dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012)  in agreement with our source identification. For layer II δ 532 and δ 355 are respectively in the range 5-10 and < 8 % with the lowest values on 27 June at 45 • N along the 10 • W CALIOP overpass. The LRs calculated from the ground-based and airborne lidars are more accurate, and they are 59 ± 5 sr at 355 nm and 60 ± 5 sr at 532 nm, respectively. The CALIOP 27 June (10 • W) and 28 June LRs at 532 nm are estimated to be 60 ± 20 sr and 50 ± 20 sr, respectively, using the L1 data analysis and are of the order of 60 sr for both layers using the L2 operational products. It also gives confidence in the LR retrieval to see the largest LR is obtained where δ 532 is minimum. The CALIOP C a is in the range 0. 4-0. 5. These aerosol optical parameters are still in the range expected for a BB layer.
Mixing of dust and BB can correspond to a 20-30 % dust contribution in the total aerosol backscatter. The comparison with the MODIS AOD distribution during this episode over the western Mediterranean Sea shows that the Canadian fire contributions were surprisingly as large as the direct northward dust outflow from the Sahara. An additional contribution from a mid-tropospheric aerosol layer due a mixture of dust and BB aerosol was found in the region of higher AOD seen by MODIS. The next step will now concern the use of all presented and analyzed data for evaluating 3-D regional models to simulate this specific event, in terms of optical properties, possible mixing and vertical extent of mineral dust and forest fire aerosol layers.
Acknowledgements. This work was funded by the  French MISTRALS program  funded by  CNRS/INSU ,  ADEME ,  CEA ,  Météo-France  and  CNES  for aerosol and cloud satellite mission validation. The NILU team led by  A. Stohl  is gratefully acknowledged for distributing the FLEXPART model. The SAFIRE team,  INSU DT  and  D. Bruneau  from  LATMOS  are gratefully acknowledged for the aircraft flight operation and the LNG lidar operation. The lidar measurements in Barcelona were supported by the 7th  Framework Programme project Aerosols, Clouds , and  Trace Gases Research Infrastructure Network (ACTRIS)  (grant agreement no. 262254 ) and by the  Spanish Ministry of Science and Innovation  and  FEDER  funds under the projects  TEC2012-34575 ,  UNPC10-4E-442  and  CGL2011-13580-E/CLI. Edited by:  O. Dubovik.
The majority of questions were closed and dichotomous, and results were reported in both proportions and total sample. To compare sexologists according to their professional category (medical doctors versus non-medical professionals) and gender, chi-square tests (or Fisher's exact test) were used for categorical variables, and the nonparametric Mann-Whitney test was used for continuous variables. All analyses were conducted using SPSS 21. 0 for Windows and for all tests the level of statistical significance was set at 0. 05.
Identification of putative TRs of SEC23B, SEC24B, and SEC24D As we observed that EGF stimulation specifically increased SEC23B, SEC24B, and SEC24D mRNA levels, we set out to identify possible TRs mediating the mRNA changes of these COP II paralogues. A genome-wide RNAi screen has previously identified 554 "hit genes" with a role in secretion  (Simpson et al. , 2012). Using the DNA-binding domain transcription factor prediction database and the Gene Ontology annotation term "DNA binding," we identified 38 putative TRs among the hit genes of this study (Table  S4 ). These putative TRs could potentially regulate secretion through transcriptional control of secretory pathway genes. To prioritize the list of TR candidates, we performed coexpression analysis and searched for TRs that show similar expression patterns with SEC23B, SEC24B, and SEC24D under various cellular conditions.
For this analysis, we used one of the largest publicly available gene expression datasets derived from human cell lines subjected to a wide range of perturbations  (Lamb et al. , 2006). We hypothesized that functional related TRs that regulate COP II-mediated EGFR sorting and transport are likely to be coexpressed with SEC23B, SEC24B, and SEC24D under different cellular conditions  (Stuart et al. , 2003; van Noort et al. , 2003). Finally, the 38 TRs were ranked based on their coexpression with SEC23B, SEC24B, and SEC24D (see Materials and methods for details; Table  S4 ), which revealed ranked candidate TRs that might participate in the regulation of the mRNA levels of SEC23B, SEC24B, and SEC24D (Fig. 6 A ).
Cell lines and reagents HeLa Kyoto cells, A431 cells, and HeLa Kyoto cells stably expressing mouse RNF11-mCherry-LAP (MCB_7996) in a bacterial artificial chromosome were cultured in DMEM (Thermo Fisher Scientific) supplemented with 10% FCS (PAA Laboratories GmbH) and 1% l-glutamine (Sigma-Aldrich) at 37°C in 5% CO 2. HeLa Kyoto cells stably expressing mouse RNF11-mCherry-LAP were generated as previously described  (Poser et al. , 2008). Experiments were performed 24 h after seeding the cells. Antibodies were used as follows: rabbit RNF11 (HPA045781; used at a 1:43 dilution; Atlas Antibodies), mouse GFP (11814460001; clones 7. 1 and 13. 1, used at a 1:100 dilution; Roche), mouse SEC31A (612351; clone 32, used at a 1:50 dilution; BD), mouse EEA1 (610457; clone 14, used at a 1:100 dilution; BD), rabbit SEC24B (12042; used at a 1:100 dilution; Cell Signaling Technology), mouse anti-VG (used at a 1:100 dilution; gift from T.
Q-RT-PCR For the analysis of mRNA levels, cells were grown in 6-well culture dishes for 24 h in complete medium at 37°C in 5% CO 2. Cells were lysed, and total RNA was extracted using the InviTrap Spin Universal RNA Mini kit (Stratec Molecular). cDNA was obtained using the Su-perScript III First-Strand Synthesis SuperMix (Invitrogen), all according to the manufacturer's instructions. The Q-RT-PCR was performed in internal triplicates with the specific and efficient (efficiency between 90 and 110%) primer pairs (Sigma-Aldrich) listed in Table  S2. The Q-RT-PCR reaction was performed using the SYBR green detection reagent (Applied Biosystems) in StepOne Real-Time PCR System machines (Applied Biosystems) using the StepOne software v2. 3, all according to the manufacturer's instructions with standard Q-RT-PCR cycling conditions.
Images of the nucleus (DAPI), total EGFR (A488), and plasma membrane EGFR (A647) were acquired on an automated ScanˆR microscope (Olympus) with an Orca R2 camera (Hamamatsu Photonics) and a UPlanAPO 20×/0. 7 NA air objective using the software ScanR 2. 1. 0. 15. 100 positions per coverslip were imaged with roughly 100 cells per position, which led to at least 10,000 imaged cells per condition. The transport efficiency to the plasma membrane was determined as the ratio of cell surface EGFR fluorescence (A647 signal intensity) to total EGFR fluorescence (A488 signal intensity) as previously described  (Simpson et al. , 2007 (Simpson et al. , , 2012)). The mean transport efficiency was calculated from individual cells for each condition, and the EGFR transport efficiency increase was calculated by the following formula: (Ratio (2h) -Ratio (0h))/Ratio (0h).
When the RUSH assay was performed after siRNA-mediated gene knockdown, cell lysates were additionally taken from the 6-well culture dishes, and Q-RT-PCR experiments were performed to confirm the siRNA-mediated mRNA reduction of the investigated genes. Only samples with at least 80% mRNA reduction were used for immunostaining. Images of the nucleus (DAPI), total EGFP-EGFR/EG-FP-VSVG (A488), and plasma membrane EGFP-EGFR/EGFP-VSVG (A647) were acquired on an automated ScanˆR microscope with an Orca R2 camera and UPlanAPO 20×/0. 7 NA air objective using the software ScanR 2. 1. 0. 15. 100 positions per coverslip were imaged with roughly 100 cells per position, which led to at least 10,000 imaged cells per condition. Images were analyzed automatically using CellProfiler version 2. 0. 11047. In brief, based on predefined size and shape parameters, stained nuclei were detected.
Data were recorded using scheduled acquisition with a fixed cycle time of 2. 5 s, and the number of coeluting transitions was limited to 100 per cycle. Data acquisition was performed using 4-min-long time windows centered around the approximate retention time of targets relative to the iRT-kit. Peptide ratios were estimated using the summed intensity of all transitions and normalized to the mean ratio of the prelamin A/C peptides across samples. Protein fold changes were estimated using linear mixed-effect models (as implemented in R package 'nlme;' version 3. 1-103) as described previously by  Ori et al. (2013). Assay refinement, recalibrations, and data analysis were performed using SpectroDive (Biognosys AG).
Large-scale coexpression analysis was performed on all 554 secretion screen "hit genes"  (Simpson et al. , 2012)  that are known or predicted to be transcription regulators, as determined by their presence in the DNAbinding domain: Transcription factor prediction database  (Wilson et al. , 2007)  or their annotation with the GO term "DNA-binding activity" (GO :0003677). The list of TRs was limited to available probe sets that were measured in the Connectivity Map resource by the HT_ HG-U133A microarray platform. In total, from the 554 hit genes, 38 TRs detected as "present" in at least two out of three cell lines were retained for further analysis. Drug-induced gene expression profiles from three cell lines were used to calculate all the pairwise Pearson's correlations between TRs and the SEC23B, SEC24B, and SEC24D COP II components.
Confocal microscopy and segmentation analysis Confocal microscopy experiments were performed with fixed and immunostained cell samples on a microscope (LSM780; ZEI SS) with a Plan Apochromat 63×/1. 4 NA oil differential interference contrast objective and the ZEN (black edition) 2012 SP1 8. 1 software (ZEI SS). DAPI was detected by using a 405-nm laser with a 407-489-nm emission filter; a 488/561-nm laser with a 497-558-nm emission filter was used for Alexa Fluor 488-labeled proteins; a 488/561-nm laser with a 559-655-nm emission filter was used for Alexa Fluor 568-labeled proteins; and a 633-nm laser with a 640-715-nm emission filter was used for Alexa Fluor 647-labeled proteins. Z stacks of images covering the entire cell thickness were acquired. Using ImageJ version 1. 46r (National Institutes of Health), the stacks were projected (maximum or sum intensity), the background was subtracted, and the images were smoothed.
Acknowledgments We are grateful to  P. Ronchi  and  C. Godlee  for critical reading of the manuscript. We thank the  Advanced Light Microscopy Facility  at the  European Molecular Biology Laboratory (EMBL ) for support. We also gratefully acknowledge support from  EMBL 's genomics and proteomics facilities. We thank  M. Reiss  for support with sample preparation,  C. Tischer  and  A. Halavatyi  for their help with image analysis,  J. Krijgsveld ,  C. Hughes , and  W. Elings  for support in the design of targeted proteomic assays, and  Biognosys AG  for access to the SpectroDive software.
can open the routes to significantly enhanced caloric response and smartly designed devices. Y. L. and B. D. acknowledge the China Scholarship Council (CSC) for funding Y. L. 's stay in France and a public grant overseen by the French National Research Agency (ANR) as part of the "Investissements d'Avenir" program (Reference No. ANR-10-LABX-0035, Labex NanoSaclay). The authors thank Z. D. Luo, W. P. Geng, X. J. Meng, and L. Bellaiche for fruitful dicussions.
80 ± 0. 13 vs NW-BS = 4. 24 ± 0. 12; p = 0. 0001) rhizospheres compared to the bulk soil samples, with significantly more Burkholderia detected in the Norway spruce rhizosphere than in the beech rhizosphere (p < 0. 05). Quantification of Collimonas revealed an enrichment of this bacterial genus in the Norway spruce rhizosphere compartment (NW-R = 4. 10 ± 0. 16 vs NW-BS = 3. 23 ± 0. 33; p = 0. 04), and analysis of the ratio (Collimonas/total bacterial community) revealed that these values were significantly higher in both the beech and Norway spruce rhizospheres than in the surrounding bulk soil. Pyrosequencing data analyses. We obtained a total of 440,000 reads for the beech and Norway spruce rhizosphere and bulk soil samples collected. After quality treatment, we obtained 26,386 reads for archaea, 203,476 reads for bacteria, and 24,540 and 14,607 reads for fungal ITS and 18S rRNA, respectively (Table  S1 ).
4% of the archaeal 16S rRNA generated are strongly related to Nitrosotalea devanaterra, a recently described obligate acidophilic ammonia oxidizer  48. This organism's optimal growth conditions are within a pH range of 4 to 5, which corresponds to the pH conditions occurring at the experimental site of Breuil-Chenue (pH = 3. 5 to 4. 5). The presence of this archaeal taxon and the lack of detection of bacterial 16S rRNA sequences affiliated with ammonia-oxidizing bacteria (Nitrosomonas, Nitrospira) in our dataset suggest that archaeal communities may contribute to ammonia oxidation at the Breuil-Chenue experimental site. Finally, regarding fungal communities, it was difficult to assess the impact of the environmental parameters tested. Nevertheless, our results clearly showed the involvement of symbiotic ectomycorrhizal fungal species in host preference, with a non-stochastic distribution of fungal species from the following genera: Amanita, Inocybe, Russula and Scleroderma.
seqs command of Mothur. For Dikarya sequences, the internal transcribed spacer 2 (ITS2) region was extracted using Fungal ITS X extractor  67  and sequences shorter than 100 bp were removed. For each organism-type considered, all sequences were pooled and sorted by decreasing length as recommended for Usearch clustering  68. Operational taxonomic units (OTUs) were generated from these reads using the cluster_smallmem command of Usearch v 6. 0. 307 68 at a 97% similarity threshold. Phylogenetic assignment was determined for each OTU representative sequence (consensus sequence) using the Basic Local Alignment Search Tool (BLAST) algorithm v 2. 2. 23  69  against the Ribosomal Data Project (RDP) database release 10. 3  70  for bacterial and archaeal sequences. For fungi, the phylogenetic assignment was determined against UNITE database release 5.
Acknowledgements This work was funded by grants from the  ANR   JCJC SVSE7 'BACTOWEATHER'  , the  EC2CO program  and a bilateral Hubert Curien van Gogh travel grant. The authors thank  Dr. S. Höppener-Ogawa  for technical assistance and  Drs F. Martin  for helpful discussions and  J. Ranger  for giving access to the experimental site of  Breuil-Chenue. We also thank  Dr. A. Legout  for the scientific management of the experimental site. The  UMR1136  is supported by the  French Agency  through the  Laboratory of Excellence Arbre  ( ANR-11-LABX-0002-01 ). We thank  C. Cochet  for technical support for soil analyses. The English language was reviewed by American Journal Experts (Certificate  AC92-9421-8654-F7FB-A3EC ). The authors thank Reviewers #1 and #2 for their helpful comments to improve and clarify our manuscript.
Bioacoustic study Collected crickets from the field were recorded with a modified condenser microphone (CM16 Avisoft Bioacoustics, Berlin, frequency range: 3-150 kHz ± 6 dB, R. Specht pers. comm. ) in a sound attenuated room controlled for temperature in the MNHN. The insects were placed over night alone in a cage made of mosquito net to avoid echoes. The microphone was placed 30 cm from the cage. Automatic recordings were made using the program Avisoft Triggering Harddisk Recorder version 2. 97 and a 8-Pre MOTU sound card at a sampling frequency of 96 kilo-samples per second (16 bit). The temporal acoustic activity of crickets was obtained in the field by ambient acoustic recordings with a SongMeter SM2 Bat sensor (Wildlife Acoustics, concord, NY, U. S. A. ) at Pic du Grand Kaori during 72 hours.
Cricket song terminology follows Ragge & Reynolds  [35] : one song unit is called a syllable and corresponds to one opening-closing cycle of the male forewings; a group of syllables forming a cricket call is named an echeme. We used the computer software Avisoft-SASLab pro V. 5. 2. 06 (Avisoft bioacoustics; Specht 2012) to automatically analyze cricket calls. We used automatic analyses to calculate syllable duration and period, number of syllable per echeme, dutycycle, and dominant frequency. Eight calling songs from 4 different males were used for the analysis (two per male). Measurements acquired from automatic analyses were verified manually in order to avoid errors or aberrant values. Sound tracks were deposited in the Sound Library of the MNHN, Paris (MNHN-  SO-2015-9, 10, 11, 12, 13, 14).
Molecular phylogeny Taxon sampling. Two molecular datasets were built aiming both to investigate the phylogenetic placement of Pixibinthus within crickets (= dataset 1), and to generate a molecular dating to estimate divergence times of Pixibinthus and its close relatives (= dataset 2). The molecular sampling of dataset 1 comprised six individuals of Pixibinthus (from three different locations). As Pixibinthus shares the typical morphology of the Lebinthini tribe within the subfamily Eneopterinae (see below), a special emphasis has been put on the taxonomical sampling of this tribe, by incorporating representatives of the genera Agnotecous Saussure, 1878 (nine species; 14 samples), Lebinthus Stål, 1877 (six species; seven samples), Cardiodactylus Saussure, 1878 (four species; five samples), and Swezwilderia Chopard, 1929 (one species).
The sampling was then completed with other species belonging to the other four current tribes of the Eneopterinae subfamily (sensu  [36] ): Eurepini (one species), Eneopterini (one species), Nisitrini (two species; three samples), and Xenogryllini (one species). A total of 26 species were then used in the ingroup sampling of dataset 1 (corresponding to 39 samples; Tables  1  and 2 ). Two species from a different cricket subfamily (Gryllinae: Acheta domesticus (Linnaeus, 1758) and Gryllus bimaculatus De Geer, 1773) were finally used as most external outgroups. The molecular sampling of dataset 2 was composed of a sub-sampling of dataset 1 to reduce each ingroup species to a single individual, except Cardiodactylus novaeguineae that occurs in New Caledonia and other Pacific islands (Tables  1  and 2 ).
We also increased the species sampling of the Grylloidea superfamily in order both to better estimate divergence times and to use an appropriate calibration point (see below). We consequently added representatives of the major Gryllidea lineages delimited in Chintauan-Marquier et al. [37] :Trigonidiidae (two species), Pteroplistinae (one species), Phalangopsidae (four species), and Gryllidae (six species). We finally used as most external outgroups, one species belonging to Mogoplistidae (Grylloidea) and one species of Gryllotalpidae (Gryllotalpoidea; Tables  1  and 2 ). Newly generated sequences are indicated with an asterisk (and would be very soon referred to a genbank accession number). The superscript numbers refer to the publication where the sequences were first published: 1  [27] , 2  [38] , 3  [39] , 4  [40] , 5  [41] , 6  [42] , 7  [43] , 8  [37] , 9  [44].
Species dataset 1 dataset 2 16S 12S Cytb CO1 CO2 28SA EF1a H3 18S Acheta domesticus (Linnaeus, 1758) x x AF248698 2 Z97611 9 AF248682 2 JX897403 1 JX897439 1 JX897465 1 GQ886692 3 KR903150* AD18SITS1* Afrophaloria amani Desutter- Grandcolas, 2015 x KR903696 8 KR903858 8 KR903353 8 N/A N/A KR903524 8 N/A KR903173 8 KR904049 8.
We selected nine DNA loci, five mitochondrial and four nuclear, proved to have been useful in previous phylogenetic studies on Grylloidea  [16, 27, 37, 40, 41]. These are fragments of the small (12S rRNA, ~400 bp) and large (16S rRNA, ~500 bp) mitochondrial ribosomal subunits, the mitochondrial gene coding for cytochrome b protein (cytb, ~400 bp), fragments of the mitochondrial cytochrome oxidase subunits 1 (CO1, ~750 bp) and 2 (CO2, ~400 bp) genes, a fragment of the small nuclear ribosomal subunit (18S rRNA, ~650 bp), a fragment of the large nuclear ribosomal subunit (28S rRNA, ~400 bp), and fragments of the genes coding for H3 protein (H3, ~330 bp) and elongation factor 1-α (EF1α, 950 bp). We used these nine DNA loci for analyses of dataset 1, to refine and improve the internal phylogenetic resolution.
However we observeda high level of missing sequences for the CO1 and CO2 regions, especially for outgroup species of dataset 2. We therefore used only the seven following loci for phylogenetic analyses of this dataset: 12S, 16S, 18S, 28SA, cytb, EF1a, and H3. Laboratory procedures and Preparation of datasets. Molecular work was performed at the Service de Systématique Moléculaire of the MNHN. All sequences were generated by using the DNA extraction, amplification and sequencing protocols of the nine DNA loci, described in Nattier at al. [27]. Some DNA extractions were made from median legs of crickets using the Epmotion 5075 robot (Eppendorf). In total we generated 106 new DNA sequences (33% of the total samplings) for this study (Tables  1  and 2 ), while other sequences were generated in previous studies and downloaded from Genbank  [27, 37, 38, 39, 40, 41, 42, 43, 44].
Primers and annealing temperatures for each DNA locus are given in S1A Table. Newly generated sequences were edited in Sequencher v. 4. 9 (Gene Codes Co. ) and BioEdit v. 7. 0. 5. 3  [45] , blasted with NCBI blast tools, and submitted to GenBank (Tables  1  and 2 ). Multiple alignments were generated for each DNA locus using the software Muscle v. 3. 7  [46]  with default parameter settings, by using the online portal Phylogeny. fr [47]. Subsequently we adjusted them manually. We concatenated the different DNA locus partitions with Sequence-Matrix v. 1. 7. 7  [48]. The datasets 1 and 2 resulted in a combined alignment of ~4750 bp and 3789 bp, respectively (S1B and S1C Table ). Several ambiguous and large indel regions in the alignments of loci 12S, 16S, 18S, and EF1α were found within the dataset 2, and attributed to the inclusion of several distantly related Grylloidea taxa.
This induced problematic homology hypotheses, and we consequently removed them from the DNA alignments (S1 Table ). Phylogenetic analyses. Preliminary single-locus and combined phylogenetic inferences were carried out using maximum likelihood (ML) and Bayesian Markov Chain Monte Carlo (MCMC) analyses for both datasets 1 and 2. Best-fit model for each DNA locus was identified using jModelTest v. 2. 1. 3 [49]  based on the Akaike Information criterion (S1B and S1C Table ). The combined datasets were partitioned to allow each locus to have its specific model parameters  [50, 51]. The ML analyses were performed in RAxML v. 7. 4. 2. [52, 53]  by running 500 likelihood searches. To evaluate node support, a supplementary bootstrap analysis (BS;  [54] ) was performed using 500 replicates. A clade with a BS value > 95% was considered as well supported.
The Bayesian MCMC analyses were performed using MrBayes v. 3. 2. 6  [51]. The single-locus and combined analyses were set as follows: four Metropolis-coupled Markov chains with an incremental heating temperature of 0. 2 were run for 50 and 100 million generations with a tree sampled every 10000th generation for dataset 1 and dataset 2, respectively. The analysis was repeated four times for both datasets, all starting with random trees. Bayesian MCMC analyses were carried out using the CIPRES Science Gateway v. 3. 3  [55]. The MCMC sampling was considered sufficientwhen the effective sampling size (ESS) was higher than 200, as verified in Tracer v. 1. 6 [56] , or when the potential scale reduction factor was reasonably close to 1. 0 for each parameter (PSRF;  [57] ). After a burn-in period of 5× 10 6 and 10 × 10 6 for dataset 1 and dataset 2, respectively, the remaining trees were used to construct a majority rule consensus tree and its associated Bayesian posterior probabilities (PP).