dataesr/scientific-paragraphs-categorization huggingface dataset:
An early stage was recorded by default for other patients. After six months, patients identified at locally advanced or early stage became at risk of relapse. Relapse was identified by the first hospital record of a local relapse of the primary HNSCC site or any new event indicative of extension (distant metastasis, locoregional extension, or chemotherapy). Patients who relapsed were followed over a relapse treatment phase (starting at first hospital record of relapse); other patients were followed by default over a continuing care phase (starting at six months after HNSCC diagnosis). The Charlson Comorbidity Index (CCI)  20  is the most widely used comorbidity index in HNSCC care  21  and we identified each comorbidity with use of ICD-10 coding algorithms validated in large hospital discharge databases  22  including the French National Hospital Discharge database.
Hospital stays were categorized with use of the DRG classification system to disentangle several main cost categories:  24  diagnostic workup; surgery (including reconstructive surgery); radiation therapy; chemotherapy (including actual expenses of targeted therapy cetuximab); supportive care (including adverse effects of treatments); and palliative care (from first record of palliative care onwards). Radiation therapy is usually performed in an outpatient setting. In the public sector, radiation therapy is also reimbursed with use of national DRG-tariffs. In the private sector, radiation therapy is directly reimbursed to physicians and cannot be identified in the hospital discharge database. However, all modality treatments other than radiation therapy administered in the private sector were observed in the study population.
Acknowledgments The  EPICORL (EPIdémiologie des Cancers ORL) Study Group includes: Sylvain Baillot, MSc, Translational Health Economics Network (THEN ), Paris, France;  Mélina Bec ,  MSc, Health Economics & Outcomes Research department, MSD France ;  Lynda Benmahammed, MD ,  Medical Advisor Oncology, MSD France ;  Caroline Even, MD, PhD ,  Department of Head & Neck Surgical & Medical Oncology, Institut de cancérologie Gustave Roussy, Villejuif, France ;  Lionel Geoffrois, MD, PhD ,  Department of Medical Oncology, Institut de cancérologie de Lorraine -Alexis Vautrin, Vandoeuvre Les Nancy, France ;  Florence Huguet, MD, PhD ,  Department of Radiation Oncology, Hôpital Tenon, AP-HP, France; Béatrice Le Vu, MD, MSc, Stratégie et Gestion Hospitalière ,  UNICANCER Fédération Nationale des Centres de Lutte Contre le Cancer, Paris, France & Translational Health Economics Network (THEN) ,  Paris, France ;  Laurie Lévy-Bachelot, PhD ,  Health Economics & Outcomes Research department, MSD France ;  Stéphane Luchini, PhD ,  CNRS ,  GREQAM-IDEP, Marseille, France & Translational Health Economics Network (THEN), Paris, France ;  Yoann Pointreau, MD, PhD ,  Department of Radiation Oncology, ILC -Institut inter-régionaL de Cancérologie, Centre Jean Bernard-Clinique Victor Hugo, Le Mans, France; Camille Robert, PharmD, Health Economics & Outcomes Research department, MSD France ;  Luis Sagaon Teyssier, PhD ,  AMU/ Inserm/IRD ,  UMR 912 ,  Marseille ,  France & Translational Health Economics Network (THEN) ,  Paris, France ;  Antoine Schernberg, MD, MPH ,  Department of Radiation Oncology, Hôpital Tenon, AP-HP, France  (at time of manuscript submission) and  Department of Radiotherapy, Brachytherapy Unit, Gustave Roussy Cancer Campus, Villejuif, France ;  Michaël Schwarzinger, MD, PhD ,  Translational Health Economics Network (THEN) ,  Paris, France ;  Stéphane Temam, MD, PhD ,  Department of Head & Neck Surgical & Medical Oncology, Institut de cancérologie Gustave Roussy, Villejuif, France.
sRNA-Seq Data Analysis In this study, small RNAs are defined as the RNA molecules with size ranging from 20 to 300 nucleotides, consistent with the definition by  Großhans and Filipowicz (2008). The raw RNA-Seq data in Fastq format were quality checked and trimmed for low-quality regions and adaptor sequences using Trimmomatic v0. 35  (Bolger et al. , 2014) , parameters of "2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:20. " The trimmed sequencing reads from P. trichocarpa and P. deltoides were mapped to the P. trichocarpa genome v3. 0 and P. deltoides WV94 v2. 1 1  , respectively, using TopHat2  (Kim et al. , 2013). Novel genes were assembled and transcript abundance in RPKM (Reads Per Kilobase of transcript per Million mapped reads) was estimated using Cufflinks  (Trapnell et al.
sORF Annotation ORFfinder  (Wheeler et al. , 2003)  was used to annotate putative ORFs within the differentially expressed sRNAs. We searched for 6 translational frames with the standard genetic code between 30 and 300 nucleotides for each prediction and selected the longest full-length sORF (including start and stop codon). The subcellular localization was predicted for each sORF using Loctree3  (Goldberg et al. , 2014).
miRNA Identification The small RNA sequencing reads were filtered to remove adaptors and low-quality bases by FastQC  (Andrews, 2010). The sequencing reads were processed to remove adaptors and cleaned by Q30 value. Reads with over 20% bases less than Q30, and N base more than 10% were filtered. The clean reads were further selected by length between 18 and 30 bp for miRNA identification process. The miRDP1. 3 package  (Yang and Li, 2011)  was used for miRNA precursor prediction with size set at 250 nt. To validate the miRNAs, the secondary structure and reads distribution were evaluated to generate the final set of miRNAs  (Yin et al. , 2016). The precursors and mature sequences were aligned with the searching toolbox in PNRD database for annotation of miRNAs  (Yi et al. , 2015). To define the conservation of miRNAs, a 17 bp match in the mature sequences were used to identify conserved miRNA families, allowing two mismatches.
To quantify the abundance of miRNA, transcripts per million (TPM) value was defined as "counts of read mapped to miRNA * 1,000,000"/"reads mapped to reference genome"  (Fahlgren et al. , 2007). To identify differentially expressed miRNA DESeq2 software was used, and expression folder change greater than 2 and p-value (Benjamini-Hochberg FDR corrected) less than 0. 01 were selected  (Love et al. , 2014). The miRNA expression data were normalized by row z-score method. Hierarchical clustering of gene expression was performed by clustergram function in MATLAB Bioinformatics toolbox with minor changes.
Target Prediction and Degradome Analysis The miRNA targets were annotated by standard settings of psRNATarget  (Dai and Zhao, 2011)  with maximum expectation value 2. 0. The transcripts of P. deltoides (WV94_445 v2. 1) and P. trichocarpa (Nisqually-1 v3. 0) were downloaded from Phytozome 12 3  while the transcripts for L. bicolor (20110203) and R. irregularis (Gloin1_GeneCatalog_transcripts_20120510. nt) was downloaded from MycoCosm 4. To validate the targets, the degradome datasets in P. trichocarpa were downloaded from NCBI Short Read Archive (SRR4010497, SRR4010498). The clean reads from degradome library were obtained for target site identification. The CleaveLand4. 0 pipeline  (Addo-Quaye et al. , 2009)  was used for target scanning. The reads were mapped to transcript datasets and the alignment scores and p-value were calculated according the signatures (abundances of potential slicing end based on reads distributions).
Novel sORFs in Populus in Response to Mycorrhizal Fungi Since sRNAs can potentially encode sORFs  (Li et al. , 2014; Ruiz-Orera et al. , 2014) , we used ORFfinder  (Wheeler et al. , 2003)   to scan the differentially expressed sRNAs for potential sORF translations. In total, we could predict 22 and 6 full-length sORFs from the treatments in P. deltoides and P. trichocarpa, respectively (Supplementary Tables  S5, S6 ). The shortest predicted sORF was 39 bp (12 AA) while the longest was 273 bp (90 AA). The transcript, coding, and protein sequences of these sORFs can be found in Supplementary Files S1, S2, S5, S6. Prediction of subcellular localization showed that 26 sORFs are secreted extracellularly while a single sORF is targeted to the nucleus and another to the chloroplast (Supplementary Table  S7 ).
Identification of miRNAs in Populus in Response to Mycorrhizal Fungi To gain insight into the role of miRNAs in mutualistic interactions, we analyzed the sRNA datasets to identify miRNAs that were responsive to the different mycorrhizal treatments. Using the miRDeep-P pipeline  (Yang and Li, 2011) , we identified 287 putative miRNAs in P. deltoides and 357 putative miRNAs in P. trichocarpa, including precursor and mature sequences (Supplementary Files S3, S4 and Supplementary Tables  S8, S9 ). In P. deltoides and P. trichocarpa, 174 and 130 miRNAs were classified into "Plant conserved" miRNA families, respectively, and 113 and 227 miRNAs were classified as "Plant novel" miRNAs, respectively (Figure  2A  and Supplementary Tables  S8, S9 ). Note that the corresponding known families for the miRNAs identified in this study can found in Supplementary Tables S8, S9 while the miRNA abundance expressed as TPM for each treatment can be found in Supplementary Tables S10, S11.
We did not find any miRNAs in common between different species colonized by a common fungus (data not shown). Also, we compared the differential transcripts listed in DATA 1 and 2 with the miRNA precursor sequences using the CAP3 program implemented in http://biosrv. cab. unina. it/webcap3/ and identified that five differential transcripts were miRNA precursors (Supplementary Table  S14 ). The miRNA expression fold-change trend can be seen in Figure  2D  and Supplementary Tables  S12, S13. In P. trichocarpa, we found that 23 and 7 miRNAs were only detected in the PTA and PTE treatments, respectively, and not in the controls.
Identification of Target Genes of miRNAs in Populus in Response to Mycorrhizal Fungi To determine the biological function of all identified miRNAs, we searched the "degradome" libraries of public databases for potential gene targets in Populus. While we identified 61 gene targets to 41 unique miRNAs in P. deltoides and 117 gene targets to 46 unique miRNAs in P. trichocarpa (p ≤ 0. 05; Supplementary Tables  S15, S16 ), there were only 12 and 59 unique gene targets (including alleles) to 7 and 25 unique differentially expressed P. deltoides and P. trichocarpa miRNAs, respectively (Supplementary Table  S17 ). We also identified putative targets to the "Plant novel" miRNAs category, i. e. , 10 miRNA-gene targets in P. deltoides and 18 miRNA-gene targets in P. trichocarpa (Table  4 ). In both P.
DATA AVAILABILITY The Department of Energy (DOE) will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy. gov/downloads/doepublic-access-plan). The RNA-Seq reads, along with the processed data (including the sequence and abundance measurements of differentially-expressed transcripts) are deposited in NCBI GEO (https://www. ncbi. nlm. nih. gov/geo/) with the accession code GSE117158.
Clinical data Demographics, aetiology and delay since the acute brain injury (ABI) were collected. In addition to CRS-R and DoC-feeling ratings, we also collected complementary metrics (such as the classical distinction between wakefulness and awareness, interaction during nursing and/ or painful care) using the same VAS approach as well as the best FOUR-score observed during each shift  18  (online supplementary material). statistics Our primary objective was to evaluate the diagnostic accuracy of the index test called 'DoC-feeling score' to detect the target condition (MCS) as defined by the standard reference (best CRS-R). First, to evaluate the association of individuals' DoC-feeling ratings with the standard reference, we computed a linear mixed model (LMM) using DoC-feeling individual ratings as the dependent variable, the state of consciousness as the fixed effect explanatory variable and patients as well as raters as random effects.
Acknowledgements We thank all the members of the  Pitié-Salpétrière hospital Neuro-ICU  led by SD (medical director), JB and LR-G (head nurses) who participated in this study (alphabetic order):  Jérémie Abitbol ,  Fatiha Ait Yata Azzi ,  Fatoumata Bah ,  Francis Bolgert ,  Sandrine Briand ,  Sandra Coelho ,  Alexia Camuzat ,  Marie-Chantal Colmar ,  Flora Cherruault ,  Cecile Chordi ,  Véronique Cottin ,  Bintou Coulibaly , KC,  Mélanie Dalibard , LD,  Estelle Dumarey ,  Bouchra El Aouni ,  Atef El Ouarghi ,  Helene Espiand ,  Cécilia Eltebert ,  Fabrice Fanhan ,  Agnès Flament ,  Marie-Suzelle Fontano ,  Pascale Fournier ,  Céline Frammezelle , GG,  Alexandra Grinéa ,  Nouara Harchaoui ,  Marie Harmancij ,  Claire Jacqueminet ,  Charlotte Janvier ,  Jamila Kebli , SL,  Aurélie Lemoal ,  Kim Louis-Joseph ,  Brice Lucas ,  Valérie Maes ,  Sophie Maillard ,  Romain Maurel ,  Madely Petit ,  Floriane Pépin ,  Isabelle Picot ,  Eva Proneur ,  Manuela Roselmac ,  Sylviane Saintini ,  Mélody Seidel ,  Yolène Sully ,  Kelly Tcha ,  Laura Verbaux ,  Nicolas Weiss ,  Kelly Yanganju.
Funding This work was supported by:  Amicale des Anciens Internes des Hôpitaux de Paris & Syndicat des Chefs de Cliniques et Assistants des Hôpitaux de Paris  (AAIHP-SCCAHP; BR),  Assistance Publique-Hôpitaux de Paris (AP-HP ; BR and LN),  Institut National de la Santé et de la Recherche Médicale  (Inserm; BH, JDS and LN),  Sorbonne Université (LN) , the  James S. McDonnell Foundation (LN) ,  Académie des Sciences-Lamonica Prize   2016  (LN) and  Philippe Foundation (BR). The research leading to these results has received funding from the  program 'Investissements d'avenir'   ANR-10-IAIHU-06.
Moreover, using the same parameter, it is possible to predict both MSE and CR. Then, it is easy to find a proper compromise depending upon priority of requirements and imposed restrictions. One question is what curves to fit and what are criteria of fitting quality to be used. There are different approaches but we employed goodness-of-the-fit R 2 and RMSE  [50]  as two main criteria (the former one has to be maximized and the latter one minimized for a given scatter plot). Without going to details, we can state the following. For each scatter plot, usually there are several functions able to provide approximately the same R 2 and RMSE. Sums of two exponentials (see an example in Figure  7 ), polynomials of low order, Fourier series, power functions are good candidates to be tested. Using the corresponding tools of Matlab or Excel, it is possible to quickly find optimal or, at least, appropriately good solution.
Experimental data for component-wise compression Let us present the results of applying the proposed approach to real-life hyperspectral data. Images of Hyperion sensor dataset EO1H1800252002116110KZ have been compressed. Hyperion sensor produces data of bad quality (very noisy) in sub-bands with indices k = 1,…,12 and k = 58,…,76. The images in these sub-bands are often discarded in analysis, so we have not compressed them. Then, two approaches to compression have been compared. Both presume component-wise compression. The first one has been proposed earlier  [11]. Images are compressed after applying variance stabilizing transform that takes into account signal-dependent noise properties and converts this noise to additive with variance approximately equal to unity. Then, the recommended QS = 3.
Furthermore, the iterated FALDOI strategy adds robustness against outliers in the initial seeds and against occlusions thanks to the forward-backward consistency check performed between two consecutive iterations. For instance, the densified flow obtained after the first iteration is incorrect on the leg that disappears behind the barrel. Nonetheless, this problem, which usually happens in the first iteration, is easy to detect and solve with the forward-backward pruning and the optical flow is improved in the subsequent iterations. After six iterations, the initial outlier regions are significantly reduced. Also notice that the ground truth of the MPI-Sintel dataset is created from the albedo pass, where no external effects (motion blur, defocus blur, atmospheric effects or shading) are present.
e. : 2 cpus). One can clearly notice that in the in-depth analysis presented in Tables  1, 2 , 3, 4, 5 and 6. We have also parallelized the global step by using detailed profiling to find the critical sections in the code that take most of the time. We have sorted them, from more to less critical. Then, we have optimized their internal for loops one at a time, measuring the improvement (in percentage) and only keeping the ones that have given a significant improvement. Finally, the chosen ones can be combined to reduce the execution time further. As for the matches, we computed both SIFT and DeepMatching in parallel (for frames I t and I t+1 ). We used the multiprocessing 2 library in Python to call the binaries for computing matches (SIFT or DeepMatching) at once in order to compute the forward and backward matches in parallel and thus halve the computation time compared to the sequential execution.
If that is the case, your execution may be stopped by the server. If so, we advise you to do one of the following things: if you are using SIFT, try DeepMatching instead as it gives more initial seeds and is less likely to yield as many empty partitions as SIFT (depends on the number of outliers); alternatively, execute the code locally with the original parameters but be aware that execution time will be notably increased (also depending on your hardware). In order to properly evaluate the speedup of the algorithm, we have averaged the timings reported by the algorithm with 17 images that include the critical cases commented above so the estimation is more accurate. It is important to notice that including such cases, the average execution time grows slightly with respect to not considering these cases.
One can see that for 11 × 11 and SIFT matches (Table  1 ), the execution time is slightly above 60 seconds due to the inclusion of a very complex example with very few seeds in the processed images. It is also important to note the speedup introduced by using partitions even when only one CPU is available: it significantly reduces the execution time. One can also see that, despite being quite slower with few CPUs, DeepMatching ends up being faster with 16 CPUs, resulting in an overall smaller execution time thanks to its better scaling (Figure  3 ). In that regard, note that the DeepMatching version already has an execution time below the 1 minute threshold while the SIFT version is still above it. Comparing it to FALDOI's original source code, we can see that the speedup factor is always above 4 and it is even larger for more CPUs which is mainly due to the fact that the original code did not scale properly when using 8 or more CPUs.
This may be due to the fact that FALDOI's original code parallelizes several for-loops which are nested inside others which yields a big memory overhead and takes more time. Additionally, in the case where DeepMatching seeds are used, the scalability commented above is not reproduced since the original code used an older version of DeepMatching that only used 1 CPU. This fact is critical as one can see in Table  7 , where the combination of a multi-threaded version of DeepMatching and parallel calls for the forward and backward image yields a 8. 7 times faster matching computation. Moreover, FALDOI' original code computed the seeds at the original image scale instead of at half-resolution, as the current method does, which produces reasonably similar results (about 5% worse for an 11 × 11 patch) but it is almost 2x faster in mean execution time (averaged over several frames) than using the original scale.
The difference is more pronounced when we use the original image scale (more computationally expensive) for which the current version of the code is 18. 6 times faster in execution time due to the null scaling of DeepMatching and the other speedup optimizations commented throughout this section. This time reduction is consistent with the other two patch sizes tested. Another reason for not using downscale = 1 is that it uses more memory than the limit specified by IPOL for the online demo. All remaining tests, if not stated otherwise, use half-resolution images to compute the DeepMatching seeds. In the second pair of tables 3, 4 (9 × 9 patch), we can see the same trend but with all timings reduced approximately by a 25% with respect to the default parameters and SIFT as the matching algorithm.
We do not describe it in this document or used it in the online demo but we have opted to leave it in the code for the sake of completeness. If you wish to test the code with occlusions, please go to the Github repository linked at the top of this article. In order to have a robust data cost, specially under illumination changes, we use a convex and continuous approximation of the data cost based on the Census transform  [12, 27, 34] , approximated by a sum of centralized absolute differences, denoted as CSAD  [30] , and defined as E 1 D (u) = Ω Ω |I t (x) -I t (y) -I t+1 (x + u) + I t+1 (y + u)|χ(x -y)dydx, (3) where χ denotes the characteristic function of a square of size P × P centered at the origin (P = 7 in our experiments, as in  [30] ). Secondly, we also consider the classical point-wise L 1 data term that imposes the well-known brightness constancy assumption, namely, E 2 D (u) = Ω |I t+1 (x + u) -I t (x)| dx.
Experimental Results The method has been tested on two publicly available databases: Middlebury  [3]  and MPI-Sintel  [6] , and on proof of concept examples, chosen in order to obtain a better understanding of the key features of the algorithm. Let us remark that all results have been obtained by using the grayscale versions of the original color frames. The color version is only used to compute the seeds in case they come from the DeepMatching algorithm and to compute the local regularization weights in case the Non-Local Total Variation as regularization term. All the results in this section have been obtained with the FALDOI minimization strategy (Algorithm 1) presented in Section 2. 1. 2. If not stated otherwise, the parameters used are the same for all experiments, listed in Appendix B.
First, we present a comparison of FALDOI against the multi-scale approach using different functionals. Our implementation of those multi-scale approaches is based on the implementation published in  [26]. In order to assure that the method is a real alternative to the coarse-to-fine warping strategy and, therefore, also valid for sequences that do not have large displacements of small objects, experiments on the Middlebury optical flow dataset  [3]  have been performed for both approaches. Table  8  shows how FALDOI achieves better results in all the samples in the dataset  5  , even if the difference is slight is some cases. Nevertheless, it is interesting to note that the functional that obtains the better results changes for some of the sequences. For instance, the sequence urban3 yields much worse results for the T V ℓ 2 -CSAD and the N LT V -CSAD energies than the simpler T V ℓ 2 -L1.
On the other hand, on RubberWhale, the opposite can be observed, with N LT V -CSAD obtaining significantly lower error. Some qualitative results are shown in Figure  7  for images of the MPI-Sintel database that contain large displacements, the initial seeds have been computed using DeepMatching. The first row displays, from left to right, two consecutive frames and the optical flow ground truth (color coded). The image (d) of the second row displays the ground truth occlusion map (the occluded points are shown in white). The multi-scale results obtained using the T V ℓ 2 -L1 energy are shown in (e), while the corresponding results obtained by the FALDOI strategy are shown in the third row. As it can be observed, the use of an advanced data term based on patches reduces the outlier area and better recovers the human shape.