dataesr/scientific-paragraphs-categorization huggingface dataset:
Spectra of cell viability under the same light irradiance level for all tested 10 nm wavelength bands (f) or after normalizing the irradiance levels to the solar spectrum reaching the retina (g). Viability was assessed by calcein staining after a 15 h-light exposure to 10 nm-wide bands of light and expressed as percentage of dark control condition. f n = 3 for 390, 460, 470, 510 nm, n = 4 for 410, 420, 450, 490, 500 nm, n = 5 for 400, 430, 440, 480, 630. g n = 5 for 390, 460, 470, 510, 520 nm, n = 7 for all other wavelengths. h Relative ratio of green M-opsin positive cone photoreceptors in darkness, or after exposure to different wavelengths with irradiance levels normalized to the solar spectrum reaching the retina as in g, n = 3. Data are expressed as mean ± SEM. Each 10 nm spectral band is designated by its central wavelength.
4-WD 0. 12, UPLSAPO 100X NA 1. 4 WD 0. 13. Control of the microscope and image acquisition were conducted using Olympus Fluoview software version 4. 2. Image acquisition was conducted at a resolution of 1024 × 1024 pixels, with a scan rate of 10 μs pixel -1. Images were acquired sequentially, line by line, in order to reduce excitation and emission crosstalk, and step size was defined according to the Nyquist-Shannon sampling theorem. Exposure settings that minimized saturated pixels in the final images were used. Twelve bit images were then processed with ImageJ or FIJI, Z-sections were projected on a single plane using maximum intensity under Z-project function and finally converted to 24 bits RGB color images. Figures were then assembled by using Adobe Photoshop CC and Adobe Illustrator software (Adobe, San Jose, CA, USA).
Statistics Experiments were repeated at least three times. Results for cell viability and cell type quantifications after light exposure were therefore the averaged values of at least three independent experiments (represented biological replicates). For cell viability measurement, each experiment was the mean of at least three technical replicates. The data were normalized to dark control and represented as mean ± SEM. Statistical analyzes were performed using GraphPad Prism 7. 03 Software (GraphPad Prism, La Jolla, CA, USA). One-way ANOVA followed by Dunnett's post hoc tests were used to compare variances of all groups. Differences between samples and dark control were considered to be significant when p < 0. 05 (*), p < 0. 01 (**), p < 0. 001 (***) or p < 0. 0001 (****).
Acknowledgements We thank  Arthur Planul  for artwork,  Kate Grieve  for carefully reading the manuscript,  Anaïs Potey  and  Elisabeth Dubus  for technical assistance. We thank the cell culture, imaging and HTS platforms at the  Institut de la Vision. We thank the  School of Surgery (Ecole de Chirurgie, Assistance Publique Hôpitaux de Paris)  for postmortem human ocular globes. We thank  Cheryl Mae Craft  ( University of Southern California ROSKI Eye Institute, Los Angeles, CA ) for providing the antibody (LUMIf-hCAR) for cone arrestin. This work was supported by  French  state funds managed by the  Agence Nationale de la Recherche  within the  Investissements d'Avenir program ,  RHU LIGHT4DEAF [ANR-15-RHU-0001 ], LABEX  LIFESENSES  [ ANR-10-LABX-65 ],  IHU FOReSIGHT  [ ANR-18-IAHU-0001 ], [ ANR-11-IDEX-0004-02 ].
Small-mammal neuroimaging offers incredible opportunities to investigate structural and functional aspects of the brain. Many tools have been developed in the last decade to analyse small animal data, but current softwares are less mature than the available tools that process human brain data. The Python package Sammba-MRI (SmAll-MaMmal BrAin MRI in Python; http://sammba-mri. github. io) allows flexible and efficient use of existing methods and enables fluent scriptable analysis workflows, from raw data conversion to multimodal processing.
INTRODUCTION The use of magnetic resonance imaging (MRI) methods in animals provides considerable benefits for improving our understanding of brain structure and function in health and diseases. The greatest advantages of preclinical MRI include group homogeneity and the opportunity to acquire a high amount of information repeated as needed. This added value, together with practical and ethical considerations, resulted in an increase of the use of small-mammal MRI in research. In human brain imaging, a large variety of high level software solutions is available for MRI preprocessing and analysis (e. g. , SPM 1 , FSL 2 , or AFNI 3 ). Less Free and Open Source Software (FOSS) are already available to analyse animal MRI. Atlas-based Imaging Data Analysis of structural and functional mouse brain MRI (AIDAmri)  (Pallast et al.
, 2019)  package allows registration of functional and diffusion mouse brain MRI with the Allen Mouse Brain Atlas (Allen Institute for Brain  Science, 2004; Lein et al. , 2007). The SAMRI (Small Animal Magnetic Resonance Imaging) package provides fMRI preprocessing, metadata parsing, and data analysis functions optimized for mouse brains  (Ioanas et al. , 2020). Today, there is still a need for other efficient and collaborative tools that would facilitate the adoption and dissemination of standardized pre-processing strategies for small animal MRI. Sammba-MRI was designed to process MR images, including anatomical, functional, and perfusion images. It allows to preprocess image dataset (conversion to NIfTI, bias correction), register images to templates or atlases, and perform perfusion measures.
Tools: Python Ecosystem and Neuroimaging Software Packages With its FOSS dependency stack and its growing neuroimaging community Python has been naturally the language of choice for our package. The scientific Python libraries used inSammba-MRI are NumPy  (Oliphant, 2006) , SciPy  (Millman and Aivazis, 2011) , the neuroimaging data analysis tools nibabel 4  , Nilearn  (Abraham et al. , 2014)  and Nipype  (Gorgolewski et al. , 2011). Visualization functionality depends on Matplotlib  (Hunter, 2007)  or Graphviz  (Gansner and North, 2000) , but neither is required to perform MRI data processing. Via Nipype, we utilize basic MRI preprocessing functions from AFNI  (Cox, 1996) , FSL  (Jenkinson et al. , 2012)  and ANTs  (Avants et al. , 2009)  packages. The dependency on the efficient but non open-source brain segmentation RATs tool  (Oguz et al.
, 2014)  is optional. More specifically, Sammba-MRI and the examples provided in its manual depends on the following libraries: Nipype ≥ 1. 0. 4; Nilearn ≥ 0. 4. 0; Numpy ≥ 1. 14; SciPy ≥ 0. 19; Nibabel ≥ 2. 0. 2; Sklearn ≥ 0. 19; matplotlib ≥ 1. 5. 1; nose ≥ 1. 2. 1; doctest-ignoreunicode; DICOM ToolKit package as well as FSL (version 5. 0), AFNI, ANTs, and RATS.
Code Design Sammba-MRI is developed within GitHub development platform  5. Coding guidelines follow the model of Nilearn and other successfully adopted packages (e. g. , Scikit-learn  Pedregosa et al. , 2011)  to make the codebase understandable and easily maintainable 6. Objects are used with parsimony: the different registration classes share all the same interface, and the brain extraction classes comply to the Nipype BaseInterface. Effort is made to keep the code uniformly formatted and to use consistent naming for the functions and parameters following the coding conventions of Nilearn. Preprocessing building blocks and pipelines are automatically tested on light MRI data samples to ensure code quality. Finally, the user is guided through Sammba-MRI with extensive documentation including installation instructions, application programming interface (API) reference, pipeline graphs, and practical examples based on publicly available small animal neuroimaging datasets.
DICOM to NIfTI Conversion Sammba-MRI allows to convert Bruker DICOM (digital imaging and communications in medicine) files to the standard Neuroimaging Informatics Technology Initiative format (NIfTI-1) and extracts extensive information using DCMTK package  (Eichelberg et al. , 2004). Bruker files conversion is an active development field, with various available tools handling DICOM (e. g. , dicomifier 7  ) or not (e. g. , bru2nii 8  , Bruker2nifti 9  , bruker2nifti 10  ). Finally, ParaVision 360 with the latest patch 1. 1 can export the NIFTI format since February 2019. Our implementation is meant to be a light helper function, allowing to handle the conversion on the fly. It has been tested only for Paravision 6 and a limited number of imaging sequences.
Bias Field Correction Intensity non-uniformity modeling is essential in preclinical studies because the intensity gradient corrupting MR images becomes particularly pronounced at high field strengths  (Boyes et al. , 2008). Sammba-MRI relies on AFNI's 3dUnifize to correct for intensity bias in anatomical images, and on N4BiasFieldCorrection function of the ANTs package  (Tustison et al. , 2010)  for the other modalities. 3dUnifize is also used to aid brain extraction, as detailed in the following paragraph.
Skull-Stripping Skull-stripping is a critical early step in processing MR images from small animals. Various automatic rodent-specific softwares  (Chou et al. , 2011; Oguz et al. , 2014)  or adaptations of human algorithms  (Wood et al. , 2013; AFNI's 3dskullstriprat)  are freely available for research purposes. We choose to rely on the LOGISMOS-based graph segmentation  (Yin et al. , 2010)  based on grayscale mathematical morphology RATS software  (Oguz et al. , 2014)  because of its good performance across a wide range of datasets  (Sargolzaei et al. , 2018). An alternative to the free but non-open source RATS tool is also available, based on an adaptation of the human histogram-based brain extraction method of Nilearn. This method can be used in any pipeline by setting the parameter use_rats_tool to False.
Because intensity inhomogeneity can hamper the performance of automatic skull-stripping, prior bias field correction is usually recommended  (Sled et al. , 1998)  and is performed by default with 3dUnifize. The helper function brain_segmentation_report from Sammba-MRI segmentation module allows to efficiently tune the initial intensity threshold used in bias correction by producing for a given set of thresholds 5 informative measures characterizing the extracted mask to bypass time consuming repetitive visual checks. The returned features consist of the total volume of the extracted mask, its anteroposterior length, its rightleft width, and its inferior-superior height as well as the sample Pearson's product-moment correlation coefficient between the brain mask image and its reflection with respect to the estimated mid-sagittal plane  (Powell et al.
PIPELINES Sammba-MRI proposes two ready-to-use pipelines to perform spatial registrations to a population or standard reference template as well as inter-modalities registration between anatomical, functional, or perfusion images. These pipelines have been tested throughout the different stages of their development process on various datasets from mice, rats and mouse lemurs and used in several publications from our lab  (Garin et al. , 2018 (Garin et al. , , 2019;; Nadkarni et al. , 2019). The two pipelines are called Coregistrator and TemplateRegistrator. All pipelines start with bias field correction for the individual images, involve skull-stripping and specific registration algorithms depending on image modality.
In all cases, it is better to only reorient the anatomical image to match the modality of interest. Finally, perslice-based registration is performed as described in section 2. 6. 2. Sammba-MRI was also designed to analyse ASL scans to perform perfusion measures. This analysis relies on Bruker-FAIR (Flow-sensitive Alternating Inversion Recovery) EPI sequences. Quantitative CBF maps are first estimated using perf_fair_nii_proc function from the modality_processor module. Then Sammba-MRI allows to preprocess functional ASL scans with the equilibrium magnetization maps (M0) used as the representative volume for registration. The M0 volume is aligned to the anatomical, first with a rigid body registration and then on a per-slice basis.
Group-Wise Registration, Registration of Anatomical Images to a Common Space, and Template Creation First, we evaluated group-wise registration and template creation using a dataset of in vivo T2-weighted images of 10 Sprague-Dawley rat brains  (Lancelot et al. , 2014). The scans were acquired using a 7. 0 T Bruker scanner at 100 × 100 × 500 µm resolution using 30 different slices. We used anats_to_common to register images from the different animals and create a group average template (Figure  3 ). For comparison purposes, the registration between images from each animal was also performed using algorithms from SPM8 12  with the SPMMouse toolbox 13   (Sawiak et al. , 2009) , a reference method for image-registrations. The brain images were segmented into gray (GM) and white matter (WM) tissue  probability maps using locally developed priors, then spatially transformed to a standard space.
Validation of Template Matching The Sprague-Dawley dataset is associated to brain segmentations into 28 regions for each animal  (Lancelot et al. , 2014). It also includes a study-template and an atlas based on segmentation of this template into 28 regions. Each image of the 12 individual animal was registered to the template using Sammba-MRI and the deformation fields were applied to the segmented images of each animal. We then measured the regional overlap between each region of the transformed atlases of each animal and the template-segmentation using Dice similarity coefficient (2 |A∩B| |A|+|B| ). The deformation fields calculated with SPM were also applied to the MR and segmented images of each animal. We also measured the regional overlap between each region of the SPM-transformed segmentations of each animal and the template-segmentation also using Dice similarity coefficient.
BIG DATA, REPRODUCIBILITY, COLLABORATION The package design facilitates big data exploration: the user is able to run an entire analysis in a single Python script. Rerunning pipelines are optimized through Nipype caching mechanism and long lasting steps (non-linear warping, perfusion fitting) are executed in parallel. We believe that reproducibility in the neuroimaging field is not possible without making the acquired images and the preprocessing code available to the community. For this reason, Sammba-MRI promotes the sharing of MRI data by providing utility functions to download public small animal brain MRI datasets and relies on it for demonstrating the package capabilities. In order to encourage external contributions, our library source code is hosted on the open collaborative GitHub platform and distributed under the CeCILL v2.
CONCLUSION By efficiently combining different existing human and animal neuroimaging tools, Sammba-MRI allows to tackle common processing issues in a fully automated fashion. High quality spatial registration can be easily performed, including template matching, between modalities registration as well as the creation of cohort-specific templates. Sammba-MRI also implements functional and perfusion MRI preprocessing methods and cerebral blood flow estimation for FAIR perfusion images. Emphasis is put on code readability and ease of use to favor contributions from the community.
Electrophysiological Recordings Brainstem preparations recovered for 40 mins in oxygenated aCSF (1. 8% CO 2 + 98. 2% O 2 ) before experiments commenced. Bursts of respiratory-related motor activity were recorded simultaneously from CNs V and X using suction electrodes constructed from borosilicate glass (0. 84 mm i. d. ; Stoelting Instrument vertical microelectrode puller, USA). Neural signals were amplified (gain = 10,000) and filtered (low cut-o↵: 10 Hz; high cut-o↵: 1 kHz) using a di↵erential AC amplifier (model 1700; AM systems, USA). Resulting signals were digitized using a DI-720 data acquisition system at a conversion of 2500 Hz (Dataq Instruments, USA). Once a stable neural signal was achieved, 20 mins of baseline activity was recorded during perfusion with oxygenated aCSF. The perfusion was then switched to aCSF equilibrated with either a hypoxic gas mixture (1.
8% CO 2 + 98. 2% N 2 ; pH 7. 9 ± 0. 15) or hypercapnia (5% CO 2 + 95% O 2 ; pH 7. 33 ± 0. 04), for 20 mins. We have previously shown that the respiratory motor rhythm of tadpoles is chemoresponsive to hypoxia, while adults respond to hypercapnia. These data informed our decision to use a hypoxic perfusate for tadpoles and a hypercapnic perfusate for adults. Note that in previous experiments, we used an oxygen micro-electrode (MI-730; Microelectrodes Inc. NH, USA) to determine the definite oxygen level within the recording chamber resulting from the mixing of room air with the hypoxic perfusate; this was typically 5-7% O 2. All solutions were maintained at room temperature (21-23 C).
Amplitude maps, amplitude and oscillation profiles of lung and buccal bursts For the 8 selected S 10 signals, we analysed the temporal grid inside the lung and buccal bursts and compared them to each other: first within the same neurograms and then between neurograms of the same preparation (i. e. we compared baseline with the stimulated condition). The results of the lung burst analysis, performed using the same processing tools applied to the buccal bursts, are presented in Fig. 9. Data for the 4 buccal and lung S 10 signals are presented in vertical pairs (Fig. 9A , baseline; Fig. 9B , stimulated condition). The same reference cycle (the cycle most correlated with all the buccal and lung bursts in the baseline condition) was used for the alignment process of the cycles recorded from a given preparation (Tad1, Tad 2, Adult 1 Adult 2).
Preprocessing The continuous MEG recording was noise-reduced using Maxfilter's SSS correction on the raw data, bandpass-filtered between 0. 5 and 40 Hz using MNE-Python's default parameters with firwin design  (Gramfort et al. , 2014)  and downsampled to 250 Hz. Epochs were then segmented between À300 ms and +1500 ms relative to stimulus onsets. After coregistering the MEG sensor data with subjects' structural MRI and the head position coils, we computed the forward model using a 1-layer (inner skull) boundary element model, for each subject separately and fit a minimum-norm inverse model (signal to noise ratio: 3, loose dipole fitting: 0. 2, with normal orientation of the dipole relative to the cortical sheet) using the noise covariance across sensors averaged over the pre-stimulus baseline across trials.
Acknowledgements This project received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement No  660086 , the  Bettencourt-Schueller Foundation , the  Fyssen Foundation , the  Philippe Foundation , the  Abu Dhabi Institute   G1001 ,  NIH   R01DC05660  and the  Dingwall Foundation. We are infinitely grateful to  Stanislas Dehaene , as well as  David Poeppel  and  Alec Marantz , for their support. We thank  Michael Landy  for his very helpful and generous feedback on a previous version of the manuscript.
Data collection The following demographic data were collected at baseline: age, gender, weight, height, occupation, and tobacco use. Medical history included: respiratory allergy, chronic disease, asthma, immunosuppression, diabetes, long-term corticosteroid treatment, obesity, rhinologic history (head trauma, chronic rhinosinusitis, olfactory dysfunction history), acute anosmia history (date and onset (sudden or progressive), severity of associated olfactory symptoms (parosmia, phantosmia), taste disorders (ageusia/dysgeusia, sweet, salty, sour, bitter taste recognition), duration and treatment received (nasal irrigation, local or general corticosteroids, antibi-otics…); other ENT symptoms. Data specifically covering COVID-19 included history and chronology of symptoms; severity (hospitalization/outpatient management) and treatment; Results for SARS-COV-2 by RT-PCR assay on nasopharyngeal swabs and contact with a confirmed case (by positive RT-PCR) as previously described.
Statistical analysis Baseline demographic and medical characteristics (symptoms, history of the disease, ENT and general history) of the patients were described by n (%) for categorical data and mean (standard deviation SD) or median (inter-quartile range IQR) for continuous data as appropriate. We determined the proportion of patients with positive SARS-COV-2 RT-PCR status among patients with acute ( < 7 days) decreased sense of smell. 95% Confidence Intervals are displayed using binomial assumption. For diagnostic accuracy of self-reported symptoms smell loss and/or taste for COVID-19 diagnostic, sensitivity, specificity, Positive and Negative Predictive Value and their Confidence Intervals were calculated and compared to that of other symptoms (cough, headache, sore throat) based all the population of all patients screened for COVID-19 during the same period in the Parisbased screening center for health professionals and government employees.
However, in the second part of the study performed in one of the participating center, we included all consecutive patients with a suspicion of COVID, with or without loss of smell and confirmed the high predictive value and specificity of self-reported smell and taste loss for the diagnosis of COVID-19, than other usual symptoms such as cough, headache and sore throat. No data was reported in the current literature about diagnostic value of loss of smell/taste or other symptoms of COVID-19. The positive predictive value is also impacted by the prevalence of COVID-19 which could evolve over time and settings. Finally, definite diagnosis of COVID-19 infection was only based on RT-PCR and it is possible that, if performed, chest CT-scan would have added more patients in the COVID-19 positive group.
Acknowledgment The authors would like to thank  Pr Sebastien Gallien ,  Henri Mondor Hospital ,  Pr Stéphane Jaurreguiberry  and  Dr Tran Khai Nhung ,  Bicêtre Teaching Hospital, Le Kremlin Bicêtre France. We thank all patients who participated in the study and nurses/doctors who performed the nasopharyngeal samples. We thank  Felicity Neilson  for linguistic revision.
This work was supported by the European Research Council (ERC) Synergy Grant Scheme (holistic evaluation of light and multiwave applications to high resolution imaging in ophthalmic translational research revisiting the helmholtzian synergies, ERC Grant Agreement 610110) and by French state funds managed by the Agence Nationale de la Recherche (ANR) within Programme Investissements d'Avenir, Laboratoire d'Excellence (LABEX) LIFESENSES (ANR-10-LABX-0065) and Institut Hospitalo-Universitaire FOReSIGHT (ANR-18-IAHU-0001).
ACKNOWLEDGMENTS. This work was supported by the European Research Council (ERC) Synergy Grant Scheme (holistic evaluation of light and multiwave applications to high resolution imaging in ophthalmic translational research revisiting the helmholtzian synergies, ERC Grant Agreement 610110) and by French state funds managed by the Agence Nationale de la Recherche (ANR) within Programme Investissements d'Avenir, Laboratoire d'Excellence (LABEX) LIFESENSES (ANR-10-LABX-0065) and Institut Hospitalo-Universitaire FOReSIGHT (ANR-18-IAHU-0001).
Intra-subject variability and stop signal reaction time ISV and SSRT were obtained by behavioral data for the stop signal fMRI task (42) (n=1,846). ISV was estimated by the standard deviation of reaction time in successful GO trials. SSRT was estimated by subtracting the mean stop signal latency from the mean correct go response time. Participants who had fewer than 50% correct hits and who had negative SSRT were excluded.