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Arceneaux JS, Brockman AA, Khurana R, Chalkley MBL, Geben LC, Krbanjevic A, Vestal M, Zafar M, Weatherspoon S, Mobley BC, Ess KC, Ihrie RA. Multiparameter quantitative analyses of diagnostic cells in brain tissues from tuberous sclerosis complex. CYTOMETRY. PART B, CLINICAL CYTOMETRY 2024. [PMID: 38953209 DOI: 10.1002/cyto.b.22194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 06/05/2024] [Accepted: 06/11/2024] [Indexed: 07/03/2024]
Abstract
The advent of high-dimensional imaging offers new opportunities to molecularly characterize diagnostic cells in disorders that have previously relied on histopathological definitions. One example case is found in tuberous sclerosis complex (TSC), a developmental disorder characterized by systemic growth of benign tumors. Within resected brain tissues from patients with TSC, detection of abnormally enlarged balloon cells (BCs) is pathognomonic for this disorder. Though BCs can be identified by an expert neuropathologist, little is known about the specificity and broad applicability of protein markers for these cells, complicating classification of proposed BCs identified in experimental models of this disorder. Here, we report the development of a customized machine learning pipeline (BAlloon IDENtifier; BAIDEN) that was trained to prospectively identify BCs in tissue sections using a histological stain compatible with high-dimensional cytometry. This approach was coupled to a custom 36-antibody panel and imaging mass cytometry (IMC) to explore the expression of multiple previously proposed BC marker proteins and develop a descriptor of BC features conserved across multiple tissue samples from patients with TSC. Here, we present a modular workflow encompassing BAIDEN, a custom antibody panel, a control sample microarray, and analysis pipelines-both open-source and in-house-and apply this workflow to understand the abundance, structure, and signaling activity of BCs as an example case of how high-dimensional imaging can be applied within human tissues.
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Affiliation(s)
- Jerome S Arceneaux
- Department of Biochemistry, Cancer Biology, Neuroscience, and Pharmacology, Meharry Medical College, Nashville, Tennessee, USA
| | - Asa A Brockman
- Department of Cell & Developmental Biology, Vanderbilt University, Nashville, Tennessee, USA
| | - Rohit Khurana
- Department of Cell & Developmental Biology, Vanderbilt University, Nashville, Tennessee, USA
| | - Mary-Bronwen L Chalkley
- Department of Cell & Developmental Biology, Vanderbilt University, Nashville, Tennessee, USA
| | - Laura C Geben
- Department of Pharmacology, Vanderbilt University, Nashville, Tennessee, USA
| | - Aleksandar Krbanjevic
- Department of Pathology, Microbiology, & Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Matthew Vestal
- Duke University Children's Hospital and Health Center, Durham, North Carolina, USA
| | - Muhammad Zafar
- Duke University Children's Hospital and Health Center, Durham, North Carolina, USA
| | - Sarah Weatherspoon
- Neuroscience Institute, Le Bonheur Children's Hospital, Memphis, Tennessee, USA
- Department of Pediatrics, University of Tennessee Health Science Center, Memphis, Tennessee, USA
| | - Bret C Mobley
- Department of Pathology, Microbiology, & Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kevin C Ess
- Department of Cell & Developmental Biology, Vanderbilt University, Nashville, Tennessee, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Section of Child Neurology, University of Colorado Anschutz Medical Center, Aurora, Colorado, USA
| | - Rebecca A Ihrie
- Department of Cell & Developmental Biology, Vanderbilt University, Nashville, Tennessee, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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2
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Blake N, Gaifulina R, Griffin LD, Bell IM, Rodriguez-Justo M, Thomas GMH. Deep Learning Applied to Raman Spectroscopy for the Detection of Microsatellite Instability/MMR Deficient Colorectal Cancer. Cancers (Basel) 2023; 15:cancers15061720. [PMID: 36980606 PMCID: PMC10046611 DOI: 10.3390/cancers15061720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/08/2023] [Accepted: 03/09/2023] [Indexed: 03/16/2023] Open
Abstract
Defective DNA mismatch repair is one pathogenic pathway to colorectal cancer. It is characterised by microsatellite instability which provides a molecular biomarker for its detection. Clinical guidelines for universal testing of this biomarker are not met due to resource limitations; thus, there is interest in developing novel methods for its detection. Raman spectroscopy (RS) is an analytical tool able to interrogate the molecular vibrations of a sample to provide a unique biochemical fingerprint. The resulting datasets are complex and high-dimensional, making them an ideal candidate for deep learning, though this may be limited by small sample sizes. This study investigates the potential of using RS to distinguish between normal, microsatellite stable (MSS) and microsatellite unstable (MSI-H) adenocarcinoma in human colorectal samples and whether deep learning provides any benefit to this end over traditional machine learning models. A 1D convolutional neural network (CNN) was developed to discriminate between healthy, MSI-H and MSS in human tissue and compared to a principal component analysis–linear discriminant analysis (PCA–LDA) and a support vector machine (SVM) model. A nested cross-validation strategy was used to train 30 samples, 10 from each group, with a total of 1490 Raman spectra. The CNN achieved a sensitivity and specificity of 83% and 45% compared to PCA–LDA, which achieved a sensitivity and specificity of 82% and 51%, respectively. These are competitive with existing guidelines, despite the low sample size, speaking to the molecular discriminative power of RS combined with deep learning. A number of biochemical antecedents responsible for this discrimination are also explored, with Raman peaks associated with nucleic acids and collagen being implicated.
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Affiliation(s)
- Nathan Blake
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
| | - Riana Gaifulina
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
| | - Lewis D. Griffin
- Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Ian M. Bell
- Spectroscopy Products Division, Renishaw PLC, Wotton-under-Edge GL12 8JR, UK
| | - Manuel Rodriguez-Justo
- Department of Research Pathology, Cancer Institute, University College London, London WC1E 6DD, UK
| | - Geraint M. H. Thomas
- Department of Cell and Developmental Biology, University College London, London WC1E 6BT, UK
- Correspondence: ; Tel.: +44-20-3549-5456
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Chen YT, Huang PY, Chai CY, Yu S, Hsieh YL, Chang HC, Kuo CW, Lee YC, Yu HS. Early detection of the initial stages of LED light-triggered non-alcoholic fatty liver disease by wax physisorption kinetics-Fourier transform infrared imaging. Analyst 2023; 148:643-653. [PMID: 36621928 DOI: 10.1039/d2an01546c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Light-emitting diodes (LEDs), particularly in the blue waveform range, are regarded as a major source of circadian rhythm dysregulation. A circadian rhythm dysregulation induced by blue LEDs is associated with non-alcoholic fatty liver disease (NAFLD). Hepatocellular accumulation of lipids is a key event in the early stages of NAFLD. Kupffer cells (KCs) have been reported to be lost in the early onset of NAFLD followed by an inflammatory reaction that alters the liver response to lipid overload. This study focused on the detection of the initial stages (subpathological stages) of LED light-triggered NAFLD. Mice were exposed to either blue or white LED irradiation for 44 weeks. Synchrotron radiation-based Fourier-transform infrared microspectroscopy (SR-FTIRM) and wax physisorption kinetic-Fourier transform infrared (WPK-FTIR) imaging were used to evaluate the ratio of lipid to protein and the glycosylation of glycoprotein, respectively. Immunohistopathological studies on KCs and circadian-related proteins were performed. Although liver biopsy showed normal pathology, an SR-FTIRM study revealed a high hepatic lipid-to-protein ratio after receiving LED illumination. The results of WPK-FTIR demonstrated that a high inflammation index was found in the high irradiance of the blue LED illumnation group. These groups showed a decrease in KC number and an increase in Bmal1 and Reverbα circadian protein expression. These findings provide explanations for the reduction of KCs without subsequent inflammation. A significant reduction of Per2 and Cry1 expression is correlated with the findings of WPK-FTIR imaging. WPK-FTIR is a sensitive method for detecting initiative stages of NAFLD induced by long-term blue LED illumination.
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Affiliation(s)
- Yi-Ting Chen
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.,Department of Pathology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.,Department of Pathology, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Pei-Yu Huang
- Life Science Group, National Synchrotron Radiation Research Center, Hsinchu 30076, Taiwan
| | - Chee-Yin Chai
- Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.,Department of Pathology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.,Department of Pathology, Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.,Institute of Biomedical Sciences, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan
| | - Sebastian Yu
- Department of Dermatology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan. .,Department of Dermatology, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Yu-Lin Hsieh
- Department of Anatomy, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.,School of Post-Baccalaureate Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan.,Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung 80708, Taiwan
| | - Hao-Chao Chang
- Taiwan Instrument Research Institute, National Applied Research Laboratories, Hsinchu 30205, Taiwan
| | - Chin-Wei Kuo
- Life Science Group, National Synchrotron Radiation Research Center, Hsinchu 30076, Taiwan
| | - Yao-Chang Lee
- Life Science Group, National Synchrotron Radiation Research Center, Hsinchu 30076, Taiwan.,Department of Optics and Photonics, National Central University, Taoyuan 320317, Taiwan.,Chemistry Department, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Hsin-Su Yu
- Department of Dermatology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 80708, Taiwan. .,National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli County 35053, Taiwan.,Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
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G. K. AV, Gogoi G, Behera B, Rila S, Rangarajan A, Pandya HJ. RapidET: a MEMS-based platform for label-free and rapid demarcation of tumors from normal breast biopsy tissues. MICROSYSTEMS & NANOENGINEERING 2022; 8:1. [PMID: 35087680 PMCID: PMC8761751 DOI: 10.1038/s41378-021-00337-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 11/07/2021] [Accepted: 11/28/2021] [Indexed: 05/09/2023]
Abstract
The rapid and label-free diagnosis of malignancies in ex vivo breast biopsy tissues has significant utility in pathology laboratories and operating rooms. We report a MEMS-based platform integrated with microchips that performs phenotyping of breast biopsy tissues using electrothermal sensing. The microchip, fabricated on a silicon substrate, incorporates a platinum microheater, interdigitated electrodes (IDEs), and resistance temperature detectors (RTDs) as on-chip sensing elements. The microchips are integrated onto the platform using a slide-fit contact enabling quick replacement for biological measurements. The bulk resistivity (ρ B ), surface resistivity (ρ S ), and thermal conductivity (k) of deparaffinized and formalin-fixed paired tumor and adjacent normal breast biopsy samples from N = 8 patients were measured. For formalin-fixed samples, the mean ρ B for tumors showed a statistically significant fold change of 4.42 (P = 0.014) when the tissue was heated from 25 °C to 37 °C compared to the adjacent normal tissue, which showed a fold change of 3.47. The mean ρ S measurements also showed a similar trend. The mean k of the formalin-fixed tumor tissues was 0.309 ± 0.02 W m-1 K-1 compared to a significantly higher k of 0.563 ± 0.028 W m-1 K-1 for the adjacent normal tissues. A similar trend was observed in ρ B, ρ S, and k for the deparaffinized tissue samples. An analysis of a combination of ρ B , ρ S , and k using Fisher's combined probability test and linear regression suggests the advantage of using all three parameters simultaneously for distinguishing tumors from adjacent normal tissues with higher statistical significance.
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Affiliation(s)
- Anil Vishnu G. K.
- Center for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, Karnataka India
| | - Gayatri Gogoi
- Department of Pathology, Assam Medical College, Dibrugarh, Assam India
| | - Bhagaban Behera
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, Karnataka India
| | - Saeed Rila
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, Karnataka India
| | - Annapoorni Rangarajan
- Department of Molecular Reproduction, Development, and Genetics, Indian Institute of Science, Bangalore, Karnataka India
| | - Hardik J. Pandya
- Department of Electronic Systems Engineering, Indian Institute of Science, Bangalore, Karnataka India
- Centre for Product Design and Manufacturing, Indian Institute of Science, Bangalore, Karnataka India
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Doherty T, McKeever S, Al-Attar N, Murphy T, Aura C, Rahman A, O'Neill A, Finn SP, Kay E, Gallagher WM, Watson RWG, Gowen A, Jackman P. Feature fusion of Raman chemical imaging and digital histopathology using machine learning for prostate cancer detection. Analyst 2021; 146:4195-4211. [PMID: 34060548 DOI: 10.1039/d1an00075f] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The diagnosis of prostate cancer is challenging due to the heterogeneity of its presentations, leading to the over diagnosis and treatment of non-clinically important disease. Accurate diagnosis can directly benefit a patient's quality of life and prognosis. Towards addressing this issue, we present a learning model for the automatic identification of prostate cancer. While many prostate cancer studies have adopted Raman spectroscopy approaches, none have utilised the combination of Raman Chemical Imaging (RCI) and other imaging modalities. This study uses multimodal images formed from stained Digital Histopathology (DP) and unstained RCI. The approach was developed and tested on a set of 178 clinical samples from 32 patients, containing a range of non-cancerous, Gleason grade 3 (G3) and grade 4 (G4) tissue microarray samples. For each histological sample, there is a pathologist labelled DP-RCI image pair. The hypothesis tested was whether multimodal image models can outperform single modality baseline models in terms of diagnostic accuracy. Binary non-cancer/cancer models and the more challenging G3/G4 differentiation were investigated. Regarding G3/G4 classification, the multimodal approach achieved a sensitivity of 73.8% and specificity of 88.1% while the baseline DP model showed a sensitivity and specificity of 54.1% and 84.7% respectively. The multimodal approach demonstrated a statistically significant 12.7% AUC advantage over the baseline with a value of 85.8% compared to 73.1%, also outperforming models based solely on RCI and mean and median Raman spectra. Feature fusion of DP and RCI does not improve the more trivial task of tumour identification but does deliver an observed advantage in G3/G4 discrimination. Building on these promising findings, future work could include the acquisition of larger datasets for enhanced model generalization.
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Affiliation(s)
- Trevor Doherty
- Technological University Dublin, School of Computer Science, City Campus, Grangegorman Lower, Dublin 7, Ireland.
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Tuck M, Blanc L, Touti R, Patterson NH, Van Nuffel S, Villette S, Taveau JC, Römpp A, Brunelle A, Lecomte S, Desbenoit N. Multimodal Imaging Based on Vibrational Spectroscopies and Mass Spectrometry Imaging Applied to Biological Tissue: A Multiscale and Multiomics Review. Anal Chem 2020; 93:445-477. [PMID: 33253546 DOI: 10.1021/acs.analchem.0c04595] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Michael Tuck
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Landry Blanc
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Rita Touti
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Nathan Heath Patterson
- Mass Spectrometry Research Center, Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232-8575, United States
| | - Sebastiaan Van Nuffel
- Materials Research Institute, The Pennsylvania State University, University Park, Pennsylvania 16802, United States
| | - Sandrine Villette
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Jean-Christophe Taveau
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Andreas Römpp
- Bioanalytical Sciences and Food Analysis, University of Bayreuth, Universitätsstraße 30, 95440 Bayreuth, Germany
| | - Alain Brunelle
- Laboratoire d'Archéologie Moléculaire et Structurale, LAMS UMR 8220, CNRS, Sorbonne Université, 4 Place Jussieu, 75005 Paris, France
| | - Sophie Lecomte
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
| | - Nicolas Desbenoit
- Institut de Chimie & Biologie des Membranes & des Nano-objets, CBMN UMR 5248, CNRS, Université de Bordeaux, 1 Allée Geoffroy Saint-Hilaire, 33600 Pessac, France
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Dantas D, Soares L, Novais S, Vilarinho R, Moreira JA, Silva S, Frazão O, Oliveira T, Leal N, Faísca P, Reis J. Discrimination of Benign and Malignant Lesions in Canine Mammary Tissue Samples Using Raman Spectroscopy: A Pilot Study. Animals (Basel) 2020; 10:ani10091652. [PMID: 32937987 PMCID: PMC7552658 DOI: 10.3390/ani10091652] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 09/06/2020] [Accepted: 09/11/2020] [Indexed: 11/16/2022] Open
Abstract
Breast cancer is a health problem that affects individual life quality and the family system. It is the most frequent type of cancer in women, but men are also affected. As an integrative approach, comparative oncology offers an opportunity to learn more about natural cancers in different species. Methods based on Raman spectroscopy have shown significant potential in the study of the human breast through the fingerprinting of biological tissue, which provides valuable information that can be used to identify, characterize and discriminate structures in breast tissue, in both healthy and carcinogenic environments. One of the most important applications of Raman spectroscopy in medical diagnosis is the characterization of microcalcifications, which are highly important diagnostic indicators of breast tissue diseases. Raman spectroscopy has been used to analyze the chemical composition of microcalcifications. These occur in benign and malignant lesions in the human breast, and Raman helps to discriminate microcalcifications as type I and type II according to their composition. This paper demonstrates the recent progress in understanding how this vibrational technique can discriminate through the fingerprint regions of lesions in unstained histology sections from canine mammary glands.
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Affiliation(s)
- Diana Dantas
- IFIMUP, Department of Physics and Astronomy, Faculty of Sciences of University of Porto, Rua do Campo Alegre 687, 4169-007 Porto, Portugal; (D.D.); (L.S.); (R.V.); (J.A.M.)
| | - Liliana Soares
- IFIMUP, Department of Physics and Astronomy, Faculty of Sciences of University of Porto, Rua do Campo Alegre 687, 4169-007 Porto, Portugal; (D.D.); (L.S.); (R.V.); (J.A.M.)
| | - Susana Novais
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Rua do Campo Alegre 687, 4169-007 Porto, Portugal; (S.N.); (S.S.); (O.F.)
| | - Rui Vilarinho
- IFIMUP, Department of Physics and Astronomy, Faculty of Sciences of University of Porto, Rua do Campo Alegre 687, 4169-007 Porto, Portugal; (D.D.); (L.S.); (R.V.); (J.A.M.)
| | - J. Agostinho Moreira
- IFIMUP, Department of Physics and Astronomy, Faculty of Sciences of University of Porto, Rua do Campo Alegre 687, 4169-007 Porto, Portugal; (D.D.); (L.S.); (R.V.); (J.A.M.)
| | - Susana Silva
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Rua do Campo Alegre 687, 4169-007 Porto, Portugal; (S.N.); (S.S.); (O.F.)
| | - Orlando Frazão
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Rua do Campo Alegre 687, 4169-007 Porto, Portugal; (S.N.); (S.S.); (O.F.)
| | - Teresa Oliveira
- Departamento de Medicina Veterinária; Escola de Ciências e Tecnologia; Universidade de Évora; Pólo da Mitra, Apartado 94, 7002-554 Évora, Portugal;
| | - Nuno Leal
- DNAtech Laboratório Veterinário, Estrada do Paço do Lumiar, N.° 22 Edifício E, 1° Andar, 1649-038 Lisboa, Portugal; (N.L.); (P.F.)
| | - Pedro Faísca
- DNAtech Laboratório Veterinário, Estrada do Paço do Lumiar, N.° 22 Edifício E, 1° Andar, 1649-038 Lisboa, Portugal; (N.L.); (P.F.)
- Centro de Investigação em BioCiências e Tecnologias da Saúde, Faculdade de Medicina Veterinária, Universidade Lusófona de Humanidades e Tecnologias, Campo Grande 376, 1749-024 Lisboa, Portugal
| | - Joana Reis
- Departamento de Medicina Veterinária; Escola de Ciências e Tecnologia; Universidade de Évora; Pólo da Mitra, Apartado 94, 7002-554 Évora, Portugal;
- Correspondence:
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