dataesr/scientific-paragraphs-categorization huggingface dataset:
The occurrence dataset was randomly split into a 70% sample, used for the calibration of the model, and a remaining 30%, used to evaluate model predictive performance, repeating the procedure 10 times and averaging the results. The predictive performance of each model was assessed by measuring the area under the receiver operating characteristic curve (AUC)  [74]  and the true skill statistic (TSS)  [75]. To avoid using poorly calibrated models, only projections from models with AUC ! 0. 7 and TSS ! 0. 4 were considered in all subsequent analyses  [76, 77]. For the current, past and future predictions we averaged the model projections to obtain final consensus maps The model averaging was performed by weighting the individual model projections respectively by their AUC score and averaging the results, as this method was shown to be particularly robust  [78].
Genetic analyses A Micro-Checker analysis provided no evidence of scoring errors due to large allele drop-out or stutter peaks in our final dataset. A presence of null alleles was detected in 19 populations (39. 6%, Table  1 ): higher frequency was observed in a Spanish population (S, P AL = 8. 3%), in Israel (K, P AL = 5. 3%), and in a southern Italian population (I, P AL = 4. 1%). The remaining populations presented a range of null alleles from 0. 8%-2. 9% (Table  1 ). The number of alleles that were detected per locus ranged from 16 to 27 (mean 22 ± 1. 53); the H O among all of the loci was 0. 6 (± 0. 12), with a range of 0. 29-0. 98; the H E ranged from 0. 42 to 0. 65 with an average of 0. 54 (± 0. 05); the F IS value across all of the loci was -0. 08 (± 0. 14), ranging from -0. 56 to 0.
The correlation between genetic and geographic distances was positive (r = 0. 28, P < 0. 001), indicating the existence of a probable isolation-by-distance pattern. Similar results were also obtained using the pairwise F ST not correcting for null alleles (data not shown). The assignment of accessions into groups or gene pools was further assessed based on the assignment tests that were carried out with STRUCTURE. These analyses identified a major peak at K = 2 and another peak at K = 4 (S3 File). The highest average pairwise similarity (H') was associated with K = 2. Even if this Bayesian method identified an optimal partition in two genetic pools (red and green), an unclear geographical pattern was observed ( According to the changes in population size, the Wilcoxon sign-rank test revealed a recent demographic bottleneck in four populations under both the TPM and the SSM model (Table  1 ): CS, Corsica-Propiano (P TPM = 0.
Conclusions Genetic data of the sea daffodil were combined with past and present climatic information to assess the role of the environment in the observed patterns of P. maritimum genetic structure. Thanks to this multi-faceted approach, we highlight the importance of the inclusion of complementary, non-genetic data, for better interpreting genetic patterns, and in order to have a better understanding of the evolution of plants in space and time. This integrated approach may be used in other organisms to create a complete and informative database, providing new tools to explore the effect of climate factors on the patterns of genetic diversity in a wider context.
File. Species Distribution Models inputs/output. Sheet 1: Dataset of the Pancratium maritimum accessions that were used in both the Species Distribution Models (SDMs) and the genetic analyses. Sheet 2: Relative contribution to the models and related response curves of each environmental predictor. (XLS) S3 File. Structure output. Graph of delta K values to determine the ideal number of groups that were present in the accessions of Pancratium maritimum and genetic clusters (K) as obtained for the 48 P. maritimum populations (867 individuals) using STRUCTURE (K = 4). (DOCX).
Acknowledgments For the field sampling of Pancratium maritimum populations, we would like to especially thank  Drs Laetitia Hugo  and  Carole Piazza  ( Conservatoire Botanique National de Corse, Corse );  Salima Benhouhou  ( Ecole Nationale Supérieure Agronomique, Algeria );  Gérard de Bélair  ( Université Badji Mokhtar, Algeria );  Giancarlo Sibilio  ( Botanical Garden of Naples, University of Naples Federico II, Italy );  Roberto Rizzieri Masin  ( Padova, Italy ); and  Hagar Leschner  (The Herbarium, The Natural History Collections, The  Hebrew University of Jerusalem, Israel ). This work was supported by the  Nando and Elsa Peretti Foundation  (grant number  2012-83 ).
S2 File. Species Distribution Models inputs/output. Sheet 1: Dataset of the Pancratium maritimum accessions that were used in both the Species Distribution Models (SDMs) and the genetic analyses. Sheet 2: Relative contribution to the models and related response curves of each environmental predictor. (XLS) S3 File. Structure output. Graph of delta K values to determine the ideal number of groups that were present in the accessions of Pancratium maritimum and genetic clusters (K) as obtained for the 48 P. maritimum populations (867 individuals) using STRUCTURE (K = 4). (DOCX).
Results The proton-sensing receptor GPR68, a target of RFX6, is expressed in EndoC-βH2 cells and human islets. Our previously published transcriptomic analyses (GEO No: GSE48101) indicated that EndoC-β H2 cells express mRNA coding for the proton-sensing receptor GPR68  24. We validated these data by Real-Time-quantitative PCR (RT-qPCR) that indicated that GPR68 mRNA expression was enriched in EndoC-β H2 cells compared to the duct cell line SKPC (Fig. 1a ). Transient transfection of EGFP tagged human GPR68 construct in EndoC-β H2 cells showed its predominant localization on the plasma membrane (Supplementary Fig. 1 ). GPR68 was almost the sole proton sensing GPCR expressed in EndoC-β H2 cells, the other ones (GPR4, GPR65, GPR132) being expressed at nearly undetectable levels (Fig. 1a ). Similar data were obtained using human islet preparations that expressed GPR68, but not GPR65 and GPR132 (Fig.
1b ). Of note, GPR4 was detected in human islets and not in EndoC-β H2 cells (Fig. 1a ), which could be due to its expression by non β -cells present in human islet preparations like endothelial cells  25, 26. RFX6 is a key transcription factor highly expressed in β -cells and required for their function. Our previous transcriptomic analyses indicated that siRNA-mediated RFX6 knock-down decreased GPR68 expression in EndoC-β H2 cells [FC, -3. 85; p = 7. 78. 10 -5 ] (GEO No: GSE59049) without effecting the expression of other proton sensing receptors  23. Further validation by RT-qPCR showed that decreased expression level of RFX6 mRNA (63. 92 ± 10. 5%) was consistently accompanied by decrease in the level of GPR68 mRNA (59. 73 ± 15%) in EndoC-β H2 cells (Fig. 2a ). Similar results were obtained in human islets where decreased RFX6 expression (79.
Preparation of buffered culture media. Culture media at different pH were prepared as described  8, 60. Briefly, low glucose DMEM without sodium bicarbonate (Sigma, D2902) was buffered with HEPES (4-(2-Hydroxyethyl) piperazine-1-ethanesulfonic acid; Sigma, H0887), EPPS RNA isolation, reverse transcription and RT-qPCR. Total RNA was extracted from EndoC-β H2 cells using RNeasy Plus Micro kit (Qiagen). First strand cDNA was prepared using Maxima First Strand cDNA synthesis kit (ThermoFisher). RT-qPCR was performed using Power SYBR Green mix (Applied Biosystems) with ABI Prism 7300 sequence detector (Applied Biosystems). Cyclophilin A transcript levels were used for normalization of each target gene. The custom primers were designed with IDT Primer-Quest online software and the amplification efficiency for each primer was determined with serial dilution of total cDNA from EndoC-β H2/human islets cDNA.
5 mM IBMX, at pH 7. 4 or 6. 4) for 30 min at room temperature. Cells were lysed using kit lysis buffer and cAMP was then measured in 384 well plates (HTRF) with TECAN Infinite F500. Immunocytochemistry and immunoblotting. EndoC-β H2 cells were cultured on Matrigel/fibronectin coated 4-well chambers slide (Nunc Lab-Tek) and processed for IL-8 and RELA immunostaining as described 62 using anti-IL-8 (1:1,000; BD554717; BD Biosciences) or anti-RELA (1:200; sc8008; Santa Cruz Biotechnology) antibodies. Images were acquired with a Leica Leitz fluorescent microscope equipped with cooled 3-chip charge coupled device camera (Hamamatsu C5810; Hamamatsu) and processed using ImageJ software. For immunoblot assays, total cellular proteins were prepared as described  23. Proteins (25 μ g) were resolved by SDS PAGE, immunoblotted with antibodies against RELA (sc8008, 1/250 dilution) and Actin (1/1000, Sigma-Aldrich).
Acknowledgements This work was supported by grants from  ANR Blanc RFX-PancInt , the  Seventh Framework Program  of the  European Union  (no. 241883 ) and the  Foundation Bettencourt Schueller  (to RS). The  RS laboratory belongs to the Laboratoire d'Excellence consortium Revive  and to the  Departement Hospitalo-Universitaire (DHU) Autoimmune and Hormonal disease. Human  islets for research were provided by the  "Plateforme de Recherche sur les Ilots de Langerhans en Ile-de-france"  ( Cell therapy Unit, Hôpital Saint-Louis ), supported by the  Fedération Française des Diabétiques.
Microfluidic chip fabrication All lithographic masks were designed using L-Edit software (Tanner EDA). The device was microfabricated by assembling an upper PDMS part with a microchannel and a lower part containing gold microelectrodes patterned on a glass substrate. Glass substrates were cleaned with a piranha solution (H 2 SO 4 /H 2 O 2 , 1 : 1) prior to use. The glass slides were then spin-coated with AZ5214 resin followed by inverted lithography on an MJB4 aligner. Substrates were then cleaned by a reactive ion etching (RIE) process to remove all resin residues and then metallized with a 200 nm layer of gold on top of a 20 nm titanium layer followed by a classic lift-off process. The fluidic circuit is obtained from a master mold made of SU-8. Fabrication of the mold starts with the cleaning of the silicon wafer and lithography of a 16 μm thick layer of SU-8.
Acknowledgements This work was supported by the  LabEx MiChem part of French  state funds managed by the  ANR  within the  Investissements d'Avenir programme  under reference  ANR-11-IDEX-0004-02. The authors would also like to thank the  "Centre de Nanosciences et de Nanostructures"  for its financial support through the clean room facilities in Marcoussis, France.
Study Design A phase I, prospective, bicentric, single-arm, open-label, doseescalating clinical trial of a single injection of autologous ASCs in patients with severe primary knee OA was conducted from March 2012 to April 2014 in two hospitals: CHRU Montpellier (France) and the Department of Orthopedic Surgery at the University of Würzburg (Germany). No placebo group was scheduled because of ethical issues (including late-stage knee OA patients associated with liposuction procedure without active therapy benefit). The study protocol was approved by the local ethics committees of both institutions (Comité de Protection des Personnes of Montpellier [UF8606-120203] and Ethik-Kommission bei der Medizinischen of Würzburg) and by the national competent authorities (TC301; EudraCT no. 2011-000183-10).
ACKNOWLEDGMENTS We thank all the patients for their participation in the study and  Dr. Mazen Hamoui  and  Prof. Andrea Facchini  for their cooperation and support during the study. M. R. and U. N. are currently affiliated with the  Department of Orthopaedic and Trauma Surgery, Evangelisches Waldkrankenhaus Spandau, Berlin, Germany. Trial registration number:  NCT01585857. The research leading to these results has received funding from the  European Union Seventh Framework Programme   FP7/2007-2013  under grant agreement  241719. Work in the laboratory INSERM U844 was also supported by the  Inserm Institute , the  University of Montpellier , and the  Agence Nationale pour la Recherche  for support of the national infrastructure: " ECELLFRANCE: Development  of a national adult mesenchymal stem cell based therapy platform.
607 and 5. 868 Mb, respectively. The contigs were annotated using the National Center for Biotechnology Information (NCBI) Prokaryotic Genome Annotation Pipeline (PGAP) at http://www. ncbi. nlm. nih. gov/genomes /static/Pipeline. html. In the genomes of #22207 and #22593, 5,567 and 5,837 coding sequences were identified, respectively. Future comparative analysis of whole-genome sequencing data from these isolates will provide insights into the attributes of emerging ESBL-producing EHEC. Nucleotide sequence accession numbers. The whole-genome shotgun projects have been deposited at DDBJ/EMBL/ GenBank under the accession numbers LMBJ00000000 and LMBK00000000. The versions described in this paper are versions LMBJ01000000 and LMBK01000000, respectively.
ACKNOWLEDGMENTS This study was supported by  Anses ,  Laboratoire de Lyon ,  Unité Antibiorésistance et Virulence Bactériennes. This work was partially funded by  BBSRC  and  Defra  (project references  BB/1017283/1  and  OD1718 ). The funders had no role in the study design, data collection and interpretation, or the decision to submit the work for publication. crossmark Genome Announcements January/February 2016 Volume 4 Issue 1 e01633-15 genomea. asm. org 1.
The LPAATd activity of cell extracts was markedly suppressed by 14-3-3g depletion (Fig. 7a ), while depletion of other 14-3-3 isoforms had no effect (Fig. 7b  and see also Supplementary Fig. 9 ). Moreover, addition to cell extracts of a characterized affinity-purified anti-14-3-3g antibody  15  also suppressed the LPAATd activity (Fig. 7c ). These data indicate that 14-3-3g is required for LPAATd activity, presumably because it stabilizes BARS in its monomeric fission-competent conformation. Finally, we repeated a few of the above experiments using a Golgi-membrane-enriched fraction (Supplementary Fig. 10a ). Here the LPAATd overexpression increased LPAAT activity by E60% over controls (Supplementary Fig. 10b ), while LPAATd-depletion or treatment of the membranes with the anti-LPAATd antibody (see also Fig.
8 ). (d) Representative photon-counting events during the time of the donor E 0 GFP-BARS fluorescence emission, measured in pixels containing 4300 photons in the Golgi area, when overexpressed alone (blue) or with tdTomato-LPAATd under steady-state (red) or during traffic pulse (green) conditions. The average fluorescence lifetime of E 0 GFP-BARS were 2. 08 ± 0. 07 ns (E 0 GFP-BARS alone), and 1. 62 ± 0. 22 ns and 1. 28 ± 0. 27 ns (with tdTomato-LPAATd) under steady-state or during traffic pulse conditions, respectively. (e) Quantification of FLIM-FRET efficiency in the Golgi area under steady-state or during a traffic pulse conditions (as indicated). Data are mean ± s. d. (n ¼ 10 cells per condition). ***Po0. 005 (Student's t-tests) versus steady state. Transduction Laboratories (610823) and the rabbit polyclonal anti-Calnexin antibody (1:500 for WB use) was from Santa Cruz (AF18 sc23954).
coli Top10 strain (Invitrogen). Colony PCR screening were used to search positive clones by using specific primers for BARS and LPAATd. The cloned fragments were then completely sequenced and verified. COS7 cells were transiently transfected with 0. 4 mg E 0 GFP-BARS and 0. 4 mg LPAATd-tdTomato using TransIT-LT1 Transfection Reagent, according to manufacturer's instructions. Sixteen hours after transfection, the cells were subjected to the VSVG TGN-exit assay. After 10 min of the 32 °C temperaturerelease block, the cells were fixed with 4% (w/v) PFA in 20 mM Hepes pH 7. 4, for 10 min at room temperature. The samples were then treated with 100 mM glycine in 20 mM Hepes for 30 min, washed three times with 20 mM Hepes and mounted in glycerol/20 mM Hepes (1:1). The lifetime values were extracted from the fitting of decay curves obtained from the donor emission (E 0 GFP-BARS) on the Golgi area, and global and pixel kinetic data analyses are computed by a commercially available software package SymPho Time, Version 5.
3. 2. 2 (PicoQuant GmbH, Berlin, Germany). Time-resolved images were acquired by the confocal microscope Leica SP5 II equipped with the White Light Laser (both Leica Microsystems GmbH, Mannheim, Germany) and the TCSPC measurements were performed with the SMD Upgrade Kit (PicoQuant GmbH). The excitation light was pulsed at 80 MHz and the donor fluorophore was excited at 475 nm and acquired from 495 to 530 nm. The measurements were carried out in fixed cells, for ease and speed of acquisition of a statistically reliable number of samples. The donor lifetime values were extracted from the fitting of decay curves obtained from the donor emission (E 0 GFP-BARS) in the Golgi area, and both global and pixel kinetic data analyses were computed by the commercially available software package SymPho Time, Version 5.
Acknowledgements We would like to thank all colleagues who kindly provided us with antibodies and reagents (as listed under 'Reagents');  Dr D. Barneda  for very preliminary experiments,  Dr C. P. Berrie  for editorial assistance and  Dr R. E. Duncan  for constructs and fruitful discussions; the  BioImaging Facility at the Institute of Protein Biochemistry  and the  Laboratory of Ultrastructure  at the  Italian Institute of Technology of Naples , for support in imaging microscopy, data processing and analysis; the  Italian Association for Cancer Research  (to  D. Corda IG10341  and  14675 , to A. L. IG15767), projects  PON01-00117  and  PON01-00862  from  MIUR ,  PNR-CNR Aging Program 2012-2014 ,  NanoMax, FaReBio di Qualita  `and ' Creme Campania Research in Experimental Medicine Project '  POR Campania   FSE 2007/2013 ,  POR  project  OcKey , for financial support.
Computational Methods The self-consistent calculations were performed using density functional theory (DFT)  [1, 12]  within the Perdew-Burke-Ernzerhof (PBE) exchange-correlation and employing the generalized gradient approximation (GGA)  [13]  and the local density approximation (LDA)  [14, 15]  as implemented in the Cambridge Serial Total Energy Package (CASTEP) code  [16]. A plane-wave basis set with energy cut-off of 380 eV was used for Pnma-GaN and their Ga 1-x Al x N alloys in these calculations. For wurtzite-GaN, we used a plane-wave basis set with energy cut-off 400 eV, which is used with GGA and LDA. The Monkhorst-Pack  [17]  k-point separation with a grid of 0. 025 Å-1 along the three axes in the reciprocal space was used in all calculations. The Broyden-Fletcher-Goldfarb-Shanno (BFGS)  [18]  minimization scheme was used in geometry optimization.
Importantly, the open microfluidic design allows the retrieval of solvent without carrying any aqueous media with it-an essential condition for sample preparation. The stability and integrity of the aqueous fluid was also validated by numerical simulation using the Evolver software (Fig. 2c ; Supplementary Fig. 1B ), which shows that during solvent retrieval, the solvent level decreases and the aqueous compartment becomes domed until the solvent breaks around an eye of aqueous media which grows in diameter eventually allowing the media to settle back in its original configuration. The solvent is then evaporated and the metabolites are analysed by LC-MS. The efficiency of extraction in the micrometabolomics platform is comparable to traditional extraction techniques such as vortexing or homogenization (Supplementary Fig.
4 ), and to get at these changes, features extracted from the chromatograms were compared using principal component analysis (PCA). As untargeted metabolomics typically yields a large number of unidentifiable features, we first narrowed the comparison: only features that could be putatively annotated as secondary metabolites based on exact mass (error o10 p. p. m. ) when compared with databases of known A. nidulans compounds were used in the PCA (Table  1 ; Fig. 3b ). Of the B1,000 features observed by LC-MS, 33 features could be putatively annotated based on databases of known A. nidulans compounds. These 33 features correspond to 19 putative metabolites; as is commonly observed in metabolomics data, a single metabolite may form multiple adducts (for example, features 14-16 were annotated as three different diorcinol adducts).
The media conditions supplemented with eicosanoids were selected with the thought that they might differentially impact the DppoA mutant. When wild type (WT) A. fumigatus grown in solid culture on blood is compared with growth on solid GMM, there are evident changes in its global metabolite profile (Fig. 5b ). In particular, zone 1 contains 56 features that are produced primarily when A. fumigatus is grown on blood. The 66 features in zone 2 are produced by A. fumigatus on both blood and GMM, and the 183 features in zone 3 are produced primarily when A. fumigatus is grown on GMM. Features in zones 1-3 were compared with databases of known A. fumigatus secondary metabolites and annotated by exact mass (error o10 p. p. m. ) (Fig. 5c ). Of these putative annotations, one metabolite from each zone was selected for further analysis.
We extended the micrometabolomics platform to allow culture and extraction of two organisms in soluble communication (Supplementary Fig. 3D ; Supplementary Data 5) to address these challenges. The coculture device is made up of two micrometabolomics platforms placed on each opposing face of a thermoplastic layer and connected via pores in the bottom of each culture well (Fig. 6a ). This allows for diffusion of secreted chemical factors between the two cultures while keeping the bulk of each culture separate. The cultures are both exposed to air, have a nearly uniform distance from each other, and can be grown on different media in the same device. To characterize the contact between the two culture wells, diffusion of a fluorescent dye, Alexa 488, across the two agar pads was measured over time.
In coculture experiments, fungal cell walls were stained (Fig. 6d ,e v, vi) by adding 10 ml calcofluor white (1 mg ml À 1 ) to the wells 1 min prior to microscopy. Cultures were collected from coculture devices by cutting the edges of the agar pads using a sterile 18 g needle and aspirating into 250 ml large orifice pipet tips (USA Scientific). Cultures were wet mounted using another 10 ml ddH 2 O and imaging was done using a Zeiss Axio Imager A10 microscope (Carl Zeiss, Oberkochen, Germany) equipped with a Zeiss A Plan-Â 10 lens, a series 120 X-Cite light source (EXFO), and a DAPI excitation/emission filter set. Images were collected with AxioVision Release 4. 7 software (Carl Zeiss). Metabolite extraction. Metabolites in the macroscale samples were extracted by removing 10 cores of 10 mm diameter from the culture and homogenizing them in 0.
They were then grouped by condition and uploaded to XCMS online  [58] [59] [60]. Multigroup analyses were performed using default HPLC/ UHD Q-TOF settings that have been optimized for an HPLC run with B60 min gradient and subsequent analysis using a high-resolution ESI-QTOF-MS. Polarity was specified to be negative. The XCMS multigroup analysis includes retention time correction, peak picking using the centWave algorithm, and peak grouping algorithms. Peak identification and PCA. Data on each of the peaks was downloaded and imported into MATLAB. Peaks were removed from analysis if they had a q value, as calculated by XCMS, of o0. 05 or were present in fewer than 66% of replicates of at least one condition in the experiment. Peaks were also removed if present in the solvent control or if their maximum intensity was o5,000 (60,000 for all peaks in the matrix experiment because of higher background in blood).