dataesr/scientific-paragraphs-categorization huggingface dataset:
Anatomical MRI Linear Processing of T1-Weighted MR Images (t1-linear) The t1-linear pipeline performs a set of steps in order to affinely align T1w MR images to the MNI space using the ANTs software package  (Avants et al. , 2014, p. 201). These steps include: bias field correction using N4ITK  (Tustison et al. , 2010) ; affine registration to the MNI152NLin2009cSym template 17 https://pypi. org/project/clinica/ (Fonov et al. , 2009 (Fonov et al. , , 2011) )  in MNI space with the SyN algorithm  (Avants et al. , 2008) ; cropping of the registered images to remove the background. This pipeline was designed to be a prerequisite for the deeplearning-prepare-data pipeline. Processing of T1-Weighted MR Images for Volume Analyses Using SPM (t1-volume * ) The t1-volume * pipelines extract voxel-based anatomical features from T1w MR images.
Diffusion MRI DWI Pre-processing (dwi-preprocessing * ) The dwi-preprocessing * pipelines correct diffusionweighted MRI (DWI) datasets for motion, eddy current, magnetic susceptibility, and bias field distortions, assuming that the data have been acquired using an echo-planar imaging (EPI) sequence. Due to the heterogeneity in acquisitions of fieldmaps and techniques to correct magnetic susceptibility distortions, several pipelines are proposed. Currently, Clinica can handle DWI datasets with fieldmap data containing a phase-difference map (case "phase-difference map and at least one magnitude image" in the BIDS specifications 18  ) (dwi-preprocessing-using-fmap) and DWI datasets with no extra data (dwi-preprocessing-using-t1), which is the case of the public Alzheimer's Disease Neuroimaging Initiative (ADNI) 19  dataset for instance.
In all cases, motion and eddy motion corrections are performed with the FSL software  (Jenkinson et al. , 2012)  using the eddy tool  (Andersson and Sotiropoulos, 2016)  with the replace outliers (--repol) option  (Andersson et al. , 2016)  while bias field is corrected with the ANTs N4 bias correction  (Tustison et al. , 2010). Regarding susceptibility correction, the FSL prelude/fugue tools were used for the dwi-preprocessing-using-fmap pipeline and the ANTs SyN registration algorithm  (Leow et al. , 2007; Avants et al. , 2008)  for the dwi-preprocessing-using-t1 pipeline. The outputs of the pipelines are the corrected DWI datasets and a brain mask of the b = 0 image. These pipelines are prerequisites for the dwi-dti and dwi-connectome pipelines.
Computation of Fiber Orientation Distributions, Tractogram, and Structural Connectome (dwi-connectome) The dwi-connectome pipeline computes a weighted graph encoding anatomical connections between a set of brain regions from corrected DWI datasets. To that aim, it relies on the MRtrix3  (Tournier et al. , 2012)  software to compute the constrained spherical deconvolution diffusion model  (Tournier et al. , 2007) , perform probabilistic tractography  (Tournier et al. , 2010)  and computes a connectome using the Desikan and Destrieux atlases from FreeSurfer  (Fischl, 2012). Its main outputs are the diffusion model, the whole-brain tractography, and the connectivity matrices.
Surface-Based Mass-Univariate Analysis With SurfStat (statistics-surface) The statistics-surface pipeline performs statistical analysis on surface-based features using the GLM. To that aim, the pipeline relies on the Matlab toolbox SurfStat designed for statistical analyses of univariate and multivariate surface and volumetric data using the GLM  (Worsley et al. , 2009). Surface-based measurements are analyzed on the FsAverage surface template from FreeSurfer. The pipeline can handle cortical thickness from the t1-freesurfer pipeline or PET measurements from the pet-surface pipeline. Currently, statistical analysis includes group comparison and correlation. The main outputs are p-value maps with different corrections for multiple comparisons (at the cluster or the vertex level using random field theory, or using the false discovery rate) and a JSON file summarizing the specified model.
They need to be used within Python code, for instance within a notebook (an example of such notebook is provided at: https://github. com/aramis-lab/AD-ML/blob/ master/Generic_Version/Experiments. ipynb). The outputs are: the estimated model parameters, the optimal value of the hyper-parameters (if any), a set of metrics regarding the classification performance (balanced accuracy, AUC, accuracy, sensitivity, specificity. ) as well as the predicted class for each subject thereby allowing to calculate any additional performance metric. More details regarding the different modules and a description of the way they can be used to perform reproducible evaluation of classification methods in Alzheimer's disease can be found in  Samper-González et al. (2018)  and its dedicated repository. 20 patially-Regularized Support Vector Machine (machinelearning-prepare-spatial-svm) The machinelearning-prepare-spatial-svm pipeline allows the preparation of T1w MRI and PET data to perform classification with an SVM with spatial and anatomical regularization  (Cuingnet et al.
Training and Validation of Deep Learning Models (ClinicaDL) The training and validation of deep learning models based on Clinica outputs can be performed using a dedicated Python library: ClinicaDL 21  This extension of Clinica contains essential features for deep learning application to 3D medical images: -modules to split data avoiding data leakage, which is a major problem in the domain  (Wen et al. , 2020) ; -a training method for autoencoders, CNN, and a multi-CNN framework which allows the use of other networks trained with ClinicaDL for transfer learning; -a testing function to evaluate the performance of classifiers on independent test sets; -saliency maps generation  (Simonyan et al. , 2013)  extensively used to interpret the outputs of deep learning networks; -basic network architecture search tools, such as random search utilities and methods to generate trivial synthetic datasets for architecture debugging.
CLINICA UTILITIES Conversion of Neuroimaging Datasets Into a BIDS Hierarchy (clinica convert) Clinica provides tools to curate several publicly available neuroimaging datasets and automatically convert them into the BIDS standardized data structure. This section explains what the user needs to download prior to running the converter and the rationale behind the selection of data when multiple acquisitions or pre-processing steps are available. For all converters, the user only needs to download the dataset. All subsequent conversion steps are performed automatically (no user intervention is required) and use parallelization for faster processing. For further details, the reader can refer to  Samper-González et al. (2018). Clinica currently provides converters for the following studies: ADNI, AIBL, NIFD, and OASIS.
Conversion of the ADNI Dataset to BIDS (adni-to-bids) The ADNI to BIDS converter requires the user to have downloaded all the ADNI study data (tabular data in CSV format) and the imaging data of interest. Note that the downloaded files must be kept exactly as they were downloaded. The imaging modalities currently being converted to BIDS include T1w MRI, FLAIR, DWI, fMRI, FDG PET, PiB PET, Florbetapir (AV45) PET, and Flortaucipir (AV1451) PET. Clinical data are also converted to BIDS. They include data that do not change over time, such as the subject's sex, education level, or diagnosis at baseline, as well as session-dependent data, such as the clinical scores. The clinical data being converted are defined in a spreadsheet that is available with the code of the converter. The user can easily modify this file if they want to convert additional clinical data.
Conversion of the AIBL Dataset to BIDS (aibl-to-bids) As for ADNI, the AIBL to BIDS converter requires the user to have downloaded the AIBL non-imaging data (tabular data in CSV format) and the imaging data of interest. For each AIBL participant, the T1w MRI and the Florbetapir, PiB, and Flutemetamol PET images are converted. As for the ADNI converter, clinical data converted to BIDS are defined in a spreadsheet available with the code of the converter, which the user can modify. Conversion of the NIFD Dataset to BIDS (nifd-to-bids) As for ADNI, the NIFD to BIDS converter requires the user to have downloaded the NIFD imaging data alongside the corresponding clinical data in CSV format. For each NIFD participant, the T1w MRI, FLAIR, PiB PET, and FDG PET images are converted. The clinical data conversion is as described in the previous sections.
Conversion of the OASIS Dataset to BIDS (oasis-to-bids) As for ADNI, the OASIS to BIDS converter requires the user to have downloaded the OASIS-1 imaging data and the associated CSV file. For each subject, among the multiple T1w MR images available, we select the average of the motioncorrected co-registered individual images resampled to 1 mm isotropic voxels. The clinical data are converted as described in the previous sections.
Syntax to Run the Converters After having downloaded the clinical and imaging data of one of these studies, the conversion of a dataset into BIDS is performed using the following syntax: clinica convert <dataset>-to-bids dataset_directory clinical_data_directory bids_directory where <dataset>-to-bids can be adni-to-bids, aibl-to-bids, nifd-to-bids or oasis-to-bids.
Data Handling Tools (clinica iotools) We also propose a set of tools that allows the user to handle BIDS and CAPS datasets. For the moment, there are five different commands: center-nifti: This command takes a BIDS directory as input and outputs the same BIDS with centered NifTI: the NifTI headers are modified to set the origin of the coordinate system at the center of the image. This correction is crucial for SPM which is especially sensitive to NifTI files whose origin does not correspond to the center of the image. check-missing-modalities: This command checks missing modalities in a BIDS directory. check-missing-processing: This command checks the outputs in a CAPS directory. create-subjects-visits: This command generates a list of subjects with their sessions based on a BIDS directory and stores the outputs in a TSV file.
USAGE EXAMPLE In this section, we propose to show how Clinica can be used to perform a group comparison of FDG PET data projected on the cortical surface between patients with Alzheimer's disease and HCs from the ADNI database. An illustrative summary of this example can be found in Figure  3. To download the ADNI dataset, it is necessary to register to the LONI Image & Data Archive 24 , a secure research data repository, and request access to the ADNI dataset through the submission of an online application form. Both the imaging and clinical data need to be downloaded, each to a folder that we will call imaging_data_dir and clinical_data_dir, respectively. The following command can be used to convert the T1 and FDG PET data of the ADNI dataset into BIDS: clinica convert adni-to-bids imaging_data_dir clinical_data_dir ADNI_BIDS -modalities T1 PET_FDG where the ADNI_BIDS folder contains the conversion of ADNI into BIDS.
DISCUSSION We proposed a software platform that aims at making clinical neuroscience easier and more reproducible. Clinica automates the processing of pipelines involving several neuroimaging modalities (currently, anatomical MRI, diffusion MRI, and PET) as well as statistics, machine learning, and deep learning tools. Additionally, Clinica provides tools to convert public neuroimaging datasets focused on dementia (ADNI, AIBL, OASIS, and NIFD) into the BIDS standard, and tools to handle raw (BIDS) and processed datasets. The use of the BIDS standard as the only prerequisite on the data and the unified command line interface across the pipelines ease the processing automation. The image analysis automation is also improved by the use of the CAPS hierarchy, which facilitates the chaining of pipelines.
, 2014). Some of these tools cover a variety of modalities while others focus on a specific one (diffusion MRI for Camino, Dipy, DTI-TK, and MRtrix; fMRI for AFNI; PET for PETPVC). However, performing a multimodal study can be difficult because one needs to combine tools from different packages. This results in complex pipelines which can be difficult to build, maintain, and distribute. Even when analyzing a single modality, one often wants to combine tools from different packages, thus facing similar difficulties. Combination of tools is made even more difficult by the fact that the input and output data are organized differently by each tool. Efforts of the community have alleviated several of these difficulties. The NeuroDebian community 26   (Halchenko and Hanke, 2012)  aims to provide and ease the installation of a large collection of software packages for the Debian distribution.
Unlike Clinica however, Brainlife is not available as a Python package. Additionally, both the upstream processing provided by Clinica (curation and transformation of common research dataset into BIDS) and downstream (preparation for ML pipelines) are not the focus of Brainlife. BrainSuite does not wrap existing tools but provides a set of innovative tools for the analysis of neuroimaging data. It can be executed using a GUI, a command line, a Nipype interface, or as a BIDS App. However, BrainSuite is limited to the processing of MRI images only. Flywheel is a cloud-based platform to capture, curate, and process medical data. In the spirit of Brainlife, it allows the composition of different components through a web-based interface. However, unlike Clinica, Flywheel is a proprietary software and does not share its source code with the community.
fMRIPrep combines software components using Nipype  (Gorgolewski et al. , 2011)  to provide a robust pipeline for pre-processing of fMRI data. It assumes input data to follow the BIDS standard  (Gorgolewski et al. , 2016)  and outputs are organized following the ongoing BIDS-derivatives initiative. As the name implies, fMRIPrep focuses on fMRI data. MIBCA is a toolbox focused on brain connectivity analysis using multimodal imaging. It is developed in MATLAB and provides pipelines for popular neuroimaging software. Pypes is probably the closest in spirit to Clinica: it also focuses on the integration of existing tools into a set of reusable pipelines built with Nipype. The user needs to specify a configuration file to describe the input data even for BIDS datasets. The output data will then follow the same structure as the input data and can be chained to other pipelines from Pypes.
Deep learning methods are also increasingly applied to neuroimaging. Even though such methods usually do not rely on pre-extracted features, they will still require some preprocessing and data conversion tools in order to use neuroimaging data in frameworks such as PyTorch. Clinica provides such tools. Reproducibility has been highlighted as a major challenge in many scientific fields including neuroimaging  (Poldrack et al. , 2017). This problem has also been highlighted in machine learning for healthcare in general  (McDermott et al. , 2019)  and for brain diseases in particular  (Samper-González et al. , 2018; Wen et al. , 2021). Clinica aims to make reproducible research easier to perform. To that purpose, it combines: (1) use of a community standard for inputs; (2) the definition of a standardized organization for outputs; (3) standardized ways to extract features; (4) extensive software testing.
As for the clinicians, we acknowledge that the use of a command line interface as well as the need to install third-party software may make it difficult for them to use Clinica. Our clinical collaborators were nevertheless able to successfully use Clinica but this is clearly a limitation. In the future, we aim to implement a visual dashboard that would simultaneously allow executing the pipelines and performing quality control (QC) as well as to ease or avoid the installation of third-party software. Clinica pipelines rely on third-party dependencies whose development and versioning are outside of our control. To avoid potential incompatibilities, each pipeline declares its (versioned) dependencies in the corresponding pipeline metadata, which are checked at runtime by Clinica. Nevertheless, the installation of third-party software may be difficult for the user and we acknowledge that this is a limitation.
We plan to add more advanced QC of outputs in the spirit for instance of MindControl  (Keshavan et al. , 2018). This is important in order to ease the QC of large datasets and to enforce the good practice of systematic QC among the users. Implementing an integrated QC system is among our priorities for the development of Clinica. The aim is to provide a visual dashboard that would allow the user to: (1) easily control which pipelines have been executed and whether they exited without error; (2) systematically review snapshots of the major outputs of each pipeline (together with typical examples of how a correct output should look like); (3) flag incorrect outputs so that they can be excluded from further statistical analysis. Another limitation is that, in order to use previously built templates, they currently need to be manually copied into the CAPS directory which is clearly suboptimal.
FUNDING The research leading to these results has received funding from the programs Investissements d'avenir ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6), ANR-11-IDEX-004 (Agence Nationale de la Recherche-11-Initiative d'Excellence-004, project LearnPETMR number SU-16-R-EMR-16), and.
Research in context Evidence before this study We searched PubMed, bioRxiv, and medRxiv for articles published in English from database inception up to Dec 31, 2019, before the COVID-19 pandemic, using search terms "pandemic" AND "microbial transmission" OR "transmission" AND "containment". 262 papers were identified, none of which met our inclusion criteria (ie, a study that described large-scale containment measures implemented during a pandemic). Although strategies for containment and reducing transmission of an epidemic or pandemic pathogen have been well described in the literature, there is currently an evidence gap regarding the extent to which large-scale containment measures implemented during a pandemic reduce the burden of infectious diseases due to pathogens other than the one causing the pandemic.
Added value of this study We used existing laboratory data to address the effect of COVID-19 and associated containment measures on the incidence of invasive diseases caused by Streptococcus pneumoniae, Haemophilus influenzae, and Neisseria meningitidis, which are all transmitted via the respiratory route, with Streptococcus agalactiae as a non-respiratory comparator microorganism. We rapidly established an international network of laboratories in 26 countries and territories, compiled a large invasive disease dataset of more than 80 700 case records, analysed data for national policy decisions and containment measures in each country using the Oxford COVID-19 Government Response Tracker, and examined the movements of people during the early months of the COVID-19 pandemic using Google COVID-19 Community Mobility Reports.
Funding Given the severity of the diseases they cause, invasive infections (eg, meningitis and sepsis) are medical emergencies that are treated in hospital without delay. Clinical microbiology laboratories in many countries are required to report invasive infections due to S pneumoniae, H influenzae, and N meningitidis to national health authorities, and many also request referral of any isolates to reference laboratories for surveillance purposes. We established the Invasive Respiratory Infection Surveillance (IRIS) Initiative with a network of reference laboratories in 26 countries and territories to rapidly analyse laboratoryconfirmed invasive bacterial infection data during the COVID-19 pandemic. We compared the incidence of invasive bacterial infection with S pneumoniae, H influenzae, and N meningitidis during the COVID-19 pandemic with rates in previous years.
Study design and participants We approached microbiology laboratories with established invasive disease surveillance systems to join the IRIS network between April 30 and June 10, 2020. All IRIS labora tories were national reference laboratories except for the laboratories in Spain (representing the territory of Catalonia) and China (representing one institute in Beijing). Confirmed cases of invasive disease due to S pneumoniae, H influenzae, or N meningitidis (detected from a normally sterile site or from a patient with invasive disease) plus the sampling date were collected. All 26 countries or territories submitted data for S pneumoniae, and most countries or territories submitted data for H influenzae (24 laboratories) and N meningitidis (21 laboratories; appendix pp 2-3). Nine laboratories also submitted data for Streptococcus agalactiae (Lancefield group B streptococcus) as a control, non-respiratory pathogen.
Data collection No patient-identifiable data were collected. A private IRIS dataview was set up as part of the PubMLST suite of databases and IRIS participants were able to upload and view study data. 15 A subset of the French N meningitidis dataset was published previously. 16 Cases of S pneumoniae, H influenzae, N meningitidis, and S agalactiae diagnosed between Jan 1, 2018, and May 31, 2020, were analysed. Case counts were summed by the International Organization for Standardization (ISO) week. The Oxford COVID-19 Government Response Tracker (OxCGRT) collects information on policies and interventions that governments have implemented during the COVID-19 pandemic. 17 Data were collected from more than 180 countries for 18 different indicators, which were combined into composite indices that measured the magnitude of government responses.
The stringency index provided a combined estimate of the stringency of public information campaigns plus containment measures, including school closures, workplace closures, cancellation of public events, restrictions on gatherings, closures of public transport, stay at home requirements, restrictions on internal population movement (eg, recommendations not to travel between regions or cities), and international travel controls. The OxCGRT dataset was downloaded on Oct 5, 2020, and the daily stringency index data were converted to a mean ISO week value and merged with the IRIS case data, with zero indicating least stringent and 100 indicating most stringent. The Google COVID-19 Community Mobility Reports provide anonymised, aggregated within-country data for the movement of people by capturing mobile device location history data from Google users in six categories (ie, grocery and pharmacy, parks, transit stations, retail and recreation, residential, and workplaces).
Data that could identify individuals are not available from Google CCMR. Daily mobility data were calculated as a percentage change from the Google CCMR baseline day, which was the median value from the 5-week period from Jan 3 to Feb 6, 2020. Google COVID-19 Community Mobility Reports will potentially underestimate mobility changes for countries that experienced widespread COVID-19 disruption during the baseline period; however, we expect that only East Asian countries and territories would be affected (ie, China, Hong Kong, and South Korea). Google COVID-19 Community Mobility Reports data were downloaded on Oct 5, 2020.