dataesr/scientific-paragraphs-categorization huggingface dataset:
X-ray diffraction studies Crystallographic data were collected at the IECB X-ray facility on a high flux microfocus Rigaku FRX rotating anode at the copper ka wavelength equipped with a Dectris Pilatus 200 K hybrid detector and Varimax HF optics at 130 K. The crystal was mounted on a cryo-loop after quick soaking on ParatonedN oil from Hampton research and flash-frozen. The data were processed using the Crystal-Clear suite version 2. 1b25. The crystal structure was solved using SHELXD and refined with SHELXL 2013 version  [56]. Full-matrix least-squares refinement was performed on F 2 for all unique reflections, minimizing w(Fo 2 -Fc 2 ) 2 , with anisotropic displacement parameters for non-hydrogen atoms. Hydrogen atoms were positioned at idealized positions and refined with a riding model, with Uiso constrained to the 1.
2 Ueq value of the parent atom (1. 5 Ueq when CH 3 ). The positions and isotropic displacement parameters of the remaining hydrogen atoms were refined freely. SIMU and DELU commands were used to restrain some side chains as rigid groups and restrain their displacement parameters. The BYPASS/SQUEEZE  [57]  procedure was used to take into account the electron density in the potential solvent area. CCDC-1041244 contains the supplementary crystallographic data for this paper. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www. ccdc. cam. ac. uk/data_request. cif.
Acknowledgements This work was supported by the  CNRS ,  Univ. Bordeaux , the  Conseil Regional d'Aquitaine  (Project # 20091102003 ),  ANR  ( Project  # ANR-12-BS07-0019 ) and UREkA. CIFRE support from  UREkA  and  ANRT  to L. M. is gratefully acknowledged. We thank  Jonathan Clayden  for sharing results with us and for fruitful discussions.
Methods The dataset is comprised of satellite imagery and a digital elevation model (DEM). A recently obtained SRTM1 dataset (Shuttle Radar Topography Mission;  Rabus et al. , 2003)  is available for the entire Lake Turkana area. This DEM is produced by radar interferometry with 1 arcsec (approximately 30 m) horizontal grid spacing and provides a maximum 5 m absolute vertical error  (Becek, 2008; Garcin et al. , 2009). In addition, high-resolution (< 1 m) PLEIADES and (5 m) SPOT 5 images were used to focus on selected areas. This dataset was processed using GIS software  (Global Mapper 15; www. globalmapper. com)  to provide a high-resolution 3-D image of geomorphological features. Topographic profiles, elevation differences, and slope values were obtained using Global Mapper 15 software. The trajectory analysis method is a recent development based on the principles of sequence stratigraphy.
Acknowledgements. This work is a contribution of the  Rift Lake Sedimentology project (RiLakS)  funded by  Total Oil Company. Satellite  images (SPOT and PLEIADES) were acquired thanks to the support of the  CNES/ISIS program. Finally, we are grateful to  Murray Hay  ( Maxafeau Editing Services ) for verifying the English within the text. Edited by:  A. Stroeven  Reviewed by: two anonymous referees.
Intriguingly, SH3 domains primary bind to peptides containing a consensus PxxP sequence that is not present in CID; however, many examples of unconventional SH3-binding peptides have previously been described  (Saksela and Permi, 2012). To understand how CD95 CID might interact with PLCg1-SH3, we undertook two computational peptide screenings. First, we performed homology modeling by using the crystal structure of PLCg1-SH3 in association with the SLP-76 heptapeptide complex (PDB:1YWO)  (Deng et al. , 2005). This structure served as a template, and the SLP-76 peptide was replaced by each heptapeptide combination present in CD95-CID. Second, we took a protein-peptide docking approach by using each aforementioned CID heptapeptide alternatively docked within the PLCg1-SH3 domain. Both methods predicted that amino acids 182-188 (TCRKHRK) would have the highest affinity for PLCg1-SH3, and the calculated binding energy of the complex consisting of PLCg1-SH3 and TCRKHRK was similar to that of the PLCg1-SH3 interaction with SLP-76 (Figure  5D  and Table  S3 ).
Cells were stimulated with cl-CD95L (100 ng/mL; arrow), and the intracellular calcium concentration ([Ca 2+ ] i ) was monitored. Data are given as means ± SD of three experiments performed independently on n = 20 cells. (E) HEK cells transfected with the indicated constructs were stimulated with CD95L (100 ng/mL) for indicated times. The CD95 protein complex was immunoprecipitated from cell lysates and subjected to immunoblotting, as indicated. Total lysates served as controls. The column marked ''B'' indicates treatment with beads alone. Data are representative of three independent experiments. (F) Activated PBLs were pre-incubated for 1 hr with TAT-control or TAT-CID (10 mM) and stimulated with cl-CD95L (100 ng/mL) for the indicated times. The CD95 protein complex was immunoprecipitated from cell lysates and subjected to immunoblotting, as indicated.
Total lysates served as controls. The column marked ''B'' indicates a treatment with beads alone. Data are representative of three independent experiments. (G) Activated PBLs from healthy donors were loaded with FuraPE3-AM (1 mM) and pre-treated for 1 hr with TAT-control or TAT-CID (10 mM). Cells were stimulated with cl-CD95L (100 ng/mL; arrow). Data represent means ± SD. (D) Predicted interaction between CID and PLCg1. Within CD95-CID, TCRKHRK is the amino acid sequence that is optimal for interaction with PLCg1-SH3. (E) Indicated cells were pre-incubated for 1 hr with TAT-control, TAT-CID, TAT-CID-N, or TAT-CID-C (10 mM) and then stimulated with cl-CD95L (100 ng/mL). [Ca 2+ ] CYT was assessed in FuraPE3-AM (1 mM)-loaded cells. Data represent means ± the SD of three independent experiments.
Transcriptomic Analysis Isolated Th17 and Treg cells from two healthy donors were treated with or without 100 ng/mL of cl-CD95L for 8 hr. Total RNA was extracted with the Nucleospin RNA XS kit (Macherey-Nagel), and quality was assessed with the RNA6000 nano chip (Agilent). For each condition, 9 ng of RNA was reverse transcribed with the Ovation PicoSL WTA System V2 (Nugen, Leek, The Netherlands). Fragmented cDNAs were hybridized to GeneChip Human Gene 2. 0 ST microarrays (Affymetrix). Then chips were scanned on a GeneChip Scanner 3000 7G (Affymetrix). Raw data and quality-control metrics were generated from scanned images using the Expression Console software (Affymetrix). Probes were mapped with Brainarray V20 CDF files (http://brainarray. mbni. med. umich. edu/) and normalized by robust multi-array averaging with R software.
Scoring was conducted by an individual blind to the objective/treatments within the study as per  (Kikawada et al. , 2003). FITC-conjugated rabbit anti-C3 polyclonal antibody (Dako) and nuclei (DAPI, sigma-aldrich) staining was performed on frozen kidney sections, and results were analyzed with NIKON Ni-E (magnification 3 200). C3 accumulation in kidneys was assessed by densitometric analysis of ten different fields via NIS-Elements AR Analysis software. Transendothelial Migration of Activated T Lymphocytes Membranes (3 mm pore size) of a Boyden chamber were hydrated in sterile D-PBS (Millipore). Activated T-lymphocytes (10 6 ) were added to the top chamber on a confluent monolayer of HUVEC in a low-serum (1%) medium. The bottom chamber was filled with low-serum (1%) medium in the presence or absence of 100 ng/ml of cl-CD95L.
(D) Predicted interaction between CID and PLCg1. Within CD95-CID, TCRKHRK is the amino acid sequence that is optimal for interaction with PLCg1-SH3. (E) Indicated cells were pre-incubated for 1 hr with TAT-control, TAT-CID, TAT-CID-N, or TAT-CID-C (10 mM) and then stimulated with cl-CD95L (100 ng/mL). [Ca 2+ ] CYT was assessed in FuraPE3-AM (1 mM)-loaded cells. Data represent means ± the SD of three independent experiments.
ACKNOWLEDGMENTS We are grateful to the  H2P2 facility of Biosit (Rennes)  for its technical assistance. This work was supported by  INCa PLBIO, Ligue Contre le Cancer ,  INSERM Transfert, Fondation ARC, Region Bretagne, Rennes Metropole. We would like to thank the  University of Nottingham Faculty of Medicine for funding of BSU  pump priming. R. J. F. was supported by the  Biotechnology and Biological Sciences Research Council  award  BB/M018369/1. AP was supported by  ANR  ( ANR-12-JSV2-0004-001 ).
(2006)  found in their investigation and is an acceptable compromise between NH 3 loss due to wall sorption effects and ammonia alterations through NH 4 NO 3 volatilization. This affects not only the concentration but also the flux because NH 4 NO 3 particles are also  deposited. In this study, we performed calibration with zero air every 8 h, i. e. , at 00:00, 08:00, and 16:00 local time for 60 s per interval (data not shown) as well as internal system calibration through the laser itself by aligning the NH 3 absorption peak of the sampled air to the standard of the HITRAN database  (Rothman et al. , 2009). An ultrasonic anemometer (model R3, Gill Instruments, Lymington, UK) was installed at 2. 5 m above ground. The inertial inlet box was mounted next to it on the northern side, a less common wind direction at the site (5.
Data acquisition, analysis, and flux calculation Sonic anemometer data were recorded by the EddyMeas software, which is an embedded application of the software package EddySoft  (Kolle and Rebmann, 2009). The QCL was controlled by Aerodyne's TDL Wintel software with ammonia concentration data being recorded at a frequency of 10 Hz on the QCL computer. Anemometer and concentration time series needed alignment to a reference timestamp before Figure  1. Schematic overview of the measurement setup. An ultrasonic anemometer (measurement height 2. 5 m) is mounted closely to the heated "inertial inlet" box (39 cm × 34 cm × 21 cm) containing a critical glass orifice to reduce the pressure regime inside the sample line (total length 3. 5 m) to 5. 3 to 8 kPa. After passing the critical orifice, a sharp turn of the flow path leads to a reduction of particles (> 300 nm) of approx.
2b ), which is attributed to diverging drifts of the system clocks of the sonic anemometer's data acquisition computer and the QCL clock. Thus, the drifting time lag was divided into subsections (one for each tooth) and a linear trend was fitted to the apparent time lag in each section. Then each half-hourly NH 3 time series was shifted with this estimated time lag using a specifically designed Rscript (R Core Team, 2012) resulting in near-zero time lags in the corrected datasets. After a further covariance maximization procedure, a smaller, less varying, time lag remained. In EddyPro a range of -2 to 4 s was chosen. The ogive method from Ammann et al. (  2006 ) was applied to empirically investigate the high-frequency damping of NH 3 concentration measurements which usually occurs due to imperfect turbulent flow regime and possible wall sorption effects in the sampling line.
Acknowledgements. Funding for this study from the  German Federal Ministry of Education and Research (BMBF)  within the framework of the  Junior Research Group NITROSPHERE  under support code  FKZ 01LN1308A  is greatly acknowledged. We thank  Jeremy Smith ,  Jean-Pierre Delorme , as well as  Ute Tambor ,  Andrea Niemeyer , and  Daniel Ziehe  for excellent technical support and conducting laboratory analyses of denuder and filter samples, respectively. Edited by: L. Zhang Reviewed by: two anonymous referees.
ACKNOWLEDGMENTS This research was supported by the  U. S. Department of Energy  Grant Nos. DE-FG02-04ER54738  and  DE-SC0008378  and CMTFO. We thank  L. Cui ,  G. R. Tynan ,  S. C. Thakur ,  P. Vaezi ,  Y. Kosuga ,  S. Kobayashi ,  M. Malkov , and  I. Cziegler  for useful discussions.
Search Strategy The online literature databases Embase. com, Medline (Ovid-SP), Pubmed Publisher, Web of Science, Google Scholar, and the Cochrane Library were used for this systemic search of studies reporting the influence of d3-GHR on the outcome in response to PEGV treatment in acromegaly patients. This was performed under the guidance of a research librarian. The performed search strategy was: (d3GHR OR d3-GHR OR 'd3-growth hormone receptor' OR 'exon 3' OR d3 OR 'exon 3-deleted' OR 'exon 3 deletion') AND (GHR OR 'GH receptor' OR 'growth hormone receptor' OR GHRs OR 'GH receptors' OR 'growth hormone receptors') AND (Polymorphism OR polymorphisms OR isoform OR isoforms OR genotype OR genotypes OR variant OR variants) AND Acromegaly AND (Pegvisomant OR somavert OR 'growth hormone receptor antagonist' OR 'GH receptor antagonist' OR 'GHR antagonist').
Data Review and Data Collection Data selection was independently assessed by the investigators S. E. Franck and S. J. C. M. M. Neggers. Besides our own cohort, two articles and one conference abstract met our selection criteria. The study of  Bianchi et al. [16]  from 2009 (n = 19) was excluded from this meta-analysis as the vast majority of the patients was also included in a larger cohort (n = 127) published by the same author group in 2012  [25]. We contacted the principal investigators of these three research groups, in order to collect raw data from these acromegaly cohorts. We were able to obtain from all three selected articles the variables needed to perform the meta-analysis: genotype coded as fl/fl, d3/fl or d3/d3, sex, age at diagnosis, PEGV monotherapy versus PEGV combined with long-acting LA-SSA and/or cabergoline (CAB) (combination treatment), lowest IGF-I levels during PEGV treatment and required PEGV dose to achieve this lowest IGF-I level.
, USA). Potential effects of the genotype GHR deletion of exon 3 were calculated by an inverse variance meta-analysis in R, version 3. 2. 1  [27]. Fixed effects meta-analyses were performed as implemented in the R package 'rmeta'  [28]. As we tested two independent medical treatment variants (PEGV monotherapy and combination treatment) as well as the total cohort, we set the significance threshold of our one-sided p values at 0. 025 in order to correct for multiple testing (Bonferroni correction).
3%); d3/fl: 122 (37. 7%); d3/d3: 39 (12. 0%). With the exception of Franck et al. [26]  (p = 0. 859), none of the included cohorts had genotype frequencies following HWE proportions. When all cohorts (n = 324) were pooled together, the genotype distribution was not in accordance with HWE (p = 0. 034). In 1 of the 4 studies, an effect of d3-GHR was observed on the duration of successful PEGV treatment, as carriers required less time to reach IGF-I normalization  [24]. 511 from online literature databases 3 studies and 1 abstract were included 14 studies were relevant for more detailed examination 497 studies were excluded on the basis of title and abstract 1 study was excluded as a majority of the patients had been included in a larger more recent cohort, which is included in this meta-analysis  [25]  9 studies were excluded on the basis of: n = 4; description/citationon of an included study in a book n = 3; abstract of an included study n = 1; review n = 1; special feature editorial However, no difference was observed in genotype distribution between normalized and nonnormalized patients regarding IGF-I levels during PEGV treatment.
Meta-Analysis Lowest IGF-I Level during PEGV Treatment The effects of d3-GHR on the lowest IGF-I level during PEGV treatment could be assessed in all 4 cohorts using the raw data sets obtained from the principal investigators and the individual effects of the studies are summarized in table 2. The mean difference in lowest IGF-I level (ULN) between d3-GHR carriers and fl/fl-GHR in the total cohort was -2. 3% (95% CI: -6. 5 to 1. 8), which reflects a small negative effect in d3-GHR carriers when compared with fl/fl-GHR in combined data from the 4 studies. However, this effect was not significant (p = 0. 270, heterogeneity p = 0. 535, fig. 2  ). Similar results were observed when the total cohort was subdivided into patients using either PEGV monotherapy or combination treatment and analyses were performed separately (PEGV  Forest plot of meta-analysis: effect of d3-GHR on IGF-I during PEGV.
Since acromegaly is a rare disease and patients treated with PEGV are limited, we could only include a relatively small number of large studies in this meta-analysis. To date, however, this is the largest data set available for this specific group of acromegaly patients. Furthermore, GHR genotyping was done locally in several laboratories using multiplex and quantitative polymerase chain reaction techniques. Similarly, different assays were used to measure IGF-I levels, and, therefore, it was chosen to express the IGF-I level as the upper limit of normal. In addition, a comparison between fl/fl-GHR and d3-GHR regarding baseline GH and IGF-I levels in untreated acromegaly patients is missing, which would have given a more complete overview. Especially, as there are data reporting an association between d3-GHR and discordant GH and IGF-I levels (high IGF-I vs.
normal GH)  [22, 30]  , however conflicting data regarding this topic are also reported  [31]. Despite these limitations, we were able to obtain all the available raw data from the studies derived from our systematic literature search on d3-GHR and PEGV treatment. Moreover, data were all collected using similar definitions, as response to PEGV treatment was objectified by the lowest IGF-I level during the PEGV treatment period, as was the associated required PEGV dose linked to these IGF-I levels. Furthermore, PEGV monotherapy and combination therapies showed similar results for both the response to PEGV treatment and the required PEGV dose. If d3-GHR carriers were to exhibit greater biological activity of GH, a dose effect for PEGV per haplotype is also to be expected. In this meta-analysis, we aimed to evaluate a dose effect regarding the required PEGV dose in a pairwise comparison between the three genotypes.
Acknowledgements The authors would like to thank all patients for their participation. They would also like to thank all authors who participated in the original studies and were willing to share the data of their patients. In this light, they would especially like to thank  Prof. Dr. P. Chanson  of the  Hôpital de Bicêtre -University Paris-Sud ,  Dr. Marazuela  of the  Clinic University Hospital -Santiago de Compostela ,  Dr. M. Filopanti ,  Prof. G. Mantovani  and  Prof. A. Spada  of the  Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico -University of Milan  for their contributions in the original studies. They would also like to thank  Dr. W. M. Bramer , the biomedical information specialist of the medical library of the  Erasmus Medical Center Rotterdam , for his support in conducting a systematic literature search.
The metabolome level To determine metabolite responses samples were analyzed by liquid chromatography/mass spectrometry (LC/ MS, LC/MS2) and gas chromatography/mass spectrometry (GC/MS) platforms. 207 biochemicals were detected in the root tissue and 241 in leaf tissue. Changes in the biochemical profile of root were far more extensive than those observed in leaf (Additional file 1: Table  S1 ). The changes were also faster because statistically significant changes were only observed in leaf after 120 min. This is similar to the transcriptome data in leaf where there were no significant changes at the earliest two time points (see below). Previous studies have shown that sugars (such as raffinose family oligosaccharides, sucrose, trehalose and sorbitol), sugar alcohols, amino acids, and amines accumulate under drought stress  [13].
Notably, the positions of the ABRE-like sequences are non-random with the majority occurring within 500 bp of the predicted ATG. In leaf after two hours, 80 % of ABRE-like sequences were within the 500 bp closest to the ATG (Fig. 8 ) and 70 % were found in the first 280 bp. This is in contrast to most other sequences detected by MEME that showed a random distribution (data not shown). MEME analyses also identified the CRT/DRE motif CAC/TCGACC that was found in ten of the root early up-regulated promoters. In addition, the sequence motif GTGCnTGC/G (C/GCAnGCAC) was found in sixteen of the leaf late up-regulated promoters. This element would appear to be novel as it bears no significant similarity to any promoter element in the current list of binding sites in the AtcisDB database. It is unclear which transcription factors bind to the GTGCnTGC/G motif and it remains to be functionally characterized but it could prove to be a useful building block for synthetic drought-inducible promoters.
Transcriptomics analyses RNA was isolated using QIAGEN© RNeasy-MIDI. 10 μg total RNA from each sample was used for micro-array analysis. A custom made 12 × plex array was designed by Roche NimbleGen, Inc. containing multiple 60mer oligomers to all genes from the GLYMAv1. 0 release of the soybean genome. Oligoarray experiments were performed at MOgene, LLC (St Louis, MO). Data analysis was performed using ArrayStar v4. Differential regulation was calculated using 90 % confidence (FDR Benjamini Hochberg) and 8-fold change. For gene enrichment analysis, agriGO  [42]  was employed and enriched GO terms were obtained using Singular Enrichment Analysis  [43]. Pathway visualization was performed by MapMan. The transcriptome data set is available in the Gene Expression Omnibus under the accession number GSE49537.
Proteomics analyses Roots tissues were processed at Bio-Proximity, LLC as described  [45, 46]. MGF data files were searched using X!Hunter against the latest library on the GPM  [47]  and also searched using X!Tandem  [48, 49]  using both the native and k-score  [50]  scoring algorithms and by OMSSA  [51]. Proteins were required to have 2 or more unique peptides with E-value scores of 0. 01 or less. The proteomics data was used for identification of differentially regulated proteins with an FDR correction of 5 %.
Acknowledgements We thank  P. G. Naveen Kumar  for help with the stomata experiments and  Ying-Sheng Huang  for help with the qRT-PCR. The authors thank  Marie Turner ,  Kathie Mathiason  and  Stephanie Hansen  for help in experimental set up and  Deena Rushton  for her help with several tables. We also thank  Nalini Desai  and  John Ryals  at  Metabolon Inc. and  Brian Balgley at Bioproximity LLC. This project was supported in part by  National Research Initiative  grants  2008-35100-04519  and  2008-35100-05969  from the  USDA National Institute of Food and Agriculture.
, 2014). Subsurface microbiota in the majority of locations, however, must have adaptations for survival in energy-and carbon-limited habitats. Culture-based studies have indeed demonstrated the ability of subsurface prokaryotic isolates to survive on extremely low energy fluxes  (Hoehler and Jørgensen, 2013). Microbial diversity in subsurface sediments includes representatives from all three domains of life. A meta-analysis of published prokaryotic small subunit ribosomal RNA gene (SSU rDNA) datasets from a range of subsurface sediments and depths shows that novel Archaea and Bacteria within the Chloroflexi, Gammaproteobacteria, Planctomycetes, and Atribacteria (the former candidate phylum JSI) dominate  (Parkes et al. , 2014). Subsurface environments are providing discoveries of new taxonomic groups, such as the recently described 'Hadesarchaea' discovered deep below hotsprings in Yellowstone National Park  (Baker et al.
, 2014). Samples analyzed for this study consisted primarily of mud-rich sediment with some marl. Organic carbon content in the Canterbury Basin samples we analyzed was lower than in the Peru Margin samples, ranging from <0. 6 to 1%  (Ciobanu et al. , 2014). In this study, we provide additional important evidence for active Fungi in the deep subsurface on the basis of iTAG analyses, metatranscriptomics, and microscopy. Fungal iTAG datasets were published previously for similar Peru Margin depths but were obtained using a broad microeukaryote-targeting approach  (Orsi et al. , 2013b). Here we aimed to apply fungal-specific PCR primers for iTAG studies to gather more in-depth information on this portion of the community. Microbial communities function as a consortium and exhibit complex interdependencies and interactions.