dataesr/scientific-paragraphs-categorization huggingface dataset:
Hi-C analysis from ED or whole Drosophila embryos Raw Hi-C sequencing data were processed using the "scHiC2" pipeline  (41). Construction of expected models, Hi-C contact scoring, and Hi-C aggregate plots were performed using the "shaman" R package [https://bitbucket. org/tanaylab/shaman, (42) ]. Hi-C replicates generated for this study cluster together and show high Pearson correlation coefficients (fig. S18 ). Moreover, our newly generated Hi-C replicates from whole 16-to 18-hour embryos cluster with the two replicates generated in  (8)  and show high Pearson correlation coefficients (fig. S18 ). Therefore, these three replicates were merged to increase the resolution of the maps. Hi-C map visualization, segmented interaction frequencies, and downstream analyses were conducted using in-house R scripts using the "misha" package (https://github.
com/ msauria/misha-package). Moreover, to facilitate the visualization of newly generated Hi-C maps, raw sequencing data were processed using the Hi-C-Pro pipeline (version 2. 11. 1, REF: 26619908) with default parameters and normalized at 2-, 5-, and 10-kb resolutions using the ice_norm flag. Last, the hicpro2juicebox utility was used to convert the allValidPairs output of the pipeline into Juicebox. hic format at fragment resolution. These processed files are available in the GEO database repository.
Alignment and differential expression analysis of Drosophila RNA-seq After initial quality checks of newly generated data using fastqc, paired-end and single-end. fastq files were aligned to the dm6 version (dmel_r6. 15 annotation) of the Drosophila genome using STAR with default parameters (v2. 4. 0i)  (43). Then, the reads were counted using htseq-count  (44) , and the differential expression analysis was performed using the "DESeq2" R package (v1. 22. 1) (45).
Collection and alignment of ESC and NPC datasets Raw ChIP-seq data for RING1B from ESCs and NPCs, H3K4me1, H3K4me3, H3K27Ac, and H3K27me3 from NPCs were collected (table  S10 ) and aligned to the mm10 version of the mouse genome using bowtie 2 with default parameters [v2. 1. 0; (31) ]. For the visualization and the quantification of ChIP-seq signals, RPKM-normalized bigWig binary files were generated with the same method used for Drosophila ChIP-seq (see upper sections). H3K4me1, H3K4me3, H3K27Ac, and H3K27me3 ChIP-seq datasets from ESCs, RNA-seq, and Hi-C data from ESCs and NPCs were taken from (24) (table  S10 ). k-Means clustering of RING1B peaks from NPCs RING1B PRC1 peak coordinates reported in  (25)  were lifted over to the mm10 version of the mouse genome, and the peaks that were closer than 500 bp have been merged.
In addition, we adapted HCP preprocessing methods to the macaque brain, including spatial minimal preprocessing of structural, functional MRI (fMRI), and diffusion MRI (dMRI). The coil provides the necessary high signal-to-noise ratio and high efficiency in data acquisition, allowing four-and five-fold accelerations for dMRI and fMRI. Automated FreeSurfer segmentation of cortex, reconstruction of cortical surface, removal of artefacts and nuisance signals in fMRI, and distortion correction of dMRI all performed well, and the overall quality of basic neurobiological measures was comparable with those for the HCP. Analyses of functional connectivity in fMRI revealed high sensitivity as compared with those from publicly shared datasets. Tractography-based connectivity estimates correlated with tracer connectivity similarly to that achieved using ex-vivo dMRI.
, 2003; Andersson and Sotiropoulos, 2016; Glasser et al. , 2013 Glasser et al. , , 2016b;; 2018; Griffanti et al. , 2014; Salimi-Khorshidi et al. , 2014). 4) Use appropriate geometrical models-surfaces for the sheet-like cerebral cortex and volumes for globular subcortical structures  (Glasser et al. , 2013). 5) Perform alignment across subjects via cortical features, not cortical folds  (Robinson et al. , 2018 (Robinson et al. , , 2014)). 6) Use a data-driven structurally and functionally sensible parcellation, ideally derived from multiple modalities  (Glasser et al. , 2016a). 7) Share results as data files in neuroimaging databases such as the Brain Analysis Library of Spatial maps and Atlases (BALSA) database  (Van Essen et al. , 2017) , not just 3D coordinates. Following the HCP-Style approach in humans investigations led to dramatic improvements in spatial localization precision relative to traditional brain imaging methods  (Coalson et al.
Methods and materials Experiments were performed using a 3T MRI scanner (MAGNETOM Prisma, Siemens, Erlangen, Germany) equipped with 80 mT/m gradients (XR 80/200 gradient system with slew rate 200 T/m/s) and a 2-channel B 1 transmit array (TimTX TrueForm). The animal experiments were conducted in accordance with the institutional guidelines for animal experiments, and animals were maintained and handled in accordance with the Guide for the Care and Use of Laboratory Animals of the Institute of Laboratory Animal Resources (ILAR; Washington, DC, USA). All animal procedures were approved by the Animal Care and Use Committee of the Kobe Institute of  RIKEN (MA2008-03-11). We also used Young Adult HCP (YA-HCP) data as a reference for data quality. The use of YA-HCP data was approved by the institutional ethical committee (KOBE-IRB-16-24).
Macaque 24-channel coil The coil frame geometry was designed using a 3D digital design software (Rhinoceros 5, McNeel, Seattle, USA) to closely fit the head of the animal with maximum head dimensions (anterior posterior 109 mm, left-right 99 mm, superior-inferior 84 mm) (Fig. 1A ) in our macaque head MRI database. The database included structural scans (T1w) of 133 individual subjects from three macaque species (Macaca fuscata, N ¼ 4; Macaca fascicularis, N ¼ 122; Macaca mulatta, N ¼ 7; weight 2-8 kg). The largest animal's MRI data was used to delineate the contour of the head surface and imported into the 3D digital design software where the inner surface of the coil was designed to closely fit the head surface (Fig. 1A ). 16 pentagonal and 8 hexagonal elements were configured over the surface (Fig.
, 2013)  with 500 directions (more than the 270 in the HCP). Pilot studies for each modality included assessments for varying spatial resolution, flip-angle, RF transmission power, pulse length, inversion time (TI), fat suppression, multiband acceleration factor, in-plane acceleration factor, repetition time (TR), echo-time (TE), echo-spacing, spectral width, phase encoding direction, phase partial Fourier, phase oversampling, image resolution and diffusion directions. The key imaging parameters and features of the data acquisition are listed in Table  1  and are compared with those in publicly available macaque database (PRIMatE Data Exchange, PRIME-DE)  (Milham et al. , 2018)  and in the YA-HCP protocol.
Functional acquisition protocol To reduce susceptibility induced geometric distortions and signal loss, the data was acquired in LR and RL directions. Functional scans were acquired using gradient-echo EPI (FOV ¼ 95 Â 95 mm, matrix ¼ 76 Â 76, 1. 25 mm isotropic, interleaved slice order, and number of slices ¼ 45 covering the whole brain). An empirical estimate of the effect of multiband slice acceleration factor on fMRI tSNR was obtained by a procedure similar to that used by the HCP  (Smith et al. , 2013). Briefly, simultaneous slice excitation enables a multiband factor fold reduction in the TR and subsequent incomplete T1-recovery leads to a reduction in the optimal (Ernst) flip angle and thus in tSNR. However, as more data volumes can be acquired in a matched time window, a more relevant estimate for the data quality can be calculated by multiplying the tSNR with the square root of acquired data timepoints.
Data analysis Data analysis utilized a version of the HCP pipelines customized specifically for use with non-human primates including structural (Pre-FreeSurfer, FreeSurferNHP (instead of FreeSurfer) and PostFreeSurfer), functional (fMRIVolume, fMRISurface) and diffusion preprocessing (Diffusion Preprocessing)  (Donahue et al. , 2016; Glasser et al. , 2013)  (for an overview flowchart, see Supplementary Fig. S1 ). The HCP-NHP pipelines require FMRB's Software Library (FSL) v6. 0, FreeSurfer v5. 3. 0-HCP and Connectome Workbench v1. 4. 2 (https://www. humanc onnectome. org/software/get-connectome-workbench) and are available at https://github. com/Washington-University/NHPPipelines.
The intensity correction was performed using FMRIB's Automated Segmentation Tool (FAST)  (Zhang et al. , 2001)  followed by scaling the whole brain intensity by a species-specific factor (¼80). This process significantly improved white and grey contrast particularly in the anterior temporal lobe as well as white surface estimation, an effect that may be associated with the so-called 'anterior temporal lobe problem' in pediatric brains, potentially due to less myelination in these white matter regions. We also improved the subcortical parcellation training dataset for the macaque brain (N ¼ 21), and trained for 21 subcortical structures: brainstem plus bilateral accumbens, amygdala, caudate, claustrum (which is not a part of the default structures for human FreeSurfer), cerebellum, diencephalon, hippocampus, pallidum, putamen, and thalamus  (Fischl, 2012).
A training dataset for brain mask extraction was also created. After parcellating the cortical and subcortical structures with these training datasets using the T1w image, the claustrum was treated as putamen, so that subsequent white surface estimation accurately estimates the white surface beneath the insular cortex, as shown in the Results. The white matter segmentation was fine-tuned by filling a white matter skeleton to accurately estimate white surface around the blade-like thin white matter particularly in the anterior temporal and occipital lobe. After the white surface was estimated, the pial surface was initially estimated by using intensity normalized T1w image and then estimated using the T2w image to help exclude dura and blood vessels, as in the HCP pipeline  (Glasser et al. , 2013).
We modified this procedure by applying an optimized value of maximal cortical thickness (¼10 mm in 'fake' space, 5 mm in real space like the FreeSurfer default) and the average curvature (¼25). The surface and volume data in 'fake' space was transformed back into the native AC-PC space, and cortical thickness was recalculated in the animals' real physical space. The PostFreeSurfer pipeline transformed anatomical volumes and cortical surfaces into the Yerkes19 standard space, performed surface registration using folding information via MSMSulc  (Robinson et al. , 2018 (Robinson et al. , , 2014)) , generated the mid-thickness surface (by averaging the white and pial surfaces), generated inflated and very inflated surfaces, as well as the myelin map from the T1w/T2w ratio on the mid-thickness surface.
The brain mask calculated in FreeSurfer was used for a waypoint mask through which paths were maintained. The step length was set to 0. 23 mm, one fourth of voxel size, and the maximum path length to 200 mm. The calculated dense connectomes were created by counting the number of streamlines that terminal on voxels within the seed regions and normalizing by the total number of generated streamlines. These dense connectomes were parcellated using a 91-area atlas  (Markov et al. , 2014)  to reduce gyral bias and the parcellated connectome matrices were fractionally scaled, symmetrized, and log 10 -transformed  (Donahue et al. , 2016). The quality of the dMRI data and the success of the tracking algorithm were evaluated with respect to quantitative retrograde tracer data by correlating log 10 -transformed tractography with the corresponding log 10 -scaled fraction of labeled neurons in a source area relative to the total number of label neurons extrinsic to the injected area (FLNe)  (Donahue et al.
(D) The boxplot shows 1/g-factor with respect to reduction factor. While geometric distortions are small with acceleration factor of 2 (1/g ¼ 1. 03 AE 0. 07), further reduction yields large signal degradations. Geometric distortions were evaluated using a phantom whose contour was matched to the average macaque brain. temporal cortex is notable in the macaque data (Fig. 3H ), which is mainly attributable to a large B 0 dephasing effect (Fig. 3F ).
Data quality of resting-state fMRI To estimate the optimum multiband factor in fMRI, we determined the relationship between tSNR Â sqrtðtimepoints) and multiband acceleration factor (Fig. 5A ) and found that tSNR Â sqrt (timepoints) increases up to a factor of 5, then decreases. This trend was more evident after the data was processed using ICA-based artefact removal algorithm FIX, which yielded approximately 25% improvement in tSNR. In the cortical ribbon, denoised tSNR Â sqrt (timepoints) is clearly highest at MBF ¼ 5 (Fig. 5B ). The resting-state fMRI runs were analyzed using single-run sICA þ FIX. The resulting sICA components (a total of number of components: 124 AE 29 for each animal, N ¼ 30) were manually classified as noise (on average 100 AE 23 components per animal) or signal (22 AE 9 components per animal).
The example RSN component (Fig. 7A ) extended positive connectivity over posterior parietal cortex (areas 7A, DP, LIP), precuneus (areas 23, 31), temporo-occipital areas (MST, PGa) and prefrontal cortex (areas 46d, 8b). Temporal properties of this component included low frequency fluctuations, less than 0. 2 Hz, which are typical of RSNs. A similar functional connectivity pattern was found using a single greyordinate seed placed over area 7A (Fig. 7B ). Both the RSN signal components (a total of 32 signals) and the dense functional connectome can be interactively viewed in Connectome Workbench after downloading data from the BALSA database (https://ba lsa. wustl. edu/3ggwG). Overall, these results demonstrate that our experimental setup enables robust functional connectivity detection and analysis.
Ltd. , Tokyo, Japan) and the data acquisition protocols (including animal management and MRI scanning) are freely available from our website (https://brainminds-beyond. riken. jp/hcp -nhp-protocol), enabling other investigators to adapt, compare and make the best use of the parallel imaging capabilities of the coil. The HCP-NHP analysis pipelines are also available on github (https://github. com/Washington-University/NHPPipelines) along with macaque specific FIX training file and datasets on the website of FSL-FIX (htt ps://www. fmrib. ox. ac. uk/datasets/FIX-training/).
, 2006). The majority of the signal variance, however, is unstructured noise (>60%), particularly in subcortical regions that are distant from the coil elements (Supplementary Fig. S7 ), which can be effectively reduced by averaging within parcels and/or applying Wishart filtering (Fig. 8B )  (Glasser et al. , 2016b). The advantages of our experimental approach was further demonstrated by the capability to identify an average (across sessions/animals) of 22 AE 9 signal (neural) components at 3T (Fig. 7 , for exemplar signal components see Fig. 7  or in BALSA at https://balsa. wustl. edu/3ggwG). A previous report using group-ICA from six anesthetized macaques at 7T identified 11 RSNs  (Hutchison et al. , 2011). Our preliminary results replicate several of these RSNs. Moreover, in comparison to publicly available macaque datasets meeting the minimum criteria for the HCP-NHP pipeline (high-resolution structural images and B 0 field-map) in PRIME-DE  (Milham et al.
, 2016b) , though a robust delineation of cortical areas into functionally distinct areas will assuredly require analysis of a more extensive neuroimaging dataset and will likely require additional types of data such as from high resolution postmortem histology. These HCP-style macaque data also provide an attractive substrate for multi-modal registration across species-in particular, macaques and humans. Just as myelin maps and resting state networks are used to register across human subjects  (Robinson et al. , 2018 (Robinson et al. , , 2014)) , they could be used to register the cerebral cortex between group averages of humans and macaques. This would allow direct comparisons between human and macaque structural and functional connectivity  (Mars et al. , 2018b). That said, we expect that the gains for cross-individual registration with areal features in the macaque will be less than those in humans simply because folding patterns and the relationships between folds and areas are less variable in macaques than they are in humans  (Van Essen et al.
, 2019). Additionally, this approach to macaque imaging acquisition and analysis can be used to generate structural and functional connectomes in the macaque for comparison with invasively measured tracer datasets  (Donahue et al. , 2016; Glasser et al. , 2016b). Such validation analyses will help to establish optimal methods for estimating structural and functional connectomes in humans  (Jbabdi et al. , 2013) , where a direct comparison with a gold standard is not available. Future work will also explore cross-species comparisons  (Eichert et al. , 2019; Norman--Haignere et al. , 2019; Premereur et al. , 2018; Xu et al. , 2019)  between alert macaques and marmosets imaged using specialized hardware and an HCP-style approach. These acquisition and analysis methods can also be applied to study disease models in primate species where controlled and invasive methods can be used to investigate causality and plasticity of structural and functional connectomes and their importance in shaping primate behavior.
Data of figures and supplementary figures are available at https://ba lsa. wustl. edu/study/show/LPDP. Raw data associated with this publication are available at PRIME-DE (http://fcon_1000. projects. nitrc. org/indi/indiPRIME. html). Supplementary Information is available in the online version of the paper. These datasets require Connectome Workbench v1. 4. 2 for visualization. CRediT authorship contribution statementJoonas A. Autio: Investigation, Formal analysis, Writing -original draft. Matthew F. Glasser: Conceptualization, Formal analysis, Software, Writing -original draft, Writing -review & editing. Takayuki Ose: Investigation. Chad J. Donahue: Software, Writing -review & editing. Matteo Bastiani: Software, Writing -review & editing. Masahiro Ohno: Investigation. Yoshihiko Kawabata: Methodology.
(LR and RL directions, FOV ¼ 95 Â 95 mm, 1. 25 mm isotropic resolution, axial Table 1 orientation, slices ¼ 45, interleaved data acquisition, TE ¼ 46. 2 ms, 6/8 MRI scanning parameters and features of the current HCP-NHP macaque protocol phase partial Fourier, bandwidth ¼ 1370 Hz/pixel, echo spacing ¼ 0. 95 in comparison with open macaque database (PRIME-DE) and Young Adult HCP ms, fat suppression and pre-scan normalization). The B 1 transmit (YA-HCP) protocols. field-map was obtained using vendor provided flip-angle sequence HCP-NHP Open Macaque Database YA-HCP (Siemens, B 1 -map) (FOV ¼ 128x128 Â 58mm, gap ¼ 2 mm, gaps ac-quired in a separate run, 2 mm isotropic, TR ¼ 10 s, target flip-angle ¼ Macaque Protocol (PRIME-DE) protocol 90 ). MRI scanner Magnetic field strength 3 3 -7 3 (T) Gradient performance 80 40-80 100 (mT/m) RF coil Number of channels 24 a 4-24 32 Structural MRI Spatial resolution (mm) 0.
5 0. 3-0. 5 0. 7 T1w Yes Yes Yes T2w Yes a No, Yes Yes Functional MRI Spatial resolution (mm) 1. 25 a 1. 4-1. 65 at 3T 2 1-1. 2 at 7T Repetition time (s) 0. 76 a 1. 6-2. 0 at 3T 0. 72 1. 0 at 7T Echo-time (ms) 30 16-30 33 Spin-echo fieldmap Yes a No Yes Gradient-echo fieldmap No No, Yes No In-plane acceleration 1 2 or NA 1 Cross-plane acceleration 5 a 1, 2, NA 8 (multiband factor) Total scan time for 102 a 13-476 (median ¼ 41) 58 resting-state fMRI (min/subject) Total number of volumes 8192 a 500-13824 (median 4800 for resting-state fMRI 1712) (volumes/subject) Diffusion MRI Spatial resolution (mm) 0. 9 a 1-1. 4 at 3T 1. 25 1. 2 at 7T Repetition time (s) 3. 4 6. 3-11. 0 at 3T 5. 5 3. 8 at 7T Echo-time (ms) 73 83-107 at 3T 90 54 at 7T Number of b-shells 3 a 1-3 3 b-value (mm 2 /s) 1000- 1000, 1600, 1000- 2000- 1250-2500,850-1650- 2000-3000 3000 a 2500 at 3T, 1500 at 7T Number of diffusion 500 a 60, 80, 100, 120, 270 270 gradient direction Opposing-phase Yes No, Yes Yes encoding acquisition In-plane acceleration 2 NA 1 a Imaging parameters that are important in the HCP-NHP protocol (left col- umn) and were not often applied in PRIME-DE database (middle column).
Empathy Task: Stimuli The task and the stimuli were the same as those used in the author's previous study (Figure  1 ;  [29] ). The stimuli included 32 Caucasian faces of 8 females and 8 males with either a neutral or painful expressions [36], and 32 sentences describing either a neutral or a painful context (e. g. , "This person owns a yellow car" for the neutral context; "This person got a finger hammered" for the painful context). The stimuli were presented at the centre of the screen on a 19" LCD monitor controlled by a computer running E-prime 2 software  [54]   CAG repeats: number of CAG repeats, n > 37 is pathological  [46] ; Activity of daily living (ADL) score was assigned as follows: 0 = normal, 1 = mild weakness of limb muscles, climbs stairs easily but aware of weakness, 2 = mild to moderate weakness, climbs stairs with difficulty and generally uses a cane, and 3 = moderate to severe weakness, uses a wheelchair most of the time or mostly recumbent  [47].
, 2017). For observations of the covariate, the data set HadCRUT4  (Jones et al. , 1999 (Jones et al. , , 2001 (Jones et al. , , 2012;; Kennedy et al. , 2011; Morice et al. , 2012)  is used and noted as X o t. For each CMIP5 model included in our analysis, we also extract this covariate by taking the mean temperature anomaly over Europe. To summarize, we have the following: -The observation T o of T , given by the Météo-France thermal index. -The observation X o t of X t , given by the HadCRUT4 data set. -A total of 26 time series of T from 26 CMIP5 models. -A total of 26 time series of X t coming from 26 CMIP5 models.
Code and data availability. The CMIP5 database is freely available. Source codes realizing the GEV fit are freely available in the R Python package of SDFC under the CeCILL-C license (https://doi. org/10. 5281/zenodo. 4263886, Robin, 2020a). Source codes to reproduce the analysis of this article are packed into the library NSSEA under the CeCILL-C license (https://doi. org/10. 5281/zenodo. 4263904, Robin, 2020b). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/ascmo-6-205-2020-supplement. Author contributions. YR performed the analyses. The experiments were codesigned by YR and AR. All the authors contributed to the writing of the paper. Competing interests. The authors declare that they have no conflict of interest. Acknowledgements. We acknowledge the World Climate Research Program (WCRP) Working Group on Coupled Modelling (WGCM), which is responsible for CMIP, and we thank the climate modeling groups (listed in Table  1 of.
this paper) for producing and making available their model output. For CMIP, the U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provided coordinating support and led the development of the software infrastructure in partnership with the Global Organization for Earth System Science Portals. Financial support. Part of this work was supported by the French Ministère de la Transition Énergétique et Solidaire, through the "Convention pour les Services Climatiques" grant, and the EU-PHEME project, which is part of ERA4CS, an ERA-NET initiative by JPI Climate and cofunded by the European Union (grant no. 690462). Review statement. This paper was edited by Dan Cooley and reviewed by Daithi Stone and two anonymous referees.