cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5551057
title: DeepBacs – Escherichia coli antibiotic phenotyping object detection dataset and YOLOv2 model

description: Training and test images of <em>E. coli</em> cells treated with different antibiotics for antibiotic phenotyping using YOLOv2 object detection. Additional information can be found on this github wiki. Example images show predictions of drug-treated <em>E. coli</em> cells. <strong>Training and test dataset</strong> <strong>Data type</strong>: Paired microscopy images (confocal fluorescence) and manual annotations <strong>Microscopy data type</strong>: Confocal fluorescence images of fixed <em>E. coli</em> cells stained for membrane (Nile Red) and DNA (DAPI) paired with annotations in PASCAL VOC format <strong>Microscope</strong>: Zeiss LSM710 confocal microscope with a Plan-Apo 63x oil objective (1.4 NA) <strong>Cell type</strong>: Chemically fixed <em>E. coli</em> NO34 cells (MreB-sfGFPsw, kindly provided by Zemer Gitai) (untreated or drug-treated); <strong>File format</strong>: .png (RGB) <strong>Image size</strong>: 400 x 400 px² (Pixel size: 84 nm) <strong>YOLOv2 model</strong> The YOLOv2 model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 97 epochs on 153 manually annotated images (image dimensions: (400, 400, 3)) with a batch size of 16 and a custom loss function combining MSE and crossentropy losses, using the YOLOv2 ZeroCostDL4Mic notebook (v 1.12) (von Chamier & Laine et al., 2020). Key python packages used include tensorflow (v0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 10.1.243). The training was accelerated using a Tesla P100GPU and data was augmented by a factor of 8 using rotation and flipping. The model weights can be used with the ZeroCostDL4Mic YOLOv2 notebook. <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup> <strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de <strong>Affiliation(s)</strong>: 1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany 2) ORCID: 0000-0001-9886-2263 3) ORCID: 0000-0002-9821-3578
DOI: 10.5281/zenodo.5551056
title: DeepBacs – Escherichia coli antibiotic phenotyping object detection dataset and YOLOv2 model

description: Training and test images of <em>E. coli</em> cells treated with different antibiotics for antibiotic phenotyping using YOLOv2 object detection. Additional information can be found on this github wiki. Example images show predictions of drug-treated <em>E. coli</em> cells. <strong>Training and test dataset</strong> <strong>Data type</strong>: Paired microscopy images (confocal fluorescence) and manual annotations <strong>Microscopy data type</strong>: Confocal fluorescence images of fixed <em>E. coli</em> cells stained for membrane (Nile Red) and DNA (DAPI) paired with annotations in PASCAL VOC format <strong>Microscope</strong>: Zeiss LSM710 confocal microscope with a Plan-Apo 63x oil objective (1.4 NA) <strong>Cell type</strong>: Chemically fixed <em>E. coli</em> NO34 cells (MreB-sfGFPsw, kindly provided by Zemer Gitai) (untreated or drug-treated); <strong>File format</strong>: .png (RGB) <strong>Image size</strong>: 400 x 400 px² (Pixel size: 84 nm) <strong>YOLOv2 model</strong> The YOLOv2 model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 97 epochs on 153 manually annotated images (image dimensions: (400, 400, 3)) with a batch size of 16 and a custom loss function combining MSE and crossentropy losses, using the YOLOv2 ZeroCostDL4Mic notebook (v 1.12) (von Chamier & Laine et al., 2020). Key python packages used include tensorflow (v0.1.12), Keras (v 2.3.1), numpy (v 1.19.5), cuda (v 10.1.243). The training was accelerated using a Tesla P100GPU and data was augmented by a factor of 8 using rotation and flipping. The model weights can be used with the ZeroCostDL4Mic YOLOv2 notebook. <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup> <strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de <strong>Affiliation(s)</strong>: 1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany 2) ORCID: 0000-0001-9886-2263 3) ORCID: 0000-0002-9821-3578
DOI: 10.6073/pasta/ea00fc37f0658dae21b817b1f93911cf
title: Interagency Ecological Program: Over four decades of juvenile fish monitoring data from the San Francisco Estuary, collected by the Delta Juvenile Fish Monitoring Program, 1976-2017

description: The United States Fish and Wildlife Service Delta Juvenile Fish Monitoring Program (DJFMP) has monitored juvenile Chinook Salmon Oncorhynchus tshawytscha and other fish species within the San Francisco Estuary (Estuary) since 1976 using a combination of surface trawls and beach seines. Since 2000, three trawl sites and 58 beach seine sites have been sampled weekly or biweekly within the Estuary and lower Sacramento and San Joaquin Rivers. As part of the Interagency Ecological Program (IEP) that manages the Estuary, the DJFMP tracks the abundance and distribution of naturally and hatchery produced juvenile Chinook Salmon of all races as they outmigrate through the Sacramento-San Joaquin Delta for over four decades. The data that DJFMP collected has been used not only to help inform the management of Chinook Salmon, but also to monitor the status of native species of interest such as the previously listed Splittail Pogonichthys macrolepidotus and invasive species such as Mississippi Silverside Menidia beryllina and Largemouth Bass Micropterus salmoides. For more information: https://www.fws.gov/lodi/juvenile_fish_monitoring_program/jfmp_index.htm
DOI: 10.5281/zenodo.5551135
title: DeepBacs – Bacillus subtilis denoising dataset

description: Live-cell time series of vertically aligned <em>B. subtilis</em> cells expressing FtsZ-GFP protein fusion. Additional information can be found in this github wiki. The example shows raw and denoised images (Noise2Void 2D) of vertically aligned <em>B. subtilis</em> cells (VerCINI) <strong>Data type</strong>: Fluorescence images of vertically oriented <em>B. subtilis </em>cells <strong>Microscopy data type</strong>: 2D widefield images (fluorescence) <strong>Microscope</strong>: Custom-built 100x inverted microscope bearing a 100x TIRF objective (Nikon CFI Apochromat TIRF 100XC Oil); images were captured on a Prime BSI sCMOS camera (Teledyne Photometrics) <strong>Cell type</strong>: <em>B. subtilis</em> strain SH130 grown under agarose pads, Cells were imaged at 1 frame/second with continuous exposure for 2 minutes at 1-8 W/cm2 <strong>File format</strong>: .tiff (16-bit) One frame was selected from each time series <strong>Image size</strong>: 1024x1024 px² (Pixel size: 65 nm) <strong>Author(s)</strong>: Mia Conduit<sup>1</sup>, Séamus Holden<sup>1,2</sup> <strong>Contact email</strong>: Seamus.Holden@newcastle.ac.uk <strong>Affiliation</strong>: 1) Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK 2) ORCID: 0000-0002-7169-907X <strong>Associated publications</strong>: Whitley <em>et al</em>., 2021, Nature Communications, https://doi.org/10.15252/embj.201696235
DOI: 10.5281/zenodo.5551134
title: DeepBacs – Bacillus subtilis denoising dataset

description: Live-cell time series of vertically aligned <em>B. subtilis</em> cells expressing FtsZ-GFP protein fusion. Additional information can be found in this github wiki. The example shows raw and denoised images (Noise2Void 2D) of vertically aligned <em>B. subtilis</em> cells (VerCINI) <strong>Data type</strong>: Fluorescence images of vertically oriented <em>B. subtilis </em>cells <strong>Microscopy data type</strong>: 2D widefield images (fluorescence) <strong>Microscope</strong>: Custom-built 100x inverted microscope bearing a 100x TIRF objective (Nikon CFI Apochromat TIRF 100XC Oil); images were captured on a Prime BSI sCMOS camera (Teledyne Photometrics) <strong>Cell type</strong>: <em>B. subtilis</em> strain SH130 grown under agarose pads, Cells were imaged at 1 frame/second with continuous exposure for 2 minutes at 1-8 W/cm2 <strong>File format</strong>: .tiff (16-bit) One frame was selected from each time series <strong>Image size</strong>: 1024x1024 px² (Pixel size: 65 nm) <strong>Author(s)</strong>: Mia Conduit<sup>1</sup>, Séamus Holden<sup>1,2</sup> <strong>Contact email</strong>: Seamus.Holden@newcastle.ac.uk <strong>Affiliation</strong>: 1) Centre for Bacterial Cell Biology, Biosciences Institute, Newcastle University, NE2 4AX UK 2) ORCID: 0000-0002-7169-907X <strong>Associated publications</strong>: Whitley <em>et al</em>., 2021, Nature Communications, https://doi.org/10.15252/embj.201696235
DOI: 10.5517/ccdc.csd.cc28d2wn
title: CCDC 2096991: Experimental Crystal Structure Determination

description: Related Article: F. W. Heinemann, A. Hille, D. Sellmann
|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28d2xp
title: CCDC 2096992: Experimental Crystal Structure Determination

description: Related Article: F. W. Heinemann, A. Hille, D. Sellmann
|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28d2zr
title: CCDC 2096994: Experimental Crystal Structure Determination

description: Related Article: F. W. Heinemann, N. Blum, D. Sellmann
|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28d30t
title: CCDC 2096995: Experimental Crystal Structure Determination

description: Related Article: F. W. Heinemann, A. Lauderbach, D. Sellmann|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28d32w
title: CCDC 2096997: Experimental Crystal Structure Determination

description: Related Article: F. W. Heinemann, N. Blum, D. Sellmann
|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28d33x
title: CCDC 2096998: Experimental Crystal Structure Determination

description: Related Article: F. W. Heinemann, N. Blum, D. Sellmann
|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28d34y
title: CCDC 2096999: Experimental Crystal Structure Determination

description: Related Article: F. W. Heinemann, M. Klimowicz, D. Sellmann|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28d35z
title: CCDC 2097000: Experimental Crystal Structure Determination

description: Related Article: F. W. Heinemann, R. Prakash, D. Sellmann|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28g00s
title: CCDC 2098824: Experimental Crystal Structure Determination

description: Related Article: Kyle Pearce|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28ghqz
title: CCDC 2099311: Experimental Crystal Structure Determination

description: Related Article: jusong yang|2021|CSD Communication|||
DOI: 10.5281/zenodo.5551123
title: DeepBacs – Artificial labeling of E. coli membranes dataset and fnet/CARE models

description: Training and test images of <em>E. coli </em>cells for artificial labeling of membranes in brightfield images using fnet or CARE, as well as trained models for prediction of super-resolution membranes. Additional information can be found on this github wiki. Example image shows an <em>E. coli</em> bright field image and PAINT membrane image predicted by the neural network (scale bar is 1 µm). <strong>Training and testing dataset</strong> <strong>Data type</strong>: Paired bright field and super-resolution images <strong>Microscopy data type</strong>: Bright field and fluorescence microscopy (widefield and point accumulation for imaging in nanoscale topography (PAINT) images) <strong>Microscope</strong>: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective <strong>Cell type</strong>: <em>E. coli </em>K12 strain derivatives <strong>File format</strong>: .tif (8-bit) <strong>Image size</strong>: 512x512 px<sup>2</sup> with different pixel sizes: 1x tube lens: 158 nm (raw) and 19.75 nm (8x upscaled for PAINT images) 1.5x tube lens: 106 nm (raw) (widefield fluorescence only) <strong>fnet model (PAINT membrane images)</strong> The fnet 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained for 200,000 steps on 33 paired images (image dimensions: (512 x 512 px²), patch size: (128 x 128 px²)) with a batch size of 4, a learning rate of 0.0004, 10% validation split and 4x data augmentation (flipping and rotation). Model weights can be used with the ZeroCostDL4Mic fnet 2D notebook. <strong>CARE model (PAINT membrane images):</strong> The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained for 300 epochs (100 steps/epoch) on 33 paired images (image dimensions: 512 x 512 px², patch size: 256 x 256 px²) with a batch size of 4, a learning rate of 0.0004, 90/10% train/validation split and 4x data augmentation (flipping and rotation). Model weights can be used with the ZeroCostDL4Mic CARE 2D notebook or the CSBDeep Fiji plugin. <br> <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup> <strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de <strong>Affiliation(s)</strong>: 1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany 2) ORCID: 0000-0001-9886-2263 3) ORCID: 0000-0002-9821-3578
DOI: 10.5281/zenodo.5551122
title: DeepBacs – Artificial labeling of E. coli membranes dataset and fnet/CARE models

description: Training and test images of <em>E. coli </em>cells for artificial labeling of membranes in brightfield images using fnet or CARE, as well as trained models for prediction of super-resolution membranes. Additional information can be found on this github wiki. Example image shows an <em>E. coli</em> bright field image and PAINT membrane image predicted by the neural network (scale bar is 1 µm). <strong>Training and testing dataset</strong> <strong>Data type</strong>: Paired bright field and super-resolution images <strong>Microscopy data type</strong>: Bright field and fluorescence microscopy (widefield and point accumulation for imaging in nanoscale topography (PAINT) images) <strong>Microscope</strong>: Nikon Eclipse Ti-E equipped with an Apo TIRF 1.49NA 100x oil immersion objective <strong>Cell type</strong>: <em>E. coli </em>K12 strain derivatives <strong>File format</strong>: .tif (8-bit) <strong>Image size</strong>: 512x512 px<sup>2</sup> with different pixel sizes: 1x tube lens: 158 nm (raw) and 19.75 nm (8x upscaled for PAINT images) 1.5x tube lens: 106 nm (raw) (widefield fluorescence only) <strong>fnet model (PAINT membrane images)</strong> The fnet 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained for 200,000 steps on 33 paired images (image dimensions: (512 x 512 px²), patch size: (128 x 128 px²)) with a batch size of 4, a learning rate of 0.0004, 10% validation split and 4x data augmentation (flipping and rotation). Model weights can be used with the ZeroCostDL4Mic fnet 2D notebook. <strong>CARE model (PAINT membrane images):</strong> The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained for 300 epochs (100 steps/epoch) on 33 paired images (image dimensions: 512 x 512 px², patch size: 256 x 256 px²) with a batch size of 4, a learning rate of 0.0004, 90/10% train/validation split and 4x data augmentation (flipping and rotation). Model weights can be used with the ZeroCostDL4Mic CARE 2D notebook or the CSBDeep Fiji plugin. <br> <strong>Author(s)</strong>: Christoph Spahn<sup>1,2</sup>, Mike Heilemann<sup>1,3</sup> <strong>Contact email</strong>: christoph.spahn@mpi-marburg.mpg.de <strong>Affiliation(s)</strong>: 1) Institute of Physical and Theoretical Chemistry, Max-von-Laue Str. 7, Goethe-University Frankfurt, 60439 Frankfurt, Germany 2) ORCID: 0000-0001-9886-2263 3) ORCID: 0000-0002-9821-3578
DOI: 10.5281/zenodo.5551153
title: DeepBacs – E. coli SIM prediction dataset and CARE model

description: Training and test images of live, membrane-labeled <em>E. coli </em>cells for prediction of SIM super-resolution images from widefield images, as well as a trained CARE model. Additional information can be found on this github wiki. The example image shows a widefield fluorescence image and SIM reconstruction of FM5-95 labelled, live <em>E. coli </em>cells. <strong>Training and test dataset</strong> <strong>Data type</strong>: Paired microscopy images (fluorescence) of low (widefield) and high resolution (SIM) <strong>Microscopy data type</strong>: Fluorescence microscopy (FM5-95) <strong>Microscope</strong>: GE HealthCare Deltavision OMX system (with temperature and humidity control, 37°C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence) <strong>Cell type</strong>: <em>E. coli </em>DH5α grown under agarose pads <strong>File format</strong>: .tif (16-bit for widefield images and 32-bit for SIM reconstructions) <strong>Image size</strong>: 1024 x 1024 px² (40 nm/px)<br> <strong>Image preprocessing</strong>: <em>E. coli</em> widefield images were scaled with a factor of 2 to match the SIM reconstruction pixel size. <strong>CARE model</strong> The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 300 epochs on 5500 paired image patches (image dimensions: (1024 x 1024 px²), patch size: (80 x 80 px²), 100 patches/image) with a batch size of 8 and a laplace loss function, using the CARE 2D ZeroCostDL4Mic notebook (v 1.12). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.1), numpy (v1.19.5), cuda (v 10.1.243). The training was accelerated using a Tesla P100GPU and data was augmented by a factor of 4 using rotation and flipping. Model weights can be used with the ZeroCostDL4Mic CARE 2D notebook or the CSBDeep Fiji plugin. <strong>Author(s)</strong>: Pedro Matos Pereira<sup>1,2</sup>, Mariana Pinho<sup>1,3</sup> <strong>Contact email</strong>: pmatos@itqb.unl.pt and mgpinho@itqb.unl.pt <strong>Affiliation</strong>: 1) Bacterial Cell Biology, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal 2) ORCID: https://orcid.org/0000-0002-1426-9540 3) ORCID: https://orcid.org/0000-0002-7132-8842
DOI: 10.5281/zenodo.5551152
title: DeepBacs – E. coli SIM prediction dataset and CARE model

description: Training and test images of live, membrane-labeled <em>E. coli </em>cells for prediction of SIM super-resolution images from widefield images, as well as a trained CARE model. Additional information can be found on this github wiki. The example image shows a widefield fluorescence image and SIM reconstruction of FM5-95 labelled, live <em>E. coli </em>cells. <strong>Training and test dataset</strong> <strong>Data type</strong>: Paired microscopy images (fluorescence) of low (widefield) and high resolution (SIM) <strong>Microscopy data type</strong>: Fluorescence microscopy (FM5-95) <strong>Microscope</strong>: GE HealthCare Deltavision OMX system (with temperature and humidity control, 37°C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence) <strong>Cell type</strong>: <em>E. coli </em>DH5α grown under agarose pads <strong>File format</strong>: .tif (16-bit for widefield images and 32-bit for SIM reconstructions) <strong>Image size</strong>: 1024 x 1024 px² (40 nm/px)<br> <strong>Image preprocessing</strong>: <em>E. coli</em> widefield images were scaled with a factor of 2 to match the SIM reconstruction pixel size. <strong>CARE model</strong> The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 300 epochs on 5500 paired image patches (image dimensions: (1024 x 1024 px²), patch size: (80 x 80 px²), 100 patches/image) with a batch size of 8 and a laplace loss function, using the CARE 2D ZeroCostDL4Mic notebook (v 1.12). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.1), numpy (v1.19.5), cuda (v 10.1.243). The training was accelerated using a Tesla P100GPU and data was augmented by a factor of 4 using rotation and flipping. Model weights can be used with the ZeroCostDL4Mic CARE 2D notebook or the CSBDeep Fiji plugin. <strong>Author(s)</strong>: Pedro Matos Pereira<sup>1,2</sup>, Mariana Pinho<sup>1,3</sup> <strong>Contact email</strong>: pmatos@itqb.unl.pt and mgpinho@itqb.unl.pt <strong>Affiliation</strong>: 1) Bacterial Cell Biology, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal 2) ORCID: https://orcid.org/0000-0002-1426-9540 3) ORCID: https://orcid.org/0000-0002-7132-8842
DOI: 10.5281/zenodo.5551141
title: DeepBacs – S. aureus SIM prediction dataset and CARE model

description: Training and test images of live, membrane-labeled <em>S. aureus </em>cells for prediction of SIM super-resolution images from widefield images, as well as a trained CARE model. Additional information can be found on this github wiki. The example image shows a widefield fluorescence image and SIM reconstruction of Nile Red labelled, live <em>S. aureus </em>cells. <strong>Training and test dataset</strong> <strong>Data type</strong>: Paired microscopy images (fluorescence) of low (widefield) and high resolution (SIM) <strong>Microscopy data type</strong>: Fluorescence microscopy (Nile Red) <strong>Microscope</strong>: GE HealthCare Deltavision OMX system (with temperature and humidity control, 37°C) equipped with an Olympus 60x 1.42NA Oil immersion objective and 2 PCO Edge 5.5 sCMOS cameras (one for DIC, one for fluorescence) <strong>Cell type</strong>: <em>S. aureus</em> strain JE2 grown under agarose pads <strong>File format</strong>: .tif (16-bit for widefield images and 32-bit for SIM reconstructions) <strong>Image size</strong>: 1024 x 1024 px² (40 nm/px)<br> <strong>Image preprocessing</strong>: <em>S. aureus</em> widefield images were scaled with a factor of 2 to match the SIM reconstruction pixel size. <strong>CARE model</strong> The CARE 2D model was generated using the ZeroCostDL4Mic platform (Chamier et al., 2021). It was trained from scratch for 300 epochs on 9400 paired image patches (image dimensions: (1024 x 1024 px²), patch size: (80 x 80 px²), 100 patches/image) with a batch size of 8 and a laplace loss function, using the CARE 2D ZeroCostDL4Mic notebook (v 1.12). Key python packages used include tensorflow (v 0.1.12), Keras (v2.3.1), csbdeep (v 0.6.1), numpy (v 1.19.5), cuda (v 10.1.243). The training was accelerated using a Tesla P100GPU and data was augmented by a factor of 4 using rotation, flipping and random zoom. Model weights can be used with the ZeroCostDL4Mic CARE 2D notebook or the CSBDeep Fiji plugin. <strong>Author(s)</strong>: Pedro Matos Pereira<sup>1,2</sup>, Mariana Pinho<sup>1,3</sup> <strong>Contact email</strong>: pmatos@itqb.unl.pt and mgpinho@itqb.unl.pt <strong>Affiliation</strong>: 1) Bacterial Cell Biology, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Oeiras, Portugal 2) ORCID: https://orcid.org/0000-0002-1426-9540 3) ORCID: https://orcid.org/0000-0002-7132-8842