dataesr/scientific-paragraphs-categorization huggingface dataset:
After all science and reference observations are complete, we perform the dedicated background observations that are used to subtract the dominant "glow stick" stray light feature  (Boccaletti et al. 2022)  as described in Section 2. 1. We begin data reduction using the Stage 0 ( * uncal. fits) files as generated by the jwst pipeline. These products are then processed to Stage 1 ( * rateints. fits) files using spaceKLIP. Similarly to NIRCam (see Section 2. 3), we explore the impact of the jump detection threshold on our analysis. For these data in particular, we found that the default jump detection threshold value of 4 is too low and leads to a number of pixels being erroneously flagged as containing a jump. Flagged pixels are interpreted differently from unflagged pixels in the ramp fitting procedure, and the resulting Stage 1 files contain a large number of pixels with unrealistic (negative) flux values as a result.
The Stage 1 products are processed further to Stage 2 ( * calints. fits) files using spaceKLIP, with some additional pixel cleaning procedures as follows. First, every pixel with a data quality flag (e. g. , indicating hot pixels, warm pixels, or unreliable data processing) is replaced by the median of its orthogonal and diagonal neighbors, with the notable exclusion of pixels with a jump flag, which are typically grouped in clusters. Following this correction, ∼30 static hot pixels remain in our images and are corrected following an identical procedure by manually providing the pixel locations to spaceKLIP. Similarly to NIRCam, these final pixels primarily impact the measured contrast performance, and not our ability to recover HIP 65426b. As shown in  Boccaletti et al. (2022) , the MIRI coronagraphic fields of view are subjected to a stray light "glow stick" feature along the horizontal edges of the FQPMs that dwarfs the residual stellar flux (see Figure  2 ).
1 pixels  (Boccaletti et al. 2022). However, the SAM has a typical uncertainty of ∼10-20 mas, leading to positional offsets between different rolls or targets. Finally, between integrations, the pointing stability of JWST (∼1 mas) and the accuracy of the small-grid dither maneuvers (∼2-4 mas) will lead to further positional shifts for both the NIRCam and MIRI coronagraphs  (Boccaletti et al. 2022; Rigby et al. 2023). For NIRCam, the absolute star position is only explicitly measured for the first science image in each filter. This position is measured using a cross correlation of a model coronagraphic PSF as obtained from webbpsf_ext to the science PSF using the scikit-image package (van der Walt et al. 2014). To best match the science PSF, the model PSF is generated using the telescope optical path difference (OPD) map as obtained on 2022 July 29th.
As a result, the stellar position is not known better than a minimum of ∼10 mas  (Boccaletti et al. 2022). Similarly, the relative alignment between images may be discrepant by ∼1 -4 mas  (Rigby et al. 2023) , or <0. 05 pixels. Attempts to estimate the absolute star position in a similar manner to the NIRCam data were unsuccessful, and this process led to significantly larger estimated shifts than the known pointing stability of JWST (∼50 mas, versus ∼1 mas). This is most likely due to the variable spatial structure of the residual PSF, which significantly changes with small pointing shifts. An effort was also made to fit each MIRI image with model PSFs across all feasible pointings using webbpsf_ext 73  within an emcee (Foreman-Mackey et al. 2013) Markov Chain Monte Carlo (MCMC) framework.
interpolate. griddata to linearly interpolate the throughput estimates across the FOV to produce a smooth 2D map matching the subarray dimensions for each MIRI FQPM. Due to the target acquisition errors described in Section 2. 5, the measured star centers are offset from the center of the coronagraphic occulter. However, in addition to these pointing errors, for NIRCam coronagraphy we expect wavelengthdependent spatial shifts of the entire image as viewed on the detector focal plane, due to refraction through the coronagraphic mask sapphire substrate along with deflections through filter optics further downstream in the optical train. Therefore, the star position in an image is not in isolation a suitable proxy for its position behind the coronagraphic mask. The wavelength-dependent shifts must be accounted for in order to more accurately determine the impact of the coronagraphic throughput across the detector focal plane and apply the correct throughput scaling to injected PSFs.
The absolute position of the star on the detector does not change significantly (<1 pixel) between NIRCam filters, and it is feasible that the measured position has a similar systematic offset in each filter (see Section 2. 5). As the dominant noise source for the NIRCam alignment is this systematic offset, it is not appropriate to propagate the uncertainties of the NIRCam astrometry in a typical fashion. We combine these new measurements with the existing astrometry from  Chauvin et al. (2017)  and Cheetham et al. (2019) to determine updated orbital parameters using the orbitize! package  (Blunt et al. 2020). As in  Cheetham et al. (2019) , we exclude the NaCo epochs due to the inconsistency with the SPHERE epochs. Additionally, we exclude the MIRI epoch, because the observations were taken just two weeks after the NIRCam observations and have larger uncertainties.
Estimates of Mass and Other Companion Properties from Hot-start Evolutionary Models The mass of HIP 65426b is estimated using a method similar to that described in  Dupuy & Liu (2017). We first built an interpolated grid of model luminosities as a function of age and mass, with 10,000 equally spaced age values spanning from 5 to 30 Myr and 10,000 equally spaced mass values spanning from 0. 3 to 21 M Jup using scipy. interpolate. griddata with cubic interpolation in Python. We adopted an age for HIP 65426b of 14 ± 4 Myr based on the Lower Centaurus-Crux age given in  Chauvin et al. (2017)  and a measured bolometric luminosity uniformly distributed between  L L log bol ( ) = -4. 14 and -4. 31 from the previous section. We then generated 1 × 10 6 samples of age and mass from a Gaussian distribution in age around 14 Myr, with σ = 4 Myr, and a uniform distribution in mass from 0.
436 MASK335R DEEP8 15 2 617. 946 1 2 1235. 892 F444W 4. 397 0. 979 MASK335R DEEP8 15 2 617. 946 1 2 1235. 892 F1140C 11. 307 0. 608 FQPM1140 FASTR1 101 41 1002. 102 1 2 2004. 204 F1550C 15. 514 0. 703 FQPM1550 FASTR1 250 60 3609. 341 1 2 7218. 682 HIP 68245 F250M 2. 523 0. 179 MASK335R MEDIUM8 4 4 166. 852 9 1 1501. 669 F300M 3. 067 0. 325 MASK335R MEDIUM8 4 4 166. 852 9 1 1501. 669 F356W 3. 580 0. 769 MASK335R MEDIUM8 4 2 83. 426 9 1 750. 835 F410M 4. 084 0. 436 MASK335R MEDIUM8 4 2 166. 852 9 1 750. 835 F444W 4. 397 0. 979 MASK335R MEDIUM8 4 2 83. 426 9 1 750. 835 F1140C 11. 307 0. 608 FQPM1140 FASTR1 52 10 126. 791 9 1 1141. 116 F1550C 15. 514 0. 703 FQPM1550 FASTR1 100 19 459. 706 9 1 4137. 356 Note. 68 https://github. com/kammerje/spaceKLIP 69 https://jwst-pipeline. readthedocs. io 70 All data were processed using pipeline version, CAL_VAR = 1.
Overview We characterized bacterial genetic structure and develop our method using real whole-genome sequencing dataset of Listeria monocytogenes strains. All analyses were conducted using unitigs inferred from the raw sequence data with compacted De Bruin Graphs (Supplementary Notes and Fig. S1 ). We first use those data to characterize the extend of linkage disequilibrium across variants, the distribution of relatedness across strains, and to examine the impact of this structure on heritability across Listeria lineages. We then present the ChoruMM approach, a multi-component linear mixed model (MLMM), that we benchmarked for both power and robustness using real unitigs and simulated phenotype data. Finally, we introduce multiple complementary innovations to assess the calibration of the type I error rate and to facilitate the visualization and interpretation of the GWAS results.
As for the correlation between genetic variants, this is illustrated using linkage disequilibrium (LD)-pruning, a common technique in human genetics which filters out variants with a squared-correlation (R²) above a given threshold. In a standard human GWAS dataset, pruning a set of (e. g. ) 1M common variants from HapMap3 SNPs  19  at an R² of 0. 05 results in roughly 50K independent SNPs. We plotted in Figure  1b  the count of unitigs remaining after LD pruning with an R² in [0. 99, 0. 05] for the Listeria cohort. Overall, less than 0. 5% of the unitigs can be considered independent (R² < 0. 05). Even using a very lenient threshold of R² < 0. 5 results in the removal of over 90% of the unitigs. Next, we assessed the variability in the realized genetic variance between the two main Listeria lineages (I and II)  23  across simulated phenotypes.
Performances in the presence of complex genetic and environmental structure In the previous section, we considered a fairly simple model, where the genetic effect was drawn independently from the actual genetic structure and assuming the residual noise was homogeneous across strains. To illustrate the potential of our approach, we investigated two additional models and assessed the performances of ChoruMM against the state-of-the-art one component model as implemented in DBGWAS and pyseer, and a simple fixed effect linear regression. First we simulated phenotypic data under the null but in the presence of residual noise confounding the genetic structure. In practice we draw 100 phenotypes as a mixture of standardized normal distribution and the first principal component of a sub-matrix of 22,673 unitigs displaying variability only in lineage II.
Visualization and statistical significance Human GWAS results are typically displayed as Manhattan plot that represent the genome-wide -log10(p-value) as a function of the chromosomal position. The hallmark of Manhattan plots are peaks of signal around associated regions due to the high correlation between neighboring variants. By construction, unitig have no clear physical position, and a given unitig can potentially display high correlation with any other unitig in the matrix. Consequently, a naïve Manhattan plot for unitigs association results presents disbursed clouds of points, substantially reducing its interpretability. Hierarchical clustering of the unitigs might allow to regroup distant (at the sequence level) but related (at the pattern level) unitigs together, but is computationally prohibitive in large datasets because it has complexity cubic with the number of unitigs.
The Listeria cohorts The Listeria monocytogenes sequencing data is a non-redundant collection of isolates of food (n = 4,551) and clinical (n = 2,791) origin collected in France between January 2005 and October 2013. The isolates were collected by the French National Reference Center (NRC) for Listeria and the French National Reference Laboratory (NRL) for Listeria. Food isolates were collected from multiple sources: 3,143 (69. 1%) from food alerts, 178 (3. 9%) in the context of investigations following neurological forms of listeriosis, 692 (15. 2%) from checks by food industries, and 538 (11. 8%) from food surveillance activities. Here we focused on two subsets that passed stringent quality control, in order to limit potential confounding effect. The first, and most stringent dataset, includes 912 Listeria strains that were collected and stored using the same conditions and for which sequencing data were produced using the same protocol.
Building the unitig matrix We defined the genetic variants using unitigs built from a De Bruijn Graph (DBG). The DBG offers an exhaustive synthesis of genetic variability across sequences in a cohort of strains. Concatenating nodes within a unique path to obtain the compacted De Bruijn Graph (cDBG) further provides a node incidence matrix of binary variables (with a value of 1 when the node is present in the strain and 0 otherwise) that share strong similarity with the single nucleotide polymorphisms (SNP) matrix of human GWAS. For each bacterial dataset, we derived the DBG using the GATB C++ library  27, 28  and derived the 32-mers from the raw sequence data. The choice of using the 32-mer was driven by recent reviews suggesting it provide an optimized accuracy over computational cost ratio  7.
We start from contigs rather than reads and, consequently, we do not need to filter out low abundance k-mers, allowing for the exploration of any variation present in the input genomes. We then compact the DBG using a graph traversal algorithm, which identifies all linear paths in the DBG -each forming a unitig in the cDBG. During this step we also associate each k-mer index to its corresponding unitig index in the cDBG. We applied the cDBG pipeline to both Listeria datasets, which resulted in a total of 432,226 and 621,244 unitigs, respectively.
In a first series, we drew 1000 replicates, each including a random sets of 2000 unitigs and estimated the distribution, mean, and median for both the FDR and 𝜆 (Fig. 3a  and Fig. S7a ). To assess the impact of the strong correlation between unitigs on those parameters, we conducted a secondary analysis using the same simulated phenotypes but after randomly shuffling the unitigs, in order to remove the correlation (Fig. S6  and Fig. S7b ). In a second series, we investigated whether pruning of unitigs i. e. , gradually removing highly correlated unitigs, would reduce the variability in both FDR and 𝜆. Here, we drew 200 random phenotypes and tested all unitigs out of 137,981 there were independent at a given R2 threshold that we varied in [1; 0. 05] (Fig. 3b  and Fig. S7c ). To assess the statistical power of our approach, we used the extended Listeria dataset including 3,481 strains.
Cellular identity during development is under the control of transcription factors that form gene regulatory networks. Ho we ver, the transcription factor s and g ene regulator y networks underlying cellular identity in the human adult pancreas remain lar gel y une xplored. Here, we integrate multiple single-cell RNAsequencing datasets of the human adult pancreas, totaling 7393 cells, and comprehensively reconstruct g ene regulator y networks. We show that a network of 142 transcription factors forms distinct regulatory modules that characterize pancreatic cell types. We present evidence that our approach identifies regulators of cell identity and cell states in the human adult pancreas. We predict that HEYL, BHLHE41 and JUND are active in acinar, beta and alpha cells, respectivel y, and sho w that these proteins are present in the human adult pancreas as well as in human induced pluripotent stem cell (hiPSC)-derived islet cells.
Method details Motif discovery of bulk A TA C-seq data. P air ed-end raw reads for bulk ATAC-seq (see Data Availability / Sequence Data Resour ces) wer e downloaded from SRA using SRA toolkit (v2. 9. 4). Reads were aligned and further analyzed using the ENCODE ATAC-seq pipeline with default parameters using the ENCODE human r efer ence genome GRCh38. 15  (Lee, 2016). Bed files containing the global open chromatin landscape of adult alpha (Alpha 1; EA4 and Alpha 2; EA28), beta (Beta 1; EA5 and Beta 2; EA29), acinar (Acinar 1; EA7 and Acinar 2; EA27) and ductal (EA11) cells or cell type specific differentially accessible regions were used as input for motif discovery by HOMER (v4. 10. 4) using the 'findMotifsGenome. pl' with options using hg38 with size gi v en  (Heinz et al. , 2010). The TFs whose motifs identified by HOMER correspond with TFs identified by p ySCENIC ar e visualized in Supplementary Figure  S2A.
Analysis of pub lic ly availab le scA TA C-seq data. Motif discovery results by HOMER of a publicly available scATACseq dataset (see Key Resource Table ) was retrie v ed from Supplementary Table  S8  of  (Rai et al. 2020 ). The TFs whose motifs identified by HOMER correspond with TFs identified by pySCENIC are visualized in Supplementary Figure  S2B  using pheatmap (v1. 0. 12). Analysis of pub lic ly availab le scRNA-seq data. Raw reads for fiv e pub licly availab le scRNA-seq datasets (see Key Resour ce Table) wer e downloaded from SRA using SRA toolkit (v2. 9. 4). Afterwards, reads were aligned to the human r efer ence genome GRCh38. 95 using STAR (v2. 5. 3a) with default parameters followed by the conversion to the coordinate sorted BAM format. Next, the feature-Counts command from the 'Rsubread' (v1.
5. 2) package in R (v3. 6. 1) was used to assign mapped reads to genomic features. Low quality transcriptomes with a mitochondrial contamina tion grea ter than 5% and less than 200 expressed genes per cell were excluded from subsequent analyses. The resulting raw count matrix was batch corrected using the FindIntegr ationAnchors and Integr ateData functions from the 'Seurat' package (v3. 1. 1) after which subsequent analyses were carried out in the R package 'Seurat' (v3. 1. 4). Gene expression was used to cluster all 7393 cells with unif orm manif old appr oximation and pr ojection (UMAP), using Seurat's function RunUMAP. Clusters for cell type annotation were defined using Seurat's shared nearest neighbor algorithm FindClusters function after which differential expression analysis was performed using Wilco x on's rank sum test with a minimum cutoff of 0.
25 average log fold change and min. pct of 0. 25. pySCENIC. GRNs were inferred using pySCENIC (python implementation of SCENIC, v0. 9. 15) in Python version 3. 6. 9  ( 91 ). Integrated read counts were used as input to run GENIE3  ( 92 )  which is part of arboreto (v0. 1. 5). GRNs were subsequently inferred using pySCENIC with the hg38 refseq-r80 motif database and default settings. To control for the stochasticity, which is inherent to pySCENIC, a consensus GRN was generated by merging results from fiv e repeat pySCENIC runs. If regulons were identified in multiple pySCENIC runs, only the regulon with the highest AUC value was retained. Regulon activity r epr esented by AUCell values was used to cluster all 7393 cells with UMAP, using Seurat's RunUMAP function. All 142 regulons within non-diabetic cell types were visualized using the 'clustermap' function of the Python package 'seaborn' (v0.
9. 0). The z-score for each regulon across all cells was calculated using the z-score parameter of the seaborn 'clustermap' function. Extended analysis of the target genes of specific regulons was conducted in Cytoscape (v3. 7. 1) using the iRegulon application (v1. 3). The list of target genes of a specific regulon was downloaded from the loom file through the SCope platform ( https://github. com/pasquelab/scP ancr easAtlas )  ( 76 ). Regulon ranking. To quantify the cell-type specificity of a regulon, we utilized an entropy-based strategy as described previously  (Suo et al. , 2018)  using the AUCell matrix as input in MATLAB R2019b. The top 10 most specific regulons were subsequently visualized using the R package ggplot2  For each siRNA treatment, 6000 cells were loaded onto the 10 × Chromium Single Cell Platform (10X Genomics) using the Next GEM Single Cell 3 library and Gel Bead Kit (v3.
1 chemistry) according to manufacturer's instructions (10 × User Guide CG000204, Revision D). RNA quality was assessed using Tapestation (Agilent). Libraries were quantified using Tapestation (Agilent), the Qubit 2. 0 (Ther-moFisher Scientific) and KAPA Library Quantification Kit for Illumina Platform (KAPA Biosystems) before pooling. Libraries were pooled in equimolar amounts for pairedend sequencing on an Illumina NextSeq 2000 instrument to yield ∼168 million (range 147-195 million) 100-bp-long reads on average per sample. scRNA-seq analysis. Raw data was processed using the 10x Genomics CellRanger software (v4. 0. 0) and mapped to the pre-built human reference genome GRCh38-2020-A. Contamination by background RNA from disrupted cells was estimated and corrected for using SoupX  ( 93 )  with Seurat identified clusters, and known cell type specific marker genes (GCG, TTR; INS, IAPP; SST; PPY; GHRL; CP A1, CLPS, CP A2, REG1A, CELA3A, CTRB1, CTRB2, PRSS2; KRT19, VTCN1).
Cells with less than 200 expressed genes and more than 25% mitochondrial reads were excluded from future analyses. DoubletFinder (v2. 0. 3) ( 94 ) was used with default settings to identify and remove potential doublets. The resulting counts were normalized, scaled and analyzed for PCA and UMAP on the first 20 principle components (PCs) using 2000 variable genes using Seurat (v3. 1. 1). Clusters for cell type annotation were defined using Seurat's shar ed near est neighbor algorithm as defined above. To compare our dataset to the sc-deri v ed and primary cell populations described in  ( 35 ) , we downloaded the countmatrix and metadata files from the Single Cell Portal from the Broad Institute (SCP1526). The resulting Seurat object was integrated with our dataset using the FindIntegrationAnchors and Integra teDa ta functions from the 'Seurat' package (v3.
1. 1) after which subsequent analyses were carried out in the R package 'Seurat' (v3. 1. 4). Gene expression was used to cluster all 57158 cells with UMAP, using Seurat's function RunUMAP. Human islets. Human islets (se v en non-diabetic donors, age 43 ± 15 years, BMI 30 ± 6 kg / m 2 and one type 2 diabetic donor, age 66 years, BMI 36. 5 kg / m 2 ) were isolated by enzymatic digestion and density gradient purification in Lille or Edmonton, with the approval of the local Ethical Committee. After overnight recovery in Ham's F-10 containing 6. 1 mM glucose, 10% FBS, 2 mM GlutaMAX, 0. 75% BSA, 50 units / ml penicillin, and 50 g / ml streptomycin, human islets were dispersed for RNA silencing. The percentage of beta cells, assessed by insulin immunofluorescence, was 50 ± 11% (mean ± SD). hiPSC-derived islet cell differentiation and immunocytoc hemistr y.
9 × 10 6 cells per well after which dif ferentia tion was carried out as described previously until stage 7  ( 97 ). Ov ervie ws of the dif ferentia tion protocol and efficiency are shown in Supplementary Figure  S4  and Supplementary Tables S2 and S3. Stage 7 dif ferentia ted islet cells were washed twice with PBS containing 0. 5 mM EDTA and incubated in Accumax (SIGMA #A7089) for 8 min at 37 • C after which 50% volume of KnockOut Serum Replacement (ThermoFisher #10828028) was added to stop the reaction. After centrifuga tion a t 400 g for 5 min a t room tempera tur e, cells wer e r e-suspended in 1 ml HAM's F-10 medium, supplemented as indicated before  (Demine et al. , 2020). 75000 cells in 500 l medium were seeded per square of a Nunc Lab-Tek II ICC chamber (ThermoFisher). Imm unohistochemistry anal yses were carried out largel y as described previously ( 99 ), using the following primary antibodies: INS (Dako (Agilent), IR002, Ready to use solution), GCG (Sigma-Aldrich, G2654, 1 / 1000) and JUND (Atlas Antibodies, HPA063029, 1 / 100) and secondary antibodies A-11075, A-21206, A-31571 (ThermoFisher, 1 / 500).
5, No. 3  Uni v ersity Hospital in Leuven (Belgium). Two pathologists (M. V. H. and T. R. ) separately evaluated all histological sections. This study was approved by the ethical committee of the Uni v ersity Hospital in Leuv en (Belgium) (S32980). Imm unohistochemistry anal yses were carried out largel y as described previously ( 108 ), using primary antibodies against the following proteins: ASCL2 (Merck, MAB4418, clone 7E2, 1 / 5000), JUND (Atlas Antibodies, HPA063029, 1 / 50), BHLHE41 (Sigma Aldrich, HPA056035, 1 / 25), HEYL (Atlas Antibodies, HPA076960, 1 / 200) and INS (Agilent, IR002, 1 / 100). Pictur es wer e taken using a Leica DMLB (Leica Microsystems). SCope. The integrated scRNA-seq data and pySCENIC results can be explored interacti v ely in SCope  ( 76 ). Loompy (v2. 0. 17) (Linnarsson Lab.