dataesr/scientific-paragraphs-categorization huggingface dataset:
e. genotype, effect of homoplasy, NCBI identifiers, gene IDs, gene names, type, position, phenotypical impact) for each node and all Fig. 7  Programs (i. e. black letters) and commands (i. e. grew letters) implemented in the 'VARCall' workflow aiming to call single nucleotide polymorphisms (SNPs) and small insertions/deletions (InDels). The scripts referring to alignment against reference genome (i. e. 'BAMmaker'), variant calling (i. e. 'VCFmaker_SNP' and 'VCFmaker_INDEL'), variant combination (i. e. 'SNP-INDEL_merge'), pairwise distances (i. e. 'VCFtoMATRIX'), variant concatenation (i. e. 'VCFtoFASTA'), pseudogenome assemblies (i. e. 'VCFtoPseudoGenome'), and report about breadth and depth coverages (i. e. 'reportMaker') were written with Python 2. 7 and are invoked by the driven script 'VARCall' (i.
e. black arrow). The script 'BAMmaker' is performed for each genome (i. e. circular arrow) Fig. 8  Programs (i. e. black letters) and corresponding effects (i. e. grew letters) implemented in the scripts 'phyloFixedVar', 'GetGOxML' and 'EveryGO' aiming to identify sensitive (Se) and specific (Sp) variants (SNPs and InDels) at each branches of corresponding phylogenetic inference, associate prokaryotic gene ontology (GO) terms with these homoplastic and/or non-homoplastic variants, and perform gene-ontology enrichment analysis based on the parent-child approach integrating hypergeometric tests and Bonferroni corrections, respectively. Online databases are queried by the scripts 'GOSlimer' and 'GOxML' (i. e. clouds). The GO database of the Gene Ontology Consortium is used by the script 'GOSlimer' to identify prokaryotic GO-terms.
The QuickGO browser of the UniProt GO annotation program is queried by the script 'GOxML' to associate the variants with the corresponding GO-terms. These scripts were written with Python 2. 7 and implement R libraries 'p. ajust' and 'phyper'. The whole workflow is semi-automated (i. e. grew arrows) and the scripts 'GetGOxML' and 'EveryGO' can be performed for each variant and each branch, respectively (i. e. circular arrow).
Availability of data and materials The scripts developed for computational analyses (i. e. 'VARCall', 'phyloFixedVar', 'FixedVar', 'GetGOxML' and 'EveryGO') can be found in the following repositories https://github. com/afelten-Anses/VARtools/tree/ master/VARCall; https://github. com/afelten-Anses/VARtools/tree/master/ FixedVarTools and https://github. com/afelten-Anses/VARtools/tree/master/ GOtools. Previously published sequencing data  [22]  is available in the European Nucleotide Archive (ENA) (www. ebi. ac. uk/ena) (Additional file 1). The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
0×10 -05 3. 4×10 -02 MF GO:0003747 translation release factor activity 5 0. 658 8 3. 0×10 -05 3. 4×10 -02 MF The identification of variants, detection of fixed variants, assignment of GO-terms to variants, and gene-ontology enrichment analysis were performed with the scripts 'VARCall' , 'phyloFixedVar' , 'GetGOxML' , and 'EveryGO' , respectively. The level, biological process (BP), molecular function (MF), and cellular component (CC) of GO-terms are represented. The p-values of hypergeometric tests were adjusted by Bonferroni correction. The lowest corrected p-values representing GO-terms highly impacted by fixed variants (i. e. < 5. 0×10 -2 ), the highest GO-levels presenting the most accurate GO-terms (i. e. ≥ 5), and the highest number of hits representing relevant GO-terms quantitatively (i.
It is a physics-based, accurate, scalable, robust and predictive model which takes into account the temperature, the bias voltage and the impedance load of the MTJ. A special care was given to establish a PDK which is compatible with standard design suites. Thus, Verilog-AMS language has been used to develop the physical compact model. The model has been qualified on different commercials Spice and fast Spice simulators engines showing compatibility and good accuracy and speed. Concerning accuracy and speed, we described in  [3]  the two possible strategies to efficiently model spintronics while highlighting pros and cons of the different modeling strategies.
Magnetic Random Access Memory (MRAM) Independently of the technology used, a standalone or an embedded memory is designed according to a combination of different blocks as shown in Figure  2. Each block fulfills a specific function and has a direct impact on the final memory performances and characteristics. The core block is the Array of BitCells where data is stored. In our case, we opt for a 1T-1MTJ BitCell architecture where each MTJ is connected in series with a n-type FET. Finally, the WordLine Driver is used to select one row of the array; the word is obtained by combining this selection with the column multiplexer or "y-mux", part of the Read/Write Driver Block. We designed, validated and characterized a full library of MRAMs ranging from 128kb to 1Mb. All instances have been simulated and validated through different Process, Voltage, Temperature (PVT) corners while analyzing their performances (in time, power and estimated area, yield.
MRAM simulation and evaluation We run simulations in the developed hybrid CMOS/magnetic environment of different size instances of memories from 128Kb to 1Mb. Accurate results with fast simulation time and no convergence issues have been obtained. Figure  4  depicts different access operations of the MRAM where data is written or read for each clock signal. Thanks to the body-biasing technique offered by the FDSSOI technology, we may decrease the static power consumption during sleep mode. It is also possible to improve the performance of the memory by boosting the back gate of the memory core to pass higher current through the bitcell and so write the MTJ more rapidly. In figure  5 , we compare the power consumption and the performance of the MRAM with and without activating the body biasing.
B. Spaces of girls who… The factory managed by "girls who…" is a common property (like Wikipedia or an open-source software) under a double license, GNU GPL and CC BY-SA. Digital spaces of the project are numerous: Moodle courses and Prezi presentations, product and service backlogs. The entry point is a public Trello board, in French (https://trello. com/b/83omDwmt/les-filles-qui). The integration of new workers in the factory occurs during the first internship (1-4 weeks) in the facility. Thanks to a partnership with an engineer school, École Nationale Supérieure de Techniques Avancées -ENSTA Bretagne, new workers are accommodated in a dedicated room at ENSTA Bretagne. Thereafter, "girls who…" will not be always on the same space-time: girls telework, go in schools by pairs, exchange synchronously and asynchronously.
125 • resolution with data assimilation consistent with the CHIMERE horizontal resolution. Emissions for the main pollutants are issued from the EDGAR-HTAP initiative  [60] [61] [62]. The EDGAR-HTAP project compiled a global emission dataset with annual inventories for CH 4 , NMVOC, CO, SO 2 , NOx, NH 3 , PM 10 , PM 2. 5 , BC (Black Carbon), OC (Organic Carbon) and covering the period 2008-2010 using, to the extent possible, official or scientific inventories at the national or regional scale that are likely to be acceptable for policy makers in each region of the world. This compilation of different official inventories from EMEP for Europe, EPA for USA, GAINS for China and REAS was first gap-filled with global emission data of EDGARv4. 2. For this period, the 2010 dataset has been used; monthly emissions of these macro pollutants are pre-processed in the CHIMERE suite to fit with the simulation grid, the model species and the final time resolution  [46].
The flux is calculated only if u * > u * t , the threshold friction velocity, u * t , is calculated using two possible schemes up to the user  [69, 70]. The corresponding u * st is this friction velocity but for a standard atmospheric density. The vertical dust flux is integrated over the whole size distribution of particles. The Dust Production Model and its parameters are fully described in  [46, 71]. To have global and highly spatially resolved surface databases, the module uses the NCAR USGS landuse dataset  [72]  and the STATSGO-FAO soil dataset  [73]. The roughness length is estimated using the global 6 km horizontal resolution "Global Aeolian Roughness Lengths from ASCAT and PARASOL" dataset  [74]. The soil erodibility is refined over desert areas using the global erodibility dataset derived from MODIS (Moderate Resolution Imaging Spectroradiometer) data  [75] , Figure  1  represents the erodibility of soils for the CHIMERE domain, deserts can be clearly identified but large agricultural zones over the US, Europe and Asia are also highlighted in light blue.
Some soil and surface parameters are added to limit the emissions fluxes, depending on the period to model and its meteorological conditions. To consider the monthly vegetation variability, the vegetation fraction is diagnosed from the USGS 30s resolution database (from U. S. Geological Survey, accessible at https://landcover. usgs. gov/) and is used as limiter to the erodibility factor. The rain may also inhibit the emission flux and a function is added to stop the fluxes after a precipitation event  [46]. The flux may also be limited by the soil humidity and the threshold friction velocity is limited using the Fecan's scheme  [76]. Higher in latitude over Alaska, Canada, Siberia and Greenland, dust emissions can potentially occur  [77]  due to additional processes specific to or enhanced in cold regions: low temperatures, humidity, strong winds, permafrost and niveo-aeolian processes.
Most of these processes are not included in CHIMERE, however for an early spring period such as in this study, the emissions would be turned off due to the snow coverage in these regions. where fbare and fclay represent the relative fraction of bare soil and clay soil content, respectively; ρa is the air density; and Cd the coefficient of erodibility. The flux is calculated only if u * > u * t, the threshold friction velocity, u * t, is calculated using two possible schemes up to the user  [69, 70]. The corresponding u * st is this friction velocity but for a standard atmospheric density. The vertical dust flux is integrated over the whole size distribution of particles. The Dust Production Model and its parameters are fully described in  [46, 71]. To have global and highly spatially resolved surface databases, the module uses the NCAR USGS landuse dataset  [72]  and the STATSGO-FAO soil dataset  [73].
The roughness length is estimated using the global 6 km horizontal resolution "Global Aeolian Roughness Lengths from ASCAT and PARASOL" dataset  [74]. The soil erodibility is refined over desert areas using the global erodibility dataset derived from MODIS (Moderate Resolution Imaging Spectroradiometer) data  [75] , Figure  1  represents the erodibility of soils for the CHIMERE domain, deserts can be clearly identified but large agricultural zones over the US, Europe and Asia are also highlighted in light blue. Some soil and surface parameters are added to limit the emissions fluxes, depending on the period to model and its meteorological conditions. To consider the monthly vegetation variability, the vegetation fraction is diagnosed from the USGS 30s resolution database (from U. S. Geological Survey, accessible at https://landcover.
• CALIPSO and CloudSat, launched in 2006 as part of NASA's A-train satellite constellation, provide detailed information on cloud and aerosol vertical profiles from tropics to the poles. Cloud vertical profiles are derived from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)  [78]. Here, we use the V4. 10 dataset  [79] , cross section of the atmosphere provides various categories separating mineral dust from anthropogenic pollution. The list of selected orbits is given in Figure  2. • CALIPSO and CloudSat, launched in 2006 as part of NASA's A-train satellite constellation, provide detailed information on cloud and aerosol vertical profiles from tropics to the poles. Cloud vertical profiles are derived from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP)  [78]. Here, we use the V4.
10 dataset  [79] , cross section of the atmosphere provides various categories separating mineral dust from anthropogenic pollution. The list of selected orbits is given in Figure  2. • MODIS (Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (originally known as EOS AM-1) and Aqua (originally known as EOS PM-1) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1-2 days, acquiring data in 36 spectral bands, or groups of wavelengths. The MODIS Aerosol Product monitors the ambient aerosol optical thickness over the oceans globally and over the continents.
Furthermore, the aerosol size distribution is derived over the oceans, and the aerosol type is derived over the continents. "Fine" aerosols (anthropogenic/pollution) and "course" aerosols (natural particles; e. g. , dust) are also derived  [80, 81]  • MODIS (Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (originally known as EOS AM-1) and Aqua (originally known as EOS PM-1) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1-2 days, acquiring data in 36 spectral bands, or groups of wavelengths. The MODIS Aerosol Product monitors the ambient aerosol optical thickness over the oceans globally and over the continents.
70 The lowest values (<1 µg m -3 ) of surface dust concentrations are simulated over the Equatorial Pacific, this is certainly due to low dust emissions downwind to the prevailing air mass fluxes from Central and South America. From Central to North Pacific the mean surface concentrations lie in the range 2-10 µg m -3 that are usual for a March period which generally exhibits the highest dust concentrations over the Pacific Ocean  [91] , these orders of magnitude are fully in line with the SEAREX experiments findings  [93]. In Alaska, the CHIMERE model exhibits dust concentrations in the fine mode of particles 5 to 10 times overestimated at IMPROVE Stations (Denali 63. 72  W) with observational data usually in the range 0. 01-0. 4 µg m -3 while CHIMERE simulates concentrations up to 1 to 5 µg m -3 , however the end of March 2014 increase of concentrations is well reproduced by the model.
inecc. gob. mx/ that is the Air quality information system in Mexico. High concentrations are also observed at this station on 19 and 20 March confirming the extension of the plume far from the emission areas in US. The comparison of CALIPSO data identified as "dusty marine" in Figure  S1  in Supplementary Materials (NAM5 trajectory) clearly confirms the dust load within the boundary layer simulated by CHIMERE over the Pacific Ocean between 10 • and 30 • N, dust is mixed with sea salts ("dusty marine" category for CALIPSO products). The three events can also be observed thanks to the MODIS data despite the presence of clouds that limits the detection of the full emission areas. As depicted in Figure  12 , MODIS data are compared with the AOD computed with CHIMERE at the same wavelength (555 nm), the pattern of over the emission areas in the southeast of Colorado on 11 March and two high concentrations area on 18 March over an area located in the intersection of Colorado, New Mexico and Kansas states and the other one over Texas.
In China, the three main events are very well simulated, the model outputs showing a sharp increase of PM concentrations over the Beijing area due to dust issued from the Gobi Desert. Over the USA, three dust storms have been identified and analyzed with several observational datasets and CHIMERE is able to simulate these events with a rather good timing. The evolution of the plumes toward Mexico and particularly Baja California is confirmed by available observations. Europe is mainly affected during end of March with a dust plume that is mainly located in the free troposphere at the beginning with some intrusions within the boundary layer; this episode affects later in April the UK  [87]. The Caribbean area is affected by a long-range transport of dust from the Sahara and again the model rather well captures the arrival timing of the plume but with a clear overestimation while the concentrations and AOD are in rather good agreement on African stations.
Processes such as wet deposition will be revised in the next version of CHIMERE to improve the model results. The implementation of a more complete aqueous chemistry module is also one of the next challenges of the developments for the CHIMERE community  [147]. The evolution of the size distribution has to be better analyzed in the next studies. The nitrate coating of coarse mineral particles could also influence their removal  [148, 149] ; this process must be implemented in the next version of the model. In addition, the capacity of the model to transport thin dust layers has to be improved, indeed the chemistry transport models tend to vertically diffuse the dust within the boundary layer and the free troposphere, more layers has to be implemented in CHIMERE in accordance with the available layers in the meteorological model driver.
Acknowledgments: This work is partly funded by the  French Ministry in charge of Ecology (MTES). IMPROVE is a collaborative association of state, tribal, and federal agencies, and international partners. US Environmental Protection Agency  is the primary funding source, with contracting and research support from the  National Park Service. The Air Quality Group at the  University of California , Davis is the central analytical laboratory, with We thank also  Terry Keating  for advising us on the use of US air quality data. We acknowledge the  Pyrenean Platform for Observation of the Atmosphere (P2OA , http://p2oa. aero. obs-mip. fr)for the data collected at the  Pic du Midi  in the frame of the  SNO (Service National d' Observation) CLAP  supported by the  CNRS  ( Centre National de la Recherche Scientifique ).
However, we also found three CpG sites cg00804078, cg02872436 and cg14956327 which were annotated to the DDO (D-aspartate oxidase) gene showing significant cis-MeQTL, but did not show significant cis-MeQTL in the BIOS QTL database. This may indicate an age specific MeQTL effect since D-aspartate content in brain is decreased during adulthood  [22]. Furthermore, we compared the MeQTL-effect size at ages 4 and 8 (Fig. 3a ), and found that almost all effects of genetic factors on methylation had the same direction at both ages (3,348 SNP-CpG pairs; 97. 9%), i. e. , genetic effects on methylation remained stable with ageing. Among these 3,348 SNP-CpG pairs, 725 had a significantly larger effect and 2,623 had a significantly smaller effect at age 4 years than at age 8 (Fig. 3b , 1-sample proportion test, P = 4.
DNA methylation changes related to disease development Early children development is considered to be the most important phase in life and also of great vulnerability to negative influences. We found that many top associated a-DMSs, and their annotated gene were linked to diseases, such as SHANK2 (cg16069986 and cg16312514) which is related to autism, and CSRP3 (cg05895618) which is linked to cardiomyopathy. We hypothesised that these a-DMSs annotated genes are crucial for the growth, and dysregulation of gene expression and eventually lead to disease development. Therefore, we further investigated if genes containing a-DMSs were more likely to be associated with disease by studying the enrichment of genes on our list (Additional file 1) in the catalogue of disease-associated genes provided by the Clinical Genomic Database  [23].
We propose that future studies could assess the association of a-DMSs with other chronic, non-communicable diseases originating in early childhood. A major strength of our study is the combination of four different European birth cohorts of general population samples. Although children from more highly educated parents may be overrepresented in our study  [32] , our study represents a reasonable representation of European populations, forming a large dataset with good statistical power. Furthermore, the paired sample approach enables conclusions on longitudinal changes, and we specifically focused on childhood ageing to complement the focus on adult ageing populations in the literature. However, our study had some limitations: the HM450 platform only covers 1. 6% of all methylation sites  [33] , and the design of HM450 may be biased towards CpG-richer regions of the genome.
Standard male and female DNA samples were included in this step as control samples. Quality control and pre-processing of microarray data DNA methylation data were pre-processed in R with the Bioconductor package Minfi  [37] , using the original IDAT files extracted from the HiScanSQ scanner. We had a total of 1,748 blood samples from four birth cohorts in the MeDALL epigenetics study. Samples that did not provide significant methylation signals in more than 10% of probes (detection P = 0. 01) were excluded from further analysis. Samples were also excluded in cases of low staining efficiency, low single base extension efficiency, low stripping efficiency of DNA from probes after single base extension, poor hybridization performance, poor bisulphite conversion and high negative control probe staining.
Differential methylation analysis Methylation levels (beta values, β) at a given CpG site were derived from the ratio of the methylated probe intensity to overall intensity (sum of methylated and unmethylated probe intensities): β is equal to M/(U + M + α), where M is the intensity of the methylated probe, U is the intensity of the unmethylated probe, and α is the constant offset with the default value of 100. To remove bias in methylation profiles due to technical variation, we implemented a correction procedure based on 613 negative control probes  [40]  present in HM450K arrays because these negative control probes did not relate to biological variation. First, we implemented principal component analysis (PCA) on control probe data according to the method proposed by Zhang et al. [41]. Then, we permuted the control probe data 10000 times and applied PCA to each of these permuted datasets.
The dMeQTL and MeQTL analysis was performed using the R package MatrixEQTL  [44]  using an additive linear model. To remove the effect of extreme outliers, we trimmed the methylation set using: (25th percentile -3*IQR) and (75th percentile + 3*IQR), where IQR = interquartile range. SNPs were included in the cis-analysis if they were located within 250 kb of the methylation probe under consideration. The methylation differences between two time points were used as molecular phenotype for dMeQTL study. The fixed-effect-model based on the inverse standard error was utilized for meta-analysing BAMSE and PIAMA dMeQTL and MeQTL results at age 4-8 by using METAL  [45]. The BAMSE and PIAMA samples from the 4-8 years-old comparison set were used as discovery cohorts, and the INMA samples from the younger comparison set were used as replication.