dataesr/scientific-paragraphs-categorization huggingface dataset:
Database Search and Quantitative Proteomic Analysis The gene ontology (GO) (http://www. geneontology. org), KEGG (http://www. genome. jp/kegg/), cluster of orthologous groups of proteins (COG) (http://www. ncbi. nlm. nih. gov/COG/), and other databases were used to annotate the identified proteins. GO annotation included the analysis of identified proteins with InterProScan v. 5. 22-61. 0 (European Bioinformatics Institute, Cambridge, UK) and databases Pfam (http://pfam. sanger. ac. uk/), ProPrInt (http://crdd. osdd. net/raghava/ proprint/), ProDom (http://prodom. prabi. fr/prodom/current/html/home. php), SMART (http://smart. embl. de/), ProSite (https://prosite. expasy. org/doc/PS51082) and PANTHER (http://www. pantherdb. org/). The KEGG and COG annotations were used in a BLAST comparison (E-value ≤ 10 -4 ) of the verified proteins, and proteins with the highest scores were annotated.
InterPro (IPR) also included the use of InterProScan software  [27]  with the Pfam, ProDom, and SMART domain databases. IPR was conducted with unknown proteins using their pattern structure or characteristics. Proteome Discoverer v. 2. 2 software was used to acquire the relative quantitative values of the PSMs of all samples. The values were based on the peak area of the plot generated by the original spectrograph. The relative quantitative values of the unique peptide fragments determined following calibration were obtained based on the quantitative data for all unique peptide fragments of each protein. For the differential analysis of proteins, the mean quantitative value of all biological repeats of each sample was used to calculate the ratio (fold change) between two samples. To determine the statistical significance of the difference, a T-test was conducted on the relative quantitative value of each protein for two samples to be compared.
When the fold change (FC) was ≥ 1. 5 and p ≤ 0. 05, protein expression was considered significantly increased (upregulated). When FC ≤ 0. 67 and p ≤ 0. 05, protein expression was considered significantly decreased (downregulated). Among all identified DEPs, protein-protein interactions (PPIs) were predicted in silico using software STRING v11. 0. (https://string-db. org/) [28]. The PPIs (more than two protein interactions) data that were highly similar to sorghum proteins were retrieved and their networks were drawn using Cytoscape V3. 6. 1 (http://www. cytoscape. org/).
Statistical Analyses The paired comparison T-test was used to determine if the differences between protein (log 2 Fold Change) and transcriptional (2 -∆∆CT ) data of seven candidate genes at 48 hpi were significant. A general linear model was fitted to all relative expression levels (2 -∆∆CT ) of each gene using the one-way ANOVA procedure, and Student-Newman-Keuls (SNK) test was performed with the mean values. All the statistical analyses were conducted with SAS version 8. 1 (SAS Institute, Cary, NC, USA).
Overview of Proteomic Profiling of Sugarcane Infected by X. albilineans iTRAQ analysis revealed 574,559 and 548,154 total spectra, 21,703 and 19,350 unique peptides, and 6126 and 5463 proteins for the non-infected control plants (R0_CK and S0_CK) and the plants inoculated with X. albilineans (R48_Xa and S48_Xa), respectively. A total of 6891 proteins (at least one unique peptide with > 95% confidence) were identified among all proteins from all samples when screened against the Saccharum spp. unigene database (P101SC18020747-01) (Table  1 ). Among those, 4295 proteins were annotated to 1099 GO terms after GO analysis. Among the 2231 proteins annotated in the biological process category, most proteins were distributed into the oxidation-reduction process (525 proteins, 24%), the metabolic process (280 proteins, 13%), proteolysis (163 proteins, 7%), and the carbohydrate metabolic process (156 proteins, 7%).
Protein-Protein Interactions (PPIs) Network Predicted in the STRING Database The STRING database is a large repository of protein-protein interaction networks, including functional interactions, regulatory interactions, and stable complexes of proteins. The PPIs of the 285 sugarcane DEPs were identified by submitting a protein query sequence in the search box of the database. Ninety-two DEPs interacted with each other in the two varieties inoculated with X. albilineans. Among those, 51 DEPs (36 upregulated and 15 downregulated) were found in LCP 83-384 whereas 61 DEPs (29 upregulated and 32 downregulated) were found in ROC20 (Figure  3 ). A majority of these proteins were enriched in the metabolic (ko01100), the biological secondary metabolic (ko01110), glutathione (ko00480) and glycolysis/gluconeogenesis pathways (ko00010).
High Level Expression of the UDP-Glycosyltransferase in the Sugarcane Cultivar Resistant to Leaf Scald The UDP-glycosyltransferase (UGTs; EC 2. 4. 1. 91) catalyzes the transfer of sugar molecules to a variety of acceptor molecules, such as hormones, lipids and other small molecules  [57, 58]. The glycoside molecule then regulates the biological activity, water solubility and stability of receptors  [57, 59, 60]. Up to now, 106 GT families have been identified in the carbohydrate-active enzyme database (CAZy; http://www. cazy. org/). The largest of these families is GT Family 1 that comprises a large number of UGT members  [61, 62]. UGTs play an important role in the regulation of plant hormone balance, detoxification of endogenous and exdogenous substances, and modification of secondary metabolites  [58, 60].
Conclusions This study provides the first global proteomic dataset of sugarcane in response to infection by X. albilineans. The DEPs that were identified are predicted to be involved in metabolic pathways and biosynthesis of secondary metabolites as well as some plant-defense related pathways, such as phenylpropanoid biosynthesis and plant immune signal transduction. Seven candidate genes coding for a UDP-glycosyltransferase, a non-specific lipid-transfer protein, an acytochrome P450 protein, a cytosolic glyceroldehyde-3-phosphate dehydrogenase, an argonaute family protein, the ubiquitin-activating enzyme E1, and the photosystem I P700 apoprotein A1 (chloroplast) were activated or repressed at the transcriptional level after inoculation with the leaf scald pathogen. These genes are good candidates for further investigations of the pathways involved in the sugarcane response to infection by X.
6 ±0. 8 ng of total RNA per sample. Assessment of RNA quality showed an RNA integrity number (RIN) of 10 for all samples, which represents the highest possible RIN score. The RNA thus obtained was then reverse transcribed and amplified using a commercially available kit, yielding 288±21 ng of cDNA per sample. Libraries were prepared from cDNA and sequenced using nextgeneration sequencing, which generated between 23. 7 and 36 million reads per sample. Reads were trimmed, mapped to the Danio rerio GRCz10 reference genome, counted, filtered and normalised. A principal component analysis (PCA) was then carried out on filtered and normalised expression data to explore patterns with respect to experimental groups. This revealed high duplication and low mapping rates for three samples from the 2 hpi experimental group, which did not cluster well with the other samples in PCA plots (Fig.
S2 ). Since inclusion of these samples would have caused signals from the remaining samples to be overwhelmed, they were excluded from further analysis. Hence, all subsequent analysis was carried out using six samples for the sham experimental group, and the three remaining samples for the 2 hpi experimental group. Filtering and normalisation were repeated for these samples before proceeding. Differential analysis was then carried out to compare gene expression between the sham and 2 hpi experimental groups. This identified 426 differentially expressed genes with a false discovery rate (FDR)<0. 01 (Fig. 1A ). Of these, 348 were upregulated and 78 were downregulated. These results show that neural injury leads to changes in the transcriptome of macrophage-lineage cells as early as 2 hpi. Importantly, the raw and processed data from our RNA-seq analysis are available through the Gene Expression Omnibus (GEO) database (accession number GSE140810).
Interestingly, the expression of odc1 is upregulated in macrophage-lineage cells after neural injury (Figs  1B  and 3 , Table  1 ), suggesting an increase in the rate of polyamine biosynthesis in the damaged CNS. To investigate whether this effects the extent of secondary cell death, we quantified cell death at 6 hpi in odc1 crispants. Intriguingly, odc1 knockout did not appear to have an effect on secondary cell death (Fig. 4D ). To confirm our result from odc1 gene knockout, we also sought to reduce odc1 function pharmacologically. For this, we used the ornithine decarboxylase inhibitor DFMO, which has been shown to inhibit polyamine biosynthesis in larval zebrafish  (Mastracci et al. , 2015; Mounce et al. , 2016). Consistent with our data from odc1 knockout, DFMO treatment had no impact on secondary cell death (Fig.
, 2019). This discrepancy might be due to the later time point of analysis in the latter study, capturing microglia in a regenerative state rather than during the acute phase of the injury. Overall, the transcriptional response of macrophage-lineage cells to acute neural injury appears to be conserved across vertebrate species, highlighting the relevance of our model system for the study of microglia and macrophage reactions to CNS injury. Importantly, all raw and processed RNAseq data from our analysis are available through the GEO database (accession number GSE140810). This dataset will provide a powerful new resource for studies aiming to understand early immune-cell reactions to CNS injury. Which aspects of the response of microglia and macrophages to CNS injury have neurotoxic or neuroprotective effects is the subject of ongoing debate  (Hu et al.
, 2014). The rawcounts table thus generated was filtered to remove genes consisting predominantly of near-zero counts and reads were normalised using the weighted trimmed mean of M-values method  (Robinson and Oshlack, 2010). A principal components analysis was then undertaken on filtered and normalised expression data to explore patterns with respect to experimental factors. The cumulative proportion of variance associated with each factor was used to study the level of structure in the data, while associations between continuous value ranges in principal components and categorical experimental factors was assessed with an ANOVA test. Differential analysis was carried out with edgeR  (Robinson et al. , 2010)  to compare gene expression between sham and 2 hpi experimental groups. Fold changes were estimated as per the default behaviour of edgeR, to avoid artefacts which occur with empirical calculation.
RT-qPCR For RT-qPCR, cDNA was synthesised from RNA using the iScript™ cDNA synthesis kit (Bio-Rad). RT-qPCR was performed on a LightCycler 96 Real-Time PCR system using SsoAdvanced™ Universal SYBR Green Supermix (Bio-Rad). Primer efficiencies were determined from cDNA dilution curves. The sequences, product sizes, optimal concentrations and efficiencies of primers used for RT-qPCR are listed in Table  S3. Analysis of RT-qPCR data was carried out using the Pfaffl Method, which takes primer efficiency into account  (Pfaffl, 2001). CRISPR/Cas9-mediated gene editing gRNAs were designed manually to target endonuclease restriction sites in exonic sequences of genes to be knocked out. At least four gRNAs were generated for each gene, and their efficiencies were tested by assessing the extent of loss of their targeted restriction sites through restriction fragment length polymorphism analysis.
Acknowledgements The authors thank the BVS zebrafish facility ( QMRI, University of Edinburgh ) for maintenance and care of the zebrafish. We are grateful to  Shonna Johnston  for help with flow cytometry; to  Pamela Brown  for help with RNA quantity and quality measurements; to  Linda Ferguson  for help with RT-qPCR; to  Jason Early  and  Katy Cole  for help with VAST screening, and to the  Becker, Lyons and Sieger labs  for sharing reagents and protocols. We thank  Jill Fowler  and  Dirk Sieger  for critical reading of the manuscript.
Funding This work was supported by a  University of Edinburgh Chancellor's Fellowship  to L. H. ; a  Wellcome Trust/The University of Edinburgh Institutional Strategic Support Fund  to L. H. ; a  Wellcome Trust Seed Award in Science  ( 207701/Z/17/Z  to L. H. ); a  Carnegie Trust Research Incentive Grant  ( 70457  to L. H. ); a  Rosetrees Trust/ Stoneygate Trust Seedcorn Award  ( M602  to L. H. ); and a  Royal Society   Research Grant  ( RG170428  to L. H. ). Analyses of RNA-seq data were carried out by  Edinburgh Genomics  at the  University of Edinburgh. Edinburgh Genomics  is partly supported through core grants from  NERC  ( R8/H10/56 ),  MRC  ( MR/K001744/1 ) and  BBSRC  ( BB/J004243/1 ).
METHODS This cross-sectional study was conducted on subjects registered for research trials and specialized assistance in the city of Salvador, Bahia, Brazil. The study was approved by the Research Ethics Committee of the Catholic University of Salvador under CAAE 49634815. 2. 0000. 5628, constituting a specific objective of a crossover clinical trial. All participants, after being informed of the collection objectives and procedures, signed the consent form that followed the recommendations of the Helsinki Declaration and National Health Council Resolution 466/12. Data collection was carried out in the biomechanics laboratory of the Catholic University of Salvador, from April 2016 to January 2017, by a trained team of physiotherapists. The evaluated population was comprised of patients seropositive for HTLV-1, with defined or likely HAM/TSP, according to the guidelines of the World Health Organization (WHO) published in 1988, revised in 1989  15  , in addition to the ability to remain in orthostasis for at least 30 seconds.
For baropodometry evaluation, through the FootWork® software, we considered the total area of oscillation (TAO) in cm², and anteroposterior (APO) and lateral oscillations (LO) in cm. Video recordings were performed during all the tests using a video camera (GoPro HERO 3. 0, GoPro Inc. ®, San Mateo, Califórnia, USA) in the sagittal, and left and right views. For the delimitation of the anatomical landmarks, the SAPO® Protocol (http://sapo. incubadora. fapesp. br/portal) was used. The anatomical points were marked using 25 mm semi-beads affixed with doublesided Scotch® adhesive tape. The video camera was adjusted using an Elgin adjustable height tripod (TEEM® TM 3180, Rio de Janeiro, Brazil). The tripod was positioned three meters from the platform and at half the height of the participant. Subsequently, the video was transferred to a laptop, and the kinematic evaluation was performed using CVMob.
83 of the Berg Scale scores, a between-group difference in the BBS of 10 points  18  , a 5% alpha value, and study power of 80%. According to these characteristics, a sample size of 12 participants was estimated for two groups, one with HAM/TSP and the other composed of healthy participants. The comparative group was composed of an HTLV seronegative family member or caregiver, systematically assessed by a multidisciplinary team at the reference center. All data were tabulated and analyzed by the Statistical Package for the Social Sciences (SPSS®, IBM SPSS, University of Chicago, USA) version 25. 0. Initially, descriptive analyses were performed through the distribution of absolute numbers and proportions for categorical variables, with mean and standard deviation for the normally distributed quantitative variables.
The Shapiro-Wilk test was used to test normality. The sociodemographic characteristics of the two groups were compared using the chi-squared test. For continuous variables, the t-test for independent samples was used. For the inferential analyses of the differences in the measures, an unpaired t-test or Mann Whitney U test was used, according to the distribution. To test the correlation, the Spearman's correlation test was applied. The construction of composite variables was based on the compute variable option in SPSS to create a new variable that results from the joining of two. The acceptable level of significance was 5%.
Thus, two-dimensional kinematics may be useful in the evaluation of this population  11  , but with restrictions in the data analysis. Despite the limitation of performing only two-dimensional analyses, the CVMob software used in the present study for kinematic analyses, proved useful in the evaluation of stabilometric oscillations at the three selected angles, with moderate correlation with the BBS and with the TUG, regarding lateral oscillation. These findings point to the indication of the use of kinematics with the aid of simple images acquired with smartphone cameras in the follow-up of these patients. With an application time (about 60 seconds for calibration and collection) less than or equal to the scales (30 minutes for BBS and 60 seconds for TUG) and baropodometry (about 90 seconds), it was able to detect the general equilibrium state.
, 2018). This bioinformatic tool provides high accuracy (>90%) functional annotation of sequencing reads, using a reference database of experimentally verified microbial enzymes. Our results highlighted significantly lower metagenome-to-metatranscriptome similarity in the early spring than in the late spring samples. We also found that in the late spring samples, the abundance of sequencing reads mapping to the components of the fatty acid synthesis-related microbial pathways significantly correlated with the experimentally determined levels of organic acids. We further observed that the rise in organic acid levels correlated with the enrichment of the geraniol degradation pathway use and the depletion of the styrene degradation pathway. This finding might represent a change in nutrient conditions during the community growth period.
| Data collection and preprocessing We obtained the metagenomic, metatranscriptomic, and chemistry (organic acid) data for 20 snow samples from the Environmental Microbial Genomics Group (see Data Availability Statement). The technical details of sampling and sequencing are described in Bergk  Pinto et al. (2019). Briefly, snow was collected over two months from mid-April to mid-June at Ny Ålesund on the Spitsbergen island of Svalbard, Norway (78°56′N, 11°52′E). The field site, a 50 m 2 perimeter with restricted access (to reduce contamination from human sources), is located along the south coast of the Kongsfjorden, which is oriented SE-NW and open to the sea on the west side. The site is in a bird sanctuary (for migratory birds that generally arrive in June) and the closest building is located 1 km away.
025, Figure  2b ). Note that for all comparisons, ~29% ECs (195 of 683) in our data could not be mapped to known KEGG pathways (Appendix B at https://doi. org/10. 6084/m9. figsh are. 12290711). The discrepancy in functional annotation of metagenomes (DNA) and metatranscriptomes (cDNA) of the same samples has previously been observed in environments such as the human gut  (Franzosa et al. , 2014)  and open ocean  (Shi, Tyson, Eppley, & DeLong, 2010). The genes observed in the metagenomes represent potential functions that may or may not be expressed in the environment at the time of sampling and could belong to inactive community members. The metatranscriptome-specific functions, on the other hand, belong to active members of the community at the time of sampling  (Yu & Zhang, 2012). The exceedingly low metagenome-to-metatranscriptome similarity (high distance/dissimilarity) in the early spring samples (Figure  2 ) suggests that the active members (organisms and molecular functions) in early spring occur at such low abundance that metagenomic sequencing fails to detect them.
We speculated that the potential functional diversity in the early spring metagenome samples (Figure  1   (Nikrad et al. , 2016; Price & Sowers, 2004). With the warming in the late spring, the active community made up a larger fraction of the sequenced reads and, thus, manifested in more homogeneity. Previous 16S rRNA-based taxonomic analysis on the same dataset also observed a shift in the community from early to late spring  (Bergk Pinto et al. , 2019). While the early spring samples contained a core community of 59 OTUs, there were only 29 OTUs in the late spring samples, with 42 early spring core OTUs disappearing from the core community of late spring samples (and 12 late spring-specific OTUs appearing)  (Bergk Pinto et al. , 2019). The early spring community thus contained a higher diversity of organisms of which only a small fraction was likely active; the inactive community members could no longer be detected in the late spring samples.
| CON CLUS IONS We defined microbial activity at low temperatures as the gene abundance level in metagenomic and metatranscriptomic datasets from snow in early and late spring. Our results highlight the novel microbial activity of complex organic compound degradation at low temperatures. A further in-depth exploration of the functionality of the cryosphere inhabitants can contribute to our understanding of microbial metabolism at low temperatures and aid in the discovery of novel enzymes with potential industrial and bioremediation value.
; Figure A1; DNA datasets) might come from the DNA of dead or inactive cells preserved in the snow. Interestingly, many microorganisms identified in snow and ice via 16S rRNA gene surveys are non-psychrophiles (Cowan & Tow, 2004) and their membership in the community needs further investigation. Meanwhile, the diversity of active microbial functionality in the early spring metatranscriptomes (Figure 1; Figure A1; RNA datasets) reflected diverse microbial activities (238 enzymatic functions involved in 84 metabolic pathways including cell size reduction, changes in fatty acid and phospholipid membrane composition, and decrease in the fractional volume of cellular water). This observation is in line with the known variety of survival strategies employed by microbes at low temperature.
To avoid this problem, we follow the paper by  Da et al. (2011) , by downloading all the GSV index data once and no data change thereafter. Downloading GSVs values several times, results in most correlations to exceed the value of 97%. We focus on firms listed on the NGSE-30 index on the floor of the Nigerian Stock Exchange as at June 2019. GT reports data from Sunday to Saturday. The raw downloaded GSV cannot be applied directly in analysis, because its value depends on the time period of downloaded data, hence, the need for standardization of GSV data relative to its past history.
Abnormal GSV This study follows the standardization formula by  Bijl et al. (2016)  and  Kim et al. (2019) , where the average of the past 52 weeks is subtracted from the weekly raw GSV and dividing their difference with the standard deviation of the previous year. Then we compute the abnormal GSV (hereafter referred to as AGSV). AGSV t ¼ X t À X n i¼1 X i n σ X (1) where; X t represents search volume of company i in week t; Note: The Wiki data are downloaded from the English language Wiki web, launched on the 15th of January 2001 and available on https://en. wikipedia. org. The data are converted to weekly data and transformed using Eqn (1) framework. Firm's Wiki page views can be downloaded daily (both in weekdays and weekend) and months.