dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017; Honarvar et al. , 2016 ). Since all three methods underestimate G in region 5 of the CIRS phantom and region G of the breast phantom, it is assumed that these values lie above this threshold in their respective data sets. Furthermore, stiffness values should decrease when high frequency noise is added, as higher frequencies correspond to lower stiffness values. Therefore, the less affected the method is by noise, the more accurate these average values of G will be. The SD metrics assess the variability of the stiffness results, in what should be homogeneous regions of the phantoms, by computing the standard deviations of G and G within those regions. The %Dec metric measures the percent decrease in the mean G value with approximately 6% uniformly distributed random noise added to every component of the displacement field.
The global method had regularization parameters optimized to α G = 10 4 , α P1 = 10 5 , and α P2 = 10 5 , which were chosen based on the second data set, as this appeared to give reasonable values for G and correlation  to anatomy. These parameters were then applied to the other two data sets. Symmetry measures for each method were computed by flipping the elastograms over the line of symmetry, subtracting from the originals, and taking the two-norm of the difference so that a lower number means more symmetry. G and G values were averaged over the three data sets. The proposed method gives 10. 4 and 7. 8 for G and G symmetry, respectively. The curl method gives 11. 6 and 8. 5 and the global method gives 11. 5 and 10. 3. Additional information is provided in Fig. 10  in order to better contextualize the brain results.
Computational cost The computational cost of the methods is presented in Table  4  and includes the pre-computation of displacement derivatives. The methods were applied to each data set 3 times and the average run time was calculated. The total number of voxels refers to the cropped image stack resolution as shown in Table  1. All reconstructions were run on a Linux workstation using MATLAB with an Intel Xeon 4-core (8-thread) 3. 60 GHz processor.
4  ) has a more significant G component with known values and distribution, potentially making this data more difficult to reconstruct. The G elastograms from all three methods give comparable accuracy to the CIRS phantom data. Here, however, the ratio between the inclusion stiffness and background stiffness is lower which reduces the ringing effect for the curl method and presumably the variability in G near the inclusions for both local methods. In the G elastograms the inclusions are not clearly identifiable in the global method reconstruction but are for both local method reconstructions. In Fig. 5  and Table  3  , the proposed method is shown to accurately predict the mean G for all regions. Overall, considering both phantom data sets, the proposed method is seen to approach the accuracy of the more expensive global method for G.
47 kPa and the global method averages 2. 11 and 0. 83 kPa in these respective regions. The other choice of pressure regularization for the global method gives 1. 81 and 0. 79 kPa in these regions. These values are near or within previously reported ranges  ( Samani et al. , 20 07; Sinkus et al. , 20 05b ) , and the lower values for the curl method may be due to noise sensitivity. The stiffness distributions themselves also differ, as the global method shows a single large area of increased stiffness while the proposed and curl methods show a more constant baseline stiffness with several localized areas of increased stiffness. The breast data was also used to demonstrate the effect of the global method's pressure regularization on reconstructed stiffness. It was found that adjusting the pressure regularization in this data set could result in local changes in G of up to about 1 kPa (45%), and in the average glandular values by about 15%.
Acknowledgments This research was funded by the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement No  668039  and by the  European Research Council  under the European Union's  Seventh Framework Programme  (FP/2007-2013)/ ERC  Grants Agreement No. 600948. The authors would like to acknowledge funding from  Engineering and Physical Sciences Research Council  (  EP/N011554/1  ). This work was supported by the  British Council  under the  Newton Fund Institutional Links  (Grant Agreement ID 172707526) and by the  Wellcome EPSRC Centre for Medical Engineering at King's College London  ( WT 203148/Z/16/Z ).
The shortage of information on snow properties in high latitudes places a major limitation on permafrost and more generally climate modelling. A dedicated field program was therefore carried out to investigate snow properties and their spatial variability at a polygonal tundra permafrost site. Notably, snow samples were analysed for surface-normal thermal conductivity (K eff-z ) based on X-ray microtomography. Also, the detailed snow model SNOWPACK was adapted to these Arctic conditions to enable relevant simulations of the ground thermal regime. Finally, the sensitivity of soil temperatures to snow spatial variability was analysed. Within a typical tundra snowpack composed of depth hoar overlain by wind slabs, depth hoar samples were found more conductive (K eff-z = 0. 22 ± 0. 05 W m -1 K -1 ) than in most previously published studies, which could be explained by their high density and microstructural anisotropy.
1 mm, so DH crystals and especially DH chains were in adwww. the-cryosphere. net/12/3693/2018/ The Cryosphere, 12, 3693-3717, 2018 dition easy to discriminate from the upper layer with smaller, mostly rounded grains (RGs). Snow depth was recorded continuously over the 2012-2013 snow season by an SR50 sensor (Campbell Scientific, ±1 cm accuracy, ±1 cm precision) located in the topographically low centre of the reference polygon (Fig. 1 ). This instrument acquires data over a circular surface of ∼ 20 cm in radius. However, this snow depth record differed from data acquired at grass-centre snow pits: on 21 April 2013 the SR50 measured 13 cm of snow while the transect, CT, and snow pit data indicated depths in excess of 17 cm for grasscentre conditions (Fig. 3 ). This difference is likely due to small-scale variability in snow depth induced by microrelief (notably vegetation tussocks) and in processes such as wind erosion immediately below the SR50 sensor: ancillary snow depth data acquired over a 14 m grass polygon transect at 20 cm spatial resolution show a 7 cm variance in snow depth and variations up to 9 cm over a 40 cm horizontal distance in centre conditions.
Wind speed was measured at 2. 5 m above ground with a 05103 Young wind monitor. Wind speed was (together with air temperawww. the-cryosphere. net/12/3693/2018/ The Cryosphere, 12, 3693-3717, 2018 ture) the only meteorological field for which likely instrumental failure was detected, characterized by periods of a few hours to a few days with null wind speed. The impact on the SNOWPACK simulations is however marginal as no major snow depth variation was observed during these periods. Snow depth data, meteorological data, and data on the ground thermal conditions at Samoylov during the 2012-2013 snow season are presented in Fig. 2. Meteorological and snow depth data are freely available  (Boike, 2017).
However, no value as low as that was recorded during the 2013 program from the wind slab layers at Samoylov, where the density is always above 305 kg m -3. Such densities are furthermore essentially achieved by wind compaction (settlement in thin arctic snowpacks is negligible). We therefore used ρ 0 = 305 kg m -3 in Eq. (  5 ). The original value for ρ( ρ = 361 kg m -3 ) was retained. For the ANISO adaptation we implemented in SNOW-PACK an alternative formulation derived from L2013  (Löwe et al. , 2013, their Eq. 5) , which by considering anisotropy, explained a larger part of the observed variability in our K eff-z measurements than formulations relying solely on density. However, L2013 requires an anisotropy parameter Q, which can be either calculated from CT images of samples or es-timated from polarimetric radar data  (Leinss et al.
Thermal implications of snow spatial variability We made use of data from the reference polygon transect to more thoroughly characterize the spatial variability in snow depth, structure, and insulating power across the polygonal www. the-cryosphere. net/12/3693/2018/ The Cryosphere, 12, 3693-3717, 2018 tundra at Samoylov. We extracted DH thickness and snow depth at 31 points with 50 cm spacing along the transect by post-processing the NIR images (Fig. 1e , Sect. 2. 2. 1). The two-layer approach by  Zhang et al. (1996)  (see Sect. 3) was then used to infer bulk K eff-z , R th , and density at these 31 points at the time of the field observation (21 April 2013), relying on Eqs. (  1 ) and (  2 ). Time series of these bulk snow properties were then computed, based on the two-layer approach and time evolution of the DH properties as simu-lated by the WIND + VEG + ANISO set-up providing the best match to observed snow characteristics.
To bypass these shortcomings and still produce reasonable SNOWPACK simulations, we adopted a phenomenological parameterization for the combined effects of snow vapour flux and vegetation on basal snow porosity. On the one hand, our adaptations to SNOWPACK are inherently local, tied to the specific Samoylov conditions and should be verified at other tundra sites comprising co-located snow and soil observations together with a complete set of meteorological driving data. On the other hand, neither this approach nor the current observational datasets allow the retrieval of any dynamics in basal snow ice depletion. A considerable uncertainty therefore remains regarding the thermal properties of snow in the early winter (cooling) period, when the sensitivity of ground thermal regimes to snow conditions is at its maximum.
, 2016) , thermal, and optical properties of the snow. A promising way to further assess the relevance of anisotropy to the conductivity and the ground thermal regime may be to incorporate remote-sensing observations. It has been recently demonstrated  (Leinss et al. , 2016)  that the depthaveraged anisotropy parameter (Q) of a snowpack can be estimated from polarimetric radar data such as, for example, those available from the TerraSAR-X satellite. Such an analysis could be used to produce global maps of the average anisotropy of snowpacks, as an indication of their metamorphic state. Our combined SNOWPACK and CG3 simulations show a cold bias during and after melt-out. Hydrological processes within the snowpack related to thaw and rain are known to have an important influence on soil thermal dynamics, as has been emphasized in a large number of publications (e.
Snow schemes used in permafrost models such as CG3 do not currently represent these processes, inducing significant biases in the melt period. Finally, we assessed the impact of snow spatial variability linked to microtopography on the ground thermal regime. Our approach disregards the spatial variability in soil properties and soil saturation, which is also related to microtopography, as well as the lateral heat fluxes among different landscape units. Distributed three-dimensional simulations that include the effect of snow redistribution by wind and spatial variations in soil conditions could, in theory, support a more consistent assessment of spatial variability in soil temperatures. However, they require a considerable number of in situ data that are currently unavailable even at the most instrumented sites  (Kumar et al.
Acknowledgements. We thank  Mathias Bavay  for his constant help and brilliant management of the SNOWPACK core structure and integration of new developments. Our sincere thanks go also to  Michael Lehning  and  Charles Fierz  for providing funding, enthusiasm, and insightful comments on development choices. We thank  Florent Dominé  for his vision of the relevant processes and the critical model limitations and  Jean-Emmanuel Sicart  for very valuable comments. Finally, we thank  Franziska Gerber  for deep scrutiny of the Samoylov meteorological dataset. The field work for  Martin Proksch  was supported by the  INTERACT-TA  project  SSTIS. Edited by:  Christian Hauck Reviewed  by: two anonymous referees.
Code availability. The adaptations to SNOWPACK used in this study are not included in the SNOWPACK distribution but the description provided in the paper allows the simulations to be reproduced in their entirety. Data availability. Meteorological and snow depth data are available at https://doi. org/10. 1594/PANGAEA. 879341 (Boike, 2017). www. the-cryosphere. net/12/3693/2018/ The Cryosphere, 12, 3693-3717, 2018.
RNA editing analysis of ASD postmortem brain samples We obtained a total of 261 RNA-Seq datasets derived from samples representing three brain regions implicated in ASD-susceptibility: frontal cortex, temporal cortex, and cerebellar vermis (Table  S1 ). These datasets were generated as part of our transcriptomic study of ASD brains that included a cohort of 68 ASD and control subjects  15. Overall, the ASD and control groups did not have significant difference in variables that could confound RNA editing analysis, such as age, gender, sequencing depth, etc (Fig. S1 ). For each brain sample, rRNA-depleted total RNA was sequenced with an average of 70 million raw read pairs (average 55 million uniquely mapped pairs, 2x50 nt, non-strand-specific, Fig. S2 )  15. We applied our previously developed methods to identify RNA editing sites using the RNA-Seq data  34, 35.
In addition, we implemented additional steps to capture editing sites located in "hyperedited" regions, which were likely missed by regular methods  36   (Methods). Combining these approaches, we identified a total of 98,477 RNA-DNA differences (RDDs) from frontal cortex, 97,994 from temporal cortex, and 134,085 from cerebellum. As expected, the number of RDDs per sample correlated with read coverage approximately (Fig. S3 ). On average, >95% RDDs were A-to-G and T-to-C editing types per sample, consistent with A-to-I editing reflected in non-strand-specific RNA-Seq data (Fig. 1a , Fig. S4a ). The remaining 5% of RDDs mainly consisted of C-to-T and G-to-A types, possibly due to C-to-U editing. Notably, most (84%) of the A-to-I editing sites identified here are included in the REDIportal database  37  (Fig.
CC-BY 4. 0 International license a across the subjects. We applied weighted gene co-expression network analysis (WGCNA)  43  to search for highly correlated clusters of editing sites (i. e. , modules) (Methods). Remarkably, we identified a module enriched in editing sites that had significant association with diagnosis based on the correlation of editing variation across subjects (Fig. 2d , Table  S4 ). We next asked whether certain trans-regulators were responsible for regulation of this module (called the "turquoise module") by examining the correlation between the module "eigengene"  44  (i. e. , eigen-editing site) and expression levels of potential trans-regulators. We included a small number of RNA-binding proteins (RBPs) in the search of trans-regulators chosen based on a parallel study (Tran, Bahn, Xiao, unpublished data).
Consistent hypoediting patterns observed for different brain regions of ASD patients In addition to the frontal cortex, the temporal cortex and cerebellum are also functionally implicated in ASD pathophysiology  57. Here we ask whether different brain regions have similar editing patterns. Similar to frontal cortex, we observed global down-regulation of RNA editing in both brain regions in ASD (Fig. 5a ). In addition, differential editing sites shared between brain regions showed significant correlation in levels of dysregulation (Fig. 5b ). We next carried out WGCNA analysis on the editing sites identified in temporal cortex and cerebellum, respectively. Similarly as for frontal cortex, the module (colored turquoise by WGCNA convention) associated with each of these brain regions was also strongly associated with ASD (Fig.
RNA-Seq data sets of Fragile X patients and carriers Postmortem frontal cortex samples of Fragile X patients and Carriers were obtained from the University of Maryland Brain and Tissue Bank. Total RNA was extracted using TRIzol (Thermo Fisher Scientific, 15596018). RNA-Seq libraries were prepared using NEBNext Poly (A) mRNA magnetic isolation module (NEB, E7490) followed by NEBNext Ultra Directional RNA library prep kit for Illumina according to manufacturer's instruction. RNA-Seq data were collected on an Illumina HiSeq 2000 sequencer. Paired end reads (2x100nt) were obtained with about 75 million read pairs per sample (Fig. S12b ).
Principal components analysis (PCA) PCA was conducted on differential editing sites in order to examine associations between PCs and potential confounding covariates. The R function prcomp was used for this purpose. Missing values in the editing level matrix were imputed using the missMDA package  77. The PCs were then correlated against technical and biological covariates such as age and gender (Fig. S5a ). The first PC was predominantly associated with ASD diagnosis, and was thus used as the PC for differential editing.
Weighted gene co-expression network analysis (WGCNA) The WGCNA package  43  in R was used to find modules of correlated editing sites. In multisample analysis, it is typical that some editing sites have no available values (missing data) in certain samples that lack read coverage at those sites. To preclude inaccurate calculations due to samples with too much missing data, we used the following requirements for editing sites to be included in WGCNA: (1) with ≥5 reads in ≥ 90% of samples and (2) with nonzero editing levels in ≥ 10% samples. In addition, to detect variation in the data, we further required that the included editing sites had a standard deviation ≥ 0. 1 in their editing levels across samples. A soft threshold power of 10 was used to fit scale-free topology. To avoid obtaining modules driven by outlier samples, we followed our previous bootstrapping strategy  15, 18.
One hundred bootstraps of the data set were carried out to compute the topological overlap matrix of each resampled network. Co-editing modules were obtained using the consensus topological overlap matrix of the 100 bootstraps. WGCNA offers a dynamic tree-cutting algorithm, which enables identification of modules at various dendrogram heights and allows delineation of nested modules  78. However, upon. CC-BY 4. 0 International license a examination of the WGCNA dendrogram (Fig. 2d ), we observed only one pronounced module of editing sites. Furthermore, most modules, identified through dynamic tree cutting, were generally unstable, highly dependent on tree cutting parameters. Therefore, we used the traditional constant height tree cutting, provided by WGCNA as cutreeStaticColor, with cutHeight set to 0.
Association of modules with ASD diagnosis and RNA binding proteins To test the association of the turquoise module with diagnosis, we first defined eigen-editing sites as the first principal component of the module, according to WGCNA recommendation  44. A linear regression model was constructed between the eigen-editing sites and diagnosis, in addition to biological and technical covariates including RIN, age, gender, sequencing batch, PMI, brain bank, 5' to 3' RNA bias, AT dropout rate, GC dropout rate, mapped bases in intergenic regions, uniquely mapped reads. The linear model was fit with backwards selection, and the module was deemed as associated with ASD diagnosis if p ≤ 0. 05 for the coefficient of this variable. We tested if a module was enriched with differential editing sites using Fisher's Exact test.
eCLIP-Seq experiment and data analysis The eCLIP experimental procedure is detailed in our previous studies  51, 79. The antibodies used for this experiment are: FMRP antibody (MBL, RN016P) and FXR1 antibody (Bethyl Laboratories, A303-892A). Flash-frozen brain tissue was cryo-ground in pestles pre-chilled with liquid nitrogen, spread out in standard tissue culture plates pre-chilled to -80°C, and UV crosslinked twice at 254 nM (400 mJ/cm 2 ). 50 mg of crosslinked tissue was then used for each eCLIP experiment, performed as previously described  51, 79. eCLIP-Seq data were analyzed using the CLIPper software  51  that generated a list of predicted binding peaks of the corresponding protein. In each replicate, peaks were further filtered to retain those whose abundance was at least 2 fold of that in the SMInput sample.
To examine the FMPR or FXR1P binding relative to RNA editing sites, we compared the distances from eCLIP peaks to turquoise editing sites compared to region-matched random adenosines. Only editing sites residing in genes containing at least 1 eCLIP peak were considered. The closest distance between an editing site/random adenosine and eCLIP peaks were calculated, and density plots were generated using the geom_density function in the ggplot2 package in R. A total of 10 sets of randomly selected control adenosines were generated. To identify the motifs enriched in eCLIP peaks, we used two alternative methods: HOMER  80  , and DREME  81. We ran DREME with all eCLIP peaks of each protein using default parameters, which creates control sequences through dinucleotide shuffling. HOMER was run with the findMotifsGenome.
pl program (-p 4 -rna -S 10 -len 5,6,7,8,9). Background controls were defined as randomly chosen sequences in the same type of genic region as the true peaks. The control sequences have one-to-one match in length with the actual peaks. Three sets of random controls were constructed. Homopolymer or dinucleotide repeats were discarded. We required the final consensus motif to be the most enriched motif identified by HOMER that was also one of the most enriched motifs resulting from DREME.
Acknowledgements: Postmortem brain samples of used in this study were obtained from the  University of Maryland Brain and Tissue Bank , which is a component of the  NIH  NeuroBioBank. We are grateful to the patients and families who participate in the tissue donation programs. This work was funded by grants from the  National Institute of Health  to X. X. ( HG009417 ,  HG006264 ), G. W. Y. ( HG004659 ,  HG007005  and  NS075449 ), S. T. ( T32HG002536 ), E. L. V. N. ( HG009530 ). S. T is supported by the  UCLA Eureka Scholarship. E. L. V. N. is a  Merck Fellow  of the  Damon Runyon Cancer Research Foundation  ( DRG-2172-13 ). G. A. P. is supported by the  National Science Foundation Graduate Research Fellowship.
Data availability All sequencing data (eCLIP-Seq of FMRP, FXR1P, RNA-Seq of Fragile-X patients and carriers) are being deposited to GEO (accession number pending). RNA-Seq data sets of ASD and control brains were obtained from our previous study  15  and are available in the PsychENCODE website (https://www. synapse. org//#!Synapse:syn4921369/wiki/235539).
Such a change causes the clam to escape deep into the mud and enter a state of inactivity. Finally, Charifi et al. [20]  described the capacity to hear in the Pacific oyster Magallana gigas (formerly Crassostrea gigas) and discussed the role this response could play in the lives of these oysters today. Based on the above dataset showing how anthropogenic noise can shape bivalve behavior, we hypothesized that this noise could also induce changes in the volume of the water flow during ventilation and thus interfere with the contaminant bioaccumulation process and/or other physiological mechanisms. The aim of the present report was to test this hypothesis in the widely distributed Pacific oyster M. gigas. Magallana gigas (Thunberg, 1793) is a common oyster species that has been introduced extensively around the world for aquacultural purposes.