dataesr/scientific-paragraphs-categorization huggingface dataset:
The results presented in Table  2  show that the identified communities correspond to some "parts" of real communities existing in other biological pathway databases, and in some cases to a complete network (percentage 100%). Therefore, all the methods were able to efficiently rebuilt communities existing in real biological pathway databases. GA-PPI-Net approach, as well as the three selected clustering approaches, achieve the highest percentage 100% in three pathway databases: Reactome , KEGG and Ec number. For example, on the Reactome database, the GA-PPI-Net approach obtained the max percentage value 100% which corresponds to a complete network. The worst percentage value is of 15. 38% which corresponds to some "parts" of the real communities. Nevertheless,GA-PPI-Net exhibited the largest Percentage Min with respect to MCL, RNSC and ClusterOne.
These tests should be supplemented on a larger scale with other datasets and different communities. With respect to the functional and structural quality of the communities, we determined the semantic similarity and the interaction score for every pair of proteins in each predicted community (i. e. clusters) and compared the distribution of these values over all communities for the four contenders. The semantic similarity between two proteins can be assessed by semantic similarity of their respective GO annotation terms  [21]. We employed the GS2 (GObased similarity of gene sets) method  [28]  to determine similarity between protein pairs in a given community. And the interaction score is selected form the string database. In this study, the semantic similarity and the interaction score of a community are summarized respectively by the average of the semantic similarity (eq 2) and the average of interaction score (eq 3) of the protein pairs in the community.
Conclusion In this paper, we have evaluated the capability of three graph-based clustering algorithms (MCL, RNSC, Cluster) and a genetic algorithm-based approach (GA-PPI-Net) to find protein communities from PPI networks. This evaluation has led us to elaborate the performance of these methods. Detailed comparisons were carried out to assess the quality of the obtained results considering three aspects. The first one was in terms of checking if the obtained communities already exist in real biological pathway databases. The second aspect concerned the functional quality of the detected communities, it was assessed using a semantic similarity based on Gene Ontology. Finally, the third aspect concerns the interactions between the genes of the identified communities. Experimental results have shown that the evolutionary based approach GA-PPI-Net deals with community identification in PPI networks.
Code availability The code for generating the Sentinel-2 preand post-fire composites can be found at https://doi. org/10. 5281/zenodo. 5646758 (Salim et al. , 2021). Cumulative national total burned area vs. burned-scar area, for Sentinel-2, Landsat 8 (official), and MODIS MCD64A1 burned-area estimates. Note the logarithmic axis. For a given segment of the x axis between scar sizes X 1 and X 2 , a difference in the slopes for any two estimates is indicative of inter-estimate differences in terms of inclusivity of scars between X 1 and X 2.
Data availability All the data including pre-and post-fire composites, all three burned-area products, and reference points with screenshots can be visualised online at this application portal: https://thetreemap. users. earthengine. app/view/ burn-area-validation-simplified (last access: 5 November 2021,  Salim, 2021). The Sentinel-based burned-area map and reference dataset are freely available for download at https://doi. org/10. 5281/zenodo. 4551243 (Gaveau et al. , 2021a). The dataset 2019_burnedarea_indonesia. shp contains the 2019 burned-area estimates that we developed for Indonesia using 20 m × 20 m time-series Sentinel-2 imagery. The reference dataset Reference_dataset. shp contains 1298 reference points that we assembled and used to validate all three burned-area products described in this study.
Figure3. Representation of the adjusted stratified-sampling design for the validation of three burned-area datasets (A, B, and C) against reference sites (dots). Panel (a) shows the stratified random sampling of reference sites (black points) over the combined burned area. Note that the density of samples is higher in the combined burned area than the unburned area. Panels (b-d) show, in cyan, the area U , being classified as unburned in a given dataset i but classified as burned in at least one other datasets = i. For a given validation of A, B, and C, the sample points in the corresponding area U (b-d) were randomly excluded until the sampling density in the area U equalled that of the larger unburned area U (area in grey). Panels (e-g) show the three final adjusted, stratified subsamples of reference points derived from the initial sample of 1298 reference points.
Introduction Since few years, data-based models and reduced order modeling  [10, 11, 47]  have been particularly studied in many physics applications. Such approaches allows to determine complex solutions in real-time. By real-time we mean a computational cost drastically reduced in comparison with a classical approach. In most reduced order approaches, an offline step is first needed in order to build the database model. For parametric studies, this means constructing and running the model for given sets of parameters wisely chosen. In the online phase, executed in near real-time, this database is used to construct the solution of a new set of parameters. Common reduced order modeling techniques classically used in computational mechanics often require structured meshes to be efficient by avoiding inaccurate projection steps between all meshes.
This is obviously encountered when geometric parameters are considered. One way to circumvent this issue is to build isotopological meshes of all the geometric instances. Previous work has been done to build the foundations of our approach  [2, 3]. Moreover, research on the same problematic has been recently published  [40] [41] [42]. For different geometric instances with the same topological properties (e. g. the same shape), being able to construct isotopological meshes of all these instances makes it very easy to compare these meshes. Consequently databased approaches with geometric parameters are mainly guided by topological information throughout the offline step. Generating isotopological quad meshes therefore seems as the right way to solve this problem. In numerous applications in domains like computational mechanics, aerodynamics, crash simulations and fluid-structure interactions, 3D quadrilateral meshes are recommended due to their highly regular structure.
( 2 ) where v i , v j ∈ V , N i is the neighborhood of v i , and w ij is the scalar weight assigned to the oriented edge e ij (v i , v j ). Different parameterization methods assign different weights w ij. The first definition of weight was introduced by Tutte  [51]. In the parameter space, each vertex is placed at the barycenter of its neighbors. Recently, Saboret et al. [50]  have implemented a CGAL package handling some of the state-of-the-art surface parameterization methods such as least squares conformal maps, discrete conformal map, discrete authalic parameterization, Floater mean value coordinates or Tutte barycentric mapping. In this context, we use mean value coordinates introduced by Floater  [18]. According to the Rado-Kneser-Choquet theorem, if weights are positive and the boundary ∂M is mapped homeomorphically in a convex square parametric space, the mapping driven by the harmonic function f has to be bijective.
Choice of the geometric criterion Shortest length of cycles on weighted triangulated meshes are computed using a Dijkstra's algorithm based on weighted tree graphs. A CGAL package exists to compute these paths  [29]. Different geometric criteria can be used to guide the pants decomposition. Geometric criterion can be adapted to the mesh M. In algorithm 2 and also in Algorithm 1 to sort loops in L, this can be changed for areas Build or reset an empty set of loops L = {0}.
Algorithmic details The software Rhinoceros 5  [43]  is used for visualization and RhinoCommon. dll [44]  for handling various Non-Uniform Rational B-Spline (NURBS) objects. Our algorithm is entirely incorporated into a Rhinoceros 5 Plug-In implemented in VB. NET. C++ processes are called from the Plug-In. Taking a B-Rep object provided from a file or constructed from the Rhinoceros 5 graphical interface, a geometric reduced order model can be built using our fully integrated workflow. We use for that purpose the ROM builder proprietary software developed by ANSYS  [1]. For instance, this builder has been used to prevent excessive compression of buttock's soft tissues by bony structures  [39]  in real-time for paraplegic persons. All computations have been performed on an Intel Core TM i7-6820HQ CPU @ 2.
Acknowledgements T. Maquart was partially supported by a  CIFRE fellowship of the french Association Nationale de la Recherche et de la Technologie  and ANSYS research. T. Maquart,  T. Elguedj  and  A. Gravouil  were also partially supported by a research contract with ANSYS. These supports are gratefully acknowledged. We especially thank  Martijn Hoeijmakers  for his help in providing the scanned meshes of the ventricle model coming from anonymous data. All the reduced order model computations are performed with the Reduced Order Model builder developed by the  ANSYS France research team.
This approach is called sequential and orthogonalized covariance selection (SO-CovSel) as recently proposed in the literature  [41]. SO-CovSel is a multi-block variable selection technique  [41] , which extends the covariance selection (CovSel) approach for feature reduction  [42]  to a multi-block scenario, borrowing the concepts of sequential inclusion of the matrices to be fused after orthogonalization from SO-PLS. Thus, the aim of the present work was to demonstrate the use of the sequential data fusion strategies SO-PLS and SO-CovSel to integrate spectra from two portable spectrometers operating in different NIR ranges, in order to predict two quality attributes (MC and SSC) of pear fruit. The two portable spectrometers were the Felix handheld spectrometer and the DLP NIR Scan Nano.
Data acquisition 2. 2. 1. Felix handheld spectrometer The VIS-NIR spectral measurements were carried out with a portable spectrometer Felix F-750 (Camas, WA, USA), which utilizes a Carl Zeiss MMS-1 spectrometer to record the reflected light in the spectral range 310-1135 nm. Illumination is provided by a xenon tungsten lamp, with a built in white painted reference standard used to calibrate each scan. Data acquisition was performed by placing the fruit belly directly on the optical window support of the instrument and manually pressing the scan button on the Felix device: each recorded spectrum was the result of the average of 6 consecutive scans. The data were radiometrically calibrated with the in-built white reference standard. The data were automatically normalized by the built-in software of the Felix device and the raw reflectance spectra were extracted as excel files utilizing the "Data-Viewer" software from Felix (Camas, WA, USA).
DLP NIR Scan Nano The SWIR measurements (900-1700 nm) were performed with a DLP NIR Scan Nano (Texas Instrument, USA). On each fruit, the SWIR spectrum was acquired immediately after collecting the VIS-NIR spectrum and exactly on the same position, by directly placing the sample on the sensor head of the DLP NIR Scan Nano. The measurements performed were in diffuse reflectance mode. The data were radiometrically calibrated with the in-built mathematical transformation for estimating the absorbance. The output of the sensor were the absorbance spectra. Spectral sampling resolution was 6. 35 nm and the integration time was 8 ms. Each recorded spectrum was the result of the average of 5 consecutive scans. The measurements were controlled using the PC software provided, which automatically corrects for the dark and white references and outputs the absorbance measurements.
Data processing Data processing involved four main steps, i. e. , spectral preprocessing, building calibration models for the prediction of MC and SSC with the separate spectral matrices using standard PLS regression, verifying whether the joint processing of both spectral matrices through sequential and orthogonalized PLS (SO-PLS) regression could lead to better results than those obtained with single data blocks and identifying which spectral variables in each wavelength range supplied relevant and complementary information for the prediction of the two quality attributes through the use of SO-CovSel. All the analyses were performed using MATLAB 2017b (The Mathworks, Natick, MA, USA).
Eventually, all the block scores (here T 1 and T 2 ) are concatenated and a final ordinary least-squares regression model is calculated between the concatenated scores and the response(s). Here it is worth stressing once again that one of the main benefits of SO-PLS is its sequential nature which, together with the orthogonalization step, allows a straightforward evaluation of whether incorporating a new block really brings additional information, to significantly improve the model quality, or not. The SO-PLS regression analysis was implemented on the free open-source MATLAB based chemometrics toolbox called MBA-GUI  [46].
Partial least-squares regression on individual spectroscopic matrices PLS models were first separately built on the individual data matrices resulting from each of the two spectroscopic sensors (Felix and DLP NIR Scan Nano). The models for the prediction of MC had an optimal complexity of 11 LVs in the case of Felix data and of 10 LVs when built on spectra recorded by DLP NIR Scan Nano; the corresponding results are graphically displayed in Fig. 2. Both models provide good MC prediction results, the one built on the Felix data shows a 20. 5% higher R 2 p and a 25% lower RMSEP when compared to those obtained from the DLP NIR Scan Nano. For the prediction of SSC, the models built on the spectra recorded on the Felix or on the DLP NIR Scan Nano devices had an optimal complexity of 11 and 9 LVs, respectively; the corresponding results are shown in Fig.
Data fusion with SO-PLS The results reported in the previous section show that good models for the prediction of MC and SSC can be obtained by calibrating NIR data (particularly the Felix ones) with single-block PLS regression. A multiblock approach based on sequential and orthogonalized partial leastsquares regression (SO-PLS) was used to integrate the complementary data from the two spectroscopic sensors to try to achieve better calibration performance and also a deeper understanding/interpretation of the models. When fusing the data matrices, one of the advantages of the SO-PLS method is that it is scale-invariant, meaning that no scaling of the blocks is needed prior to integration. The order in which the blocks are introduced into the model can be relevant and, in the present case, the optimal choice was found to be Felix data first and DLP NIR Scan Nano afterwards.
The results of SO-PLS multi-block analysis for the calibration of MC and SSC are shown in Fig. 4 , respectively. In both predictive models, most of the scores are extracted from the Felix block, which was the one giving the better performance when used individually. The sequential multi-block approach showed better predictive ability with higher R 2 p and lower RSMEP values when compared to building calibration models on individual data matrices, thus confirming that integrating the information from the two spectral matrices substantially improves the regression performance. For MC prediction, compared to the models built on the Felix or DLP NIR Scan Nano data individually, R 2 p were increased by 2. 5% and 23%, respectively and RMSEP were reduced by 6. 5 and 30%, respectively. Similarly, for SSC prediction, compared to the models built on the Felix or DLP NIR Scan Nano data individually, the RMSEP were reduced by 6% and 47%, respectively.
Discussion In the present work, data from two portable spectrometers (Felix and DLP NIR Scan Nano) were sequentially fused for improving the prediction of MC and SSC in individual pear fruit. The results show that both spectral ranges carry complementary information in terms of functional group overtones (OH and CH) responsible for predicting MC and SSC content in pear fruit. SO-PLS was able to extract complementary LVs from each spectral range and resulted in model with improved prediction performance (higher R 2 p and lower RMSEP) compared to those obtained on the individual blocks. A major strength of the approach is that the sequential strategy first modelled the data from the Felix spectrometer and then, once it could not find any further improvement, it explored the DLP NIR Scan Nano data.
Conclusions The study showed that fusion of NIR and the SWIR data can improve model performance when the spectroscopic profiles are used to predict key quality parameters (MC and SSC) in pear fruit. In particular, by selecting the VIS-NIR block as the one to be modelled first, complementary information related to 2nd overtones of functional groups present in the SWIR could be integrated to substantially improve the predictive ability of the MC and SSC models. Moreover, the introduction of a further variable selection step selection step identified a minimum set of predictors that still showed good model performance. This minimum set of predictors could be used to build lower cost multispectral sensors or be integrated into existing portable devices such as the Felix. In general, the sequential fusion of spectral data from the Felix and the DLP NIR Scan Nano instruments increased the R 2 p by 2.
Acknowledgments We would like to thank  Mariska Nijenhuis ,  Manon Mensink ,  Najim El Harchioui  and  Marcel Staal  for their help in sample preparation and analysis for MC and SSC. Pear samples measurements with Felix sensor were part of a bigger study funded through Foundation TKI, The Netherlands Horticulture and  Starting Materials  and other private partners in the project ( TU-16025  ( 1605-043 ) Humistatus).
Here we evaluate the accuracy of prediction for eye, hair and skin pigmentation in a dataset of > 6500 individuals from Mexico, Colombia, Peru, Chile and Brazil (including genome-wide SNP data and quantitative/ categorical pigmentation phenotypes -the CANDELA dataset CAN). We evaluated accuracy in relation to different analytical methods and various phenotypic predictors. As expected from statistical principles, we observe that quantitative traits are more sensitive to changes in the prediction models than categorical traits. We find that Random Forest or Linear Regression are generally the best performing methods. We also compare the prediction accuracy of SNP sets defined in the CAN dataset (including 56, 101 and 120 SNPs for eye, hair and skin colour prediction, respectively) to the well-established HIrisPlex-S SNP set (including 6, 22 and 36 SNPs for.
In addition to these, novel pigmentation SNPs with genome-wide significant association were also identified in that study. These included SNPs polymorphic only in East Asians and Native Americans, consistent with the independent evolution of skin pigmentation in West and East Eurasia  [7, 29, 30]. The admixed ancestry of Latin America and the finding in the region of pigmentation variants not present in Europeans emphasizes the need to evaluate the accuracy of tools currently available for prediction of pigmentation traits in this population. Here we aimed to evaluate the accuracy of prediction of pigmentation traits in a large Latin American dataset from Mexico, Colombia, Peru, Chile and Brazil  [12, [31] [32] [33] [34] [35] [36]  characterized for eye colour, comparing methods, model predictors, and training datasets.
We compared results from the widely-used HIrisPlex-S with sets of SNPs selected from the CANDELA data (and including a larger number of SNPs than HIrisPlex-S). We find that, when trained in the CANDELA data, the HIrisPlex-S SNP set has a performance similar to the CAN SNPs in the prediction of eye and hair colour, but its performance is somewhat lower for skin colour. This observation is consistent with the polygenicity of skin colour, relative to hair and (particularly) eye colour and the large variation in skin colour across the world. The work presented here sets the stage for the optimization of tools for the prediction of pigmentation traits across Latin America, for forensic purposes.
Study sample: phenotypes, genetic data and covariates We analyzed data previously studied by the CANDELA consortium for GWAS of pigmentation traits  [12, [31] [32] [33] [34]. The consortium gathered genetic and phenotypic data from over 6500 individuals recruited in five Latin American countries: Mexico (N = ~1200), Colombia (N = ~1700), Peru (N = ~1230), Chile (N = ~1730) and Brazil (N = ~630). Pigmentation traits evaluated directly on the research subjects consists of (A) hair pigmentation (recorded in four categories: 1-red/reddish, 2-blond, 3-dark blond/light brown or 4-brown/black. However, due to their very low frequency (<0. 6%), individuals in the 'red/reddish' category were not included here), (B) eye colour, recorded as five ordered categories: 1-blue/grey, 2-honey, 3-green, 4-light brown, 5-dark brown/black.
For increasing consistency with previous publications  [23, 26, [37] [38] [39] , here we recoded these data into just three categories: 1-Blue/Grey, 2-Intermediate (honey or green) and 3-Brown/Black (light brown or dark brown/black) and (C) a quantitative measure of skin pigmentation from an area unexposed to sunlight (the Melanin Index MI, obtained by reflectometry). We also had available additional measures of iris pigmentation, extracted from digital photographs, using the HCL colour space (Hue, Chroma and Luminance). Hue being an angle (recorded in arc degree), we linearized this trait with cosine and shifted the angle by 15 • in order to maximize the number of samples in the range [0,180 • ]; hence the trait considered is cos(Hue+15). The frequency distribution for these traits in the CANDELA dataset is shown in Supplementary Fig.
S1. To enable comparison with previous studies on categorical skin colour  [26, 37] , we converted the quantitative MI values into a three-level categorical trait (Fair, Intermediate and Dark skin colour). For this, we used the individual genetic ancestry estimates in the CANDELA dataset to select individuals with estimated 100% European ancestry (N = 70) and individuals with African ancestry higher than both the European and Native estimates (i. e. > 39% African ancestry; N = 23). Based on the MI distribution in these two groups, we defined MI values of 33 and 47 as thresholds for three skin colour categories: Fair (MI < 33; N = 2506), Intermediate (MI 33-47; N = 3840) and Dark (MI > 47; N = 180) (Supplementary Fig. S2 ). These thresholds are in line with values obtained in a previous study of Brazilians  [40].
The genetic data consisted of ~9 million genotypes, ~700k of which were obtained experimentally by genotyping Illumina's Omni Express chip, the remainder obtained by imputation as described in Adhikari et al. [12]. We applied several filters to the CANDELA dataset prior to the trait prediction analyses. Firstly, we retained only individuals aged 18-45. Secondly, we removed 8 pairs of individuals whose pairwise probability of IBD was estimated close to 1, to discard potential sample mix-ups (hence, 16 individuals removed), and individuals whose estimated African ancestry was more than European and native ancestry estimations  (23 individuals) , as those were considered as genetic outliers and thus excluded. Finally, we excluded all individuals with missing data on any of the covariates (age, sex, BMI).