dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding -This work was supported by a grant from  French National Research Agency  under the LabEx  ANR-10-LABX-0003-BCDiv , in the program 'Investissements d'avenir' number  ANR-11-IDEX-0004-02 , attributed to OS, by a  Paris city council grant Emergence  to VL. ME acknowledges funding by the  French National Research Agency  ( ANR grant ,  CLEARWING ,  16-CE02-0012 ) and by the  Human Frontier Science Program  ( HFSP research  grant  RGP0014/2016 ). TGA was supported by a grant from the  Swiss National Science Foundation  and from the  National Science Foundation (USA). Conflict of interests -We declare no competing interests.
Quantification of apical polarity Fluorescent signals of polarity markers in 3D cell cultures were scored visually for apical localization on an Olympus IX83 microscope equipped with a 40x objective (NA = 0. 95), using a binary scale. At least 100 acini were scored per condition in each experiment. Alternatively, images were recorded with a sCMOS camera (Hamamazu Orca-Flash 4. 0) at 10x magnification (NA = 0. 3). Imaging of 96-well culture plates was automated using the WellNavigator function of CellSens (Olympus), with software-based autofocus on the DAPI staining. The RadialProfiler MATLAB software  24  (https://osf. io/e37st/) was used for automated analysis of apical polarity. Briefly, RadialProfiler identifies acini based on the DAPI (or Hoechst) nuclear stain, divides the segmented acini area into concentric regions, and retrieves the intensity of the polarity marker in each of them.
ACKNOWLEDGEMENTS We are grateful to  Jill Henry  at the  Komen Tissue Bank  for her help with the Virtual Tissue Bank and tissue procuration. This work was funded by the  Department of Defense Breast Cancer Research Program  ( BC170905  to PAV and  BC190271  to KLC), the  Vera Bradley Foundation and IU Simons Cancer Center  (to PAV and BSH), the  Wake Forest Center for Molecular.
A more precise description of the microplate setup can be found in  Grassino et al. (2022). The maximum specific uptake rate (k M ) was estimated by minimization of the residual sum of squares, using the function lsqnonlin in Matlab (MATLAB R2018b, The MathWorks Inc. , Natick, MA). 95% confidence intervals were calculated using the function nlparci. A value of the half saturation constant (K S ) of 20 mg COD⋅L -1 was assumed  (Puyol et al. , 2017). The biomass yield (Y) was assumed as 1 g COD biomass ⋅g COD consumed -1  (Puyol et al. , 2017). Biomass decay was considered in the model, using a first rate decay constant of 0. 09 d -1  (Puyol et al. , 2017). The resulting kinetic parameters were used as criteria to identify efficient photoheterotrophic growth  (Batstone et al. , 2002; Henze et al.
Implementing light attenuation in mechanistic models for PPB growth The equation from Section 2. 4. 1. providing the best I Z prediction was included in the mechanistic PAnM, presented in  Puyol et al. (2017) , based on a system of ordinary differential equations and algebraic equations. Using the inhibition function and the kinetic parameters obtained from Section 2. 3, the model was improved to consider light attenuation at different biomass concentrations, and a more realistic value for the light half-saturation coefficient (K I ). Matlab was used for the simulations. In the modified PAnM, once the attenuation effect was accurately predicted, the average light intensity in the growth media (along the reactor) was calculated according to Eq. (  8 )  (Béchet et al. , 2013; Ruiz-Martínez et al.
For the VIS fraction a factor of 4. 57 µmol⋅J -1 was applied, and for NIR wavelengths the value was 7. 52 µmol⋅J -1  (Mavi and Tupper, 2004). These conversions factors imply that for an equivalent PFD, the power density of NIR wavelengths is almost halved compared to that of VIS light. Experimental datasets from photoheterotrophic tests from Capson-Tojo et al. (  2021 ) were used for validating the modified PAnM. The experiments consisted of batch tests (in triplicate) carried out in 250 mL Schott bottles. The reactors were fed with domestic wastewater and spiked with acetic acid at 500 mg COD⋅L -1 to provide a readily available source of organics and to optimise the COD:N:P ratio of the media. The tests were started with 250 mL of a 1:4 vol:vol mixture of enriched PPB inoculum (1. 2 ± 0. 4 g VS⋅L -1 ) and domestic wastewater (see  Capson-Tojo et al.
(2021)  for details on the inoculum preparation). Each reactor was stirred at 150 rpm (RCT basic, Kika Labortechnik) and covered with UV-VIS absorbing foil (ND 1. 2 299, Transformation Tubes). Continuous illumination was provided by 150 W fluorescence lamps (Nelson Portable Flood Light) at an incident intensity of 120-130 W⋅m -2 (with a distribution of 17% below 700 nm, 15% at 700-800 nm, 20% at 800-900 nm and 48% at 900-1000 nm, measured with a Stel-larNet BLUE-Wave Spectrometer). The Matlab code corresponding to the modified PAnM function, and the run file can be found in GitHub (https://github. com/GabrielCapson/ PPB_Light_attenuation. git).
The resulting sequences were analysed using QIIME v1. 8. 0  (Caporaso et al. , 2010) , and were de-noised with DADA2  (Callahan et al. , 2016) , after which the relative frequencies of the amplicon sequence variants (ASV) were calculated. The taxonomy was assigned by uclust against the SILVA rRNA gene database (128_release)  (Quast et al. , 2013). ASVs with only one-two reads were filtered from the ASVs table. The resulting ASVs were processed according to  Hülsen et al. (2018b).
Runtime The runtime of a superpixel algorithm is of utmost importance, as it is a pre-processing algorithm. Figure  5  shows the runtime for the SLIC algorithm itself and the connectivity enforcement procedure. These are measured on an Intel Core i7 CPU @ 2. 60 GHz, the C++ code being compiled with GCC 7. 5 under Linux. It can be observed that the connectivity step is always a fraction of the SLIC step. The general trend is a decreasing processing time with respect to K. For large images with proportionally few superpixels, the runtime is too large as many iterations are necessary before the superpixels stabilize. However, even for 16 Mpixels, the runtime goes below 10 s, which is probably small compared to further processing.
Convergence There is no theoretical guarantee of convergence of the K-means procedure, though in practice a few iterations are sufficient (precise measures appear below). Still, we ran over a pathological case (one among several thousands of images) where the SLIC repeats the same periodic pattern indefinitely after 354 iterations, see Figure  6. To have a finite runtime, our code limits to 1000 iterations. If the algorithm stops because of this limit, a warning message is displayed.
Comparison with the Original Implementation The code from the authors of  [1]  is generally faster than ours, since it systematically stops after ten loops. This allows it to be fast whatever the size of the image. For example, a 16 Mpx image can take over a minute of treatment with our program, against a few seconds with theirs. Regarding precision, we noticed a few shortcomings in their program. As can be seen on Figure  7 , our superpixels adapt better to the contours of the guitar head's top and body. Images treated by both algorithms are presented in Figure  8  for comparison. Note that the original version displays the original image in the background of the output image, while our version has been programmed to return an image in which the superpixels are displayed in their average color.
Benchmark Protocol We use the Berkeley Segmentation Dataset  [8]  (BSDS), a collection of 500 small size images (481 × 321), among which 200 are in the test section. These come with user-annotated segmentations, at least five for each image (one image has only four segmentations). The score is computed following  [11]. For each image in the test set, the worst measures, that is, lowest BR, higher UE, and lowest EV, when comparing the superpixel segmentation to the five ground truth segmentations is recorded. This score is averaged over all images of the test set. This is performed for 18 values of K, from 200 to 5200, yielding a curve for BR, UE, and EV. The area under the curve (AUC) for 1-BR, UE and 1-EV gives finally a compound score for each criterion, where lower is better.
Comparison of Implementations The code associated to  [11]  includes the evaluation routines along with wrappers for 28 superpixel algorithms. Among them, three implementations of SLIC are included: the original one from  [1] , the reimplementation in VLFeat  [14]  and a fast variant, preemptive SLIC  [10]. This allows a quantitative comparison of the implementations. The original implementation contains a connectivity enforcement function, not mentioned in the article, that relabels pixels following Algorithm 10. Notice that, contrary to our method, there is no guarantee that the number of superpixels cannot increase after relabeling. For instance, consider initially K = 2 superpixels, with two connected components of 5/16 × w h pixels for the first one, and two connected components of respective size 5/16 × w h and 1/16 × w h pixels for the second one.
Since 5/16 × w h > 4/16 × w h = 1/2 × w h/K, we get after relabeling K = 3 > K superpixels. Algorithm 10: Connectivity enforcement in the code of  [1]. The implementation of VLFeat has a similar function but the threshold for the number of pixels is user-defined and defaults to 1, which does not enforce connectivity. It is important to set it as the same value as the original SLIC: by default, the benchmark code of  [11]  relabels each connected component with a unique label, which increases greatly the number of superpixels and the metrics do not make sense any more. Input To perform our tests, we use a fork of D. Stutz's code 5  , which allows the build with recent versions of OpenCV and fixes a few bugs: measure of AUC was faulty and led even to NaN ("Not a Number") in some situations; the results on the last image of the dataset were not incorporated in the statistics.
Sassy) The Big Lez Show by Jarrad Wright CC-BY-SA 3. 0 (2012) (Spiral) photograph by Thomas Williamson and Robin Gay, CC-BY-SA 4. 0 (2017) (Pond) photograph by Robin Gay, CC-BY-SA 4. 0 (2017) (Guitar) photograph by Thomas Williamson, CC-BY-SA 4. 0 (2017) (Castle, Boy, Mushroom, Fish, Woman) Berkeley Segmentation Dataset [8] 6.
( Sassy) The Big Lez Show by Jarrad Wright CC-BY-SA 3. 0 (2012) (Spiral) photograph by Thomas Williamson and Robin Gay, CC-BY-SA 4. 0 (2017) (Pond) photograph by Robin Gay, CC-BY-SA 4. 0 (2017) (Guitar) photograph by Thomas Williamson, CC-BY-SA 4. 0 (2017) (Castle, Boy, Mushroom, Fish, Woman) Berkeley Segmentation Dataset [8] 6.
Materials and Methods We report on two main experiments. First, we describe the phenotyping, genotyping, and GWAS analyses of the SAM population across three environments. This allows us to examine the genetic bases of heterosis and to show that the results are repeatable across a broad range of environments. We then describe a smaller scale greenhouse experiment to generate transcriptome data and to observe phenotypic responses in XRQ and PSC8, as well as their hybrid INEDI. We chose these three genotypes for two reasons. Most importantly, chromosome level reference genomes have been generated for XRQ and PSC8 (https://www. heliagene. org/), which permitted us to characterize PAVs and to know which PAVs are expected to be complemented in INEDI. In addition, a previous study  [38]  showed that INEDI exhibits heterosis under both control and drought conditions.
Phenotyping and genotyping of the SAM population The SAM population includes 288 sunflower cultivars that represent circa 90% of the allelic diversity in the cultivated sunflower gene pool  [51, 52]. The population includes a mix of inbred and open-pollinated lines, as well as oilseed and confectionary varieties. Here we analyze phenotypic data that were collected for the population in the summer of 2010; experimental design and GWA analyses of two traits (branching and days to flower) have been described previously  [51, 53]. Phenotyping was conducted at three locations: Watkinsville, GA and Ames, IA in the USA and Vancouver, BC, in Canada using 271 lines that were available at the time. A total of 19,512 seeds were planted across the three sites using an alpha lattice design (12 seeds per plot Â 271 lines Â 2 replicate sites Â 3 locales).
5. 3 (github. com/Cibiv/NextGenMap). Variant calling was performed following the Genome Analysis Tool Kit best practices (https://gatk. broadinstitute. org/hc/en-us) on non-repetitive regions on 17 chromosomes of the reference genome. In order to remove low-quality variants, we conducted Variant Quality Score Recalibration (VQSR) using the 20 samples with the highest sequencing depth in the SAM population as a ''gold set". The variant data set was filtered to retain only bi-allelic SNPs in the 90% tranche with minor allele frequency > 0. 05 and genotyping rate > 50% for GWA analyses. Detection of gene PAVs in the SAM population used read depth values from Illumina reads aligned to the HA412-HOv2 reference genome. For each 100 bp non-overlapping window across the genome, we measured the read depth in each sample, ignoring mapping quality.
GWA analyses We initially conducted a standard GWA analysis using EMMAX (https://genome. sph. umich. edu/wiki/EMMAX), in which we searched for associations between PAVs, and phenotypic data for the 16 traits in Table  1. To identify relatedness between samples, we used a SNP table for the SAM population filtered to include only biallelic sites sequenced in ! 50% of samples with a minor allele frequency ! 5%, and further pruned for linkage disequilibrium (LD < 0. 2 in 500 kb windows) using bcftools (https://samtools. github. io/bcftools/bcftools. html) and SNPrelate (https://github. com/samtools). This SNP dataset was used to create a PCA of samples using SNPrelate, as well as a kinship matrix using EMMAX. The first two principal components were used as covariates in EMMAX, as well as the kinship matrix to control for relatedness between samples.
Therefore, we surveyed the genome for stop mutations in protein-coding genes, since we expected such mutations to be most similar to PAVs in effect sizes. Based on a recently-refined annotation of the reference genome (https://sunflowergenome. org/), we removed RNA genes, putative pseudogenes (including those without suitable ORF sequences and all genes without introns) and genes with an ORF<75 amino acids, leaving a total of 36,856 genes. Using the filtered set of proteincoding genes, we annotated the SNPs with the program snpEff (https://pcingola. github. io/SnpEff/) and extracted ''stop_gained" and ''stop_lost" mutations. Only stop codon mutations with MAF > 20% were chosen for further analyses because sites with skewed allele frequencies are likely to generate unreliable GWAS results. We then searched for associations between stop condons and the 16 phenotypic traits listed in Table  1.
Genomic prediction The following genomic prediction model was trained with mixed. solve function in R package rrBLUP version 4. 6. 1 (https:// CRAN. R-project. org/package = rrBLUP) for the 16 traits in Table  1  and PAV markers: y ¼ 1b þ Zg þ e Table 1 Phenotypic traits assayed and predicted phenotypic expression in hybrids. Traits phenotyped in XRQ, PSC8, and INEDI by  [38]   b Traits were classified as heterotic or not based on reports from the literature (e. g. ,  [54] ). where y is a vector of the phenotypic trait (in this case Z-scores of the phenotypic trait); Z is a 286*7541945 incidence matrix containing the allelic states of the markers (Z = {-1, 1}); À1 and 1 represent the absent and present allele, respectively; b is a vector of fixed effects; g is the vector of marker effects; and e is a vector of residuals.
Illumina RNA-Seq library preparation We generated 100 bp paired-end RNA sequencing libraries of each sample (36 libraries total from the two experiments). The libraries were prepared using the TruSeq sample preparation kit (Illumina San Diego, CA, USA) following the manufacturer's instructions. RNA sequencing was performed on the Illumina HiSeq 2000 by DNAVision (Charleroi, Belgium). FASTQ files from the Illumina sequencing have been deposited in the National Center for Biotechnology Information Sequence Read Archive under BioProject number of PRJNA345532.
Analyses of gene expression and alternative splicing The 100 bp paired-end raw reads from the 36 RNA-Seq libraries were assessed for quality using FastQC (bioinformatics. babraham. ac. uk/projects/fastqc), filtered and trimmed using Trimmomatic v0. 36 (usadellab. org/cms/?page = trimmomatic), and checked using FastQC again to confirm read quality. Processed reads were then mapped against the XRQ sunflower reference genome  [60]  using STAR v2. 4. 2 (github. com/alexdobin/STAR) with non-default parameters (--alignIntronMax 10,000). Analyses of expression levels and alternative splicing (AS) employed a custom pipeline (Fig. 1A ) following the approach of Lee and Adams  [61]. Read count metrics and AS events were determined by quantifying reads mapped against constitutive and alternatively spliced forms using reference genome gene models  [60].
constitutive isoforms were counted, and their relative abundance or percent splicing index (PSI) was calculated for each sample. Logistic regression using a custom R script (https://github. com/dejonggr/ differential_as) was employed to identify differentially spliced genes (DAS) (FDR < 0. 01, log 2 FC > 0. 25 or log 2 FC < -0. 25). UpSetR v1. 4. 0 (https://cran. r-project. org/web/packages/UpSetR/index. html) was used to visualize the extent of overlap in differentially expressed and differentially spliced genes across the two experiments (Fig. 1B ). Because the AS data is in a binomial distribution, a higher log2 fold change underestimates DAS detection.
Detection of genes exhibiting expression complementation due to PAV We have newly sequenced reference genomes of both the XRQ (version 2) and PSC8 genotypes (https://www. heliagene. org/). In order to identify genes exhibiting expression complementation due to PAV, we re-mapped the RNA-Seq data of all three genotypes (13HP02) against both reference genomes. We classified genes as expressed if Transcripts Per Million (TPM) > 2 and not expressed if TPM < 0. 1. We used the thresholds based on the even distribution of reads on Integrative Genomics Viewer (https://software. broadinstitute. org/software/igv/). Genes expressed in one parent and hybrid, but not the other parent, were blasted (BLASTN, E-value < 1 e À 100, sequence identity > 95%, skipping shorter than 200 bp, minimum bit-score of 200) against the reference genome of the latter to ensure they were truly missing.
RNA-Seq data We generated $ 1. 8 billion 100 bp paired-end reads from 36 RNA-Seq libraries from three genotypes (PSC8, XRQ, and their F 1 hybrid INEDI). As detailed above, these libraries were developed from seedling leaf tissue in both control and drought treatments and across two independent experiments (12S01 and 13HP02). After filtering out low-quality reads and genes with low constant expression levels, 417 million clean Illumina mRNA reads were obtained for 35,278 genes (an average of 11. 6 million reads per sample).
Heterosis for morpho-physiological traits in INEDI Re-analyses of the morpho-physiological trait data from Rengel et al. [38]  confirmed previous observations  [65] ; INEDI exhibits heterosis in vegetative performance (plant height, stem diameter, third-leaf area, and total leaf area) under control and drought stress, but not for specific leaf area, when compared with the parental lines, PSC8 and XRQ (Suppplementary Figure  S5 ). Notably, INEDI significantly out-performed the parental lines in almost every category under control conditions and was significantly taller than both parental lines under drought stress (Wilcoxon rank- sum test, p < 0. 05). For the other categories under drought stress, INEDI was above the mid-parent mean, but not significantly larger than the high parent.
Expression complementation of PAVs The GWA analyses described above indicate that absence alleles typically reduce the performance of sunflower cultivars. Complementation of such PAVs therefore offers a simple explanation for heterosis. To confirm predicted expression complementation, we mapped the transcriptome data against both parental reference genomes and identified genes exhibiting presence absence variation in gene expression. Mapping on the XRQ genome, we found 170 XRQ genes that were expressed in XRQ and INEDI but not in PSC8 in the control treatment, and 57 XRQ genes that were expressed in XRQ and INEDI but not in PSC8 under drought stress. Mapping on the PSC8 genome, we found 180 PSC8 genes that were expressed in PSC8 and INEDI but not in XRQ under control conditions, as well as 200 PSC8 genes that were expressed in PSC8 and INEDI but not in XRQ in the drought treatment.
Differential expression in response to drought stress Transcriptome data from the 13HP02 experiment, which consisted of an extreme drought stress treatment (FTSW = 0. 1) and a well-watered control (FTSW = 1. 0), resulted in detection of 7,973 differentially expressed genes (FDR < 0. 01) responding to drought stress. Likewise, analyses of transcriptomic responses to the drought stress gradient in the 12S01 experiment identified 8,402 differentially expressed genes, of which approximately two thirds (5,621 genes) were also found on the 13HP02 DEG list (Fig. 1B ). The genes in common between the two datasets were used for downstream analyses. Of the genes responding to drought stress, 2,523 were upregulated, and 3,098 were down-regulated. For the up-regulated DEGs (Supplementary Table  S2A ), the most significantly enriched GO terms were Response to chemical (GO:0042221, FDR = 4.
Differential AS in response to drought stress Focusing on the 13HP02 data because of its greater replication, we employed a custom pipeline of Python (v2. 7. 3) scripts (including the filtering;  [66] ) to discover 33,186 alternative splicing events in 11,630 genes. The majority of these events were classified as intron retention (30,615 events), followed by alternative acceptor (859 events), alternative donor (743 events), alternative position (213 events), exon skipping (602 events), and other (153 events). The latter represent complex and less easily categorized AS events. A total of 3,493 differential splicing events (2,980 up-regulated and 513 down-regulated) were found as responses to drought stress in sunflower cultivars. Up-regulated DAS (Supplementary Table  S2C ) were most significantly enriched in Photosynthesis (GO:0015979, FDR = 7.
96E-42) in biological process, in Chloroplast (GO:0009507, FDR = 2. 47E-167) in cellular process, and in Protein domain specific binding (GO:0019904, FDR = 5. 23E-16) in molecular function. The Metabolic pathways category (map01100, FDR = 4. 76E-47) was most significantly mapped from the KEGG pathway database. On the other hand, down-regulated DAS (Supplementary Table  S2D ) were mostly enriched in Small molecule metabolic process (GO:0044281, FDR = 3. 24E-07) in biological process, Chloroplast (GO:0009507, FDR = 1. 07E-14) in cellular process, and Nucleotide binding (GO:0000166, FDR = 2. 49E-4) in molecular function. These genes also mapped most significantly on Metabolic pathways (map01100, FDR = 2. 94E-08). Differential splicing appears to negatively correlate with differential expression, most notably for photosynthesis related genes.