dataesr/scientific-paragraphs-categorization huggingface dataset:
Covariance Analysis Since 186 channels is too many to perform the proposed LBP descriptors, we propose to spectrally downsample the 112 radiance images. We selected, for each radiance image, I, L channels whose indexes uniformly ranged in  [K]. The choice of a uniform distribution of channels was made to comply with the datasets and hyperspectral devices presented in Table  1  that provide spectral channels whose associated bands are uniformly distributed in their respective spectral range. The output is a spectrally sampled radiance image, I L = {I k } k∈C L where C L = (l + 1) K-1 L+1 l∈[L]. Note that respective band centers, {λ k } k∈C L , range uniformly in ]405. 37, 995. 82[ nm. In order to determine the minimum number of channels, L, required to provide the best discrimination between channels, we studied the normalized covariance matrix between each couple of channels over the whole dataset computed in Section 4.
Classification Scheme A texture feature assessment was performed on our dataset by considering the 112 texture images as 112 different classes. Each 8-bit hyperspectral radiance texture image, I L , of size 1024 × 1024 × L was divided into 25 subimages of size 204 × 204 × L (without overlap), among which 12 were randomly considered to be training images and the 13 others as test images. Supports of subimages according to this random sampling and corresponding classes are available as Supplementary Material as two text files (train. txt and test. txt). In a learning phase, LBP histogram features were extracted from each training image. Then, to assign a test image to one of the classes, the same features were extracted from each test image and compared to those of each training image. This comparison was performed using a similarity/dissimilarity measure between test and train features.
In order to study the influence of channel distribution, we selected 10 sets of 10 random channels. All of these provided performances that were less than or close to performances obtained with a uniform distribution of 10 channels for the marginal LBP operator. We also considered the 10 channels with the highest variance among our dataset. They provided weak classification performances compared to uniform or random distributions, since all the related band centers are in the near infrared domain and consequently, do not take into account the textural information observed in the visible range. 11 )), Opponent Band LBP (OBLBP)  [76]  (Equation (  12 )), Luminance-Opponent Band Angle LBP (L-LCCLBP)  [78]  (Equation (  15 )), L-OBALBP  [79]  (Equation (  16 )). LBP-PCA refers to the marginal LBP operator applied to the PCA channels.
PCA channels are computed for each image of the dataset by first projecting the 186-channel reflectance image onto each principal axis, such that the first PCA channel is associated with the eigenvector with the highest eigenvalue and so on. For a fair comparison with the previous operators, PCA channels are then quantized on 8-bits using the same quantization function for all PCA channels of all 112 images. The LBP-PCA accuracy is shown Figure  16 , where L is the number of sorted (according to decreasing eigenvalues) PCA channels that are considered for the LBP operator. By taking only the PCA channel with the highest variance (1st channel), classification performances are lower than by taking one real channel (93th centered at 699 nm). This can be explained by the specificity of our dataset.
Conclusions In this paper, we introduced and described a hyperspectral reflectance dataset of close range textured object surfaces. The spectral range of this dataset is from 405 to 995 nm, thus providing spectral information in both visible and NIR regions. There are five classes of objects, and a total of 112 images are provided. We discussed the hyperspectral image acquisition protocol and the corrections that were applied to the data. We performed a spectral analysis to quantify the spectral complexity of the samples and showed that a limited number of components permits a good description of their reflectance. Furthermore, we extended local binary pattern operators to a hyperspectral texture analysis. We observed that increasing the number of bands permits better texture classification.
Acknowledgments: This work was supported (in part) by the  National French  project  CNRS-INS2I-JCJC-2017 MOSAIC , the  HyPerCept  project funded by the  research council of Norway  and the conseil régional  Bourgogne-Franche-Comté, France. We greatly thank  Olivier Losson  and  Ludovic Macaire from CRIStAL laboratory  for their advice on reflectance computation and LBP-based classification.
Hirvonen, T. ; Orava, J. ; Penttinen, N. ; Luostarinen, K. ; Hauta-Kasari, M. ; Sorjonen, M. ; Peiponen, K. E. Spectral image database for observing the quality of Nordic sawn timbers. Wood Sci. Technol. 2014, 48, 995-1003. [CrossRef].
50 1392 × 1040 420-720 UGR (2015) [32] Outdoor scenes Photon V-EOS 14 1392 × 1040 400-1000 Apogee Alta U260 with CAVE (2008) [35] Materials and objects VariSpec liquid crystal 32 512 × 512 400-700 tunable filter East Anglia (2004) [36] Everyday objects placed in viewing booth Applied Spectral Imaging Spectracube camera 22 Various resolutions 400-700 SIDQ (2015) [40] Pseudo-flat objects HySpex-VNIR-1600 9 500 × 500 400-1000 160 Kodak KA4200 CCD Brainard (1998) [37] Indoor scenes with Optical Thin 9 2000 × 2000 400-700 Films filter Nordic sawn timbers (2014) [44] Wood samples N/A 107 320 × 800 300-2500 440 Scien (2012) [45] Various objects, scenes and faces N/A 106 Various resolutions Various range Various channels Paintings (2017) [42] Paintings IRIS II filter wheel camera 23 2560 × 2048 360-1150 Ancient manuscripts (2012) [38] Printed documents Hamamatsu C4742-95-12ER tunable filter with VariSpec liquid crystal 3 1344 × 1024 400-700 Apple tree leaves (2018) [43] Near infrared images of healthy & infected leaves HySpex SWIR-320m-e N/A Various resolutions 960-2490 256 SpecTex (2017) [39] Textiles ImSpector V8 60 640 × 640 400-780 Honey (2017) [41] Honey samples Surface Optic Corporation SOC710-VP 32 520 × 696 400-1000 126 Singapore (2014) [34] Outdoor images of natural objects, man made objects, buildings Specim PFD-CL-65-V10E 66 Various resolutions 400-700 HyTexiLa Our proposed dataset Textured materials from 5 different categories HySpex VNIR-1800 112 1024 × 1024 400-1000 186.
Subsequently, one clone from each culture exhibiting faster growth than the parental ΔpedH strain was used for PCR amplification of the pedS2 gene and multiple-sequence alignment analysis with the native sequence of the gene from the Pseudomonas Genome Database (52). (D and E) Visualization of domain composition of PedS2 of P. putida (D) and MxaY of Methylomicrobium buryatense 5GB1C (E) using the prediction from the Simple Modular Architecture Research Tool (53). (F) Amino acid sequence alignment of the PedS2 and MxaY proteins generated with Clustal Omega (50).
ACKNOWLEDGMENTS The work of  Matthias Wehrmann  and  Janosch Klebensberger  was supported by an individual research grant from the  Deutsche Forschungsgemeinschaft (DFG)  ( KL 2340/ 2-1 ). The work of  Charlotte Berthelot  and  Patrick Billard  was supported in part by  Labex   Resources 21  ( ANR-10-LABX-21-01 ). We thank  Bernhard Hauer  for his continuous support. We declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Xper3  (Vignes Lebbe et al. 2016 ) is a collaborative knowledge base publishing platform that, since its launch in november 2013, has been adopted by over 2 thousand users  (Pinel et al. 2017). This is mainly due to its user friendly interface and the simplicity of its data model. The data are stored in MySQL Relational DBs, but the exchange format uses the TDWG standard format SDD (Structured Descriptive Data Hagedorn et al. 2005). However, each Xper3 knowledge base is a closed world that the author(s) may or may not share with the scientific community or the public via publishing content and/or identification key (Kopfstein 2016). The explicit taxonomic, geographic and phenotypic limits of a knowledge base are not always well defined in the metadata fields. Conversely terminology vocabularies, such as Phenotype and Trait Ontology PATO and the Plant Ontology PO, and software to edit them, such as Protégé and Phenoscape, are essential in the semantic web, but difficult to handle for biologist without computer skills.
These ontologies constitute open worlds, and are expressed themselves by RDF triples ( Resource Description Framework). Protégé offers vizualisation and reasoning capabilities for these ontologies  (Gennari et al. 2003 , Musen 2015). Our challenge is to combine the user friendliness of Xper3 with the expressive power of OWL (Web Ontology Language), the W3C standard for building ontologies. We therefore focused on analyzing the representation of the same taxonomic contents under Xper3 and under different models in OWL. After this critical analysis, we chose a description model.
01). All teams reprocessed their photometry at the end of the season using the difference image analysis (DIA) technique : RoboNet used DanDIA  (Bramich 2008; Bramich et al. 2013) , OGLE and MOA used their own implementation of DIA  (Udalski et al. 2015; Bond et al. 2001). The K band of VVV was rereduced using pySIS  (Albrow et al. 2009 ). The VVV pySIS photometry were roughly calibrated to an independent VVV catalog  (Beaulieu et al. 2016 ) by adding an offset of 0. 6 mag. Note that the VVV K light curve is nearly flat, so we did not use this dataset in the first round of modeling. In total, 7659 data points are available for the analysis, as summarized in Table  1.
Unfortunately, in this case, the observations taken around HJD ∼ 2457087 were in SDSSi band only in order to reduce the impact of the moonlight. Moreover, the caustic crossings are not intensively covered by the data. For these reasons, we investigated a simpler model, the Uniform Source Binary Lens (USBL)  (Bozza 2010; Bozza et al. 2012 ) and use pyLIMA  (Bachelet et al. 2017)  to perform the modeling. A detailed description of this binary fitting code is given in  Bachelet (2018, in prep). We did not use the standard grid approach to locate the global minimum, but instead ran a global search on all parameters using the differential evolution method  (Storn & Price 1997; Bachelet et al. 2017). Briefly, this method uses a set of starting points in parameter space and maintains an ordered population of candidate solutions while exploring potential new solutions by combining existing ones.
Error bar rescaling It is common practice to rescale the uncertainties in microlensing using (in mag unit in the present work): σ = k σ 2 + e 2 min (1) where σ is the rescaled uncertainty, k and e min are parameters that need to be tuned to reach a certain metric to optimize. The usual metric used is to force the χ 2 /dof for each dataset to converge to 1  (Bachelet et al. 2012b; Miyake et al. 2012; Yee et al. 2013 ). However,  Andrae et al. (2010)  show that the use of the reduced χ 2 , for model diagnostic, is relevant only for linear models, which is not the case in the present work. Instead, they recommend the use of normality tests of residuals, like  Bachelet et al. (2015). The physical reasons that motivate the rescaling are to account for photometric low-level systematics and potential underestimation of the uncertainties.
There are multiple causes coming from both intrumentation and software reductions. The impact is expected to be different for each dataset and therefore, instead of automatically rescaling the errorbars of each dataset blindly, we assessed wether this was necessary. To do so, we use the approach describe below. First, we rescaled OGLE-IV uncertainties using the custom method of Skowron et al. (  2016 ) 2. We then analyzed the residuals around the best model using three test of normality : a Kolmogorov-Smirnov test, an Anderson-Darling test and a Shapiro-Wilk test. We considered to rescale a dataset if any of these test were not successful (i. e, the p-value associated to the test was less than 1%). All datasets, except MOA Red , passed the three normality tests. The majority of datasets present a relative small number of observations (≤ 100), any deviations to normality would be then hard to detect.
On the other hand, it might indicate that uncertainties reproduce the data scatter accurately. Note that the OGLE-IV dataset also passed the three tests after the rescaling process. V 43 2. 1 ± 0. 2 0. 0 VVV K 14 3. 5 ± 0. 5 0. 0 LSCA i 30 1. 1 ± 0. 1 0. 0 LSCB i 23 1. 9 ± 0. 2 0. 0 LSCC i 21 1. 7 ± 0. 2 0. 0 CPTA i 21 1. 1 ± 0. 1 0. 0 CPTB i 21 1. 2 ± 0. 1 0. 0 CPTC i 12 1. 2 ± 0. 2 0. 0 COJA i 29 1. 7 ± 0. 1 0. 0 COJB i 18 2. 1 ± 0. 3 0. 0.
Further details can be found at http://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GPL17071. RNA sequencing (RNAseq) was performed at the University of North Carolina at Chapel Hill (NC, USA). Total FFPE RNA was used to create a total RNAseq library using the Illumina TruSeq Total RNA library Prep Kit with Ribo-Zero Gold Kit. Libraries were then sequenced two per lane on a HiSeq2500 with a 48x7x48 bp configuration. Alignment with the Human Genome Sequence GRCh37 was done using MapSplice v 2. 2. 1 and RSEM v1. 3. 0. Analysis of RNAseq data was done in collaboration between the University of North Carolina and the Hospital Gregorio Marañon. RSEM normalized data was uploaded into the TNBCtype online tool: http:// cbc. mc. vanderbilt. edu/tnbc in order to get Lehmann's subtype distribution  (23, 24).
On the other hand, LAR tumors have consistently shown low response rates and pCR in other series  (30) , and, however inconsistent across studies, AR TNBC seem to show a better long-term prognosis, supporting that TNBC/LAR tumors have a distinct biology compared to non-LAR TNBC  (15, (30) (31) (32). AR-driven TNBC represent a subset of tumors for which pCR might not be prognostic, and thus, that may display a favorable outcome despite residual disease after NACT  (33, 34). This chemoresistance of AR-driven TNBC could be filled by molecularly targeted therapies directed against the androgen receptor (13), which have been evaluated both in the metastatic and early setting  (18). For instance, several trials are evaluating adjuvant enzalutamide in TNBC patients with AR+ disease (NCT02750358), as well as in the neoadjuvant setting in combination with chemotherapy (NCT02689427).
Although adjuvant antiandrogen therapy in patients with residual disease following NACT seemed like an interesting option to consider, the phase III ENDEAR trial (NCT02929576) was prematurely discontinued. In addition to the use of the former TNBCtype classification in most of the published data, there is little evidence regarding TNBCtype performance in patients treated with nonanthracycline, platinum salt-containing regimens. Table  4. A recent phase 2 study evaluating the addition of neoadjuvant everolimus to cisplatin and paclitaxel, performed TNBCtype in 48 of their patients, exhibiting similar trends of response to our cohort overall, although it included the MSL subtype  (28). We then analyzed the TNBCtype and PAM50 distribution among carriers of mutation in a panel of 7 homologous repair genes, with all our tumors classified as basal-like by PAM50 and as BL1, BL2 and M with TNBCtype-4.
Acknowledgments Acknowledgement of research support: Funding for  M Martin  was supported by research grant from  Instituto de Salud Carlos III (ISCIII ,  PI 12/02684 ),  FEDER  ( RETICC-RD12/0036/0076 ). "Ayuda cofinanciada por el  Fondo Europeo de Desarrollo Regional (FEDER). Una manera de hacer Europa" and CiberOnc). CM Perou was supported by funds from the  NCI Breast SPORE  program  P50-CA58223.
| Population diversity and mating system statistics For each of the 36 temporal or spatial samples, the expected heterozygosity, H e , and the fixation index, F IS , were estimated in the genepop 1. 0  (Rousset, 2008 ) R package. The same software was used to test for departures from Hardy-Weinberg equilibrium in each sample, with the p-values computed using enumeration for the RAD-seq panel, and using the Markov chain algorithm (100,000 dememorization steps, 1,000 batches and 50,000 iterations per batch) for the microsatellite panel. Population selfing rate, s, was derived from g 2 , estimated in in-breedR  (Stoffel et al. , 2016). g 2 measures the extent to which heterozygosities are correlated across loci  (David, Pujol, Viard, Castella, & Goudet, 2007). Under no inbreeding, the heterozygosities at different loci are expected to be statistically independent.
py script. As U. pinnatifida is a partial selfer (see Results and Table  1 ), other clustering algorithms, such as the one implemented in INSTRUCT, which estimates individuals' inbreeding coefficients while simultaneously grouping them into distinct genetic clusters  (Gao, Williamson, & Bustamante, 2007) , may perform better. However, INSTRUCT is based on a likelihood method and is, therefore, highly computationally intensive, which prevented us to analyse our RAD-seq panel with this software. We instead used the computer program snmf developed by  Frichot, Mathieu, Trouillon, Bouchard, and François (2014). Besides computational efficiency, snmf does not rely on Hardy-Weinberg equilibrium assumptions, in contrast to fastSTRUCTURE. It is thus particularly relevant to use in selfing species as shown by  Frichot et al.
(2014). Using simulated and empirical data sets, with the partial selfing species Arabidopsis thaliana as a case study, these authors showed that the snmf algorithm performs particularly well with high levels of inbreeding. The snmf algorithm is implemented as a function in the LEA package which has been optimized to process large population genomics data sets  (Frichot & François, 2015). The snmf function was run with default parameters, with the number of iterations set to 1,000, and its outcomes were compared with those of fastSTRUCTURE. Admixture proportions for the optimal values of K were visualized in pophelper  (Francis, 2017). For the microsatellite panel, population structure was investigated amongst 789 individuals collected in 2015 utilizing DAPC and STRUCTURE version 2. 3. 4  (Falush, Stephens, & Pritchard, 2003).
For both sample sets, the "mahalanobis" method was used to compute the p-values. At last, we used OutFLANK  (Whitlock & Lotterhos, 2015) , which identifies outliers by comparing differentiation at each locus against a trimmed null distribution of F ST values for loci that are deemed neutral. For sample set 1, OutFLANK was run with default parameters except that LeftTrimFraction = 0. 4. For sample set 2, the programme was run with LeftTrimFraction = 0. 7, RightTrimFraction = 0. 1. Settings were chosen so as to improve the fit of the inferred neutral distribution of F ST. When needed, to correct for multiple tests, we used the FDR correction implemented in the R package qvalue  (Storey, 2002). Only loci detected with three of four methods, excluding OutFLANK (see Results), were considered putative outliers.
| Outlier detection With sample set 1, comprising 460 individuals collected in 2015 from 22 natural rocky reef and marina samples spanning two biogeographical provinces, 240 RAD-seq loci (2. 44% of the investigated loci) were identified as outliers across all methods (Supporting Information Figure  S4A ). OutFLANK did not detect any positive selection outliers at a q-value of 0. 05. A single locus was detected as an outlier by all four remaining approaches F I G U R E 3 Bayesian clustering analyses (fastSTRUCTURE software) using the RAD-seq panel (a) over the whole data set collected in 2015 and (b) in the Bay of St. Malo only (bay no. 12 in Figure  1 ). Each individual is represented by a vertical line divided into K coloured segments, the length of which indicates the individual's membership fraction to each of K clusters.
These data suggest an impairment of the early follicular growth in juvenile mutant ovaries. The size distribution of follicles on sections was thoroughly analyzed in mutant and wild-type ovaries. Comparison of the resulting profiles revealed different size distributions profiles in mutant and wild-type ovaries. In mutant ovarian sections, the number of small follicles appeared higher compared to wild-type, with a significant increased in the 50-100 µm size class, whereas the number of follicles in the larger diameter class (200-350 µm) tended to decrease, although not significantly. This clearly suggests an important defect of the early follicular growth in juvenile miR-202-/-females, leading to an accumulation of small and medium oocytes (i. e. pre-vitellogenic and early-vitellogenic oocytes) to the detriment of larger late-vitellogenic and maturing follicles.
Establishment of the miR-202 mutant medaka line For the CRISPR/Cas9 knock-out analysis, the target genomic sequence was identified with the help of the ZiFiT online tool (http://zifit. partners. org/ZiFiT/) and using the medaka genome reference available on the Ensembl genome database (Ensembl gene: ENSORLG00000021212). A short sequence in the mature miR-202-3p was selected as followed: GG-(N)18-NN. Two inverse-complementary primers (Forward 5'-TAGGCATAAGGCATGGGAAAAT-3' and (Reverse 5'-AAACATTTTCCCATGCCTTATG-3') were annealed and cloned into the pDR274 vector (Addgene plasmid 42250) in the BsaI cloning site. The modified pDR274 vector was digested with DraI and the miR-202 specific guide RNA (mir202-sgRNA) was transcribed using the T7 RNA polymerase (P207, Promega). For the Cas9-RNA in vitro synthesis, the pCS2-nCas9n vector (Addgene plasmid 47929) was linearized with NotI and capped RNA encoding the Cas9 was transcribed with the mMessage mMachine SP6 Kit (AM1340, Life Technologies) following manufacturer's instructions.
QPCR was performed using the Step One Plus system (Applied Biosystems, USA) with the following conditions: 95°C for 20 sec; and 40 cycles of 95°C for 1 sec and 60°C for 20 sec. The relative expression of miRNA within a sample set was calculated from standard curve using Applied Biosystem StepOne V. 2. 0 software. All QPCR were performed in duplicates. MiR-26a (hsa-miR-26a-5p, A25576, Thermofisher Scientific) or cel-miR-39-3p was used for normalization. For mRNA and pri-miR-202 expression analysis, 2µg of total RNA was reversetranscribed using the Maxima First Strand cDNA Synthesis Kit (K1671, ThermoFisher Scientific). The cDNA was diluted (1:20). The SyberGreen QPCR was performed using 4 µl of diluted cDNA, 5 µl of GoTaq QPCR Master Mix 2x (A600A, Promega) and 100 nM of each primer (S3 Table ), in a total volume of 10 µl.
The QPCR was performed using the Step One Plus system (Applied Biosystems, Foster City, USA) with the following conditions: 95°C for 2 min; and 40 cycles of 95°C for 15 sec and 60°C for 1 min. Standard curves were generated using five serial cDNA dilutions (from 1:2 to 1:32) of a pool of all samples. The relative abundance of target cDNA was calculated from standard curve using Applied Biosystem StepOne V. 2. 0 software. All QPCR were performed in triplicates and the rpl7 gene was used for normalization.
Microarray analysis Gene expression profiling was conducted using an Agilent 8x60K microarray as previously described  (60). Samples were randomly distributed on the microarray for hybridization. The data were processed using the GeneSpring software (Agilent v. 14. 5) using gMedianSignal values. The gene expression data was scale normalized and log  (2)  transformed before the statistical analysis. Corresponding data were deposited in Gene Expression Omnibus (GEO) (https://www. ncbi. nlm. nih. gov/geo/) database under the reference GSE111388. The differences between the groups were analyzed with unpaired t-test after application of minimum two-fold change filter with the significance level of 5 % (p < 0. 05) after Benjamini-Hochberg correction.