information to evaluate the correlation between randomness tests have recently been demonstrated. However, it has been pointed out that the high complexity of this method limits its application in batteries with a greater number of tests. The main objective of this work is to reduce the complexity of the method based on mutual information for analyzing the independence between the statistical tests of randomness. The achieved complexity reduction is estimated theoretically and verified experimentally. A variant of the original method is proposed by modifying the step in which the significant values of the mutual information are determined. The correlation between the NIST battery tests was studied, and it was concluded that the modifications to the method do not significantly affect the ability to detect correlations. Due to the efficiency of the newly proposed method, its use is recommended to analyze other batteries of tests.
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Affiliation(s)
- Jorge Augusto Karell-Albo
- Instituto de Criptografía, Facultad de Matemática y Computación, Universidad de la Habana, Habana 10400, Cuba; (J.A.K.-A.); (C.M.L.-P.)
| | - Carlos Miguel Legón-Pérez
- Instituto de Criptografía, Facultad de Matemática y Computación, Universidad de la Habana, Habana 10400, Cuba; (J.A.K.-A.); (C.M.L.-P.)
| | - Raisa Socorro-Llanes
- Facultad de Ingeniería Informática, Universidad Tecnológica de la Habana José Antonio Echeverría (CUJAE), Habana 19390, Cuba;
| | - Omar Rojas
- Facultad de Ciencias Económicas y Empresariales, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, Jalisco, Mexico;
| | - Guillermo Sosa-Gómez
- Facultad de Ciencias Económicas y Empresariales, Universidad Panamericana, Álvaro del Portillo 49, Zapopan 45010, Jalisco, Mexico;
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15
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Charest N, Shen Y, Lai YC, Chen IA, Shea JE. Discovering pathways through ribozyme fitness landscapes using information theoretic quantification of epistasis. RNA 2023; 29:1644-1657. [PMID: 37580126 PMCID: PMC10578471 DOI: 10.1261/rna.079541.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 07/29/2023] [Indexed: 08/16/2023]
Abstract
The identification of catalytic RNAs is typically achieved through primarily experimental means. However, only a small fraction of sequence space can be analyzed even with high-throughput techniques. Methods to extrapolate from a limited data set to predict additional ribozyme sequences, particularly in a human-interpretable fashion, could be useful both for designing new functional RNAs and for generating greater understanding about a ribozyme fitness landscape. Using information theory, we express the effects of epistasis (i.e., deviations from additivity) on a ribozyme. This representation was incorporated into a simple model of the epistatic fitness landscape, which identified potentially exploitable combinations of mutations. We used this model to theoretically predict mutants of high activity for a self-aminoacylating ribozyme, identifying potentially active triple and quadruple mutants beyond the experimental data set of single and double mutants. The predictions were validated experimentally, with nine out of nine sequences being accurately predicted to have high activity. This set of sequences included mutants that form a previously unknown evolutionary "bridge" between two ribozyme families that share a common motif. Individual steps in the method could be examined, understood, and guided by a human, combining interpretability and performance in a simple model to predict ribozyme sequences by extrapolation.
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Affiliation(s)
- Nathaniel Charest
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California 93106, USA
| | - Yuning Shen
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California 93106, USA
| | - Yei-Chen Lai
- Department of Chemistry, National Chung Hsing University, Taichung City 40227, Taiwan
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California 90095, USA
| | - Irene A Chen
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California 93106, USA
- Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, California 90095, USA
| | - Joan-Emma Shea
- Department of Chemistry and Biochemistry, University of California, Santa Barbara, California 93106, USA
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16
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Quétant G, Belousov Y, Kinakh V, Voloshynovskiy S. TURBO: The Swiss Knife of Auto-Encoders. Entropy (Basel) 2023; 25:1471. [PMID: 37895592 PMCID: PMC10606332 DOI: 10.3390/e25101471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 10/13/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023]
Abstract
We present a novel information-theoretic framework, termed as TURBO, designed to systematically analyse and generalise auto-encoding methods. We start by examining the principles of information bottleneck and bottleneck-based networks in the auto-encoding setting and identifying their inherent limitations, which become more prominent for data with multiple relevant, physics-related representations. The TURBO framework is then introduced, providing a comprehensive derivation of its core concept consisting of the maximisation of mutual information between various data representations expressed in two directions reflecting the information flows. We illustrate that numerous prevalent neural network models are encompassed within this framework. The paper underscores the insufficiency of the information bottleneck concept in elucidating all such models, thereby establishing TURBO as a preferable theoretical reference. The introduction of TURBO contributes to a richer understanding of data representation and the structure of neural network models, enabling more efficient and versatile applications.
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Affiliation(s)
- Guillaume Quétant
- Centre Universitaire d’Informatique, Université de Genève, Route de Drize 7, CH-1227 Carouge, Switzerland; (Y.B.); (V.K.)
| | | | | | - Slava Voloshynovskiy
- Centre Universitaire d’Informatique, Université de Genève, Route de Drize 7, CH-1227 Carouge, Switzerland; (Y.B.); (V.K.)
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17
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Lee IH, Le HA, Yang SRE. Mutual Information and Correlations across Topological Phase Transitions in Topologically Ordered Graphene Zigzag Nanoribbons. Entropy (Basel) 2023; 25:1449. [PMID: 37895570 PMCID: PMC10606814 DOI: 10.3390/e25101449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 10/11/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023]
Abstract
Graphene zigzag nanoribbons, initially in a topologically ordered state, undergo a topological phase transition into crossover phases distinguished by quasi-topological order. We computed mutual information for both the topologically ordered phase and its crossover phases, revealing the following results: (i) In the topologically ordered phase, A-chirality carbon lines strongly entangle with B-chirality carbon lines on the opposite side of the zigzag ribbon. This entanglement persists but weakens in crossover phases. (ii) The upper zigzag edge entangles with non-edge lines of different chirality on the opposite side of the ribbon. (iii) Entanglement increases as more carbon lines are grouped together, regardless of the lines' chirality. No long-range entanglement was found in the symmetry-protected phase in the absence of disorder.
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Affiliation(s)
| | | | - S.-R. Eric Yang
- Department of Physics, Korea University, Seoul 02841, Republic of Korea; (I.-H.L.); (H.-A.L.)
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18
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Kurtin DL, Araña‐Oiarbide G, Lorenz R, Violante IR, Hampshire A. Planning ahead: Predictable switching recruits task-active and resting-state networks. Hum Brain Mapp 2023; 44:5030-5046. [PMID: 37471699 PMCID: PMC10502652 DOI: 10.1002/hbm.26430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 06/08/2023] [Accepted: 07/05/2023] [Indexed: 07/22/2023] Open
Abstract
Switching is a difficult cognitive process characterised by costs in task performance; specifically, slowed responses and reduced accuracy. It is associated with the recruitment of a large coalition of task-positive regions including those referred to as the multiple demand cortex (MDC). The neural correlates of switching not only include the MDC, but occasionally the default mode network (DMN), a characteristically task-negative network. To unpick the role of the DMN during switching we collected fMRI data from 24 participants playing a switching paradigm that perturbed predictability (i.e., cognitive load) across three switch dimensions-sequential, perceptual, and spatial predictability. We computed the activity maps unique to switch vs. stay trials and all switch dimensions, then evaluated functional connectivity under these switch conditions by computing the pairwise mutual information functional connectivity (miFC) between regional timeseries. Switch trials exhibited an expected cost in reaction time while sequential predictability produced a significant benefit to task accuracy. Our results showed that switch trials recruited a broader activity map than stay trials, including regions of the DMN, the MDC, and task-positive networks such as visual, somatomotor, dorsal, salience/ventral attention networks. More sequentially predictable trials recruited increased activity in the somatomotor and salience/ventral attention networks. Notably, changes in sequential and perceptual predictability, but not spatial predictability, had significant effects on miFC. Increases in perceptual predictability related to decreased miFC between control, visual, somatomotor, and DMN regions, whereas increases in sequential predictability increased miFC between regions in the same networks, as well as regions within ventral attention/ salience, dorsal attention, limbic, and temporal parietal networks. These results provide novel clues as to how DMN may contribute to executive task performance. Specifically, the improved task performance, unique activity, and increased miFC associated with increased sequential predictability suggest that the DMN may coordinate more strongly with the MDC to generate a temporal schema of upcoming task events, which may attenuate switching costs.
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Affiliation(s)
- Danielle L. Kurtin
- NeuroModulation Lab, Department of Psychology, Faculty of Health and Medical SciencesUniversity of SurreyGuildfordUK
- Department of Brain Sciences, Faculty of MedicineImperial College LondonLondonUK
| | | | - Romy Lorenz
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
- The Poldrack LabStanford UniversityStanfordCaliforniaUSA
- Department of NeurophysicsMax‐Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Ines R. Violante
- NeuroModulation Lab, Department of Psychology, Faculty of Health and Medical SciencesUniversity of SurreyGuildfordUK
| | - Adam Hampshire
- Department of Brain Sciences, Faculty of MedicineImperial College LondonLondonUK
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19
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Witter J, Houghton C. Estimating Mutual Information for Spike Trains: A Bird Song Example. Entropy (Basel) 2023; 25:1413. [PMID: 37895534 PMCID: PMC10606342 DOI: 10.3390/e25101413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/31/2023] [Accepted: 09/19/2023] [Indexed: 10/29/2023]
Abstract
Zebra finches are a model animal used in the study of audition. They are adept at recognizing zebra finch songs, and the neural pathway involved in song recognition is well studied. Here, this example is used to illustrate the estimation of mutual information between stimuli and responses using a Kozachenko-Leonenko estimator. The challenge in calculating mutual information for spike trains is that there are no obvious coordinates for the data. The Kozachenko-Leonenko estimator does not require coordinates; it relies only on the distance between data points. In the case of bird songs, estimating the mutual information demonstrates that the information content of spiking does not diminish as the song progresses.
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Affiliation(s)
- Jake Witter
- Faculty of Engineering, University of Bristol, Bristol BS8 1TR, UK;
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20
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Fernandez Martinez R, Okariz A, Iturrondobeitia M, Ibarretxe J. The determination of optimum segmentation parameters using genetic algorithms: Application to different segmentation algorithms and transmission electron microscopy tomography reconstructed volumes. Microsc Res Tech 2023; 86:1237-1248. [PMID: 36924345 DOI: 10.1002/jemt.24318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 02/08/2023] [Accepted: 03/03/2023] [Indexed: 03/18/2023]
Abstract
A method for optimizing an automatic selection of values for parameters that feed segmentation algorithms is proposed. Evolutionary optimization techniques in combination with a fitness function based on a mutual information parameter have been used to find the optimal parameter values of region growing, fuzzy c-means and graph cut segmentation algorithms. To validate the method, the segmentation of two transmission electron microscopy tomography reconstructed volumes of a carbon black-reinforced rubber and a polylactic acid and clay nanocomposite is carried out (i) using evolutionary optimization techniques and (ii) manually by experts. The results confirm that the use of evolutionary optimization techniques, such as genetic algorithms, reduces the computational operation cost needed for a total grid search of segmentation parameters, reducing the probability of reaching a false optimum, and improving the segmentation quality. HIGHLIGHTS: A new approach to optimize 3D segmentation algorithms. Methodology to optimize segmentation parameters and improve segmentation quality. Improvement on the results when using region growing, fuzzy c-means and graph cuts algorithms.
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Affiliation(s)
- Roberto Fernandez Martinez
- Department of Electrical Engineering, College of Engineering in Bilbao, University of the Basque Country UPV/EHU, Bilbao, Spain
| | - Ana Okariz
- Department of Applied Physics, College of Engineering in Bilbao, University of the Basque Country UPV/EHU, Bilbao, Spain
| | - Maider Iturrondobeitia
- Graphic Design and Project Engineering Department, College of Engineering in Bilbao, University of the Basque Country UPV/EHU, Bilbao, Spain
| | - Julen Ibarretxe
- Department of Applied Physics, College of Engineering in Bilbao, University of the Basque Country UPV/EHU, Bilbao, Spain
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21
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Glanz RM, Sokoloff G, Blumberg MS. Neural decoding reveals specialized kinematic tuning after an abrupt cortical transition. Cell Rep 2023; 42:113119. [PMID: 37690023 PMCID: PMC10591925 DOI: 10.1016/j.celrep.2023.113119] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/08/2023] [Accepted: 08/24/2023] [Indexed: 09/12/2023] Open
Abstract
The primary motor cortex (M1) exhibits a protracted period of development, including the development of a sensory representation long before motor outflow emerges. In rats, this representation is present by postnatal day (P) 8, when M1 activity is "discontinuous." Here, we ask how the representation changes upon the transition to "continuous" activity at P12. We use neural decoding to predict forelimb movements from M1 activity and show that a linear decoder effectively predicts limb movements at P8 but not at P12; instead, a nonlinear decoder better predicts limb movements at P12. The altered decoder performance reflects increased complexity and uniqueness of kinematic information in M1. We next show that M1's representation at P12 is more susceptible to "lesioning" of inputs and "transplanting" of M1's encoding scheme from one pup to another. Thus, the emergence of continuous M1 activity signals the developmental onset of more complex, informationally sparse, and individualized sensory representations.
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Affiliation(s)
- Ryan M Glanz
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA 52242, USA
| | - Greta Sokoloff
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA 52242, USA; Iowa Neuroscience Institute, University of Iowa, Iowa City, IA 52242, USA
| | - Mark S Blumberg
- Department of Psychological & Brain Sciences, University of Iowa, Iowa City, IA 52242, USA; Iowa Neuroscience Institute, University of Iowa, Iowa City, IA 52242, USA.
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22
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Dai Q, Wong Y, Kankanhali M, Li X, Geng W. Improved Network and Training Scheme for Cross-Trial Surface Electromyography (sEMG)-Based Gesture Recognition. Bioengineering (Basel) 2023; 10:1101. [PMID: 37760203 PMCID: PMC10525369 DOI: 10.3390/bioengineering10091101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/14/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023] Open
Abstract
To enhance the performance of surface electromyography (sEMG)-based gesture recognition, we propose a novel network-agnostic two-stage training scheme, called sEMGPoseMIM, that produces trial-invariant representations to be aligned with corresponding hand movements via cross-modal knowledge distillation. In the first stage, an sEMG encoder is trained via cross-trial mutual information maximization using the sEMG sequences sampled from the same time step but different trials in a contrastive learning manner. In the second stage, the learned sEMG encoder is fine-tuned with the supervision of gesture and hand movements in a knowledge-distillation manner. In addition, we propose a novel network called sEMGXCM as the sEMG encoder. Comprehensive experiments on seven sparse multichannel sEMG databases are conducted to demonstrate the effectiveness of the training scheme sEMGPoseMIM and the network sEMGXCM, which achieves an average improvement of +1.3% on the sparse multichannel sEMG databases compared to the existing methods. Furthermore, the comparison between training sEMGXCM and other existing networks from scratch shows that sEMGXCM outperforms the others by an average of +1.5%.
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Affiliation(s)
- Qingfeng Dai
- College of Computer Science and Technology, Faculty of Computer, Zhejiang University, Hangzhou 310058, China; (Q.D.); (X.L.)
| | - Yongkang Wong
- School of Computing, National University of Singapore, 21 Lower Kent Ridge Rd, Singapore 119077, Singapore; (Y.W.); (M.K.)
| | - Mohan Kankanhali
- School of Computing, National University of Singapore, 21 Lower Kent Ridge Rd, Singapore 119077, Singapore; (Y.W.); (M.K.)
| | - Xiangdong Li
- College of Computer Science and Technology, Faculty of Computer, Zhejiang University, Hangzhou 310058, China; (Q.D.); (X.L.)
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Maciejewska M, Azizah A, Szczurek A. Co-Dependency of IAQ in Functionally Different Zones of Open-Kitchen Restaurants Based on Sensor Measurements Explored via Mutual Information Analysis. Sensors (Basel) 2023; 23:7630. [PMID: 37688085 PMCID: PMC10490683 DOI: 10.3390/s23177630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/24/2023] [Accepted: 09/01/2023] [Indexed: 09/10/2023]
Abstract
High-quality indoor air is essential in open-kitchen restaurants for ensuring a healthy workplace and comfortable conditions for visitors. In this study, indoor air quality interdependence between the kitchen and the dining zones in open-kitchen restaurants was analyzed. The method was based on measurements of selected air parameters using a sensor technique and mutual information (MI) analysis. A long-term approach (based on a several-hour time series) and a short-term approach (based on a several-minute time series) were applied. This study involved four open-kitchen restaurants. The indoor conditions were represented by the temperature, relative humidity, CO2 concentration, and content of the total volatile organic compounds (TVOC) in the air. The MI analyses showed that the long-term co-dependence of the indoor conditions between the kitchen and the dining zones was smaller during business hours (MI = 0.12 ÷ 0.40) compared to night hours (MI = 0.24 ÷ 0.58). The ranking of the long-term MI values for the individual air parameters was MICO2 (0.34) ≅ MIT (0.34) > MIRH (0.28) > MITVOC (0.23). The short-term interdependencies were smaller during night hours (median MI = 0.01 ÷ 0.56) compared to business hours (MI = 0.23 ÷ 0.61). Additionally, the short-term MI was subject to high temporal variability. The ranking of the short-term MI values for the individual air parameters was MICO2 (0.48) > MIT (0.46) > MIRH (0.37) > MITVOC (0.26). Due to the weak and highly variable co-dependence of the air parameters between the kitchen and dining areas, multi-zone monitoring of air parameters with an emphasis on TVOC measurements is recommended to ensure proper indoor conditions in open-kitchen restaurants. The presented approach may be applied to design indoor air quality monitoring and ventilation systems not only in open-kitchen restaurants but also in other interiors with functionally different zones.
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Affiliation(s)
- Monika Maciejewska
- Faculty of Environmental Engineering, Wroclaw University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wroclaw, Poland; (A.A.); (A.S.)
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24
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Pichardo-Carmona EY, Reyes-Lagos JJ, Ceballos-Juárez RG, Ledesma-Ramírez CI, Mendieta-Zerón H, Peña-Castillo MÁ, Nsugbe E, Porta-García MÁ, Mina-Paz Y. Changes in the autonomic cardiorespiratory activity in parturient women with severe and moderate features of preeclampsia. Front Immunol 2023; 14:1190699. [PMID: 37724103 PMCID: PMC10505439 DOI: 10.3389/fimmu.2023.1190699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 08/03/2023] [Indexed: 09/20/2023] Open
Abstract
Background Cardiorespiratory coupling (CRC) is a physiological phenomenon that reflects the mutual interaction between the cardiac and respiratory control systems. It is mainly associated with efferent vagal activity from the central autonomic network. Few studies have explored the autonomic changes of CRC in preeclampsia, a critical obstetric complication related to possible autonomic dysfunctions and inflammatory disturbances. This study examined the autonomic mechanisms of CRC in women with severe and moderate preeclampsia and healthy controls by applying nonlinear methods based on information theory, such as mutual information (MI) and Renyi's mutual information (RMI) and the linear and nonlinear analysis of the Pulse-Respiration Quotient (PRQ). Methods We studied three groups of parturient women in the third trimester of pregnancy with a clinical diagnosis of preeclampsia without severe symptoms (P, 38.5 ± 1.4 weeks of pregnancy, n=19), preeclampsia with severe symptoms (SP, 37.5 ± 0.9 weeks of pregnancy, n=22), and normotensive control women (C, 39.1 ± 1.3 weeks of pregnancy, n=20). 10-minutes of abdominal electrocardiograms (ECG) and respiratory signals (RESP) were recorded in all the participants. Subsequently, we obtained the maternal beat-to-beat (RR) and breath-to-breath (BB) time series from ECG and RESP, respectively. The CRC between RR and BB was quantified by nonlinear methods based on information theory, such as MI and RMI, along with the analysis of the novel index of PRQ. Subsequently, we computed the mean PRQ (mPRQ) and the normalized permutation entropy (nPermEn_PRQ) from the PRQ time series generated from BB and RR. In addition, we examined the vagal activity in the three groups by the logarithm of the median of the distribution of the absolute values of successive RR differences (logRSA). Results The MI and RMI values were significantly lower (p<0.05) in the preeclamptic groups compared to the control group. However, no significant differences were found between the preeclamptic groups. The logRSA and nPermEn_PRQ indices were significantly lower (p<0.05) in SP compared to C and P. Conclusion Our data suggest that parturient women with severe and mild preeclampsia may manifest an altered cardiorespiratory coupling compared with normotensive control women. Disrupted CRC in severe preeclampsia could be associated with vagal withdrawal and less complex cardiorespiratory dynamics. The difference in vagal activity between the preeclamptic groups may suggest a further reduction in vagal activity associated with the severity of the disease.
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Affiliation(s)
| | | | | | | | - Hugo Mendieta-Zerón
- School of Medicine, Autonomous University of the State of Mexico (UAEMéx), Toluca, Mexico
- Mónica Pretelini Sáenz Maternal-Perinatal Hospital, Health Institute of the State of Mexico (ISEM), Toluca, Mexico
| | | | - Ejay Nsugbe
- Nsugbe Research Labs, Swindon, United Kingdom
| | | | - Yecid Mina-Paz
- Faculty of Health Sciences, Universidad Libre Seccional Cali, Cali, Colombia
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25
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Zhang R, Wu H, Li Y, Huang Z, Yin Z, Yang CX, Du ZQ. GWLD: an R package for genome-wide linkage disequilibrium analysis. G3 (Bethesda) 2023; 13:jkad154. [PMID: 37431944 PMCID: PMC10468308 DOI: 10.1093/g3journal/jkad154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/14/2023] [Accepted: 06/26/2023] [Indexed: 07/12/2023]
Abstract
Linkage disequilibrium (LD) analysis is fundamental to the investigation of the genetic architecture of complex traits (e.g. human disease, animal and plant breeding) and population structure and evolution dynamics. However, until now, studies primarily focus on LD status between genetic variants located on the same chromosome. Moreover, genome (re)sequencing produces unprecedented numbers of genetic variants, and fast LD computation becomes a challenge. Here, we have developed GWLD, a parallelized and generalized tool designed for the rapid genome-wide calculation of LD values, including conventional D/D', r2, and (reduced) mutual information (MI and RMI) measures. LD between genetic variants within and across chromosomes can be rapidly computed and visualized in either an R package or a standalone C++ software package. To evaluate the accuracy and speed of LD calculation, we conducted comparisons using 4 real datasets. Interchromosomal LD patterns observed potentially reflect levels of selection intensity across different species. Both versions of GWLD, the R package (https://github.com/Rong-Zh/GWLD/GWLD-R) and the standalone C++ software (https://github.com/Rong-Zh/GWLD/GWLD-C++), are freely available on GitHub.
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Affiliation(s)
- Rong Zhang
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
| | - Huaxuan Wu
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
| | - Yasai Li
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
| | - Zehang Huang
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
| | - Zongjun Yin
- College of Animal Science and Technology, Anhui Agricultural University, Hefei 230036, Anhui, China
| | - Cai-Xia Yang
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
| | - Zhi-Qiang Du
- College of Animal Science, Yangtze University, Jingzhou 434025, Hubei, China
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26
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Martínez Vásquez DA, Posada-Quintero HF, Rivera Pinzón DM. Mutual Information between EDA and EEG in Multiple Cognitive Tasks and Sleep Deprivation Conditions. Behav Sci (Basel) 2023; 13:707. [PMID: 37753985 PMCID: PMC10525564 DOI: 10.3390/bs13090707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/05/2023] [Accepted: 08/22/2023] [Indexed: 09/28/2023] Open
Abstract
Sleep deprivation, a widespread phenomenon that affects one-third of normal American adults, induces adverse changes in physical and cognitive performance, which in turn increases the occurrence of accidents. Sleep deprivation is known to increase resting blood pressure and decrease muscle sympathetic nerve activity. Monitoring changes in the interplay between the central and autonomic sympathetic nervous system can be a potential indicator of human's readiness to perform tasks that involve a certain level of cognitive load (e.g., driving). The electroencephalogram (EEG) is the standard to assess the brain's activity. The electrodermal activity (EDA) is a reflection of the general state of arousal regulated by the activation of the sympathetic nervous system through sweat gland stimulation. In this work, we calculated the mutual information between EDA and EEG recordings in order to consider linear and non-linear interactions and provide an insight of the relationship between brain activity and peripheral autonomic sympathetic activity. We analyzed EEG and EDA data from ten participants performing four cognitive tasks every two hours during 24 h (12 trials). We decomposed EEG data into delta, theta, alpha, beta, and gamma spectral components, and EDA into tonic and phasic components. The results demonstrate high values of mutual information between the EDA and delta component of EEG, mainly in working memory tasks. Additionally, we found an increase in the theta component of EEG in the presence of fatigue caused by sleep deprivation, the alpha component in tasks demanding inhibition and attention, and the delta component in working memory tasks. In terms of the location of brain activity, most of the tasks report high mutual information in frontal regions in the initial trials, with a trend to decrease and become uniform for all the nine analyzed EEG channels as a consequence of the sleep deprivation effect. Our results evidence the interplay between central and sympathetic nervous activity and can be used to mitigate the consequences of sleep deprivation.
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Affiliation(s)
- David Alejandro Martínez Vásquez
- Electronic Engineering Faculty, Universidad Santo Tomás, Bogotá 110231, Colombia
- Department of Technology, Universidad Pedagógica Nacional, Bogotá 110221, Colombia;
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Xu Z, Zhai Y, Kang Y. Mutual information measure of visual perception based on noisy spiking neural networks. Front Neurosci 2023; 17:1155362. [PMID: 37655008 PMCID: PMC10467273 DOI: 10.3389/fnins.2023.1155362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 06/06/2023] [Indexed: 09/02/2023] Open
Abstract
Note that images of low-illumination are weak aperiodic signals, while mutual information can be used as an effective measure for the shared information between the input stimulus and the output response of nonlinear systems, thus it is possible to develop novel visual perception algorithm based on the principle of aperiodic stochastic resonance within the frame of information theory. To confirm this, we reveal this phenomenon using the integrate-and-fire neural networks of neurons with noisy binary random signal as input first. And then, we propose an improved visual perception algorithm with the image mutual information as assessment index. The numerical experiences show that the target image can be picked up with more easiness by the maximal mutual information than by the minimum of natural image quality evaluation (NIQE), which is one of the most frequently used indexes. Moreover, the advantage of choosing quantile as spike threshold has also been confirmed. The improvement of this research should provide large convenience for potential applications including video tracking in environments of low illumination.
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Affiliation(s)
| | | | - Yanmei Kang
- School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China
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28
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Jwo DJ, Cho TS, Biswal A. Geometric Insights into the Multivariate Gaussian Distribution and Its Entropy and Mutual Information. Entropy (Basel) 2023; 25:1177. [PMID: 37628206 PMCID: PMC10453792 DOI: 10.3390/e25081177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 07/31/2023] [Accepted: 08/04/2023] [Indexed: 08/27/2023]
Abstract
In this paper, we provide geometric insights with visualization into the multivariate Gaussian distribution and its entropy and mutual information. In order to develop the multivariate Gaussian distribution with entropy and mutual information, several significant methodologies are presented through the discussion, supported by illustrations, both technically and statistically. The paper examines broad measurements of structure for the Gaussian distributions, which show that they can be described in terms of the information theory between the given covariance matrix and correlated random variables (in terms of relative entropy). The content obtained allows readers to better perceive concepts, comprehend techniques, and properly execute software programs for future study on the topic's science and implementations. It also helps readers grasp the themes' fundamental concepts to study the application of multivariate sets of data in Gaussian distribution. The simulation results also convey the behavior of different elliptical interpretations based on the multivariate Gaussian distribution with entropy for real-world applications in our daily lives, including information coding, nonlinear signal detection, etc. Involving the relative entropy and mutual information as well as the potential correlated covariance analysis, a wide range of information is addressed, including basic application concerns as well as clinical diagnostics to detect the multi-disease effects.
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Affiliation(s)
- Dah-Jing Jwo
- Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, 2 Peining Rd., Keelung 202301, Taiwan;
| | - Ta-Shun Cho
- Department of Business Administration, Asia University, 500 Liufeng Road, Wufeng, Taichung 41354, Taiwan;
| | - Amita Biswal
- Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, 2 Peining Rd., Keelung 202301, Taiwan;
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29
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Zhang J, Zhou K. Identification of Solid and Liquid Materials Using Acoustic Signals and Frequency-Graph Features. Entropy (Basel) 2023; 25:1170. [PMID: 37628200 PMCID: PMC10453644 DOI: 10.3390/e25081170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/24/2023] [Accepted: 08/03/2023] [Indexed: 08/27/2023]
Abstract
Material identification is playing an increasingly important role in various sectors such as industry, petrochemical, mining, and in our daily lives. In recent years, material identification has been utilized for security checks, waste sorting, etc. However, current methods for identifying materials require direct contact with the target and specialized equipment that can be costly, bulky, and not easily portable. Past proposals for addressing this limitation relied on non-contact material identification methods, such as Wi-Fi-based and radar-based material identification methods, which can identify materials with high accuracy without physical contact; however, they are not easily integrated into portable devices. This paper introduces a novel non-contact material identification based on acoustic signals. Different from previous work, our design leverages the built-in microphone and speaker of smartphones as the transceiver to identify target materials. The fundamental idea of our design is that acoustic signals, when propagated through different materials, reach the receiver via multiple paths, producing distinct multipath profiles. These profiles can serve as fingerprints for material identification. We captured and extracted them using acoustic signals, calculated channel impulse response (CIR) measurements, and then extracted image features from the time-frequency domain feature graphs, including histogram of oriented gradient (HOG) and gray-level co-occurrence matrix (GLCM) image features. Furthermore, we adopted the error-correcting output code (ECOC) learning method combined with the majority voting method to identify target materials. We built a prototype for this paper using three mobile phones based on the Android platform. The results from three different solid and liquid materials in varied multipath environments reveal that our design can achieve average identification accuracies of 90% and 97%.
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Affiliation(s)
- Jie Zhang
- School of Computer Science & Technology, Xi’an University of Posts & Telecommunications, Xi’an 710121, China;
- School of Information Science and Technology, Northwest University, Xi’an 710127, China
| | - Kexin Zhou
- School of Computer Science & Technology, Xi’an University of Posts & Telecommunications, Xi’an 710121, China;
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30
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Gupta A, Daniel R, Rao A, Roy PP, Chandra S, Kim BG. Raw Electroencephalogram-Based Cognitive Workload Classification Using Directed and Nondirected Functional Connectivity Analysis and Deep Learning. Big Data 2023; 11:307-319. [PMID: 36848586 DOI: 10.1089/big.2021.0204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
With the phenomenal rise in internet-of-things devices, the use of electroencephalogram (EEG) based brain-computer interfaces (BCIs) can empower individuals to control equipment with thoughts. These allow BCI to be used and pave the way for pro-active health management and the development of internet-of-medical-things architecture. However, EEG-based BCIs have low fidelity, high variance, and EEG signals are very noisy. These challenges compel researchers to design algorithms that can process big data in real-time while being robust to temporal variations and other variations in the data. Another issue in designing a passive BCI is the regular change in user's cognitive state (measured through cognitive workload). Though considerable amount of research has been conducted on this front, methods that could withstand high variability in EEG data and still reflect the neuronal dynamics of cognitive state variations are lacking and much needed in literature. In this research, we evaluate the efficacy of a combination of functional connectivity algorithms and state-of-the-art deep learning algorithms for the classification of three different levels of cognitive workload. We acquire 64-channel EEG data from 23 participants executing the n-back task at three different levels; 1-back (low-workload condition), 2-back (medium-workload condition), and 3-back (high-workload condition). We compared two different functional connectivity algorithms, namely phase transfer entropy (PTE) and mutual information (MI). PTE is a directed functional connectivity algorithm, whereas MI is non-directed. Both methods are suitable for extracting functional connectivity matrices in real-time, which could eventually be used for rapid, robust, and efficient classification. For classification, we use the recently proposed BrainNetCNN deep learning model, designed specifically to classify functional connectivity matrices. Results reveal a classification accuracy of 92.81% with MI and BrainNetCNN and a staggering 99.50% with PTE and BrainNetCNN on test data. PTE can yield a higher classification accuracy due to its robustness to linear mixing of the data and its ability to detect functional connectivity across a range of analysis lags.
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Affiliation(s)
- Anmol Gupta
- Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India
| | - Ronnie Daniel
- Department of Applied Mechanics, Indian Institute of Technology Madras, Chennai, India
| | - Akash Rao
- School of Computing and Electrical Engineering, Applied Cognitive Science Laboratory, Indian Institute of Technology Mandi, Mandi, India
| | - Partha Pratim Roy
- Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, India
| | - Sushil Chandra
- Department of Biomedical Engineering, INMAS Defence Research and Development Organization, New Delhi, India
| | - Byung-Gyu Kim
- Division of Artificial Intelligence Engineering, Sookmyung Women's University, Seoul, South Korea
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31
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Doyle J, Green BF, Eminizer M, Jimenez-Sanchez D, Lu S, Engle EL, Xu H, Ogurtsova A, Lai J, Soto-Diaz S, Roskes JS, Deutsch JS, Taube JM, Sunshine JC, Szalay AS. Whole-Slide Imaging, Mutual Information Registration for Multiplex Immunohistochemistry and Immunofluorescence. J Transl Med 2023; 103:100175. [PMID: 37196983 PMCID: PMC10527458 DOI: 10.1016/j.labinv.2023.100175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 03/24/2023] [Accepted: 05/08/2023] [Indexed: 05/19/2023] Open
Abstract
Multiplex immunohistochemistry/immunofluorescence (mIHC/mIF) is a developing technology that facilitates the evaluation of multiple, simultaneous protein expressions at single-cell resolution while preserving tissue architecture. These approaches have shown great potential for biomarker discovery, yet many challenges remain. Importantly, streamlined cross-registration of multiplex immunofluorescence images with additional imaging modalities and immunohistochemistry (IHC) can help increase the plex and/or improve the quality of the data generated by potentiating downstream processes such as cell segmentation. To address this problem, a fully automated process was designed to perform a hierarchical, parallelizable, and deformable registration of multiplexed digital whole-slide images (WSIs). We generalized the calculation of mutual information as a registration criterion to an arbitrary number of dimensions, making it well suited for multiplexed imaging. We also used the self-information of a given IF channel as a criterion to select the optimal channels to use for registration. Additionally, as precise labeling of cellular membranes in situ is essential for robust cell segmentation, a pan-membrane immunohistochemical staining method was developed for incorporation into mIF panels or for use as an IHC followed by cross-registration. In this study, we demonstrate this process by registering whole-slide 6-plex/7-color mIF images with whole-slide brightfield mIHC images, including a CD3 and a pan-membrane stain. Our algorithm, WSI, mutual information registration (WSIMIR), performed highly accurate registration allowing the retrospective generation of an 8-plex/9-color, WSI, and outperformed 2 alternative automated methods for cross-registration by Jaccard index and Dice similarity coefficient (WSIMIR vs automated WARPY, P < .01 and P < .01, respectively, vs HALO + transformix, P = .083 and P = .049, respectively). Furthermore, the addition of a pan-membrane IHC stain cross-registered to an mIF panel facilitated improved automated cell segmentation across mIF WSIs, as measured by significantly increased correct detections, Jaccard index (0.78 vs 0.65), and Dice similarity coefficient (0.88 vs 0.79).
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Affiliation(s)
- Joshua Doyle
- Department of Astronomy and Physics, Johns Hopkins University, Baltimore, Maryland
| | - Benjamin F Green
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland; The Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University, Baltimore, Maryland; Bloomberg∼Kimmel Institute for Cancer Immunotherapy and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland
| | - Margaret Eminizer
- Department of Astronomy and Physics, Johns Hopkins University, Baltimore, Maryland; Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, Maryland
| | - Daniel Jimenez-Sanchez
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Steve Lu
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Elizabeth L Engle
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland; The Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University, Baltimore, Maryland; Bloomberg∼Kimmel Institute for Cancer Immunotherapy and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland
| | - Haiying Xu
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland; The Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University, Baltimore, Maryland; Bloomberg∼Kimmel Institute for Cancer Immunotherapy and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland
| | - Aleksandra Ogurtsova
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland; The Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University, Baltimore, Maryland; Bloomberg∼Kimmel Institute for Cancer Immunotherapy and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland
| | - Jonathan Lai
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Sigfredo Soto-Diaz
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jeffrey S Roskes
- Department of Astronomy and Physics, Johns Hopkins University, Baltimore, Maryland; Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, Maryland
| | - Julie S Deutsch
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Janis M Taube
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland; The Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University, Baltimore, Maryland; Bloomberg∼Kimmel Institute for Cancer Immunotherapy and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Joel C Sunshine
- Department of Dermatology, Johns Hopkins University School of Medicine, Baltimore, Maryland; Bloomberg∼Kimmel Institute for Cancer Immunotherapy and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland; Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland; Johns Hopkins Center for Translational Immunoengineering, Johns Hopkins University School of Medicine, Baltimore, Maryland.
| | - Alexander S Szalay
- Department of Astronomy and Physics, Johns Hopkins University, Baltimore, Maryland; The Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University, Baltimore, Maryland; Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, Maryland
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32
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Pandey B. The Time Evolution of Mutual Information between Disjoint Regions in the Universe. Entropy (Basel) 2023; 25:1094. [PMID: 37510040 PMCID: PMC10378379 DOI: 10.3390/e25071094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 07/18/2023] [Accepted: 07/19/2023] [Indexed: 07/30/2023]
Abstract
We study the time evolution of mutual information between mass distributions in spatially separated but casually connected regions in an expanding universe. The evolution of mutual information is primarily determined by the configuration entropy rate, which depends on the dynamics of the expansion and growth of density perturbations. The joint entropy between distributions from the two regions plays a negligible role in such evolution. Mutual information decreases with time in a matter-dominated universe, whereas it stays constant in a Λ-dominated universe. The ΛCDM model and some other models of dark energy predict a minimum in mutual information beyond which dark energy dominates the dynamics of the universe. Mutual information may have deeper connections to the dark energy and accelerated expansion of the universe.
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Affiliation(s)
- Biswajit Pandey
- Department of Physics, Visva-Bharati University, Santiniketan 731235, India
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Liu J, Yang S, Zhang H, Sun Z, Du J. Online Multi-Label Streaming Feature Selection Based on Label Group Correlation and Feature Interaction. Entropy (Basel) 2023; 25:1071. [PMID: 37510018 PMCID: PMC10377943 DOI: 10.3390/e25071071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Revised: 07/10/2023] [Accepted: 07/14/2023] [Indexed: 07/30/2023]
Abstract
Multi-label streaming feature selection has received widespread attention in recent years because the dynamic acquisition of features is more in line with the needs of practical application scenarios. Most previous methods either assume that the labels are independent of each other, or, although label correlation is explored, the relationship between related labels and features is difficult to understand or specify. In real applications, both situations may occur where the labels are correlated and the features may belong specifically to some labels. Moreover, these methods treat features individually without considering the interaction between features. Based on this, we present a novel online streaming feature selection method based on label group correlation and feature interaction (OSLGC). In our design, we first divide labels into multiple groups with the help of graph theory. Then, we integrate label weight and mutual information to accurately quantify the relationships between features under different label groups. Subsequently, a novel feature selection framework using sliding windows is designed, including online feature relevance analysis and online feature interaction analysis. Experiments on ten datasets show that the proposed method outperforms some mature MFS algorithms in terms of predictive performance, statistical analysis, stability analysis, and ablation experiments.
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Affiliation(s)
- Jinghua Liu
- Department of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
- Xiamen Key Laboratory of Computer Vision and Pattern Recognition, Huaqiao University, Xiamen 361021, China
- Fujian Key Laboratory of Big Data Intelligence and Security, Huaqiao University, Xiamen 361021, China
| | - Songwei Yang
- Department of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
- Xiamen Key Laboratory of Computer Vision and Pattern Recognition, Huaqiao University, Xiamen 361021, China
- Fujian Key Laboratory of Big Data Intelligence and Security, Huaqiao University, Xiamen 361021, China
| | - Hongbo Zhang
- Department of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
- Xiamen Key Laboratory of Computer Vision and Pattern Recognition, Huaqiao University, Xiamen 361021, China
- Fujian Key Laboratory of Big Data Intelligence and Security, Huaqiao University, Xiamen 361021, China
| | - Zhenzhen Sun
- Department of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
- Xiamen Key Laboratory of Computer Vision and Pattern Recognition, Huaqiao University, Xiamen 361021, China
- Fujian Key Laboratory of Big Data Intelligence and Security, Huaqiao University, Xiamen 361021, China
| | - Jixiang Du
- Department of Computer Science and Technology, Huaqiao University, Xiamen 361021, China
- Xiamen Key Laboratory of Computer Vision and Pattern Recognition, Huaqiao University, Xiamen 361021, China
- Fujian Key Laboratory of Big Data Intelligence and Security, Huaqiao University, Xiamen 361021, China
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Bahamonde AD, Montes RM, Cornejo P. Usefulness and limitations of convergent cross sorting and continuity scaling methods for their application in simulated and real-world time series. R Soc open sci 2023; 10:221590. [PMID: 37448474 PMCID: PMC10336384 DOI: 10.1098/rsos.221590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 06/21/2023] [Indexed: 07/15/2023]
Abstract
Causality detection methods are valuable tools for detecting causal links in complex systems. The efficiency of continuity scaling (CS) and the convergent cross sorting (CSS) methods to detect causality was analysed. Usefulness and limitations of both methods in their application to simulated and real-world time series was explored under different scenarios. We find that CS is more robust and efficient than the CSS method for all simulated systems, even when increasing noise levels were considered. Both methods were not able to infer causality when time series with a marked difference in their main frequencies were analysed. Minimum time-series length required for the detection of a causal link depends on intrinsic system dynamics and on the method selected to detect it. Using simulated time series, only the CS method was capable to detect bidirectional causality. Causality detection, using the CS method, should at least include: (i) causality strength convergence analysis, (ii) statistical tests of significance, (iii) time-series standardization, and (iv) causality strength ratios as a strength indicator of relative causality between systems. Causality cannot be detected by either method in simulated time series that exhibit generalized synchronization.
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Affiliation(s)
- Adolfo D Bahamonde
- Interdisciplinary Center for Aquaculture Research (INCAR), University of Concepción, O'Higgins 1695, Concepción, Chile
| | - Rodrigo M Montes
- Interdisciplinary Center for Aquaculture Research (INCAR), University of Concepción, O'Higgins 1695, Concepción, Chile
| | - Pablo Cornejo
- Interdisciplinary Center for Aquaculture Research (INCAR), University of Concepción, O'Higgins 1695, Concepción, Chile
- Mechanical Engineering Department, University of Concepción, Concepción, Chile
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Cruz-Ramos C, García-Avila O, Almaraz-Damian JA, Ponomaryov V, Reyes-Reyes R, Sadovnychiy S. Benign and Malignant Breast Tumor Classification in Ultrasound and Mammography Images via Fusion of Deep Learning and Handcraft Features. Entropy (Basel) 2023; 25:991. [PMID: 37509938 PMCID: PMC10378567 DOI: 10.3390/e25070991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/15/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023]
Abstract
Breast cancer is a disease that affects women in different countries around the world. The real cause of breast cancer is particularly challenging to determine, and early detection of the disease is necessary for reducing the death rate, due to the high risks associated with breast cancer. Treatment in the early period can increase the life expectancy and quality of life for women. CAD (Computer Aided Diagnostic) systems can perform the diagnosis of the benign and malignant lesions of breast cancer using technologies and tools based on image processing, helping specialist doctors to obtain a more precise point of view with fewer processes when making their diagnosis by giving a second opinion. This study presents a novel CAD system for automated breast cancer diagnosis. The proposed method consists of different stages. In the preprocessing stage, an image is segmented, and a mask of a lesion is obtained; during the next stage, the extraction of the deep learning features is performed by a CNN-specifically, DenseNet 201. Additionally, handcrafted features (Histogram of Oriented Gradients (HOG)-based, ULBP-based, perimeter area, area, eccentricity, and circularity) are obtained from an image. The designed hybrid system uses CNN architecture for extracting deep learning features, along with traditional methods which perform several handcraft features, following the medical properties of the disease with the purpose of later fusion via proposed statistical criteria. During the fusion stage, where deep learning and handcrafted features are analyzed, the genetic algorithms as well as mutual information selection algorithm, followed by several classifiers (XGBoost, AdaBoost, Multilayer perceptron (MLP)) based on stochastic measures, are applied to choose the most sensible information group among the features. In the experimental validation of two modalities of the CAD design, which performed two types of medical studies-mammography (MG) and ultrasound (US)-the databases mini-DDSM (Digital Database for Screening Mammography) and BUSI (Breast Ultrasound Images Dataset) were used. Novel CAD systems were evaluated and compared with recent state-of-the-art systems, demonstrating better performance in commonly used criteria, obtaining ACC of 97.6%, PRE of 98%, Recall of 98%, F1-Score of 98%, and IBA of 95% for the abovementioned datasets.
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Affiliation(s)
- Clara Cruz-Ramos
- Escuela Superior de Ingenieria Mecanica y Electrica-Culhuacan, Instituto Politecnico Nacional, Santa Ana Ave. # 1000, Mexico City 04430, Mexico
| | - Oscar García-Avila
- Escuela Superior de Ingenieria Mecanica y Electrica-Culhuacan, Instituto Politecnico Nacional, Santa Ana Ave. # 1000, Mexico City 04430, Mexico
| | - Jose-Agustin Almaraz-Damian
- Escuela Superior de Ingenieria Mecanica y Electrica-Culhuacan, Instituto Politecnico Nacional, Santa Ana Ave. # 1000, Mexico City 04430, Mexico
| | - Volodymyr Ponomaryov
- Escuela Superior de Ingenieria Mecanica y Electrica-Culhuacan, Instituto Politecnico Nacional, Santa Ana Ave. # 1000, Mexico City 04430, Mexico
| | - Rogelio Reyes-Reyes
- Escuela Superior de Ingenieria Mecanica y Electrica-Culhuacan, Instituto Politecnico Nacional, Santa Ana Ave. # 1000, Mexico City 04430, Mexico
| | - Sergiy Sadovnychiy
- Instituto Mexicano del Petroleo, Lazaro Cardenas Ave. # 152, Mexico City 07730, Mexico
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36
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Lamberti M, Tripathi S, van Putten MJAM, Marzen S, le Feber J. Prediction in cultured cortical neural networks. PNAS Nexus 2023; 2:pgad188. [PMID: 37383023 PMCID: PMC10299080 DOI: 10.1093/pnasnexus/pgad188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 04/18/2023] [Accepted: 05/25/2023] [Indexed: 06/30/2023]
Abstract
Theory suggest that networks of neurons may predict their input. Prediction may underlie most aspects of information processing and is believed to be involved in motor and cognitive control and decision-making. Retinal cells have been shown to be capable of predicting visual stimuli, and there is some evidence for prediction of input in the visual cortex and hippocampus. However, there is no proof that the ability to predict is a generic feature of neural networks. We investigated whether random in vitro neuronal networks can predict stimulation, and how prediction is related to short- and long-term memory. To answer these questions, we applied two different stimulation modalities. Focal electrical stimulation has been shown to induce long-term memory traces, whereas global optogenetic stimulation did not. We used mutual information to quantify how much activity recorded from these networks reduces the uncertainty of upcoming stimuli (prediction) or recent past stimuli (short-term memory). Cortical neural networks did predict future stimuli, with the majority of all predictive information provided by the immediate network response to the stimulus. Interestingly, prediction strongly depended on short-term memory of recent sensory inputs during focal as well as global stimulation. However, prediction required less short-term memory during focal stimulation. Furthermore, the dependency on short-term memory decreased during 20 h of focal stimulation, when long-term connectivity changes were induced. These changes are fundamental for long-term memory formation, suggesting that besides short-term memory the formation of long-term memory traces may play a role in efficient prediction.
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Affiliation(s)
- Martina Lamberti
- Department of Clinical Neurophysiology, University of Twente, PO Box 217 7500AE, Enschede, The Netherlands
| | - Shiven Tripathi
- Department of Electrical Engineering, Indian Institute of Technology, Kanpur 208016, India
| | - Michel J A M van Putten
- Department of Clinical Neurophysiology, University of Twente, PO Box 217 7500AE, Enschede, The Netherlands
| | - Sarah Marzen
- W. M. Keck Science Department, Pitzer, Scripps, and Claremont McKenna College, Claremont, CA 91711, USA
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37
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Ullmann D, Taran O, Voloshynovskiy S. Multivariate Time Series Information Bottleneck. Entropy (Basel) 2023; 25:e25050831. [PMID: 37238586 DOI: 10.3390/e25050831] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 05/10/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023]
Abstract
Time series (TS) and multiple time series (MTS) predictions have historically paved the way for distinct families of deep learning models. The temporal dimension, distinguished by its evolutionary sequential aspect, is usually modeled by decomposition into the trio of "trend, seasonality, noise", by attempts to copy the functioning of human synapses, and more recently, by transformer models with self-attention on the temporal dimension. These models may find applications in finance and e-commerce, where any increase in performance of less than 1% has large monetary repercussions, they also have potential applications in natural language processing (NLP), medicine, and physics. To the best of our knowledge, the information bottleneck (IB) framework has not received significant attention in the context of TS or MTS analyses. One can demonstrate that a compression of the temporal dimension is key in the context of MTS. We propose a new approach with partial convolution, where a time sequence is encoded into a two-dimensional representation resembling images. Accordingly, we use the recent advances made in image extension to predict an unseen part of an image from a given one. We show that our model compares well with traditional TS models, has information-theoretical foundations, and can be easily extended to more dimensions than only time and space. An evaluation of our multiple time series-information bottleneck (MTS-IB) model proves its efficiency in electricity production, road traffic, and astronomical data representing solar activity, as recorded by NASA's interface region imaging spectrograph (IRIS) satellite.
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Affiliation(s)
- Denis Ullmann
- Faculty of Science, University of Geneva, CUI, 1227 Carouge, Switzerland
| | - Olga Taran
- Faculty of Science, University of Geneva, CUI, 1227 Carouge, Switzerland
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38
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Liou JW, Liou M, Cheng PE. Modeling Categorical Variables by Mutual Information Decomposition. Entropy (Basel) 2023; 25:e25050750. [PMID: 37238505 DOI: 10.3390/e25050750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 04/24/2023] [Accepted: 04/26/2023] [Indexed: 05/28/2023]
Abstract
This paper proposed the use of mutual information (MI) decomposition as a novel approach to identifying indispensable variables and their interactions for contingency table analysis. The MI analysis identified subsets of associative variables based on multinomial distributions and validated parsimonious log-linear and logistic models. The proposed approach was assessed using two real-world datasets dealing with ischemic stroke (with 6 risk factors) and banking credit (with 21 discrete attributes in a sparse table). This paper also provided an empirical comparison of MI analysis versus two state-of-the-art methods in terms of variable and model selections. The proposed MI analysis scheme can be used in the construction of parsimonious log-linear and logistic models with a concise interpretation of discrete multivariate data.
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Affiliation(s)
- Jiun-Wei Liou
- Department of Electrical Engineering, Ming Chi University of Technology, New Taipei City 243, Taiwan
| | - Michelle Liou
- Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan
| | - Philip E Cheng
- Institute of Statistical Science, Academia Sinica, Taipei 115, Taiwan
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39
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Basanisi R, Marche K, Combrisson E, Apicella P, Brovelli A. Beta Oscillations in Monkey Striatum Encode Reward Prediction Error Signals. J Neurosci 2023; 43:3339-3352. [PMID: 37015808 PMCID: PMC10162459 DOI: 10.1523/jneurosci.0952-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 02/22/2023] [Accepted: 03/17/2023] [Indexed: 04/06/2023] Open
Abstract
Reward prediction error (RPE) signals are crucial for reinforcement learning and decision-making as they quantify the mismatch between predicted and obtained rewards. RPE signals are encoded in the neural activity of multiple brain areas, such as midbrain dopaminergic neurons, prefrontal cortex, and striatum. However, it remains unclear how these signals are expressed through anatomically and functionally distinct subregions of the striatum. In the current study, we examined to which extent RPE signals are represented across different striatal regions. To do so, we recorded local field potentials (LFPs) in sensorimotor, associative, and limbic striatal territories of two male rhesus monkeys performing a free-choice probabilistic learning task. The trial-by-trial evolution of RPE during task performance was estimated using a reinforcement learning model fitted on monkeys' choice behavior. Overall, we found that changes in beta band oscillations (15-35 Hz), after the outcome of the animal's choice, are consistent with RPE encoding. Moreover, we provide evidence that the signals related to RPE are more strongly represented in the ventral (limbic) than dorsal (sensorimotor and associative) part of the striatum. To conclude, our results suggest a relationship between striatal beta oscillations and the evaluation of outcomes based on RPE signals and highlight a major contribution of the ventral striatum to the updating of learning processes.SIGNIFICANCE STATEMENT Reward prediction error (RPE) signals are crucial for reinforcement learning and decision-making as they quantify the mismatch between predicted and obtained rewards. Current models suggest that RPE signals are encoded in the neural activity of multiple brain areas, including the midbrain dopaminergic neurons, prefrontal cortex and striatum. However, it remains elusive whether RPEs recruit anatomically and functionally distinct subregions of the striatum. Our study provides evidence that RPE-related modulations in local field potential (LFP) power are dominant in the striatum. In particular, they are stronger in the rostro-ventral rather than the caudo-dorsal striatum. Our findings contribute to a better understanding of the role of striatal territories in reward-based learning and may be relevant for neuropsychiatric and neurologic diseases that affect striatal circuits.
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Affiliation(s)
- Ruggero Basanisi
- Institut de Neurosciences de la Timone, Aix Marseille Université, Unité Mixte de Recherche 7289 Centre National de la Recherche Scientifique, Marseille 13005, France
| | - Kevin Marche
- Institut de Neurosciences de la Timone, Aix Marseille Université, Unité Mixte de Recherche 7289 Centre National de la Recherche Scientifique, Marseille 13005, France
- Wellcome Center for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Etienne Combrisson
- Institut de Neurosciences de la Timone, Aix Marseille Université, Unité Mixte de Recherche 7289 Centre National de la Recherche Scientifique, Marseille 13005, France
| | - Paul Apicella
- Institut de Neurosciences de la Timone, Aix Marseille Université, Unité Mixte de Recherche 7289 Centre National de la Recherche Scientifique, Marseille 13005, France
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone, Aix Marseille Université, Unité Mixte de Recherche 7289 Centre National de la Recherche Scientifique, Marseille 13005, France
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Alzoubi H, Alzubi R, Ramzan N. Deep Learning Framework for Complex Disease Risk Prediction Using Genomic Variations. Sensors (Basel) 2023; 23:s23094439. [PMID: 37177642 PMCID: PMC10181706 DOI: 10.3390/s23094439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 04/05/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023]
Abstract
Genome-wide association studies have proven their ability to improve human health outcomes by identifying genotypes associated with phenotypes. Various works have attempted to predict the risk of diseases for individuals based on genotype data. This prediction can either be considered as an analysis model that can lead to a better understanding of gene functions that underlie human disease or as a black box in order to be used in decision support systems and in early disease detection. Deep learning techniques have gained more popularity recently. In this work, we propose a deep-learning framework for disease risk prediction. The proposed framework employs a multilayer perceptron (MLP) in order to predict individuals' disease status. The proposed framework was applied to the Wellcome Trust Case-Control Consortium (WTCCC), the UK National Blood Service (NBS) Control Group, and the 1958 British Birth Cohort (58C) datasets. The performance comparison of the proposed framework showed that the proposed approach outperformed the other methods in predicting disease risk, achieving an area under the curve (AUC) up to 0.94.
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Affiliation(s)
- Hadeel Alzoubi
- Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Raid Alzubi
- Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia
| | - Naeem Ramzan
- School of Computing, Engineering and Physical Sciences, University of the West of Scotland, High Street, Paisley PA1 2BE, UK
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41
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Jaenada M, Miranda P, Pardo L, Zografos K. An Approach to Canonical Correlation Analysis Based on Rényi's Pseudodistances. Entropy (Basel) 2023; 25:e25050713. [PMID: 37238468 DOI: 10.3390/e25050713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/16/2023] [Accepted: 04/21/2023] [Indexed: 05/28/2023]
Abstract
Canonical Correlation Analysis (CCA) infers a pairwise linear relationship between two groups of random variables, X and Y. In this paper, we present a new procedure based on Rényi's pseudodistances (RP) aiming to detect linear and non-linear relationships between the two groups. RP canonical analysis (RPCCA) finds canonical coefficient vectors, a and b, by maximizing an RP-based measure. This new family includes the Information Canonical Correlation Analysis (ICCA) as a particular case and extends the method for distances inherently robust against outliers. We provide estimating techniques for RPCCA and show the consistency of the proposed estimated canonical vectors. Further, a permutation test for determining the number of significant pairs of canonical variables is described. The robustness properties of the RPCCA are examined theoretically and empirically through a simulation study, concluding that the RPCCA presents a competitive alternative to ICCA with an added advantage in terms of robustness against outliers and data contamination.
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Affiliation(s)
- María Jaenada
- Interdisciplinary Mathematics Institute, Complutense University of Madrid, 28040 Madrid, Spain
| | - Pedro Miranda
- Interdisciplinary Mathematics Institute, Complutense University of Madrid, 28040 Madrid, Spain
| | - Leandro Pardo
- Interdisciplinary Mathematics Institute, Complutense University of Madrid, 28040 Madrid, Spain
| | - Konstantinos Zografos
- Probability-Statistics and Operational Research Unit, Department of Mathematics, University of Ioannina, 45110 Ioannina, Greece
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Murakami H, Yamada N. Human Information Processing of the Speed of Various Movements Estimated Based on Trajectory Change. Entropy (Basel) 2023; 25:e25040695. [PMID: 37190483 PMCID: PMC10138091 DOI: 10.3390/e25040695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 04/04/2023] [Accepted: 04/18/2023] [Indexed: 05/17/2023]
Abstract
Fitts' approach, which examines the information processing of the human motor system, has the problem that the movement speed is controlled by the difficulty index of the task, which the participant uniquely sets, but it is an arbitrary speed. This study rigorously aims to examine the relationship between movement speed and information processing using Woodworth's method to control movement speed. Furthermore, we examined movement information processing using an approach that calculates probability-based information entropy and mutual information quantity between points from trajectory analysis. Overall, 17 experimental conditions were applied, 16 being externally controlled and one being self-paced with maximum speed. Considering that information processing occurs when irregularities decrease, the point at which information processing occurs switches at a movement frequency of approximately 3.0-3.25 Hz. Previous findings have suggested that motor control switches with increasing movement speed; thus, our approach helps explore human information processing in detail. Note that the characteristics of information processing in movement speed changes that were identified in this study were derived from one participant, but they are important characteristics of human motor control.
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Affiliation(s)
- Hiroki Murakami
- Graduate School of Health and Sport Sciences, Chukyo University, 101 Tokodachi, Kaizu-cho, Toyota, Aichi 470-0393, Japan
| | - Norimasa Yamada
- Graduate School of Health and Sport Sciences, Chukyo University, 101 Tokodachi, Kaizu-cho, Toyota, Aichi 470-0393, Japan
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43
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Fullwood J. An Axiomatic Characterization of Mutual Information. Entropy (Basel) 2023; 25:e25040663. [PMID: 37190451 PMCID: PMC10137661 DOI: 10.3390/e25040663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 04/10/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023]
Abstract
We characterize mutual information as the unique map on ordered pairs of discrete random variables satisfying a set of axioms similar to those of Faddeev's characterization of the Shannon entropy. There is a new axiom in our characterization, however, which has no analog for Shannon entropy, based on the notion of a Markov triangle, which may be thought of as a composition of communication channels for which conditional entropy acts functorially. Our proofs are coordinate-free in the sense that no logarithms appear in our calculations.
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Affiliation(s)
- James Fullwood
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China
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44
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Hollerbach R, Kim EJ. Effects of Stochastic Noises on Limit-Cycle Oscillations and Power Losses in Fusion Plasmas and Information Geometry. Entropy (Basel) 2023; 25:e25040664. [PMID: 37190453 PMCID: PMC10137813 DOI: 10.3390/e25040664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/01/2023] [Accepted: 04/13/2023] [Indexed: 05/17/2023]
Abstract
We investigate the effects of different stochastic noises on the dynamics of the edge-localised modes (ELMs) in magnetically confined fusion plasmas by using a time-dependent PDF method, path-dependent information geometry (information rate, information length), and entropy-related measures (entropy production, mutual information). The oscillation quenching occurs due to either stochastic particle or magnetic perturbations, although particle perturbation is more effective in this amplitude diminishment compared with magnetic perturbations. On the other hand, magnetic perturbations are more effective at altering the oscillation period; the stochastic noise acts to increase the frequency of explosive oscillations (large ELMs) while decreasing the frequency of more regular oscillations (small ELMs). These stochastic noises significantly reduce power and energy losses caused by ELMs and play a key role in reproducing the observed experimental scaling relation of the ELM power loss with the input power. Furthermore, the maximum power loss is closely linked to the maximum entropy production rate, involving irreversible energy dissipation in non-equilibrium. Notably, over one ELM cycle, the information rate appears to keep almost a constant value, indicative of a geodesic. The information rate is also shown to be useful for characterising the statistical properties of ELMs, such as distinguishing between explosive and regular oscillations and the regulation between the pressure gradient and magnetic fluctuations.
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Affiliation(s)
- Rainer Hollerbach
- Department of Applied Mathematics, University of Leeds, Leeds LS2 9JT, UK
| | - Eun-Jin Kim
- Centre for Fluid and Complex Systems, Coventry University, Priory St, Coventry CV1 5FB, UK
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45
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Soriano J, Marzen S. How Well Can We Infer Selection Benefits and Mutation Rates from Allele Frequencies? Entropy (Basel) 2023; 25:e25040615. [PMID: 37190403 PMCID: PMC10137336 DOI: 10.3390/e25040615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 03/24/2023] [Accepted: 03/26/2023] [Indexed: 05/17/2023]
Abstract
Experimentalists observe allele frequency distributions and try to infer mutation rates and selection coefficients. How easy is this? We calculate limits to their ability in the context of the Wright-Fisher model by first finding the maximal amount of information that can be acquired using allele frequencies about the mutation rate and selection coefficient- at least 2 bits per allele- and then by finding how the organisms would have shaped their mutation rates and selection coefficients so as to maximize the information transfer.
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Affiliation(s)
- Jonathan Soriano
- W. M. Keck Science Department, Pitzer, Scripps, and Claremont McKenna College, Claremont, CA 91711, USA
| | - Sarah Marzen
- W. M. Keck Science Department, Pitzer, Scripps, and Claremont McKenna College, Claremont, CA 91711, USA
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46
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De Clercq P, Vanthornhout J, Vandermosten M, Francart T. Beyond linear neural envelope tracking: a mutual information approach. J Neural Eng 2023; 20. [PMID: 36812597 DOI: 10.1088/1741-2552/acbe1d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 02/22/2023] [Indexed: 02/24/2023]
Abstract
Objective.The human brain tracks the temporal envelope of speech, which contains essential cues for speech understanding. Linear models are the most common tool to study neural envelope tracking. However, information on how speech is processed can be lost since nonlinear relations are precluded. Analysis based on mutual information (MI), on the other hand, can detect both linear and nonlinear relations and is gradually becoming more popular in the field of neural envelope tracking. Yet, several different approaches to calculating MI are applied with no consensus on which approach to use. Furthermore, the added value of nonlinear techniques remains a subject of debate in the field. The present paper aims to resolve these open questions.Approach.We analyzed electroencephalography (EEG) data of participants listening to continuous speech and applied MI analyses and linear models.Main results.Comparing the different MI approaches, we conclude that results are most reliable and robust using the Gaussian copula approach, which first transforms the data to standard Gaussians. With this approach, the MI analysis is a valid technique for studying neural envelope tracking. Like linear models, it allows spatial and temporal interpretations of speech processing, peak latency analyses, and applications to multiple EEG channels combined. In a final analysis, we tested whether nonlinear components were present in the neural response to the envelope by first removing all linear components in the data. We robustly detected nonlinear components on the single-subject level using the MI analysis.Significance.We demonstrate that the human brain processes speech in a nonlinear way. Unlike linear models, the MI analysis detects such nonlinear relations, proving its added value to neural envelope tracking. In addition, the MI analysis retains spatial and temporal characteristics of speech processing, an advantage lost when using more complex (nonlinear) deep neural networks.
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Affiliation(s)
- Pieter De Clercq
- Experimental Oto-Rhino-Laryngology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Belgium
| | - Jonas Vanthornhout
- Experimental Oto-Rhino-Laryngology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Belgium
| | - Maaike Vandermosten
- Experimental Oto-Rhino-Laryngology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Belgium
| | - Tom Francart
- Experimental Oto-Rhino-Laryngology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Belgium
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Tripathi U, Mizrahi L, Alda M, Falkovich G, Stern S. Information theory characteristics improve the prediction of lithium response in bipolar disorder patients using a support vector machine classifier. Bipolar Disord 2023; 25:110-127. [PMID: 36479788 DOI: 10.1111/bdi.13282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
AIM Bipolar disorder (BD) is a mood disorder with a high morbidity and death rate. Lithium (Li), a prominent mood stabilizer, is often used as a first-line treatment. However, clinical studies have shown that Li is fully effective in roughly 30% of BD patients. Our goal in this study was to use features derived from information theory to improve the prediction of the patient's response to Li as well as develop a diagnostic algorithm for the disorder. METHODS We have performed electrophysiological recordings in patient-derived dentate gyrus (DG) granule neurons (from a total of 9 subjects) for three groups: 3 control individuals, 3 BD patients who respond to Li treatment (LR), and 3 BD patients who do not respond to Li treatment (NR). The recordings were analyzed by the statistical tools of modern information theory. We used a Support Vector Machine (SVM) and Random forest (RF) classifiers with the basic electrophysiological features with additional information theory features. RESULTS Information theory features provided further knowledge about the distribution of the electrophysiological entities and the interactions between the different features, which improved classification schemes. These newly added features significantly improved our ability to distinguish the BD patients from the control individuals (an improvement from 60% to 74% accuracy) and LR from NR patients (an improvement from 81% to 99% accuracy). CONCLUSION The addition of Information theory-derived features provides further knowledge about the distribution of the parameters and their interactions, thus significantly improving the ability to discriminate and predict the LRs from the NRs and the patients from the controls.
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Affiliation(s)
- Utkarsh Tripathi
- Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel
| | - Liron Mizrahi
- Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Gregory Falkovich
- Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot, Israel
| | - Shani Stern
- Sagol Department of Neurobiology, Faculty of Natural Sciences, University of Haifa, Haifa, Israel
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48
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Liu L, Huo J. PCNN Model Guided by Saliency Mechanism for Image Fusion in Transform Domain. Sensors (Basel) 2023; 23:2488. [PMID: 36904693 PMCID: PMC10007409 DOI: 10.3390/s23052488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 02/16/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
In heterogeneous image fusion problems, different imaging mechanisms have always existed between time-of-flight and visible light heterogeneous images which are collected by binocular acquisition systems in orchard environments. Determining how to enhance the fusion quality is key to the solution. A shortcoming of the pulse coupled neural network model is that parameters are limited by manual experience settings and cannot be terminated adaptively. The limitations are obvious during the ignition process, and include ignoring the impact of image changes and fluctuations on the results, pixel artifacts, area blurring, and the occurrence of unclear edges. Aiming at these problems, an image fusion method in a pulse coupled neural network transform domain guided by a saliency mechanism is proposed. A non-subsampled shearlet transform is used to decompose the accurately registered image; the time-of-flight low-frequency component, after multiple lighting segmentation using a pulse coupled neural network, is simplified to a first-order Markov situation. The significance function is defined as first-order Markov mutual information to measure the termination condition. A new momentum-driven multi-objective artificial bee colony algorithm is used to optimize the parameters of the link channel feedback term, link strength, and dynamic threshold attenuation factor. The low-frequency components of time-of-flight and color images, after multiple lighting segmentation using a pulse coupled neural network, are fused using the weighted average rule. The high-frequency components are fused using improved bilateral filters. The results show that the proposed algorithm has the best fusion effect on the time-of-flight confidence image and the corresponding visible light image collected in the natural scene, according to nine objective image evaluation indicators. It is suitable for the heterogeneous image fusion of complex orchard environments in natural landscapes.
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Affiliation(s)
- Liqun Liu
- College of Information Science and Technology, Gansu Agricultural University, Lanzhou 730070, China
| | - Jiuyuan Huo
- School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
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49
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Xiang X, Jin B, Wu Y. Change-Point Detection in a High-Dimensional Multinomial Sequence Based on Mutual Information. Entropy (Basel) 2023; 25:355. [PMID: 36832721 PMCID: PMC9955467 DOI: 10.3390/e25020355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 02/04/2023] [Accepted: 02/13/2023] [Indexed: 06/18/2023]
Abstract
Time-series data often have an abrupt structure change at an unknown location. This paper proposes a new statistic to test the existence of a change-point in a multinomial sequence, where the number of categories is comparable with the sample size as it tends to infinity. To construct this statistic, the pre-classification is implemented first; then, it is given based on the mutual information between the data and the locations from the pre-classification. Note that this statistic can also be used to estimate the position of the change-point. Under certain conditions, the proposed statistic is asymptotically normally distributed under the null hypothesis and consistent under the alternative hypothesis. Simulation results show the high power of the test based on the proposed statistic and the high accuracy of the estimate. The proposed method is also illustrated with a real example of physical examination data.
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Affiliation(s)
- Xinrong Xiang
- School of Management, University of Science and Technology of China, Heifei 230026, China
| | - Baisuo Jin
- School of Management, University of Science and Technology of China, Heifei 230026, China
| | - Yuehua Wu
- Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
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Verdú S. The Cauchy Distribution in Information Theory. Entropy (Basel) 2023; 25:346. [PMID: 36832712 PMCID: PMC9955388 DOI: 10.3390/e25020346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/05/2022] [Accepted: 02/02/2023] [Indexed: 06/18/2023]
Abstract
The Gaussian law reigns supreme in the information theory of analog random variables. This paper showcases a number of information theoretic results which find elegant counterparts for Cauchy distributions. New concepts such as that of equivalent pairs of probability measures and the strength of real-valued random variables are introduced here and shown to be of particular relevance to Cauchy distributions.
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Affiliation(s)
- Sergio Verdú
- Independent Researcher, Princeton, NJ 08540, USA
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