dataesr/scientific-paragraphs-categorization huggingface dataset:
Due to the lower number of GENCODE lncRNAs annotated in mouse compared to human, each file contains ~2,000 lncRNAs and 5,000 mRNAs. For "non-model organisms", lncRNAs belonging to the antisense and intergenic classes (NONCODE codes 1000 and 0001, respectively) were downloaded from the latest version of the NONCODE database (NONCODE 2016)  (28)  while mRNAs were retrieved from the EnsEMBL database (v84). A summary of the number of mRNAs/lncRNAs per species is available (Supplementary Table  1 ). Whole transcriptome sequencing of dog samples (n = 20) were generated in the frame of the LUPA consortium. The biological samples were obtained from the "Cani-DNA_CRB" (http://dog-genetics. genouest. org) and XXX biobanks. The dog owners consented to the use of data for research purposes anonymously.
The sequencing libraries were constructed using paired-end stranded polyA selection and sequenced by HiSeq-2000 Illumina technology. The 20 samples correspond to 16 unique tissues and 7 breeds (Supplementary Table  2 ) with about 50 Millions reads per sample. The RNASeq data is available in the short read archive (SRA) under NCBI bioproject PRJNA327075 and SRA accession SRP077559.
FEELnc coding potential predictors Among the three FEELnc modules, the second one aims at computing a coding potential score given the assembled sequences following transcriptome reconstruction. To deal with the incompleteness of ORF annotation where both the reference and the reconstructed transcripts may not be full-length, FEELnc can compute five types of ORF from the stricter "type 0" which corresponds to the longest ORF having both a start and stop codons, to the more relaxed "type 4" that is the whole input RNA sequence (see Supplementary Material for detailed description of the ORF types). Because the size of the protein-coding ORF is generally correlated with the length of the input RNA sequence, we used the ORF coverage, i. e. the proportion of the transcript size covered by the ORF, as the first predictor to discriminate mRNA/lncRNAs in the FEELnc model.
Note that with increasing length of k, it is possible that Ushuffle could not permute some input sequences because of the constraint to preserve k-mer frequency. Finally, the strategy cross-species makes use of lncRNA sets annotated in other species to extract noncoding predictors and train the RF model. For the latter strategy, we used the NONCODE lncRNA catalogues in 13 species and train FEELnc model using human mRNAs and NONCODE species-specific lncRNAs. For all strategies, we assessed the performance on the HT datasets.
Performances We evaluated the performances of FEELnc and five other programs: CNCI (version 2 Feb 28, 2014), CPC (version 0. 9-r2), CPAT (version 1. In addition, we used two complementary metrics that could be considered as more global values of performance. -F1-score = 2× !"#$%&%'(×!"#!$%$&$%' !"#$%&%'(!!"#!$%$&$%' , which is a statistic measuring the harmonic mean of precision and sensitivity. - MCC (Matthews Correlation Coefficient) = !"×!"!!"×!" !"!!" !"!!" !"!!" !"!!" , which is particularly useful when the two classes are of very different sizes (often the case between mRNA and lncRNA in non-model organisms) and which could be seen a correlation coefficient between trained and tested datasets. For each performance metric, we considered lncRNAs as the negative class and mRNAs as the positive class.
As a binary classification, the mRNA specificity corresponds to the lncRNA sensitivity (and conversely). For the CPAT and PLEK programs, which allow training their models, we used species-specific set of mRNAs/lncRNAs for training (called CPAT train and PLEK train , denoted as CPAT_train and PLEK_train in Figures and Tables ). For CPAT train , we referred to the optimal CPS cut-off mentioned on the website to discriminate between coding and non-coding RNAs  (18). A detailed description of the command lines used to run each program is given in Supplementary Materials.
FEELnc filter filters out short transcripts (default 200 nt) and can deal with single-exon transcripts depending on whether the protocol used to construct libraries is stranded or not. Remaining transcripts are thus candidates to be new lncRNAs or mRNAs. The second module (FEELnc codpot ) computes a coding potential score for every candidate transcripts based on a RF model trained with several predictors such as ORF coverage, multi k-mer frequencies and RNA sizes (see Methods). Using the "goldstandard" GENCODE human learning set (HL) for training, we evaluated the performances of FEELnc on 5,000 lncRNAs and 5,000 mRNAs from the test dataset with respect to the five ORF types and multi k-mer combinations. We observed that "type 1" and "type 3" ORF models, which extract the longest ORF even in the absence of stop codon, consistently display better achievements (mean MCC = 0.
4 ). Third, in order to model incompletes RNA reconstruction, we removed 10%, 25% and 50% from the 5' of each sequence from HT data set. Even if FEELnc codpot performance decreased with increasing degradation percentage (MCCs = 0. 827, 0. 749 and 0. 53 for 10%, 25% and 50%, respectively), it performed better than other tested methods (Supplementary Fig. 5 ). Fourth, we were able to replicate the performance with mouse GENCODE dataset ML and MT (see materials) composed of 5,000 mRNAs and 2,000 lncRNAs where FEELnc achieves 0. 938 in sensitivity, 0. 941 in specificity and a MCC of 0. 856. Finally, among the manually curated list of 35 "well-characterized" lncRNAs from  (42)  The third FEELnc module (FEELnc classifier ) formalizes the annotation of lncRNAs based on neighbouring genes in order to predict lncRNA functions and RNAs partners (see classes in methods).
Benchmarking FEELnc in comparison with existing tools We next compared the performance of FEELnc with five state-of-the-art programs either alignment-free (CPAT, CNCI and PLEK) or alignment-based (PhyloCSF and CPC). As for FEELnc, CPAT and PLEK allow building their models using userspecified learning dataset. Hence, we used the balanced HL dataset (5,000 lncRNAs and 5,000 mRNAs) to construct the models for these two programs denoted CPAT train and PLEK train. Contrary to FEELnc, PLEK and CPAT required to a priori extract the CDS of the mRNA input file to learn the coding parameters. We also used default pre-built models for PLEK and CPAT although some of the transcripts from the human GENCODE dataset test file (HT) could have been used for building these models. For all programs, performance metrics were calculated according to the human GENCODE dataset (HT).
It showed that FEELnc had the highest classification power (AUC value 0. 97) compared to the others tools as illustrated by the ROC curve (Fig. 3A ). Figure  3. A) ROC curve analysis of FEELnc versus ncRNA annotation programs based on GENCODE human dataset (HT). B) Empirical cumulative distribution of FEELnc codpot feature scores with the true set of human lncRNAs (lncRNA) in comparison with the "shuffle" and "intergenic" approaches. C) FEELnc codpot MCC values tested on human set and trained using human mRNAs and species-specifc NONCODE lncRNAs. In x-axis is represented the time of speciation between human and NONCODE species as given in  (57). Species abbreviations are the following: Atha: Arabidopsis; Btau: Cow; Cele: Nematode; Dmel: Fly; Drer: Zebra; Ggal: Chicken; Ggor: Gorilla; Hsap: Human; Mdom: Opossum; Mmul: Rhesus; Mmus: Mouse; Oana: Platypus; Pabe: Orangutan; Ptro: Chimpanzee; Rnor: Rat.
Accordingly, FEELnc displays the highest sensitivity (0. 923) and secondly ranked specificity (0. 915) values among all tools while PLEK displays the highest specificity (0. 985) and precision (0. 981). In general, alignment-based methods have lower classification metrics than alignment-free programs and it appears that most of the CPC misclassified lncRNAs are lincRNAs (37%) versus antisense lncRNAs (10%). Finally, FEELnc also shows the highest performance metrics for classification accuracy (0. 919), F1-score (0. 919) and MCC values (0. 838) indicating that it performs well on the human GENCODE dataset in comparison to other tools (Table  1A ). We further investigated performance on the mouse datasets composed of 2,000 lncRNAs and 5,000 mRNAs in order to replicate the analysis in another organism and using an unbalanced dataset.
For this benchmark, we included all the same programs except PhyloCSF due to the labour-intensive task to extract input cross-species multiple alignments. Again, FEELnc displays the highest classification accuracy, F-score and MCC (Table  1B ) while CPC shows the best specificity (0. 992) and precision (0. 996) despite a weak sensitivity (0. 744). As for human dataset, the CPAT program performs well even if we consider both the re-trained and prebuilt models. Interestingly, CPAT with the trained mouse model has only slightly better performance than using the human prebuilt model indicating that cross-species training achieves relatively few performance gains. Finally, we assessed the computational time of each program including the time required for computing the model for trained-based tools.
For this strategy that we called "shuffle", we took the 5,000 HL mRNAs and shuffled the sequences while preserving k-mer frequencies using Ushuffle  (39). We tested different size of k and showed that preserving 7-mer frequencies gave the best MCC values on the HT set while sustaining a high number of permuted sequences (Supplementary Fig. 6 ). In order to evaluate the intergenic and shuffle strategies on the training sets, we computed their predictor scores in comparison with the true set of 5,000 HL lncRNAs. We first observed that the cumulative distribution of predictor values for the shuffle strategy tend to be closer to the one observed in the true set of human lncRNAs compared to the intergenic approach (Supplementary Fig. 7 ). We then confirmed on the HT dataset that the shuffle method outperformed the intergenic approach (MCCs = 0.
7 ). In order to directly evaluate FEELnc performance for "non-model" organisms, we used the FEELnc shuffle strategy where the coding parameters were learned on speciesspecific mRNAs and the noncoding ones on species-specific mRNAs shuffled by Ushuffle with preserved 7-mer frequencies. All tests were further assessed on the catalogues of lncRNAs annotated in NONCONDE database. We also compared performances with CNCI knowing that NONCODE uses both CNCI and matching protein-coding coordinates from RefSeq database to remove all ncRNAs annotated as protein coding. In Supplementary Table  3 , we showed that FEELnc without annotated lncRNAs achieved good classification metrics in many species (MCC ranging from 0. 70 for rat to 0. 903 for cow) and even better than CNCI in five species (nematode, fly, gorilla, orangutan and rhesus macaque).
As a third approach to model noncoding sequences, we sought to use cross-species lncRNAs for learning FEELnc model parameters. To this end, NONCODE lncRNAs from 13 organisms were used to serve as proxy for human lncRNAs and performance were then evaluated on the HT dataset. As expected, FEELnc performance is negatively correlated with the time of speciation between human and NONCODE species (spearman rho = -0. 85; p-val = 5. 6e-05) with MCC values of 0. 374 when using Caenorhabditis elegans lncRNAs to 0. 823 with chimpanzee lncRNAs (Supplementary Table  4 ). This probably reflects the variability in term of lncRNA sequence conservation (and thus in noncoding k-mer frequencies) between human and NONCODE species  (35, 42). Interestingly, the shuffle strategy that has a MCC of 0. 748 would correspond to performance obtained with lncRNAs that diverged about 100 Myr indicating that it constitutes an interesting approach when no lncRNAs from closely related species are available.
Application to identify an extended catalogue of canine lncRNAs Within the framework of the LUPA consortium  (29) , we performed 20 whole transcriptome sequencing of 16 tissues (Supplementary Table  1 ). After QC, ~1,3 billions reads were mapped onto the canfam3 genome using the STAR mapper  (47). Cufflinks  (11)  was further used to reconstruct the transcriptome models in each tissue separately guided by a consensus reference annotation given by the BROAD  (48)  and EnsEMBL v83  (22)  annotations (called canFam3. 1). Then, the cuffmerge tool merged the tissue samples files into a single GTF file containing 211,794 transcripts and 50,547 genes. In order to annotate new transcribed loci, we used the FEELnc filter module to remove all transcripts which overlap exons (in sense) from the reference annotation, single exonic intergenic transcripts and transcripts with a size below 200 nt.
DISCUSSION In this study, we designed a new program to identify and annotate lncRNAs called FEELnc for FlExible Extraction of Long non-coding RNAs. Using the gold standard GENCODE annotation in human and mouse  (27) , we showed that FEELnc performs well to discriminate long noncoding versus protein-coding RNAs. FEELnc include predictors (multi k-mer frequencies and ORF coverage) that are general enough to capture all lncRNAs classes whereas alignmentbased methods will be biased towards misclassifying species-specific transcripts or coding transcripts that are not referenced in peptide databases. The integration of a random forestbased model in FEELnc contributes to the good achievements of the tool because of the intrinsic properties of randomly sub-sampling features thereby encompassing diverse lncRNA characteristics.
Third, we expect the FEELnc classifier module to be of great interest to researchers in order to automatically annotate novel lncRNAs thus directly providing candidate pairs of lncRNAs and (m)RNA partners to be investigated for experimental validations. For non-model organisms, we have shown that FEELnc performs similarly to CNCI although the benchmark was done on NONCODE lncRNAs that were a priori filtered by the CNCI tool. As for human and mouse where the manually curated GENCODE annotation is considered as a standard, this stressed the importance of also defining gold-standard sets of lncRNAs/mRNAs to correctly evaluate programs for "non-model" organisms annotation. This could be envisaged within the framework of collaborative projects such as the FAANG (30) for instance. Finally, we illustrated the usefulness of FEELnc on the dog transcriptome for which 20 RNA-seq samples were sequenced in the frame of the LUPA consortium.
622) is clearly in-between lncRNAs and mRNAs CPS (mean CPS = 0. 085 and 0. 923, respectively) suggesting that it would be feasible to discriminate pseudogenes from lncRNA and mRNA. Similarly, eRNAs defined as non-coding transcripts derived from enhancer elements  (55, 56)  should harbor small ORFs and specific patterns of k-mers corresponding to the transcription factor binding sites which could be caught by FEELnc predictors. All together, the FEELnc tool provides a standardized and exhaustive protocol to identify and annotate lncRNAs.
ACKNOWLEDGEMENT The authors are grateful to referring veterinarians, all dog owners and breeders for sample collection and also people from the European LUPA consortium (www. eurolupa. eu). We also thank FEELnc users and more particularly Kevin Muret, Sandrine Lagarrigue, Mark cock and Sarah Djebali. Finally, we are grateful to Jacques Nicolas for useful discussions and the GenOuest Bioinformatics core facility (www. genouest. org) for storing sequencing data, hosting the Cani-DNA website and for the use of the bioinformatic cluster to analyse the data.
2. 3 Scanning Nanofocus X-Ray Diffraction (nXRD) As we show below, the limitations of both SEM and GI-WAXS outlined above can be overcome using Scanning Nanofocus XRD (nXRD), which we use to study the same perovskite films deposited at different temperatures. Measurements were performed at the ID13 beamline (ESRF, Grenoble, France), where each sample was placed on a holder mounted on high-speed xyz piezo scanning stages. An optical microscope with a 50× magnification was first used to image a specific region of interest and thus define the centre of the nXRD scan (Figure  3a ). Figure  3c, f, i  show the optical micrographs corresponding to the region in which nXRD measurements were performed. To analyse the data, we developed a MATLAB application (see Experimental Section and Figure  S6 ).
[36] A MAR 165 CDD detector (2048 rows × 2048 columns) was placed on the diffractometer arm at a distance of 223 mm from the sample (calibrated as in ref. [72, 73]  ). Single diffraction patterns were obtained by illuminating the samples for 10 s with an Xray beam having a wavelength of ~1. 2384 Å. Data processing was performed using Matlab software. [74] or the calculation of the GI-WAXS penetration depth we used the following expression:  [37]  Λ ≈ [√2𝑘 √√( 𝛼 𝑖 2 -𝛼 𝑐 2 ) 2 + 4𝛽 2 -(𝛼 𝑖 2 -𝛼 𝑐 2 )] -1 where k = 2π/λ is the wavenumber (λ is the X-ray wavelength), αc is the critical angle, αi is the out-of-plane incident angle, and β is the imaginary part of the index of refraction. The index of refraction is defined as n = 1 -δ + iβ. [37] The critical angle was calculated as αc = √(2δ). The δ and β parameters for CH3NH3PbI3 at λ = 1.
nXRD Measurements and Data Analysis During data collection, samples were scanned within the y-z laboratory plane (with the X-ray beam along the x-axis), here indicated as x-y. A picture of the beamline configuration used during the nanofocus raster scan is shown in Figure  S5  Supporting Information. Each scan consisted of a 40 × 40 μm 2 raster scan of 101 × 101 pixels, with a step size Preprint of 400 nm. A single scan resulted in 10201 2167 × 2070 pixel diffraction images, for a total of ~170 GB of data saved in the ESRF Data Format (EDF). Data Analysis was performed with a MATLAB  [78]  application specifically designed by S. Lilliu for nXRD measurements (Figure  S6 ). We used a custom NNB-XB0057 (NOVATECH, UK) notebook with a quad-core Intel® Core™ i7-4790K CPU 4GHz, 32GB of RAM, and Windows 7 (64bit).
The time taken for data conversion was mainly limited by the low data speed transfer between the external hard drive and the processor (~50 MBps). Therefore, parallel processing did not result in any improvement. The conversion process, where the 10201 image were saved in a struct  [78]  variable, took approximately 1 hour. This step compressed data ~2. 4 × 10 4 times (from ~170 GB to ~7 MB). The average diffraction pattern of the 10201 images was calculated (in ~10 s) as the sum of the diffraction patterns divided by the number of diffraction images in a scan. From the average diffraction pattern we observed some residual noise and broad diffused scattering from the substrate (Si/SiO2). We therefore reprocessed all the diffraction patterns by applying a 'clean' filter, which set to zero all the pixels surrounded by zeroes (Figure  S7c ).
5±0. 5) × 10 -3 Å -1. As a consequence, a step size of Δq < 1 × 10 -3 Å -1 would result in oversampling and noisy data. In the binning step we collected all the elements from the triplet that fell into the (m,n)-th bin, i. e with q coordinates between mΔq and (m-1)Δq and χ coordinates between nΔχ and (n-1)Δχ, and calculated the average of the intensity of these elements. The obtained 'polar' matrix I(q,χ) was then used for the calculation of the 'average azimuthally integrated line profiles': 𝑖(𝑞) = ∑ 𝐼(𝑞, 𝜒) 𝑁 𝑞 360°𝜒 =0° (9) where, here, the azimuthal integration was performed from 0° to 360°, and Nq is the number of elements with the same q. In practice, the line profile was simply calculated as mean(I) in MATLAB. Line profiles extracted for the cold, medium, and hot sample are shown in Figure  S11.
With our MATLAB application it is possible to inspect single diffraction patterns in real time by simply selecting the image index, the column and row of the raster scan, or by simply pressing up/down or left/right arrows. The line profile extraction, which includes 'remapping' and 'binning' is performed in real time. The processing time for these two steps is ~1. 2 ms per image (average value calculated on the 10201 images of the cold sample). Note that the processing speed is inversely proportional to the density of the sparse matrices. Parallel processing or GPU processing  [78]  was not an option for highly sparse dataset, as the time taken to dispatch/collect the job or to generate a GPU variable and retrieve results after processing was longer than the actual processing time. The line profile extracted from the average diffraction pattern of the three samples were converted into 2θ coordinates.
This allowed us to consider only data from the (002) and (110) reflections. The analysis followed two conceptual steps: (i) diffraction spots clustering in the single diffraction patterns and extraction of their features including the peak position, (ii) spots clustering on the full dataset based on the spatial distance in the raster scan and on the spot orientation (χ). We now discuss the first step. The following steps were repeated for each of the 10201 diffraction patterns in a scan. The row, column and intensity vectors were extracted from the sparse matrix representing the ROI described above in a single diffraction pattern. Rows and columns were remapped (see equations above) into q and χ coordinates. Row and column vectors defining the pixel positions in ROI of the diffraction pattern were clustered based on the pairwise Euclidean distance between the pixels.
Diffraction patterns were collected at each point of the raster scan using a 2D detector. A full scan consisted of 10201 diffraction images. Further details are available in the Experimental Section. Pictures of the setup are shown in FigureS5Supporting Information. To analyse the data, we developed a MATLAB application (see Experimental Section and FigureS6). The 10201 diffraction patterns of a raster scan were de-noised as described in the Supplementary Information and FigureS7and S8. Montages of the diffraction images were constructed by placing each diffraction image at the spatial coordinate at which it was collected (see inset Figureband FigureS9). The sparse patterns of these montages are shown in Figure3d, g, j for the three samples. Note that the development of software that could handle sparse matrices was essential for this step.
Acknowledgements This work was funded by the  UK Engineering and Physical Sciences Research Council  via grants  EP/M025020/1  ' High resolution mapping of performance and degradation mechanisms in printable photovoltaic devices ',  EP/J017361/1  ( Supersolar Solar Energy Hub ) and the  E-Futures Doctoral Training Centre in Interdisciplinary Energy Research   EP/G037477/1. This work was partially funded by The President of the  UAE 's  Distinguished Student Scholarship Program  (DSS), granted by the  Ministry of Presidential Affairs ,  UAE  ( M. A. PhD  scholarship). This work was also partially funded by  Masdar Institute  through the grant  Novel Organic Optoelectronic Devices. XMaS is a mid-range facility supported by the  Engineering and Physical Sciences Research Council (EPSRC). We are grateful to all the beam line team staff for their support.
This platform provides a protocol for urban simulation that is able to depict the varying territorial configurations produced by a myriad of individual trip trajectories and its ambition is to help planners formulate new public policies. f In this paper, we will focus on the specific issue of air quality and traffic emission when considering individual daily mobility. Our purpose is to start from the individual point of view, i. e. , modelling the daily activities within a urban environment with resources and simulate the emerging traffic and the impact on air quality. The paper is organised in four sections: we first give a state of the art of different modelling approaches, finishing with ours; a second section presents the construction of the virtual city from our database; then we describe the design of the model and its functioning; and in the last section we present the first results of simulation, which complete the overview of what is possible with GaMiroD.