dataesr/scientific-paragraphs-categorization huggingface dataset:
collections. One data collection (SDSU) provided field map-corrected version of its R-fMRI and DTI data. The two pilot longitudinal collections include sMRI and R-fMRI datasets collected at two time points (1-2 years apart) for 23 individuals with ASD and 15 controls. Consistent with its popularity in the imaging community and prior usage in FCP/INDI efforts, the NIFTI file format was selected for storage of the ABIDE II MRI datasets. With the exception of a single collection (IP_1, 1. 5 Tesla), all MRI data were acquired using 3 Tesla scanners. Table  1  lists the specific MRI scanners and head coils utilized for each collection, along with the number of individuals available for each MRI modality within diagnostic groups (i. e. , ASD and controls). Specific MRI sequence parameters for the various data collections are summarized in Table  2  and detailed on the ABIDE II website.
Across collections, R-fMRI acquisition durations varied from five to eight minutes (6:21 ± 0. 04 min) per individual; in all but four collections, individuals were verbally asked to keep their eyes open. For 12 collections, exposure to scan simulators prior to scanning was also used for habituation, as documented in the narratives. users regardless of data quality  27. The rationale of this decision includes the lack of consensus on optimal quality criteria in regards to specific measures or their combinations and cutoffs. Additionally, depending on the study goal, the availability of scans with a range of quality can facilitate the development of artifact correction techniques  18. For initiatives focusing on clinical populations like ABIDE II, the inclusion of datasets with artifacts such as motion are valuable, as they enable investigators to determine impact of such real-world confounds on reliability and reproducibility.
To facilitate quality assessment of the ABIDE II collections and selection of datasets for analyses by individual users, we used the Preprocessed Connectome Project quality assurance protocol  49  (http://preprocessed-connectomes-project. github. io). These encompass quantitative metrics commonly used in the imaging literature for assessing data quality, particularly for multisite projects, e. g. , ref. 50. They include spatial metrics of scanner performance such as contrast to noise ratio  50  artifactual voxel detection  51  as well as temporal metrics including those quantifying head motion  52  ; all metrics are summarized in Table  5  and all are available in the data release. As expected by design, within-and between-site variation exists across quality metrics (see Figs  3  and 4  and Supplementary Fig.
1  for examples of spatial and temporal metrics in sMRI, R-fMRI and DTI). It is important to note that the field remains without consensus standards for the usage of QA measures. Additionally, differences in some measures across collections may reflect purposeful tradeoffs in the design of an imaging protocol, which may not be readily obvious at times. As such, caution should be taken in over-interpretation of between-collection differences in QA measures. At a minimum, the various QA measures provided can be used to find outlier datasets for a given site; though, potentially they may be used to provide insights into the impact of differences in acquisition protocols on quality measures as well.
Usage Notes As data aggregation followed independent data collections across multiple sites, various sources of heterogeneity exist between collections. They can range from inclusion/exclusion criteria, recruitment/ sampling strategies, MRI scanner types, data acquisition parameters and instructions (e. g. , eyes open versus closed). Users must be aware of such factors when designing their research questions and selecting data for analyses accordingly. Care should be taken when attempting to draw comparisons across ABIDE I and ABIDE II, as they are independently created aggregate datasets, bringing with them both commonalities and differences. Nine institutions participated in both initiatives with either related collections in regard to both phenotypic and imaging protocols (e. g. , NYU_1 in ABIDE II is a continuation of NYU in ABIDE I) or collections acquired through independent protocols (e.
g. , KUL_3 in ABIDE II). We suggest consideration of the commonalities and differences among contributions when attempting to combine datasets from the two ABIDE initiatives. The narratives included in the ABIDE II website should facilitate this process-see Supplementary Fig. 2  for the collections distributed among the ABIDE initiatives. As a general rule, for aggregate data analyses datasets should be selected to ensure that the number ASD and TDC data are balanced at each collection, unbalanced designs (e. g. , all typical  For parallel acquisition the acceleration technology and dimension are listed followed by the acceleration factor, SP, SENSE acceleration in the phase encoding direction. For partial Fourier, the under-sampled dimension is listed with the undersampling factor, P, phase encoding.
g. , refs 53,56,57. Users can also employ meta-analytic approaches that have been shown to be fruitful for examination of cortical thickness or structural volumes e. g. , ref. 58. Awareness of site-related variability should also be reflected in the presentation of findings. For example, effects within each data collection should be reported along with those obtained across collections e. g. , refs 29,56,59. Inconsistencies that arise may be informative and provide insights into known or unknown differences in samples including and beyond data acquisition protocols. Finally, we note that along with the challenges related to its multisite post-hoc data aggregation, ABIDE II also offers a unique opportunity to develop analytical approaches to address these challenges. For example, a recent effort based on ABIDE I demonstrated the ability to optimize classifiers for the prediction of data from previously unseen imaging sites  23.
The need for careful consideration of variation in acquisition parameters also applies to the use of the quality assurance (QA) metrics available for the ABIDE-II sample. Some QA measures may be more or less comparable across data collections. Mean FD is an example of a measure commonly used for QA in resting state fMRI studies, albeit without significant considerations on the impact of the specific acquisition protocol employed. Motion-induced fluctuations in the BOLD signal are primarily due to spin history effects and partial voluming, which are proportional to the amount of tissue displacement between subsequent excitations. From this perspective, one might expect that factors capable of impacting spin history effects or partial voluming, would in turn impact meanFD. Importantly, these relationships.
g. , DVARS), across collections. Users may also employ this and other shared multisite datasets e. g. , refs 60,61 to explore the impact of possible differences related to acquisition parameters, such as TR and other, on motion metrics. MeanFD computed in DTI data should not be used for comparisons between different collections with different MRI protocols. Mean FD in DTI is the result of the combination of both eddy current effects and head motion. As a result, meanFD can be used to compare and select data within collections obtained with the same scanning protocols and equipment. Finally, to facilitate replications among studies using ABIDE data, we encourage users to provide the ID list utilized for their published manuscripts in the manuscript section of the ABIDE website (http://fcon_1000.
projects. nitrc. org/indi/abide/manuscripts. html). Users are also requested to   5  for details on this and the other quality metrics released. The colored scatterplots illustrate the quality metrics distribution for spatial MRI dataset within a given ADBIE II collection (17 cross-sectional and 2 longitudinal collections). The black and white violin plots represent a kernel density estimation of the distribution across all datasets for each quality metrics. The midline thick gray line represents the value that occurs most commonly in the distribution. For each plot the horizontal gray lines mark the 1st, 5th, 25th, 50th (solid gray line), 75th, 95th and 99th percentiles starting from the bottom. , (c) signal to noise ratio (SNR)  50. Temporal metrics are: (d) mean framewise displacement  52  ; (e) standardized DVARS  63  , and (f) global correlation (GCORR)  64  -See Table  5  for details on this and the other quality metrics released.
The colored scatterplots illustrate the quality metrics distribution for spatial MRI dataset within a given ADBIE II collection (17 cross-sectional and 2 longitudinal collections). The black and white violin plots represent a kernel density estimation of the distribution across all datasets for each quality metrics with its midline thick gray line representing the value that occurs most commonly in the distribution. For each plot, the horizontal gray lines mark the 1st, 5th, 25th, 50th (solid gray line), 75th, 95th and 99th percentiles starting from the bottom.
The median FIQ across collections (112) is depicted with a thick red dashed line; 25th, 75th, and 90th percentiles (101, 122, and 130, respectively) are represented by thin red dashed lines. (e) Tukey's box-whiskers plots depict the distribution of Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) total calibrated severity scores (CSS) for ASD datasets in the nine collections sharing them (ordered by mean CSS per collection). The black plus sign depicts the mean CSS for each collection. (f) Distribution of Social Responsiveness Scale (SRS) total T scores (gray = controls; blue = ASD) in the 12 collections sharing them. For each collection, red dashed and solid lines indicate mean SRS total T scores of ASD and controls, respectively.
Spatial and temporal indices of MRI data quality selected from the Preprocessed Connectome Project. They have been computed for all structural MRI (sMRI), Diffusion Tensor Imaging (DTI), and Resting State functional MRI (R-fMRI) datasets unless indicated otherwise. All are released for each dataset in ABIDE II (file and link of release pending). See Figs3 and 4and Supplementary Fig. 1for illustrations of the distribution of a selection of spatial and temporal metrics within and across collection. *For all R-fMRI data temporal metrics have been computed after discarding the first 5 time points of the time series which were field map corrected if field maps were provided (only in the SDSU_1 data collection). Computation of all spatial metrics excluded absolute zero background values. † For R-fMRI data these metrics are computed on mean functional data.
Acknowledgements We would like to thank the numerous contributors at each donating institution (see Supplementary Table 1 and http://fcon_1000. projects. nitrc. org/indi/abide/)as well as the www. nitrc. orgteam for providing the data-sharing platform. We are particularly thankful to  Tanmay Nath  for support in aspects of website organization and assistance in some aspects of MRI data review along with  Dorothea Floris ,  Yuta Aoki ,  Lindsay Alexander  and  Erica Ho. Support  for ABIDE II coordination and data aggregation was provided by  NIMH  ( 521MH107045 ) to A. D. M. and gifts from  Joseph P. Healey ,  Phyllis Green  and  Randolph Cowen  to M. P. M. (M. P. M. is a  Randolph Cowen and Phyllis Green Scholar ). Support for data collection at each site was provided by  NIH  ( MH084961-GU_1 ;  MH094409-IU_1 ;  NS048527 ,  MH085328 ,  MH078160-KKI_1 ;  MH102660 ,  MH087770 ,  MH084126 ,  MH081218 ,  HD065282-NYU_1 ;  MH102660-NYU_2 ;  MH095888-ONRC_2 ;  MH096773 ,  MH091238 ,  MH096773-03S1 ,  MH086654-OHSU_1 ;  MH081023 ,  MH097972-SDSU_1 ;  MH099250-01-UCD_1 ;  HD055784-UCLA_1 ;  MH092697 ,  MH080826 ,  MH60450 ,  DC008553 ,  NS34783-USM_1 ;  HD065280-01-UCLA_Long ;  MH081191 ,  MH67924 ,  HD55748-UPSM_Long ),  Autism Speaks  ( KKI_1 ,  04593 ; UPSM_Long), the  Simons Foundation  ( 307280 ;  EMC_1 ,  OHSU_1 ),  IDDRC  ( HD040677-07 ;  GU_1 ),  State of Arizona Alzheimer's Consortium, BNI and Department of Defense  ( AR140105 ;  BNI_1 ),  Dutch ZonMw TOP  grant ( 91211021 ;  EMC_1 ),  European Community 's  7th Framwork Programme  ( FP7/2008-2013 ,  212652 ;  EMC_1 ),  Physical Sciences Division, SURFsara , the  Municipal Health Service Rotterdam area , the  Rotterdam Homecare Foundation , and the  Stichting Trombosedienst & Artsenlaboratorium Rijnmond STAR-MDC  ( EMC_1 ),  Institut Pasteur ,  CNRS ,  INSERM ,  AP-HP, University Paris 7 Diderot , the  BioPsy Labex , the  DHU PROTECT , the  Bettencourt-Schueller Foundation , the  FondaMental Foundation , and the  ANR SynDiv  ( IP_1 ),  Branco Weiss fellowship of the Society in Science ETH Zürich, Marguerite-Marie Delacroix Foundation, Flanders Fund for Scientific Research  ( FWO  project  1521313N , G.
(d) Distribution of full scale IQ (FIQ) standard scores per collection (ordered by lowest FIQ included per collection) for all datasets, irrespective of diagnostic group. The median FIQ across collections (112) is depicted with a thick red dashed line; 25th, 75th, and 90th percentiles (101, 122, and 130, respectively) are represented by thin red dashed lines. (e) Tukey's box-whiskers plots depict the distribution of Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) total calibrated severity scores (CSS) for ASD datasets in the nine collections sharing them (ordered by mean CSS per collection). The black plus sign depicts the mean CSS for each collection. (f) Distribution of Social Responsiveness Scale (SRS) total T scores (gray = controls; blue = ASD) in the 12 collections sharing them.
e. , datasets collected in a given institution from individuals not included in ABIDE I) and in the longitudinal pilot collections (i. e. , data from the same individuals scanned twice: Time 1 data originally released in ABIDE I and Time 2 data released in ABIDE II). *For longitudinal collections Diffusion Weighted Imaging (DWI) data were not included ABIDE I, thus are not applicable (NA) here. †For IP one individual ID only has sMRI available. ‡For KKI_1 an 8-channel head coil was used for n = 149 datasets and a 32-channel head coil was used for n = 62 datasets-a list of the IDs with the corresponding head coil is provided in the ABIDE II website page for this collection under 'Additional scan Information'. §SDSU also includes field maps corresponding to the R-fMRI and all DTI datasets but two.
PCR product was subsequently subcloned behind the CAG promoter into the pCX-MCS2 vector  (Morin et al. , 2007) , or behind the BLBP promoter  (Schmid et al. , 2006). The pCAGGS-human Zic2 plasmid was described by  García-Frigola et al. (2008). Downregulation experiments: the shZic1 clone, which was subsequently used for the in vivo experiments, was selected among a set of 5 shZic1 clones provided by Sigma-Aldrich. For this selection step, Zic1 cDNA was subcloned into the peGFP-C1 vector (Clontech, GenBank: U55763. 1) to fuse Zic1 and eGFP proteins. This Zic1-eGFP construct was cotransfected in mycoplasma free HeLa cells together with a Zic1 shRNA and a pCAGGS-tomato expression vector (all 3 plasmids in equal ratio) using Lipofectamin2000 (ThermoFisher Scientific, 11668019) following the manufacturer's instructions.
Two days after transfection images in fluorescence (GFP and Tomato) were randomly acquired in all transfected wells. Using ImageJ software GFP and tomato fluorescences were measured on all images and GFP/tomato ratios were used to evaluate the efficiency of each Zic1 shRNA to target the mouse Zic1 sequence. shZic1 targeting the sequence CCAGCGCT GCTTTCAATTCTA was finally chosen as it induced a 95% decrease in GFP fluorescence. The same sequence has been independently identified, selected, and successfully used in another study  (Urban et al. , 2015). The mouse shZic2 was described by  Escalante et al. (2013). For all in vivo functional experiments overexpression vectors and shRNA plasmids were electroporated in P1 pups, as described by  Boutin et al. (2008) , together with a pCX-eGFP vector  (Morin et al.
GFP-expressing neuronal cells were subsequently sorted by flow cytometry (MoFlow, Beckman-Coulter). Numbers of FACS isolated cells and experimental repetitions are listed in Table  1. Ten to 15 mice were used per condition. RNA was extracted, amplified and hybridized (2 colors protocol, each color corresponding to a targeted region, dorsal or lateral wall) on a G4122F Whole Mouse Genome (4 ϫ 44K) Oligo Microarrays (Agilent). Microarray results (referenced in GEO as GSE69693) were analyzed using R software (RRID:SCR_001905) and Limma package (RRID: SCR_010943;  Gentleman et al. , 2004). Briefly, data were first normalized using quantile normalization. Differential gene expression was analyzed at each time-point using a moderated t test applied on a filtered subset of data. The filtering selected genes for which the average of dorsal/lateral intensity ratio (log2) was Ͼ2.
This resource is accessible under GEO reference GSE69693. Recent work suggested that the postnatal neuronal lineages are specified early in the developing cortical and striatal ventricular walls and maintained in a quiescent state for postnatal use in OB neurogenesis  (Fuentealba et al. , 2015). We asked whether this spatial relationship is detectable at the molecular level in the postnatal OB neurogenic lineages. Comparison of the expression ratio in the dorsal versus lateral lineages revealed that key transcription factors (TFs) implicated in cortex regionalization, cortical layer specification or glutamatergic differentiation were strongly overrepresented in the dorsally generated postnatal OB lineage (heat map: Fig. 1C ; space/time diagram: Fig. 1D ). These  factors included Emx1, Emx2, Pax6, Tbr1, Tbr2, NeuroD2, Neu-roD6, Neurog2, Lhx2, Tcfap2c,  and Bhlhe22   Englund et al.
In vivo electrophysiology Head direction firing was sampled from presubicular neurons in vivo in rats, with higher channel counts and unit yield compared to mice (cf. ref. 3  ). Briefly, tetrodes were implanted in 4 months old Long-Evans rats at AP 2. 2 mm in front of the transverse sinus, ML 3. 7 mm from the midline, and DV 1. 5 mm below the dura. Tetrodes were lowered progressively until reaching presubicular layers. Recording sites in presubiculum were confirmed from post-hoc Nissl, parvalbumin and calbindin stained sections. Head direction was tracked with two light-emitting diodes while the animal collected randomly distributed food crumbs from a 100 cm wide square box. Spikes were sorted offline with cluster cutting Axona software. Head direction was calculated from projections of the relative position of the two LEDs on the horizontal plane.
ACKNOWLEDGEMENTS This work was supported by the  Région Ile-de-France  and  Fondation pour la recherché Medicale  (J. S. ) and by  ANR  Grant  JCJC R10206DD  (D. F. ). The research leading to these results also benefitted from the program " Investissements d'avenir "  ANR-10-IAIHU-06  and we gratefully acknowledge financial support from the  ERC  ( 322721 , RM). We thank  Karl Deisseroth  for making available AAV-hSyn-hChR2( H134R )-EYFP. We thank  Dominique Debanne  for discussions, and  Matthew Nolan  and  Bruno Delord  for comments on the manuscript. In vivo spike train data were collected in the lab of Edvard and  May-Britt Moser Norway ).
The actual spectra of the diodes were measured by a Thorlabs CCS175 spectrometer. To calculate the averaged extinction coefficients of oxy-and deoxyhemoglobin (ǫHbO and ǫHbR), the spectrophotometric data of rat blood absorption was used  (Zijlstra et al. , 1994). Based on our calculation of hemoglobin extinction coefficients we may conclude that OIS in the GREEN spectrum mainly reflects blood volumetric changes, in the RED spectrum-mainly blood oximetric changes and in the IR spectrum-mainly tissue transparency changes. However, to facilitate diode notations, they are referred to only by their spectral ranges: GREEN, RED, and IR. Example images of the neonatal cortex in the reflectance mode for each spectrum are shown in Figure  1B. Natural sensory stimulation was performed as described previously  (Peterson and Goldreich, 1994; Borgdorff et al.
, 2015; Bai et al. , 2016; Cerro-Herreros et al. , 2016). Twenty-five years after discovery of the DM1 mutation and with these validated proofs of concept, the first phase 1/2a clinical trial was launched in December 2014 using gapmer-ASOs aiming at destroying DMPK transcripts in muscles (trial NCT02312011). Beside strategies targeting toxic DMPK RNA to release MBNL proteins, CELF proteins are also targets to consider. The inhibition of PKC or GSK3β, proteins known to regulate the phosphorylation of CELF proteins, allowed the reversal of muscle and heart phenotypes in DM1 mouse models  (Wang et al. , 2009; Jones et al. , 2012). CELF proteins (and particularly CELF2) are known to be significant players in the CNS and thus represent important proteins to target, in particular with pharmacological means  (Ladd, 2013).
A phase 2 trial was recently launched to evaluate the safety and efficacy of tideglusib, a GSK3ß inhibitor (trial NCT02858908). The recent development of genome editing using TALENs or CRISPR-Cas nucleases provides for DM a very appealing new field of investigation  (Richard, 2015; Lee et al. , 2016; Long et al. , 2016). One can dream to be able to remove the deleterious expansion directly on DNA correcting definitively the mutation. Recent studies in yeast, and cell systems have demonstrated the feasibility to remove or reduce CTG repeats with TALEN or CRISPR-Cas nucleases  (Richard et al. , 2014; Cinesi et al. , 2016; van Agtmaal et al. , 2017). Furthermore, dual CRISPR-Cas9 cleavages on both side of the CTG expansions in DM1 myoblasts were able to eliminate RNA foci and to restore splicing defects and myogenic capacity  (van Agtmaal et al.
We then compared the performance over time of human participants and model C neurorobots not only for the first canceled target (Figure  7 -10) but also across all the remaining targets. We performed a Bayesian repeated measures ANOVA (JASP software, version 0. 8. 2), with agents (human, neurorobots C) as between-group factor, and the spatial position (X coordinate) of the sequence of all the five canceled targets as within-group factors. The Inclusion Bayes Factor, which compares ANOVA models that contain a given effect to equivalent models stripped of the effect, showed decisive evidence (BFInclusion ϭ 2. 137e ϩ42) for the cancellation order main effect. Thus, the order of cancellation of all the five targets depended on their spatial position (Figure  7 -10). Importantly, this effect was statistically equivalent for the human and the neurorobot C populations.
Background: Observational studies are widely used in pharmacoepidemiology. Several designs can be used, in particular self-controlled designs (case-crossover and self-controlled case series). These designs offer the advantage of controlling for time-invariant confounders, which may not be collected in electronic healthcare databases. They are particularly useful in pharmacoepidemiology involving healthcare database. To be valid, they require the presence of some characteristics (key validity assumptions), and in such situations, these designs should be preferred. We aimed at describing the appropriate use and reporting of the key validity assumptions in self-controlled design studies. Methods: Articles published between January 2011 and December 2014, and describing a self-controlled study design involving electronic healthcare databases were retrieved.