dataesr/scientific-paragraphs-categorization huggingface dataset:
An Intuitive Front Office Interface Another challenge is to provide a fast and efficient use compatible with all kinds of computer supports. WKI must ensure pertinent answers according to the user's operational needs, while leading them to implement progressively innovation processes. This feature brings us to think on a smart design which promotes an intuitive browsing easily understandable "by and for the whole stakeholders", especially for our main specific target: SMEs owners. In addition of classical research functions based on keywords, we integrate an advanced research browser offering a smart intuitive and precise need formulation to satisfy each individual's expectations. The figure 3 illustrates an example of a WKI advanced research sequence. This aspect will enable us to build a dynamic observatory of organizations practices relying on an empirical and real-time data collection of real individual's and enterprises strengths or expectations.
Securing Environment Our wish for an open and free-access to useful knowledge for the society at whole shouldn't mask the importance we attach to confidentiality and data securing, being both essential requirements for our tool. Indeed, the maximal reduction of the risk is a necessary prerequisite to achieve the creation of a secure environment. This must absolutely be seriously considered, especially when dealing with economic organizations and SMEs to ensure a free flow of information exchanges and the development of innovation processes in a safe climate based on confidence within the new community. Regarding to this topic, we design so a controlled access for users by making them log in to WKI using their professional e-mail address or their professional social networks account. A beta test of WKI is already planned within a selected community.
Integration of a Data-Extraction System to Drag up Information We integrate a back-office module to collect qualitative and quantitative data concerning contributors' profiles, their web pages research, the evolution of their consultations and the nature of the information they filled on the WKI. In a second time, the data collected will be processed to build a global diagnostic. The purpose is to build statistical outputs to implement most efficient regional policies toward innovation incentives relying on a qualified raw organization's needs, practices and individuals expectations.
Users Motivating Process To motivate users, we intend to offer an incentivizing system by unlocking "premiums functions" all along their evolution toward new status and through an incremental information filling process of their profiles and affiliated organizations. We offer so a plurality of users profiles starting from the simple "contributor" to most knowledgeable expert ones as "reviewers", having then the possibility to qualify others users contributions in order to facilitate the validation work dedicated to the "administrators". This attempts to enhance progressively the pertinence and the quality of the contributions provided by the users. Indeed, the evolution within the WKI community will be motivated by reaching progressively the access to refined data and much more pertinent and personalized information.
Acknowledgements. Authors would like to acknowledge our partner  AGEFA PME  for its support concerning the Chair on SMEs 3. 0 and the  TRANSCREATIVA  project which has been co-financed by the  "Fonds européen de développement régional" (FEDER)  in the frame of the  Interreg IV B program , " le programme de coopération territoriale de l'espace Sud-ouest européen " (SUDOE).
Protein-RNA complexes provide a wide range of essential functions in the cell. Their atomic experimental structure solving, despite essential to the understanding of these functions, is often difficult and expensive. Docking approaches that have been developed for proteins are often challenging to adapt for RNA because of its inherent flexibility and the structural data available being relatively scarce. In this study we adapted the RosettaDock protocol for protein-RNA complexes both at the nucleotide and atomic levels. Using a genetic algorithm-based strategy, and a non-redundant protein-RNA dataset, we derived a RosettaDock scoring scheme able not only to discriminate but also score efficiently docking decoys. The approach proved to be both efficient and robust for generating and identifying suitable structures when applied to two protein-RNA docking benchmarks in both bound and unbound settings.
Specially designed coarse-grained force-fields based on statistics  [28] [29] [30] [31] [32]  have shown great promises and coarse-grained versions for reducing the initial exploration phase of coarse-grained search are interesting  [33, 34]. The optimization is however often based on relatively simple statistics measurements and rarely benefits from the variety of structural datasets recently made available to the community. The Protein-RNA interface database  [35]  offers high quality curated datasets for statistical analysis. Both available in a redundant and non-redundant version it allows for fine measurements on high-resolution structures. The three protein-RNA benchmarks available in the literature  [36] [37] [38]  also offer a great opportunity to assess and review high-resolution structures and predictions.
We then used the Protein Interface Database  [35]  as reference dataset to generated near-native and plausible docking conformations. We then optimized the Rosetta-Dock high-resolution scoring function using supervised machine learning. After cross-validation and carefully handling tests, we assessed the obtained protocol on the protein docking benchmarks I and II  [36, 38]. We show that the obtained RosettaDock RNA protocol performs better than in the previous attempts  [49]  in a semi-rigid body approach for both bound and unbound docking and can undoubtedly be used for successful protein-RNA predictions. Figure  1. Flowchart of the machine learning strategy. The procedure is made of six steps: a) data processing using the non-redundant PRIDB to generate candidates, b) splitting of candidates into test decoys, decoys, near-native structures according to their Irmsd so as to define the perturbation set, c) definition of the sample set using 30 near-native structures and 30 decoys per native structure -randomly chosen, d) leave-onepdb-out evaluation, e) scoring function learning using ROGER and f) result analysis.
Protein-RNA complexes training and evaluation sets for RosettaDock Protein-RNA native X-ray structures for learning were downloaded from the Protein-RNA Interface Database (PRIDB)  [35]. The non-redundant PRIDB (RB199) contains 199 RNA chains extracted from the PDB in 2010. From the 134 complexes described in this set, we only kept the binary complexes: one protein and one RNA molecule. We also discarded complexes involving the ribosome because of their redundancy and to avoid biasing towards ribosome data but also to avoid computationally expensive procedures. The resulting native structure dataset from the PRIDB is made of 120 complexes (Table  S1 ). We also used the two protein-RNA benchmarks  [36, 38]  as a validation set in bound and unbound (protein and RNA when available) settings. Among the 45 complexes contained in the Benchmark I  [36] , 11 complexes are not found in the PRIDB.
Native and near-native configurations are recovered A data based docking procedure for protein-RNA complexes should first be able to recover the native and close-to-native states for a reference set of complexes. This is assessed by a careful crossvalidation setting. In this study we assessed the performance of our learning procedure by plotting Energy vs. Irmsd and checking the enrichment scores of our procedure relatively to the Rosetta CAPRI default. Figure  2  shows detailed results for nine different complexes (the remaining plots can be found in Figure  S2 ). Interestingly, while only one complex (2e9t) shows a funnel in the default Rosetta version, none of the others do. Funnels can be found however on all the optimized scoring function plots that correlate to a high Enrichment Score.
While not all complexes in the dataset display such a good conformation selection, the optimized scoring always performs better than the default RosettaDock setting and seems suitable for prediction. The machine learning procedure was also assessed separately by plotting the ROC curves in the leave-one-pdb-out setting. Figure  3  (panels a and b) shows the ROC curves for the optimized and default RosettaDock scoring functions respectively. While the default strategy does not show any discrimination power, our optimized function performs very well. In particular, at the origin, the ROC curve is very steep. This is especially interesting as in the CAPRI challenge only 10 putative conformations can be submitted and in any experimental setting, not more than 100 can be easily tested. Table  1  reports the statistics for the previously mentioned complexes and confirms that a large number of nearnative conformations can be found in the top10 and top100 conformations, making the optimized score suitable for prediction (Results on the whole reference set are available in S3).
Limits A current limit of our approach is the way RNA flexibility is handled. Handling RNA flexibility for RNA during docking is a very difficult task  [13]. Thus, aside from hydrogen atoms and protein rotamers, flexibility is not well taken into account. This can however be handled by geometric sampling  [55]. For small RNA molecules this lack of flexibility handling is a limitation that cannot allow for good results despite a good high-resolution scoring function as it calls for a preliminary sampling experiment. Modelling electrostatics is also a major issue when modelling RNA molecules: solvent and ions are often found at the interface and are still hard to predict  [56]. In our reference set, the interaction between the mRNA binding domain of elongation factor SelB from E. coli in complex with SECIS RNA (PDB code 2pjp) is an example where the interface is mediated by sodium ions that our model does not take into account and for which we obtained very poor results (See Figure  S5 ).
Conclusions Protein-RNA complexes are undoubtedly a real challenge for the design of good docking scoring functions. Using a well curated dataset and a well-designed optimization strategy, we show that we could set up of an efficient protein-docking scoring function that can be used in RosettaDock and that can perform better than the existing option in both bound and unbound settings. While scoring can be improved, the nature of RNA makes the prediction experiment still difficult. Electrostatics plays a large role in RNA interactions and ions have to be modelled. Like ours, the data based approaches are limited by the relatively small number of structures available to take ions into account carefully. RNA flexibility modelling for docking is then the next challenge: while some strategies allow for conformation sampling, selection of one or several putative bound states for large cross-docking experiments are still out of reach for both modelling and computational reasons.
To check the correctness of our computations, and more specifically of the ScMult and ScExp functions and the modular root, we developed a simple PHP script  6. By programming an encoding-free version of Elgamal, we successfully verified that the computation of the local identifiers in the route proposal and initialization protocols yield the right output. The choice of the language was motivated by the facility and rapidity to develop with PHP. The code should not, in any case, be considered secure or be used for any other purpose than checking correctness of the arithmetic operations involved in the protocol. Although a highly non-optimized coding, we report in Table  1  the timings for a toy instantiation of Elgamal with security parameter λ = 10, without taking in account any notion of communication delay.
Acknowledgment This work is partially funded by the  Collège International Doctoral de l'Université Européenne de Bretagne  ( International Doctoral College of the European University of Brittany, France ) and the  Regional Council of Brittany, France. The authors would like to thank  Simon Boche  and  Cristina Onete  for their insightful suggestions and discussions on various components of the protocol and cryptographic-related topics, as well as for the precious advices brought during the writing of this paper.
Tree ring data All tree ring data (Fig. 4 ) used for the inversion are latewood measurements previously published by  Etien et al. (2008). Thirty living, dominant oaks were sampled in the Fontainebleau Forest (two stations, N = 15/station). On each tree, three cores were sampled at 1. 3 m height. Each series was measured and cross-dated by the phytoecology team of the National Institute of Agronomical Research (INRA, Nancy, France) using a master chronology constructed from more than 400 oaks sampled in the nearby area. The living tree chronology was standardized using the adaptive regional growth curve technique  (Nicault et al. , 2010)  to produce an average chronology (Fig. 4a ). Tree rings were then cut using a scalpel to separate latewood from earlywood. Samples were pooled and then milled with an 80 µ sieve.
The resulting series was shifted by 1 ‰ towards lighter ratios to match values observed during the common period at Schauinsland. δ 18 O of precipitation. In MAIDENiso, δ 18 O p is modeled statistically using linear regressions, with daily temperature and precipitation used as regressors  (Danis et al. , 2012). Regression parameters were calibrated using 42 years of δ 18 O p (dependant variable), temperature and precipitation data extracted from the REMO(REgional MOdel)iso mesoscale climate model  (Sturm et al. , 2005). The regression equation was directly incorporated into MAIDENiso's code so that the modeling of δ 18 O p could be achieved at the daily time step. Such parameters are site-specific.
Acknowledgements. The first author sincerely thanks the  Fonds de Recherche Nature et Technologies (FRQNT)  for its financial support. This work is a contribution to the  Labex OT-Med  ( ANR-11-LABEX-0061 ) funded by the  Investissements d'Avenir, French Government program  of the  French National Research Agency (ANR)  through the A *  Midex  project ( ANR-11-IDEX-0001-02 ). Financial support was also provided by the  ARCHIVES  project funded by the  Natural Sciences and Engineering Research Council of Canada (NSERC)  as part of a collaborative research and development grant with  OURANOS and Hydro-Québec. Edited by:  V. Brovkin.
Microarray Analysis We analysed samples with the LLMDAv2 microarray, developed at the Lawrence Livermore National Laboratory (LLNL), USA and described elsewhere  [14, 19, 41]. The LLMDAv2 contains 388,000 oligonucleotides probes designed from all sequenced viruses and bacteria  [14]. Labelling and microarray hybridization was performed according to manufacturer protocols (Gene expression analysis, Roche NimbleGen) with the exception that 8 mg, instead of 2 mg, of labelled material was used for hybridization. Microarray data was analysed using a simple Excelbased data analysis method developed at SSI (SSI analysis) as described previously  [19]. Since the SSI analysis is not optimized for bacteria, any bacterial hits were excluded from the results. Non-human, non-zoonotic pathogens were also excluded since they are assumed to be clinically irrelevant in a diagnostic setting.
To better understand the source of these additional predictions, we used the CliMax software to generate sequenceprobe alignment plots, where the intensity of each probe is plotted against its alignment position in the viral genome. These plots clarify whether identification of a virus is based on presence of the whole genome or may be due to cross-hybridization from probes matching sub-regions of other genomes present in the sample. For example, the sequence-probe alignment plots for the Puumala virus sample show the positive probes to be uniformly distributed across all three Puumala virus genome segments, indicating the presence of the whole viral genome (Figure  3B , top). Probe hits for the bovine viral diarrhea virus 1 (BVDV-1) genome show a different pattern, landing in only a narrow region suggesting nonspecific-or cross-hybridisation (Figure  3B , bottom).
The presence of rotavirus A was confirmed by rotavirus A-specific PCR (data not shown). BVDV, SV5 and porcine rotavirus C were considered clinically irrelevant and therefore not confirmed by PCR. In summary, the LLMDAv2 correctly identified single and multiple viruses present in non-clinical samples with a very low level of false positive signals. The CLiMax analysis method identified every virus present in the samples whereas the simpler SSI analysis method only identified clinically relevant human pathogens.
Microarray Detection of Emerging Viruses in Clinical Samples We tested the LLMDAv2 microarray on 18 clinical samples previously identified as positive by real-time PCR for emerging viruses. The correct virus was identified in 17 samples using both the SSI (Table  3 ) and CLiMax analyses (data not shown). The sample identified only as a parapoxvirus was determined to be Orf virus, a member of the Parapoxvirus genus. Seven of the eight DENV-positive samples were clearly determined by the microarray analysis to be positive for DENV type 2, DENV type 1 or DENV type 3. DENV type 4 was not identified in any of the clinical samples. Additional DENV subtypes were detected in four of the samples, but at very low probe signal intensities compared to the correct DENV subtype probe signal (Figure  4A+4B ).
However, the use of PCR requires a specific hypothesis as to the causative agent, which is not the case with the LLMDA array. We use a modified random WTA method to amplify the RNA virus and show that least 10 5 copies/ ml of amplified material is needed in order to have a successful identification by the LLMDAv2. This is equivalent to the recently published data that show detection of 10 5 copies of vaccinia virus DNA without any amplification prior to hybridization to the 4x72K version of the LLMDA  [46]. The samples used in this study to measure sensitivity were all dilutions of viral samples or supernatants and do not represent clinical samples containing low viral concentrations. Therefore, further experiments to investigate clinical sensitivity are warranted. Previous reports have shown high clinical sensitivity (86-97%) and specificity (98-99%) of another microarray, the Virochip  [15] , when it was applied to samples from different respiratory virus infections that were confirmed by specific PCR  [20, 21].
Both data analysis methods had difficulty in distinguishing between the four different DENV subtypes (Table  1  and Figure  3B ). This was not surprising, since viral strain subtyping was not a goal of the LLMDAv2 design  [14]. Nevertheless, our work shows that improvements to LLMDA probe specificity are needed to increase its value for diagnosis and outbreak detection. The CLiMax software is numerically intensive and requires a large-memory LINUX server harbouring a library of probe-target binding probabilities that are the basis for pathogen identification  [14, 41]. The CLiMax analysis is sophisticated and powerful, providing a user-friendly web interface to a database that keeps track of requested analyses and their results. In addition to a list of probable viruses, the CLiMax software can generate a target sequence-probe alignment plot showing probe fluorescence intensities together with the location of probe hits across each viral genome detected.
This can help to distinguish the presence of whole viral genomes from non-specific probe hits and crossreactivity. The analysis developed in-house at SSI processes microarray feature intensities produced by the NimbleScan software within a Microsoft Excel framework  [19]. While the CLiMax analysis is designed for broad-spectrum detection of all microbial targets represented on the LLMDA, the Excel-based SSI analysis is more focused toward identification of human-infecting viral pathogens. The relative simplicity of the SSI analysis is attractive for a clinical diagnostic environment, since it requires less costly computing hardware, and provides a clearer diagnostic result for clinicians, because clinically irrelevant non-human and non-zoonotic pathogens are excluded from the analysis. The CLiMax software is a more sophisticated, precise tool for data analysis in a research environment.
Acknowledgments We thank  Solvej Jensen ,  Birgit Knudsen ,  Bente Østergaard  and  Britt Christensen  for expert technical help. We thank the  CCH Fever Network  supported by the  European Commission  under the  Health Cooperation Work Program of the 7th Framework Program  (no. 260427 ) for contributing with CCHFV-positive clinical samples. This work was performed in part under the auspices of the  U. S. Department of Energy  by  Lawrence Livermore National Laboratory  under Contract  DE-AC52-07NA27344.
The gut microbiome of a French CRC study population To explore associations between the gut microbiome and colorectal carcinoma (CRC), we first analyzed fecal metagenomes from a popu-lation of 156 participants recruited in France (study population F in the following, see Table  1 , Supplementary Table  S1  and Supplementary Dataset S1 for patient data), who underwent colonoscopy to either diagnose colorectal neoplasia in the form of adenoma(s) (polyps) or CRC, or confirm the absence of these. Carcinomas were further classified according to established staging systems (AJCC and TNM)  (O'Connell et al, 2004). We first analyzed global community properties: While CRC-associated dysbiosis did not result in significant changes of microbial community diversity or richness  (Supplementary Fig S1D and F) , the distribution of enterotypes, as a descriptor of global community structure  (Arumugam et al, 2011) , varied slightly, but significantly between patient groups (Supplementary Fig  S1A  and B ).
Validation of the CRC classifier using patients and controls from different countries The broad utility of gut microbial markers might be limited by geographical or ethnical particularities or by technical variations in experimental procedures  (Sunagawa et al, 2013; de Vos & Nieuwdorp, 2013) , as illustrated by distinct microbial associations with type 2 diabetes in Chinese and Swedish studies  (Qin et al, 2012; Karlsson et al, 2013). We therefore sought to validate the metagenomic CRC classifier in an independent group of individuals from different countries (see Materials and Methods). To assess whether it maintains high specificity in a large control population, we applied the classifier to five samples from healthy individuals living in Germany and 292 published fecal metagenomes from Danish and Spanish individuals who were diagnosed with neither CRC nor inflammatory bowel disease (IBD)  (Qin et al, 2010; Le Chatelier et al, 2013)  (study population H, see Table  1 , Supplementary Table  S1  and Supplementary Dataset S1).
At a decision boundary of 0. 275 (i. e. , the value above which the classifier predicts CRC, Fig  1 ), the resulting false-positive rate (i. e. , 1specificity) varied slightly from 8. 0 to 7. 7% between cross-validation on study population F and independent validation on study population H  (Supplementary Fig S6A). In these validation populations, the true error rate might be slightly overestimated, because absence of CRC has not been confirmed by colonoscopy. To also independently validate the sensitivity of the classifier, we sequenced an additional 38 fecal metagenomes from individuals who were diagnosed with CRC by colonoscopy in a German hospital (study population G, see Table  1 , Supplementary Table  S1  and Supplementary Dataset S1). On this dataset, the sensitivity (i. e. , true-positive rate) of the metagenomic CRC classifier was 52.