dataesr/scientific-paragraphs-categorization huggingface dataset:
Domain-general function: The uniform function of adaptive control or error-based learning attributed to the cerebellum across motor and cognitive domains. tween cerebral networks of interest and the cerebellum. These measures reflected different aspects of the total cerebellar influence within distinct large-scale brain networks. We hypoth-Large-scale brain network: A collection of widespread brain regions having strong functional connectivity and performing the same function. esized that FC between cerebral networks, which are involved in control and reward brain processes, and the cerebellum could be associated with impulsivity. To test this hypothesis, we employed an open dataset comprising highly sampled resting-state fMRI data (four runs, ≈ 15 min/run) and self-reports assessing different elements of impulsivity, from a large group of healthy participants (N = 134, 62 females).
We decomposed the rs-fMRI data into separate cerebral and cerebellar resting-state networks (RSNs), using data-driven techniques, to account for the functional heterogeneity present in both structures. Then, we estimated static FC among the identified RSNs and used hidden Markov models (HMMs) to model whole-brain dynamics Hidden Markov models: A probabilistic method to analyze sequential data (e. g. , time series) and find hidden patterns (i. e. , states) that underlie them. and estimate subject-specific dynamic FC matrices (brain states;  Vidaurre, Smith, & Woolrich, 2017). Finally, we calculated summary measures of the total strength and temporal variability of FC between distinct cerebral RSNs of interest and the cerebellum and evaluated them against self-reported measures of impulsivity using multivariate general linear models.
Participants A total of 134 healthy participants (62 females, ages 20-40 years) from the Neuroanatomy and Connectivity protocol (N&C), which is part of the Max Planck Institute Leipzig Mind-Brain-Body (MPILMBB) dataset, were included in this study. All included participants were healthy with no past or present signs of any neuropsychiatric condition, fulfilled the MRI safety requirements, and provided written informed consent prior to their participation  (Mendes et al. , 2019). Originally, the dataset included fully preprocessed MRI data and a battery of behavioral assessments from 188 participants. However, because of a gap in the age distribution, we excluded 26 subjects that were older than 55, and 28 subjects for missing data from source. Details on the inclusion and exclusion criteria can be found in  Mendes et al.
MRI Data Acquisition The resting-state fMRI acquisition parameters are described in full detail in  Mendes et al. (2019). In summary, four resting-state fMRI scans were acquired for each individual in axial orientation using T2*-weighted gradient-echo echo planar imaging (GE-EPI) with multiband acceleration. Sequences were identical across the four runs, with the exception of varying slice orientation and phase-encoding direction. The phase-encoding direction was anteriorposterior (AP) for Runs 1 and 3, and posterior-anterior (PA) for Runs 2 and 4. The complete set of parameters was set as follows: voxel size = 2. 3-mm isotropic, FOV = 202 × 202 mm 2 , imaging matrix = 88 × 88, 64 slices with 2. 3-mm thickness, TR = 1,400 ms, TE = 39. 4 ms, flip angle = 69 • , echo spacing = 0. 67 ms, bandwidth = 1,776 Hz/Px, partial Fourier 7/8, no pre scan normalization, multiband acceleration factor = 4,657 volumes, duration = 15 min 30 s per run.
, 2011)  (f) application of all transformation parameters to each volume in the four resting-state runs in one interpolation step; (g) inclusion of six motion parameters, their first-order derivatives, and outliers from Nipype's rapidart algorithm as nuisance regressors in a general linear model (GLM); (h) The aCompCor method to remove physiological noise from residual data from the previous denoising step  (Behzadi, Restom, Liau, & Liu, 2007) ; and finally (i) band-pass filtering [0. 01-0. 1 Hz]. All included subjects exhibited relatively low in-scanner motion-mean framewise displacement <0. 5-mm across all resting-state scans  (Power, Barnes, Snyder, Schlaggar, & Petersen, 2012). Preprocessed data were obtained from https://ftp. gwdg. de/pub/misc/MPI-Leipzig_Mind-Brain-Body/derivatives/.
Group Independent Component Analysis (GICA) Preprocessed data from all subjects were analyzed using group independent component analysis (GICA) as implemented in the GIFT toolbox software http://mialab. mrn. org/software/gift/. GICA decomposes the rs-fMRI data into linear mixtures of spatially independent components (ICs) that exhibit unique time course profiles  (Allen et al. , 2014). In order to investigate cerebrocerebellar FC, we decomposed the cerebrum and cerebellum, separately, into spatially independent components. By applying a "cerebellum-only" GICA approach, we can extract cerebellar ICs and signals that are usually overpowered by signals of cortical and subcortical origin when performing a whole-brain GICA  (Dobromyslin et al. , 2012). Moreover, parcellating the cerebellum permits modeling the patterns of static and dynamic FC among functionally diverse cerebellar networks and their cerebral counterparts with enhanced accuracy, relative to what is possible when considering the cerebellum as one homogeneous region of interest.
Cerebellum-only GICA. To isolate the cerebellum from the brain, we generated a cerebellar mask in MNI152 space using the standard cerebellar MNI152 anatomical template from FSL. Concatenated cerebellar rs-fMRI data across all subjects and scans were demeaned and analyzed using principal components analysis (PCA) to reduce the dimensionality to 100 subjectlevel PCs (retaining > 99% of the variance in the data) and 25 group-level PCs. Then, we applied the Infomax algorithm 20 times using the ICASSO toolbox to automatically estimate and select the most reliable set of 25 independent components in the cerebellum. We then used the group information guided ICA, or GIG-ICA, to estimate subject-specific ICs and time series. GIG-ICA extracts subject-specific ICs and time series with better accuracy and correspondence than dual regression  (Salman et al.
Cerebral GICA. A similar approach to the cerebellum-only GICA was performed in order to estimate cerebral RSNs. A brain mask in MNI152 space was generated from a standard FSL MNI152 brain anatomical template after setting all cerebellar voxels to 0  (Jenkinson et al. , 2012). Concatenated, demeaned data from all subjects and runs were analyzed using PCA to estimate 120 subject-level PCs (retaining > 99% of the variance in the data) and, subsequently, 30 group-level PCs. We applied the Infomax algorithm 20 times using ICASSO to estimate and automatically select the most reliable set of 30 ICs, and GIG-ICA to estimate subject-specific ICs and time series. The choice of the number of components was driven by our interest in largescale brain networks that were suitable for subsequent FC analysis in terms of dimensionality, complexity, and interpretability.
Static functional connectivity. To construct static FC matrices, we computed pairwise Pearson's full and partial correlation coefficients in each of the four resting-state fMRI runs using the Ledoit-Wolf estimator as implemented in the nilearn and scikit-learn Python packages  (Abraham et al. , 2014; Ledoit & Wolf, 2004 ). Pearson's full and partial correlation coefficients quantify and reflect different types of FC: Full correlations measure direct and indirect functional connections,whereas partial correlations measure direct functional connections only Direct functional connections: Functional connectivity between any two brain regions after statistically removing the effect of all other regions. (Varoquaux & Craddock, 2013). This is informative because cerebro-cerebellar networks form closed-loop circuits, and it is likely that cerebellar networks directly connect to singular brain Closed-loops circuits: The set of neuronal connections that carry signals from cerebral regions to the cerebellum and back to the same regions.
Dynamic functional connectivity. To model whole-brain FC dynamics among the identified cerebro-cerebellar RSNs, we applied the hidden Markov models (HMMs) on temporally concatenated BOLD time series from all runs and subjects as implemented in the hidden Markov model multivariate autoregression (HMM-MAR) toolbox https://github. com/OHBA-analysis/ HMM-MAR. The HMMs method is a windowless dynamic FC approach that bypasses the limitations of sliding-windows and k-means clustering by being directly applied to the BOLD time series. The method uses variational Bayesian inference to estimate a set intermittently recurring brain states at the group level, each described as a multivariate Gaussian distribution with a mean representing a spatial activation pattern and a covariance matrix representing a FC pattern  (Vidaurre et al.
Assessing the robustness of FC dynamics. In order to confirm the presence of robust and genuine dynamic FC in the rs-fMRI data, we generated 100 null datasets from a multivariate Gaussian distribution fitted to the rs-fMRI data of each individual subject as in  Vidaurre et al. (2017). According to the authors, the correlations between brain regions in the null data are similar to those in the rs-fMRI data but are presumed to be stationary and exhibit no dynamic structure. We applied the HMMs method, with unchanged parameters, on each set of null data and extracted a metric that allowed us to compare the resultant dynamics with to those obtained in the rs-fMRI data. One important metric that has been used in a previous study is the maximum fractional occupancy  (Vidaurre et al. , 2017). Generally speaking, fractional occupancy (FO) is defined as the proportion of time each state is visited by each subject, whereas maximum fractional occupancy (maxFO) is the maximum proportion of time spent by each subject visiting the most occurring state  (Vidaurre et al.
, 2017). High values of maximum fractional occupancy close to 1 indicate that a single state describes the entirety of the data and hence the absence of dynamics in FC. On the contrary, low values of maxFO indicate that multiple recurring states describe the data and hence the presence of dynamics. In this context, we compared the distributions of maximum fractional occupancy values in the rs-fMRI data with those in the null data to assess the presence/absence of genuine FC dynamics. More detailed theoretical and practical information on the use of HMMs to study brain FC dynamics can be found in  Baker et al. (2014) ,  Ryali et al. (2016) ,  Vidaurre et al. (2017 ), Quinn et al. (2018) , and https://github. com/OHBA-analysis/HMM-MAR/wiki/User-Guide.
Statistical Analysis To test for associations between cerebro-cerebellar FC and impulsivity, we used multivariate general linear models (GLMs) that included the impulsivity scales as predictors and the static and dynamic FC measures as response variables in a multiple linear regression framework. In addition, age, gender, and mean framewise displacement were included as nuisance covariates. To test for significance and correct for multiple comparisons, we applied nonparametric permutation testing with 10,000 permutations and a maximum z-statistic procedure to obtain family-wise error adjusted p values across all tests. Particularly, we performed random rearrangements of the labels in the observed data and estimated an empirical distribution of maximum z-statistics obtained from all tests and permutations.
Temporal Variability of Cerebro-Cerebellar FC and Impulsivity The details of statistically significant findings are reported in Table  2 , whereas scatterplots are illustrated in Figure  4. We demonstrated the inferred group-level brain FC matrices (states) along with distribution profiles of the maximum fractional occupancy values obtained from the rs-fMRI data and the generated null data in Supplementary Figure  1. The distribution profiles and the group-average frequency of occurrence values (percentages) indicated the presence of genuine FC dynamics in the rs-fMRI data as opposed to the null data, which were mostly described by a single state. In accordance with our hypothesis, only dynamic FC measures pertaining to cerebral RSNs that encompass regions involved in control and reward brain processes, notably the frontoparietal, salience, attention, subcortical, and default mode networks, were evaluated against self-reported measures of impulsivity using GLMs.
, 2018). Briefly, experiments were performed using a nano RSLC Dionex U3000 system fitted to a Q-Exactive mass spectrometer (Thermo Scientific, San Jose, United States). The spectra of eluted peptides were recorded in full MS mode and selected within a mass range of 250-2000 m/z for MS spectra and a resolution of 70,000 at m/z 200. For each scan, the ten most intense ions were selected for fragmentation. Protein identification was performed as previously described  (Huang et al. , 2018). Peptides were identified from the MS/MS spectra using X!Tandem pipeline software  (Langella et al. , 2017). The search was performed against the proteome of the P. freudenreichii CIRM-BIA129 strain (ITGP20) (downloaded from NCBI. nlm. nih. gov on 23 August 2018). For each peptide identified, a minimum score corresponding to an e-value below 0.
05 was considered as a prerequisite for peptide validation, and a minimum of two peptides were required for protein identification. Protein quantification was performed as previously described  (Huang et al. , 2018). Each peptide identified by tandem mass spectrometry was quantified using the free MassChroQ software before data treatment and statistical analysis under R software (R 3. 2. 2, Project for statistical computing). A specific R package called 'MassChroqR' was used to automatically filter dubious peptides for which the standard deviation of their retention time was longer than 30 s and to regroup peptide quantification data into proteins. For peak counting analysis, variance analysis was performed on proteins with a minimum peak ratio of 1. 5 between the two culture conditions. Proteins with an adjusted p-value < 0.
Determined using a database composed of the proteome of P. freudenreichii CIRM-BIA129 (downloaded from NCBI. nlm. nih. gov on 23 August 2018). (b) Ratio between protein levels in the soy ultrafiltrate versus the cow's milk ultrafiltrate. This ratio indicates induction (ratio > 1. 5) or repression (ratio < 0. 66) during growth in soy ultrafiltrate.
ACKNOWLEDGMENTS The authors would like to thank  Agnès Burel  for her involvement in the TEM Transmission Electron Microscopy analyses performed at the  Microscopy -Rennes Imaging Center (MRic-Rennes, France). BIOSIT, Université de Rennes1. The authors thank  CNIEL  ( Centre National Interprofessionnel de l'Economie Laitière ) for providing the P. freudenreichii ITG  P20  ( CIRM-BIA129 ) strain. The authors offer their thanks to  Victoria Chuat , from  CIRM-BIA , for storing, checking and maintaining the bacterial strains. The authors finally thank  Benoît Foligné ,  Otto Phu ,  Pipon Lévèque , and  Firmin Steir  for useful discussions and advices.
FUNDING FGa was the recipient a joint doctoral " CIFRE" fellowship  from  Bioprox  and the  French ANRT  ( Association Nationale de la Recherche et de la Technologie ). NI and FC are recipients of post-doctoral fellowships from the  French FUI  ( Fond Unique Interministeriel) program. The Asylum Research MFP3D-BIO atomic force microscope was funded by the  European Union (FEDER) , the  French Ministry of Education and Research, INRA ,  Conseil Général 35 , and  Rennes Métropole.
( a ) a Determined using a database composed of the proteome of P. freudenreichii CIRM-BIA129 (downloaded from NCBI. nlm. nih. gov on 23 August 2018). (b) Ratio between protein levels in the soy ultrafiltrate versus the cow's milk ultrafiltrate. This ratio indicates induction (ratio > 1. 5) or repression (ratio < 0. 66) during growth in soy ultrafiltrate.
Galaxy Alignment and Angular Scaling Table  1  provides galaxy distances, scaling and rotation data for the following discussion. Each image (Q and U maps both PBcorrected and PB-uncorrected) had to be rotated and scaled in angular size in preparation for stacking. However, since the polarization images tend to be complex, it was not possible to scale/rotate them based on the appearance of the polarization. For this reason, the orientation (position angle) of a galaxy was initially taken from the K s passband as given in the NASA Extragalactic Database (NED) 5  but minor adjustments were made by eye (mostly zero but all ă 5 deg) to fine-tune the disk alignment so that the total intensity radio images aligned with the x axis. We next wish to scale all galaxies to have the same angular size.
These RM ranges should be similar in a source without Faraday depolarization. Also the magnetic field vectors corrected for the observed RM at the corresponding frequency band also look somehow different at L-band when compared to those at C-band. In general, the vectors at C-band show a very smooth, regular structure, while those in two of the three blobs at L-band look rather irregular. All these findings strongly indicate that the L-band data are strongly affected by Faraday depolarization and even completely depolarized over large areas in NGC 3044. Faraday depolarization can be caused by thermal electrons together with a regular magnetic field within the emitting source (differential Faraday rotation) and/or by random magnetic fields within the source (internal Faraday dispersion) or between the source and the observer (external Faraday dispersion)  (Burn 1966; Sokoloff et al.
They can be regarded as a reliable tracer of the parallel magnetic field component along the whole line-of-sight of the galaxy, especially outside the midplane where the thermal electron density is expected to be largest. Hence, we only used the C-band data for our further analysis. After RM-synthesis, the vectors are corrected for the observed RM and the Galactic foreground rotation measure RM fg (see Sect. 3. 1) and hence are the intrinsic magnetic field vectors. When analyzing the C-band D-array maps we detected polarized intensity PI in each of the 35 CHANG-ES galaxies except in NGC 4244. This galaxy is extremely faint even in total intensity (TP) with values of 12 µJy/beam as extended emission at 12 2 HPBW. Hence we cannot expect to observe any PI from NGC 4244 with the sensitivity of our survey.
Introduction The EXFOR library  [1]  has served as the unique repository of experimental nuclear reaction data for many decades. Data exchange of the neutron-induced reaction data (neutron data) in the EXFOR format was started in July 1970  [2] , following the agreement among the Four Neutron Centres in 1969  [3]. Currently the EXFOR library contains neutron data, charged-particle induced reaction data (charged-particle data) and photon induced reaction data (photonuclear data) measured in more than 22,000 experimental works. It is maintained and developed by the 13 data centres belonging to the International Network of Nuclear Reaction Data Centres (NRDC)  [4] , under the coordination of the IAEA Nuclear Data Section. This article summarizes the status and progress in our collaboration since our report to the last Nuclear Data conference (ND2016)  [5].
Status of collaboration The NRDC Protocol  [6]  defines the work shared among us such as compilation scope, compilation responsibility, procedures of dictionary and manual updates, data * e-mail: n. otsuka@iaea. org transmission, and code development. The EXFOR scope has remained unchanged: Data for neutron and light-ion (A ≤ 12) beams below 1 GeV belong to the category of compulsory compilation. The highest priority is given to compilation of recently published data in this category. The NDS regularly scans about 40 journals to identify articles with data suitable for EXFOR and to monitor progress in their compilation. All new and old articles are listed on the Article Allocation List  [7]  until they are compiled. Table  1  summarizes the compilation responsibility of each data centre, which ensures that all data in the category of compulsory compilation are compiled by us.
These compilations are further reviewed by the NDS and NEA DB to rigorously check the logical and physical consistency of the files and to verify the bibliographic data. NEA DB also performs visual inspection and comparison with evaluated nuclear data and/or other EXFOR entries to identify potential data outliers. As an example, Figure  1  shows digitized points in a fission fragment mass yield curve, identified in checking, where the use of linear scales in the article results in unphysical data for highly asymmetric fission. Figure  2  shows the cumulative numbers of EXFOR entries. While neutron data have been prioritized since the formation of the NRDC, the recent focus on chargedparticle data has resulted in a near-identical number of EX-FOR entries for both. The relative contributions from all of the centres is shown in Figure  3 , where the total number of entries is displayed.
Retroactive compilation The NDS started regular journal scanning for experimental data of reactions induced by all projectiles (neutron, lightand heavy-ions, photon) in 2004. Before this, CINDA  [10]  served as the main list of articles for EXFOR compilation. However, CINDA covered only neutron data and there was no mechanism to ensure completeness of charged-particle and photonuclear data. Additionally, some neutron data (e. g. , fission product yields) were not prioritised by all centres in past decades. To improve the EXFOR coverage, the NRDC is performing retroactive compilation in parallel with compilation of newly published data. Three examples of our retroactive compilation projects are introduced in the following subsections.
Fission product yields The Four Neutron Centres discussed compilation of fission product yields in 1975, and concluded that they did not have sufficient resources to devote to the compilation of this data [18], while the US and UK pioneers of the fission product yield evaluation developed their own compilation in 1970s  [19] [20] [21] [22]. In order to establish a common experimental database, EXFOR compilation of fission product yields was actively discussed in two IAEA meetings  The NNDC approach  [27]  was to check EXFOR against their bibliographic database, NSR, which contains over 230,000 entries. At the first stage, about 650 fission product yield articles potentially missing in EXFOR were extracted from NSR. All data with identical values in an EXFOR entry were removed. All remaining datasets were compared with all EXFOR data within a 10-year window to identify datasets that were linked (e.
Digitization tools Collection of original data from experimentalists is the primary role of the NRDC. Experimentalists are asked by journal editors to limit the number of figures and data tables, reducing the published data to a small fraction of the valuable output from their work. EXFOR is designed to be a complete data library and such unpublished data are routinely included through direct engagement with experimentalists. Unfortunately, a considerable fraction of the numerical data are not available from the experimentalists, even if they have been published, and in many cases it is necessary to use whatever published materials are available. This is a very typical situation when compiling differential cross sections of charged-particle reactions, which were published when the NRDC was not uniformly prioritising charged-particle data compilation.