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Holistic Characterization of a Salmonella Typhimurium Infection Model Using Integrated Molecular Imaging

JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021年第12期32卷 2791-2802页

作者： Strittmatter, Nicole Kanvatirth, Panchali Inglese, Paolo Race, Alan M. Nilsson, Anna Dannhorn, Andreas Kudo, Hiromi Goldin, Robert D. Ling, Stephanie Wong, Edmond Seeliger, Frank Serra, Maria Paola Hoffmann, Scott Maglennon, Gareth Hamm, Gregory Atkinson, James Jones, Stewart Bunch, Josephine Andren, Per E. Takats, Zoltan Goodwin, Richard J. A. Mastroeni, Pietro Univ Cambridge Dept Vet Med Cambridge CB3 0ES England AstraZeneca R&D Biopharmaceut Discovery Sci Alderley Pk Macclesfield SK10 4TG Cheshire England Imperial Coll London Fac Med Dept Metab Digest & Reprod London SW7 2AZ England AstraZeneca Clin Pharmacol & Safety Sci R&D Cambridge CB4 0WG England Uppsala Univ Med Mass Spectrometry Imaging Dept Pharmaceut Biosci S-75124 Uppsala Sweden Uppsala Univ Sci Life Lab Spatial Mass Spectrometry S-75124 Uppsala Sweden Imperial Coll London Div Digest Dis Sect Pathol St Marys Hosp London W2 1NY England Charing Cross Hosp Dept Cellular Pathol London W6 8RF England AstraZeneca Biol Engn R&D Cambridge CB4 0WG England Univ Edinburgh BHF Ctr Cardiovasc Sci Queens Med Res Inst Edinburgh EH16 4TJ Midlothian Scotland Natl Phys Lab Natl Ctr Excellence Mass Spectrometry Imaging NiC Teddington TW11 0LW Middx England Univ Glasgow Inst Infect Immun & Inflammat Coll Med Vet & Life Sci Glasgow G12 8TA Lanark Scotland Tech Univ Munich Fac Chem D-85748 Garching Germany

A more complete and holistic view on host-microbe interactions is needed to understand the physiological and cellular barriers that affect the efficacy of drug treatments and allow the discovery and development of new therapeutics. Here, we developed a multimodal imaging approach combining histopathology with mass spectrometry imaging (MSI) and same section imaging mass cytometry (IMC) to study the effects of Salmonella Typhimurium infection in the liver of a mouse model using the S. Typhimurium strains SL3261 and SL1344. This approach enables correlation of tissue morphology and specific cell phenotypes with molecular images of tissue metabolism. IMC revealed a marked increase in immune cell markers and localization in immune aggregates in infected tissues. A correlative computational method (network analysis) was deployed to find metabolic features associated with infection and revealed metabolic clusters of acetyl carnitines, as well as phosphatidylcholine and phosphatidylethanolamine plasmalogen species, which could be associated with pro-inflammatory immune cell types. By developing an IMC marker for the detection of Salmonella LPS, we were further able to identify and characterize those cell types which contained S. Typhimurium.

关键词： mass spectrometry imaging imaging mass cytometry Salmonella infection network analysis multimodal data integration

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An Overview of data integration in Neuroscience With Focus on Alzheimer's Disease

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第4期28卷 1824-1835页

作者： Turrisi, Rosanna Squillario, Margherita Abate, Giulia Uberti, Daniela Barla, Annalisa Univ Genoa I-16146 Genoa Italy IRCCS Osped Policlin San Martino LISCOMP Lab I-16132 Genoa Italy Univ Brescia I-25121 Brescia Italy

This article represents the first attempt to provide an overview of how to face data integration as the result of a dialogue between neuroscientists and computer scientists. Indeed, data integration is fundamental for studying complex multifactorial diseases, such as the neurodegenerative diseases. This work aims at warning the readers of common pitfalls and critical issues in both medical and data science fields. In this context, we define a road map for data scientists when they first approach the issue of data integration in the biomedical domain, highlighting the challenges that inevitably emerge when dealing with heterogeneous, large-scale and noisy data and proposing possible solutions. Here, we discuss data collection and statistical analysis usually seen as parallel and independent processes, as cross-disciplinary activities. Finally, we provide an exemplary application of data integration to address Alzheimer's Disease (AD), which is the most common multifactorial form of dementia worldwide. We critically discuss the largest and most widely used datasets in AD, and demonstrate how the emergence of machine learning and deep learning methods has had a significant impact on disease's knowledge particularly in the perspective of an early AD diagnosis.

关键词： multimodal data integration multidisciplinary neurodegenerative diseases Alzheimer's disease machine and deep learning

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Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy

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NETWORK NEUROSCIENCE 2024年第2期8卷 466-485页

作者： Wirsich, Jonathan Iannotti, Giannina Rita Ridley, Ben Shamshiri, Elhum A. Sheybani, Laurent Grouiller, Frederic Bartolomei, Fabrice Seeck, Margitta Lazeyras, Francois Ranjeva, Jean-Philippe Guye, Maxime Vulliemoz, Serge Univ Hosp Div Neurol EEG & Epilepsy Unit Geneva Switzerland Univ Geneva Geneva Switzerland Aix Marseille Univ CNRS CRMBM 7339 Marseille France CHU Timone AP HM CEMEREM Marseille France IRCCS Ist Sci Neurolog Bologna Bologna Italy UCL Queen Sq Inst Neurol Queen Sq London England Univ Geneva Swiss Ctr Affect Sci Geneva Switzerland Aix Marseille Univ UMR 1106 INS INSERM Marseille France CHU Timone AP HM Serv Hepatogastroenterol Marseille France Univ Geneva Dept Radiol & Med Informat Geneva Switzerland

Whole-brain functional connectivity networks (connectomes) have been characterized at different scales in humans using EEG and fMRI. multimodal epileptic networks have also been investigated, but the relationship between EEG and fMRI defined networks on a whole-brain scale is unclear. A unified multimodal connectome description, mapping healthy and pathological networks would close this knowledge gap. Here, we characterize the spatial correlation between the EEG and fMRI connectomes in right and left temporal lobe epilepsy (rTLE/lTLE). From two centers, we acquired resting-state concurrent EEG-fMRI of 35 healthy controls and 34 TLE patients. EEG-fMRI data was projected into the Desikan brain atlas, and functional connectomes from both modalities were correlated. EEG and fMRI connectomes were moderately correlated. This correlation was increased in rTLE when compared to controls for EEG-delta/theta/alpha/beta. Conversely, multimodal correlation in lTLE was decreased in respect to controls for EEG-beta. While the alteration was global in rTLE, in lTLE it was locally linked to the default mode network. The increased multimodal correlation in rTLE and decreased correlation in lTLE suggests a modality-specific lateralized differential reorganization in TLE, which needs to be considered when comparing results from different modalities. Each modality provides distinct information, highlighting the benefit of multimodal assessment in epilepsy. The relationship between resting-state hemodynamic (fMRI) and electrophysiological (EEG) connectivity has been investigated in healthy subjects, but this relationship is unknown in patients with left and right temporal lobe epilepsies (l/rTLE). Does the magnitude of the relationship differ between healthy subjects and patients? What role does the laterality of the epileptic focus play? What are the spatial contributions to this relationship? Here we use concurrent EEG-fMRI recordings of 65 subjects from two centers (35 controls, 34 TL

关键词： Temporal lobe epilepsy Concurrent EEG-fMRI Resting state Functional connectome multimodal data integration

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Mirror Mirror on the Wall ... An Unobtrusive Intelligent Multisensory Mirror for Well-Being Status Self-Assessment and Visualization

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IEEE TRANSACTIONS ON MULTIMEDIA 2017年第7期19卷 1467-1481页

作者： Henriquez, Pedro Matuszewski, Bogdan J. Andreu-Cabedo, Yasmina Bastiani, Luca Colantonio, Sara Coppini, Giuseppe D'Acunto, Mario Favilla, Riccardo Germanese, Danila Giorgi, Daniela Marraccini, Paolo Martinelli, Massimo Morales, Maria-Aurora Pascali, Maria Antonietta Righi, Marco Salvetti, Ovidio Larsson, Marcus Stromberg, Tomas Randeberg, Lise Bjorgan, Asgeir Giannakakis, Giorgos Pediaditis, Matthew Chiarugi, Franco Christinaki, Eirini Marias, Kostas Tsiknakis, Manolis Univ Cent Lancashire Preston PR1 2HE Lancs England CNR IFC Inst Clin Physiol I-56124 Pisa Italy CNR ISTI Inst Informat Sci & Technol I-56124 Pisa Italy CNR ISM Inst Struct Matter I-00185 Rome Italy Linkoping Univ S-58183 Linkoping Sweden Norwegian Univ Sci & Technol N-7491 Trondheim Norway Fdn Res & Technol Hellas Inst Comp Sci Iraklion 70013 Greece Technol Educ Inst Crete Dept Informat Engn Iraklion 71410 Greece

A person's well-being status is reflected by their face through a combination of facial expressions and physical signs. The SEMEOTICONS project translates the semeiotic code of the human face into measurements and computational descriptors that are automatically extracted from images, videos, and three-dimensional scans of the face. SEMEOTICONS developed a multisensory platform in the form of a smart mirror to identify signs related to cardio-metabolic risk. The aim was to enable users to self-monitor their well-being status over time and guide them to improve their lifestyle. Significant scientific and technological challenges have been addressed to build the multisensory mirror, from touchless data acquisition, to real-time processing and integration of multimodal data.

关键词： Cardio-metabolic risk unobtrusive well-being monitoring multimodal data integration 3D face detection and tracking 3D morphometric analysis psychosomatic status recognition multispectral imaging breath analysis

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Wize Mirror - a smart, multisensory cardio-metabolic risk monitoring system

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COMPUTER VISION AND IMAGE UNDERSTANDING 2016年 148卷 3-22页

作者： Andreu, Yasmina Chiarugi, Franco Colantonio, Sara Giannakakis, Giorgos Giorgi, Daniela Henriquez, Pedro Kazantzaki, Eleni Manousos, Dimitris Marias, Kostas Matuszewski, Bogdan J. Pascali, Maria Antonietta Pediaditis, Matthew Raccichini, Giovanni Tsiknakis, Manolis Univ Cent Lancashire Sch Comp Engn & Phys Sci Robot & Comp Vis Res Lab Preston PR1 2HE Lancs England Natl Res Council Italy Inst Informat Sci & Technol Via G Moruzzi 1 I-56124 Pisa Italy Fdn Res & Technol Hellas FORTH Inst Comp Sci N Plastira 100 GR-70013 Iraklion Crete Greece Technol Educ Inst Crete Biomed Informat & eHlth Lab GR-71004 Iraklion Crete Greece

In the recent years personal health monitoring systems have been gaining popularity, both as a result of the pull from the general population, keen to improve well-being and early detection of possibly serious health conditions and the push from the industry eager to translate the current significant progress in computer vision and machine learning into commercial products. One of such systems is the Wize Mirror, built as a result of the FP7 funded SEMEOTICONS (SEMEiotic Oriented Technology for Individuals CardiOmetabolic risk self-assessmeNt and Self-monitoring) project. The project aims to translate the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, multispectral images, and 3D scans of the face. The multisensory platform, being developed as the result of that project, in the form of a smart mirror, looks for signs related to cardio-metabolic risks. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. This paper is focused on the description of the part of that system. utilising computer vision and machine learning techniques to perform 3D morphological analysis of the face and recognition of psycho-somatic status both linked with cardio-metabolic risks. The paper describes the concepts, methods and the developed implementations as well as reports on the results obtained on both real and synthetic datasets. (C) 2016 The Authors. Published by Elsevier Inc.

关键词： Unobtrusive health monitoring 3D face detection Tracking and reconstruction 3D morphometric analysis Psycho-somatic status recognition multimodal data integration

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Improving streamflow predictions across CONUS by integrating advanced machine learning models and diverse data

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ENVIRONMENTAL RESEARCH LETTERS 2024年第10期19卷 104009页

作者： Tayal, Kshitij Arvind, Renganathan Lu, Dan Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 37830 USA

Accurate streamflow prediction is crucial to understand climate impacts on water resources and develop effective adaption strategies. A global long short-term memory (LSTM) model, using data from multiple basins, can enhance streamflow prediction, yet acquiring detailed basin attributes remains a challenge. To overcome this, we introduce the Geo-vision transformer (ViT)-LSTM model, a novel approach that enriches LSTM predictions by integrating basin attributes derived from remote sensing with a ViT architecture. Applied to 531 basins across the Contiguous United States, our method demonstrated superior prediction accuracy in both temporal and spatiotemporal extrapolation scenarios. Geo-ViT-LSTM marks a significant advancement in land surface modeling, providing a more comprehensive and effective tool for better understanding the environment responses to climate change.

关键词： streamflow prediction remote sensing machine learning in hydrology multimodal data integration

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Orthogonalized Kernel Debiased Machine Learning for multimodal data Analysis

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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2023年第543期118卷 1796-1810页

作者： Dai, Xiaowu Li, Lexin Univ Calif Berkeley Dept Econ Berkeley CA 94720 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA USA Univ Calif Berkeley Dept Biostat & Epidemiol Berkeley CA USA

multimodal imaging has transformed neuroscience research. While it presents unprecedented opportunities, it also imposes serious challenges. Particularly, it is difficult to combine the merits of the interpretability attributed to a simple association model with the flexibility achieved by a highly adaptive nonlinear model. In this article, we propose an orthogonalized kernel debiased machine learning approach, which is built upon the Neyman orthogonality and a form of decomposition orthogonality, for multimodal data analysis. We target the setting that naturally arises in almost all multimodal studies, where there is a primary modality of interest, plus additional auxiliary modalities. We establish the root-N-consistency and asymptotic normality of the estimated primary parameter, the semi-parametric estimation efficiency, and the asymptotic validity of the confidence band of the predicted primary modality effect. Our proposal enjoys, to a good extent, both model interpretability and model flexibility. It is also considerably different from the existing statistical methods for multimodal data integration, as well as the orthogonality-based methods for high-dimensional inferences. We demonstrate the efficacy of our method through both simulations and an application to a multimodal neuroimaging study of Alzheimer's disease. Supplementary materials for this article are available online.

关键词： Basis expansion High-dimensional inference multimodal data integration Neuroimaging analysis Neyman orthogonality Reproducing kernel Hilbert space

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Intricacies of single-cell multi-omics data integration

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TRENDS IN GENETICS 2022年第2期38卷 128-139页

作者： Rautenstrauch, Pia Vlot, Anna Hendrika Cornelia Saran, Sepideh Ohler, Uwe Max Delbruck Ctr Mol Med Berlin Inst Med Syst Biol D-10115 Berlin Germany Humboldt Univ Dept Comp Sci D-10117 Berlin Germany Humboldt Univ Dept Biol D-10117 Berlin Germany

A wealth of single-cell protocols makes it possible to characterize different molecular layers at unprecedented resolution. Integrating the resulting multi modal single-cell data to find cell-to-cell correspondences remains a challenge. We argue that data integration needs to happen at a meaningful biological level of abstraction and that it is necessary to consider the inherent discrepancies between modalities to strike a balance between biological discovery and noise removal. A survey of current methods reveals that a distinction between technical and biological origins of presumed unwanted variation between datasets is not yet commonly considered. The increasing availability of paired multimodal data will aid the development of improved methods by providing a ground truth on cell-to-cell matches.

关键词： single-cell omics multimodal data integration single-cell multi-omic assays cell type identity method development multi-omics data integration

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STASCAN deciphers fine-resolution cell distribution maps in spatial transcriptomics by deep learning

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GENOME BIOLOGY 2024年第1期25卷 1-28页

作者： Wu, Ying Zhou, Jia-Yi Yao, Bofei Cui, Guanshen Zhao, Yong-Liang Gao, Chun-Chun Yang, Ying Zhang, Shihua Yang, Yun-Gui China Natl Ctr Bioinformat Beijing 100101 Peoples R China Chinese Acad Sci Beijing Inst Genom Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Chinese Acad Sci Coll Future Technol Beijing 100049 Peoples R China Chinese Acad Sci Acad Math & Syst Sci CEMS RCSDSNCMIS Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Univ Chinese Acad Sci Hangzhou Inst Adv Study Sch Life Sci Key Lab Syst Hlth Sci Zhejiang Prov Hangzhou 310024 Peoples R China Univ Chinese Acad Sci Sino Danish Coll Beijing 101408 Peoples R China Chinese Acad Sci Inst Stem Cell & Regenerat Beijing 100101 Peoples R China

Spatial transcriptomics technologies have been widely applied to decode cellular distribution by resolving gene expression profiles in tissue. However, sequencing techniques still limit the ability to create a fine-resolved spatial cell-type map. To this end, we develop a novel deep-learning-based approach, STASCAN, to predict the spatial cellular distribution of captured or uncharted areas where only histology images are available by cell feature learning integrating gene expression profiles and histology images. STASCAN is successfully applied across diverse datasets from different spatial transcriptomics technologies and displays significant advantages in deciphering higher-resolution cellular distribution and resolving enhanced organizational structures.

关键词： Spatial transcriptomics Cell annotation Deep learning Imputation multimodal data integration

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A novel deep learning algorithm for real-time prediction of clinical deterioration in the emergency department for a multimodal clinical decision support system

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SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Choi, Arom Lee, Kwanhyung Hyun, Heejung Kim, Kwang Joon Ahn, Byungeun Lee, Kyung Hyun Hahn, Sangchul Choi, So Yeon Kim, Ji Hoon Yonsei Univ Dept Emergency Med Coll Med 50-1 Yonsei Ro Seoul South Korea Yonsei Univ Inst Innovat Digital Healthcare 50 Yonsei Ro Seoul South Korea AITRICS Corp 218 Teheran Ro Seoul South Korea Yonsei Univ Dept Internal Med Div Geriatr Coll Med Seoul South Korea

The array of complex and evolving patient data has limited clinical decision making in the emergency department (ED). This study introduces an advanced deep learning algorithm designed to enhance real-time prediction accuracy for integration into a novel Clinical Decision Support System (CDSS). A retrospective study was conducted using data from a level 1 tertiary hospital. The algorithm's predictive performance was evaluated based on in-hospital cardiac arrest, inotropic circulatory support, advanced airway, and intensive care unit admission. We developed an artificial intelligence (AI) algorithm for CDSS that integrates multiple data modalities, including vitals, laboratory, and imaging results from electronic health records. The AI model was trained and tested on a dataset of 237,059 ED visits. The algorithm's predictions, based solely on triage information, significantly outperformed traditional logistic regression models, with notable improvements in the area under the precision-recall curve (AUPRC). Additionally, predictive accuracy improved with the inclusion of continuous data input at shorter intervals. This study suggests the feasibility of using AI algorithms in diverse clinical scenarios, particularly for earlier detection of clinical deterioration. Future work should focus on expanding the dataset and enhancing real-time data integration across multiple centers to further optimize its application within the novel CDSS.

关键词： Clinical decision support system Emergency department Deep learning multimodal data integration Real-time prediction Patient deterioration

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