dataesr/scientific-paragraphs-categorization huggingface dataset:
UV data We reduced the UVM2 OM data with the standard OM SAS commands omfchain and omichain. These commands automatically produce images of the individual exposures as well as magnitudes and fluxes of the detected sources in these images. We confirmed by visual inspection that Mrk 1018 and the three reference stars are always present and detected in the images. The OM data reduction also provides averaged magnitudes and fluxes (including their statistical uncertainties) for all four targets of interest in both XMM-Newton observations. We then evaluated the systematic uncertainties, comparing the magnitudes of the reference stars before and after the 2020 outburst and assuming no changes in their UV output. We show the corresponding magnitudes in Table  5. Systematic offsets of up to 0. 05 magnitudes are found between the reference stars.
Pixels We used the image-fitting software Imfit 14  (Erwin 2015 ) with a Levenberg-Marquardt algorithm to model Mrk 1018. Our goal was to remove the host galaxy contribution at and around the AGN, rather than creating a highly accurate model over the entire extent of the galaxy. Firstly, we masked the central region containing the AGN (20 × 20 pixels, or 4. 1 × 4. 1 ) for the initial fitting to determine a best fit of the host galaxy only, which we fitted with a single Sérsic function  (Sérsic 1963). A Sérsic function is given by the following equation: I(R) = I e exp        -b n        R R e 1/n -1              . (A. 1) We note that I e is the half-light intensity, R e is the half-light radius, n is the Sérsic index (the higher the index, the more centrally concentrated the galaxy's luminosity) and b n is a constant, dependent on the Sérsic index used  (Ciotti & Bertin 1999).
So far, we only considered the VIMOS image with the faintest AGN contribution for the clearest view of the galaxy's features. However, any image from the VIMOS data set could have been chosen and may have yielded slightly different fitting results. Moreover, we have a set of high quality images from the Gemini Multi-Object Spectrograph South (GMOS-S) which, although not as high-resolution as the VIMOS images, can also be used for modelling. To quantify this systematic uncertainty on the host galaxy modelling, we chose eight VIMOS and two GMOS images from the set of 20 VIMOS images and 21 GMOS images. We chose them to span the observing time from August 2016 to January 2019. We then repeated all the steps summarised under Sects. A. 1 & A. 2 for each of these images. Next, we did photometry with a 10 radius aperture on the central region of the (AGN-subtracted, STELLAquality) host galaxy images.
Acknowledgements. R. B. acknowledges that this research was supported by  DLR  grant number  FKZ 50 OR 2004. M. K. is supported by  DFG  grant number  KR3338/4-1. D. H. acknowledges support from  DLR  grant  FKZ 50 OR 2003. M. G. acknowledges partial support by  HST GO-15890. 020/023-A  and the  BlackHoleWeather program. This work is based on data obtained with the STELLA robotic telescopes in Tenerife, an  AIP  facility jointly operated by  AIP  and  IAC. This work includes observations obtained with XMM-Newton, an  European Space Agency (ESA) science  mission with instruments and contributions directly funded by  ESA Member States  and  NASA. This work includes observations from FORS2, an instrument on the VLT commissioned by  ESO. This paper uses data taken with the MODS spectrographs built with funding from  NSF  grant  AST-9987045  and the  NSF   Telescope System Instrumentation Program  (TSIP), with additional funds from the  Ohio Board of Regents  and the  Ohio State University Office of Research.
It has made use of data from the  Asteroid Terrestrial-impact Last Alert System (ATLAS)  project. The  Asteroid Terrestrialimpact Last Alert System (ATLAS)  project is primarily funded to search for near earth asteroids through  NASA  grants  NN12AR55G ,  80NSSC18K0284 , and  80NSSC18K1575 ; byproducts of the NEO search include images and catalogues from the survey area. This work was partially funded by  Kepler/K2  grant  J1944/80NSSC19K0112  and  HST GO-15889 , and  STFC  grants  ST/T000198/1  and  ST/S006109/1. The ATLAS science products have been made possible through the contributions of the  University of Hawaii Institute for Astronomy , the  Queen's University Belfast , the  Space Telescope Science Institute , the  South African Astronomical Observatory , and The  Millennium Institute of Astrophysics (MAS), Chile.
METHODS We included patients from the EBMT registry who underwent first allogeneic HSCT for an inborn error between 2006 and 2018 to investigate determinants of survival. We retrieved survival data and patient, donor, and transplantation characteristics known at the time of HSCT from the registry. Given that some of the terminology discussed below will be unfamiliar to physicians without extensive knowledge of machine learning, a glossary explaining these terms has been included in the Supplementary Data. To predict survival, we evaluated a simple linear Cox model without regularization or term interactions, an elastic net regularized Cox model with relevant interactions  [14] , a random survival forest model to fit nonparametric survival curves  [15] , and an XGBoost model optimizing the Cox negative log-likelihood  [16].
Covariate selection was based on data availability and known effects in the literature (Supplementary Table  S2 ). For all models except the XGBoost model, MissForest imputation was used to account for missing data  [17]. The XGBoost model can natively incorporate missing values. The full model configuration, including hyperparameter search space and optimization strategy, can be found in Supplementary Tables  S1  and S2. In cases of near ties, hyperparameters that led to the least complex model were preferred. Iteration counts for both the XGBoost model and random forest model were determined by inspecting learning curves. To compare models, we determined the area under the receiver operator characteristic curve (AUC) and calculated the changes in AUC, CIs, and P values using the riskRegression R package  [18].
Patient Characteristics A total of 10,888 patients with survival data who underwent HSCT for an inborn error were included. Diagnosis groups consisted of hemoglobinopathies and inborn errors of immunity and metabolism. Among these, 28% of the patients had thalassemia, 27% had a non-SCID inborn error of immunity, 16% had SCID, 8% had a histiocytic disorder, 10% had an inborn error of metabolism, and 1% had a congenital bone marrow failure syndrome. Donors were mostly HLA-identical siblings (43%) and unrelated donors (38%). Bone marrow was the most common stem cell source (61% of cases), with peripheral blood used in 25% of cases and cord blood used in 12% of cases (Table  1 ). When splitting data in groups and predicting using survival curves, diagnosis was the best predictor of survival, with an AUC of.
Similarly, the excellent results using an HLAidentical sibling donor may be augmented by the benefit of HSCT more often outweighing the risks if an identical sibling donor is available, causing low-risk patients to be treated with an identical sibling donor more often. Although simple scores can be calculated using only weights that can be published in a table, our scores cannot be calculated in this manner, and so an application to calculate risk is needed. When designing this application, future availability was a key concern. Sustainably maintaining a web application that processes patient data available is a challenge in academia, as demonstrated by the fact that 2 previously available applications are no longer available at the time of this report  [11, 27]. To avoid this, we took an approach in which the application can be run both online and offline using Shiny  [28] , which by being open source serves as a reference implementation to easily allow future use in large datasets.
A limitation of the model lies in data collection, which ended in 2018 to ensure a decent duration of follow-up. Innovations and changes in HSCT practice over time may change the accuracy of the model, necessitating refitting. In an analysis that included year of transplantation as a covariate, prediction accuracy did not improve, but we cannot anticipate if this may change in a more recent or future dataset. For example, the increasing use of post-transplantation cyclophosphamide for HLA-mismatched transplantation may change the influence of donor type on survival probability. Coronavirus disease 2019 (COVID-19) also might have an impact, both because patients may be at risk for COVID-19 itself, and because of the increased use of cryopreserved grafts during the COVID-19 pandemic  [29]. Missing data remained a challenge during model development, and the ability of XGBoost to incorporate missing data into the model and make predictions based on incomplete data may be one reason for its superior performance over the other modeling approaches, which rely on imputation of missing data both for fitting and for prediction.
Acknowledgments. The authors and the research team would like to thank  Dr. Aimee Nasser Karam  for participation in reflection and conceptualization of the study along with regular and clinical feedback on the implications of the study. They would also like to thank  Ms. Marie Therese Nacouzi ,  Ms. Stephanie Loukie ,  Ms. Roula Ounaissi ,  Ms. Josleen Al Barathie , and  Ms. Marina Tcheurekjian  for helping with data collection and finalization of the manuscript. Author contribution. E. K. : Conceptualization, data collection, review and reflection of analyses, article writing and editing. D. S. : Supervision of data analyses and article writing. S. J. : Data collection, literature search, article writing. G. K. : Conceptualization, data collection, revision of article. E. H. and J. A. : Conceptualization, Review of results and discussions, review of draft.
RNA-seq Data Processing RNA-seq data were obtained from previously published datasets  (9, (29) (30) (31)  of NEPC (n = 27), metastatic CRPC (n = 72), localized prostate cancer (n = 68), and benign prostate tissue (n = 66). Normal tissue RNA-seq data were obtained from the Genotype-Tissue Expression Project [GTEx  (32) , n = 30]. The GTEx Project was supported by the Common Fund of the Office of the Director of the NIH, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this article were obtained from: the GTEx Portal on March 1, 2015 and/or dbGaP accession number phs000424. vN. pN on March 1, 2015. Reads were mapped to the human genome reference sequence (hg19/GRC37) using STAR v2. 3. 0e  (33). For each sample, HTSeq  (34)  and Cufflinks  (35)  were then used to generate read counts and fragments per kilobase of transcript per million mapped reads (FPKM), respectively.
Low-quality reads were removed using Trimmomatic  (40)  with a sliding window size of 4 bp and a quality threshold of 20. The resulting reads were aligned to mm10 using STAR  (33). Reads were sorted and indexed using SAMtools  (41). Transcript abundance was calculated in FPKM using Cufflinks  (35)  and in gene counts using HTSeq  (34). Differential gene expression was assessed using DESeq2  (36). For variant calling, GATK's Van Emmenis et al. best practices pipeline  (42, 43)  was followed including the alignment method as described previously  (44). Reads with less than 30 sequence length were removed before alignment. In brief, reads were aligned to the mm10 reference genome with STAR in paired-end and two-pass mode. PCR duplicates were removed using Picard tools and reads were split into exon segments keeping the grouping information by SplitNCigarReads of GATK  (42).
Identification of Gene Transcripts Encoding Surface Proteins that are Specifically Upregulated in NEPC On the basis of RNA-seq data of tissues from benign prostate, localized prostate cancer, metastatic CRPC (adenocarcinoma) and NEPC  (9, 31) , we developed a multi-step bioinformatics pipeline to identify NEPC-specific, overexpressed gene transcripts that encode surface proteins. Using adjusted P value and foldchange cut-off criteria to identify transcripts of surface protein genes expressed in NEPC tumors (n = 27), but not in critical normal tissues [GTEx Project  (32) , n = 30], benign prostate tissue (n = 66), locally advanced prostate cancer (n = 68), or CRPC (n = 72)). Specifically, RNA-seq data were aligned to the human genome using STAR and was further processed using HTSeq Count and Cufflinks to generate FPKM values for each gene's transcript.
We then used DE-Seq2 to test for differential expression of transcripts comparing the data from NEPC and benign prostate tissue (adjusted P value less than 0. 05 and a foldchange cutoff of greater than or equal to 2. 0). Gene transcripts were filtered out if the transcript per million (TPM) values were greater than 1. 0 in the benign prostate tissue dataset or less than in 1. 0 in NEPC dataset. We also used DE-Seq2 to test for differential expression of transcripts comparing the data from NEPC and prostate cancer (adjusted P value less than 0. 1 and a fold-change cutoff of greater than or equal to 1. 5). Gene transcripts were filtered out if the TPM values were greater than 1. 0 in the prostate cancer dataset or less than in 1. 0 in NEPC dataset. This led to the identification of 996 gene transcripts.
We then relied on Human Cell Differentiation Molecules (HCDM), Cell Surface Protein Atlas (CSPA; ref. 46) , and Gene Ontology (GO) nomenclature to restrict our analyses to transcripts that encode putative cell surface (genes with a "CD" nomenclature, n = 437), mass spectrometric-derived CSPA (n = 3152), ion transmembrane transport (GO:0034220, n = 246), transmembrane transport (GO:0055085, n = 1,119) or membrane-associated (GO:0016020, n = 3,160; Supplementary Fig. S1A ) proteins. This restricted the list to 117 gene transcripts. As the ultimate goal was to identify targetable surface proteins, we further filtered out gene transcripts if they were expressed in normal tissue. For this, we relied on data from the GTEx (32) dataset, which at the time of these analyses housed expression data from 2,921 samples of 30 different tissue types.
To determine whether Hmmr and Celsr are differentially expressed in the poorly differentiated/NEPC tumor foci compared with the adenocarcinoma tumor foci, we performed bulk RNA-seq on histologically distinct tumor foci (n = 6 adenocarcinoma foci and n = 6 poorly differentiated/NEPC foci; ref. 58). These poorly differentiated/NEPC tumor foci had transcriptomic NEPC scores (9) that were on par with values obtained from a cohort of patient NEPC tumors  (58). On the basis of this dataset, we found that both Hmmr and Celsr were expressed at significantly higher levels in the differentiated/NEPC tumor foci compared with the adenocarcinoma tumor foci (Fig. 2D ).
For this, we captured a total of two separate regions of sgCELRS3, adenocarcinomalike cells as well as two separate regions of sgGFP NEPC cells from FFPE tissue sections. RNA-seq data from each batch of tumor foci were pooled for further analyses. On the basis of gene set enrichment analyses (GSEA), we found that the sgCELSR3 adenocarcinoma-like tumor cells were enriched for expression of genes associated with epithelial differentiation and genes downregulated in the EMT upon TGFβ stimulation (Fig. 4D ). We also found that the sgCELSR3 adenocarcinoma-like tumor foci were depleted of expression of neural lineagerelated genes. In agreement with these data and the IHC data, we also found that epithelial markers KRT, EPCAM, and CDH gene expression levels were upregulated while NEPC-associated genes INSM, POUF (encodes BRN2) and the neuronal gene OLIG were downregulated in sgCELSR3 adenocarcinomalike cells compared with sgGFP NEPC cells (Fig.
Other avenues that may provide additional and pertinent data to nominate NEPC-specific neopeptides could be through direct proteomics  (72)  or a combination of transcriptomic and proteomic (73) approaches. Here, we describe the identification of surface protein-encoding genes whose expression is enriched in NEPC compared with CRPC, locally advanced prostate cancer, benign prostate tissue or tissue from other organs. For this, we performed a bioinformatics analysis of RNA-seq data obtained from a large cohort of well-characterized tissue samples of benign prostate, localized prostate adenocarcinoma, CRPC, or NEPC tumors  (9, 31). Our analyses also included screening out genes that are expressed in normal tissue beyond prostate using transcriptomic data from GTEx  (32). Limitations of this approach are that it could filter out relevant targets expressed in some normal tissues at lower levels but may still be attractive therapeutic targets or not consider targets that are not annotated as a cell surface protein in the HCDM, CSPA, or GO databases.
3 FIGURE 1 A 31 FIGURE 1 A, Schematic of the bioinformatics pipeline that was used to analyze bulk RNA-seq data with the aim of identifying cell surface protein encoding genes specifically enriched in CRPC and NEPC compared with localized prostate cancer (PCA), benign prostate (Prost. ) and across the different normal tissues that are represented in the GTEx database. B, Heat map summarizing the average expression levels of the indicated genes from RNA-seq data from the normal tissue from the GTEX database, benign prostate, prostate cancer, CRPC, or NEPC tumors from our cohort. Box plot showing distribution of expression of the indicated gene (C) or CDH2 transcript isoform (D) based on RNA-seq data from the clinical cohort of normal tissue from GTEX, benign prostate, prostate cancer, CRPC, or NEPC.
Statistical modelling Mixed effects models were constructed to examine the relative contributions of all potential predictors on CRISPR evolution. A binomial dataset was constructed where CRISPR evolution was coded as 1 or 0 for each clone per replicate. Next, a binomial generalised linear mixed effects model with fixed effects of glucose, cell density, phage density and initial phage inoculum, and treatment replicate as a random effect, was run for each time point (days 1 to 3). Cell and phage density data were log transformed. A maximal model was generated and all possible candidate models were compared using the AIC method with dredge from the MuMIn package  [44]. AIC values assess the fit of a model by looking at the likelihood of a model given the data, penalising for increased number of parameters (as increased complexity of the model increases parameter uncertainty).
We selected the most parsimonious model (i. e. , the model with fewest parameters) within 2 delta AICs for each time point. Model comparisons based on AIC are presented in S1 Table, with the selected models highlighted. Model estimates for the selected models are presented in S2 Table. For the selected models, prediction data frames with 95% confidence intervals were generated using ggpredict from the package ggeffects  [45] , model dispersion was tested and scaled residuals were examined using DHARMa residual diagnostics  [46] , and the final predictions were visualised with ggplot2 v3. 3. 2 and the wesanderson package. All statistical analyses were performed in R v4. 0. 2, and code and data are available at https://doi. org/10. 5281/zenodo. 8193506.
1%: p = 0. 9995 (ns, not significant), 10% vs. 0. 1%: p = 0. 0164 (*), 10% vs. 0. 001%: p = 0. 0011 (**), 1% vs. 0. 1%: p = 0. 0130 (*), 1% vs. 0. 01%: p = 0. 0009 (***), 0. 1% vs. 0. 01%: p = 0. 6463 (ns). Data are available at https://doi. org/10. 5281/zenodo. 8193506. https://doi. org/10. 1371/journal. pbio. 3002122. g002.
Funding: Alnylam Pharmaceuticals. Trial Registration: Registered at Clinical-Trials. gov with study number NCT04152200 and at EudraCT with study number 2019-001346-17. P rimary hyperoxaluria type 1 (PH1) is a rare, pro- gressive, autosomal recessive genetic disease characterized by excessive hepatic oxalate production. 1, 2 PH1 is caused by mutations in the AGXT gene, which encodes the liver peroxisomal enzyme alanine-glyoxylate aminotransferase. 1, 2 When alanine-glyoxylate aminotransferase is deficient, glyoxylate is oxidized to oxalate, which is mainly excreted by the kidneys. Oxalate, in the form of its calcium salt, is insoluble and can readily crystallize in the renal parenchyma and urinary tract. In PH1, excess urinary oxalate (UOx) results in recurrent kidney stones, progressive kidney disease, and, ultimately, kidney failure (Fig  S1 ).
14 It targets and promotes degradation of the messenger RNA encoding glycolate oxidase, an enzyme upstream of alanine-glyoxylate aminotransferase. ] [15]  Current evidence supports the use of UOx and POx measurements as surrogate end points for clinical trials in patients with primary hyperoxaluria. 16, 17 POx level is a useful predictor of the risk of kidney failure and systemic oxalosis in patients with an eGFR <45 mL/min/1. 73 ] [18]  In the phase 3 ILLUMINATE-A (ClinicalTrials. gov identifier, NCT03681184; EudraCT identifier, 2018-001981-40) and ILLUMINATE-B (ClinicalTrials. gov identifier, NCT03905694; EudraCT identifier, 2018-004014-17) studies, lumasiran resulted in substantial reductions in UOx and POx levels, with an acceptable safety profile in patients with PH1 whose kidney function was relatively preserved.
Methods Study Design and Patients ILLUMINATE-C (ClinicalTrials. gov identifier, NCT04152200; EudraCT identifier, 2019-001346-17) is a multicenter, multinational, single-arm, phase 3 study of lumasiran in patients with PH1 and advanced kidney disease consisting of a 6-month primary analysis period followed by a long-term (54 months) extension period (Fig  S2 ). Cohort A included patients who were not receiving hemodialysis at study enrollment. Cohort B included patients who were receiving hemodialysis at study enrollment; elective changes to the hemodialysis regimen (changes in dialysis frequency or duration, filter size or type, and blood flow) were not permitted during the screening and 6-month primary analysis except when medically necessary.
The primary estimate is the least-squares mean of the percent change in POx from baseline to month 3 through month 6 averaged over these time points. Subgroup analyses by age and by weight-based dosing groups were performed to further understand treatment effect. For patients in cohort B, a secondary end point of percent change from baseline in POx area under the curve from 0 to 24 hours (AUC 0-24h ) was evaluated at month 6. Analyses of secondary end points of absolute change in POx, percent and absolute change in spot urinary oxalatecreatinine ratio, and 24-hour UOx were also performed using the mixed-effect model repeated-measures approach. Full details of the analyses are provided in the Statistical Analysis Plan (Supplementary File 3). Statistical analyses were performed using Statistical Analysis Software (SAS Institute) version 9.
Therapies that can effectively reduce hepatic oxalate production have the potential to halt or even reverse disease progression and be effective in patients of all ages and at all stages of the disease, including those receiving dialysis, which may lead to improvements in their quality of life. Additional data will be gathered during the extension period of this study. ILLUMINATE-C evaluated the efficacy and safety of lumasiran in patients with PH1 who have advanced kidney disease. Lumasiran was generally well tolerated and resulted in a substantial reduction in POx in patients with  Change from baseline to month 6 was calculated as the change across months 3 through 6. The least-squares mean with corresponding SEM and 95% CI were derived using the restricted maximum likelihood-based mixed model for repeated measures model.