dataesr/scientific-paragraphs-categorization huggingface dataset:
The HDR+ Dataset In 2018,  [9]  was completed by a very large dataset captured with several smartphones, simply named the HDR+ dataset  13. It is comprised of 3640 raw bursts (28461 images total), for a total of 765 GB. Each burst contains the following files: • a set of raw images stored in the open and commonly used Adobe Digital Negative (. dng) format, which can be read by rawpy and contains useful metadata such as exposure time, ISO, black level as well as white balance gains estimated by the camera. • additional metadata stored in separate files, such as maps for lens shading correction and color correction matrices. A curated subset of 153 bursts is also available. Since the HDR+ algorithm is not learning-based, we mostly focused on that subset for the purposes of this article. Two sets Google's own results are available on the dataset website: 2016 results, said to correspond to the algorithm and parameters described in  [9] , while the 2017 ones correspond to a more recent version of the pipeline with "algorithm refinements and updated tuning".
We instead decided to perform comparisons against the 2017 results. Even though they are said to feature different tunings Figure  11:  The Google 2016 results seem to feature less residual noise except around strong high contrast features / specular highlights, which is typical of spatial denoising as stated in  [9]. and algorithm refinements (we do not know all the 2016 tuning factors either), the alignment and merging results seem more in line with what we were able to obtain with our own implementation. Using the subset of 153 bursts provided in the HDR+ dataset, we thus took the bursts where the raw reference image and the 2017 Google "merged. dng" result were of the same size (for an unknown reason, some Google images are larger than the reference), and set out to compute two PSNRs: • the PSNR between Google's result and the noisier reference image it is on.
Visual Comparison to Google's Final Results The presence of their final results also enables visual comparison of their consumer-grade raw to JPEG finishing pipeline against our own. That said, given the simplified nature of our own finishing step, if we compare our final images to Google's cleverly chroma denoised, tone-mapped, sharpened, dehazed, color corrected (and so on and so forth) final HDR+ results, such comparison is definitely not in our favor in the vast majority of cases. It would certainly make sense to fully implement these additional finishing steps, but that would require a lot of additional development, testing and fine-tuning time to reach a similar level of quality. The added complexity would also likely imply longer processing times in our Python implementation, and would stir this article away from our main subject of focus, the raw burst alignment and merging algorithm.
Results on GoPro Bursts In order to further verify the appeal of the HDR+ raw burst denoising algorithm and the improvements provided by parts of its finishing pipeline, we set out to test our own implementation on bursts that were not part of the HDR+ dataset. We used GoPro HERO 8 Black and Hero 9 Black action cameras as an alternative to the Android devices used by Google. They feature similar sensors (Sony CMOS sensor with relatively small pixels) but much wider lenses when compared to the main camera typically found in modern smartphones. We captured sequences of 10 raw images using the dedicated burst mode of the HERO 8 and 9, all while trying to follow the base principles described in  [9]  (underexposed bursts, with identical focal length and short exposure time for all images, and scene and camera motion that is manageable with respect to exposure time).
Reference Ours final Google final Bottom row : Google also seems to perform hot pixel removal, which we do not (look at the top right corner of the crops). we converted them to Adobe Digital Negative (. dng) files using Adobe DNG Converter 16 , which is the same format used for the HDR+ dataset. We then simply executed the exact same code we ran on the images provided by Google, without changing any tuning parameter. We applied our own finishing pipeline (described in Section 5. 2) to both the reference image and the result of the alignment and merging steps in order to assess their impact on noise reduction. We also added a minimally processed version of the reference image (with no tone-mapping) to show how bright the original raw image actually is. We can see that noise is significantly reduced by the alignment and merging procedure (even on image edges, where these steps might perform worse because of image distortion induced by the wide lens).
It should be noted that for 2 of the 292 complexes for which the prediction is correct (4I9W 30 and 4HG4  31  ), the four bestranked regions contain less than 30% of the epitope, which could be considered as low. However, the prediction still gives the approximate position of the epitope on these rather large targets (309 and 501 residues, respectively, Figure  S1 ). On the complete dataset, on average 83% of epitope residues are predicted, meaning that they belong to one of the top 4 regions (Figure  1 ). For 199 complexes, more than 80% of the amino acids belonging to the crystallographic epitope belong to one of the top 4 regions. It is easier to predict the correct epitope for small targets than for large ones, since the surface ratio covered by the top 4 peptides is larger for small proteins.
However, for the 50 smallest proteins of our dataset, the predicted epitope covers only 30% of the total accessible area. This ratio falls to 6% for the 50 largest targets of the benchmark. The total accessible surface area of targets and that of predicted epitope are given in Table  S1. Very comparable results are obtained on the different tested subsets (Table  1 ), antigen-binding fragments (Fabs) (or singlechain variable fragments (scFvs)) and VHH, but also complexes that belong or not to the learning dataset (see Material and Methods). Finally, when using the negative controls dataset, on average only 42% of the crystallographic epitope is correctly predicted, which is significantly different from the   S2  shows the same average values as indicated in Table  1 , together with the standard deviations and the results of pairwise statistical tests.
Impact of sequence identity To evaluate the effect of sequence identity between query and template sequences, for a given query variable domain, we built models using as templates antibodies with known 3D structure, having the same CDR lengths, and decreasing overall sequence identities. We built a dataset of 20 randomly selected  complexes of our dataset for which the epitope was correctly predicted in our first experiment, and for which we were able to find more than one possible template. It should be noted that even though the different templates have different sequences (as compared to the query and between each other), the query always has the same sequence. The number of models generated for each antibody varied from 2 to 107, accounting for a total of 767 models (Table  S2 ). The lowest overall sequence identity between query and template is 60%, which is very low for variable domains of antibodies.
Determining the epitope of dupilumab To further demonstrate the ability of MAbTope to determine the epitope of modeled antibodies, we chose to work on dupilumab, which targets an unknown epitope on the interleukin 4 receptor α subunit (IL4Rα). Marketed as DUPIXENT®, dupilumab is approved for therapeutic use in eczema, atopic dermatitis and several forms of severe asthma. Dupilumab's structure was modeled using templates 6AZM for heavy chain and 5BK5  33  for light chains, targeting circumsporozoite protein NANP 5-mer and circumsporozoite protein 663, respectively. For IL4Rα, we used the 3D structure given in PDB entry 1IAR. 34 Dupilumab's epitope was determined using MAbTope (Figure  4 (a) and 4(b)). This epitope appears to be scattered among 4 regions of IL4Rα: P1 from T56 to P86, P2 from H87 to Q107, P3 from L103 to P123, and P4 from S193 to S113.
The actual epitope residues from the crystallographic structure (PDB:6WGL) solved after the submission of this study are shown by stars. (e). HEK293 cells were transiently transfected with the IL4Rα WT or mutated. IL4Rα expression at the membrane was monitored with a PE-coupled anti-Flag antibody (y-axis) and dupilumab binding was measured in APC (x-axis). An isotype IgG was used as a control (Figure  S6 ). (f). APC +PE+ double positive cells percentages were collected from 3 independent experiments and normalized by the total PE+ cells. Results are shown as mean ± sem. Different letters indicates statistical difference at p ≤ 0. 05. graph in Figure  S6 ), indicating a little instability even though they reach the membrane. The 3 new mutants showed a decrease in dupilumab binding, but the inhibition was lower than with the first series of mutants, indicating that epitope residues remain present in all the regions and confirming that the epitope is scattered across the 4 mutants.
To conclude, here it is shown that: 1) Dupilumab's epitope is conformational since it is located over 4 discontinuous regions of IL4Rα; and 2) MAbTope efficiently predicted epitope residues of a modeled antibody. As the structure of the complex between dupilumab and IL4Rα (PDB:6WGL  36  ) was published after we performed our prediction and validation, we compared the crystallographic epitope with the one we predicted. Peptides 1 and 2 indeed constitute the core of the crystallographic epitope (Figure  4c ). They are also those most impairing binding of the antibody when mutated.
To evaluate the accuracy of the predicted competitions, we compared with crystallographic structures of the antibody-antigen complexes available in the PDB, and with results from Sivasubramanian et al,  39  who measured the competition between 7 different antilysozyme antibodies using high-throughput SPR. As shown in Figure  5 (a) and 5(b), there is a very good concordance between the crystallographic overlap Xo and the value of the RawS score, which measures the overlap between the predicted epitopes. Over the 21 possible pairs, the average error, defined as the absolute difference between Xo and RawS is only 0. 14. There is only one case, for the pair (D1. 3, HyHEL-10), where this error leads to the incorrect prediction of an overlap (Figure  5(c)  and 5(d) ). Similarly, on the 20 antibodies dataset, the average error between Xo and RawS is 0.
Test datasets Antibody-antigen complexes of known structure were extracted from the PDB (www. rcsb. org; version of January 2017). A test dataset was built gathering the antibodyantigen complexes fulfilling the following criteria: 1) Unbound 3D structure of antigen is available; 2) Unbound 3D structure of antibody is not available; 3) No missing residues in paratope and epitope of 3D structure. The third criterion is very important. From different tests we concluded that missing residues are an important cause in failure of prediction (data not shown). The dataset used for this study contained 292 complexes, 245 were Fabs or scFv and 47 were VHH (Table  S1 ). Importantly, some of the complexes of this test dataset are also present in the learning dataset, as shown in the last column of Table  S1.
15 µg of biotinylated mouse anti-human IgG4 antibody (Invitrogen, MH1542)-and left 45 min at 4°C. After a wash in PBS-EDTA-SVF, cells were incubated with 0. 03 µg of APCcoupled streptavidin (BioLegend, BLE405207) for 15 min. Cells were firstly washed with 2 ml PBS-EDTA-FBS, and secondly washed with 2 ml PBS-EDTA before final suspension in 150 μl PBS-EDTA. Samples were run in a MACSQuant Analyzer 10 (Miltenyi Biotec). Cytometry data from 4 independent experiments were analyzed using FlowJo. 35 The number of APC+PE+ double-positive cells was collected from every sample and normalized on the total PE + cells within the sample. Histograms and ANOVA statistical analyses were realized with Prism software (GraphPad Software, San Diego, CA).
Acknowledgments This study was funded with support from the  French National Research Agency  under the program " Investissements d'avenir "  Grant Agreement LabEx MabImprove :  ANR-10-LABX-53 ; " ARD2020 Biomédicaments" projects GPCRAb ,  GPCRAb2  and  BIO-S  and " APR-IR MABSILICO " grants from  Région Centre Val de Loire.
The approach eliminates ad hoc decisionmaking about membrane replacement and, in our opinion, sets a new gold-standard for membrane maintenance management. The crux of our approach, and the model that underlies it, is the modeling and quantification of rate-state interactions in the degradation of multi-element systems  [19, 20]. Rate-state interaction parameters are estimated using real data on vessel performance. A further novel aspect of the model is that the wear-states of the individual elements are themselves hidden (unobservable). Nonetheless, we are able to estimate them, and thereby quantify the effect (on performance) of element replacements and swaps. Although we focus on biofouling, our model is not limited to this, and potentially can be used in cases of mineral scaling. The structure of the paper is as follows.
The American Society for Testing Materials (ASTM) defines the standard practice for standardizing RO performance data in ASTM Methods 4516, but there is no specification for PD normalization in this standard  [22]. We use a normalization equation that was specified by the membrane manufacturer. RO real-time performance data is monitored continuously and recorded in intervals of 60 s. The normalized pressure differential (NPD) is calculated from the hourly means of the recorded data. Then, to reduce noise from e. g. , stoppages, at 00:00 h every day the mean NPD is calculated over the previous 24 h. This daily mean NPD provides a measure of the level of deterioration of the vessels in an RO train (Fig. 1 ). The effect of bio-fouling is continuous and long-lasting and regarded as the most severe and the most difficult to manage.
Such interventions may extend over the entire product life cycle. We built a DT of an RO vessel using the mathematical model of degradation described above. The DT is part of a decision support system (DSS). The DSS is a tool, developed in MATLAB, with visualization for planning restoration (Fig. 5 ). The DSS has three modules: a data analysis module, a planning module, and the DT. The data analysis module allows the user to import real data and maintenance history, estimate parameters, and tune parameters manually. At the visualization (Fig. 5 ), a simulated trajectory can be compared with a real trajectory. The planning module allows for different restoration policies to be studied. It uses the DT to simulate NPD and cost trajectories for a specific policy. The DSS displays the modelled NPD (red pen, Fig.
Results and discussion Root penetration and access to water depends on soil density. Parameters of the root system architecture are extracted from the voxelised root phase identified above. The first step consists in the 3D skeletonization of the root system with the python module skimage. morphology. skeletonise_3D  24 -Supplementary Figure  S1  provides an example of root skeleton. Then, an in-house developed algorithm identifies and labels each hierarchical root element, determines their respective length and calculates the size of the root cross-section at each voxel that makes up the root skeleton. At this early stage of growth, the root size is independent from the soil conditions considered here, with a size of the primary root about 0. 6 mm for all the root systems (Fig. 3 ). This value is in agreement with published data that report a mean size usually ranging between 0.
The position of the small volume of soil used for the analysis is shown in the top right corner of each row. the total specimen volume containing fine sand is divided into a structured grid of about 158401 correlation windows, with a side length of 1. 3 mm. Displacements and strains are computed using the open source software SPAM -Software for Practical Analysis of Materials 34 -at each measurement point (centre of mass for coarse sand and centre of correlation window for fine sand). Concerning the fine sand, local strains are calculated from the displacements obtained from image correlation in the finite strain framework, using a 3D implementation of the method proposed by Geers et al. 35. The first invariant -the volumetric strain ε v -is calculated as the determinant of the transformation gradient (F -I).
Acknowledgements The authors would like to acknowledge the  French National Research Agency  in the framework of the " Investissements d'avenir" programs referenced   ANR-15-IDEX-02  ( IDEX Université Grenoble Alpes ) and  ANR-11-LABX-0030  ( LABEX Tec 21 ) for the financial support. We thank  Glyn Bengough  at the  James Hutton Institute -Dundee - and  Robert Peyroux  at Laboratoire 3SR -Grenoble -for the discussion had on the interpretation of the plant roots response. We would also like to acknowledge the anonymous reviewers for their comments, which helped improve the manuscript.
The HLA system plays a pivotal role both in transplantation and immunology. While classical HLA genotypes matching is made at the allelic level, recent progresses were developed to explore antibody-antigen recognition by studying epitopes. Donor to recipient matching at the epitopic level is becoming a trending topic in the transplantation research field because anti-HLA antibodies are epitope-specific rather than allele-specific. Indeed, different HLA alleles often share common epitopes. We present the HLA-Epi tool (hla. univnantes. fr) to study an HLA genotype at the epitope level. Using the international HLA epitope registry (Epregistry. com. br) as a reference, we developed HLA-Epi to easily determine epitopic and allelic compatibility levels between several HLA genotypes. The epitope database covers the most common HLA alleles (N = 2976 HLA alleles), representing more than 99% of the total observed frequency of HLA alleles.
| INTRODUCTION With a constantly increasing world population, average life expectancy and growing prevalence of chronic diseases (kidney, lung, etc. ), more and more individuals will need organ transplantation during their lifetime. In 2018, around 150,000 transplantations were performed worldwide, representing 17 solid organ transplants per hour. 1  Currently, most graft allocation systems are partly based on generic HLA donor-recipient compatibilities, mainly for hyperimmunized patients. HLA corresponds to the human major histocompatibility complex (MHC). It is a major component of selfrecognition by the immune system and determines the tissue compatibility in organ transplantation. HLA genes show a high level of polymorphisms with more than 30,000 described alleles  2, 3  listed in the IPD-IMGT/HLA Database (www.
Several informatics programs have become available to study epitopic matching between donor and recipient. 15 LAMatchmaker 16 is used to identify three-dimensional eplets on the HLA molecular surface toward which antibodies can be directed. This Excel-formatted program for HLA antibody analysis and eplet-based matching defines an eplet according to differences within repeating triplets of linear amino acid sequence. The OneLambda Incorporated (OLI) Fusion MatchMaker software is based on HLAMatchmaker. It uses the same database and the same calculation process but also includes additional eplet data, and offers a user-friendly eplet calculation tool. HLA-EMMA  17  analyzes HLA class I and class II compatibilities between donor and recipient on amino acid level focusing on the antibody-accessible amino acid mismatches, with both single or batch analysis options.
In the present work, we aimed at creating a user-friendly and freely available web tool to improve the study of donor/recipient tissue compatibility levels with an up-to-date public database. Our tool allows the complete study of a genotype regardless of its resolution (low resolution or one field, e. g. , A*01, high resolution, or two fields, e. g. , A*01:01). Figure  1B  summaries alloantigen recognition mechanisms covered by the HLA-Epi and PIRCHE algorithms while highlighting their differences. HLA-Epi has the ambition to become a ready-to-use tool for the graft allocation system, and is currently oriented for research applications. HLA-Epi is based on the international reference eplet registry database: epregistry (www. epregistery. com. br). 14.
| Web development HLA-Epi is integrated into the Easy-HLA website (hla. univ-nantes. fr). 20 We developed this new tool using HTML5, PHP7. 4 (www. php. net), Javascript1. 8. 5 languages and implemented a PostgreSQL database (www. postgresql. org). This eplet database is queried through a PostgreSQL database management system and will gradually be updated as new eplets are identified. Easy-HLA website and related database are hosted on Nantes Université secure server. All reference data are anonymized and coming from public database. Database access is restricted to authorized person only. Submitted data are deleted immediately after analysis completion and the output data files are safely conserved on our server for 1 week. Source code and used data to generate our results are available on the following git repository https://gitlab.
| Eplets database The HLA-Epi tool is based on a public database including 560 distinct eplets correspondences carried by 2976 HLA alleles coming from EpRegistry3. 0, the International Registry of HLA Epitopes (http://www. epregistry. com. br). 21 mong them, 154 have been antibody checked. EpRegistry represents a rich resource to study histocompatibility at the epitope level and antibody responses against HLA mismatches in transplant patients. Overall, although 10% of the total described HLA alleles are represented in the eplet database, it represents more than 99% of the HLA alleles observed frequency. This database contains epitopes carried by proteins coded by seven HLA genes HLA-A, -B, -C, -DRB1, -DQB1, -DQA1, and -DPB1. Depending on the user's goal, different epitopes properties such as exposed, cryptic, antibody checked and unchecked eplets can be considered.
MMload indicates global compatibility levels between donor and recipient. The main calculations are summarized in the following equations: All calculations are made through specific PHP functions calling SQL requests and retrieving results from the PostgreSQL eplet database. High resolution HLA genotype imputation function is using previously developed HLAupgrade tool. High resolution genotypes are inferred using the National Marrow Donor Program haplotype frequencies database published in 2013 for uses in clinical transplant and immunological research. 22 MMCL I ¼ X epletsI d = 2epletsI r ð Þ MMCL II ¼ X epletsII d = 2epletsII r ð Þ MM load ¼ MMCL I þ.
| Distribution of eplet variability study In order to characterize the eplet distribution variability in different populations, we ran HLA-Epi on two different datasets: The 1000 Genomes (1KG) public database  23  and a local clinical database of kidney transplanted patients (DIVAT, www. divat. fr). Data from the DIVAT cohort are freely available to academic researchers and were obtained after approval by its scientific board (http://www. divat. fr/ access-to-data). All donors were informed of the final use of their blood and signed an informed consent form. First, we prepared datasets to analyze the number of mismatch distribution between two HLA genotypes. (1) We retrieved high-definition HLA genotypes from three large ancestral populations from the 1KG database: European (n = 332), East Asian (n = 335) and African (n = 388).