dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2007) , BIMBAM  (Servin and Stephens, 2007)  or MultiPhen  (O'Reilly et al. , 2012) , are unable to achieve, because they only allow for the combination of P-values from the testing of multiple biomarkers at the same time point in a cross-sectional setting. In this work we present the first GWAS of an Alzheimer's disease DPS derived from two imaging modalities [T 1 -weighted MRI and amyloid PET ( 18 F-florbetapir)] on a large cohort from the ADNI database  (Jack et al. , 2008). We investigated the DPS properties as an endophenotype and compared GWAS results to single-modality cross-sectional biomarkers. We also compared association strengths to genome-wide polygenic risk scores across phenotypes. Lastly, we investigated our GWAS results in the National Alzheimer's Coordinating Center (NACC) database, to assess their effect on the risk of conversion from healthy ageing to mild cognitive impairment (MCI) or Alzheimer's disease.
Materials and methods Data used in the preparation of this article were obtained from the ADNI database (http://adni. loni. usc. edu). The ADNI was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of the ADNI has been to test whether serial MRI, PET, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early Alzheimer's disease. For up-to-date information, see www. adni-info. org.
Genotyping and imputation At the time of this study single nucleotide polymorphism (SNP) genotyping data were available for 1674 subjects across all ADNI phases. Genotyping was conducted using three different platforms: Human610-Quad, HumanOmniExpress and Omni 2. 5M (Illumina)  (Saykin et al. , 2010). The proportions of study participants genotyped with each platform and their relationship with diagnosis at baseline can be found in Supplementary Table  1.
X chromosome variants Variants on the X chromosome underwent recommended preimputation quality control provided with the X-wide analysis toolset (XWAS version 1. 1)  (Gao et al. , 2015)  using the following parameters: exclusion of genotype calls with MAF 5 5%, missingness rate 4 10%, deviation from Hardy-Weinberg equilibrium with P 5 0. 05 (Bonferroni corrected); exclusion of samples with 410% missing genotypes and high relatedness (GRM off-diagonal relatedness coefficient 4 0. 1). At the time of manuscript preparation, the Sanger server did not support imputation of X chromosome variants. Thus, we applied an in-house imputation pipeline for this task, using the 1000 Genomes Project data as a reference panel  (Durbin et al. , 2010) , SHAPEIT  (Delaneau et al. , 2011)  for phasing and IMPUTE2 for imputation  (Marchini and Howie, 2010).
Cortical amyloid burden At the time of this study, 2034 preprocessed longitudinal 18 F-florbetapir PET scans for 1089 subjects were available in the ADNI database (date accessed 24/03/2016). Preprocessing of florbetapir PET scans in ADNI has been described in detail elsewhere  (Jagust et al. , 2010). Briefly, four 5-min frames were acquired 30-60 min post-injection; dynamic frames were co-registered to the base frame image, averaged, reoriented into a standard space (voxel grid size 160 Â 160 Â 96, voxel size 1. 5 Â 1. 5 Â 1. 5 mm 3 ), and smoothed down to the lowest resolution available in ADNI (8 mm full-width at half-maximum uniform isotropic). To compute the standardized uptake value ratio (SUVR), information from structural magnetic resonance scans was used. A total of 1931 preprocessed T 1 -weighted magnetic resonance scans (voxel size 1 Â 1 Â 1.
Genome-wide association analysis Genome-wide association tests were conducted with PLINK on the imputed SNP data (5 137 219 markers). We tested three quantitative phenotypes (via the -linear command): bilateral hippocampal volume at first PET scan (926 subjects), florbetapir SUVR at first PET scan (936 subjects) and DPS (944 subjects). Sample sizes differences resulted from exclusion of poorly registered scans. The larger sample size for DPS was due to the model's ability to deal with missing data: if a baseline value of hippocampus or amyloid was excluded from the cross-sectional GWAS, GRACE was still able to work with the remaining time points and return a DPS (see 'Results' section). Table  1  reports demographics and clinical outcomes for the DPS GWAS sample. SNP-trait associations were tested under an additive model.
, 2011). Variants passing the genome-wide suggestive threshold (P = 10 À5 ) were annotated using the Ensembl Variant Effect Predictor  (McLaren et al. , 2016). Genome-wide significant variants were tested for effects on gene expression levels in 10 brain tissues by accessing data from the UKBEC (www. braineac. org, Ramasamy et al. , 2014)  and followed up in the GTEx database  (Aguet et al. , 2017)  (date accessed: 9 May 2017) with a targeted query for a specific triplet SNP-gene-tissue and correcting for the number of tests using the Bonferroni method.
SNPs effect on risk of conversion Following up on our GWAS results, we hypothesized that variants related to disease progression through significant association with DPS might exert an influence on clinical conversion from healthy control to MCI or Alzheimer's disease. To test this hypothesis, we ran a Competing Risks regression analysis using the model developed by  Fine and Gray (1999)  to evaluate the risk of conversion to MCI or Alzheimer's disease of top SNPs, while accounting for death as a competing risk. This analysis compared carriers of the minor allele (heterozygotes and homozygotes) to non-carriers, and was carried out using the cmprsk package in R  (Scrucca et al. , 2010). Competing risks analysis is a type of time-to-event analysis that aims to accurately estimate the marginal probability of an event in the presence of competing events such as death.
This approach is relevant in this study because participants are elderly and death may occur before the event of interest (i. e. conversion to MCI or Alzheimer's disease) is observed, which can produce bias in risk estimates. This analysis used data from three NACC Alzheimer's disease centres, collected between 2005 and 2015. We included all participants from the NACC dataset who were healthy at baseline, had genotyping data available, and had at least 1 year of follow-up, to assess the effect of top SNPs on risk of conversion. Age at entry, APOE "4 status, sex, years of education, first two principal components (Supplementary Fig. 1 ), and Alzheimer's disease centre were included as covariates in the analysis (n = 911, Table  2 ). P-values were corrected for multiple comparisons using the Bonferroni procedure.
SNP imputation and quality control After imputation and quality control, the available genetic data comprised 5 082 878 autosomal and 54 340 Xchromosome SNP markers and 1674 subjects, with a total genotyping rate of 0. 99. No subjects were missing more than 10% of the genotypes. After ancestry adjustment and removal of related individuals (144 individuals that appeared to be related), the final sample comprised 1499 unrelated Central European individuals. For the X chromosome, there were 1329 Central European unrelated subjects because of additional X-specific pre-imputation quality control  (Gao et al. , 2015).
SNPs effect on risk of conversion Competing risks regression analysis in the NACC dataset was conducted for the two top SNPs rs6850306 (GWAS b = À0. 07; P = 1. 03 Â 10 À8 ) and rs114368656 (GWAS b = À0. 05; P = 1. 70 Â 10 À6 ). Given the relatively low minor allele frequency of both rs6850306 and rs114368656, the analysis was conducted on minor allele carriers (recessive homozygotes and heterozygotes) versus non-carriers (major allele homozygotes). rs6850306 appears to be protective against conversion [hazard ratio (HR) = 0. 593, 95% CI = 0. 387-0. 907, n = 911, P Bonf = 0. 032], conferring decreased risk to homozygous and heterozygous carriers of the minor allele A and confirming the negative direction of effect reported in the GWAS (Fig. 5A  and Supplementary Table  3 ). However, results suggest that rs114368656 does not modulate conversion risk between carriers and non-carriers of the minor allele T (HR = 1.
4B ). Having shown the advantage of the DPS over individual biomarkers in tracking disease progression, the DPS was used as a quantitative phenotype in a GWAS. Genomewide significant rs6850306 and its haplotype fall in the LCORL gene on chromosome 4p15. 31. For this variant we identified a tissue-specific effect on LCORL expression levels in the hippocampus, with expression levels increasing with each copy of the minor allele; this cis-eQTL behaviour was consistently found in two independent gene expression databases. The protein encoded by LCORL is a direct interaction partner of CTBP1  (Szklarczyk et al. , 2015) , which mediates activity-dependent synapse-to-nucleus communication  (Ivanova et al. , 2015). Furthermore, the CTBP1/BARS complex co-localizes with amyloid precursor protein (APP) in macropinosomes at the cell surface  (Tang et al.
, 2006; Otaegui et al. , 2009; Zaltieri et al. , 2015). However, some caution is required when interpreting these association signals due to scarce coverage on some genotyping platforms and lack of information on LD structure in the 1000 Genomes reference panel and all resources based on it. In fact, the ADNI whole-genome sequencing data called no variants in the 5 kb region surrounding rs114368656. We believe this is due to the general architecture of the locus, that is characterized by several insertions/deletions and copy-number variations of different lengths. We demonstrated the first successful application of a multimodal DPS as quantitative phenotype in a GWAS. There remain, however, some limitations. First, the DPS relies heavily on the choice of considered biomarkers. In this study, amyloid burden and hippocampal volume were used owing to their specificity to Alzheimer's disease pathology.
However, larger or different sets of biomarkers may provide more granular estimates of disease progression, which may translate into increased statistical power for reaching genome-wide significance. Second, the multimodal nature of the DPS complicates validation in independent cohorts. In fact, for this work we could not secure a sufficiently sized cohort with genotyping, longitudinal amyloid and structural imaging data to validate our findings. Although lacking a formal replication cohort, a supporting evidence for our genome-wide significant association in chromosome 4 comes from the conversion risk analysis performed on an independent dataset (NACC), confirming the protective role of rs6850306 revealed through GWAS and linking rs6850306 genotype, lower DPS values and decreased risk for conversion.
The i-Select chips was funded by the  French National Foundation on Alzheimer 's disease and related disorders. EADI was supported by the  LABEX (laboratory of excellence program  investment for the future) DISTALZ grant,  Inserm ,  Institut Pasteur de Lille, Universitede Lille 2  and the  Lille University Hospital. GERAD was supported by the  Medical Research Council  (Grant n  503480 ),  Alzheimer's Research UK  (Grant n  503176 ), the  Wellcome Trust  (Grant n  082604/2/07/Z ) and  German Federal Ministry of Education and Research (BMBF) :  Competence Network Dementia (CND)  grant n  01GI0102 ,  01GI0711 ,  01GI0420. CHARGE was partly supported by the  NIH/NIA  grant  R01 AG033193  and the  NIA   AG081220  and  AGES  contract  N01-AG-12100 , the  NHLBI  grant  R01 HL105756 , the  Icelandic Heart Association , and the  Erasmus Medical Center and Erasmus University.
ADGC was supported by the  NIH/NIA  grants:  U01 AG032984 ,  U24 AG021886 ,  U01 AG016976 , and the  Alzheimer's Association  grant  ADGC-10-196728. The NACC database is funded by  NIA/NIH  Grant  U01 AG016976. NACC data are contributed by the  NIA -funded ADCs:  P30 AG019610  ( PI Eric Reiman, MD ),  P30 AG013846  ( PI Neil Kowall, MD ),  P50 AG008702  ( PI Scott Small, MD ),  P50 AG025688  ( PI Allan Levey, MD, PhD ),  P50 AG047266  ( PI Todd Golde, MD, PhD ),  P30 AG010133  ( PI Andrew Saykin, PsyD ),  P50 AG005146  ( PI Marilyn Albert, PhD ),  P50 AG005134  ( PI Bradley Hyman, MD, PhD ),  P50 AG016574  (PI  Ronald Petersen, MD, PhD ),  P50 AG005138  (PI  Mary Sano, PhD ),  P30 AG008051  ( PI Thomas Wisniewski, MD ),  P30 AG013854  ( PI M. Marsel Mesulam, MD ),  P30 AG008017  ( PI Jeffrey Kaye, MD ),  P30 AG010161  ( PI David Bennett, MD ),  P50 AG047366  (PI Victor Henderson, MD, MS),  P30 AG010129  ( PI Charles DeCarli, MD ),  P50 AG016573  ( PI Frank LaFerla, PhD ),  P50 AG005131  ( PI James Brewer, MD, PhD ),  P50 AG023501  ( PI Bruce Miller, MD ),  P30 AG035982  ( PI Russell Swerdlow, MD ),  P30 AG028383  ( PI Linda Van Eldik, PhD ),  P30 AG053760  ( PI Henry Paulson, MD, PhD ),  P30 AG010124  ( PI John Trojanowski, MD, PhD ),  P50 AG005133  ( PI Oscar Lopez, MD ),  P50 AG005142  ( PI Helena Chui, MD ),  P30 AG012300  ( PI Roger Rosenberg, MD ),  P30 AG049638  ( PI Suzanne Craft, PhD ),  P50 AG005136  ( PI Thomas Grabowski, MD ),  P50 AG033514  ( PI Sanjay Asthana, MD, FRCP ),  P50 AG005681  ( PI John Morris, MD ),  P50 AG047270  ( PI Stephen Strittmatter, MD, PhD ).
The  Genotype-Tissue Expression (GTEx) Project  was supported by the  Common Fund  of the Office of the Director of the  National Institutes of Health , and by  NCI ,  NHGRI ,  NHLBI ,  NIDA ,  NIMH , and  NINDS. The data used for the analyses described in this manuscript were obtained from: the GTEx Portal on  08/05/2017  and dbGaP accession number phs000424. v6. p1 on  08/05/2017.
Funding M. A. S. acknowledges financial support by the  EPSRCfunded UCL Centre for Doctoral Training in Medical Imaging  ( EP/L016478/1 ). M. D. G. was supported by the  NIH  ( P50 AG047366 ). J. M. S. acknowledges the support of the  National Institute for Health Research Queen Square Dementia Biomedical Research Unit , the  NIHR UCL/H Biomedical Research Centre ,  Wolfson Foundation ,  Engineering and Physical Sciences Research Council  ( EP/ J020990/1 ),  Medical Research Council  ( CSUB19166 ),  Alzheimer's Research UK  ( ARUK-Network 2012-6-ICE; ARUK-PG2014-1946 ),  Brain Research Trust  ( UCC14191 ) and  European Union 's  Horizon 2020 research and innovation programme  (Grant  666992 ). S. O. receives funding from the  EPSRC  ( EP/H046410/1 ,  EP/J020990/1 ,  EP/ K005278 ), the  Medical Research Council  ( MR/J01107X/ 1 ), the  EU - FP7  project  VPH-DARE@IT  ( FP7-ICT-2011-9-601055 ), the  National Institute for Health Research Biomedical Research Unit (Dementia)  at  UCL  and the  National Institute for Health Research University College London Hospitals Biomedical Research Centre  ( NIHR BRC UCLH/UCL High Impact Initiative-BW.
Thus, integrating multiple approaches is essential to gain in depth understanding of mechanisms shaping assembly of communities across environmental gradients. In the present study, we analyzed the species composition, abundance and functional trait data of tropical tree species from 96 plots, each 1 ha in size, distributed along a broad range of environmental conditions with the varying level of human disturbance, in a phylogenetic framework, to assess the relative importance of neutral (dispersal limitation, external factors) and niche based processes (environmental filtering and interspecific competition) in assembly of forest communities along a large scale environmental gradient in the region. We specifically address following questions: (i) How does the spatial variation in environmental variables influence the relative composition, richness, abundance, phylogenetic structure and functional trait strategies of tree communities with different leaf phenology? (ii) Do the phylogenetic structure and functional trait strategies of varying forest types differ, thereby indicating the importance of niche based processes (environmental filtering and competition) in sorting of lineages into specific forest types from regional species pool? iii) Do phylogenetic alpha and beta diversity differ among plots that experienced the different level of human disturbance and (iv) Does the phylogenetic beta diversity turnover between tree communities faster or slower between plots than that expected given the species turnover along climatic gradients? and v) Are leaf phenological traits (evergreen and deciduous) phylogenetically conserved among tree species in the Western Ghats?.
2009) , and the detailed description of the study area is given in  (Ramesh et al. 2010a (Ramesh et al. , 2010b)). We recorded the diameter at breast height (dbh) of all living trees ≥10 cm in each plot along with the phenological classification of each tree as evergreen or deciduous based on existing literature  (Pascal 1986 ) and botanists' expertise. Finally, we calculated distribution and abundance of deciduous and evergreen tree species by summarizing floristic data of 96 plots through a site-by-species abundance matrix. First, we classified each tree species into either evergreen or deciduous category and calculated the proportional abundance and species richness of deciduous and evergreen tree species of each plot.
Abiotic variables We chose abiotic variables related to temperature, precipitation, evapotranspiration and aridity, which are considered as relevant factors influencing the patterns of plant species diversity and community dynamics  (Wright 1983; Currie 1991). We evaluated 23 variables of which 19 were bioclimatic, 2 evapotranspiration related, one variable related to aridity and number of dry months (Additional file 1: Table  S1 ). We removed variables with multicollinearity through forward selection and stepwise selection procedures based on Variance Inflation Factor (VIF) as implemented in Ordistep  (Blanchet et al. 2008)  and MASS  (Ripley et al. 2011 ) in R statistical software packages (http://cran. r-project. org) and retained 12 variables for further analysis (Additional file 1: Table  S1 ).
Functional trait data We selected a set of continuous and discrete functional traits often considered essential for the maintenance of woody plant form in both wet and dry conditions  (Westoby et al. 2002). In total, we collected data on five continuous traits and two discrete traits reflecting morphology, physiology, growth and reproduction (Additional file 1: Table  S2 ). The five continuous traits included maximum dbh (m), wood density (g•cm -3 ), leaf size (cm), seed size (mm 2 ) and seed mass (g), and the discrete traits were seed dormancy type (orthodox and recalcitrant) and leaf phenology (deciduous and evergreen). See methods section in Supporting Information for further details on functional trait data collection.
Functional trait metrics We used four community level functional trait metrics following the method of  Kraft and Ackerly (2010). Of these four metrics, the community trait range (RANGE) and variance (VAR) are sensitive to habitat filtering  (Kraft and Ackerly 2010). The other two metrics, the standard deviation of nearest neighbor distance and the single direction normalized to the range (SDNDr, referred to as even spacing) and kurtosis are sensitive to niche differentiation (competition)  (Cornwell and Ackerly 2009). All functional trait metrics were calculated using the R script from  Kraft and Ackerly (2010). The five continuous traits were considered independently for the analysis. The species without a given trait value were excluded from that analysis. We tested for nonrandom patterns of functional traits among plots distributed across environmental gradient and in different forest types by creating 999 null communities of equal richness to the sampled plot by drawing species at random from regional species pool (included all species occur in our study plots), weighted by plot-wide species abundance.
Hierarchical partitioning computes all possible regression models and estimates the increase in the fit of all possible models with a particular predictor compared to the equivalent model without that variable and averages model improvements across all hierarchies, thus permits a ranking of variables by their independent effects. Hierarchical partitioning was conducted using the R package 'hier. part' version 1. 0-4 (Walsh and Mac Nally 2013). To identify non-redundant variables in the set of five most contributed variables selected from hierarchical partitioning, we conducted multiple regression analyses using generalized linear modelling (GLM)  (McCullagh and Nelder 1983)  with log link and Poisson errors. All five selected variables were included in the multi-model analyses, and the best model was chosen from the 31 models representing all possible combinations of the additive variables based on the lowest AICc value  (Anderson et al.
The cells were washed three times with PBS and incubated with streptavidin-APC diluted 1:500 in blocking buffer containing DAPI (Santa Cruz Biotechnology) for 30 min at room temperature in dark. After washing with PBS, coverslips were mounted using Prolong Gold Antifade Reagent (Ther-moFisher Scientific). Cells were photographed with a Zeiss confocal microscope (Axio Observer Z1, LSM 800) using a 63× oil immersion objective and analyzed using Fiji-ImageJ software. Acquisition parameters were the same for nontransfected cells (negative control) and transfection with the four biotinylated RNAs.
An inspection of the Modomics database (http://modomics. genesilico. pl/) of mature tRNA post-transcriptional modifications did not show reported modified bases in the tRNA precursors that could interfere with base pairing interactions in the dimerization interface (Figure  2C ). To provide further insight into the ability of these 5 tRNA halves to form dimers, unrestrained ab initio threedimensional folding simulations using simRNA (25) were performed as described in Supplementary Methods. Such calculations provide 3D structural models that can be further used to explore dimer dynamics and interactions with other macromolecules or ligands. The main folded state predicted by this method corresponded to bifold predictions for tRNA Gly homodimers (Figure  2D  and Supplementary Figure  S1 ). The main folded state for Gly/Glu heterodimers also resembled bifold predictions, with equivalent population as for Gly/Gly homodimers (Figure  2E ).
Noteworthy, the ability of these structures to hide their 3 ends could render them less prone to degradation by cellular 3 -5 exonucleases. In agreement with bifold, tRNA Glu homodimers were less favored by simRNA, with a 2-fold reduction in the population associated to the main folded state (Supplementary Figure  S1 ). To address whether tRNA 5 halves dimerization ability is a consequence of the high G/C/U content of tRNAs instead of a specific feature of tRNA Gly  GCC and tRNA Glu CUC, bifold calculations were applied to all predicted human tRNA genes from the genomic tRNA database (GtR-NAdb, comprising 610 predicted tRNA genes in the human genome version hg19), to assess the capacity of their 5 halves to form homodimers. For ranking this in a genomewide scale, mature tRNA sequences were truncated to thirty nucleotides from their 5 end and then the percentage of bases forming intra-or inter-molecular base pairing interactions was computed.
With the empirically calculated molar extinction coefficient of the RNA, we determined a dissociation constant of approx. 3M for these dimers in PBS. Of note, the RNAs aggregated under conditions typically used for native gel electrophoresis, as evidenced by SEC analysis in 90 mM Trisborate buffer pH 8. 3 (data not shown). Thus, SEC was considered as the most suitable technique to study these tRNAderived fragments under pH and osmolarity conditions representative of the extracellular space (PBS). To validate our predictions, we designed a mutant in the dimerization interface by substituting a uracil for a cytosine (25U/C mutant). As expected, the behavior of this mutant was exclusively monomeric (Figure  4A ). However, this mutant was designed in order to keep complementarity with tRNA Glu CUC 5 halves, which do not form homod- imers.