dataesr/scientific-paragraphs-categorization huggingface dataset:
2) There is no direct way to evaluate the dataset towards its effectiveness for the real-world application. 3) It suffers from what is known as concept drift, as the annotation process is detached from the target application, the dataset might get obsolete over time. In this regard, we have dealt specifically with the semantic annotation problem, using an adaptive, integrated, and personalized annotation process. By adaptive, we mean that target applications do not require pre-existing training data, rather it depends on the usage data from the user. The machine learning model then adapts towards the actual goal of the application over time. Instead of developing a standalone annotation tool, the collection of training examples is integrated inside a real-world application. Furthermore, our approach is personalized in a sense that the training examples being collected are directly related to the need of the user for the application at hand.
As far as our knowledge concerned, PAR4SEM is the first tool in the semantic and NLP research community, where adaptive technologies are integrated into a real-world application. PAR4SEM is open source 1 and the associated data collected for the lexical simplification use-case are publicly available. The live demo of PAR4SEM is available at https://ltmaggie. informatik. uni-hamburg. de/par4sem. 2 System Architecture of PAR4SEM The PAR4SEM system consists of backend, frontend, and API components. The backend component is responsible for NLP related pre-processing, adaptive machine learning model generation, data storage, etc. The frontend component sends requests to the backend, highlights target units, presents candidate suggestions, sends user interaction to the database and so on. The API component transforms the frontend requests to the back-1 https://uhh-lt.
The Backend Component The backend component consists of several modules. For the adaptive paraphrasing system, the first component is to identify possible target units (such as single words, phrases, or multi-word expressions). For our lexical simplification use-case, the target units identification component is instantiated with the datasets obtained from  Yimam et al. (2017a Yimam et al. ( ,b, 2018)). The adaptive target identification unit then keeps on learning from the usage data (when the user highlights portions of the text to get paraphrase candidate suggestions). Once target units are marked or recognized (by the target unit identification system), the next step is to generate possible candidate suggestion for the target unit (paraphrase candidates). The candidate suggestion module includes candidate generation and candidate ranking sub-modules.
Paraphrasing Resources Paraphrase resources are datasets where target units are associated with a list of candidate units equivalent in meaning, possibly ranked by their meaning similarity. One can refer to the work of  Ho et al. (2014)  about the details on how paraphrase resources are produced, but we will briefly discuss the different types of paraphrase resources that are used in generating candidate suggestions for PAR4SEM. PPDB 2. 0: The Paraphrase Database (PPDB) is a collection of over 100 million paraphrases that was automatically constructed using a bilingual pivoting method. Recently released PPDB 2. 0 includes improved paraphrase rankings, en-tailment relations, style information, and distributional similarity measures for each paraphrase rule  (Pavlick et al. , 2015). WordNet: We use WordNet synonyms, which are described as words that denote the same concept and are interchangeable in many contexts  (Miller, 1995) , to produce candidate suggestions for a given target unit.
We have retrieved the top 10 similar words to the target units as candidate suggestions. The ranking adaption model first generates candidate paraphrases using the paraphrase resource datasets (see Section 2. 1. 1). As all the candidates generated from the paraphrase resources might not be relevant to the target unit at a context, or as the number of candidates to be displayed might be excessively large (for example the PPDB 2. 0 resource alone might produce hundreds of candidates for a target unit), we re-rank the candidate suggestions using a learning-to-rank adaptive machine learning model. Figure  2  displays the process of the adaptive models while Figure  3  displays the pipeline (as a loop) used in the generations of the adaptive models. The whole process is iterative, interactive, and adaptive in a sense that the underlying models (both target adaption and ranking adaption) get usage data continuously from the user.
Backend Technologies The backend components are fully implemented using the Java programming language. Text segmentation such as sentence splitting, tokenization, lemmatization, and parts of speech tagging is handled using the Apache OpenNLP 2  library. For the target unit identification system, we have used Datumbox 3  , a powerful open-source machine learning framework written in Java. We have used specifically the Adaboost classification algorithm. For the ranking model, RankLib, which is the well-known library for the learning to rank algorithms from the Lemur 4  project is integrated. All the data related to PAR4SEM interactions (usage data, time, and user details) are stored in a MySQL database.
RESTful API Component Semantic technologies, those like PAR4SEM incorporates highly dynamic dimensions. One dimension is that the paraphrase resources can be varied depending on the need of the application. Another dimension is that the application can be in different languages. If the backend and the frontend technologies are highly coupled, it will be difficult to reuse the application for different languages, resources, and applications. To solve this problem, we have developed PAR4SEM using a RESTful API (aka. microservices) as a middleware between the backend and the frontend components. The API component consumes requests (getting target units and candidate suggestions) or resources (saving usage data such as selection of new target units, user's preference for candidate ranking, user and machine information) from the frontend and transfers them to the backend.
Difficult words or phrases are automatically highlighted so that workers can click and see possible candidate suggestions. The experiment was conducted over 9 iterations, where the ranking model is updated using the training dataset (usage data) collected in the previous iterations. The first iteration does not use ranking model but candidates are presented using a default languagemodel-based ranking. In  (Yimam and Biemann, 2018)  we have shown that the adaptive paraphrasing system adopts very well to text simplification, improving the NDCG  (Wang et al. , 2013)  score from 60. 66 to 75. 70. Figure  6  shows the learning curve for the different iterations conducted in the experiment.
Conclusion In this paper, we have described PAR4SEM, a semantic writing aid tool based on an embedded adaptive paraphrasing system. Unlike most annotation tools, which are developed exclusively to collect training examples for machine learning applications, PAR4SEM implements an adaptive paraphrasing system where training examples are obtained from usage data. To the best of our knowledge, PAR4SEM is the first of its kind where machine learning models are improved based on usage data and user feedback (correction of suggestions) for semantic applications. PAR4SEM is used in a text simplification use-case. Evaluation of the system showed that the adaptive paraphrasing system for text simplification successfully adapted to the target task in a small number of iterations. For future work, we would like to evaluate the system in an open task setting where users can paraphrase resp.
Colocalization and SG Quantification, and Statistics Colocalization analysis was performed using a custom-made Matlab script to measure the Manders Colocalization coefficients (MCC)  (Manders et al, 1993). Briefly, nuclei were segmented in the DAPI channel using a watershed algorithm. Cell boundaries were then approximated by dilating the nuclear mask. Within each cell, G3BP1 (SG) and 18S rRNA signal were segmented by intensity thresholding using the Otsu method. MCCs between each channel (G3BP1 (green, G), and 18S rRNA (red, R)) were then calculated as follows: 𝑀 ! = ! !,!"#"!$# ! ! , where G i, colocal = G i if R i >0 and G i, colocal = 0 if R i = 0; and 𝑀 ! = ! !,!"#"!$# ! ! , where R i, colocal = R i if G i >0 and R i, colocal = 0 if G i = 0; Only the M G is shown for simplicity, and because the 18S signal was diffuse resulting in only a small fraction colocalizing with SG even under arsenite-treated conditions.
Each data point represents the Manders coefficient for each cell spanning 8 fields per condition. Microscopy was performed with a 100X objective. A Student's t-test was used to measure statistical significance (p<0. 001, ***). Results from all panels are representative of a minimum of three experimental replicates. Figure  3 : Chronic nutrient starvation induces static stress granules that do not exchange material with translating mRNPs. A, U2OS cells were either treated with 250µM arsenite for 1h or starved for 16h prior to fixation and staining with antibodies against G3BP1 (green) and TiaR (red). During the last hour of stress, 20µM emetine or 50µM cycloheximide were added to trap SG constituents within translating mRNPs by stalling elongating ribosomes on mRNAs to assess exchange of material with SG.
Strikingly, $5,000 other transcripts were either upregulated or downregulated in the KO, when compared to the WT embryos, while up to half that number was altered in the KO versus HET comparison (Figures  5C, 5D , and S6D). Examination of these altered-gene lists indicates that up to 1,000 genes are either commonly up-or downregulated in the KO when compared to both WT and HET embryos (Figure  5E ). A previous study identified key transcription and chromatin factors that define specific developmental stage transcriptomes  (Mohammed et al. , 2017). Examination of these factors in our datasets did not reveal any altered expression profile between the genotypes (Figures  S6B  and S6C ). A Gene Ontology (GO) term analysis of the altered transcripts revealed an upregulation in splicing-related factors (Table  S3 ), but analysis of splice junction reads did not reveal any changes in the KO embryos (Figure  5F ).
Constructs for mammalian cell expression Coding sequence for full-length (FL) human METTL16 (hMETTL16; 562 aa; Accession number NP_076991) was amplified from human HeLa cell total RNA by reverse transcription-PCR (RT-PCR). A mammalian expression vector (pCI-neo vector backbone) allowing production of 3xFLAG-HA tagged proteins from a cytomegalovirus (CMV) promoter was used. Sequence of the tag: ATG GACTACAAAGACCATGACGGTGATTATAAAGATCATGATATCGATTACAAGGATGACGATGACAAGggcggcagcggcTACCCATATG ATGTTCCAGATTACGCT.
Constructs for bacterial expression Constructs covering only the core methyltransferase domain of hMETTL16 (1-291 aa) or its point mutant/deletion versions were cloned into the bacterial expression vector (pETM-11-SUMO vector; EMBL Protein Expression and Purification Core Facility) as 6xHis-Strep-SUMO-TEV fusions. The following constructs were prepared: METTL16-DN: 40-291 aa, N-terminal deletion version similar to that used in PDB ID: 2H00 [Structural Genomics Consortium (SGC)]. METTL16-core: 1-291 aa. METTL16-core mutants 1. Single amino acid changes: K5A, R10A, R12A, K14A, K16A, K5E, K10E, K10D, K47E, R74E, R82E, F187G, R279E, R282E. 2. MUT1: five residues (K5, R10, R12, K14, and K16) mutated to As. 3. MUT2: two residues (K26 and K31) mutated to As. 4. MUT3: combination of MUT1 and MUT2 sites mutated to As.
Crystallization and data collection Optimal crystallization conditions for full-length human METTL16 (1-562 aa) and the human METTL16-core (1-291 aa) proteins were sought by robot screening at the High Throughput Crystallization Facility at EMBL Grenoble, France. Only the METTL16-core gave crystals in this screen. Once conditions were identified, crystals were manually produced: 2 mL protein solution at 13 mg/ml was manually mixed with 2 mL reservoir solution using the hanging drop method at room temperature. The reservoir conditions used were either 0. 2 M di-sodium tartrate, 20% (w/v) PEG 3350 or 0. 1 M Bis-Tris propane, pH 6. 5, 0. 2 M potassium-sodium tartrate, 20% (w/v) PEG 3350. We additionally crystallized the human METTL16-DN (40-291 aa) version using conditions previously described in PDB ID: 2H00 [Structural Genomics Consortium (SGC)].
Structure determination and refinement The hMETTL16-core (1-291 aa) structure was solved by molecular replacement using the METTL16-DN, N-terminal deletion structure (PDB ID: 2h00) as a search model with  Phaser (McCoy et al. , 2007). Several rounds of manual building with Coot  (Emsley et al. , 2010) , and structure refinement with BUSTER  (Bricogne et al. , 2016)  were carried out for all structures. MOLPROBITY  (Chen et al. , 2010)  was used for model validation and all the crystallographic information is summarized in Table  1. The atomic coordinates and structure factors have been deposited in the Protein Data Bank with the accession codes: 6GFN (METTL16-core, crystal form 1), 6GT5 (METTL16-core, crystal form 2) and 6GFK (METTL16-DN). For modeling a bound RNA into the METTL16-core structure we used a tRNA from PDB ID: 2ZZM  (Goto-Ito et al.
, 2009). Structural figures were prepared with PyMOL (Schro ¨dinger, LLC). The electrostatic potential was calculated using PDB2PQR  (Dolinsky et al. , 2004)  and displayed in PyMOL using the APBS plugin. For modeling of adenosine into the METTL16-core structure we used a 20-mer DNA from the complex structure of the MTase EcoP15I (PDB: 4ZCF, chain B)  (Gupta et al. , 2015). While this study was in preparation, Ruszkowska et al. reported the crystal structure (PDB ID: 6B92) of METTL16 core MTase domain  (Ruszkowska et al. , 2018). A comparison with our structure (PBD ID: 6GFN) reveals a very high degree of overlap (rmsd = $0. 38 A ˚for superimposition of 187 C a atoms) (Figure  S1F ). Nevertheless, there are some differences. First, the N terminus in our structure is longer by four amino acids.
Second, there are differences in labile loops between a4-b3 (96-99 disordered in ours); a7-b6 (poor density in ours); and b6-a8 (the long catalytic loop). The catalytic NPPF residues (in both our METTL16-core structures, Table  1 ) are more similar to their apo form (PDB ID: 6B91) than their SAH-bound form (PDB ID: 6B92)  (Ruszkowska et al. , 2018). They also have eight additional residues on a8, but these are not helical as in our METTL16-DN structure (PBDB ID: 6GFK) (Figure  1D ). In vitro transcription of RNA substrates for methylation assay Templates for in vitro transcription (of full-length human MAT2A mRNA hairpin 1 and human U6 snRNA RNA)  (Pendleton et al. , 2017)  were amplified in a PCR reaction to prepare a single-stranded DNA template with T7 promoter sequence. The T7 promoter sequence 5ʹ-TAATACGACTCACTATAGGG À3ʹ was introduced at the 5ʹ end of forward primer followed by a specific sequence.
Gel was run at 12 W for 1 h 30 min. Then, dried in a gel dryer (Bio-Rad, model 583) with a gradual heating and cooling program, 80 C for 3 h. Dried gel was exposed to a phosphor screen BAS (GE Healthcare) for 24 h. The phosphor screen was scanned in a Typhoon FLA 9500 laser scanner (GE Healthcare) at 700V and 100 mm pixel size using control software (1. 1 version) for Typhoon FLA 9500. Scans were analyzed using ImageQuant TL 8. 1 software (GE Healthcare). The quality of RNAs used for methylation assays were verified by Methylene Blue staining. In some experiments, after the methylation reaction products were resolved by urea-PAGE, the gel was stained with Methylene Blue, imaged to verify integrity of RNAs present in the reaction (Figure  S3A ) and then dried for exposure to the phosphor storage screen to detect radioactivity signals (Figure  3A ).
Tubes were placed around 4 cm from UV lamp (254 nm) and irradiated for 5 min. (UV Stratalinker 2400, Stratagene). After UV irradiation, samples were boiled for 5 min in SDS loading buffer and resolved by 12% SDS-polyacrylamide gel electrophoresis. Gel was dried in a gel dryer (Bio-Rad, model 583) with a gradual heating and cooling program, 80 C for 3 h. Dried gel was transferred to the cassette and exposed with a phosphor screen BAS (GE Healthcare) for 24 h. After exposure, phosphor screen was scanned in a Typhoon FLA 9500 laser scanner (GE Healthcare) at 700V and 50 mm pixel size. Control software for Typhoon FLA 9500 was at 1. 1 version. Scans were analyzed using ImageQuant TL 8. 1 software (GE Healthcare).
no. 11815016001). After, beads were collected by gentle centrifugation (500 x g for 1 min at 4 C) and eluate was discarded. Beads were washed 5 times with wash buffer (50 mM Tris pH 7. 4, 150 mM NaCl, 0. 1% Triton X-100, 1 mM EDTA) Then, beads were transferred to fresh 1. 5 mL eppendorf tubes and 40 mL of 2x Laemmli buffer (4% SDS, 20% glycerol, 120 mM Tris-HCl pH 6. 8, 10% b-mercaptoethanol, 0. 02% bromophenol blue) was added. Beads were boiled at 95 C for 5 minutes and stored at À20 C. Proteins were identified by mass spectrometry at the Functional Genomics Center Zurich (ETH Zurich) (Figure  S5F ). Database searches were performed using the Mascot (SwissProt, human) search program. Applied settings: 1% protein false detection rate (FDR), min. 2 peptides per protein, 0. 1% peptide FDR. Isolation of METTL16 complexes from mouse testes and spleen An aliquot of 80 ml of Dynabeads Protein A slurry (ThermoFisher, 10001D) was transferred to a fresh Eppendorf tube and washed three times with 1 mL of 20 mM sodium phosphate with 0.
8, 10% b-mercaptoethanol, 0. 02% bromophenol blue) was added. Beads were boiled at 95 C for 5 minutes and stored at À20 C. Proteins in the samples were identified at the Functional Genomics Center Zurich (ETH Zurich) with the shotgun mass spectrometry analysis (Figure  S5G ). Database searches were performed using the Mascot (SwissProt, human) search program. Applied settings: 1% protein false detection rate (FDR), min. 1 peptides per protein, 0. 1% peptide FDR.
Mass spectrometry Mass spectrometry to confirm purified recombinant proteins were carried out at the Proteomics Core Facility, EMBL, Heidelberg. Identification of components within an immunoprecipitated complex was carried out at the Functional Genomics Center Zurich (ETH Zurich) using the shotgun mass spectrometry analysis. Database searches were performed using the Mascot (SwissProt, all species) search program. Applied settings if not stated differently are 1% protein false detection rate (FDR), min. 2 peptides per protein, 0. 1% peptide FDR.
Figure6. A Model for METTL16 Function during Early Embryonic Development (A) Structural comparison of METTL16 core and METTL3/METTL14 complex. METTL3 (PDB 5IL2), colored purple, was superimposed on the METTL16-core-SAH (PDB 6GFN), colored in green (core) and red (N-terminal). Gate loops 1 and 2, and the interface loop of METTL3, are colored in blue, orange, and yellow, respectively. The disordered loop in our METTL16-core is shown as a dotted line. (B) Surface charge representation of human METTL16-core domain with a modeled tRNA (from PDB: 2ZZM). See STAR Methods. The SAH bound in the catalytic pocket is shown. (C) A methyl-acceptor adenosine (orange) was modeled into the SAH binding site of METTL16 core (PDB 6GFN) by superimposition of an m 6 A DNA MTase, EcoP151 (PDB: 4ZCF, chain B). The sulfate binding site (as in Figure1F) overlaps with the adenosine base moiety.
ACKNOWLEDGMENTS We thank  Olivier Fazio  and  Leonardo Beccari  for help with mouse embryo collection,  Margot Riggi  for scientific illustrations,  Magdalena Wojtas  for the METTL3/14 protein complex, and  Johanna Mattay  for critical reading of the manuscript. We thank the following facilities: iGE3  Genomics Platform, University of Geneva , and the  EMBL Genomics core facility  for sequencing; the  Transgenic Mouse Facility, University of Geneva  for mouse creation; the  Functional Genomics Center, Zurich  for proteomics analyses. M. M. is grateful for a PhD Fellowship from the  Boehringer Ingelheim Fonds. This work was supported by grants to R. S. P. from the  Swiss National Science Foundation :  ERC Transfer Grant  (GermMethylation), Project Grant (Origin-of-pi), and funding from the  NCCR RNA & Disease.
2017). Library construction and sequencing were processed by Novogene (Beijing, PR China) using an Illumina HiSeq X Ten system (Illumina, San Diego, CA), according to the manufacturer instructions. We performed a de novo assembly as described previously  (Hinsinger and Strijk 2017; Jiang et al. 2016). Genome annotation was performed using CpGAVAS  (Liu et al. 2012). We reconstructed the 164,767 bp long chloroplast of Z. fischeri (GenBank accession number MH311043). It contained a LSC, SSC, and a pair of inverted repeats (IRa and IRb) of  90,226, 23,223, and 25,659  bp, respectively (Figure  1 ). We identified 127 genes, including 88 protein-coding genes, 31 tRNA genes, and 8 ribosomal RNA genes. Of these genes, 13 genes were duplicated in the IR regions, including 4 protein-coding genes (ndhB, ycf2, rps7, rps12), 5 tRNA genes (trnH-GTG, trnL-CAA, trnN-GTT, trnR-ACG, trnV-GAC), and 4 rRNA genes (4.
5S, 5S, 16S, 23S). Five genes (ndhA, ndhB, rpl2, rpoC1, rps12) contained one intron while two genes contained two introns (clpP, ycf3). The overall GC content of the plastome of Z. fischeri was 39. 7%, while the GC content in LSC, SSC, IRa, and IRb regions were 38. 8%, 36. 9%, 42. 4%, respectively. Fourteen plastomes of cycads were retrieved from GenBank (accessed 2018/05/15), plus Ginkgo biloba as an outgroup (Figure  1 ), and aligned with MAFFT  (Katoh and Standley 2013). We built a maximum likelihood (ML, TPM1uf þ I þ G model, 1000 bootstraps) tree using PHYML v3. 3  (Guindon et al. 2009 ). All but one nodes were highly supported (BP 94%), with the two Zamia clustering together. The results of this phylogenetic analysis are highly consistent with a previous plastome-based study  (Jiang et al.
quintana and B. henselae infectious status by real-time PCR, and the second half was subjected to MALDI-TOF MS analysis. Comparison of MS spectra from infected fleas and uninfected fleas revealed distinct MS profiles. Blind queries against our MALDI-TOF MS arthropod database, upgraded with reference spectra from B. quintana and B. henselae infected fleas but also non-infected fleas, provided the correct classification for 100% of the different categories of specimens tested on the first model of flea infection with Bartonella quintana. As for Bartonella henselae, 81% of exposed qPCR-positive fleas, 96% of exposed qPCR-negative fleas and 100% of control fleas were correctly identified on the second model of flea infection. MALDI-TOF MS successfully differentiated Bartonella spp. -infected and uninfected fleas and was also able to correctly differentiate fleas infected with Bartonella quintana and fleas infected with Bartonella henselae.
However, the number of entomologists specialized in flea's taxonomy is very small  [7]. Also, the accurate identification of most flea species requires specimens to be slide mounted for stereoscopic examination, which makes them unusable for further molecular studies  [8]. During entomological surveys, the determination of the infection status of fleas is also a critical step in assessing the risk of disease transmission. Molecular biology has been used in the last 20 years for arthropod identification and detection of their associated pathogens  [8]. However, this approach is limited by the availability of reference sequences in the GenBank database. The length of time and the cost associated with molecular biology approaches can be limiting as well. Recently, Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight Mass Spectrometry (MALDI-TOF MS), a technique based on the analysis of protein fingerprints, has revolutionized clinical microbiology, enabling the rapid identification of bacteria and fungi by comparing their protein profiles to a reference database  [9].