dataesr/scientific-paragraphs-categorization huggingface dataset:
Post-processing We completed our segmentation pipeline with a post-processing step to reject false positives. We tested three machine learning classifiers: the random forest, the support vector machine and the k-nearest neighbors as shown in Tab 4    Finally, we compared our pipeline performances with approaches encountered in the literature as shown in Tab 5. We observed an improvement of over 10% and 5% on Rashno et al. [2]  and Ganjee et al. [16]  methods respectively for the UMN DME dataset. A smaller improvement was obtained for the Kermany dataset with a gain of 4% and 1% compared with Ganjee et al. [16]  and Lu et al. [7]. However, our pipeline did not reach the state of art performance of Chen et al for the fluids segmentation on the UMN AMD dataset.
Quantification We evaluated the performance of our fluid quantification step using two metrics: the Pearson correlation coefficient ρ and the coefficient of determination R 2. We could not estimate the fluid surface for the Kermany dataset, because the resolution of the B-Scans was unavailable. For the UMN datasets, comparison with assessment by expert graders trained in retinal analysis showed a good correlation with automated September 17, 2021 9/12 quantification of fluid in B-Scans as reported in Tab 6.
Discussion We present a fluid segmentation method on OCT B-Scans based on a cascade of deep convolutional neural networks using ENets. To evaluate the performance of the proposed method, we have compared it to the fluid segmentation methods encountered in the literature. As shown in Tab 3, the proposed method achieves good results on OCT B-Scans related to DME and AMD affected patients. On the UMN DME dataset, we outperform the unsupervised approaches of Rashno et al. [2]  and Ganjee et al  [16]  by up to 10% for the dice score. Both papers proposed unsupervised approaches and therefore tested their method on the whole dataset. They detailed their results for each volume by taking the mean Dice Score of each B-Scan composing the volume. Thus, we were able to compare our performance on our test set.
Our method slightly improved the results obtained by Ganjee et al. [16]  with their unsupervised approach on the Kermany dataset and matched the performance of Lu et al. [7]. However, this comparison must integrate the fact that the dataset was unknown to Lu et al. as their model was trained on another dataset provided by the Medical Image Computing and Computer Assisted Intervention conference (MICCAI). We also evaluated our network on AMD OCTs. We compared our performance on the 5 OCT volumes of our test set with the one of Rashno et al  [2]  and found a very slight improvement in results of 1%. We had more difficulties to compare our results to those of Chen et al. [11] , who report a mean Dice Score of 94%, because it was not made clear which OCT volumes were considered in the training and testing phases of their work.
Conclusion In this paper, we have described a novel approach to automatically segment fluid in OCT B-Scans. The proposed pipeline starts with a preprocessing step to reduce the September 17, 2021 10/12 speckle noise thanks to the BM3D algorithm. Then, it consists of a cascade of ENets where the first one extracts the region of interest between the ILM and BM and the second one segments fluid accumulations. We complete our network with a post-processing step by training a random forest classifier to remove false positive pixel detections and thus to improve our segmentation performances. The proposed method showed good performances with a DSC over 80% on three different datasets associated with different types of diseases. In the future, we plan to test our method on a "real life" dataset to assess the generalization and clinical benefit of the proposed framework.
, 2016;; Afilhado et al. 2008; Sallarès et al. 2011; Martínez-Loriente et al. 2014, and references therein). A number of models for the Earth's structure have been derived from these rich seismic data sets for the Rif  (Gil et al. 2014) , the Atlas  (Ayarza et al. 2014) , the Cantabrian Mountains  (Pulgar et al. 1996; Pedreira et al. 2015) , Variscan Iberia  (Flecha et al. 2009; Ehsan et al. 2015) , the Iberian Rift System  (Seillé et al. 2015)  as well as Central Iberia  (Andrs et al. 2019). However, a single model that covers the entire region and has a good description of Moho topography, as well as of P and S wave speeds, in the crust and in the mantle, is still missing. In this paper, we merge the most up-to-date seismic models   Becker et al. 2009). The parameters of M > 4 earthquakes are taken from the SHARE database up to 2006  (Grünthal et al.
2013; Stucchi et al. 2013 ) and complemented with those from the compilation of  Custódio et al. (2016)  from 2007 to 2016. The magnitudes of these earthquakes are shown by the size of the circles. High-quality instrumental epicentres of smaller magnitude (M < 4) earthquakes that occurred between 1996 and 2016 are displayed as dots  (Custódio et al. 2016). Epicentres are colour-coded according to earthquake depth, with a colourscale saturated at a depth of 80 km. Earthquakes with undetermined depths are plotted in brown. (b) Simplified geological units of Iberia, NW Africa and SW France  (USGS et al. 1997a,b). Also displayed are the Eurasia-Africa Plate boundary  (brown, Bird 2003) , potentially active faults documented in the SHARE database (black,  Basili et al. 2013; Vilanova et al. 2014 ) and high-resolution fault traces (including probable and debated faults, blue,  Zitellini et al.
Then, depth slices were computed every 0. 5 km by linear interpolation. Note that since the grid node spacing of the input models is in all cases larger than that of PRISM3D, no anti-alias filtering was needed prior to that stage. Validity limits provided by the authors were used to define polygons enclosing regions of full weight for each model (Fig. 3 ), with a weight of 1. 0 inside the polygon and 0. 0 outside. In order to prevent edge effects, those full weight regions were smoothed out by means of a Gaussian lowpass filter with 50 km standard deviation. Weights denote the level of confidence assigned to each model. Although quantitative by nature, they result essentially from a qualitative ranking of the models based on the methodology and type of data they were obtained from, allowing to implicitly account for the differences in resolution of the different models.
2014). Samples of Moho depth profiles from that experiment were part of the input Moho depth values used to build PRISM3D, as they were included in the compilation of  Diaz et al. (2016). So, as expected, the crustal thicknesses are in fair agreement. The match, however, is not perfect, due to the presence of multiple data sets used as input to PRISM3D. Yet it is worth noting that this area corresponds to that covered by the profiles EE' and DD' on Fig. 7 , where the good fit between the input Moho depths and those from PRISM3D is evident.
1997a,b). Also displayed are the Eurasia-Africa Plate boundary (brown, Bird 2003), potentially active faults documented in the SHARE database (black, Basili et al. 2013; Vilanova et al. 2014) and high-resolution fault traces (including probable and debated faults, blue, Zitellini et al. 2009; Cabral 2012; García-Mayordomo et al. 2012). Labelled regions: Gorringe bank, Iberian massif, Gulf of Cadiz, Gibraltar strait, Atlas mountains, Rif, Alboran sea (Alb. ), Betics, Valencia trough and Pyrenees.
(b) Simplified geological units of Iberia, NW Africa and SW France (USGS et al. 1997a,b). Also displayed are the Eurasia-Africa Plate boundary (brown, Bird 2003), potentially active faults documented in the SHARE database (black, Basili et al. 2013; Vilanova et al. 2014) and high-resolution fault traces (including probable and debated faults, blue, Zitellini et al. 2009; Cabral 2012; García-Mayordomo et al. 2012). Labelled regions: Gorringe bank, Iberian massif, Gulf of Cadiz, Gibraltar strait, Atlas mountains, Rif, Alboran sea (Alb. ), Betics, Valencia trough and Pyrenees.
The least significant difference (LSD) test showed that whatever the trait, the average PA of genomic prediction with LD threshold of 0. 25 was systematically lower than that with the other three LD thresholds. Likewise, whatever the trait, the average PA with r 2 0. 75 was either equal or significantly higher than that with the other LD thresholds, including r 2 1. Average PA for GY was always significantly higher than that for DTF and AsG. Trait × LD interaction was the highest for AsG and GY. The highest trait × irrigation-system interaction was observed for AsG. Given the systematically high PA obtained with the genotypic dataset selected with LD threshold of 0. 75, we decided to use this dataset (17 449 SNPs) for the further analysis.
Marker density and use of trait-specific markers Analysis of the effect of four levels of marker density on the PA of three single-environment genomic prediction models confirmed the superfluity of very dense genotyping when the LD extent is large. Indeed, simulation studies  (Habier et al, 2009; Zhong et al, 2009)  and empirical studies in various plant species including rice (ben Hassen et al, 2017; Bhandari et al, 2019) have shown very little, if any, effect of marker densities, as long as the distance between adjacent markers remains below the extent of LD. Given the size of the rice genome (430 Mb), the theoretical minimum number of markers needed would be 3K evenly distributed SNPs. However, the 4K SNPs dataset (LD threshold of 0. 25) led, systematically, to lower PA than the ones with higher numbers of SNPs.
This is probably due to the uneven distribution of the SNPs, and the local variation of LD along each chromosome. The 17K dataset (LD threshold of 0. 75) provided PA equal to the one observed with the 1600K dataset. Its SNP density compensated for the unevenness of their distribution and for the possible low local LD in each chromosome. Whatever the size of the genotypic dataset and the phenotypic trait considered, PA obtained with the three prediction methods (BL, GBLUP and RKHS) was not significantly different. Given the fact that several QTLs of rather large effect (up to 5% for DTF, 10% for AsG and 25% for GY) were detected in the BAAP population  (Norton et al, 2018 (Norton et al, , 2019)) , BL method should perform better than GBLUP and RKHS. This is probably due to the strong linkage of each QTL of rather large effect with a large number of SNPs, taking off the shrinkage edge of BL method.
On the other hand, in their study, the share of total phenotypic variance explained by the traitspecific markers did not exceed 19% and they did not attribute a specific weight to those markers. In our study, the share of total variance explained by the trait-specific markers was much higher. For instance, for AsG, the three most important QTLs (represented by 8 SNPs out of the 64 AsG-specific SNPs) each explained 10% to 18% of the total variations. It is also noteworthy that the simple inclusion of the trait-specific SNPs in the genotypic dataset did not improve the PA of genomic predictions. Attribution of a distinct weight is necessary. We empirically opted for four levels of trait-specific weight and, at each level, attributed the same weight to each of the 64 trait-specific SNPs, regardless of the effect of the underlying QTLs.
Phenotypic data Phenotypic data were produced in Mymensingh, Bangladesh during the dry seasons of 2013 (Year 1) and 2014 (Year 2). Each year, two water management systems, AWD and CF, were conducted. The target traits included DTF, GY and AsG. Details of the phenotyping procedures are provided in  Norton et al (2017b Norton et al ( , 2018)). Briefly, in each trial, the experimental design was a randomised complete block with four replicate blocks. The individual plot was composed of one 4 m-long row. At maturity, grains and shoots of each accession were hand-harvested from the six central hills of each row. Methods for the measurement of AsG are described in  Norton et al (2007a Norton et al ( , 2019)). The dried grain powder were digested using nitric acid and hydrogen peroxide and total As content was measured by inductively coupled plasma-mass spectroscopy (ICP-MS).
Genotypic data and marker selection for genomic prediction Genotypic data were extracted from the BAAP SNP database, containing 2 053 863 SNPs  (Norton et al, 2018). Using Gigwa software  (Sempéré et al, 2019) , the BAAP database was filtered for minor allele frequency (MAF > 5%), heterozygosity (H < 5%) and absence of missing data. The filtering yielded 1 603 611 SNPs hereafter referred to as 1600K SNP dataset. BAAP database is available as a project called BAAP at the SNP-Seek database (http://snp-seek. irri. org/). Using the 1600K SNP dataset, two types of smaller datasets were extracted with the aim of identifying the threshold of marker density below which the accuracy of GEBV declines, and analysing the effect of accounting for trait-specific markers on the performances of genomic predictions.
For DTF, 2 3 SNPs were selected along each of the 24 chromosomic segments with 'notable association' detected in one of the two years of field experiment; the total number of DTF-specific SNP was 64. For GY, two SNPs were selected at the two extremities of each of the 32 chromosomic segments with 'notable association' detected in at least one of the two years of field experiment, under at least one of the two water management systems. For AsG, 64 SNPs were selected out of the 74 most significant SNPs (P < 0. 0001) detected in at least one of the two years of experiment and at least under one of the two water management systems; the remaining 10 most significant SNP were not present in the 1600K SNP dataset. The 192 traitspecific markers are provided in Table  S12.
Prediction model using trait-specific genomic relationship matrix Trait-specific GRM (G') were constructed following the method proposed by  Zhang et al (2014). Briefly, it consists in (i) building the standard GRM of Van Raden (2008) (G), with the set of markers that are assumed to have identical small effects not specific to the phenotypic trait considered. (ii) For each trait, building a specific relationship matrix (S), using markers known for their rather large effect on the trait, while specifying the effect w i of each of those markers. (iii) Finally, building for each trait the weighted GRM as: G' w = W × S + (1 -W) × G where w is the overall weight for trait-specific markers w [0, 1]. In this study, the G matrix was built with the 17 449 SNPs dataset (LD threshold of 0. 75). For each trait, four G' W GRM were computed, corresponding to w values of 0.
Indeed, many propositions have been made in recent years concerning the formation of UDGs, including the potential role of halo angular momentum, feedback, tidal interactions, ram-pressure stripping (RPS), and collisions (e. g. ,  Amorisco & Loeb 2016; Burkert 2017; Martin et al. 2019; Di Cintio et al. 2019; Tremmel et al. 2020; Silk 2019). In this paper, we therefore analyze the full system including the UDG galaxy NGVS 3543 and the associated blue knots in its vicinity. In Sect. 2, we discuss details of the multi-wavelength data we use, as well as measurements performed on the images. In Sect. 3 we present the results obtained during this study, and in Sect. 4 we provide a detailed discussion on the implications of these results along with a comparison of existing data and models. We conclude in Sect.
Selection of blue regions To understand the nature of the young stellar systems associated to NGVS 3543 and AGC 226178 seen in Fig. 1 , we need first to identify them and then to characterize their spectro-photometric properties. For this purpose, we followed two different selection criteria. The first one is based on the NGVS u-band image, Radius (kpc) Fig. 2 : Radial surface-brightness profiles of NGVS 3543 measured in eight bands, shown as blue filled dots. The surface brightness units are in mag arcsec -2 for all the bands except for Hα which is in erg s -1 cm -2 arcsec -2. The blue shaded area marks the 1σ error (for data points) and upper-limits (3σ). The black dotted line indicates the best-fit model described in Sect. 3. 1 for a ram-pressure stripped galaxy (V = 42 +8 -4 km s -1 , λ = 0.
Acknowledgements. We are grateful to the whole CFHT team who assisted us in the preparation and in the execution of the observations and in the calibration and data reduction:  Todd Burdullis ,  Daniel Devost ,  Bill Mahoney ,  Nadine Manset ,  Andreea Petric ,  Simon Prunet ,  Kanoa Withington. We acknowledge financial support from " Programme National de Cosmologie and Galaxies" (PNCG)  funded by  CNRS/INSU-IN2P3-INP ,  CEA  and  CNES, France , and from  "Projet International de Coopération Scientifique" (PICS)  with  Canada  funded by the  CNRS, France. Co-author Matteo Fossati  has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  757535 ).
(2018)  In this work we will consider networks corresponding to three temporal contexts: (i) the spatial networks obtained when T is the entire period 2011-2017, (ii) the yearly spatiotemporal networks formed by the seven spatial networks obtained when T 1 encompasses the year 2011, T 2 encompasses 2012 and so on, and (iii) monthly spatiotemporal networks formed by the twelve spatial networks obtained when T 1 represents every January from 2011 to 2017, T 2 every February from 2011 to 2017, and so on. In all these cases, we consider that the length of the time interval was 1 to easily compare the inferred networks (i. e. , |T | " 1 in Equations (  11 ) and (  12 )). 1 https://www. ncdc. noaa. gov/data-access/model-data/model-datasets/global-data-assimilation-system-gdas.
Such predictions may be carried out by various software, including proprietary or free methods, available on local computers or through web interfaces, with possible automated use or not, and with performances that can be difficult to evaluate. CNMR Predictor, NMRPREDICT, ChemDraw, nmrshiftdb2 are such software, among which nmrshiftdb2 may be used for free in an automated may on a local computer while CNMR Predictor is a commercial product renowned for its accuracy.
Databases The results of the chemical study of living beings are diluted among a profusion of specialized scientific journals. The construction of a collective memory about NPs is not a spontaneous process, so that the initial dilution of results has to be counterbalanced by efforts for the re-concentration of the knowledge at some well-defined places named databases. Databases that link structural, spectroscopic and taxonomic knowledge should constitute the basis of well-managed NP chemistry. A must-read article recently published focuses on where to find data about NPs in 2020  [25]. Open databases for NP research containing structural and spectroscopic data were reported earlier in  [26]. The grouping of structural, taxonomic, and experimental spectroscopic data of natural products was undertaken in the '90s in the framework of the SISTEMAT project  [27].
The data and software resulting from this visionary undertaking are unfortunately not accessible to the general public  [28]. Other databases dedicated to the study of NP chemistry always miss some aspect. Biological activity studies are purposely left aside in this article as they do not constitute an entry point for dereplication. As well, bibliographic databases are not considered here, as the creation of NP databases from primary literature is not discussed, even though everyone understands that this is an important aspect of NP research.
KnapsakSearch The KNApSAcK website exposes multiple databases searchable by organism name, metabolite name, and other commonly used compound identifiers  [29]. Searching KNAp-SAcK for a given genus name displays a series of lines, each one showing a compound identifier, the related CAS identifier, metabolite name, molecular formula, molecular weight, and the name of the species in which it was reported. A genus name refers to a set of species names and each species is related to a set of compounds, each one, as discussed earlier, being possibly present in organisms from different species. Directly querying KNApSAcK for family names of organisms fails. About 54,000 compounds are referenced in KNApSAcK, thus giving access to an incomplete but still non-negligible part of the chemical space of NPs.
When starting the chemical study of an organism, the search for taxonomy-related ones may not be limited to those of the same species and may extend to the entire family, to parts of it, or to super-sets of it. The goal of the KnapsackSearch (KS) project is to process a list of user-defined genera to produce a list of chemical compounds that are related to one or more of these genera. The result is obtained as an SDF file, so that each compound is associated to a 2D molecular structure with chiral center flags and to a taxonomic and a spectroscopic description. The SDF chemistry file format is not a database format by itself but is sufficiently widespread to be read by most of chemistry software and computational toolkits. The source code of KS is made of Python scripts that rely on the RDKit library of functions for cheminformatics  [30].
The freely available EdiSDF software is useful for the viewing of 2D structures and related tag-value pairs  [31]. The workflow of KS (Figure  S1 ) starts with the collection of all pairs made of a compound identifier and a binomial name obtained as replies to queries for the queried genus names. Each compound identifier (C_ID) is associated to a list of organism binomial names. As an example, Figure  S2a  shows the beginning of the list of compounds (columns 1-5) and organisms (column 6) related to genus Galanthus. C_IDs are then used as keys for compound search. Figure  S2b  shows the result of such a query for galanthamine, C_ID C00001570. The resulting in data aggregates containing a compound name, a molecular formula, a molecular weight, a CAS number (if any), an InChI string, the InChIKey hashed form of the InChI, and a SMILES string.
Each compound is then associated to the taxonomic information retrieved during the first stage of the data collection process and to the 13 C-NMR chemical shifts as predicted by the nmrshiftdb2 software. The data record of galanthamine, as displayed by the EdiSDF software, is presented in Figure  S2c. The source code of KS is freely available  [32]  and a few KS-generated SDF files are given with the corresponding lists of organism genera related to a taxonomic family. The 13 C-NMR chemical shifts included in KS files may be reformatted to be imported by NMR spectroscopy software by ACD/Labs and to facilitate compound selection according to chemical shift values, thus allowing the user to benefit from the easy prediction of 13 C-NMR chemical values on a massive scale (massive meaning without one-by-one manual operation on structure records) by nmrshiftdb2 and from the friendly graphical interface of software from ACD/Labs.
Predicted NMR Data for Natural Products (PNMRNP) This section reports the transformation of a discontinued NP database, the Universal Natural Product Database (UNPD), into a "two-pillar" NP database, PNMRNP, in which biological taxonomy data is missing. Chemical classification is tentatively proposed as a remedy to this lack. The initial data used in this process is a set of Comma Separated Value (CSV) files from UNPD, provided as a part of the In Silico Data Base (ISDB) dedicated to MS-based dereplication  [34]. Most of the data transformations were carried out using RDKit and locally developed Python scripts. The Comma Separated Values (CSV) files from UNPD contain SMILES and InChI character strings as structure descriptors of NPs (213,210 compounds). Attempts to decode the SMILES chains led to the detection of a non-negligible amount of badly formed chains, so that only InChIs were considered for 2D structure generation with retained chirality information.