dataesr/scientific-paragraphs-categorization huggingface dataset:
This research is partly based on observations made with the Mercator Telescope, operated on the island of  La Palma  by the  Flemish Community , at the  Spanish Observatorio del Roque de los Muchachos  of the  Instituto de Astrofísica de Canarias. HERMES is supported by the  Fund for Scientific Research of Flanders (FWO) ,  Belgium , the  Research Council of K. U. Leuven, Belgium , the  Fonds National de la Recherche Scientifique (F. R. S. -FNRS) ,  Belgium , the  Royal Observatory of Belgium , the  Observatoire de Genève, Switzerland , and the  Thüringer Landessternwarte, Tautenburg, Germany. This research made use of the SIMBAD and VIZIER databases at the  CDS, Strasbourg (France) , and  NASA 's Astrophysics Data System Bibliographic Services. We made use of the Python programming language ( Rossum 1995 ) and the open source Python packages numpy (van der Walt et al.
Acknowledgements This study is part of a consortium working within the  INTENSE  project, partially supported by  BPI France , within the  'Investments for the Future ' program in France. F. Bottausci ,  N. Verplanck ,  C. Pudda ,  F. Baleras ,  G. Grenot ,  M. C. Gentet  ( CEA Leti DBTS ) designed and manufactured the parylene/platinum electrode arrays and the instrumentation set up. M. D'Orchymont  ( CEA Leti CLINATEC ) performed the surgery on rodents.
Subjects Three hundred and fifty-three subjects from the AddNeuroMed study were included  (113 HC, 121 MCI, 119 AD). The AddNeuroMed study is an integrated project funded by the European Union Sixth Framework Program aimed at establishing and validating novel biomarkers of disease and treatment based upon in vitro and in vivo human and animal models of AD. Data were collected from six participating sites across Europe: University of Kuopio, Finland; University of Perugia, Italy; Aristotle University of Thessaloniki, Greece; King's College London, United Kingdom; University of Łódź, Poland; and University of Toulouse, France  (Lovestone et al. , 2009). Subjects were patients who attended local memory clinics and received a diagnosis of MCI and early AD, while HC subjects were recruited from non-related members of the patient's families, caregiver relatives, and social centers for the elderly or General Practitioner (GP) surgeries.
MRI Acquisition Standardized MRI data acquisition techniques were in place for AddNeuroMed to ensure homogeneity across data acquisition sites, using the ADNI protocol 1. The imaging protocol included a 1. 5T high-resolution T1 weighted sagittal 3D MP-RAGE volumes (voxel size 1. 1 × 1. 1 × 1. 2 mm 3 ), and axial proton density with T2 weighted fast spin echo images. A comprehensive quality control procedure was carried out on all MR images according to the AddNeuroMed quality control framework  (Simmons et al. , 2009).
Sample Preparation and Data Acquisition All participants were required to fast for 2 h before blood sample collection; only water or fluids containing no milk or sugar were allowed during the fasting period. Plasma samples were collected using EDTA coated tubes and centrifuged at 3000 rpm for 8 min at 4 • C before being aliquoted and then frozen at -80 • C. Plasma samples were analyzed with ELISA (Bio-Rad Laboratories, Hercules, CA, United States) for the assessment of peripheral IL-4. The samples were prepared according to the manufacturer's instructions. All samples and standards were run in duplicate and were measured as pg/ml. The system running protocol was set according to the manufacturer's guidelines.
Statistical Analysis The observed data are presented as means ± SD. To approximate a normal distribution, plasma IL-4 levels were logarithmically transformed for all calculations. Chi-square test, One-way ANOVA followed by Bonferroni post hoc analysis and correlation by r Pearson were performed where appropriated. Linear regression analysis was conducted using hippocampal subfield volumes as dependent variables and log-transformed IL-4 measures along with age, education, APOE4 status and MMSE score as independent variables. All p-values presented are 2-tailed, and p ≤ 0. 05 was chosen for levels of significance. Statistical analysis was conducted using SPSS Statistics (Version 21. 0, SPSS Inc. , United States).
, 2010; La Joie et al. , 2013; Boutet et al. , 2014; Voevodskaya et al. , 2014). Carlesimo et al. (2015) , using an automated hippocampal segmentation protocol in a smaller cross-sectional imaging dataset, reported the early and severe involvement of the subiculum and presubiculum in patients with MCI and AD compared to HC. According to our findings, other studies have also shown that CA1 and subiculum atrophy was present at a very early stage of AD, as it was detectable in cognitively healthy individuals that later developed MCI or AD  (Csernansky et al. , 2005). Altogether, our report stresses the importance of hippocampal subfield measurements as sensitive markers of AD processes and emphasizes their potential role as biomarkers for early AD detection. IL-4 is a cytokine that functions as a potent regulator of immunity, secreted primarily by mast cells, Th2 cells, eosinophils, and basophils.
0 mM, and TEA-Cl 10. 0 mM. For K dr measurements, the pipette solution contained: CaCl 2 0. 1 mM, EGTA 1. 1 mM, glucose 25. 0 mM, HEPES 10. 0 mM, K + -gluconate 100. 0 mM, Mg 2+ -ATP 3. 0 mM, MgCl 2 5. 0 mM, and NaCl 5. 0 mM. The bath solution consisted of: CaCl 2 1. 8 mM, glucose 16. 0 mM, HEPES 10. 0 mM, KCl 5. 4 mM, MgCl 2 0. 8 mM, and NaCl 110. 0 mM. The recordings were performed at room temperature using an L/M-EPC7 amplifier (List Medical, Darmstadt, Germany). Signals were filtered using the EPC7 10 kHz lowpass filter and obtained data were analyzed with PClamp 10 software (Version 10. 1. 0. 10, Molecular Devices, Sunnyvale, CA, USA). Due to deteriorating cells accompanied by increasing leakage currents, the maximal measurement period for each cell culture dish was limited to 60 min.
In the following runs, we proceeded in the same way, but we further added 0. 5 µM of either H6HT-SOS1cat fusion proteins to the reaction mix, thus determining their impact on nucleotide exchange rates. For data analysis, we exported the data to an analysis software (OriginPro). We normalized the fluorescence intensity for the first time point after THE addition of GDP to 1 and, according to that, we calculated the relative fluorescence of later time points. Because of the large excess of GDP over H-RAS and the exponential decrease of fluorescence due to mant-GDP release from H-RAS, we calculated a pseudo-first order rate constant (observed rate constant, K obs ) using a "Dissociation-One-phase exponential decay"-fitting model  [17].
Fluorescein-labeled BSA without HaloTag™ and thereby unable to bind to HTL-MNPs served as negative control. Each fusion protein was mixed in a 1:1 ratio with HTL-functionalized or non-functionalized MNPs as an individual sample and equilibrated for 10 min. Before applying the sample on a 35 mm glass bottom cell-culture dish, the glass bottom was flamed with a Bunsen burner in a laminar flow hood to remove fluorescent impurities. FCS measurements were performed at 25 • C on a Leica TCS SP8 at 594 nm wavelength in combination with the multichannel picosecond event timer and TCSPC module HydraHarp 400 (PicoQuant, Berlin, Germany). Data were analyzed using the SymphoTime 64 software and a 3D Triplet autocorrelation function (PicoQuant).
Electrophysiological Characterization of Induced Neurons mDA neurons derived from human neural progenitor cells were evaluated for electrophysiological function using whole-cell patch clamping after two weeks of maturation. We distinguished between pyramidal and bipolar neurons and showed peak Na v current densities, average current-voltage relationships normalized to cell capacitances, and the steady-state inactivation of Na v channels fitted with modified Boltzmann equation. In all graphs, for each data point error, bars represent the standard error of mean (SEM). The peak Na v current densities were averaged and normalized to the capacitances (Figure  5A ). The current-voltage relationships of Na v channels followed a bell curve-like shape and allowed a conclusion about the channel opening probability depending on the applied voltage.
Acknowledgments: We are grateful for the support of  Christian Herrmann  and his group ( Ruhr-Universität Bochum, Bochum, Germany ) in the GEF activity measurements and providing us E. coli strain BL21 RIL cells and mant-GDP-loaded RAS. We thank  David Gnutt  for his advices in FCS measurements and critical discussion. We thank  Annika Haak ,  Heiko Leßlich  and  Lars Klapal  ( Electrobiochemistry of Neural Cells, Ruhr-Universität Bochum, Bochum, Germany ) for constant support during electrophysiological recordings and data evaluation. We thank  Sebastian Neumann  ( Molecular Biochemistry, Ruhr-Universität Bochum, Bochum, Germany ) for critical proofreading. We are thankful to all partners of the  MAGNEURON  project by name  Jacob Piehler  and  Domenik Liße  ( University of Osnabrück, Osnabrück, Germany ),  Mathieu Coppey  ( Institut Curie, Paris, France ),  Neil Telling  and  Monte Gates  ( Keele University, Keele, UK ),  Alicia El Haj  ( University of Birmingham, Birmingham, UK ) and  Pierre Joly  ( Efficient Innovation, Paris, France ).
We calculated an overall average for each participant, which is thought to reflect how much each individual was thinking off-task thoughts. In this way, we assessed both off-task frequency (i. e. , the number of moments when attention was not directed towards the reading task) and the content of these experiences. Prior to data analysis, all variables were z-transformed and outliers more than 2. 5 standard deviations above or below the mean were identified. Using this criterion, there was one outlier for text-based memory, three outliers for off-task frequency and no outliers in the content of off-task thought questionnaire. These outlying values were imputed with the cut-off value (i. e. , +/-2. 5 standard deviations above or below the mean). No participant was removed from the data analysis as a result of this process.
Neuroimaging data acquisition. A 9-minute resting-state fMRI scan was used in Experiment 2, recorded using single-shot 2D gradient-echo-planar imaging (TR = 3 s, TE = minimum full, flip angle = 90°, matrix size = 64 × 64, 60 slices, voxel size = 3 mm × 3 mm × 3 mm, 180 volumes). During resting-state scanning, the participants were instructed to focus on a fixation cross with their eyes open and to keep as still as possible, without thinking about anything in particular. Neuroimaging data for Experiments 1 and 2 were collected in the same session, with the resting-state sequence presented first, so that measures of intrinsic connectivity could not be influenced by the words and nonwords that were presented in the sentence-reading task. There was a break of a few minutes between these scans which allowed us to remind participants of the task requirements and set up the scanning.
Neuroimaging data pre-processing. All functional and structural data were pre-processed using a standard pipeline and analysed via the FMRIB Software Library (FSL version 6. 0, www. fmrib. ox. ac. uk/fsl). Individual FLAIR and T1-weighted structural brain images were extracted using FSL's Brain Extraction Tool (BET). Structural images were linearly registered to the MNI152 template using FMRIB's Linear Image Registration Tool (FLIRT). The sentence-reading functional neuroimaging data were pre-processed and analysed by using FSL's FMRI Expert Analysis Tool (FEAT). A standard pre-processing pipeline was applied, including motion correction via MCFLIRT, slice-timing correction using Fourier space time-series phase-shifting, and spatial smoothing using a Gaussian kernel of FWHM 6 mm. In addition, for the task-based fMRI data in Experiment 1, high-pass temporal filtering (sigma = 100 s) was applied in order to remove temporal signal drift.
Neuroimaging analysis. Task-based fMRI analysis (Experiment 1). This analysis identified sites in which activation during the sentence reading task was modulated by individual differences in text-based memory, off-task frequency or the content of off-task thought (i. e. , NYC-Q), measured outside the scanner. In the first-level analysis of the sentence reading task performed in the scanner, we identified voxels responding to (i) meaning and (ii) orthographic inputs devoid of meaning, through the contrasts of Meaningful > Baseline, Meaningless > Baseline, Meaningful > Meaningless, plus the reverse, for each participant. In the higher-level analysis at the group level, z-transformed behavioural data for text-based memory, off-task frequency and NYC-Q were added as explanatory variables, using FMRIB's Local Analysis of Mixed Effects (FLAME1), with automatic outlier de-weighting  57.
nmr. mgh. harvard. edu/fswiki/CorticalParcellation_Yeo2011). This identified a region of middle temporal gyrus (MTG) within DMN, and a region of inferior temporal gyrus (ITG) in the FPN, as well as another region of inferior temporal region (ITG)/lateral occipital cortex (LOC) in the DAN. These regions were taken as seeds in a subsequent analysis of intrinsic connectivity. We extracted the time series from the seeds and used this data as explanatory variables in whole-brain connectivity analyses at the single-subject level. Sixty-four participants were included in this analysis (five participants without intrinsic connectivity data were excluded). These functional connectivity maps were then related to individual differences in behaviour using a multiple regression model, in which z-transformed scores for text-based memory, off-task frequency and NYC-Q were added as explanatory variables.
Acknowledgements We would like to thank  Tirso Gonzalez Alam ,  Theodoros Karapanagiotidis  and  Charlotte Murphy  for their help with data analysis. E. J. was supported by the  European Research Council  (Project ID: 771863 -FLEXSEM), J. S. was supported by the  European Research Council  (Project  ID :  646927-WANDERINGMINDS ), and M. Z. was supported by the  China Scholarship Council (CSC) Scholarship  (No. 201704910952 ).
(Broadly speaking, this line gives the direction in which the cloud of data points is most elongated. ) The second component is obtained by finding another one-dimensional space that is orthogonal to the first one while maximising the variance of data projected onto the former. And so on for subsequent components, stopping after a small number of components have been extracted. In many cases, these few first components are sufficient to recover a large proportion of the overall multidimensional variability present in the original data set, thereby performing a reduction of dimension while keeping most of the information. In practice, these directions (given by the weights of each linear combination) are easily obtained thanks to a linear algebra tool called the Singular Value Decomposition (SVD); see  (Meyer 2000, Figure 5.
(2010)  and their associated R package glmnet  (Friedman et al. 2018). By imposing the assumption of sparsity these methods provide stable estimates of the coefficients, and often have improved predictive performance. However, ultra high dimensional data sets (p >> n) pose a computational challenge in fitting these methods. This problem has only been addressed recently by  Zeng & Breheny (2017a)  but only for an univariate re-sponse (see their R package biglasso  (Zeng & Breheny 2017b)). In this article, we consider big data extensions to PLS methods which incorporate sparsity in fitting the PLS weights. Similarly to standard PLS methods, these sparse approachs permit dimension reduction by constructing components as linear weighted combinations of the original data. However, sparse PLS approachs allow for some of these weights to be set to zero.
Consequently, a variable in the original dataset might well be used in constructing only a subset of the PLS components. An advantage of having several components in PLS is to visualise the data projected on various 2D planes spanned by pairs of components (as illustrated on our Figure  3 ) in order to gain better insight into the underlying structure of the data. Also, these sparse approaches are often only considered for two blocks of data in a regression context, whereas standard PLS can deal with several blocks of data in a more general analysis. Closely related to Lasso regression is (multivariate) ridge regression  (Brown & Zidek 1980 ) that shrinks the coefficients using a L 2 norm (instead of a L 1 norm for the Lasso). This different type of shrinkage does not lead to sparsity of the coefficients though, and consequently does not perform a reduction of dimension.
Gathering an accurate description of all these methods in a single document, along with their complete mathematical proofs, constitutes a valuable addition to the literature; see also  (Lafaye de Micheaux et al. 2017 , Wegelin 2000). In Section 3 we present the sparse versions of the four types of PLS, as well as a recent group and sparse group version. A new unified algorithm is then presented in Section 4 to perform all four types of PLS including the regularised versions. Various approaches to decrease the computation time are proposed. We explain how the whole procedure can be made scalable to big data sets (any number of measurements, or variables). In Section 5, we demonstrate the performance of the method on simulated data sets including the case of a categorical response variable. Our algorithm is implemented in the R programming language (R Core Team 2017) and is available at https://github.
SVD when p or q is very large The main step of our algorithm is the computation of the first triplet (δ 1 , u 1 , v 1 ) in the SVD of the (p × q) matrices M h-1. The irlba (Baglama & Reichel 2015) R package can be used to compute quite easily this triplet for values of p and q as big as 50, 000. This package is based on an augmented implicitly restarted Lanczos bidiagonalisation method. When p (or q) is much larger, another approach is necessary to compute the SVD of M h-1 ; see e. g. ,  (Liang et al. 2016). Suppose that p is large but not q, which is common in several applications. We thus suppose that p q. The Algorithm 1 in  Liang et al. (2016)  is now presented to highlight the elements needed in our algorithm. We can partition a large matrix M : p × q by rows into a small number s of submatrices (or chunks): M T = M T 1 M T 2 • • • M T s.
The EMNIST data set The now famous MNIST data set (LeCun & Cortes 2010) has become a standard benchmark for classification systems. The Extended MNIST (EMNIST), another freely available data set, "constitutes a more challenging classification task involving letters and digits, and that shares the same image structure and parameters as the original MNIST task"  (Cohen et al. 2017). This data set consists of n = 280, 000 handwritten digit images. It contains an equal number of samples for each digit class (0 to 9). The images are already split into a training set of 240,000 cases and a test set of 40,000 cases. Each case is a 28 × 28 pixels gray-scale image whose intensity values range in  [0, 255]. Overall, if imported into R, they would use around 1. 6 GB. We applied our PLS-DA algorithm on this data set, where the matrix X : n × p (p = 784) contains the images, and the matrix Y : n × q (q = 10) indicates the label of each image.
We ran a PLS-DA model with 20 latent components which enabled us to get an accuracy (percentage of correct classification) of 86% in around 3 minutes using 2 cores. Note that in  Cohen et al. (2017) , linear classifiers produced an accuracy of around 85% for the EMNIST data. We also considered a larger version of the EMNIST data set where each image in the training set is rotated 5 and 10 degrees in both a clockwise and counterclockwise direction. The enlarged data set contains 1,200,000 images and uses 7. 5 GB of memory. It is more representative of data sets which Wine plot of the elapsed computation time for fitting a single component of the PLS discriminant analysis algorithm using 2, 4 or 6 cores (on a laptop equipped with 8 cores). On the vertical axis, ngx indicates that x chunks were used in our algorithm.
Introduction Health administrative databases (HAD) are a valuable source of data for studying the association between treatment and disease outcome  [1] [2] [3] [4]. In France, the national inter-regime information system on health insurance (SNIIRAM) database covers the entire French population (65 million inhabitants)  [2] [3] [4]. This database includes demographic (age, gender, city of residence) and out-hospital reimbursement (drug dispensing and long-term diseases). A 1/97th random permanent sample of SNIIRAM, the general sample of French health insurance beneficiaries (Echantillon Generaliste des Beneficiaires, EGB), which is representative of the national population of health insurance beneficiaries, was composed in 2005 to allow a 20-year follow-up. These administrative databases are readily available for epidemiological research.
The large number of patients without loss of follow-up allows for sufficient powering of studies. Furthermore, the information is plentiful, comprehensive and detailed, without any exclusions  [2] [3] [4]. Administrative databases are not without limitations. Information on potential confounders is often missing and recent reviews have described current strategies to control for unmeasured confounding in HAD  [5] [6] [7]. Sensitivity analyses  [8] [9] [10]  have been helpful in the past to adjust for bias due to unmeasured confounders, but they are limited because they cannot control for multiple unmeasured confounding variables. Occasionally, detailed confounding information missing from the HAD (main sample) can be procured from a validation sample that may be a subsample of the HAD (internal validation sample) or another cohort assumed to be representative of the same population (external validation sample).
Study population Main sample. The main database was EGB  [2]. The data consisted of 60,243 subjects of at least 69 years of age in 2006 who were alive and had not dropped out of the EGB before 2009. BZD users were defined as subjects who had at least one reimbursement for BZD between October 1 and December 31, 2006. We identified 15,638 BZD users while the remaining 44,605 were considered as unexposed. The outcome of interest was fractures of all types arising in the three years following the measure of exposure, that is, between January 1, 2007 and December 31, 2009. Using information on hospital diagnoses, we found 3,260 subjects with at least one fracture. The observed potential confounders were age, gender, exposure to antihypertensive and non-BZD psychotropic medications. Validation samples.
The Paquid project, initiated in 1988, was designed to study the risk factors of age-related health conditions  [23]. The cohort includes 3,777 subjects of at least 65 years old, from two French departments. Subjects randomly selected from the electoral rolls who agreed to participate were interviewed at their homes by trained neuropsychologists at baseline and subsequently every two or three years. At each visit, information on fractures since the last visit and drugs used at the time of the visit was collected. The validation sample consisted of 1,342 subjects visited in the 5th (T5, in 1993-94) and 8th (T8, in 1996-97) followup years with complete data regarding drugs used and confounding factors at T5 and fractures at T8. This ensured that the validation sample was as close as possible to the EGB sample while optimizing the sample size.