dataesr/scientific-paragraphs-categorization huggingface dataset:
Shuttle Radar Topography Mission X-band synthetic aperture radar DEM The Shuttle Radar Topography Mission (SRTM)-acquired data from 11 to 22 February 2000 with two interferometric synthetic aperture radar sensors: the American SIR-C sensor and the German-Italian X-SAR sensor. The SIR-C (λ = 5. 6 cm) covers a 225 km swath width and provides an almost complete DEM of the earth's surface between latitudes 60 • N and 56 • S  (Farr et al. , 2007). The X-SAR sensor (λ = 2. 8 cm) has a narrower ground track (swath width of 50 km), and covers approximately half of the area sampled by SIR-C. The two data sets were processed independently. The SRTM C-band DEM was released by NASA Jet Propulsion Laboratory (JPL) in 2003 with an initial spatial resolution of 3 arcsec  (Rodriguez et al. , 2006). The DLR X-SAR SRTM DEM (hereafter SRTM-X) with a spatial resolution of 1 arc-second was produced by the DLR and has been freely available for scientific purposes since 2010  (Hoffmann and Walter, 2006).
Pléiades DEM The Pléiades DEM (hereafter PLEI) was generated from a triplet (back, nadir and front) of Pléiades images acquired on 21 April 2012 with the software PCI Geomatica v2013  (Berthier et al. , 2014 ). An output DEM was generated from the pixel values with higher correlation scores between the three DEMs derived from the different combinations of images (nadir-back, nadir-front, and back-front). A post-processing scheme was applied to eliminate anomalous values  (Ruiz and Bodin, 2015). The final DEM has a spatial resolution of 2 m and accuracies of 0. 5 and 1. 06 m (RMSE) in horizontal and vertical directions. Accuracy was estimated using more than 2000 GPS elevation data collected on bare ground with a Trimble DGPS receiver on dynamic mode.
A four-degree polynomial fit was used to make the correction but only within an acceptable range of curvature. For extreme curvature values, the relationship between elevation difference and curvature is noisy; thus these extreme values were discarded (Fig. 2c ). The time span between mid-February (acquisition time of SRTM-X) and mid-April (acquisition time of PLEI) represents a significant proportion of the ablation season. Thus, to estimate glacier mass balance over an integer number of years, it was also necessary to take into account the mass balance change of this period. To determine this seasonality correction, we used preliminary seasonal mass balance data for Alerce glacier available since 2013. At this site, summer measurements are made at intervals that range between 15 and 35 days, so we could estimate the mass loss rate during the ablation season.
Mean elevation changes and mass balance calculation Before calculating the height and mass balance changes, we excluded all the void pixels in SRTM-X and PLEI, as well as those with extreme curvature values (Fig. 2c ). The excluded cells represent ∼ 25 % of the area covered by glaciers in 2000. Here we briefly summarize the procedure to obtain the volume and glacier-wide mass balance (see Appendix A for a detailed description of the calculations). To calculate the volume change for each glacier, elevation changes were analysed for 50 m altitude bands. Within each altitude band, we averaged the elevation change (dh n ) after excluding pixels for which absolute height differences differed by more than 3 standard deviations from the mean   (Berthier et al. , 2004 ) (Fig. 3 ). This is an efficient way to exclude outliers (less than 10 % of the remaining data after eliminating the voids and the extreme curvature values), based on the assumption that elevation changes should be similar at a given altitude of the glacier.
Acknowledgements. We would like to acknowledge the support from  Ministerio de Ambiente y Desarrollo Sustentable de Argentina  ( Inventario Nacional de Glaciares ),  Agencia de Promoción Científica  (projects PICT 2010(projects PICT -1438;;  PICT 2014 PICT -1794 ) ) and  CON-ICET  for funding. JICA (Japan International Cooperation Agency ) also provided funding and equipment which were critical for the completion of this work. Etienne Berthier  acknowledges support from the  French Space Agency (CNES)  through the  TOSCA programme  and from the  Programme National de Telédétection Spatiale (PNTS). Maximiliano Viale  acknowledges supports from  FONDECYT 11151009. Pléiades images were provided at no cost by  Airbus Defense and Space  through the  Pléiades User Group initiative. Administración de Parques Nacionales  kindly provided permission and logistical assistance to work at  Monte Tronador inside Parque Nacional Nahuel Huapi.
The contamination present in the negative controls was identified as being mostly Proteobacteria (69%) at the phylum level and Pseudomonas (30%), Halomonas (18%) and unknown (12%) at the genus level. After this filtering step and at a rarefaction of 1000 sequences per sample, we obtained sequence data for 24 out of 27 serum samples from patients and for eight out of 11 ascitic fluid samples and no sequence data were recovered from healthy controls. Supplementary Fig. 2  shows the taxonomic profiling of the three sample types at the phylum level before and after the sequence-filtering step. Beta-diversity analysis, which studies the variation in composition between samples, showed a similar microbial composition between serum and ascitic fluid samples. However, the microbial community differed greatly between these two sample types and the stools (Fig.
2a ), although 89% and 86% of the serum and ascitic fluid microbiome was shared with the stool microbiome at the genus level (Supplementary Fig. 3 ). Euryarchaeota (phylum level) was detected only in stool samples and Thermi and Deinococcus-Thermus were detected only in ascitic fluid (Fig. 2b ). Firmicutes and Bacteroidetes were the two most dominant phyla in the three sample types. From serum and ascitic fluid, we detected six and eight groups of microbes at the phylum level, 26 and 28 groups at the family level, and 36 and 38 groups at the genus level, respectively. At the phylum level, Firmicutes (41%), Bacteroidetes (37%) and Proteobacteria (14%) accounted for 92% of the sequence data of the serum microbiome, whereas in ascitic fluid Firmicutes (46%), Bacteroidetes (27%), Thermi (10%) and Proteobacteria (8%) accounted for 92%.
On the basis of our analysis done with Primer Prospector software, the V4 primer pairs used in this study were expected to amplify almost 100% of the bacterial and archaeal domains. The 5′ ends of the forward (V4F_515_19: 5′ -GTGCCAGCMGCCGCGGTAA-3′ ) and reverse (V4R_806_20: 5′ -GGACTACHVGGGTWTCTAAT-3′ ) primers targeting the 16S gene were tagged with specific sequences as follows: 5′ -{AATGATACGGCGACCACCGAGATCTACACTATGGTAATTGT}  3, 15, 18  {GTGCCAGCMGCCGCGGTAA}-3′ and 5′ -{CAAGCAGAAGACGGCATACGAGAT} {Golay barcode} {AGTCAGTCAGCC} {GGACTACHVGGGTWTCTAAT}-3′. Multiplex identifiers, known as Golay codes, had 12 bases and were specified downstream of the reverse primer sequence (V4R_806_20)  19, 20. Standard PCR using 0. 75 units of Taq polymerase (Roche) and 20 pmol/μ L of the forward and reverse primers was run in a Mastercycler gradient (Eppendorf) at 94 °C for 3 min, followed by 35 cycles of 94 °C for 45 sec, 56 °C for 60 sec, 72 °C for 90 sec, and a final cycle of 72 °C for 10 min.
A total of 3,393,253 high quality sequences were finally recovered (2,910,686 for feces and 482,567 for serum and asctic fluid samples). UCLUST algorithm based on 97% of similarity was used to cluster similar sequences into Operational Taxonomic Units (OTUs) or taxa. Representative sequences of each OTU were aligned using PyNAST against Greengenes template alignment (gg_13_8). Chimeric sequences were then identified and removed with ChimeraSlayer. Finally, a taxonomical assignment for each OTU was performed with the basic local alignment search tool (BLAST) and the combination of two microbial databases (Greengenes and PATRIC). A phylogenetic tree was obtained with the FastTree program. The general OTU table was split into various tables in order to individually analyze feces, serum, and ascitic fluid samples.
In order to avoid false positive OTUs in stool samples, we eliminated those that did not represent at least 0. 2% of the sequences. For samples with a low biomass, such as serum and ascitic fluid, we removed the OTUs that did not account for at least 0. 2% of the sequences in at least 3 samples. Moreover, OTUs detected in negative controls were also removed for downstream analyses. Unknown bacteria assigned by BLAST against Greengenes and PATRIC databases were additionally checked against the NCBI database, and OTUs identified as from human origin were removed from the dataset. The final total, mean, minimum and maximum number of sequences per sample type were computed, and OTU tables were rarefied at several rarefaction depths (Supplementary Table  2 ). Statistical analyses. The characteristics of patients with and without ascites were compared using Fisher's exact test for categorical variables and Mann-Whitney test for quantitative variables.
Experiment and data analysis 2. 1 The ALICE detector The results presented in this paper are obtained from the data collected by the ALICE experiment at the LHC. Its performance and the description of its various subsystems are discussed in detail in Refs. [23, 24]. The ALICE detector has excellent particle identification capabilities. The main detectors used in this analysis are the Time Projection Chamber (TPC)  [25] , the Time-Of-Flight detector (TOF)  [26] , and the Inner Tracking System (ITS)  [27]. All detectors are positioned in a solenoidal magnetic field of B = 0. 5 T. As the main tracking device, the TPC provides full azimuthal acceptance for tracks in the pseudo-rapidity region |η| < 0. 8. In addition, it provides particle identification via the measurement of the specific energy loss dE/dx.
Event and track selection For this analysis, the data collected in the year 2010 are used. In total, the data sample consists of nearly 14 million Pb-Pb collisions at √ s NN = 2. 76 TeV and 380 million minimum-bias triggered events for pp collisions at √ s = 7 TeV after off-line event selection. A pair of forward scintillator hodoscopes, the V0 detectors (2. 8 < η < 5. 1 and -3. 7 < η < -1. 7), measured the arrival time of particles with a resolution of 1 ns and was used for triggering purposes and for centrality determination of Pb-Pb collisions. In pp collisions, the data were collected using a minimum-bias trigger requiring at least one hit in either of the V0 detectors or in the two innermost layers of the ITS (Silicon Pixel Detector, SPD). The trigger condition during the Pb-Pb data taking was changed with time to cope with the increasing luminosity delivered by the LHC.
Efficiency and acceptance The final p T -spectra of nuclei are obtained by correcting the raw spectra for tracking efficiency and acceptance based on Monte-Carlo (MC) generated events. Standard event generators, such as PYTHIA  [29] , PHOJET  [30] , or HIJING  [31]  do not include the production of (anti-)nuclei other than (anti-)protons and (anti-)neutrons. Therefore, nuclei are explicitly injected into underlying PYTHIA (in case of pp) and HIJING (in case of Pb-Pb) events with a flat momentum distribution. In the next step, the particles are propagated through the ALICE detector geometry with the GEANT3 transport code  [32]. GEANT3 includes a basic description of the interaction of nuclei with the detector, however, this description is imperfect due to the limited data available on collisions of light nuclei with heavier materials.
Due to the unknown interaction of anti-nuclei with material, these processes are not included for anti-nuclei heavier than anti-protons. In order to account for these effects, a full detector simulation with GEANT4 as a transport code  [33, 34]  was used. Following the approach described in  [35] , the correction for interaction of (anti-)nuclei with the detector material from GEANT3 was scaled to match the expected values from GEANT4. An alternative implementation to correct for this effect and the relevant uncertainties related to these corrections are discussed in Section 3. 1. The acceptance×efficiency is then obtained as the ratio of the number of particles detected by the detector to the number of generated particles within the relevant phase space. Figure  5  shows the acceptance×efficiency for deuterons (left) and 3 He (middle) as a function of p T for Pb-Pb collisions at √ s NN = 2.
The second set of genotypes followed the first set on the same ridges, with a 1. 5 m alley between sets. Farmers applied an equivalent of 100 kg ha -1 diammonium phosphate (DAP) and 50 kg ha -1 urea on their trials and hand weeded their trials according to their own schedule and practice. A total of 34 trials were conducted in 20 villages (Fig. 1 ; Table  1 ). Panicle yield was determined by weighing harvested panicles dried at ambient temperatures for at least 2 wk in all trials, both on-farm and on-station. Threshed grain yield was not used in this study, so as to facilitate rapid data collection and minimize errors due to grain loss during threshing in the on-farm trials. On-Farm Trials, 2011 and 2012  The 21 selected short-internode genotypes along with two (2011) or four (2012) check varieties were tested in a-lattice designs (incomplete block size 5) with three replicates in 2011 and 2012 (Supplemental Fig.
Selection of the 12 progenies with highest yield from the 2010 on-station testing of 100 short-internode progenies was also done using grain yield data from a low (-P) and a high (+P) phosphorous-managed trial. A simple index composed of standardized (m = 0, s = 1. 0) BLUEs for grain yield in -P and +P trials, with each value weighted by the respective trial repeatability, was computed. The highest ranking 12 genotypes for their yield index values were retained for advanced testing. All selected progenies were advanced by selfing and bulking panicles within families from plants of similar height and panicle aspects. The 21 progenies used for the advanced generation yield testing in 2011 and 2012 were thus in the S 3 or S 4 generation.
The means of the two check varieties Grinkan and Ngolofing were dropped from the analysis since they were not present in all datasets. A stability analysis was conducted for yield performance of 21 selected genotypes over the second-stage on-farm trials of 2011-2012 using the stability variances  (Shukla, 1972)  and linear regression of each genotype on the average yield of all genotypes in the studied environments  (Finlay and Wilkinson, 1963) , which are strongly related to the static and dynamic concept of stability, respectively. 30 genotypes. Each farmer trial evaluates one subset in an a-lattice design with two replicates. Five incomplete blocks occurred within each replicate, with random allocation of six test genotypes and one check variety to each block, and five additional check plots were randomly allocated to different blocks over the two replicates to complete the total of 75 plots available per farmer.
Acknowledgments The contributions of Malian farmers and field staff of the  Association des Organisations Paysans Professionels (AOPP) ,  Union Local de Producteurs de Cereals (ULPC) ,  Association d'Eveil Malienne pour la Developpement Durable (AMEDD) ,  Cooperatives des semenciers du Mande (COOPROSEM) ,  Institut d'Economie Rural  and  International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)  for the conduct of the yield trials is greatly appreciated. The support of the McKnight.
Strikingly, the overexpression of RNA splicing and processing factors was a prominent feature of the poorly differentiated cells and was also observed in clinical datasets. A genome-wide analysis of splice isoform expression revealed many alternative splicing events that were specific to the differentiation state of the cells, including an unexpectedly high frequency of events on chromosome 19. The poorly differentiated cells exhibited alternative splicing in many genes associated with tumor progression, as exemplified by the preferential expression of the short isoform of telomeric repeat-binding factor 1 (TERF1), also known as Pin2. Our findings demonstrate the utility of the ALI method for probing the molecular mechanisms that underlie NSCLC pathogenesis and provide novel insight into posttranscriptional mechanisms in poorly differentiated lung cancer cells.
Alternative Splicing SpliceArray™ micorarrays (Diaxonhit) contain body probes that monitor exon bodies and are designated F, T, and B, and junction probes that monitor exon-exon junctions are designated C, D, and E. Preprocessing steps including robust multi-array average background correction, quantile normalization, and mean probe summarization were performed on the CEL files using Partek software (Partek Inc. , St. Louis, MO). Microarray processing methods have been published  [13]  and are described in Supplemental Information. Gene level expression from SpliceArray™ was calculated as the geometric mean of all F and T probes, wherease gene level expression from Affymetrix platform was summarized as median of all probe sets. Correlation analysis of gene level expression change between differentiated and undifferentiated condition (log2ratio) was performed using Pearson method.
Event level change for each alternatively spliced event was calculated as the ratio of geometric means between probes that target the variant transcript to the reference transcript (B, C, D, and E). Differential event level change between the differentiated and undifferentiated conditions was analyzed by analysis of variance method using Partek software (Partek Inc). Significantly altered events are defined as those that with a fold-change ≥ 2 and false discovery rate (FDR) ≤ 0. 05 in both cultures. The RNAseq method was not feasible in this scenario because of limited quantities of RNA.
TERF1 Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) Short-and long-form TERF1 transcripts were detected by PCR with splice variant specific primer sets previously developed  [14]. PCRs were run on an Applied Biosystems Veriti thermocycler. PCR products were resolved on 20% polyacrylamide gels, stained with Sybr Green (Life Technologies), and imaged with the ChemiDoc XRS. Band intensities were quantitated using ImageJ software.
Overexpression of mRNA Processing Factors in Poorly Differentiated Cells Additional bioinformatic analyses were performed with the full gene expression profiles and thus were independent of GeneSig2. Gene Set Enrichment Analysis (GSEA; Broad Institute) demonstrated that the expression profiles of the poorly differentiated state were significantly enriched for gene sets of poor clinical prognosis (Figure  3A ), most notably one from the Directors' Challenge  [18]  which is the same dataset used above in the independent GeneSig2 analysis. In addition, the identification of stem cell factors demonstrates that the poorly differentiated state of the culture was associated with metastasis factors (Figure  3A , Supplemental Figure  S2A ), which is consistent with a large body of data that supports the links between metastatic stem cells and poor survival  [19, 20].
Higher expression of the short form was observed in the poorly differentiated state in both cultures; expression relative to the differentiated state was 4. 26-fold (FDR = 0. 024) in TUM110 and 3. 92-fold (FDR = 0. 067) in TUM449 (Figure  6 , B and C). TERF1 was selected for confirmation of the SpliceArray data by RT-PCR because of prior characterization of its two isoforms, the documented function of TERF1 in stem cells  [27] , and the association between telomeres and tumorigenesis  [28, 29]. We performed RT-PCR with two probe sets: one that specifically amplifies the short form and the other that specifically amplifies the long form. The results were consistent with the SpliceArray data (Figure  6 , C-E). In fact, a subtle distinction was observed in both array and RT-PCR: the short form was upregulated in the poorly differentiated cells in both cultures; the long form was upregulated in the differentiated cells in TUM110 but unchanged during differentiation in TUM449.
Discussion The novel strategy employed in this study to characterize the differentiation hierarchy in NSCLC revealed the upregulation of RNA processing factors and a distinct mRNA splicing profile in poorly differentiated cells, including the preferential expression of the short form of TERF1. Our strategy circumvents specific liabilities of other methods such as cell sorting and intact tissue analysis and thus is complementary to such approaches. Our analysis of samples that represent the two major subtypes of NSCLC suggests that the results may apply broadly to NSCLC. Indeed, the overexpression of RNA processing factors that we identified in this study was also observed in the larger datasets that represent hundreds of human NSCLC samples.
ALI Method to Study NSCLC Inducing differentiation of stem cells by exposure to serum is a common method  [3] , but our results indicate that the ALI method constitutes a cleaner, more precise technique in NSCLC. As appreciated previously, the 3D structures formed by cells at the ALI resemble solid tissues more than the spindly low-density cultures of cells exposed to serum. Consistent with the morphological observations, three independent lines of analysis demonstrated the greater precision and relevance of the ALI method, in particular BEGM-ALI. First, the BEGM-ALI gene signature showed the strongest HRatios when compared with clinical datasets, whereas the serum signature had the weakest values despite being comprised of a larger number of genes. Second, GSEA revealed that BEGM-ALI exhibited the strongest enrichment of clinical and stem cell gene signatures, whereas, in contrast, serum was the only condition to enrich for cell death.
Spot detection and differential protein expression analysis were performed using Delta 2D software (Decodon). Differentially expressed spots (Student's t-test value of less than p < 0. 05) were excised from the gel. In gel digestion, analysis by MALDI-TOF/TOF MS and database searching were performed by Plateforme Prot eomique Strasbourg Esplanade as described previously  [12]. Proteins were identified by searching data against the SwissProt non-redundant protein sequence database. In all results, the probability scores were greater than the score fixed as significant with a pvalue of 0. 05.
Bacterial TusA homologues contain a highly conserved cysteine residue (C19) which is responsible for persulphide formation and sulphur transfer  [30] ; AFE_2600/AFE_3117 contain this conserved cysteine residue (data not shown). Given that the expression of AFE_2599/AFE_3116 and AFE_2600/AFE_3117 was upregulated in the presence of U(VI), there is a strong likelihood that these proteins interact together in A. ferrooxidans with a role in sulphur relay. Low molecular weight thiols maintain the reducing environment of the cytosol and are critical for preventing the oxidation of cysteine residues in proteins. Transcriptional regulation of genes of interest from the proteomics study was analysed by quantitative-RT-PCR analysis (data not shown). Unfortunately, we did not find any Fig. 1. Two identical copies of two genes encoding proteins with increased expression in the presence of U(VI).
Chromatographic procedure The chromatographic system was an Agilent 1260 (Agilent Technologies, Santa Clara, CA, USA), with a 1260 quaternary pump, RR/SS or RS/SR: the amine has two stereogenic centers, hence four possible enantiomers, the indicated enantiomers were considered. Hept: heptane; IPA: isopropyl alcohol; BA: butylamine; ACN: acetonitrile; MeOH: methanol; AA: acetic acid; TEA: triethylamine; EtOH:ethyl alcohol. All mobile phase compositions are given in % v/v. a 1260 autosampler, thermostated column compartment, and a 1260 diode array detector (DAD). Data was evaluated using the Agilent Chemstation software. A Shimadzu SCL 10A refractive index detector (Shimadzu, Columbia, MA, USA) was used to detect the seven primary amines lacking UV chromophores (Solutes #33 to #39 in Table  1 ).