dataesr/scientific-paragraphs-categorization huggingface dataset:
HOWP1 and AO2 are macrogametocyte proteins that are conserved in T. gondii The fundamental biological processes of sexual stage development and oocyst formation are broadly conserved in the Coccidia. It would be expected, therefore, that many of the E. tenella gametocyte proteins identified in the present study would be biologically conserved in T. gondii. Indeed, sequence homology searching of the T. gondii genome database (www. toxodb. org) revealed that TGME49_316890 (referred to herein as TgHOWP1) may be the structural orthologue of EtHOWP1, although the two proteins share only 24. 7% sequence identity (and a predicted signal peptide) (Additional file 8). Importantly, TgHOWP1 was identified previously in a proteomic analysis of purified T. gondii oocyst walls  [55]. In the present study, polyclonal antisera raised against a TgHOWP1-GST fusion protein specifically stained T.
In the present study, a fold change of equal to or greater than eight (log2 fold change ≥ 3) was used to identify differentially expressed genes, excluding false positives represented by a p-adjusted value cut-off of 0. 05 or over. Pair-wise hierarchical clustering between the vectors of read counts of all pairs of samples was carried out using the heatmap. 2 function (available in the gplots package from CRAN, cran. r-project. org) according to default settings. An additional measurement of transcript abundance, FPKM (fragments per kilobase of exon model per million mapped reads), was calculated for each gene using the formula "FPKM = 10 6 × C/(N × L)" where C is the number of paired-reads mapped to the exon model of a gene, N is the total number of mapped reads in each library and L is the length of the gene in kilobases.
Western blot Protein samples were prepared from E. tenella and uninfected host caecal samples and analysed by Western blot under reducing and alkylating conditions as described previously  [17]. Again, the initial lysis of oocyst samples was achieved by vortexing with glass beads (as described above). Protein concentration was determined using a NanoDrop ND-1000 spectrophotometer. Membranes were probed with either mouse or rabbit sera (at dilutions described in the results) and subsequently with goat anti-mouse or anti-rabbit IgG (heavy and light chain), horseradish peroxidase conjugates (Life technologies) diluted at 1 in 5,000. Probed membranes were developed using CPS-1 Chemiluminescent Peroxidase Substrate-1 (Sigma) according to manufacturer's instructions. The membranes were visualised using the VersDocTM Imaging System (BioRad) and images analysed using the Quantity One® 1-D Analysis Software (BioRad).
Gene annotation and sequences analysis Where possible, existing gene annotations available on www. toxodb. org were used for E. tenella genes described in this study. For hypothetical genes, additional annotation was achieved through Blast2Go analysis (www. blast2go. com) of the predicted protein sequences, using a cut-off of E-Value < 0. 0005. The identification of potential homologues of E. tenella proteins was also carried out using BlastP on the non-redundant NCBI database or on www. toxodb. org. Multiple alignments of protein and DNA sequences were performed using ClustalW (http://www. ebi. ac. uk/Tools/msa/clustalw2/). The prediction of signal peptides in protein sequences was carried out using the SignalP 4. 1 Server (www. cbs. dtu. dk/services/SignalP/).
Acknowledgements We are grateful to the staff at the  Functional Genomics Centre Zurich  for carrying out RNA Seq experiments and for providing ongoing technical support. We are grateful to the staff of the  Ernst Facility at the University of Technology, Sydney , for their care of the chickens used in this study. NCS gratefully acknowledges the  Australian Research Council for Discovery Project funding  ( DP0664013 ) and  NCS  and FB are grateful for an  Australian Government Department of Innovation, Industry, Science and Research International Science Linkage  grant ( FR110089 ). NCS is supported by a grant from the  Bellberry Foundation. RAW was supported by a  Swiss Government Excellence Scholarship: Postdoctoral Scholarship  from the  Swiss Confederation. PAS was supported by an  Australian Postgraduate Award Scholarship  and an  Australian Society for Parasitology Researcher Exchange, Training  and  Travel Award.
2009). A set of candidate models was defined using all possible combinations of factors and 2-way interactions (Table  1 ). Generalised linear models (GLMs) were fitted for each response variable on all candidate models. Normality in the model residuals was verified using Q-Q plots and histograms of the residuals. Model selection for fixed terms was performed by selecting the candidate model with the lowest corrected Akaike Information Criterion (AIC c , a version of AIC corrected for small sample size; Symonds & Moussalli 2011) with the R package 'AICcmodavg' (Mazerolle 2013). AIC c results are presented for the top 3 ranked models. Unless otherwise specified, parameter estimates and statistical sig nificance of model terms are presented for the top-ranked model only.
Acknowledgements. This study was supported by  IRD  and the  INSU EC2CO PNEC and CYTRIX IBANOE program. We thank the crew of the  RV 'ALIS'  for their help during the two 2012 cruises. We are grateful to  ReefTEMPS (Réseau de capteurs de température dans le domaine côtier) , the  GOPS  and  D. Varillon  from  US191 IMAGO  and  IRD Nouméa  for sea surface temperature at the Amédée reef. Finally, we thank the  LAMA laboratory  and  P. Gerard  for ammonium and nitrates analyses,  P. Dumas  for helpful discussions on trochus, and  P. Douillet  for providing the map through the software MapCamelia.
One approach is to pull viral signals out of microbial genomic datasets, with single-cell amplified genomes (SAGs) likely offering the best hope of obtaining complete viral genomes for uncultivated hosts (e. g. ,  Roux et al. , 2014a). Another is to use assembled genomic contigs from viral metagenomes (viromes) to explore the genomic context for novel viral groups (e. g. ,  Emerson et al. , 2012; Minot et al. , 2012a,b; Dutilh et al. , 2014). Among viruses infecting bacteria (bacteriophages or phages), the T4-like superfamily (Tevenvirinae) is one of the most widespread, abundant, and extensively studied group. The Tevenvirinae are members of the Myoviridae order, tailed bacteriophages with a double-stranded DNA genome, and were first isolated and characterized on Escherichia coli  (Miller et al.
, 2014). T4-like phage sequences were also mined from the Global Ocean Sampling (GOS) expedition microbial metagenomic dataset (i. e. , the viral signal here originate from actively infected cells captured on filters) to design new degenerate PCR primers which revealed a new T4 phage group-the "Far-T4" phages  (Comeau and Krisch, 2008). This clade includes a very peculiar phage: RM378, isolated on the thermophilic bacterium Rhodothermus marinus  (Hjorleifsdottir et al. , 2014). Morphologically, phage RM378 is similar to a T4-like phage (A2 morphology) and encodes a T4-like capsid protein gene, but its genome contains only half of the 38 (core) genes conserved in 26 T4-like phage genomes available for comparative study  (Sullivan et al. , 2010). Moreover, the RM378 genome lacks a readily identifiable structural or replication module that is discernible among all other Tevenvirinae.
We next evaluated the diversity and novelty of genes within all Far-T4 contigs. On average, clade 1 genome fragments are the most closely related to database representatives, ∼80% of genes have a hit against the NCBI NR database, whereas this frequency is only ∼70, 60, and 47% for clades 5, 4, and 3 respectively (Figure  S2 ). Of the novel genes (not hitting anything in databases) genes, 60 to 100% remain conserved within a clade for clades 1, 4, and 5. In contrast, most (80%) of the novel genes in clade three remain unique to each contig and appear not to be conserved within this clade, even though the contigs cover the same genome region including the MCP. This difference in genome content associated with the long distance separating these two sequences on the MCP tree (Figure  1 ) and the lower bootstrap support for this clade compared to other Far-T4 clades (84% vs.
Currently, the most straight-forward in silico approach is to look for sequences similar to the newly described virus in microbial genomic datasets, either as prophage (viral genome integrated in the host's genome), as separate contig in a single-amplified genome from an infected cell, or as a CRISPR spacer (i. e. , 15-50 bp viral sequence(s) stored in a microbial genome from past infections). Here, no sequence closely related to Far-T4 phages could be detected in public databases of microbial genomes: the most similar genome sequences display only 50-60% amino acid identity (hits from genomic fragments in Proteobacteria SAGs, NCBI gis 655454702 and 655453257), and best hits to CRISPR spacers still displayed 2 mismatches when only 100% matches can be trusted on such short nucleotide sequences (hits to a Clostridia: Peptoclostridium difficile, gi 484228666, and a Bacilli: Streptococcus pneumoniae, gi 452723578).
Thus, no putative host could be predicted for Far-T4 phages based on a search of microbial genomic datasets. Another approach available is to use genome composition similarities between viral genome(s) and reference microbial genomes (especially tetranucleotide frequencies,  Pride et al. , 2006). In the Far-T4 case, 3 sequences from the Far-T4 clade 1 display tetranucleotide frequencies close to the small Alphaproteobacteria Ehrlichia chaffeensis (Table  1 ), an obligate intracellular parasite mostly found in animals and ticks. Based on a previous evaluation on more than 15,000 known virus-host pairs, such similarity in tetranucleotide frequencies correspond to host family predictions 88 to 98% accurate, and in that case would link Far-T4 phages with small Alphaproteobacteria from the Anaplasmataceae family.
, 2012). Both virome read sets are available on the Short Read Archive (accession number: ERP000339). For this study, two samples were taken at 4 and 8 m on Lake Pavin in July 2013 and sequenced (after preparation) with Illumina HiSeq (GATC Biotech, Germany). For 454 viromes, reads were first clustered using Uclust  (Edgar, 2010)  at a 100% identity level, in order to remove duplicate sequences, and sequence assembly was conducted with Newbler using threshold of 90% identity on at least 35 nucleotides. Illlumina sequences were trimmed by quality score (cutoff at 30 using FASTX v0. 0. 13) and then assembled using IDBA-UD  (Peng et al. , 2012).
Sequence Analysis Using proteins predicted from the Far-T4 contigs identified, phylogenies were inferred for different T4 phages conserved genes, namely the ones coding for the major capsid protein Gp23 (Figure  1 ), the portal protein Gp20, the large subunit of the terminase Gp17 and PhoH (Figure  S1 ). For all these trees, reference sequences were obtained from the NCBI Refseq database of complete phage genomes, except for the g23 PCR amplicons that were obtained from the NCBI Genbank database. All phylogenies were based on (predicted) protein sequences. Mutiple alignments were computed with Muscle  (Edgar, 2004 ) and manually curated. The Gp23 multiple alignment was trimmed around the PCR amplicon boundaries to avoid artificially increased distances between sequences. FastTree2  (Price et al.
Detection of Far-T4 Phages in Other Viral Metagenomes Sequences similar to the large Far-T4 contigs assembled from Lake Pavin Illumina viromes, were searched in a large collection of viromes using tblastx (bit score >50, e-value < 0. 001). Sequences similar to Far-T4 contigs were detected in seawater viromes from the Pacific Ocean Viromes (POV,  Hurwitz and Sullivan, 2013) , the Indian Ocean  (Williamson et al. , 2012) , and the Atlantic Ocean (Chesapeake Bay, part of the GOS dataset  Yooseph et al. , 2007). Far-T4 sequences were also detected in several freshwater viromes from lakes in Europe  (Roux et al. , 2012) , in Asia  (Ge et al. , 2013; Tseng et al. , 2013) , Antarctica  (Lópezbueno et al. , 2009) , and in freshwater ponds in the USA  (Rosario et al. , 2009b). Conversely, no sequences similar to the Far-T4 were detected in other types of samples including human gut  (Kim et al.
, 2011; Minot et al. , 2012a) , airborne samples  (Whon et al. , 2012)  or plant samples  (Coetzee et al. , 2010). Recruitment plots were generated with ggplot2 module in R, considering only BLAST hits with an amino-acid identity higher than 60% (in addition to bit score > 50 and e-value < 0. 001). Coverage was calculated as the log10 of the number of reads mapped to the contig on sliding windows corresponding to a 30th of the contig length (i. e. , for a contig of 30 kb, sliding windows of 1 kb would be used).
Host Prediction Prophages or phages infecting single-cells (SAGs) closely related to Far-T4 were searched in microbial draft genomes by comparing predicted proteins from Far-T4 to the bacterial and archaeal genomes in Refseq and WGS NCBI database (blastp, bit score >50 and e-value < 0. 001). In addition, CRISPR spacers were predicted on the bacterial and archaeal genomes available at Refseq and WGS (as of January 2014) with CRT  (Bland et al. , 2007)  and then compared to the Far-T4 contigs with blastn. As CRISPR spacers are short sequences, more stringent thresholds were applied: only hits that covered more than 80% of the CRISPR spacer with more than 90% of nucleotide identity were considered significant. Three matches were identified: contigs Pavin_2013_4m_335 and Pavin_2013_4m_328 were similar to a CRISPR spacer from a Streptococcus pneumoniae genome (gi 452723578) at 92% of identity, and contig Pavin_2013_4m_8 matches a CRISPR spacer from another Firmicutes, Peptoclostridium difficile (gi 484228666), at 90% of identity.
Host prediction based on genomic signature was also computed using tetranucleotide frequency as in  (Roux et al. , in revision). First, tetranucleotide frequency vectors were calculated for each Far-T4 contig with Jellyfish  (Marçais and Kingsford, 2011). The euclidean distance between these vectors and the tetranucleotide frequency vectors from bacterial and archaeal genomes in Refseq and WGS (as of January 2014) were then calculated. A previous analysis of more than 12,000 virus-host pairs indicated that in the absence of the exact host species in the database (which is the most likely case for freshwater viruses), host family could be predicted with 95% of success if the distance between virus and host tetranucleotide frequency vectors was below 4. 10 -04 , and with 84% of success if it was between 4.
Acknowledgments This work was performed under the auspices of the  EC2CO program  through the  CAVIAR  project led by  FE , partially supported by a  Gordon and Betty Moore Foundation  grant (# 3790 ) to MS, SR was partially supported by the  University of Arizona Ecosystem Genomics Institute  through a grant from the  Technology and Research Initiative Fund  through the  Water, Environmental and Energy Solutions Initiative. An allocation of computer time from the  UA Research Computing High Performance Computing (HPC)  and  High Throughput Computing (HTC)  at the  University of Arizona  is gratefully acknowledged.
Immunoblot Cells were solubilized in lysis buffer: 10 mM Tris-HCl (pH 7. 5), 1% Nonidet P-40, 0. 5% deoxycholate supplemented with a protease inhibitor cocktail (Roche Applied Science) and 2 mM AEBSF. Cell lysate homogenization, SDS-PAGE, and Western blot were performed as previously described  [22]. Bands were quantified by densitometry using Scion Image software. To compare sets of data, we used Dunnett's Multiple Comparison Test. Each result reported in this paper is representative of at least 3 independent experiments.
Iodide efflux The gating of CFTR Cl -channels was assessed by the iodide ( 125 I -) efflux technique as already described  [23]. Curves were constructed by plotting the rate of 125 I -versus time. All comparisons were based on maximal values for the time-dependent rates (k = peak rates, min -1 ), excluding the points used to establish the baseline (k peak -k basal , min -1 ). Result significances were calculated by two-tailed t test or ANOVA test, using GraphPad Prism 6 software. Forskolin and genistein were purchased from LC Laboratories, USA and diluted in DMSO.
Labelled CFTR protein amounts were assessed in the resulting samples by densitometric quantification of Western blot images (representative examples in the bottom left panel). In control RNAi condition, labelled CFTR amounts extracted from the same amount of whole cell lysates appeared higher after 24h CHX treatment: stability differences between the various cellular proteins may account for this paradox. We estimated PM-targeting efficiency by calculating (+) to (-) ratios, the Ctrl RNAi value being used as 100% of apparent PM-targeting efficiency. In the bottom right panel, histogram expresses the relative CFTR cell surface targeting efficiency. Data represent means ± SEM of 3 independent experiments, each performed in duplicate.
Although there have been major migrations of farmers from the Mossi plateau since the 1970s, the population densities of rural areas are lower in western Burkina Faso than in the rest of the country; densities range from 20 to 40 people per square kilometre versus 60 to 80 people per square kilometre on the Mossi plateau, which is considered to be overexploited. Since Independence, regional development has been based on agriculture, particularly on cotton  (Schwartz 1997; Bassett 2001). But cattle breeding by both farmers and pastoralists has become a key issue in the region  (Petit 2000; Botoni Liehoun et al. 2006; Augusseau 2007). In 2012 and 2013, we conducted field research in the western region of Burkina Faso to collect data on the territorial impact of land tenure policies, livestock husbandry policies and rural development policies on livestock rearing.
First, we gathered data on the definition and implementation of the policies. We reviewed Burkinabé laws, policy reports and national and colonial archives. We interviewed top civil servants at the Ministry of Livestock (Ministère des Ressources Animales, MRA) along with 40 experts (NGO staff, livestock administration agents, private consultants, local political representatives). Second, we collected specific data on the consequences of land tenure policies, livestock husbandry policies and rural development policies on herders' action spaces and on cattle mobility. To review changes in the extent of pasturelands (Figure  2 ), we processed Landsat images acquired in 2010 c using a supervised classification based on samples of cropped areas, orchards, pastures in the plains, pastures in the hills and woodlands.
In Madagascar, similar experiments with Dicranopteris spp. are also underway (J. Aronson et al. , unpublished data). Data collected after 18 months showed high levels for density, basal area and height of balsa trees and a significant reduction in living rhizome biomass for all treatments with bracken weeding. Furthermore, rhizome biomass was negatively correlated with balsa basal area, leaf litter, and light intensity in the understory  [29]. In our study, we achieved total eradication of this invasive fern in all treatments (direct sowing of seeds, broadcasting of seeds, transplanting of seedlings and control) four years after initiating the experiment. This is the first report of successful, short-term complete bracken eradication of bracken by using fast-growing trees in tropical regions.
Acknowledgments We thank  Manuel Castellanos Chan 'kin,  Adolfo Chan 'kin,  Antonio Sánchez González , and  Miguel Angel Villatoro Juárez  for technical field support, and  Ann Greenberg  for style corrections. This research was funded by the  Instituto Nacional de Ecología y Cambio Climático (INECC) ,  Etnobiología para la Conservación A. C. and El Colegio de la Frontera Sur (ECOSUR). We did not receive funds to publish in open access.
These models effectively place a prior on how phylogenetic trees grow. Probability distributions over models of diversification were employed in some of the earliest attempts to use likelihood techniques to reconstruct genealogies  [23]. The two most commonly used BPPs are the Yule (also called 'pure-birth') process, which models tree formation with a constant rate of speciation and no extinction, and the birth-death process, which includes speciation as well as a constant rate of lineage extinction. Birth-death priors have been used in Bayesian phylogenetics  [24] ; however, most published analyses use the Yule prior, perhaps because it was initially the only prior for the diversification process implemented in the widely used Bayesian software package BEAST  [16]. Although the birth-death prior has recently been integrated into this software  [25, 26] , many phylogenetic analyses still use the Yule prior (probably because the BEAST manual recommend the use of the Yule prior, see p.
Even if there is a possibility that fossils instead should be assigned to stem, they cast doubts on the dating. At the level of the whole group, different studies using similar fossil calibrations (but different taxon and molecular sampling) have produced different age estimates, notably for the origin of the cycad crown: ≈200 Ma for Nagalingum et al. [6] , and ≈ 230 Ma for Salas-Leiva et al. [43]. Therefore further dating analyses are needed to estimate the cycad age and validate the recent generic radiations. In reconstructing the phylogeny of cycads, Nagalingum et al. [6]  sampled about two-thirds of all known species and used a molecular dataset composed of two chloroplast genes and one nuclear gene (although most of the taxa had the nuclear gene only). In addition to Penalized Likelihood and a strict molecular clock, they inferred the age and divergence times using the BRC approach implemented in BEAST, calibrated with four fossil constraints and a birth-death process as the BPP.
Taxon sampling and molecular dataset We extended the molecular dataset of Nagalingum et al. [6]  that initially contained 199 cycad species. We added molecular data for 38 additional species and the same genes retrieved from Genbank  [52] [53] [54] [55] [56] [57]. Our dataset comprises three genes covering two plastid genes: the maturase K (matK, 2,387 nucleotides, 82 taxa) and the ribulose 1,5-bisphosphate carboxylase large subunit (rbcL, 1,398 nucleotides, 80 taxa), and one nuclear gene: region 1 of phytochrome (PHYP, 1,802 nucleotides for 201 taxa). This resulted in a total of 237 species out of 331 described species (71%), representing all extant genera with the following number of species per genus: 2 of the 2 Bowenia (B. serrulata and B. spectabilis), 65 of the 107 Cycas, 24 out of 27 Ceratozamia, 8 of the 14 Dioon, all of the 65 Encephalartos, 2 of the 2 Lepidozamia (L.
hopei and L. peroffskyana), 26 of the 41 Macrozamia, the monotypic Microcyas calocoma, the single Stangeria eriopus, and 43 out of 71 Zamia (including the junior synonym genus Chigua) (Figure  1 ). Following Nagalingum et al. [6] , the dataset also included six species as outgroups: Ginkgo biloba (Ginkgoales), which is recognised as the sister lineage of cycads  [58] ; plus five conifer species (Abies firma, Araucaria heterophylla, Cryptomeria japonica, Pinus strobus and Pseudotsuga menziezii). The phylogenetic analyses were thus performed on a dataset containing a total of 243 species (of which 237 are cycads) and 5587 nucleotides. The information on each sampled species is presented on Additional file 1: Table  S1.