dataesr/scientific-paragraphs-categorization huggingface dataset:
Cortical co-methylation modules associated with ASD are enriched for immune, synaptic and neuronal processes We next used weighted gene co-methylation network analysis (WGCNA)  45  to characterize systems-level differences in DNA methylation associated with ASD. We built co-methylation networks using all 'variable' DNA methylation sites (defined as those where the range of DNA methylation values for the middle 80% of individuals was greater than 5%; N = 251,311) using cross-cortex (PFC and TC) data from all donors (see Methods). WGCNA identified 61 co-methylation modules (Supplementary Table  16 ) and we used the 'module eigengene' (i. e. the first principal component) for each module to explore differences between controls (n = 29), individuals with iASD (n = 30), individuals with dup15q (n = 6), and a combined ASD group (n = 36).
DNA methylation profiling and data quality control All samples were randomized with respect to phenotypic status, age, sex and brainbank to avoid batch effects throughout all experimental procedures. Genomic DNA (500ng) from each sample was treated in duplicate with sodium bisulfite using the Zymo EZ DNA Methylation-Lightning Kit™ (Zymo Research, Irvine, CA, USA). Genome-wide DNA methylation was quantified using the Illumina Infinium HumanMethylation450 BeadChip ("450K array) (Illumina, San Diego, CA, USA) and scanned on an Illumina HiScan System (Illumina, San Diego, CA, USA). Illumina GenomeStudio software (Illumina, San Diego, CA, USA) was used to extract signal intensities for each probe, generating a final report that was imported in to the R statistical environment 3. 0. 2 (www. r-project.
org)  57 using the methylumi 58 package. Data quality control and pre-processing were performed using the wateRmelon package as described previously  59. Multidimensional scaling plots of sex chromosome probes were used to check that the predicted sex corresponded with the reported sex for each individual and comparison of 65 SNP probes on the array confirmed that matched tissues were sourced from the same individual. Stringent filtering of the pre-normalized Illumina 450K data was performed. First, we removed the 65 SNP probes, cross-reactive probes and probes overlapping polymorphic CpGs containing an SNP with minor allele frequency>5% within 10 bp of the single base extension position as detailed in the Illumina annotation file and identified in recent publications  60, 61. Second, CpG sites with a detection p-value of >0.
05 in 1% of samples identified by the pfilter function within the wateRmelon R package were removed. Third, polymorphic single nucleotide polymorphism (SNP) control probes (n=65) located on the array were used to confirm that matched cortex and cerebellum tissues were sourced from the same individual. The final dataset consists of a total of 417,460 probes from 76 PFC samples (n=36 iASD patients, n=7 dup15q patients, n=33 controls), 77 TC samples (n=33 iASD patients, n=6 dup15q patients, n=38 controls) and 70 CB samples (n=34 iASD patients, n=7 dup15q patients, n=29 controls) (Supplementary Table  1 ) was normalized with the dasen function of the wateRmelon R package and then batch-corrected with the ComBat function of the ComBat R package  62.
Identification of autism-associated differential methylation All statistical analyses were conducted using R statistical package (version 3. 1. 1). Analyses were performed to test for differential methylated positions (DMPs) and differentially methylated regions (DMRs) associated with disease status for each brain region. The R package Cell EpigenoType Specific (CETS) mapper  63  designed for the quantification and normalisation of differing neuronal proportions in genomewide DNA methylation data sets was used to estimate brain cellular heterogeneity in cortex (both PFC and TC) samples. CETS based neuronal cell composition estimate was not applied on the cerebellum samples given the known high proportion of non-NeuN-expressing neurons in this brain region  41, 63. To model the effect of samplespecific variables, we performed linear regression for each probe using age, gender, brain bank, CETS (for PFC and TC, but not CB) and diagnosis as independent variables.
Weighted gene correlation network analysis For the co-methylation network analyses, modules of co-methylated probes were identified using the WGCNA package in R  45  and network was constructed for crosscortex samples. For each network, the input probe set was pruned to remove probes with minimal variability across samples. We required that each probe have a minimum range of methylation values of 5% within the middle 80% of samples, resulting in 251,311 probes for cortex. In the cross-cortex network, to enrich for modules relating to diagnosis, we regressed out the effect of CET score and age after fitting a linear model for each probe with diagnosis, age, sex, cortical region, CET score, and brain bank as independent variables. A signed network was constructed using the biweight midcorrelations between probes with a soft power of 7.
This network was generated blockwise, using the WGCNA function blockwiseModules with the parameters: maxBlockSize=15000, mergeCutHeight=0. 1, deepSplit=4, and minModuleSize=100. For each module, a linear mixed effects model was fit between its eigengene and diagnosis, age, sex, cortical region, batch, CET score, and brain bank as fixed effects with individual ID as a random effect. This LME model was fit for 3 subsets of samples: (iASD + dup15q) vs control, iASD vs control, and dup15q vs control.
Acknowledgements This work was funded by  PsychENCODE  Grant  1R01MH094714  to DHG. JM is supported by  Medical Research Council  grants  MR/R005176/1 ,  MR/K013807/1 , and  MR/M008924/1. We are grateful to the patients and families who participate in the tissue programs from which our samples are obtained. Human tissue was obtained from the  Autism BrainNet  (sponsored by the  Simons Foundation  and  Autism Speaks , formerly the  Autism Tissue Program)  and the  University of Maryland Brain  and  Tissue Bank , which is a component of the  NIH NeuroBioBank  and the  Oxford Brain Bank.
In our case, the goal of the study is just to obtain general tendencies. In order to predict the evolution of salt weathering with climate change several methods have been proposed. We compared results that were obtained when applying some of these different estimation methods. Salt weathering has been estimated for single salt and salt mixture for past climate data and also for future "data" obtained by the MeteoFrance model Aladin under 4. 5 scenario. The procedure, detailed in the next chapter, was applied to 41 locations in France, representative of the different climates of France. To quantify salt weathering degradation some simplifications need to be done. Some authors use only halite salt to quantify salt weathering evolution. This is the case in the Noah's Ark project. The reason of this choice is that NaCl is a very common salt, which is present in more of weathering material and also that has only a single stable phase, which makes calculations very simple.
Classe Tmean Tsd Tmin Tmax RHmean RHsd Next, we will explain how salt weathering has been calculated. For NaCl we just consider the number of times relative humidity change in two consecutive days between higher than 73. 3% and lower than this deliquescence relative humidity. To do the calculations in all locations and for all the salts and salt combinations we used a scilab program that reads the data, performs the calculations and writes a file with the results. Even if we did not use it in this paper, the seasonality of the transitions has also be calculated (distribution of the different parameters from January to December). For Na 2 SO 4 , we consider the number of times that thenardite (anhydrous phase) will convert into mirability (decahydrated phase)  [25]. This occurs when environmental conditions cross the line with arrows in Figure  2.
This line can be fitted by a straight line of equation RH (%) = 0. 72 × T ( • C) + 60. 4. More details about the method can be found in  [26]. Geosciences 2018, 8, 401 5 of 14 lower than this deliquescence relative humidity. To do the calculations in all locations and for all the salts and salt combinations we used a scilab program that reads the data, performs the calculations and writes a file with the results. Even if we did not use it in this paper, the seasonality of the transitions has also be calculated (distribution of the different parameters from January to December). For Na2SO4, we consider the number of times that thenardite (anhydrous phase) will convert into mirability (decahydrated phase)  [25]. This occurs when environmental conditions cross the line with arrows in Figure  2.
Acknowledgments: We thank  Météo-France  for the provided data especially  Michel Déqué  for the climate model realizations. We thank also  Christian David  ( University of Cergy-Pontoise ) for his help in writing the article. Acknowledgments: We thank  Météo-France  for the provided data especially  Michel Déqué  for the climate model realizations. We thank also  Christian David  ( University of Cergy-Pontoise ) for his help in writing the article.
of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there.
When run as a prediction system, CanSIPS, combining CanCM3 and CanCM4, is able to show multi-month skill in seasonal forecasts of detrended sea ice area anomalies, comparable to that obtained in other modelling systems  (Merryfield et al. , 2013b) , and generally enhanced skill relative to a statistical persistence forecast  (Sigmond et al. , 2013). The assessed skill depends on the verification dataset  (Sigmond et al. , 2013) , especially for total (non-detrended) anomalies. Such issues will be revisited in this study. Our assessment of CanSIPS and CanESM2 is enhanced by two recent research products arising from CanSISE: the Blended-5 snow water equivalent (SWE) dataset of  Mudryk et al. (2015)  and the CanESM2-LE (large ensemble) of simulations from CanESM2. The Blended-5 dataset addresses the need for a SWE verification dataset and, potentially, for initialization of snow-related parameters in CanSIPS and other prediction systems.
Blended-5 builds on long-term work of ECCC (e. g. Brown et al. , 2010; Derksen and Brown, 2012; Brown and Derksen, 2013)  and consists of an ensemble of gridded SWE datasets over 1981-2010 from a variety of sources including remote sensing, land-surface assimilation systems, and reanalysis-driven snow models. The papers of  Mudryk et al. (2015 Mudryk et al. ( , 2017) )  detail the components, quality assessment, and characteristics of the Blended-5 dataset. The use of large initial-condition ensembles has afforded a renewed assessment of the impacts of natural climate variability on recent and projected climatic variability and trends (e. g. Deser et al. , 2012; Kay et al. , 2015). The CanSISE team designed the CanESM2-LE (e. g. Sigmond and Fyfe, 2016) , which consists of four sets of 50 simulations each of CanESM2 that examine the impact of natural and anthropogenic forcings over the period 1950-2100 in the presence of internal climate variability.
Besides CanESM2, the other CMIP5 models referenced below are the same as those in  Mudryk et al. (2018)  (see Table  2  of the paper). Observed SWE climatology, variability, and trends are relatively non-robust compared to variables such as landsurface temperature  (Mudryk et al. , 2015 (Mudryk et al. , , 2017) )  and for this reason we assess some aspects of the spread across the Blended-5 SWE datasets. Individual observational datasets contributing to Blended-5 also show stronger spatial gradients than the Blended-5 mean (circles filled with light brown in the Taylor diagram in Figs. 1c  and 2c ). This is in part expected because the observational mean will cancel random errors. However, this also suggests that there is considerable uncertainty in the spatial variance, and so it is difficult to assess how realistically spatial variance is captured in CanESM2 and the other CMIP5 models.
This observational uncertainty is also evident in the seasonal cycle of total snow mass aggregated for Canada and the Northern Hemisphere, as well as geographic subregions (Fig. 3a-h , grey shading). For example, for the Northern Hemisphere (panel a), the range in Blended-5 estimates of peak snow mass in  Unlike the observational uncertainty, the uncertainty represented for the models is the impact of internal variability on estimated trends. In addition, the IQR of the observations is obtained from only five datasets, which represents a less robust estimate of uncertainty than that from the 30-50 simulated realizations in the large ensembles. Spread from these distinctive sources of uncertainty is indicated schematically by the extent of the vertical grey lines. February is over 50 % of the average and is driven mainly by uncertainty in Arctic (panel c) and alpine regions (panel d).
The individual datasets in the Blended-5 product are not shown in Fig. 3 , but their characteristics are discussed in  Mudryk et al. (2015). The NASA Global Land Data Assimilation System (GLDAS) provides an estimate well below the multi-dataset mean, the MERRA reanalysis dataset typically provides a central estimate, and the maximum estimate varies with region among the remaining three datasets. After accounting for the considerable observational uncertainty in total snow mass, it is nevertheless possible to assess the realism of CanESM2's simulation. The CanESM2 snow mass for the Northern Hemisphere is plotted as originally available on the model's land grid (light teal points, shown only in Fig. 3a ) and as adjusted to reflect the observational mask which is on a finer scale (dark teal points).
Our assessment is that, especially in midlatitudes, CanESM2 simulates excessive springtime snow associated with excessive wintertime precipitation building up throughout winter and into spring (middle rows of Figs. 1  and 2 ). The seasonal cycle of snow cover extent (SCE) is shown in Fig. 3i -l for North America and Fig. 3m -p for Canada. Here, observed SCE is derived from the Blended-5 dataset by converting SWE to SCE using a threshold of 4 mm; this threshold was tested in  Mudryk et al. (2017). For the observational products in the Blended-5 dataset, the relative uncertainty in SCE is generally less than for snow mass. For example, the observational range in peak northern hemispheric SCE in January is about 15 % and is dominated by uncertainty in midlatitude and Arctic regions. A modest positive springtime excess of SCE is evident for CanESM2 for Canada and the Northern Hemisphere.
Observed and simulated trends in terrestrial snow A standard target for snow process analysis in climate models is trends of SCE, which are strongly temperature controlled (e. g. Brutel-Vuilmet et al. , 2013; Mudryk et al. , 2017). Assessing the ability of models to capture these trends needs to account for natural variability, forced variability, observational uncertainty, and inter-model differences. We show in Fig. 4a  and b  the trends in SCE derived from the Blended-5 dataset for the Northern Hemisphere in January-March and April-June. In both seasons, there is a spread of observed seasonal snow cover reduction estimates from 0. 0 to -0. 5 million km 2 per decade in winter (based on a simple interquartile range for this small number of observational datasets) and from -0. 1 to -0. 6 million km 2 in spring.
The red horizontal line in the box plot represents the median over the Blended-5 datasets. Over the 25-year observational period, the trends correspond to an approximate snow loss that is at most 4 % of JFM SCE and in the range of 5-40 % of AMJ SCE. The range of trends from the CanESM2-LE, NCAR CESM1, and NCAR CCSM4 suggests that internal variability alone provides an uncertainty range of about 0. 5 million km 2 per decade. Assuming internal variability is realistic in the models, this is the limit of precision we can expect in assessing recent trends. CanESM2 consistently produces greater snow loss than NCAR CCSM4 and CESM1, especially in AMJ. We conclude that all the models displayed fall within wintertime snow retreat estimates, that NCAR CCSM4 and CESM1 overlap with estimates of observed snow retreat in spring, but that CanESM2 exhibits more spring snow retreat than our best estimate of the observations.
6 to +0. 6 (Taylor diagram in the left panel of Fig. 5c ), suggesting that these patterns are affected by significant internal variability  (Deser et al. , 2012). There are more realizations with positive than negative spatial correlation in winter land-surface temperature trend patterns, which is consistent with the anticipated ef- fect of anthropogenic forcing. Wintertime land-surface temperature trends systematically show greater spatial variance than the estimated warming pattern from the single observational land-surface temperature dataset employed here. This could be related to stronger (more negative) meridional gradients in land-surface temperature and its trends in the models compared to the observational dataset. Springtime landsurface temperature trend patterns (left panel of Fig.
6a ) feature anomalously negative changes in the Canadian prairie regions and positive changes around the coastal regions. It is harder to find realizations in the spring that correspond to the observed pattern (left Taylor diagram in Fig. 6d ), and spatial variance of the land-surface temperature trends appears to be biased high as in winter. For precipitation, there is little evidence of consistent pattern matching between the observations and individual realizations, for winter or spring (middle column of panels a and d of Figs. 5  and 6 ). For SWE spatial patterns (right column of panels a and d of Figs. 5  and 6 ), the structural details of the trend maps are also not readily found in the models compared to the mean of the Blended-5 SWE. Compared to the ensemble mean of the Blended-5 observations, the simulations show greater spatial variance in SWE trends, but this is partially due to smoothing of spatial structure in observational errors, as is shown by the scatter of SWE trends by individual contributors to the Blended-5 dataset (light brown circles in Taylor diagrams).
38 for the middle panel of Fig. 6b ) versus wintertime land-surface temperature change (r = 0. 01 for the left panel of Fig. 5b ). The worst match case exhibits a similar range of correlations, on the negative side, and generally looks quite different from the best match case. This preliminary analysis of intra-ensemble variability suggests limits on how much regional-scale information about changes for snow cover and related climate variables can be extracted from ESMs. The key point is that caution is needed in judging a model on its ability to reproduce spatial patterns of trends in SWE and related climate parameters, even on these multidecadal timescales. The spatial pattern of CanESM2 land-surface temperature and precipitation trends is generally representative of that found in individual realizations of the CMIP5 datasets, in the sense that the individual realizations of CanESM2 and other CMIP5 models have positive pattern correlations with the CMIP5 multi-model mean (Taylor diagrams not shown).
Current operational practice in CanSIPS uses observed atmospheric forcing to bring the land surface (including soil moisture and snow cover) into a realistic state. Although this procedure performs reasonably well for snow (within observational uncertainty), potential remains for improving the initialization and forecasting of snow and other land variables by assimilating observation-based land data directly in real time. Blending different sources of data from highly uncertain observations has led to improved characterization of the forecast skill of the CanSIPS system. Figure  13  shows the degree of agreement between SWE forecasts from CanSIPS and several SWE products over Canada (similar results are found for other regions). The degree of agreement is measured as the anomaly correlation coefficient for a 1-month forecast (with lead 0 from initialization).
The five datasets are the Blended-5 dataset (blue) and four individual datasets including two components of the Blended-5 dataset. Even though all observational datasets are being compared to the same forecast, it is the Blended-5 dataset, capturing the mean of several observational datasets, that agrees best with the forecast. It is clear that improving verification datasets through blending, which can be reasonably expected to lead to cancellation of independent errors in observational estimates, impacts assessed agreement with the forecast. To reiterate, in this case, improved calculated skill is derived from an apparent improvement in the quality of the verification data and not an improvement of the forecast  (Sospedra-Alfonso et al. , 2016b). Whether or not such improved consistency might be found for the prediction of other quantities, the broader point is that there is a need to ensure that verification data are continually updated in order to fairly compare predictions to the best available data  (Massonet et al.
, 2016). Recent research in snow analysis and observational datasets is expected to support operational improvements in CanSIPS and hence in ECCC's operational prediction capacity. For example, CanSISE work has led to new efforts to develop an operational real-time snow amount forecast for the coming months, which could be used in several impacted sectors such as outdoor recreation, water resource planning, and agriculture (Fig. 14 , snow amount forecast shown as above and below normal SWE amounts). In this successful proof of concept, we note satisfactory general agreement with the MERRA analysis, which is independent of Can-SIPS and is itself subject to some uncertainty. This indicates promise for this new forecast product, while highlighting issues of observational uncertainty addressed in part by our recent research.
These are based on predictors available in real time together with historical SIT values represented by the pan-Arctic Ice and Ocean Modelling System, or PIOMAS  (Zhang and Rothrock, 2003) , which is frequently used as a reference dataset for SIT due to the sparseness of historical SIT observations. The first such model (known as "SMv1"), described in  Dirkson et al. (2015) , uses a statistical approach to find an optimal combination of sea ice concentration and sea level pressure information to provide useful sea ice thickness information. While this model reduces temporal-and spatial-mean absolute errors in the SIT initial conditions by 48 % relative to the original CanSIPS initialization (when validated against PIOMAS SIT values) and shows consistent skill estimat-ing ice volume in all months, much of this improvement in skill emerges from a more accurate representation of local negative trends in SIT.