dataesr/scientific-paragraphs-categorization huggingface dataset:
Structure validation was performed with the programmes PHENIX  42  , SFCHECK  46  , PROCHECK  47  , WHAT_CHECK  48  and RAMPAGE  49. Potential hydrogen bonds and van der Waals contacts were analysed using the programmes HBPLUS  50  and LigPlot þ (ref. 51). All crystal structure superpositions of backbone a-carbon traces were performed using the CCP4 programme LSQKAB  39. All molecular graphics representations in this work were created using PyMol  52. Superscripts following receptor residue numbers denote N-terminus (NT), Helix 8 (H8), cytoplasmic loop and GPCR numbering according to Ballesteros and Weinstein  53. The most conserved residue in a helix among rhodopsin-like GPCRs is designated x. 50, where x is the helix number. All other residues on the same helix are numbered relative to this reference residue.
0. 'Extra Meta II' analysis was performed at 0 °C using a Shimadzu UV3000 instrument  30. Note that all peptides used for crystallization were first functionally verified by the absorbance 'Extra Meta II' assay. Samples consisted of 10 mM ROS-P in 100 mM HEPES buffer pH 8. 0 and increasing concentrations of peptide. The photo-flash activated B20% of the rhodopsin. The titration data were fit to the expression (B max *x/K D þ x), where the final amplitude of the plots was fixed to the theoretical maximum absorbance change (that is, 100% Meta II stabilization). The resulting apparent K D value was corrected for Meta I/Meta II equilibrium at pH 8. 0 (refs 30,55). The true K D value of peptide binding to Meta II was derived from the apparent K D value, as described by Schleicher et al. 30, 55  , using a Meta II/Meta I ratio of B0.
Peptides were solubilized in 50 mM HEPES buffer, and the pH was adjusted to 7. 0. Peptide competition against arrestin A366NBD (1 mM) for binding to OpsP (2 mM) was measured by a centrifugal pull-down assay (50 mM HEPES, pH 7. 0, 22 °C)  54, 58. Briefly, arrestin and OpsP were mixed with increasing concentrations of peptide (120 ml sample volume). Following an equilibration period of B30 min, samples were centrifuged at 16,000 g for 10 min (room temperature). The absorbance spectrum of each supernatant was then measured. Given the redshifted absorbance of the NBD label (l max ¼ 500 nm) and its defined extinction coefficient (0. 025 mM À 1 cm À 1 ), the concentration of arrestin remaining in the supernatant could be accurately determined. The K D of peptide binding to OpsP was determined by fitting the peptide titration data to the expression: y ¼ s*(1-(P/(P þ K Dp *(1 þ A/K Da ))), where P is the peptide concentration, K Dp is the K D of the peptide, A is the arrestin concentration (1 mM), K Da is the K D of arrestin binding to OpsP (400 nM)  54.
(d) Finger loop in basal rod photoreceptor arrestin (PDB entry 1CF1) in 'closed' (olive) and 'open' (dark-green) conformations. (e) Finger loop in b-arrestin-1 (blue) bound to peptide analogue of the phosphorylated receptor C-terminus (red; PDB entry 4JQI). (f) Finger loop peptide ArrFL-1 (purple) bound within the crevice of R*. In d-f, the backbone Ca-Ca distance between Asp71/67 and Leu77/73 (numbering refers to rod photoreceptor arrestin/b-arrestin-1) is indicated (Å).
Acknowledgements We thank  Anja Koch  and  Brian Bauer  for assistance in purifying reagents and opsin purification. We are grateful to  Uwe Mu ¨ller ,  Manfred Weiss  and the scientific staff of the  BESSY-MX/Helmholtz Zentrum Berlin fu ¨r Materialien und Energie  at beamlines  BL14. 1 ,  BL14. 2  and  BL14. 3 operated by the  Joint Berlin MX-Laboratory at the BESSY II electron storage ring (Berlin-Adlershof, Germany ) and the scientific staff of the  European Synchrotron Radiation Facility (ESRF, Grenoble ) at beamlines ID14-1, ID23-1,  ID23-2 ,  ID14-4  and  ID29 , where the data were collected, for continuous support. We thank  Antoine Royant  for extensive support with the usage of the microspectrophotometer on the crystals at the  ID29S-Cryobench (ESRF, Grenoble). This work was supported by grants from the  Deutsche Forschungsgemeinschaft  ( SFB740  to K.
1 ArrFL- 1 Figure 4 | 114 Figure 4 | Structural and functional comparison of G t aCT and ArrFL-1. (a) Side view of the active receptor Ops* crevice (black ribbons and sticks) with bound G t aCT-HA peptide (blue ribbon and sticks; PDB entry 3DQB). (b) Side view of the active receptor Ops* crevice (orange ribbons and sticks) with bound ArrFL-1 peptide (purple ribbon and sticks). (c) Stabilization of Meta II by the high-affinity peptide G t aCT-HA and (d) ArrFL-1 peptide. (e) Competition against rod photoreceptor arrestin for binding to phosphorylated opsin by G t aCT-HA and (f) ArrFL-1 peptide. In panels c-f, data points from independent experiments are represented as differently shaped symbols. The peptide titration experiments which measure Meta II-stabilization (c,d and Supplementary Fig.
low prevalence of herpes infections (which can be translated into "herpes susceptibility or resistance") was then determined. For the HLA frequency in ethnically and geographically distinct populations; the databases from the following websites were consulted: http:// www. ncbi. nlm. nih. gov/projects/gv/mhc/ [38, 39] , http://www. allelefrequencies. net/ default. asp [38, 39] , and http://hla-net. eu [40] [41] [42].
Statistical analyses Data for each assay were compared by analysis of variance (ANOVA) and Student's t-test using Graph Pad Prism 5 software (San Diego, CA). Differences between the groups were identified by ANOVA, multiple comparison procedures, as we previously described  [14, 24, 25, 27, [43] [44] [45] [46] [47] [48] [49] [50] [51] [52] [53] [54]. Statistical analysis also included Fisher's exact test and multivariate analyses, using logistic and linear regression models. Results are considered statistically significant if p < 0. 05.
This study presents spatial patterns in glacier area and elevation changes in the monsoon-influenced part of the Himalaya (eastern Nepal and Sikkim) at multiple spatial scales. We combined Corona KH4 and topographic data with more recent remote-sensing data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+), the Advanced Spaceborne Thermal Emission Radiometer (ASTER), QuickBird (QB) and WorldView-2 (WV2) sensors. We present: (1) spatial patterns of glacier parameters based on a new "reference" geospatial Landsat/ASTER glacier inventory from ∼ 2000; (2) changes in glacier area  and their dependence on topographic variables (elevation, slope, aspect, percent debris cover) as well as climate variables (solar radiation and precipitation), extracted on a glacier-by-glacier basis and (3) changes in glacier elevations for debris-covered tongues and their relationship to surface temperature extracted from ASTER data.
, 2014)  provides a global dataset of glacier outlines intended for large-scale studies; in some regions the quality varies and the outlines may not be suitable for detailed regional analysis of glacier parameters. Among recent glacier inventories, we cite those in the western part of the Himalaya  (Bhambri et al. , 2011; Kamp et al. , 2011; Frey et al. , 2012) , and a few in the eastern Himalaya  (Bahuguna, 2001; Krishna, 2005; Bajracharya and Shrestha, 2011; Basnett et al. , 2013). Parts of the monsoon-influenced eastern Himalaya (the eastern extremity of Nepal, Sikkim and Bhutan) still lack comprehensive, multi-temporal glacier data needed for accurate change detection. The use of remote sensing for glacier mapping in this area is limited by frequent cloud cover and sensor saturation due to unsuitable gain settings and the persistence of seasonal snow, which hampers satellite image acquisition.
Furthermore, this area has very limited baseline topographic data needed for comparison with recent satellite-based data, as discussed in detail in  Bhambri and Bolch (2009a). The earliest Indian glacier maps date from topographic surveys conducted by expeditions in the mid-nineteenth century  (Mason, 1954) , but these are limited to a few glaciers. The Geologic Survey of India (GSI) inventory based on Survey of India maps  (Sangewar and Shukla, 2009)  is not in the public domain. For eastern Nepal, 1970's topographic maps from Survey of India 1 : 63 000 scale are available, but their accuracy is not known with certainty. Given these limitations,  de-Figures Back  Close Full classified Corona imagery from the 1960's and 1970's has increasingly been used to develop baseline glacier datasets, such as in the Tien Shan  (Narama et al.
, 2007) , Nepal Himalaya  (Bolch et al. , 2008a)  and parts of Sikkim Himalaya  (Raj et al. , 2013). While some theoretical understanding of the climate-glacier relationship at a mountain-range scale is starting to emerge  (Scherler et al. , 2011; Bolch et al. , 2012; Gardelle et al. , 2013) , updated, accurate glacier inventories are continuously needed to advance our understanding of the climatic, topographic and glaciological parameters that govern glacier fluctuations across the Himalaya. In this study, we evaluate spatial patterns in glacier area and elevation changes using multi-temporal satellite data from Corona KH4, Landsat 7 Enhanced Thematic Mapper Plus (ETM+), the Advanced Spaceborne Thermal Emission Radiometer (ASTER), QuickBird (QB) and WorldView-2 (WV2) sensors. We constructed a new "reference" geospatial glacier inventory based on 2000 Landsat ETM+ coupled with ASTER imagery, and extracted glacier parameters on a glacier-by-glacier basis.
We evaluated spatial trends in glacier area and elevation changes over almost five decades based on high-resolution datasets  (1962 Corona KH4 and 2006  QB/WV data), and investigated them using statistical analysis. A particular emphasis was placed on the behavior of clean glaciers vs. debris-covered glaciers. For a subset of the debris-covered tongues, we computed glacier elevation changes  (1960s to 2000)  on the basis of topographic maps and recent DEMs and related them to ASTER-derived surface temperature and debris cover characteristics on a pixel-bypixel basis. In previous studies,  Sakai et al. (1998 Sakai et al. ( , 2002) )  and  Fujita and Sakai (2014)  illustrated the importance of supraglacial surface temperature as well as surface features (ice walls, ablation cones, supraglacial lakes) for inducing differential ablation on the surface of the debris-covered tongues.
The Corona KH4 system was equipped with two panoramic cameras (forward-looking and rear-looking with 30 • separation angle), and acquired imagery from February 1962 to December 1963  (Dashora et al. , 2007). We chose images from the end of the ablation season (October/November in this part of the Himalaya), suitable for glaciologic purposes. Six Corona stripes were scanned at 7 microns by USGS from the original film strips. The nominal ground resolution reported for the KH4 mission is 7. 62 m  (Dashora et al. , 2007) ; however, we calculated an actual pixel size of approximately 2 m using the scale of the photos and the scan resolution. Raw, unprocessed Corona images obtained from USGS are known to contain significant geometric distortions due cross-path panoramic scanning. Furthermore, the Frame Ephemeris Camera and Orbital Data (FECOD) camera/spacecraft parameters (roll, pitch, yaw, speed, altitude, azimuth, sun angle and film scanning rate) for Corona missions, needed to construct a camera model and to correct these distortions are not available.
This is consistent with accuracies obtained on Corona images in Mexico, using a fitting software and the FECOD parameters (H. Snyder, personal communication, 2011). (2) The orthorectified Landsat ETM+ scene from December 2000, obtained from the USGS Eros Data Center was used as "reference" dataset. The Landsat ETM+ scene has seven spectral channels at 30 m spatial resolution, a thermal channel at 60 m and a panchromatic channel at 15 m, a revisit time of 16 days and a large swath width (185 km). In addition, six orthorectified ASTER scenes from 2000 to 2005 were obtained at no cost through the Global Land Ice Monitoring from Space (GLIMS) project  (Raup et al. , 2007). The ASTER scenes have 3 channels in the visible wavelengths (15 m spatial resolution), 3 channels in the short-wave infrared at 30 m, and four thermal channels at 90 m, a swath width of 60 km and a revisit time of 16 days.
Elevation datasets Two elevation datasets were used in this study: (1) the hydrologically-sound CGIAR SRTM DEM (90 m spatial resolution) (CGIAR-CSI 2004) was used to extract glacier parameters for the 2000 decade. The SRTM accuracy and biases have been quantified in several studies  (Berthier et al. , 2006; Fujita et al. , 2008; Gardelle et al. , 2012). Its vertical accuracy in our study area, calculated as root mean square (RMSEz) with respect to 25 field-based GCPs was 31 m ± 10 m. The GCPs were obtained in the field on non-glacierized terrain including roads and bare land outside the glaciers using a Trimble Geoexplorer XE series. (2) The Swiss topographic map (1 : 150 000 scale), compiled from Survey of India maps from the 1960s to 1970s, published by the Swiss Foundation for Alpine Research was used to extract 1960's glacier elevation contours.
Analysis extents We defined two spatial domains for our study area (Fig. 1 ). Spatial domain 1 includes the Sikkim province of India, eastern Nepal (Tamor and Arun basins), as well as parts of Bhutan and China (Table  2 ). This spatial domain was split into four sub-regions on the basis of east-west and north-south climate/topographic/political barriers, as shown in Fig. 3. Rainfall averages from the Tropical Rainfall Measuring Mission (TRMM) data 2B31 product  (Bhatt and Nakamura, 2005; Bookhagen and Burbank, 2006)  are used to characterize the sub-regions climatically. The dataset contains rainfall estimates calibrated with groundcontrol stations derived from local and global gauge stations (Bookhagen and Burbank, 2006) with a spatial resolution of 0. 4 • , or ∼ 5 km. Given the well-known biases in the TRMM data  (Bookhagen and Burbank, 2006; Andermann et al.
For the 2006/09 QB/WV2 images, ice masses were delineated using band ratios 3/4, then isodata clustering with a threshold of 1. 07 (snow/ice > 1. 07), and a majority filter of 7 × 7 to remove noise. Debris-covered tongues for this dataset were delineated manually on the basis of supraglacial features (lakes and ice walls) visible on the high Introduction.
Glacier area changes 1962-2006 (spatial domain 2) Out of the 487 glaciers in spatial domain 1, a sample of 50 glaciers from Tamor basin in Nepal and Zemu basin in Sikkim (spatial domain 2) were used for deriving area changes from 1962 (Corona imagery) to 2000 (Landsat/ASTER) and 2006 (Quickbird). To minimize uncertainties due to differences in rock outcrops specific to each dataset, we used the same rock outcrops for each dataset. We obtained an area change of -10. 3 % (-0. 24 % yr -1 ± 0. 08 % yr -1 ) from 1962 to 2006 for the 50 glaciers (Table  5 ), with double the rates from 2000 to 2006 (-0. 43 % yr -1 ± 0. 9 % yr -1 ) compared to the previous period 1962 to 2000 (-0. 20 ± 0. 16 % yr -1 ). Our rates of change are within the range of those reported in other studies, if we consider the uncertainties in the change estimates.
In this study, we could not perform a validation using high-resolution imagery, since this was used as source imagery for area change. Instead, using the 1-pixel variability (±30 m for Landsat/ASTER, ±6 m for Corona and ±3 m for QB outlines), we obtained a total estimate of image classification errors of 3-6 % for our datasets. Highest uncertainties were associated with the larger pixel size (Landsat). The.
Uncertainties in area change were computed as the RMSE of the uncertainties embedded in each dataset, from classification errors above, and amounted to 3-6 % of the glacierized area. Uncertainties related to differences in internal rocks in each dataset were derived by comparing area changes computed with internal rocks specific to each dataset, vs. "merged" internal rocks from each datasets. The differences in glacier datasets due to rock inconsistencies amounted to ∼ 2 % glacier area, inducing differences in the rates of change in area of ±0. 05 % yr -1. For simplicity, here we neglected the area change that might be due to exposure of new internal rock due to glacier ice thinning. The glacier area changes computed from 1962 to 2006 suggest a higher rate of retreat in the last decade (-0.
Comparison with other areas Comparison of our results to other studies in this area of Himalaya is subject to inconsistencies and inaccuracies in the classification methods used. For example, in Sikkim, our study estimated 569 km 2 ± 70 km 2 of glacierized area for Sikkim in 2000 period based on Landsat/ASTER data (Sect. 4. 1). For the same time period,  Mool et al. (2002)  reported 285 glaciers with an area of 577 km 2 based on the same source imagery (Landsat ETM+) (Table  8 ). These two area estimates differ only by 8. 2 km 2 (1. 4 %) of our estimated area. The significant difference in glacier numbers (186 glaciers in our study compared to 285 glaciers in ICIMOD study) are most likely due to the way in which ice masses were split and how glaciers were counted. Methodology differences and inconsistencies in glacier estimates are quite common in multi-temporal image analysis performed by different analysts, and were previously noted in other areas of the world  (Racoviteanu et al.
, 2009). Similarly, for the 1962 decade, our analysis of Corona 1962 imagery for Sikkim yielded 178 glaciers with an area of 658 km 2 ±20 km 2. In a recent publication  (Racoviteanu et al. , 2014) , we reported 158 glaciers with an area of 742 km 2 in 1960s based on the Swiss topographic map, which is 13 % higher than our new estimates based on Corona imagery from 1962. We consider the Corona estimates more reliable than the topographic map, and we use this dataset as the baseline for comparison with more recent imagery. Our 1962 Corona glacier inventory differs by 48. 3 km 2 , or ∼ 7 % from the inventory published by the Geological Survey of India (GSI)  (Sangewar and Shukla, 2009) , which reported 449 glaciers with an area of 706 km 2 for approximately the same time period. We consider both the GSI and the Swiss topographic map to overestimate the glacier area, potentially due to classifying Introduction.
Summary and conclusions In this study we combined remote sensing data from various sensors to construct a new glacier inventory for the eastern Himalaya, and to quantify glacier area and elevation changes in the last four decades. We have constructed two updated glacier inventories for this part of the Himalaya, based on 1962 Corona and 2000 Landsat/ASTER imagery, respectively. Spatial trends of glacier area and elevation changes in the last decades include: -Larger percent of debris cover on glaciers on the southern slopes of the Himalaya (14 % of the glacierized area) compared to northern slopes (2 %), with supraglacial lakes constituting about 6 % of the debris covered area; -Glacier area change amounts to -0. 24 % ± 0. 08 % yr -1 from the 1960's to the 2006's, with a higher rate of retreat in the last decade (-0.
m)(1951 to 1980)  (IMD, 1980), and 164 rainy days per year(Nandy et al. , 2006). Mean minimum and maximum daily temperatures at this station were reported as 11. 3• C and 19. 8• C, with an average of 15. 5• C based on the same observation record(IMD, 1980Remote sensing datasets used in this study are summarized in.
Over the last two decades, there has been a strong interest in the cosmic ray positron fraction in both particle physics and astrophysics  [1]. The positron fraction is defined as the ratio of the positron flux to the combined flux of positrons and electrons. The first results from the Alpha Magnetic Spectrometer (AMS) on the positron fraction were reported in  [2]. They generated widespread interest  [3]. In this Letter, we report new results based on all the data collected during 30 months of AMS operations on the International Space Station (ISS), from 19 May 2011 to 26 November 2013. Because of the excellent and steady performance of the detector, and an increase of the data sample by a factor of 1. 7, the measurement of the positron fraction is extended up to 500 GeV with improved precision.
In each energy bin, the two-dimensional reference spectra for e AE and the background are fit to data in the [TRD estimator-logðE=pÞ] plane by varying the normalizations of the signal and the background. This method provides a datadriven control of the dominant systematic uncertainties by combining the redundant and independent TRD, ECAL, and tracker information. The reference spectra are determined from high statistics electron and proton data samples selected using tracker and ECAL information including charge sign, track-shower axis matching, and the ECAL estimator. The purity of each reference spectrum is verified using Monte Carlo simulation. The fit is performed simultaneously for the positive and negative rigidity data samples in each energy bin yielding the number of positrons, the number of electrons, the number of protons, and the amount of charge confusion, where charge confusion is defined as the fraction of electrons or positrons reconstructed with a wrong charge sign.
The systematic uncertainties due to these two effects are obtained by varying the background normalizations within the statistical limits and comparing the results with the Monte Carlo simulation. They were examined in each energy bin. The proton contamination in the region populated by positrons is small. It is accurately measured using the TRD estimator. The amount of proton contamination has a negligible contribution to the statistical error. The systematic error associated with the uncertainty of the data derived reference spectra arises from their finite statistics. It is measured by varying the shape of the reference spectra within the statistical uncertainties. Its contribution to the overall error is small compared to the statistical uncertainty of data and is included in the total systematic error.
Data sources and searches Overall methods were adapted from MOOS and PRISMA guidelines for meta-analyses  [11, 12]. A literature search was conducted to identify randomized controlled trials (RCTs) and observational studies of 90 Y-ibritumomab tiuxetan + BEAM (Z-BEAM) and autologous stem cell transplantation in adult DLBCL. On 30 June 2013, a systematic literature search was performed on the following databases: the National Library of Medicine PubMed (http://www. ncbi. nlm. nih. gov/pubmed), the American Society of Hematology (http://www. hematologylibrary. org), the European Haematology Association (http://www. haematologica. org), and the American Society of Clinical Oncology (http://meetinglibrary. asco. org/abstracts). The search terms were B-cell lymphoma [MeSH] and Zevalin â (Spectrum Pharmaceuticals Inc.
Study selection The literature searches, study selection, and data extraction were carried out independently by two investigators (S. A. , P. Q. ), discrepancies were solved by discussion until consensus was reached. Studies were included in the meta-analysis if they matched all prespecified eligibility criteria. Studies were excluded for the following reasons: (1) lymphoma other than DLBCL (e. g. , mantle cell lymphoma, Richter syndrome, or indolent lymphoma [except for transformed follicular lymphoma]), (2) age <18 years, (3) consolidation without ASCT, and (4) allogeneic stem cell transplantation. Studies with patient populations of mixed lymphomas were included if the required data for patients with DLBCL were extractable from the reports. Finally, studies enrolling fewer than five patients were not included due to risk of extreme bias.