dataesr/scientific-paragraphs-categorization huggingface dataset:
HeLa GFP (control) Sho/Mito 58 ± 0. 3% HeLa TRAP1-GFP (Tet-induced) Sho/Mito 36 ± 0. 5% Table  1. Amount of Sho colocalizing with mitochondria. Quantification of immunofluorescence assays of Supplementary Fig. S6. Colocalization between Sho and Mitochondria was determined in at least 82 cells from three different experiments under control (HeLa GFP) or TRAP1 induced cells (HeLa TRAP1-GFP). The number of colocalizing red (Sho) and green pixels was normalized for the total number of pixels from an extracted region of interest in the confocal image. The results are expressed in percentage ± S. D; P < 0. 05. (22 kDa), reinforcing the data by which TRAP1 over-expression results in an improved ER translocation of Sho and consequently in its increased maturation. However, although TRAP1 over-expression seems to not affect the natural PK-resistance of the mature Sho isoform (Fgly), we cannot exclude that TRAP1 expression levels could impact on the folded/unfolded Sho isoforms ratio.
Acknowledgements The confocal microscopy was performed at confocal microscopy platform Dynamic Imaging (DIM) " CEINGE-Biotecnologie avanzate scarl ". We acknowledge  Elena Dobrikova  and  Matthias Gromeier  (at  Duke University Medical Center ) for the establishment of the HeLa Flp-In T-Rex cell line. This work has been supported in part by POR Campania FSE 2007-2013, Project  CREME , by Ministero per l′Università e la  Ricerca Scientifica ,  PRIN 2010-2011  to L. N. , and research grants to C. Z. from  European Commission   FP7 PRIORITY 222887, MI CARNOT ICSA/PMI , grants from the  region Ile-de-France  (IDF DIM-MALINF 2013), and  Equipe FRM (Fondation Recherche Medicale)   2014  ( DEQ20140329557 ).
We trained one ENM for each of the 151 GWA and 70 aGWA drought-associated SNPs to predict which allele, either the high or the low survival one, is more likely, given a set of environmental variables (all ENM 5CV accuracy >92%; Table  S3 -4, Supplementary Fig. 13 14 15 16 ). Consequently, from each model, we geographically mapped the potential distribution of the high survival allele using available environmental datasets (www. worldclim. org, ref. 17). Finally, concatenating the resulting 221 maps, we inferred the most likely individual genotype at each location. At present, individuals from both northern and southern edges of the species' Eurasian and N. African range are predicted to harbor more drought-survival alleles than those located in between (Fig. 3C , Supplementary Fig. 15 16 , with the quadratic term in a regression of allele count on latitude being positive at p=10 -3 ), corroborating our previous observations.
3E ; with the quadratic term in a regression of the allele count difference on latitude being negative at p<10 -10 ). Central European populations may therefore be under threat of lagging adaptation by the end of the 21 st century. In the end, for a population to persist, not only must drought-survival alleles be present locally, but they also need to increase in frequency51. The chance of this occurring will depend on current local allele frequencies and the strength of natural selection favouring the drought-survival alleles. Therefore, we studied current allele frequencies at three representative locations with the highest sampling density in our dataset (40 samples within a 50 kilometer area): Madrid (Spain), Tübingen (Germany) and Malmö (Sweden), which are near the southern edge, center and northern edge of the Eurasian and N.
Genetic structure To understand the genetic structure of Arabidopsis thaliana we ran, on the 762 samples, the software ADMIXTURE v1. 2 (ref. 21)  assuming two to 20 groups and using a 5-fold crossvalidation procedure. The number of groups with the smallest cross-validation error was 11 (Fig. 2 , Fig. Supplementary Table 1 , Supplementary Fig. 8 ). We computed a genomic PCA using PLINK v1. 9 (ref. 53). The three first PC axes explained 33. 5% of the genomic variance (see Supplementary Methods 3). We used genomes with probability >0. 9 of assignment to one of the 11 ADMIXTURE groups to run MSMC v. 3 (ref. 22). This was done in quartets of genomes, i. e. four genomes for within-population coalescent mode, and two genomes of each of two populations for the cross-coalescent mode (Fig. 2 , Supplementary Fig.
Terminal drought experiment Stratified seeds from the selected 211 natural lines were sown in greenhouse pots and abundantly watered every three days during two weeks. Thereafter watering only occurred every three weeks, which dramatically reduced soil water content (Fig. 1 , Supplementary Methods 1. 2). Top-view photographs of the potting trays were done at 20 timepoints during the whole experiment with a high resolution Panasonic DMC-TZ61 digital camera mounted in a closed black box setting to ensure image consistency (Supplementary Methods 2). Using customized Python scripts and the module Open Computer Vision, we segmented the green plant-leave pixels from the brown soil background to monitor plant area over time (Supplementary Video). Starting from the day with the largest rosettes areas, until the end of the experiment, we modeled the decay of green area (i.
e. # pixels) using a polynomial generalized linear mixed model with Poisson link as described in the MCMCglmm R package v. 2. 25 (see Supplementary Methods 2). The random genotype effects captured the average deviation of each genotype from a general intercept, slope and quadratic curvature. After calculating the heritability of each of the three coefficient deviations and their correlation with the genotype's climate variables of origin, we understood that it was the quadratic curvature that was the most suitable to use as index of survival (Supplementary Methods 2).
Genome-Wide Association (GWA) Using the index of survival per genotype as the trait and the SNPs with a minimum allele frequency > 5% as predictors (n=879,654 SNPs), we carried out associations using the linear mixed model implemented in EMMAX software25 to find SNPs that excessively contributed to the prediction of survival of genotypes (Supplementary Table  3 ) (see Supplementary Methods 3. 3). To corroborate the identified top SNPs we also performed a Bayesian Sparse Linear Mixed Model (BSLMM) with GEMMA software28. Both approaches, EMMAX and GEMMA, fit a model as: Y = X i β + Zu + є, where Y is the vector of trait values, X is the alternative allele dosage at SNP i and β the allelic effect of SNP i on the trait. Population structure is corrected with a random genotype term (of 211 levels) represented by u, which follows a Multivariate Normal distribution where is A the relationship matrix between all individual genotypes built from SNP information and is the genotypeassociated variance.
Environmental Niche Models We used classification and regression Random Forest models implemented in the randomForest R package, available environmental databases www. worldclim. com v. 1. 4 (ref. 17, 19 bioclimatic variables at 2. 5 arc-minutes resolution), and geographic locations of GWA-identified alleles, to fit environmental niche models (ENM). To evaluate model's predictive ability for each allele, we used a 5-fold cross-validation procedure in which ⅘ parts of the data were used to train the model and ⅕ was used to test it. This enabled us to assign a percentage of successful assignment of an allele given the environmental variables at a location (Supplementary Tables  3 4 ). The fitted Random Forest model was used to generate potential geographic distributions of survival-associated alleles which, all overlapped, provided a geographic map of density of survival alleles.
Acknowledgements We thank  R. Wedegärtner  for assistance with the greenhouse drought experiment,  I. Henderson  for the recombination map, the  Petrov, Coop ,  Ross-Ibarra , Gaut and Schmitt labs for discussions. We thank  J. Lasky ,  X. Picó ,  A. Hancock ,  H. Thomassen ,  T. Mitchell-Olds ,  J. Mujica ,  P. Lang , and  D. Seymour  for comments and the  Weigel and Burbano labs for discussion. This work was supported by the  President's Fund of the Max Planck Society , project " Darwin " to HAB and by central  Max Planck Society  funds and the  ERC (AdG IMMUNEMESIS)  to DW.
Code availability Code for the image analysis pipeline available at http://github. com/MoisesExpositoAlonso/ hippo with DOI: https://doi. org/10. 5281/zenodo. 1039888, code for ancestryGWA is available at https://github. com/MoisesExpositoAlonso/aGWA with DOI: https://doi. org/ 10. 5281/zenodo. 1039882, code for Wright-Fisher population simulations at http:// github. com/MoisesExpositoAlonso/popgensim with DOI: https://doi. org/10. 5281/zenodo. 1039886.
Variant selection Missense substitution variants from BRCA1 (n=19) and BRCA2 (n=33) were selected by ENIGMA for inclusion on the iCOGS genotyping array based on frequency in the ENIGMA database  (15)  (Supplementary Tables  S2 ). Variants are defined by Human Genome Variant Society (HGVS) nomenclature and are based of Refseq transcripts (BRCA1: NM_007294. 3 ; BRCA2: NM_000059. 3).
Models were fitted under maximum likelihood theory using a modified version of the LINKAGE genetic analysis package  (17). Non-carriers were assumed to be at population risks with incidence rates taken from cancer registry data obtained from Cancer Incidence in Five Continents, VIII (IARC, Lyon) and risk ratios (RR, the age specific breast cancer incidence rate in carriers divided by the relevant population rate) were estimated. Reported breast cancers with unknown age at diagnosis were excluded from all analyses. Cancers other than breast (including ovarian cancer) were treated as unaffected at the age of their cancer diagnosis.
Electrophoretic Mobility Shift Assay (EMSA) The ssDNA substrate (oAC423) used for DNA binding was obtained from Sigma and purified by polyacrylamide gel electrophoresis (PAGE). Purified wildtype or mutated BRCA2 at concentrations 0, 1, 5, 10, 20 nM was mixed with the ssDNA oligonucleotide oAC423 167-mer (0. 2 µM nt), labeled with 32 P at the 5' end, in a buffer containing 25 mM TrisAcO (pH 7. 5), 1 mM MgCl 2 , 2 mM CaCl 2 , 1mM DTT, 1 mM ATP, 100 µg/ml BSA, and incubated for 60 min at 37°C. Reaction products were resolved by 6% PAGE, imaged on a Typhoon PhosphorImager (Amersham Biosciences), and analyzed with Image Quant software. The relative amount of product was calculated as labeled complex divided by the total labeled input DNA in each lane. The protein-free lane defined the value of 0% complex.
Importantly, functional analysis of Y3035S using multiple independent assays consistently revealed partial activity. Y3035S showed intermediate BRCA2 HDR activity in VC8 cells, failed to restore HDR activity in ES cells (Fig. 3A ), and only partially rescued sensitivity to PARP inhibition in ES cells (Fig. 3B ). Similarly, Y3035S showed significantly reduced ssDNA complex formation (Fig. 2B  and 2C ) consistent with the location of Y3035 in the crystal structure of the BRCA2 DBD (28) (Supplementary Fig. S4 ). Together the functional studies indicate that Y3035S is a hypomorphic BRCA2 variant. Overall the case-control association study and functional analyses provide the first evidence that a hypomorphic BRCA2 missense variant can confer a moderate risk of breast cancer. However, Y3035S is consistently reported as "likely benign" and "benign" in the ClinVar public database.
Since this database is widely used by researchers and clinicians, this under-appreciation of moderate risks of breast cancer associated with this variant has the potential to impact patient care. Further prospective studies are required to estimate age-dependent risks of cancer and to inform management protocols for carriers of this and other hypomorphic BRCA2 variants. The BRCA2 G2508S (OR=2. 68, p=0. 004) variant was associated with a moderate risk of breast cancer in Asian women, but could not be evaluated in the Caucasian population (Table  1 ). The variant was predicted neutral by a protein likelihood prediction model  (31) , but was predicted deleterious by other in silico prediction models including MetaLR, MetaSVM, Vest3, and A-GVGD (prior probability of pathogenicity of 0. 66) (Supplementary Table  S2 ).
BRCA1 and BRCA2 have been reported in the Clinvar database but no validated high throughput methods for clinical classification of missense on March 28, 2017. © 2017 American Association for Cancer Research. cancerres. aacrjournals. org Downloaded from Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 10, 2017; DOI: 10. 1158/0008-5472. CAN-16-2568.
Acknowledgements We thank  Shyam Sharan  for the Pl2F7 conditional mBrca2 knockout mES cell line and  Maria Jasin  for the DR-GFP targeting construct. on March 28, 2017. © 2017  American Association for Cancer Research. cancerres. aacrjournals. orgDownloaded from Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 10, 2017; DOI:  10. 1158/0008-5472. CAN-16-2568.
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on March 10, 2017; DOI: 10. 1158/0008-5472. CAN-16-2568 the BRCA1 variants, 12 have been classified as Class 1-neutral, 6 as Class 3-uncertain, and 2 as Class 5-pathogenic, by the quantitative multifactorial likelihood model that the ENIGMA consortium (www. enigmaconsortium. org) uses for expert panel review of BRCA1 and BRCA2 variants for ClinVar (http://www. ncbi. nlm. nih. gov/clinvar) and BRCA Exchange (http://brcaexchange. org) (Supplementary Table S2). Among the Class 3 variants, BRCA1 c. 5207T>C, p. Val1736Ala (V1736A) is known to disrupt BRCA1 activity (22), and both V1736A and c. 5363G>A, p. Gly1788Asp (G1788D) are annotated as pathogenic by multiple sources in the ClinVar database.
, 2008; Lannoo and Van Damme, 2014). So far, the amaranthin family is considered as one of the smallest plant lectin families. The first lectin from the amaranthin family was purified from the seeds of Amaranthus caudatus and represents a 66 kDa homodimeric protein further referred to as amaranthin. The three-dimensional structure of the A. caudatus agglutinin was resolved and revealed two carbohydrate-binding sites located at the interface of the two subunits  (Transue et al. , 1997). In recent years, sequences homologous to amaranthin have also been identified outside the family Amaranthaceae, suggesting that the occurrence of amaranthin-like proteins is much wider and more amaranthin-like proteins remain to be discovered in the plant kingdom. Fortunately, publicly available genome databases enabled us to extend the knowledge about the amaranthinlike sequences in a wide range of species and understand the distribution of amaranthin-like proteins in different plant families.
Identification of Amaranthin Domains in Different Plant Genomes The protein sequence for amaranthin (GenBank: AAL05954) was used to perform BLASTp searches against different plant genome databases (Supplementary File 1) with default settings to identify sequences containing amaranthin domains (pfam: Agglutinin/PF07468). BLASTp searches were repeated with the top hits to obtain all the possible candidate sequences. The sequences retrieved were then screened with InterProScan 5 for the presence of amaranthin domains and other protein domains attached  (Mulder and Apweiler, 2007). Different domain architectures were summarized according to the results of InterProScan 5. The presence of signal peptides and transmembrane regions was analyzed with the SignalP 4. 1 server and TMHMM Server v. 2. 0, respectively  (Krogh et al.
Phylogenetic Analysis of AA Domains and Aerolysin Domains from Amaranthin-Like Proteins The tandem arrayed amaranthin (AA) domain sequences from all identified amaranthin-like proteins in different plant genomes were used for the phylogenetic analysis of AA domains. Similarly, sequences encoding the toxin (T) domains were used for the phylogenetic analysis of aerolysin domains (Pfam: PF01117). Aerolysin domains from different cucumber AAT proteins as well as from proteins with similar domain architectures were included in the analysis: Hfr-2 (GenBank: AAW48295) and Dln1 (PDB: 4ZNR_A) from the Pfam database; aerolysin (GI: 113485), LSLa (GI:241161788), LSLb (GI:32261218), LSLc (GI:32261220) from  Moran et al. (2012). Multiple sequence alignment was conducted with MUSCLE using the default settings  (Edgar, 2004).
The primers for target and reference genes (clathrin adapter complex subunit and Protein phosphatase 2A) for qPCR analysis are available in Supplementary Table 2  (Migocka and Papierniak, 2011). The results of qPCR were analyzed using Relative Expression Software Tool-384 version 2 (REST-384), which also determined the statistical significance of the results  (Pfaffl et al. , 2002). All experiments were performed with two independent biological replicates, each containing three technical replicates.
The clathrin adapter complex subunit gene and protein phosphatase 2A gene were used as reference genes for qPCR analysis. The stability of these reference genes was analyzed by qBase PLUS software  (Hellemans et al. , 2007). REST-384 software was used to analyze the qPCR results and determine the statistical significance  (Pfaffl et al. , 2002). All experiments were performed with two independent biological replicates, each containing two technical replicates.
Molecular Modeling of AAT4 The cucumber lectin AAT4 was modeled using the X-ray coordinates of amaranthin from A. caudatus (PDB code 1JLY;  Transue et al. , 1997) , the aerolysin-like protein Dln1 from zebrafish (Danio rerio, PDB code 5DI0;  Jia et al. , 2016) , the hemolytic lectin from the mushroom Laetiporus sulphureus (PDB code 1W3F;  Mancheno et al. , 2005) , the parasporin-2 toxin from Bacillus thuringiensis (PDB code 2ZTB)  (Akiba et al. , 2009) , and the Cry23Aa1/Cry37Aa1 toxin complex of B. thuringiensis (4RHZ) as templates. Homology modeling of AAT4 was performed with the YASARA Structure program  (Krieger et al. , 2002) , running on a 2. 53 GHz Intel duo core Macintosh computer. Nine residues (Leu27, Phe28, Arg141, Asp220, Asn248, Ser326, Lys328, Glu329, Asp369) out of 518 amino acids, of the hybrid model built for AAT4, occurred in the non-allowed regions in the Ramachandran plot.
Domain Architectures for Sequences with Amaranthin Domains Sequence analyses revealed different domain arrangements among the sequences with amaranthin domains. Databases such as Pfam and Superfamily were also checked to obtain more comprehensive information about the protein domains. Based on all information retrieved, 21 types of domain architectures can be distinguished in all identified sequences (Figure  2 ). The most prevalent domain combination consists of two amaranthin domains in combination with one aerolysin domain (AAT) and was identified in 113 out of 265 identified sequences. Other protein domains identified in combination with an amaranthin domain include the fascin domain, the Bet v1 domain, the TRAF-like domain, an alpha/beta hydrolase domain and the B box type zinc finger domain.
Background: The aim of this systematic review with meta-analysis was to analyze the effects of intra-pregnancy nonsurgical periodontal therapy on periodontal inflammatory biomarkers and adverse pregnancy outcomes. Methods: On June 5, 2017, we searched PubMed, Cochrane, SCOPUS, Web of Science, LILACS, ProQuest, Open Grey, and Google Scholar databases. Randomized clinical trials in which pregnant women with chronic periodontitis underwent nonsurgical periodontal therapy, compared with an untreated group, tested for inflammatory biomarkers, and followed till delivery were included. Primary outcomes were preterm birth, low birth weight, and preeclampsia. Meta-analysis was performed with 5. 3. 5 version of Review Manager software. Results: We found 565 references in the databases, 326 after duplicates removal, 28 met criteria for full text reading, and 4 met eligibility criteria for quantitative and qualitative synthesis.
Search strategy On June 5, 2017, a broad search of articles without language or time limits was performed in the following databases: PubMed, Cochrane Library, SCOPUS, Web of Science, LILACS (Latin American and Caribbean Literature), and gray literature through ProQuest, Open Grey, and Google Scholar. The Medical Subject Headings (MeSH) terms were used to develop the search strategy and to acquire the main strategy on PubMed. When words with different spelling appeared, synonyms that were in the MeSH terms were used. This strategy was adapted for the other databases. On LILACS, the same keywords in Spanish and Portuguese were used. The search strategy used is in Appendix I (see Additional file 2). Manual searches of reference lists of relevant articles, theses, and dissertations were also performed.
Data extraction The article selection was performed in two phases. On phase 1, two independent reviewers (HECS and CMS) evaluated titles and abstracts of all articles, according to the eligibility criteria. On phase 2, both reviewers (HECS and CMS) independently read the full texts according to the inclusion and exclusion criteria. In case of disagreements, both reviewers discussed and, if consensus was not reached, a third reviewer (NSM) analyzed the articles to reach a final decision.
Tabulation of findings Data extraction was also performed by two independent reviewers (HECS and CMS) and posteriorly compared. Extracted data comprised author, year, country, sample, and age in years (mean ± SD and/or range); case definitions of periodontal disease (PD) and premature birth (PTB)/low birth weight (LBW); groups (n and treatments); inflammatory biomarkers tested and source; primary outcomes (incidence of PTB/LBW, expressed as number of cases/total deliveries per group, percent and p value between groups); inflammatory biomarker level differences between groups (p value); and main conclusions of each paper.
Search findings The electronic search of five databases resulted in 565 references. Removal of duplicated studies resulted in 326 references. Titles and abstracts from these studies were read and those not fulfilling the eligibility criteria were excluded. In addition, gray literature was searched. ProQuest returned 5 references and Open Grey none. From Google Scholar, the first 60 references were considered for evaluation. At the end of phase 1, 28 studies remained for full text reading (phase 2). Manual search of reference lists did not provide additional studies. Full text reading resulted in 4 eligible studies for qualitative analysis. Appendix II presents excluded articles and reasons for exclusion (see Additional file 3). A flowchart of the complete process is shown in Fig. 1. Included studies were conducted in Ireland  [13] , Australia  [16] , USA  [22] , and India  [23].
Existing MLE method In the Maximum Likelihood Estimation (MLE) method  [25] [26] [27] , the basic idea is to associate the failure or the damage state event with a Bernoulli random variable Y. The use of MLE to identify the distribution of Y leads to a fragility curve. The step-by-step procedure is as follows: -step 1: generate or choose a set of N independent ground accelerations a i (t), i = 1. N. Their values of ground motion index are computed from a i (t) and equal to a i ; -step 2: simulate structural responses subjected to the ground accelerations a i (t) and verify whether the failure or the damage state is reached. Consider a Bernoulli random variable Y ; if a i (t) gives rise to failure or damage, Y is given the value y i = 1; otherwise, y i = 0; -step 3: formulate the likelihood function L( A m , β) L( A m , β) = N i=1 [F r (a i ; A m , β)] y i [1 -F r (a i ; A m , β)] 1-y i (5) -step 4: identify A m and β from the optimization problem below ( Âm , β) = arg min A m ,β (-L(A m , β)) (6) As previously analyzed in section 1, the data used for the MLE method are N couples (a i , y i ), where a i are values of the ground motion index, calculated from the chosen ground accelerations a i (t) for a given site.