dataesr/scientific-paragraphs-categorization huggingface dataset:
Utilising this time-series of SAR imagery in combination with optical imagery where available, enables the generation of targeted land cover maps quantifying the spatial distribution of key land cover types influencing small mammal distributions. Multi-date SAR data has previously been utilised for other land cover mapping activities  [33] [34] [35] [36]. However, it has not been employed within an epidemiological context. Optical and Synthetic Aperture Radar (SAR) remote sensing datasets characterise target features in different ways, but deliver complementary information that, if used in combination, generally leads to an increase in land cover mapping accuracy  [37]. Multispectral optical remote sensing imagery delivers rich spectral information about the scattered energy (visible and infrared) from the target surface, which enables the discrimination of different land cover classes on the basis of spectral variations in the specific features in question  [38].
In contrast, SAR characterises the physical structure of target features, with the differing spatial structure of these features scattering the SAR signal at differing levels of intensity and amplitude. The ground surface texture strongly influences SAR backscatter [39], and the varying characteristics of SAR backscatter can be used to derive information about that target feature. By using SAR-based texture (structural) and optical (spectral) information synergistically to exploit the different physical principles, which they characterise, improved classifications of land cover have been observed  [37, 40] , compared to where either optical or SAR data have been used independently  [38]. This has been illustrated for examples such as mapping of winter wheat in China  [35] , maize, soybeans, and sunflowers in Ukraine  [41, 42] , forestry studies  [43] [44] [45] [46] , and mapping land cover  [47] [48] [49] [50] [51] [52].
The Sentinel remote sensing satellite series launched as part of the European Space Agency's Copernicus program offers cost-free, broad-area, high temporal frequency coverage. Sentinel-1 offers cloud-penetrating SAR data, which guarantees data collection even in heavily cloud-affected areas. The availability of Sentinel satellite data at 10 m resolution, along with the six-day repeat frequency of Sentinel 1, provide yet more advantages, and enable mapping of key land cover types present in the study areas using the differing phenological characteristics of the vegetation communities and land cover types present  [53, 54]. By utilising both Sentinel-1 SAR and optical remote sensing data in combination (for example, from Sentinel-2 or Landsat OLI), misclassifications, which commonly occur using single-date imagery due to the spectral similarity of features, should be reduced by using the additional phenological backscatter profile data from the SAR time-series.
Key advancements of this research are, first, to increase our fundamental understanding of the distribution and ecology of the intermediate hosts involved in the Em transmission processes and apply this knowledge for improved disease control targeting, and second, utilise new remote sensing and modelling methodologies for characterisation of small mammal habitats. Specifically, we pose two research questions: (1) Can time-series SAR data generate land cover maps of equivalent quality as optical imagery? (2) What are the key land cover drivers impacting Em host Ellobius tancrei and Microtus gregalis distributions in the study area?.
The data was then sub-set to the study area extent using the SNAP software package. The time series of SAR images were then layer-stacked in order of the imagery acquisition date using ERDAS Imagine to generate a 28-band dataset, which formed the SAR only dataset for analysis. The time-period of this study pre-dated Sentinel-2 imagery availability. Therefore, a 30 m resolution Landsat OLI optical image (acquisition date 22 July 2014) was also acquired and was the closest available cloud and snow-cover free image of the study area to the 2014 small mammal survey data collection period. This was acquired from the EarthExplorer data access portal (https://earthexplorer. usgs. gov) and was downloaded as a level-2 atmospherically corrected surface reflectance data product. Landsat OLI bands 2 (blue, 0.
452-0. 512 µm), 3 (green, 0. 533-0. 590 µm), 4 (red, 0. 636-0. 673 µm), 5 (near infrared, 0. 851-0. 879 µm), 6 (shortwave infrared, 1. 566-1. 651 µm), and 7 (shortwave infrared, 2. 107-2. 294 µm) were used to generate the single-date (OLI only) dataset for analysis. The OLI only imagery and time-series SAR only 28-band dataset were layer-stacked together to generate the combined SAROLI (34-band) dataset using ERDAS Imagine. All data was projected to the Universal Transverse Mercator (UTM) WGS84 zone 43N.
Land Cover Classification Three land cover classifications were then generated using identical training data to assess their relative abilities to discriminate the land cover classes present using (1) Landsat OLI optical image only, (2) Sentinel-1 SAR time-series data only, (3) Sentinel-1 SAR time-series data, and Landsat OLI optical image in combination. The land cover classifications were performed using a random forest classifier using the R statistical package, and used an eight-class classification nomenclature using the classes: built-up, bare, water, dry grassland, alpine grassland, steppe, bushes, and agriculture (hay and low productivity barley fields). Reference locations of known land cover types were used for training the classification and accuracy assessment, respectively, with field photographs illustrating the different land cover classes within the study area displayed in Figure  3.
These data were generated from four sources: (1) field-collected land cover survey locations, (2) reference locations derived from field photos, (3) reference locations derived from very high-resolution Bing aerial and Google Earth imagery, and (4) expert knowledge and direct interpretation of reference locations of clear imagery features (for example, water). The use of higher resolution imagery accessible via public portals such as Google Earth for identification of reference data points is an established technique  [38]. For each land cover class, the reference data points were allocated on an alternating basis as training or independent validation locations, which created two equal-sized datasets providing 352 locations for both the training and validation datasets (704 in total) distributed across all land cover classes (built up 7.
They are well-suited to complex, non-linear ecological datasets  [67]  handling large datasets with correlated predictor variables  [68] , are non-parametric [69], handle a variety of data types  [70] , make no assumption of independence concerning the data being analysed [71], and are robust to outliers, noise, and over-fitting  [66]. Their use in ecology is becoming more widespread  [72] , including studies of landscape dynamic influences on parasite hosts  [14, 28]. Random forest analysis was performed six times, using the land cover maps derived from the combined SAROLI data, the SAR only data, and the OLI data only, for E. tancrei and M. gregalis, respectively. To achieve the final six random forest models, stepwise removal of the initial 80 nested land cover variables was performed, using the %IncMSE (percentage increase in mean squared error (MSE)) values produced for each variable.
It is also possible to examine the statistical significance of the relative importance values of the predictor variables using a permutation-based random forest approach. This works by constructing a large number of random forest models from an identical dataset, and permuting the response variable to obtain the probability distributions of the relative importance measures of the respective predictor variables [73] under the null hypothesis of no relationship between the predictor and response variables. p-values for each predictor variable are also generated, which enables assessment of whether the observed relationships are statistically significant. The random forest analysis performed in this case built on methods previously applied in Reference  [74]  and was performed in R using the 'randomForests' package  [75].
470, which confirm no statistically significant difference in classification accuracy between these two classifications. Therefore, where both SAR and optical remote sensing data are available, the best results are achieved by using both datasets in combination to generate land cover classification. Where optical remote sensing data is not available due to factors such as persistent cloud cover, McNemar's test results show that SAR-only based classifications perform as well as single-date Landsat OLI derived classifications, which produce similar classification accuracies (SAR-only = 90. 91%, OLI-only = 88. 92%). This offers a viable alternative method of land cover characterisation based on vegetation phenology where cloud cover prevents the use of optical imagery. statistical significance are calculated individually for each land cover class at each nested buffer size for all three land cover classifications for both E.
Partial dependence plots were generated for the statistically significant variables in relation to E. tancrei relative density scores, as identified by the random forest permutation analysis to illustrate the nature of the relationships present. For brevity, only the SAROLI partial dependence plots are presented here, with the SAR-only and OLI-only plots displayed in Supplementary Information. For the SAROLI classification, Figure  5  consistently shows that E. tancrei relative density scores increase as the area of agricultural land present increases. This clearly illustrates that E. tancrei relative density scores are positively related to increasing areas of agricultural land. Similar patterns are observed for the SAR-only derived land cover classification of RF partial dependence plots (Supplementary Information Figure  S1 ), and the OLI-only derived land cover classification partial dependence plots (Supplementary Information Figure  S2 ), which demonstrate that this pattern is consistently observed for the land cover classifications derived using three different input datasets.
Discussion This research examined a critical phase of the Echinococcus multilocularis (Em) transmission cycle, and adopted an analytical approach using RF to model and predict E. tancrei and M. gregalis small mammal presence in relation to landscape characteristics within a highly endemic Em area in Sary Mogol, Alay Valley, Kyrgyzstan. This approach successfully identified the key land cover types driving small mammal distributions and enabled the prediction of small mammal distributions based on land cover maps derived from combined SAR time-series and optical OLI, time-series SAR-only, and single-date optical OLI-only datasets. Our first question 'Can time-series SAR data generate land cover maps of equivalent quality as optical imagery?' can be answered from our analysis. McNemar's test results showed no statistically significant difference between classification accuracies indicating that the performance of both time-series SAR data (90.
91% accuracy) and single-date Landsat OLI imagery (88. 92% accuracy) in generating land cover maps using identical training data are similar. However, in this study, the synergistic use of time-series SAR and Landsat OLI data in combination has produced statistically improved land cover classifications over each dataset used independently by using an identical training dataset. This shows that, where data availability allows, the combination of optical imagery (capturing spectral variability) and time-series SAR data (capturing structural variability and its dynamics resulting from seasonal vegetation phenology) is the best option for producing high accuracy land cover maps. Similar results have been observed in previous studies, which found higher classification accuracies when using SAR and optical data in combination  [35, 37, 40, 42, 44, 50]  rather than using each dataset individually.
These improved classification accuracies are due to the respective capabilities of optical and SAR datasets, which detect different physical properties of land cover features, provide complementary information across the electromagnetic spectrum, and compensate for limitations of using either sensor alone  [40]. This is particularly relevant for the agriculture class identified as being of key importance, with its specific SAR backscatter profile exhibited over the growing cycle (based on sowing, growth, and harvesting of crops and ploughing of fields) differing from the typical phenological growth and senescence cycle of other vegetation types such as grassland. This provides additional information on which to classify this class. Although the three land cover classifications generated are broadly similar, differences did exist in the distributions of the classified land cover types due to the way in which SAR and optical sensors characterise target features.
The two key land cover classes of agriculture and bushes did vary in both producer's and user's accuracies for each of the SAROLI, SAR-only, and OLI-only classifications, although accuracy levels were generally high. For agriculture, producer's accuracies (98. 0% (SAROLI), 88. 0% (SAR-only), and 96% (OLI-only)) and user's accuracies (98. 0% (SAROLI), 91. 7% (SAR-only), and 82. 8% (OLI-only)) showed that this class was classified well using all three input datasets. Likewise, bushes producer's accuracies (89. 7% (SAROLI), 94. 9% (SAR-only), and 61. 5% (OLI-only)) and user's accuracies (97. 2% (SAROLI), 97. 4% (SAR-only), and 92. 3% (OLI-only)) showed that this class was being classified well, with the exception of producer's accuracy for OLI, which performed less well. Although there were some differences in the classification accuracies and spatial distributions of patches of these land cover classes, the random forest analysis consistently identified these classes of being of the highest importance in relation to E.
When applied predictively across the study area, all three E. tancrei predicted models identified the same areas as being of high relative density. The models identified these areas as likely hot-spots for active Em transmission foci due to the denser presence of intermediate hosts (Figure  7 ). Since the predicted random forest relative density maps all identified similar areas as the highest E. tancrei relative density, this further supports the suitability of applying SAR-only datasets for generating land cover classifications for small mammal distribution modelling in this region. This shows that similar results can be achieved by using optical remote sensing imagery, while overcoming the challenges of persistent cloud cover and phenological vegetation dynamics. For Microtus gregalis, the results were more varied.
Spoligotyping High-throughput spoligotyping was performed using the microbead-based method using flow-cytometry suspension arrays (Luminex 200, Luminex Corp, Austin, TX), as previously described  [21]. TB-SPOL and TB-SPRINT kits were purchased from Beamedex (Beamedex, Orsay, France; www. beamedex. com). Given the low DNA content of the samples, the PCR protocol was adjusted by increasing PCR cycles from 20 to up to 35 cycles. Each spoligotyping profile was verified at least twice and doubtful profiles were rejected. Quality control was conducted by three experts (MKG, BMM, CS) who independently assessed individual spoligotyping profiles. Full or empty squares represents the presence or absence of the 43 classical CRISPR spacers. The spoligotyping patterns were further labelled using the SITVITWEB database  [22].
Map building and spatial representation A Mac OSX Version of QGIS (v2. 18 Las Palmas de Gran Canaria) was downloaded and installed from: http://www. kyngchaos. com/software/qgis. A licence-free Nigerian map with administrative level 1 was downloaded as a shapefile from http://maplibrary. org/library/stacks/ Africa/Nigeria/index. htm; Latitude and longitude of the main Nigerian cities was downloaded from http://www. downloadexcelfiles. com/wo_en/download-list-latitudelongitude-cities-nigeria #. WUPbwYXSWDQ and http://simplemaps. com/data/world-cities. Nigerian Population records and density was obtained by collecting data from: http:// www. iplussolutions. org/isolutions-leads-consortium-streamline-patient-access-essentialtreatments-nigeria-0 (assessed on November 2017, 20th) and https://www.
Acknowledgments We are deeply grateful to all State TB program coordinators for the collection of samples in Nigeria. BMM was a PhD Student of the  Universitat Autonoma de Barcelona. LS was a Postdoctorate fellow from the  University Federal of Rio de Janeiro, Brazil , and MKG was Postdoctorate fellow from the  Centre Muraz ,  Bobo-Dioulasso ,  Burkina Faso , working in CSs's  Orsay Team.
Immunocytochemistry Cells, seeded on chamber slides, were fixed, incubated for 2 h with the indicated primary antibody and the primary antibody was detected using a fluorescein isothiocyanate (FITC)-or Alexa 594-conjugated secondary antibody (Santa Cruz Biotechnology). Stained cells were mounted on microscope slides in 4′,6-diamidino-2-phenylindole (DAPI) containing mounting medium and imaged on a Zeiss confocal microscope using the LSM 510 software (Zeiss, Oberkochen, Germany).
Detection of ROS ROS detection in the cells was performed by confocal microscopy or flow cytometry after staining with dichlorodihydrofluorescein diacetate (DCF-DA, Sigma-Aldrich, MO, USA). Cells were seeded in 6-well plates at a density of 3 × 10 5 cells/well. After 24 h at 37 °C, the cells were treated with 5-FU for various amounts of time. Cells were treated with 25 µM DCF-DA, trypsinized, and analyzed using a flow cytometer (Becton Dickinson, CA, USA) and the CellQuest™ software (Becton Dickinson) or confocal microscopy. H 2 O 2 detection in the cells was performed by confocal microscopy after staining with Amplex ® red reagent (Invitrogen). Cells were seeded and, after 16 h, the dye (50 μM of Amplex ® red reagent and 0. 1 U/mL of horseradish peroxidase in phosphate buffer) was added to each well to a final volume of 100 μl and samples were incubated for 30 min in the dark.
Pair-end ChIP and input DNA libraries were sequenced using the Illumina HiSeq. 2500 system (Illumina, San Diego, CA, USA) according to the manufacturer's instructions. Initial quality-control analysis and adapter of raw data was performed using the Cutadapt v1. 15  [14] , Trim Galore v0. 4. 5 (http://bioinformatics. babraham. ac. uk) and FastQC v0. 11. 7 (http://bioinformatics. babraham. ac. uk). Finally, about 28 million mapped reads by BWA aligner v0. 7. 12  [15]  for each ChIP group were analyzed using the HOMER v4. 7 software  [16]  (http://homer. ucsd. edu/homer/) for peak calling, annotation, Gene Ontology, and signal pathway analyses, including 30 million mapped input reads as control. Both IPA (http://www. ingenuity. com) and DAVID softwares were used for the ontology analysis of the TET1 occupied targets.
PCR conditions were as follows: 95 °C for 10 min followed by 40 cycles of 95 °C for 15 s, and 59-61 °C for 1 min. PCR reactions were performed in 96-well plates in Amplification was performed with a Bio-Rad iQ5 real-time PCR detector system (Bio-Rad, Richmond, CA, USA). Data analysis was performed using Bio-Rad iQ5 Optical System Software V1. 0.
Quantitative methylation specific PCR (MSP) using qPCR and bisulfite sequencing analysis The MethPrimer software (http://www. urogene. org/methprimer/) [17]  was employed to design primers for the human DUOX2 promoter, which contains abundant CpG sites (potential DNA methylation targets) around the transcription start site  [18]. For methylation analysis, a standard phenol-chloroform method was used to extract DNA. The EZ DNA methylation kit (Zymo Research, Irvine, CA, USA) was used for Bisulfite modification of genomic DNA. The Maxima SYBR Green qPCR kit (Fermentas, Carlsbad, CA, USA) and the CFX96 qPCR detection system (Bio-Rad) were used for quantitative analyses. The amplification consisted of an initial 10 min denaturation (95 °C), followed by 40 cycles of denaturation at 95 °C (15 s) and annealing and extension for 30 s and 60 s, respectively.
Cytosolic Ca 2+ detection Cells were seeded on a culture plate at a concentration of 2 × 10 5 cells/mL. After 24 h, cells were treated with 10 µM of fluo-4 A. M. (Thermo Scientific) for 30 min at 37 °C, and fluorescence of the cells was measured using the flow cytometer (Becton Dickinson) and analyzed using the CellQuest™ (Becton Dickinson) software or confocal microscopy.
55%) in TET1-silenced cells compared with control cells (Fig. 5G ). Finally, since we have previously shown that 5-FU induces the expression of TET1 in SNUC5 cells  [21] , we investigated the effect of 5-FU on DUOX2 mRNA expression in these cells. It showed that 9 μM 5-FU, the 5-FU IC 50 in SNUC5, induced the expression of DUOX2 in SNUC5 cells (Fig. 5H ). Therefore, our data suggested that TET1 overexpression, upon 5-FU treatment or in 5-FU resistant cells, leads to the transcriptional induction of the NADPH oxidase DUOX2 by regulating the methylation of its promoter. Human DUOX enzymes contain both an extracellular peroxidaselike domain and a canonical calcium-binding helix-loop-helix-hands  [26]. Moreover, ROS generation by DUOX2 is dependent on Ca 2+ mobilization  [27]. To assess whether calcium-dependent DUOX2 activation might participate to the increased ROS generation in SNUC5/ FUR cells, we first analyzed the intracellular calcium concentration ([Ca 2+ ] i ).
Acknowledgements Kyoung Ah Kang ,  Hee Kyoung Kang ,  Young Sang Koh ,  Guillaume Bossis ,  Sung Young Yoon ,  Seong Bong Kim  and  Jin Won Hyun  conceived and designed the experiments. Kyoung Ah Kang ,  Yea Seong Ryu ,  Mei Jing Piao  and  Joo Mi Yi  performed the experiments. Kyoung Ah Kang ,  Joo Mi Yi ,  Kristina Shilnikova ,  Mathias Boulanger ,  Rosa Paolillo  and  Jin Won Hyun  analyzed the data and wrote the paper.
Funding The work was supported by grant from the  Basic Research Laboratory Program  [ NRF-2017R1A4A1014512 ], the  Science and Technology Amicable Relationships  [ NRF-2017K1A3A1A21013557 ] and NRF-2016R1A2B4007934 by the  National Research Foundation of Korea [NRF ] grant funded by the  Korea government. The study was also supported by the  R&D Program of Plasma Advanced Technology for Agriculture and Food (Plasma Farming)  via the  National Fusion Research Institute of Korea (NFRI) , funded by the  Korea Government.
Among the top upregulated genes in the U2987MG-Snail cells were astrocytic differentiation markers, the intermediate filament glial acidic fibrilary protein (GFAP) and the extracellular matrix glycoprotein secreted protein, acidic, cysteine rich-like-1 (SPARCL1) (Fig. 1a, b ). In order to corroborate that Snail can induce astrocytic fate in GBMs, we compared the differentially expressed genes in our dataset to those differentially expressed genes in normal mouse astrocytes versus other brain cell lineages (oligodendrocyte progenitors, oligodendrocytes, neurons, cultured astroglia) reported by Cahoy et al. [20]  We chose a murine dataset as this one contained several brain cell types and allowed us to discriminate genes expressed in astrocytes from those in other brain cell types. The resulting 130 common genes were plotted in a heatmap relative to the biological samples, named according to cell clone and their respective duplicates (bis, Fig.