dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS S. B. acknowledges financial support from the  Italian Space Agency  under grant  ASI-INAF I/037/12/0  and from the  PRIN MIUR  project " Black Hole  winds and the  Baryon Life Cycle of Galaxies : the stone-guest at the galaxy evolution supper", contract # 2017PH3WAT. D. J. W. acknowledges support from  STFC  in the form of an  Ernest Rutherford fellowship  ( ST/N004027/1 ).
Study Design and Populations We examined 1759 subjects from the MetaCardis cohort and 1549 from the Healthy Life in an Urban Setting (HELIUS) cohort for whom nutritional and gut microbiota data were available. For the MetaCardis cohort, subjects were recruited between 2013 and 2015 in clinical institutions in France (Pitié-Salpêtrière Hospital, Center of Research for Clinical Nutrition (CRNH) and Institute of Cardiometabolism And Nutrition (ICAN)), Germany (Integrated Research and Treatment Center (IFB) Adiposity Diseases in Leipzig), and Denmark (Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR) in Copenhagen) for the European project MetaCardis (www. MetaCardis. net, accessed date 1 July 2021), as described previously  [11]. All subjects provided written informed consent and the study was conducted in accordance with the Helsinki Declaration.
The study is registered at: https://clinicaltrials. gov/show/NCT02059538. The Ethics Committee of each participating country approved the clinical investigation. The study was approved by the Comite de Protection des Personnes (CPP) Ile de France III n • IDRCB2013-A00189-36. For HELIUS cohort, subjects were recruited between 2011 and 2015 from the municipality of Amsterdam, The Netherlands as previously described  [24]. The HELIUS study was performed in accordance with the Helsinki Declaration and approved by the Institutional Research Board of the Amsterdam University Medical Centre. More information about the HELIUS study can be found on http://www. heliusstudy. nl/en/researchers/publications, accessed date 1 July 2021  [24]. A detailed list of prescribed medications, anthropometric data, clinical history, fecal sample, and a fasting blood sample were obtained at enrollment.
Dietary Intake Data Assessment Dietary data for the MetaCardis cohort was collected via a validated food-frequency questionnaire that was adapted to the cultural habits of each of the countries of recruitment. A validation study against repeated 24-h dietary records among 324 French MetaCardis participants indicated a good validity for macronutrients and protein food groups  [28]. Portion size and nutrient composition were derived from national food consumption surveys and food composition databases. Data on physical activity were collected using a validated Recent Physical Activity Questionnaire (RPAQ)  [29]. Dietary data for the HELIUS cohort was obtained via a validated, ethnicity-specific food-frequency questionnaire, as previously described  [30]. For each subject, basal metabolic rate (BMR) was estimated using the Harris and Benedict Formula  [31].
Statistical Analysis All nutrient data are originally expressed as grams of intake per day. In all the analysis, residuals of each nutrients to total energy intake were calculated as described previously  [34]  to correct for total caloric intake. Participants characteristics were analyzed using chi-square tests for categorical variables and t-tests for numerical variables, using the TableOne Package in R. High protein eaters were defined as protein intake >20% of total energy intake. This number is based on the fact that the average protein consumption is 14 energy percent in the Netherlands and 13 energy percent of total caloric intake in France (https://ourworldindata. org/diet-compositions, accessed date 1 July 2021). This is also in agreement with nutritional recommendations to consume a 10-20% range of total energy intake from protein  [35].
The link between nutritional data and metabolic status was assessed using logistic regression with adjustment for confounders. The model used was metabolic status ~protein_residual + carbohydrate_residual + fat_residual + fibre_intake + total_kcal_intake + age + sex + physical_activity. To investigate the links between macronutrients intake and the gut microbiota, residuals of macronutrients adjusted for energy intake and other gut microbiota confounders were computed. The model used to compute residuals was: macronutrient ~total_kcal_intake + age + sex + Body Mass Index (BMI) + T2D status + anti diabetic treatment + lipid lowering treatment. Gut microbiota alpha diversity was assessed by computing Shannon index. Correlations between residuals of each macronutrient and alpha diversity were computed.
Acknowledgments: Metacardis consortium collaborators:  Rohia Alili ,  Renato Alves ,  Chloe Amouyal ,  Ehm Astrid Andersson Galijatovic ,  Fabrizio Andreelli ,  Olivier Barthelemy ,  Jean-Philippe Bastard ,  Jean-Paul Batisse ,  Magalie Berland ,  Randa Bittar ,  Hervé Blottière ,  Matthias Blüher ,  Frederic Bosquet ,  Rachid Boubrit ,  Olivier Bourron ,  Mickael Camus ,  Cecile Ciangura ,  Luis Pedro Coelho Jean-Philippe.
Funding: This research was funded in part by the  TransAtlantic Networks of Excellence Program  ( 33. 17CVD01 ) from the  Fondation Leducq  to M. N. and K. C. , I. A. and P. B. L. are supported  JPI MICRODIET  grant  2017  ( 5290510105 ). M. N. is supported by a personal ZONMW  VICI grant   2020 [09150182010020 ]. This work was also supported by  European Union 's  Seventh Framework Program  for research, technological development and demonstration under grant agreement  HEALTH-F4-2012-305312  (METACARDIS). Assistance Publique-Hôpitaux de Paris  ( AP-HP ) is the promoter of the clinical investigation (MetaCardis). Funding supports were also  SFN  ( Société Française de Nutrition), F-CRIN-FORCE  network for support, INSERM via ITMO, study and the  Fondation pour la Recherche Médicale  ( FDT202106012793 ).
Acknowledgements This work was supported by the  Institut National de la Sant e et de la Recherche M edicale (INSERM)  and by government grants managed by the  Agence National de la Recherche  as part of the " Investment for the Future" program  ( Institut Hospitalo-Universitaire Imagine , grant  ANR-10-IAHU-01 ,  Recherche Hospitalo-Universitaire , grant  ANR-18-RHUS-0010 ), the  Centre de R ef erence D eficits Immunitaires H er editaires (CEREDIH) , the  Agence National de la Recherche  ( ANR-14-CE14-0026-01  " Lumugene ";  ANR-18-CE17-0001  " Action "), the  Ligue Contre le Cancer e Comit e de Paris, Fondation ARC pour la recherche sur le CANCER , the  Fondation pour la Recherche M edicale  (FRM:  EQU202103012670 ). NGS was supported by a funding program of the  Fondation Maladies Rares  ( AAP2012 High  throughput sequencing).
Lenadogene nolparvovec contains a complementary DNA (cDNA) sequence that encodes the human wild-type ND4 protein under the control of a cytomegalovirus (CMV) immediate early promoter in an intron-containing expression cassette (beta globin intron, HBB2) flanked by the viral inverted terminal repeats from the vector. The construct includes the cis-acting elements of human cytochrome c oxidase 10 (COX10) mitochondrial ribonucleic acid (mtRNA), thus ensuring efficient delivery of the corresponding hybrid mRNA to the mitochondrial surface and the translocation of the translated human ND4 protein into the mitochondria. [3] [4] [5] [6] [7]  Two randomized, double-masked, sham-controlled phase 3 clinical trials (REVERSE, ClinicalTrials. gov: NCT02652780, and RESCUE, ClinicalTrials. gov: NCT02652767) and their long-term follow-up study (RESTORE, ClinicalTrials.
Lenadogene nolparvovec DNA was still quantifiable in the optic nerve and detected in the aqueous humor and anterior segment of the noninjected eyes at 6 months. Overall, the data from individual animals were consistent, as lenadogene nolparvovec DNA was recovered in contralateral tissues of the noninjected eye in 3 out of 3 animals at 3 months and in 2 out of 3 animals at 6 months following unilateral IVT. Lenadogene nolparvovec was quantified or detected in the visual pathway and associated brain tissues, including optic chiasm, optic tract, visual cortex, lateral geniculate nucleus, and sporadically in the cerebellum, thalamus, and auricular lymph node at 3 and 6 months. Because lenadogene nolparvovec DNA was detected and quantified in the optic chiasm, a possible anatomic route for the transfer of viral vector DNA from the treated eye to the nontreated eye was via the optic nerve and chiasm, through anterograde and subsequent retrograde movement along the anterior visual pathways.
For all samples, Ct (Cycle threshold) values were plotted according to the base 10 logarithms of the calibration point copy numbers, and the resulting curve was linear fitted using Microsoft Excel software. Sample test item DNA copy numbers in QC, study, and control samples were then interpolated from the standard curve. Potential PCR inhibition was monitored in all samples by spiking a known quantity (2. 5 Â 10 3 copies) of an exogenous DNA (Drosophila melanogaster antp gene). The spiked samples were then assayed by qPCR, using a qPCR assay developed and validated in house. PCR was considered as inhibited when the recovery percentage of the measured antp copy number was lower than 50% of the nominal value. Acceptance criteria were set for all qPCR runs. Assay 1: Validation of the CMV qPCR method for quantification of AAV2/2-ND4 vg in NHP samples The qPCR method was validated for the determination of AAV2/2-ND4 copies with a dynamic range of 250 to 2.
qRT-PCR analysis of RNA from study samples (100 or 500 ng of total RNA/qRT-PCR reaction) was done using the QuantiTect Probe RT-PCR Kit (QIAGEN). The qRT-PCR plates were run using an Applied Biosystems 7900HT instrument. Samples were run in duplicate in 96well plates in parallel with the following: $ a calibration curve containing 8 calibration standards ranging from 100 to 10 7 copies of a reference analyte containing the amplicon sequence, prepared in 100 ng of a surrogate RNA matrix (yeast RNA), $ 2 sets of QCs at the low, mid, and high levels, $ no RNA controls, For all samples, Ct values were plotted according to the base 10 logarithms of the calibration point copy numbers, and the resulting curve was linear fitted using Microsoft Excel software. ND4 transcript quantity in QC, study, and control samples was then interpolated from the standard curve.
ACKNOWLEDGMENTS We extend our thanks to  Joanna Moore ,  ELS  ( Charles River Laboratories, Evreux, France ), for help in the preparation of this manuscript. N. J. N. is supported in part by an  Ophthalmology Department core grant  from the  NIH/NEI  ( P30 EY006360 ). P. Y. -W. -M. is supported by a  Clinician Scientist Fellowship Award  ( G1002570 ) from the  Medical Research Council (UK)  and also receives funding from  Fight for Sight (UK) , the  Isaac Newton Trust (UK) ,  Moorfields Eye Charity , the  Addenbrooke's Charitable Trust , the  National Eye Research Centre (UK) , the  International Foundation for Optic Nerve Disease (IFOND) , the  UK National Institute of Health Research (NIHR)  as part of the  Rare Diseases Translational Research Collaboration , the  NIHR Cambridge Biomedical Research Centre  ( BRC-1215-20014 ), and the  NIHR Biomedical Research Centre based at Moorfields Eye Hospital NHS Foundation Trust  and  UCL Institute of Ophthalmology.
V. C. is supported by grants from the  Italian Ministry of Health  ( RF-2018-12366703 ), the  Italian Ministry of Research  ( 20172T2MHH ), and  Telethon-Italy  ( GUP15016 ). V. C. is also supported by patients' organizations MITOCON and IFOND and patients' donations. J. A. S. is supported by the  Agence Nationale de la Recherche (France)  within the  Programme Investissements d'Avenir, Institut Hospitalo Universitaire FOReSIGHT  ( ANR-18-IAHU 0001 ), and  LabEx LIFESENSES  ( ANR-10-LABX-65 ).
Mineralogical studies Mineralogical investigations include phase identification using powder X-ray diffraction (XRD) analyses, which were carried out at the X-ray services, Centre Européen de Recherche et d'Enseignement de Géosciences de l'Environnement (CEREGE) and Centre Interdisciplinaire de Nanoscience de Marseille (CINaM), CNRS -Aix Marseille University. XRD patterns were recorded mainly on two diffractometers; Philips, θ-2θ geometry, Co radiation (λ = 0. 179 nm), and Panalytical X'Pert Pro MPD θ-θ geometry, Cu radiation (λ = 0. 15418 nm). The diffraction patterns were then analyzed using the X'Pert Highscore program and the PDF-2 -ICDD database (released 2004). In addition, XRD analyzes were made on sample Cham 6 before and after decarbonatation using diluted HCl, in order to remove the speleothem calcite compound around the black seam.
Environmental monitoring in the cave Some preliminary measures and monitoring of microclimatic parameters were done. We acquired temperature (T) of cave air and water, relative humidity (RH), airflow velocity (V), and carbon dioxide concentration (CO 2 ). For punctual measurements, we used Testo 425 and Testo 888 anemometers (V, T), Senseair pSENSE analyzer (T, RH, CO 2 ); and for continuous monitoring dataloggers Reefnet Sensus (T) and Temtop TemLog 20H (T, RH). The detailed exploitation of these climatic data is beyond the topic of this paper. However, in the Microclimate distribution subchapter a general overview of the environmental conditions and the controlling processes is provided.
Radiometric dating of speleothems The detailed U-series dataset spanning the period 107 to 38 ka is available in Supplementary, Table  S1. Samples GC10-10A, GC10-10B, GC10-8A, Cham J, and Cham E have a low 230 Th/ 232 Th (<120), that indicates detrital contamination. Thus, their ages cannot be used for precise chronology. Chameav A1 and A2, which age can be considered as reliable, have two ages at 97. 6 ± 0. 6 and 107 ± 1 ka, for the same flowstone from the upstream part of the River Gallery. The stalagmites fossilizing the debris talus at the downstream end of Amelineau Gallery, GC10-10A, 10B, 8B, and 8A, provided ages between 65 and 50 ka. Only 8B at 50. 1 ± 4. 4 can be considered as relatively reliable, however all ages are grouped in a rather narrow period indicating their consistence.
ACKNOWLEDGEMENTS This study was granted by the  Fondation de la Vallée de Zegzel. Pavel Bosák  was supported by the  Institutional financing of the Inst. Geol. , Czech Acad. Sci. no. RVO6798531. We particularly thank  Christian Dodelin , " Bat delegate " of the  French Federation of Speleology (FFS) , for identification of the bat species;  Jean-Jacques Bahain ,  Yann Callot , and  Jacques Martini  for fruitful discussions;  Daniel Borschneck  for XRD analyses of samples Cham 3 to 9b at CEREGE;  Roger Notonier  for MEB images and EDX analyses at PRATIM;  Olivier Grauby  for the SEM-TEM analysis at CINaM, all three at  Aix Marseille University ;  Daniel Chailloux  for participating at the survey of Chameau Cave;  Hubert Camus  for providing Morocco's cave inventory. The inhabitants of Zegzel village are especially acknowledged for their warm welcome during each field session.
b. The Amount of Variability Two different gait patterns can still exhibit equivalent variability, while two similar gait patterns do not necessarily duplicate variability. Knowing how variable the person's gait is provides insights about the stability of their gait pattern and could help define when to transition to using an assistive device. As mentioned above, when all adaptation capabilities are in play, the variability will inevitably increase, and an assistive device will become necessary. With limitations in gathering long-term FRDA patient datasets, it is not possible to build on nonlinear analysis methods to define when variability reflects perturbation or regulation. However, if regulation is seen, it is typically an answer to a perturbation. Therefore, the global amount of variability should be representative of both regulation and perturbation, at quite a similar level.
Participants All FRDA subjects were participants of the University of Florida's clinical trial titled Biomarkers in Friedreich's Ataxia. Nineteen participants with FRDA, 11 to 65 years old (only one older than 49 years) were included. Demographics and clinical information are available in Table  1. To be eligible, participants were required to provide a confirmed genetic diagnosis of Friedreich's ataxia and be between the ages of 8 and 70 (inclusive). Exclusionary criteria included pregnancy, unstable heart disease, heart transplant, or any other concurrent medical condition, that in the opinion of the principal investigator would make the subject unsuitable for participation. All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Institutional Review Board of the University of Florida and declared on ClinicalTrials.
The scale consists of 4 sections that focus on specific areas of the body: bulbar function, upper limb coordination, lower limb coordination, and upright stability. It is utilized to measure change at single points in time over time. Gait data collection utilized the Zeno Walkway (6. 10 m × 0. 61 m; ProtoKinetics, Havertown, PA 19083, USA). Researchers asked participants to balance and initiate gait once an automatic light turned on, after ten seconds, signaling them to ambulate. Once gait was initiated, participants completed three passes on the Zeno Walkway, turning off the mat after each pass. Evaluators instructed participants to walk at a speed they felt comfortable and safe doing. Once the walk was captured, trials were processed with the PKMAS software (ProtoKinetics, Havertown, PA 19083, USA).
Results At baseline, nine participants ambulated without aids, three with a cane, and seven with a rollator. Due to the limited sample of cane users, statistical analysis was run only between unassisted walkers and rollator users. Based on the demographics and data collected, participants who utilized a rollator were older than the unassisted walkers (95% Confidence Interval, CI95%: 18. 4-49. 5 vs. 11. 9-16. 8 years-old). Additionally, those utilizing a rollator had lived with FRDA for the longest time (CI95%: 9. 8-25. 9 vs. 4. 9-9. 6 years) and had worse SARA-GS (3. 0 ± 0. 7 vs. 6. 1 ± 0. 7). Figure  3  visually highlights the different levels of organization and variability through all participants. Table  1  provides mean, standard deviation, and CI95% for the parameters and the scores computed in this study, as well as significant differences be- Circular curves represent the value for the Organization Score.
Results At baseline, nine participants ambulated without aids, three with a cane, and seven with a rollator. Due to the limited sample of cane users, statistical analysis was run only between unassisted walkers and rollator users. Based on the demographics and data collected, participants who utilized a rollator were older than the unassisted walkers (95% Confidence Interval, CI95%: 18. 4-49. 5 vs. 11. 9-16. 8 years-old). Additionally, those utilizing a rollator had lived with FRDA for the longest time (CI95%: 9. 8-25. 9 vs. 4. 9-9. 6 years) and had worse SARA-GS (3. 0 ± 0. 7 vs. 6. 1 ± 0. 7). Figure  3  visually highlights the different levels of organization and variability through all participants. Table  1  provides mean, standard deviation, and CI95% for the parameters and the scores computed in this study, as well as significant differences between the group of independent ambulators and the group of individuals who used a rollator.
Coefficients for walking aids one, two, and four were arbitrarily selected for unassisted walk, use of a cane, and use of a rollator, respectively. While some other factors may have been possible, the Organization and Variability Scores are both mathematically independent of the coefficient for walking aids. On the other hand, assigning coefficients according to the type of assistive device raises the question about the effectiveness of the method and the potential for distortive effects on longitudinal follow-up when current clinical tools solely evaluate performance by time to walk a certain distance. Regarding the normative dataset used to derive the scores from gait parameters recorded in the control population, an adjustment would be required for patients younger than the youngest subject included in our study (i.
, 2017; Hernandez-Pajares et al. , 1997; Hu & Zhang, 2018; Li, Zhao, He, et al. , 2020; Li, Zhao, Shen, & Zhang, 2020; Maltseva & Glebova, 2020; Mansilla, 2019; Sivavaraprasad et al. , 2020; Tulasi Ram et al. , 2018). ANN was first applied to develop the foF2 model over the Grahamstown Ionosonde (33°S, 26°E) using data from 1973 to 1983. The root mean square error (RMSE) of the simulated residuals was about 0. 95 MHz, and the monthly averaged RMSE value reached 0. 48 MHz  (Williscroft & Poole, 1996). Subsequently, the ANN technique has been widely utilized to build regional ionospheric models in Japan, Southern Africa, China, the Asia/Pacific sector and the auroral zone. An improved Artificial Neural Network-Based Ionospheric Model (ANNIM) was developed using the combined data from COSMIC, GRACE, CHAMP and the global Ionosondes by Tulasi  Ram et al.
Previous studies demonstrate that the ANN technique has a great advantage in developing an ionospheric model dependent on the geographic position and solar-terrestrial environment, and the performances of the ANN model are significantly better than some traditional physical or empirical models. However, the performances of ANN-based models are controlled by the input data source, network structure and algorithm. It is believed that the performance of the ANN model could be improved substantially with the development of a data assimilation method. This study aims to develop a completely global ionospheric three-dimensional model based on the ANN technique by utilizing the long-term observations from space-borne GNSS RO systems (COSMIC and FY-3C) and the Digisonde Global Ionosphere Radio Observatory (GIRO).
The organization of the paper is as follows. The datasets and data quality control method are introduced in Section 2. 1, and the ANN methodology including the neural network structure and the model's performances are described in Section 2. 2. The validations of ANN model performances compared with incoherent scatter radars for quiet and disturbed space environments are presented in Section 3. The predicted capability of the ANN model in capturing some prominent ionospheric dynamic structures, including the Weddell Sea anomaly and the mid-latitude summer nighttime anomaly (MSNA), are validated in Section 4. Finally, the merits and limitations of this model along with expectations for future improvement are discussed in Section 5.
Data and Analysis In this study, the global distributed ionospheric electron density profiles provided by FORMOSAT-3/COSMIC constellation, FY-3C, and global Digisonde stations are utilized to develop a global ionospheric electron density model. The COSMIC profiles are obtained from the UCAR community programs (https://www. cosmic. ucar. edu/), the FY-3C profiles are downloaded from the National Satellite Meteorological Center (http:// satellite. nsmc. org. cn/portalsite/default. aspx) and the Digisonde measurements are downloaded from the Digisonde GIRO (http://spase. info/ SMWG/Observatory/GIRO). The periods of three datasets are  COSMIC-1 (2007 COSMIC-1 ( -2016)) ,  FY-3C (2014 -2019 ), and Digisonde (2005 -2019). The FOR-MOSAT-3/COSMIC constellation is an international collaborative project between NSPO (National Space Program Office) of Taiwan and UCAR (University Corporation for Atmospheric Research) of the United States of America.