dataesr/scientific-paragraphs-categorization huggingface dataset:
(20) Massalia Data from 13 apparitions were at our disposal to model (20) Massalia, although some of them were grouped close together in ecliptic longitudes. Massalia displayed regular, bimodal lightcurve shapes with amplitudes from 0. 17 to 0. 27 mag. New data gathered within the SBNAF and GaiaGOSA projects significantly improved the preliminary convex solution that exists in DAMIT  (Kaasalainen et al. 2002) , which has a much lower pole inclination and a sidereal period of 0. 002 hours shorter. If we consider the long span (60 years) of available photometric data and the shortness of the rotation period, such a mismatch causes a large shift in rotational phase after a large number of rotations. The two SAGE mirror solutions have a smooth shape with a top shape appearance. Their fit to the occultation record from 2012 led to two differing size solutions of 106 +6 -3 and 113 +6 -10 km (Fig.
C. 3); both are smaller and outside the combined error bars of the 145 ± 2 km solution that was obtained from the TPM. The full TPM details and the PACS data will be presented in Ali-Lagoa et al. (in preparation). The SAGE shapes fit the thermal data much better than the sphere, which we consider as an indication that the model adequately captures the relevant shape details. We note that (20) Massalia is one of the objects for which the stellar occultation data are rather poor. This provides rough size determinations and underestimated uncertainties.
(64) Angelina The lightcurves of (64) Angelina display asymmetric and variable behavior, with amplitudes ranging from 0. 04 mag to 0. 42 mag, which indicates a spin axis obliquity around 90 degrees. Data from ten apparitions were used to calculate the SAGE model. The synthetic lightcurves that were generated from the shape are in good agreement with the observed ones. Although the low value of the pole's latitude of 12 • is consistent with the previous solution by  Ďurech et al. (2011)  (see Table  A. 2 for reference), the difference of 0. 0015 hours in the period is substantial. We favor our solution given our updated, richer dataset since  Ďurech et al. (2011)  only had dense lightcurves from three apparitions that were complemented by sparse data with uncertainties of 0. 1 -0. 2 mag (i.
(308) Polyxo The available lightcurve data set has been very limited for Polyxo, so no model could have been previously constructed. However, thanks to an extensive SBNAF observing campaign and the observations collected through GaiaGOSA, we now have data from six apparitions, covering five different aspects. The lightcurves were very irregular and had a small amplitude (0. 08-0. 22 mag), often displaying three maxima per period. To check the reliability of our solution, we determined the model based on the simple lightcurve inversion method. Then, we compared the results with those from the SAGE method. All the parameters are in agreement within the error bars between the convex and SAGE models. Still, the SAGE shape model looks rather smooth, with only small irregularities, and it fits the visible lightcurves reasonably well.
(381) Myrrha In the case of Myrrha, there were data from seven apparitions, but only five different viewing aspects. The lightcurves displayed a regular shape with a large amplitude from 0. 3 to 0. 36 mag. Thanks to the observing campaign that was conducted in the framework of the SBNAF project and the GaiaGOSA observers, we were able to determine the shape and spin state. Without the new data, the previous set of viewing geometries would have been limited to only 1/3 of the Myrrha orbit, and the earlier model by  Hanuš et al. (2016)  was constructed on dense lightcurves supplemented with sparse data. As a consequence, the previous model looks somewhat angular (cf. both shapes in Fig. 6 ). Due to a very high inclination of the pole to the ecliptic plane (high value of |β|), two potential mirror pole solutions were very close to each other.
(721) Tabora Together with new observations that were gathered by the SB-NAF observing campaign, we have data from five apparitions for Tabora. Amplitudes ranged from 0. 19 to 0. 50 mag, and the lightcurves were sometimes strongly asymmetric, with extrema at different levels. A model of Tabora has been published recently and it is based on joining sparse data in the visible with WISE thermal data (bands W3 and W4,  Ďurech et al. 2018a ), but it does not have an assigned scale. The resulting shape model is somewhat angular, but it is in agreement with the SAGE model with respect to spin parameters. Stellar occultations are also lacking for Tabora, and the TPM only gave a marginally acceptable fit (χ 2 = 1. 4 for pole 1) to the thermal data, which is nonetheless much better than the sphere. Thus, the diameter error bar, in this case, is not optimal (∼ 6%) and additional IR data and/or occultations would be required to provide a better constrained volume.
In a few cases, our solutions are the first in the literature. The shape models, spin parameters, diameters, volumes, and corresponding uncertainties derived here are already available on the ISAM webpage. Table  1. Spin parameters of asteroid models obtained in this work, with their uncertainty values. The first column gives the sidereal period of rotation, next there are two sets of pole longitude and latitude. The sixth column gives the rms deviations of the model lightcurves from the data, and the photometric dataset parameters follow after (observing span, number of apparitions, and number of individual lightcurve fragments).
The research leading to these results has received funding from the European Union's Horizon 2020 Research and Innovation Programme, under Grant Agreement no 687378 (SBNAF). Funding for the Kepler and K2 missions is provided by the NASA Science Mission directorate. L. M. was supported by the Premium Postdoctoral Research Program of the Hungarian Academy of Sciences. The research leading to these results has received funding from the LP2012-31 and LP2018-7 Lendület grants of the Hungarian Academy of Sciences. This project has been supported by the Lendület grant LP2012-31 of the Hungarian Academy of Sciences and by the GINOP-2. 3. 2-15-2016-00003 grant of the Hungarian National Research, Development and Innovation Office (NKFIH). TRAPPIST-South is a project funded by the Belgian Fonds de la Recherche Scientifique (F.
R. S. -FNRS) under grant FRFC 2. 5. 594. 09. F. TRAPPIST-North is a project funded by the University of Liège, and performed in collaboration with Cadi Ayyad University of Marrakesh. EJ is a FNRS Senior Research Associate. "The Joan Oró Telescope (TJO) of the Montsec Astronomical Observatory (OAdM) is owned by the Catalan Government and operated by the Institute for Space Studies of Catalonia (IEEC). " "This article is based on observations made with the SARA telescopes (Southeastern Association for Research in Astronomy), whose node is located at the Kitt Peak National Observatory, AZ under the auspices of the National Optical Astronomy Observatory (NOAO). " "This project uses data from the SuperWASP archive. The WASP project is currently funded and operated by Warwick University and Keele University, and was originally set up by Queen's University Belfast, the Universities of Keele, St.
Acknowledgements. The research leading to these results has received funding from the European Union's Horizon 2020 Research and Innovation Programme, under Grant Agreement no 687378 (SBNAF). Funding for the Kepler and K2 missions is provided by the NASA Science Mission directorate. L. M. was supported by the Premium Postdoctoral Research Program of the Hungarian Academy of Sciences. The research leading to these results has received funding from the LP2012-31 and LP2018-7 Lendület grants of the Hungarian Academy of Sciences. This project has been supported by the Lendület grant LP2012-31 of the Hungarian Academy of Sciences and by the GINOP-2. 3. 2-15-2016-00003 grant of the Hungarian National Research, Development and Innovation Office (NKFIH). TRAPPIST-South is a project funded by the Belgian Fonds de la Recherche Scientifique (F.
Deployment of the national healthcare database (Système national des données de santé; SNDS), which includes both outpatient and hospital healthcare, allows real-life evaluation of compliance with guidelines  [14]. However, while a real-life study is often synonymous with a comprehensive population, the inclusion of certain subgroups with lower outpatient healthcare utilization or a global funding mode can impact estimation of the levels of healthcare utilization. This is the case for patients institutionalized in skilled nursing homes, psychiatric hospitals and rehabilitation units, as well as for short-stay hospitalized or deceased patients. As HF affects mostly elderly patients, exclusion of institutionalized patients from analysis is all the more relevant when evaluating compliance with HF guidelines.
Data source The SNDS database collects the individual characteristics of the beneficiaries of the various national health insurance schemes, as well as prescriptions and procedures performed on an outpatient basis or in healthcare institutions and funded or reimbursed by French national health insurance  [15]. The SNDS database does not record any clinical information concerning results relating to consultations, prescriptions or examinations. However, it includes information on the presence of long-term disease (Affection de Longue Durée [ALD]) eligible for 100% reimbursement of healthcare for the disease concerned. Reimbursed drugs are identified by their Anatomical Therapeutic Classification (ATC) code, and procedures are identified by national nomenclatures. This information is individually linked by a pseudonymous identifier to information concerning private and public hospital stays recorded in the Programme de médicalisation des systèmes d'information (PMSI; medical information systems programme), specific to short-stay hospitals, rehabilitation units and psychiatric hospitals.
Population and case identification The main national health insurance scheme in France is the general scheme, which covers about 77% of the 66 million inhabitants; this proportion reaches about 86% with the addition of local mutualist section beneficiaries. The other main schemes are the Mutualité Sociale Agricole (agricultural workers scheme) and the Régime Social des Indépendants (self-employed workers scheme), together representing almost 10% of the population. Today, most studies conducted on SNDS data are limited to the general scheme with the addition of local mutualist section beneficiaries, because of the absence of certain data in the other schemes. Healthcare non-users, defined by the absence of reimbursed healthcare or health insurance coverage during a given year, are also often excluded from analyses, such as when using mapping algorithms.
Data analysis The results are presented in the form of frequencies of at least one reimbursed healthcare utilization during the target period. Medians and interquartile ranges (IQRs) (Q1; Q3) and the intervals between healthcare utilization before or after the index hospitalization (date of admission or date of discharge) were calculated for patients with at least one healthcare utilization. Departmental values of certain indicators for non-institutionalized patients, after adjustment for age and sex, were represented by maps, together with their medians and IQRs. Geographical correlations were studied by Pearson's correlation coefficient (r). The Caisse Nationale de l'Assurance Maladie, as a health research institute, has permanent access to the SNDS database, approved by decree and the French data protection authority (Commission Nationale de l'Informatique et des Libertés).
Strengths and limitations One of the strengths of this study was the large size of the study population, as general scheme and local mutualist section beneficiaries correspond to 75% of all patients hospitalized for HF in France. These beneficiaries may present specific characteristics, exposures and healthcare utilization compared with beneficiaries of the other schemes, as the various French health insurance schemes are based on occupation. Nevertheless, this study was a real-life observational study. A limitation common to all reimbursement database studies is underestimation of the number of admissions for HF  [27]. However, ICD-10 codes are highly predictive of acute HF, with high positive and negative predictive values, good sensitivity and excellent specificity  [28]. A French study comparing medical charts and computer files using the ICD-10 I.
Conclusions. - - Abbreviations: ACE, angiotensin-converting enzyme; ALD, affection de longue durée (long-term disease); ARB, angiotensin II receptor blocker; HF, heart failure; ICD-10, International Classification of Diseases, 10th revision; ICU, intensive care unit; IQR, interquartile range; SNDS, Système national des données de santé (national healthcare database).
Literature Search Methodology A literature search was conducted using four electronic databases (PubMed, ISI Web of Knowledge, Web of Science and SPORTDiscus) from inception until January 2019. The following key terms (and synonyms searched for by the MeSH database) were included and combined using the operators "AND", "OR", "NOT": "fasting"*"body weight", "body mass", "body fat", "weight loss", "health", "training", "exercise", "endurance", "performance", "metabolism", "glucose", "lipid", "protein", "obesity", "cardiovascular disease", "caloric restriction", "diet", "restricted feeding", "Ramadan". Our search identified 6671 records. Only randomized controlled trials that examined the effects of fasting on performance and selected health indices in healthy sedentary individuals, untrained and trained subjects were eligible for inclusion.
24, 25 The data generated thus did not suggest which of these ICR protocols elicited the greatest impact. Additional confounding factors included age, weight and gender, as ICR studies generally included middle-aged or older adults. 15 he data in Table  4  show that body weight,  30, 31  fat mass,  32, 33  and also fat free mass,  32  are significantly lowered by ADF. Moreover, ADF-mediated weight loss occurred in obese and overweight individuals,  30, 34  and also in normalweight persons. 35 Variations in results may be related to differences in study design, duration of fasting, subject characteristics, as well as differences in weight (normal weight, overweight, obese), age, and gender. Older adults, obese individuals and women can experience a greater reduction in weight and loss of percentage fat body after fasting.
fMRI Acquisition and Preprocessing Briefly, all dat a were acquired using 3T MRI scanners, and were motion corrected, had cerebrospinal fluid and white matter signals linearly removed, and were spatially normalized to a standard M NI152 template, except for Study 2 and Study 5, which were acquired at 1. 5T and did not have cerebrospinal fluid or white matter signals removed. Please refer to studies referenced in table 1 for further details on data preprocessing.
Data Standardization We remove between-study voxel variability (i. e. the study mean maps), and standardize all single trial images within-trial (mean voxel value is zero, standard deviation of voxel values within trial image is 1). This removes global intensity differences between images, but preserves relative spatial differences within images. Thus, individual images differ from one another in the spatial configuration of their pain response but are matched on net response. Although global intensity information may be task related, it is also affected i n idiosyncratic ways by preprocessing decisions, image acquisition parameters, and spurious physiological noise. We also z-score pain ratings within study to partially control for quantitative differences in the rating scales and calibrations used between studies.
This preserves between-subject differences within-study. Following standardization, imaging data and pain ratings are averaged within the pain rating quartiles for each subject. This removes some portion of the overall variance in our data, and all reported r 2 statistics should be interpreted accordingly as variance explained in quartiled data, but this is necessary for computational tractability. Incidentally, this also partially aligns variability in our brain data with our outcome variable (pain rating), improves the sensitivity of principal component analysis to pain relevant features, and results in better multivariate models when we subsequently employ principal component regression (see MVPA "General Method Description" below).
We estimate model performance using Pearson correlation between predicted and observed values computed within-subject. This means the scope of this study is limited to within-subject EVOKED PAIN MVPA META-ANALYSIS 14 variance in pain rather than between individual differences. Subject-wise Pearson r-values are normalized by Fisher's z transformation. We repeat this entire nested cross validation procedure twice to stabilize results with respect to fold slicing (2x5 repeated k-fold cross validation). Results shown are averaged over repetitions. PCR implementation is available at https://github. com/bogpetre/mlpcr0. We employ MVPA in two methods: (1) in a mega-analysis which pools data across studies and yields a single model for each hypothesis space (and an associated performance estimate), and (  2 ) in an analysis of the learning capacity of each hypothesis space, which trains a separate model for each study (with seven associated performance estimates for each hypothesis space, one for each study's model).
The former gives us the best performing models we can obtain given our large dataset, and tells us how well we can expect them to perform ("model generalization performance"). The latter estimates how much can be learned from more typically sized datasets and allows for inference to how much will be learned by new models trained in new studies ("learner generalization performance"). Each method is described below in turn. Bayesian hyperparameter optimization samples from the hyperparameter space (PCR dimension) and estimates loss (MSE) at each sampled point using (5-fold) cross validation. Gaussian process regression estimates the posterior distribution over loss functions after each sampling using the MSE from all thus-far obtained sample estimates, stopping after 30 iterations (our implementation's default stop condition).
There are two major advantages to this approach over other methods like grid sampling. First, cross validation is subject to sampling variance (different fold slicing produces slightly different results). Gaussian process regression models this variance as noise and takes this noise into account when selecting global optima. Second, this approach estimates a loss function after each sampling iteration, allowing for intelligent selection of the next best point to sample (for instance sampling in the domain which maximally improves precision of posterior loss function estimates). We used the algorithm as implemented in the BayesianOptimization toolbox in Matlab  (Snoek, Larochelle, and Adams 2012).
MVPA Mega-analysis Brain Map Visualizations This study uses bootstrap resampling to generate brain maps representative of the study population. Bootstrap resampling identified statistically significant voxels (uncorrected, bootstrapped 95% CI does not include 0) for multisystem predictive models (neurosynth and pain pathways) and the full brain predictive model. Additionally, we used bootstrapping resampling to identify significant voxels for each region, cRSN and fRSN selected as optimal by any inner cross validation loop. For visualization we fit predictive maps to the entire dataset (balanced across studies and subjects but not resampled ), and then only show voxels identified as statistically significant under the bootstrap analysis. For any bootstrapped analysis it is problematic to identify the PCR hyperparameters, namely the dimensionality of the underlying PCA.
Study-wise MVPA and learner generalization performance We define the learner as the combination of algorithm (PCR) and the hypothesis space (regional, cRSN, fRSN, pain pathways, neurosynth or full brain). For learner performance estimates we do not aggregate data across studies, instead defining 7 non-overlapping datasets, one for each study (using the complete subject sample available in each case, in contradistinction with the mega-analysis where subjects were balanced across studies), and train separate patterns for each. This study-wise MVPA uses a total of 171 subjects and 9773 trials (16-30 subjects, 256-2592 trials per study MVPA, table  2 ). As for the mega-analysis, we average data within pain rating quartile within subject, and evaluate the performance of MVPA patterns using 2x5 repeated k-fold nested cross validation.
Bayesian hypothesis tests We assess support for a null hypothesis using Bayes factors. including associated random effects, in null models. Following  (Jeffreys 1998)  , this study considers a Bayes factor greater than 10 in favor of the null "strong" enough to confirm the null. We implement Bayesian models using markov chain monte carlo (MCMC) sampling algor ithms specified in the STAN programming language  (Carpenter et al. 2017 ). The rstanarm package efficiently and reproducibly converts our linear mixed effects model specifications used for frequentist testing into STAN source code for null hypothesis confirmation  (Goodrich et al. 2018). We subsequently perform Bayes factor tests using bridge sampling  (Gronau, Singmann, and Wagenmakers 2017). We evaluate four MCMC chains with a burn in of 5000 samples and subsequent drawing of 5000 samples each, and visually confirm chain convergence by inspecting parameter trace plots and by confirming unitary Rhat statistics.
Learner generalization performance Although the above mega-analysis and associated mediation analysis provides an estimate of expected predictive performance for our models in novel data, and informs the sources of information these models capture, it does not show if multisystem and full brain hypothesis spaces will systematically confer an advantage in pattern training in general. To establish whether multisystem and full brain patterns in general (rather than the specific patterns obtained here) outperform modular models developed in new datasets, we evaluate learner performance across individual studies (n = 16-30 subjects each, 7 studies, 171 subjects total). Regardless of a study's design or experimental manipulation there is a systematic tendency for patterns trained on multisystem and full brain hypothesis spaces to outperform patterns trained on modular hypothesis spaces (F 1,6.
Validating these models through direct application to novel datasets (without model retraining) illustrates the rigid scope of this and traditional MVPA approaches. We circumvent the problems of using a model instance to estimate the learning capacity of a class of models by simply examining performance of independent models retrained for each study separately (i. e. 42 different models, 6 hypotheses, 7 studies), following precedent established in machine learning  (Dietterich 1998; Boulesteix et al. 2015)  , and applications in genomics (de Souza, de Carvalho, and Soares 2010). Variability in model performances across studies informs the performance of new unseen models trained on unseen new subjects in new studies, and quantifies the information content which can be recovered at each scale across experimental contexts in a practical sense.