dataesr/scientific-paragraphs-categorization huggingface dataset:
Model Building, Refinement and Stereochemistry Analysis of Proteins Predicted With B-cell Epitopes The tertiary structure of the selected proteins which possess B-cell epitopes was built using RaptorX which is a web portal for protein structure and function prediction  (41). RaptorX uses a threading-based modeling approach for model building. RaptorX server predicts three-dimensional structures for protein sequences without close homologs in the Protein Data Bank (PDB). RaptorX server can predict secondary and tertiary structures, contacts solvent accessibility, disordered regions and binding sites. It can also assign some confidence scores to indicate the quality of a predicted 3D model such as p-value for the relative global quality, GDT (global distance test) and uGDT (unnormalized GDT) for the absolute global quality, and modeling error at each residue.
Prediction of T Cell Epitope(s) and Their Specific MHC-I Restriction Element(s) The prediction of cytotoxic T-cell (CTL) epitopes was done using NetCTL 1. 2 server  (42). The threshold was set to 0. 98 for high sensitivity and accuracy. All antigenic proteins were analyzed for the presence of CTL epitopes. The CTL epitopes identified from the previous step were further screened for bovine MHC-I restriction element identification using HLArestrictor 1. 1  (43). All 77 bovine MHC-I alleles present in the database were analyzed for their ability to bind to identified CTL epitopes and their binding affinity for respective MHC-I allele was also calculated. The threshold for strong binder (IC 50 ) was set to 50 and percentile rank was set to 0. 5. The threshold for weak binder (IC 50 ) was set to 500 and percentile rank was set to 2.
Model Building, Refinement, and Stereochemistry Analysis of Bovine MHC-I The sequences of bovine MHC-I alleles, namely BoLA-3 * 05101, BoLA-1 * 00901, and BoLA-1 * 00902 were downloaded from the Immuno Polymorphism Database (IPD)  (44). Since crystal structures for these proteins were not present, a reliable model was built using homology modeling. The best template for the homology modeling was obtained through protein BLAST (pBLAST) against Protein Data Bank (PDB) and the three-dimensional model of BoLA-MHC proteins were built using modeler 9. 19  (45). The generated model was then refined by ModRefiner  (46)  and stereochemistry analysis was done using PROCHECK, a protein parameters analysis tool  (47).
Structure Characterization of the Multi-Epitope Vaccine Candidate and Bovine TLR4 A template-based tertiary structure prediction of the multiepitope vaccine candidate was carried out by RaptorX server  (41). The further refinement of modeled multi-epitope vaccine candidate was done using ModRefiner server as described previously  (46)  and stereochemistry analysis was done using PROCHECK  (47). The sequence of bovine TLR4 was downloaded from UniProt. Since crystal structure for this protein was not present, a reliable model was built using homology modeling. The best template for homology modeling was obtained through BLASTp against PDB database. A three-dimensional model of bovine TLR4 protein was built on modeler 9. 19 using full-length human TLR4 as a template. The generated model was then refined by using ModRefiner and stereochemistry analysis was done using PROCHECK.
Molecular Dynamics Simulation of Bovine TLR4 Receptor With Multi-Epitope Vaccine Candidate To determine the stability of a protein-ligand complex molecular dynamics (MD) simulation study of the complex is an important tool. Molecular dynamics simulation was carried out for the docked complex of the TLR4 and multi-epitope vaccine protein for 100 ns with a time interval of 2fs using Maestreo software. The simulation was done with Single Point Charge (SPC) water as a solvent model and 0. 15 M NaCl buffer. The boundary conditions were set to orthorhombic (a=b=c=10 Angstrom; α = β = γ = 90 • ). The isothermal-isobaric pressure (NPT) ensemble were set at 300 K (temperature) and 1 atm pressure. Nose-Hoover Thermostat method was used to maintain the temperature constant. To examine the standard deviation and fluctuation of the backbone of the protein, the root mean square deviation (RMSD) and root mean square fluctuation (RMSF) analysis were performed.
Identification of Plasma Membrane and GPI Anchored Proteins of Theileria Parasite A systematic workflow based on the immuno-informatics approach was designed to identify the potential vaccine candidates which would elicit an effective antibody response and to design a multi-epitope based vaccine candidate which would generate effective cell-mediated immune response against Theileria parasites (Figure  1A ). The first step in this workflow was to screen for T. annulata proteins having the potential to be localized on the cell surface. The complete transcriptome/proteome of T. annulata comprising total 3,783 protein sequences was downloaded from NCBI database for this purpose (Figure  1B ). The paralogous sequences with more than 90% similarity were identified using CD-HIT server from all the protein sequences.
Twenty-eight protein sequences were removed as paralogs while remaining 3755 protein sequences were selected for further analysis. The selected 3755 protein sequences were submitted to the CELLO sub-cellular localization server to screen for the proteins predicted to be localized on the plasma membrane. The output of this software is in the form of different numerical values as confidence scores for predicting the probability of localization of the proteins in the cell. A total of 711 proteins obtained with the confidence score greater than 2 were selected for further analysis (Figure  1B ). Furthermore, the criteria chosen for identification of proteins predicted to be localized on the cell surface was that it should have a signal peptide along with a GPI anchor. All the 3755 protein sequences were screened for the presence of a signal peptide sequence and a GPI-anchor.
Homology Modeling and Model Refinement of Bovine TLR4 and Multi-Epitope Vaccine Candidate The three dimensional model of the multi-epitope vaccine candidate was generated using the RaptorX server as no template was found in the PDB database. The RaptorX server predicted only a single domain for this multi-epitope vaccine candidate. The best template for the multi-epitope vaccine candidate was found to be 4AV3'A' with its p-value 6. 61e -05 (Figure  4A ). The Ramachandran plot of this protein revealed that 89. 1% amino acids were located in the core region, 8. 3% amino acids were located in the allowed region, 1. 6% amino acids were located in the generously allowed region and none was located in the disallowed region (Figure  4C ). The predicted secondary structure of the multi-epitope vaccine candidate was found to possess only helices (Figure  S3A ).
As the crystal structure of bovine TLR4 was not available, the tertiary structure of bovine TLR4 was modeled using 4G8A (human TLR4) as a template by Modeler 9. 19 software (Figure  5A ). Both the query coverage and identity were found to be 72%. The structure was then refined using ModRefiner and validated with PROCHECK analysis. The Ramachandran plot of the modeled bovine TLR4 revealed that 79. 6% amino acids were located in the core region, 17. 4% amino acids were located in the allowed region, 1. 8% amino acids were located in the generously allowed region and only 1. 2% amino acids were located in the disallowed region.
Similar to the CTL epitopes, all the predicted B-cell epitopes were also analyzed for their nonhomology to the bovine and it was found that none of these B-cell epitopes were present in the bovine proteome database. These results suggest that this potential multi-epitope vaccine protein is non-homologous to bovine and does not possess any predicted B-cell and CTL epitopes from bovine. Hence, the putative multiepitope vaccine candidate would not generate an autoimmunity or tolerance in bovine.
DISCUSSION Various factors contributing to the failure of any antigen as a vaccine candidate under development stages are: (a) antigen not being exposed to immune response (b) poor antigenic/immunogenicity response of antigen and, allergenicity to the host. Recently, with the advancement of in silico analysis it has been possible to identify molecule(s) which would likely possess maximum qualities/properties to qualify as a good antigen. The primary aim of this study was to identify the candidate gene(s) having the potential to induce an immune response against macroschizont stage of T. annulata. We utilized a systematic immuno-informatics approach in this study to predict potential vaccine candidate(s) in Theileria parasites using the proteome databases of T. annulata and T. parva, to design a multi-epitope based vaccine.
One important criteria for selecting a potential antigen for vaccine development is that it should be located on the surface of the cell so that it is more accessible for both the humoral and the cellular immune system. The proteins are translocated to the cell surface using a signal sequence for plasma membrane translocation or could be anchored to the Glycosylphosphatidylinositol (GPI) moieties. These proteins which are exposed to the extracellular surface are easily recognized and are likely to elicit an immune response when used as a target antigen for vaccine development. Proteins from the Theileria proteome database were thus examined for their translocation to plasma membrane using CELLO software. CELLO is based on support vector machine (SVM) system to predict the probability distribution for possible localization of the protein which is more reliable than the other sub-cellular localization prediction tool, PSORTb.
Furthermore, PSORTb program is trained only for the bacterial proteome  (56). We selected a confidence score of 2 or more for the sub-cellular localization prediction based on our observation that in many cases the values lesser than 2 resulted in predicting two different sub-cellular localizations for the same protein. A total of 711 proteins were predicted to be localized at the cell surface using the CELLO prediction server. To take into account the proteins that may also be present on the cell surface using GPI moieties, we first looked for the proteins having signal peptide sequences using the SignalP 4. 1 server. Few research groups have also analyzed the proteome of T. annulata and T. parva for the identification of proteins containing signal peptide sequence and GPI anchor using the older version of SignalP  (23, (57) (58) (59).
First, we trimmed residual removed adapters and merged read pairs according to  29. BWA software package version 0. 7. 8  30  was used to map merged reads as single-end reads against the revised Cambridge Reference Sequence (rCRS)  31, 32  (GenBank: NC_012920). The ratio of reads mapping to Y and X chromosomes (R y ) (with mapping quality greater than 30) was calculated to assign molecular sex to the individuals sequenced on the Illumina platform  33. FASTX-Toolkit (http://hannonlab. cshl. edu/fastx_toolkit/) was used to demultiplex sequences generated by the PGM Ion Torrent. Cutadapt v. 1. 8. 1  34  was then used to remove long (-M 110), short (-m 35), and lowquality sequences (-q 20). The filtered reads were analyzed with FastQC v 0. 11. 3  35  using the options described previously  36.
Statistical analysis. For comparative studies we used ancient mtDNA data obtained from the literature, the European Nucleotide Archive (www. ebi. ac. uk/ena) and NCBI GenBank (www. ncbi. nlm. nih. gov) web databases. All comparative populations used for principal component analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), pairwise genetic distances (F ST ), and AMOVA analysis are described in detail in Supplementary Tables S2-S5. We divided the population associated with the Corded Ware culture into a western group encompassing comparative German samples, and an eastern group comprising individuals linked with the Corded Ware culture from this study. Similarly individuals associated with the Yamnaya horizon were divided into western and eastern groups according to their geographic localization.
Due to overlapping dating of the samples and their common origin, the western Yamnaya horizon group encompassed individuals associated with Yamnaya culture and Late Eneolithic from this study, and additional comparative samples from present day Ukraine and Bulgaria  24, 45, 46. Because of potential maternal kinship between two Catacomb culture-associated individuals from this study, only one of them was used in the PCA and in a comparative Catacomb group. The eastern Yamnaya horizon group consisted of Yamnaya samples from the Samara region in Russia  14, 17, 46. The map with archaeological sites from which the studied individuals originated, was generated using QGIS 2. 12. 2  47  (Fig. 1 ). PCA for frequencies of mtDNA hgs was calculated using Python 3. 5 and Scikit-learn v. 0. 18. 1 package  48.
We utilized Matplotlib 1. 5. 1 Python package  49  for plotting the PCA results and mtDNA hgs loadings. We have used a centroid-based clustering approach to examine the PCA results and search for logical clusters within our data. We applied the k-means method (as implemented in Scikit-learn v. 0. 18. 1 Python package)  48  to the first 5 principal components from the PCA analysis (for details see Supplementary Information Text). All k-means variants can be found in the Supplementary Material Text and Supplementary Fig. S27. To further explore the relatedness of populations using the mtDNA hg frequencies, we ran the t-Distributed Stochastic Neighbor Embedding (t-SNE) analysis  50  as implemented in Scikit-learn (18. 1) Python package. F ST values were computed in Arlequin 3. 5  51. In total, 18 populations were used in F ST and following AMOVA analyses, consisting only of ancient individuals with complete mtDNA genomes (Supplementary Table  S3 ), using Nei's average number of pairwise differences  52  and 10,000 permutations to estimate the p-values.
To visualize F ST values we employed multidimensional scaling (MDS) analysis with the use of Python Scikit-learn 0. 18. 1 package  48. We have used the traditional method of analysis of molecular variance (AMOVA)  53  to assess the population differentation using complete mt genomes. We ran AMOVA using the Arlequin 3. 5 software package (for details see Supplementary Information Text). Availability of data and material. Mitochondrial genome sequences were deposited in GenBank under accession numbers MH176332, MH176333, MH17635 -MH176355.
Results Ancient mitochondrial genomes. Out of the 45 analyzed samples, we successfully obtained 23 mtDNA genomes, belonging to individuals associated with the Corded Ware culture (N = 11), and with the Late Eneolithic (N = 3) and Bronze Age (N = 9) from western Pontic region (Table  1  and Supplementary Table  S1 ). Eleven of the mtDNA genomes were retrieved from the Illumina shotgun screening data with the depth-of-coverage (DoC) ranging between ca. 5. 4× to 64×. The remaining twelve mtDNA genomes were retrieved from the hybridization capture enrichment followed by PGM Ion Torrent sequencing, and yielded DoC ranging between 11× to 194×. Nucleotide misincorporation patterns assessed using MapDamage showed characteristic aDNA damage involving C-T and G-A transitions at the 5′ and 3′ ends of DNA fragments, respectively (Supplementary Fig.
S26 ). Schmutzi estimations conducted for each individual showed low levels of contaminations (1-3%) (Supplementary Table  S1 ). Additionally, we found no contamination in the extraction blanks and PCR negative controls. The mitochondrial DNA data are deposited in GenBank under accession numbers MH176332, MH176333, MH17635-MH176355. In general, the individuals associated with the Corded Ware culture and the Yamnaya horizon were assigned to mtDNA lineages common among modern-day west Eurasian groups (hgs H, I, J, T, U2, U4, U5, W, X). Individuals associated with the Eneolithic and Yamnaya cultures were assigned to hgs U2e1a1, U5a2b, H2a1 and U5a1i1, U4c1, W3a1, W3a1a, respectively (Table  1  and Supplementary Table  S1 ). Other Bronze Age individuals from the western Pontic region belonged to hg X4 (two individuals associated with Catacomb culture) and J2b1a, J1c2m, H1e (three individuals associated with Babyno culture).
Acknowledgements We are grateful to  Maanasa Raghavan  from the  Cambridge University , for helpful comments on the text. We also thank to  Sylwia Łukasik  from  Adam Mickiewicz University in Poznan , for help in collecting bone samples. The project was supported by the  National Programme for the Development of Humanities  [ NPRH 0108/NPH3/ H12/82/2014 ]. E. E. was supported by  ELIXIR CZ research infrastructure project  ( MEYS  Grant No:  LM2015047 ) including access to computing and storage facilities, and M. J. and H. M. were supported by  Knut and Alice Wallenberg Foundation.
In urban areas, dense atmospheric observational networks with high-quality data are still a challenge due to high costs for installation and maintenance over time. Citizen weather stations (CWS) could be one answer to that issue. Since more and more owners of CWS share their measurement data publicly, crowdsourcing, i. e. , the automated collection of large amounts of data from an undefined crowd of citizens, opens new pathways for atmospheric research. However, the most critical issue is found to be the quality of data from such networks. In this study, a statistically-based quality control (QC) is developed to identify suspicious air temperature (T) measurements from crowdsourced data sets. The newly developed QC exploits the combined knowledge of the dense network of CWS to statistically identify implausible measurements, independent of external reference data.
The evaluation of the QC is performed using data from Netatmo CWS in Toulouse, France, and Berlin, Germany, over a 1-year period (July 2016 to June 2017), comparing the quality-controlled data with data from two networks of reference stations. The new QC efficiently identifies erroneous data due to solar exposition and siting issues, which are common error sources of CWS. Estimation of T is improved when averaging data from a group of stations within a restricted area rather than relying on data of individual CWS. However, a positive deviation in CWS data compared to reference data is identified, particularly for daily minimum T. To illustrate the transferability of the newly developed QC and the applicability of CWS data, a mapping of T is performed over the city of Paris, France, where spatial density of CWS is especially high.
The benefit of crowdsourced data in atmospheric research lies in high spatial resolution and extended coverage in urban areas, potentially long-term measurements, and low cost that it provides compared to traditional networks. However, the most critical issue is found to be the quality of data from such networks  (Bell et al. , 2015; Chapman et al. , 2017; De Vos et al. , 2017; Meier et al. , 2017). As crowdsourced data are directly collected by citizen weather stations (CWS) owned by individuals of the crowd, there is no or very little external control to ensure the quality of the obtained data set and hence data quality assessment is a crucial step before any analysis can be carried out  (Chapman et al. , 2017; Meier et al. , 2017). Chapman et al. (2017) , e. g. , studied T in London, United Kingdom, using CWS data by removing measurements from their data set if the temporal average deviated more than three times the standard deviation of the average of all stations.
Meier et al. (2017)  accurately addressed the issue of quality assessment of crowdsourced CWS data, suggesting a detailed approach to identify suspicious crowdsourced T and filter these from the data set. Their method relies mostly on data from a quality-controlled reference network that allows them to exclude the suspicious CWS measurements. Main error sources were not due to the quality of the sensor itself but mostly related to the siting of the CWS  (Meier et al. , 2017) , i. e. , (i) the CWS are not always set up outside, and (ii) some stations are influenced by solar radiation leading to radiative errors. Recently,  Hammerberg et al. (2018)  applied a modified version of this QC approach to quality-control CWS data in Vienna, Austria, also including a statistically-based QC step that relies on the distribution of T data.
While these QC procedures  (Meier et al. , 2017; Hammerberg et al. , 2018)  specifically address and identify common error sources in CWS data by using reference data, the methods can only be applied in other urban regions where high-quality reference data are available from multiple stations in different environments. This, of course, hinders the transferability due to the lack of detailed reference data in most cities. Therefore, in the current study, a new statistically-based QC approach is proposed for crowdsourced T data that is independent of such detailed reference networks. Crowdsourced T data from Netatmo CWS (https://www. netatmo. com/) are used in this study, as these stations are distributed worldwide with a high spatial density especially in European cities, and the data can easily and freely be accessed via an API  (Meier et al.
, 2017). The objectives of this study are to address the quality assessment of this data set by developing a new QC procedure based on a statistical analysis that (i) does not need a set of reference data, (ii) is robust and can be applied in different cities, and (iii) can easily be applied in future studies. The cities of Berlin, Germany, and Toulouse, France, are in this way investigated during a 1-year period (July 2016 to June 2017). First, the newly developed QC is evaluated and compared with the method developed by  Meier et al. (2017). Second, a detailed comparison between T observed by CWS and reference networks is performed for an assessment of the quality-controlled crowdsourced data sets. Third, to illustrate the transferability and a possible application of CWS, a mapping of T is performed over the city of Paris, France, using quality-controlled CWS data.
Study Areas and Period Two cities are investigated in this study for the development of the QC: Berlin, Germany (52. 52 • N, 13. 40 • E) and Toulouse, France (43. 60 • N, 1. 45 • E). They were chosen for two reasons: (i) their climatic and topographic similarities, i. e. , weak influence of mountains or seas, overall relatively flat topography, and a humid warm temperate climate (Cfb) according to Köppen's classification  (Kottek et al. , 2006) , (ii) the availability of CWS data with a high number of stations, and (iii) the availability of reference networks (more details in section Reference data sets). However, the two cities also exhibit morphologic differences, i. e. , size, population and thus density of their CWS networks. The main characteristics of the two cities are summarized in Table  1.
Reference Data Sets Quality-controlled reference measurements were used to evaluate the performance of the new QC. In Berlin, the reference data set consists of six stations maintained by the Chair of Climatology at Technische Universität Berlin (Urban Climate Observation Network-UCON;  Fenner et al. , 2014) , and four stations maintained by the German weather service (DWD) (Figure  1 , right and Table  A1  in Supplementary Material). Note that only stations set up in built-up local climate zone (LCZ) classes were considered, using the LCZ classification in  Fenner et al. (2017). UCON measurements are taken by Campbell Scientific CS215 T and relative humidity probes (specified accuracy ± 0. 4 K in range 5-40 • C) at 1-min resolution, fixed in white radiation shields that are actively ventilated during sunlit periods.