dataesr/scientific-paragraphs-categorization huggingface dataset:
We successfully validate these estimates and illustrate the broader nature of this claim by training new models in new studies on new subjects and obtaining an identical performance hierarchy favoring multisystem models. This provides a blueprint for exploiting large heterogeneous datasets to practically quantify information content in the brain, one which can just as readily be applied to other brain phenomena besides pain. Although our work primarily supports a multisystem representation of pain, local perspectives have also been advanced, and our findings support the notion that specific regions have a privileged role in pain representation. Different subregions of mostly ACC, insula, midbrain and cerebellum significantly predict pain on their own. Previously some of these areas have been suggested to be pain specific (a24pr)  (Lieberman and Eisenberger 2015)  or "fundamental" to pain (OP 1)  (Segerdahl et al.
2010 ) and  (Atlas et al. 2014 ) who use Study 2 and Study 5 (respectively) to identify expectation mediators in dorsolateral PFC, amygdala, pons, and dorsal striatum , thermal stimulus mediators in somatomotor areas and cerebellum, and mediators of both components in dorsal ACC, anterior insula and thalamus. This illustrates the spatial distribution of multidimensional pain signals in our dataset broadly consistent with theoretical conceptions of a pain "neuromatrix", and lends support to the notion that multisystem models subsume, integrate and expand upon a diverse constellation of unique regional signals. Several studies suggest traditional methods of functional localization may miss subtle representations within and across brain areas, and that pain representations might be embedded in areas typically associated with unrelated processes  (Haxby et al.
above) by selecting regions related to pain in previous literature (Apkarian et al. 2005 ; Duerden and Albanese 2013) and regions that play important roles in non-human pain studies. The areas included are ventral posterolateral, intralaminar and mediodorsal thalamic nuclei, hypothalamus, parabrachial nuclei, periaqueductal gray matter (PAG), rostroventral medulla, amygdala, dorsal posterior, middle and anterior insula, primary and secondary somatosensory cortices, and anterior medial cingulate cortex/medial prefrontal cortex (PFC). Regions were selected based on the prior knowledge of the field (by T. W. D. ). The map and labels are available online in the Cognitive and Affective Neuroscience Laboratory Github repository at https://github. com/canlab/Neuroimaging_Pattern_Masks/.
Post-hoc comparison shows a full brain model outperforms the best single region (FullBrain -Regions = 0. 34(0. 10), F 1,7. 9 = 12. 35, p = 0. 008). Models are trained after pooling data across seven datasets, yielding 90 subjects per training fold. (B) We retrain cross-validated models separately for each study to infer whether findings generalize to new PCR models trained on new datasets. All obtained models show significant predictive performance (p < 0. 001, random subject and study intercept, random study effect), but comparison of performance across models from each study shows that distributed representations will continue to outperform modular representations (planned comparison, mean(PainPathway, Neurosynth, FullBrain)mean(regions, fRSN, cRSN) = 0. 27(0. 09), F 1,6. 1 = 9. 04, p = 0.
Data storage supported by the  University of Colorado Boulder "PetaLibrary". Research  funded by  NIMH   R01 MH076136 ,  NIDA   R01 DA046064  and  NIDA R01 DA035484. Lauren Atlas  is supported in part by funding from the  Intramural Research Program of the National Center for Complementary and Integrative Health, National Institutes of Health  ( ZIA-AT000030 ). Marina Lopez-Sola  is supported by a  Serra Hunter fellow lecturer program. We would like to thank  Dr. Christian Buchel  for contributing data to this project, and  Dr. Marta Čeko  for comments and feedback on the manuscript.
( above) by selecting regions related to pain in previous literature (Apkarian et al. 2005 ; Duerden and Albanese 2013) and regions that play important roles in non-human pain studies. The areas included are ventral posterolateral, intralaminar and mediodorsal thalamic nuclei, hypothalamus, parabrachial nuclei, periaqueductal gray matter (PAG), rostroventral medulla, amygdala, dorsal posterior, middle and anterior insula, primary and secondary somatosensory cortices, and anterior medial cingulate cortex/medial prefrontal cortex (PFC). Regions were selected based on the prior knowledge of the field (by T. W. D. ). The map and labels are available online in the Cognitive and Affective Neuroscience Laboratory Github repository at https://github. com/canlab/Neuroimaging_Pattern_Masks/.
EVOKED PAIN MVPA META-ANALYSIS 8 whole brain. Formal model comparisons at each spatial scale provide a quantitative measure of the extent to which modular features, distributed networks and the whole brain represent pain experience. By repeating this process across a selection of datasets with varying data acquisition and pain manipulations we are able to generalize our findings to novel studies and contexts. METHODSParticipantsThis study aggregated data from 10 previously published studies involving 289 participants. Institutional review boards at the University of Colorado Boulder, Columbia University or the ethics committee of the Medical Chamber Ha mburg approved thes e studies. Please refer to Table1for details on sample size, age ranges and gender. All subjects are right handed. DataAll data in this st udy have been previously published.
However, cross valid ation is not valid on bootstrapped samples due to likely redundancy of some resampled observations in both training and test splits. A common approach is EVOKED PAIN MVPA META-ANALYSIS 16 to dataset, and draw an instance f* from the posterior distribution over f , which varies according to the gaussian process model's estimate of uncertainty in f , compute the global minima of f* , and use that as the hyperparameter for the bootstrapped PCR. This method is novel, results in greater MVPA map variance across bootstrap iterations, and more conservative bootstrap correction than typical in other MVPA studies.
, 2019). This approach constrains the recovered topography to match all direct topographic observations. This constraint helps account for geological factors, such as variations in crustal thickness, sedimentary basins, or intrusions. We acknowledge that away from direct topographic observations the uncertainties in the bathymetric estimate due to geological factors increase. However, we suggest that using a well-constructed gravityderived bathymetry is preferable to unconstrained interpolation across sub-ice-shelf bathymetric data gaps many tens of kilometres wide. Such use of gravity data is routine for predicting topography in unsurveyed parts of the ocean using satellite data  (Smith and Sandwell, 1994)  and is being used in the Arctic where higher-resolution airborne data are included  (Abulaitijiang et al.
, 2019). Our results confirm the shape and position of the previously identified troughs  (Millan et al. , 2017). Differences in the inversion results beneath the inboard parts of some of the ice shelves are identified, reflecting the higher resolution of the new gravity data set and differences in the methods used. Our improved topographic estimate reveals variations in subice-shelf cavity thickness, which have implications for the rate at which the warm ocean water can access the presentday grounding lines and the mechanism of grounding line retreat in these and other areas. (Rignot et al. , 2017)  with grounding lines from interferometric synthetic aperture radar (InSAR) shown in colour  (Rignot et al. , 2014; Milillo et al. , 2019) ; grey lines represent rock exposure. TGT and EIS denote the Thwaites Glacier tongue and Eastern Ice Shelf, respectively.
(b) Line gravity data coverage, with regions of known topography and an inset showing Antarctic context. ASE is Amundsen Sea Embayment, and WAIS is the West Antarctic Ice Sheet. (c) Integrated free air gravity anomaly grid. (d) Topographic constraints, with direct observations shown in strong colors. Onshore these were taken from airborne radar including Operation Ice Bridge (OIB)  (Paden et al. , 2010 (Paden et al. , , updated 2018) )  and the ITGC airborne survey. Offshore bathymetric data was taken from ship-borne swath coverage  (Hogan et al. , 2020). Pale colours show BEDMAP2 DEM  (Fretwell et al. , 2013). Yellow areas highlight post-1993 ice shelves. Red boxes show the locations of Figs. 4  and 6.
S1  in the Supplement) and do not appear to give rise to any anomalous signals in the integrated free-air gravity dataset (Fig. 1c ). Marine gravity data from cruise NBP19-02 matched the pattern of the airborne anomalies but were offset by ∼ 7. 14 mGal above the level of the airborne data. The majority of this offset is due to the difference between geoid (marine) and ellipsoid (airborne) references used for the different systems. In the area of overlap the geoid-ellipsoid difference results in a ∼ 9 mGal discrepancy, based on the GOCO3s satellite gravity model  (Pail et al. , 2010). The residual ∼ 2 mGal difference may reflect drift in the marine system, and potential discrepancies in base station ties between the different surveys. Alternatively, unconsidered shorter wavelength variability in the gravity field not resolved by the GOCO3s model (<∼ 160 km) or temporal changes in the geoid associated with glacio-isostatic adjustment and mass loss may account for the residual shift.
Such features do not impact the locally recovered bathymetry and are beyond the scope of this paper. The average measured shift of 7. 14 mGal was therefore subtracted from the marine line data as a single offset value. All line data were then merged into a single database, interpolated onto a 1 km mesh raster, and filtered with a 5 km low-pass filter removing residual line-to-line noise. This filter wavelength is justified, as anomalies with wavelengths < 5 km are not resolved by the airborne gravity systems used. The final, integrated free-air gravity map (Fig. 1c ) shows a clear pattern of high and low anomalies, which first-order match the main 5-10 km wavelength features visible in the available onshore subglacial topography and offshore bathymetry (Fig. 1d ). The topographic observations onshore were taken from OIB line radar data  (Paden et al.
, 2010 (Paden et al. , , updated 2018)) , augmented with new depth sounding radar collected along with the gravity data during the ITGC campaign (Fig. 1d ). These new bed elevation data were collected using a 600-900 MHz accumulation radar provided by the Center for Remote Sens-ing of Ice Sheets (CReSIS). Bed elevations were picked from synthetic aperture radar (SAR)-processed radargrams in a semi-automated fashion. Although the primary target of this radar system was shallow ice sheet structures, bed elevation was resolved through ice up to ∼ 1900 m thick. Visual inspection revealed a few incorrect onshore bed picks in the OIB dataset on Bear Island, which gave bed elevations above the highly accurate Reference Elevation Model of Antarctica (REMA) surface digital elevation model (DEM)  (Howat et al.
, 2019). These points were deleted from the integrated line bed elevation dataset. The line bed elevation data were corrected to the GL04c Geoid  (Forste et al. , 2008) , and the data were then interpolated onto a 1 km mesh raster. This gridded dataset was carefully masked to remove regions that are now covered by the floating ice shelf based on the most up-to-date grounding lines  (Rignot et al. , 2014; Milillo et al. , 2019). Bed elevation values over local sub-shelf pinning points were also excluded. This masking mitigates the risk of the base of a floating ice shelf being misidentified as a bed elevation point and biasing the inversion. Beyond the ice shelves we took the values constrained by a new compilation of shipborne multibeam swath bathymetric data  (Hogan et al. , 2020) , which was downsampled to a 1 km mesh raster for this study (Fig.
2b ). As our inversion did not use any additional data (swath, seismic, or radar) to constrain the elevation at these isolated pinning points within the ice shelves, the fact that many appear to be within error of their grounding level provides qualitative support for the reliability of our inversion. The revealed sub-ice-shelf cavity is > 500 m thick in many areas. Adjacent to parts of Thwaites Glacier this deep cavity reaches to within 0-10 km of the grounding line. In contrast, the inboard parts of the Dotson and Crosson ice shelves formed since 1993 overlie a cavity typically < 150 m thick (Fig. 2b ), and the thick (> 450 m) cavity lies more than 10-30 km from the current grounding line. Profiles of the bathymetry beneath the ice shelves confirm the complex sub-ice-shelf pattern (Fig. 3 ).
5 km was achieved over the Dotson and Crosson ice shelves. This study therefore only recovers bathymetric features with a wavelength of ∼ 5 km and upwards. To best quantify the uncertainty in the sub-ice-shelf bathymetric estimate in our study region we utilised the new shipborne multibeam bathymetric data collected predominantly by a recent ITGC cruise, NBP19-02 (Fig. 1a )  (Hogan et al. , 2020). The topographic shift method was rerun with these multibeam data excluded from the constraining bathymetric dataset (Fig. 4a ). The difference between the results with and without this test dataset (Fig. 4a  and b ) provides a snapshot of the errors associated with our recovered bathymetry (Fig. 4c ). In this region the mean error is -40 m, with a standard deviation of 100 m. We take this standard deviation to be representative of the expected error in our modelled bathymetry.
This error is within the typical range for that quoted for gravity-derived bathymetry, for example error estimates of ∼ 60 m have been suggested in Greenland  (An et al. , 2019) , while errors of up to ∼ 160 m are suggested for the Larsen Ice Shelf where, unlike our study, no account had been made for the underlying geology (Brisbourne et al. , 2014;  Cochran and Bell, 2012). The mean error we find indicates that the bathymetry constrained by the swath data is deeper than predicted by the gravity inversion alone, and hence that there are geological features in this region distorting the recovered bathymetry. It is apparent that the largest errors are associated with higher-frequency topography revealed by the new multibeam data. Such errors resulting from comparison of datasets with fundamentally different resolutions is to be expected, highlighting the need for multibeam bathymetry in regions where sub-kilometre-scale resolution of bathymetry is required.
Conclusions Airborne gravity provides a good first-order estimate of subice-shelf bathymetry. Despite the relatively high uncertainty (∼ 100 m standard deviation), comparisons with different gravity inversion techniques, the location of ice shelf pinning points, and new observational bathymetric data indicate that the pattern of sub-ice-shelf bathymetry is well resolved. Thwaites Glacier is connected to Pine Island Bay to its east by a major trough that is > 800 m deep and 20 km wide. In contrast, the grounding lines of the of Dotson and Crosson ice shelves are accessible through relatively narrow channels and thin sub shelf cavities. In the Thwaites, Dotson, and Crosson region, areas of ice shelf that developed before and after 1993 form distinct populations. The most recently ungrounded areas are underlain by thin cavities (average 112 m) where the ice shelf base closely tracks the underlying bed topography.
We propose that these systems represent a transient phase of ice margin development that may act to slow future changes, which is indicated by but not fully captured in present models. Data availability. The new calculated bathymetry, along with input topography and gravity grids, is available from the UK Polar Data Centre (https://doi. org/10. 5285/7803DE8B-8A74-466B-888E-E8C737BF21CE, Jordan et al. , 2020a). The associated ITGC line airborne gravity and magnetic data are available from the same source (https://doi. org/10. 5285/776612D1-573C-49C4-AFF5-23B0FBA48271, Jordan et al. , 2020b ; https://doi. org/10. 5285/B9B28A35-8620-4182-BF9C-638800B6679B, Jordan et al. , 2020c). Other data are from sources cited in the text. Supplement. The supplement related to this article is available online at: https://doi.
org/10. 5194/tc-14-2869-2020-supplement. Author contributions. All authors contributed to the discussion and production of the final manuscript. Additional specific contributions are as follows: gravity data processing, topographic shift bathymetric inversion, and primary manuscript preparation was carried out by TAJ. Airborne gravity and magnetic data collection was led by DP. Discussion of bathymetry inversion techniques, including previous inversion methods, was aided by KT and RM. Discussion of gravity inversion and implications of ice sheet cavity findings was with the assistance of AM. Preparation of multibeam swath bathymetry compilation was carried out by KH. RDL provided shipborne gravity and multibeam swath data. Processing and preparation of multibeam swath bathymetry data on NBP19-02 was carried out by AGCG.
Airborne radar system development, data processing, and ice bottom tracking was led by JDP. support for this work is from Lamont-Doherty Earth Observatory, NSF grant no. NSF1842064 (David Porter, Kirsty Tinto) and the THOR (Kelly Hogan, Robert D. Larter, Alastair G. C. Graham, and cruise NBP19-02), TARSAN (Atsuhiro Muto), and MELT (John D. Paden) projects, components of the International Thwaites Glacier Collaboration (ITGC). This is paper no. ITGC:009. Financial support. This research has been supported by the UK Natural Environment Research Council (NERC) (grant nos. NE/S006664/1, NE/S006419/1) and US National Science Foundation (NSF) (grant nos. NSF1842064, NSFPLR-NERC-1738942, NSFPLR-NERC-1738992, NSFPLR-NERC-1739003). Review statement. This paper was edited by Joseph MacGregor and reviewed by two anonymous referees.
2), 2. 5 mM MgCl 2 , each dNTP at 200 M, 320 nM each primer and 1 U of Platinum Taq Polymerase (Invitrogen), two mg of HotStart-IT Binding Protein (Affymetrix), and 1. 5 l of bisulfite-treated template DNA. Forty cycles were run under following conditions: 94 • C for 20 s, 58 • C for 50 s, and 72 • C for 60 s. At least three primary PCRs were performed for each sample to exclude amplification of one template molecule. Non-converted DNA did not provide bands. Several non-template controls were included in each bisulfite PCR reaction. Amplification products were cloned in the pGEM-T-EasyVector System (Promega, Madison, WI, USA) and sequenced. Analysis was performed using the Quma (Quantification tool for Methylation Analysis) software (http://quma. cdb. riken. jp/). Only PCR clones with at least 95% conversion of cytosines outside CpGs were taken into account.
Gene enrichment analysis We applied the function enrichKEGG involved in the R package clusterProfiler  (50)  to assay the enrichment Kyoto Encyclopedia of Genes and Genomes (KEGG) categories from less frequently HIV-targeted genes and genes harboring non-RNA expressing provirus with the false discovery rate (FDR) control represented by adjusted P-values. An adjusted P-value takes multiple statistic tests (individual Pvalues) into account in one entire dataset  (51, 52). We used the Benjamini-Hochberg procedure  (53)  to compute adjusted P-values for each enriched KEGG pathway in given gene sets. The Gene Ratio was calculated by taking the number of unique genes overlapping with those involved in a specific KEGG pathway (value k) divided by the number of unique genes overlapping with those in the collection of tested KEGG pathways (value n), based on the definition in the R package clusterProfiler.
Quantification and statistical analysis GraphPad Prism version 7. 00 was used for statistical analysis (GraphPad Software, La Jolla, CA, USA, www. graphpad. com). Differences in distribution of integration sites across chromosomes and different genome categories were assessed by the Chi square test. The non-parametric Kruskal-Wallis with Dunn's multiple comparison test was used to compare distances of integration sites to certain features. In case other tests were used, the information is specified in the main text and figure legends.
Bioinformatic analyses ChIP-seq reads were mapped on GRCh37/hg19 with BWA-MEM with default parameters and a minimum mapping quality of 20. The targets were identified with Zerone v1. 0 (54) with options '-list-output' and '-confidence 0. 99'. Sequencing reads from Jurkat and SupT1 mRNA were mapped to Ensembl cDNA assembly GRCh37 (release 75) with kallisto (55) 46 with options '-single' (single-end mode), '-bias' (sequence bias correction, '-s300' (fragment length 300 nucleotides) and '-l100' (s. d. 100 nucleotides). The counts of the different isoforms were summed, thus generating a total count per gene copy in transcripts per million. Identification of barcodes and HIV integration sites was performed as described before  (43). The human genome was partitioned into six types: active genes, silent genes (genes refer to only protein-coding genes), active promoters, enhancers, intergenic regions and repeats.
Data and code availability All data-processing steps are documented in a Docker virtual machine available at https://github. com/ezorita/bhive. The datasets generated during this study are deposited at Gene Expression Omnibus (GEO), series GSE135295. Additional ChIP-Seq data for H3K36me3, H3K79me3, H3K27ac, H3K4me1, RNAPII, H3K4me3, H3K9me3 (GSE65687)  (56) , H3K79me2 (GSE60104) (57), Med1 (GSE59657)  (58)  and CBP (GSE17954)  (59)  were downloaded from GEO.
ACKNOWLEDGEMENTS We would like to thank  A. Bruggemans ,  M. Carlon ,  F. Christ  and  J. Janssens  for critical reading of the manuscript. We are grateful to  B. Van Remoortel  and  P. Van de Velde  ( Molecular Virology  and  Gene Therapy ,  KU Leuven ) for the technical assistance and to  K. Jacobs  (Center for cancer biology,  VIB-KU Leuven)  for helping with fluorescence sorting. We would like to thank the  Genomics Core facility of the Center for Genomic Regulation (CRG, Barcelona)  for the support of Illumina sequencing. LEDGINs were synthesized by Cistim/CD3 (courtesy of  Dr A. Marchand ). Author contributions: Z. D. and H. C. conceived the study. G. V. , H. C. , K. T. , D. M. and Z. D. designed experiments. G. V. , H. C. and K. T. conducted experiments. H. C. and E. Z. performed bioinformatic analysis.
The instrument line shape (ILS) is part of the state vector and retrieved simultaneously along with the O 3 profile, with an ideal ILS being applied as the a priori input. The temperature, pressure and H 2 O profiles are from the National Centers for Environmental Prediction (NCEP) 6hourly reanalysis data. For the a priori profiles of O 3 and other interfering species, we use the mean of the WACCM model data between 1980 and 2020. Since the broadening effect of absorption lines is related to the pressure and temperature, we can obtain limited vertical information of O 3 by fitting the spectra. Figure  2  shows an example of the typical averaging kernel of the FTIR O 3 retrieval at Xianghe. The retrieved O 3 profile is mainly sensitive to the vertical range between 10 and 40 km. The degrees of freedom for signal (DOFS) is 2.