dataesr/scientific-paragraphs-categorization huggingface dataset:
Autoencoder is used for representation of tweets while fuzzy c-means is the clustering part of method. e authors report their results on Berita dataset which is an Indonesian news dataset from Twitter. Utilization of these methods, which are all based on deep learning, is a new field in NLP, specially transfer learning based ones that use Transformers to have a semantic understanding of text. is semantic understanding is a missing part of other methods. e semantic clustering used by various methods can categorize texts with different words into a single cluster if they have close meaning. Language models and pretrained transformer based architectures that can capture semantic similarity such as SBERT and USE are successful examples of these approaches. ese approaches are well known for their ability to understand complex sentences.
Any word such as W that has been encoded into a vector in previous layers is separated in terms of its characters, and each character is encoded into another vector such as r chr m. Matrix vector multiplication of set r chr 1 , r chr 2 ,. , r chr n   gives r chr for each character that would be character embedding in this layer. Sentence level representation and scoring are applied as described in character and word level. CharSCNN has been applied to two distinct short sentence datasets of Movie Reviews and Twitter posts with word embedding size of 30. Sentiment-specific word embedding for Twitter sentiment classification that is proposed in  [24]  uses C&W method of  [59]. ree different neural networks (SSWE h , SSWE r , and unified model of SSWE u ) are proposed in this manuscript for different strategies to overcome task of Twitter sentiment classification.
TwitterMonitor identifies emerging topics in real-time in Twitter and provides meaningful analytical information that can be further used to extract a topic to detected event. A StreamListener listens to Twitter API data stream and detects bursty keywords; these keywords are then grouped and along with an index are passed into Trend Analysis module. All of described steps form the backend of system while a user interface sums up all of information and presents it to user. Other implementations such as AllTop, Radian6, Scout Lab, Sysomos, oora, and TwitScoop have a user interface to represent information gathered from different social media, newswire, and other data stream lines to the front end user. TwitterStand is another electronic medium that, with use of Naïve Bayes classifier, separates news from irrelevant user generated tweets  [12].
Data and Evaluation Issues Twitter by its nature possesses unstructured and unlabeled data stream that can be obtained from online or offline sources. Online Twitter data source is the Twitter API, and offline data is the offline Twitter data obtained from different snapshots. ese snapshots possess better properties to evaluate differences between algorithms or systems that aim to find events or topics on Twitter. Evaluation of an online Twitter event extraction system is doable if the input data is the same input data snapshot that is recorded. Another drawback of event detection and tracking algorithms that has indirect relation to the previous issue, is the event detection time. Suppose that two algorithms or systems such as A 1 and A 2 both have the same precision and recall on finding events and tracking them on Twitter data snapshot but have different detection times.
Misdetection of events and identification of a nonevent phenomenon also constitute a huge problem. e reason this issue possesses bigger threads is that a real-time disaster informing system can be fooled and misdetect a disaster or even not detect it. With all of these in mind, an evaluation/scoring system for TEDT requires quantities of HITS, MISSES, recall, precision, and speed to be calculated on a specific data snapshot of Twitter. Otherwise, the systems cannot be compared to each other. A typical scoring system can be known as 10 with α, β as weights. Other scores of Score 2 and Score 3 are the precision and recall of algorithm on the dataset. Score 1 (t) � α × Hits(t)β × Misses(t) + Speed. (10).
At 6 hours post-infection, cells were fixed with 4% formaldehyde and 0. 2% glutaraldehyde for 10 min. Cells were then washed with 1´ PBS (Gibco®, Life Technologies) and stained with 10 µg/µL DAPI (Invitrogen, ThermoFisher Scientific) in 1´ PBS solution (Invitrogen, ThermoFisher Scientific) for 5 min. Fixed cells were mounted on a glass slide with Fluoromount-G mounting media (Southern Biotech). Images were acquired using confocal LSM780 (Zeiss) inverted microscope with an argon laser (514x nm) and with ´40 immersion oil objective. All pictures obtained from BiFC experiments were treated using FigureJ software (NIH).
step (dithiothreitol 5 mM -iodoacetamide 10 mM), proteins were digested overnight with sequencing-grade porcine trypsin (1:25, w/w, Promega, Fitchburg, MA, USA). The resulting vacuum-dried peptides were resuspended in water containing 0. 1% (v/v) formic acid (solvent A). One sixth of the peptide mixtures were analyzed by nanoLC-MS/MS an Easy-nanoLC-1000 system coupled to a Q-Exactive Plus mass spectrometer (Thermo-Fisher Scientific, USA) operating in positive mode. Five microliters of each sample were loaded on a C-18 precolumn (75 μm ID × 20 mm nanoViper, 3 µm Acclaim PepMap; Thermo) coupled with the analytical C18 analytical column (75 μm ID × 25 cm nanoViper, 3 µm Acclaim PepMap; Thermo). Peptides were eluted with a 160 min gradient of 0. 1% formic acid in acetonitrile at 300 nL/min. The Q-Exactive Plus was operated in data-dependent acquisition mode (DDA) with Xcalibur software (Thermo-Fisher Scientific).
Survey MS scans were acquired at a resolution of 70K at 200 m/z (mass range 350-1250), with a maximum injection time of 20 ms and an automatic gain control (AGC) set to 3e6. Up to 10 of the most intense multiply charged ions (≥2) were selected for fragmentation with a maximum injection time of 100 ms, an AGC set at 1e5 and a resolution of 17. 5K. A dynamic exclusion time of 20 s was applied during the peak selection process. Database search and mass-spectrometry data post-processing Data were searched against a database containing Human and Viruses UniProtKB sequences with a decoy strategy (GFP, Human and Sindbis Virus SwissProt sequences as well as Semliki Forest Virus SwissProt and TrEMBL sequences (releases from January 2017, 40439 sequences)). Peptides were identified with Mascot algorithm (version 2.
3, Matrix Science, London, UK) with the following search parameters: carbamidomethylation of cysteine was set as fixed modification; N-terminal protein acetylation, phosphorylation of serine / threonine / tyrosine and oxidation of methionine were set as variable modifications; tryptic specificity with up to three missed cleavages was used. The mass tolerances in MS and MS/MS were set to 10 ppm and 0. 02 Da respectively, and the instrument configuration was specified as "ESI-Trap". The resulting. dat Mascot files were then imported into Proline v1. 4 package (http://proline. profiproteomics. fr/) for post-processing. Proteins were validated with Mascot pretty rank equal to 1, 1% FDR on both peptide spectrum matches (PSM) and protein sets (based on score). The total number of MS/MS fragmentation spectra (Spectral count or SpC) was used for subsequent protein quantification in the different samples.
Exploratory and differential expression analysis of LC-MS/MS dataMass spectrometry data obtained for each sample were stored in a local MongoDB database and subsequently analyzed through a Shiny Application built upon the R/Bioconductor packages msmsEDA (Gregori J, Sanchez A, Villanueva J (2014). msmsEDA: Exploratory Data Analysis of LC-MS/MS data by spectral counts. R/Bioconductor package version 1. 22. 0) and msmsTests (Gregori J, Sanchez A, Villanueva J (2013). msmsTests: LC-MS/MS Differential Expression Tests. R/Bioconductor package version 1. 22. 0). Exploratory data analyses of LC-MS/MS data were thus conducted, and differential expression tests were performed using a negative binomial regression model. The p-values were adjusted with FDR control by the Benjamini-Hochberg method and the following criteria were used to define differentially expressed proteins: an adjusted p-value < 0.
Acknowledgments The authors would like to thank members of the  Pfeffer laboratory for discussion, Pr. Bryan Cullen  for the kind gift of the HEK293T NoDice and NoDice/DPKR cell lines and  Dr. Oliver Vugrek  for the BiFC plasmids. This work was funded by the  European Research Council  ( ERC-CoG-647455 RegulRNA ) and was performed under the framework of the  LABEX :  ANR-10-LABX-0036_NETRNA  and  ANR-17-EURE-0023 , which benefits from a funding from the state managed by the  French National Research Agency  as part of the  Investments for the future program. This work has also received funding from the  People Programme  ( Marie Curie Actions ) of the European Union's  Seventh Framework Program  ( FP7/2007-2013)  under  REA  grant agreement n°  PCOFUND-GA-2013-609102 , through the  PRESTIGE program  coordinated by  Campus France  (to EG), and from the  French Minister for Higher Education, Research and Innovation.
Although phytoplasma studies are still hampered by the lack of axenic cultivation methods, the availability of genome sequences allowed dramatic advances in the characterization of the virulence mechanisms deployed by phytoplasmas, and highlighted the detection of signal peptides as a crucial step to identify effectors secreted by phytoplasmas. However, various signal peptide prediction methods have been used to mine phytoplasma genomes, and no general evaluation of these methods is available so far for phytoplasma sequences. In this work, we compared the prediction performance of SignalP versions 3. 0, 4. 0, 4. 1, 5. 0 and Phobius on several sequence datasets originating from all deposited phytoplasma sequences. SignalP 4. 1 with specific parameters showed the most exhaustive and consistent prediction ability.
However, the configuration of SignalP 4. 1 for increased sensitivity induced a much higher rate of false positives on transmembrane domains located at N-terminus. Moreover, sensitive signal peptide predictions could similarly be achieved by the transmembrane domain prediction ability of TMHMM and Phobius, due to the relatedness between signal peptides and transmembrane regions. Beyond the results presented herein, the datasets assembled in this study form a valuable benchmark to compare and evaluate signal peptide predictors in a field where experimental evidence of secretion is scarce. Additionally, this study illustrates the utility of comparative genomics to strengthen confidence in bioinformatic predictions.
, 2004). In SignalP 4. 0 the HMM part was removed, and two neural networks were made available, including one that was trained with transmembrane sequences as negative data to improve the discrimination between signal peptides and transmembrane domains  (Petersen et al. , 2011). SignalP 4. 0 was updated to 4. 1 when a greater choice of options was allowed  (Nielsen, 2017). Compared to SignalP version 4, SignalP 5. 0 benefits from an internal algorithm more suited to signal peptides, a modified output score, and training on a dataset grouping (rather than separating) sequences from Eukarya, Gram-negative bacteria, Gram-positive bacteria, and Archaea  (Almagro Armenteros et al. , 2019). SignalP 5. 0 also simultaneously differentiates Sec and Tat (twin-arginine translocation pathway, unidentified in phytoplasmas so far) substrates without having to rely on specialized separated software.
Benchmark tests indicated that incremental versions of SignalP showed superior performance to previous versions, with the notable exception that SignalP 5. 0 ranked second after SignalP 4. 1 regarding the prediction of type I signal peptides (i. e. , cleaved by type I signal peptidase) in Gram-positive bacteria  (Almagro Armenteros et al. , 2019). Phobius was released in 2004 and is based on a HMM. It was designed with an emphasis on the separation of transmembrane domains and signal peptides, and is available online  (Käll et al. , 2004 (Käll et al. , , 2007)). An important point is that the training datasets of SignalP 4. 0 (and presumably of other versions) did not include sequences from Mycoplasma and related genera  (Petersen et al. , 2011). On the other hand,  Bai et al. (2009)  showed that SignalP 3.
0 efficiently detected a signal peptide in a set of Mollicutes proteins. It therefore appeared of special interest to determine how these software products perform on phytoplasma sequences. In this work, we compared the prediction results of these signal peptide predictors, in order to enable informed decisions when mining phytoplasma genome sequences for candidate effectors. We restricted our study to software and configurations already used in previous reports about phytoplasma effector analyses (i. e. , SignalP versions 3. 0, 4. 0, 4. 1, 5. 0, and Phobius), with the aim to provide a reasonably thorough presentation of the results, and included the TMHMM software for comparison. We examined prediction results for all publicly available phytoplasma sequences that code for the documented effectors Tengu, SAP11, and SAP54 and homologs, for substrate-binding proteins, putative effectors, and specific membrane proteins.
MATERIALS AND METHODS Identification of the phytoplasma homologous sequences was performed using the BLASTP software  (Camacho et al. , 2009)  against the 'non-redundant' database  (NCBI Resource Coordinators, 2018)  with default parameters at the NCBI website. For the SAP54 dataset, the sequences from the phyl-B group of  Iwabuchi et al. (2020)  were excluded as they did not show the phyllody inducing phenotype observed with other members, although they may still have a functional signal peptide and yetto-discover functions. For Amp and Imp, that can be highly variable, we first extracted from draft or complete phytoplasma genomes the coding sequences located between groEL and nadE, and DnaD and PyrG respectively. We then used the translated sequences as BLASTP queries to retrieve the full dataset of Amp and Imp homologous sequences.
To ensure that our dataset was as exhaustive as possible, a keyword search ("antigenic membrane protein phytoplasma" and "imp" respectively) was also performed at Genbank, and validated hits from both strategies were merged. For all datasets, sequences that were obviously truncated in Nor C-terminal, or exceeding the expected size, were removed from the final sets of hits. Sequence alignments were built with either Muscle 3. 8. 31 (Edgar, 2004)  or Clustal Omega 1. 1. 0 (Sievers et al. , 2011). Alignments were visualized and formatted with Seaview 5. 0. 4  (Gouy et al. , 2010)  and Jalview 2. 11. 1. 0 (Waterhouse et al. , 2009). Pred-Lipo  (Bagos et al. , 2008)  and LipoP 1. 0  (Juncker et al. , 2003)  were queried online at http://bioinformatics. biol. uoa. gr/PRED-LIPO and http://www. cbs.
dtu. dk/services/LipoP respectively. The eulerr package 6. 0. 0 (Larsson, 2020) was used to draw initial Euler diagrams that were then adapted. Color schemes used in Figures  2, 4  have been elaborated by Paul Tol 1. The parameters used for the various versions of SignalP were set following common practice reported for phytoplasma sequences. SignalP 3. 0  (Nielsen and Krogh, 1998; Bendtsen et al. , 2004)  was configured as described in  Bai et al. (2009). Thus, in the present study, "SignalP3HMM" refers to the use of the online version of SignalP 3. 0 at https://services. healthtech. dtu. dk/service. php?SignalP-3. 0, using the following parameters: organism group: "Gram-positive bacteria"; method: "Hidden Markov Models, " Truncation: "Truncate each sequence to max. 70 residues" (default).
1. We took care to check that the various predictions were identical for identical signal peptides, and only very few exceptions were found: BAD04276. 1 and WP_069028310. 1 from the DppA family for SignalP3HMM predictions (data not shown), and the accessions mentioned in Figure  5. In such cases, the sequences were split into two subgroups and each subgroup was then considered as a group. To build the phytoplasma gene families, the get_homologues software was used  (Contreras-Moreira and Vinuesa, 2013). Phytoplasma genomes downloaded from Genbank were provided 2 https://services. healthtech. dtu. dk/service. php?SignalP-4. 1 as input to get_homolog using the "-G -t 0" options and leaving all other options as default.
0, 4. 0, 4. 1, and 5. 0), as well as Phobius (signal peptide prediction referred to as "Phobius_SP" in this study). For SignalP version 4 we collected predictions using either the default thresholds (mentioned as "SignalP4. 0"), or the 'Sensitive' 4. 1 cut-off values (see section "Materials and Methods"). Given that both signal peptide and transmembrane segments share the presence of a hydrophobic core, predictions performed by the TMHMM software package (devoted to detection of transmembrane segments) were also included, as well as the cumulated predictions of signal peptide and transmembrane domain by Phobius (see section "Materials and Methods"; indicated by "Phobius_SP_TM" in the text). In both cases, prediction of a transmembrane domain in the 50 N-terminal amino acids was interpreted as a possible signal peptide.
The prediction results for effector homologs with functional evidence readily point to some potential shortcomings for specific predictors (Table  4 ). Indeed, not all of the sequences were predicted to contain a functional signal peptide by all predictors. The performance of the various predictors differed depending on the homologous sequence set, revealing sequencedependent sensitivity. For the Tengu dataset, the accession WP_011412450. 1 that was not detected by SignalP3HMM and SignalP4. 0 is present notably in the AYWB (aster yellows witches' broom) phytoplasma genome (Table  1  and Supplementary  Table 1 ). Consistently, this sequence was not listed as a putative effector in the original publication on AYWB effectors  (Bai et al. , 2009) , which relied on SignalP 3. 0 HMM. Available evidence suggest that the corresponding mature protein is active in planta  (Sugawara et al.
, 2013). In the SAP11 dataset, SignalP3HMM also did not detect a putative signal peptide for three related sequences, unlike all other predictors. The associated mature protein from onion yellows strain OY-M was demonstrated to induce a bushy phenotype and to destabilize expected targets  (Chang et al. , 2018). Regarding the SAP54 WP_011160837. 1 Y 1 Y BAN15032. 1 Y 2 Y* TKA87909. 1 Y 3 Y* BAN15035. 1 Y 3 - WP_011412450. 1 N (SignalP3HMM, SignalP4. 0) 4 Y WP_024563540. 1 Y 5 Y BAN15038. 1 Y 6 Y The second column indicates if a signal peptide is predicted by all of the SignalP3HMM, SignalP4, SignalP4. 1sensitive, Phobius_SP, Phobius_SP_TM software (mention "Y" = yes). If not, cells were highlighted in gray (for better readability) with a "N" (no), and predictors that did not produce a positive prediction are mentioned.
1 Y 12 Y WP_054598480. 1 Y 12 - ARE29789. 1 Y 12 - WP_011412651. 1 Y 13 Y WP_069028078. 1 Y 14 Y WP_017191934. 1 Y 13 - QKX95086. 1 Y 15 - WP_017193717. 1 Y 15 Y WP_012504341. 1 Y 16 Y AKL79269. 1 Y 17 Y* QGX07542. 1 Y 17 - Column content is described in the caption of Table  1. dataset, Phobius_SP predicted a signal peptide in only 11 out of 14 sequences. However, for the remaining three sequences, that originate from AYWB phytoplasma, Japanese hydrangea phyllody phytoplasma, and Vc33 phytoplasma, Phobius predicted a N-terminal transmembrane domain instead of a signal peptide. This suggests that transmembrane segment identification could provide, in some cases, useful indication of putative signal peptides. This is independently confirmed by the fact that TMHMM identified a putative transmembrane segment for all sequences of the datasets.
If homologs without functional evidence are included in the analysis, further cases of discrepancies between predictors are revealed. Phobius_SP detected a signal peptide for 86% of unique N-terminal sequences, followed by SignalP3HMM (93%), SignalP4. 0 and SignalP5 (98%) (Table  4 ). Predictions from SignalP4. 1sensitive, TMHMM, Phobius_SP_TM suggested the presence of a signal peptide in all (100%) of the sequences. Only two accessions were predicted to be devoid of a signal peptide by at least two predictors: WP_011412450. 1 (Tengu dataset) and BBF24902. 1 (SAP54 dataset). A common theme between these two sequences is the presence of a negatively charged residue in the n-region of the putative signal peptide (Figure  1 ). The three sequences from the SAP11 dataset with a WP_011412506. 1 N (Phobius_SP) 1 Y PQP79243.
Predictions for SBP Genes In their seminal paper,  Bai et al. (2009)  emphasized that the detection of signal peptides in sequences similar to solute-binding proteins (SBP) strengthened the signal peptide predictions. Sequence redundancy at the N-terminus was addressed by counting only unique signal peptide sequences (each signal peptide sequence is associated with a signal peptide group as indicated in Tables  1 2 3 ). Indeed, SBP are components of ATP-binding cassette (ABC) transporters that bind the substrate to be translocated. In bacteria, SBP are usually located in the periplasm (Gram-negative bacteria) or tethered to the membrane as lipoproteins (Grampositive bacteria) and thus usually possess a signal sequence allowing translocation across the plasma membrane. In order to evaluate the performance of signal peptide predictors on SBP sequence datasets, we used the BLASTP software to identify phytoplasma sequences similar to the AYWB SBPs described in  Bai et al.
1 and WP_050337100. 1 from two different strains of 'Ca. P. phoenicium, ' znuA gene family). In addition, the sequences identified by using BLASTP showed sequences signatures associated with SBP activity (accessions cl21456, cd00995, cl01709, or cl00262 of NCBI Conserved Domain Database). We confirmed that their encoding genes are located at close proximity (presumably within same operon) to other ABC transporter components, i. e. , ATP-binding and permease subunits. Exceptions were dppA sequences of 'Ca. P. oryzae, ' Cynodon dactylon phytoplasma, and sugarcane grassy shoot phytoplasma, that are isolated but with the corresponding operon being devoid of SBP gene (Supplementary Figure  1  and data not shown). Finally, a few SBP sequences of the DppA family were found to be duplicated or even triplicated within their operon, notably in the 16SrI and 16SrXII groups, explaining the higher number of sequences for this family (Supplementary Table  4 ).
, where most of the identified SBP are predicted to contain a type II signal peptide by Pred-Lipo and LipoP (Supplementary Table  7 ), and contain a typical lipobox (data not shown). In addition, Acholeplasma genomes possess readily detectable lgt and lspA genes, and protein acylation was demonstrated in this genus  (Serebryakova et al. , 2011). These observations raise interesting questions about the evolution of phytoplasmas from acholeplasma-like ancestors and how the transition from lipoprotein SBP to non-lipoprotein SBP occurred. On a functional point of view, if phytoplasma SBPs are not lipoproteins, how does it happen that they do not diffuse away from phytoplasma cells when their peptide signal is cleaved? A hypothetical scenario could be that the signal peptide is not cleaved, acting as a membrane anchor.