dataesr/scientific-paragraphs-categorization huggingface dataset:
For SNP calling, the only bases used to support a SNP were those with quality score ≥20, and SNPs were only called if the 11 bp centered around the putative SNP had an average quality ≥15 and did not contain more than two SNPs and/or gaps. We combined all SNPs identified in each accession to obtain a unique set for the ten P. persica accessions and the P. ferganensis accession that included only those variants that were present in nucleotide positions informative in at least four accessions. Nucleotide diversity (π) was computed among the 11 P. persica accessions (including the dihaploid PLov2-2N accession) and the P. ferganensis accession with internally developed scripts using the formula from Nei and Li 56 where x i and x j are the respective frequencies of the ith and jth sequences, π ij is the number of nucleotide differences per nucleotide site between the ith and jth sequences, and n is the number of sequences in the sample.
r-project. org/web/packages/genetics/index. html). Decay of LD over distance was calculated according to the formula of Hill and Weir  59. Background LD was estimated extracting one random SNP from each pseudomolecule and computing r 2 between SNPs. The procedure was iterated 50,000 times, and summary statistics were calculated. Average values of r 2 were calculated at every given distance from 1 bp to 50,000 bp and aggregated in bins of size 100 bp. If the sample size of a bin was lower than 1,000, larger bins were created. Analyses of linkage disequilibrium were performed using R  60. Comparative analysis. Duplicated regions in the peach genome were analyzed using SynMap at the CoGe 61 website using BLASTZ  62  and DAGchainer  39  as the underlying software. Only syntenic groups including at least six gene pairs where the distance between two adjacent matches was lower than 200 kb were considered.
The orthologous regions among species were detected using Mercator  40  , which uses orthologous coding exons to define blocks of orthologous segments. The orthologous segments identified are those with one-to-one orthology relationships rather than any syntenic regions in which one region can have many syntenic regions. In finding orthologous segments, Mercator uses BLAT-similar anchor pairs in a modified k-way reciprocal besthit algorithm  63. In our analysis, two exons from each genome were selected as being similar if the BLAT score  64  of the pair was below 1 × 10 -10. The BLAT scores were computed using the translated protein sequences. The whole-genome sequence and annotation data of grape used in this analysis were downloaded from Genoscope (http://www. genoscope. cns. fr/). The whole-genome sequences of peach and grape were masked for repeats using the RepeatMasker program (see URLs), as well as the NMerge, WU-BLAST distribution and faSoftMask, Mercator 40 distribution utilities.
AcknowledGmenTs This work was jointly supported by the  Office of Science of the US Department of Energy  under contract number  DE-AC02-05CH11231  and the  Ministero delle Politiche Agricole Alimentari e Forestali-Italy  (MiPAAF, http://www. politicheagricole. it)through the project ' DRUPOMICS ' (grant  DM14999/7303/08 ). We would also like to thank the  US Department of Agriculture (USDA)  for their support of the peach genomics program through  USDA National Institute of Food and Agriculture (NIFA) Specialty Crop Research Initiative (SCRI)  grant  2010-2010-03255 , the  Robert and Louis Coker Chair for Plant Molecular Genetics  for their grant to  Clemson University , the Chilean government for supporting this work through  FDI G02P1001  ( Chilean Genome Initiative ),  Basal ProgramPB-16  and  FONDAP CRG15090007 , the Consolider- Ingenio 2010 Program  ( CSD2007-00036 ) from the  Spanish Ministry of Science and Innovation , the  French National Research Agency (ANR)  for supporting this work through  Chex-ABRIWG ANR/ INRA 22000552 ,  G.
Entomological Data Collection and Mapping We conducted our study in Paucarpata, the largest district in the city of Arequipa, Peru. The Ministry of Health of Arequipa applied insecticide to 13,917 households in Paucarpata between November 2006 and April 2009. During the insecticide application campaign, household-level data on the presence or absence of T. infestans and relevant risk factors for vector infestation were collected. Risk factors included a description of construction materials in each house and the presence of guinea pigs, dogs, and other domestic animals. We mapped the position of all households and the delimitation of city blocks in the district comparing satellite imagery in Google Earth TM  [52]  to field maps drawn by the personnel of the Ministry of Health. Households were then snapped to their city block according to their respective coordinates.
The latent infestation is then imperfectly revealed by the inspection: an infested household is observed as infested by an inspector k with a probability q k : the sensitivity of the inspector. v i ~X k C ik c k zE i When infestation data are not available (non-inspected houses), the sensitivity is set to 0 (see Section 4 in Text S1 for more details on the implementation and Section 2 in Text S2 for an analysis of the sensitivity of the results to this modeling choice). Fitting and validation. We fit the Gaussian Field Latent Class model on a fraction of the map consisting of all of the households inspected between September and December 2007 (Fig. 2 ). We used the remaining households as a validation dataset. For all priors, we use flat or weakly informative priors  [57]. Further We used the validation dataset to determine how well our parameterized model predicted the presence of vectors in unobserved households.
To do so, we randomly selected 5% of the houses in the validation set and removed them. We set the sensitivity of inspectors and the spatial parameters to their estimated means, remove the cofactors from the model and refit the spatial component, predicting the observation of infestation in the withheld households. We repeated the process 20 times, without replacement, so that all houses had been selected for prediction exactly once. We then evaluated the predictions using the McFadden index  [58]. As a second check, we verified that the Gaussian Field Latent Class model properly reproduced the global autocorrelation of the observed infestation by generating 1,000 vector infestation maps across the validation dataset and repeating the Moran's I analysis on each. All analyses were performed in R  [59] ; the code is available in Data S1 and updated versions are available at https://github.
First our Gaussian Field Latent Class model explicitly accounts for the main known cofactors of infestation by Chagas vectors as identified previously  [27, 52, 78, 79]. Second, taking into account these known cofactors has a limited influence on the estimated influence of streets (Section 3 in Text S2). We were only able to obtain binary data on the presence or absence of vectors; data on vector densities could provide more information with which to assess the effect of streets. While our analysis is tailored to binary observations, it could be extended to consider discrete measurements. Our Gaussian Field Latent Class model can be applied to a wide variety of datasets without adaptation of the priors; however, care should be taken to correctly choose the order of magnitude when assigning a prior on the shape factor of the spatial autocorrelation kernel.
Supporting Information Data S1 Annotated code in R with data. Two datasets are given. One corresponds to the original, de-identified data (realData. R) where spatial relationships are given through the distance matrix between points. The second dataset (JitteredDa-taPaucarpata. csv) corresponds to a generated dataset having the same spatial characteristics as the original dataset. The given examples with this second dataset (example_structuredMI. R and example_fit_GMRF. R) provide a model for application of these methods to new datasets. Please see the README file for details. Updated code can be accessed through github: https://github. com/cbarbu/spatcontrol. (ZIP) Table  S1  Detailed parameters estimates. Estimates for each spatial kernel of all fitted parameters with their 95% credible intervals.
Acknowledgments We thank  Dr. Tony Smith ,  Dr. Sebastien Goubie  `re,  Dr. Vitaliano A. Cama ,  Ian Spalding ,  Dr. Chris Paciorek  and  Katelyn Levy  for insightful comments;  Katty Borrini-Mayori ,  Danitza Pamo ,  Giovanna Moscoso ,  Lina Mollesaca ,  Maria Luz Hancco ,  Manuel Burgos ,  Renzo Salazar , and the laboratory staff at  UPCH-LID. We gratefully acknowledge the work of the following organizations that have organized and conducted the Chagas disease control program in Arequipa:  Ministerio de Salud del Peru (MINSA) , the  Direccion General de Salud de las Personas (DGSP) , the  Estrategia Sanitaria Nacional de Prevencion y Control de Enfermedades Metaxenicas y Otras Transmitidas por Vectores (ESNPCEMOTVS) , the  Direccion General de Salud Ambiental (DIGESA) , the  Gobierno Regional de Arequipa , the  Gerencia Regional de Salud de Arequipa (GRSA) , the  Pan American Health Organization (PAHO/OPS)  and the  Canadian International Development Agency (CIDA).
According to databases, TMEM165 presents a cytoplasmic loop of 52 amino acids organized in a coiled coil domain, two EXGDK/R motifs and a putative lysosomal-targeting sequence YNRL (belonging to the classical YXXØ lysosomaltargeting signal) (Fig. 1 ). Lysosome-targeted membranous proteins indeed possess cytosolic tyrosine-based sorting signals, such as NPXY or YXXØ, and di-leucine motifs (DXXLL or  [.
When mutated, TMEM165 leads to type II CDG, a glycosylation disorder. It is reasonable to think that the double localization of TMEM165 suggests that this protein would have a function required both in Golgi apparatus and in lysosomes, organelles involved in glycoproteins synthesis and catabolism, respectively. Concerning the function of this protein, the ProtFun software assigns to TMEM165 a role of transporter. Moreover, the enrichment in acidic residues in E  108  LGDK and E 248 WGDR motifs, at the end of the first and the fourth transmembrane domains oriented in antiparallel manner, could play a role in putative cation recognition signals. As CDG linked to homeostasis disruption has previously been identified  (33) , we suppose that the glycosylation defect originates from a homeostasis dysfunction in Golgi apparatus and/ or lysosomes.
The membranes were then incubated with the peroxidase-conjugated secondary antibody (P0448, Dako; used at a dilution of 1:10 000) in blocking buffer for 1 h at room temperature and afterward washed two times for 5 min in TBS-T. The signal was detected using a electrochemiluminescence reagent (ECL, PerkinElmer) on imaging film (GE Healthcare). Quantitative analysis of wt and mutants TMEM165 transiently expressed in HeLa cells was performed using the Quantity One software on a GS800 Calibrated Imaging Densitometer (Bio-Rad Laboratories, UK) and normalized to the actin intensity.
FUNDING The work was supported by a grant from the  Agence Nationale de la Recherche  ( ANR-JC-SWEET-CDG ) to F. F. , by grants  G. 0553. 08  and G. 0505. 12 of the  FWO (Fonds voor Wetenschappelijk Onderzoek ,  Vlaanderen, Belgium)  to G. M. and by grant  ERARE 11-135  ( EURO-CDG ) of the  ERA-Net for Research Programmes on Rare Diseases Joint Transnational Call   2011  to F. F. and G. M.
2 ( http://www. broadinstitute. org/). The HapMap database provides information on millions of SNPs that determine the variation among human beings. This database provides the frequency for each SNP located in the noncoding region of the genome. Based on the above criteria, we examined HapMapdesignated-informative SNPs in ARHG, CHRNA10, and CD81 genes. 4  summarizes the results of association analysis of alloimmunization with 27 SNPs, all located in the noncoding regions of appropriate genes. Overall, allelic distribution was similar to that reported in the general population in Sub-Saharan region (HapMap; NCBI). For some patients, we were unable to establish a genotype despite repeated attempts. These patients may have previously undescribed/novel alleles or additional polymorphisms in the primer-binding regions.
Discussion Blood and blood product transfusion most frequently either provoke no obvious immune reaction or induce immune suppression. The latter, known as transfusion-related immuno-   [41] , (http://www. ncbi. nih. gov/snp/). The Single Nucleotide Polymorphism database (dbSNP) is a public domain archive for a broad collection of simple genetic polymorphisms. dbSNP reports many cases of SNPs genotyped by HapMap and other projects which provide additional genotype and allele frequency information. * * NCBI database describes two additional alleles, A/G with very low frequencies (<0. 5%) for rs708564. This might be due to a mutational mechanism that leads to the simultaneous creation of two new base pairs at the same site which is beyond the scope of this study  [42]. Our results, however, are based on the two reference SNPs alleles (C/T) of rs708564.
Criteria related to the SNPs selection: (a) known association of a polymorphism with an immune phenomenon; (b) SNP frequency difference (HapMap database) between Yoruba (YRI) in Ibadan, Nigeria, and CEPH (Utah residents with ancestry from northern and western Europe: CEU) [44]; (c) characterization of SNPs by the HapMap database as informative for the linkage disequilibrium in haplotype analysis using the software.
) Criteria related to the SNPs selection: (a) known association of a polymorphism with an immune phenomenon; (b) SNP frequency difference (HapMap database) between Yoruba (YRI) in Ibadan, Nigeria, and CEPH (Utah residents with ancestry from northern and western Europe: CEU) [44]; (c) characterization of SNPs by the HapMap database as informative for the linkage disequilibrium in haplotype analysis using the software.
851861 43/33/23 (25) rs2237863 CD81 CC/CT/TT 3/22/5 15/10/6 0. 001518 ## 15/47/38 (26) rs11022565 CD81 GG/GT/TT 17/14/4 21/7/5 0. 280369 61/32/7 (27) rs11022567 CD81 AA/AG/GG 5/14/16 5/7/25 0. 120284 7/33/60 * Patient numbers are displayed in the order shown in the column labeled genotypes. # Relative distribution of genotypes in Sub-Saharan population, as reported by the HapMap. This research utilizes the NCBI SNP database.
1. Goswami U, Sarangi S, Gupta S, Babbar S. Comparative evaluation of two doses of tranexamic acid used prophylactically in anemic parturients for lower segment cesarean section: a double-blind randomized case control prospective trial. Saudi J anaesth 2013;7:427-31. 2. McCormack Pl. tranexamic acid: a review of its use in the treatment of hyperfibrinolysis. Drugs 2012;72:585-617. 3. Henry da, Carless Pa, Moxey aJ, o'Connell d, Stokes BJ, McClelland B, et al. Anti-fibrinolytic use for minimising perioperative allogeneic blood transfusion. Cochrane database Syst rev. 2007;17:Cd001886. 4. CraSH-2 trial collaborators, Shakur H, roberts i, Bautista r, Caballero J, Coats t, et al. Effects of tranexamic acid on death, vascular occlusive events, and blood transfusion in trauma patients with significant haemorrhage (CRASH-2MC, Foreman KJ, Naghavi M, ahn SY, Wang M, Makela SM, et al.
Finally, the CA04-WT virus, which is an H1N1 isolate from the 2009 pandemic season, induces lower pathogenicity in mice relative to VN1203  [5, 22]. The extensive systematic analysis of transcriptomic and proteomic pulmonary responses to wild-type and mutant viruses we report here provides an unprecedented opportunity to assess the effect of specific virus attenuating mutations on global host responses in vivo, as well as the opportunity to directly examine the effects of dosage on the elicited host response. Here, we sought to specifically address how the kinetics of the host responses to the different viral infections differs, and how this kinetics is related to the outcome of infection. To address this, we used a systems biology approach to analyze this dataset, and we developed a new geometrical representation method and two criteriathe magnitude and velocity coefficientsto visualize and quantify the kinetics of the host response to the different viral infections.
Assembly of phenotypic variables and lung transcriptomic and proteomic profiles from mice infected with low and high pathogenicity influenza viruses We collected 230 transcriptomic and 198 proteomic profiles from the lungs of 20-week old C57BL/6 mice infected with CA04-WT, VN1203-WT, VN1203-HAavir, VN1203-PB2627E, VN1203-NS1trunc, or VN1203-PB1F2del. Mice were infected with a range of inoculation dosages (from 10 2 to 10 6 Plaque Forming Unit -PFU) and lungs were harvested at 1, 2, 4 and 7 days post-infection (dpi) to measure gene expression using oligonucleotide arrays and obtain proteomic profiles by liquid chromatography-mass spectrometry (LC-MS). Time-matched lung samples from mock-infected mice were collected and a total number of 300 transcriptomic profiles and 266 proteomic profiles are represented in our dataset.
Throughout this article, we use the following definitions to describe the different conditions: (i) biological condition refers to all samples infected by the same virus, at the same inoculation dose, and from the same dpi; (ii) dosage condition refers to samples infected by the same virus and at the same inoculation dose; (iii) strain condition refers to all samples infected by the same virus. Based on these criteria, the dataset is divided into 51 transcriptomic biological conditions and 42 proteomic biological conditions, representing 13 transcriptomic and 11 proteomic dosage conditions, and 6 strain conditions. Additional file 1: Figure  S1  provides a schematic representation of the collected infected 'omics profiles across the time course. The different viruses can be ranked based on their pathogenicity as determined by Median Lethal Dose (MLD) values in 6-week-old BALB/c mice (Figure  1A ).
The VN1203-WT (MLD < 1 PFU) and the VN1203-PB1F2del (MLD = 3. 2 PFU) viruses were associated with the highest level of pathogenicity. The CA04-WT (MLD = 630,957 PFU; previously determined  [22] ) and the VN1203-HAavir (MLD = 316,228 PFU) viruses were associated with the lowest level of pathogenicity. The VN1203-NS1trunc (MLD = 631 PFU) and the VN1203-PB2627E (MLD = 6,310 PFU) viruses showed an intermediate level of pathogenicity. Morbidity associated with infection, determined by mouse body weight loss (Figure  1B ), viral replication quantified by viral titration (Figure  1C ), viral messenger RNA (Figure  1D ), and viral genomic RNA (Figure  1E ) measured by quantitative RT-PCR were used to characterize the viruses in our study. These data were collected in the same experiment in which mouse lung tissues were collected for global transcriptome and proteome profiling.
Altogether, these results demonstrate that it is mainly the magnitude of expression changes within the same transcripts or proteins, rather than the induction of different gene sets, that differentiate the host response to CA04-WT, VN1203-WT, and H5N1 VN1203 mutants. Thus, isogenic viruses and different inoculation dosages likely trigger the same differentially regulated genes, but with different degrees of magnitude at both the transcriptomic and proteomic levels. This analysis was performed on the transcriptomic and proteomic levels independently, and in parallel. Though we noted this magnitude effect was stronger in the proteomic dataset compared to the transcriptomic dataset, we found the same conclusions in each separate analysis.
(C) Heatmap of the transcriptomic expression values, ratioed to mock-infected samples, for each infected sample. The heatmaps have been restricted to the lists of transcripts found as specific for each viral strain and the different subsets of transcripts specific are indicated. For each set of specific transcripts, hierarchical clustering have been performed and represented using dendrograms. Biological samples have been ordered by strain conditions, sorted by inoculation concentrations and then by increasing days post-infection. (D) Heatmaps showing the statistical over-representation of the canonical pathways based on the lists of transcripts found as differentially expressed (compared to the mock-infected conditions) for each of the 51 biological conditions. This heatmap has been restricted to only display the top canonical pathways over-represented across all the dataset.
Previous work by Chang and coworkers, using a compendium of about 200 publicly available transcriptomic profiles of mouse lungs, showed differences in pathogenicity among respiratory viruses were explained in part by the changes in magnitude of gene expression  [37]. However, due to constraints of the meta-analysis, time was not taken into consideration. Moreover, the different microarray platforms and mouse genetic backgrounds used in the different datasets introduced noise into the analysis. Here, we have expanded upon the findings by Chang et al. through a kinetic analysis that incorporates the dimension of time, uses a single mouse genetic background and isogenic respiratory viruses that differ based on mutations to known pathogenicity determinants. Transcriptomic and proteomic samples were collected in a systematic manner to generate a comprehensive dataset that provides a powerful resource for modeling pathogen-host interactions.
The large sample number allows to infer co-expression and co-regulation networks for identification of unknown associations and dynamic interactions between biological components. Moreover, the extensive collection of sampled time-points allows to model causality of the biological system for discovery of novel biological events. In addition, a large panel of machine learning and data mining algorithms can be used, trained and tested based on this assembled dataset. While our study focuses on acute responses to H5N1 virus, other transcriptomic datasets, such as the one reported by Pommerenke and coworkers, encompass acute and adaptive host responses to influenza virus  [38]. It will be important to consider changes during adaptive host responses to fully appreciate the impact of magnitude and velocity kinetic effects on the outcome of viral infection.
With more traditional methods (i. e. gene modules, gene heatmaps or PCA representations in gene space), this kind of representation is not systematic and could produce large unintelligible representations. The MDS Reference Maps and Projections representations that we developed and used in the context of this study were particularly useful to drive our analysis. By using the VN1203 wild-type virus -omics profiles as a reference representation, we have been able to observe the behavior of the host response to the other variants. Traditional visualization representation methods do not naturally allow fixed positions of a subset of highdimensional objects. In the context of the development of novel visualization methods  [40] [41] [42] , we showed the relevance of this approach for the analysis of large datasets.
RNA isolation and microarray processing Lung tissues were directly submerged in RNA-Later stabilization solution (Life Technologies) following dissection, placed at 4°C overnight, followed by freezing at -80°C. Lung tissues were thawed, transferred to 1 ml TRIzol (Life Technologies) and homogenized using a TissueLyser or MagnaLyser. RNA was isolated using QiagenRNeasy Mini columns and the manufacturer's recommended protocol (Qiagen Inc. , Valencia, CA). Fluorescently labeled probes were generated from each RNA sample using Agilent one-color Low Input Quick Amp Labeling Kit (Agilent Technologies). Individual cRNA samples were hybridized to oligonucleotide microarrays for gene expression profiling using Whole Mouse Genome Microarray Kit (Agilent Technologies). All the microarray experiments passed the quality control criteria of the Agilent Feature Extraction Software.