dataesr/scientific-paragraphs-categorization huggingface dataset:
Mean subband entropy was equivalent for outdoors and indoors (2. 91 ± 0. 15 and 2. 91 ± 0. 15, respectively; F 1,18 < 1). Stimuli were elaborated using the image processing toolbox on MATLAB (Mathworks Inc. , Sherborn, MA, USA). Each scene was filtered with three low-pass filters, with cutoff frequencies corresponding to 0. 5, 1, and 2 cycles per degree (cpd; i. e. 12, 24, 49 cycles per image) and three high-pass filters with cutoff frequencies corresponding to 3, 6, and 12 cpd (i. e. 71, 144, 293 cycles per image), or left unfiltered (NF). Cut-off frequencies followed a logarithmic scale in order to obtain a better sampling of the amplitude spectrum of natural scenes (see  [58]  for a similar procedure). Furthermore, these values were chosen in order to include cut-off frequencies of 2 cpd for LSF and 6 cpd for HSF, as used in Schyns & Oliva's pioneer study on spatial frequency processing during scene perception  [10].
The resulting images were then normalized to obtain a mean luminance equal to 128 on a grey-level scale ranging from 0 to 256. This resulted in 7 versions of each scene (1 NF, 3 LSF and 3 HSF, see  Fig 1). Stimuli were displayed using E-prime software (E-prime Psychology Software Tools Inc. , Pittsburgh, USA) and backprojected onto a translucent screen positioned at the rear of the magnet. Participants viewed this screen at a distance of about 222 cm via a mirror fixed on the head coil. We used a backward mask, built with 1/f white noise, to prevent retinal persistence of the scene. We used a block-design paradigm with the NF, LSF, and HSF scenes. The Spatial Frequency experiment consisted of four functional runs. Each functional scan lasted five minutes and was composed of nine 25-second task blocks (one block per spatial frequency cut-off and three blocks of NF scenes), including 10 scenes (5 indoors and 5 outdoors), interspersed with three 25-second blocks with a fixation dot in the center of the screen (Fixation condition) displayed against a grey background.
fMRI Acquisition Experiments were performed using a whole-body 3T Philips scanner (Achieva 3. 0T TX Philips -Philips Medical Systems, Best, NL) with a 32-channel head coil at the Grenoble MRI facility IRMaGe in France. For all functional Spatial Frequency scans, the manufacturer-provided gradient-echo/T2 Ã weighted EPI method was used. Forty-four adjacent axial slices parallel to the bi-commissural plane were acquired in sequential mode. Slice thickness was 3 mm. The inplane voxel size was 2. 5×2. 5 mm (220×220 mm field of view acquired with a 88×85 pixel data matrix; reconstructed with zero filling to 96×96 pixels). The main sequence parameters were: TR = 2. 5 s, TE = 30 ms, flip angle = 80°. Finally, a T1-weighted high-resolution three-dimensional anatomical volume was acquired, by using a 3D Modified Driven Equilibrium Fourier Transform (MDEFT) sequence (field of view = 256×224×175 mm; resolution: 1.
Data Analysis Data analysis was performed using the general linear model  [61]  for block designs in SPM12b (Wellcome Department of Imaging Neuroscience, London, UK, www. fil. ion. ucl. ac. uk/spm/) implemented in MATLAB 7 (Mathworks Inc. , Sherborn, MA, USA). Functional volumes were realigned to correct for head movements to the mean functional image using a rigid body transformation. The T1-weighted anatomical volume was then realigned (affine transformation) to match the mean functional image of each participant, and was then normalized (non rigid, non linear transformation) into the MNI space. A default 4th degree B-Spline interpolation was applied. The anatomical normalization parameters were subsequently used for the normalization of functional volumes. Finally, each functional volume was smoothed by an 8-mm FWHM (Full Width at Half Maximum) Gaussian kernel.
Left and right PPA were defined independently for each participant as regions of interest (ROIs) using the [Scenes > Faces + Objects] contrast. Significant voxel clusters on individual t maps were identified using a false-discovery correction at qFDR <. 05 to control for the overall false-positive rate. As the clusters were generally large and involved several regions of interest, small sphere ROIs (3 mm radius) were created at individual peaks of activation in each scene-selective region and in each hemisphere. These clusters were selected as ROIs for the data analysis in the Spatial Frequency experiment. Parameter estimates (% signal change relative to the global mean intensity of signal) of block responses were extracted from the two sphere ROIs for each participant. The average parameter of activity was calculated for the NF, LSF, and HSF.
ROI analysis The PPA ROIs were defined in each individual, based on the independent Localizer experiment. This served as the structural constraint for the analysis of the data in the Spatial Frequency experiment. No recordings were obtained in one young participant due to technical problems. The PPA was localized for all other participants (11 young and 12 elderly participants) in the two hemispheres (see Fig  5A  for an illustration on representative young and elderly participants) based on the ([Scenes > Faces + Objects] contrast). Parameter estimates (% signal change relative to the global mean intensity of signal) of block responses were extracted from the two sphere ROIs for each participant. The average parameter of activity was calculated for each experimental condition. These values were submitted to a first ANOVA for mixed designs with Groups (young and aged participants) as between-subject factor and Hemisphere (Left and Right) and Spatial frequency (NF, LSF, and HSF) as within-subject factors.
This work was supported by  Agence Nationale pour la Recherche  grant  ANR-12-JHS2-0002-01  RECOR. Grenoble MRI facility IRMaGE was partly funded by the  French program  " Investissement d'Avenir " run by the " Agence Nationale pour la Recherche": Grant "Infrastructure d'Avenir en Biologie Santé " ( ANR-11-INBS-0006 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Subjects Subjects were Princeton University students who either signed up to perform the experiment for pay, or for course credit. A total of 14 subjects participated in the experiment (4 males, mean age 20 yrs). The first six  (6)  subjects performed the experiment before we had access to the eye-tracking equipment. An additional eight  (8)  subjects performed the experiment with eye tracking. The experimental protocol was approved by the Princeton University Institutional Review Board, and all subjects gave written informed consent before participating in the experiment. Data from three subjects were excluded from the behavioral data analyses (see below), and the eye-tracker data file was corrupt for one subject. Thus N = 7 for subjects contributing both behavioral and eye-tracker data, and N = 11 for subjects contributing behavioral data.
Eye tracking Eye tracking was performed on 8 of the 11 subjects who participated in the experiment using an infrared eye-tracking system (Applied Science Laboratories, Bedford, MA), sampling at 60Hz. This particular eye-tracking device can resolve differences in relative eye position on the order of 0. 25°. The eye-tracker data file for one subject was corrupted and could not be read, leaving N = 7 for eye-tracking analyses. During eye tracking, the subject's head was secured using a chin/forehead rest (Applied Science Laboratories). Prior to each experiment, the eye tracker was calibrated on the 6°x 6°frame in which the stimuli would appear on the computer display. The eye-tracking camera was positioned just to the left of the cardboard divider, and monitored the subject's left eye.
We make use of the "advantageous wager" metric (with due caution) when a single-trial measure is called for. Although both Wd' and PAW are problematic (and have been criticized  [1, 19, 29] ), the primary finding reported here does not depend on the particular value(s) that these measures take on, but rather on the way that they interact with ocular activity. Eye tracker data. Analyses of ocular activity were performed on the 1. 5-second time window immediately following stimulus onset, but before a response was made (stimulus duration was 1. 33 sec and subjects rarely responded earlier than 300 ms after stimulus cutoff). Pre-processing of the eye-tracking data was carried out using the ILAB software application  [30] , and further analyses were carried out using MatLab (The MathWorks, Inc).
Correcting for multiple comparisons For the post-hoc sliding window analysis (see below) it was necessary to correct for multiple comparisons and temporal non-independence. This was done using a cluster-based permutation test. The analysis was repeated 1000 times shuffling the OCULAR-ACTIVITY trial labels for each subject on each iteration. A sample-wise threshold of p < 0. 05 was used to identify positions of the sliding window at which the relevant dependent measure (the difference between ϕ AW and ϕ CR ) was significantly different from zero. The largest number of contiguous significant values was recorded on each iteration, and the distribution of these temporal cluster sizes was compared against the temporal cluster sizes in the observed data in order to arrive at a corrected p value.
1 ). Stimuli were presented and behavioral data were acquired using a real-time acquisition system (ADwin-Pro, Keithley Instruments, Cleveland, OH) controlled by laboratory-made software (Docometre) by courtesy of Franck Buloup (Institut des Sciences du Mouvement, Marseille). Pre-stimulus delays (time between the lightening of the central fixation point and stimulus presentation, i. e. , the time during which proactive inhibitory control had to be sustained) varied from two to four seconds in steps of one second. In cue trials, Cue-Target Onset Asynchrony (CTOA) was manipulated so that the cue-target delay varied randomly across nine conditions: from 100 to 2100 ms by steps of 250 ms. Since a lengthening of the time necessary to release the inhibitory control after a cue has been presented can be expected as a possible effect of clonidine intake, CTOA duration was increased with respect to the landmark study  [14]  to maximize the chances of catching the complete dynamics of the process.
Maze task The main experimental task was a VE maze, created using Virtools 5. 0, a 3D software toolkit. The maze was laid out in a grid of 16 'blocks'. Fifteen landmarks featured on the paths in the maze. There were two conditions: Rich and Sparse, each with the same layout and same number of landmarks. In the Sparse condition, the blocks were uniform brickwork and each appeared identical to every other (Fig. 1a ). In the Rich condition, the blocks appeared as detailed and realistically rendered street blocks and each block was unique (Fig. 1b ). In both conditions, there were three 'sheds' hidden in recesses within particular blocks. Each participant started the maze at a brown shed, which contained a locked box. There were two further sheds in each condition, a blue one and a green one, both of which contained a hidden key.
Route knowledge Predictors of route knowledge Each time that the participant hit a barrier, this counted as an error. The cumulative number of errors made across learning trials for the two routes was calculated for each participant. Correlations between overall route knowledge errors and other measures were calculated for each group separately (see Table  2  for descriptive statistics of the attention measures). There were extreme values on various measures in the DS and WS groups. However, there were no grounds for believing that these values reflected error rather than genuine cognitive variability. Therefore, these values were retained, but these groups' data were analysed non-parametrically to avoid undue influence by individual data points. Correlations are shown in Table  3. These analyses were repeated for route knowledge errors in rich and sparse mazes separately (the sum of errors made across learning trials for the two learnt routes); there was little difference in the patterns of correlation, so these separate analyses have been omitted for brevity.
Trajectory analyses To compare developmental trajectories, parametric methods were necessary. Therefore, participants scoring more than 3 standard deviations above the group mean of the dependent variable (overall route knowledge errors) were excluded. Using this criterion, one participant was excluded from the WS group. In order for the ranges of the covariate to be similar across the groups, the four participants with the lowest RCPM scores in the DS group were also excluded. The ANCOVA model included interaction terms between the RCPM covariate and the between-subjects factor to explore whether route knowledge performance developed at a different rate in each group with reference to non-verbal ability. The data were analysed with respect to RCPM rather than chronological age because chronological age ranges were largely nonoverlapping between the TD and disorder groups.
Acknowledgements Thank you to  Boris Gambet  for programming the mazes and  Gauthier Billiet ,  Riya Marwaha  and  Hannah Ward  for help with data coding. Thank you to  St Catherine's School, Barnet , the  Williams Syndrome Foundation UK , and the  Down's Syndrome Association , for enabling this research to take place. Special thanks go to the participants for taking part in the study. This research was supported by  ESRC  Grant  RES-062-33-0005  and  Agence Nationale de la Recherche (ANR)  Grant  ANR-09-FRBR-035.
Acknowledgments This study was supported by the  Conseil Régional de Picardie  (CRP; IHDADAL and EPIGENALCO projects), the  National Institute of Health and Medical Research (INSERM) , the  National Agency for Research  ( SAMENTA 2011  grant  N°ANR-12-SAMA-008-01   SENSIBALCO ), and IREB. J. J. is supported by a postdoctoral fellowship from the  INSERM/CRP. We thank  Dr. Maria del Carmen Gonzalez Marin  for her technical support during the immune-histo-chemistry experiment. We thank  Ludovic Didier  for his technical assistance. The authors declare no conflict of interest and the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
A STRING (http://string-db. org) analysis of 46 of the 101 differentially expressed genes that encode proteins predicted to interact with at least one other. Each protein is indicated by a circle, connecting lines indicate evidence of interaction. A red line indicates evidence of fusion; a green line is evidence of a shared neighborhood; a blue line is evidence of co-occurrence in phyletic profile; a purple line is experimental evidence of interaction; a yellow line is co-occurrence in published abstracts; a light blue line is co-occurrence in databases; a black line is evidence of co-expression days before parturition, and estrogen levels rise and stay high during the last half of the pregnancy  [55, 56]. Pgmrc1 has been shown to be expressed in mature VSNs by immunohistochemistry  [41]. Esr1, but not Esr2, protein and mRNA expression has also been reported in the VNO  [57].
RNAseq data processing and analysis VNOs from pregnant and control females were dissected and immediately frozen on dry ice. RNA was extracted using the RNeasy mini kit (Qiagen) with on-column DNAse digestion. mRNA was prepared for sequencing using the TruSeq RNA sample preparation kit (Illumina). All samples were multiplexed together and sequenced on four lanes on the Illumina HiSeq 2500 platform, to generate 100 bp paired-end reads. All data is available through the European Nucleotide Archive and the corresponding accession numbers can be found in Additional file 2. Sequencing reads were mapped using STAR 2. 3 to the GRCm38 mouse reference genome, annotation version 72 in the Ensembl mouse genome database. The annotation file was modified to substitute the gene models for the olfactory and vomeronasal receptors by those described previously  [68] , which include extended UTRs and additional non-coding exons.
The number of fragments aligned to each gene was counted using the HTSeq package, with the script htseq-count (mode intersectionnonempty). Any reads that map to multiple locations in the genome (also called multireads) were not counted towards the expression estimates since they cannot be assigned to any gene unambiguously. To compare the expression values across samples, raw count data was normalized to account for the depth of sequencing. Size factors were calculated using DESeq2's function estimate-SizeFactorsForMatrix, and raw counts were divided by the corresponding size factor for each sample. In order to analyze the molecular influence of neurogenesis on VR genes we used expression of Dcx as criterion to determine the precise neurogenesis peak. Pregnant females displaying less than 1.
Gene Analysis In the 5-week-old SHRSP rats, 162 genes were differentially expressed compared with 5-week-old WKY rats in frontal and midcoronal brain sections (Table  II  in the online-only Data Supplement). 14 e used the following databases to match the SHRSP Illumina IDs to human genes (Materials and Table II in the online-only Data Supplement): Ensembl-http://www. ensembl. org, GeneCardshttp://www. genecards. org, Illumina ID search-http://www. genscript. com, NCBI-http://www. ncbi. nlm. nih. gov, and Rat Genome Database-http://www. rgd. mcw. edu. Of the 162 SHRSP genes, 132 had an equivalent human gene, 8 transcripts were mapped to the same gene, 20 were uncharacterized in humans, and 2 had no human homologue. Of the 132 genes, 126 were available for association testing using the Versatile Gene-based Association Study (VEGAS) test.
3  The VEGAS software summarized evidence for association with WMH in subjects from LBC1936 per gene by considering the P values of all 543 050 SNPs that were located within 17 681 unique autosomal genes (including SNPs±50 kb outside of genes to include regulatory regions). For a more direct comparison with CHARGE (which used imputed data), we also performed a gene-based test on LBC1936's 2 447 226 HapMap2 derived P values (after removing SNPs with a minor allele frequency of <0. 01 and imputation quality of <0. 3) with VEGAS software as above.
Replication in Subjects From CHARGE We then tested whether any of the 126 SHRSP genes were also associated with WMH in subjects from CHARGE by using data from CHARGE's published genome-wide meta-analysis of WMH in 9361 stroke-free individuals from 7 community-based cohorts. 7 We performed a gene-based test using VEGAS software, which summarized the evidence for association with WMH burden on a per gene basis, as above, by considering the associated P values of all HapMap2 SNPs located within 17 787 autosomal genes (including SNPs±50 kb outside of genes to include regulatory regions).
Replication of Previous CHARGE Findings in Subjects From LBC1936 To demonstrate our ability to detect WMH-gene associations in subjects from LBC1936, we attempted replication of CHARGE's genome-wide associations with WMH  7, 8  in the subjects from the LBC1936 Cohort in a genome-wide association analysis using the 2 534 887 SNPs imputed to HapMap2, with WMH (volume and Fazekas score) in Mach2QTL software. 28 e applied Bonferroni correction for multiple testing (P=0. 05/126 genes=0. 0004). We did not include the 2 WMH phenotypes in the Bonferroni correction as they are highly correlated (r 2 =0. 77). Because of the overconservative nature of Bonferroni correction for multiple testing,  29  a nominal significance threshold of P value of <0. 05 was required for replication efforts.
We did not test gene associations with other SVD features in addition to WMH because a total SVD burden score was not available for CHARGE. Although it is a relevant model of spontaneous SVD  11, 12  and of human hypertension and metabolic disorders,  15  like any model, the SHRSP has translational limitations, arguing for additional studies at different ages and brain regions, with or without environmental stressors. This work has the following strengths: accurate LBC1936 WMH phenotyping  17  and genetic information in this relatively large narrow age-range older population. 16 The Glasgow SHRSP colony is long established, with carefully controlled environments. The mRNA data were obtained from the same rats that provided protein expression data. 13 Replication in other SHRSP colonies and examination of related strains (eg, SHR's) may be informative.
Acknowledgments We thank the Lothian Birth Cohort 1936 participants and research team members,  Wellcome Trust Clinical Research Facility  (http:// www. wtcrf. ed. ac. uk, subject testing and genotyping), and  Brain Research Imaging Centre  (http://www. bric. ed. ac. uk, brain imaging and analyses). Cohorts for Heart and Aging Research in Genomic Epidemiology thanks the staff and participants of the  Aging Gene-Environment Susceptibility-Reykjavik Study, Atherosclerosis Risk in Community Study (ARIC), Austrian Stroke Prevention Study (ASPS), Cardiovascular Health Study , and  Framingham Heart  and  Rotterdam Studies  for their important contributions. ASPS thanks  Birgit Reinhart  for her long-term administrative commitment and  Ing Johann Semmler  for the technical assistance at creating the DNA bank.
The SHRSP analysis was funded by  Medical Research Council  ( Dr Bailey ),  British Neuropathological Society, Newby Fund, Scottish Funding Council Scottish Imaging Network A Platform for Scientific Excellence Collaboration  (http:// www. sinapse. ac. uk), and  European Union Community 's  FP7/2007-2013 , grant agreement HEALTH-F4-2010-241504 EURATRANS. The  Aging Gene-Environment Susceptibility-Reykjavik Study  was funded by  National Institutes of Ageing (NIA) , contract  N01-AG-12100 ,  National Eye Institute, National Institute on Deafness and Other Communication Disorders, National Heart, Lung, and Blood Institute (NHLBI) , the  NIA   Intramural Research Program ,  Hjartavernd  (the  Icelandic Heart Association) , and the  Althingi (the Icelandic Parliament). The  Atherosclerosis Risk in Communities Study  is a collaborative study supported by  NHLBI  contracts ( HHSN268201100005C ,  HHSN268201100006C ,  HHSN268201100007C ,  HHSN268201100008C ,  HHSN268201100009C ,  HHSN268201100010C , and  HHSN2682011 00011C, HHSN268201100012C ),  R01HL087641 ,  R01HL59367  and  R01HL086694 ;  National Human Genome Research Institute  contract  U01HG004402 ; and  National Institutes of Health (NIH)  contract  HHSN268200625226C ;  NIH  grant number  UL1RR025005 , and  NIH Roadmap for Medical Research  and grant number  HL093029.
The  Austrian Stroke Prevention Study  was funded by the  Austrian Science Fond  grant numbers  P20545-P05  and  P13180. The  Medical University of Graz  supports the databank of the ASPS. The  Cardiovascular Health Study  was supported by  NHLBI  contracts  N01-HC-85239 ,  N01-HC-85079  through  N01-HC-85086 ,  N01-HC-35129 ,  N01 HC-15103 ,  N01 HC-55222 ,  N01-HC-75150 ,  N01-HC-45133 ,  HHSN268200960009C , and  HHSN268201200036C  and  NHLBI  grants  HL080295 ,  HL087652 ,  HL105756  with additional contribution from  National Institute of Neurological Disorders and Stroke ,  AG-023629 ,  AG-15928 ,  AG-20098 , and  AG-027058  from the  NIA  (http://www. chs-nhlbi. org/pi. htm). DNA handling and genotyping at  Cedars-Sinai Medical Center  was supported in part by  Cedars-Sinai Board of Governors' Chair in Medical Genetics (JIR) , the  National Center for Research Resources , grant  UL1RR033176 , and is now at the  National Center for Advancing Translational Sciences, Clinical and Translational Science Institute  grant  UL1TR000124 ; in addition to the  National Institute of Diabetes and Digestive and Kidney Disease  grant  DK063491  to the  Southern California Diabetes Endocrinology Research Center.