dataesr/scientific-paragraphs-categorization huggingface dataset:
L. Girod et al. :  Precise DEM extraction from high oblique imagery of Svalbard in 1936 position of fiducial marks) and (2) location information in the form of ground control points (GCPs) or navigation data. -Stereo overlap is low (typically 60 % along-track and 20 % cross-track) compared to modern standards (typically around 80 % along-track and 60 % cross-track). Challenging scenarios are, however, common when metadata are impossible to find or image quality is particularly bad. For instance, large datasets over Antarctica include regions with very little off-glacier stable areas to provide sufficient GCPs, as well as large areas of low-contrast snow fields yielding no tie points and bad or no correlation  (Child et al. , 2017). Another common type of imagery is oblique imagery. It was acquired primarily in areas previously unmapped or only superficially so.
Absolute orientation The absolute orientation requires GCPs, but no such points were recorded at the time of acquisition in 1936. It is therefore necessary to rely on modern data coverage of the area from which to gather GCPs. To do so, prominent points in stable areas are identified both in the historical images and in a modern source. For our case study, at the time of processing the orientation, the 1990 DEM from the Norwegian Polar Institute represented the best data available and was therefore used. The GSD is 20 m and the SD with the 2008 DEM is below 2 m. This step is very challenging since most of the lowlands, on top of being generally unrecognizable by a lack of strong structures, cannot be considered stable over 50 years: icecored moraines and permafrost areas, thermokarst processes, unstable cliffs, or simply erosion renders the lowlands unstable.
Specifically, one should mask the sea, the sky, clouds, and background land visible in the image but not in the area of interest. The masking can be completely manual, but it is then a very time-consuming task, especially for large datasets. It is possible to automate the detection of sea, sky, and clouds by running an entropy-based texture analysis, but differentiating foreground and background is more difficult (Nielsen, 2017) (see Fig. 4 ). Additional masking through the use of a polygon defining the area of interest in ground coordinates is also possible, either by back-projecting a known DEM of it into the individual, then-known image geometries or by filtering out-of-area points during the iterative dense correlation process. These latter approaches were not used in our case study.
Data quality A first approach to determine the data quality is to check the residuals of the orientation. Here, after using GCPs to reevaluate the calibration, the average residual of the tie points was 0. 803 pixels, and the GCP reprojection error was of 4. 108 pixels with an RMSE of 18. 15 m. The relatively high RMSE value is most likely due to the relatively soft DEM used as an input for GCP identification. The quality of the data is better estimated by pulling statistics from various dif-  In order to determine their accuracy, our 1936 elevation data were compared to other DEMs. The NPI most modern DEM product (S0 product) over the area is extracted from the 2008 aerial survey of Svalbard and is also at 5 m GSD. It has a declared accuracy of 2-5 m (https://doi. org/10. 21334/ npolar. 2014.
Alpine glacier example: Millerbreen In this example, we show the data gathered for the alpine glacier Millerbreen. In 1936, it was land terminating, reaching nearly to its moraine, while in 2008 (and up to today), a large part of the front calves into a lake that formed inside the barrier formed by the moraine. Figure  9  shows the dDEM over the glacier and a profile line over the two DEMs and the dDEM. We can see that during the 72 years separating the two datasets, the glacier retreated by approximately 900 m (12. 5 m year -1 ), losing up to 95 m of ice thickness. The dH curve is very typical for a retreating glacier, linearly going from zero at the old tongue edge to a maximum loss at the new tongue edge and then reaching back to zero at a slower rate up to the top edge of the accumulation zone.
Marine-terminating glacier example: Søre Buchananisen Søre Buchananisen is a large marine-terminating glacier that experienced a significant retreat (up to 2. 0 km) in the 72 years separating the two datasets (27. 8 m year -1 ). The dDEM shows the new ice edge very clearly as the elevation loss is total in the retreated area and only partial from the edge of the ice cliff upwards. The change in the glacier seems to display several basins, with the northern and southern parts retreating fast, separated by a (now) land-terminating middle section. The southern part itself seems to experience contrasting behavior, with the land-terminating part retreating at a significantly lower rate than the marine-terminating front. A quite sharp rectangular indent in the glacier front is visible (see the red circle in Fig.
Acknowledgements. The study was funded by the  European Research Council  under the European Union's  Seventh Framework Program  (FP/2007-2013)/ ERC  grant agreement no. 320816  and the  ESA projects Glaciers_cci  ( 4000109873/14/I-NB ),  DUE GlobPermafrost  ( 4000116196/15/IN-B ), and CCI_Permafrost (4000123681/18/I-NB). Data scanning and distribution was funded by the  Norwegian Polar Institute. Couderette's internship was financed in part through the  Erasmus program. We are very grateful to the  Norwegian Polar Institute , specifically to  Harald F. Aas  for providing the raw imagery data for this study and the modern validation data. We also acknowledge the help from  Marc Pierrot-Deseilligny  and the  MicMac team at IGN/ENSG  for their help in the development of the software. Edited by:  Nicola Masini Reviewed  by:  Simon Buckley  and two anonymous referees.
Similar findings of a blood signature in active TB patients have since been reported  23, [24] [25] [26] [27] [28] [29]  , and our meta-analysis of 16 datasets, including many of these studies, identified 380 genes differentially abundant in active TB across all datasets  30. However, there is a relative lack of concordance across studies that have reported a reduced and optimised diagnostic gene signature, although an agreement exists for some of the pathways they represent  23, 24, 25, 31, 32. While some genes overlap between the different reduced signatures, the overall composition of each reduced signature is unique, both in size and transcript profile. In this respect, we note that a consistent statistical approach to optimising gene selection has not been used across studies, and where the approach was consistent, a different optimal reduced signature was reported for discriminating active TB from either LTBI and controls or other diseases  24.
32  and the 27-and 44-gene signatures by Kaforou et al. 24  (Fig. 3a ). Only the Kaforou 44-gene signature was developed to discriminate between active TB and other diseases (including infectious meningitis, pneumonia, gastric diseases and malignancies)  24  , rather than LTBI. The composition of all three signatures  24, 32  is dominated (≥50% of the signature) by IFN-inducible genes (Supplementary Table  2 ), raising the possibility that they are not TB specific, but may also be expressed in acute viral infections. We therefore evaluated the enrichment of these signatures in two independent published datasets of influenza infection from Parnell et al. 35  and Zhai et al. 36  (Supplementary Table  3 ; Fig. 3b ). Subjects with influenza at baseline showed a high enrichment score for the three TB signatures as compared with healthy controls, which diminished with time in keeping with recovery (Fig.
We therefore hypothesised that methodologies that incorporate information from the entire transcriptome may better inform the development of a unique biosignature for TB. Weighted gene coexpression network analysis  37  (WGCNA) is a well-validated clustering technique for reducing high dimensional data into modules that preserve intrinsic relationships between variables within a network structure. When applied to the blood transcriptome, modules of co-ordinately expressed genes with a coherent functional relationship are generated. The complete transcriptome is thus expressed as a signature defined by the relative perturbation of individual modules. We applied WGCNA analysis to the blood transcriptional data from our Berry and Leicester TB cohorts, those TB cohorts published by Zak and Kaforou  24, 32  , and to several others that included sample sets of other viral and bacterial infections  35, 36, 38, 39  , together with our previous cohorts of sarcoidosis and lung cancer  10  as conditions that may mimic TB, all compared against their healthy controls (Fig.
32  , and the 27-gene (TB vs. LTBI) and 44-gene (TB vs. other diseases (OD)) signatures from Kaforou et al. 24  in tuberculosis datasets (Berry London, Berry South Africa and Leicester), and b in datasets of other infections-severe influenza from Parnell et al. 35  , Influenza A from Zhai et al. 36  and bacterial pneumonia from Parnell et al. 35. The box represents the 25th to 75th percentile, with a line inside the box indicating the median and the whiskers representing the minimum to the maximum points in the data (dark turquoise) module and underabundance of genes associated with Haematopoeisis (pink) in severe influenza, but not in TB (Fig. 4 ). In this context, the classical approaches of gene signature reduction algorithms  [40] [41] [42]  used by Kaforou et al. 24  to distinguish TB from LTBI or TB from other diseases, and Zak et al.
This provides scope to select genes from this dominant module that can be used to develop a TB signature, while retaining discriminant value from viral infection. Using this rationale as a proof of principle, we identified and extracted 303 unique gene candidates in the Berry London TB dataset that were selectively perturbed in TB, but not in any confounding viral infections, from all modules that contributed to and exhibited consistency across the TB datasets that we analysed (Fig. 4 ; Supplementary Figure  6a ; Supplementary Figure  6b ). Using this gene set, we developed a reduced gene signature to distinguish active TB from LTBI. We applied the Boruta algorithm  41  based on random forest to this set of genes, yielding 61 genes (Supplementary Figure  6c ) that was further reduced by selecting the top 20 genes, ranked according to the GINI score using Random Forest (Supplementary Figure  6d ).
This group was reproduced using RNA-seq in the Berry cohorts (10. 9%), and a similar proportion was also identified in our new Leicester cohort (10%) (Fig. 2 ). To compare and contrast the signature of this group with active TB and the majority of LTBI resembling healthy controls (Supplementary Figure  2d  and e ), we specifically examined the WGCNA modular signature in the LTBI outliers using the combined Berry London and South Africa datasets and Leicester dataset, respectively, compared with healthy controls (Fig. 9a ). The modular signature of LTBI outliers in both datasets showed overabundance of the lightgreen (IFN/ Pattern recognition receptors) and yellow (IFN/Complement/ myeloid) modules, as seen in active TB (Fig. 9a, b ). This is entirely in keeping with our earlier finding (Figs.
2c  and 3a ) that gene enrichment scores using the published signatures  24, 32  , all of which are comprised primarily of genes from the yellow module (Supplementary Figures  4  and 5 ), were consistently higher in the LTBI outliers. Of note, these reduced gene signatures from published signatures  24, 32  were not present in the lightgreen (IFN/ 4 2 0 -2 -2 4 2 0 -2 4 2 0 -2 4 2 0 -2 4 2 0 4 2 0 4 2 0 4 2 0 4 2 0 4 2 0 -2 4 3 2 0 1 -1 4 4 3 2 2 0 0 4 2 0 4 2 0 4 2 0 4 2 0 4 2 0 4 2 0 4 2 0 4 2 0 1 -1 4 3 2 0 1 -1 4 3 2 0 1 -1 4 3 2 0 1 -1 4 3 2 0 1 -1 Fig. 5  Gene expression in the yellow module in TB compared to TB cohorts and to other viral and bacterial infections. a Log 2 -fold changes for genes in the yellow module from Berry London cohort (active TB vs. controls; y-axis) compared to the log 2 -fold changes in other datasets (respective cases vs.
controls; x-axis) in TB and b other infections (Herberg et al. 38  , Suarez et al. 39  , time-course data from Zhai et al. 36  (influenza A) and Parnell et al. 35  (severe influenza and bacterial pneumonia)). Shapes and colours represent significantly differentially expressed genes (FDR p-value < 0. 05) in either Berry London only (orange squares), respective dataset only (cyan diamonds), both dataset (yellow circles) or significant in neither (black triangles) Pattern recognition receptors) module. In addition the overabundance of the IFN modules, the LTBI outlier group of the Leicester dataset showed changes in other modules also perturbed in active TB, suggesting a host response that is evolving towards the phenotype typically observed in active TB (Fig. 9a ). Of particular interest was the observation of underabundance in the tan module (Th1 and NK cells) that is associated with IFN-γ expression, a cytokine required for protection against TB  16, [45] [46] [47] [48] [49] [50] [51].
Underabundance of this module was a consistent finding across all the TB datasets that we analysed (Figs. 4  and 9 ). We performed differential gene expression analysis between the active TB, LTBI outliers, and LTBI with outliers removed, and identified a set of 70 genes that was consistently upregulated in active TB and LTBI outliers, compared to LTBI (without outliers), in both the Berry and Leicester datasets (Fig. 9d ; Supplementary Data 3), which were enriched for the IFN signalling pathway and innate immunity. Dynamic transcriptional heterogeneity in recent TB contacts. Longitudinal RNA-seq was performed in a subset of our Leicester cohort (Methods; Fig. 10a ) that included 15 IGRA -ve contacts, 16 IGRA +ve contacts, both of whom remained healthy, and 9 subjects recruited as contacts that were subsequently diagnosed with microbiologically confirmed TB during prospective observation (Fig.
In transcriptomic studies of the disease, there has been focus on deriving reduced gene signatures to develop clinical diagnostics, with inconsistencies in both deriving and defining the optimal reduced gene signature. Studies defining signatures distinguishing active TB and LTBI  24, 25, 27, 32, 43, 55  are illustrative of this issue. We evaluated the diagnostic performance of some of the published reduced gene signatures  24, 27, 32, 43, 44  on our independent TB cohorts and confirmed excellent specificity and sensitivity to distinguish active TB patients from those with LTBI. However, we identified dominance of IFN-inducible genes in these signatures and demonstrated the enrichment of these signatures in published datasets of acute influenza infection  35, 36  , which represents the immune response globally observed in viral infections.
a Modules of co-expressed genes tested in LTBI outliers from the Combined Berry and Leicester cohorts. Fold enrichment scores derived using QuSAGE are depicted, with red and blue indicating modules over-or underexpressed, compared to the controls. Colour intensity and size represent the degree of enrichment, compared to the controls. Only modules with fold enrichment scores with FDR p-value < 0. 05 were considered significant and depicted here. b Gene networks depicting the top 50 'hub' genes, i. e. genes with high intramodular connectivity, for the yellow, light green and c tan modules. Each gene is represented as a square node with edges representing correlation between the gene expression profiles of the two respective genes (minimum Pearson correlation of 0. 75). A key describing the four different partitions within each square node is shown, with each partition representing log 2 -fold changes for active TB (without outliers) and LTBI outliers from the Berry Combined and Leicester cohorts, compared to respective controls (without outliers).
We developed the WGCNA-derived modular signature for active TB across our cohorts and determined consistency of the signature in our cohorts and those from other published datasets. When all 23 modules were taken into consideration, the signature in active TB was distinct from both viral and bacterial infections. In keeping with our earlier findings of an IFN-inducible signature of active TB  9  , we here also demonstrate an IFN-inducible gene signature in both the active TB and LTBI outliers. The IFNinducible signature, however, is now distributed across the three different modules; two overabundant modules; the yellow module that includes BATF2, AIM2, FCGR1A and B, and a number of GBPs; the light green module, which we show is also strongly overabundant in influenza infection, includes many IFITs, ISGs and OASs, and very reminiscent of type-I IFN-inducible genes induced during viral infections.
RNA-seq data analysis. The raw paired-end RNA-seq data obtained for Berry London, Berry South Africa and Leicester cohorts were processed separately and subjected to quality control using FastQC (Babraham Bioinformatics) and Mul-tiQC  57. Trimmomatic 58 v0. 36 was used to remove the adapters and filter raw reads below 36 bases long, and leading and trailing bases below quality 25. The filtered reads were aligned to the Homo sapiens genome Ensembl GRCh38 (release 86) using HISAT2 59 v2. 0. 4 with default settings and RF rna-strandedness including unpaired reads resulting from Trimmomatic. The mapped and aligned reads were quantified to obtain the gene-level counts using HtSeq 60 v0. 6. 1 with default settings and reverse strandedness. Raw counts were processed using the bioconductor package edgeR 61 v3.
14. 0 in R. Genes expressed with counts per million (CPM) > 2 in at least five samples were considered and normalised using trimmed mean of Mvalues (TMM) to remove the library-specific artefacts. Only protein-coding genes were considered for subsequent analyses. Differentially abundant genes were calculated using the likelihood ratio tests in edgeR by fitting generalised linear models to the non-normally distributed RNA-seq data. Genes with log 2 fold change >1 or < -1 and false discovery rate (FDR) p-value < 0. 05 corrected for multiple testing using the Benjamini-Hochberg (BH) method  62  were considered significant. For subsequent analysis, voom transformation was applied to RNA-seq count data to obtain normalised expression values on the log 2 scale. For Berry Combined dataset, the raw counts from Berry London and South Africa cohorts were combined as one dataset and processed in edgeR, as described above, and the batch effects were removed from log 2 expression values using surrogate variable analysis (sva) using the bioconductor package sva 63 in R.
RNA-Seq data obtained from Zak et al. 32  in the SRA format were converted to fastq files using the SRA toolkit and processed as above. Microarray data analysis. External microarray datasets retrieved from GEO as non-normalised matrices were processed in GeneSpring GX v14. 8 (Agilent Technologies). Flags were to filter out the probe sets that did not result in a 'present' call in at least 10% of the samples, with the 'present' lower cut-off of 0. 8. Signal values were then set to a threshold level of 10, log 2 transformed, and perchip normalised using 75th percentile shift algorithm. Next, per-gene normalisation was applied by dividing each messenger RNA transcript by the median intensity of all the samples. The training, test and validation sets in Bloom et al. 10  were combined and the batch effects were removed using sva  63.
In Kaforou et al. 24  , HIV+/-groups were combined and analysed as one dataset. In all datasets, multiple probes mapping to the same gene were removed and the probe with the highest inter-quartile range across all samples was retained to match with the RNA-seq data. Differentially expressed genes were identified using the bioconductor package limma  64  in R and only the genes with FDR p-value < 0. 05 corrected for multiple testing using the BH method  62  were considered significant. Gene signature enrichment analysis. Enrichment of the TB gene signatures was carried out on a per sample basis using ssGSEA  34  using the bioconductor package gsva 65 in R. The enrichment scores were obtained similar to those from Gene Set Enrichment Analysis (GSEA), but based on absolute expression rather than differential expression  34  to quantify the degree to which a gene set is over-represented in a particular sample.
Weighted gene co-expression network analysis. Modular analysis was performed using the WGCNA package in R. The modules were constructed using the Berry Combined dataset (combined Berry London and South Africa sets) using 5000 genes with highest covariance across all samples using log 2 RNA-seq expression values. A signed weighted correlation matrix containing pairwise Pearson correlations between all the genes across all the samples was computed using a soft threshold of β = 14 to reach a scale-free topology. Using this adjacency matrix, the topological overlap measure (TOM) was calculated, which measures the network interconnectedness and is used as input to group highly correlated genes together using average linkage hierarchical clustering. The WGCNA dynamic hybrid tree-cut algorithm  66  was used to detect the network modules of coexpressed genes with a minimum module size of 20.
All the modules were assigned a colour arbitrarily and annotated using Ingenuity Pathway Analysis (IPA) (QIAGEN Bioinformatics) and Literature Lab (Acumenta Biotech, Massachusetts, USA). Literature Lab mines the PubMed literature and identifies the significant associations in 20 MeSH (Medical Subject Headings) domains including pathways, diseases and cell biology. Significantly enriched canonical pathways from IPA (pvalue < 0. 05) and strongly associated terms from Literature Lab were obtained. Modules were assigned annotation terms based on the pathways and the processes that showed corroboration between both tools (Supplementary Table  4 ). Representative terms were then selected and assigned to the modules (Fig. 4 ). For each module, module eigengene (ME) values were calculated, which represent the first principal component of a given module and summarise the gene abundance profile in that module.
For each module, top 50 hub genes with high intramodular connectivity and a minimum correlation of 0. 75 were calculated and exported into Cytoscape v3. 4. 0 to create interaction networks. WGCNA module enrichment analysis. Fold enrichment for the WGCNA modules was calculated using the quantitative set analysis for gene expression (QuSAGE)  67  using the bioconductor package qusage in R to identify the modules of genes over-or underexpressed in a dataset, compared to the control group. Linear-mixed models were incorporated in the analysis using QGen algorithm in QuSAGE, and patients in the datasets with repeated measures were modelled as random effects. Only modules with FDR p-value < 0. 05 were considered significant. To test the modules in the microarray datasets, only those modules were analysed that had at least 70% of total genes within the module with a match in the filtered microarray data.
To obtain a modular profile of a disease group, singlesample enrichment scores were calculated using ssGSEA and the average enrichment score of the control group was subtracted from the average enrichment score of the disease group. To obtain a modular profile on a single sample basis, the average enrichment score of the control group was subtracted from the enrichment score of the sample. Class prediction. In order to develop a TB-specific gene signature, only genes significantly differentially expressed in Berry London set and not in other flu cohorts were considered from only those modules that were perturbed in TB (a module was considered perturbed in TB if it followed a similar profile (up or down compared to the control) in at least four of the five TB datasets (Berry London, Berry South Africa, Leicester cohort, Kaforou et al.