dataesr/scientific-paragraphs-categorization huggingface dataset:
Dried polar phases were shaken for 1. 5 h at 30 °C in 40 µl of MeOX (40 mg ml -1 methoxyaminhydrochloride in pyridine) followed by 30 min shaking at 37 °C in 80 µl of BSTFA mixture [70 µl of N,O-bis(trimethylsilyl)trifluoroacetamide+10 µl of alkane mix]. The derivatized metabolites were subjected to GC-MS analysis (Agilent Technologies 7890, USA) coupled with MS. Measurement from an injection volume of 1 µl was taken in split-less mode in a DB-5 column (30 m×0. 25 mm, 0. 25 µm film thickness, Agilent) using helium as carrier gas. Metabolites were separated as described by  Wagner et al. (2013). Qualitative analysis of chromatograms was performed on the MassHunter Qualitative analysis Sp1 workstation (Agilent, USA). Identification and annotation of each compound was supervised manually using AMDIS software and the NIST08 database (https://www.
nist. gov/srd/niststandard-reference-database-1a-v17). Data were normalized to sample weight and internal control (sorbitol). Statistical analysis was performed as described previously  (Quanbeck et al. , 2012). The log2 ratio of metabolite abundances in -Fe was plotted against +Fe. Delta method approximation was used to calculate the SEs of the log ratio, SE log ratio=1/ ln 2√[(SE T /T) 2 +(SE C /C) 2 ], where SE T and SE C are standard errors of average -Fe and +Fe metabolite abundances.
Differential expression analysis, homoeologue (A, B, and D) induction, and expression bias during -Fe response The effect of Fe starvation on the wheat root transcriptome has not been investigated to date. To perform this study, RNA-seq technology was used to identify the changes in the transcripts of wheat roots, where plants were grown in the presence or absence of optimal Fe (20 DAS). Our analysis resulted in 87 million quality filtered reads, with an average of nearly 22 million reads from each sample (>87% of reads had a quality score >30). Filtered reads from the four libraries had a mapping rate ranging from 81. 7% to 85. 4% when mapped against release-37 of the wheat genome using TopHat (Supplementary Table  S2 ). As a quality check, a strong correlation within the two biological replicates from each condition was observed, while a clear variation was seen between the two conditions (Fig.
2A ). We thereafter analysed the expression values as FPKMs, calculated by using Cufflinks software. DEGs were then identified by calculating logFC and performing statistical tests between FPKM values from control and stressed samples using CuffDiff. A total of 50 610 genes with an FPKM of ≥1 in at least one of the two conditions were considered to be 'expressed transcripts'. In all, 7221 genes had logFC >0 and 8010 had logFC <0 (Fig. 2B ). On setting up a criterion of logFC >1 for up-regulated genes, and of < -1 for down-regulated genes, and an FDR of <0. 05, a total of 3478 genes were highly expressed, whereas 2376 were down-regulated under -Fe conditions in wheat roots (Fig. 2C ). Interestingly, 45 genes were also induced exclusively during starvation conditions when compared with the control samples (Fig.
2C ; Supplementary Table  S3 ). Our data allowed us to analyse the chromosomal distribution of the DEGs under the -Fe condition. While all chromosomes contributed to the DEGs, the highest number of genes was mapped on chromosome 2 of the wheat genome (Fig. 3A ). Equal representation of transcripts was observed for chromosomes 7 and 5. The remaining chromosomes, 1, 3, 4, and 6, showed 13, 15, 12, and 10% distribution of DEGs, respectively. In polyploidy crops such as wheat, homoeologue induction bias could impact plant response to various stresses  (Liu et al. , 2015; Powell et al. , 2017). To determine the extent of induction bias (from A, B, and D subgenomes) in wheat during Fe starvation, homoeologue-specific expression analysis was performed. Starting from a list of 8473 gene triplets present/expressed selected as 'accepted triplets' (Supplementary Table  S4 ), most of the homoeologue triplets (8349) showed no significant bias  in expression (A=B=D).
5A ). Overall, catalytic activity and binding activities were the most significantly enriched GO terms in the -Fe condition (Fig. 5B ). Further mapping of up-regulated genes to databases such as the Kyoto Expression changes within triads with respect to iron stress were observed to identify the presence of differential regulation among homoeologues from each triad, thus contributing towards genome induction bias. The table gives a descriptive list of homoeologues that were up-/down-regulated under iron starvation only in the A subgenome, followed by those only in the B subgenome Table  2. Continued Encyclopedia of Genes and Genomes (KEGG) pathway  (Xie et al. , 2011)  and MapMan  (Thimm et al. , 2004)  revealed enrichment related to phenyl-propanoid biosynthesis, amino acid biosynthesis, carbon metabolism, and glutathione metabolism (Fig.
For expression analysis, FPKM values were obtained using Cufflinks, and CuffDiff was used to identify DEGs by calculating significant changes in transcript expression between the stressed and normal samples (FDR ≤0. 05). genes involved fall into the category 'response to phosphate starvation'. In the enriched network of down-regulated DEGs, genes involved in Fe and other metal homeostasis were associated with two different clusters, mainly including genes related to lipid, ketone, and carboxylic metabolism, in addition to those involved in nitric acid and the salicylic acid response (Fig. 6B ). A second cluster contains transcripts related to purine and adenine nucleotide binding and pyrophosphatase activity. Thus, the whole set of significant up-and down-regulated DEGs were clustered in distinct cellular processes, suggesting a differential transcriptional response under Fe starvation.
GSTs are involved in the response to Fe starvation in wheat Glutathione-mediated conjugation of multiple metabolites plays an important role during metal stress  (Zhang et al. , 2013). Our transcriptome data revealed the enhanced expression of multiple GSTs in Fe-starved wheat root as compared with control (Supplementary Table  S9 ). To correlate the expression response with its enzymatic activity, the temporal response of GST was measured in wheat roots under -Fe conditions. Using the GST functional assay, the activity was determined in wheat roots of plants grown for 10, 15, and 20 d under Fe starvation. Our activity assays showed a significant increase in the GST activity under -Fe conditions, which peaked at 10 d and 20 d after the beginning of Fe starvation compared with control plants (Fig.
Fe starvation causes an accumulation of organic acids and polyhydroxy acids in wheat To obtain a comparative insight into the metabolite profile of wheat roots of plants grown in the absence or presence or Fe, GC-MS profiling analysis was performed. Metabolites were extracted from the roots of three replicate pools of plants each containing eight seedlings. Qualitative processing of each chromatogram for peak area and identification was performed with MassHunter version B. 05. 00 software coupled with the NIST11 compound library. The compound annotation was determined by comparing individual resolved peaks with library searches based on mass spectra and compound chromatographic retention indices. Interestingly, analysis resulted in the identification of 54 metabolites, and a further 39 annotated metabolites were analysed for their response ratio (Supplementary Table  S10 ).
, 2011). Altogether, based upon the high similarity and response under Fe starvation, it is tempting to propose wheat ZIFL (ZIFL4) as a functional transporter of DMA. However, other ZIFL genes were also significantly expressed, but a functional wheat transporter for DMA needs to be deciphered. PS secretion was also observed during our analysis of the roots of C306 exposed to Fe starvation. In this study, a high PS release of ~40-45 nmol g -1 root biomass (3 h) -1 was observed, reinforcing our RNA-seq data showing that DMA biosynthesis genes and its transporters were highly up-regulated. Control plants show accumulation of ~1-2 nmol g -1 , suggesting that wheat releases a basal level of PSs to maintain the constant flux of Fe in roots. Upon careful analysis of the RNA-seq data, it was observed that multiple such efflux transporters are represented in the list that are highly abundant under Fe starvation.
Acknowledgements The authors thank the Executive Director,  NABI  for facilities and support. This research was funded by a  NABI-CORE  grant to AKP and partial support from a  DST-SERB  grant ( PDF/2016/001355 ) to PG. GK and AK acknowledge  NABI-SRF Fellowships. Technical help from Fe starvation response in hexaploid wheat roots | 6159 Jagdeep Singh  and  Dr. Rupam K. Bhunia  for the GC-MS analysis is highly appreciated. The  DBT-eLibrary Consortium (DeLCON)  is acknowledged for providing timely support and access to e-resources for this work. The wheat genome resources developed by the  International Wheat Genome Sequencing  Consortium are highly appreciated. We also acknowledge the critical comments provided by  Dr Kerry Pedley ,  USDA, USA. We would like to thank  Abhijeet Panwar  ( CDAC-Pune ) for his help in the preparation of the Sankey plot.
For the ICP-MS analysis, samples were dissolved using a mix of nitric and perchloric acids (HNO 3 +HClO 4 ), in an Open beaker system. The instrumentation used was a ICP/MS-DRC-Elan 6100, Perkin-Elmer, Norwalk, Connecticut  (Linderholm, 2010 ). Here we report data from sulfur (S), phosphorous (P) and calcium (Ca) as these elements partly represent bone matter in the analyzed context. 3. F I G U R E 3 (a) All materials analyzed by NIR were also subjected to wet chemical analysis (see Section 2. 3).
| JDSU MicroNIR field spectrometer measurement The JDSU MicroNIR 1700 is a portable instrument to be connected to a tablet (Windows 7 based software) or laptop computer for field work. The probe is 42 mm (diam) by 42 mm (length) in size (see also  Linderholm, Geladi, & Sciuto, 2015). It is based on an InGaAs detector and two built in quartz halogen lamps and it gives absorbances for 125 wavelengths (908-1,676 nm). This is approximately a wavelength every 6 nm. The obtained data set was thus 16 × 125. Calibration was done by using dark current for 0% and reflections from a Spectralon (Labsphere, Inc. , North Sutton, NH) standard for 100%. The spectra in reflectance were transformed into absorbance. Three measurements per sample were collected and subsequently averaged. The JDSU is the only portable instrument used for the data in this paper and it is therefore important because it has a high potential for field use.
For each sample, dark current was recorded from all measured spectra and subtracted. A Spectralon (Labsphere, Inc. ) white reference was used as a reference to standardize spectra from nonuniformities of all components of the instrumentation. Three measurements per sample were collected and subsequently averaged to give a 16 × 2151 data matrix of absorbance values  (Araújo et al. , 2015 , Cambou et al. , 2016).
| Hyspex SWIR measurements The NEO Hyspex (Norsk Elektro Optikk, Lørenskog, Norway), a pushbroom hyperspectral camera was set up on a translation stage operated by a stepper motor and a computer supported by NEO software (Norsk Elektro Optikk). An illumination unit including 150 W tungsten halogen source mounted at a 45°angle was used to illuminate the sample. The hyperspectral camera covers the spectral range from 1,000-2,500 nm in 256 bands (spectral resolution of 6 nm) with 320 pixels over the cross-track field of view (FOV). The camera was equipped with a 30 cm lens that produced a nominal pixel size of approximately 0. 287 mm across and 0. 427 mm along the FOV, respectively  (Mishra et al. , 2016). Settings of acquisition parameters were performed by means of the software provided by NEO.
| Multivariate data analysis The data produced by the NIR spectrometer and imaging equipment consisted of many variables and required multivariate data analysis. Before all analysis, all wavelengths below 1,000 nm were removed because these only represent the visible light reflection (color) of the samples. Principal component analysis (PCA) was done to get an overview of large multivariate data sets with possibly correlated variables. A few principal components were extracted to represent the spectral information contained in the data  (Wold, Esbensen, & Geladi, 1987). The Evince software was used for making and testing PCA models (Prediktera 2. 7. 4). PCA was used on average spectra from the NIR probes, but also on the hyperspectral images from the NEO images. Especially in imaging, a correction for scattering and offset of each pixel spectrum is often useful.
| Multivariate analysis of NIR spectra The PCA was performed on the ASD and MicroNIR spectra. Components 1 and 2 are shown for the ASD data (Figure  6 ). In Figure  6 , the first component (t1) separates the samples in two clusters (positive-negative). On the negative side, there is also a separation in two clusters in the t2-component (positive-negative). Similar PCA clustering was also obtained with the Hyspex (not shown here). For the MicroNIR, separation of objects was found only for Component 1. The second and higher components were too noisy to show anything useful. In Figure  6 , two objects (B10-B11) do not belong to the neighboring group. This is purely based on visual observation in the.
| DISCUSSION The real world situation of archaeological observations is different from that of process industry or food quality assurance in that all samples are unique and final and that repeats are not possible to acquire. We only have data from the endpoint state of all previous events as in this case to understand the process forming the archaeological record. The conditions are not fully controllable or repeatable, which is a very different situation to most experimental situations. Still, the results in this study show that it is possible to use a data analysis approach similar to the process industrial one to get new information from archaeological contexts. It was shown the NIR spectral information combined with multivariate analysis could be used to find soil horizon traits. The NIR measurements on the soils samples show that classes similar to those obtained by wet chemistry can be obtained that clearly shows the potential of using NIR spectroscopy in archaeological contexts.
, 2016) , maintaining biological activity for Wnt3 and Wnt3a, and partial activity for Wnt1, Wnt7a, Wnt7b, Wnt8a, Wnt9b and Wnt10b, which play a crucial role in cell differentiation during embryogenesis and are involved in the development of various diseases including cancer  (Nusse & Varmus, 2012 , Nile & Hannoush, 2019). A compelling computational model of lipid-bound afamin in a monoclinic crystal form (PDB entry 5OKL) docked with a homology model of Wnt3A indicated that afamin can capture the acyl chain of palmitoylated Wnt3A serine 209 in a deep hydrophobic pocket  (Naschberger et al. , 2017). The preparation and crystallization of afamin has posed significant challenges and is non-trivial  (Altamirano et al. , 2018). In contrast to human serum albumin (HSA), which is exported from the liver as a non-glycosylated chain, human afamin in vivo is highly and variably enzymatically glycosylated.
Data collection Prior to dehydration experiments, 348 crystals were screened (Figure  2 ) on the fully autonomous ESRF beamline MASSIF-1  (Bowler, Nurizzo, et al. , 2015 , Nurizzo et al. , 2016)  at a fixed wavelength of 0. 9660 Å. Data were processed by the EDNA automated data processing pipeline  (Monaco et al. , 2013)  employing XDS and XSCALE  (Kabsch, 2010)  and pointless/aimless/ctruncate from the CCP4 program suite  (Winn et al. , 2011)  and xtriage from the phenix suite  (Adams et al. , 2011). Strategy calculations accounted for flux and crystal volume in the collection parameter prediction for complete data sets and in the case of the monoclinic mP lattice, the symmetry was pre-set  (Svensson et al. , 2015). Large scale screening was required as a wide variety of crystal volumes was observed with very few diffracting to high resolution (Figure  2 ).
MeTHodS We conducted this systematic review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. 17 A full protocol is available on the PROSPERO database. definition HPV self-sampling is a process where a woman who wants to know whether she has HPV infection uses a kit to collect a (cervico-)vaginal sample, which is then sent for analysis by a laboratory. Collection methods include lavage, brush, swab and vaginal patch. While HPV self-sampling cannot provide a diagnosis of cervical (pre-)cancer, it identifies those women at higher risk. The privacy afforded by self-sampling may encourage more people to get tested compared with Pap smears that, especially in LMICs, still have low coverage due to limited population awareness and lack of availability.
Search strategy and screening process We searched the electronic databases PubMed, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Latin American and Caribbean Health Sciences Literature (LILACS) and Embase through 19 October 2018 using search terms for HPV and self-sampling. The full search strategy is available in online supplementary file 1. Secondary reference searching was also conducted on all studies included in the review and on four relevant systematic reviews. 12 14-16 Ongoing randomised controlled trials (RCTs) were searched for through ClinicalTrials. gov, the WHO International Clinical Trials Registry Platform, the Pan African Clinical Trials Registry and the Australian New Zealand Clinical Trials Registry. After initial title-abstract screening, full-text articles were obtained of all potential studies.
data extraction and analysis Two reviewers independently extracted data and conducted the quality assessments. Standardised data extraction forms included fields for study citation, objectives, location, population characteristics, description of the type of HPV sampling, description of any additional intervention components, study design, sample size, follow-up periods and loss to follow-up, analytic approach, reported numerical outcomes, results, and limitations. For RCTs, risk of bias was assessed using the Cochrane Collaboration's tool for assessing risk of bias. 18 For observational studies that presented comparative data, study rigour was assessed using the Evidence Project risk of bias tool for intervention evaluations. 19 here available, comparative data were stratified by the following categories: supervised (defined as direct support through inperson demonstration of how to perform self-sampling and follow-up support, counselling, referral and linkage to care) versus unsupervised (defined as indirect support through video, instructions for use, package inserts, hotline, or other information which provides counselling, support, referral and linkage to care); age (<50 vs ≥50 years); vulnerabilities (relating to poverty, disability, living with HIV, sex work or literacy); and high-income versus low-income or middle-income countries.
Data were analysed according to coding categories and outcomes. Where multiple studies reported the same outcome, we conducted meta-analysis using random-effects models to generate pooled relative risk (RR) using the Comprehensive Meta-Analysis program. 20 Where studies reported both intention-to-treat and per-protocol analyses, we used intention-to-treat data. Where studies reported multiple follow-up periods, we used the longest follow-up time. Heterogeneity was assessed using both Q and I-squared statistics, and funnel plots were created to examine the potential for publication bias. Data from RCTs and non-randomised studies were analysed separately. For the uptake of cervical cancer screening outcome, we combined authors' reported data on screening participation, attendance, response and compliance.
ReSulTS Electronic database searching retrieved 1847 citations. Searching trial registries, contacting experts in the field and secondary searching identified an additional 11 citations (figure  1 ). After removing duplicates, there were 1099 unique citations. After initial screening of titles and abstracts, 361 citations remained. After two independent reviewers screened in duplicate and gained consensus, 149 articles were pulled for full-text review. Of these, 33 studies (reported in 34 articles) were included in the review. 93  from high-income countries. Studies occurred in urban (n=14), mixed urban/rural (n=15) and rural settings (n=5).
Strengths and limitations This study had several strengths. We included both randomised and non-randomised studies, searched multiple databases, considered studies in any location or language, and used meta-analysis to present pooled effect sizes. Other reviews have examined the impact of HPV self-sampling on screening participation among women who are never or infrequently attending cervical cancer screening services, reporting that self-sampling has great potential to increase attendance among underscreened women compared with invitations to have a Pap test done by a health professional. 12 14-16 This review expands on previous work by incorporating evidence from general populations and other segments of the population, BMJ Global Health finding similar increases in uptake overall, not just among non-responders.
Acknowledgements We thank  Laura Ferguson  for her contribution to conceptualising this review, and  Patricia Garcia ,  Gina Ogilvie  and  Nandi Siegfried  for their thoughtful comments on the protocol. We thank  Maribel Almonte ,  Nathalie Broutet  and  Anandi Yuvraj  for reviewing an early draft of this manuscript. We also thank our  Johns Hopkins Bloomberg School of Public Health  graduate students ( Melissa Camila Alamo ,  Chergai Gao Rittenberg ,  Priyanka Mysore ,  Holly Nishimura  and  Laura Graf ) for their assistance in searching trial registries, screening citations and extracting data. Finally, we thank  Janet Austin ,  Louise Cadman  and  Zahra Modibbo  for graciously providing additional stratified data for our meta-analyses. Contributors MN conceptualised the study. CEK and PTY designed the protocol, with feedback from HdV.
Furthermore, because there are clear differences in host range and virulence/ pathogenicity patterns across the three pathotypes (as mentioned above) but only a handful of genes associated with A* and A w phenotypes have been found so far  [13, 14] , it is important to expand the search for pathotypeassociated suspected genes. Given the presence of well-supported yet contrasting resolutions of pathotype relationships in the phylogenomic studies of Zhang et al. [13]  and Gordon et al. [14] , together with the availability of more genomes from the outgroup studied by Bansal et al. [21] , we aimed at a more inclusive phylogenomic dataset in terms of both ingroup (XCC) and outgroup (remaining XCP), also including five new A and one new A* genomes sequenced by our group. Besides minimizing artifacts such as some types of long-branch attraction, this inclusive analysis allows finer-grained analyses of presence/absence of genes, biogeographic patterns, and divergence dating estimates due to an increased number of nodes along the phylogeny, making evolutionary transitions detectable at a finer scale.
More specifically, our analyses considered different sources of phylogenetic and molecular dating bias (method, dataset, and effect of recombination) that may be impacting the resolution of pathotype relationships and inference of divergence times. At the same time, we inferred the ancestral XCC host, where it originated and when, and whether dispersion or vicariance was the most dominant force in the evolution of XCC. We also assessed 120 pathogenicityrelated genes that could have contributed to the evolution of XCC lineages: 63 effectors from the Xanthomonas. org database; and 57 genes with virulence effects whose presence or absence across XCP had not been systematically verified  [33] [34] [35] [36] [37] [38].