dataesr/scientific-paragraphs-categorization huggingface dataset:
By contrast, our program which is freely available at (https://github. com/EA2106-Universite-Francois-Rabelais/Expression-network-analysis) was able to calculate PCC-HRR in less than 3 h for both datasets. As PCC-HRR resulted in relevant networks, this tool can be useful for further studies requiring many computations such as analyzing sample size impact on PLC or testing other normalization methods. The present work demonstrates that Pearson's Correlation Coefficients (PCC) on which highest reciprocal ranking (HRR) was applied can be used to construct reliable global and targeted networks. When considering Pathway Level Correlation (PLC) with a set of guide genes, three reliable measures can be used for evaluation: NV AUROC as a global indicator of GO recovery (expecting values > 0. 5), modularity (between 0 and 1, 1 being the best network partition) and normalized Chi statistic (between 0 and 1, 1 indicating a perfect match with an expected partition).
Methods Microarray data preparation. Experiment accessions (GSE) for GPL198 (Arabidopsis ATH1, 22,746 genes) were retrieved from ArrayExpress (Supplementary Table  6 ). Signal intensities per probe were generated with R 36 using the 'arrayexpress' package  35. The function 'getAE' was used to convert the raw signal CEL files. Array normalization was performed per GSE using the 'justRMA' function of the 'affy' package. This procedure applies a background correction together with a quantile normalization to correct for biases within arrays and finally returns log2-transformed corrected signal intensities. All 10,095 arrays were combined into a single file and subjected to a quality control based on upper quartile dispersion (75%) and Kolmogorov-Smirnov statistical testing for outliers using an empirical cumulative distribution function as described previously  37.
A total of 142 arrays were considered outliers in the two tests and discarded from the final matrix. Each array was finally centered and scaled individually. RNA-seq data preparation. 2,549 RNA-seq accessions obtained for A. thaliana were retrieved from ArrayExpress. Fastq files were obtained from the SRA after converting. sra files with the SRA ToolKit function 'fastq-dump' with the -split-files option for paired-end sequencing runs. Reads were systematically trimmed with Trimmomatic using adapter files according to the Illumina platform used for the runs  38. Trimmed reads were pseudo-aligned to predicted transcripts from the representative gene models of Arabidopsis TAIR genome v10 (33,604 transcripts) with Salmon v0. 7. 2 using the variational Bayesian EM algorithm mode to improve abundance estimation  39.
Only samples displaying a mapping rate of reads >30% were kept, resulting in a final matrix containing 1,676 samples (Supplementary Table  6 ). RNA-seq counts were used as non-normalized raw counts or expressed as Transcript per Million to correct for sequencing depth. Normalization by Variance Stabilizing Transformation (VST) was performed with the DESeq2 R package. This normalization method aims at limiting the variance dependence to the mean  40. Distance calculations. Before calculations, zero-variance genes were discarded. CCs (Pearson or Spearman) are computationally intensive particularly in the case of large matrices. Highest Reciprocal Ranking (HRR) of CCs for genes A and B is calculated as max(rank(CC(A,B)), rank(CC(B,A)). For each gene, all CC values are first transformed as ranks, with 0 corresponding to the gene rank against itself.
Ranks are subsequently compared and the highest value is retained for each gene pair. We developed a tool written in C allowing the easy parallelization of these computations. Briefly, for a given initial matrix containing n genes and p samples, the number of cores c allocated is used to split the dataset into n/c submatrices. In case of non-integer value, the last line of the matrix is replicated (without incidence on PCC or rank values) so that n/c is an integer. PCC or HRR are then calculated for each gene pair using communication between CPUs with Message Passing Interface. The program delivers c files containing n/c x n values corresponding to PCC or HRR. This program is freely available on Github (https:// github. com/EA2106-Universite-Francois-Rabelais/Expression-network-analysis). To calculate SCCs, expression values were first ranked in R.
Mutual information (MI) which is reported to better capture non linear relationships  9  were calculated with the 'knni. all' function of the Parmigene R package  41. This function estimates MI using a k-nearest neighbor. Partial correlations were challenging to compute on genome scale expression matrices. Partial correlations are usually calculated from multiple linear regressions or by inverting the correlation matrix and used in Graphical Gaussian Models  42. Our expression matrices had many more variables (genes) than samples therefore regression methods would have required a Lasso or Ridge penalization to estimate coefficients. However, this procedure generally leads to memory errors when considering more than 30,000 variables. We found that the most computationally appropriate method in our case was to estimate shrinkages of partial correlations with the R package 'corpcor' (http://strimmerlab.
Global Network analysis. Construction: For each dataset × distance combination, we dynamically set a threshold to obtain arbitrary lists of 10 million best gene pairs (with CC above or HRR below that threshold), i. e. less than 2% of the total possible edges. Networks were then constructed with the 1, 5, 10, 20, 40, 60 or 80% best pairs from these lists. Thresholds used to get the 10 million gene pairs are reported in Table  S2. Global networks were analyzed as adjacency matrices in R. Network characteristics: besides classical topological characteristics such as vertex and edge numbers and mean node degree (the average number of connections for each vertex), we evaluated network quality by comparison with the reference dataset (Fig. 2 ). In a first approach, we built a confusion matrix by classifying edges as false or true positives, considering edges as valid if both genes were annotated with at least one same GO term.
A mean NV AUROC was calculated for each network. In addition to ROC analysis, we counted GO terms that were significantly enriched with gene pairs using a hypergeometric test with R. Pathway Level Correlation. Construction: In PLC, subnetworks were constructed from global networks (see above) by keeping edges connecting at least one guide gene. Guide gene lists are indicated in Supplementary Table  2. The R package 'igraph' 49 v1. 0. 1 was used to construct and visualize these targeted networks with a force-directed layout (Fruchterman-Reingold). Community Detection: Modules containing densely connected vertices were estimated within each network by using a fast greedy approach which aims at maximizing modularity of the detected communities  30. Modularity measures how good a network partition is by calculating for each gene the number of edges within its community against its total node degree.
Each point in the lower panels (scatterplots) represent one network for which 2 characteristics (eg NV AUROC and modularity) are compared. Data are presented for each pathway separately with a specific color. (C) The expected partitioning of phenylpropanoid related guide genes was compared to two PLC: (D) higher predictability and lower modularity (microarrays raw PCC) and (E) lower predictability and higher modularity (microarrays PCC-HRR). In (D and E), colored vertices correspond to genes encoding enzymes catalyzing steps of similar color in (C). Community (surrounded by grey polygons) numbers in (E) are indicated in deep blue and can be used to access Supplementary Table3.
Acknowledgements We deeply acknowledge the  Fédération CaSciModOT (CCSC Orléans-Tours, France) ,  Jean-Louis Rouet  and  Laurent Catherine  for help and access to the  Région Centre computing grid. We also thanks  Yann Jullian  for access and help on University computer resources. This study was supported by the  Région Centre-Val de Loire, France  ( SiSCyLi grant ). Doctoral Fellow attributed to F. L. and D. D. was jointly funded by the  Région Centre-Val de Loire, France  and the  Ministére de l'Enseignement Supérieur et de la Recherche, France.
5 and RCP 8. 5. RCP 4. 5 is a scenario that stabilizes radiative forcing at 4. 5 W/m 2 in the year 2100, with emissions peak around 2040, whereas in RCP 8. 5, emissions continue to rise throughout the 21st century. The simulations, available on the portal DRIAS (http://www. drias-climat. fr), were performed with the ALADIN-Climate model of Météo-France. These data were then used to analyze the impact of the climate into the hazards intensity, as described below. Modeling hazards: The previous exposure (land use/land cover) and triggering (climatic) factors are then introduced within the considered hazard models. For the landslide hazard analysis, ALICE (Assessment of Landslides Induced by Climatic Events) has been used  [27, 28]. The mechanical model is based on a 2D slope stability analysis, for which the main physical characteristics of the soils and surfaces are quantified.
Conceptual Aspects and Architecture The platform developed in the SAMCO project is able to store and manage a large number of modelling results stored as different categories of maps, e. g. , exposure, hazards, and potential damage indexes. All of this information has been computed during the SAMCO project by using the presented hazard/impacts modelling tools and stored in the database, so that they can be searched according to the scenarios criterions and displayed thanks to the web-mapping capability of the platform. Thanks to the web-based interface, the users have the possibility to browse, consult, and analyze these data through clean and user-friendly screens. This platform has been designed from a set of open-source components dedicated to data storage, filtering, browsing, visualizing, and analyzing, as shown in Figure  4.
In this architecture, the input data selected by the user correspond to a given scenario for which modelling results have been computed. Results can therefore be visualized as hazard or risk maps. Each scenario constitutes a collection of maps called "simulation" in the database. From a data management point of view, a simulation is described by (i) CSV files that describe the simulation characteristics and (ii) GIS files, composed by vectors and raster layers, which are used to draw the computed map. Climate 2018, 6, x FOR PEER REVIEW 8 of 14.
In this architecture, the input data selected by the user correspond to a given scenario for which modelling results have been computed. Results can therefore be visualized as hazard or risk maps. Each scenario constitutes a collection of maps called "simulation" in the database. From a data management point of view, a simulation is described by (i) CSV files that describe the simulation characteristics and (ii) GIS files, composed by vectors and raster layers, which are used to draw the computed map. All these elements are stored and managed into a PostgreSQL (https://www. postgresql. org/) database. This open-source Relational Database Management System (RDBMS) is one of the most popular solutions available on the market, and is used and maintained by a wide community of data managers. In addition, this RDBMS can manage geographical data thanks to its PostGIS (http://postgis.
). A record is also connected to repository of GIS layers. With such architecture, simulations cannot share the same set of layers, and avoid any conflict in the simulation management. The tables only store metadata needed to use GIS web services, mostly links to external/internal databases, for example, GIS data stored into the PostGIS area. The filter function the interface provides is based on the information stored in the simulation table, which ensure that all user requests are completely controlled. As an example, shown in Figure  5 , in the case of the site "Ubaye", the possibility for the user to choose a period is restricted to "2040" or "2010" since no simulation is available for the period "1950" or "2100" on that specific site. All these elements are stored and managed into a PostgreSQL (https://www.
postgresql. org/) database. This open-source Relational Database Management System (RDBMS) is one of the most popular solutions available on the market, and is used and maintained by a wide community of data managers. In addition, this RDBMS can manage geographical data thanks to its PostGIS (http: //postgis. net/) addon. This capability is a crucial point for the SAMCO platform that has to manage geo-localized rasters. Data are stored according a specific structure dedicated to manage simulations, flexible enough to welcome a large number of test-sites studied within the project (Figure  4 ). This structure is used to hold a set of records, i. e. , the features of a simulation: The spatial extent of the site, the selected period, as well as the hazard maps and climate/socio-economic scenarios considered during the modeling phase.
A simulation record is linked to a set of master data tables storing common repository values. Each record is referenced in a secondary table, linked to a collection of master data tables describing the possible values that simulation's features can take (list of the sites, list of periods, list of hazards, etc. ). A record is also connected to repository of GIS layers. With such architecture, simulations cannot share the same set of layers, and avoid any conflict in the simulation management. The tables only store metadata needed to use GIS web services, mostly links to external/internal databases, for example, GIS data stored into the PostGIS area. The filter function the interface provides is based on the information stored in the simulation table, which ensure that all user requests are completely controlled.
The Web Approach Finally, the platform is hosted by a web-server offering PHP solutions. Application servers like Apache (https://httpd. apache. org/) or Nginx (https://www. nginx. com/) were benchmarked successfully. On one hand, Apache is a Web server component of the very popular LAMP stack (Linux, Apache, MySQL, PHP) which is quite attractive. On the other hand, Nginx is designed to handle a large number of simultaneous connections. Depending on the number of foreseen users, Apache or Nginx can be used. Their advantages are to be open-source and to be compatible with Windows and Linux operating systems. To exploit the layers stored in the PostGIS area of the database, a geographic server needs to be activated to generate GIS web services. Our choice fell on GeoServer (http://geoserver.
org/), an open source server written in Java that allows users to share and modify geographic data. Designed for interoperability, it publishes data from all major sources of spatial data using open standards. Vector and raster data is thus delivered through WFS (http://www. opengeospatial. org/standards/wfs) and WMS (http://www. opengeospatial. org/standards/wms) services, respectively. For the moment, the GeoServer only provides simulation data computed in the framework of the SAMCO project. Additional GIS layers, i. e. , topographic map, road maps, etc, is queried from external servers. At the end of the chain, the platform implement visualization functionalities. Again, these services are carried out thanks to a PHP code developed by the mean of open source components. We used Laravel (https://laravel.
com/ https://github. com/laravel/laravel) for the coding, an open-source web framework written in PHP respecting the model-view-controller principle and fully developed in object-oriented programming. The Bootstrap (http://getbootstrap. com/) framework and the JQuery (https://jquery. com/ https://github. com/jquery/jquery) library take in charge the HTML5 contents and the graphic interface client respectively. Finally, OpenLayers3 (http://openlayers. org/, https://github. com/openlayers/ol3) JavaScript library help in developing the web mapping tools. As a consequence, all these options make the application very responsive and easy to use for stakeholders.
com/ https://github. com/laravel/laravel) for the coding, an open-source web framework written in PHP respecting the model-view-controller principle and fully developed in object-oriented programming. The Bootstrap (http://getbootstrap. com/) framework and the JQuery (https://jquery. com/ https://github. com/jquery/jquery) library take in charge the HTML5 contents and the graphic interface client respectively. Finally, OpenLayers3 (http://openlayers. org/, https: //github. com/openlayers/ol3) JavaScript library help in developing the web mapping tools. As a consequence, all these options make the application very responsive and easy to use for stakeholders.
As a consequence, the proposed scenarios from which hazards evolutions are computed are particularly realistic and are in good agreement with the scientific and the stakeholders' visions. Based on different hazard and risk assessment models dealing with landslides, rockfalls, and floods, the platform allows also to consider their combination in a multi-hazards approach, the risk being, at the end, evaluated from the potential damage index method. The web application was developed using different open-source components. PostgreSQL database was used to manage a table referencing the diverse simulations. GeoServer and PHP technologies provide the web capabilities that make the application able to run from different platforms, including PC, tablets, or smartphones. We had a particular attention to the graphic user interface that is very fast and easy to manipulate.
Acknowledgments: The authors would like to thank all collaborators that participate to this project: G. A. F. , F. B. , F. B. , M. F. , D. G. , T. H. , C. L. , J. P. M. , A. M. , D. P. , A. P. , Y. T. and R. V. , and the reviewers that contributed to improve the quality of the paper.
Results and Discussion Reaction mechanism. Figure  1  shows the chemical reaction network (CRN) used in this study which is based on the PEN toolbox, a full description of which can be found here  19. It is composed of sequences of DNA oligonucleotides, called templates, which encode the reactions and a mediator strand called input. It relies on three enzymes: a polymerase, a nickase and an exonuclease to produce an out-of-equilibrium CRN of two interconnected reactions. While the input and output strands can get degraded by the exonuclease, the templates are protected from this thanks to 3 phosphorothioate (PTO) modified nucleotides (nt) on their 5′ end. The reaction, shown in Fig. 1a , is based on 22 nt DNA template which produces an autocatalytic amplification of the input. When an input strand hybridizes on the 3′ end, it gets elongated by the polymerase and cut by the nickase to produce 2 copies of itself.
The reaction mixture was injected in both microfluidic channels. The device was placed on a heating plate (Tokai hit) at 38 °C and the fluorescence signal was measured using an inverted fluorescence microscope (Olympus IX71, 1. 25× objective), equipped with CoolLED pE-2 (CoolLED) excitation system. Fluorescence images were processed using the ImageJ software  23. Electrochemical desorption from active surface. Initial electrochemical cleavage of the thiol-bound DNA on the gold surface was investigated vs Ag/AgCl electrodes. For ease of fabrication purposes, a study was thereafter conducted to optimize the conditions for cleavage vs a gold electrode. While not ideal from the electrochemical point of view, this is made possible by the fact that the reaction buffer is always the same and that we can afford to work a slight over-potential without going out of the electrochemical window of the solvent.
Acknowledgements This work was partially supported by the  French RENATCH network. I. K. would like to thank the  CNRS  and the  region Haut de France  for financial support. A. V. would like to thank the region  Haut de France  for support for funding the  NanoTrami (Project Emergent ). The authors would like to thank  Andre Estevez-Torres  for providing the ttRecJ enzyme and  Jean-Christophe Galas  for fruitful discussions and feedback on the manuscript.
We extract candidate substitutes for the identified complex words from SimplePPDB  (Pavlick and Callison-Burch, 2016) , a database of 4. 5 million English simplification rules linking English complex words to simpler paraphrases. We select the substitutes that best fit each context using a word embeddingbased lexical substitution model  (Melamud et al. , 2015). An example sentence, along with the complex words identified by our model and the proposed replacements, is shown in Figure  1. We show that our complex word identification classifier and substitution model improve over several baselines which exploit other types of information and do not account for context. Our approach proposes highly accurate substitutes that are simpler than the target words and preserve the meaning of the corresponding sentences.
In our work, we opt for an approach which addresses lexical substitution in a direct way and does not include an explicit disambiguation step. Lexical substitution systems perform substitute ranking in context using vector-space models  (Thater et al. , 2011; Kremer et al. , 2014; Melamud et al. , 2015). Recently,  Apidianaki (2016)  showed that a syntax-based substitution model can successfully filter the paraphrases available in the Paraphrase Database (PPDB)  (Ganitkevitch et al. , 2013)  to select the ones that are adequate in specific contexts. In the same line,  Cocos et al. (2017)  used a word embedding-based substitution model  (Melamud et al. , 2015)  for ranking PPDB paraphrases in context. We extend this work and adapt the  Melamud et al. (2015)  model to the simplification setting by using candidate paraphrases extracted from the Simple PPDB resource  (Pavlick and Callison-Burch, 2016) , a subset of the PPDB that contains complex words and phrases, and their simpler counterparts that can be used for incontext simplification.
Data The first step for lexical simplification is to identify the complex words that should be simplified. The bulk of prior work on text simplification has addressed the complex word identification problem by training machine learning algorithms on the parallel Wikipedia Simplification (PWKP) corpus  (Zhu et al. , 2010). The PWKP corpus, however, has several shortcomings, as described in  Xu et al. (2015). Namely, it was determined that 50% of the parallel sentences in PWKP were either not aligned correctly, or the simple sentence was not in fact simpler than the complex sentence. Xu et al. (2015)  created a more reliably annotated dataset, which uses a corpus consisting of 1,130 articles, manually rewritten by experts at Newsela 3  at four different reading levels. Xu et al. (2015)  also aligned sentences from these texts, extracting 141,582 complex/simple sentence pairs.
We use the Newsela corpus to create a goldstandard dataset of complex and simple words for training and testing our models. We do this by hiring crowdsourced annotators through Amazon Mechanical Turk, and asking them to identify complex words in the context of given texts. We randomly select 200 texts from the Newsela corpus, and take the first 200 tokens from each to be labeled by nine annotators. We preprocess the texts using the Stanford CoreNLP suite  (Manning et al. , 2014)  for tokenization, lemmatization, part-of-speech (POS) tagging, and named entity recognition. The annotators are instructed to label at least 10 complex words they deem worth sim- plifying for young children, people with disabilities, and second language learners. After filtering out stop words (articles, conjunctions, prepositions, pronouns) and named entities, we are left with 17,318 labeled tokens.