dataesr/scientific-paragraphs-categorization huggingface dataset:
A suite of multivariate methodologies exist to link biotic and abiotic multidimensional plots and can be used to understand which driver(s), plays a role in shaping the structure of fish assemblages  (Diniz-Filho and Bini 1996). Unsupervised ordination methods: There are statistical techniques that should be used when there is no great ecological knowledge of the targeted species and its ecosystem, and therefore when a preliminary and explorative screening is being conducted. Canonical Correlation Analysis (CcorA) is an appropriate tool to analyse datasets (including multivariate timeseries) that show no clear dependency between biological (i. e. species visual counts) and environmental parameters. CcorA is a way of measuring the linear relationship between two multidimensional variables. The method identifies two bases, one for each variable, that are optimal with respect to correlations, whilst simultaneously identifying the corresponding correlations.
2006). RELATE calculates rank correlations between two dissimilarity matrices, producing a matching coefficient, which is then used in a permutation test. BIO-ENV identifies subsets of variables from one dataset (usually environmental data) that show the best match with patterns from a second dataset (usually community compositions). It is reasonable to assume that certain taxa of a community may reflect the underlying environmental forcing more clearly than others. Some of these species could be efficiently monitored over the long term by cabled observatories. Example 1: Faunal list and video-biodiversity estimates A primary target of marine observational technology is the implementation of systems capable of delivering exhaustive species lists for ecosystems exploration  (Bouchet 2006 ).
Animals may disappear from the imaging material when displaying cryptic behaviour by sheltering under rocks at inactive phases (e. g. nocturnal drops in counts for D. sargus may be due to that sheltering behaviour). Conversely, other species as S. cabrilla may move in and away from the OBSEA zone according to a differential habitat use at day and night. Also for Scorpaena sp. , visual counts time series should be evaluated carefully in relation to activity or resting phases. Different animals inhabit the OBSEA platform surface just in front of the camera and counts may increase at night when individuals are engaged in feeding activity  (Harmelin-Vivien et al. 1989 ). Waveform analysis integrating fish counts along with light data is hence of relevance, as it allows for the identification of overall thresholds in environmental illumination at which one should expect to successfully sample (or not) certain species in coastal zones.
D. dentex was only present at daylight hours and usually in association with peaks in counts for the fish assemblage. Also, Dentex presence at the OBSEA seems to be more frequent during periods of stable meteorological conditions. These data are complementary to those presented in Figure  7 , and seem to be promising to elucidate the role of diurnal predators in the day-night structuring of coastal fish communities. Dentex presence always coincides with peaks in assemblage counts at daytime, which is the product of an increase in the presence of several other species as potential prey. One of the triggers of coastal predator activity may be light intensity, which implies a higher efficiency in localizing preys by sight  (Becker et al. 2013). Therefore, the temporal forecasting data associated with fish presence into coastal areas based on certain average levels of light intensity (via their activity rhythms), is of relevance for their behavioural ecology in relation to trophic relationships.
Water temperature and salinity plus wind speed and rain, were therefore measured at each image acquisition, as potential proxies of water and air column conditioning on fish behaviour according to species specific tolerances and sensitivity  (Albouy et al. 2012; Jørgensen et al. 2012 ). These factors contribute in shaping the seasonal patterns of fish abundance and assemblage composition. We could not carry out waveform analysis on data sets since we had only a one year time series. Therefore, the presence of seasonal rhythms was evaluated by averaging number of detected fishes per each month. In doing so, we considered all pictures for each imaging position at once. The number of available images was variable month by month and the photo-sampling constancy was disrupted by water turbidity over such an extended monitoring period.
Visual count data were then plotted with MESOR (i. e. as yearly threshold mean), in order to identify at which months significant increases took place (see Univariate methods Section). Mean counts presented seasonal fluctuations for all species considered (Fig. 9 ). Most of these species showed a single and compact seasonal increase lasting several months (e. g. , D. annularis and D. cervinus), while others showed a more punctual increase during one month (e. g. , S. mediterraneus, O. melanura). Other species showed a scattered seasonal pattern with sparse peaks over the year (e. g. , S. cabrilla and S. cantharus). One should notice that seasonal changes in the behaviour of coastal fish were evaluated by computing of averages at each month and not by simply summing up all counted fishes. Anyway, temporal gaps in photo-acquisition did not impair the detection of a seasonal variation in fish counts, as a proof of the efficiency of the applied monitoring method (Fig.
Conclusions Fish species assemblages in coastal areas vary at diel and seasonal scales as result of activity rhythms of constituting species. Sampling has to be repeated at one location over temporal scales that match with the rhythmic behavioural patterns of targeted species. In this framework, cabled observatory video-technology such the one enforced at the OBSEA, may be used efficiently to monitor fish behaviour, providing important means to link the temporal dynamism in coastal ecosystems to the changing environment. Cabled observatories provide long-term datasets and new opportunities for tracking and understanding temporal changes in biodiversity. These data can be used for multiple purposes, and to answer challenging questions in ecological research, such as the need to estimate both natural and anthropogenic variability at the different biological and ecological levels.
Acknowledgements This research was funded by  RITFIM  ( CTM2010-16274 ) and  European Multidisciplinary Seafloor Observation (EMSO Preparatory Phase -FP7  Infrastructures-2007-1, Proposal  211816 ). Researchers from  CSIC-UPC  are members of the  Associated Unit Tecnoterra. English revision was carried out by  V. Radovanovic.
However, if the sheep has repeatedly large litter sizes, the genotype is clearly BB, and GCMTBLUP can "see" this information to obtain a more accurate estimate of the genotype. Estimation of genotypes is useful not only for genetic evaluation but also for mating purposes. Meuwissen and Goddard  [6]  suggested an approximate hybrid method in which (approximate) iterative peeling was weighted by the likelihood given the quantitative trait, but their method is complex to implement and, to the best of our knowledge, has not been tested for genetic evaluation. An additional advantage of GCMTBLUP over iterative peeling is that GCMTBLUP can handle very large datasets (provided the effect of the gene is known), whereas iterative peeling is not exact and slow. More refined procedures can be devised that would couple peeling with BLUP style genetic evaluation.
However, it was not until recently that major genes were included in regular evaluations  [9, 12]. Also, livestock pedigrees are extraordinarily complex, which precludes the use of methods such as exact peeling. Lack of availability of good software for peeling also complicates research into these methods, whereas efficient public software and, particularly, source codes exist for BLUP. Deviations from linearity are common for the effects of major genes  [3]. If the gene has a dominant action, then the substitution effect is α = a + d(qp). Under selec- tion or drift, this substitution effect changes as allele frequencies change. Thus, in practice, if there are large deviations due to dominance, the GCMTBLUP method proposed here could be used by including only data from a few generations back, so there is no major change in frequencies.
Acknowledgements We thank  Mehdi Sargolzaei  for providing a modified version of QMSim. Discussions with  Jean Michel Elsen  and  Loys Bodin  ( INRA Toulouse ) are greatly acknowledged. We are grateful to the  Genotoul bioinformatics platform Toulouse Midi-Pyrenees  for providing computing resources. Research financed by projects  X-Gen and EpiSel from SelGen INRA metaprogram. Two reviewers are thanked for useful comments.
This will be considered as a predecessor measurement to one with radioactive projectiles, such as 8 Li. The final goal, for both stable and radioactive projectiles, will be the breakup measurement, which can be easily accessible in inverse kinematics since all the ejectiles are confined at forward angles and MAGNEX is a powerful tool, capable of detecting them with good angular and energy resolutions. The elastic-scattering measurement is then an inevitable study for probing the potential at the same conditions. Moreover by setting MAGNEX close to zero, and thanks to its large acceptance, we can span almost a full angular range in the center-of-mass frame, all in one go, facilitating our normalization via Rutherford scattering at the most forward angles. This can validate or not the normalization adopted in the previous data and remove possible uncertainties.
That is, within our experimental approach we focus on a precise, clear from previous data ascribed uncertainties, elastic-scattering measurement, predecessor to measurements on MAGNEX with radioactive projectiles in inverse kinematics. This measurement will be the first step for obtaining the optical potential and subsequently in a future work for describing the various reaction processes. Another goal of this Brief Report is to establish the reliability of the CDCC method at lower energies than in previous studies  [27, 28]  for a safe prediction of cross-section data. In the following sections we will first present the experimental details and the data reduction with comparisons with previous data (Sec. II), then we will proceed with details of our theoretical calculations (Sec. III), and we will finalize with the concluding remarks.
Experimental Details and Data Reduction. The experiment was performed at the MAGNEX facility of the Istituto Nazionale di Fisica Nucleare Laboratori Nazionali del Sud (INFN-LNS) in Catania, Italy. Beams of 6 Li 3+ were accelerated by the Tandem accelerator at the energies of 16, 20, 25, and 29 MeV and impinged on a 240-μg/cm 2 CH 2 target. Measurements were repeated with a 12 C target of similar thickness for estimating the carbon background. The elastically scattered lithium ions were momentum analyzed by the MAGNEX spectrometer  [31, 34, 35] , whose optical axis was set at θ opt = 4 • and were detected by its focal plane detectors  [33]. MAGNEX worked in a full horizontal angular acceptance but with a reduced vertical acceptance.
(Color online) A reconstructed E-θ correlation plot for 6 Li + p at a projectile energy of 29 MeV. The two kinematical solutions of the reaction were obtained in three different runs with three sets of magnetic fields. The plot shows the superposition of these runs, designated with different colors. The black solid line represents the theoretical prediction which seems to describe perfectly well the data, giving further support to the accurate spectrum reconstruction. for protecting the focal plane detectors from the elastic high counting rate. Our data reduction technique, based on a differential algebraic method  [36]  and the performances of the whole system are described in Refs. [32, 37]. The two kinematical solutions of the ejectiles were measured by the application of three different magnetic fields.
Absolute differential cross sections for the elastic scattering of 6 Li + p were obtained at  16, 20, 25, and 29  MeV in inverse kinematics and are found to be in very good consistency with previous data in direct kinematics. Our technique, for measurements at a close to zero angle, obtained with the MAGNEX spectrometer, is well established, and it can be applied in future measurements with radioactive beams. JLM calculations fail to reproduce the data, whereas the validity of CDCC calculations is extended to a lower-energy regime (2. 5-4. 5 MeV/u) than before (5-72 MeV/u). It is proven that even in the present low-energy region CDCC calculations can be adapted for reproducing the data. However the degree of applicability of the CDCC will be tested in the future with the measurement of breakup and other direct and/or compound reaction measurements.
5 lanes of sequencing reads in total. The raw sequencing data reported in this work have been deposited in the NCBI GEO with accession numbers GSE67587, GSE67861 and GSE67862 for D. sechellia (Tuson, #14021-0248. 25), D. sechellia  (k-s10; #14021-0248. 25) and D. simulans (k-s05; #14021-0251. 194) , respectively. The raw reads data of D. melanogaster from  Shiao et al. (2013)  were included for analysis. Reference genomes version r5. 48 (D. melanogaster), r1. 3 (D. simulans), and r1. 3 (D. sechellia) were used. For RNA-seq analysis, the 100-bp paired-end sequencing reads were mapped to the reference genome using TopHat 2. 0. 6  (Trapnell et al. 2009) , with allowance of two mismatches in read mapping to the reference genome. The expression levels of genes were measured in Fragments Per Kilobase of exon per Million fragments mapped (FPKMs) by Cufflinks with bias correction ("-multiread-correct" and "-frag-bias-correct").
Generating Orthologous Gene Set and Identifying DEGs Gene annotation tables of the three species were retrieved from FlyBase (version 2012_06) with individual versions 5. 48, 1. 3 and 1. 3 for D. melanogaster, D. simulans and D. sechellia, respectively. The orthologous gene set (OGS) was manually curated by excluding genes duplicated in any of the species and only single copy genes in all three species were selected. The potential bias from sequencing between different samples was normalized by upper quartile implemented in NOISeq  (Tarazona et al. 2012). NOISeq requires technical replicates to perform the calculation. To identify DEGs, we used sequencing data from three lanes as technical repeats. As Dmel_TW had RNA-seq data from only a single sequencing lane for each sex, we generated technical replicates of Dmel_TW artificially in the software for comparison, using one function of NOISeq, NOISeq-sim.
DEGs Validated by qRT-PCR To verify DEGs, we conducted qRT-PCR using the NanoString technology. A total of 18 differentially expressed chemosensory genes were analyzed, along with 20 other genes to be discussed later in this section. The same total RNA used for RNA-seq was used for qRT-PCR. Targeting probes were designed in the consensus regions of the three species for all the genes selected. The Act42A, Cyp33, mRpL20, RpL32, and SdhA genes were selected as endogenous controls to normalize the expression levels. In addition, lush and Orco were included as positive controls. All the endogenous genes selected for qRT-PCR had similar expression levels (FPKMs) in the three species. For most of these genes, the qRT-PCR data corresponded very well to the RNA-seq data except for Or9a, Or42b, Or67b, and CheA75a (supplementary table  S13 , Supplementary Material online).
The qRT-PCR data for Or9a showed that the expression level was not significantly different between D. sechellia and the two other species because the log2ratios ranged only from À0. 13 to 0. 08. On the other hand, the RNA-seq data suggested a much lower expression level for Or9a in D. sechellia. We checked the genomic location and found that a putative gene, FBgn0166364, overlapped with Or9a in the gene annotation table. We then calculated FPKMs by using a different version of annotation table (gene_ortho-logs_fb_2013_01. tsv. gz) that did not include FBgn166364 in the table (data not shown). The log2-ratios of FPKM values then suggested no significant differences in expression levels between species, consistent with the qRT-PCR results. Thus, some of the incongruences between RNA-seq and qRT-PCR analysis might be due to annotation artifacts (e.
However, CheA75a had a sequence identify of 98% (gi190067942) and 99% (gi190008972) with two clones from a full-length cDNA in the cDNA library of D. simulans adult males in GenBank. Thus, the expression level of CheA75a in D. simulans remains unclear. In addition to chemosensory genes, we also studied the expression levels of 18 genes with conspicuous differential expression patterns between D. sechellia and the other two species (CG16799, CG4797, and Listericin) with potentially interesting functions (supplementary table  S13 , Supplementary Material online), such as sense perception (nompC), olfactory learning (vsg), protein binding (Nup50), hormone catabolic process (Jheh1, Jheh2, Jheh3, and to), and insecticide metabolism (Cyp308a1 and GstE9). For most of these genes, the qRT-PCR data corresponded very well to the RNA-seq data.
The significance of clustering was tested by multiscale bootstrap resampling implemented in the pvclust R package  (Suzuki and Shimodaira 2006). The results showed that the expression profiles of Or and Ir genes in D. sechellia antennae were significantly divergent from the two sibling species (figs. 2 and 3, approximately unbiased [au] P value > 0. 95). The Obp genes also showed an  expression profile divergent from the two sibling species, although D. sechellia was closer to D. melanogaster than to D. simulans (fig. 4 ). We did not observe any significant divergence in Gr genes or the rest of the transcriptome (supplementary figs. S2-S3, Supplementary Material online). The nonsignificant expression divergence in the Gr genes was likely due to the very low expression level of the gene family in the antennae.
Acknowledgments This work was supported by  Postdoctoral Fellows Program ,  Academia Sinica, Taiwan  (to M. -S. S. );  Plan Nacional  ( BFU2011-28575  to C. N. and J. -M. C. );  Spanish Ministry of Economy and Competitiveness, "Centro de Excelencia Severo Ochoa   2013-2017" (SEV-2012-0208  to C. N. and J. -M. C. ); " Fundacio' Obra Social la Caixa " (to J. -M. C. ); and.
Motivation and related work Large-scale experimental approaches in the field of biology shift the focus of researchers towards transversal questions that involve very diverse biological knowledge. The researcher needs new tools to deal with the growing number of relevant publications. The domain of text-mining for biology (BioNLP) develops automatic methods to assist the analysis of knowledge expressed in natural language scientific articles. Periodic shared tasks measure the progress of the community methods by formally comparing the method predictions to a reference annotation on test data  [1, 2]. The goals of the shared tasks evolve with the advances in BioNLP, moving towards a better adaptation to the needs of biologists. It is reflected through the diversity of biology questions (e. g.
This motivates the use of a large set of categories organized in a hierarchical structure. Moreover,  [14]  has shown that the lexical information contained in ontologies can make the task easier. OntoBiotope is an ontology of microorganism habitats  [15]. Its modeling principle and its lexicon reflect the usual biotope classification used by biologists to describe microorganism isolation sites (e. g. GenBank, GOLD, EnvO)  [16] [17] [18]. OntoBiotope is developed and maintained by the Meta-omics of Microbial Ecosystems (MEM) network in which 30 microbiologists from INRA (French National Institute for Agricultural Research) from all fields of applied microbiology participate. The relevance of OntoBiotope terms has been evaluated through the PubMedBiotope semantic search engine  [19]. It identifies and categorizes biotopes in a collection of 600 000 PubMed abstracts by applying the ToMap method (Text to Ontology Mapping)  [20]  to the Onto-Biotope ontology.
e. , proportional to the square of the number of taxa). In all cases, very large input trees (> 10,000 taxa) can easily be processed and transformed into time-scaled trees. We compare these algorithms to standard methods (root-to-tip, r8s version of Langley-Fitch method, and BEAST). Using simulated data, we show that their estimation accuracy is similar to that of the most sophisticated methods, while their computing time is much faster. We apply these algorithms on a large data set comprising 1194 strains of Influenza virus from the pdm09 H1N1 Human pandemic. Again the results show that these algorithms provide a very fast alternative with results similar to those of other computer programs. These algorithms are implemented in the LSD software (least-squares dating), which can be downloaded from http://www.
With rooted trees, the time complexity is nearly linear (i. e. , proportional to the number of taxa), while with unrooted trees, it becomes nearly quadratic (i. e. , proportional to the square of the number of taxa). In both cases, very large trees (>10,000 taxa) can easily be processed and transformed into time-scaled trees. The article is organized as follows: we first define the model and show its ability to handle uncorrelated rate variations among tree branches, as is commonly assumed with virus data. We then present our two main algorithms, distinguishing the unconstrained setting and the case where the temporal precedence constraints (i. e. , an ancestral node must be older than its daughter nodes) are accounted for. Last, we compare these algorithms to standard approaches using simulated data and a large influenza data set.
Our algorithms are implemented in the LSD program (least-squares dating), which can be downloaded (along with all data and results reported VOL. 65 here) from http://www. atgc-montpellier. fr/LSD/ (last accessed October 2015). An Online Appendix, providing additional algorithm descriptions, tables, and figures, can be found in the in the Supplementary Material available on Dryad at http://dx. doi. org/10. 5061/dryad. 968t3.
In the following, we first assume that the tree is rooted, and then summarize the rooting procedure, which is described in more details in the Online Appendix (available as Supplementary Material on Dryad at http://dx. doi. org/10. 5061/dryad. 968t3). Given a set of n serially dated sequences, let R be the input rooted binary phylogenetic tree on these sequences with known branch lengths. Enumerate the internal nodes of R by 1,2,. n-1 and the leaves by n, n+1,. ,2n-1. Node 1 corresponds to the root. The date of node i is denoted by t i. So t n , t n+1 ,. ,t 2n-1 are known. Times are measured from the origin, that is, t i t j when i is more recent than j. For every node i different from the root (i = 1), let a(i) be the parent node of i. For every internal node i, let s 1 (i) and s 2 (i) be the two direct descendants of i.
Outline of the Approach The rate ω is positive, and we can fix in LSD the minimum value of the estimated rate to ω ω min > 0. Moreover, time is measured forward from the root to the tips of the tree, so it must satisfy the temporal precedence constraints t i t a(i) for every node i that is not the tree root (i = 1). In other words, any daughter node (i > 1) is more recent than its parent node (a(i)). This is an obvious requirement, analogous to the positivity of branch lengths in phylogenetic trees. However, not all dating methods comply with this requirement (e. g. , see our example below with BEAST and the influenza data set), just as some phylogenetic algorithms (e. g. , NJ) infer trees with negative branch lengths. The reasons for this are mostly computational. Imposing positivity constraints has a computational cost, as we shall see below in our dating context.