dataesr/scientific-paragraphs-categorization huggingface dataset:
A signal corresponding to Nε-Hε of R295 (numbering based on BxGH99), and which is close to the enzyme active site, was chosen as binding reporter. In the case of BxGH99, this signal shifts from δ 7. 33, 87. 5 ppm ( 1 H, 15 N) in the free state to δ 7. 41, 87. 2 ppm ( 1 H, 15 N) in the bound state. Bound and free protein populations at different protein/ligand ratios were calculated from peak intensities. NMR measurements were made in 50 mM potassium phosphate pH 7. 0, 50 mM KCl with 5% D 2 O added. Protein concentration was determined by measuring 280 nm UV absorbance after denaturing the solution in 6 M guanidinium chloride (average protein concentration: 58 μM). Ligand concentrations were cross verified by integrating the 1 H peaks against the internal standard TSP-d 4 (Sigma-Aldrich). The NMR data were processed and integrated using NMRPipe.
51 The dissociation constants (K D values) were estimated using in-house Matlab 2015b scripts, using the following equation: = + K [PL] P [L] [L] T D where L is the free ligand concentration and ([PL]/P T ) is the ligandbound protein fraction. Duplicate experimental points were used for error-bar estimation. Errors were propagated using a Monte Carlo algorithm to estimate the uncertainty in the K D values. A distribution of K D values (n = 10 000) were obtained from data sets randomly varying within the error bars, and the standard deviation of was used for the K D error estimation. Quantum Chemical Calculations. To obtain the conformational free energy landscapes of ddMan, Glucal, Chex, and NOE, quantum mechanical calculations were performed using density functional theory based molecular dynamics (MD), according to the Car-Parrinello (CP) method.
23 ). To gain further insight, we compared the distribution of average selection scores for genes (Supplementary Data 3) across gene ontology (GO) terms with the R-package TopGO  30. Testing for enrichment using the relative rank of all scores yielded stronger signals than testing genes with the highest scores against the background, suggesting that weaker, polygenic effects contribute strongly to the signal of selection (Supplementary Fig. 24 ). We found 157 significantly enriched GO terms, many of which are related to RNA transcription and cell signalling (Fig. 3 , Supplementary Fig. 25 , Supplementary Data 4). These GO enrichments show partial overlap with similar scores comparing human populations (Supplementary Fig. 26 )  13. However, there are many more significantly enriched GO terms for vervets than for humans and shared enrichments are generally much more significant in the current data set, suggesting that vervet taxa provide a powerful model to study diversifying selection across closely related primate taxa.
To test more specifically for virus-related selection signals, we looked for enrichment of signals among the orthologs of human HIV-interacting genes. Indeed, 43 out of 166 gene sets in the NCBI HIV-1 human interaction database  35  show significant enrichment for high selection scores (Supplementary Fig. 31 , Supplementary Data 5). However, we note that 21 and 71 partly overlapping gene categories from this database also show enrichment for human selection scores and conserved elements, respectively (Supplementary Figs. 31  and 32 , Supplementary Note 3), suggesting that these gene sets are not very specific.
99 and p>0. 99, respectively). To further investigate the underlying mechanisms, we grouped differentially expressed genes by coexpression patterns using weighted gene co-expression network analysis (WGCNA, Supplementary Figs. 34  and 35 )  42. Five out of 33 gene co-expression modules show a significant enrichment for genes with high selection scores (FWER<0. 05; Fig. 4a , Supplementary Fig. 35 , Supplementary Data 6). Remarkably, the significant modules share similar expression patterns with strong changes in vervets post-infection, and weak, inconsistent signals in macaques. In particular, all the modules that are enriched for diversifying selection show changes in gene expression in vervets six days post-infection, which is around the time that the virus becomes detectable and activates early immune responses.
Two modules also show expression differences in the chronic stage (115 days post infection), which is most relevant for progression to immunodeficiency. We ran GO enrichment analysis separately on the genes in the enriched WGCNA modules showing early ("acute") and late ("chronic") expression changes (Fig. 4 ). We found 30 and 20 significantly enriched GO categories, respectively, many of which are involved in response to HIV in humans (Supplementary Data 7 and 8). For example, for early expression response, enriched GO categories include clathrin-mediated endocytosis  43  , autophagosome assembly, positive regulation of type I interferon (IFN-I) production  40, 41  and innate immune response (marginally significant at p=0. 01004). This is consistent with recent findings that in macaques the IFN-I response is delayed in response to SIV infection and inhibited during the first week of SIV infection  44  , while natural hosts mount a very early and transient IFN-I response  40, 41.
Finally, we carry out a screen for diversifying selection across vervet taxa, our primary goal being to look for signs of adaption to SIV. To this end, we use a method that tests whether the change in allele frequency between taxa at a locus occurred too quickly to be due to random drift. This approach is expected to be sensitive to both recent and relatively older selection events, and to pick up signals of recurrent adaptation  29. Gene ontology analysis yields strong enrichment of selection scores in multiple biological processes, generally driven by polygenic signals. Our data have the potential to yield insights into taxon-specific adaptations (e. g. , altitude adaptation in aethiops). In the present study, we focus on loci that show signals of repeated (but differential) adaptations across multiple taxa, consistent with host-pathogen coevolution.
Sample collection and sequencing All blood samples were collected under approved country specific permits that meet standardized bioprospecting regulations. DNA samples were obtained from blood (PaxGene DNA tubes, ACD tubes or archival blood cell pellet collection) except for sample AG23 that was obtained from a B lymphoblastoid cell line transformed with herpesvirus papio. Individuals were sequenced at variable coverage (Supplementary Data 1) on a Illumina HiSeq2000 platform obtaining 100bp paired-end reads.
1 ). We used VCFtools diff-indv-discordance to compare the genotype calls from Warren et al. 2015  11  and the current dataset for the five individuals that are shared between the studies. For non-filtered SNP the discordance rate was 0. 12% -0. 26% and for all non-filtered sites it was 0. 006% -0. 012%. Given the large differences in coverage between individuals (2X-45X), a stringent control on false positive rate would have led to strong a bias towards lower diversity (and especially a lower number of singletons) in low coverage samples. We suggest that for population genomic analysis it is conservative to reduce bias at the cost of increased noise. Our dataset does show correlation between coverage and individual heterozygosity (Pearson's r=0. 48, p=10 -10 , Supplementary Fig. 3 ), especially for individuals with less than 4X coverage, but cross-individual sequence divergence within and between taxa is not strongly affected by coverage (Pearson's r=0.
SIVagm phylogenetic analyses A 602 bp pol integrase fragment of SIVagm, obtained as described previously  9  , was used for phylogenetic analyses of a large sample of SIVagm strains from the different subtaxa of vervet with different origin. pol nucleotide sequence alignments were obtained from the Los Alamos National Laboratory HIV Sequence Database (see URLs). Newly derived SIV sequences were aligned using MUSCLE  73  and alignments were edited manually where necessary. Regions of ambiguous alignment and all gap-containing sites were excluded. Phylogenetic trees were inferred from the nucleotide sequence alignments by the neighborjoining method using the HKY85 model of nucleotide substitution  73, 74  implemented using PAUP*  75. The reliability of branching order was assessed by performing 1,000 bootstrap replicates, again using neighbor-joining and the HKY85 model.
Cross-taxon gene flow across time Missing genotypes in our autosomal SNP calls were imputed using beagle 4  77  , version 03Oct15. 284: java -jar beagle. jar gl=biallelic_pass_snps. vcf out=beagle_out. vcf ibd=false Samples were phased using shapeit. v2. r837. GLIBCv2. 12. Linux 78 : shapeit -phase --input-vcf beagle_out. vcf --window 0. 1 -O phased. tmp shapeit -output-vcf --input-haps phased. tmp -O phased. vcf For each geographic sample group of interest, we chose the individual with the highest coverage for further analysis. For pairs of individuals from different groups, we extracted the alleles that segregate within or between the two individuals and their phase as needed as input to MSMC. The number of informative sites between two segregating variants was determined for each pair of individuals separately from the all-sites VCF (the whole genome including non-variant sites) by counting the number of non-filtered sites for which both individuals had genotype calls.
Gene set enrichment analysis z-score transformed selection scores across genes (exons and introns) were used for gene enrichment analysis. Gene locations were extracted from NCBI Chlorocebus sabaeus Annotation Release 100 (see URLs). To test for enrichment in gene ontology (GO) terms, we first used the the R-package TopGO 30 with the "weight01" algorithm which allows to account for the hierarchical structure (and thus overlap) of GO terms when testing significance and thereby implicitly corrects for multiple testing. GO annotations were obtained from the R package org. Hs. eg. db (Bioconductor 3. 2). We restricted the analysis to 5777 GO terms with more than ten genes in our annotation. Note that our gene scores are not biased by gene length because we are calculating the average score across genes rather than taking the maximum score.
Gene expression analysis We conducted Weighted Gene Co-expression Network Analysis (WGCNA) on gene expression data of vervets and macaques essayed at different time points pre-and postinfection with SIVagm and SIVmac, respectively  40  , using the WGCNA R package as previously described  42, 79. We used as a starting point the list of genes with differentially expressed transcripts in CD4+ cells before and after SIV infection in either vervet or rhesus  40. Correlation coefficients were constructed between expression levels of genes, and a connectivity measure (topological overlap, TO) was calculated for each gene by summing the connection strength with other genes. Genes were then clustered based on their TO, and groups of coexpressed genes (modules) were identified. Each module was assigned a color, and the first principal component (eigengene) of a module was extracted from the module and considered to be representative of the gene expression profiles in a module.
We identified 36 modules (Supplementary Figs. adapted from Hill (1966)  17. Distribution maps are adapted from IUCN (2017)  18. Enrichment map network of Gene Ontology (GO) categories enriched for high average gene selection scores. Edges represent overlap in genes. Colors represent p-values on a log scale (red most highly significant, TopGO Kolmogorov-Smirnov weight01 p<0. 001). Node size represents number of genes in a category (capped at 474). Terms are grouped using Cytoscape clustermaker  34. Gene co-expression modules with differential expression pre-and post-SIV-infection which are also significantly enriched for high selection scores. Genes that were differentially expressed in CD4+ blood cells in vervet or macaque as response to SIV infection were grouped into 36 co-expression modules using WGCNA (shown in Supplementary Fig.
35 ). (a) Expression pattern of the five co-expression modules which are significantly enriched for high selection scores (p<0. 01, FWER <0. 05). (b) Joint enrichment map network of GO enrichments of the genes in the top three panels of (a) ("acute", circles) and the bottom two panels of (a) ("chronic", diamonds). GO enrichment was tested using TopGO Fisher's exact test with weight01 algorithm. Edges represent overlap in genes. Node size represents number of genes in a category (capped at 474).
Acknowledgments Samples were collected through the  UCLA Systems Biology Sample Repository  funded by  NIH  grants  R01RR016300  and  R01OD010980  to  N. F. For  permits allowing us to collect samples, we thank the  Gambia Department of Parks & Wildlife Management; Botswana Ministry of Environment & Wildlife and Tourism; Ghana Wildlife Division ,  Forestry Commission ; Zambia Wildlife Authority; Ethiopian Wildlife Conservation Authority; Ministry of Forestry & the Environment,  Department of Environmental Affairs, South Africa; Department of Economic Development and Environmental Affairs ,  Eastern Cape ;  Department of Tourism, Environmental and Economic Affairs, Free State Province ; the  Ezemvelo KZN Wildlife in KwaZulu Natal Province ; and the  Department of Economic Development, Environment and Tourism, Limpopo Province.
SPIRE OBSERVATIONS The HERUS SPIRE photometry observations were carried out between 16th August 2011 (Herschel Operational Day (OD) 825) and 16th May 2012 (OD 1022). The observations are summarised in Table  1  where the source name, redshift and infrared luminosity (L I R = L 8-1000µm ) are tabulated along with the Herschel-SPIRE observation I. D. s (obsid) for the corresponding photometric observations and their OD. The SPIRE photometer observations were carried out in Small Map mode  (SpirePhotoSmallScan, POF10, Dowell et al. , 2010)  with fixed, 3 repetition (445s), cross-linked 1×1 scan legs covering a field of 4 ′ radius. Images are taken simultaneously in the SPIRE 250µm (PSW), 350µm (PMW), and 500µm (PLW) bands. Note that 3C273 only has photometric data and the photometric data for IRAS 06035-7102 was extracted from the Open Time Key Programme: KPOT mmeixner observations of the Large Magellanic Cloud, Level 2.
DATA REDUCTION AND RESULTS All SPIRE photometry observations (listed in Table  1 ) were processed through the standard Small Map User Pipeline with HIPE 11. 2825, using SPIRE Calibration Tree 11. 0 with default values for all pipeline tasks. Target positions in the map were found by the HIPE SUSSEXtractor task  (Savage & Oliver, 2007) , assuming a Full Width Half Maximum (FWHM) of 18. 15, 25. 2, 36. 9 arcseconds for the PSW, PMW, PLW bands respectively. These positions were then input into the SPIRE Timeline Fitter task within the HIPE environment  (Pearson et al. , 2014)  that fits a Gaussian function to the baseline-subtracted SPIRE timelines. The background is measured within an annulus of between 300 and 350 arcsec and then an elliptical Gaussian function is fit to both the central 22, 32, 40 arcsec (for the PSW, PMW, PLW bands respectively) and the background annulus.
For the present photometric atlas, which includes just the combination of IRAS and SPIRE photometry from 60 to 500µm that is available for the complete dataset, we restrict ourselves to the simplest of the parametric models available, a modified black body function where the emission as a function of frequency, ν is given by: F ν [ν, T, β] ∝ ν β B ν (ν, T) (1) where F ν is the flux density in Jy, β is an emissivity parameter that typically has a value between 1 and 2, T is a parameter specifying the dust temperature, and B ν (ν, T) is the standard black body function. We do not claim that the dust SED in the HERUS ULIRGs, or in fact any galaxy, is explained by dust of a single type at a single temperature, we simply use this particular commonly used SED parameterisation as a way of comparing the far-IR properties of the HERUS ULIRGs to those of other galaxy samples that have widely been fit using a similar parameterisation.
The only significant issue we find is a bias towards higher dust temperatures for fits using PACS+SPIRE data for input dust temperatures >50K, where the output temperature is high compared to that input. There were no substantial biases for the IRAS+SPIRE models comparable to the HERUS data. Since there are no PACS+SPIRE sources with T > 50K in our comparison samples we conclude that no significant biases are introduced to our analysis as a result of the slightly different wavelength bands being used.
Fit Results Plots of the fitted SEDs compared to the observational data can be found in Appendix B. The distribution of temperature and β values derived from these fits are shown in Figs. 3  and 4 , where they are also compared to similar results from the HRS survey  (Cortese et al. , 2014). The ULIRGs clearly have warmer temperatures than the lower luminosity HRS sources (median temperatures of 37. 9K, standard deviation 4. 7K for the ULIRGs compared to 21. 3K with a standard deviation of 3. 4K for the HRS sources). They also have very similar median β values (medians of 1. 7 and 1. 8 respectively), though the HRS sources have a wider range of β values. Comparing our results with previous ULIRG SED stud-ies such as the  Clements et al. (2010) , which are based on IRAS data combined with submm photometry at 850µm, and in some cases 450µm, shows broad agreement between the fits, with typical differences of less than 5K between the SPIRE and submm derived temperatures.
Deriving population averages from such samples, though, is difficult since previous work is largely based on incomplete samples with non-detections at submm for many of the fainter objects. The SPIRE derived temperatures are usually lower than the submm temperatures. There are three outliers where there is a large difference in derived temperature, with T S PI RE -T submm > -10K. One of these (IRAS08572+3915), which has the largest temperature discrepancy in the fitting (T S PI RE -T submm = -12. 5 K), had only a very low significance 850 µm detection (2. 7σ) so we might expect its submm SED fit to be unreliable. IRAS 10378+1108, with T S PI RE -T submm = -11 K, has a 4σ 850µm detection which, while comparatively low, was not unusual in the  Clements et al. (2010)  dataset. The final discrepant object, with T S PI RE -T submm = -10.
Temperature-β Relation The relation between dust temperature and β is shown in Fig. 5 , which also shows similar data for the HRS from  Cortese et al. (2014). As can be seen the HRS sources and the HERUS ULIRGs both show an anticorrelation between T and β, with hotter sources having a shallower β. The correlation coefficient for the HERUS sources is -0. 51 with a probability of this arising at random of 0. 00062. However, the relationships are quite different. The gradient of the anticorrelation is significantly shallower for ULIRGs, and there is a clear offset in temperature of about +15K between the HERUS and HRS samples. The temperature offset might simply come from increased dust heating in the higher luminosities of ULIRGs relative to the HRS galaxies, or possibly result from an AGN contribution to the luminosity, but the different relationship to β is more interest- ing.
fixed rather than variable β values), suggested that high redshift sources such as those of  Chapman et al. (2005) , lying at z ∼2, or those of  Huang et al. (2014) , lying at z > 4, occupied part of this diagram largely disjoint from more local high L F I R sources, with the SMGs having lower temperatures than nearby ULIRGs. The current data sets still show this to some extent, but the gap between the SMGs and more local ULIRGs is being filled, largely by sources from HERUS and  Magdis et al. (2014). The highest redshift SMGs in this plot, from  Huang et al. (2014) , also appear rather warmer than the lower redshift SMGs of  Chapman et al. This suggests that the earlier distinction between the two populations was likely a result of sample incompleteness, with earlier observations failing to cover cooler local ULIRGs or warmer high z SMGs.
What are the real Dust SEDs of ULIRGs? While we have fitted a parameterised single (T, β) model to the SEDs of the HERUS galaxies, it is far from certain that this simple model is adequate for fitting the data. A single temperature and β value for all of the dust in a galaxy is almost certainly an over simplification, especially since physical dust properties are likely to evolve differently in different environments in the same galaxy (see eg. Köhler et al. , 2015). Our current investigation uses only the five photometric points from Herschel and IRAS. More complex models can be fitted to those sources where more data is available -an example for this can be found for Arp220 in  Rangwala et al. (2011). Such an analysis will be the subject of a future HERUS paper, but this additional photomet-ric data is inhomogeneously distributed, with some HERUS sources being very well studied while others having little more data than is presented here.
Conclusions from such a study might thus be biased in some statistical sense towards, for example, brighter or more nearby ULIRGs. The use of a simple model that can be applied uniformly to a larger data set, as done here, can thus have substantial value. We can gain insights into the appropriateness of our simple parametric SED model by searching for trends in the flux residuals between our fits and the real data. We plot the relative residuals, (F model -F obs )/F obs against model dust temperature in Figure  8. The SPIRE and IRAS data behave quite differently. The model generally underpredicts SPIRE fluxes by about 20%, but there is no correlation between the residual and the modelled dust temperature -the results of fitting a straight line to the data finds correlation coefficients of -0.
This is not a surprise in itself since there are several ways in which the SED of a genuine object is likely to differ from a single temperature parameterization. These include the presence of dust components at other temperatures, whether cooler than the temperature found in our single T fits (eg. Clements et al. , 2010; Dunne & Eales, 2001) , the contamination of the 60µm band by significantly warmer dust (eg. Mrk1231 and Mrk273), or a flattening of the 60 to 250µm SED as a result of the dust being optically think at these wavelengths, as has been found for Arp220 by  Rangwala et al. (2011). A full analysis of these possibilities would require more data points than the five we have for the complete HERUS sample. However we we may gain some insight into these more complex models by seeing if the amplitude of the residuals and the correlation with temperature can be reproduced by simple toy models of more complicated dust SEDs.
Acknowledgements SPIRE has been developed by a consortium of institutes led by  Cardiff Univ. (UK)  and including:  Univ. Lethbridge (Canada) ;  NAOC (China) ;  CEA ,  LAM (France) ;  IFSI ,  Univ. Padua (Italy) ;  IAC (Spain) ;  Stockholm Observatory (Sweden) ;  Imperial College London ,  RAL ,  UCL-MSSL ,  UKATC ,  Univ. Sussex (UK) ; and  Caltech ,  JPL ,  NHSC ,  Univ. Colorado (USA). This development has been supported by national funding agencies:  CSA (Canada) ;  NAOC (China) ;  CEA ,  CNES ,  CNRS (France) ;  ASI (Italy) ;  MCINN (Spain) ;  SNSB (Sweden) ;  STFC ,  UKSA (UK) ; and  NASA (USA). HIPE is a joint development by the  Herschel Science Ground Segment Consortium , consisting of  ESA , the  NASA Herschel Science Centre , and the  HIFI, PACS  and SPIRE consortia. This research has made use of the NASA/IPAC Extragalactic Database (NED) which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration.