dataesr/scientific-paragraphs-categorization huggingface dataset:
For all data, T1weighted Magnetic Resonance Imaging (MRI), sex and age at image acquisition were available. For the test datasets, Fluid Attenuated Inversion Recovery (FLAIR) MRI and results from the Symbol Digit Modalities Test (SDMT)  (7)  were available. The SDMT is a brief test, designed to measure information processing speed, that is attractive for its psychometric properties  (17) , quick administration  (8)  and its capability of predicting scores on other cognitive tests  (18). Finally, MS_test data also contained EDSS, disease duration and type of MS. A summary of all data is available in table 1.
B. Ethics. Participants of the BRUMEG study, Brussels, all provided their written informed consent prior to MRI assessment. The study protocol (B. U. N. 143201423263) was approved by the ethical committee of the UZ Brussel (Commissie Medische Ethiek (O. G. 016), Reflectiegroep Biomedische Ethiek) on 2015/02/25. For the patients from Greifswald, Germany, the study was approved by the ethics committee of the Medical Faculty of the University of Greifswald (BB159/18), and all participants gave their written informed consent. HC_train data consists of publicly available data originating from other projects, listed in supplementary table S1. Ethical approval was received by each project separately. C. MRI preprocessing and brain age pipeline. Several preparatory steps were involved in the construction of our brain age model.
They are summarized below. • Brain MRI segmentation. From the HC_train dataset, we extracted 3D T1-weighted MRI, sex and chronological age. Next, the T1-weighted MR images were evaluated by the FDA cleared icobrain software (version 4. 4) of icometrix NV (Leuven, Belgium). This is an end-to-end automatic software that segments and subsequently quantifies distinct regions of the brain. For a comprehensive description of the pipeline, originally published in Jain et al. 2015  (19) , we refer to Golan et al 2020  (20). The pipeline relies on T1-weighted MR images, and when available, also uses FLAIR images to segment white matter lesions in the brain. These lesions are filled in the T1 image with white matter intensities, and the T1 image is subsequently segmented by fitting a probabilistic model for grey matter, white matter and cerebrospinal fluid image intensities.
However, an important hurdle in the path of brain age models to clinical practice is nicely illustrated by a statement in Ribeiro et al 2016: "if the users do not trust a model or a prediction, they will not use it"  (24). First, to maximise trust of the MS clinician in our model, we used a model that is based on simple linear regression. The MS clinician has been familiarised with this method by decades of research adopting it for various purposes, e. g. studying the relation between MRI and cognitive performance in multiple sclerosis  (25, 26). This contrasts with other studies on brain age in MS, mostly adopting models that are common in machine learning, but not in clinical research, such as Gaussian processes regression  (13)    that our results were not driven by this methodological issue, we recalculated brain age in the MS_test dataset on the same set of features, except that lesion volume was subtracted from white matter and whole brain volume.
We first calculated raw brain age on the HC_test and MS_test dataset by calculating the weighted sum of the features (i. e. sex and brain volumes from icobrain) with their respective weight, which were then corrected by using the correction formula of preparatory step 3. In the remainder of this paper, "brain age" consistently refers to the corrected brain age. To test the complexity of the brain age measure, we compared it to the first principal component (P C 1 ) of the feature space in the MS_test data by means of Pearson correlation.
Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article:  Stijn Denissen  is funded by a Baekeland grant appointed by  Flanders Innovation and Entrepreneurship  (HBC. 2019. 2579,www. vlaio. be), Guy Nagels  received research grants from  Biogen  and  Genzyme , and is a senior clinical research fellow of the FWO Flanders (1805620N, www. fwo. be), and  Jeroen Van Schependom  is a senior research fellow of VUB.
We first calculated raw brain age on the HC_test and MS_test dataset by calculating the weighted sum of the features (i. e. sex and brain volumes from icobrain) with their respective weight, which were then corrected by using the correction formula of preparatory step 3. In the remainder of this paper, "brain age" consistently refers to the corrected brain age. To test the complexity of the brain age measure, we compared it to the first principal component (P C 1 ) of the feature space in the MS_test data by means of Pearson correlation. D. Statistical Analyses. Statistical analyses and visualiza-tions were performed in Python and R. Significance level alpha was set to 0. 05 for all reported test results. Results A. The brain age pipeline. Linear regression between brain age, obtained with 10-fold cross-validation on HC_train, and chronological age yielded the following cor- rection weights: β (13)nel A) of the final brain age model, i.
e. establishing the final feature weights on the full HC_train data using ridge regression. Next, we investigated whether the learned weights generalise to other datasets as well (figure1, panel B). Where Pearson correlation was used, the distributions of the variables were checked for normality with a Shapiro-Wilk test. Raincloud plots were generated with use of the PtitPrince package(23). First, to compare brain age, BPAD and chronological age distributions between MS_test and HC_test, we used a Mann-Whitney U test. To compare BPAD of both test sets with 0, we used a one-sample Wilcoxon signed rank test. Second, we calculated the error of predicting age from brain images with the mean absolute error (MAE) between true and predicted age for every dataset. Next, to establish the association between brain age and SDMT in the MS_test data, we used Pearson correlation.
To assess whether it contains unique information beyond chronological age, we used two approaches. First, we considered brain age and chronological age together in a multivariate linear regression equation: SDMT = β 0 + β 1 BrainAge + β 2 ChronologicalAge +. Second, previous literature used the "Brain-Predicted Age Difference" (BPAD)(13). It quantifies brain age overestimation by subtracting chronological age from brain age. It was calculated in our test sets from the corrected brain age values (figure1). Pearson correlation was used to establish the relationship between BPAD and SDMT in the MS_test data. 0 =8. 32, β 1 =0. 80. In the same dataset, the mean absolute error (MAE) between corrected brain age and chronological age was 7. 84 years. The result of the brain age correction was added to the supplementary material as figureS2.
In a next step, represented by panel A in figure1, we fitted a final brain age model by using all available HC_train data for training. The model's feature weights and lambdaD R A F TFig. 1. Brain age pipeline. The pipeline is subdivided in a Training phase (panel A) and a Testing phase (panel B), where "Train Data" refers to the HC_train data, and "Test Data" represents either the HC_test or the MS_test dataset. A silo-like shape represents a dataset, whereas green diamonds represent some kind of operation, specified by the text inside. Other text are either variables or images. valuecanbe consulted in table 2. To test its quality, we applied it to the HC_test and MS_test set, represented in figure1by panel B. The corrected brain age and BPAD values are summarised in table 3, and visually displayed for HC_test and MS_test in the raincloud plots of figure 2.
(27)extreme gradient boosting(12). Second, trust in the prediction of our model, i. e. predicted brain age, was enhanced by showing that brain age explains the majority of the variance in MRI-derived volumetric features and sex, used to train our brain age model. This observation is logical in light of what is known about the aging brain, shrinking as people get older(27). Our results imply that brain age has the potential to explain cognitive status in people with Multiple Sclerosis. Yet, the cross-sectional nature of the data limited us to investigate the potential of brain age to predict future decline in processing speed. Another limitation concerns the difference in segmen- tation method between the HC_train data and all test data. In HC_train, segmentation relied solely on T1-weighted images, whereas lesion filling in the T1-weighted image occurred in the test datasets with the use of FLAIR images.
1  ). This hypothesis was evaluated using diffusion Magnetic Resonance Imaging (dMRI) tractography in two successive samples of right-and left-handed subjects randomly selected from the Human Connectome Project (HCP) database. The dorso-posterior parietal hand control region (DPPC hand ) was used as the seed region of interest (ROI) for the tractography. This region was defined as the 95% confidence area of the parietal sites that disrupted hand movements in our previous per-operative study  ( Desmurget et al. , 2018 )  ( Fig. 1  ). To avoid ambiguity, it may be worth mentioning that this area is likely to be oversized, i. e. to be substantially larger than the parietal region truly involved in hand control. Indeed, DPPC hand was computed from a limited number of per-operative points ( n = 12) collected in brain tumor patients  ( Desmurget et al.
Material and methods The HCP is a unique dataset aiming to be used by the scientific community to shed light on the anatomical and functional connectivity of Fig. 1. Putative anatomy of the parietal inhibitory network. S1: primary somatosensory cortex. M1: primary motor cortex. DPPC: dorso-posterior parietal region. Confidence ellipsoid of DPPC hand was computed from the parietal sites where electrical stimulation was found to trigger a selective disruption of hand movements in a previous per-operative study  ( Desmurget et al. , 2018 ). The yellow border displays the 95% confidence border of this ellipsoid. the human brain  ( Glasser et al. , 2013 ; Van Essen et al. , 2013 ). Tractography analyses were carried out on dMRI multi-shell data of 50 righthanded subjects (22 to 35 years old; 31 females) randomly selected from the HCP database.
To evaluate the generality of the results obtained from this main sample and to evaluate the existence of potential hemispheric asymmetries related to handedness, we also considered an additional population of 25 left-handed subjects (randomly selected from the HCP database; 25 to 33 years old; 16 females). The 75 multi-shell data-sets (18 images with b = 0 s. mm-2 and 270 diffusion-weighted images with b = 1000, 2000 and 3000 s. mm-2 applied in 90 non-collinear diffusion directions) were acquired with an isotropic spatial resolution of 1. 25 mm and corrected for distortion artifacts. ( Sotiropoulos et al. , 2013 ). Tissue segmentations as well as cortical pial and white matter surfaces computed for each subject using FreeSurfer  ( Fischl, 2012 )  were taken from the structural HCP database.
Within this dataset, precentral (PreC) and postcentral (PostC) ROIs were defined from Destrieux parcellation  ( Destrieux et al. , 2010 ). The DPPC hand ROI was defined on each individual pial surface, in MNI coordinates, as the 95% confidence area of all the parietal sites that disrupted hand movements when electrically stimulated in our per-operative study  ( Desmurget et al. , 2018 ). Tractography analyses were performed from the parietal portion of the ellipse (i. e. after the small anterior fraction of the ellipse involving the postcentral gyrus was truncated) ( Figs. 1  and S1 ). For dMRI tractography, we used the state-of-the-art MRtrix3 software  ( Tournier et al. , 2019 ). First, for all tissue types (white matter (WM), grey matter (GM) and cerebrospinal fluid) we computed the response function estimations using an automated unsupervised algorithm  ( Dhollander et al.
To this end, for the right-hand sample, we superimposed our sDPPC hand /PostC and sDPPC hand /PreC bundles with the three subdivisions of the SLF tract, using the white matter atlas available through the XTRACT toolbox  ( Warrington et al. , 2020  ; see methods). As illustrated in Fig. 6  , the longitudinal portion of the sDPPC hand /PostC and sDPPC hand /PreC bundles closely follow the SLF tract. 75% of the overlap occurs with SLF-II. This is much more than the percentages found for SLF-III (22 %) and SLF-I (3%). Anatomically, SLF-II originates from the posterior-inferior parietal region and predominantly targets the dorsal premotor cortex and dorsolateral prefrontal cortex  ( Catani and Thiebaut de Schotten, 2012 ; Parlatini et al. , 2017 ; Schmahmann and Pandya, 2006 ). This tract has been associated with various functions, including motor control, spatial attention and some aspects of language production  ( Hecht et al.
Funding This work was supported by the  Centre National de la Recherche Scientifique (CNRS) , the  Agence Nationale de la Recherche  (  ANR-16-CE37-0017-01  ) and the  Laboratory of Excellence programs CORTEX  ( ANR-11-LABX-0042   Université de Lyon)  and  TRAIL  ( ANR-10-LABX-57   Université de Bordeaux ).
Thrashing assays For body bends frequency (BBF) measurements, 1-day-old or 13-day-old worms prepared as described in "ageing cohorts" were gently transferred into a 12-wells cell culture plate (ten worms per well) containing 1. 5 mL of 2 % agarose in M9 buffer (3 g KH2PO4, 6 g Na2HPO4, 5 g NaCl, 0. 25 g MgSO4•7 H2O, and distilled water up to 1 liter) and 2 mL of M9 buffer per well. Two minutes after transfer, animal movements were recorded for 30 s. BBF was then quantified using the open-source wrMTrck plugin on Fiji (ImageJ (v2. 0. 0)) software.
Microscopy and image processing For all observations, animals were mounted on 2 % agarose dry pads with 4 % polystyrene beads (Polybeads, Polysciences) in M9 buffer. For images corresponding to Figure  1a , 2, 5 and S1, worms were observed using an Andor spinning disk system (Oxford Instruments) installed on a Nikon-IX86 microscope (Olympus) equipped with a 40x/NA 1. 3 and a 60×/NA 1. 42 oil-immersion objectives and an Evolve electron-multiplying charge-coupled device camera. Each animal was imaged with IQ software (APIS Informationstechnologien) as a stack of optical sections (0. 3 μm apart) across the whole thickness of the worm (Figure  1a  and S1a) or across a specific tissue (Figure  2  and 4 ). All images were processed using the Fiji (ImageJ) software and correspond to the sums of the same number of slices (for each strain with or without auxin) except for images of the intestine (Figure  2g  and S1h) which correspond to z projection of maximum intensity.
Statistical analysis All statistical analyses were performed with R version 4. 0. 1 (2020-06-06). The R Test Survival Curve Differences (package survival_3. 2-3) was used to analyze lifespan assays. This test is based on the Grho family of tests which makes use of the Kaplan-Meier estimate of survival  (Fleming et al. 1982). Thrashing assays were analyzed using the non-parametric Kruskal-Wallis Rank Sum Test, followed by Dunn's Test of Multiple Comparisons (package rstatix_0. 6. 0) with FDR adjusting method as post-hoc tests. To compare between different conditions for the mitochondria morphology assay a Fisher exact test was performed, followed by pairwise tests with FDR adjusting method as post-hoc tests (package rcompanion_2. 4. 1). For all tests compared samples were considered different when statistical test gave an adjusted P-value <0.
05 (*p<0. 05; **p<0. 01; ***p<0. 001), ns: non-significant. S1  for strain description and Table  S2  for detailed lifespan data, replicates and statistics. For the intestine (f), images were taken in apb-3(ok429) mutant background in order to reduce unspecific intestinal autofluorescence (arrows indicate the specific DAF-2::AID::mNG associated signal). In all images, the remaining staining of the gut after auxin treatment corresponds to nonspecific autofluorescence that varies between animals. Similar results were obtained in 7-day-old animals (data not shown). Scale bars: 20 µm. epidermis (e-f), intestine (g-h), neurons (i-j) or neurons and intestine (k). Numbers (ex: Muscle 1, Muscle 2) refer to distinct alleles driving TIR1 expression (See Table  S1  for complete strain description). For each condition, n is about 80 individuals.
Acknowledgement We thank the  Caenorhabditis Genetic Center  (which is funded by  NIH Office of Research Infrastructure Programs ,  P40 OD010440 ) for strains. We are grateful to  Laure Granger  for technical assistance. This work was supported by the  European Research Council  (ERC_Adg C. NAPSE # 695295 ), within the framework of the  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon  operated by the  French National Research Agency (ANR). CR was supported by a PhD fellowship from the  French Ministry of Higher Education and Fondation de la recherche médicale  ( FDT202106012780 ). Biorender. comimages were used to generate the original graphical abstract.
Moreover, the combination of visual stimulations and olfactory training may improve recovery results  [9]. No data about olfactory training for persistent olfactory dysfunction are available on patients with SARS-CoV-2 infection and persistent olfactory dysfunction, but most patients who experience hyposmia or anosmia for 30 days or more seem to have a low rate of spontaneous recovery  [4]. In order to quantitatively study the time course of olfactory scores during olfactory training in real life, we developed a web application dedicated to olfactory training and visual stimulations as well as the self-assessment and follow-up of olfactory scores. We assessed the results in a real-life observational study.
Methods The web application users were recruited via a national media campaign in France that was disseminated through social media, radio, and magazines between January 30 and  February 15, 2021. This observational, data-based study was approved by the French National Health Data Institute, which reviews the ethical conduct of human subjects research, data confidentiality, and safety. To participate, individuals were required to connect to the free covidanosmia. eu web application and provide electronic agreement. Respondents anonymously self-entered sociodemographic data and real-time polymerase chain reaction test results and confirmed a diagnosis of SARS-CoV-2-related olfactory dysfunctions. Patients were also asked to complete items about comorbidities, the duration of olfactory symptoms, and the self-assessed intensity of olfactory dysfunction by providing subjective ratings with a visual analogue scale of 0 (no smell) to 10 (no smell alteration)  [10].
They were asked to keep a daily diary on the web application, in which they rated their overall olfactory abilities for each oil with subjective ratings on a visual analogue scale. We assessed the rate of self-assessed improvement in overall olfactory function along with training times by using data that were collected anonymously from the web application diaries of patients. Improvement was defined as an increase of ≥2 points on the olfactory visual analogue scale. The analysis was performed when the mean olfactory training time of the study population was at least 4 weeks and when at least 500 patients were assessable for primary outcome assessment. Categorical variables were summarized by using frequencies and percentages, and chi-square tests or Fisher exact tests were used to make comparisons.
There was no control group in this study; therefore, it remains unclear whether the incidence of spontaneous recovery distorted the results. The scale that was used to measure olfactory dysfunction and changes was subjective, as it was a self-assessment analogue scale; scores were self-reported and data about olfactory assessment were not confirmed by physicians and objective tests. However, the possibility of conducting olfactory training at home increased the number of recruited patients and resulted in higher levels of olfactory function recovery compared to those of spontaneous improvement. Olfactory training and visual stimulation assisted by a dedicated web application was associated with significant olfactive improvement in persistent olfactory dysfunction following SARS-CoV-2 infection, especially after 28 days of olfactory training.
Acknowledgments We thank the users for their participation in this study as well as  Magali Balavoine , MSc, from  Weprom in Angers, France , and the anosmie. orgpatients' association. No one received compensation for their contributions. The sponsor (Weprom) designed and conducted the study; collected, managed, analyzed, and interpreted the data; prepared, reviewed, or approved the manuscript; and made the decision to submit the manuscript for publication.
Animals were scanned before and 2 days after LPS administration, and the difference in SUV uptake was estimated with voxel-wise statistical parametric mapping. This revealed a striato-cortical cluster, which surprisingly reflected a significant decrease of the TSPO-PET signal rather than an increase. As this result was not further supported by any ex vivo or in vitro data, additionally considering both the limitations of the SUV measure in the absence of any blood data and the low specificity of [ 11 C]-R-PK11195, interpretation of these data needs to be considered cautiously. A far more complete study in the rat brain was performed by Schmidt et al. , who evaluated the time and regional course of the [ 11 C]PBR28 TSPO-PET signal after a systemic (i. p. ) LPS challenge (3 mg/kg) in the context of a genetic risk factor for Parkinson's disease (more details in the paragraph "Parkinson's disease" below)  [22].
Models of multiple sclerosis The PubMed search used for this paragraph was "(omega* OR TSPO OR PBR OR PK11195) AND (PET OR positron) AND (brain OR cereb*) AND (multiple AND sclerosis) AND (rat OR mouse OR mice OR animal* OR experimental OR pig OR gerbil OR rabbit OR guinea OR model OR dog OR cat) AND (English[ lang])"; it returned 54 references, including 23 reviews; once Molecular Imaging and Contrast Agent Database entries, clinical studies and reviews were excluded, 11 preclinical studies were kept for this review. All these studies are summarized in Table  7. Various studies investigated the neuroinflammatory profile of different models of MS (EAE or WM lesion) using [ 18 F]PBR111  [188] , [ 18 F]VC701  [189] , [ 18 F]GE-180  [190] [191] [192]  or [ 18 F]DPA-714  [193, 194]  PET tracers.
Study setting {9} Four clinical sites in four countries will participate: the Department of anesthesiology of the Cliniques universitaires Saint-Luc of the Université catholique de Louvain in Belgium (Principal Investigator: Patricia Lavand'homme), the Center for Biomedicine and Medical Technology Mannheim (CBTM) of the University of Heidelberg in Germany (Principal Investigator: Rolf-Detlef Treede), the Department of Neurology and Psychiatry of the Sapienza University of Rome in Italy (Principal Investigator: Andrea Truini), and the Nuffield Department of Clinical Neurosciences (NDCN) of the University of Oxford in the United Kingdom (Principal Investigator: Irene Tracey). Details on the study sites can be obtained on the EudraCT clinical trials register (2019-001204-37). All sites are academic hospitals and/or academic laboratories conducting research in human volunteers.