dataesr/scientific-paragraphs-categorization huggingface dataset:
In total, 122 trials were presented. Participants were allowed a short break after each block. Participants received 100 CHF for their participation and all received a bonus of 20 CHF, in total, every participant was paid 120 CHF. There were two laboratory sessions with one week in between. The sessions were identical in terms of experimental task, stimuli, and timing. However, in one of the two sessions, a Social Presence condition was added (presence of a confederate in the lab room). As the data for the social presence condition are to be reported in a separate article, only the data from the alone sessions are reported here. As a manipulation check, at the end of the experiment, and only after the second session, participants rated each stimulus concerning the felt pleasantness (nine-point scale from 1 [very unpleasant] to 9 [very pleasant]).
Data Acquisition The experimental task and behavioral data acquisition were administered by using E-Prime 2. 0 (Psychology Software Tools, Inc. , Pittsburgh, PA). Facial EMG was applied to assess the predicted facial muscle innervations produced by the manipulated appraisals. The surface electrodes were placed over the brow region (mainly targeting the corrugator supercilii muscle) and the cheek region (mainly targeting the zygomaticus major muscle, according to the established guidelines;  Fridlund & Cacioppo, 1986). Signals were recorded (bandwidth 0. 1-417 Hz, sampling rate: 2048 Hz) with a BIOSEMI Active-T w o a m p l i f i e r s y s t e m ( B i o S e m i B i o m e d i c a l Instrumentation, Amsterdam, the Netherlands). 2 s mentioned in the "Introduction," surface EMG on the face does not allow recording of the activity of individual muscles, but only the overall activity over a circumscribed region, in this case the brow and cheek regions.
Data Analysis Preprocessing of the EMG data (Brain Vision Analyzer software, Brain Products, Gilching, Germany) followed the standard procedure  (Fridlund & Cacioppo, 1986). First, bipolar montages were calculated for each pair of electrodes of each facial muscle region (brow and cheek region) by subtracting the recorded activity of one electrode from the activity of the neighboring electrode. Next, the continuous waveforms of the EMG data were bandpass filtered (20-400 Hz, 12 db/octave), full-wave rectified, low-pass filtered (40 Hz, 12 db/octave), and cut into segments (including 500-ms baseline and 1. 6-s post-stimulus intervals) for each experimental condition. The EMG data were then downsampled to 512 Hz and exported to a commercial software package (MATLAB R2012a). Separately, for each facial muscle region, artifacts and outliers Participants were instructed to breathe in and then breathe out by counting 3-2-1.
Survey of CFAV-Derived EVEs in Aedes aegypti Genome Sequences In order to inventory CFAV-derived EVEs, we used BLAST search to identify CFAV-like sequences in publicly available Ae. aegypti genome assemblies, RNA sequencing data, and whole-genome sequencing data. We identified several potential EVE structures based on samples for which reads aligned only to segments of the CFAV genome, in addition to samples for which reads covered the entire CFAV genome, presumably representing true CFAV infections (Data S1). The predicted structure of two of these putative EVEs, which we designated CFAV-EVE1 and CFAV-EVE2, was obtained by de novo assembly (Figure  1A ). These two putative EVEs were confirmed in an outbred Ae. aegypti colony derived from a wild population in Thailand and maintained in our laboratory since 2013.
Survey of CFAV-related EVEs in public sequencing data of Aedes aegypti The accession numbers for the Ae. aegypti sequencing data were selected using the web platform of the SRA database  [61]. We used BLAST (megablast) search  [60]  implemented in the SRA Toolkit  [61]  to search for CFAV-like sequences in the preselected SRA data. The BLAST search resulted in 796 RNA-seq and 709 WGS runs tested, released before January 30 and February 6, 2020, respectively. Fullgenome CFAV sequences from Thailand CFAV-Bangkok (European Nucleotide Archive (ENA): LR694074)  [62]  and CFAV-KPP (ENA: LR596014)  [32]  were used as queries. Visualization of positive hits was performed in R v3. 6. 1 (http://www. r-project. org/). Using the online BLAST tool (megablast), the CFAV-EVE1 sequence was detected in the supercontig 1.
109 of the AaegL3 genome assembly (GenBank: GCA_000004015. 3) but absent from the AaegL5 genome assembly (GenBank: GCA_002204515. 1). The CFAV-EVE1 sequence was reconstructed from a published WGS dataset (SRA: SRR5562867) using metaSPAdes v3. 11. 0  [63]. Reads from the WGS dataset were first quality trimmed with Trimmomatic v0. 36  [64]  and aligned against the AaegL5 genome assembly with Bowtie2 v2. 3. 4. 3 (-end-to-endvery-fast)  [65]  to filter out all non-EVE reads. The CFAV-EVE2 sequence was reconstructed from SRA: SAMN04480331, SAMN04480332, SAMN04480333. Reads were trimmed with Cutadapt v1. 18  [66]. Relaxed local Bowtie2 v2. 3. 4. 3 alignment (-local -D 20 -R 3 -L 11 -N 1-gbar 1-mp 3) was used in order to preselect CFAV-derived reads, which were then used for de novo assembly with Ray v2.
The selected reads were converted into FastA format, trimmed at the 3 0 end to 20 nt and converted to RNA letters using the corresponding FastA/FastQ tools. The processed reads were used as input for the Weblogo tool available in the Galaxy toolshed  [75]. For the analysis of ping-pong signatures, aligned reads were loaded into the Mississippi instance of Galaxy (https://mississippi. snv. jussieu. fr/). SAM files containing the reads of 26-30 nt in length were used as input for the Small RNA signatures tool. The Z-scores of the calculated overlap probabilities were plotted with Graphpad Prism. All sRNA sequencing library sizes with the number of CFAVmapped reads are reported in Table  S5. All data are available at SRA: PRJNA588447.
ACKNOWLEDGMENTS We thank  Catherine Lallemand  for assistance with mosquito rearing and  Fabien Aubry  for technical aid during review. We are grateful to  Catherine Bourgouin  and  Nicolas Puchot  for assistance with the microinjection apparatus and to  Anavaj Sakuntabhai  for facilitation of the mosquito genome sequencing. R. P. v. R. was supported by the  Netherlands Organisation for Scientific Research  ( VICI grant   016. VICI. 170. 090 ). P. M. was supported by a short-term fellowship of the  European Molecular Biology Organization (EMBO  grant  ASTF 449-2016 ). Work in the laboratory of L. L. was supported by  Agence Nationale de la Recherche  (grants  ANR-16-CE35-0004-01  and  ANR-17-ERC2-0016-01 ) and the  City of Paris Emergence (s) program in Biomedical Research. Work in the laboratory of M.
-C. S. was supported by the  European Research Council  ( FP7/2013-2019 ERC CoG 615220 ). L. L. and M. -C. S. were financed by the  French Government 's  Investissement d'Avenir program Laboratoire d ' Excellence Integrative Biology of Emerging Infectious Diseases  (grant  ANR-10-LABX-62-IBEID ). The funders had no role in study design, data collection, and interpretation or the decision to submit the work for publication.
Data and Code Availability Statement The genome sequence of the Aedes aegypti isofemale line is available at Sequence Read Archive (SRA): SRR01437595. All sRNA sequencing data are available at SRA: PRJNA588447. The code and data of the SRA survey were deposited to a public repository (https://github. com/artembaidaliuk/SRA_search_CFAV_EVE_sequences).
sRNA. Isofemale line derived knockout line with This paper HJ5CNBGX9, SRA: SAMN13244312 CFAV-EVE1 (À/À), ovary, mock IT injected. sRNA. Isofemale line derived knockout sister line This paper HJ5CNBGX9, SRA: SAMN13244310 with CFAV-EVE1 (+/+), ovary, mock IT injected. sRNA. Thai Aedes aegypti isofemale line with This paper SRA: SRR01437595 CFAV-EVE1 (+/?). WGS. SRA accessions and BLAST search output This paper, https://github. com/ N/A of SRA search for CFAV related sequences artembaidaliuk/SRA_search_CFAV_ EVE_sequences/ Experimental Models: Cell Lines Deposited Data Aedes albopictus C6/36 ATCC SUPPLEMENTAL INFORMATION Cat# CRL-1660 Outbred Thai Aedes aegypti with CFAV-Experimental Models: Organisms/Strains This paper HGW27BGXX, SRA: SAMN13244317 EVE1 (+/?) and CFAV-EVE2 (+/?), full body, naturally infected with CFAV.
sRNA. Aedes aegypti outbred colony Supplemental Information can be found online at https://doi. org/10. 1016/j. cub. 2020. 06. 057. https://doi. org/10. 1371/journal. pgen. 1006111 N/A Outbred Thai Aedes aegypti with CFAV-Aedes aegypti isofemale line EVE1 (+/?) and CFAV-EVE2 (+/?), full body, uninfected with CFAV. sRNA. Thai Aedes aegypti isofemale line with CFAV-EVE1 (+/?), full body, CFAV IT Isofemale line derived knockout line with injected, replicate 1. sRNA. Thai Aedes aegypti isofemale line with injected, replicate 2. sRNA. with CFAV-EVE1 (+/+) CFAV-EVE1 (+/?), full body, CFAV IT CFAV-EVE1 (À/À) Isofemale line derived knockout sister line This paper https://doi. org/10. 1371/journal. pgen. 1006111 This paper https://doi. org/10. 1371/journal. pgen. 1003621 This paper This paper This paper HG7CHBGXX, SRA: SAMN13244318 Line D HVV5HBGXX, SRA: SAMN13244306 N/A HVV5HBGXX, SRA: SAMN13244307 N/A Thai Aedes aegypti isofemale line with Oligonucleotides This paper HVV5HBGXX, SRA: SAMN13244308 CFAV-EVE1 (+/?), full body, CFAV IT All primer sequences are listed in Table S1 [57] N/A injected, replicate 3.
sRNA. Repair template sequence is listed in Table N/A Thai Aedes aegypti isofemale line with S3 This paper HVV5HBGXX, SRA: SAMN13244303 CFAV-EVE1 (+/?), full body, mock IT Software and Algorithms injected, replicate 1. sRNA. BLAST [60] N/A Thai Aedes aegypti isofemale line with SRA Toolkit v2. 9. 6 This paper https://doi. org/10. 1093/nar/gkq1019 HVV5HBGXX, SRA: SAMN13244304 N/A CFAV-EVE1 (+/?), full body, mock IT https://github. com/ncbi/sra-tools injected, replicate 2. sRNA. metaSPAdes v3. 11. 0 https://doi. org/10. 1101/gr. 213959. 116 N/A Thai Aedes aegypti isofemale line with CFAV-EVE1 (+/?), full body, mock IT R v3. 5. 2 This paper http://www. r-project. org/ HVV5HBGXX, SRA: SAMN13244305 N/A injected, replicate 3. sRNA. Trimmomatic v0. 36 https://doi. org/10. 1093/bioinformatics/ N/A btu170 (Continued on next page) d RESOURCE AVAILABILITY B Lead Contact B Material Availability B Data and Code Availability Statement d EXPERIMENTAL MODEL AND SUBJECT DETAILS B Mosquito origin and maintenance d METHOD DETAILS B Ethics statement B Survey of CFAV-related EVEs in public sequencing data of Aedes aegypti B Live Aedes aegypti mosquitoes B CRISPR/Cas9-mediated genome engineering B CFAV experimental infections in vivo B Small-RNA sequencing d QUANTIFICATION AND STATISTICAL ANALYSIS (Continued on next page).
Discussion To demonstrate the effect of the harmonic beatnote on the modulation of the QCL, numerical simulations based on the open-source software package mbsolve  28, 29  were realised to investigate the electric field evolution in a structure reproducing the QCL characteristics. The simulation model is based on the full-wave Maxwell-Bloch (MB) equations  30, 31  for the three-level system (i. e. , without the rotating wave and slowly varying amplitude approximations), taking into account counterpropagating waves and spatial hole burning. Further details about the specific parameters used for the simulations as well as the time response can be found in the supplementary section. The simulations of the spectral response were realised in two cases: (i) the harmonic beatnote introduces loss regions along the structure and (ii) where the active region is above the threshold as a whole (i.
We constrain the rest-frame FUV (1546Å), NUV (2345Å) and U-band (3690Å) luminosity functions (LFs) and luminosity densities (LDs) with unprecedented precision from z ∼ 0. 2 to z ∼ 3 (FUV, NUV) and z ∼ 2 (U-band). Our sample of over 4. 3 million galaxies, selected from the CFHT Large Area U-band Deep Survey (CLAUDS) and HyperSuprime-Cam Subaru Strategic Program (HSC-SSP) data lets us probe the very faint regime (down to M FUV , M NUV , M U -15 at low redshift) while simultaneously detecting very rare galaxies at the bright end down to comoving densities < 10 -5 Mpc -3. Our FUV and NUV LFs are well fitted by single Schechter functions, with faint-end slopes that are very stable up to z ∼ 2. We confirm, but self-consistently and with much better precision than previous studies, that the LDs at all three wavelengths increase rapidly with lookback time to z ∼ 1, and then much more slowly at 1 < z < 2-3.
2013b; Madau & Dickinson 2014; Bouwens et al. 2015a; Bouwens et al. 2016; Ono et al. 2018; Khusanova et al. 2019, and many others). The advent of multi-wavelength datasets that contain flux measurements at a great many wavelengths (sometime as many as several dozen -e. g. ,  Laigle et al. 2016 ) allow the estimation of physical quantities for each galaxy, such as its stellar mass, and the construction of related global descriptors, such as the galaxy stellar mass functions (SMFs) and stellar mass densities (SMDs, ρ M ) -e. g. ,  Ilbert et al. (2013) ;  Muzzin et al. (2013) ;  Moutard et al. (2016b) ;  Davidzon et al. (2017). Such "physical" measurements are an extremely powerful tool to help us understand galaxy evolution, but they suffer from some important limitations: they rely heavily on the assumptions that underpin stellar population synthesis models (e.
Data This study uses the U + grizy data from the Canada-France-Hawaii Telescope (CFHT) Large Area U-band Deep Survey (CLAUDS) and the HyperSuprime-Cam Subary Strategic Program (HSC-SSP). These surveys are described in detail in  Sawicki et al. (2019, CLAUDS)  and in  Aihara et al. (2018b, and  references therein; HSC-SSP), and the procedures for merging the datasets are described in  Sawicki et al. (2019). Consequently, here we give only a summary of the key details. The CLAUDS and HSC-SSP imaging data overlap over four well-studied fields, namely E-COSMOS, ELAIS-N1, DEEP2-3, and XMM-LSS, each spanning ∼4-6 deg 2. The U-band data cover 18. 60 deg 2 to a depth of U AB =27. 1 (5σ in 2 apertures), with selected ultra-deep sub-areas within the E-COSMOS and XMM-LSS fields that cover 1. 36 deg 2 to a depth of U=27.
7 (5σ in 2 apertures). CLAUDS U-band data were obtained in two somewhat different CFHT/MegaCam filters: data in the ELAIS-N2 and DEEP2-3 fields were taken with the new u filter, while those in XMM-LSS were taken with the older u * filter. The E-COSMOS field contains data in the u filter except in the central region where both u and u * data overlap. The u and u * data are kept separate, even in areas where they overlap. The image quality of the CLAUDS data is excellent, with median seeing of 0. 92. For the details of CLAUDS data see  Sawicki et al. (2019). The HSC-SSP project  (Aihara et al. 2018b ) provides deep Subary/HSC imaging in the grizy wavebands in the same fields imaged by CLAUDS. Here we use images from the S16A internal HSC-SSP data release that are deeper than the HSC-SSP public data release 1 (PDR1,  Aihara et al.
29 deg 2 with U ≥ 26. 8, g ≥ 26. 5, r ≥ 26. 1, i ≥ 25. 7, z ≥ 25. 1 and y ≥ 24. 2, respectively; • while the Ultra-Deep layer covers an area of 1. 54 deg 2 with U ≥ 27. 5, g ≥ 27. 1, r ≥ 26. 9, i ≥ 26. 6, z ≥ 26. 3 and y ≥ 25. 0, respectively. We use the SExtractor-based multi-band catalog described in  Sawicki et al. (2019). For object detection, this uses the signalto-noise image, ΣSNR constructed from all the available uu * grizy images as ΣSNR = N i=1 f i -µ i σ i , (1) where f i is the flux in each pixel, σ i is the RMS width of the background sky distribution, and µ i is its mean. Here the index i runs over MegaCam bands u or u * (or both, where available -i. e. , in the central area of E-COSMOS) as well as the HSC bands grizy. Once the SExtractor software  (Bertin & Arnouts 1996 ) has detected objects in the ΣSNR image, the multiband catalog is then created by running SExtractor in dual image mode, with various measurements recorded for each object, including positions, fluxes (in Kron, isophotal, and fixed-radius circular apertures), fiducial radii, ellipticities, position angles, and central surface brightnesses.
To properly constrain the FUV and NUV luminosities at low redshift, we complemented our photometric dataset with FUV (135-175 nm) and NUV (170-275 nm) observations from the GALEX satellite  (Martin et al. 2005). Both in the XMM-LSS and E-COSMOS fields, the GALEX observations we used were reduced with the EMphot code  (Guillaume et al. 2006 ; Conseil et al. 2011) dedicated to extract UV photometry by using the CFHTLS (T0007) u * -band detections as a priors down to u * ∼25. Consequently, the astrometry of the resulting GALEX photometry is that of the CFHTLS, which enabled a straightforward position matching with our photometric dataset (with 0. 5 tolerance).
89. Doing so, we discarded ∼7. 4% of the sources as stars. See A. Golob et al. (submitted to MNRAS) for details of the method and its application to our CLAUDS+HSC-SSP dataset. Our photometric redshifts are computed using a hybrid approach that combines a nearest-neighbours machine-learning method (hereafter kNN; A. Golob, in preparation; see also  Sawicki et al. 2019)  with the template-fitting code Le Phare  (Arnouts et al. 2002; Ilbert et al. 2006). The kNN method uses the 30-band COSMOS photometric redshifts from  Laigle et al. (2016)  as a training set. For each object in our catalog it identifies 50 nearest neighbours in colour space and then fits a weighted Gaussian kernel density estimator (KDE), with each neighbour's redshift weighted by (d NN × ∆z) -1 ; here d NN is the Euclidean distance in colour space to the object under consideration, and ∆z is the width of the 68% confidence interval of the neighbour's redshift in the  Laigle et al.
FUV, NUV & U-band LF fitting As can be seen in Figs. 3  and 4 , the Schechter parametric form  (Schechter 1976)  appears to be well suited to the fitting of the FUV and NUV LFs down to the completeness limits of our survey and, at least, between z = 0. 05 and z = 3. 5. We therefore fitted the FUV and NUV LFs with the classical Schechter function defined as φ(L) dL = e -L L φ L L α dL L , (11) which, in term of absolute magnitude, can be written as φ(M) dM = ln 10 2. 5 φ 10 0. 4∆M α+1 exp -10 0. 4∆M dM , with ∆M = M -M. While all three Schechter parameters are well constrained at 0. 05 < z ≤ 1. 8, the slope α and normalisation φ start being poorly constrained at 1. 8 < z ≤ 2. 5 and are no longer constrained at z > 2. 5. One of the strengths of our dataset is its ability to probe the bright end of the LF, thanks to the large area covered, the location of which is well traced by the characteristic absolute magnitude M.
Comparison with previous studies Often simply referred to as the UV LF, the FUV LF has been extensively studied up to redshift z ∼ 9, where rest-frame (not dustcorrected) UV can be constrained from mid-infrared observations. In Fig. 8 , we compare our best-fit Schechter parameters for the FUV LF with values from the literature across redshift. As one can see, our results are in overall good agreement with the literature at 0. 05 < z ≤ 3. 5. Given its unsurpassed combination of depth and area, our homogeneous dataset provides the definitive reference measurement of the rest-frame FUV LF out to z ∼ 3 at this time. It is remarkable how well-behaved the values of the Schechter parameters are with redshift in Fig. 8  over the redshift range we measured them: M increases monotonically with lookback time, while both α and φ remain essentially constant.
SUMMARY In this paper we presented our measurements of the 0 < z 3 restframe FUV (1546Å), NUV (2345Å), and U-band (3690Å) galaxy luminosity functions and luminosity densities using more than 4. 3 million galaxies from the CLAUDS and HSC-SSP surveys. The unprecedented combination of depth (U∼27) and area (∼18deg 2 ) of this dataset allows us to constrain the shape and evolution of these LFs with unmatched statistical precision and essentially free of cosmic variance. The main results of this paper are the LF and LD measurements presented in the Figures and Tables  in Section 3. In addition to these main products, we wish to highlight again the following observations: (i) The rest-frame FUV and NUV luminosity functions are de- (ii) In contrast to the FUV and NUV LFs, the rest-frame Uband luminosity functions are best described by a double Schechter model, M U, Doub , φ U,1 , φ U,2 , and α U,1 evolving continuously through 0.
2 < z < 2, assuming that α U,2 is simultaneously stable with redshift, which is confirmed to at least z ∼ 0. 5 (we are unable to measure it independently beyond this redshift). We speculate that the second Schechter component in the rest-frame U-band LF is due to the population of quiescent galaxies -a topic we are currently investigating in a companion paper (T. Moutard, in prep. ). (iii) We measured the rest-frame FUV, NUV, and U-band luminosity densities by integrating the corresponding LFs down to M = -15 at z ∼ 0. 2. At all three wavelengths we confirm previous results but with much better statistical precision afforded by our wide-and-deep CLAUDS+HSC-SSP dataset: at all three rest wavelengths the luminosity density increases monotonically and rapidly with lookback time from z ∼ 0. 2 to z ∼ 1 and then flattens to a much gentler slope at z > 1.
ACKNOWLEDGEMENTS We gratefully acknowledge the anonymous reviewer, whose insightful comments helped in improving the clarity of the paper. We thank the  CFHT observatory  staff for their hard work in obtaining these data. The observations presented here were performed with care and respect from the summit of Maunakea which is a significant cultural and historic site. We thank  Guillaume Desprez  and  Chengze Liu  for helpful suggestions. This work is based on observations obtained with  MegaPrime/ MegaCam , a joint project of CFHT and CEA/DAPNIA, at the  Canada-France-Hawaii Telescope (CFHT)  which is operated by the  National Research Council (NRC)  of  Canada , the  Institut National des Science de l'Univers of the Centre National de la Recherche Scientifique (CNRS) of France , and the  University of Hawaii.
3  Stress adaptation responsive genes in K. marxianus are enriched for young genes. a A simplified phylogenetic tree for K. marxianus showing speciation events and organisms used in orthology queries. b The transcripts detected via RNAseq from this study were grouped based on ortholog presence in the groups shown (described in detail in Supplementary Fig. 4 ). c Differentially expressed genes for K. marxianus were parsed by their grouping shown in a and b, then normalized to the group size and the total measured DE % (dashed line). Transcripts in groups IV and V were assessed for shared upregulated genes (d) or downregulated genes (e). suggesting that young genes could adapt more rapidly compared to ancient genes. To test this, amino acid sequence identity was compared between homologous proteins from members of the same genus using BLAST+.
g. , S. cerevisiae GAL1 and GAL3) (Supplementary Fig. 2 ). Protein groups were matched to their underlying genes for gene expression analyses. This grouping strategy was carried out to sort each species protein-coding genes into a single group. Results of these gene sorting analyses are shown in Supplementary Data 1, Supplementary Data 2 and Supplementary Data 3. For Figs. 2  and 3 , and Supplementary Fig. 7 , OrthoFinder was used to identify orthologs between each yeast and a set of eukaryotic organisms. This is shown in Supplementary Fig. 4  and is discussed in more detail in Supplementary Method 1. The results of these gene sorting analyses are shown in Supplementary Data 4, Supplementary Data 5 and Supplementary Data 6. RNAseq preparation and mapping. RNA extractions were performed on samples that were mechanically lysed with 0.