dataesr/scientific-paragraphs-categorization huggingface dataset:
Environmental data The environmental factors studied included hours of daylight, distance to water bodies, average daily precipitation, the normalized difference vegetation index (NDVI), and land surface temperature at daytime (LSTday). Data on hours of daylight and average daily precipitation were acquired from the China Meteorological Data Sharing Service System (http://cdc. cma. gov. cn/home. do). Inverse distance weighting (IDW) interpolation was applied to analyse the meteorological data available at 610 weather stations nationwide by ArcGIS 10. 4 (ESRI, Redlands, CA, USA), then selecting the data within the study area. NDVI and LSTday were downloaded from the Level 1 and Atmosphere Archive and Distribution System (http://ladsweb. nascom. nasa. gov) with all 8-day global 1-km products for LSTday and monthly global 1-km products for NDVI.
In this study, we investigated the variability in several pig genes (ACE2, ANPEP, DPP4 and TMPRSS2) that can serve as receptors or protease for priming the infection of coronaviruses. We also evaluated their relevance in conferring potential differences in susceptibility to coronavirus diseases, also considering a comparative analysis between the corresponding human genes and the information available in other species. Analysis of variability included a total of 22 European pig breeds and wild boars and two Asian pig populations using next generation sequencing data (NGS). This dataset covered a broad number of pig genetic resources raised in Europe  65, 66  in comparison with a few Asian populations. The obtained results could be useful (i) to establish a risk evaluation system in a "One Health" approach, including information on the diversity of pig populations, (ii) to define cross species evolutionary analyses of genes involved in coronavirus infections and (iii) to identify natural genetic variability within the Sus scrofa species that could help to design genetic improvement strategies to increase genetic resistance in commercial and autochthonous pig populations against emerging and re-emerging coronavirus diseases.
dbSNP" and "novel". The KS test measures the similarity of the two distributions in a quantitative way via the D-statistics (a metric ranging from 0 to 1). Lower values of D indicate more similar distributions. Different cut-off values, in the range 0-50 with steps of 1, were tested. The CRISP quality score (Q CRISP ) minimizing the D value was selected as the best score; • third step: to globally evaluate the quality of our dataset, the transition-to-transversion ratio (Ts/Tv) was used as quality indicator (1000 Genomes Project Consortium). Variant detection in the wild boar DNA pool was carried out with Samtools v. 1. 7  76  considering a RD min equal to 3. Polymorphisms were detected in four porcine candidate genes (ACE2, ANPEP, DPP4 and TMPRSS2) involved in coronavirus infections considering a region spanning 5 kbp upstream and 5 kbp downstream the corresponding gene coordinates as reported in Ensembl database (http://www.
ensem bl. org/). Information on the annotated features of these genes in the Sscrofa11. 1 genome version as retrieved in Ensembl database (release 100, April 2020) are reported in Table  1. Variants were annotated using the Variant Effect Predictor (VEP) v. 95. 0  77  , by predicting with SIFT v. 5. 2. 2  78  their impact to the protein function. Variants that affected the protein coding regions were manually checked. Pipelines were developed either in Python v. 2. 7. 12 or in R v. 3. 4. 4  79  ; the Kolmogorov-Smirnov test was carried out with the function "ks. test". SNP allele frequencies (AF) were estimated by counting the number of reads covering the SNP position. Mining sequence data from other whole genome resequencing datasets in public databases. As European and Asian pigs derives from independent domestication routes (e.
g. 80 ), for comparative analyses with information obtained from European pig breeds, sequence data of five Chinese Meishan pigs and two Asian wild boars were retrieved from the EMBL-EBI European Nucleotide Archive (ENA) repository (http://www. ebi. ac. uk/ena), project PRJEB9922. Reads were aligned with BWA-MEM and detection of variants was carried out with Samtools, considering a RD min equal to 3. Variants affecting the protein coding regions of the same four candidate genes (ACE2, ANPEP, DPP4 and TMPRSS2) were manually checked, were annotated using VEP, and their impact was predicted with SIFT v. 5. 2. 2. A summary of whole genome sequencing data statistics is reported in Supplementary Table  S2. Variants in porcine candidate genes retrieved from Ensembl database. Genome variants affecting the protein coding sequence (i.
e. missense, frameshift and stop gain/loss variants) and the related single amino acid polymorphisms (SAPs) of the ACE2, ANPEP, DPP4 and TMPRSS2 porcine genes were downloaded from Ensembl database (release 100, April 2020)  81  , as information annotated against the Sscrofa11. 1 reference genome version of Sus scrofa and derived from dbSNP. The impact on the protein function was predicted with SIFT v. 5. 2. 2.
Comparative analysis between pig and human ACE2, ANPEP, DPP4 and TMPRSS2 protein sequences. Sequence identity between the pig and human ACE2, ANPEP, DPP4 and TMPRSS2 proteins was obtained via sequence alignments carried out with Clustal Omega  82  as implemented in UniProt  83. Details about genes, transcripts and protein accessions numbers used in this analysis are reported in Table  1. The iden-Table  1. Candidate genes investigated in the present study. 1  Porcine chromosome, starting position, ending position, gene orientation. Coordinates are based on the Sscrofa11. 1 reference genome; 2 Ensembl gene identifier;  3  Ensembl canonical transcript identifier (it is defined as the longest CCDS translation with no stop codons);  4  UniProtKB accession number related to the Ensembl canonical transcript.
The number of residues of the protein is reported; 5 UniProtKB accession number. *Alternative reviewed entry (Swiss-Prot). tification of protein residues functionally relevant for coronavirus disease infections in humans (SARS, MERS and the novel COVID-19) was carried out through a survey of the literature that focused on human ACE2, ANPEP, DPP4 and TMPRSS2 proteins. Our attention was focused on all protein residues either interacting with coronavirus proteins or functional for the biological activity of the selected proteins, including active sites, substrate sites, ions binding sites, residues in interaction patches and glycosylation sites. These protein residues were selected according to 3D structural analyses and related literatures that identified key roles of these sites in the interaction with the virus spike proteins and the functions of the host protein in virus infections (see Supplementary material for details and the extensive references).
Then, we evaluated the impact of protein coding variants identified in pigs and derived by combining the different datasets explored in this study (DNA pools from European breeds and wild boars; Asian pig genomes; Ensembl database). Figure  3  reports the position of the identified and analysed protein coding variants located in the four encoded proteins. Pig vs human protein sequence comparisons. Overall sequence homology between the pig and human ACE2, ANPEP, DPP4 and TMPRSS2 proteins showed that the two species share 81. 7, 74. 7, 81. 0, 68. 5% identical residues, respectively. In these proteins, a total of 82 (ACE2), 19 (ANPEP), 24 (DPP4) and 30 (TMPRSS2) key residues are considered essential either for the virus-host interaction or for the functional activity (Supplementary Table  S4 ).
Protein coding variants deduced from whole genome resequencing datasets. A total of 25 variants affecting the protein sequence of the four candidate genes were identified by mining whole genome resequencing data obtained from the 22 European pig breeds and from the European wild boars (Table  2 ). Variants were located in all four investigated candidate genes: 11 were in the ACE2 gene (10 were then considered; see below), four in the ANPEP gene, two in the DPP4 gene and eight in the TMPRSS2 gene. Allele frequencies of these protein coding variants in the analysed pig breeds and wild boars are reported in Fig. 4  and Supplementary Table  S8. All these variants were reported in the European pig breeds and nine segregated in the European wild boars. Based on this information, European breeds and wild boars were represented in multidimensional scaling plots that showed some contrasting differences among breeds for the information derived by the four genes separately (Fig.
Two other novel frameshift mutations (13:g. 204881920_20488192insT and 13:g. 204881920_20488192insG) would completely change the peptidase coding region of the canonical gene transcript. However, considering an alternative transcript for this gene (transcript ENSSSCT00000026685. 3; Uni-ProtKB I3LBF8), these two variants would be annotated as splice donors (as they might change the 2 nd base pair region at the 5′-end of an intron). It is worth to mention that at this position, the reference allele was not found in any resequencing dataset in which, instead, all three genotypes insG/insG, insG/insT and insT/insT were called. Mining whole genome resequencing data retrieved from the Chinese Meishan breed and from Asian wild boars identified other four variants affecting protein sequences (DPP4: p.
I383V and p. S704L; ANPEP: p. V32A and p. E359D). Considering also the other variants described above for the European breeds and wild boars, a total of 15 and 14 variants affecting proteins were identified in the Chinese Meishan breed and in the Asian wild boars, respectively (Table  2  and Fig. 4 ). Putative functional effects of the porcine protein variants. For a comprehensive analysis of the effects of protein coding variants in the four analysed genes, the 29 variants affecting proteins and identified in the European pig breeds and wild boars and in the Asian pig populations (described above) were combined with information on polymorphic sites available in the Ensembl database for the same genes. The Ensembl database reported a total of 60 functional coding variants (10 in the ACE2 gene, 28 in the ANPEP gene, 9 in the DPP4 gene and 13 in the TMPRSS2 gene) that combined with the mentioned variants accounted for a total of 64 variants affecting the protein encoded by the four genes (11 in the ACE2 gene, 28 in the ANPEP gene, 10 in the DPP4 gene and 15 in the TMPRSS2 gene; Supplementary Table  S9 ).
Few of these variants might change the 3D structure or the function of the protein domain in which they are inserted and may potentially modify, at least in part, their role in the infection routes of the targeted coronaviruses in pigs. It is worth to mention that   S8. Information for the European breeds and wild boars is obtained from the sequenced DNA pools. Information for the Asian populations is obtained from whole genome sequencing data of individual animals and the right part of the figure reports the carrier status of the alternative allele. most of the DNA polymorphisms identified in the three genes are located in non-coding regions or do not affect the encoded proteins. It could be possible that some of these variants play regulatory roles but here we did not analyze the sequencing data for this purpose.
10mber of Meishan pigs carrying the variants;7Number of Asian wild boars carrying the variant; 8 dbSNP identification number;9Single Amino-acid Polymorphism. Protein coordinates refer to UniProtKB accession number listed in Table1;10SIFT prediction. LC means low confidence prediction; 11 SIFT prediction score. * ariants rs703692808 and rs713746699, both affecting residue S657, are in complete linkage disequilibrium resulting in the SAP p. S675K †. § The reference allele was not present in our sequencing. data.
Acknowledgements SB received a fellowship from the  Europe-FAANG COST Action. MB was supported by the  Ministry of Foreign Affairs of Italy. This work has received funding from the  University of Bologna  RFO 2016-2019 programmes, the Italian MIUR 2017  PigPhenomics  project, from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 634476  for the project with acronym TREASURE, from the  European Open Science Cloud (EOSC) Secretariat , project "Application of animal genomics and data mining to predict and monitor novel coronavirus potential infections (VirAnimalOne)", the  EGI call  for  COVID-19 research projects  ( AnGen1H  project) and from the  Por Fesr Emilia-Romagna   2014-2020  (actions 1. 1. 4and 1. 2. 2- Bando per sostenere progetti di ricerca ed innovazione per lo sviluppo di soluzioni finalizzate al contrasto dell ' epidemia da COVID-19-Project  LIVESTOCK-STOP-COVI ).
Data availability Sequence data generated and analysed in the current study from DNA pools are available in the EMBL-EBI European Nucleotide Archive (ENA) repository (http://www. ebi. ac. uk/ena), under the study accession PRJEB36830. From the same repository we retrieved sequence data of five Meishan pigs (samples: ERS804949, ERS804950, ERS804951, ERS804953 and ERS804955) and two Asian wild boars (samples: ERS804971 and ERS805009) deposited with the study accession PRJEB9922. The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.
10mber of Meishan pigs carrying the variants;7Number of Asian wild boars carrying the variant; 8 dbSNP identification number;9Single Amino-acid Polymorphism. Protein coordinates refer to UniProtKB accession number listed in Table1;10SIFT prediction. LC means low confidence prediction; 11 SIFT prediction score. * ariants rs703692808 and rs713746699, both affecting residue S657, are in complete linkage disequilibrium resulting in the SAP p. S675K †. § The reference allele was not present in our sequencing. data. Gene SSC 1 Position 2 Ref/Alt 3 AF Pigs(Europe) 4 AF WB(Europe) 5 Meishan 6 WB(Asia) 7 RefSNP 8 SAP 9 SIFT 10 SIFT-score 11 ACE2 X 12103359 G/A 0. 011 0. 000 0/5 0/2 rs713862336 P738L Deleterious-LC 0. 04 ACE2 X 12103425 C/T 0. 043 0. 000 0/5 0/2 rs323807708 R716H Tolerated-LC 0. 08 ACE2 X 12105547 T/C 0.
An extended study of different accessible conformations using accelerated molecular dynamics (aMD) and a classical force field is conducted. Selected snapshots from the most populated minima will be relaxed with ab initio quantum mechanics using a quantum mechanics/molecular mechanics molecular dynamics (QM/MM MD) approach to refine the chemical interactions on the interface in solution. Knowledge of the role that other possible conformations may play in stabilizing the b12-gp120 interface in solution will provide a deep insight into the most probable interactions of the complex structures. These insights will facilitate the design of novel HIV vaccines by emulating an activity similar to or better than that exhibited by the b12 antibody. ■ METHODS b12-gp120 Protein Complex Model. The 2. 3 Å resolution structure of the Fab region of the IgG1-b12 antibody complexed with the HIV CD4-binding domain of gp120 was taken from the protein data bank (pdb code 2NY7).
16 The missing loop regions of gp120, which are distant from the b12-gp120 heavy chain interface, were modeled using the MODELLER  18  web service integrated into UCSF chimera 1. 13. 1,  19, 20  picking the conformation with the lowest Z-DOPE energy (discrete optimized protein energy) score. The amino acid labeling is following the nomenclature of Zhou et al. 16  The CDR-H3 of b12 contains a 10 residue insertion, which in the Kabat and Wu numbering are designated as 100a, 100b,. , and 100j, respectively. 21 e b12-gp120 protein complex was placed in a triclinic box of dimensions 115. 3 Å × 120. 2 Å × 125. 7 Å; the system was neutralized by adding 14 Cl -ions and solvated with 46 056 TIP3P water molecules. Classical Molecular Dynamics Protocol. The AMBER 18 simulation package  22, 23  was chosen to perform all of the classical simulations.
To set up the simulation input files, the AmberTools Leap program was used. The simulation parameters used for protein components of the system were obtained from the ff14SB Amber force field,  24  whereas glycan parameters were taken from the GLYCAM06 force field. 25 ater molecules were modeled using the TIP3P force field  26  and the solvated free ions were described by Merz and coworkers. 27 he first step in the classical set of simulations was a minimization of 2000 cycles, followed by heating gradually from 0. 1 up to 298. 0 K using 25 000 time steps of 2 fs with NVT ensemble conditions. Next, an NPT ensemble along 0. 5 ns (2 fs of time step) at 1 atm and 298 K was applied to reach constant system density. Covalent bonds involving hydrogen atoms were constrained using the SHAKE algorithm,  28  and long-range electrostatic interactions were treated with particlemesh Ewald using a real-space cutoff of 10 Å.
All aMD productions were carried out with an Amber simulation package and the values of the (E, α; in kcal/mol) parameter couple used were 12 784. 52, 618. 4 and -415 123. 05, 45 023. 7 for the dihedral and system potential, respectively. The parameters' values were derived following the methodology proposed by Miao et al. 31  Similarly to the previous cMD simulations, Langevin dynamics  32  and its thermostat with a relaxation time of 1 ps -1 under an NVT ensemble were used. Clustering of aMD trajectories was performed using the DBSCAN  33  clustering algorithm as it is implemented in the cpptraj program of AMBER 18  23  to ensure the aMD convergence. The minimum number of points to define a cluster was set to 9 and the epsilon parameter was set to 1. 6. Moreover, the distance metric to be used in the clustering analysis was the root-mean-square deviation (RMSD) value of the whole antigen plus the three CDRs of the antibody.
The quantum region was mainly defined by means of a contacting residue analysis on the protein-protein interface and considering crystallographic structural data from previous studies, where the three heavychain complementarity-determining regions (CDRs) appear to show a higher degree of interaction with the antigen. 16 The final stage considered a quantum region containing 158 atoms and was made of 11 amino acid residues, as shown in Figure  2. All QM/MM MD simulations were conducted by means of the AMBER-PUPIL-NWChem tandem, where the PUPIL program  37, 38  links the classical engine (AMBER 18) with the quantum engine (NWChem  39  ) by calculating and managing all of the QM/MM coupling within the QM/MM MD approach. All starting conformations were subjected to the same protocol along 0. 6 ps of equilibration followed by 5 ps of production run under NVT dynamics.
The usable data were collected from a total production run of 60 ps of the QM/MM MD trajectory. An NVT ensemble at 298 K with a temperature regulation using a Langevin thermostat (collision frequency of 10 ps -1 ) and a time step of 1 fs was used. The remaining simulation parameters and force fields used for the QM/MM MD simulations were the same as those used for a classical MD protocol. The MM region was parameterized using the ffSB14 Amber force field,  24  while the QM region was modeled by means of density functional theory (DFT) using the M06-2X functional  40  in combination with a 6-31G basis set for all quantum atoms. It should be noticed that medium-range (i. e. , ≤5 Å) noncovalent interactions, such as conventional (e. g. , N-H•••O and H-O•••H) and nonconventional (e. g. , C-H••• O) hydrogen bonds, are better described by the M06-2X functional than usual DFT functionals.
Similar to what was observed experimentally in the crystal structure,  16  no representative contacts were obtained in the CDR domain of the IgG1 light chain. The Kabat numbering scheme to label the amino acids belonging to the CDRs of mAb IgG1-b12 is used, and these were obtained from the SabDab structural antibody database. 46, 47 The highest numbers of contacts involving the IgG1-b12 antibody are localized on the polar amino acids N31 (H1) and N100g (H3). Thus, persistent hydrogen bond formation across the interface involving these residues is expected. The 12 snapshots extracted from the global minimum and the two adjacent local minima of the PMF profile plotted against the RMSD variable (Figure  3a ) were used as starting points for ab initio QM/MM MD simulations. These conformations sample a short range of RMSD values (∼3.
Methods We did a systematic literature search and meta-analysis of five databases (PubMed, Embase, Global Health [CABI. org], Web of Science, and Global Index Medicus) to identify studies analysing the association between HIV infection and cervical cancer. We estimated the pooled risk of cervical cancer among women living with HIV across four continents (Africa, Asia, Europe, and North America). The risk ratio (RR) was combined with country-specific UNAIDS estimates of HIV prevalence and GLOBOCAN 2018 estimates of cervical cancer to calculate the proportion of women living with HIV among women with cervical cancer and population attributable fractions and agestandardised incidence rates (ASIRs) of HIV-attributable cervical cancer. Findings 24 studies met our inclusion criteria, which included 236 127 women living with HIV.
Identification and selection of relevant studies We systematically searched five databases (PubMed, Embase, Global Health [CABI. org], Web of Science, and Global Index Medicus) to identify studies analysing the association between HIV infection and cervical cancer. The detailed search strategy can be found in the appendix (pp 3-6). In short, we used a WHO validated search strategy for "HIV" and "AIDS" and combined these terms with "cervical cancer" and standard WHO epidemiological search terms (eg, "incidence" and "prevalence"). The search was done on April 29, 2019. We did not restrict our search by language or year of publication. We included published epidemiological studies (cohort, case-control, and registry linkage studies) reporting the risk of cervical cancer (International Classification of Diseases 10th revision [ICD-10] code C53) as either a risk ratio (RR), odds ratio (OR), hazard ratio (HR), incidence rate ratio (IRR), or standardised incidence ratio (SIR) among women living with HIV compared with either women without HIV infection or the general female reference population.
Data extraction and risk of bias assessment The published literature was screened for inclusion by two of us (DS and LFT) independently. These same reviewers extracted data from the included studies. Discrepancies were resolved by consensus between the two reviewers. In quantitative analyses, only one study per cohort was included to avoid double inclusion of patients. We selected the study with either the longest follow-up time or the largest sample size. We listed all studies that met inclusion criteria and studies used for the calculation of the pooled RR (appendix pp 7-18); for those excluded, we reported the main reason. The flowchart for study selection and the MOOSE checklist are in the appendix (pp 19-21). Risk of bias was assessed based on the Newcastle-Ottawa scale, a tool used to appraise observational studies.
We ran these analyses for all countries and integrated the results into regional and global estimates. We report the results by country, WHO region, UNAIDS region, and UN Africa subregion (appendix pp 49-60). World maps were created to display these estimates by country, divided into five levels, with the darkest shades representing the highest estimates. Statistical analyses were done in R (version 3. 6. 1) using the packages metafor (version 2. 4-0) and rworldmap (version 1. 3-6). The protocol of this systematic review was published on PROSPERO, number CRD42019139813. or writing of the report. DS and SD had full access to all data in the study. SD had final responsibility for the decision to submit for publication.