dataesr/scientific-paragraphs-categorization huggingface dataset:
Region of Interest-Based Diffusion Analysis To minimize bias from differences in b values and voxel sizes  42  between Prisma and Skyra platforms, diffusion volumes with b = 1500 s/mm 2 for Prisma and b = 1000 s/mm 2 for Skyra were extracted for diffusion tensor calculation. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were subsequently calculated using FSL DTIFIT (FSL v6. 0) in 25 WM regions of interest (ROIs) for each subject in their native space as previously described. 11 For the list and visualization of the 25 ROIs, see the first figure in our prior publication. 11  bias was noted in the diffusivity values in the Skyra (up to 20% higher) compared to Prisma data. To address this, diffusion data were acquired with the Prisma and Skyra protocols in the same session on a Prisma scanner from 3 healthy volunteers.
Statistical Analysis MR and clinical data from control, preataxic, and ataxic groups were compared using nonparametric Kruskal-Wallis test. SARA scores were only compared between control and preataxic groups, because SARA was used to classify preataxic and ataxic groups. Probability values were Holm adjusted for multiple comparisons within each MR analysis (for 55 volumes in FreeSurfer volumetry, 26 volumes in CERES volumetry, 25 regions in each diffusion metric, 19 metabolites in MRS across both VOIs). The metrics that showed statistical significance (p < 0. 05) in the group comparisons underwent pairwise comparisons between the 3 groups in the SCA1 and SCA3 cohorts separately using 2-tailed t tests with Dunn adjustment for multiple pairwise comparisons. Cohen d was computed for each group comparison as the difference in means divided by the pooled standard deviation, which was calculated as the square root of the mean of each group's variance.
51, 52 Although this interpretation is problematic in regions with many crossing fibers,  52, 53  in highly ordered WM tracts, such as those in SCP (FA $ 0. 67), and in the absence of edema, the prominent RD abnormalities are supportive of early oligodendrocyte pathology proposed in SCA3. 54, 55 arly GM damage was limited to select regions of the cerebellar cortex, namely lobules involved in motor processing (IV, I/II) and vestibulocerebellum (lobule X)  56  and subcortical forebrain structures (thalamus, pallidum, putamen, nucleus accumbens). Importantly, despite large overlap in cerebellar/brainstem regions involved in SCA1 and SCA3, the pattern of tissue damage was genotype-specific; therefore, imaging data collected in clinical trials should not be pooled across genotypes. Cerebral involvement was detected with higher sensitivity by diffusion than structural MRI, consistent with microstructural changes preceding tissue loss.
We further observed moderate-to-strong associations of cerebellar and pons MR metrics with estimated ataxia duration. Existing statistical models to estimate time to ataxia onset in preataxic individuals utilize CAG repeat length and current age. 17, 18 Although they perform well at the group level, these estimates a large uncertainty in estimating onset age of individual patients. Noninvasive imaging metrics can enrich these parametric models and are expected to reduce the uncertainty of these estimates, which will be critical in subject stratification in trials. The predictive value of MR metrics for ataxia onset will be better understood with longitudinal follow-up data that are currently being collected in the READISCA project. A limitation of the study was the smaller sample size in the SCA1 group, but the ratio of SCA1 and SCA3 sample sizes was consistent with prior multisite studies.
Acknowledgments This work was supported by the  NIH National Institute of Neurological Disorders and Stroke  (grant  U01 NS104326 ). The Center for Magnetic Resonance Research is supported by the  National Institute of Biomedical Imaging and Bioengineering  (grant  P41 EB027061 ) and the  Institutional Center Cores for Advanced Neuroimaging  (awards  P30 NS076408  and  S10 OD017974 ). Research reported in this publication was also supported by an  Academic Investment Research Program award at the University of Minnesota  and the  National Center for Advancing Translational Sciences  of the  NIH  (award  UL1TR000114 ). A portion of this work was performed in the  McKnight Brain Institute of the University of Florida  at the  National High Magnetic Field Laboratory's Advanced Magnetic Resonance Imaging and Spectroscopy Facility , which is supported by the  National Science Foundation  (cooperative agreement  DMR-1644779 ) and the State of Florida and was supported in part by an  NIH  award ( S10 OD021726 ) for  High End Instrumentation.
Electroencephalographic (EEG) data EEG activity was recorded by four active scalp electrodes (Cz, Fz, FP1, FP2) connected to a V-Amp amplifier (Brain Products, Germany; digitisation 256 Hz, bandwidth 0. 05-5 Hz), the patients being asked to limit body and eye movements as much as possible to reduce artefacts. Changes in brain activity related to breathing condition were identified using the Riemannian evaluation of cortical connectivity previously described  [8] [9] [10] [11]. EEG classifying performance was evaluated by a cross-validation process: the reference period of the "unassisted breathing" condition was divided into 10 equal EEG segments and comparisons between nine of these segments and the data from the "ventilatory support" condition were repeated nine times to take all combinations into account.
Statistical Analysis Statistical analyses were performed using Prism 6 (GraphPad Software, Inc. , USA). The variables were expressed as median and interquartile range [Q1;Q3]. All analyses were performed using nonparametric tests (Wilcoxon for paired comparisons, Spearman for correlations). SpO2 data were compared in a "before-after" manner (end of neuropsychological test vs. beginning). Breathing pattern was continuously recorded during the experiments. Breathing pattern data were compared between baseline conditions (in the absence of cognitive tasks) and cognitive loading conditions. The recordings obtained during the whole duration of the cognitive tests were used for analysis and compared to 10 minutes of baseline recordings. The two-stage step-up method of Benjamini et al. [17]  was used to control the false discovery rate (FDR) which was set up at 5%.
Study limitations. The main limitation of our study is the small number of patients, reflecting the very rare nature of the disease and the relative complexity of the experimental setup. The patient population in this research represents one third of the known adult CCHS population nationwide at the time of the study in France. The study must therefore be considered a pilot, and more data will need to be collected using simpler designs. In particular, we could not study putative relationships between breathing-cognition interferences and genotypic and phenotypical characteristics (e. g. severity of hypoventilation or chemosensitivity blunting), which warrants further investigation. As a second limitation, we only assessed changes in alveolar ventilation through changes in SpO2. It was indeed impossible to measure the endtidal partial pressure in carbon dioxide during unassisted breathing in our setup, the time dynamics involved would have made transcutaneous measures unreliable, and arterial puncture would not have been acceptable for the patients.
Statistical Analyses Categorical variables are presented using frequencies (%). The quantitative variables are presented using mean (± standard deviation, SD) if the distribution was considered as gaussian or median (25-75th percentiles) in case of a skewed distribution. All the analyses were based only on the measurements made by the senior radiologist; similar results were obtained from the junior radiologist (data not shown). The inter-rater reliability between the junior and senior radiologist was assessed by intraclass correlation coefficients (ICC, two-way model with random effects, single unit by rater). Scatter plots and Bland-Altman plots are presented for all five parameters of interest. To assess separately the association of the different study parameters with the group (case or control), we used univariable conditional logistic regression.
A multiple conditional logistic regression was also proposed using both the palate's height and width in the model. All quantitative parameters were standardized (f : x → (xmean (x))/sd(x)), so the associated odds ratios (OR) were expressed for an increase of 1 SD. The associated P-values were calculated using a Wald test. These calculations were also completed by analyzing the area under the receiver operating characteristics (AUROC) curves, presented in the Supplementary Material. Considering a global alpha risk  set to 5%, the unfavorable hypothesis of 5 uncorrelated tests, and the conservative Bonferroni's correction, a P-value <0. 01 (0. 05/5) was considered statistically significant. All analyses were performed using the R statistical software version 4. 0. 0, with the "survival" package  (13, 14).
Imaging The mean height of the hard palate was 4. 1 millimeters (mm) (± 0. 7) in the deceased children versus 3. 2 mm (± 0. 6) in the living children, with an OR (+1 SD) = 4. 30 in univariable analysis (95% CI, 2. 04-9. 06, P = 0. 0001) and an OR (+1 SD) = 3. 23 Categorical data are expressed using frequencies. (%). Quantitative data are given using mean (± SD) when the distribution was gaussian, except for age (25-75th percentile). Distribution of quantitative variables was assessed on histograms. Only the age was not considered as following a gaussian distribution. The ORs were calculated using a conditional logistic regression accounting of 2:1 matching. For quantitative variables, all ORs are expressed for a +1 SD increase after standardization f : x → (xmean (x))/sd(x)). P-values were calculated using a Wald test.
To determine whether the functional pupillary responses relate to the structural deficits, we computed the correlation coefficients between the SPPf and retinal thickness (RNFL) measured by optical coherence tomography (OCT, see Methods), at 3 different eccentricities. The two variables were correlated significantly (r=0. 73, p<0. 001) in healthy participants, suggesting that pupillary responses can provide a proxy to retinal thickness. The correlation coefficients were not as high in participants with retinal disease (RP, r=0. 45, p<0. 012; SD, r=0. 56, p<0. 02) and not significant in optic nerve disease (LHON, r=0. 42, p<0. 11; Supplementary Figure  4 ), possibly because retinal thickness as measured herein does not distinguish between different retinal layers (although this information existed in the data set, it was not possible to retrieve it in the present study).
Apparatus: The stimuli were back-projected at 60 Hz using a Titan 1080p video projector (Digital Projection, Ldt) on a translucent screen (55,5 x 41. 63 degrees of visual angle). Participants were conformably installed at 128 cm from the screen, while resting their head on a chin-rest. Pupil modulations and eye-movements of both eyes were recorded with an eye-tracker (EyeLink II, 500 frames/s. , SR Research, Ldt), and down-sampled to 60 frames/s. Each eye could be stimulated independently, using a large mobile black cardboard that masked the display screen to one eye. Dedicated custom software (Jeda) was used for stimulus generation and eye-movement recordings. (Note that similar results were found in other studies using a conventional monitor and a different eye-tracker). Stimuli: Three different spatial distributions of annulus-sectors were used, with each sector being coupled with a sinusoidal luminance modulation at a specific temporal frequency (TMF).
To evaluate test-retest variability, the large-field and quadrant MFPT tests were repeated twice for each eye, with a delay of about 45 to 60 minutes between recordings, during which other pupillary tests were given to the participants. Overall, a session lasted about 2 hours, but the MFPT tests (2 eyes x 2 repetitions for the large field MFPT; 2 eyes x 2 trials for the quadrant MFPT) only took about 12 min. A MFPT test started with a brief full screen flash (150 msec. ) at the maximum screen luminance, (181. 10 cd/m²) followed by a dark screen for 3 seconds, before the static MFPT grey stimulus appeared on the screen for 3 seconds, followed by 45 second of temporal modulations. In this way, a PLR was recorded as a reference baseline before each MFPT run. Data analyzes: All recorded data were included in analyzes, performed with Matlab R2018a (The MathWorks, Natick, MA, USA).
This was not the case (Supplementary Figure  7B ), demonstrating that stimulus FOIs did tag the pupillary response in each case, although with varying power depending on the disease at stake. Time Frequency maps were also computed to verify that, on average, the power spectrum at FOIs was sustained during a run and did not result from short episodes of oscillatory activity (data not shown; see examples in supplementary Figure  8 ). We used the spectral FOI power distribution to compute the area under the receiving operating characteristic curves (AUC of ROC). The area under the curve (AUC) and the corresponding sensitivity, Ss, and specificity, Sp, were computed using the fitglm and perfcurve Matlab functions: first, a logistic regression model was first computed with the Matlab fitglm function using the power spectrum at all FOIs.
Second, the probability distribution returned by the fitglm function as scores was then used to compute the ROC curves with the Matlab function perfcurve. ROC curves were computed separately for each eye using: i. the spectral power of all FOIs from both trials to discriminate the different patient groups (SD, RP and LHON) from healthy participants (HP), ii. The spectral power of all FOIs of each eye and each trial iii. AUC of ROC were also computed with the spectral power of each eye at each frequency-tagged sub-region of the large-field and quadrant multipartite stimuli, resulting in a synthetic pupillary field maps describing the power distribution over space for each participant, as well as the corresponding AUC distribution of ROC for group analyses (Figure  2 ). Test-retest variability was assessed by computing the Pearson's coefficient correlation between the normalized FOI spectral power collected during the first and second MFPT trials (ran with a delay of about >60 min), as well as Bland-Altman plots.
Analysis All data were analyzed using Linear Mixed Effects (LME) or Generalized Linear Mixed Effects (GLME) models in R studio with the packages lme4  (Bates et al. , 2014) , car  (Fox and Weisberg, 2020) , and lmerTest  (Kuznetsova et al. , 2017). When appropriate, we corrected the skewness of distributions by log-transforming the variables. The raw data can be retrieved from osf. io/57chk. Details of kinematic analyses could be found in Supplementary Results. To analyze the performance, we measured error rates for front-back discrimination task and average 3D errors for sound localization in front space. 3D errors represent the distance in centimeters between perceived positions of sources and the actual speaker's location. We then analyzed the average error on azimuth (in degrees), elevation (in degrees), and distance (in centimeters).
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: FP was supported by a grant of the  Agence Nationale de la Recherche  ( ANR-16-CE17-0016 ,  VIRTUALHEARING3D , France), by a  prize of the Foundation Medisite (France) , by the  Neurodis Foundation (France) , and by a grant from the  Italian Ministry for Research and University (MUR ,  PRIN 20177894ZH ). CV was supported by a grant of the  Università Italo-Francese/Université Franco-Italienne , the  Zegna Founder's Scholarship  and  Associazione Amici di Claudio Demattè. EGe, FP, and CV were supported by a grant of the  Velux Stiftung  (n. 1439). FP, CV, and AF were supported by a grant of the  Agence Nationale de la Recherche  ( ANR-16-CE17-0016 ,  VIRTUAL-HEARING3D , France) and by a prize of the  Fondation Medisite (France).
Generation of TgShI112 mouse lines 460 and 445 After isolation by PCR amplification from genomic DNA, extracted using GeneJET ™ Genomic DNA Purification Kit (Fermentas) from a sheep brain tissue sample using 5' CCG GAA TTC CGG CGT ACG ATG GTG AAA AGC CAC ATA GGC 3' and 5' CTA GTC TAG ACT AGG CCG GCC CTA TCC TAC TAT GAG AAA AAT G 3' as primers, the open reading frame (ORF) of the sheep PRNP gene with A136, R154 and Q171 polymorphisms was cloned into the pDrive vector (Qiagen), being the sequence identical to that of the Genbank entry NP_001009481. 1. The genetic construct containing the sheep M112I substitution was carried out by two-step PCR site-directed mutagenesis using the pDrive sheep construct as template, using primers 5' CCG GAA TTC CGG CGT ACG ATG GTG AAA AGC CAC ATA GGC 3' with 5' GCT CCT GCC ACA TGC TTA ATG TTG GTT TTT GG 3' and 5' CTA GTC TAG ACT AGG CCG GCC CTA TCC TAC TAT GAG AAA AAT G 3' with 5' CCA AAA ACC AAC ATT AAG CAT GTG GCA GGA GC 3'.
Semi-quantification and data analysis Mouse histological lesions (i. e. spongiform change) and PrP res immunolabeling were evaluated under a light microscope by a single pathologist. A semi-quantitative approach was used as previously described  [72]. Spongiform lesion and PrP res immunolabeling were separately scored. Fourteen different brain regions were chosen: piriform cortex (Pfc), hippocampus (H), occipital cortex (Oc), temporal cortex (Tc), parietal cortex (Pc), frontal cortex (Fc), striatum (S), thalamus (T), hypothalamus (HT), mesencephalon (M), medulla oblongata (Mobl), cerebellar nuclei (Cm), cerebellar vermis (Cv) and cerebellar cortex (Cc). Scores ranging from (0) absence of spongiosis or immunolabeling: (1) mild, (2) moderate, (3) intense and (4) maximum intensity of lesion or immunolabeling were assigned to each brain area studied.
After blocking non-specific antibody binding of the membranes by incubation in 5% non-fat milk powder for 1 h at room temperature, monoclonal antibodies D18 (1:5000)  [80]  or 12B2 (1:5000) (Central Veterinary Institute, Wageningen UR) were added and incubated for 1 h at room temperature, prior to washing. After incubation with peroxidase-conjugated secondary goat anti-human IgG (H+L, Thermo Scientific or anti-mouse antibody (m-IgGκ BP-HRP, Santa Cruz Biotechnology)), membranes were washed again and developed with an enhanced chemiluminescent horseradish peroxidase substrate (West Pico Plus, Thermo Scientific), using a FluorChem Q (Alpha Innotech) for image acquisition and the software AlphaView (Alpha Innotech) for image processing. For bank vole samples WB analysis, brain homogenates (20% w/v) were prepared as previously described  [62].
Acknowledgements The authors would like to thank the following for their support: IKERBasque foundation, vivarium and maintenance from  CIC  bioGUNE and  Patricia Piñeiro  for technical support. Maria de la Sierra Espinar ,  Samanta Giler  and  Marta Valle  ( IRTA-CReSA ) for technical support,  Dr. Bernat Pérez de Val  for his assistance in data analysis, IRTA-CReSA's biocontainment unit staff for excellent care and maintenance of the animals, and also  Dr. Olivier Andréoletti  from  UMR INRAE-ENVT, Toulouse  for providing the sheepBSE inoculum.
Funding The present work was partially funded by "Ministerio de Economía y Competitividad" (Spanish Government), Grant Nos. AGL 2013-46756-P, PID2021-122201OB-C21, and PID2020-117465 GB-I00 which are partially supported by EU FEDER funds, by the Infectious diseases edition of the Fundació La Marató de TV3 Grant No. ATYPRION 201821-30-31-32 and by the Project No. EFA148/16 REDPRION. The project EFA148/16 REDPRION was 65% cofinanced by the European Regional Development Fund (ERDF) through the Interreg V-A Spain-France-Andorra program (POCTEFA 2014-2020). POCTEFA aims to reinforce the economic and social integration of the French-Spanish-Andorran border. Its support is focused on developing economic, social and environmental cross-border activities through joint strategies favouring sustainable territorial development.
Study Population and Available Data We conducted a single-center study on all consecutive adult patients transplanted or retransplanted at Nantes University Hospital between January 1, 2009, and October 31, 2020, for whom the primary renal disease was ADPKD. Follow-up was finished on March 31, 2021. Patients with primary nonfunctioning allograft were excluded from the study. All data were extracted from the French multicenter, observational, and prospective DIVAT cohort of transplanted patients (www. divat. fr). The reported clinical and research data are in line with the Principles of the Declaration of Istanbul on Organ Trafficking and Transplant Tourism. The DIVAT cohort received CNIL (Commission Nationale de l'Informatique et des Libertés) (No914184) and the CCTIRS (Advisory Committee on Information Processing in Material Research in the Field of Health) approval.
Regarding cyst infection episodes, although the infectious episodes were recorded prospectively, each medical records of the patients included in the study were retrospectively reviewed to identify patient with history of cyst infection before transplantation and during the post-transplantation period. Furthermore, clinical, biological, imaging, and cyst infection parameters and cyst infection outcomes were collected. Corticosteroid exposure was calculated using prescription data collected in parallel in our electronic system. Each prescription was reviewed in terms of start (excluding intravenous steroids used concomitantly with thymoglobulin antibodies) and stop (censored to death, graft failure, last follow-up, or cyst infection).
Given the paucity of event (cyst infection) in our cohort, few clinical covariables were analyzed in a multivariable approach. Baseline covariables in the model included age, sex, history of diabetes, history of cardiovascular event, history of nephrectomy, body mass index, cold ischemia, delayed graft function, type of donor (living, standard criteria, or extended criteria), and lymphocyte depleting or nondepleting treatment in the induction regimen. Corticosteroid exposure $ 1 year before cyst infection and the absence or presence of corticosteroids treatment during the follow-up were also included in the analysis. For death-censored graft survival analysis, de novo donor specific antibodies and rejection were included in the follow-up data only if they appeared after the first cyst infection episode.
DISCUSSION In our large cohort of kidney transplant recipients with ADPKD, the incidence of cyst infection after renal transplantation was 1. 6 per 100 person-years, and the only predictive factor of cyst infection was a history of cyst infection before renal transplantation. In one-fifth of cases, cyst infection in renal transplant patients presented as isolated fever and was frequently associated with acute kidney injury. To best of our knowledge, this study is the first to investigate specifically cyst infection in patients with ADPKD with renal allograft. Sallée et al. 11  identified retrospectively cyst infection in patients with ADPKD with or without renal replacement therapy using a single-center computerized patient database and estimated an incidence rate of 1 per 100 person-years, which is consistent with our data.