dataesr/scientific-paragraphs-categorization huggingface dataset:
Proteomics raw data processing All datasets were analysed with MaxQuant (version 1. 6. 3. 4)  [51, 52]  on a Linux highperformance computing cluster for peptide/protein identification and protein quantification. Input parameters for each dataset, such as MS1 and MS2 tolerances, digesting enzymes, fixed and variable modifications, were set as described in their respective publications, with two missed cleavage sites. The FDR at the PSM (peptide spectrum match) and protein levels were set to 1%. The MaxQuant parameters were otherwise set to default values: the maximum number of modifications per peptide was 5, the minimum peptide length was 7, the maximum peptide mass was set to 4,600 Da, and for the matches between runs the minimum match time window was set to 0. 7 seconds and the minimum retention time alignment window was set to 20 seconds.
The MaxQuant parameter files are available for downloading from Expression Atlas. The Mus musculus UniProt Reference proteome release-2021_04 (including isoforms, 63,656 sequences) and Rattus norvegicus UniProt Reference proteome release-2021_04 (including isoforms, 31,562 sequences) were used as the target sequence databases for mouse and rat datasets, respectively. The built-in contaminant database within MaxQuant was used and a decoy database was generated by MaxQuant by reversing the input database sequences after the respective enzymatic digestion. The datasets were run separately in multi-threaded mode.
Post-processing The post-processing of results from MaxQuant is explained in detail in  [15]. In brief, the protein groups labelled as potential contaminants, decoys and those with less than 2 PSMs were removed. Protein intensities in each sample were normalised by scaling the iBAQ intensity values to the total amount of signal in each MS run and converted to parts per billion (ppb). !!"_$%&' ! = ) $%&' ! ∑ $%&' ! # !$% , -. 1,000,000,000 The 'majority protein identifiers' within each protein group were mapped to their Ensembl gene identifiers/annotations using the Bioconductor package 'mygene'. For downstream analysis only protein groups whose isoforms mapped to a single unique Ensembl gene ID were considered. Protein groups that mapped to more than one Ensembl gene ID are provided in Supplementary file 1.
Protein abundance comparison across datasets To compare protein abundances, the normalised protein abundances (in ppb) from each group of tissues in a dataset were converted into ranked bins. In this study, 'tissue' is defined as a distinct functional or structural region within an 'organ'. For example, hippocampus, cerebellum and cortex are defined as 'tissues' that are part of the brain (organ) and similarly sinus node, left atria, left ventricle, right atria, right ventricle are defined as 'tissues' in heart (organ). Protein abundances were transformed into bins by first grouping MS runs from each tissue within a dataset as a batch. The normalised protein abundances (ppb) for each MS run within a batch were sorted from lowest to highest abundance and ranked into 5 bins. Proteins whose ppb abundances are ranked in the lowest bin (bin 1) represent lowest abundance and correspondingly proteins within bin 5 are of highest abundance in their respective tissue.
Comparison of protein expression across species The g:Orth Orthology search function in the g:Profiler suite of programs  [55]  was used for translating gene identifiers between organisms. Since a custom list of gene identifiers could not be used as the background search set, the mouse and rat genes were first mapped against the background Ensembl database. The resulting list of mouse and rat genes mapped to human orthologs were then filtered so that they only included parent gene identifiers of the protein groups from mouse and rat organs identified in this study and the parent genes of human organs described in our previous study (Supplementary file 2 in  [15] ), respectively. The orthologs were grouped into various categories denoting the resulting mapping between identifiers: "one-to-one", "one-to-many", "many-to-one", "many-to-many", and "no mappings" between gene identifiers.
Acknowledgements First of all, we would like to thank all data submitters who made their datasets available via PRIDE and ProteomeXchange. We would also like to thank  Andrew Leach  and the rest of the team involved in the Open Targets target safety project, for helpful discussions. This work has been funded by  Open Targets  (project  OTAR-02-068 ),  Wellcome Trust  [grant number  208391/Z/17/Z ],  BBSRC  [ BB/T019670/1  and  BB/T019557/1 ] and EMBL core funding.
iBAQ values. The samples were hierarchically clustered on columns and rows using Euclidean distances. (B) PCA of all samples coloured by the organ types. (C) PCA of all samples coloured by their respective dataset identifiers. The numbers in parenthesis indicate the number of datasets for each organ. Binned values of canonical proteins quantified in at least 50% of the samples were used to perform the PCA. A Datasets Organs 1 Datasets PXD000288 0. 8 PXD000867 PXD003155 PXD004496 0. 6 PXD004612 PXD005230 0. 4 PXD008736 PXD009639 PXD009909 0. 2 PXD012307 PXD012636 PXD019394 0 PXD019431 PXD022614 -0. 2 Organs ArticularCartilage Brain Eye Heart Kidney Liver Lung Pancreas Spleen Tendon Testis TricepsMuscles B 25 50 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 22 2 2 22 2 2 2 2 2 2 2 2 2 10 10 10 10 10 10 10 10 C 25 50 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 4 4 4 4 4 4 4 44 4 4 44 4 4 4 4 6 6 5 2 2 2 PC2 0 -25 2 2 2 2 2 2 2 3 3 6 6 6 6 6 6 1 1 1 1 1 1 PC2 0 -25 14 14 14 14 14 14 12 9 8 13 13 13 13 13 13 12 8 -50 -50 4 4 4 4 4 4 4 4 4 4 12 12 12 7 7 7 9 11 5 4 6 -75 1111 11 11 7 7 7 7 7 7 10 10 12 12 12 12 12 12 -75 66 6 6 -50 0 50 100 -50 0 50 100 PC1 PC1 1 PXD000288 6 PXD005230 11 PXD012636 2 PXD000867 7 PXD008736 12 PXD019394 1 ArticularCartilage ( 1 ) 4 Heart ( 3 ) 7 Lung ( 2 ) 10 Tendon ( 1 ) Datasets 3 PXD003155 8 PXD009639 13 PXD019431 Organs 2 Brain ( 5 ) 5 Kidney ( 1 ) 8 Pancreas ( 1 ) 11 Testis ( 1 ) 4 PXD004496 9 PXD009909 14 PXD022614 3 Eye ( 2 ) 6 Liver ( 3 ) 9 Spleen ( 1 ) 12 TricepsMuscles ( 1 ) 5 PXD004612 10 PXD012307.
A corollary of the theory is an alternative definition of inbreeding coefficients that allows us to extend F ST to adjusted or surrogate genotypes, such as genotype likelihoods and other modifications of allele counts  [27]. To illustrate the new definition, we compute F ST for ancient human DNA samples after performing correction for genomic coverage and for distortions due to difference in sample ages  [28]. In a second application, we compute F ST for Scandinavian samples of Arabidopsis thaliana after removing genetic variation associated with environmental variables taken from a climate database  [29, 30].
Genetic differentiation explained by bioclimatic factors. To provide a second illustration of the use of spectral estimates of inbreeding coefficients, we studied the role of bioclimatic factors in shaping population genetic structure in plants  [29]. Here, the objective was to evaluate the proportion of differentiation explained by temperature and precipitation, which may influence adaptive genetic variation in those taxa. For 241 Swedish accessions of Arabidopsis thaliana taken from The 1,001 Genomes database  [30] , population structure was first evaluated by using a spatially explicit Bayesian algorithm. The individuals were clustered in two groups located in southern and northern Sweden (Figure  6A ). For the groups estimated by spatial population structure analysis, the mean value of F ST across loci was equal to 7.
Chromatin immunoprecipitation (ChIP) ChIP was carried out as described previously  (Rivosecchi et al, 2019) , using the primers listed in Table  S2. GFP-tagged proteins were immunoprecipitated with the A11122 antibody (Thermo Fisher Scientific); Myc-tagged proteins were immunoprecipitated with the 9E10 antibody (Merck); Rpb1 was immunoprecipitated using the 8WG16 antibody (Merck). Mathematical modelling and simulations (see also the Supplemental Data 1) We considered a genomic locus of 10 kbp that we modelled as a unidimensional array of N = 100 bins (1 bin = 100 bp). This region contains a 2-kbp-long gene between bin 1 and bin 20. We investigated the interplay between RNAP elongation and condensin translocation using two models.
Acknowledgements We are very grateful to  François Bachand  ( University of Sherbrooke ) for sending the tfs1-DN over-expression plasmid. This work was supported by a " Chaire d'Excellence" (Project TRACC ,  CHX11 ) and by a  "Projet de Recherche Collaborative" (PRC)  (project  19-CE12-0016-04 ) awarded to V Vanoosthuyse by the  Agence Nationale pour la Recherche (ANR) , and by the  PRC  project ( ANR-15-CE12-0002-01 ) and the project (PJA 20191209370) awarded to  P Bernard  by the  Agence Nationale pour la Recherche (ANR)  and the  Association pour la Recherche sur le Cancer (ARC) , respectively. D Jost acknowledges additional funding by the  Agence Nationale pour la Recherche  ( ANR-18-CE12-0006-03 ,  ANR-18-CE45-0022-01 ).
Bioinformatics Analysis SignalP 5. 0 Server (http://www. cbs. dtu. dk/services/SignalP/) was used to predict the signal peptide cleavage site of porcine TLR8-ECD. NetNGlyc 1. 0 Server (http://www. cbs. dtu. dk/services/NetNGlyc/) was used to predict pTLR8-ECD N-glycosylation sites. NetPhos 2. 0 Server (http://www. cbs. dtu. dk/services/NetPhos-2. 0/) was used to forecast pTLR8-ECD phosphorylation sites. PLMD-protein Lysine to modify the Database Server 3. 0 (http://www. cbs. dtu. dk/services/NetPhos-2. 0/) was used to forecast pTLR8-ECD ubiquitination sites. GPS-PAIL Online Service (http://pail. biocuckoo. org/online. php) was used to forecast pTLR8-ECD acetylation sites. Then, CSS-Palm Online Service (http:// csspalm. biocuckoo. org/showResult. php) was used to forecast pTLR8-ECD palmitoylation sites.
Molecular Cloning and Gene Mutations PCR primers were designed on Benchling (https://benchling. com/) for the following genes: Porcine TLR8 (pTLR8, GenBank: EF583903. 1), bovine TLR8 (bTLR8, GenBank: EF583902), human TLR8 (hTLR8, GenBank: NM_138636. 5), porcine TLR3 (GenBank: NM_001097444. 1), porcine TLR5 (GenBank: Nm_001348771. 1), glycoprotein D (gD) of bovine herpes virus type I (BoHV-1) (GenBank: MH751901. 1), porcine STING (GenBank: NM_001142838. 1), human GM130 (GenBank: AF248953. 1), human LAMP1 (GenBank: J04182. 1), and porcine UNC93B1 (GenBank: XM_003122436. 6). All the primers are available in Supplementary Table  S1. The porcine TLR8, porcine TLR8-ECD, bovine TLR8-ECD, human TLR8-ECD, porcine TLR3-ECD, porcine TLR5-ECD, gD, and gD-ECD sequences were all amplified using PCR from the corresponding plasmids in our lab.
Individual points were then weighted for robustness according to their distance from the newly defined background curve, with iterative applications further refining the dataset. Apadula et al. (2019)  developed an algorithm to subtract outliers from hourly CO 2 datasets. They first removed all values which differed by more than a specified threshold ρ from the median value within a sliding 21 d window and subsequently rejected values that differed by more than ρ from the mean value of the remaining points. Such methods have been widely applied to estimate baseline concentrations of atmospheric trace gases and, in some instances, to identify the occurrence of short-term signal spikes in time series  (e. g. , El Yazidi et al. , 2018). Lacking in the current literature, however, is a comprehensive statistical framework for the extraction of non-background events occurring at synoptic (1-2 d to several weeks) to seasonal timescales.
We thus present here a novel approach to identify exceptional non-background events ("anomalies") in atmospheric time series based on statistical curve-fitting, LOESS smoothing and outlier detection with the aim of developing a protocol for the detection of anomalous episodes of synoptic and seasonal duration. The methodology is designed for application to station data from the Integrated Carbon Observation System (ICOS) network. In particular, our goal is to investigate whether seasonal-and synoptic-length deviations from background concentrations can be discerned in near real time (NRT) through statistical filtering and cross-referencing observations from multiple sites and to present a framework for communicating information about such events to station managers and other end users. We focus primarily on CO 2 , although we validate our detection of wintertime CO 2 signal peaks by applying our methodology to concurrent CH 4 time series.
Finally, we present the methodology in the context of a near-real-time anomaly detection algorithm (ADA) developed and employed at the ICOS Atmospheric Thematic Centre (ICOS ATC). The algorithm is concise and portable and is intended to be used with multi-year datasets consisting of validated (level 2) and NRT (level 1) daily datasets from sites in the ICOS network. Both R and Python implementations of the algorithm currently exist, but the methodology can theoretically be adapted to any programming language by any user with access to the ICOS Carbon Portal (ICOS CP) or other standardized GHG data. The methodology described in the following sections refers to the R implementation of the algorithm.
Materials and methods We conduct our analysis using daily aggregated CO 2 and CH 4 data from 10 European sites. At each, we approximate the background signal of both trace gases using the curvefitting method of  Thoning et al. (1989). We then define an "envelope" representing the range of normal or expected seasonal variability in the signal. The envelope is calculated from the smoothed cycle, which consists of a polynomial function fit to the data and a short-term residual filter. The upper and lower bounds of the envelope are defined by the second standard deviation (2σ ) of the smoothed cycle about the background signal and are adjusted to account for seasonal effects on signal stability. We then smooth the daily data using a LOESS function and evaluate the smoothed daily data in relation to the ±2σ envelope.
Observations We analyze continuous time series data from 10 stations, which are part of the Atmosphere network of the European ICOS research infrastructure  (ICOS RI, 2020a, b). ICOS provides high-precision, long-term and standardized observations of the carbon cycle such as GHG concentrations in the atmosphere and GHG exchanges between the atmosphere, ecosystems and oceans. All ICOS stations are rigorously assessed before being labeled, i. e. , before receiving approval to join the network  (Yver-Kwok et al. , 2021). Daily CO 2 and CH 4 records for the 10 stations are available through the ICOS CP (https://www. icos-cp. eu, last access: 5 July 2021) from varying start dates, depending on the date an individual station joined the ICOS network. Table  1  summarizes the stations selected for the analysis and gives the time range of analyzed data at each.
For four of the selected sites, we use only the complete level 2 (L2) records available through the ICOS CP. At the other sites, indicated in Table  1 , we use slightly longer historical records obtained from the ICOS ATC data products database. We first concatenate all available pre-L2 and L2 daily data (ICOS RI, 2020b) together with daily near-realtime (L1) data (ICOS RI, 2018), which are typically available for the past year or so. We then extract and aggregate the afternoon (12:00-17:00 CET) values for each site except for the two mountain sites (JFJ, PUY), where we extract and aggregate nighttime values only (20:00-05:00 CET). These time periods can be set by the user; however the general convention in our field is to select the afternoon mean for nonmountain sites since this generally represents optimal mixing conditions of boundary layer air (e.
g. ,  Morgan et al. , 2015; El Yazidi et al. , 2018). At the mountain sites, nighttime values are used to capture the properties of subsiding air from the free troposphere. The concatenated datasets are stored as R data frames for the ensuing analysis. The sites chosen are distributed throughout central and northern Europe and include a mix of rural and mountain sites -which are fairly remote and minimally affected by nearby pollution sources -and sites in closer proximity to large urban settlements or other sources of anthropogenic contamination. Figure  1  shows the locations of the 10 sites.
CCGCRV curve fitting CCGCRV  (Thoning et al. , 1989 ) is a curve fitting application for long-lived GHG time series maintained at the Carbon Cycle Greenhouse Gases (CCGG) group of the Global Monitoring Laboratory (GML) of the National Oceanic and Atmospheric Administration (NOAA, USA). The version of CCGCRV used here is applied as a stand-alone function in R and is available from the NOAA GML server at https://gml. https://doi. org/10. 5194/amt-14-6119-2021 Atmos. Meas. Tech. , 14, 6119-6135, 2021  The method is succinctly summarized by  Pickers and Manning (2015). Basically, a fit to a time series is first obtained using a linear least squares regression following the "LFIT" protocol, in which a linear function describing the data is determined from an x 2 minimization of the residuals  (Press et al.
The selection of a ±2σ envelope width represents a compromise between the desire to disregard smaller, site-specific signal excursions (which we term "localized fluctuations") to the extent possible while retaining the capacity to capture the true magnitudes of atypical regional events. Figure  3  shows the CCGCRV harmonic and the 30 and 90 d smoothed, detrended seasonal cycle of CO 2 at TRN for the period 2013-2020. The algorithm next smooths the raw data via a LOESS (locally estimated scatterplot smoothing) function  (Cleveland, 1979)  implemented via the R stats package (R Core  Team, 2019). This is done as a way of filtering the daily data and attenuating the influence of short-duration, high-intensity signal spikes when categorizing deviations from the background as anomalies vs. normal signal instabilities.
Sites with longer historical records will have a larger residual dataset to draw from, allowing for more precision when extracting the mean annual cycle and resultant ±2σ envelope, while sites with shorter records may produce less precise results. The potential drawbacks of this are twofold; (1) very slight anomalies might tend to be obscured at sites with a limited number (∼ 3-4 years) of relatively capricious measurements, and (2) lowamplitude localized fluctuations might be detected at sites where the full range of expected seasonal variability is underestimated by the ±2σ envelope, or the background curve is an imprecise fit to the true seasonal cycle. Anomaly patterns at sites with shorter records should thus be regarded with caution and cross-validated with patterns from other nearby sites if possible.
The robustness of the results is reliant on cross-validation of anomaly detection across multiple sites. However, we note that anomalies of sufficient size -e. g. , ∼ 3 ppm CO 2 greater than our ±2σ envelope boundary when using a 30 d bandwidth or ∼ 0. 5 ppm CO 2 when using a 90 d bandwidth -will usually register simultaneously at multiple sites within the same region and seem generally unlikely to stem from localized contamination. The method also has a low computational cost, and the process of including additional sites in an analysis is relatively straightforward. As new NRT (level 1) GHG data are uploaded to the ICOS CP, users have only to concatenate these to existing datasets and reinitiate the method beginning with the steps outlined in Sect. 2. 3. A fully automated Python implementation of the code is available online in which L2 and L1 data for a given station are extracted directly from the ICOS CP and concatenated to form a continuous multi-year time series for all dates up to the present.
Users also have the option to extract longer historical records than those available through the ICOS CP and preprocess these themselves before running the code. The current default implementation is to produce time series plots in the style of Figs. 6, 7 and 9. The ability to detect in NRT the occurrence of nonbackground signal events at multiple timescales is central to an improved understanding of GHG variability and regional carbon cycling processes at multiple timescales, and the ADA represents an important step toward this end. Eventually, our aim is to make results available online so that sta-tion managers and other end users can be alerted in NRT when anomalous signal events occur, i. e. , through the automated generation of data files and time series plots to display on the sites' respective panel board pages.
Acknowledgements. This work was made possible by the  Copernicus Atmosphere Monitoring Service (CAMS) , which is funded under regulation no. 377/2014  of the European Parliament and of the  Council of 3 April 2014 establishing the Copernicus program  ("the Copernicus Regulation") and operated by the  ECMWF  under an agreement with the  European Commission   dated 11 November 2014 ("ECMWF Agreement "). The authors also acknowledge the persons in charge of the 10 monitoring stations, which are part of the  ICOS Atmosphere network. ICOS Atmosphere  stations are supported through national consortia and funding agencies. JFJ observations are supported through  ICOS Switzerland , which is funded by the  Swiss National Science Foundation  (grants  20FI21_148992  and  20FI20_173691 ) and in-house contributions.
GAT, HPB and LIN observations are supported through  ICOS Germany  and funded by the  German Ministry of Transport and Digital Infrastructure (DWD)  and in-house contributions. OPE, PUY and TRN are supported through  ICOS France ,  ANDRA ,  CEA  and  CNRS. HTM and NOR are supported by  ICOS Sweden and Lund University. SMR is supported by  ICOS Finland  and the  University of Helsinki. We also wish to thank all members of the  ICOS Atmosphere Monitoring Station Assembly  for their contributions to the discussions surrounding anomalous signal detection techniques. Financial support. This research has been supported by the  Commissariat à l'énergie atomique et aux énergies alternatives (CEA)  (grant no. ECMWF/Copernicus/2016/CAMS_26 ). Review statement. This paper was edited by  Can Li  and reviewed by two anonymous referees.
Code availability. The ADA is intended to be open-source, and the R code is currently accessible via a GitHub repository page (https://github. com/hellonskis/ICOS_ATC_anomaly_detection) (DOI: https://doi. org/10. 5281/zenodo. 4639780, Resovsky, 2021a). Python code is available internally via the ICOS Jupyter hub at https://jupyter3. icos-cp. eu/ (last access: 16 April 2021) and is available upon request. An open-source version of the Jupyter code is also available online at https://doi. org/10. 5281/zenodo. 5166711 (Resovsky, 2021b). Data availability. The ADA is developed at the ICOS ATC (LSCE) in Gif-sur-Yvette, France. The code is intended to be applied to datasets consisting of validated (level 2) hourly values and NRT (level 1) measurements. Level 2 data are available online (https://doi.
Results For this study, we leveraged publicly-available data from sample NA12878 generated on Oxford Nanopore and Pacific Biosciences platforms. Each tool that was benchmarked, including GraphMap, minimap2, and NGMLR, produced the same alignment file each time. However, the different tools widely disagreed on which reads to leave unaligned, affecting the end genome coverage and the number and locations of discoverable breakpoints. Only minimap2 was computationally lightweight enough for use at scale. No alignment from one tool independently resolved all large structural variants (10,000-100,000 basepairs) present in the Database of Genome Variants (DGV) for sample NA12878. For variants larger than 1,000,000 basepairs, nanopore sequence aligned with minimap2 and NGMLR, and single-molecule real-time sequence aligned with NGMLR contained more breakpoints than are present in DGV.