dataesr/scientific-paragraphs-categorization huggingface dataset:
Mexican Teachers Physical Activity Questionnaire (MTPAQ) This is a 5-item questionnaire, and asks for the average number of hours per week spent walking, and doing moderate and vigorous physical activities during work and leisure time over the past 12 months (Fig. 2 ). The Spanish version is included as a Additional file 1. Criteria for MTPAQ data cleaning were as follows: (1) data collected in hour per category were converted into minutes: not performing physical activity (0 min), < 1 h (30 min), 1:00 h (60 min), > 1:01 to 2:59 h (120 min), 3:00 to 4:59 h (210 min), 5:00 to 6:59 h (330 min), 7:00 to 10:00 h (510 min), > 10:00 h (600 min); (2) "do not know", "refused" or "missing data" for duration or frequency were removed from the analysis. As used in the IPAQ, truncation was performed for all daily duration values exceeding 180 min for walking, moderate and vigorous physical activity.
IPAQ-long form The International Physical Activity Questionnaire is a 31-item questionnaire that could be self-administered, conducted face-to-face, or answered by telephone  [8]. The questionnaire asks about physical activities achieved during leisure time, at home, at yard and at work. In addition, this questionnaire inquiries about active transportation, walking and sedentary activities performed for at least 10 continuous minutes in the previous 7 days  [8]. The IPAQ protocol was used to clean up IPAQ values  [9]. Information related to data cleaning procedure has been published elsewhere  [9]. Minutes per week of moderate (MPA), walking (WPA) and vigorous physical activities (VPA) performed during the four domains (leisure time, home, work and transportation) were summed to generate a measure of MVPA minutes per week.
Actical ® accelerometer This is a waistband-mounted omnidirectional device. The Actical accelerometer has been used and validated in adults  [10]. The Personal Activity and Location Measurement System (University of California, San Diego, California, United States) and IBM SPSS software; version 24 (SPSS Inc. , an IBM company Chicago Illinois, United States) were used to clean the dataset. Compliance criteria was defined based on previous studies  [11]. Accelerometer data output is expressed in total counts (countsmin -1 ). These counts were stratified as sedentary (≤ 1. 5 Mets, < 100 accelerometer counts), light (1. 5 to 2. 9 Mets, 100 to < 1500 accelerometer counts), moderate (3. 0 to 5. 9 Mets, 1500 to < 6500 accelerometer counts), or vigorous (≥ 6 Mets, ≥ 6500 accelerometer counts)  [10, 11].
Conclusion The MTPAQ had a strong reliability for MWPA and MVPA. High correlation values were observed between MTPAQ and IPAQ-long forms for MWPA and MVPA (r > 60). Minutes per week of MVPA for MTPAQ1 had fair validity values when compared to accelerometer estimates (average, AC1 and AC2). MTC is one of the few women cohort studies in Mexico, this information will serve to estimate and/or correct physical activity levels within the study. This work was supported by the American Institute for Cancer Research grant number 10A035. CM received support from the CONACYT scholarship (CVU 359065).
Acknowledgements This work was supported by the  American Institute of Cancer Research. The authors want to thank  Dr. Martin Lajous , the EsMaestras women for their participation in the study, and the field workers:  Elsa Yunes-Díaz ,  Erin Comerford ,  Jaime Jaquez ,  Jose Isaias Rangel Rosas ,  Maricruz Morales ,  Martha Tamez ,  Nataly Mercado , and  Rocío Santoyo Vistrain.
Statistical Analysis The statistical analysis had two successive steps. First, the data collected from all survey respondents were characterized in terms of frequency and percentage distributions. Next, responses were subdivided according to HD status to see if there were any statistically significant differences in the way individuals with HDRisk and PreHD answered the survey. This latter step provides critical information for the design and implementation of actions tailored to each specific HD group. When examining the differences between the HDRisk and the PreHD groups, nominal/categorical variables were compared resorting to chi-square tests of independence. Comparisons of quantitative variables between the two groups were performed using Mann-Whitney U tests. The analysis was carried out using IBM SPSS Statistics 24, and all calculations adopted a significance level of α = 0.
Acknowledgments: The authors gratefully acknowledge all survey participants. The authors would like to acknowledge the following HD family members and HD researchers for their relevant input to the topic of research participation in HD:  Mara Andrade ,  Alina Baierl ,  Anna Castaldo ,  Lucy De Cat ,  João Freixo ,  Mark Guttman ,  Anne Elizabeth Magnusson ,  Álvaro Mendes ,  Lorenzo Nanetti ,  Dag Nordbo ,  Julia Oblitsova ,  Milena Paneque ,  Carmen Peiró ,  Dimitri Poffé ,  Hugh Rickards ,  Yury Seliverstov ,  Jorge Sequeiros , and  Osvaldo Tassano.
3 ). Our ITC experiments revealed that Zn binding is highly selective and specific. On the other hand, our kinetic data show that zinc is not necessary for L-Asn hydrolysis. The enzyme kinetic data contradict, therefore, the conclusion about the role of the Zn 2+ cation that might be drawn from its open coordination sphere. We cannot exclude, of course, that ReAV might also have other catalytic properties and act as an enzyme with dual (or even multiple) activity and that for the other activity zinc might be necessary. This speculation, however, would need a lot of experimental work (and even serendipity) to be verified. To date, we have only checked the potential βlactamase and glutaminase activities and received negative results. Finally, R. etli is also interesting as an evolutionary curiosity.
The pH activity profile was additionally confirmed by Berthelot reaction  53. Protein identity was confirmed by MS as follows. Protein was reduced with NH 4 HCO 3 and DTT at 95 °C; alkylation was carried out with iodoacetamide. Protein was digested overnight at 37 °C with sequencing-grade trypsin (Promega). Peptide solutions were deposited on a MALDI AnchorChip plate (Bruker Daltonics GmbH) with saturated solution of α-cyano-4-hydroxycinnamic acid in 50% ACN and 0. 1% TFA (matrix). Spectra were acquired with a minimum of 2500 laser shots in a MALDI TOF/TOF UltrafleXtreme mass spectrometer (Bruker Daltonics GmbH). The MS/MS data of combined peptide mass fingerprints were used for searching UniProt database installed on Mascot server (Matrix Science, London, UK). Site-directed mutagenesis of ReAV was carried out according to Q5-site mutagenesis protocol (New England Biolabs) using site-specific non-overlapping mutagenic primers (Supplementary Table  1 ) to introduce substitutions into the following positions: S48A, S80A, K51A, K263A, and C135A.
After incubation, TPEN was removed by dialysis and gel filtration on a HiLoad 16/600 Superdex 200 (GE Healthcare) column (50 mM Tris pH 8. 0, 100 mM NaCl, 1 mM TCEP). Titrations were performed at 25 °C using a MicroCal iTC200 calorimeter (GE Healthcare). The protein in the cell at ~100 µM concentration (determined at 280 nm) was titrated with 2 µl aliquots of 1 mM XCl 2 salts (X = Zn, Mg, Ni, Mn, Cd). Both, metal and protein were in 25 mM Tris buffer pH 8. 0, with 100 mM NaCl and 1 mM TCEP. The raw ITC data were analyzed with the Origin 7. 0 software (OriginLab) to obtain the thermodynamic parameters of the complexation reactions: stoichiometry (N), dissociation constant (K d ), as well as changes in enthalpy (ΔH) and entropy (ΔS). The measurements were performed in duplicate. Measurements of enzymatic activity of ReAV.
Next, the differential power change (dQ/dt) arising from the turnover of the substrate into product was determined in a heat-rate shift experiment, in which the substrate at 100 mM concentration (in the syringe) was injected in nineteen 2 µL aliquots with short 60 s intervals (to minimize substrate depletion) into the cell with the enzyme kept at ~14. 7 nM concentration. All measurements were taken at 37 °C, with stirring at 700 rpm and differential power set to 5 µcal/s. The raw rate data were analyzed using the Enzyme Assay Method 2 included in the ITC DataAnalysis option of the Origin 7. 0 software (OriginLab). Briefly, they were transformed into reaction rates and L-asparagine concentrations and fitted to Michaelis-Menten equation. Final kinetic parameters at pH 7. 5, 8. 0 and 9. 0 were calculated by averaging the values obtained in three separate experiments.
2 M Li 2 SO 4 , 0. 01% (w/v) heptane-1,2,3-triol, 0. 1 M Tris pH 8. 0 (orthorhombic P2 1 2 1 2 1 , OP, START); 25% PEG3350, 0. 2 M Li 2 SO 4 , 0. 1 M Tris pH 8. 0, ~20 mM n-octyl-β-D-thioglucoside (monoclinic P2 1 , MP1); 2% PEG8000, 20% PEG400, 5 mM Mg(CH 3 COO) 2 pH 6. 5, ~6 mM sucrose monolaureate (monoclinic P2 1 , MP2); and 2% PEG8000, 20% PEG400, 5 mM Mg(CH 3 COO) 2 pH 6. 5, ~0. 5 mM CYMAL-3 (monoclinic C2, MC). Before data collection, crystals were cryoprotected in their mother liquor supplemented with ethylene glycol or glycerol and vitrified in liquid nitrogen. X-Ray data collection, structure solution and refinement. Structural analysis. X-Ray diffraction data were collected using synchrotron radiation from beamlines at Petra III EMBL/DESY in Hamburg or BESSY  55  in Berlin. Initially, the diffraction data were collected at EMBL beamline P13  56  at the Petra III storage ring.
Since in the 367-residue amino acid sequence of ReAV there are as many as 21 Cys/Met residues, long-wavelength (λ = 2. 0664 Å, 6. 0 keV) anomalous X-ray diffraction data were collected for S-SAD phasing. The data were measured at seven positions of a rod-shaped crystal with approximate dimensions 350 × 200 × 150 μm. At each position, 3600 frames of 0. 1°oscillation and 40 ms exposure were collected. The anomalous data (as well as all subsequent X-ray diffraction images) were indexed and integrated with XDS and further processed with XSCALE  57. Out of the seven anomalous data sets, the best four were selected, scaled and merged together to serve as an experimental S-SAD data set (START). A high-resolution native data set (1. 29 Å, OP) was collected at λ = 0. 9762 Å for the same crystal. The data were input to SHELXC and SHELXD  58  for sulfur-substructure determination using the HKL2MAP interface  59.
Structures were refined with REFMAC5 using anisotropic (OP) or TLS (START, MP1, MP2, MC) protocols. The electron density maps were inspected in Coot. The statistics of data collection and structure refinement are summarized in Supplementary Table  2. The structures were analyzed and visualized using PyMOL  64  , DALI  22  and PISA  65. The electrostatic potential distribution was calculated using Adaptive Poisson-Boltzmann Solver (APBS) algorithm  [66] [67] [68]  as implemented in the PyMOL APBS Plugin. Due to low sequence similarity, superpositions of ReAV and its structural homologs identified by DALI were calculated using the Secondary Structure Matching tool  69  from the ccp4 package  70. Sequence similarity between ReAV and selected structural homologs was assessed by pairwise sequence alignments in EMBOSS Needle  71.
Acknowledgements We thank  Dr. Milosz Ruszkowski  for crystal vitrification and participation in synchrotron data collection at  EMBL/DESY  and  Dr. Jakub Barciszewski  for data collection at BESSY. We thank  Dr. Piotr Bonarek  and  Dr. Jakub Nowak  for help with the CD and nanoDSF experiments. We acknowledge  Dr. Lukasz Marczak  for carrying out the MS measurements. We thank the  Helmholtz-Zentrum Berlin für Materialien und Energie  for the allocation of synchrotron beamtime, and  Dr. Thomas Schneider  for his interest in the S-SAD experiment at  EMBL/DESY. Work supported by  National Science Centre (NCN, Poland)  grant  2020/37/B/NZ1/03250.
Data availability Atomic coordinates and structure factors corresponding to the final crystallographic models of ReAV generated in this study have been deposited in the Protein Data Bank (PDB) under accession codes 7os3 (START), 7os5 (OP), 7os6 (MP1), 7ou1 (MP2), and 7oz6 (MC). The corresponding raw diffraction images have been deposited in the Macromolecular Xtallography Raw Data Repository (https://mxrdr. icm. edu. pl) under DOI numbers: 10. 18150/74YTYQ (START), 10. 18150/MUKYJI (OP), 10. 18150/XBX6JE (MP1), 10. 18150/VQQIHQ (MP2), and 10. 18150/6W9HGI (MC). Source data are provided with this paper.
Pairs of reviewers abstracted data from the included studies, including study design, baseline characteristics, PD subtyping methodology and results. We assessed the methodologic quality and clinical applicability of the included studies using two tools developed for the current study: a 13-item checklist for Methodologic Quality (item score range: 0-2, higher score being better, see Table  4 ) and an 11-item checklist for Clinical Applicability (items rated as Unknown, Limited/Low, Moderate, and Strong, see Supplementary Table  1 ). We compared rating frequencies between two publication periods  (1980-2014 vs. 2015-2019)  and two subtyping methodologic approaches (datadriven vs. hypothesis-driven) using Fisher's exact tests. The definition of publication periods was pragmatic to allow for a balanced distribution of included studies into a more recent group (representing the current state of the field) and older studies allowing for a sufficient sample size in each group to test for temporal trends.
Research methodology: Can we do better? Quality ratings for subtyping studies revealed clear areas for improvement. More extensive use of longitudinal data is critical for an understanding of the stability of proposed subtypes, and their prognostic value. The use of longitudinal data to define or evaluate subtypes does appear to be more common in the last 5 years  [4] [5] [6] [7] [8] , making use of large, publicly available cohorts  [4, 9]. To our knowledge, only one study has used longitudinal profiling as the basis for defining subtypes  [7] , incorporating data on the evolution of clinical or biological features across time into the definition of subtypes. Alternatively, serial cluster analyses could provide data about the stability of proposed subtypes and the influence of disease duration on their characteristics.
ACKNOWLEDGMENTS The authors would like to acknowledge the support of the  International Parkinson and Movement Disorders Society (MDS)  for facilitating meetings of the Task Force and, particularly, the assistance of  Sarah Smith  from the  MDS  for assistance in coordination and communications. from  Parkinson's UK  and supported by the  National Institute for Health Research (NIHR) Oxford Biomedical Research Centre  based at  Oxford University Hospitals NHS Trust  and  University of Oxford  and the  Dementias and Neurodegenerative Diseases Research Network (DeNDRoN). Nicholas Bohnen received research funding from the  NIH, Department of Veterans Affairs  and the  Michael J. Fox Foundation. Glenda Halliday  is a  National Health and Medical Research Council of Australia Leadership Fellow  (# 1176607 ).
g. Berger et al. 2020 ). However, interpolation can become computationally expensive at high input dimensions and grid size, and evaluating the likelihood using modern Bayesian sampling techniques is slow. Therefore, we use machine learning to map stellar model inputs to observables to provide a fast way to sample the HBM. In this work, we train an artificial neural network (ANN) on a large grid of stellar models. There have been similar applications of ANNs in asteroseismology  (Verma et al. 2016; Bellinger et al. 2016; Hon et al. 2017 Hon et al. , 2018;; Hendriks & Aerts 2019)  but not yet in the context of an HBM. Using the machine learning speed-up, we demonstrate a scalable method for obtaining fundamental stellar parameters. In Section 2, we describe the data for the sample of 81 Kepler dwarfs and subgiants studied in this work.
DATA For this study, we selected the sample of 415 stars from the first APOKASC catalogue of dwarfs and subgiants  (Serenelli et al. 2017, hereafter S17). This sample provides an extensive set of dwarfs and subgiant stars with asteroseismic detections observed by the Kepler mission. S17 used grid-based modelling to determine the ages (), masses (), radii (), and surface gravity (log ) of stars in the sample, using global asteroseismic parameters, effective temperature ( eff ), and metallicity ([M/H]) as inputs. Using five independent pipelines, S17 determined values for global asteroseismic parameters -the large frequency separation Δ and the frequency at maximum power,  max with median uncertainties of 1. 7 per cent and 4 per cent respectively. We chose to adopt the Δ determined in their work as inputs for our method.
2020 ) with K S -band photometry, Gaia DR2 parallaxes, ASPCAP [M/H] and  eff , and asteroseismic log  as inputs. This involved computing absolute K S -band magnitudes using the Gaia DR2 parallaxes and extinctions determined by the 3D galactic reddening maps of  Green et al. (2018). We determined absolute bolometric magnitudes by interpolating the MIST bolometric correction tables as a function of  eff , log  and [M/H] (Dotter 2016;  Choi et al. 2016). We adopted the uncertainty of 0. 02 mag assumed by for both the extinctions and bolometric corrections, corresponding to typical systematic errors in extinction maps and bolometric fluxes (e. g. Zinn et al. 2019b; Tayar et al. 2020). We obtained luminosities for the sample with a median uncertainty of 3. 4 per cent. The final sample comprised 81 stars for which we had complete data for  eff , [M/H], Δ, and  to use as inputs for our stellar modelling method -see Table  1.
Statistical models We devised three Bayesian models, each with varying levels of parameter sharing (pooling) between stars in the population. Initially, we tested the models and demonstrated reduction of statistical uncertainties in the stellar fundamental parameters by analysing a random sample of 100 synthetic stars modelled using MESA. Then, we applied the models to the sample of stars in Table  1  (with and without solar data for two of the models) and compared the results with that of S17. Our first model was equivalent to modelling each star individually and featured no pooling; henceforth, we refer to it as the no-pooled (NP) model (see Section 3. 2. 1). We then derived two hierarchical Bayesian models (HBMs) which use population-level parameters to describe their distribution in the sample.
Naturally, the uncertainty is still limited by observational precision. However, hierarchical modelling as demonstrated in this work, allows us to get the most out of our data and paves the way for a data-driven analysis of model systematics. Including all-sky data from TESS and in anticipation of PLATO  (Rauer et al. 2014)  we can expect our sample size of asteroseismic dwarfs and subgiants only to increase. There is also scope to extend our grid of models to include red giants, for which there are vast catalogues of stars already studied with Kepler  (Pinsonneault et al. 2018). but also constrained it to  < 15 Gyr because we consider ages above ∼ 15 Gyr unphysical and such points are sparse in the training data. The test dataset was set aside and evaluated on the final ANN.
A2 Tuning We trained an ANN to reproduce stellar observables according to our choice of physics with greater accuracy than typical observational precisions. We experimented with a variety of ANN parameter choices, such as the architecture, activation function, optimization algorithm, and loss function. We tuned the ANN parameters by varying them in both a grid-based and heuristic approach, each time evaluating the accuracy using the validation dataset. During initial tuning, we found that having stellar age as an input was unstable, because it varied heavily with the other input parameters. We mitigated this by introducing an input to describe the fraction of time a star had spent in a given evolutionary phase,  evol. 𝑓 evol = 𝑓 MS , 𝑓 MS ≤ 1 1 + 𝜏 -𝜏 MS 𝜏 end -𝜏 MS , 𝑓 MS > 1 (A4) where  end is the age of the star at the end of the track, 𝑓 MS = 𝜏 𝜏 MS , ( A5 ) and  MS is the MS lifetime.
After preliminary tuning, we chose the ANN input and output parameters to be X = {  evol , ,  MLT ,  init ,  init } and Y = {log(),  eff , , Δ, [M/H] surf } respectively. A generalised form of our neural network is depicted in Fig. A1. The inputs corresponded to initial conditions in the stellar modelling code and the outputs corresponded to surface conditions throughout the lifetime of the star, with the exception of age which is mapped from  evol. We standardised the training dataset by subtracting the median,  1/2 and dividing by the standard deviation,  for each input and output parameter. We found that the ANN performed better when the training data was scaled in this way as opposed to no scaling at all. We present the parameters used to standardise the training dataset in Table  A1. We found that the optimal choice of architecture ( and ) varied depending on our choice of other ANN parameters.
We compared the performance of two optimisers: Adam (Kingma & Ba 2014) and stochastic gradient descent (SGD; see e. g. Ruder  2016 ) with and without momentum  (Qian 1999). Both optimizers required a choice of learning rate which determined the rate at which the weights were adjusted during training. We found that Adam performed well but the validation loss was noisy as a function of epochs as it struggled to converge. The SGD optimizer was less noisy than Adam, but it was difficult to tune the learning rate. However, SGD with momentum allowed for more adaptive weight updates and outperformed the other configurations. There are several ways to reduce over-fitting, from minimising the complexity of the architecture, to increasing the size and coverage of the training dataset. One alternative is to introduce weight regularisation.
So-called L2 regularisation adds a term, ∼     2 , to the loss function for a given hidden layer,  which acts to keep the weights small. We varied the magnitude of   and found that if it was too large it would dominate the loss function, but if it was too small then performance on the validation dataset was poorer. We compared the choice of two error functions: mean squared error (MSE) and mean absolute error (MAE). The former is widely used among ANNs because it is more sensitive to large errors. However, we tracked both metrics regardless of which was added to the loss function and found that MAE converged faster. Although MAE is less effective at large errors, we found that these were typically at the edges of the grid and the accuracy was good enough everywhere else. After extensive tuning, we opted for an ANN with  = 128 neurons in each of  = 6 hidden layers.
A3 Testing The test dataset contained ∼ 2 × 10 6 stellar models evolved in the same way as the training dataset, but with initial conditions chosen randomly across the range of the grid. We made predictions for the test dataset, deriving luminosity from the output radius and effective temperature, using the final trained ANN as described in Appendix A2. We then evaluated the accuracy of the ANN by taking the difference between the test truth and ANN prediction,  true - pred. We found good agreement between the test dataset and ANN predictions, within typical observational uncertainties. We noted that the largest errors lay at the boundaries of the training data and in areas sparsely populated by the grid. This is apparent in Fig. A3  where we plot the test error against each parameter. For example, the spread in error increases for [M/H] surf < -0.
The median error,  1/2 and median absolute deviation of the error,  MAD = 1. 4826 • median( | (Y) - 1/2 |) for a given ANN output parameter, Y from the test dataset. The error,  (Y), is given in the table header, where Y = Y -Y. Error  / (%)  eff (K) / (%)  / (%) Δ ( Hz)  [M/H] surf (dex).