dataesr/scientific-paragraphs-categorization huggingface dataset:
RESULTS Across all datasets, the negative binomial and Poisson lognormal distributions had very similar average log-likelihoods (within 0. 01 of one another; Fig. 2 ). The log-likelihoods for each of these distributions averaged 0. 8 units higher than for the log-series distribution and 5 units higher than for the Zipf distribution (corresponding to likelihoods that were twice as high and 140 times as high, respectively). Although the negative binomial and Poisson lognormal distributions matched the data most closely, the likelihood provides a biased estimate of these distributions' ability to generalize to unobserved species. AICc approximately removes this bias by penalizing models with more degrees of freedom (e. g. , the negative binomial and Poisson lognormal distributions, which have two free parameters instead of one like the log-series and Zipf distributions).
After applying this penalty, the log-series distribution would be expected to make the best predictions for 69. 2% of the sites. The Poisson lognormal and negative binomial distributions were each preferred in about 12% of the sites, and the Zipf distribution was preferred least often (6. 0% of sites; Fig. 3 ). Across all datasets and taxonomic groups, the log-series distribution had the highest AICc weights more often than any other model. The negative binomial performed well for BBS, but was almost never the best fitting model for plants (FIA and Gentry), butterflies (NABA), Acintopterygii, or Coleoptera. The Poisson lognormal performed well for the bird datasets (BBS and CBC) and the Gentry tree data, but was almost never best in the FIA and Coleoptera datasets (Fig. 4 ). The Zipf distribution only performed consistently well for Arachnida.
Because datasets differ in both taxonomic groups and sampling methods care should be taken in interpreting these differences. The full distribution of AICc weights shows separation among models (Fig. 5 ). Although the log-series distribution had the best AICc score much more often than the other models, its lead was never decisive: across all 16,209 sites, it never had more than about 75% of the AICc weight (Fig. 5 ). Most of the remaining weight was assigned to the negative binomial and Poisson lognormal distributions (each of which usually had at least 12-15% of the weight but was occasionally favored very strongly). The Zipf distribution showed a strong mode near zero, and usually had less than 7% of the weight.
, 2014)  and are generally consistent with a third recent analysis  (Alroy, 2015). Ulrich, Ollik & Ugland (2010)  analyzed ∼500 SADs and found support for three major forms of the SAD that changed depending on whether the community had been fully censused or not. They found that ''fully censused'' communities were best fit by the lognormal, and ''incompletely sampled'' communities were best fit by the Zipf and log-series  (Ulrich, Ollik & Ugland, 2010). In contrast we find effectively no support for the Zipf across ecosystems and taxonomic groups, including a number of datasets that are incompletely sampled. Our AICc value results also do not support the conclusion that the lognormal outperforms the log-series in fully censused communities. The Gentry and FIA forest inventories both involve large stationary organisms and were collected with the goal of including all trees above a certain stem diameter.
(2014). The explanation for these differences is being explored elsewhere (SR  Connolly et al. , 2016, unpublished data). Alroy (2015)  compared the fits of the lognormal, log-series, Zipf, geometric series, broken stick, and a new model dubbed the ''double geometric'', to over 1,000 terrestrial community datasets assembled from the literature. To incorporate the geometric series, broken stick, and the double geometric, this research used non-standard methods for evaluating the fits of the models to the data, however the results were generally consistent with those presented here. The central Kullback-Leibler divergence statistics results showed that: (1) the Zipf, geometric series, and broken stick all perform consistently worse than the other distributions; (2) the double geometric, log-series, and lognormal all provide the best overall fit for at least one taxonomic group; and (3) the lognormal and double geometric fit the data equivalently well and slightly better than the log-series when not controlling for differences in the number of parameters (Alroy's Table  S1, S2,  and S3 ).
ACKNOWLEDGEMENTS We thank all of the individuals involved in the collection and provision of the data used in this paper, including the citizen scientists who collect the BBS, CBC, and NABC data, the  USGS  and CWS scientists and managers, the  Audubon Society , the  North American Butterfly Association , the  USDA Forest Service , the  Missouri Botanical Garden , and  Alwyn H. Gentry. We also thank all of the scientists who published their raw data allowing it to be combined in Baldridge (2013).
This step was repeated with 500 ll Buffer AW2. The spin column was dried by centrifugation for 3 min at 20,000 g in a clean 2-ml collection tube. The DNA was eluted in 200 ll of Buffer ATE, directly pipetted on the QIAamp membrane, and collected in a clean Eppendorf tube. After DNA extraction from the fecal pellets, the Thermo Fisher Ion 16S TM Metagenomics Kit was used to amplify the hypervariable regions V2, V3, V4, V6, V7, V8, and V9 of bacterial 16S rRNA. The amplified fragments were then bar-coded, sequenced on the Ion PGM sequencer system, and analyzed with the Ion Reporter Software.
Statistical analysis Results are presented as mean AE SEM. GraphPad Prism version 6. 00 (GraphPad Software, San Diego, CA; www. graphpad. com) was used to analyze the statistical significance of differences by one-way ANOVA and Tukey's multiple comparisons test or a two-tailed Student's t-test, where appropriate. Data with P < 0. 05 were considered statistically different: *P < 0. 05, **P < 0. 01, and ***P < 0. 001. Expanded View for this article is available online.
The present paper describes the bioinformatic identification of several barley and non-barley miRNA/miRNA * sequences that were shown, through database mining and computational prediction, to have potential to interact with B/CYDV genome. A total of 5,000 mature miRNA sequences were used in the study. These miRNAs belong to three groups: (a) Group I, consisting of 71 mature barley miRNAs (hvu-miRs), was collected from miRBase 22 (http://www. mirbase. org/cgibin/query. pl?terms=hvu&submit=Search); (b) Group II was initially made of 2,441 mature miRNAs from 15 MIR families, conserved among 67 plant species, among which barley. From this preliminary set, twelve hvu-miRs already contained in group I were excluded, keeping 2,429 mature miRNAs in group II; and (c) Group III was built from 2,500 complement reverse miRNAs (miRNAs * ) belonging to groups I and II.
Using RNA hybrid software version 2. 2 (http://bibiserv. techfak. unibielefeld. de/rnahybrid/submission. html)  23  , a total of 19 sequences were shown to target B/CYDV viral ORFs. These miRNAs/miRNAs * , their binding positions and targets are described in Table  1. Thirteen out of 19 sequences belonged to barley (hvu-miRNAs/miRNAs * ), three to A. thaliana, two to rice, and a unique miRNA * sequence to Lotus japonicus. Among this identified set, eight miRNAs were barley-specific miRNAs (group I), eight were miRNAs conserved across several plant genomes (group II) and three were miRNAs * (group III). The targeted regions were associated with replication (ORF1), RNAdependent RNA polymerase (ORF2), coat protein (ORF3), viral transmission (ORF3-ORF5) and movement protein (ORF4-ORF5)  6. In strain BYDV-PAV genome, eleven miRNAs and two miRNAs* were predicted to target all ORFs except ORF6.
In strain BYDV-MAV genome, only three ORFs (ORF1, ORF2 and ORF5) had putative miRNAs/miRNAs * counterparts. Finally, in strain CYDV-RPV genome, only two miRNAs, osa-miR166a-5p and hvu-miR6199, were able to target ORF1 and ORF 2 of this strain, respectively. The identification of three miRNA * species with potential to silence ORFs of B/CYDV, provides a valuable support to the hypothesized role of miRNAs * in host defense  14, 20, 24. In animals, it has been demonstrated that certain miRNAs * can be functionally active  25, 26. If such a mechanism operates in plants, we expect that the present study will broaden our understanding of miRNAs * as potential contributors to host-pathogen interactions. Using psRNATarget software (http://plantgrn. noble. org/psRNATarget/), predicted targets of miRNAs/miRNAs * were obtained from H.
Step two was repeated for the eight major ions detected in step one. Dynamic exclusion was set to 40 s. Lock mass option was chosen "best," MS resolution 70,000 at m/z 400, auto gain control was 1e 6 , maximum injection time 250 ms. For MS2 the resolution was 17,500 at m/z 400, auto gain control was 2e 5 , maximum injection time 120 ms, isolation window m/z ϭ 3, normalized collision energy: 30, underfill ratio 0. 5%, intensity threshold 8. 3e 3. Charge state: 2, 3, 4; dynamic exclusion 40 s. Data Processing and Bioinformatics Analysis-The R. solanacearum strain GMI1000 database was downloaded from Database site (https://iant. toulouse. inra. fr/bacteria/annotation/cgi/ralso. cgi, October 2012, 5122 protein entries). This database was merged and in conjunction with reverse and contaminant databases, were searched by X!Tandem (Sledge Hammer version 2013.
In order to be compared, samples were normalized dividing each PAI by the total sum of PAIs on the corresponding sample. Thus normalized PAI values were log-scaled to be able to adjust a linear model for every protein. With the aim of better handling multiple comparisons, general linear hypothesis tests (glht)  (32)  were subsequently run on the model to discriminate proteins differentially quantified between bacterial strains. All statistical procedures were carried out under [R] v3. 1. 1 environment (33) using base packages as well as "multcomp"  (32) , "limma"  (34) , and "venneuler"  (35)  packages. The R source code created for this study is available upon request. Type 3 Secretion Assays and Immunoblot Analysis-The secretion pattern of thirteen T3Es was followed in five R. solanacearum backgrounds.
To compare the disease development of two given strains, we used the Kaplan-Meier survival analysis with the Gehan-Breslow-Wilcoxon test. A p value smaller than 0. 05 was considered significant, indicating that the Ho hypothesis of similarity of the survival experience of the tested strains can be rejected. Statistical analyses were done with Prism version 5. 00 (GraphPad Software). Each experiment was repeated at least three times. Internal Growth Curve-Internal Growth curve was determined mostly as previously described  (39). Fourteen days post-inoculation, at least three pools of three plants were harvested, sterilized in 70% ethanol and rinsed three times in water. Plants were weighted, grinded and then resuspended in sterile water. Bacterial concentrations were determined by plating dilutions on B medium with antibiotic if necessary.
Secretome of R. solanacearum GMI1000 Grown in Type 3 Secretion Inducing Conditions-Using LC-MS/MS, we analyzed the secretome of the R. solanacearum GMI1000 wildtype strain. Bacteria were grown in secretion inducing media, and four independent biological secretome samples were generated by a shotgun approach with label-free quantification. The experimental procedure scheme is shown in supplemental Fig. S2A. Identification of peptides was done using X!TandemPipeline developed by PAPPSO platform (http:// pappso. inra. fr/). Protein identification was done using the R. solanacearum genome database (https://iant. toulouse. inra. fr/ bacteria/annotation/cgi/ralso. cgi) and a contaminant database (trypsin, keratins, etc). A protein was considered as present in the supernatant when spectra were detected in at least two independent replicates.
The number of spectra corresponding to each detected protein, the normalized protein abundance index (PAI), and all the peptide sequences are provided in supplemental Table  S1. The analysis of strain GMI1000 secretome identified 228 proteins which were classified into functional groups according to the R. solanacearum proteome database (see supplemental Table S1; Fig. 1 ). A Bonferroni-corrected hypergeometric test (with a p value Ͻ 0. 001) revealed a significant enrichment for three categories corresponding to macromolecule metabolism, structural elements, and type 3-associated proteins (Fig. 1 ). Among the 32 proteins belonging to the "macromolecule metabolism" class many previously characterized R. solanacearum secreted proteins were found, e. g. plant cell wall degrading enzymes (endoand exoglucanases, polygalacturonases) (supplemental Table  S1 ).
5A ). The hpaB mutant appears therefore strongly altered in its secretion of effectors but not of the components of the T3SS machinery. The hpaD mutant shows a secretion pattern close to the wild-type strain, with only four Rip significantly differentially detected (Fig. 5B ). In contrast, the hpaG mutant secretome reveals a global over-secretion pattern. Indeed, even if few Rips are differently secreted in the mutant compared with the wild-type (RipAB, RipF1_1, RipF1_2, and RipR), we can observe that numerous Rips are more secreted by the hpaG mutant than the wild-type (Fig. 5C ). Six Rips were detected only in the hpaG mutant (RipV1, RipAK, RipAS, RipA4, RipAR, and RipAZ1). Three Main Classes of Type 3 Effectors Based on Secretion Pattern Specificities-We used the log-normalized PAI data to perform a double hierarchical clustering in order to classify both the secreted Rips and the bacterial samples, according to the secretion of those Rip proteins.
We fitted a linear model to explain the log-normalized PAI values by the squared rpkm values from the RNA-Seq dataset. We obtained the following formula log(normalized PAI) ϭ 0. 1509 ϩ 0. 0093*rpkm 2 where the p values for intercept (0. 1509) and slope (0. 0093) are 0. 07 and 6. 10 Ϫ4 , respectively (supplemental Fig. S7G ). Therefore, except for the RipA and RipS family which correspond to very large proteins, no apparent differences could be attributed between detected and not-detected Rips in term of pI or MW, the threshold of Rip detection being very probably dependent on gene transcription, and thus to the amount of intracellular proteins produced. Altered Secretion of Rip Proteins in the hpaB and hpaG Mutants is Associated with Impaired Virulence on M. truncatula-We then wanted to evaluate the impact of these modulations of Rip secretion on R.
Combining the data obtained for the four biological replicates of the five strains analyzed in this study, we could detect 320 secreted proteins. Among them, 66% were predicted to be secreted using SecretomeP 2. 0 (http://www. cbs. dtu. dk/ services/SecretomeP/), SignalP 4. 0 (http://www. cbs. dtu. dk/ services/SignalP-4. 0/), and MaGe (Magnifying Genomes Microbial Genome Annotation System) (http://www. genoscope. cns. fr/agc/microscope/mage/index. php). We also showed with Western blot experiments that the non-detection of some Rips using MS is not because of a non-secretion in our experimental conditions, but is because of the MS detection threshold. Detection of 60% of the R. solanacearum GMI1000 known effectors (43 out of 72  (11) ) is satisfactory compared with other T3-secretome studies  (51) (52) (53) (54) (55)  and considering that several Rip effectors may certainly be low-abundant proteins produced at the host cell contact.
We observed a direct correlation between rip gene transcription and the quantitative detection of the corresponding proteins through MS analysis. Quantitative MS data were also supported by Western blot experiments. Absence or residual spectral counting for Rips in the hrcV mutant was confirmed by the absence of detection of Rips in bacterial supernatants after immunoblot experiments whereas these proteins were well produced in the hrcV bacterial pellets. Contrary to all the other Rips tested, RipW was detected as slightly secreted by the hrcV mutant as shown by MS (Fig. 2C ) or Western blot experiments (Fig. 7A ). The contrasting secretion patterns of the hpaB and hpaG mutants, with specific proteins either less or more secreted than the wild-type, were also validated by specific immunodetections.
Acknowledgments-We thank  Jean Luc Pariente  and  Claudette Icher  for tobacco plant preparation. We thank  Elise Gay  for technical assistance for construction of strains with effector-HA tag, and  Lenaïck Belliot  for LBy5 and LBy14 plasmids. We thank  Alice Guidot  and  Laure Plener  for generously providing transcription data of Rip genes. We thank  Laurent Deslandes  for the gift of the PopP2 antibody. We thank  Anne Aubert Frambourg  and  Michel Zivy  for fruitful discussions. * This work was supported by a  French Agence Nationale de la Recherche  grant ( ANR-2010-JCJC-1710-01 ) to F. V. This work benefited from interactions promoted by  COST Action  FA 1208 https:// www. cost-sustain. org. Our work is performed at the  Laboratoire des Interactions Plantes-Microorganismes  that is part of the  Laboratoire d'Excellence (LABEX)  entitled  TULIP  ( ANR-10-LABX-41 ).
This paper describes the goals, design and results of a shared task on the automatic linguistic annotation of German language data from genres of computer-mediated communication (CMC), social media interactions and Web corpora. The two subtasks of tokenization and part-of-speech tagging were performed on two data sets: (i) a genuine CMC data set with samples from several CMC genres, and (ii) a Web corpora data set of CC-licensed Web pages which represents the type of data found in large corpora crawled from the Web. The teams participating in the shared task achieved a substantial improvement over current off-the-shelf tools for German. The best tokenizer reached an F 1score of 99. 57% (vs. 98. 95% off-the-shelf baseline), while the best tagger reached an accuracy of 90. 44% (vs. 84. 86% baseline).
Annotation guidelines For tokenization, we developed a guideline with detailed rules for handling CMC-specific tokenization issues  (Beißwenger et al. , 2015a). It was tested and refined for a range of CMC and Web genres with the help of several student annotators in Berlin, Darmstadt, Dortmund and Erlangen. For PoS tagging, we used the 'STTS IBK' tag set which had been defined as a result from discussions in the Empirikom network and at three workshops dedicated to the adaptation and extension of the canonical version of the Stuttgart-Tübingen-Tagset ('STTS 1. 0';  Schiller et al. , 1999)  to the peculiarities of "non-standard" genres  (Zinsmeister et al. , 2013 (Zinsmeister et al. , , 2014)). STTS IBK introduces two types of new tags: (i) tags for phenomena that are specific to CMC and social media discourse, (ii) tags for phenomena that are typical for spontaneous (spoken or "conceptually oral") language in colloquial registers (cf.
AM Despite these issues, most errors and misinterpretations were caught by our adjudication of the four-fold annotation. A fifth independent tagging carried out by annotator SM at a later stage showed an agreement of acc = 95. 90% with the final gold standard. The training data of the Web corpora subset were manually tokenized by three independent annotators and tagged by five independent annotators, with adjudication by one of the task organizers after each stage. Agreement between annotators and the gold standard is similar to the test data.
Availability All gold standard data sets, the specification of the extended STTS tag set and the guidelines for tokenization and PoS tagging have been published on the EmpiriST Web site 7  and will remain available for use in future research. We used simple UTF-8 encoded text formats for both raw and annotated versions of the data. Annotated files are provided in one-token-per-line format with empty lines serving as posting or paragraph boundary markers. Corresponding PoS tags are given in an additional column separated from the token text by a single tab stop. Metadata for each posting or Web page are inserted as empty XML elements on separate lines. A small excerpt from one of the files is shown in Fig. 1. Apart from the actual contents, the EmpiriST 2015 data package comes with a description of the tag set, evaluation scripts and licensing informa-  The preparation stage started with the release of the annotation guidelines together with roughly 2,000 tokens of trial data from each subset in October 2015.
The trial data were intended to illustrate the required input and output file formats and to give an impression of the specific characteristics of the CMC and Web texts to be processed. They were based on preliminary versions of the guidelines and were produced without multiple annotation. Participants were instructed that they should not be relied on for training the final systems. During the preparation stage, there was also a fruitful dialogue between interested parties and the shared task organizers, leading to clarifications and corrections of the guidelines. The second stage was dedicated to the training and adaptation of the competing systems. It started with the release of the complete training data on the shared task Web site in December 2015. The registration deadline fell within this stage, enabling participants to make an initial assessment of their performance before registering.
in order to improve correct tokenization of special characters and punctuation. We had six submissions for the PoS tagging subtask, two of them non-competitive. 11 From the four regular submissions, one (bot. zen) was sent in after the submission deadline and is thus not included in the official ranking. In contrast to tokenization, all systems competing in the PoS tagging subtask made use of statistical models specially trained or re-trained for the purpose of Em-piriST 2015. The types of models employed reflect all state-of-the-art approaches to the task of PoS tagging. All approaches have in common that they extend the EmpiriST training data with additional corpora and linguistic resources. The three UdS systems built on a classical hidden Markov model (HMM;  Rabiner, 1989). In addition, they focused on improvements in the analysis of out-of-vocabulary (OOV) words by adding domain-specific training material and a list of likely PoS tags for OOV items.
Results In order to put the performance of the shared task submissions into perspective, we also evaluated several widely-used off-the-shelf tools as baselines: • the WASTE tokenizer  (Jurish and Würzner, 2013) ; 12 • TreeTagger v3. 2  (Schmid, 1995) ; 13  • Stanford tagger v3. 6. 0  (Toutanova et al. , 2003) ; 14 tations to account for the tokenization principles and extended tag set of EmpiriST. It may therefore be more appropriate to compare COW with the baseline systems than with the other task participants. 12 We used WASTE as shipped with the moot package (v2. 0. 13, http://kaskade. dwds. de/waste/) and trained a model solely using the EmpiriST training data. 13 We used the German UTF-8 parameter file downloaded from http://www. cis. uni-muenchen. de/ ˜schmid/tools/TreeTagger/ on 21 June 2016.
14 We used the german-dewac parameter file from the distribution released on 9 Dec 2015. Substantial automatic and manual post-editing was required to undo character transformations made by the tokenizer, replace non-STTS tags (e. g. $[ instead of $(), and account for the systematic mistagging of parentheses and brackets as TRUNC. • the COW pipeline  (Schäfer and Bildhauer, 2012; Schäfer, 2015). 15 ab. 7 (tokenization) and Tab. 8 (PoS tagging) show the results obtained by all task participants and baseline systems on the CMC and Web corpora subsets. Within each subset, results are micro-averaged across the text samples. The overall score is the macro-average over both subsets, ensuring that CMC and Web corpora carry the same weight. For systems that submitted multiple runs, only the best run is shown in the table (indicated by a subscript appended to the team name).