dataesr/scientific-paragraphs-categorization huggingface dataset:
Vice-versa, structural residues S, in orange, are highly conserved and co-evolved in the artificial evolution, but poorly in the natural ones. Residues with a comparable signal between natural and artificial analysis are classified as overlapping-functional-structuralresidues OFSR and visualised in yellow. At this point we want to focus only on the F and the S residues because of their strong signal compared to the one of the OFSR. The latter, can be refined by improving the definition of the cluster algorithm  [78]. We use the information found in literature (both experimental and from predicting software) to mark the residues important for functional processes and/or for structural stability  [51, 52, 53, 54, 58, 59, 60, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89]. Given that information, we highlight in blue the residues that show a match with our prediction, while in red the ones identified as key by literature but missing in our analysis.
Conclusion We have analysed three major protein families: FKBP, PDZ and Response reg. From the natural sequences, we extracted the information relative to highly conserved sites and highly co-evolved residue pairs. The analysis resulted in a long list of residues that could potentially have a functional and/or a structural role for the proteins. For each protein family, we produced an artificial set of sequences that are equivalent solutions to the folding problem. The researchers that are interested in using our methodology at the moment need three tools all of which are available as open software. The first information necessary is the alignment of the target protein. Then the target protein structure is stripped of the sequence and redesigned using the protein package ViPS available online  [94].
At this point, the user will have two lists of aligned proteins one natural and the other artificial. In order to generate the Entropy and FAPC weight, the user can use the Matlab DCA script, free for download [95], and run it on both lists. Finally, the last step is to use the Matlab script in the supplementary materials to compare the weights of the two lists. We applied the same conservation and co-evolution analyses performed on the natural alignments, obtaining a correlation pattern with many features in common with the natural ones. However, it is in the differences that we find the main result of this work. The residues that have a strong co-evolution signature only in the natural alignments are residues that do not have a structural role in the protein because otherwise, they would show a similar signature in the analysis of the artificial set.
It is important to notice that, for the entropy distribution, outliers that are larger than the average S c correspond to sites of high variability, but since we are interested in the highly conserved ones we exclude such points. Similarly we focus only on the outliers corresponding to strong co-evolution events, hence we exclude the F AP C (i, j) outliers below the distribution average F AP C. We perform a standard Principal Component Analysis (PCA), using residue index (indices) and conservation (co-evolution) signals, implemented in the open-source software Octave (see PCA source code section in SI for the source). The residue index are used to map back the strong signals into the protein sequence. Once the S u (i) and the F AP C (i, j) are projected over their respective principal components, we prune for the outliers.
Bioinformatic analysis Conversion and demultiplexing of reads were performed using bcl2fastq. FastQC was used for quality controls of the raw data. Reads were cleaned using the Trimmomatic software with a minimum quality threshold of Q30  (Bolger et al. , 2014). Cleaned reads were aligned and counted to the annotated Homo sapiens genome (GRCh38. p11) and the annotated HAdV C5 genome (accession number AY339865) using STAR software  (Dobin et al. , 2013). Principal component analysis and identification of differentially expressed genes were performed using the package DESeq2  (Love et al. , 2014). Genes with an absolute fold change greater than two and an adjusted p-value less than 0. 05 (FDR < 5%) were considered to be differentially expressed. Ontologic analyses were performed with IPA software (Qiagen).
Descriptive statistics of RNAseq data To obtain a broad picture of the A549 cells response to infection with HAdV and BCV treatment, RNAseq was used to analyse differential gene expression at the mRNA level. In total, there were four experimental conditions including mock-infected lung epithelial cells (condition A549, n = 2), A549 cells infected with C5 ATCC HAdV (condition ADV, n = 3), A549 cells infected with C5 ATCC HAdV with BCV treatment (condition ADV+BCV, n = 3), and mock-infected A549 cells with BCV treatment (condition BCV, n = 3). Read counts for every sample is detailed in Table  1. A median of 94,642,847 reads per sample were sequenced. For each sample, 92 to 98 percent of reads were mapped on human genome. In total, more than 20,000 human genes covered by sequencing have been analyzed for each condition.
2007 ). The flux and bandpass were calibrated with the quasar 3C 286. J1822-0938 was used for the phase and gain calibration (see also  Beuther et al. 2016). To recover the large scale structure we combined the Cconfiguration data with the H i Very Large Array Galactic Plane Survey (VGPS,  Stil et al. 2006) , which consists of VLA Dconfiguration data combined with single-dish observations from the Green Bank Telescope (GBT). We subtracted the continuum in the visibility datasets (with the CASA command uvcontsub), and used the multiscale CLEAN in CASA 2 to image the three adjacent tiles of continuum-subtracted C-configuration data together with D-configuration data. A pixel size of 4 , a spectral resolution of 1. 5 km s -1 , and a robust weighting value of 0. 45 were used. The resulting images, which have a synthesized beam between 20 to 40 over the entire coverage of the THOR survey, were all smoothed to a common resolution of 40.
The fit was performed with the python package Powerlaw  (Alstott et al. 2014). The fitted parameters of the log-normal functions and power-laws, when applicable, are listed in Table  2. The N-PDF from the H i emission peaks at 2. 2×10 21 cm -2 , corresponding to A V = 1  (Güver & Özel 2009). Studies towards nearby clouds have found that the N-PDFs from H i emission there peak around ∼ 1 -2 × 10 21 cm -2  (Imara & Burkhart 2016) , slight lower than we find here for our target. The widths of the fitted log-normal models vary among the tracers; the H i emission has the smallest width, followed by HISA and the 13 CO that has the widest width (see Table  2 ). The narrowness of the H i emission N-PDF indicates that H i emission is relatively smoothly distributed without large variations and substructure (Fig.
Acknowledgements. The  National Radio Astronomy Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc. Y. W. , H. B. , S. B. , and J. D. S. acknowledge support from the  European Research Council  under the  Horizon 2020 Framework Program  via the  ERC Consolidator Grant   CSF-648505 , and  R. S. K. via the  ERC Advanced- Grant  339177  (STARLIGHT). H. B. , S. C. O. G. , and M. R. acknowledge support from the  Deutsche Forschungsgemeinschaft in the Collaborative Research Center  ( SFB 881 ) "The  Milky Way System " (subproject  B1 ,  B2 ,  B8 ). R. S. K. and S. C. O. G also acknowledge support from the  DFG via Germany's Excellence Strategy   EXC-2181/1 -390900948  (the  Heidelberg STRUCTURES Cluster of Excellence ).
This work was carried out in part at the  Jet Propulsion Laboratory  which is operated for NASA by the  California Institute of Technology. N. S. acknowledges support by the french  ANR  and the german DFG through the project " GENESIS"  ( ANR-16-CE92-0035-01/DFG1591/2-1 ). F. B. acknowledges funding from the  European Union 's  Horizon 2020 research and innovation program  (grant agreement No  726384 ). This research made use of Astropy and affiliated packages, a community-developed core Python package for Astronomy (The Astropy Collaboration et al. 2018),  Python package SciPy 3 , APLpy , an open-source plotting package for Python ( Robitaille & Bressert   2012 ), and software TOPCAT (Taylor 2005). The authors thank the anonymous referee for the constructive comments that improve the paper.
This paper provides an overview of the scientific background and the research objectives of the Research Unit "VolImpact" (Revisiting the volcanic impact on atmosphere and climate -preparations for the next big volcanic eruption, FOR 2820). VolImpact was recently funded by the Deutsche Forschungsgemeinschaft (DFG) and started in spring 2019. The main goal of the research unit is to improve our understanding of how the climate system responds to volcanic eruptions. Such an ambitious program is well beyond the capabilities of a single research group, as it requires expertise from complementary disciplines including aerosol microphysical modelling, cloud physics, climate modelling, global observations of trace gas species, clouds and stratospheric aerosols. The research goals will be achieved by building on important recent advances in modelling and measurement capabilities.
Observational data Central for VolImpact is the analysis and exploitation of satellite data. Satellite measurements will be used in two different ways in VolImpact. Some of the research activities will employ raw satellite data (Level 1 data) to retrieve relevant aerosol parameters (aerosol extinction and aerosol particle size information), e. g. , in VolARC. In addition, we will also make use of existing Level 2 satellite data of aerosol parameters, chemical composition and the atmospheric background state. We expect to greatly benefit from: (a) the daily near-global observations from the wellestablished limb-scatter instruments (OSIRIS  (Llewellyn et al. , 2004) ; SCIAMACHY  (Bovensmann et al. , 1999) ; OMPS-LP  (Jaross et al. , 2014) ) and the recently launched and upcoming satellite missions including SAGE III/ISS (launched in 2017), Earth-CARE (scheduled for launch in 2021) and ALTIUS  (Fussen et al.
, 2016)  (currently scheduled for launch in 2022), which provide high-resolution observations of aerosols, clouds, and aerosol-cloud interactions. (b) multi-spectral observations of the scattered or transmitted solar radiance which allow the retrieval of more than one parameter of the aerosol particle size distribution -a key variable for volcanic forcing constraints. (c) advanced optical observations to retrieve the volcanic plume geometry, such as MISR multi-angle observations and geostationary observations with high spatio-temporal resolution. (d) cloud property retrievals by active remote sensing (CloudSat and CALIPSO) for evaluation of the simulation of perturbed and unperturbed liquid and ice clouds. Table  1  provides an overview of the satellite datasets we plan to use within the individual projects.
It will, e. g. , be capable  of distinguishing clouds and different types of aerosols and even detect vertical motion within clouds. From the remote sensing perspective, the next moderate to large volcanic eruptions will very likely be well characterized through the retrieval of optical, microphysical and geometrical properties of both volcanic ash and sulfate aerosol provided by a range of complementary Earthobserving platforms. In VolImpact other observational data will also be used. Collaborations with different in-situ and remote sensing measurement groups are planned including the Network for Observation of Volcanic and Atmospheric Change (NOVAC), the Network for the Detection of Atmospheric Composition Change (NDACC), IAGOS-CARIBIC and the upcoming Strateole-2 balloon campaign.
Models and Forcing data In VolImpact the ICON modeling framework will be applied to better understand the impacts of volcanic eruptions from the dynamics of the eruption plume to global climate effects. The ICON model is a joint development of the German Weather Service (DWD) and the Max Planck Institute of Meteorology Hamburg (MPIM). ICON scales very efficiently on massively parallel computers and is therefore especially suited to run on current high performance computing architectures. The atmospheric component of the ICON model system has been developed around a dynamical core that solves the fully compressible non-hydrostatic equations, and includes a mass conserving tracer transport scheme. Three packages for parameterisations of subgrid-scale diabatic and turbulent processes have been developed for climate simulations at a grid resolution of ∼100 km (ICON-A, Giorgetta et al.
VolImpact projects will focus on the late 20th century and early 21th century. This period includes a large volcanic eruption, i. e. , Mt. Pinatubo in 1991, but also several small to moderate ones (Figure  1 ). A set of volcanic eruptions has been selected from this VolImpact core period  based on eruption characteristics and data availability (Figure  5  and Table  2 ) which will be studied across the different projects, including Mt. Pinatubo (1991 ), Sarychev Peak (2009) ,  Eyjafjallajökull (2010 ), Nabro (2011 ) and Holuhraun (2014). VolImpact will also address the Raikoke eruption in June 2019 which happened after the start of the project.
Conclusions and perspectives The DFG Research Unit VolImpact will improve the scientific understanding of key aspects of the volcanic influence on atmosphere and climate, taking advantage of new developments in observational and modelling capabilities. This will enhance our capacity to understand observed past climate variability, to quantify potential consequences of the next large volcanic eruption and to design observing systems, software tools and strategies that will allow us to learn the most from future eruptions. The overall success of the VolImpact research unit does, however, not depend on the occurrence of major volcanic eruptions during the project period. Independent of the occurrence of a major eruption during the project period, the new remote sensing and modelling capabilities will help improving the scientific understanding of poorly known processes related to volcanic eruptions.
In addition, the developed tools can also be applied to study pyrocumulus events, which occur much more frequently than major volcanic eruptions (e. g. ,  Fromm et al. , 2005; Siddaway and Petelina, 2011; Peterson et al. , 2017). Research of the described phase 1 (4/2019-3/2022) of VolImpact will pave the way for a potential 2nd phase. Physical and chemical modules developed in phase 1 will be combined into one modelling suite. Experiments with convection-permitting resolution will be performed on the global scale with an ultra-fine nest around the volcano. Such simulations could become an early demonstrator for the potential benefits of the approach to use extreme computing for understanding natural extreme events. On the observational side a special emphasis in a 2nd phase of VolImpact will be on the exploitation of future data sets relevant for the VolImpact research goals.
This includes in particular upcoming satellite missions such as ESA's EarthCARE, comprising different instruments that are highly relevant for several of the VolImpact science projects. In addition, ESA's ALTIUS mission (scheduled for launch in 2022) as well as the U. S. mission OMPS JPSS 2 (scheduled for launch in 2021) will continue satellite limb observations and fill the looming "limb-gap", i. e. , the potential interruption of the vertical profiling capability of the middle atmosphere using satellite sensors. More information on VolImpact and instructions on how to access datasets created within the VolImpact projects are provided on the VolImpact website (www. uni-greifswald. de/volimpact).
Acknowledgements We are deeply indebted to the  Deutsche Forschungsgemeinschaft (DFG)  for funding the VolImpact research unit proposal (FOR 2820). We thank  Thor Hansteen  ( GEOMAR ) for serving as an external Steering Committee member,  Bjorn Stevens  ( MPIM ) for inspiring discussions and  Alicja Buszkiewicz  ( University Greifswald ) for editorial assistance with this manuscript. We also thank the OSIRIS and OMPS-LP science teams for providing the data sets shown in Figure 1.
Methods Study design and participants. The EPIC-InterAct study is a large-scale prospective study nested in the European Prospective Investigation into Cancer (EPIC) study, facilitating the investigation of genetic and lifestyle factors on the risk of T2D among European populations. A total of 26 research centres located in eight different European countries (France, Italy, Spain, UK, the Netherlands, Germany, Sweden, and Denmark) were included. The study design, sample collection and genotyping have been described in detail previously  5, 6. In brief, the EPIC-InterAct study adopted a nested case-cohort design. A total of 340,234 participants with stored blood and information reported on diabetes status from the wider EPIC study were followed up for 3. 99 million person-years. During the follow-up, researchers from participating study centres ascertained and verified 12,403 incident cases of T2D through self-reported history of T2D, doctor diagnosed T2D and diabetes medication use, linkage to primary care registers, secondary care registers, medication use (pharmacy/ drug registers), hospital admissions and mortality data or local and national diabetes and pharmaceutical registers 5.
Related individuals in the Illumina 660 W genotyping array group were identified based on an identity by descent (IBD) pi-hat threshold of 0. 1875 (mid-point between second-degree (0. 25) and third-degree (0. 125) relatives), and those with the largest number of relatives or the lowest call rate were removed preferentially. A total of 9,290 samples genotyped on the Illumina 600 W array passed initial sample quality control (QC). A total 13,474 individuals from the remaining of EPIC-InterAct samples (including the Danish samples) were genotyped using the Illumina core-exome 12v1 and 24v1 arrays at Cambridge Genomic Services in the Department of Pathology at the University of Cambridge. The two core-exome arrays are very similar; hence the genotype data were merged for further analyses. Following comparable QC procedures as above, a total of 13,202 samples genotyped using the core-exome arrays passed initial sample QC.
Genotype imputation. Prior to imputation, single nucleotide polymorphisms (SNPs) were removed if they had Hardy Weinberg p-value < 10 -6 or were not found in the Haplotype Reference Consortium (HRC) reference panel version 1. 0 8 , were A/T or G/C with minor allele frequency (MAF) >0. 4, had an allele frequency difference >0. 2 with the reference panel, or were short insertion-deletion mutations (indels). A total of 553,115 and 366,044 SNPs passed pre-imputation SNP QC in the Illumina 660W-Quad BeadChip and combined Illumina core-exome arrays, respectively. Imputation was performed using the HRC reference panel and IMPUTE v2. 3. 2 software  9. Monomorphic and singleton SNPs and those with imputation quality (info) <0. 3 were excluded prior to genetic analyses. Genome-wide association meta-analysis.
Data records Genome-wide association summary statistics from the meta-analysis of T2D in the EPIC-InterAct study and Cox-regression analysis results for the 370 top T2D SNPs from the recently published DIAMANTE study 2 are available to download from the Dryad Digital Repository (https://doi. org/10. 5061/dryad. qnk98sfcg)  13. The genome-wide summary statistics are in tab-delimited TXT format, including rsID (based on the HRC reference panel), chromosome, position (using the reference genome GRCh37 (hg19)), effect allele, other allele, frequency of effect allele, effect estimate, standard error of the effect estimate, p-value, assessment of heterogeneity across the two genotyping arrays, total sample size and effective sample size for the SNP. The Cox-regression analysis results are in tab-delimited TXT format, including MarkerName (hg19), rsID (based on the HRC reference panel), chromosome, position (using the reference genome GRCh37 (hg19)), effect allele, other allele, frequency of effect allele, beta, standard error of beta, hazard ratio (HR), lower-bound of 95% confidence interval (CI) of HR, upper-bound of 95% confidence interval (CI) of HR, p-value, imputation quality, total sample size.
Alternatively, the genome-wide summary statistics data is also available in NHGRI-EBI's GWAS Catalog with accession ID GCST90006934 14. It can be downloaded via the following ftp link: ftp://ftp. ebi. ac. uk/pub/databases/ gwas/summary_statistics/GCST90006934. In addition, access to individual-level EPIC data is available through the International Agency for Research on Cancer (IARC): https://epic. iarc. fr/access/, where there is a controlled-access repository. A clear and open access request mechanism and data use agreement is in place.
Acknowledgements We thank all EPIC participants and staff for their contribution to the study. We thank  Nicola Kerrison  ( MRC Epidemiology Unit, Cambridge ) for managing the data for the InterAct Project and staff from the  Laboratory Team, Field Epidemiology Team , and Data Functional Group of the  MRC Epidemiology Unit in Cambridge, UK , for carrying out sample preparation, DNA provision and quality control, genotyping, and data-handling work. The funding of the  EPIC-InterAct study  was provided by the  EU   FP6 Programme  [grant number  Integrated  Project  LSHM_CT_2006_037197 ]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors acknowledge support from the  Medical Research Council Epidemiology Unit  (grants  MC_UU_12015/1  and  MC_UU_12015/5 ) and  Wellcome   WT206194.
Code availability IMPUTE v2. 3. 2: https://mathgen. stats. ox. ac. uk/impute/impute_v2. html QUICKTEST Version 0. 98: http://toby. freeshell. org/software/quicktest. shtml METAL: https://genome. sph. umich. edu/wiki/METAL All other analyses, including the Prentice-weighted Cox-regression analyses, were performed using R 3. 4. 2  17.
METHODS The measurement of the gingival exposure was performed on frontal extraoral photographs of the posed smile before (T1) and after treatment (T2) in each patient, using the Corel Draw X5® software (CorelCorporation, Ottawa, Canada). A spontaneous smile for staged photography is difficult to replicate. This way, patients were trained to smile as extreme as possible, until producing a squint with a noticeable decrease in the palpebral fissure, which refers to the distance between the open eyelids. Three photographs were taken at each time point for each patient, and the photograph with the greatest gingival exposure was chosen for measurement. Gingival exposure (GING EXP) was defined as the distance between the stomion of the upper lip (UL) to the incisal edge of the maxillary central incisor (UL-Mx1) minus the length of the maxillary central incisor (Mx1).
Statistical Analysis Normality of the data was tested with the Shapiro-Wilk test and all variables present normal distribution, and then parametric tests were used. The comparability of the groups of sex distribution was performed by Fisher's exact test. Comparability of the mean age of the groups was performed by an independent t-test. Intergroup comparison of gingival exposure before (T1) and after treatment (T2) and changes with treatment (T2-T1) was performed by independent t-tests. The tests were performed using Statistica software (Statistica for Windows, version 7, Statsoft, Tulsa, Oklahoma, USA). The data were considered significant for P <0. 05.