dataesr/scientific-paragraphs-categorization huggingface dataset:
There is considerable disagreement among observational datasets on the amount and geographical structure of SWE in the satellite era. The CanESM2 simulation of SWE performs as well as available international models in this area. Even accounting for this observational uncertainty, however, there is a bias towards excessive seasonal snow cover and unrealistic spatial distribution of SWE in the spring over the Canadian land mass and over the Northern Hemisphere as a whole. Excessive precipitation over the Canadian land mass contributes to this bias. -Accounting for observational uncertainty, CanESM2 simulates a greater retreat of springtime snow over the satellite era than most of the available observations assessed here and other models that include large initialcondition ensembles. The spatial pattern of the observed temperature, precipitation, and SWE trends is strongly influenced by internal variability.
The results suggest that there can be several benefits to updated multi-source observational datasets for climate pre-diction, monitoring, and assessment. Our focus in this paper has been on recently produced multi-source snow observational datasets, but our results suggest that there are benefits of multi-source temperature, precipitation, and sea ice datasets that follow a similar approach  (Massonet et al. , 2016). We have articulated the tradeoffs involved in constraints on CanESM2's resolution in light of limitations of available advanced computing resources. Running the model at 2 • latitude-longitude permits the creation of the CanESM2-LE set, but can entail under-resolution of key features of interest in applications, such as the Canadian Arctic Archipelago's channels and islands. We suggest that similar large ensembles be considered based on future model versions, accounting for these tradeoffs, and being complemented by ECCC regional climate model simulations (e.
In JFM precipitation (panel a of Figs. 1 and 2), the general pattern and spatial gradient strength are captured in the model, but there is excessive wintertime precipitation over most of Canada, including the Western Cordillera, sub-Arctic and Arctic, in both seasons. This excessive precipitation contributes towards a bias of excessive SWE over much of western Canada and the Canadiansub-Arctic that is particularly pronounced in spring (panel b of Figs. 1 and 2). CanESM2 SWE has greater spatial variance than the Blended-5 SWE ensemble mean and most of the individual component datasets of the Blended-5 (panel c of Figs. 1 and 2). Generally speaking, the Taylor diagrams in Figs. 1 and 2 suggest that CanESM2 is well within the state of the art of current models for the climate parameters related to seasonal snow cover.
Panels(i-l) and (m-p) are similar to (a-d) and (e-h), but for snow cover extent in 10 6 km 2. The estimate of observed snow cover extent is derived from the Blended-5 SWE dataset using the approach ofMudryk et al. (2017)  and is based on a 4 mm SWE threshold for the presence of snow cover; the simulated snow cover extent is based on snow cover fraction directly produced by the models.
Figure10. (a) Seasonal cycle of Arctic average sea ice drift speed (solid, in units of km day -1 ) and near-surface wind speed (dashed, in units of m s -1 ) from a historical run of CanESM2 averaged over 1979-2005. The spatial domain used for the calculation is the region north of 68 • N for longitudes east of 103 • E and west of 124 • W and north of 79 • N at all other longitudes, excluding grid points within 150 km of a coastline. This focuses on regions of year-round drifting ice and excludes landfast ice. (b) as in (a) but using non-gridded drift speed measurements from the International Arctic Buoy Programme (solid; Tschudi et al. , 2016)  and near-surface wind data from ERA-Interim (dashed;Dee et al. , 2011).
Acknowledgements. This work represents " Deliverable 1" of the CanSISE Network. CanSISE was funded under the auspices of the  Natural Science and Engineering Research Council of Canada's Climate Change and Atmospheric Research Program , with additional support provided by  Environment and Climate Change Canada , the  Pacific Climate Impacts Consortium , and the  University of Toronto. Comments by  Richard L. H. Essery  and an anonymous reviewer were helpful in improving the manuscript. Edited by:  Martin Schneebeli Reviewed  by:  Richard L. H. Essery  and one anonymous referee.
cirad. fr) enables accurate prediction and comparison of T4Es. Search parameters and threshold can be customized by the user to work with any genome sequence, whether publicly available or not. Applications range from characterizing effector features and identifying potential T4Es to analyzing the effectors based on the genome G+C composition and local gene density. S4TE 2. 0 allows the comparison of putative T4E repertoires of up to four bacterial strains at the same time. The software identifies T4E orthologs among strains and provides a Venn diagram and lists of genes for each intersection. New interactive features offer the best visualization of the location of candidate T4Es and hyperlinks to NCBI and Pfam databases. S4TE 2. 0 is designed to evolve rapidly with the publication of new experimentally validated T4Es, which will reinforce the predictive power of the algorithm.
A new module has been added in the workflow to locate phosphorylation (EPIYA-like) domains. S4TE 2. 0 consists of the S4TE 1. 4 tool and a web interface available to noncommercial users at http://sate. cirad. fr. The web interface is designed to make S4TE 2. 0 easy to use for biologists and more time efficient. Most of the genomes and plasmids available in the NCBI database of pathogenic bacteria that have a type IV secretion system have been loaded into the S4TE 2. 0 database so effectors can be predicted in only a few clicks. S4TE 2. 0 offers advanced users an expert mode (S4TE-EM) they can use to customize S4TE 2. 0 search parameters (e. g. exclude modules, modify module weightings). In this mode, S4TE 2. 0 can be used as 14 independent programs to search for particular features in a given bacterial genome (e.
SOFTWARE AND ALGORITHM Programming S4TE 2. 0 software consists in a graphical interface (website) to use the S4TE 1. 4 algorithm for genome analysis, Type IV effectors (T4Es) prediction and comparison of effectomes. S4TE 1. 4 is an update of S4TE 1. 0  [1]. It is written in Perl programming language and uses NCBI, Pfam, EMBOSS, BioPerl and MitoFates libraries and its own proper programs and database. It was developed to improve the prediction performances. CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted April 14, 2018. ; https://doi. org/10. 1101/301150 doi: bioRxiv preprint of S4TE 1.
0 and to provide new functionalities to search for new features, enable interactivity and comparative genomics. The 10 S4TE search modules in S4TE 1. 0 were kept in S4TE 1. 4. However, some modules have been modified (promoter motif search, homology, MLS, NLS, E-block and Pfam database) to improve their predictive power. A supplementary module (EPIYA search) has been added to the workflow. In this paper, only the EPIYA search module and the revised modules are described.
Promoter motif search As several T4Es in a given bacterium can be subjected to coordinated regulation with the same protein, e. g. PmrA  [2] , we used S4TE 2. 0 to conduct a search for conserved motifs (potential regulatory motifs) in the short promoter regions of the genes. The aim was to improve S4TE 2. 0 prediction of possible regulons of T4Es. Enriched DNA motifs were searched in a window of 100 nucleotides (nt) placed upstream of the start codon, using MEME  [3]. Eight consensus motifs were identified in different bacteria (table  1 ). The corresponding motif search module of S4TE 2. 0 extracts the 5' Flanking intergenic regions (5' FIRs) and searches for all these motifs thanks to a position-specific scoring matrix generated from multiple sequence alignments with the promoters of known T4Es.
Homology BLAST 2. 2 was used to compare proteins to search for homologies with known T4Es  [4]. The cut-off of the S4TE 1. 0 homology module was changed. S4TE 2. 0 compares the database containing all known T4Es with the query proteome and returns all homologs with a cut-off of the expected value (E) <10-4. This E-value cut-off was selected to find real homologs between phylogenetically distant bacterial species. Databases containing proven effectors have also been updated (Table  S1 ).
Nuclear localization signals (NLS) NLS are protein sequences that target proteins in the nucleus of eukaryotic cells  [5]. We assume that the occurrence of NLS in a bacterial protein sequence would be a good indicator of secretion. There are two classes of NLS, monopartite and Bipartite. In S4TE 2. 0, the search for monopartite NLS has been improved according to Ruhanen et al. [6]. We rewrote this module to add more known NLS motifs in the search. Monopartite NLS consist of [KR]-[KR]-[KR-][KR]-[KR], X-K-[KR]-[KRP]-[KR]-X, X-R-K-[KRP]-[KR]-X, X-R-K- X-[KR]-[KRP], X-K-[KR]-[KR]-X-[KRP], X-R-K-[KR]-X-[KRP], X-K-[KR]-X-[KR]-X-X, X-R-K- X-[KR]-X-X, X-K-[KR]-[KR]-X-X-X and X-R-K-[KR]-X-X-X motifs. Bipartite NLS were also searched with S4TE 1. 0 motif (K-[KR]-X(6,20)-[KR]-[KR]-X-[KR] ). The new module was tested with a dataset of 32 NLS and 32 no-NLS containing proteins (dataset 1).
E-block The E-block domain consists of a glutamate sequence rich in C-terminal 30 amino acids and is associated with T4Es translocation in L. pneumophila. Huang et al. showed that an E-block motif is also important for the translocation of T4SS substrates  [9]. In S4TE 2. 0, the E-block module was modified according to Lifshitz et al. [10]. The E-block was searched in a window of 22 amino acids between position -4 C-terminal and -26 C-terminal. The motif that is searched for is a motif of 10 amino acids containing three or more glutamate (E) residues. The module was tested on 98 E-block and 98 no-E-block containing proteins (dataset 2). This module selected 60 TP and only 6 FP (Sensitivity of 61%, Specificity of 94%).
EPIYA search EPIYA search is a new module implemented in S4TE 2. 0. The EPIYA domain is an eukaryotic phosphorylation motif  [11]. In H. pylori, EPIYA has been shown to contribute to. CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted April 14, 2018. ; https://doi. org/10. 1101/301150 doi: bioRxiv preprint the secretion of a CagA effector  [12]. We searched for conserved EPIYA motifs (EPIYA, ENIYE, NPLYE, EHLYA, TPLYA, EPLYA, ESIYE, EDLYA, EPIYG, EPVYA, VPNYA, EHIYD) in different bacteria that have a type IV secretion system and we searched for hypothetical EPIYA motifs using the motif E-X-X-Y-X.
Validation S4TE 2. 0 is a software program with 14 independent modules. We tested all the modules independently. The 14 modules were weighted to make S4TE 2. 0 efficient. The weighting of each module was calculated according to its performance in finding effectors in L. pneumophila Philadelphia I which has been shown to have the most extensive repertoire of T4Es ever identified, with 286 confirmed effectors  [10]. Each module has its own weighting in S4TE 2. 0 searches. The weightings were calculated for each module based on their Positive Predictive Value (PPV [PPV=TP/(TP+FP)]) for L. pneumophila (Table  2 ). The S4TE 2. 0 prediction threshold was then defined to enable the best prediction by disregarding homology with known effectors. The threshold was chosen by examining the Sensitivity (Se), Specificity (Sp), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Accuracy (Acc) for thresholds ranging from 40 to 120 on the test dataset (Figure  2 ).
Design and general features The S4TE 2. 0 website is powered from scratch on the 'CIRAD web server'. All the features of the web site were tested on common web browsers. S4TE 2. 0 found T4Es in large genome databases (Table  S3 ) available to all users. A user account is available and necessary to keep your jobs up to three months, to import your own genome in a S4TE 2. 0 temporary database and to ask to add a new proved effector in the database. The addition of an effector to the database must be accompanied by a reference (scientific article) and will be checked manually before the effector is added to the database. Those who subscribe to the newsletter will be notified by email about the addition of new effectors to the database and the effector will be visible in the S4TE 2. 0 tab strip.
This free account allows users to search for proteins in the S4TE 2. 0 database using the name, the locus tag or NCBI number of a protein in the search bar. The account also allows the user to subscribe to the S4TE newsletter that summarizes any changes made to the software, and provide updates on the latest research on Type IV Effectors. CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted April 14, 2018. ; https://doi. org/10. 1101/301150 doi: bioRxiv preprint S4TE 2. 0 is a simple and user-friendly tool S4TE 2. 0 is a web-based user-friendly tool that gets results in only a few clicks.
The user can locate a chromosome in more than 340 bacterial genomes and plasmids available in the database and the results can be viewed by clicking on run S4TE 2. 0 (Table  S3 ). If the desired genome is not available in the databases, the user can import it with a GenBank file (. gbk). S4TE 2. 0 will import the file to a temporary database for three months. All S4TE tools (S4TE-EM and S4TE-CG) can then be used on the genome by the owner. The S4TE 2. 0 web page allows users to read some of the news published in the newsletter. Five news items are visible on the S4TE2. 0 web page, but all the news can be found by clicking on the bottom right link. Figure  4  presents some results obtained with S4TE 2. 0. All the proteins in the selected genome are represented on the S4TE 2. 0 web results page.
CONCLUSION This paper presents updated S4TE software. The computational tool is designed to predict the presence of T4SS effector proteins in bacteria. The identification of T4Es and some characteristics are improved in this update. Compared with a machine learning approach, using S4TE 2. 0 to predict T4Es in Legionella and Coxiella species  [10, 13, 14]  improved sensitivity (98% for S4TE 2. 0 and 89% for Wang et al. ) and equivalent specificity (97% for Wang et al. and 93% for S4TE 2. 0). S4TE 2. 0 is easy to use. Only an internet connection and a few clicks are needed to search for T4Es in more than 340 bacterial genomes and plasmids. The results are displayed instantaneously for easy reading. An automated pipeline is also provided to analyze and visualize effector distribution in the genome according to G+C content and local gene density.
S4TE 2. 0 results are linked to bioinformatics databases like NCBI and Pfam. The S4TE 2. 0 database is designed to evolve and will be updated by adding new proven effectors and new bacterial genomes. S4TE 2. 0 not only predicts the T4Es but also their subcellular localization (NLS, MLS, prenylation) and the function of these proteins (Coiled coils, EPIYA, Euk-like, etc. ). All these features make S4TE 2. 0 a powerful software for studies of T4Es. S4TE 2. 0 also offers an expert mode, which allows users to make manual adjustments to the weight of the modules. Each module that searches for a feature or a characteristic can be used independently. S4TE EM can be viewed and use as 14 independent programs. This could facilitate the annotation of new genomes by looking for specific features such as NLS, prenylation domains, etc.
0 is a web interface and the S4TE 1. 4 package is freely available to noncommercial users at http://sate. cirad. fr/S4TE-Doc. php. All programming was done using Perl 5. 18 and BioPerl 1. 6. 1. The software runs on Linux platforms (Ubuntu 14. 04 and Mac OS X). All required packages and the installation process are described in the user guide included in the package. The user guide also details S4TE options for running S4TE. By default, the command line to launch S4TE is. /S4TE. pl -f "Genbank_file" in the S4TE folder (cd way_to_S4TE/S4TE/). Some options are available for the user to launch S4TE: -c, suppression of a module in the pipeline; -w, modification of the weight of each module in the pipeline; -t, imposition of a threshold for effector selection. Each S4TE module creates a. txt file in the folder way_to_S4TE/S4TE/Jobs/ job<Name_of_genome_folder><year><month><day><hour><min> All the results are compiled in the CompilationFile.
CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under EM will find all proteins with an NLS in the genome, meaning users can use S4TE-EM as a new genome analysis tool. -CG is a new tool designed to compare different effectomes predicted by S4TE 2. 0. Users can choose up to four effectomes in S4TE 2. 0 databases or upload a genome present in the temporary database. S4TE-CG displays results in a Venn diagram and in an interactive table. Users can easily find different subsets of information in the appropriate table by referring to the different colors in the Venn diagram (Figure.
S4TE 2. 2 0 is a web interface and the S4TE 1. 4 package is freely available to noncommercial users at http://sate. cirad. fr/S4TE-Doc. php. All programming was done using Perl 5. 18 and BioPerl 1. 6. 1. The software runs on Linux platforms (Ubuntu 14. 04 and Mac OS X). All required packages and the installation process are described in the user guide included in the package. The user guide also details S4TE options for running S4TE. By default, the command line to launch S4TE is. /S4TE. pl -f "Genbank_file" in the S4TE folder (cd way_to_S4TE/S4TE/). Some options are available for the user to launch S4TE: -c, suppression of a module in the pipeline; -w, modification of the weight of each module in the pipeline; -t, imposition of a threshold for effector selection. Each S4TE module creates a.
S4TE-CG Comparative genomics to compare effectomes S4TE. CC-BY-NC-ND 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under EM will find all proteins with an NLS in the genome, meaning users can use S4TE-EM as a new genome analysis tool. -CG is a new tool designed to compare different effectomes predicted by S4TE 2. 0. Users can choose up to four effectomes in S4TE 2. 0 databases or upload a genome present in the temporary database. S4TE-CG displays results in a Venn diagram and in an interactive table. Users can easily find different subsets of information in the appropriate table by referring to the different colors in the Venn diagram (Figure bioRxiv preprint doi: ; https://doi.
Experiment results show that our approach converges faster and learns models which are much smaller than those learned by existing approaches while providing better or similar analysis results. We consider it is an advantage to learn smaller models as they are often easier to comprehensive and easier to model check. Secondly, we develop a software toolkit Ziqian, realizing previously proposed learning approaches for PMC as well as our approach so as to systematically study and compare them in a fair way. The tool is written in a generic manner and is friendly to extension. Thirdly, we conduct an empirical study on comparing different model learning approaches against a suite of benchmark systems, two real-world systems, as well as randomly generated models (Sect. 6). One of our findings suggests that learning models for model checking might not be as effective compared to SMC given the same time limit.
We evaluate whether we can learn precise models in such a scenario. For PBN, we use the tool ASSA-PBN  [38]  to generate the data to learn from. First, a set of steady states which represents whether a gene node is activated are generated. Notice Fig. 7  Convergence of AA and GA over time. The numbers after the system of legends are one kind of system configuration Fig. 8  The comparison of accuracy of AA, GA, and SMC given same amount of time, which varies from 30 s to 30 min. The horizontal-axis point represents a benchmark system with certain configuration in Fig. 7  that the number of states depends on the number of gene nodes. Then, transition probabilities are assigned between each state. In this way, we can evaluate the accuracy of the learned model directly with the generating model by comparing the differences between the transition probabilities.
Eventually we managed to identify an optimal value and both PST and GA learn the same models given the same training data. A closer look at the learned models reveals that they are all first-order Markov chains. This makes sense in the way that sensor readings in the real SWaT system vary slowly and smoothly. Applying the learned models to predict the probability of the test data (from another day with length 7000), we observe a very good accuracy. In our experiment, the average generating probability using the learned model for ait502 and pit501 is over 0. 97, and the number is 0. 99 for ait504, which are reasonably precise. Finding 4. GA learns models with much fewer states than AA. Models with fewer states are better than models with many states since they are easier for humans to understand the system.
This research is partly supported by  T2MOE1704  Singapore. Q. Yuan was supported by the  National Research Fund (FNR) ,  Luxembourg  (Grant  7814267 ). J. Pang  was partially supported by the project  SEC-PBN  (funded by the  University of Luxembourg)  and the  ANR-FNR  project  AlgoReCell  ( INTER/ANR/15/11191283 ).
(e) Identification of differentially expressed transcripts The reads were mapped to the gene models from Aiptasia  [26]  with the use of KALLISTO v0. 42. 4  [27]. To reduce ambiguity in read-mapping downstream, a custom Python script was written to pick the longest isoform on a per-loci basis, leading to the decrease of overall Aiptasia gene models from 29 269 to 27 504. Based on the transcripts per million (TPM) values produced from KALLISTO, sleuth  [28]  was used to identify genes that were differentially expressed (Benjamini -Hochberg-corrected p value , 0. 05) under heat stress relative to control conditions. (f ) Sequencing the proteome of Aiptasia Protein fractions were labelled using an iTRAQ labelling method according to manufacturer's protocol. Purified, digested and labelled samples were taken through high performance liquid chromatography (HPLC) for elution of peptides into 15 fractions and then pooled down to five fractions.
Mobile phase A was 0. 1% formic acid in H 2 O and mobile phase B was 80% acetonitrile and 0. 1% formic acid. The MS was operated in positive and data-dependent mode, with a single MS scan (350 -1400 m/z at 60 000 resolution (at 200 m/z in a profile mode) followed by MS/MS scans on the 10 most intense ions at 15 000 resolution. Ions selected for MS/ MS scan were fragmented using higher energy collision dissociation (HCD) at normalized collision energy of 28% and using an isolation window of 1. 8 m/z. MAXQUANT software 1. 5. 2. 8  [29]  was used for peptide and protein identification. Raw files were processed using the MAX-QUANT software interlinked with the local MASCOT server (Matrix Science, London, UK). Mascot searches were done against Aiptasia gene models; contaminants were excluded. Precursor mass tolerance of 20 ppm and a fragment ion mass tolerance of +0.