dataesr/scientific-paragraphs-categorization huggingface dataset:
org/10. 5194/gmd-13-2723-2020 Geosci. Model Dev. , 13, 2723-2742, 2020 tively small number of reference vectors. Doing so allows us to take the nonlinearities of the data set into account and to filter the noise, which can make the classification spurious. This reduced number of dataset vectors enables an HAC to determine the highly nonlinear borders between the different SOM clusters. This procedure has been used with success in several studies  (Farikou et al. , 2015; Jouini et al. , 2016; Niang et al. , 2003 Niang et al. , , 2006;; Sawadogo et al. , 2009). Note that the use of an HAC directly on the initial data set would not be efficient in the present study because the number of degrees of freedom (here the grid points of the initial domain) is too large for this method to work efficiently.
Such a procedure could advantageously be used by individual modeling groups to identify, analyze and therefore hopefully reduce their model biases in a targeted region. Secondly, from a physical point of view, an identified model group can be used to analyze the targeted region (here the SMUS) in terms of processes, with the advantages of a subset of models which have been selected from quantitative criteria. Such an application has been briefly ilhttps://doi. org/10. 5194/gmd-13-2723-2020 Geosci. Model Dev. , 13, 2723-2742, 2020 lustrated by showing how the selected model group represents an important additional characteristic of the SMUS not used for the selection, namely Ekman pumping. A promising reduction of biases of the full multi-model mean ensemble has been identified, opening possibilities for process studies based on this sub-ensemble of the CMIP5 database.
, 2014)  and varied in mean annual temperature, mean annual precipitation and seasonality (Table  S1 ; calculated based on data from 1998 to 2008 from the closest possible meteorological station). Four different leaf litter species of common temperate forest trees were chosen for this study: alder Alnus glutinosa (L. ) Gaertn. , grey willow Salix cinerea L. , beech F. sylvatica L. and holly Ilex aquifolium L. The four leaf litter species differed broadly in chemical and physical litter traits (Table  1 ; for details on analyses of litter traits see  Makkonen et al. , 2012). The selected four species only occurred in the temperate forest restricting potential home-field advantage effects to the temperate forest site and avoiding it in the subarctic, boreal and Mediterranean forest sites. Leaf litter was collected during the time of litter fall in autumn 2006 at the temperate forest site in the Netherlands using litter traps placed at time of leaf fall.
This study was funded by the 'BioCycle' research project (part of the ESF EUROCORES programme EuroDIVERSITY). We thank the many colleagues and student helpers in the 'BioCycle' research project for the establishment of the experimental field sites, for collecting the leaf litter, the analyses of initial leaf litter quality and for their support in the field and the laboratory. Shixing Zhou acknowledges financial support by the China Scholarship Council via a joint PhD program grant (201706910038). We also declare that all coauthors have no conflict of interest. AUTH O R S ' CO NTR I B UTI O N S S. S. , O. B. and S. H. initiated the idea and designed the study; O. B. , S. B. , I. T. H. , M. M. and V. V. conducted the research and collected the data; S. Z. and O. B. performed statistical analysis; S.
For instance, the popular "Photoshop Clone Stamp Tool" uses Gaussian blurring to produce smoother fusion and transitions. Being able to detect these operations is a step toward detecting more complex and challenging tampering. Hence, this work focuses on these elementary operations. A handful of effective detectors have been developed in the last decade  [1] ,  [2] , targeting various basic image manipulation operations. These classifiers often rely on image statistical models, which may be altered by a resizing prior to manipulation. Most of public databases (both train and test sets) for image forensics are full-sized images, which may not reflect some typical scenarios in the wild. For example, the image to be manipulated may originate from a social media platform, where images are typically resized to save storage space.
1) Objective and main idea Our goal is to come up with a simple method to adapt quickly GMM covariance matrices to target dataset (i. e. pre-resized), starting from a model learned on the source dataset (i. e. without pre-resizing). Inputs of our method are: • C 1 : a set of N = 2 × K covariance matrices from two GMMs trained on the source dataset (the two GMMs, each having K components, are trained respectively on original patches and manipulated patches); • C 2 : empirical estimations of the covariance matrices for original and manipulated patches on target pre-resized dataset  2. We seek to obtain C adp 1 , a set of N covariance matrices adapted to the pre-resized target domain. C adp 1 , along with the weights of original GMMs in the source domain, constitute two adapted GMMs which have improved forensic performance on the target domain.
C adp 1 will be obtained by transforming C 1 using information from C 2. Of course, as an unsupervised adaptation approach, our method does not use any ground-truth label of the target domain, neither for the estimation of C 2 nor for the transformation of C 1. Formally we have: C 1 = {C (1),ori 1 ,. , C (1),ori K , C ,. , C , C 2 = {C (2),ori 1 ,. , C (1),mnp K } (2),mnp 1 ,. , C (2),mnp M 2 }. The idea is to find some transformation of |C 1 | to bring it "closer" to |C 2 |, under the constraint of increasing the likelihood of adapted GMMs on the target pre-resized dataset. Here, we have been inspired by the work of Rodrigues et al. [25]  in the brain-computer interface field. Authors propose the Riemannian Procrustes Analysis (RPA) method, an adaptation between sets of covariance matrices.
With a little abuse of notation, for original patches this means satisfying the following equation (similarly for manipulated patches): K i=1 π i × C trg,ori i = 2 M M/2 j=1 C (2),ori j , (5) where C trg,ori i is the i-th covariance matrix for original patches in C trg 1 , π i are weights of GMM of original patches in source domain, and C (2),ori j is the j-th estimated covariance of original patches in target domain. This transformation considers the fit of second-order statistics (i. e. , the covariance matrix) to the target dataset. In fact, it can be shown, with the assumption of a perfect fit of GMM and centered patches, that the weighted sum of Gaussian components' covariance matrices of a GMM (the left-hand side of Eq. (  5 )) is equal to the covariance matrix of the data (the right-hand side of Eq.
This estimator is unbiased, yet it suffers from a high variance. As explained earlier, in order to counterbalance the variance of estimation, we perform M estimations of empirical covariance matrices on separate subsets of the target dataset of patches from pre-resized images. It is worth mentioning that we estimate matrices separately for the two original/manipulated classes, by using the so-called pseudo-labels in the target domain. Pseudo-labels are an important feature of GRAFT and are described in the next subsection. 3) Pseudo-labels In the unsupervised framework of GRAFT, we do not have access to ground-truth labels of target domain. However, it turns out that the accuracy of the GMM-based classifier does not drop to 50%, i. e. random guessing. It means that classifier is still able to label accurately some pre-resized test samples.
V. EXPERIMENTS A. IMPLEMENTATION DETAILS OF GRAFT Code is available online  4  to reproduce experiments. We use 8 × 8 patches. They are flattened (vectorized) and centered (mean of each patch is removed), as in  [5]. We use Scikitlearn  [30]  implementation of Gaussian Mixture Model. Each GMM has 75 components. This number is a good trade-off between model complexity for training phase and performance on testing phase. In order to counterbalance weaknesses of EM algorithm, we perform five times of initialization for mixture weights π k and covariance matrices Σ k. Initialization is done using K-means. The GMM means µ k are initialized to zeros and forced again to be zero after training  [5] ,  [28]. We keep among the five trials the initialization with the highest log-likelihood on training data.
Grid searches for interpolation parameters α 1 and α 2 are performed for speed purposes with black-box optimization technique from  [31]. It does not necessarily produce better results than regular grid search but is quicker as the more interesting sub-spaces are selected automatically to then perform grid search. For empirical covariance matrices on target domain, we have M = 2 × 30 = 60 estimations. The number of 30 has been set experimentally to have a stable geometric mean around different runs. Method is robust against this number and could work with less. Images are from Dresden database  [32]. It is a relatively big source of images with about 1200 images, in RAW format, coming from 5 different cameras. Images are in relatively high resolution (typically of 2000 × 3000 pixels), with quite diverse exposures and contents.
Some image examples can be found in Figure  5. We randomly select 30% of images for testing purposes. These images are resized, before applying considered manipulation. The remaining 70% of images are for training and selection of hyper-parameters. This set is not resized at any stage. As mentioned in Section I, we have selected a number of manipulation operations that we consider as elementary. They are summarized and described in Table  1. We apply manipulations to every training images, and it allows us to learn on pair of patches: original vs. manipulated. To prepare experimental data, raw Dresden images are demosaicked using rawpy (https://github. com/ letmaik/rawpy), then converted to grayscale with Python interface of OpenCV (https://opencv. org/). Finally we draw randomly 400000 8 × 8 patches of each class, which makes 800000 training patches (balanced case) for each considered binary classification problem.
Despite the differences in methodologies and scenarios, we compare GRAFT with the weaklysupervised method of  [19]  and its unsupervised variant. SPAM-based method. A branch of methods to detect image manipulation are based on steganalytic features, typically the SPAM  [17]  and SRM  [18]  features. In this work, We compare with a SPAM-based method. SPAM features are extracted with code from http://dde. binghamton. edu/download/ feature_extractors, translated in Python, with a quantization order T = 4 and two-neighbor co-occurrences. We perform classification with the popular XGBoost framework  [33]. It is worth mentioning that although existing methods based on steganalytic features consider larger patches, such methods can directly cope with 8 × 8 patches because the feature extraction and classifier training still work without any problem on small patches.
We think that pooling as spatial reduction may have a good effect for big patches of for example 256 × 256 pixels, but not anymore on 8 × 8 patches where we should probably avoid information loss caused by pooling. Learning rate has also been tuned for better accuracy from 10 -3 (value suggested in the original paper  [16] ) to 10 -4. We use Caffe implementation from authors (https://gitlab. com/ MISLgit/constrained-conv-TIFS2018) on exactly the same training and testing patches as GMM (same number as well).
The good performance of GRAFT is mainly due to the formulation of unsupervised adaptation as a problem of likelihood maximization of GMMs on pseudo-labeled target samples. This work raises new questions on the weaknesses of existing forensics detectors regarding their generalization capabilities to pre-processing and more generally image development processes, which is a timely and important research problem. It would be interesting to study the effect of preresizing on detection of more complex tampering operations like splicing or copy-move. In machine learning, in order to obtain better accuracy and generalization, one usually uses larger and more diverse databases. However, this may not be the best answer here and is even sometimes impossible due to lack of labeled target samples. An alternative and flexible solution is therefore needed as illustrated in this paper.
Animal use and generation of Cstf2 D50A mice All animal treatments and tissues obtained in the study were performed according to protocols approved by the Institutional Animal Care and Use Committee at the Texas Tech University Health Sciences Center in accordance with the National Institutes of Health animal welfare guidelines. TTUHSC's vivarium is AAALAC-certified and has a 12-/12-h light/dark cycle with temperature and relative humidity of 20-22 • C and 30-50%, respectively. B6;Cstf2 em1Ccma-D50A founder mice (herein Cstf2 D50A ; hemizygous males are designated Cstf2 D50A/Y ) were generated by Cyagen US Inc. (Santa Clara, CA, USA). To create C57BL/6 mice with a point mutation (D50A) in the Cstf2 locus, exon 3 in the mouse Cstf2 gene (Gen-Bank accession number: NM 133196. 6; Ensembl: ENS-MUSG00000031256) located on mouse chromosome X was selected as the target site.
Fluorescence polarization samples were equilibrated for 2 h at room temperature in 96-well black plates (Greiner Bio) and measured on Infinite M1000 PRO instrument (Tecan Inc) with excitation set at 470 nm (5 nm bandwidth) and emission 520 nm (5 nm bandwidth). At least three technical replicates were performed. The apparent dissociation constants were calculated by fitting the data to a modified version of the Hill equation (39) using GraphPad Prism version 5. 2 software for Windows (GraphPad Software). Unpaired t-test was performed using Microsoft Excel (Microsoft Corp).
NMR techniques NMR experiments were recorded in 10 mM phosphate buffer pH 6. 0 with 1 mM TCEP, 0. 05% (w/v) sodium azide, 0. 1 mg/ml 4-(2-aminoethyl)benzenesulfonyl fluoride (AEBSF), and 10% D 2 O using an Agilent 600 MHz (14. 1 T) DD2 NMR spectrometer equipped with a room temperature HCN z-axis gradient probe. Data were processed with NMRPipe/NMRDraw  (40)  and analyzed with CCPN Analysis  (41). Backbone 13 C␣, 13 C␤, 13 C , 15 N and 1 HN resonance assignments of RRM wild type and D50A mutant, both in the apo and SVL-bound states were obtained from standard gradient-selected, triple-resonance HNCACB, HN(CO)CACB, HNCO, HN(CA)CO (  42 ) and CHH-TOCSY (43) at 27 • C. Assignment data were collected with a random nonuniform sampling scheme and processed by Sparse Multidimensional Iterative Lineshape-Enhanced (SMILE) algorithm  (44).
Samples were equilibrated at 27 • C for 1 h before recording the NMR spectra. The DLPC/CHAPSO bicelle samples were stable at 27 • C for 3 days. Wild type (BMRB id: 30652) and D50A (BMRB id: 30653) RRM backbone assignments and amide RDC data from Pf1 bacteriophage were submitted to the CS-ROSETTA webserver (https://csrosetta. bmrb. wisc. edu/ csrosetta/submit) for structure calculation  (47). The 10 lowest energy structures from CS-ROSETTA were fitted to the RDC data collected in DLPC/CHAPSO bicelles with the PALES program  (48). The best wild type and D50A mutant structures are available at protein data bank (PDB codes: 6Q2I for wild type and 6TZE for D50A). The surface electrostatic potential for each structure was calculated with the Adaptive Poisson-Boltzmann Solver (APBS), and figures were generated with PyMol  (49).
RNA titration experiments were performed by adding unlabeled SVL RNA to the 15 N-labeled proteins and monitoring the change in amide chemical shifts in 2D 15 N, 1 H HSQC spectra until complete saturation. Binding affinities were calculated from spectra using a two-state ligand binding model in the TITAN program  (50). RNA binding onand off-rates were calculated for both wild type and D50A RRM from amide peaks in the fast and intermediate ex-change regimes by TITAN. Backbone 15 N R 1 (longitudinal spin-lattice relaxation rate), R 2 (transverse spin-spin relaxation rate) and heteronuclear { 1 H}-15 N NOE relaxation data  (51, 52)  for RRM wild type and D50A mutant in the absence and presence of SVL RNA were acquired at 600 MHz and 27 • C. 15 N R 1 , R 2. NOE values were used to calculate the backbone order parameters (S 2 ) and the global correlation time ( c ) from the model-free approach (53,54) using 'model 2' in modelfree v4.
RNA-seq and 3 -seq Total brain tissues were collected from three wild type and five mutant 50-day-old male animals from the same litter. Brains were immediately treated with TRIzol reagent (ThermoFisher). RNA isolation, Poly(A) + selection, RNAseq library preparation (NEBNext ® UltraTM RNA Library Prep Kit) and high-throughput sequencing (pair end 150-nucleotide sequencing, PE150, 20M unstranded reads per sample), data filtering and quality, mapping on the mouse reference genome (mm10) using the STAR aligner  (55)  and differential gene expression analysis (DE-Seq2)  (56)  were performed by Novogene Corporation. Over-Representation Enrichment Analysis of the differentially expressed genes were assessed using WebGestalt  (57)  with settings: Mus musculus; Overrepresentation Enrichment Analysis; Geneontology; Biological Process.
Reference Gene List was set on genome. Cellular Component and Molecular Function did not return any gene ontology terms with a false discovery rate (FDR) smaller than or equal to 0. 05. 3 End sequencing and library preparation were performed by Admera Health Inc. 3 -seq libraries were prepared using the QuantSeq 3 mRNA-Seq Library Prep Kit REV for Illumina from Lexogen. Libraries were sequenced with a specific sequencing primer as per the manufacturer recommendations. At least 40M stranded reads were obtained per sample. The sequences were aligned using STAR aligner  (55). Aligned reads were normalized and the genes changing the C/P sites were identified. Analysis of APA was performed by Admera Health following the protocol and using the latest polyA DB database developed by the laboratory of Dr.
The single amino acid deletion identified in STARD8 is present in 180 control males in the Genome Aggregation Database (gnomAD, nonneuro control individuals, https://gnomad. broadinstitute. org/)  (61, 62) , and was therefore interpreted as a benign polymorphism. ALG13 is an established epilepsy-associated gene in females  (63) , but the ALG13 missense mutation in Family P167 (chrX:110951509G>A, NCBI Ref-Seq NM 001099922. 3:c. 638G>A, p. S213N, rs374748006) has been reported in four control males (gnomAD, nonneuro) and was predicted as a polymorphism or benign variant by MutationTaster2 (64) and the ClinVar database  (65). Mutations in COL4A5 have been associated with X-linked Alport syndrome, also known as hereditary nephritis. The COL4A5 single nucleotide mutation (chrX:107846262C>A, NM 000495.
4:c. 2215C>A, NP 203699. 1:p. P739H) has not been reported in any publicly available database. However, the same proline residue substituted by an alanine (p. P739A) is most likely a benign polymorphism (44 hemizygous males in gnomAD). In contrast, the CSTF2 missense mutation (exon 3, chrX:100077251A>C (GRCh38. p12), RefSeq NM 001306206. 1:c. 149A>C) was identified as novel and was not reported in >14 000 control individuals from publicly available databases including gnomAD. The mutation was predicted as disease causing or pathogenic by several prediction tools, including MutationTaster2 (disease causing), DANN (pathogenicity score of 0. 9956 with a value of 1 given to the variants predicted to the most damaging  (66) ) and a high CADD score of 26. 9  (67). Furthermore, at the gene level, CSTF2 is highly constrained with zero known loss-of-function mutations and a Z-score of 3.
e. above the mean, 0. 032 ppm for apo and 0. 048 ppm for RNA-bound) were observed in the RNA-bound domain for the N-terminus (residues 5-7) near binding site-I (25 and 85), site-II (residues 19 and 91; Figures  1C  and 3C ), and in the ␤2-strand preceding the mutation (Figure  3B  and C , red bars). The on-and off-rates of RNA binding were determined with a simple two state ligand binding model using the 2D NMR line shape analysis program (TITAN, Supplementary Figure  S3  (50)). From the RNA-induced perturbations that were in the fast and intermediate exchange regimes, we calculated a K d of 0. 70 ± 0. 02 M for wild type and 0. 54 ± 0. 02 M for CSTF2 D50A , which followed the trend established by fluorescence polarization and ITC (Supplementary Table  S1 ). The off-rates (k off ) were similar for wild type (305.
ACKNOWLEDGEMENTS The authors wish to acknowledge  R. Bryan Sutton ,  Michaela Jansen ,  Kerri A. White ,  Charles Faust ,  Anne Rice ,  Jeffrey Thomas  and the members of the  Latham laboratory  for technical and intellectual contributions. We thank  Admera Health  for 3 -seq services and  Ruijia Wang ,  Bin Tian  and  Yun Zhao  for assistance with 3 -seq analysis. Microscopy was performed in the Image Analysis Core Facility supported in part by  TTUHSC. We thank  Helene Maurey  (Bicêtre),  Florence Pinton (Bicetre) ,  Brigitte Simon-Bouy  ( CH Versailles ), and  Cecile Zordan  (Bordeaux), who performed genetic counselling for this family. The authors would also like to thank the Genome Aggregation Database (gnomAD) and the groups who provided exome and genome variant data to this resource.
DATA AVAILABILITY High-throughput sequence data has been deposited to the Gene Expression Omnibus (GEO) database repository at NCBI with the accession numbers GSE152976 (RNA-seq) and GSE152975 (3 -seq). The structures of the wild type CSTF2 (PDB 6Q2I) and CSTF2 D50A (PDB 6TZE) RRM domains were deposited to the Research Collaboratory for Structural Bioinformatics Protein Data Bank.
For beef, strategic choices have been made in order to consider real consumers' expectations, not well estimated correctly by lab approaches. Thus, Australia set up the Meat Standards Australia grading scheme based on beef assessment by untrained consumers. The ambitions of the International Meat Research 3G Foundation is to develop beef ontology, to set up an international database with a large number of consumers' scores related to beef palatability and collected according to standard protocols of the Meat Standards Australia methodology. The Foundation also aims to support the beef industry by offering an international predictive model of beef palatability, flexible enough to take into account any local livestock characteristics or regional consumer specificity. This approach is supported by the United Nations Economic Commission for Europe, which promotes development of regulations and norms, technical cooperation, and exchange of best expertise and practices.
, 2009 (Oury et al. , , 2010)) , indicated that the biochemical properties of the muscle explained between 12% and 23% of the variability of the sensory descriptors of this muscle evaluated by panelists. However, thanks to a meta-analysis with much more data from different breeds, different cuts, and different sources,  Chriki et al. (2013)  concluded that the variability in tenderness that could be explained by some muscle biochemical properties is, in reality, limited (i. e. , less than 6%). Furthermore, the contribution of the different biochemical traits to the sensory quality of meat varies according to the cut and to the animal type  (Chriki et al. , 2013). This underlines the necessity to analyze as much data as possible to get robust results and to avoid any bias due to limited datasets restricted, e.