dataesr/scientific-paragraphs-categorization huggingface dataset:
The proposed two-stage framework is first validated on the data from the CamCAN repository and subsequently further applied to two further open-access repositories: the HCP and ATR Wide-Age-Range repositories. The use of data from two additional repositories serves to provide a clear empirical indication of the generalization capabilities of the proposed approach. This is especially relevant in the context of fMRI data, where artefacts such as scanner noise can often cause significant challenges  (Poldrack et al. , 2011). We note that the brain age prediction errors presented in this work are not competitive with alternative methods which are based on alternative imaging modalities, such as structural imaging data  (Cole et al. , 2017, Cole and Franke, 2017). This is to be expected for two reasons.
For all dataset, most of the temperature analysis increments are observed below 750 m and span the range -5 to 5 K. The largest increments are observed between 100 and 300 m altitude for which around 20 % of the analysis minus background differences are larger than 2 K in absolute values. We can note significant differences in the shape of the increment distributions depending on the forecast score. While the distribution of good detections is quite symmetric, it is not the case for missed fog profiles and false alarm distributions. In the case of missed fog events, the distribution is negatively-skewed close to the ground whereas it is positively-skewed above 100 m altitude. This asymmetry means that the largest analysis increments in magnitude tend to decrease the temperature close to the ground and increase the temperature above 100 m.
It is more symmetric in the case of false alarms. The correlation coefficient with respect to the MWR multi-channel retrieval (fig. 12 ) is sligthly increased from 0. 97 to 1. The RMSE is improved from 1. 30 to 0. 71 kg. m -2. The impact of MWR observations is thus positive on IWV, though the good quality of AROME humidity forecast leaves little room for improvement. This could be explained by the assimilation of observations sensitive to the total column water vapor like Global Navigation Satellite System (GNSS) zenith total delay. Further investigation on multiple sites would be needed to confirm this hypothesis. This worked has proved MWRs to be potential good candidates to be assimilated into current mesoscale models with a special focus on fog forecast improvement. However, our conclusions are currently limited by the small dataset (only one winter at one site) and the lack of impact studies on fog forecast.
An increased density of MWRs is found at the super-site with two co-located MWRs and a third humidity profiler deployed approximately 7 km away from the super-site to document the impact of surface heterogeneities on fog characteristics. The methodology introduced in this paper will be extended to the 8 MWRs deployed during SOFOG3D. This large dataset will help quantifying the spatiotemporal variability of fog parameters (thermodynamics and microphysics) between the different sites, better understand the main processes playing a role in fog formation / dissipation / development and run real data assimilation experiments using the operational 3D-Var assimilation scheme of the AROME model to quantify the expected fog forecast improvement thanks to ground-based MWRs.
Acknowledgements. This article is based upon work from  COST Actions ES1303 (TOPROF)  and  CA18235  (PROBE), supported by  COST (European Cooperation in Science and Technology)  -www. cost. eu. The authors thank  ANDRA  for providing access to the atmospheric platform observations. The deployment of the SOFOG3D MWR network was funded by the  ANR   SOFOG3D  (SOuth west  FOGs 3D experiment for processes study ,  ANR-18-CE01-0004 ). Thomas Rieutord is thanked for helpul discussions on statistical analysis. Yann Seity is thanked for providing the map of the AROME 500m domain.
Data availability. The AROME forecasts are available on request on the website https://donneespubliques. meteofrance. fr/. Instrumental data are available on request to: frederic. burnet@meteo. fr. Author contributions. PM supervised the MWR deployment during the field experiment, processed all the data, led the scientific analysis and wrote the paper. DC participated to the development of the 1D-Var algorithm and scientific analysis of the results. FB supervised the Bure experiment and participated to the scientific analysis of the results. VU was in charge of the technical deployment of the MWR during the experiment. BM developed and provided the support for the software used to derive background-error-covariance matrices from ensemble data assimilation. YM provided the AROME ensemble data assimilation outputs to compute the background-error-covariance matrix and participated to the redaction of section 3.
microRNAs are noncoding RNAs which downregulate a large number of target mRNAs and modulate cell activity. Despite continued progress, bioinformatics prediction of microRNA targets remains a challenge since available software still suffer from a lack of accuracy and sensitivity. Moreover, these tools show fairly inconsistent results from one another. Thus, in an attempt to circumvent these difficulties, we aggregated all human results of four important prediction algorithms (miRanda, PITA, SVmicrO, and TargetScan) showing additional characteristics in order to rerank them into a single list. Instead of deciding which prediction tool to use, our method clearly helps biologists getting the best microRNA target predictions from all aggregated databases. The resulting database is freely available through a webtool called miRabel 1 which can take either a list of miRNAs, genes, or signaling pathways as search inputs.
Receiver operating characteristic curves and precision-recall curves analysis carried out using experimentally validated data and very large data sets show that miRabel significantly improves the prediction of miRNA targets compared to the four algorithms used separately. Moreover, using the same analytical methods, miRabel shows significantly better predictions than other popular algorithms such as MBSTAR, miRWalk, ExprTarget and miRMap. Interestingly, an Fscore analysis revealed that miRabel also significantly improves the relevance of the top results. The aggregation of results from different databases is therefore a powerful and generalizable approach to many other species to improve miRNA target predictions. Thus, miRabel is an efficient tool to guide biologists in their search for miRNA targets and integrate them into a biological context.
, 2009) , the conservation of sequences among species  (Brennecke et al. , 2005)  and the accessibility of binding sites  (Long et al. , 2007). This resulted in the creation of more than 187 target prediction tools (as of September 2019, from OMICtools' database  (Henry et al. , 2014) ), all of which have their strengths and weaknesses  (Marbach et al. , 2012; Le et al. , 2015). These tools are useful to reduce the number of potential targets in order to streamline the experimental validations  (Witkos et al. , 2011). However, their predictions suffer from a poor accuracy and sensitivity as revealed by experimental data  (Thomas et al. , 2010; Pinzon et al. , 2017)  and are very divergent from one tool to another  (Min and Yoon, 2010). So far, no single method consistently outperforms others, thus supporting the idea that databases content combination is an efficient way to improve prediction relevance.
Assuming that an interaction predicted by more than one algorithm is more likely to be functional, databases such as miRWalk  (Dweep et al. , 2011; Dweep and Gretz, 2015; Sticht et al. , 2018) , miRSystem  (Lu et al. , 2012) , miRGator  (Nam et al. , 2008)  or, more recently, Tools4miRs  (Lukasik et al. , 2016) , store and/or compare results predicted by several popular tools using statistics and mRNA/ protein expression data. Interestingly, it has been demonstrated that targets resulting from the intersection of two lists of predictions are not more likely to be present in the intersection of two other lists  (Ritchie et al. , 2009). Therefore, intersecting results does not increase the probability of retaining true positives and it may lead to decreased sensitivity because of possibly omitting valid interactions  (Sethupathy et al.
, 2006; Oliveira et al. , 2017). In order to circumvent these limitations, we computed a new score based on the aggregation of the interaction ranks taken from other well-known prediction algorithms. The goal being to give all available predicted interaction to the biologist for a given miRNA while highlighting the most relevant ones. To test our hypothesis, we aggregated four prediction algorithm results which enabled us to show that this new score significantly improves miRNA targets prediction compared to other prediction tools. To allow a more comprehensive analysis, the results of this aggregation were eventually linked to their respective cellular pathways using KEGG database, and implemented in a web tool named miRabel.
Aggregated Databases Computationally predicted human miRNA::mRNA interaction databases generated by miRanda  (Betel et al. , 2010) , PITA  (Kertesz et al. , 2007) , SVMicrO  (Liu et al. , 2010)  and TargetScan  (Agarwal et al. , 2015)  were used. These publicly available online algorithms have been chosen because each of them uses different and complementary features of miRNA:: mRNA interactions such as seed match, interspecies conservation, free energy, site accessibility and target-site abundance (Table  S1 )  (Peterson et al. , 2014). Only precomputed data were used since this is what is mostly accessible online to biologists. The ranks of each predicted interaction retrieved from one or more of these databases have been aggregated using the R package RobustRankAggreg (RRA, v1. 1)  (Kolde et al.
Testing Data Sets Two types of testing data sets were used for each of the comparisons described in this paper. First, to compare the different aggregation methods, we used one million randomly selected interactions within aggregated data. Validated interactions accounted for 5% of the testing data set. The use of all common interactions between compared databases resulted in extremely large data sets (>500,000 interactions) which reduced the amount of possible analysis due to computation time (several weeks). This led us to design a second type of data sets of 50,000 interactions randomly picked from the corresponding larger data set. For each large data set, 10 smaller ones were created (Figure  1 ). The amount of experimentally validated interactions within these randomly picked ones was set so as to remain close in proportion to the main, larger data set.
Performance Analysis Methods On each data set, a receiver operating characteristic (ROC) analysis was done using the area under curve (ROC_AUC) as implemented in the R package pROC  (Robin et al. , 2011). To analyze top prediction results, a specificity of 90% was set as a threshold in order to compute partial ROC (pROC 90% ) and the corresponding AUC (ROC_pAUC 90% ) and sensitivity. To focus on which classifier better identifies true positive interactions, data sets were further compared with precision and recall (PR) curves using R programming as well. For the same purpose as with the pAUC of the ROC analysis, we calculated the harmonic mean between the precision and the recall (F-score) for different percentages of the top interactions.
Mirabel Overview miRabel: A Database for Microrna Target Predictions The database was designed with MySQL 3  using InnoDB motor and includes predictions from miRanda  (Betel et al. , 2010) , PITA (v. 6. 0) (Kertesz et al. , 2007) , SVMicrO  (Liu et al. , 2010) , and TargetScan  (Agarwal et al. , 2015). It contains tables for 2,587 human miRNAs which have target mRNAs, 19,799 genes and FIGURE 1 | Testing data sets design and databases performance analysis methodology. A large data set containing all common interactions between compared databases is created. For ease of use, 10 smaller data sets of 50,000 interactions were randomly picked from all common ones. Predictions performance are then compared using receiver operating characteristic (ROC) and a precision and recall (PR) analysis on all data sets.
275 pathways. This represents more than 14. 7 million predicted interactions from which 351,298 are experimentally established. These experimentally validated interactions are annoted using miRTarBase (v. 6. 0) (Hsu et al. , 2011)  and miRecords  (Xiao et al. , 2009) , whereas 5'UTR and CDS predictions were identified with miRWalk database (v. 2. 0)  (Dweep et al. , 2011). Genes and pathways information as well as their relationships were retrieved from KEGG's database while miRNA data were from miRBase (release 22. 1) and linked with miRNA target predictions. Since the annotation of miRNAs has changed over the years, a tool was developed to convert the names of miRNA queries in the latest version used by miRBase. In order to standardize gene names from the different tools, they were converted to the NCBI gene ID and a table containing their synonyms has been built.
The Web Interface The web interface was designed with PHP 4  and CSS 5. It enables users to query the system directly by miRNA, gene or pathway name (Figure  2B ). Multiple queries are allowed in order to identify common prediction results. Queries by pathways are easily made thanks to asynchronous database queries and name completion. The results are visualized by using the DataTable plugin of the JQuery framework which allows to create tables that can be easily filtered and sorted. Results can be copied, printed or exported in tabulated-separated or pdf formats. An online documentation is also provided. miRabel is freely available 6.
1% of total interactions (1,204,591 / 14. 7 million) are common to each other. The example of hsa-miR-16 (Figure  2B ) also illustrates very well these divergences of predictions. Moreover, because some of these algorithms have not been updated recently, some more refined features found in recent prediction approaches such as TarPmiR  (Ding et al. , 2016) , are not emphasized in our aggregated results if not at all present. Only human miRNAs were used initially to limit the amount of data as well as the FIGURE 2 | Overview of miRabel. Predictions results from miRanda, PITA, SVMicrO, and TargetScan for 3'UTR are aggregated using Robust Rank Aggreg. 5'UTR and CDS predictions are retrieved from miRWalk database. Experimentally validated interactions are identified using miRTarBase and miRecords.
Links between predictions and pathways are established based on KEGG information (A). An example of miRabel web interface is shown using predictions for hsa-miR-16. Predicted targets are ranked according to miRabel's score. Rank found for this interaction in each database are indicated as well as its experimental validation status and sub-localization in the mRNA (5'UTR and CDS) (B). FIGURE 3 | Performances comparison of aggregation methods. Receiver operating characteristic (ROC) curve analysis (A), showing the sensitivity and the specificity for five aggregation methods from the RobustRankAggreg (RRA) R package, and their respective area under curves (AUC, B) have been calculated using the pROC R package on ten random data sets containing 50,000 interactions. Using the same data set, PR analysis (C) with PR_AUC (D) has been carried out.
The cumulative harmonic mean between precision and recall (F-score) was also plotted (E) for each ranked interaction of this data set. The average F-score is reported for all interactions (F). The higher are the ROC_AUC, PR_AUC and F-score, the better are the performances of the tested method. Highest values are in bold font. showing the sensitivity and the specificity for miRabel and MBSTAR, and their respective area under curves (AUC) (B) have been calculated using the pROC R package on 82,867 common interactions. Using the same data set, a precision and recall (PR) analysis (C) with PR_AUC (D) has been carried out. The cumulative harmonic mean between precision and recall (F-score) was also plotted (E) for each ranked interaction of this data set. The average F-score is reported for all interactions (F).
The higher are the ROC_AUC, PR_AUC, and F-score, the better are the performances of the tested algorithm. Highest values are in bold font. FIGURE 6 | Performances comparison of miRabel and miRWalk. Receiver operating characteristic (ROC) curve analysis (A), showing the sensitivity and the specificity for miRabel and miRWalk, and their respective area under curves (AUC) (B) have been calculated using the pROC R package on 761,354 common interactions. Using the same data set, a precision and recall (PR) analysis (C) with PR_AUC (D) has been carried out. The cumulative harmonic mean between precision and recall (F-score) was also plotted (E) for each ranked interaction of this data set. The average F-score is reported for all interactions (F). The higher are the ROC_AUC, PR_AUC and F-score, the better are the performances of the tested algorithm.
Highest values are in bold font. associated computation times, but our approach is generalizable to miRNAs of all origins. Although there is so far a high prevalence of miRNA interaction sites found in the 3'UTR, recent papers have shown that some miRNAs can also regulate mRNAs by binding with the 5'UTR and CDS region of their targets  (Moretti et al. , 2010; Qu et al. , 2016). Even though the value of these sites is not yet clearly established in the literature, this information remains important to get an integrated view of the predicted miRNA interaction sites on the mRNA. Since the score generated by the RRA package is also representative of the significativity of the ranking (the lower the score, the better) for a given interaction, we suggest to use miRabel with a threshold of 0. 05.
However, further analyses are required to really define an optimal threshold for miRabel. Finally, the choice of algorithms is also limited by the free availability of their database. To further improve predictions, it would therefore be interesting to include promising tools such as ComiR  (Coronnello and Benos, 2013)  whose prediction algorithm have been shown to perform well  (Fan and Kurgan, 2015). RNAHybrid  (Rehmsmeier et al. , 2004)  and rna22  (Miranda et al. , 2006)  are also of particular interest because they allow the prediction of targets in CDS and 5'UTRs and have been used successfully in predicting targets that were later validated experimentally. We chose the RRA package for its ability to handle incomplete rankings and its robustness to noise due to divergent lists  (Kolde et al.
, 2012). Comparing five of the aggregation methods included in the RRA package shows that the "mean" method is best for aggregating miRNA prediction lists (Figure  3 , Table  S2 ). However, although statistically significant, these values are relatively close to one another. These results are similar to those obtained in studies designed to compare the performance of several rank aggregation methods  (Burkovski et al. , 2012; Wald et al. , 2012; Dittman et al. , 2013). Among other aggregation methods, Cross Entropy Monte-Carlo has been found to be inadequate for our study due to too extensive computation times with large lists of items  (Lin, 2010). Another method that could be evaluated is the Borda count algorithm  (Dwork et al. , 2001)  which has already been used to aggregate cancer expression microarrays and proteomics data sets into a single optimal list  (Jurman et al.
, 2008). MiRabel performs better than each of the individual aggregated algorithms (Figure  4 ). Prediction improvement is also visible in the top ranked interactions (Table  S3 ), thus showing that our aggregation moves validated interactions up in ranking. This is in line with multiple studies that combine data to obtain the most relevant interactions  (Shirdel et al. , 2011; Marbach et al. , 2012; Friedman et al. , 2014; Andres-Leon et al. , 2015; Sedaghat et al. , 2018). A recent study in particular shows that the union of the predictions of three tools among four (TargetScan, miRanda-mirSVR, RNA22) increases the performance of the analyses  (Oliveira et al. , 2017). However, our work goes further since prediction lists were aggregated and reranked in a unique list. The performance of their method was evaluated using only ten miRNAs and 1,400 genes but not the entire database.
In order to avoid selection bias of the data sets, we analyzed all 1,204,591 interactions common to miRabel and the four aggregated algorithms, which represent 514 miRNAs and 15,343 genes. Furthermore, even though miRabel aggregates mostly older databases, it shows equal (vs. miRmap) or better (vs. MBSTAR and miRWalk) performances than up-to-date algorithms, thus clearly establishing that our method, even though simple, has a great potential. Interestingly, from all evaluations done with our data sets and methodology, we found other algorithm performances to be quite different from what was originally described in their respective original publications. This is in agreement with previous studies that highlighted the importance of testing prediction results on multiple, independent data sets and with a standardized evaluation protocol  (Fan and Kurgan, 2015; Sedaghat et al.
, 2018). This is also one of the strengths of our study. Indeed, throughout all comparisons, miRabel was tested on 66 different data sets, which gives more robustness to the performance values calculated for our method. As a conclusion, miRabel is a new efficient tool for the prediction of miRNA target mRNAs and their associated biological functions. Using an aggregation method, we improved the relevance of the predictions of three available algorithms. This promising approach can easily be extended to all publicly available databases or to other species. Moreover, the integrated biological pathways provide a more comprehensive view into the complex regulatory network of miRNAs. Eventually, there is no doubt that this method will greatly contribute in helping biologists gather all available predictions for a given miRNA and to highlight the most relevant potential interactions.
Data preprocessing Anatomical MRI data preprocessing and structural reconstruction. The reconstruction of the subjects' individual brain network topography and connection topology within the 3D physical space was performed using an in-house pipeline for automatic processing of multimodal neuroimaging data based on publicly available neuroimaging tools and customized for TVB (https://github. com/the-virtual-brain/tvb-recon). The current version of the pipeline used in this study evolved from a version described previously in  Proix et al. (2016). In short, the pipeline proceeds as follows for each subject: First, the brain anatomy was reconstructed from the T1-weighted images using the recon-all command from the FreeSurfer package (version 6. 0. 0; Fischl, 2012). The T1-weighted images and the generated parcellation volume were then aligned with the diffusion-weighted images (DWIs) using the linear registration tool flirt from FSL package  (FMRIB Software Library, version 6.
0; Jenkinson et al. , 2012). The correlation ratio cost function was used for the alignment with 12 degrees of freedom. The tractography was performed using the tools from the MRtrix package  (version 0. 3. 15; Tournier et al. , 2012). The fiber orientation distributions were estimated from the DWI using spherical deconvolution  (Tournier et al. , 2007)  by the dwi2fod tool with the response function estimated by the dwi2response tool using Tournier's algorithm  (Tournier et al. , 2013). Afterward, 15 million tracts were generated using the tckgen tool by a probabilistic tractography algorithm, iFOD2, based on a second-order integration over fiber orientation distribution  (Tournier et al. , 2010)  with the streamlines seeded randomly within the brain volume. Finally, the SC weights matrix C ij was generated by the tck2connectome tool by counting the number of tracts connecting the regions in the Desikan-Killiany parcellation generated by FreeSurfer including 70 cortical regions and 14 subcortical regions  (Desikan et al.