dataesr/scientific-paragraphs-categorization huggingface dataset:
Thin gray lines represent the 5th, median, and 95th from the distribution made of all median PMTH obtained from surrogate raster plots from the sessions included in the group. Black asterisk indicate that the median value is above the 95th percentile or below the 5th percentile from the surrogate dataset (B). Same as (A), but illustration is made with one P12 Gad1 Cre/+ mouse pup and PMTHs are built with all imaging sessions from pups aged between P10 and P12 (N = 7 mice, n = 9 imaging sessions). Note the presence of red labeled processes in the neuropil of the stratum pyramidale of P12 in contrast to P5. Results to build the PMTHs, as well as Calcium Imaging Complete Automated Data Analysis (CICADA) configuration files to reproduce the analysis, are available in Figure3-source data 1. The online version of this article includes the following source data and figure supplement(s) for figure3:Source data 1.
Analysis was performed using CICADA(Cossart lab GitLab, group ID 5948056, project ID 14048984), a custom-made open-source Python toolbox allowing for the automatic analysis of calcium imaging data in the NWB:N format. CICADA offers a user-friendly graphical user interface allowing the user (1) to select the NWB files of the recording sessions to include in a given analysis, (2) select the analysis to run and set up the parameters, and (3) generate result tables and/or ready to use figures. In addition, each analysis run in CICADA generates a configuration file that can be loaded in CICADA with the option 'Load a set of parameters' allowing for the replication of the analysis. CICADA can be installed following the installation guidelines presented at https://gitlab.
Acknowledgements This work was supported by the  European Research Council  under the European Union's,  Horizon 2020 research and innovation program  grant#  646925 , the  Fondation Bettencourt Schueller , and an  NWB  seed grant#  R20046AA. The project leading to this publication has received funding from the  «Investissements d'Avenir» French government program  managed by the  French National Research Agency  ( ANR-16-CONV-0001 ) and the  Excellence Initiative of Aix-Marseille University  -A*MIDEX. RFD was funded by the  "Ministere de l'Enseignement Supérieur, de la Recherche et de l'Innovation"  and the  Fondation pour la Recherche Médicale  Grant  FDT202106012824. EL was funded by the  "Ministere de l'Enseignement Supérieur, de la Recherche et de l'Innovation"  and  A*Midex  foundation and the  French National Research Agency  funded by the  French government «Investissements d'Avenir» program  (NeuroSchool, nEURo *AMU ,  ANR-17-EURE-0029  grant).
JD was supported by the  Fondation pour la Recherche Médicale  Grant  FDM20170638339. MP was supported by the  Fondation pour la Recherche Médicale  Grant  ARF20160936186. TD and TS were funded by « Investissements d'Avenir» French government program  managed by the  French National Research Agency  ( ANR-16-CONV-0001 ) and from the  Excellence Initiative of Aix-Marseille University  -A*MIDEX. We would like to thank  Dr. Pierre-Pascal Lenck-Santini  for providing valuable feedback on our research project. We thank  S Pellegrino-Corby ,  F Michel , and  S Brustlein  from the  INMED animal and imaging facilities (InMagic). We would also like to thank  Marion Sicre  for her help with GAD1 Cre/+ experiments. We are grateful to  Pr. Hannah Monyer  for providing the GAD1 Cre/+ mouse lines. We thank the  Centre de Calcul Intensif d'Aix-Marseille  for granting access to its high-performance computing resources.
Analysis was performed using CICADA(Cossart lab GitLab, group ID 5948056, project ID 14048984), a custom-made open-source Python toolbox allowing for the automatic analysis of calcium imaging data in the NWB:N format. CICADA offers a user-friendly graphical user interface allowing the user (1) to select the NWB files of the recording sessions to include in a given analysis, (2) select the analysis to run and set up the parameters, and (3) generate result tables and/or ready to use figures. In addition, each analysis run in CICADA generates a configuration file that can be loaded in CICADA with the option 'Load a set of parameters' allowing for the replication of the analysis. CICADA can be installed following the installation guidelines presented at https://gitlab. com/cossartlab/cicada (copy archived at swh:1:rev:2ef0c25d7da5b69849c663ed56a0033cfe8488ca; Dard, 2022).
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. -(J EE - 1 τ E )(J II + 1 τ I ) + J EI J IE > 0 =⇒ J EI J II + 1/τ I > J EE -1/τ E J IE (10) Funding Combining the inequalities above gives the constraints for stable nonzero rates Funder European Resuscitation Grant reference number Author 646925 Rosa Cossart JE0 JI0 > JEI JII+1/τI > JEE-1/τE JIE (11) Council Fondation Bettencourt Rosa Cossart Schueller Neurodata Without R20046AA Michel A Picardo Borders Agence Nationale de la ANR-16-CONV-0001 Dmitrii Suchkov Recherche Rosa Cossart Ministère de l'Education MESR Robin F Dard Nationale, de Erwan Leprince l'Enseignement Superieur et de la Recherche Fondation pour la FDT202106012824 Robin F Dard Recherche Médicale Fondation pour la FDM20170638339 Julien Denis Recherche Médicale Fondation pour la ARF20160936186 Michel A Picardo Recherche Médicale 78116 31 of 32 Dard et al.
Data Collection: An Ethnographic Enquiry Each case study was approached through two periods of fieldwork conducted between December 2013 and August 2015, and follow up field visits were performed in 2016 and 2017. The corpus of data, Table  2 , was collected through ethnographic methods and iteration, including: (i) Participant observation in the production and market of origin cheese, including on-farm cheesemaking with selected producers, farmers markets and Slow Food events, to unfold the practical experience of different communities and the actor's point of view; (ii) Semi-structured interviews with producers, technicians, trainers and public administrators to determine the governance ideal type of both Presidia and GIs (motivation associated to launch of the initiative, actors' involvement and management system), and the most relevant challenges they are facing (estimated results and pitfalls); (iii) Informal interviews with producers, consumers and cooks designed along 3 axes: background (context, motivation associated to the initiative, estimated results), discourses on CB (understanding and value), practices of CB (learning experiences, production practices, changes); (iv) Written sources that helped to design and develop the field analysis and the following data analysis.
Funding: This work has been conducted as part of a PhD thesis project supported by the  Agricultural Transformation by Innovation (AGTRAIN) Erasmus Mundus Joint Doctorate Programme , funded by the  EACEA (Education, Audiovisual and Culture Executive Agency)  of the  European Commission. We also acknowledge the financial support of the ' Economic assessments of the sustainability of agri-food systems ' project, by  UNICT   2016-2018  Piano per la  Ricerca. Linea  di intervento 2-Seconda annualità  P7/WP2  ( 5A722192141 ).
Six to eighteen images of TH-immunostained alternated slices from each animal were then acquired on a fluorescence microscope (Leitz Aristoplan light microscope) equipped with a Nikon high resolution digital camera (756 Â 581 pixels; Nikon, Tokyo, Japan) with a 10x objective, interfaced to a PC computer and Image software (Lucia, Nikon). The number of THpositive DAergic neurons in SNc was quantified in sections taken from À2. 80 to À3. 28 mm from bregma. Slices were chosen to cover the entire rostral to caudal extension of the SNc. TH-positive cells in the SNc were counted manually in four regions of interest (ROIs, 1100 Â 1100 lm) per hemisphere, using the cell counter plugin of FIJI software (ImageJ, National Institutes of Health). ROIs were determined to cover the whole medio-lateral extension of the structure based on the stereotaxic mouse atlas  (Paxinos and Franklin, 2001).
ACKNOWLEDGMENTS The authors declare no competing financial interests. This research was supported by the  CNRS, Aix-Marseille University, French Ministry of Education and Research, France Parkinson Association , by  European Union's Horizon 2020 Research and Innovation Program (FETOPEN)  under grant agreement  N°767092 , and within the context of the  DHUNE  project supported by A* MIDEX  project ( ANR-11-IDEX-0001-02 ). We would like to thank  V. Gilbert ,  E. Mansour  and  JL Fina  for animal care,  D. Paleressompoulle  for technical assistance, the histology platform headed by  Dr. N. Turle-Lorenzo  and  A. Tonetto  for assistance in confocal microscopy. Data are available via request to the authors. None of the authors has a conflict of interest.
Fractions containing ␣-syn were identified by SDS-PAGE and Coomassie staining, and then dialyzed overnight into PBS buffer (Life Sciences). A NanoDrop 2000 (Thermo Fisher) was used to determine the protein concentration by OD 280 reading and protein was concentrated to 5 mg/mL using a Vivaspin protein concentrator spin column with a MWCO of 5 kDa (GE Healthcare). Aliquots of 500 L were stored at -80 • C until use. For amyloid fibril assembly, purified recombinant ␣-syn was thawed and subjected to continuous shaking at 1,000 r. p. m at 37 • C in a Thermomixer (Eppendorf) for 7 days. The monitoring of fibril assemblies was performed by Thioflavin T (Sigma) fluorescence reading (data not shown). Fibrils were aliquoted and frozen at -80 • C until use.
5 g, BioLegend). For splenocyte isolation and staining, spleens were isolated from mouse and kept on ice. In cold 1× RBC lysis buffer (eBioscience), spleens were macerated with the base of a 3 mL syringe plunger on a 70 m cell strainer in a 10 cm petri dish on ice. 30 mL of PBS was added to stop the reaction. Cells were spun at 350 × g for 5 min at 4 • C. Pellet was resuspended in 1 mL of PBS for ∼5x10 6 cells/mL. Staining of splenocyte cells was performed on 100 L cells as described for blood. Following antibody incubation, blood and spleen cells were washed in flow buffer and spun at 300 × g for 10 min. Pellets were resuspended in 300 L of flow buffer with 1 g/mL DAPI. Samples were acquired on a CytoFLEX S (BeckmanCoulter). Data analysis was performed using FlowJo v10. 5. 3. After gating on single live cells, T cells and B cells were identified in plots of CD3 vs SSC and CD19 vs SSC respectively.
Assessment of numbers of microglia and their morphology Microglia morphology was assessed as previously described  [51]. Color (RGB) images of Iba-1-stained striatum, substantia nigra and frontal cortex tissues were acquired bilaterally at 60x magnification (oil immersion 1. 40 N. A. ) using a Nikon Eclipse Ni-U microscope. A total of nine images/animal were analyzed; three images from three sequential brain sections through the striatum (as described above). To assess the morphology of microglia in the samples, RGB color images were processed by a custom MATLAB (Mathworks) script. Cell bodies and other large regions were segmented first. Dynamic thresholds were determined for both the blue intensity and saturation channels of the image. Upper and lower bounds of the thresholds were set to match the full width at half maximum of curves fit to the histograms of each channel.
Stereological counting We used a computer-assisted cell quantification program (StereoInvestigator, MBF Bioscience) coupled to a Nikon Eclipse Ni-U microscope (Nikon). In each mouse (3-7 animals per group), we analyzed 5-7 sections containing substantia nigra. They were spaced by 240 m (section interval = 6). Contours of the region were drawn at 10x magnification (air immersion, N. A. 0. 45). Quantifications were performed at 60x (oil immersion, N. A. 1. 40) using a counting frame of 100 m×100 m, grid size set to 200×200 m, with a guard zone of 2 m, and dissector height set at 12 m.
Statistical analysis The data represented in Fig. 2  includes pilot 1 data for all groups except NSG PFF B. In Pilot 2, the NSG PFF T group was repeated and included 3 additional mice to determine if the same reduction in phosphorylated ␣-synuclein pathology would be observed in Pilot 1. As there were no statistical differences in the NSG PFF T cell group between pilots 1 and 2, the data for this group was pooled. (see Supplementary Figure  1 ). In pilot 2, the experiment focused on the NSG PFF B group, to determine how the reconstitution of B cells affects phosphorylated ␣-synuclein pathology. These mice were compared directly with the other groups in Pilot 2, as represented in Fig. 3  and Supplementary Figure  1. All values are expressed as mean ± SEM. Differences in means between the groups were analyzed using a Kruskal-Wallis test with Dunn's multiple comparisons test as posthoc (GraphPad Prism software, version 9).
Microgliosis in mice reconstituted with T cells To understand whether microglia were involved in the reduction of phosphorylated ␣-syn pathology in NSG mice reconstituted with T cells, we analyzed the morphology of microglia. We immunostained for Iba-1 to compare microglial morphology between all groups (Fig. 4a, b ). We compared the morphology of Iba-1 immunoreactive microglia from the same anatomical level of the ipsilateral and contralateral striatum, substantia nigra and frontal cortex in mice from each experimental group. Specifically, we used a MATLAB script to define the ratio between the perimeter and surface area of Iba-1-immunoreactive microglia, allowing us to calculate the hydraulic radius of each cell as an index of the activation state as previously  [51]. An example of the image analyzed by MATLAB is presented in Supplementary Figure  5a.
ACKNOWLEDGMENTS We thank  Dr. Corinne Esquibel  from the  Optical Imaging  core for developing the MATLAB algorithm for microglia morphology analysis,  Emily Wolfrum  from the  Bioinformatics and Biostatistics  for assistance with the statistical analysis and the staff of the  Vivarium of Van Andel Research Institute  for animal care. We acknowledge the  Van Andel Institute  and the many individuals and corporations that supported financially the neurodegenerative research at  Van Andel Research Institute. S. G. was supported by the  Peter C. and Emajean Cook Foundation  and the  Farmer Family Foundation. Research reported in this publication was supported by the  National Institute On Deafness And Other Communication Disorders of the National Institutes of Health  under Award Numbers  R01DC016519  (P.
New findings on pathophysiology, epidemiology, and outcome have raised concerns on the relevance of the apnoea-hypopnoea index (AHI) in the classification of obstructive sleep apnoea (OSA) severity. Recently, a multicomponent grading system decision integrating symptomatology and comorbidities (Baveno classification), was proposed to characterise OSA and to guide therapeutic decisions. We evaluated whether this system reflects the OSA population, whether it translates into differences in outcomes, and whether the addition of AHI improves the scheme. A total of 14 499 OSA patients from the European Sleep Apnoea Database cohort were analysed. The groups were homogeneously distributed and were found to clearly stratify the population with respect to baseline parameters. Differences in sleepiness and blood pressure between the groups were analysed in a subgroup of patients after 24-36 months of treatment.
However, criticism and concerns regarding the clinical and prognostic relevance of the AHI have recently been raised  [13, 14]. First, experimental data and clinical experiences have demonstrated that the pathophysiology of OSA is not reflected by upper airway obstruction alone. Second, several cluster analyses of large databases have consistently shown that the classical phenotype of obesity, male sex, older age and severe daytime sleepiness represents only one-quarter of the OSA population  [15] [16] [17] [18] [19] [20] [21]. Other phenotypes, such as breathing disturbances associated with movement phenomena or insomnia, young age with comorbidities or female sex with less distinct or atypical symptoms, may also be identified  [15] [16] [17] [18] [19] [20] [21] [22] [23]. Finally, the AHI is a poor predictor for end-points such as cardiovascular comorbidities or mortality.
Groups B and C may warrant treatment either due to reduced quality of life or presence of relevant comorbidities. However, this innovative proposal requires further validation before reshaping OSA routine care. Therefore, we evaluated whether the multicomponent grading system, namely the Baveno classification, can be transferred to a real-life situation in OSA patients. A slightly modified version of the Baveno classification system was applied to the large clinical cohort of the European Sleep Apnoea Database (ESADA) to determine whether: • the Baveno classification A-D sufficiently reflects the distribution of the patients in the ESADA cohort; • the addition of the AHI to the classification system might improve the characterisation of sleep apnoea patients; and • the classification translates in clinically relevant outcome parameters.
Methods The ESADA registry is a multicentre, prospective patient cohort reflecting a network of 37 sleep centres in 20 countries in Europe and Israel  [31, 32]. The overall objective of the ESADA is to generate a clinically representative cohort of subjects recently evaluated for suspected sleep-disordered breathing. A central web-based platform is applied to record patient information from the participating sleep centres. In brief, unselected patients (age 18-80 years) with suspected OSA are eligible for inclusion in the registry. At the time of initial sleep laboratory diagnostic work-up, anthropometric characteristics, information on daytime symptoms and health-related lifestyle, such as smoking and alcohol consumption, blood tests, medical history, medications and sleep data are collected.
Corresponding follow-up data are collected when patients return as part of the clinical routine. The research ethics committee at each participating centre approved the ESADA protocol, and informed consent is obtained from all included patients. For this analysis, we considered all patients with an AHI ⩾5 h -1 and included all complete datasets for the parameters needed for allocation to the four Baveno groups. These parameters comprise symptoms and cardiometabolic comorbidities. Symptoms include daytime sleepiness, insomnia and hypersomnia. Comorbidities include atrial fibrillation, (uncontrolled) arterial hypertension, heart failure, stroke, and diabetes mellitus (see supplementary material for further details). For the baseline analysis, patients were classified as positive airway pressure (PAP) users based on the clinical decision to initiate any kind of PAP therapy, including continuous (CPAP), automatic (APAP) and bilevel PAP.
According to the heterogeneity of clinical practice and local healthcare regulations, follow-up data were available in a subgroup of the cohort. Follow-up data of 12-24 months were available for 1724 patients, 24-36 months for 1081, and >36 months for 953 patients. For the longitudinal analysis in this study, we used the data of those patients treated with CPAP (n=474) or APAP (n=468) at the time of their follow-up visits that occurred within a time window of 24-36 months. These groups and time windows were selected to generate a substantial number of patients and a relevant time frame for evaluation of an objective cardiovascular outcome parameter. Patients treated with bilevel PAP (n=95), or non-PAP therapies (n=29) were excluded. In some part of the analyses, data were grouped according to the established severity levels of the baseline AHI (mild ⩾5 to <15 events•h -1 , moderate ⩾15 to <30 events•h -1 and severe ⩾30 events•h -1 ).
Baseline characteristics of the patients The ESADA database currently includes 30 235 patients. After exclusion of files with missing data required for allocation to Baveno groups, 14 499 datasets were included for further evaluation, among which 943 were considered for follow-up analysis (figure  2 ). Study characteristics were as follows: 28% female, median age 55 (46; 63) years (median (quartile 1; quartile 3)) and body mass index (BMI) 31. 6 (28. 0; 36. 2) kg•m -2. The median ESS score was 10 (6; 13); 47% of the patients had a score <11 and 53% had ⩾11. The median AHI was 30. 8 events•h -1 (16. 3; 52. 9) and the oxygen desaturation index (ODI) was 27. 0 events•h -1 (12. 0; 50. 8). The mean arterial oxygen saturation (S pO 2 ) was 93% (91; 95), the minimum S pO 2 was 81% (73; 86), and the cumulative sleep time when S pO 2 was <90% (t 90 ) was 15 min (2; 65).
Groups B and C differed only mildly from each other, while the figure for group D was remarkably higher (table  1 ). Data on glycated haemoglobin (HbA 1c ) were available in 6104 patients. It was slightly and significantly ( p<0. 001) elevated in groups C (5. 7% (5. 4; 6. 2)) and D (5. 9% (5. 5; 6. 5)) as compared to the groups A (5. 6% (5. 3; 5. 8)) and B (5. 6% (5. 4; 5. 9)).
Discussion The aims of this study focussed on the translation of the Baveno classification system of OSA to the ESADA cohort, a large real-life database. First, the findings demonstrate that integration of clinical symptoms and comorbidities as proposed in the Baveno system  [30, 37]  stratifies the population, independent of the AHI without considerable over-or underrepresentation any of the groups. The groups with severe symptoms and less comorbidity (B) and less symptoms and severe comorbidity (C) were similar regarding hypoxic burden while the most severe group in both parameters (D) clearly differed in most measurements of hypoxaemia  and treatment indication. Second, symptoms seemed to drive the indication to treat more strongly than did comorbidities as shown by the different proportions of treated and untreated patients.
In contrast, group A differs in hypoxic burden, symptoms and comorbidities from all other groups and fails to show any benefit in the parameters investigated here despite similar compliance and thus, PAP treatment for these patients does not seem to be indicated. While the observed changes in blood pressure and ESS, especially in groups B and D, may in part be due to a regression to the mean, the (albeit lesser) improvement of ESS in groups A and C and the absence of an increase in median blood pressure in groups A and B strongly suggest that this is to a large extent an actual treatment effect. Our data reflect blood pressure recordings during clinical routine assessments with and without concomitant antihypertensive medication and thus, a large variability between blood pressure assessments in a proportion of patients could be expected.
Limitations The retrospective design generally limits the conclusions of any study, mainly to incomplete or insufficient data. However, a strict exclusion process allowed for analysing only patient files with all relevant data required for the Baveno classification. The classification of patients based on dichotomisation of continuous biological parameters might be seen as an oversimplification. However, the cut-off values for the ESS score and blood pressure we used are well established and substantiated in that they relate to outcome. Dichotomous cut-offs were deliberately chosen to make the Baveno system intuitive and accessible in the clinical routine. It may also be extended at a later stage by the introduction of parameters such as hypoxic load and/or therapeutic recommendations. The ESADA database has its greatest strength in the baseline data while follow-up data are available only for a subgroup of patients.
Data are presented as median (quartile 1; quartile 3) unless otherwise stated. BMI: body mass index; BP: blood pressure; ESS: Epworth Sleepiness Scale; AHI: apnoea-hypopnoea index; ODI: oxygen desaturation index; S pO 2 : oxygen saturation as measured by pulse oximetry; t 90 : percentage of sleep time with S pO 2 <90%. # : scale 0-24. ***: p<0. 001 versus D; ¶ : p<0. 001 versus C; + : p<0. 001 versus B. https://doi. org/10. 1183/23120541. 00928-2020.