dataesr/scientific-paragraphs-categorization huggingface dataset:
It also has built in warnings if data are out of range, incorrect, or misclassified. This design also assists in extracting data in a format that can be used for matching of cases and controls for age, gender, and ethnicity, and for extraction for data analysis (e. g. , SPSS). To ensure that case and control groups overall are comparable for age and gender, we have adopted a stringent strategy to match cases to controls at the time of collection. For this, a semi-automated Excel-based algorithm is designed to match each case with a control for age, gender, and ethnicity. The system can match hundreds of controls to cases simultaneously, both site by site and globally. Participants with mismatched ethnicity are excluded from matching. Controls are matched for age within À3 to +5 years of cases.
Data and Sample Collection Data collection uses a combination of face-to-face interview and medical-record sources. Data are collected regarding demographics, ancestry, alcohol and tobacco history, education, clinical symptoms, blood biochemistry, severity and date of diagnosis of cirrhosis, medication, drug use, anthropometrics, and other potential covariates that might affect development of cirrhosis (Table  S1 ). We have set up a central database at the QIMR Berghofer Medical Research Institute (QIMR) in Brisbane, Australia, using existing facilities for managing epidemiological data with appropriate data security (backup, encryption, and access control). Data from the collection centers are uploaded after local entry and stored at QIMR. Each data file is checked by the site PI followed by the study coordinator to control quality of data received.
, 1997) , consistent with previous studies showing familial aggregation and significant heritability of alcohol problems. In conclusion, we report that through the establishment of our international GenomALC Consortium, it has proven feasible to recruit and collect data and samples from chronic high-risk drinkers for our overall aim to identify genetic loci for risk of ALC. In future, identification of relevant loci is expected to give insight into the mechanisms of ALD and may (depending on the size of allelic effects) allow identification of at least some high-risk people. In the meantime, our recruitment of a substantial number of people allows us to assess disease associations and potential risk factors using self-report, clinical, and laboratory data. Analysis of these aspects on the entire collection including this cohort is anticipated once recruitment is complete.
ACKNOWLEDGMENTS The GenomALC study is funded by the  U. S. National Institutes of Health  through grant  U01-AA018389  to the  Southern California Institute for Research and Education (SCIRE). Additional GenomALC members:  Prof. Sylvie Naveau ,  Department of Antoine Beclere University Hospital, Clamart, France ;  Dr. Philippe Podevin ,  Leon Binet Hospital, Provins, France ;  Dr. Pierre Nahon ,  Hopital Jean Verdier, Bondy, France ;  Prof. Clive Harper ,  University of Sydney, Sydney, Australia. NIAAA P60 AA007611  supported the  Genomics and Bioinformatics Core of Indiana Alcohol Research  and  1I01CX000361-01  from the  Veterans Affairs Research and Administration (SL) , and  W81XWH-12-1-0497  from  U. S. Department of Defense  (SL). We would also like to express our appreciation to  Tammy Graves  for the technical support.
Cells were washed twice with PBS and the ABTS solution (Roche Diagnostics) was added for 45 min at room temperature. The absorption at 415 nm was determined with an ELISA reader EXL 808 (Biotek). Experimental data of the negative controls (biotinylated soybean trypsin inhibitor) were directly subtracted from the experimental data of the biotinylated human HGF and VEGF 165.
5 ml/min and the column was thermostatted at 20 • C. The quadrupole-time-of-flight mass spectrometer was tuned and calibrated in 2 GHz "extended dynamic range mode" for a mass range up to 3200 m/z and operated in data-dependent "Auto MS/MS" mode. Positive electrospray ionization was carried out using a capillary voltage of 4500 V and a fragmentor voltage of 175 V. Nitrogen was used as drying gas with 8 l/min at 300 • C and sheath gas respectively with 11 l/min at 350 • C, nebulizer pressure was 35 psig. Data-dependent data acquisition was carried out using "Auto MS/MS" mode. Alternating acquisitions in MS and MS/MS were carried out detecting both intact peptide masses and peptide fragment masses respectively after collision-induced dissociation (CID) using nitrogen as a collision gas. The scan rate for both MS and MS/MS was set to 5 Hz, the collision energy was ramped depending on precursor m/z with an offset of 3 V and a slope of 4 V/100 Da (Dalton).
In vitro monolayer wound closure assay Cells were seeded in 12-well plates at a density of 1. 5×10 5 cells per well and serum-starved the next day for 24 h. After starvation a scratch was made into the confluent cell layer using a sterile pipette tip. The medium was changed and replaced by fresh medium or medium containing 25 μg/ml human CD44s ectodomain or 25 μg/ml rat CD44v6 ectodomain. After 5 min at 37 • C, induction with 20 ng/ml HGF was performed. Pictures were taken directly after the scratch and 24 h after induction using a PowerShot S620 digital camera (Canon). The computer program ImageJ (National Institutes of Health) was used for quantitative evaluation. The area covered by cells that migrated into the wound was quantified.
ACKNOWLEDGEMENTS We are thankful to NanoTemper for their support and discussion. Else we would like to thank  Ermanno Gherardi  ( University of Pavia, Pavia, Italy ) for providing HGF and  Kurt Ballmer-Hofer  ( Paul Scherrer Institute, Villigen, Switzerland ) for providing the CD44v6 and VEGFR-2 expression vectors. Finally, we would like to acknowledge  Giuseppina Pace  and  Katharina Fuchs  for their help with protein production and purification. Declaration of conflict of interest:  Alexandra Matzke-Ogi  and  Véronique Orian-Rousseau  have shares in the company amcure that develop a modified version of the CD44v6 peptide for clinical trial for pancreatic cancer. This work was not funded by amcure in any way. Alexandra Matzke-Ogi  is now employed by amcure. When this work was performed  Alexandra Matzke-Ogi  was employed by the  KIT.
Anatomy and Histological Analysis For topographic and histological analysis the full length of the cervico-thoracic region of the aorta of the rabbits were obtained by Rasmusen et al. [14]. In brief, the areas of lesions were determined by using NIH Scion Image Software (Scion, Frederick, MD, USA). The areas were measured three times. The aorta was then prepared to 2 mm-thick fragments transversely cut into four segments (aortic arch, thoracic aorta, abdominal aorta and bifurcation), which were classified into distal lesions and lesions of the total aorta.
Statistical Analysis Results were tested upon distribution using Kolmogorov-Smirnov Test and QQ-plots. When normally distributed, results are presented in mean ± standard error of the mean (SEM) and in median and interquartile range when data were not normally distributed. The Student's t-test or Mann Whitney U (according to data distribution) test was used to determine significant differences in arginine and ADMA concentrations between groups. The one-sample t-test was used to test whether the values were significantly different from the beginning of the treatment. Correlations between the continuous variables were assessed by using bivariate analysis to estimate the Pearson's coefficient. SPSS for Windows software was used to perform statistical analysis. p < 0. 05 was considered to indicate a significant difference.
The resulting reduction of XTT was measured at 492 nm using a microtitre plate reader (Awareness Technology Inc. , USA). The percentage of viable cells was calculated by comparing the reduction of XTT in drug treated cells to that in the untreated control cells. The cytotoxicity was expressed as IC 50 , which is the concentration at which the agent reduces the cell viability by 50 % relative to the control cells. These values were calculated using GraphPad Prism 4. 03 software (GraphPad Inc. ).
Statistical analyses The data are expressed as a means ± S. D. Analysis of variance (ANOVA) with Tukey post hoc test was used for multiple comparisons. All statistical parameters were calculated using the STATISTICA program (StatSoft, Tulsa, OK, USA). A p value of < 0. 05 was considered significant. The viability curves were fitted according to the sigmoidal dose-response model (four-parameter logistic equation) using the GraphPad Prism 4. 03 software (GraphPad Inc).
Release of cytochrome c from mitochondria in DOX and DOX-TRF treated normal and leukemia-derived cells We monitored the efflux of cytochrome c from mitochondria to cytosol using an ELISA assay kit at 6, 12 and 24 h in DOX-Fig. 2  The effect of DOX and DOX-TRF without (left panel) and with (right panel) NAC on the level of early and late apoptotic and necrotic cells. NAC was added 1 h before DOX or DOX-TRF treatment. The number of cells in apoptosis and necrosis were counted by flow cytometry using YO-PRO-1 iodide/PI double staining, after which the percentage fractions of the respective cells were calculated. Data are mean ± SD of five samples. * p < 0. 05 indicates significant differences between drug-treated and control (untreated) cells; # p < 0. 05 indicates significant differences between cells treated with DOX and DOX-TRF; + p < 0.
We investigated mitochondrial membrane potential, the level of intracellular Ca 2+ and the intracellular fraction of cytochrome c. Dissipation of Δψ m is one of the markers for a mitochondrial involvement in apoptosis  [30]. Our data show that a drop in mitochondrial membrane potential (Δψ m ) occurred during the first 6 h following DOX or DOX-TRF treatment in the leukemia-derived cell lines tested. In PBMC and in the doxorubicin-resistant erythroleukemia cell line K562/DOX, the Δψ m decreased after 24 and 12 h of DOX-TRF treatment, respectively. After a prolonged incubation time (48-72 h), irrespective of cell type, the Δψ m gradually recovered. These observations suggest that Δψ m loss is an early event in the apoptotic cascade, induced by both DOX and DOX-TRF. We also found that DOX-TRF was more effective than DOX in the depolarization of mitochondria in malignant cells.
Fluorescence in situ Hybridization Colorless filaments were prepared for FISH analysis according to previously described protocols  [13]. After hybridization, samples were observed in MilliQ water with a drop of Vectashield using an epifluorescence Nikon microscope Eclipse 80i. FISH analyses were performed using universal probes for Bacteria (EUB 338)  [33] , NON338  [34] , as negative control and BEG572 (5'-CAACCGCCTACGTACGCT-3') and BEG282 (5'-GGATTGCTGTCTTGGTAAGC-3') for morphotype 1 and morphotype 2, respectively, that were specifically designed from the 16S rDNA bacterial sequences obtained in this study. The specific probes (labelled with Cy3) were designed manually. Probes 16S ss-rRNA localization was optimized according to Fuchs et al. [35]. The probe's specificity was further tested with the online Probes Match tool provided by the Ribosomal Database Project  [36].
DNA extraction and PCR amplification DNA was extracted from colorless filaments using DNeasy Blood & Tissue kit (Qiagen) according to the manufacturer's instructions. 16S rDNA were amplified using primers 8F/ 907R (for morphotype 1) and 8F/1492R (for morphotype 2) as previously described  [38, 39]. PCR amplifications were performed as follows: 95°C for 5 min, 35 cycles of 94°C 30 s, 58°C 45 s, 72°C 1min 30 sec and finally 72°C 7 min. PCR products were purified using QIAquick PCR purification Kit (Qiagen) and cloned with pGEM-T cloning kit (Promega) according to manufacturer's instructions. Inserts from 20 positive clones of each construction were fully sequenced by Genoscreen (http://www. genoscreen. com) using vector primers T7 and SP6. The sequences obtained in this study were deposited in the GenBank database under accession no.
Phylogenetic analysis The 16S rDNA gene sequences obtained were compared with the National Center of Biotechnology information (NCBI) (http://www. ncbi. nlm. nih. gov) database using BLAST  [40]. Best hits were included in phylogenetic analyses. The phylogenetic analyses were conducted using MEGA version 5  [41]. Sequences were aligned using SINA alignment service  [42]  of the SILVA web site (http://www. arb-silva. de) and alignments were checked manually. The phylogenetic tree was constructed from the multiple-aligned data using the Neighbor Joining (NJ) method with Tamurai-Nei as genetic distance model. Nodes robustness was assessed by performing 1000 bootstrap replicates, and only bootstrap values above 49% are indicated at the nodes of the tree. Leucothrix mucor, Thiothrix nivea, and Achromatium spp were used as outgroup.
Mat was collected from the field the day after and transferred immediately (within 1 hour) into the mesocosm on the sediment (see S1 Fig. ). Oxygen and sulphide profile measurements were carried out using Clark-style oxygen (Oxy100) and sulphide (H 2 S100) microsensors with a 10μm tip manufactured by Unisense (Aarhus, Denmark) connected to a four channel Unisense picoammeter. Calibrations were performed according to Unisense instructions. The pH was measured with autonomous probe similar to the one described  [44]  fixed to the micromanipulator. Vertical profiles were determined by moving the microelectrodes using a micromanipulator into the mat and recording the electrical current with SensorBasic software. Total sulphide concentrations (S 2- tot = H 2 S+HS -+S 2-) were calculated taking into account the measured pH and salinity  [45]  using a pK = 6.
Our data suggest that morphotype 1 and   Genbank. According to some authors, percentages lower than 95% could indicate a new genus  [48, 49]. Furthermore, all the Isobeggiatoa spp. sequences available are from cold temperate water species (Denmark and Germany), and Antarctic environments  [46]. The results of the lipid analysis (Table  1 ) confirmed the genetic results, as the main fatty acids detected contained 16 and 18 carbon atoms with 0-1 ω-7 double bonds, concurrent with previously published results for Beggiatoa  [50, 51]  and other sulphur-oxidizing bacteria Thioploca and Thiomargarita  [51, 52]. Interestingly, Jacq and co-authors  [53] , who characterized two types of filamentous bacteria retrieved from subtidal hydrothermal vents in southern California, were the only ones to also report small, but significant amounts of polyunsaturated C 20 fatty acids in both phenotypes.
Measurements and multivariate analysis Our data set includes 888 measured specimens of Anasibirites from the Noe Tobe block. We also included in our analyses measurements of additional specimens assigned to A. kingianus from the Salt Range and Spitsbergen, as well as measurements of specimens assigned to A. multiformis from Utah and Idaho (Fig. 2 ), resulting in a data set with 955 sets of measurements. All of these specimens were split into morphological groups on the basis of their similarity with the four recently validated species of Anasibirites  (Br€ uhwiler and Bucher 2012a; Brayard et al. 2013) , and these groups were then statistically compared. Each set of measurements includes classical geometric parameters of the ammonoid conch such as the shell diameter (D) and corresponding whorl height (H), whorl width  (W)  and umbilical diameter (U).
The descriptive, exploratory and multivariate analyses of the biometric ratios have been performed with the free scientific and statistical environment R version 3. 0. 3 (R Core Team 2014) and with the package epaleo (CM unpub. ). Additional details on the methods are found in  Reyment and Savazzi (1999) ,  Davis (2002)  and  Hammer and Harper (2006) , among others.
Acknowledgements. Thomas Br€ uhwiler and  Markus Hebeisen  are thanked for their technical support with the mechanical preparation of the material. Rosemarie Roth  is acknowledged for her photographic work. We also thank  Veronica Piazza  ( University of Oslo ) who kindly shared her data on Anasibirites from  Spitsbergen. Mrs Monte Brough  is thanked for allowing access to her land in  Lower Weber Canyon , Utah. This work benefited from the constructive and useful reviews of  Paul Smith  and two anonymous colleagues. This is a contribution to the  Swiss NSF  project  200020-160055  (to HB) and to the French  ANR-13-JS06-0001-01  project (to AB). We also gratefully acknowledge the support of the University of Z€ urich ( Prorector Prof. D. Wyler ) for the acquisition of the Timor material.
lapatinib reduces the expression of e6 and e7 oncogenes SCC-47 and SCC-154 cells are depicted as HPV positive. While the effective transcription and levels of the E6 and E7 oncogenes have been reported for SCC-47 cells,  5  they have not been previously described for SCC-154 cells. Therefore, we performed quantitative RT-PCR, which showed similar levels of E6 and E7 in the two HPV-positive cell lines (data not shown). We evaluated if lapatinib treatment could affect the expression of the two transcripts. As shown in Figure  1 , lapatinib significantly decreased E6 and E7 mRNA levels in both cell lines, with the exception of E6 in SCC-47, which was nevertheless close to statistical significance. As expected, E6 or E7 expression was not detected in SCC-9 and SCC-15, confirming the HPV-negative status of these cells (data not shown).
, , 2011;; Blonda et al. , 2013). A further obstacle is that there has been no systematic approach to the classification of habitats from EO data that is applicable to all sites and available as a standard. Indeed, many areas are mapped and monitored using a range of different data sources, types and classification schemes. Furthermore, the schemes used have often generated classes of limited value for conservation purposes. To address these issues, the FP7-funded BIO SOS project sought to develop the Earth Observation Data for Habitat Monitoring (EOD-HAM) system, with this providing a standardized framework for consistent land cover and habitat mapping and monitoring inside and around protected areas with particular focus on European Natura 2000 sites and their surroundings. A key component of the system is the inclusion of decision rules within a hierarchical classification structure with these generated from expert knowledge from both ecologists and remote sensing scientists.
The EO component of the EODHaM system (Fig. 2 ) is based on geographic object-based image analysis (GEOBIA;  Hay and Castilla, 2008; Blaschke and Strobl, 2001)  and also incorporates a pixelbased analysis. The system is comprised of (a) data input involving preparation and pre-processing (orthorectification, radiometric, atmospheric and/or topographic correction), (b) spectral feature extraction, segmentation and classification to LCCS Level 2 (1st stage), (c) classification to Level 3 and beyond (2nd stage), with this involving expert knowledge, and (d) translation of these classes to GHCs and Annex I Classes (3rd stage) of conservation importance (EU Habitats Directive). An additional module focuses on change detection and validation of outputs, which include maps of land cover, habitats and changes in these.
The output products feed subsequently into modules that perform ecological modeling at the landscape level, biodiversity indicators extraction, and biodiversity indicators change detection. The processing within the EODHaM is automated, with the exception of threshold value determination, and is undertaken primarily using the Remote Sensing and GIS Library (RSGISLib) software  (Bunting et al. , 2014) , the Geospatial Data Abstraction Library (GDAL), and the ORFEO Toolbox  (Inglada and Christophe, 2009) , with XML and PYTHON scripting. The classification system also makes use of the KEA image file format  (Bunting and Gillingham, 2013) , which allows for processing within a raster attribute table (RAT). Within the RAT, which has been developed such that large datasets can be efficiently analyzed  (Clewley et al.
g. , using the 6S code;  Vermote et al. , 2003)  and corrected for topographic and/or bidirectional effects (e. g. , using the algorithm of  Shepherd and Dymond (2003). The system has the advantage of being able to bring in knowledge from the users (e. g. , whether water bodies are persistent → → → → → Fig. 2. Overview of the EO components within the EODHaM system. The system is hierarchical and top down and commences with the classification of Levels 1 and 2, with this based on spectral processing alone, and continues to LCCS Level 3 and beyond. The final LCCS categories are then translated to GHCs and subsequently to Annex I. Change analyses are then performed. or otherwise) and also from other data sources, including digital terrain models (DTMs), canopy height models (CHMs), estimates of the number of vegetation strata, and outputs from hydrological models (e.
, 2013). These objects, observed in VHR data, include individual trees, hedgerows, roads and ponds. Their detection is typically validated through reference to existing thematic layers (e. g. , buildings extent or ground observations; e. g. , of tree crowns). For example, in Wales, over 76% of mobile and static caravans were detected using this approach. However, the success in extraction depends upon factors such as the ground surface topography and solar illumination as well as the contrast of the features with others in the scene. The reliability of extraction will also decrease for composite objects (e. g. , farmyards). As the resolution decreases, most of these objects become less distinct  (Marceau et al. , 1994). Hence, the number of objects of different type that can be extracted from, for example, 10 m SPOT HRG or 30 m Landsat sensor data decrease with the spatial resolution.
Segmentation Following automated extraction of landscape objects, two algorithms are available within the EODHaM 1st stage for segmenting the remaining areas, with these based primarily on spectral information. However, LIDAR data can be integrated as this often leads to improvements in the segmentation of forest areas. Within RSG-ISLib, and for image segmentation, a clustering and small object elimination algorithm is applied whilst the ORFEO Toolbox uses the algorithm of  Comaniciu and Meer (2002). At this stage, the concept of 'small' and 'large' objects is introduced. 'Small' objects are typically those that can be discerned within the imagery and include (in the case of the VHR data) previously extracted objects (e. g. , buildings, trees, hedgerows). These 'small objects' are extracted using the procedures outlined in the object extraction section.
g. , airport runways, large industrial buildings) and reservoirs. These larger objects are generated through parameterization of the segmentation algorithm and delineation of specific features (e. g. , field or forest boundaries) can often be validated through reference to existing thematic layers. The landscape objects already extracted (e. g. , buildings, individual trees) are ignored in this segmentation process such that these larger units are captured in their entirety. However, reference can be made interchangeably between the large and small objects through the RAT of both the large and small object layers. The small and large segmentation generated using the RSGISLib code is illustrated in Fig. 4. In this segmentation, which is based on an unsupervised K-means clustering of the imagery, objects below a certain size are eliminated following each iterations and associated pixels are reassigned to new clusters in subsequent iterations.