dataesr/scientific-paragraphs-categorization huggingface dataset:
1. 7, http://archive. broadinstitute. org/cancer/cga/mutect), by using a skin sample from a WT mouse not exposed to UV as the "normal sample" for paired analysis. Only somatic mutations passing Mutect internal filters were considered for the analysis. The VCF files are annotated with Annovar by using the MutSpec Annot Tool in Galaxy  [42]. Variants were then filtered based on SegDup databases from UCSC (version from 4 May 2014, http://hgdownload. cse. ucsc. edu/goldenPath/ mm10/database/genomicSuperDups. txt. gz), as well as Tandem Repeat and Repeat Masker (version from 9 February 2012, http://hgdownload. soe. ucsc. edu/goldenPath/mm10/bigZips/). House-made scripts were then used to keep only SNPs that have a functional impact and fall in exonic or splicing regions. Non-negative matrix factorization mutational signatures were inferred with MutSpec-NMF tools, as previously reported.
The pathway analysis was performed using the EnrichR web application (http://amp. pharm. mssm. edu/Enrichr/; citations Ã 2). The input gene list was made by merging the mutations detected in the pre-malignant lesions (n = 3) or cSCCs (n = 3) of the K14 HPV38 E6/E7 Tg animals. The analysis included only genes harbouring mutations that are likely to alter the biological properties of the encoded products, i. e. , 3111 genes in the pre-malignant lesions and 6372 genes in the cSCCs. The gene lists were then loaded into the EnrichR software, and the result from the KEGG database (version 2016) was considered. Only pathways with a significant adjusted p-value are shown in S1 Table. The list of pathways is ranked by combined score (combined score is computed by taking the log of the p-value from the Fisher exact test and multiplying it by the z-score of the deviation from the expected rank).
Comparison with epigenetic driver/modifier genes and Cancer Gene Census list The list of epigenetic driver and modifier genes was constructed on the basis of genes reported in different publications  [19] [20] [21] [22] [23]. The Cancer Gene Census list was downloaded from the COSMIC website (12 November 2016, http://cancer. sanger. ac. uk/census) and is based on a previous publication  [24]. The comparison of the mouse data with the human data  [25, 26]  was done with Bioconductor (release 3. 4, https://www. bioconductor. org/) in R (version 3. 3. 2, "Sincere Pumpkin Patch"). The module BioMart  [43, 44] , version 2. 3 enables the conversion of nearly 87. 86% of human gene names from the Chitsazzadeh et al. publication  [26]  to their corresponding mouse gene names.
Acknowledgments We are grateful to  Dr Christopher P. Wild  for his constant support and to all members of our laboratories at  DKFZ  and  IARC  for their cooperation. We thank the  High-Throughput Sequencing unit of the Genomics & Proteomics Core Facility ,  German Cancer Research Center (DKFZ) , for providing excellent sequencing services. We are also grateful to  Nicole Suty  for her help with preparation, and  Dr Karen Mu ¨ller  for editing this manuscript.
was reported by the TIMED/SABER satellite in Uruguay. On 21 October, the poor ozone air mass was still over the region of interest, according to the OMI-ERS satellite, data from the sounding balloon launched in Santa Maria/RS-Brazil, and measurements made by the AURA/MLS satellite. Furthermore, the influence of ozone on the stratosphere temperature was observed during this period. Despite a continuous decrease detected in height, the temperature should have followed an increasing pattern in the stratospheric layer. Finally, the TIMED/SABER and OMI-ERS satellites showed that on 23 October, the air mass with low ozone concentration was moving away, and its layer, as well as the temperature, in the stratosphere was re-established. Keywords. Atmospheric composition and structure (middle atmosphere -composition and chemistry; instruments and techniques).
Therefore, this multi-instrumental study aims to show/prove the occurrence of a secondary effect of the ozone hole in southern Brazil, especially in the city of Santa Maria, RS (29. 68 • S, 53. 80 • W). Additionally, the aim is to verify the temperature behaviour during the event by analysing data from six different instruments, which are the AURA/MLS, TIMED/SABER and OMI-ERS satellites, a ground-based instrument (Brewer), a sounding balloon, a GPS-PRO, as well as a forecast model.
NASA's Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) satellite has a circular orbit of 625 km at 74. 1 • from the Equator  (Cooper, 2004). The data obtained from this satellite allow the need to study the influence of solar radiation and human activities in the upper layers of the atmosphere (from 60 to 180 km). The radiosonde cannot reach this region and this satellite is the only one that can provide global measurements of this wide range of altitude. The sounding of the atmosphere was done using broadband emission radiometry (SABER), which is one of the instruments on board the TIMED satellite. This device is a multi-spectral radiometer that measures daily vertical profiles through near-infrared measurements between 1. 27 and 17 µmm, with a typical accuracy above 5 % in most channels and a vertical resolution of about 2 km  (Bageston et al.
, 2011). The data collected through the TIMED/SABER satellite are available from altitudes between 10 and 120 km. Another satellite used in the present study was AURA, which is part of NASA's Earth Observing System (EOS). The Aura orbit is Sun-synchronous at an altitude of 705 km with 98 • inclination. One of the instruments on board AURA is the Microwave Limb Sounder (MLS), which measures the emission of thermal microwaves from different regions and scans the Earth every 24. 7 s  (French and Mulligan, 2010). The AURA/MLS satellite measures the atmospheric composition, such as temperature, humidity and ozone. In addition, it is also possible to obtain measurements during the day and night around the globe by means of the AURA/MLS satellite, which acquires data even in the presence of glacial clouds and aerosol from infrared, visible and ultraviolet measurements  (AURA, 2018).
The temperature was also compared with data obtained from the Cosmic Satellite Constellation which utilize the radio occultation (RO) technique to retrieve the temperature profiles  (Foelsche et al. , 2006). The RO technique uses the effect of the radio signal refraction in the atmosphere when the satellites of the Global Navigation Satellite System (GNSS) are occult for the receivers on the ground. Through this technique it is possible to retrieve, besides temperature, other important variables, such as the atmospheric density, water vapour and densities of several constituents (GNSS) (COS-MIC, 2018). The last instrument used in this study was the Ozone Monitoring Instrument (OMI) on board the ERS-2/NASA satellite. It was launched to replace the Earth Probe Satellite (TOMS-EP), which ended its activities in 2015  (Antón et al.
, 2009). The OMI instrument records data from the total ozone column and other atmospheric parameters related to ozone chemistry and climate, such as NO 2 and SO 2. This instrument can also distinguish different types of aerosols, including smoke, dust and sulfates, in addition to measuring pressure and cloud cover and creating a hyperspectral image in the visible and ultraviolet spectrum  (OMI, 2018). The measurements obtained by the AURA/MLS and TIMED/SABER satellites are in mixing ratio, although the balloon measurements are in partial pressure. Thus, it was necessary to convert the satellite data unit from mixing ratio to partial pressure for a reliable comparison between the two sets of data. This paper presents a full multi-instrumental analysis of a strong ozone secondary effect over southern Brazil and Uruguay between 21 and 23 October 2016.
The ozone and temperature profiles observed during the occurrence of this ozone depletion event were compared to the climatology for Santa Maria between 1996 and 1998  (Guarnieri et al. , 2004). If the measurements of the instrument were not simultaneous, it could imply poor data correlation. However, the instruments in this study did not perform simultaneous measurements at the same location and on/at the same day/time. Nevertheless, the results were efficient in identifying poor ozone air mass displacement and analysis of this event. According to the instruments presented above, data were collected in southern Brazil and Uruguay. The locations of each ozone and temperature profile collected by the satellites and sounding balloon are in Fig. 2. The TIMED/SABER satellite, which is denoted by a blue circle, passed over the regions of interest on 19 October in Uruguay and in the coastal region of Rio Grande do Sul on 23 October.
Results Ozone profiles are shown in Fig. 3  for 19, 20, 21 and 23 October. There were no satellites passing over the regions on 22 October. The vertical dashed lines delimited the peak positions from the climatology and observations at around 24 km height. The ozone profile was obtained over the Uruguayan region (near the coast) at around 14:00 UT on 19 October by the AURA/MLS satellite (red profile), which is depicted in Fig. 3a. This shows a regular behaviour (main peak around 24 km height) that is very similar to the climatology for the state of Rio Grande do Sul (grey profile). On 20 October the TIMED/SABER satellite (blue profile) carried out measurements at about 110 km to the north of the previous day over Uruguay. These sounding data reveal a considerable decrease in ozone concentration when compared to the climatology (Fig.
Despite the measurements not being performed at the exact same place, both the balloon and the AURA/MLS satellite presented very similar ozone profiles. These profiles displayed less intense reduction than the previous day over Uruguay and approximately 60 µhPa below the climatology peak (half of the previous day's reduction). Nevertheless, this apparatus unveiled a reversal of the ozone layer at around 24. 5 km height and showed the influence of the ozone hole over Santa Maria. As previously mentioned, there were no measurements for the ozone and temperature profiles on 22 October. However, the Brewer Spectrophotometer registered that the total ozone content was still low on this specific day (Fig. 5 ). The ozone for 23 October recovered considerably according to the TIMED/SABER satellite data retrieved over the coast of the state of Rio Grande do Sul (Fig.
Discussion The influence of the Antarctic ozone hole may reach midlatitudes. Results of this study corroborate previous ones such as performed by  Kirchhoff et al. (1996) , who reported an ozone content reduction in Santa Maria for a couple of days in October of 1993. Their results suggested that the ozone content presented two minimums: ∼ 250 DU on 19 and 28 October. In that study, the monthly average data were taken for two stations, one with data collected for 19 years and the other for 13 years, with typical total ozone content reported around 290 DU. In the present study, a larger decrease was observed when compared to Kirchhoff's, with a minimum of approximately 225 DU on 20 October, which can be considered a very intense influence of the ozone hole over southern Brazil. Similarly, validation of the Brewer Spectrophotometer data using daily averages from the TOMS satellite is also presented and, therefore, a positive correlation was observed.
Additionally, the present study also confirmed the influence of the Antarctic ozone hole at midlatitudes by using validated profiles of the ozone sounding with the TIMED/SABER and AURA/MLS satellites. The Brewer Spectrophotometer, backward trajectories by HYS-PLIT/NOAA and images from the OMI-ERS2 satellite were also used and exhibited excellent agreement, thus corroborating data obtained from our sounding balloon. Another study that showed ozone profiles created by ozonesonde for the region of Santa Maria was by  Guarnieri et al. (2004) , with data from November 1996 to April 1998, which were used to build the climatology used in this study. They also built the ozone climatology by seasons (autumn, winter, spring and summer), based on 35 ozonesonde profiles, showing that ozone content is higher in winter and spring (with a peak of ∼ 135 µhPa at ∼ 23 km height) than in summer and autumn.
Data availability. All SABER satellite data used in this work have been downloaded using the SABER Custom Data Services tool available at http://saber. gats-inc. com/data. php. AURA/MLS satellite data have been downloaded by a user, available at https:// aura. gsfc. nasa. gov (AURA, 2018). OMI-ERS2 satellite data are available at https://aura. gsfc. nasa. gov/omi. html (OMI, 2018). Ra-dio occultation satellite data are available at http://www. cosmic. ucar. edu/ (COSMIC, 2018). The climatological data were obtained by  Guarnieri et al. (2004). The ground-based data (Brewer Spectrophotometer) are not available online, but can be solicited directly by e-mail to José Valentin Bageston (bageston@gmail. com). The balloon data are not available online, but can be solicited directly by e-mail to Damaris Kirsch Pinheiro (damaris@ufsm.
Soil samples were collected 18 days after the drought stress had stopped, at day 46 of the experiment. Soil was sampled with a small auger by taking ca. 100 gram (based on dry soil) from 5 to 7 places in the 7L mesocosms. Then, this soil sample was homogenized, after which a subsample was taken, put on ice and stored in the -80 • C freezer as soon as possible after sampling. The nonfrozen soil samples were used for the following measurements: Inorganic Nitrogen, Arginine Ammonification, basal respiration, substrate induced respiration, pH, ergosterol as fungal biomass indicator and total microbial biomass. The data have been published in a previous study (see  Meisner et al. , 2013b for details).
Thereafter, chimeric sequences were detected and removed using the UCHIME algorithm  (Edgar et al. , 2011)  implemented in VSEARCH. Lastly, taxonomic classification for each OTU was obtained by using the RDP Classifier version 2. 10 using the bootstrap value of 80% and classification was done on fulllength entries  (Cole et al. , 2014). The pipeline was made with Snakemake  (Koster and Rahmann, 2012)  as available at DOI: https://doi. org/10. 5281/zenodo. 597131 (de Hollander, 2016). This pipeline was also used for ITS with the following adjustments: (1) ITS2 regions where extracted using ITSx 1. 0. 11 (Bengtsson-Palme et al. , 2013). (2) Using the UNITE database  (Kõljalg et al. , 2013)  provided by RDP the sequences were classified. OTUs without affiliation to at least kingdom level were excluded from the downstream beta-diversity analysis as the carry little information.
Sequences Summary tables describing each sample are presented in Supplementary Tables  S1, S2. The raw sequencing counts were used directly to estimate the sequencing depth completeness via rarefaction curves using the package vegan in R (Supplementary Figure  S1 ). Although amplicon profiles didn't achieved asymptotic approximation (Supplementary Figure  S1 ), sequencing depth was sufficient to analyze the core majority of this soil microbial diversity with a satisfactory rarefaction threshold  (Schöler et al. , 2017). There were on average 23808 OTUs per sample for 16S (Supplementary Table  S1 ) of which three were excluded from further analysis due to too few counts or too low richness. For ITS, there were on average 4106 OTUs per sample (Supplementary Table  S2 ) of which one sample was excluded from further analysis due to too few counts.
Richness and Evenness Analysis Diversity indices were analyzed as described in previous work  (Jacquiod et al. , 2016; Nunes et al. , 2016). The Shannon diversity index (H) was calculated as H = -p i ln p i , where p i is the proportional abundance of OTU i in the mesocosms. Shannon diversity indices (H, Gaussian glm model fit) and sample richness (R, Poisson glm model fit) were calculated on rarefied data at 50,000 counts per sample for 16S rRNA gene profiles and 12,000 counts per sample for ITS profiles in order to avoid biases that may come from uneven numbers. Univariate statistical analysis was done in R version 3. 1. 1. We used the multcomp package version 1. 3. 6  (Hothorn et al. , 2008)  for the ANOVA corrected with Tukey post hoc test.
Multivariate Analysis and Constrained Ordination The multivariate analysis was done with the raw and nonrarefied contingency tables using the R software version 3. 0. 2 with the functions vegdist, hclust, rda, Adonis in package vegan and function dudi. pca, bca, randtest, s. class, in package ade4. A log10 transformation was needed to improve normality of data. Principal Component Analysis (PCA) was performed after center-scaling normalization. A pattern search was applied to the original PCAs by grouping replicates together in order to perform a Between Group Analysis (BGA). The statistical significance of the selected grouping factor was tested with a Monte-Carlo simulation involving 10,000 permutations. Complementing PERMANOVA tests were performed on the Euclidean distance profiles using 10,000 permutations in order to assess differences across the four legacies.
An ANOVA was performed with phyla as response variables and treatment as explanatory variable. Differences between the four treatments were identified using a post hoc Tukey-Kramer test and false discovery rate multiple correction test (functions glm, cld, gltht in package multcomp, FDR, P < 0. 05). Redundancy analysis (RDA) were performed as described before  (Nunes et al. , 2016)  using function rda in vegan package, after center-scaling normalization for both ITS and 16S rRNA gene amplicon profiles using the following explanatory variables: four moisture legacies as well as the soil measurements, inorganic nitrogen, arginine ammonification, basal respiration, substrate induced respiration, pH, ergosterol, total microbial biomass (see for details:  Meisner et al. , 2013b). A permutation test was performed using 999 permutations to test the robustness of the model for discrimination of 16S or ITS profiles.
We apply a machine learning algorithm to the output from seven fire-enabled DGVMs and a suite of satellite and other observation-based data sets in order to derive emergent relationships between a number of potential drivers of burned area. By comparing the model-and data-derived emergent relationships, we assess the degree to which DGVMs reproduce these relationships. While we make no assumption about the actual physical controls on burned area, this comparison allows us to pinpoint relationships between drivers and burned area that are unrealistically represented in fireenabled DGVMs.
Climate The satellite-derived global relationships showed expected patterns between burned area and climate variables: burned area increased exponentially with maximum temperature, decreased with an increasing number of wet days per month, and increased with diurnal temperature range (Fig. 4a-c ). ables were robust among the burned area data sets (Fig. S11 ). However, burned area data sets show offsets between the relationship curves: for example, the curves that were derived from the GFED4s and CCI_MERIS data sets usually show higher burned area than the curves from the other data sets (Fig. S11 ). These positive offsets are caused by the fact that GFED4s and CCI_MERIS include more small fires and therefore have an overall higher burned area than the other data sets. RF experiments that either use the "full" or "fm" set of predictor variables resulted in largely similar relationships (Fig.
Acknowledgements. We the institutions, initiatives, and researchers listed in Table A1 for providing data sets. We thank  Stephane Mangeon  for providing JULES-INFERNO model results to FireMIP. Matthias Forkel  was supported by a  Living Planet Fellowship  from the  European Space Agency , and  Matthias Forkel  and  Wouter Dorigo  were funded by the  TU Wien Wissenschaftspreis 2015 , a personal science award to  Wouter Dorigo. Niels Andela  received funding from the  Gordon and Betty Moore Foundation  (grant no. GBMF3269 ). The MERIS and MODIS Fire_CCI data sets were generated under the  ESA Climate Change Initiative. Sandy P. Harrison  acknowledges the support from the  ERC -funded project  GC2. 0  (Global Change 2. 0: Unlocking the past for a clearer future, grant number  694481 ). The authors acknowledge the  TU Wien University Library  for financial support through its  Open Access Funding Program.
This is potentially due to GWAS limitations and the complexity of this particular genetic locus. GWAS samples have high levels of genetic diversity and are underrepresented in reference panels of genetic variation  (Leffler et al. , Malaria Genomic Epidemiology Network, 2017). Also, GWAS studies mainly use SNPs to determine association and this would be challenging to evaluate more complex loci without genetic imputation method. E756del is in such a locus with multiple short tandem repeats (Figure  6C ), so that imputation of this mutation into current GWAS datasets is not straightforward. In this regard, our experimental data provide promising clues for association analysis: sequencing this particular locus in endemic population can determine whether E756del is associated with protection against severe malaria.
150 μl supernatant was transferred to 96-well flat bottom plate for absorbance reading at 540nm using Cytation3 (BioTek, Winooski, VT). The data was analyzed using 4-parameter sigmoidal nonlinear regression. Hematological properties from mice were analyzed by hematology analyzer at Ruggeri lab, Dept. of Molecular and Experimental Medicine, The Scripps Research Institute, La Jolla, USA) Gain-of-function Piezo1 mice generation-The targeting strategy was based on the NCBI transcript NM_001037298. 1. Wildtype exons 45-51, including the complete 3′ untranslated region (UTR) were flanked by loxP sites. An additional polyadenylation signal (nucleotide sequence of the synthetic polyA: gagctccctggcgaattcggtaccaataaaagagctttattttcatgatctgtgtgttggtttttgtgtgcggcgcg) was inserted between the 3′ UTR and the distal loxP site in order to prevent downstream transcription of the mutated exon 51 in the conditional allele.
2504 genomes were analyzed, 661 from African and 1843 from non-African ancestries. Wright's fixation index (F ST ), a measure of population differentiation, was calculated as follows: F ST = σ s 2 p (1 -p) where p̄ is the mean allele frequency and σ s 2 is the allele frequency variance between the populations. The 1000 Genomes browser (http://phase3browser. 1000genomes. org) was used to determine that no alleles were in linkage disequilibrium with E756del (estimated r 2 values were <0. 05). Hardy-Weinberg equilibrium was estimated using the classical binomial expansion to determine the expected genotype frequencies and x 2 tests. PIEZO1 sequences were obtained from the following sources: modern humans (Homo sapiens, Genbank NG_042229. 1), neandertals (Homo neanderthalensis, Neandertal Ensembl ENSG00000103335), and Denisovans (Homo sapiens ssp.
Denisova, previously generated reads  (Meyer et al. , 2012; Reich et al. , 2010)  aligning to humans [GRCh37/hg19] using the UCSC Genome Browser,  (Kent et al. , 2002) ). All non-human primate amino acid sequences were obtained from GenBank. The sequences were aligned in Geneious using MAFFT  (Katoh, 2002).