dataesr/scientific-paragraphs-categorization huggingface dataset:
1 Cycle Sequencing Kit (Applied Biosystems, CA, USA), and the ABI 3730 sequencing instrument, as described by the manufacturer. All primer seq u e n c e s a r e g i v e n i n S u p p l e m e n t a r y M a t e r i a l (Supplementary Tables  2  and 3 ). Ninety-five Caucasian controls were sequenced using the PSQ HS96A pyrosequencing system (Qiagen, Hilden, Germany), according to manufacturers' instructions using the PSQ 96 SNP Reagent Kit (Biotage AB) on a PSQ HS96A instrument (Qiagen, Hilden, Germany) and analyzed by pyrosequencing software (PSQ HS96A1. 2). Segregation analysis was done by Sanger sequencing. Further genotyping was performed in a control cohort consisting of 751 individuals of European descent (94 Spanish, 94 Finnish, 94 Turkish, 94 Dutch, 188 Italian, and 188 German) using the ABI TaqMan® 7900 Real-Time PCR System.
Acknowledgments We would like to thank  Dr. Gary Cutting  for providing an expression construct for hClC-2;  Drs. Alexi Alekov ,  David Ewers ,  Martin Fischer ,  Jasmin Hotzy ,  Peter Kovermann ,  Jan-Philipp Machtens , and  Daniel Wojciechowski  for helpful discussions; and  Petra Kilian  and  Toni Becher  for excellent technical assistance. We furthermore thank all patients for participating in the genetic study and all physicians of the  Epicure consortium (Supplemental Text 2)  for providing the samples for mutation screening. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
Tropical sites in the PRISM3 data set collectively indicate a mean anomaly of 11. 08uC. The aforementioned sites have a mean anomaly of 13. 21uC. Removal of these sites greatly reduces the magnitude of the mean tropical D PRISM (to 10. 4uC) and supports the Dowsett et al. 28  finding that low latitude SSTs, away from upwelling centers, were indistinguishable from modern conditions. Documenting the tropical sea surface temperature stability in the Pliocene, particularly given evidence of substantial warming at high latitudes, is of considerable importance for the appraisal of the primary climate drivers for this period. The multi-model ensemble from the IPCC 4 th assessment report shows that greenhouse gas forcing of less than a double-CO 2 equivalent still yields a tropical warming of. Although the existence of a tropical thermostat mechanism cannot be entirely ruled out given uncertainties in modeled cloud responses, most of the IPCC GCM's would support the lower-end estimates for Pliocene atmospheric CO 2.
Models are generally in good agreement with estimates of Pliocene SST in most regions except the North Atlantic and upwelling regions in the tropics and subtropics (Figures  1, 2 ). Despite the complications of variability within the PRISM time slab and the spread shown by the different PlioMIP simulations, there is a fundamental divergence between the two data sets that increases with increasing latitude. This decoupling may stem from a number of sources, including the resolution of the models, differing model parameterizations of subgrid-scale features such as clouds, the highly variable nature of the midlatitude North Atlantic, or the nature of the Gulf Stream -North Atlantic Drift Current  45. Polar amplification of SSTs is one of the principal signatures of the PRISM data set, with a maximum temperature increase observed in the North Atlantic.
Data have been assessed using the l-confidence scheme  45. This provides a measure of confidence based upon chronology, sample density, sample quality and type as well as performance of quantitative method used. l for included localities ranges from Very High to High to Medium and the percentage of sites corresponding to each level are 29%, 34% and 36% respectively. Figure  2  shows the spatial distribution and confidence of the 100 PRISM localities (see also Supplementary Table  S1 ). 1  and in the corresponding publications. The GISS-E2-R, CCSM4 and IPSL CM5A models are the same versions used in IPCC 5 th assessment future climate simulations. Coupled ocean-atmosphere model simulations for the PlioMIP experiment will be available from the PMIP3 project https://pmip3. lsce. ipsl. fr/.
Models In an attempt to standardize the PlioMIP simulations, all models were initialized and run using an identical (to the extent possible) experimental design and protocol (Table  2 ; see also  43  ). Of the eight models, three (MIROC4m, COSMOS and GISS-E2-R) used the preferred Pliocene boundary conditions, meaning each model's land/sea mask was altered to reflect a 25-meter increase in Pliocene sea level, the existence of ocean in place of the West Antarctic Ice Sheet, and the removal of Hudson Bay (which is a result of Pleistocene ice sheet geography). For each model in the PlioMIP ensemble, the Pliocene simulations were run until the individual modeling groups determined that their models had achieved an equilibrium state; integration times varied from 500 to 1500 years (Table  1 ). Twelve monthly SST fields were then averaged over the last 30 years of the run to develop a mean annual SST data set.
Acknowledgments HJD, KMF, DKS and MMR acknowledge the continued support of the  U. S. Geological Survey Climate and Land Use Change Research and Development Program ; HJD,  MAC  and MMR thank the  USGS John Wesley Powell Center for Analysis and Synthesis  for support of the PlioMIP initiative; HJD and CRR acknowledge support from the  USGS Mendenhall Post-doctoral  Fellowship Program; MAC and LES acknowledge support from the  NASA   Climate Modeling Program , and the  NASA High-End Computing (HEC) Program  through the  NASA Center for Climate Simulation (NCCS)  at  Goddard Space Flight Center. Financial support was provided by grants to US and AMH from the  Natural Environment Research Council ,  NERC  ( NE/I016287/1 ); DJL and FJB acknowledge  NERC  grant  NE/H006273/1. BLO and NAR recognize NCAR is sponsored by the  U.
S. National Science Foundation  (NSF) and computing resources were provided by the  Climate Simulation Laboratory at NCAR's Computational and Information Systems Laboratory (CISL)  sponsored by the  NSF  and other agencies. AMH and AMD acknowledge research leading to these results received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement no. 278636. AMD acknowledges the  UK Natural Environment Research Council  for the provision of a  Doctoral Training Grant. CS and GL received funding from  AWI  and  Helmholz  through the programmes POLMAR, PACES and REKLIM. WLC and AAO acknowledge financial support from the  Japan Society for the Promotion of Science  and computing resources at the  Earth Simulator Center, JAMSTEC.
Participants. In total, 29 young adults participated in two sessions: a session in which both MEG and EEG were recorded, and a session in which both structural and functional MRI were recorded. Several datasets were excluded from subsequent analysis on the basis of excessive artifact (one fMRI, five EEG/MEGs) and technical problems (four EEG/ MEGs). Here we report data from 28 fMRI datasets (17 males, 11 females; mean age, 21 years; range 18 -27 years) and 20 EEG/MEG datasets (11 males, 9 females; mean age, 21 years; age range, 18 -24 years). All participants were native speakers of American English without prior history of neuropsychiatric disorders, and were right-handed as assessed by the Edinburgh Handedness Inventory  (Oldfield, 1971). The order of EEG/ MEG and MRI sessions was counterbalanced across participants, and participants were assigned different lists in EEG/MEG and fMRI sessions, so that no participant saw the same list twice.
, 2005). In the MEG session, short intervals were inserted between trials (at least 700 ms) to allow participants time to blink. In the MRI session, additional fixation trials of varying length summing to a total of 64 s per block were added to the intertrial interval to optimize deconvolution of event-related activity  (Burock et al. , 1998). In both sessions, the paradigm was presented in two equal blocks of ϳ5 min, separated by a break. EEG/MEG. The EEG/MEG data were acquired while participants were seated inside a magnetically shielded room (Imedco). The MEG data were acquired with a Neuromag VectorView system (Elekta-Neuromag) with 306 sensors arranged in 102 triplets of two orthogonal planar gradiometers and one magnetometer. EEG, EOG, and electrocardiogram (ECG) data were acquired at the same time using a 70-channel MEGcompatible scalp electrode system (BrainProducts) and referenced to an electrode placed on the left mastoid; an electrode was also placed on the right mastoid to confirm that the reference did not incorporate measurable lateralized brain activity.
Impedance was kept Ͻ10 k⍀ for all scalp, ECG, and EOG sites, and Ͻ2. 5 k⍀ for mastoid sites. Both EEG and MEG data were acquired with an online bandpass filter of 0. 03-200 Hz and were continuously sampled at 600 Hz. To record the head position relative to the MEG sensor array and to coregister the EEG/MEG and MRI coordinate frames, the locations of three fiduciary points (nasion and two auricular), four head position indicator coils, the EEG electrodes, and at least 100 additional points were digitized using a 3Space Fastrak Polhemus digitizer integrated with the Vectorview system. During the EEG/MEG recording, the coils were used to measure the position and orientation of the head with respect to the MEG sensor array at the beginning of each block of trials. Structural MRI/fMRI. Structural MRI and fMRI images were acquired using a 3 T Siemens Trio scanner and a 32-channel head coil.
Structural MRI processing. The two T1-weighted structural images for each participant were averaged together after coregistration to increase the signal-to-noise ratio, and a cortical surface for each participant was reconstructed from the resulting average using the Free-Surfer software package developed at the Martinos Center (Charlestown, MA; http://surfer. nmr. mgh. harvard. edu). Each participant's cortical surface was morphed to a template brain created by averaging the cortical surface of 40 individuals scanned by the Buckner Laboratory (fsaverage) using an algorithm designed to align individual sulcal-gyral patterns while minimizing distortion  (Fischl et al. , 1999). This morphed surface was used for visualization, group averaging, and statistical analysis. EEG preprocessing and analysis.
Averaged event-related EEG signals, time-locked to target words, were computed offline from trials free of ocular and muscular artifacts after application of a 20 Hz offline low-pass filter. A 100 ms prestimulus baseline was subtracted from all waveforms before statistical analysis. Data from noisy or disconnected electrodes were interpolated with signals from neighboring electrodes using the Laplacian surface estimate. We computed a repeated-measures type III sum of squares ANOVA on mean ERP amplitudes between 300 and 500 ms poststimulus onset, the time window in which the N400 effect to visually presented words is classically observed. To assess broad differences in topographical distribution, ERP analyses were conducted on a subset of 48 electrodes divided equally among the four quadrants of the scalp, resulting in a 2 ϫ 2 ϫ 2 repeated-measures design (relatedness ϫ hemisphere ϫ anteriority).
MEG preprocessing and analysis. Signal-space projection (SSP) was applied to MEG magnetometer data to suppress environmental noise and biological artifacts  (Uusitalo and Ilmoniemi, 1997). For three participants in which a strong cardiac artifact was observable in the MEG recording, an additional SSP was computed for gradiometers and magnetometers during heartbeat events (detected using the bipolar ECG electrodes) and applied to the MEG data. Averaged event-related MEG signals time-locked to target words were computed offline from trials free of ocular and muscular artifacts after application of a 20 Hz offline low-pass filter. A 100 ms prestimulus baseline was subtracted from all waveforms before statistical analysis. Noisy MEG sensors were marked and excluded from the generation of sensor waveforms and source estimates.
The MNE software package (www. martinos. org/mne) was used for source estimation on the reconstructed cortical surface (derived as described above) for MEG event-related signals in each participant. The high-resolution cortical surface reconstruction was decimated into ϳ10,000 vertices in each hemisphere. A three-compartment boundary element model with the linear collocation approach was used in the forward calculation  (Ha ¨ma ¨la ¨inen and Sarvas, 1989; Mosher et al. , 1999). The scalp and skull surfaces were estimated on the basis of a 5°FLASH structural MRI sequence for most participants; for one participant, in which a FLASH sequence was not collected, the MPRAGE structural data were used instead. The amplitudes of the dipoles at each cortical location were estimated for each time sample using the dynamic statistical parametric mapping (dSPM) approach, with anatomically constrained linear estimation  (Dale et al.
, 2000). Noise covariance estimates were derived from data recorded in the 100 ms baseline period before the presentation of the prime word for all trials. The orientations of the dipoles were approximately constrained to the cortical normal direction by reducing the variance of the source components tangential to the cortical surface by a factor of 0. 5  (Lin et al. , 2006). dSPM source estimates of activity at the cortical surface for the Ϫ100: 700 ms surrounding target word presentation were computed for each participant for the two conditions of interest (directly associated and unrelated). Next, a dSPM contrast map was created for each participant by subtracting the unrelated-associated activity estimates across all vertices, resulting in a dSPM map representing the masked priming effect.
Before translating individual participant data into the common template space, a smoothing operation was applied to the individual data, using seven iterative steps to spread estimated activity to neighboring vertices. Then the activity estimates were averaged across the N400 time window in each of these maps to provide an estimate of the mean differential activity associated with the priming effect (unrelated-associated). In the cortical surface analysis, we looked across all vertices (5095 in total) within bilateral temporal, inferior frontal, inferior parietal, and occipitotemporal cortices, which have all been previously implicated in language processing, using the Desikan-Killiany atlas  (Desikan et al. , 2006)  included in the FreeSurfer distribution to delineate the regions. We used a nonparametric permutation test based on spatial clustering to estimate which differences in the remaining vertices were reliable across participants  (Pantazis et al.
FreeSurfer's Functional Analysis Stream software was used to analyze the fMRI data. After slice-time correction, functional images were motion corrected to the middle time point of each functional run using the AFNI (analysis of functional neuroimages; afni. nimh. nih. gov/afni) 3dvolreg program  (Cox and Jesmanowicz, 1999). Nonbrain voxels were masked out of the analysis using the FSL (fMRI of the Brain Software Library; www. fmrib. ox. ac. uk/fsl) Brain Extraction Tool  (Smith 2002). Images were corrected for temporal drift, normalized, and spatially smoothed on the surface by 10 mm full-width/ half-maximum using an iterative technique  (Hagler et al. , 2006). The group-averaging transform described above was used to map general linear model (GLM) parameter estimates and residual error variances of each participant's functional data to a common spherical coordinate system before smoothing.
DATA SETS T1-weighted images of 18 patients (8 females, mean age: 37. 3 years, age range: 16-66 years) with brain tumors, acquired as part of an fMRI language presurgical evaluation, and 64 neurologically normal subjects (age range: 21-75 years) were included in this study. The heterogonous sample of patients consisted of 9 patients with low grade gliomas (World Health Organization (WHO) grade I: 1 dysembryoblastic neuroepithelial tumor; WHO grade II: 2 oligoastrocytomas, 3 oligodendrogliomas, 1 astrocytoma and 2 diffuse astrocytomas), 8 patients with high grade gliomas (WHO grade III: 3 anaplastic astrocytomas, 1 glioneuronal tumor with neuropil-like islands, 1 diffuse astrocytoma with anaplastic progression; WHO grade IV: 3 glioblastomas) and one meningioma. Tumors varied in size and location, in both hemispheres, as listed in Table  1.
e. , a lesion image) that fully defines the degree of abnormality at each voxel of the brain. Following fuzzy logic rules (e. g. ,  Ibrahim, 1996) , the latter is done using the operator "max" over all voxels of the GM and WM lesion images. This fuzzy set can be thresholded at a given U-value to generate a 3D-binary image of the lesion. Given the difficulty in defining an objective universal threshold that works on any dataset, we previously recommend that U thresholds are typically set at around 0. 3 to provide accurate lesion delineation when ALI is used on images from chronic stroke patients  (Seghier et al. , 2008). Below is a succinct description of the outlier detection algorithm used in ALI. The core of its algorithm is a non-iterative Fuzzy Clustering with fixed Prototypes (FCP) method previously introduced by  Seghier and colleagues (2007).
VALIDATION The outputs from our recursive ALI procedure were compared to the gold standard: manual segmentations of the same tumors. Practically, outline of the lesion was drawn on the outer borders of abnormally intense regions. The lesions were identified on a slice by slice basis on the T1-weighted images in the native space using MRIcron software (at http://www. mccauslandcenter. sc. edu/mricro/). Abnormal gyri were included in the lesion definition when there was a clear asymmetry in gyrus width between the lesioned and non-lesioned hemisphere, excluding areas of abnormality far from the lesion that could be related to mass effects. Periventricular regions were defined as lesioned only when there was a clear signal intensity change in the area and the cortical lesion extended all the way to the periventricular space.
g. , see Figure  2 ). The use of the fuzzy clustering algorithm, however, allows patient-specific tissue classes to be compared to a set of images from matched healthy controls (e. g. , with similar demographics and/or acquisition protocols). Last but not least, we found that Dice indices were higher when the threshold on the degree of abnormality U ranged from 0. 1 to 0. 3 (Figure  4 ). For similar datasets, we thus recommend the use of U thresholds within this range if users are interested in generating binary definitions of their patients' tumors. This threshold needs to be increased (e. g. , at 0. 5) if a small number of controls were used in the outlier detection algorithm. As the case for any method that relies upon fuzzy or soft clustering techniques, the optimal threshold can be adjusted for a given dataset (see  Shen et al.
In addition, it may also be applied for the identification of other types of lesions that are problematic in the standard ALI implementation and may help to achieve accurate spatial normalization of brains with tumors for robust group analyses. The present paper also illustrates that ALI is a versatile tool that can be adapted for automated identification of any type of brain damage. under request. A final version of ALI will be made public as a toolbox after the release of the software package SPM12 (at [http:// www. fil. ion. ucl. ac. uk/spm/ext/]).
ACKNOWLEDGMENTS Data collection was funded by the  Wellcome Trust , the  James S. McDonnell Foundation  (as part of the brain network recovery group) and funding from the  Comprehensive Biomedical Research Council. Data analysis was funded by the  County Council Department of Education, Culture and Sports of the Valencian Community, Spain  (grant number  APOSTD/2012068 ) and the  Universitat Jaume I, Spain  (grant number  P1-1B2012-38 ) awarded to the first author. A beta version of ALI is available.
We did not observe any obvious effect of nitrate provision on RNA size distribution (Additional file 2), suggesting that nitrate treatments under our experimental conditions do not have a global effect on sRNA population structure. Next, valid sequences were classified according to the genomic regions they match. Most sRNA sequences matched intergenic regions (8,415,076 sequences, 50%), followed by miRNA (3,189,443 sequences, 19%) and rRNA genes (2,469,734 sequences, 14% of the total valid reads) (Figure  1A ). We were able to detect 142 distinct mature Arabidopsis miRNA sequences, corresponding to 98 different miRNA families, according to the miRBase database v. 17 (www. mirbase. org) (Additional file 3). The number of miRNA sequences identified represents 66. 7% of the 212 miRNAs reported in miRBase v.
In order to determine how our sequencing data compares with data obtained with the Affymetrix ATH1 GeneChip, we used the same RNA samples for Illumina library preparation and ATH1 microarray hybridization. We used the affy package library from Bioconductor (www. bioconductor. org) to determine the number of present calls in the ATH1 microarrays as a measure of gene detection. We were able to find 13,964 probes with a present call, approximately 67% of the gene specific probes that are present in the ATH1 microarray (Additional file 5). The Illumina sequencing data detected 13,411 of these genes (96%, at least one read matching the gene) and 3,022 annotated elements that were called absent in the ATH1 array. We found that these 3,022 elements had low expression values when compared with the 13,411 Illumina-detected elements that had present calls in Affymetrix (Additional file 6A,B).
Additionally, Illumina was able to detect 4,215 elements that had no probe on the ATH1 microarray (Additional file 5). In order to determine how data on nitrate-responsive genes obtained with RNA-seq and Affymetrix ATH1 chips correlated, we calculated the correlation between the KNO 3 /KCl ratio for RMA normalized Affymetrix gene expression and the KNO 3 /KCl ratio obtained for normalized libraries at different average gene coverages (AGCs). We defined AGC as the number of reads matching a gene multiplied by read length and divided by gene length. We found correlation between KNO 3 /KCl ratios increase hyperbolically as average gene coverage increases (Additional file 7). This indicates correlation between the two techniques depends on gene expression levels. We found excellent correlation (r 2 ≥ 0.
Deep sequencing reveals a new nitrate-responsive component of the arabidopsis root transcriptome In order to identify known miRNAs that are N-regulated under our experimental N-treatment conditions, we used the DESeq package in R to analyze digital gene expression in the RNA-sequencing data  [55]. Replicates were used independently for statistical analysis of gene expression. Surprisingly, we were not able to identify known miRNAs (reported in miRBase v17) regulated by nitrate in roots based on our RNA-sequencing data. In order to distinguish between a technical and biological explanation for this result, we calculated an RNA rarefaction curve considering increasing number of random sequences from our sRNA libraries and the number of different sequences that could be determined from each sample (Figure  2A ).