dataesr/scientific-paragraphs-categorization huggingface dataset:
Castalia includes some typical radio interfaces for WSNs, such as CC1010, CC2420 or CC2430, but it also accepts the implementation of new ones. According to the OMNET++, Castalia uses modules and messages to implement a WSN. Castalia nodes connect to each other through the wireless channel module which is responsible for delivering the messages. In addition, a physical process module implements the events simulation that occurs in the scenario. The node is a composite module. Each node contains a communication module (radio, MAC and routing layers) connected to the wireless module. The application layer is plugged on the communication module. Finally, each node has a resource manager that gathers information about the memory, energy, CPU consumption from every module in the node.
Quantification of fluorescence For the quantification of the relative myelin content of OSC during demyelination, we assessed the intensity of GFP expression in living OSC at different time points during each experiment: Using ImageJ software, we assessed the area of fluorescence signal exceeding a defined threshold in digital images acquired with an Olympus BX51 microscope (Hicksville, NY) at 49 magnification. The threshold was chosen according to the background intensity, and only the specific GFP expression was quantified. For each individual OSC, results are depicted relative to time point 0, prior to the beginning of the experiment.
At first glance, the lack of a BBB in our experimental model seems to limit the interpretation of our findings with regards to a therapeutic in vivo situation. We found few data on the actual amount of systemically administered IVIG reaching the CNS compartment: van Engelen detected a mean increase of 44% in cerebrospinal fluid (CSF) IgG concentration in epilepsy patients after systemic IVIG delivery, with the extent of increase correlating with Q albumin, a measure of blood CSF permeability. 61 Wurster and Haas measured an intrathecal Ig concentration reaching close to 1% of the serum Ig concentration (0. 317 g/L vs. 32. 1 g/L) in a patient with polyradiculitis after infusion of 30 g IVIG. 62 More recent studies in rodents by Sha Mi and colleagues established that systemically applied therapeutic antibodies could reach the CSF in therapeutic concentrations.
Acknowledgments Grants of the  German Ministry for Education and Research (BMBF   31P7398 , to NG) and the  Forschungskommission of the Medical Faculty of the Heinrich-Heine-University Duesseldorf (HHU)  funded this study. CB received a PhD stipend of the iBrain graduate school of the  HHU. The support of the  Biomedical Research Center of the HHU  is gratefully acknowledged. The Multiple Sclerosis Center at the  Department of Neurology, HHU , is supported in part by the  Walter-and-Ilse-Rose Stiftung. HPH is a member of the " German Competence Network Multiple Sclerosis " (KKNMS), supported by the  BMBF.
net/projects/lstm_agreement. 6 The network was optimized using Adam (Kingma and Ba, 2015) and early stopping based on validation set error. We trained the number prediction model 20 times with different random initializations, and report accuracy averaged across all runs. The models described in Sections 5 and 6 are based on 10 runs, with the exception of the language model, which is slower to train and was trained once. 7 The size of the vocabulary was capped at 10000 (after lowercasing). Infrequent words were replaced with their part of speech (Penn Treebank tagset, which explicitly encodes number distinctions); this was the case for 9. 6% of all tokens and 7. 1% of the subjects. line is to withhold the syntactic information carried by function words, verbs and other parts of speech. We explore two variations on this baseline: one that only receives common nouns (dogs, pipe), and another that also receives pronouns (he) and proper nouns (France).
, 2016) , which we refer to as the Google LM. 12 The Google LM represent the current state-of-the-art in language modeling: it is trained on a billion-word corpus  (Chelba et al. , 2013) , with a vocabulary of 800,000 words. It is based on a two-layer LSTM with 8192 units in each layer, or more than 300 times as many units as our LM; at 1. 04 billion parameters it has almost https://github. com/tensorflow/models/ tree/master/lm_1b 2000 times as many parameters. It is a fine-tuned language model that achieves impressive perplexity scores on common benchmarks, requires a massive infrastructure for training, and pushes the boundaries of what's feasible with current hardware. We tested the Google LM with the methodology we used to test ours. 13 Due to computational resource limitations, we did not evaluate it on the entire test set, but sampled a random selection of 500 sentences for each count of attractors (testing a single sentence under the Google LM takes around 5 seconds on average).
Acknowledgments We thank  Marco Baroni ,  Grzegorz Chrupała ,  Alexander Clark ,  Sol Lago ,  Paul Smolensky ,  Benjamin Spector  and  Roberto Zamparelli  for comments and discussion. This research was supported by the  European Research Council  (grant  ERC-2011-AdG 295810 BOOTPHON ), the  Agence Nationale pour la Recherche  (grants  ANR-10-IDEX-0001-02 PSL  and  ANR-10-LABX-0087 IEC ) and the  Israeli Science Foundation  (grant number  1555/15 ).
mSR experiments were carried out on the general purpose surface-muon instrument (GPS) at the Swiss muon source (SmS) of the Paul Scherrer Institute (PSI), Switzerland. Longitudinal-field (LF) muon-spin relaxation signals were collected for the powder sample (z1 g) in the temperature range between 50 and 450 K using a closed cycle refrigerator. The mSR data was analyzed using the musrfit package  [38].
Acknowledgment The authors thank  Anton Lefering  and  Bert Zwart  for their help with the sample preparation, and acknowledge the technical assistance of  Jouke Heringa  for mSR data analysis. Part of this work was performed at the  Swiss Muon Source ,  Paul Scherrer Institut, Villigen, Switzerland. This work is part of the  Industrial Partnership Program  of the  Dutch Foundation for Fundamental Research on Matter (FOM) , and co-financed by  BASF New Business. This paper is dedicated to the memory of  Peter Brommer  e a former physicist of the  University of Amsterdam.
In this study we have modified the recombination routine RECFAST  [96]  to include energy injection, employing the detailed cascade results of Ref. [97]. We assume that 1/3 of the energy of E < ∼ 10 keV e -'s and γ's goes into ionization, whereas 2/3 into heating. The modified recombination routine was then used with the CMBR code CAMB and the Markov-Chain Monte-Carlo (i. e. MCMC) code COSMOMC  [98]  to derive constraints on dark photons and dark Higgses decaying around recombination. In our MCMC analysis we take very generous priors on cosmological parameters, in particular generous on n s and A s , to avoid missing degenerate good fits to the data. Results are produced typically by calculating of the order 2 × 10 6 to 5 × 10 6 models, and calculating their liklihood that they may produce the observed data.
A. CMB Constraints on Dark Photons Fig. 10  shows by the shaded area dark photon models in the mass -mixing angle plane which are ruled out by the precise observations of the angular anisotropies of the CMBR. Here we used recent Planck data  [99]  for our analysis. In general about as much parameter space is ruled by the CMBR as by BBN, however, at lower masses and smaller mixing angles, leading to decay around recombination. A small island of allowed models surrounded by disallowed models is noted in this figure. This is due to a statistical fluctuation, since the entire ruled out region is not ruled out by a large likelihood degradation. There are no constraints on dark photon parameters from current FIRAS limits on deviations from a blackbody spectrum. In Fig. 11  possible limits are shown after a sucessful PIXIE mission  [94].
Acknowledgments: We thank  D. Finkbeiner  for collaborating during the early stages of this project. We also thank  R. Caldwell ,  D. Curtin ,  S. El Hedri ,  R. Mohapatra ,  S. Nussinov , and  T. Slatyer  for useful conversations. JB is supported by by the  U. S. Department of Energy  under the contract  DE-FG-02-95ER40896. DW is supported by a grant from the  University of Washington. Part of this work was completed at  SLAC , which is operated by  Stanford University  for the  US Department of Energy  under contract  DE-AC02-76SF00515.
Repeat annotation We used the TEdenovo pipeline  [23]  from the REPET package v2. 2 with default parameters (with a minimum of five sequences per group) on the contigs of size > 100 kb in the SL2. 40 assembly (representing approx. 340 Mb, gaps excluded) which generated a library of 818 consensus sequences. We used RepeatScout (version 1. 0. 5) [24]  on the contigs of size < 100 kb with default parameters which generated a second library comprising 9085 consensus sequences. The whole SL2. 40 assembly was annotated using the TEannot pipeline  [25]  from the REPET package v2. 2 with the TEdenovo library as input. Blaster sensitivity was set to "3" and threshold scores were calculated for each consensus as the 99th percentile value of scores obtained against a simulated genome consisting of the reversed (not complemented) assembly (REPET annotation).
Repeat profile of the tomato genome Genome coverage in genes (CDS) and transposable elements was calculated by 500 kb window with an overlap of 50 kb. Genes and transposable elements (REPET + RepeatScout + Tallymer) annotations and a karyotype were also needed. The resulting files were formatted to be visualized graphically through the Circos software  [27]. A clustering step of the different coverage windows by the kmean method using R (kmeans), based on the gene content and repeat content in each window has allowed distinguishing three types of genomic regions. Finally, each TE and each gene was located on the genome. The repeats or genes that straddle two regions have been excluded.
To detect each of these associations, the BEDtools  [28]  closest tool has been used with different options as required: -io to ignore overlapping, -iu to ignore upstream associations, and -id to ignore downstream associations. The associations of interest are selected depending on the distance, between the gene and the nearest repetition, provided by the software in the last column of the result file. In the case of DMR / TE or DMR / RIN associations, only overlaps between the two types of sequences were selected with R after using bedtools closest. To analyze the expression of genes associated with repeats, only genes with a single repeat close to their sequence, i. e. less than 1 kb, have been preserved. Once associations were identified, Mann Witney statistical tests with continuity correction (wilcox.
JBrowse Coordinates for the repeats identified in this paper using REPET, RepeatScout, Tallymer were converted to SL2. 50 reference space from SL2. 40 using a script (https://github. com/solgenomics/Bio-GenomeUpdate/ blob/master/update_coordinates_gff. pl) and chromosome accessioned genome path files for SL2. 40 and SL2. 50 assemblies (ftp://ftp. solgenomics. net/tomato_genome/wgs/assembly/). Please note that 15 repeats identified by REPET, RepeatScout and Tallymer in SL2. 40 were not ported over to SL2. 50 since they straddled scaffolds in SL2. 40 that were rearranged in SL2. 50. All the repeats mapped to SL2. 50 are available for analysis in the JBrowse genome browser at https://solgenomics. net/jbrowse.
RIN binding site analysis A RIN binding sites analysis was performed using the binding peaks provided in data from Zhong et al. [10] , in the Table  S8 , column "RIN binding peak", and which have been extended to plus or minus 10 bp. First, we determined which RIN sites are associated with DMRs with the command line bedtools closest -d -a RINs -b DMRs from the BEDtools software  [28]. RIN binding sites and DMRs involved in overlap were then identified and recovered in R, in bed files, column 19 indicates the distance between the two entities, so we selected distance equal to 0. For DMRs involved in these associations, we then searched for those associated to repeats, using bedtools closest -d -a DMRs -b TEs command line again. The results of interest, i. e. the overlap between DMRS and repeats, were again identified and recovered in R (column 19 of the table is equal to 0).
Availability of data and materials TE annotations are available on Sol Genomics Network (https:// solgenomics. net/jbrowse), and genes and DMR annotations can be found on Tomato Epigenome Database (http://ted. bti. cornell. edu/cgi-bin/gb2/ gbrowse/tomato/). Gene Ontology annotation comes from Gene Ontology Consortium (http://geneontology. org). Gene expressions are provided Pr. James J. Giovanonni and Dr. Zhangjun Fei. The three sets of genes that derived from phylogenetic reconstruction were obtained by courtesy of Alexandra Louis (Ecole Normale Supérieure, Paris, France). Finally, TE-genes annotation and chromosomic bands are included within the article and its additional files.
Proteome alignments RepeatModeler and RepeatMasker were used on the final assembly to find repetitive regions, which were subsequently classified into families of transposable elements or short tandem repeats (Source code 2). We found 1473 different repeat element sequences representing 57% of the assembly (Figure  4 -source data 1). The Parhyale assembly comprises of 133,035 scaffolds (90% of assembly), 259,343 unplaced contigs (4% of assembly), and 584,392 shorter, potentially allelic contigs (6% of assembly), with a total length of 4. 02 Gb (Table  2 ). The N50 length of the scaffolds is 81,190 bp. The final genome assembly was annotated with Augustus trained with high confidence gene models derived from assembled transcriptomes, gene homology, and ab initio predictions. This resulted in 28,155 final gene models (Figure  4B ; Source code 3) across 14,805 genic scaffolds and 357 unplaced contigs with an N50 of 161,819, bp and an N90 of 52,952 bp.
, 2013)  from mixed stage transcriptomes. Almost certainly the high number of predicted gene models and proteins is an overestimation due to fragmented genes, very different isoforms or unresolved alleles, that will be consolidated as annotation of the Parhyale genome improves. We also included additional high confidence gene predictions that were not found in the transcriptome (Figure  4C ). The canonical proteome dataset was annotated with both Pfam, KEGG, and BLAST against Uniprot. Assembly quality was further evaluated by alignment to core eukaryotic genes defined by the Core Eukaryotic Genes Mapping Approach (CEGMA) database  (Parra et al. , 2007). We identified 244/248 CEGMA orthology groups from the assembled genome alone and 247/248 with a combination of genome and mapped transcriptome data (Figure  4 -figure supplement 1).
A comparative genomic analysis of the Parhyale genome Assessment of conservation of the proteome using BLAST against a selection of metazoan proteomes was congruent with broad phylogenetic expectations. These analyses included crustacean proteomes likely to be incomplete as they come from limited transcriptome datasets, but nonetheless highlighted genes likely to be specific to the Malacostraca (Figure  5C , Figure  5 -source data 2). To better understand global gene content evolution we generated clusters of orthologous and paralogous gene families comparing the Parhyale proteome with other complete proteomes across the Metazoa using Orthofinder  (Emms and Kelly, 2015)  (Figure  5D ; Figure  5 -source data 3). Amongst proteins conserved in protostomes and deuterostomes we saw no evidence for  widespread gene duplication in the lineage leading to Parhyale.
From an evolutionary perspective, it is likely that GH7 coding genes were acquired by these species via horizontal gene transfer from a protist symbiont. Parhyale is a detrivore that can be sustained on a diet of carrots (Figure  11C ), suggesting that they too may be able to depolymerize lignocellulose for energy (Figure  11A  and B ). We searched for GH family genes in Parhyale using the classification system of the CAZy (Carbohydrate-Active enZYmes) database  (Cantarel et al. , 2009)  and the annotation of protein domains in predicted genes with PFAM  (Finn et al. , 2006). We identified 73 GH genes with complete GH catalytic domains that were classified into 17 families (Figure  12 -source data 1) including 3 members of the GH7 family. Phylogenetic analysis of Parhyale GH7s show high sequence similarity to the known GH7 genes in L.
Characterisation of the innate immune system in a Malacostracan Immunity research in Malacostracans has attracted interest due to the rapid rise in aquaculture related problems  (Vazquez et al. , 2009; Stentiford et al. , 2012; Hauton, 2012). Malacostracan food crops represent a huge global industry (>$40 Billion at point of first sale), and reliance on this crop as a source of animal protein is likely to increase in line with human population growth  (Stentiford et al. , 2012). Here we provide an overview of immune-related genes in Parhyale that were identified by mapping proteins to the ImmunoDB database  (Waterhouse et al. , 2007). The ability of the innate immune system to identify pathogen-derived molecules is mediated by pattern recognition receptors (PRRs)  (Janeway and Medzhitov, 2002).
Several groups of invertebrate PRRs have been characterized, i. e. thioester-containing proteins (TEP), Toll-like receptors (TLR), peptidoglycan recognition proteins (PGRP), C-type lectins, galectins, fibrinogen-related proteins (FREP), gram-negative binding proteins (GNBP), Down Syndrome Cell Adhesion Molecules (Dscam) and lipopolysaccharides and beta-1, 3-glucan binding proteins (LGBP). The functions of PGRPs have been described in detail in insects like D. melanogaster  (Werner et al. , 2003)  and the PGRP family has also been reported in Vertebrates, Molluscs and Echinoderms  (Liu et al. , 2001; Rehman et al. , 2001). Surprisingly, we found no PGRP genes in the Parhyale genome. PGRPs were also not found in other sequence datasets from Branchiopoda, Copepoda and Malacostraca (Figure  13A ), raising the possibility of their close phylogenetic relationship (like the GH7 genes).
Species without a SID-1 ortholog can silence genes only in a cell-autonomous manner  (Roignant et al. , 2003). This feature has important implications for future design of RNAi experiments in Parhyale. We also assessed the miRNA and putative long non-coding RNAs (lncRNA) content of Parhyale using both MiRPara and Rfam  (Wu et al. , 2011; Nawrocki et al. , 2015). We annotated 1405 homologues of known non-coding RNAs using Rfam. This includes 980 predicted tRNAs, 45 rRNA of the large ribosomal subunit, 10 rRNA of the small ribosomal subunit, 175 snRNA components of the major spliceosome (U1, U2, U4, U5 and U6), 5 snRNA components of the minor spliceosome (U11, U12, U4atac and U6atac), 43 ribozymes, 38 snoRNAs, 71 conserved cis-regulatory element derived RNAs and 42 highly conserved miRNA genes ( Source code 6).
Parhyale long non-coding RNAs (lncRNAs) were identified from the transcriptome using a series of filters to remove coding transcripts producing a list of 220,284 putative lncRNAs (32,223 of which are multi-exonic). Only one Parhyale lncRNA has clear homology to another annotated lncRNA, the sphinx lncRNA from D. melanogaster  (Wang et al. , 2002). We then performed a more exhaustive search for miRNAs using MiRPara (Source code 6) and a previously published Parhyale small RNA read dataset  (Blythe et al. , 2012). We identified 1403 potential miRNA precursors represented by 100 or more reads. Combining MiRPara and Rfam results, we annotated 31 out of the 34 miRNA families found in all Bilateria, 12 miRNAs specific to Protostomia, 4 miRNAs specific to Arthropoda and 5 miRNAs previously found to be specific to Mandibulata (Figure  14 ).
melanogaster and flatworms  (Goll et al. , 2006; Jaber-Hijazi et al. , 2013) , while DNMT2 is absent from nematodes C. elegans and C. briggsae. The Parhyale genome encodes members of all 3 families DNMT1, DNMT3 and DNMT2, as well as 2 orthologs of conserved methyl-CpG-binding proteins and a single orthologue of Tet2, an enzyme involved in DNA demethylation  (Hackett et al. , 2013)  (Figure  15A  and Figure  15 -source data 1). We used genome wide bisulfite sequencing to confirm the presence and also assess the distribution of CpG dinucleotide methylation. Our results indicated that 20-30% of Parhyale DNA is methylated at CpG dinucleotides (Figure  15B ). The Parhyale methylation pattern is similar to that   -7, mir-980, mir-153, mir-193, mir-278, mir-315, mir-375, mir-31, mir-34, mir-92, mir-252, mir-   observed in vertebrates, with high levels of methylation detected in transposable elements and other repetitive elements, in promoters and gene bodies (Figure  15C ).
Although more high-quality genomes with a broader phylogenetic coverage are still needed for meaningful evolutionary comparisons, our observations from analysing the Parhyale genome and other crustacean data sets also contribute to the ongoing debate on the relationships between crustacean groups. While the analysis of shared orthologous groups did not provide clear support for either the Allotriocarida hypothesis (uniting Branchiopoda with Hexapoda) or the Vericrustacea hypothesis (uniting Branchiopoda with Malacostraca), we noted the presence of GH7 genes and the absence of PGRP genes in branchiopod and multicrustacean genomes supporting the Vericrustacea hypothesis. It still remains to be proven how reliable these two characters will be to distinguish between these alternative phylogenetic affinities.
Materials and methods Raw genomic reads are deposited at NCBI with the project accession: PRJNA306836. All supplemental data including IPython notebook can be downloaded from this figshare link: https://figshare. com/articles/supplemental_data_for_Parhyale_hawaniensis_genome/3498104 Alternatively, the IPython notebooks and associated scripts can also be viewed at the following github repository: https://github. com/damiankao/phaw_genome.
Analysis of polymorphism and repetitiveness The Parhyale raw data and assembled data are available on the NCBI website. Genome assembly was done with Abyss  (Simpson et al. , 2009)  at two different k-mer settings (70, 120) and merged with GAM-NGS. Scaffolding was performed with SSPACE  (Boetzer et al. , 2011). We chose cut-offs of >95% overlap length and >95% identity when removing shorter allelic contigs before scaffolding as these gave better scaffolding results as assessed by assembly metrics. Transcriptome assembly was performed with Trinity  (Haas et al. , 2013). The completeness of the genome and transcriptome was assessed by blasting against CEGMA genes  (Parra et al. , 2007)  and visualized by plotting the orthologue hit ratio versus e-value. K-mer analysis of variant and repetitive branching was performed with String Graph Assembler's preqc module  (Simpson, 2014).
Gene model prediction and canonical proteome dataset generation Gene prediction was done with a combination of Evidence Modeler  (Haas et al. , 2008)  and Augustus  (Stanke and Waack, 2003). The transcriptome was first mapped to the genome using GMAP  (Wu and Watanabe, 2005). A secondary transcriptome reference assembly was performed with STAR/Cufflinks  (Trapnell et al. , 2010; Dobin et al. , 2013). The transcriptome mapping and Cufflinks assembly was processed through the PASA pipeline  (Haas et al. , 2008)  to consolidate the annotations. The PASA dataset, a set of Exonerate  (Slater and Birney, 2005)  mapped Uniprot proteins, and Ab inito GeneMark  (Lukashin and Borodovsky, 1998)  predictions were consolidated with Evidence Modeler to produce a set of gene annotations. A high confidence set of gene models from Evidence Modeler containing evidence from all three sources was used to train Augustus.