dataesr/scientific-paragraphs-categorization huggingface dataset:
The tests using two surveys conducted close in time both in 1960 and 1980 (online supplementary Table  S1 ) show excellent agreement of glacier-wide mean elevation changes between each dataset and the 2010 lidar DEM using the SGSim-correction. The difference between glacier-wide elevation changes when utilizing different datasets is <1 and <2 m for  1960-2010 and 1980-2010 , respectively, which is to a large degree explained by the seasonal elevation change between the respective surveys. Table  S1 ). This strengthens the potential of these datasets in geodetic mass-balance studies. A comparison with an independent DEM from the source of GCPs (EMISAR DEM) shows limited distortion of the DEM (NMAD = 5. 73 m), including areas far away from GCPs. It also reveals the changes in Eyjafjallakökull, Tindfjallajökull, Torfajökull and the western side of Vatnajökull between 1980 and 1998 (Fig.
6 ), in particular the advances of some outlets of west Vatnajökull due to their surges in the early 1990s  (Björnsson and others, 2003). We did not run SGSim between KH-9 and EMISAR DEMs, since no further analysis of a particular area of interest was carried out based on this comparison. The KH-9 spy satellites imaged the entire country in 1977-1980, and previous spy satellite missions imaged large areas of the country since the 1960s (data not scanned, USGS, https://www. usgs. gov/), making these datasets useful for expanding knowledge of the state of Icelandic glaciers at a time of lower temperatures and higher precipitation than at present (Fig. 7 ). The time series of elevation differences were completed using spaceborne, pushbroom-based stereo imagery from the last two decades. ASTER offers high potential for producing a series of elevation differences from 2000 to present  (Nuimura and others, 2012; Willis and others, 2012; Berthier and others, 2016; Brun and others, 2017)  and enabled constraining the signal of ice-volcano interaction to the period 2009-2010.
SPOT5 provided a DEM observation in 2004, resulting on larger temporal resolution for 2000-2010. Both ASTER and SPOT5 DEMs were processed with open-source software, and the uncertainty obtained was comparable to or better than other studies using similar datasets (e. g. Korona and others, 2009; Berthier and others, 2016). Our methodology relies on an accurate and high-resolution DEM as a source of GCPs for the frame imagery and for co-registration for the pushbroom imagery. Alternatively to the lidar DEM used here, other data sources could be used, such as a Pléiades-based data  (Papasodoro and others, 2015)  or  WorldView-based data (Fieber and others, 2018). The relative precision of Pléiades-and WorldViewbased DEMs is <0. 5 m (slopes <10°) and overall <1 m (e. g. Berthier and others, 2014; Shean and others, 2016; Belart and others, 2017) , often close to the precision of lidar (e.
7 ), which is due to the delayed response of the glacier to the climatic forcing (e. g. Bahr and others, 1998). Our study underlines the importance of seasonal corrections in the interpretation of records of geodetic mass balance, in particular for maritime glaciers with a high mass-balance amplitude  (Ágústsson and others, 2013; Magnússon and others, 2016; Belart and others, 2017). Without seasonal corrections, the geodetic mass balances can be misleading over short time periods, for instance in the 1980s when the surveys were conducted during various times in the season (Table  1 ). However, the seasonal corrections are negligible in long (⩾20 years) time periods. Our B EOS (cyan lines) and mass balance deduced from linear regression (Eqn 7) (black lines). The plotted mass balance and area involving the 1980 dataset is calculated using the KH-9 series because it is closer to the end of the summer.
All dates are fixed to the hydrological year starting on 1 October. The mass balance 2009-2010 was heavily affected by the volcanic eruption and is therefore not included in the mass balance obtained from the linear regression. approach to correct seasonal volume changes refined the degree-day model of  Magnússon and others (2016)  by accounting for snowfall and snow melt at high elevation, and used bootstrapping to infer the uncertainty, which is generally 30-50% of the correction (Fig. 3 ). The uncertainty increases with snow correction (generally in late summer) due to the uncertainty in the snow parameters, i. e. the temperature threshold between rain and snow, ddf and snow density. The seasonal correction applied to the 2010 dataset was, however, significantly affected by the 2010 volcanic eruption.
07 m, or 3% of the calculated seasonal correction for the the 1980 LMÍ dataset (the largest seasonal correction), well within the uncertainty estimates. The obtained geodetic mass balances agree within the error bars with the results from  Guðmundsson and others (2011)  for Eyjafjallajökull. In comparison to Drangajökull ice cap (northwest Iceland), the geodetic mass balance 1945-2014 follows a similar trend to that calculated for 1946-2011 by  Magnússon and others (2016). However, Eyjafjallajökull reveals larger decadal variations, which is consistent with its proximity to the persistent paths of the North Atlantic low-pressure system, with high precipitation rates, and the warm sea surface temperatures from the Irminger ocean current that largely controls the temperatures over the ice cap.
CONCLUSIONS This study has described a workflow to extract decadal geodetic mass balance for the last ∼70 years based on records of airborne and satellite stereo images from frame and pushbroom sensors, thus producing a series of DEMs, orthoimages and dDEMs with robust uncertainty estimates. Seasonal differences between dates of survey were corrected using a simple degree-day model that considers possible snowfall in summer. All the processing was carried out with opensource software (e. g. MicMac, ASP and GSLib). Among the datasets, we processed six frames from the declassified Hexagon KH-9 Mapping Camera and obtained a DEM covering ∼1/3 of Iceland from 1980, including ∼3400 km 2 of ice masses. These tools are useful for in-depth regional studies of geodetic mass balance and for studying glacier-climate relationship in Iceland and elsewhere.
ACKNOWLEDGEMENTS This study was funded by the  University of Iceland (UI) Research Fund. Collaboration and travels between IES and LEGOS were funded by the  Jules Verne  research fund. We thank  David Shean  and two anonymous reviewers for their valuable comments, which greatly improved the manuscript. We thank  Carsten Kristinsson  at  LMÍ  for scanning the aerial photographs,  Oleg Alexandrov  for his helpful tips and advice on ASP,  Luc Girod  for his help in the MicMac forum and  Deirdre Clark  and  Ken Moxham  for the Englishlanguage editing of the manuscript. Pléiades images were acquired at research price thanks to the  CNES ISIS program  (http://www. isis-cnes. fr). This study uses the lidar mapping of the glaciers in Iceland, funded by the  Icelandic Research Fund , the  Landsvirkjun research fund , the  Icelandic Road Administration , the  Reykjavík Energy Environmental and Energy Research Fund , the  Klima-og Luftgruppen research fund of the Nordic Council of Ministers , the  Vatnajökull National Park , the organization Friends of Vatnajökull, LMÍ,  IMO  and the UI research fund.
Based on the mast cell activation gene expression signature derived from our in vitro mast cell stimulation with IL-33, we analyzed a publically available data set (DErrico Gastric data set; GEO: See also related Supplementary Fig. 5  GSE13911) for differential expression of the mast cell signature comprising CCL2, CCL3, CCL4, IL1a, IL4, IL6, IL13, CSF2, and CCL7. With the exception of IL-4 and IL-13, we found that all other factors were at least 1. 6-fold increase in the cancer samples (p ≤ 0. 05) (Fig. 8a ). We therefore assigned the differentially expressed factors (i. e. , CCL2, CCL3, CCL4, IL1a, IL6, CSF2, and CCL7) to an IL-33/mast cell activation gene expression signature for a Kaplan-Meier survival analysis (KMplot. com)  53. → FF; St2 -/- FF; St2 -/- → FF; St2 -/- Fig. 7  Tumor burden is reduced in St2 receptor-deficient gp130 FF mice.
Biotin-labeled secondary antibodies from the Avidin Biotin Complex ABC-kit (Vector Laboratories) were used according to the manufacturer's instructions. Visualization was achieved using 3,3-Diaminobenzine (DAB, DAKO). Images were generated with Aperio ImageScope v11. 2. 0. 780 software. Primary antibodies used: rabbit anti-mouse CD31 (1:200 dilution, Abcam, Cat# 28364, RRID: AB_726362), rat anti-mouse F4/80 (1:200 dilution, Abcam, Cat# 6640, RRID: AB_1140040), rat anti-mouse B220 (1:200 dilution, BD Biosciences, Cat# 550286, RRID:AB_393581), rabbit anti-mouse CD3 (1:200 dilution, Abcam, Cat# ab5690, RRID:AB_305055), rat anti-mouse CD8 (1:150 dilution, eBioscience, Cat# 14-0808-82, RRID: AB_2572861), and rat anti-mouse Foxp3 (1:100 dilution, eBioscience, Cat# 12-5773-80, RRID: AB_465935). Immunofluorescence staining.
0. 4 software (PerkinElmer). Quantification. For quantification of mast cell numbers at least three fields of view (10x ocular) of three toluidine blue section per mouse were counted, submucosal area was measured and data represented as mast cells number/area tissue. Similarly, for quantification of CD3, BrdU, and ApopTag staining, either entire tumors or three fields of view of three sections were counted and analyzed comparing to tissue area. F4/80, CD31, B220, and Hypoxia immune-staining was quantified using Metamorph software (Molecular Devices) or Fiji scripts (ImageJ, htps://Fiji. sc/) determining percentage positive area per area tissue. Isolation of gastric epithelial cells, immune cells, and flow cytometric analysis. Glandular stomachs or antral tumors were dissected, cut into very small pieces and incubated at 37 °C for 30 min in Ca 2+ -and Mg 2+ -free HBSS medium plus 2.
Quantitative RT-PCR analyses were performed in technical triplicates with SensiMix SYBR kit (Bioline, Cat# QT605-20) using the ViiA™ 7 Real Time PCR System (life technologies). Further details and the sequences of the used oligonucleotides are described in the supplementary methods and Supplementary Table  S1. Kaplan-Meier survival analysis. All Kaplan-Meier survival analysis was performed with KMplot (KMplot. com)  53. Data from 179 intestinal-type gastric cancer patient were analyzed and following settings were applied: probes = all probe sets, Laurens classification = intestinal-type, Data sets included: GSE14210, GSE15459, GSE22377, GSE29272, and GSE51105, but excluding GSE62254 (as recommended; due to markedly different survival and shifted expression compared to the other data sets).
All other settings were kept as default. Statistics. Unless otherwise stated, data are presented as mean values ± standard error of the mean (M ± SEM). Exact n values are depicted for every data set in the figure legends and they are always true biological replicates, not technical replicates. All data sets were tested for normality with Shapiro-Wilk normality test. Comparisons between two mean values were performed by two-tailed unpaired Student's t-test using Prism6 software (GraphPad Software, California, USA). When data sets had significantly different standard deviations according to Prism's F-test of variances, Welch correction was applied. T-test t values and corresponding degree of freedom (df) are given in figure legends. For comparisons of more than two groups one-way ANOVA test was performed with multiple comparison correction (either Tukey or Bonferroni).
ANOVA F values are depicted in each figure legend as F (DFn, Dfd), where DFn is the df nominator and Dfd the df denominator. P values of less than 0. 05 were considered statistically significant. Sample size determination. G*power3. 1 software was used to estimate the minimal required sample sizes. Based on previous mean values and SD from previous studies using the gp130 FF mouse model, we calculated a minimal sample size for eight mice per group, when the mean difference is 40% and the SD 20% (unpaired two-tailed t-test; α = 0. 05, Power = 0. 95). Due to difference in availability and frequency of target genotypes born, the number of mice analyzed between cohorts differed. Sample size for subsequent analysis of tissue section staining was limited to number available, as tissues were required for several downstream processes including FACS analysis, RNA and protein expression in addition to histological and immunohistochemical analysis.
Acknowledgements We thank the animal facility staff of  La Trobe University  and the  Ludwig Institute for Cancer Research  as well as the Austin and WEHI histology department for technical assistance. We also acknowledge the excellent support for cell sorting by  David Baloyan  ( ONJCRI ) and image generation and analysis by  Cameron Nowell  ( Monash University ) and Lachlan Whitehead (WEHI). This work was supported through the  Victorian State Government Operational Infrastructure Support , the  Nation Health and Medical Research Council (NHMRC) of Australia  grants  1092788 ,  1067244 ,  1069024  and  Cancer Council Victoria 's Grant-in-Aid 1160708. M. E. is a  NHMRC Principal Research Fellow  and also received funding from  Ludwig Cancer Research.
Data availability The Stat3 chromatin immunoprecipitation-sequencing (ChIP-Seq) data have been deposited in the NCBI gene expression omnibus (GEO) under the accession code GSE48285. The source data underlying Figs. 1b, d , 2b-h, 3b-e, 4b-e, 5a-c, 6a, c, d, f, 7a-g, and 8a and Supplementary Figs 1b, 2a, b, g, i, 3d, 5a, b, g, h, and 6e, f are provided as a source data file. All the other data supporting the findings of this study are available within the article and its supplementary information files and from the corresponding author upon reasonable request. A reporting summary for this article is available as a.
Study design and participants The Tsimane sample includes all individuals who met the inclusion criteria of self-identifying as Tsimane and who were aged 40+ years (n = 507; 48% female; age range: 41-94 years; see Appendix for additional details and Appendix 1-table  18  for descriptives of all study variables). 185 of the 245 participating women (76%) were post-menopausal. No participant reported ever using hormonal contraception or dietary supplements with consistency. No Tsimane was excluded based on any health condition that can affect BMD or fracture risk. Comparative American BMD data were collected among asymptomatic subjects from greater Los Angeles as part of a different study (described in  Budoff et al. , 2010). Briefly, 9585 subjects (43% female; mean age = 56) underwent coronary artery calcification (CAC) scanning for evaluation of subclinical atherosclerosis, after exclusion of participants with vertebral deformities or fractures.
Data analysis The two outcome variables indicating bone strength are thoracic vertebral BMD and thoracic vertebral fracture. Sexes are analyzed separately given the sex-specific nature of hypotheses and to minimize confounding by unobserved factors. General linear models are used to test for associations between Tsimane women's BMD and reproductive effort (see Appendix for additional details). We compared BMD of Tsimane and age-and sex-matched Americans using age-standardized means. We use a parametric test (one sample t test) to evaluate whether population-level differences in mean BMD within each decade are significant (p<0. 05), specifying as the test value the US means published in  Budoff et al. (2010). We compare Tsimane and American fracture prevalence using age-standardized values, and using log-binomial generalized linear models (GLMs).
To address potential sources of sample bias, analyses comparing Tsimane who received CTs and those who did not but who participated in the THLHP's medical exams in Tsimane villages were conducted. There were no significant differences in sex, blood pressure or body fat (see  Kaplan et al. , 2017  for additional participant details) and thus the CT sample analyzed here is thought to be representative of all Tsimane aged 40+ years. Of the 731 Tsimane who received a CT scan, CT data from 507 (69%) were selected with no particular criteria to estimate thoracic vertebral bone mineral density (BMD) and fracture (scans from 224 Tsimane were not assessed due to radiologist time constraints). Among these 507 individuals, some data were missing because of either broken equipment, missing supplies, participant recall problems, absent or sick team personnel who were unable to collect data, or because all relevant thoracic vertebrae did not appear in the CT image field of view (see Appendix 1-table 18 for sample sizes and descriptives for all study variables).
detector row CT scanner (LightSpeed VCT; GE Medical Systems) at the Los Angeles Biomedical Research Institute. Parameters for electron-beam CT scanning were 130 kVp, 630 mA, and 2. 5 or 3 mm slice thickness. The multidetector CT parameters were 120 kVp, 200-600 mA, and 2. 5 mm slice thickness. 156 subjects underwent scanning with electron-beam CT and 64-detector row CT on the same day for comparison and normalization of BMDs between scanners. CT scanning to collect MESA sub-sample data, used here to assess fracture prevalence and severity, was performed at the Los Angeles Biomedical Research Institute; procedures involved the use of a C-150 GE-Imatron electron-beam scanner and were otherwise identical to those described in  Budoff et al. (2010). Supplementary CT data for assessment of fracture prevalence and severity were also collected at the Los Angeles Biomedical Research Institute using similar parameters.
Acknowledgements We thank the  Tsimane  for participating and THLHP personnel for collecting and coding data. Jay Stock  and two anonymous reviewers provided detailed comments that improved the quality of this manuscript. Funding was provided by the  National Institutes of Health/National Institute on Aging  ( R01AG024119 ), the  Center for Evolutionary Medicine at Arizona State University , and the  University of California-Santa Barbara Academic Senate. JS acknowledges IAST funding from the  French National Research Agency (ANR)  under the Investments for the  Future (Investissements d'Avenir) program , grant  ANR-17-EURE-0010.
Funding Funder Grant reference number Author National Institutes of Health  R01AG024119   Jonathan Stieglitz Benjamin C Trumble Caleb E Finch Hillard Kaplan Michael D Gurven Arizona State University Benjamin C Trumble University of California, Santa Barbara Michael D Gurven Agence Nationale de la Recherche   ANR-17-EURE-0010  Jonathan Stieglitz The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
005) Total e 163. 85 179. 91 À8. 93 176. 21 176. 64 À0. 24 170. 37 177. 61 À4. 08 (245) (3908) (---) (262) (5122) (---) (507) (9030) (---) a US data represent asymptomatic subjects from greater Los Angeles (described in Budoff et al. , 2010). Briefly, 9585 subjects (43% female; mean age = 56) underwent coronary artery calcification (CAC) scanning for evaluation of subclinical atherosclerosis, after exclusion of participants with vertebral deformities or fractures. Subjects had no known bone disease. Parameter b Maximum age for Tsimane women and men = 91 and 94 years, respectively (US maximum Std. b p Std. b p Std. b p Short mean IBI (<29. 7 months; vs. ! 29. 7) Age at 1 st birth (years, logged) Age (years) age = 90 for both sexes). À0. 208 0. 124 À0. 665 For women (n = 245 Tsimane and 246 US), sample sizes for ages 40-49 are 80 Tsimane and 77 0.
Single revertant phage clones were isolated and sequenced to identify compensatory mutations. Plasmid construction. Plasmid pPT290 was constructed by cloning a PCR fragment spanning genes 11 to 13 (coordinates 6863 to 8815 of the SPP1 genome sequence; GenBank accession number X97918. 3  40  ) downstream of the inducible promoter P N25/0 present in the pHP13 derived plasmid pIV2  41. The PCR product was cleaved at EcoRI and PstI sites and ligated to the vector cut with the same restriction endonucleases. Restriction sites were engineered in the sequence of primers 11CSminus1 (GTGAATTCGCGTGAGGTGTGACACG; the restriction site is shown in italics and the SPP1 sequence in bold) and 13NCS4 (CTACTG CAGCTTTCAAAAAAAGAGAGGCG) used for PCR amplification. Plasmids pCC40  38  and pPT290 were used as templates for site-directed mutagenesis with the QuikChange Site-Directed Mutagenesis Kit (Stratagene) to engineer mutations in gene 13.
After 45 s the grids were blotted for 2-4 s (blot force 1), plunged into liquid ethane and then stored in liquid nitrogen. Data were collected using a Tecnai G2 Polara (FEI), operated at 300 kV, and equipped with a Gatan K2 Summit direct detector (Gatan, Inc. ) at a defocus range from -0. 8 to -3. 5 μm. Images were acquired in super-resolution mode using Leginon 42 at ×31,000 nominal magnification corresponding to a 0. 64-Å pixel size at object scale. The electron dose was set to 2. 5 counts/pixel/s for using the K2 summit in counting super-resolution mode. For each exposure, 25 frames with 200 ms exposure time resulting in an electron dose of 1. 1 e-/Å 2 per frame and a total dose of 27. 5 e -/Å 2 per 5 s exposure. Two independent data sets were collected, one of procapsids and another of mature viral particles.
Image processing and structure analysis. Movie frames from the K2 Summit were aligned using MOTIONCORR-2  43. Data were 2 × 2 binned, yielding a pixel size of 1. 28 Å. All 25 frames were included for motion correction and determination of CTF parameters. For image analysis frame correction was done only for frames 4-21, the first three and last four frames were excluded being affected by the beam induced movement and radiation damage, respectively. The total accumulated dose was 23. 1 e -/Å 2. Particles were selected manually using EMAN-2  44. In all, 18,395 procapsid particles and 6000 mature capsid particles were extracted in frames of 800 × 800 pixels. Assessment of the contrast transfer function (CTF) of the microscope was done using CTFFIND4  45  and phase flipping was applied in SPIDER  46.
Refinement was done iteratively using for the next round a 3D map obtained from the images with the lowest errors  47. Approximately 75% images from each data set were used to reconstruct the final 3D maps, which were subsequently sharpened retaining spatial frequencies between 1/7 and ~1/2. 75 Å 47 (Supplementary Table  1 ). The local resolution for each 3D map was evaluated using the RESMAP program  48. Model building. Tracing of the SPP1 MCP polypeptide chain was done using one ASU of the mature capsid (~4. 0 Å resolution). Extraction of the ASU was done in CHIMERA  49  using the fitting of the previous atomic model of gp13 (PDB 4AN5  21  ). Each gp13 subunit of the ASU was then extracted individually. The initial model allowed defining the ASU boundaries sufficiently well although this was an incomplete model that missed the N-terminal arm (Met1-Thr37), the E-loop (Asp61-Lys79), and the C-terminus (Gly290-Ala 324).
Acknowledgements We would like to thank  I. Vinga  for kindly providing plasmid pIV2,  Dr. D. Houldershaw ,  Dr. R. Westlake  and  Y. Goudetsidis  for computer support throughout the duration of the project. Dr. A. Pandurangan  and  L. Simons  are acknowledged for assistance with building the atomic models. We would like to thank  M. Ouldali  for imaging electron microscopy of particles with mutant gp13 forms in Supplementary Fig. 7c-g, from the platform Cryo-EM of I2BC supported by the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INBS-05. We thank  Dr. H. White  for useful comments and for proof reading and  Dr. S. Roche  for the comments on the manuscript. This work was supported by the  Biotechnology and Biological Sciences Research Council  to E. V. O. and A.