dataesr/scientific-paragraphs-categorization huggingface dataset:
As shown in Figure  4B , fingolimod produced an anti-inflammatory effect by downregulating CD40L, CD40, IRF4, CR2, IL-23A, CXCR5, CD24, CD2, CD27, and CD19, as well as an increase in the expression of the antiinflammatory cytokine genes and their receptors (IL-10, IL10RA, IL10RB, and IL-15) and the TNFRSF1A receptor gene, a regulator of inflammation. We also found an upregulation of antioxidant genes implicated in the control of reactive oxygen species (ROS), such as SOD2, CAT, MT1X, MGST1, and MAOA. All data for the expression levels, fold changes, and statistical significance in the DEG analysis are shown in published repository  (26)  and all data of RNA-seq are included in SRA database SRP132699  (28).
acKnOWleDgMenTs The authors wish to thank the donors and the Biobank Hospital Universitario Puerta De Hierro Majadahonda (HUPHM)/Instituto de Investigación Sanitaria Puerta De Hierro (IDIPHIM) for the human specimens used in this study. We thank the Radiology Unit of Hospital Universitario Puerta De Hierro Majadahonda (HUPHM) for assistance and technical support with acquisition and recording of magnetic resonance imaging and Ana Royuela for assistance with statistical analysis. The English language of this manuscript was revised by the American Journal Experts service. Editorial certificate ABAC-0C08-6B47-C915-9CEA.
Exome sequencing Genomic DNA was extracted from peripheral blood using standard methods. An exome sequencing approach was applied to the discovery cohort in order to identify genes associated with benign and aggressive disease courses. Exome sequencing was based on an Illumina HiSeq2000 sequencing platform and an Agilent's SureSelect Target Enrichment System for 51 Mb. Sequencing was done with a 50× of coverage and reads were aligned against the human reference genome (GRCh37/hg19 assembly) using the Burrows-Wheeler Alignment tool (BWA)  [10]. After reads mapping, low-quality reads and sequences flagged as PCR duplicates were removed from the BAM file using the Sequence Alignment/Map (SAM)  [10]  and Picard Tools. Unmasked variants were annotated considering all possible transcripts for each target gene, and in some cases variants located within a coding sequence when considering one isoform could be positioned within a non-coding region when considering another isoform, thus resulting also in the identification of intronic variants.
Selection of candidate single-nucleotide polymorphisms for validation For the variant calling process, different algorithms were applied, including VarScan  [11]  and the Genome Analysis Toolkit (GATK)  [12]. Python scripts were developed to combine variants. Variants annotation was based on Ensembl and NCBI databases. For the selection of significant variants, a Fisher exact test was applied to the benign and aggressive phenotypes. For prioritization and selection of the most promising variants, the following criteria were applied: (i) presence of two or more statistically significant variants per gene; (ii) odds ratio difference of the prevalence for the variant between disease phenotypes equal or higher than 2; (iii) absence of the variant in one disease phenotype and presence of the variant in ≥ 50% of patients belonging to the counterpart phenotype; (iv) missense variants, splice region variants, and variants reported as possible deleterious mutations; and (v) biological and functional relevance of the target genes to MS, as reported in the literature.
TaqMan OpenArray genotyping Genotyping of selected variants in the first validation cohort was performed using an OpenArray technology (Thermo Fisher Scientific, Massachusetts, USA) and following the manufacturer's instructions. Briefly, DNA samples were loaded into custom designed arrays using an OpenArray® AccuFill System (Thermo Fisher Scientific). QuantStudio™ 12K Flex system (Thermo Fisher Scientific) was used for sample amplification and fluorescent data collection. Hapmap samples with known genotype were included as internal controls of the process. Genotype was assigned using Taqman Genotyper Software (Thermo Fisher Scientific). Genotyping was performed by the Human Genotyping laboratory of the Spanish National Cancer Research Centre (CNIO).
The first validation cohort comprised 194 MS patients, 107 with benign and 87 with aggressive disease courses, and genotyping was conducting using an OpenArray technology. The second validation cohort consisted of 257 MS patients, 224 with benign and 33 with aggressive disease courses, and genotyping was performed on a MassArray iPLEX platform. Finally, a meta-analysis was performed in the two validation cohorts PBMC differed between MS patients carrying the minor allele associated with disease course and non-carrier patients. CPXM2 and NLRP9 expression was not detected in PBMC from MS patients (data not shown). IGSF9B expression was detected in PBMC from MS patients, although no differences were observed between carriers and non-carriers of the minor allele associated with benign disease course (Additional file 1: Figure  S1 ).
Funding This work was supported by (a) grants  PI10/02099 ,  FIS PI13/0879 ,  PI15/00513 ,  PI15/00587 ,  PI16/01259 ,  RD16/0015/0001 ,  RD16/0015/0002 ,  RD16/0015/0004  integrated in the  Plan Estatal de Investigación Científica y Técnica de Innovación I+D+I  and co-funded by the  Instituto de Salud Carlos III-Subdirección General de Evaluación  and the  Fondo Europeo de Desarrollo Regional (FEDER) , and  Red Española de Esclerosis Múltiple (REEM) ; (b)  Junta de Andalucía (JA)-FEDER  grant  CTS2704 ,  Ministerio de Economía y Competitividad  (grant number  SAF 2016-80595-C2-1-P ); (c)  MS Society of Canada Scientific Research Foundation  as part of the  CCPGSMS , and research undertaken thanks to funding from the  Canada Research Chair  ( 950-228408 ) program,  Michael Smith Foundation for Health Research  ( 16827 ) and  Canadian Institutes of Health Research  ( MOP-137051 ).
Results Four hundred and seventeen classes were created with a maximum of 14 children classes (average = 5). The ontology was then converted into a Web Ontology Language (. owl) format and made available online on Bioportal and GitHub under an Apache 2. 0 License. We extracted all structures delineated in our department since the opening in 2001. 20,758 structures were exported from our "record-and-verify" system, demonstrating a significant heterogeneity within a single center. All structures were matched to the ROS ontology before integration into our clinical data warehouse (CDW).
Background Large Hospital Information Systems (CERNA, EPIC) have no functionality specific to radiation oncology. There are currently several domain-specific softwares for radiation oncology planning: MOSAIQ (Elekta, Stockholm, Sweden), ARIA (Varian, Palo Alto, California, USA), Multiplan and Tomotherapy Data Management System (Accuray, Sunnyvale, California, USA) and iPlan (Brainlab, Munich, Germany). Each of these treatment planning and recordand-verify systems has its own anatomical structures labelling, which is not consistent across platforms, making it difficult to extract and analyze dosimetric data on a multicenter large scale. Using knowledge management with concept recognition, classification and mapping would lead to an accurate ontology that could be used to unify data in clinical data warehouses, thus facilitating data reuse and study replication in cancer centers.
The CARPEM (CAncer Research and PErsonalized Medicine) program is one of these eight SIRICs and HEGP is strongly involved in CARPEM. Data from 750,000 patients are stored in the HEGP clinical data warehouse, including 14,000 cancer patients treated with radiation. The HEGP clinical data warehouse (CDW) relies on the Informatics for Integrating Biology and the Bedside (i2b2) model-an open source infrastructure developed by Harvard Medical School and adopted by more than 130 academic hospitals around the world  [12, 13]. The i2b2 warehouse uses an Entity-Attribute-Value (EAV) data model for its adaptability and dynamic nature. Concepts are stored separately in a hierarchical data model. We designed the Radiation Oncology Structures ontology to unify data extracted from our Record-and-Verify System (ARIA) before integrating the data into our i2b2 CDW  [14].
Data integration into the clinical data warehouse (CDW) We evaluated the coverage of our ontology by using it to annotate all HEGP radiation oncology data with ROS concepts and integrate them into the HEGP clinical data warehouse. This study was approved by the IRB and ethics committee CPP Ile-de-France II. IRB Committee # 00001072. Study reference # CDW_2015_0024. Data on treatment planning and delivery in our institution (2001-2016) were extracted from the ARIA system using reverse engineering and the VARIAN ESAPI  [15]  for dose-volume histograms (DVH). Structures labels were sorted and filtered by number of occurrences. Each of them was then matched to the ROS ontology before integration into the CDW.
For the Clinical Target Volumes, subclasses were created for each risk level (low, intermediary and high). The Miscellaneous Volumes class includes an applicator volume (for brachytherapy), an external volume and a pacemaker volume. We mapped the ROS to the Unified Medical Language System (UMLS) using the UMLS terminology service Application Programming Interface (UTS API). The mapping was realized in four steps: first, we tried searching ROS terms using an exact match strategy (case insensitive, but whitespace sensitive). Then, we normalized the unmatched terms using the normalization function provided by the UMLS and attempt to match them against similarly normalized terms in the UMLS. In some cases, the UMLS normalization is too conservative and fails to identify an existing concept of the UMLS.
Use case from HEGP With the aim of integrating data from our Treatment Planning and Record-and-Verify System (ARIA, Varian, Palo Alto, California, United States) into our institutional CDW, we extracted the following dosimetric data: Course ID, Plan Setup ID, Reference Point ID, Total Dose Limit, Daily Dose Limit, Session Dose Limit. For each treatment fraction we included Date/ Time, duration of the activity and text comment. We also needed to include Dose-Volume Histograms (DVH) for each treatment. We ensured that structures labeling was consistent across the population. A total of 20,758 different structures labels were created for treatment planning between 2001 (opening of our department) and 2017. We applied the ROS ontology to our data to correct inconsistencies in spelling, laterality, or lymph node levels before integration.
Why did we need a specific ontology? Several anatomical ontologies are already available, and it would be easier to re-use existing ontologies. But no existing ontology matches the classifications we use on a daily basis for delineating lymph node areas. We needed to reuse our data from our CDW, and since all our labels have been created according to lymph node areas guidelines, we decided to create a new ontology for that purpose. The ROS ontology describes commonly contoured (anatomical and treatment planning) structures for radiation treatment planning. We extracted 20,758 structures labels (created over a 16 years period in our radiation department), classified and categorized them to produce the ontology. Lymph nodes delineation international guidelines are provided. This ontology was created to standardize the integration of radiation oncology data into clinical data warehouses for multicenter studies.
The high granularity of this ontology will allow precise dosimetric evaluations. A key benefit of biomedical ontologies is sharing and reuse: We have validated the coverage and the consistency of the ROS ontology by using it to annotate the radiation oncology data of 14,000 patients treated at HEGP radiation oncology department between 2001 and 2016. The ontology is now made available to be used in other hospitals. The ROS ontology can be downloaded from BioPortal and re-used in other centers and applications  [24]. Ontologies are separate from application code that uses them. This separation of ontologies from applications permits both the commercial and public sectors to cooperate in developing and using these ontologies for the benefit of the entire scientific community. We believe that similar benefits will accrue for the ontology that we have developed.
However, we must stress that using detailed level to delineate and create target volumes is standard-of-care, since an international consensus is now available  [10]. The ROS ontology will be used to harmonize structures labels generated by the five radiation oncology departments from AP-HP, the largest public health service in Europe, with 39 hospitals. Five radiation oncology departments treat each year around 8,000 patients (122,000 fractions). AP-HP has a CDW that includes data from 6. 5 million patients as of February 2017. Following the successful example of HEGP, data from all AP-HP radiation oncology departments will be integrated within the central AP-HP CDW.
In Appendix A we exemplify timed automata usage. Throughout our modelling, we have used the specification and analysis tool Uppaal  [Bengtsson et al. , 1995] , which provides the possibility of designing and simulating timed automata networks on top of the ability of testing networks against temporal logic formulae. Temporal Logics and Model Checking. Model checking is one of the most common approaches to the verification of software and hardware (distributed) systems  [Clarke et al. , 1999]. It allows to automatically prove whether a system verifies or not a given specification. In order to apply such a technique, the system at issue should be encoded as a finite transition system and the specification should be written using propositional temporal logic. Formally, a transition system over a set AP of atomic propositions is a tuple M = (Q, T, L), where Q is a finite set of states, T ⊆ Q × Q is a total transition relation, and L : Q → 2 AP is a labelling function that maps every state into the set of atomic propositions that hold at that state.
, 2017). Apart from the SMT system itself, we also describe our work on parallel-corpus preprocessing and filtering, an aspect which has gained importance in WMT18 with the addition of the much larger and noisier parallel corpus ParaCrawl. Regarding data augmentation, we report as well how we extended the provided parallel dataset with data based on synthetic source sentences generated from the provided target-language monolingual corpora (in compliance with this shared task's "constrained" conditions). This paper is organized as follows: in Section 2, we outline the data preparation techniques that were used (corpus preprocessing, corpus filtering, and data augmentation with synthetic source sentences); Section 3 shows the architecture and parameters of our NMT system and our system combination; in Section 4, we report our experiments and results (including on data preparation and on final system evaluation); and we draw our final conclusions in Section 5.
Data preparation In this section, we describe the techniques that we used to prepare the provided WMT18 German↔English data (parallel and monolingual) to improve our SMT system results: corpus preprocessing (Section 2. 1), corpus filtering (Section 2. 2) and parallel data augmentation with synthetic source sentences (Section 2. 3). Corpus preprocessing and filtering has acquired a new relevance in WMT18, due to the addition of the new ParaCrawl parallel corpus, which sextuplicates the amount of German↔English parallel data that was available in WMT17 and previous editions: there are approx. 36 million sentence pairs in ParaCrawl, versus approx. 6 million in the rest of the parallel corpora (for a total sum of approx. 42 million sentence pairs in the full WMT18 training data). This is illustrated in Table  1 , which summarizes the number of sentences of each corpus in the provided parallel dataset.
While the large size of the ParaCrawl parallel corpus makes it a valuable resource for system training in WMT18, it is much noisier than we have observed that preprocessing and filtering the ParaCrawl corpus is necessary in order to make it useful as training data with the goal of increasing translation quality. In fact, using the ParaCrawl corpus "as is", we not only did not find any improvement in translation quality, but we even observed a degradation in all metrics of quality (as we will detail in Section 4. 2). Regarding data augmentation, the usage of relevant in-domain monolingual data has been shown to be important in order to improve NMT system results  (Sennrich et al. , 2016a). The provided WMT18 dataset contains large amounts of monolingual data which we can take advantage of to increase system accuracy.
Corpus filtering In regard to data filtering, we aimed to filter out noisy sentence pairs from the parallel corpora. To this end, we trained two separate 9-gram character-based language models (one for German, one for English) on the newstest2014 development set, based on which we computed the perplexity for each sentence in the full WMT18 dataset (including ParaCrawl), in a manner similar to the techniques described by  Yasuda et al. (2008 ), Foster et al. (2010)  and  Axelrod et al. (2011). The software used was the SRI Language Modelling Toolkit  (Stolcke et al. , 2011). The idea was that the lower the perplexity for a given sentence with respect to a reference news test corpus, the lower the odds of this sentence being noise (for the purpose of training a German→English SMT system). At the same time, this method could be considered to provide some degree of domain adaptation, since we score the sentences with respect to an in-domain reference corpus.
To produce the final score for each sentence pair, we combined the perplexity scores (s, t) with the geometric mean ( √ s • t). The geometric mean of two character-based perplexities can be interpreted as the character-based perplexity of the concatenation, assuming both sentences have the same number of characters. This is usually not the case exactly, but it is a good enough approximation. As the square root is a monotonic function, it does not alter the order of the scores. We then ranked all the sentence pairs in the full WMT18 dataset according to their combined perplexity score, and selected subsets of different sizes, taking in each case the n lowest-scored (less noisy) sentence pairs.
Synthetic source sentences We augmented the WMT18 German↔English parallel training dataset (while keeping it under "constrained" conditions) with synthetic source sentences generated from the provided targetlanguage monolingual corpora. To this end, we followed the approach outlined by  Sennrich et al. (2016a). In particular, we trained an English→German NMT system based on our best system configuration for German→English. Then, we used this system to generate our synthetic source sentences (German) from a subset of the WMT18 targetlanguage monolingual corpora (English), which provided us with a significant amount of new sentence pairs to use as in-domain synthetic training data.
Results on corpus filtering We show here the results obtained with the corpus filtering techniques explained in Section 2. 2. Table  2  summarizes the results in translation quality obtained with different subsets of the WMT18 parallel dataset. We can see that using the full WMT18 parallel dataset (42M sentence pairs), including the ParaCrawl corpus "as is", leads to a significant degradation in all metrics of quality compared to using the WMT18 dataset excluding ParaCrawl (6M sentence pairs; our baseline system for system evaluations in Section 4. 4). Furthermore, we see that if we restrict ourselves to an excessively small training dataset (5M sentence pairs) using our filtering approach, there is also a degradation in quality with respect to using the unfiltered WMT18 dataset excluding ParaCrawl (6M).
We can also see (focusing on the test set results, newstest2017) that our filtering approach is effective at selecting useful training data from ParaCrawl, in the fact that the filtered datasets with sizes over the baseline's 6M sentence pairs provide significant improvements in quality (even if we limit ourselves to the small increase in size of the 7. 5M subset). At the other extreme, in our experiments, going over 15M filtered sentence pairs meant setting the threshold for noise too low, as quality metrics degraded again. As Table  2  shows, we obtained our best test set results with the 10M and 15M subsets. As results were very similar in both cases, we considered that any possible improvements in quality obtained from using the larger 15M subset were too small to justify using it instead of the 33% smaller 10M subset (which has a significantly faster convergence time for system training).
Synthetic source sentence setup Here we detail how we augmented the WMT18 German↔English parallel training dataset, based on the technique introduced in Section 2. 3. We created an English→German NMT system using our best parameters for German→English (as described in Section 3), and trained it with the 10M-sentence filtered WMT18 parallel dataset that had shown the best performance for German→English (as described in Section 4. 2). For reference, the resulting English→German NMT system obtained 27. 4 BLEU on new-stest2017. While improving this "inverse" system with further experiments could result in better synthetic training data  (Sennrich et al. , 2016a) , we settled on this configuration (which obtains reasonable results with respect to the best WMT17 systems) in order to be in time for participation in this shared task.
Conclusions The MLLP group of the Universitat Politècnica de València has participated in the German→English WMT18 news translation shared task with an ensemble of neural machine translation models based on the Transformer architecture. Our models were trained using a filtered subset of the provided parallel training dataset, plus augmented parallel data based on synthetic source sentences generated from the provided monolingual corpora. Our primary submission was an ensemble of four independent training runs of our best model parameters. Our results point to the usefulness of the Transformer NMT architecture to obtain highly competitive SMT results with a relatively low computational cost (which can contribute to "democratizing" access to state-of-the-art research in NMT to a higher number of research groups, even those with more modest computational equipment).
Acknowledgments The research leading to these results has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 761758  ( X5gon ); the  Spanish government 's  TIN2015-68326-R (MINECO/FEDER ) research project  MORE, university collaboration grant  programme  2017-2018 , and faculty training scholarship  FPU13/06241 ; the  Generalitat Valenciana 's predoctoral research scholarship  ACIF/2017/055 ; as well as the  Universitat Politècnica de València 's  PAID-01-17 R&D support programme.
We present JSExplain, a reference interpreter for JavaScript that closely follows the specification and that produces execution traces. These traces may be interactively investigated in a browser, with an interface that displays not only the code and the state of the interpreter, but also the code and the state of the interpreted program. Conditional breakpoints may be expressed with respect to both the interpreter and the interpreted program. In that respect, JSExplain is a double-debugger for the specification of JavaScript.