dataesr/scientific-paragraphs-categorization huggingface dataset:
Earth system models (ESMs) are the main tools to describe the effect on climate of raised atmospheric GHGs, and interactions and feedbacks on global biogeochemical cycles. Such models contain a land surface component, e. g. the JULES model  (Clark et al. , 2011)  within the HadGEM2-ES ESM  (Jones et al. , 2011). HadGEM2-ES ESM carries ozone as an atmospheric tracer, to which JULES responds  Sitch et al. (2007). ESMs contribute to global model databases, most recently the fifth phase of the Coupled Model Intercomparison Project, CMIP5  (Taylor et al. , 2012) , which inform the United Nations Intergovernmental Panel on Climate Change reports (e. g. , IPCC, 2013). If a substantial fraction of vegetation responses to elevated tropospheric ozone contain stomata sluggishness, this requires implementation in large-scale terrestrial vegetation models and ESMs to assess global implications.
Acknowledgements. Chris Huntingford ,  Rebecca J. Oliver  and  Lina M. Mercado  acknowledge support from the  NERC-CEH National Capability Fund. Rebecca J. Oliver  and  Lina M. Mercado  acknowledge support from  UK Natural Environment Research Council  grant  NE/N017951/1. Lina M. Mercado  and  Stephen Sitch  acknowledge support from  UK Natural Environment Research Council  grant  NE/R001812/1. Edited by: Sönke Zaehle Reviewed by: two anonymous referees.
2 ). To evaluate the robustness of the 3,010 ten-species orthologs, we screened each transcriptome for variants of these orthologs. Variants are transcripts with the same ORF, but that contain sufficiently many SNPs, deletions or insertions of variable length in any part of the ORF to result in separately assembled transcripts in the de novo assembly (our ortholog detection pipeline retained the longest ortholog if multiple options are available, see Materials and Methods for details). Surprisingly, the proportion of orthologs with variants was significantly higher for the asexual than sexual Timema species (mean asex: Consequences of Asexuality in Natural Populations. doi:10. 1093/molbev/msy058 MBE 0. 076, mean sex: 0. 013; paired t-test, t ¼ 7. 29, df ¼ 4, P < 0. 001; supplementary data 2, Supplementary Material online).
We mapped synonymous substitutions along the Timema phylogeny and estimated the per-lineage number of weak-to-strong (A/T ! G/C), strong-to-weak (G/C ! A/T), and GCconservative (A $ T, G $ C) substitutions. As expected Asexual lineages ranked from youngest to oldest as estimated from Jukes-Cantor corrected divergence between sexual-asexual sister-species (depending on the species pair, using 5,329-5,908 pairwise orthologs). Tte: Timema tahoe, Tms: Timema monikensis, Tdi: Timema douglasi, Tsi: Timema shepardi, Tge: Timema genevievae. The oldest asexual lineage Timema genevievae, was previously estimated to be $1. 5 My old  (Schwander et al. 2011). under gBGC arrest, asexuals always harbored a higher proportion of strong-to-weak substitutions and a lower proportion of weak-to-strong substitutions than to sexuals (supplementary data 3, Supplementary Material online).
Purifying Selection is Less Effective in Asexuals To test the prediction that selection acts less effectively on asexuals than sexuals, we focused on mutations that are expected to affect protein function and are likely deleterious. This should be the case for nonsynonymous mutations in the 3,010 orthologs shared among the ten species. Indeed, since the Timema species diverged $30 Ma, the vast majority of these orthologs should be under purifying selection (or they would be too diverged to be identified as orthologs). Thus, we quantified whether more nonsynonymous mutations segregate in asexual than sexual populations and whether these mutations accumulate faster in asexual than sexual species. We identified nonsynonymous and synonymous SNPs per transcript by mapping the read data (derived from three pooled individuals) to the 3,010 ten-species orthologs (see Materials and Methods).
2015). Our results also suggest that the reduced effectiveness of selection in asexuals may result in an increase of transcriptional errors (or transcriptional noise). Indeed, an interesting and unexpected finding is that asexual species harbor more variants of orthologous transcripts than sexuals. Analyses using genomic data suggest that these variants do not stem from duplicated genes in the genomes. Moreover, the majority of variants is unlikely to represent different haplotypes, given that they vary considerably in the length of the ORF and the extremely low levels of segregating polymorphism in populations of Timema asexuals. Additional studies are required to corroborate our interpretation of higher transcriptional error rates, reduced stability or more frequent posttranscriptional alteration of transcripts in asexuals.
Assembly and Assembly Cleaning All available trimmed reads per species were pooled and used as input for assembly with Trinity with the addition of the following option: -min_kmer_cov 2 (v 2. 2. 0; Grabherr et al. 2011). Assembly often results in an unrealistically high number of transcripts. To remove likely erroneous contigs, we applied a minimum expression filter following  Moghadam et al. (2013)  and  Harrison et al. (2015). We mapped the pooled reads for each sample separately back to the assembly with RSEM v1. 2. 20 using bowtie2 v2. 2. 4  (Li and Dewey 2011; Langmead and Salzberg 2012). Ambiguously mapping reads were assigned to the most likely transcript by RSEM. We discarded contigs shorter than 300 bp and with low coverage (reads per kilobase per million mapped, RPKM 2). After filtering, all assemblies had a comparable number of isotigs and N 50 (supplementary table 1, Supplementary Material online).
For subsequent analyses in this study, the longest isoform per gene/graph was selected. ORFs were identified from the filtered transcriptome set using transdecoder v2. 1 running TransDecoder. LongOrfs and TransDecoder. Predict (with strand-specific option). In cases where multiple ORFs were predicted for a single transcript the longest ORF per transcript was selected (supplementary table 1, Supplementary Material online). Annotation and Contamination Filtering  NCBI's BLAST client (v. 2. 2. 30þ ) was used to BLAST local versions of the nt (using blastN, default options excepttask blastn, -max_target_seqs 10) and nr (using blastX, default options except, -max_target_seqs 10) databases (downloaded: 07/08/2016). BLAST hits were first filtered so that any hit with an e-value >0. 0000001 was discarded.
Alignments Alignments of the orthologs shared between sister species were generated to calculate divergence estimates. Alignments were also generated for each of the 3,010 tenspecies orthologs for analyses of dN/dS and CDC. For all types of alignments, orthologous sequences were aligned on the protein level using M-coffee for a consensus alignment from multiple software (clustalw2_msa muscle_msa kalign_msa mafftgins_msa t_coffee_msa) implemented in t-coffee v11. 00. 8  (Wallace et al. 2006). In almost all cases, all methods converged to a similar alignment, as indicated by a quality score > 75. Resulting protein alignments were backtranslated into nucleotide alignments using t-coffee and curated with Gblocks v0. 91b (type ¼ codons; minimum block length set to 4; Talavera and Castresana 2007) to remove gaprich and poorly aligned regions.
Distance Estimates Pairwise Jukes-Cantor distances of curated alignments from orthologous sequences between sister species were calculated using the distmat program from the EMBOSS suite v6. 6. 0. 0  (Rice et al. 2000). gBGC Ancestral GC3 was estimated for all nodes of the Timema tree for the 3,010 orthologs using the NHML program  (Galtier and Gouy 1998). This method makes use of a nonhomogeneous and nonstationary Markov model of DNA evolution to estimate branch-specific equilibrium GC-content in a maximumlikelihood framework  (Romiguier et al. 2010; Figuet et al. 2014). The substitution mapping procedure was used to calculate the number of synonymous substitutions in the terminal branch of each Timema species, and categorized them into weak-to-strong (A/T ! G/C), strong-to-weak (G/ C ! A/T) and GC-conservative (others;  Dutheil et al.
2012; Romiguier et al. 2012). This consisted in first fitting the YN98 codon model, as implemented in the bppML program  (Dutheil and Boussau 2008) , to each alignment of proteincoding sequences. The estimated model parameters were then used to map substitutions on each branch of the tree in the empirical Bayesian framework. The total number of substitutions in a branch for a given category was obtained by summing substitution counts across genes.
Divergence x (dN/dS) Analyses Curated back-translated nucleotide alignments of the 3,010 ten-species orthologs were used as input for dN/dS ratio estimates. To estimate gene-specific x (dN/dS) ratios, we first calculated per-gene branch-lengths using RAxML v8. 2. 8  (Stamatakis 2014)  given a fixed, unrooted species tree and the GTRCATI model. Gene-specific branch-lengths, respective alignments and the fixed, unrooted species tree were then parsed as input for codeml, which is included in the paml v4. 8 package  (Yang 2007) , using a custom script that also subsequently collected the output for each gene (see Code availability). We ran both a "free" model, allowing one x value for every branch, and a "three-rate" model, allowing one rate for all asexual and one for all sexual terminal branches as well as one rate for internal (sexual) branches to separate mutations occurring at terminal branches from internal ones.
Polymorphism (pN/pS) Analyses We used the SNP data (details above under "polymorphism") to identify nonsynonymous and synonymous segregating Consequences of Asexuality in Natural Populations. doi:10. 1093/molbev/msy058 MBE polymorphism in the ten Timema populations. For this, we identified the fold-degenerate position of resulting filtered nucleotide variants using a custom script following  Li et al. (1985)  from which pN, pS and (pN/pS) was calculated per gene (see Code availability).
Acknowledgments We thank  David Dylus  and  Cl ement Train  for helping with orthology detection and  Chlo e Larose  and  Bart Zijlstra  for helping with sample collection. This study was supported by a  DFG research fellowship  (grant number  BA 5800/1-1  to J. B. ); by funding from the  University of Lausanne and Swiss SNF  (grant number  PP00P3_170627  to T. S. ).
Data Availability Raw reads are deposited in SRA under accession codes SRR5748941-SRR5749000. The transcriptome assemblies are deposited at DDBJ/EMBL/GenBank under the BioProject PRJNA380865 with the following accession codes: GFPP00000000, GFPR00000000, GFPS00000000, GFPT00000000, GFPU00000000, GFPV00000000, GFPW00000000, GFPX00000000, GFPY00000000, and GFPZ00000000. Data will be publically released upon acceptance.
Previous reports on the stimulus-tuning of noise correlations examined only similarly tuned cells and reported mostly M-shaped modulation, which does not distinguish between the models. Therefore, we conducted our own analyses of a previously published dataset of V1 responses in the awake monkey  (Ecker et al. , 2010)  (Figure  6E ). The modulation of these correlations by the stimulus could only be accounted for by the SSN. First, we found that correlations between similarly tuned cells were significantly modulated by the stimulus (Figure  6E , orange; repeated-measures ANOVA Fð2; 274Þ = 5:29, p = 0:006), and this modulation had a U rather than an M shape. More critically, also in agreement with the predictions of the SSN but not of the attractor model, correlations between orthogonally tuned cells (E) Same as (B) and (D), for data from awake monkey V1  (Ecker et al.
, 2006 (Churchland et al. , , 2010;; Finn et al. , 2007; Poulet and Petersen, 2008; Gentet et al. , 2010; Poulet et al. , 2012; Tan et al. , 2014; Chen et al. , 2014). Moreover, the drop in variability was tuned to specific stimulus features in a model of V1/MT, also capturing recent experimental findings  (Ponce-Alvarez et al. , 2013; Lin et al. , 2015; Lombardo et al. , 2015)  as well as our own analyses of a previously published dataset  (Ecker et al. , 2010). The main insight of our analysis was that in a network of nonlinear neurons with an expansive firing rate nonlinearity, increasing the input increases the effective connection strengths of the network, which in turn modulates the variability of responses. We identified two opposing effects of increasing effective connectivity on variability: the amplification of variability by excitatory-inhibitory interactions (balanced amplification), which dominates at very low (spontaneous) levels of input, and the quenching of variability by increased inhibitory feedback, which dominates for stimulus-driven input.
Simulated spike counts To relate the firing rate model to spiking data in Figures  4  and 6 , we assumed that action potentials were emitted as inhomogeneous (doubly stochastic) Poisson processes with time-varying rate rðV m Þ given by Equation  3. Unlike in the full spiking model (see below), spikes did not ''re-enter'' the dynamics of Equation 2, according to which neurons influence each other through their firing rates. Spikes were counted in 100 ms time bins and spike count statistics such as Fano factors and pairwise correlations were computed as standard.
ACKNOWLEDGMENTS This work was supported by  NIH  grant  R01-EY11001  (K. D. M. );  NSF  award  DBI-1707398  (K. D. M. ); the  Gatsby Charitable Foundation  (K. D. M. );  Medical Scientist Training Program  grant  5 T32 GM007367-36  (D. B. R. ); the  Swartz Program in Computational Neuroscience at Columbia University  (Y. A. ); the  Postdoc Program of E ´cole des Neurosciences, Paris, France  (Y. A. );  Swiss National Science Foundation  Advanced Postdoctoral Fellowship P300P3. 154636(G. H. );  Wellcome Trust   Investigator Award   095621/Z/11/Z  (M. L. , G. H. ); and  Wellcome Trust   Seed Award   202111/Z/16/Z  (G. H. ). We are grateful to  A. Ecker ,  P. Berens ,  M. Bethge , and  A. Tolias  for making their data publicly available. We thank  L. Abbott ,  A. Tan ,  S. Solomon ,  A. Renart , and  P.
Deposited Data Awake monkey V1 dataset Ecker et al. , 2010 http://bethgelab. org/datasets/v1gratings Software and Algorithms OCaml (for all simulations) Open source http://www. ocaml. org Sqlite3 (for V1 data analysis) Sqlite Consortium https://sqlite. org/index. html Mathematica Wolfram https://www. wolfram. com/mathematica Gnuplot Open source http://www. gnuplot. info.
QUANTIFICATION AND STATISTICAL ANALYSIS Dataset We analyzed neural recordings from the V1 of two awake monkeys  (Figures 4, 6, and 7). A full description of the experimental protocol and recordings can be found in the original publication  (Ecker et al. , 2010). We discarded all cells that were poorly isolated (contamination >5%), leaving us with 330 cells to analyze. The stimuli consisted of moving gratings of various orientations, all at 100% contrast. We fitted orientation tuning curves (Figure  S11 ; average firing rate in the first 500 ms following stimulus onset, as a function of stimulus orientation) of the form fðqÞhf 0 + f m exp½kðcosð2ðq À q pref ÞÞ À 1Þ, where q is the stimulus orientation (thus, we neglected the direction of motion, which could be in either of the two directions orthogonal to the orientation of the grating).
Factor analysis We performed factor analysis of spike counts, either for a single stimulus condition in the model (the model had a natural rotational symmetry), or separately for each stimulus condition (direction) in the V1 dataset, subsequently averaging the reported quantities across conditions. We worked with normalized spike counts, defined as cik = c ik = ffiffiffiffiffiffiffiffiffi ffi hc ik i p k where c ik is the spike count of neuron i in trial k and h,i k denotes averaging across trials. Note that the variances of these normalized spike counts are exactly the Fano factors, i. e. , the usual measure of spike count variability. This prevented the normalized spike count covariance matrix C from being contaminated by a rank-1 pattern of network covariance merely reflecting the tuning of single-neuron firing rates (the ''Poisson'' part of variability, which tends to scale with the mean count).
A couple of genetic selection techniques in bacteria have sought to overcome this drawback, but they are limited in scope  (Tanner et al, 2009; Woolstenhulme et al, 2013). In addition, the bacterial culture volumes and sequencing depth necessary to perform ribosome profiling are incompatible with the systematic screening of potential antibiotic leads or small molecules likely to act as coinducers of arrest. To overcome these limitations, we developed a new, highly scalable in vitro method to investigate ribosomal stalling by nascent peptides encoded within transcript libraries of any given complexity. Inverse toeprinting is a versatile selection strategy that relies on a highly processive 39 to 59 RNA exonuclease to degrade the mRNA downstream of the leading ribosome on a transcript. This makes it possible to determine the position of stalled ribosomes on the mRNA with codon resolution, while protecting the entire upstream peptide-encoding region.
An additional advantage of our in vitro method is that it enables us to dissect molecular processes in isolation by giving us direct control over the reaction conditions. Being able to remove certain molecular processes from their cellular context is a double-edged sword however, and care must be taken to validate results obtained by inverse toeprinting with in vivo assays. For the systems studied here, the correlation between in vitro and in vivo data is generally good, but the small discrepancies observed could be due to the NNS 15 library's intrinsic focus on the early cycles of translation, where the nascent peptide is not yet fully threaded inside the ribosomal exit tunnel. These and other as yet unforeseen considerations will need to be taken into account as the method is developed further.
Read assembly and trimming Read pairs were assembled using PEAR v0. 9. 10 (Zhang et al, 2014) on a Mac Book Pro with a 2. 7 GHz Intel Core i7 processor and 16 GB 1,600 MHz DDR3 memory, with the maximal proportion of uncalled bases in a read set to 0 (-u option) and the upper bound for the resulting quality score set to 126 (-c option). Regions immediately upstream of the start codon and downstream of the point of cleavage by RNase R were removed using a modified version of the adaptor_trim. py script written by Brad Chapman (https://github. com/chapmanb/bcbb/blob/master/align/adaptor_trim. py). The 59 flanking region was defined as GTATAAGGAGGAAAAAAT, whereas the 39 flanking region was GCGATCTCGGTGTGATG for the NNS 15 and ErmBL libraries, and GGTATCTCGGTGTGACTG for all other samples. A maximum of two mismatches within each of these flanking regions was tolerated, whereas all other reads were discarded.
Calculation of 3-aa motif frequencies All possible 3-aa motifs centered on the P-site of the stalled ribosomes were identified and counted within the translated sequences, using the process-kmers. py script with a word size of three. In each case, the frequency of occurrence (F 3aa ) of the 3-aa motif was as follows: F 3aa = n 3aa n total ; where n 3aa is the number of occurrences of this 3-aa motif at the C-terminus of the translated sequences and n total is the total number of processed reads in the sample.
Calculation of 3-codon motif frequencies All possible 3-codon motifs centered on the P-site codon of stalled ribosomes were identified and counted within the codon-based sequences, using the process-codon-kmers. py script with a word size of 3 codons. In each case, the frequency of occurrence (F 3codon ) of the 3-codon motif was as follows: F 3codon = n 3codon n total ; where n 3codon is the number of occurrences of this 3-codon motif at the C-terminus of the translated sequences, and n total is the total number of processed reads in the sample.
Calculation of fold changes and propagation of inter-replicate errors The fold change in 3-aa or 3-codon motif frequency between two samples was calculated using the read-analyzer. py script and was defined as F Fg /F Bg , where F Fg is the frequency of occurrence of a sequence, 3-aa or 3-aa motif in the "foreground" sample, and F Bg is its frequency in the "background" sample. For the comparison between Ery-treated and untreated samples, the mean frequency of occurrence of each 3-aa motif in the presence or absence of the drug was as follows: F Ery = À F Ery1 + F Ery2 Á 2; and F NoAb = ðF NoAb1 + F NoAb2 Þ=2; respectively. Similarly, the errors between replicates were as follows ΔF Ery = F Ery1 -F Ery2 ; and ΔF NoAb = jF NoAb1 -F NoAb2 j: The fold change in 3-aa motif frequency on addition of Ery was given as F Ery =F NoAb , and the combined error of the fold change in 3-aa frequency was as follows: Δ À F Ery F NoAb Á = ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ΔF NoAb F NoAb 2 + ΔF Ery F Ery 2 s × F Ery F NoAb ; The histogram in Fig 3A was built using the ΔðF Ery =F NoAb Þ values for all well-measured 3-aa motifs.
Acknowledgments We thank  A Malhotra  for providing RNase R,  N Vazquez-Laslop , and  AS Mankin  for providing the pERMZα reporter plasmid, E. coli TB1 cells and oleandomycin, and  AC Seefeldt  for purifying fully modified EF-P. We thank  A Buskirk  for providing the pause scores for PP-containing motifs calculated from ribosome profiling data. CA Innis,  B Seip , and  G Sacheau  received.
Former studies performed on Placozoa  [5, 13, [20] [21] [22]  and sponges  [23] [24] [25]  initiated the study of candidate epithelial genes in the different lineages. The conservation of critical functions was not assessed, however, due to the lack of detailed analyses of key protein interaction domains and residues. On the other hand, studies on the epithelial organization of Ctenophora were neglected in favor of studies focused on the mesoderm and nervous system  [26] [27] [28] [29] [30] [31] [32]  due to the previously unquestioned position of this phylum among eumetazoans  [33] [34] [35]. In the present study, we first sequenced the genomes of two additional sponge species, O. lobularis (belonging to the Homoscleromorpha class) and O. minuta (the first Hexactinellida), and used RNA-seq data to help with the annotation procedure.
This new data was then combined with information available from public databases to carefully identify and analyze homologues of genes coding for proteins known to compose polarity complexes and adherens junctions for all classes of Porifera (Calcarea, Demospongiae, Hexactinellida, Homoscleromorpha), several genera of Ctenophora with contrasted features  [14]  and Placozoa. Classical cadherins (E-type) and catenins  [5, 36] , Par, Crumbs (Crb) apical polarity complexes and Scribble (SCRIB) lateral polarity complex were identified and analyzed  [8, [37] [38] [39] [40]. We hypothesize that sponge species exhibit highly contrasted tissue features related to molecular divergence of some of their polarity complex proteins. Finally, we revealed an unexpected lack of conservation of the epithelial toolkit in Ctenophores asking for a profound revision of our understanding of Ctenophore biology.
Results New genomic and transcriptomic data from Oopsacas minuta (Hexactinellida) and Oscarella lobularis (Homoscleromorpha) We used two platforms (Pacific Bioscience and Illumina) and a combination of paired end and mate pair sequencing approaches (see Materials and Methods) to generate and assemble the data. Concerning Oopsacas minuta, the assembly yielded a total of 61. 46 Mb of unique haploid genome sequence distributed in 365 contigs longer than 1 kb (N 50 length = 676,369 bp, L 50 number = 31, mean coverage = 381). Following the mapping of 207,529,788 RNAseq reads from a polyA+ cDNA library (mean Open Reading Frame (ORF) coverage = 1443), we predicted the presence of 17,043 protein-coding genes. The small final number of contigs and the above coverage values suggest that our delineation of the (proteincoding) gene content is very close to 100% completion.