dataesr/scientific-paragraphs-categorization huggingface dataset:
We evaluate our approach by measuring how close our synthesised summaries can be to actual summaries in Wikipedia against two other baselines of different natures: a language model, and an information retrieval template-based solution. Our model substantially outperforms all the baselines in all evaluation metrics in both Esperanto and Arabic. In this work we present the following contributions: i) We investigate the task of generating textual summaries from Wikidata triples in underserved Wikipedia languages across multiple domains, and ii) We use an end-toend model with copy actions adapted to this task. Our datasets, results, and experiments are available at: https://github. com/pvougiou/ Wikidata2Wikipedia.
Implementation and Training Details We implemented our neural network models using the Torch 1 package. We included the 15,000 and 25,000 most frequent tokens (i. e. either words or entities) of the summaries in Esperanto and Arabic respectively for target vocabulary of the textual summaries. Using a larger size of target dictionary in Arabic is due to its greater linguistic variability -Arabic vocabulary is 47% larger than Esperanto vocabulary (cf. Table  1 ). We replaced any rare enti-1 Torch is a scientific computing package for Lua. It is based on the LuaJIT package. ties in the text that participate in relations in the aligned triples set with the corresponding property placeholder of the upheld relations. We include all property placeholders that occur at least 20 times in each training dataset.
Dataset In order to train our models to generate summaries from Wikidata triples, we introduce a new dataset for text generation from KB triples in a multilingual setting and align it with the triples of its corresponding Wikidata Item. For each Wikipedia article, we extract and tokenise the first introductory sentence and align it with triples where its corresponding item appears as a subject or an object in the Wikidata truthy dump. In order to create the surface form tuples (i. e. Section 2. 3), we identify occurrences of entities in the text along with their verbalisations. We rely on keyword matching against labels from Wikidata expanded by the global language fallback chain introduced by Wikimedia 2  to overcome the lack of non-English labels in Wikidata  (Kaffee et al. , 2017). For the property placeholders, we use the distant supervision assumption for relation extraction  (Mintz et al.
Results and Discussion We evaluate the generated summaries from our model and each of the baselines against their original counterparts from Wikipedia. Triples sets whose generated summaries are incomplete 3 (i. e. summaries for which the special end-of-summary <end> token is generated) are excluded from the evaluation. We use a set of evaluation metrics for text generation: BLEU  (Papineni et al. , 2002) ,  METEOR (Denkowski and Lavie, 2014)  and ROUGE L  (Lin, 2004). As displayed in Table 4, our model shows a significant enhancement compared to our baselines across the ma-3 Around ≤ 1% and 2% of the input validation and test triples sets in Arabic and Esperanto respectively led to the generation of summaries without the <end> token. We believe that this difference is explained by the limited size of the Esperanto dataset that increases the level of difficulty that the trained models (i.
Study Design The TAKEmeds project was a pre-post multicenter interventional pilot study conducted in a community health care center in Shanghai and a tertiary hospital in Hainan Province, China (see Figure  1  for the flowchart of the study). The study was conceived and developed as a part of the World Heart Federation's Emerging Leader program. Implementation in China was led by the Global Health Research Center, Duke Kunshan University, in local partnership with the School of Public Health, Fudan University, and Hainan Provincial Nongken General Hospital (HPNGH). The study obtained ethical approval from the institutional review boards at Duke University Health System, Fudan University, and HPNGH. The trial was registered in the clinicaltrials. gov database on November 2015 (NCT02597205).
Quantitative Data We collected patient quantitative data from two sources. The first was the self-reported survey at baseline and 12 weeks at follow-up. The survey was administered by trained researchers in Shanghai and resident physicians in Hainan, and they collected data on primary and secondary outcome measures. In Shanghai, trained researchers visited the primary health care centers at baseline and follow-up to collect the data. The second was the TAKEmeds app into which demographic and clinical history were entered by physicians at the time of enrollment. In addition, we estimated communication costs based on the recorded number of messages and the estimated duration of voice calls. The pre-post paired analyses were done at the individual patient level. As for the primary outcome, we built an ordinal (ordered) logistic regression model to test the intervention effect on medication adherence, and both univariate and covariate analyses were conducted.
Qualitative Data The qualitative data were collected through process evaluations at the middle and end of the study. Two focus group discussions were conducted among physicians in Shanghai (n=4) and Hainan (n=6), each lasting around 1 hour. Individual interviews were conducted among randomly selected patients (n=24, 12 from each site) at both sites, each lasting 20 min. The qualitative component was to understand the perceived barriers to improving medication adherence and their feedback on the usability and acceptability of the intervention. Interviews and focus group discussions were recorded and transcribed. We used the thematic framework to analyze the qualitative results by using the NVivo version 10. 0 qualitative data analysis software (QSR International Pty Ltd). Specifically, we coded the transcripts into different nodes of meaning and then grouped the nodes with similar meaning under one theme.
Acknowledgments The authors would like to acknowledge their academic partners including  McMaster University  and the  Population Health Research Institute ,  Canada , which were involved in the study throughout the entire stage, and  School of Public Health, Fudan University , which was in charge of the study implementation in Shanghai. The authors would also like to thank the  Department of Cardiology, Hainan Nongken General Hospital, Haikou ,  Hainan Province , for their support of its implementation in Hainan. Specifically, the authors would like to thank the physicians in the Longhua Street Community Health Center and the physicians and residents in Hainan Nongken General Hospital that participated in and contributed to this study. The authors would also like to thank the  Zhengjiang Innovation Zhejiang University Kunshan Innovation Institute  for their help with app and message development.
Most of the queries submitted to search engines are composed of keywords but it is not enough for users to express their needs. Through verbose natural language queries, users can express complex or highly specific information needs. However, it is difficult for search engine to deal with this type of queries. Moreover, the emergence of social medias allows users to get opinions, suggestions or recommendations from other users about complex information needs. In order to increase the understandability of user needs, tasks as the CLEF Social Book Search Suggestion Track have been proposed from 2011 to 2016. The aim is to investigate techniques to support users in searching for books in catalogs of professional metadata and complementary social media. In this context, we introduce in the current paper a statical approach to deal with long verbose queries in Social Information Retrieval (SIR) by taking Social Book Search(SBS) as a study case.
Experimental data To evaluate our approach, the data provided by CLEF SBS suggestion track 2016 3  are used. • Documents: The documents collection consists of 2. 8 millions of books descriptions with meta-data from Amazon and LibraryThing. Each document is represented by book-title, author, publisher, publication year, library classification codes and user-generated content in the form of user ratings and reviews. • Queries: the collection of queries from 2011 to 2016 the organizers of SBS have used Librarything forum to extract a different set of queries with relevance judgments for each year. In our case, we chose to combine the title with the narrative as a representation of the queries. For fair comparison, the queries and the corresponding relevance judgments in the others years are utilized as the training set when evaluating on the each-year dataset, which is the same to the 6-year SBS evaluation campaigns.
Experimental setup In our experiments, we present experimental results on SBS 2014 and 2016 dataset with Amazon collections to compare the performances of different components of our system. First, we used Terrier Information Retrieval framework developed at the University of Glasgow  [22]. Terrier is a modular platform for rapid development of largescale information retrieval applications. It provides indexing and retrieval functionalities. The BM25 model was used  for querying with the usual parameter values (b = 0, k3 = 1000, k1 = 2). Using the BM25 model, the relevance score of a book d for query Q is given by: S (D, Q) = t∈Q (K 1 + 1)w(t, d) K 1 + w(t, d). id f (t). (K 3 + 1)w(t, Q) K 3 + w(t, Q) (5) Where w(t, d) and w(t, Q) are respectively the weights of terms in document D and in query Q.
1429 to 0. 1518 in the set of 2014 and from 0. 1549 to 0. 1688 in the set of 2016. From the overall perspective, the best performance is obtained by QEART strategy with the greatest improvements of the score of 24. 9% in the score of NDCG@10. This can be explained by the fact that the added terms are chosen based on the confidence of the association rule where we consider only the rules of high confidence, i. e. , using the strongest correlations inter-terms lead to generate precise terms. Tables 5. 3 and 5. 3 present the comparative results. Our run is best-performed in the evaluation on 2014 datasets, and our best results in 2016 are ranked second in term of NDCG@10. As to map, we can notice that our approach outperforms the best runs in the year 2014 but is still lower than the best runs of the year 2016.
Synthetic biology is typified by developing novel genetic constructs from the assembly of reusable synthetic DNA parts, which contain one or more features such as promoters, ribosome binding sites, coding sequences and terminators. While repositories of such parts exist to promote their reuse, there is still a need to design novel parts from scratch. PartsGenie, freely available at http://parts. synbiochem. co. uk, is introduced to facilitate the computational design of such synthetic biology parts. PartsGenie has been designed to bridge the gap between optimisation tools for the design of novel parts, the representation of such parts in community-developed data standards such as Synthetic Biology Open Language (SBOL), and their sharing in journal-recommended data repositories. Consisting of a drag-and-drop web interface, a number of DNA optimisation algorithms, and an interface to the well-used data repository JBEI ICE, PartsGenie facilitates the design, optimisation and dissemination of reusable synthetic biology parts through a single, integrated application.
Introduction The computational design of synthetic biology parts is greatly aided by the increasing availability of open access tools, standards and data repositories. A number of excellent tools are available to synthetic biologists for designing novel parts, including the RBS Calculator  [1]  and RedLibs  [2]  for ribosome binding site (RBS) optimisation, and numerous academic and commercial packages for codon optimisation of coding sequences (CDS) for expression in a given host  [3]. In addition to this, the introduction of community-developed standards for representation of synthetic biology designs, such as Synthetic Biology Open Language (SBOL) [4] and SBOL Visual  [5]  have greatly facilitated the sharing and reuse of synthetic DNA. Indeed, this journal now recommends the sharing of genetic designs, and has a dedicated repository to which synthetic biologists are encouraged to submit synthetic DNA designs to support submitted manuscripts  [6].
Despite these advantages, practical problems exist for the synthetic biologist in utilising these packages. First, there is still as yet little integration of tools, such that separate tools are required for RBS optimisation, codon selection, and the optimisation of designs to enable simple (and therefore cost-effective) synthesis by manufacturers. Secondly, tools that support the assembly of synthetic biology parts adhering to data standards  [7]  typically prioritise the re-use of existing "features", such as RBSs, CDSs, promoters, terminators, etc. , and do not interface with tools that design novel features and designs. Finally, upon submitting designs to manufacturers for synthesis, a further check or optimisation step is typically required to comply with vendor-specific synthesis constraints.
Following such a step-wise approach, which is heavily reliant on the use of numerous software packages and manual cut-and-pasting of DNA sequences between tools, is unnecessarily time consuming and potentially prone to user error. PartsGenie is therefore introduced for the design of synthetic biology parts, integrating many of the above challenges in a single web application, which can subsequently be extended to cover a more comprehensive pipeline for applications such as metabolic engineering. Driven by an intuitive dragand-drop interface, the user can assemble multiple DNA parts from a range of synthetic DNA features, which can then be sequence optimised simultaneously, with the final results directly exportable to the well-used data repository JBEI ICE  [8] , following journal recommendations.
In specifying amino acid sequences, PartsGenie offers an integrated UniProt search tool, allowing sequences, ids, NCBI Taxonomy terms and enzyme classification (EC) numbers to be automatically extracted and associated with the CDS, facilitating future traceability. Extracted sequences can subsequently be edited directly in the Feature panel, to introduce mutations and truncations etc. , if necessary. A host organism is needed for designs that include RBS or CDS features, in order to determine the optimum Shine-Dalgarno sequence for RBSs, and prefered codon usage for CDSs. (Supported prokayotic organisms are selected from a simple autofill field, and the backend algorithm extracts Shine-Delgarno sequences and codon usage tables automatically from the RBS Calculator and Codon Usage Database  [9]  respectively.
Invalid sequences and rogue RBSs are removed from the design through optimisation before designed parts are returned. Optimised designs can be viewed in an interactive Results panel (Figure  2 ), showing the result of each submitted design and allowing each individual feature to be investigated. Designs may be saved to any JBEI ICE repository, including the ACS Synthetic Biology Registry. PartsGenie therefore supports data sharing with standards such as SBOL, Genbank and fasta via its integration with ICE, from which these formats may be exported. Figure  1 : The PartsGenie drag-and-drop design interface. The Parts panel allows designs to be assembled from the palette of feature buttons above. Upon selecting a feature, the Feature panel allows for parameters necessary for the design and optimisation of that feature to be specified.
Missing parameters and incorrectly ordered features (in this instance, an RBS not directly followed downstream by a CDS) are highlighted in red, and the job Submit button disabled until such errors are corrected, preventing the submission of invalid designs to the optimisation algorithm. PartsGenie has been designed to be sufficiently generic to be of use in a range of synthetic biology studies. However, we envisage that over time PartsGenie will be integrated into a larger computational design pipeline for metabolic engineering within the SYNBIOCHEM centre. Such a pipeline will include a number of existing and novel software tools, including the upstream modules RetroPath  [12]  for pathway elucidation and Selenzyme  [13]  for enzyme selection. Downstream of PartsGenie, tools for reducing experimental complexity through Design of Experiments approaches, and automated methods for the assembly of genetic constructs through interfacing with robotics liquid handling systems, will be introduced.
Methods PartsGenie is written as a two-tier, single-page web application, using Python, Javascript and the Bootstrap and AngularJS web development libraries. The multi-objective optimisation algorithm follows a simulated annealing approach. TIR values for RBSs are calculated using a performanceoptimised version of the RBS Calculator, which relies on the NUPACK library  [14]  and caches calculated TIRs to prevent unnecessary and expensive recalculations. The system interfaces with ICE through its RESTful API (http://ice. jbei. org/api/). PartsGenie is hosted on Google Compute Engine and is distributed as a Docker file with source code openly available under MIT License at https://github. com/synbiochem/PathwayGenie. Figure  2 : Results panel showing an optimised design. The whole design, including sequence and associated metadata is visible by default.
Acknowledgment All authors acknowledge the funding from the  Biotechnology and Biological Sciences Research Council (BBSRC)  and the  Engineering and Physical Sciences Research Council (EPSRC)  under grant  BB/M017702/1 , " Centre for synthetic biology of fine and speciality chemicals (SYNBIOCHEM) ". NS is grateful for help received from  James McLaughlin ,  Neil Wipat ,  Hector Plahar ,  Nathan Hillson  and the SBOL development team. This is a contribution from the  Manchester Centre for Synthetic Biology of Fine and Speciality Chemicals (SYNBIOCHEM).
Second, we show that RDA can identify important environmental gradients that explain adaptive variation in the data. It is therefore a proof of concept of the idea of using constrained ordination method as an environmental genomic tool to identify relevant selective gradients in the environmental data  (DeKort et al. , 2014 , Steane et al. , 2014). Finally, to illustrate the pertinence of such method in concrete example, we apply it to a Populus trichocarpa SNP dataset obtained from  Geraldes et al (2013). Our results mostly concord with the ones obtained with the Fst based methods used in their study but we also show some new genetic markers specifics to the RDA-based approach. Additionally, RDA identifies the main selective gradients along the natural populations of Populus trichocarpa in the north-western coast of North America.
, 2015). A genetic dataset (e. g. SNPs genotypes 0, 1, 2) is used as response matrix Y and a set of environmental variables is used as explanatory matrix X. RDA amounts at constructing a matrix Y' of predicted genotypes based on the environmental variables and at performing principal component analysis on the matrix Y' (Legendre and Legendre 2012, section 11. 1). After this constrained ordination step, we follow the methodology implemented in pcadapt to find outliers loci  (Luu et al. , 2017). First we recover from the RDA analysis the z-scores obtained when regressing the SNPs by the RDA axes. We obtained vectors of z-scores denoted z j = (z j1 ,. , z jK ) where z jk corresponds to the z-score obtained when regressing the j-th SNP by the K-th ordination axis. Only the z-scores of the most informative ordination axis are kept for the rest of the procedure.
To evaluate the capacity of the RDA based approach to detect loci underlying environmental selective pressure we first test the method with a simulated dataset. We look at its capacity to detect positive loci (loci simulated under selection) and to return a controlled number of false positives (neutral loci detected as outliers by the analysis). Then, to emphasize the utility of RDA, we compared it to the PCA-based procedure implemented in pcadapt that does not use environmental variables  (Luu et al. , 2017).
Real dataset The Populus trichocarpa dataset is a sample of 424 individuals genotyped on 33,070 SNPs from 25 drainages (i. e. , topographic units separated by watershed barriers)  (Geraldes et al. , 2014). Genotyping of each accession was performed with a 34K Populus SNP array targeting 34,131 SNPs mostly within (plus 2kb upstream and downstream) 3543 genes  (Geraldes et al. , 2013). A total of 21 climatic variables are available on each sampling site (Table  S2 ). From the 33,070 SNPs, we removed the SNPs with missing values and a minor allele frequency below 5% resulting in 17,224 SNPs. We explored the adaptive genetic variability of Populus trichocarpa dataset using both PCA-based (pcadapt) and RDA-based method using the 21 environmental variables as explanatory variables (Table  S2 ). We finally compared the list of outliers found with these two ordination approaches to the ones found using Fst based methods  (Geraldes et al.
RESULTS 1. Genome scan on simulated dataset using RDA or PCA based methods The procedure was first performed on one simulation (sim1) with both PCA and RDA based approaches. To perform RDA, we considered environmental variables 1-10 as explanatory variables. For the two approaches, we retained the first four ordination axes to compute Mahalanobis distances as indicated by the scree plots (Fig. S1 ). The software package pcadapt is successful at detecting QTL2 SNPs (Fig. 2A ) but fails entirely at detecting QTL1 and QTL3 SNPs. The RDA-based approach also detects QTL2 SNPS but also detects most of the QTL1 SNPs (Power=75%) by contrast to pcadapt (Fig. 2B ). For the SNPs associated with QTL3, both methods have negligible power to detect them (PCA Power=0%, RDA Power=25%). When looking at the projection of each SNP in the RDA space we can detect links between QTL SNPs and environmental variables (Fig.
Populus trichocarpa For the RDA and pcadapt analysis on P. trichocarpa dataset, we retained respectively K=3 and K=6 axes as indicated by the screeplots (Fig. S2 ). Analysis with a FDR of 0. 1 leads to a list of 151 outliers for RDA and 117 for pcadapt. Interestingly, both methods have 29 outliers in common so that 122 and 88 SNPs are outliers specific to respectively RDA and pcadapt. When we compared RDA genome scan results with the outliers found by  Geraldes et al. (2014)  based on Fdist, bayescan and bayenv methods, we found a substantial overlap between them (Fig. 6 ). Among the 151 outliers found with the RDA-based method, 69 have already been picked up by Fst-based methods (see Table  S3 ). Interestingly, the loci showing the lowest p-values with the RDA analysis are also pointed out as outliers by Fst and pcadapt methods (Fig.
DATA ACCESSIBILITY STATEMENT • Simulations datasets are available at Dryad Digital Repository at the following address: https://doi. org/10. 5061/dryad. 1s7v5 • Scripts used for simulations and Populus trichocarpa analysis are available on Github: https://github. com/Capblancq/RDA-genome-scan • Populus trichocarpa SNP data are available on Dryad (http://datadryad. org/resource/doi:10. 5061/dryad. 1051d).
Bazin designed the study. E. B. and T. Capblancq performed the analysis and treatments. M. Blum, E. B. and T. C. wrote the manuscript, and all authors contributed substantially to revisions. 0004) and MB acknowledges Grenoble Alpes Data Institute, which is supported by the French National Research Agency under the "Investissements d'avenir" program (ANR-15-IDEX-02). that would perform well in common gardens, (ii) patterns of adaptation to local climate across the geographic range or (iii) future adaptive landscape in a context of climate change. E.