dataesr/scientific-paragraphs-categorization huggingface dataset:
Data set and reduction We process the images of the Cosmos field from the ultradeep part of the Subaru Strategic Program, acquired between 2014 and 2019. The ultra-deep part of the survey is geared at obtaining extremely deep images by co-adding a large number of observations, as well as detecting high-redshift supernovae and measuring their light curves. The sample of images covers the five bands: g, r, i, z and y of the camera, and also covers a broad range of image qualities. We use data acquired with the new i and r filters, which are called r2 and i2 in HSC parlance and we stick to these names. This image sample constitutes a representative playground for testing the quality of the brighter-fatter correction on HSC science images. The image reduction we perform is fairly standard for the brighter-fatter correction.
Computer codes Our code is split in three different parts: code to measure covariances and fit the covariance curves, code to perform the electrostatic fit, and code to process the images and in particular corrects those for the BF effect. Our public repository 1  contains the python code for the two first steps. The code that measures covariances did not evolve significantly since it was developed for A19. We publish here the electrostatic modeling code for the first time. Our image correction code is fairly straightforward and would probably run much faster with the convolution in Eq. (  14 ) computed in Fourier space. The parts of the analysis that are not in the repository can be made available upon request.
Acknowledgements. We are indebt to the  Subaru Telescope technical  staff that very efficiently operates the observatory and its instruments. HSC images are made available on the SMOKA server (https://smoka. nao. ac. jp/index. jsp) that we have used extensively. We perform all our reductions and store our results at the  Centre de Calcul de l'IN2P3  (https://cc. in2p3. fr),a A118, page 16 of 17  P. Astier  and N. Regnault: Correction of the brighter-fatter effect on the  CCDs of Hyper Suprime-Cam computing facility of CNRS. This work benefited from useful discussions with  N. Suzuki  (LBL) and  N. Yasuda (IPMU) , and our colleagues from the Paris team. The manuscript eventually benefited from excellent suggestions from our referee.
Data Collection In order to obtain collective and individual knowledge, we conducted several focus groups during four months of field work; 43 individual interviews in the urban site, 50 in the peri-urban site and 49 in the rural site. A consent form has been signed by all of the people surveyed. Interviews focused on local nomenclature (the template used is provided as Supplementary Electronic Material 1). In general, plant local nomenclature is based on a series of well-known morphological descriptors, but also on perceived descriptors, that are more labile or subjective (e. g. fruit taste;  Boster, 1985). In this study, we also pay attention to the knowledge gap, by considering the varieties that are known or observed but not (yet) named. To do so, we asked the respondents during individual interviews about the varieties of African plum trees they knew ('known' varieties) or had planted ('observed' varieties) on her/his property and about the detailed morphological and organoleptic description of their fruits (including fruit taste and texture; also see  Rimlinger, Duminil, et al.
For each interview, the respondent was questioned on his/her status (gender, age, family and residence status, ethnicity, main activity) and on the attributes of the household's fields (field size area, number of fruit tree species). We chose people who owned at least one plum tree in their field or home garden as respondents and did so through a snowballing sampling. We conducted a total of 142 interviews with a balanced number of men and women (53% and 47% respectively), as well as seven focus groups in the three study sites. Focus groups aimed to present the researchers, the research project and its objectives to local populations in the different sites: although data collected during focus groups were not used per se, they helped understand the local context regarding history, cultivation, consumption and marketing of African plum in the different sites.
The decision tree (DT) designed in the present study was based on the standard CART algorithm build with the function rpart from the package rpart  (Therneau et al. , 2013). The DT is a treelike structure, where each internal node denotes a test on an attribute, each branch represents conjunctions of input features that resulted in those outcomes of the test, and each leaf node (terminal node) holds a class label. The two classes are fruit purchase (1) or rejection of fruit purchase (0).
Purchase Preferred Characteristics Among the 16 potential predictors, the decision tree identified four descriptors and eight sub-classes as key criteria in predicting purchase behavior: fruit taste (tasty/sour), fruit size (large), skin color (blue, pink, white, black), and pulp texture (oily pulp) (Fig. 6 ). The CART decision tree achieved a correct prediction of purchase or rejection in 88. 4% of cases in the training datasets and 87% in the test datasets (crossvalidation). The decision tree determined that all interviewed individuals, except one would buy a tasty fruit. Furthermore, if the fruit was both tasty and large, ten people would purchase it. Similarly, if the fruit had skin color other than pink (white, blue or black) and was considered oily in texture, it would be purchased by 104 individuals.
Funding Open access funding provided by  University of Lausanne. This project has been supported by  Agropolis Fondation  under the reference  ID 1605-042  through the  « Investissements d'avenir» program  ( Labex Agro :  ANR-10-LABX-0001-01 ), under the frame of  I-SITE MUSE  ( ANR-16-IDEX-0006 ). Doctoral fieldwork was supported by the  Agence Universitaire de la Francophonie, Bureau d'Europe de l'Ouest.
5 Oily pulp 17 Fruit smell 0. 8 Anst smell 3. 5 Symbolic 19. 7 History 35 Planter's name 28 Name of a third party 37 Topgraphic Environment 12 Fig. 3 Proportion of local names for the entire dataset and for each site individually. The results of the chi-squared test are represented by letters (sites sharing the same letter indicate no significant differences). p < 0. 05 *, ** p < 0. 01; *** p < 0. 001. The X 2 gradient cor- responds to differences in nam- ing classes along the gradient.
Simulation Results and Discussion The performance of the proposed control system is tested in this section using numerical simulation. The simulation results are carried out using MATLAB/Simulink software and the parameters listed in Tables  1  and 2 , in order to show the superiority of the proposed controller in terms of tracking and stability. Remark 4. The fractional-order dynamic approximation used in this article is the CRONE toolbox  [26] , which was developed in the 1990s by the CRONE team. This method is a MATLAB toolbox developed for fractional operators. Thus, the fractional-order terms in the proposed control approach are approximated using a two-order Oustaloup modified filter with a frequency range from 0. 02 to 100 rad/s. The simulation is achieved using a scenario to track the desired trajectory, as shown in Table  3 , using the following starting circumstances: [x 0 y 0 z 0 ] T = [0 0 0] T and [φ 0 θ 0 ψ 0 ] T = [0 0 0] T.
Method An Array CGH using Agilent plateform with 4 × 180K resolution was performed for this patient in the Cytogenetic Service of CHU of Lyon, France. The 180k slides were scannned on agilent DNA microarray scanner and images were extracted with feature extraction software 12. 0. 1. 1. Data analysis was carried out with cytogenomics v3. 0. 3. 3. Data interpretation analysis used workbench v3. 4. 2. 7.
Discussion The 4. 56 Mb 7p22. 3-p22. 1 deletion To our knowledge, this is the first report of a patient with 4. 56 Mbp deletion on chromosome 7p22. 3-p22. 1 inherited from a maternal reciprocal translocation. In the literature, large genetic deletions or duplications have been associated with Neurodevelopment disorders. Singh et al. have described a 1. 6 Mbp deletion on chromosome 3q29, including 21 genes which deletion may increase apoptosis that disrupts cellular organization and brain morphology  (Singh et al. , 2020). In our patient, the analysis of the deleted region on chromosome 7p22. 3-p22. 1 using ALAMUT software showed that this 4. 56 Mbp deletion encompasses eight genes: INT1, FAM20C, LFNG, MAD1L1, DNAAF5, BRAT1, IQCE, CARD11, and AP5Z1. Bioinformatic functional analysis showed that of these genes, only BRAT1 (BRCA1-associated ATM activator 1) was related to the patient's phenotype.
Senescence-Associated β-Galactosidase Activity Quantification To detect senescence-associated acidic β-galactosidase (SA-β-Gal)-positive cells, the Senescence Cells Histochemical Staining Kit (Sigma-Aldrich, St. Louis, MI, USA; Cat#CS0030) was used according to the manufacturer's instructions. Specifically, after 14 days of treat-ment, 12 × 10 3 MSCs were plated on 18 mm coverslips for 24 h, fixed with the kit's fixation solution at room temperature for 8 min and incubated overnight with the SA-β-Gal staining solution (pH = 6. 0) to reveal SA-β-Gal activity. Ten images of random fields from each sample were taken by microscopy, and at least 100 cells were counted for each sample. Cells with blue staining in the cytosol were counted as SA-β-Gal-positive using the ImageJ software v1. 50b.
All measurements were performed in duplicate for each sample. All qPCR amplifications were run on a LightCycler 480 real-time PCR system (Roche) with the primer sequences shown below. Raw data (Ct values) were analyzed using the comparative Ct method. Gene expression values were calculated relative to the housekeeping gene RSP9 expression level. The comparative threshold cycle method (∆∆CT) was used to quantify the relative gene expression, and the obtained data were transformed to exponential 2-∆∆CT values. The results were compared with the t-test and p-values < 0. 05 were considered significant.
Microarray Analysis of Gene Expression To perform RNA analysis with the Affymetrix GeneChip Human Gene 2. 1 ST array (Cat#902136), total RNA was extracted from MSC samples using the RNeasy ® Mini kit (Qiagen, Hilden, Germany, Cat#52304,) followed by incubation with RNase-Free DNase (Cat#79254 Qiagen) according to the manufacturer's instructions. Samples were stored at -80 • C until analysis. After RNA quality and quantity assessment by spectral analysis (A260/280 nm) and determination of the RIN number using a BioAnalyzer (Agilent Technologies, Santa Clara, CA, USA, Cat#5067-1511), only samples with RIN >8 were used. CEL file treatments and conversion into CHP format were conducted with GCRMA software on Console v1. 4. 1. Following gene expression data acquisition, CHP files were analyzed using the Transcriptome Analysis Console (TAC) 4.
2. 6. Gene Ontology Analysis, Gene Set Enrichment Analysis (GSEA) and Venn Diagram Representation GO analyses were obtained using the web-based Gene Set Analysis Toolkit (http: //www. webgestalt. org, accessed on 1 January 2019) with default parameters  [13]. GSEA analysis was performed using the publicly available software GSEA v. 4. 0. 2 (http://www. broad. mit. edu/gsea, accessed on 1 January 2019). For each array dataset, gene expression levels were converted into a ranked-based gene matrix with the Signal2Noise metric method. Following the GSEA official website instructions, when required, probe sets were collapsed to a single gene expression signal using the Collapse Probe Utility with default settings. Most of the gene sets were downloaded from the Broad Institute website (http://software.
broadinstitute. org/gsea/msigdb/, accessed on 1 January 2019), with the exception of the MSC_SI_SIGN gene set that was created by our team using the list of common differentially expressed genes (DEGs) in the IFN-γ-and TGFβ1-treated MSC samples. According to GSEA protocol, gene sets with sizes >500 or <10 were excluded. Phenotype permutation was applied because the number of samples available for each condition was at least equal to 3. The p-values for gene sets were computed by permuting the genes 1000 times and were considered relevant if <0. 05. The 6-set Venn diagram was designed using the R software v. 4. 3. 1 and the publicly available nVenn algorithm (https://github. com/vqf/nVenn, accessed on 1 January 2019). Affymetrix data obtained in this study are available online: ArrayExpress accession E-MTAB-11669.
Statistical Analysis All data are presented as median or mean ± SEM. Student's t-test was used for comparisons between experimental groups. For Affymetrix analysis, a one-way analysis of variance (ANOVA) was performed after the eBayes correction method. Data were analyzed using the Prism software v9 (GraphPad Software Inc. , La Jolla, CA, USA); p-values < 0. 05 were considered significant (* p < 0. 05; ** p < 0. 01; *** p < 0. 001; **** p < 0. 0001).
87%; p = 0. 01) (Figure  1B ). Conversely, the percentage of senescent cells hallmarked by the SA-β-Galstaining was increased in IFN-γ-treated cells (mean ± SEM increase = 35. 3% ± 4. 38%; p = 0. 004) (Figure  1C ). These data suggest that this chronic treatment triggers senescence only in parts of the MSC population. This assumption was next confirmed by an RT-qPCR analysis of senescence-regulatory cell cycle inhibitors. The results show a significant upregulation of the CDKN2A, CDKN1A, CDKN1B, CDKN1C, and ARF genes and a slight decrease in the level of CDKN2B in IFN-γ-treated MSCs compared with control (Figure  1D ). Next, to identify differentially expressed genes (DEGs) between IFN-γ-treated and control MSCs, a genome-wide transcriptomic analysis was performed using the Affymetrix Human Gene ST 2.
1 array on total RNAs isolated from three different IFN-γ-treated and untreated MSCs. Principal Component Analysis (PCA) mapping followed by Volcano chart plotting allowed us to show the different gene distribution [-log10(p-value)] vs. Fold change in the two groups (Figure  1E, F ). The heatmap of DEGs (Fold change < |1. 3|, p < 0. 05) (Figure  1G ) showed that treated and control samples could be differentiated based on their expression profiles (1388 upregulated genes and 670 downregulated genes). (Figure  1C ). These data suggest that this chronic treatment triggers senescence only in parts of the MSC population. This assumption was next confirmed by an RT-qPCR analysis of senescence-regulatory cell cycle inhibitors. The results show a significant upregulation of the CDKN2A, CDKN1A, CDKN1B, CDKN1C, and ARF genes and a slight decrease in the level of CDKN2B in IFN-γ-treated MSCs compared with control (Figure  1D ).
Interestingly, the most downregulated identified gene in this common set encodes for the anti-angiogenic and pro-immunomodulating cytokine Interleukin-12 sub-unit α (IL-12a), with so far no link to OA development  [15]. To better understand the significance of such a signature, a Gene Ontology (GO) analysis was conducted (Figure  3B ). Of note, the results found a significant association with skeletal development (p < 0. 01). To evaluate whether this core signature can also be linked to rheumatic diseases, a GO analysis was performed using the Associated Diseases datasets. Remarkably, the results highlighted a significant association with OA pathology (Figure  3C ). Our findings support the idea that OA disease is the result of combining low-grade inflammation and an altered tissue support microenvironment in the affected joint  [5].
Next, the results prompted us to evaluate the presence of that newly identified senescence-driven signature in MSCs, this time isolated from OA patients. To achieve that, the transcriptomic profiles of three OA MSC samples and three MSC samples from age-matched healthy donors were obtained by an Affimetrix analysis. In order to validate the data clustering, PCA mapping was performed for both MSC groups (Figure  3D ). Heatmaps and Volcano Plots were also created (Fold change >|1. 3|, p < 0. 05) highlighting the different expression profiles found between OA MSCs and healthy MSCs (Figure  S1A ). Next, the question of whether the commonly identified senescence-driven MSC signature is also present in the transcriptome of OA MSCs was elucidated using the gene signature enrichment analysis (GSEA) method.
Richter syndrome (RS) is the transformation of chronic lymphocytic leukemia (CLL) into aggressive lymphoma, most commonly diffuse large B-cell lymphoma (DLBCL). We characterize 58 primary human RS samples by genomewide DNA methylation and whole-transcriptome profiling. Our comprehensive approach determines RS DNA methylation profile and unravels a CLL epigenetic imprint, allowing CLL-RS clonal relationship assessment without the need of the initial CLL tumor DNA. DNA methylation-and transcriptomicbased classifiers were developed, and testing on landmark DLBCL datasets identifies a poor-prognosis, activated B-cell-like DLBCL subset in 111/ 1772 samples. The classification robustly identifies phenotypes very similar to RS with a specific genomic profile, accounting for 4. 3-8. 3% of de novo DLBCLs.
In this study, we perform genome-wide DNAm analysis and wholetranscriptome profiling for a large series of primary human RS samples, and comprehensively compare our findings to those in CLL and DLBCL. We extensively characterize the epigenetic architecture of the RS samples and find the majority retain a CLL imprint. Remarkably, applying DNAm-and gene expression-based classifiers to datasets from landmark studies identifies a subset of "RS-type" DLBCL that is not previously described at the genomic level, is enriched in cases with an ABC-like COO signature, and has an unfavorable prognosis  7, 8, 10.
Differential clusters are functionally annotated to the right. Mutational statuses as reported with NGS, or abnormalities determined with CNV analysis on DNAm data, are added below sample annotation for a selected panel frequently described in CLL and RS. c Sample partitioning according to IGHV mutational status. Unsupervised PCA clustering of U-RS, M-RS, U-CLL, M-CLL, and DLBCL according to the 10,000 most variable CpGs in the dataset. The focus is made on the most variable CpGs because these are highly representative of the IGHV signature in CLL (59% of these CpGs are strongly differential between U-CLL and M-CLL). Indeed, PC1 separates IGHV-unmutated from IGHV-mutated B cell malignancies, with U-CLLs and U-RS segregating in the same area. Conversely, M-RS partition with DLBCLs, clearly separated from M-CLLs on PC2.
, and 24/562 (4. 3%; series from Wright and colleagues)  10  samples harboring the CLL-derived RS signature with a score above the threshold, for a total of 80/1342 (5. 9%) samples. In the four datasets, 91. 6% to 100% of these samples were of ABC-like subtype. We cross-compared this 215-gene signature and a discriminant 44-gene signature of ABC-type DLBCL  38  , identifying LMO2 as the only common gene. LMO2 is an important gene of the ABC signature but holds no more weight in our classifier than the other 214 genes. Indeed, instead of just outlining every ABC-subtype DLBCL, our classifier extracted DLBCL with outstanding features, enriched in, but not exclusively, ABC-subtype DLBCL, with an overlap between ABC and GCB DLBCL and a subset of ABC-subtype DLBCL associated with a low LCS (Supplementary Fig.
In particular, this shorter survival was unrelated to international prognostic index distribution (Supplementary Fig. 30 ). We next explored whether this effect might be due to the enrichment of a previously described genomic subgroup of ABC-like DLBCL associated with unfavorable prognosis  10. In the 562-sample dataset from Wright and colleagues, the 25 cases with top LCS scores were enriched in formerly unassigned (1. 5-fold relative enrichment; p = 0. 04) and N1 subgroups (6-fold; p = 6e-3) while depleted in EZB subtype (p = 0. 03). These cases were also strongly enriched (6. 74-fold; p = 4. 1e-4) in samples collected at relapse, raising the hypothesis of the ability of our classifier to identify DLBCL prone to relapse. Thus, the extreme LCS values seemed to characterize a distinct subset of ABCtype DLBCL, accounting for 4.
3-8. 3% of de novo DLBCL, with poor prognosis. The highest 25% scores in the series from Wright and colleagues showed biased distributions in genomic subgroups, dominated by unclassified cases, and associated with shorter PFS and OS (Fig. 6 ). These findings suggest an ability of the LCS classifier to: (i) identify high-scoring DLBCL samples as a separate DLBCL entity within de novo DLBCL, associated with ABC phenotypes and other features comparable to RS; and (ii) linearly classify other samples according to survival and overall prognosis (Supplementary Figs. 29  and 31 ). Interestingly, while absent from the 215-gene list, the CLL-associated marker CD5 was overexpressed in RS versus DLBCL (2. 4-fold; FDR = 2. 13e -3) and high CLL-derived versus DLBCL-like RS (2. 3-fold; FDR = 0. 01). In the dataset from Wright and colleagues 10 , CD5 expression was higher in samples within the top 25% LCS than in other samples (p = 5.
8e-7), corroborating our results. Last, in a dataset with concomitant transcriptome and CD5 immunochemistry staining GSE66770, the majority (17/22; 77. 2%) of the top 25% samples were CD5+ DLBCL (2. 1-fold enrichment) while this proportion was significantly lower (16/68; 23. 5%) in the rest of the cohort (p = 4. 73e-3).
Discussion In this study, by using genome-wide DNAm analysis and wholetranscriptome gene expression profiling, we extensively characterized the epigenetic architecture of primary human RS samples. We identified a CLL epigenetic imprint that can act as a surrogate for identifying whether an RS is clonally related to CLL or has arisen de novo. Discovery of the CLL imprint in an RS sample avoids reliance on obtaining tumor DNA at the CLL stage. Considering de novo DLBCL, DNAm-and gene expression-based classifiers delineated an RS-like subset in datasets from several landmark studies that was not previously described at the genomic level, was enriched in cases with an ABC-like COO signature, and had an unfavorable prognosis  7, 8, 10. Previous extensive explorations with exome or full genomesequencing had found differences in genomic landscapes between DLBCL-subtype RS and de novo DLBCL  [16] [17] [18] [19] [20].
We provide a gene expression-based, stable, reproducible, and potentially widely applicable classifier, on the basis of a CLLderived RS epigenetic imprint. The classifier differentiates a particular DLBCL subgroup from supposedly de novo DLBCL datasets. Of clinical importance, cases assigned to this subgroup are frequently not detected by recently described genomic and gene expression classifiers of DLBCL, and they are associated with an unfavorable prognosis. These cases were ABC-like DLBCL, enriched in unclassified or N1 DLBCL genomic subtypes  7, 10. This is in line with the association of RS with a particular gene expression profile and with NOTCH1 mutations and NOTCH pathway activation. Given the efficacy of ibrutinib plus R-CHOP chemotherapy in N1 subtype DLBCL  49  , the enrichment in N1 profile within RS samples supports research into whether these patients may also benefit from BTK inhibition combined with R-CHOP chemotherapy.