dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussions Preprint. Discussion started: 30 September 2022 c Author(s) 2022. CC BY 4. 0 License. (Ludwig, 2012). However, a limitation of these approaches is that they ignore analytical uncertainties, leading to suboptimal results where these analytical uncertainties do in fact constitute a significant component of the total data point scatter. In place of these algorithms, DQPB offers an algorithm for computing weighted averages that is based on a 1-dimensional version of the spine algorithm and is capable of accounting for assigned analytical uncertainties and uncertainty correlations (see Appendix A). Like the spine regression algorithm, the spine weighted average gives full analytical weighting to ages at the centre of the distribution, and progressively down-weights those lying away from this central 'spine' according to the Huber loss function.
Error propagation DQPB employs Monte Carlo methods to estimate uncertainties for all age types. For concordia-intercept and "classical" isochron datasets fitted either using robust or model 1 algorithms, regression parameters are randomised within uncertainties according to a bivariate Gaussian distribution for each iteration, accounting for uncertainty correlation between the slope and y-intercept. For model 1x, model 2 and model 3 fits, (i. e. , "excess scatter" fits) regression parameters are instead randomised within their "observed scatter" uncertainties, i. e. , 1σ internal errors multiplied by √ mswd according to a bivariate t distribution with n -2 degrees of freedom, n being the number of data points. Activity ratios, either as initial or present-day values, are then randomised according to univariate Gaussian distribution, and an age is computed for each combination of inputs.
Where a present-day activity ratio value is given, the initial activity ratio value is also computed for each iteration as part of the numerical solving procedure. Age uncertainties are reported as a 95% confidence interval, estimated from the 2. 5 and 97. 5 percentiles of simulated ages. In most cases these upper and lower bounds are symmetric and Gaussian distributed, although this is not necessarily the case for concordia-intercept ages with large regression parameter uncertainties. For single-analysis Pb/U ages, isotope ratios for each data point are first randomised within their uncertainties according to 7 Data visualisation and plotting DQPB outputs highly customisable plots for all diagrammatic and weighted average U-Pb age calculations. For isochron ages, a plot showing data points as 95% confidence ellipses is provided, along with the regression line.
DQPB allows users to customise a wide range of plot settings, export figures in a variety of image file formats, and access all numeric data used to construct plots via an Excel spreadsheet (see Supplementary Information for further details). is computed, along with an initial 234 U/ 238 U value of 0. 749 (0. 731, 0. 766) (95% CI). Age uncertainties are estimated by Monte Carlo simulation using 50,000 trials (Fig. 3b ).
6 ± 39 ka (95% CI), with a mswd of 3. 54, indicating a very high probability of excess scatter in the dataset under the assumption of Gaussian distributed residuals. On the other hand, the robust spine weighted average algorithm gives a weighted average age of 94. 2 ± 10. 9 ka (95% CI) (Fig. 5b ), with a spine width value of 1. 28 which lies within the upper 95% confidence limit of s (1. 57, n = 6). This suggests that the dataset contains a well-resolved central spine of data, and thus the weighted average is likely to carry age significance under the assumption that crystallisation of these zircons constitutes a geologically discrete event (e. g. , see  Ickert et al. , 2015). Note, the spine weighted average algorithm down-weights the single point lying away from the average age line, and thus it has little influence on the computed weighted average.
Availability and distribution DQPB is released under a MIT license, permitting modification of the source code and re-distribution with minimal restrictions. The source code may be viewed via an online code repository (see: https://github. com/timpol/DQPB). This repository also contains links to downloadable installers for macOS and Windows and online documentation. Suggestions for bug fixes and new features, as well as pull requests, are also accepted via this repository. In addition to the stand-alone GUI version of the software, DQPB is also available as part of a pure Python package named Table  B1. Simulated 95 % confidence intervals for √ mswd and spine width, s, as a function of the number of data points, n. DQPB outputs the one-sided upper 95% confidence limit on s (here denoted *) along with s to assess suitability of the spine weighted average algorithm for use with a particular data set.
Not covered is how best to proceed if in fact a dataset is not consistent with the confidence interval. Whereas the argument developed in  Powell et al. (2020) , and, by extension here, is that datasets which are consistent are likely to have age significance, this becomes progressively more awkward to argue as the spine width increases. The view taken in this section is that the calculations advocated are for datasets that are considered to have age significance, commonly by geological inference, even though the spine width is outside the confidence interval. Once the spine width is too large, the data-fitting should plausibly not depend on the analytical uncertainties on the data as these are deemed insufficient to account for the observed scatter. A clear-cut and robust way to proceed is then to discard the analytical uncertainties and rely on the scatter of the data-specifically the spine width-to provide the data uncertainties.
Model 2 in Isoplot provides a framework how to proceed. As outlined in the Appendix of  Powell et al. (2020) , for the Isoplot model 2, in which analytical uncertainties are discarded, data are fit y on x, and x on y, and the results combined, circumventing the potentially deleterious effects of error-in-variables effects (e. g. ,  Fuller, 1987). In Isoplot, such calcula- In the equivalent of model 2 using the spine algorithm, the analytical uncertainties are discarded, then the spine width is calculated from the scatter of the data about the line, s = nmad(e). The development in Appendix B of  Powell et al. (2020)  can be applied as-is to the two calculations required: y on x, and x on y, except that two definitions need to be changed: eq B5 1  should involve W e with diagonal elements, 1/s, and eq B13 should involve W with diagonal elements, w(r k )/s 2.
Tissue was subjected to bulk RNA sequencing and digital spatial profiling (DSP) for single-cell RNA and protein analysis, and immunohistochemistry was performed. Fecal samples were also used for colonization experiments in IL10 -/- germ-free UC mouse models. RESULTS: Proteomics identified negative associations between HBOT response and neutrophil azurophilic granule abundance. DSP identified an HBOT-specific reduction of neutrophil STAT3, which was confirmed by immunohistochemistry. HBOT decreased microbial diversity with a proportional increase in Firmicutes and a secondary bile acid lithocholic acid. A major source of the reduction in diversity was the loss of mucusadherent taxa, resulting in increased MUC2 levels post-HBOT. Targeted database searching revealed strain-level associations between Akkermansia muciniphila and HBOT response status.
HBOT Response Status Associated With Reductions in Azurophilic Granule Related Proteins Proteomics of mucosal tissue and fecal samples both identified an enrichment of effect for HBOT on reducing neutrophil degranulation, with specificity of effect for reducing azurophilic granule related proteins (P <. 001 for both mucosal and fecal proteome datasets). This effect was more pronounced among HBOT responders compared with non-responders (Figure  1E-G ). In mucosal samples, a significant negative correlation was observed between response to HBOT and azurocidin-1 (r ¼ -0. 65, P ¼. 04), and HBOT responders had significantly lower normalized protein abundances for azurocidin-1 at day 10 follow-up compared with HBOT non-responders (Figure  1F ). In fecal samples, reductions in azurophilic granule proteins observed with HBOT exposure were greater than would be expected with changes in disease activity alone, and this was Figure  1.
Translational sequencing studies identify effect for hyperbaric oxygen on hypoxia response pathways and neutrophil degranulation. (A and B) Boxplots of immunohistochemistry score for epithelial HIF-1a and HO-1 activity for sham (n ¼ 8) and HBOT (n ¼ 10) treated UC patients. Upper and lower limits represent sample score range (A, P <. 001, unpaired t test; B, not significant). (C) Post-treatment bulk RNA sequencing results of mucosal biopsies for sham (n ¼ 8) and HBOT (n ¼ 5) treated UC patients enrolled in the phase 2A clinical trial. Metallothioneins are a family of highly conserved stress-induced proteins that, by control of cellular zinc homeostasis, protect against oxidative stress. (D) Boxplot of prostaglandin E 2 synthase proteome abundance in mucosal biopsies is demonstrated to be significantly lower in UC patients compared with healthy controls using a publicly available proteomics dataset (left bar graph, n ¼ 20; P ¼.
04) and non-responders (P ¼. 01), whereas non-responders were not significantly different from pre-HBOT condition (n ¼ 5, P >. 99). (G) Fecal proteome datasetbased neutrophil-associated protein network generated by protein-protein interaction engine STRING (reformatted in Cyto-Scape), segregated by subnetworks of granule type-specific proteins. *P <. 05; **P <. 01; ***P <. 001, analysis of variance adjusted. Range represents minimum and maximum sample measurements. observed in both HBOT clinical trial datasets independently (Figure  1G ). Separate analyses linked proteins related to NOD-, LRR-and pyrin domain-containing protein 3 (NLRP3) with a positive response to HBOT (data not shown). To further probe neutrophil response, DSP on formalinfixed paraffin-embedded mucosal tissue was then performed with a specific focus on neutrophils because of observed effects of HBOT on neutrophil degranulation.
HBOT Reduces Mitogen-Activated Protein Kinase and Markers of Reactive Oxygen Species and Increases the Pro-apoptotic BIM and the NLRP3 Inhibitor BCLXL In the neutrophil transcriptome datasets, a significant decrease was observed in CXCR6 with HBOT (logFC -0. 61, adjusted P ¼. 002), known to be involved in reactive oxygen species (ROS) mediated injury via mitogen-activated protein kinase (MAPK). 6 In the neutrophil proteome datasets, HBOT-treated patients had a significant reduction in protein abundance for phosphorylated p38 MAPK (logFC -1. 11, adjusted P ¼. 007), phosphorylated ERK (logFC -0. 95, adjusted P ¼. 016), and phosphorylated JNK (logFC -0. 70, adjusted P ¼. 032) relative to controls. In the proteome datasets, a significant effect for HBOT was observed for neutrophil apoptosis (P <.
001) and programmed cell death (P <. 01). Alongside the above noted neutrophil changes in MAPK and ROS mediated pathways, we observed a significant effect for HBOT on increasing neutrophil protein expression for B-cell lymphoma-extra large (BCLXL) (logFC 0. 69, adjusted P ¼. 008) and bcl-2 interacting protein (BIM) (logFC 0. 44, adjusted P ¼. 033). No significant effect was seen for HBOT on neutrophil protein expression for bcl-2-associated death promoter (adjusted P ¼. 62), and in the neutrophil transcriptome dataset no significant effect was seen for HBOT on neutrophil gene expression for bcl-2 (adjusted P ¼. 18).
HBOT Reduces Akkermansia and Increases MUC2 in UC Patients HBOT resulted in a relative increase in the Firmicutes:Bacteroidetes ratio within the metagenome (P ¼. 076 for linear trend over time) and a reduction in host proteins relative to Firmicutes in the metaproteome (P ¼. 013 for linear trend over time). Fecal metabolomics observed an association between HBOT response status and lithocholic acid (LCA) levels (r ¼ 0. 9 change in partial Mayo and LCA, P ¼. 0002). Within the 16S dataset, we observed this proportional increase in Firmicutes to be a result of a significant reduction in Shannon diversity with HBOT responders relative to sham-treated patients. The degree of reduction in Shannon diversity was linearly related to degree of HBOT response (r ¼ -0. 72, P ¼. 04) and number of HBOT treatment sessions used (r ¼ -0.
56, P ¼. 03). The 2 most reduced families by HBOT were Muribaculaceae (formerly Bacteroidales S24-7) and Akkermansiaceae (Figure  3A ), both known to be mucinophiles. Reductions in A muciniphila specifically were significantly associated with reductions in partial Mayo scores with HBOT (Figure  3B ). We also observed a significant accompanying increase in MUC2 with HBOT in the mucosal proteomics dataset (Figure  3C , left), and the relative increase in MUC2 was greater in HBOT responders compared with HBOT non-responders (Figure  3C , right). Fecal proteomics confirmed an increase in MUC2 with HBOT, which was incremental, that was based on duration of exposure (data not shown). In the fecal proteomic and metagenomic datasets we observed an association between reductions in A mucinophilia and higher MUC protein values (data not shown).
Taken together, HBOT impacts HIF-1a, but changes in HIF-1a alone do not explain mechanisms underlying improvements in UC disease activity with HBOT. Hypoxic microenvironments enhance neutrophil degranulation, particularly for azurophilic granules, and this has been thought to be partially HIF-dependent. 10  HIF-1a is the predominant HIF subtype found in neutrophils; however, hypoxia-augmented degranulation could not be redemonstrated with pharmacologic HIF-1a stabilization and activation alone. 10 HIF-independent pathways involved in neutrophil degranulation under hypoxic conditions include ROS, MAPK, and STAT3 signalling. 10, 11 Using TMTmultiplexed proteomics, we identified a consistent and highly significant association between HBOT response status and neutrophil degranulation, with specificity of effect for the STAT3-NLRP3 inflammasome-related azurophilic granules in both the fecal and mucosal proteomics datasets.
NLRP3 is directly linked to azurophilic granules, with mutations of NLRP3 being responsible for hypersensitive inflammasome function disorders through an azurophilic granule-selective process. 12 STAT3 plays a central role in NLRP3 inflammasome-mediated inflammation, and prior studies have demonstrated that STAT3 inhibition abrogates NLRP3 protein expression and IL1ß release. 13, 14 In the DSP dataset, we observed a significant reduction in neutrophil STAT3 mRNA abundance, phosphorylated MAPK protein expression, and markers of ROS-mediated injury. STAT3 and NLRP3 are known targets of JNK, and inhibition of MAPK signalling in neutrophils abrogates the release of azurophilic granule contents. 11, 13 At the single-cell level in the proteome dataset, we observed HBOT to significantly increase the proapoptotic protein BIM alongside BCLXL.
This suggests that HBOT response mechanisms involve direct effects on host neutrophil STAT3 pathways and separate effects on the microbiome, which may further augment improvements in disease activity. Our study has multiple strengths, which include the use of baseline and follow-up mucosal and fecal samples from 2 prospective clinical trials, separate healthy and disease severity matched control samples, use of novel and emerging techniques for proteomics and mRNA transcriptomics, our ability to study tissue neutrophils, and the consistency of observation and strength of associations for our proposed mechanisms of action across sequencing datasets and prior literature. Most notably, we provide the first application of DSP in UC to study neutrophils, and it was this approach that linked observations toward a unifying central hypothesis related to neutrophil degranulation and STAT3.
Clinical Trial Overview We used clinical data and human biospecimens collected from phase 2 clinical trials  3, 47  for HBOT in UC patients hospitalized for a medically refractory moderate to severe UC flare (Full Mayo score !6, all subscores 2-3). All patients received intravenous steroids along with either HBOT (2. 4 atmospheres absolute (sea level), 100% oxygen, 90-minute sessions, one session per day) or sham air (1. 34 atmospheres absolute, 21% oxygen, 90-minute sessions, one session per day) over 3-5 days. Disease activity was measured by using the Mayo score. Endoscopy subscores were available at study days 0 and 10, with rectal bleeding and stool frequency subscores available at days 0, 3, 5, and 10. The institutional review board at each center approved the protocols, and all patients gave written informed consent for participation in the trial and the translational research outlined using human biospecimens collected.
14. 0) and mapped to a database of repetitive elements (RepBase 18. 05) using STAR (2. 4. 0i). Reads that went unmapped to repeat elements were then mapped to the human genome (hg19) using default parameters. Aligned reads were annotated using featur-eCounts (v1. 5. 3) with Gencode V19 annotations. Differential expression was performed with DESeq2 (1. 22. 1). Gene Ontology enrichment was performed against significant (adjusted P. 05) dysregulated genes using a background of all expressed genes (average DESeq2 normalized count ! 1). Ontology data were downloaded from the Ensembl (GRCh37) Biomart API.
Last, the photocleaved oligos from the spatially resolved ROI in the microplate were quantified using next-generation sequencing Illumina workflows. Data were pre-processed in Nanostring DSP Software v 2. 1. 0. 106 normalizing to ERCC counts and housekeeping genes to adjust for systemic variations to enable expression counts to be compared across genes and samples. Quality control metrics were assessed, and 2 low-quality regions were removed because of low nuclei counts <20. The distribution of the pre-processed count data reflected a negative binomial distribution as expected, with a need for further normalization with TMM and voom. After TMM and voom normalization, the data met the assumptions necessary for downstream statistical analyses including differential expression analysis using limma-voom accounting for within case repeated measures (ROIs) and spatial deconvolution analysis.
After fractionation samples were concatenated into 24 samples. 12 fractions were used in the analysis of each sample. Labeled peptides were resuspended in 8 mL 5% formic acid, 5% acetonitrile wherein 3 mL was loaded onto an inhouse laser-pulled 100 mm ID nanospray column packed to w30 cm with 1. 8 mm C18 beads (Sepax Technologies, Newark, DE). Peptides were separated by reversed-phase chromatography on a Thermo Easy Nano-LC for 3 hours per fraction. Buffer A of the mobile phase contained 0. 1% formic acid in high-performance liquid chromatography-grade water, whereas buffer B contained 0. 1% formic acid in acetonitrile. The high-performance liquid chromatography flow rate was 0. 300 mL/minute. Samples were run on a Thermo Fusion mass spectrometer that collected mass spectrometry (MS) data in positive ion mode within the 600-1200 m/z range (þ2 charge) or 500-1200 m/z (þ3/4 charge).
Precursor tolerance was set to 50. 00 ppm, and fragment tolerance was set to 0. 6 Da. Resulting peptide spectral matches were filtered at a 0. 01 Statistics were calculated and visualized using R and the following packages: ggplot2 2. 2. 1, Hmisc 4. 0-3, psych 1. 7. 8, ggpubr 0. 1. 5, and RColorBrewer 1. 1-2. Univariate statistical analysis (P <. 05) for HBOT (repeated-measures t test) samples and the public dataset (independent t test) were done by GraphPad Prism 9. 0. 0 (San Diego, CA). Protein abundance was normalized to a pooled TMT "bridge" channel. In addition, each channel was further median scaled. Proteins with more than 50% of abundance values initially present in pre-and post-HBOT samples were subjected to imputation using the missForest R package (v1. 4). Enrichment analysis was done by first recording statistically significant (P <.
Antibodies used for immunostaining were anti-HIF-1a [EP1215Y] [1:100, rabbit monoclonal antibody] and anti-anti-heme oxygenase 1 antibody [EP1391Y] rabbit monoclonal antibody]. Immunostaining was visualized with a labeled streptavidin-biotin using 3,3 0 -diaminobenzidine as a chromogen and counterstained with hematoxylin. Samples were quantitatively analyzed and scored on the basis of presence (positive) or absence (negative) of staining by a blinded pathologist with expertise in gastrointestinal diseases. Data are displayed as frequency of staining score, and a c 2 test was used to determine significance. Immunohistochemical slides were cut at 4 mm from formalin-fixed paraffin-embedded tissue and air dried at room temperature before baking at 60 C for 30 minutes. Automated protocol was performed on the Leica Bond Rx and included paraffin dewax, antigen retrieval, and staining.
Fecal Multi-Omics Experiments Fecal samples from 43 participants (n ¼ 8 sham and n ¼ 25 HBOT from phase 2 trials, n ¼ 16 outpatient UC severity matched controls [IRB#150675], n ¼ 4 healthy controls recruited through the American Gut Project  50  ) underwent 16S rRNA gene amplicon sequencing, shotgun metagenomics, metabolomics, and metaproteomics. Samples in the phase 2A sham-controlled trial were available at days 0 and 10, and samples from the phase 2B dose-finding trial were available from days 0, 3, 5, and 10. Analyses were adjusted for shifts in disease severity over time and matched against healthy controls and the outpatient UC cohort not treated with HBOT or sham air. In addition to our standard Qiime2-based shotgun metagenomic search pipeline, samples were further searched with a targeted database containing 279 strains of A muciniphila in addition to the previous species-level entries found in our metagenomic database.
5% acetic acid. Concentration of desalted peptides was determined, and 50 mg aliquots of each sample were dried in a speed-vac. In addition, bridge channels consisting of 25 mg from each sample were created, and 50 mg aliquots of this solution were used in the 126 channels for each TMT (Thermo Fisher Scientific) 10 plex MS experiment. These bridge channels were used to control for labeling efficiency, interrun variation, mixing errors, and the heterogeneity present in each sample. Mass defects for each TMT set were accounted for in the database searches according to manufacturer's report per lot number. The lot numbers for TMT reagents were TA262347 for the first cohort of samples and VA296083 for the second cohort of samples. Each sample or bridge channel was resuspended in 30% dry acetonitrile in 200 mmol/L HEPES, pH 8.
5 for TMT labeling with 8 mL of the appropriate TMT reagent. Reagents in the 126 channels were used to bridge between mass spec runs, and remaining reagents were used to label samples in random order. Labeling was carried out for 1 hour at room temperature and quenched by adding 9 mL of 5% hydroxylamine (Sigma-Aldrich). Labeled samples were acidified by adding 50 mL of 1% trifluoroacetic acid. After TMT labeling each 10-plex experiment was combined and desalted through C18 Sep-Paks and dried in a speed-vac. Basic pH reverse-phase liquid chromatography (LC) followed by data acquisition through LC-MS 2 /MS 3 was performed. Briefly, 60-minute linear gradients of acetonitrile were performed on C18 columns using an Ultimate 3000 HPLC (Thermo Fisher Scientific). Subsequently, 96 fractions were combined, and further separation of fractions was performed with an in-line Easy-nLC 1000 (Thermo Fisher Scientific) and a chilled autosampler.