dataesr/scientific-paragraphs-categorization huggingface dataset:
29 In this 1-stage analysis, a study-specific random effect was used to account for withinstudy homogeneity in outcomes and a fixed treatment effect. 30 he CLOSE Trial, in which some patients were randomized to antiplatelet vs device and others to anticoagulant vs device, was treated as 2 trials. The Breslow method was used for tied survival times. 29 Safety analyses were based on comparisons of event proportions between treatment groups, using the Cochran-Mantel-Haenszel test (stratified by trial). 31, 32 For all hypothesis-testing analyses, a 2-sided P value significance threshold of. 05 was used, without adjustments for multiple testing. 33 All analyses were conducted using SAS (version 9. 4; SAS Institute) and R (version 4. 0. 2; The R Foundation). The primary efficacy analysis was adjusted for the following covariates: age, sex, prior myocardial infarction, diabetes, hypertension, hyperlipidemia, prior stroke or TIA, smoking status, index event (stroke vs TIA), atrial septal aneurysm (≥10 mm of excursion from midline, definition in eAppendix 5 and eTables 3 and 4 in the Supplement), PFO shunt size (large vs small, definition in eAppendix 5 and eTables 3 and 4 in the Supplement), and presence vs absence of a visible superficial infarction on neuroimaging.
Missing Data In the primary analysis, patients who exited the trials early were assumed to have outcome events that were noninformative under the missing at random assumption and were censored at last follow-up. Two sensitivity analyses were performed: a multiple imputation analysis (eAppendix 1 and eTable 1 in the Supplement) with covariate adjustment 34 and a tippingpoint analysis. 35, 36 or the conditional (adjusted) analysis and for subgroup analyses, multiple imputation to impute missing covariates was used as needed. 37.
The results illustrate several core concepts of HTE analyses. Even in this pooled analysis of 6 clinical trials, most conventional one-variable-at-a-time subgroup analysis did not generally have estimated effects that would be considered clinically or statistically significant, even while estimated effects were consistently in the theorized direction (eg, attenuated effects for each subgroup with a vascular risk factor). Combining variables creates greater contrast in effects between treatment-favorable and -unfavorable patients, and could help personalize decision-making by more comprehensively describing individuals. The clinical reasoning incorporated into the PASCAL Classification System was developed over decades, including the RoPE Score, which was derived on an observational database independent from this study.
eAppendix 1. Analysis Details This analysis includes all trials that were identified for a systematic review of studies looking at recurrent stroke with patent foramen ovale (PFO). The SCOPE PI (Kent)  The PI of this study (Kent) developed an initial list of variables based on variables used in a prior 3-trial individual patient meta-analysis 2 and variables that make up the Risk of Paradoxical Embolism (RoPE) Score  3, 4. The list was further expanded and refined at an investigator meeting in February 2020. eTable 1 displays the variables collected. eAppendix 9 provides the patient-level characteristics for each study, and note where data was missing. All analyses were conducted using SAS (Version 9. 4) and R (Version 4. 0. 2).
This is different from the other three and directly germane to the current study. Among patients with no major defined cause of ischemic stroke, the PASCAL classification system integrates information regarding: While data regarding many of the patient features used in the extended PASCAL Classification system were collected in the RCTs analyzed in the SCOPE project, two were not: 1) the presence of a thrombus straddling the PFO opening (supporting Definite causal relatedness), and 2) the occurrence of a PE or DVT shortly before or concurrent with the index ischemic stroke (supporting Highly Probable or Probable causal relatedness). Accordingly, for the current pooled analysis a simpler PASCAL classification system was developed by censoring those two uncollected patients' features and using the collected patient features to algorithmically assign patients to three levels of likelihood of causal relationship: Probable, Possible, and Unlikely (main manuscript Table  1B ).
was part of the team that performed this systematic review, which was updated in August 2019 for guideline development by the American Academy of Neurology (AAN). And subsequently updated to September 2021 for this article. Based on this systematic search performed of Medline and Embase, these studies represent the totality of available randomized evidence on the use of percutaneous implanted devices for PFO closure versus medical therapy in patients with PFO-associated cerebral ischemic events. Complete information about the search strategy and systematic review can be found in the original guidance. 1 Appendix 6 shows a PRISMA flowchart of all studies identified. All RCTs identified in the systematic review provided individual patient-level study data. Data entered into the central SCOPE database were a limited dataset (LDS), with all high-level patient identifiers removed.
PRISMA IPD Flow Diagram eFigure 2. PRISMA IPD Flow Diagram. Number of studies identified through database searching 1,191 (through 6/30/2019) MEDLINE (PubMed), Closure: 481 MEDLINE (PubMed), Anticoagulation: 518 Cochrane Library, both questions: 192 Screening Available data Eligibility Number of additional studies identified in updated search 07/01/2019-09/24/2021 (n =50) Number of studies identified through SCOPE investigators (n=0) Number of studies after duplicates removed n=678 Number of studies screened for eligibility n=678 Number of studies excluded (give reasons) 672 studies were excluded; reasons are unavailable Number of studies for which IPD were sought n=6 Number of eligible Studies for which IPD were not sought (give reasons) n=0 Number of studies for which IPD were provided n=6 Number of participants for whom data were provided n=3750 Number participants for whom no data were provided (give reasons) n=0 Number of studies for which IPD were not provided (give reasons) n=0 Number of participants Number of studies for which aggregate data were available N/A Number of participants IPD (report for each main outcome) Number of studies included in analysis n=6 Number of participants included in analysis n=3740 Number participants excluded (give reasons) • Patients in CLOSE with contraindications to PFO closure n=10 Participants for whom no data were provided (n=0) Analysed data Obtaining data Aggregate data (report for each main outcome) Number of studies included in analysis: N/A Number of participants included in analysis Number participants excluded (give reasons) Participants for whom no data were provided: N/A Identification © 2021 American Medical Association.
Dr Jüni reported serving as an unpaid member of the steering group of trials funded by Abbott Vascular, AstraZeneca, Biotronik, Biosensors, St Jude Medical, Terumo, and The Medicines Company; receiving research grants to the institution from AstraZeneca, Appili Therapeutics, Biotronik, Biosensors International, Eli Lilly, and The Medicines Company; and receiving honoraria to the institution for participation in advisory boards and/or consulting from Amgen, Ava, and Fresenius, but has not received personal payments by any pharmaceutical company or device manufacturer. Mr Koethe reported receiving grants from PCORI during the conduct of the study. Dr Mattle reported receiving grants from Abbott for the PC and PRIMA Trials during the conduct of the study and personal fees from Servier, Bayer, Medtronic, and Cerenovus outside the submitted work.
eAppendix 6. 6 PRISMA IPD Flow Diagram eFigure 2. PRISMA IPD Flow Diagram. Number of studies identified through database searching 1,191 (through 6/30/2019) MEDLINE (PubMed), Closure: 481 MEDLINE (PubMed), Anticoagulation: 518 Cochrane Library, both questions: 192 Screening Available data Eligibility Number of additional studies identified in updated search 07/01/2019-09/24/2021 (n =50) Number of studies identified through SCOPE investigators (n=0) Number of studies after duplicates removed n=678 Number of studies screened for eligibility n=678 Number of studies excluded (give reasons) 672 studies were excluded; reasons are unavailable Number of studies for which IPD were sought n=6 Number of eligible Studies for which IPD were not sought (give reasons) n=0 Number of studies for which IPD were provided n=6 Number of participants for whom data were provided n=3750 Number participants for whom no data were provided (give reasons) n=0 Number of studies for which IPD were not provided (give reasons) n=0 Number of participants Number of studies for which aggregate data were available N/A Number of participants IPD (report for each main outcome) Number of studies included in analysis n=6 Number of participants included in analysis n=3740 Number participants excluded (give reasons) • Patients in CLOSE with contraindications to PFO closure n=10 Participants for whom no data were provided (n=0) Analysed data Obtaining data Aggregate data (report for each main outcome) Number of studies included in analysis: N/A Number of participants included in analysis Number participants excluded (give reasons) Participants for whom no data were provided: N/A Identification © 2021 American Medical Association.
eTable 4. Variable Definition for Large Shunt Size. RESPECT Small: Grade 0 (none), Grade 1 = 1-9 bubbles; Grade 2 = 10 to 20 bubbles; Large: Grade 3 = over 20 bubbles PENN RE-READ FROM TEE (IF MISSING (~20% of time), USED ORIGINAL DATA FROM GORE): REDUCE *Small :(0)Grade 0[no bubbles], (1)Grade 1 [1-9 bubbles], (2)Grade 2 [10-20] bubbles, Large: (3)Grade3 [>20 bubbles] DEFENSE Small: (<20 Microbubbles), Large (>20 microbubbles) CLOSE Small : <30 Bubbles on TTE or TEE, Large: >30 microbubbles on TTE or TEE PFO indicates patent foramen ovale; TTE, transthoracic echocardiogram; TEE, transesophageal echocardiogram. Systematic, Collaborative, PFO closure Evaluation Target: Large shunt size was defined in our database as >20+ bubbles (values below in BLUE coded as 'large' in our database) (SCOPE) CLOSURE Small: (1) None; (2): Trace, 1~10 bubbles, (3) Moderate, ~10-25 bubbles, Large: (4) Substantial, ~25 or more bubbles PC TrialSmall: grade 0 = none; grade 1 = minimal (1-5 bubbles), grade 2 = moderate (6 to 20 bubbles), Large: grade 3 = severe (>20 bubbles)© 2021 American Medical Association.
Patescibacteria represent more than 50% of all prokaryotes in Hainich groundwater Cand. Patescibacteria dominated the groundwater community representing on average more than 50 ± 18% (range 23-79%) prokaryotes across 32 metagenomes obtained from groundwater of six wells that was sequentially filtered through 0. 2 µm and 0. 1 µm filters, based on the proportion of the quality-controlled metagenomic reads mapped to the 16S rRNA database (SILVA SSU rRNA Ref NR99)  [34]. Three major classes within the phylum were detected: Cand. Parcubacteria/Paceibacteria (36. 2 ± 17. 1%, range 13-65. 7%), Cand. Microgenomatia (7. 2 ± 3. 1%, range 2-12%) and candidate division ABY1 (3. 2 ± 1. 4%, range 1. 1-5. 1%). Patescibacteria were found to be highly abundant in both filter fractions. Their relative abundances were significantly higher (two-proportions z-test, p-value 1.
The sequence coverages of these highlighted genome pairs or clusters across the metagenomes are compared in Additional file 8, Figure  S3   Patescibacteria MAGs to ORFs from all other bacterial and archaeal MAGs in the present study using blastn  [40] , we found various ORFs from other taxa highly similar to Patescibacteria ORFs (95% sequence identity covering 85% length of the query and hit sequences). The most ORFs that matched were between members of genus UBA10092 of Patescibacteria (class Paceibacteria) and two members of the family UBA12090 of Omnitrophota (34 and 16 ORFs, respectively). They included genes encoding for twitching motility protein PilT (K02669), Ptype Cu+ transporter (K17686) and lipopolysaccharide export system permease protein (K11720). Between members of genus UBA11707 of Patescibacteria (class ABY1) and genus UBA1573 of Proteobacteria (family Micavibrionaceae), 14 such ORFs, including gene encoding for ABC-2 type transport system ATP-binding protein (K01990), were observed.
Conclusions The Candidate Phyla Radiation represent the largest phylogenetic diversity within the bacterial domain, which has not been reflected in the metabolic versatility of genomic representatives studied to date. Here we leveraged a well characterized aquifer transect, that is dominated by members of the CPR and spans large biogeochemical gradients, to explicitly explore genomic adaptations to environmental conditions. The most significant and surprising result was the high level of similarity in predicted metabolic functions and expected lifestyles that spanned large redox gradients from fully oxic to completely anoxic groundwater, both within the larger CPR clade as well as at finer phylogenetic resolutions. One noteworthy exception was a differential abundance in superoxide dismutase, a potentially useful indicator of oxygen exposure in CPR genomes recovered from other environments or already deposited to sequence databases.
Metagenomic assembly, genome binning and refinement The quality controlled reads of each individual filter fraction replicate were assembled and scaffolded using metaSPAdes v3. 13  [57]. Scaffolds larger than 1 kb were used for downstream analyses. Genome binning was carried out using three binning algorithms -Abawaca v1. 07  [15] , ESOM  [58, 59]  and Maxbin2 v2. 2. 4  [60]. The values 3000 and 5000 bp as well as 5000 and 10000 bp were used as -min and -max parameters to calculate 4-mer frequencies for Abawaca and ESOM (the script esomWrapper. pl, https://github. com/tetramerFreqs/Binning), and both the 40 and 107 marker gene sets were utilized in Maxbin2. DASTool v1. 1  [14]  was used to determine the best bins among these approaches. Bins were further refined manually inside the Anvi'o workflow v6.
Taxonomic assignments, gene annotations and pathway predictions Overall community composition of each metagenome was determined using phyloFlash v3. 4  [62]  based on proportions of reads mapped to SILVA SSU rRNA Ref NR99 database, Release 138  [34]. Taxonomic classification of individual MAGs was performed by GTDB-Tk v0. 3. 2  [63]  using GTDB Release 89 as reference database. Out of the 1275 genomes GTDB-Tk classified 587 genomes as Cand. Patescibacteria at phylum level. We used anvi-script-gen-CPR-classifier script from Anvi'o v6. 1  [35, 36]  which uses supervised machine learning model (random forest classifier) to train the program and anvi-script-predict-CPR-genomes for predicting the probability of the MAGs to confirm the CPR genomes. The training is based on the profile of previously published 139 single copy core genes from hundreds of CPR genomes from Brown et al.
[15]  and Campbell et al. [37]  as input. This model confirmed 291 out of 587 genomes as CPR with a high confidence score (75% or more). While the model was inconclusive in case of the remaining 174 genomes based on low confidence score (less than 75%) and the remaining 122 genomes were discarded due to their completion levels below 50%. The gene annotations, coding sequences, respective protein sequences, coverage calculations and other mapping statistics for all the genomes were exported by anvi-summerize program from within the Anvi'o workflow. The annotations were also carried out using Prodigal v2. 6. 3  [64]. Distilled and Refined Annotation of Metabolism (DRAM)  [65]  was used to generate pathway / metabolism summaries. At least one proper (other than hypothetical, uncharacterized or gene with unknown function) annotation from KEGG  [66] , MEROPs  [67] , Pfam  [68]  or dbCAN  [69]  was considered.
Co-occurrence network analysis We used normalized average genome coverages of all the 1275 MAGs across all the metagenomes as the approximation of abundance profiles of species from respective metagenomes. This abundance matrix was used to calculate proportionality of the coverage profiles in R package propR v4. 2. 6  [74]. A ⍴ cutoff of 0. 95 was used for network creation to highlight only the most relevant co-occurrences. The network was generated using the R package igraph v1. 2. 6  [75]  and exported to Cytoscape v3. 8. 2  [76]  for visualization using R package Rcy3 v2. 8. 1  [77].
Acknowledgements We thank  Patricia Geesink  and  Falko Gutmann  for filtration and DNA extraction of groundwater samples, and the Hainich CZE site manager  Robert Lehmann  for their assistance with sample preparation, collection, and filtration. Additionally, we thank  Ivonne Görlich  and  Marco Groth  from the  Core Facility DNA sequencing of the Leibniz Institute on Aging -Fritz Lipmann Institute in Jena  for their help with Illumina sequencing. We also thank  Syrie Hermans  for providing preliminary data from time-series analysis of 16S rRNA gene amplicon sequencing for some of the groundwater wells.
Funding This study is part of the  Collaborative Research Centre AquaDiva  of the  Friedrich Schiller University Jena , funded by the  Deutsche Forschungsgemeinschaft (DFG ,  German Research Foundation ) - SFB 1076 - Project Number  218627073. NMC gratefully acknowledges the support of the  German Centre for Integrative Biodiversity Research (iDiv) Halle -Jena-Leipzig  funded by the  German Research Foundation  ( FZT 118 -202548816 ). MT gratefully acknowledges funding from the  DFG  under  Germany 's  Excellence Strategy - EXC 2051 -Project-ID 390713860. AJP, TLVB and PAFG were supported by the  Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen  (' Nachwuchsgruppe   Dr. Alexander Probst '). The data analysis has been partly carried out at the  High-Performance Computing (HPC) Cluster.
In molecular cell biology, reporter assays are frequently used to investigate gene expression levels. Reporter assays employ a gene that encodes a light-emitting protein, of which the luminescence is quantified as a proxy of gene expression. Commercial parties provide reporter assay kits that include protocols and specialized detection machinery. However, downstream analysis of the output data and their presentation are not standardized. We have developed plotXpress to fill this gap, providing a free, open-source platform for the semi-automated analysis and standardized visualisation of experimental gene reporter data. Users can upload raw luminescence data acquired from a reporter gene assay with an internal control. In plotXpress, the data is corrected for sample variation with the internal control and the average for each condition is calculated.
PlotXpress was built following the philosophy of transparent and state-ofthe-art data visualization implemented in the data visualization app PlotsOfData  (Postma and Goedhart, 2019). In addition to a streamlined analysis, plotXpress enables transparent communication of the data. Instead of only providing averaged data and error bars to summarize gene reporter data in bar graphs, plotXpress produces dot plots maintaining individual data points. Data in both wide and tidy format  (Wickham, 2014 ) can be provided. As coding skills are not required, plotXpress is a readily available low entry-level application that democratizes the processing and visualization of dual reporter expression data.
Implementation The plotXpress code is written in the R programming language using the following packages: ggplot2  (Wickham, 2016) , tidyr, magrittr, readr, stringr, dplyr and readxl which are all part of the tidyverse suite of packages version 1. 3. 1  (Wickham et al. , 2019) , and Shiny and DT. This manuscript documents version 1. 0. 0 of the webtool which is archived (together with the example data) at Zenodo  (Goedhart and Galland, 2021). Background information, updates of the code and version releases will be published on GitHub: https://github. com/ ScienceParkStudyGroup/PlotXpress. GitHub is the preferred channel for communication regarding issues and feature requests. An example dataset with measurements and a dataset with conditions is automatically loaded when the plotXpress app launches.
Tidy data upload Instead of uploading a GloMax spreadsheet in 96-well format and an additional design file, users can choose to upload a single tidy dataset (. CSV) containing both experimental conditions and firefly and renilla signals. An example file is available online  (Goedhart and Galland, 2021). All the relevant data from one experiment (design and measurements) should be present in one tidy table. Users are free in the size of the experiment: any number of cell types, sequences of interest, or treatments may be added. The minimal information that is required is a column with wells (in the format A01, B01,. ), a column with intensity data and a column with the conditions. An additional column with reference data is optional and is used to normalize the data if supplied.
Data processing In dual luciferase assays, each replicate consists of a firefly luciferase and renilla luciferase intensity reading. To normalize the reporter gene expression to the internal control, plotXpress calculates the ratio of firefly signal to renilla signal, resulting in the firefly/renilla ratio. An average firefly/renilla ratio is then calculated for each group of readings that share identical names in the "condition" column. Finally, the fold change is calculated by dividing the firefly/renilla ratio by the firefly/renilla ration of a selected reference condition: Fold Change ¼ firefly=renilla ð Þ firefly=renilla ð Þ reference (1).
Operation The plotXpress web tool can be accessed at https://huygens. science. uva. nl/PlotXpress/ or https://goedhart. shinyapps. io/ PlotXpress. The online web app runs on any computer platform with a browser and an internet connection. Instructions to run the app locally in R/Rstudio can be found in the README file on the Github page. Upon launching the app, the example data is loaded and the users can choose to visualize the example data by selecting the 'Plot' tab or to upload their own data (Figure  2 ). The data upload accepts both GloMax xlsx files, as well as tidy data in CSV format. After data upload, PlotXpress offers flexibility in the selection of conditions to filter the data before plotting. Individual wells can be removed from the analysis or specific conditions can be filtered.
The combined data from an experiment can be downloaded as a CSV file in tidy format. This data format can be used for further processing, such as statistical testing and plotting. The tidy format is well handled by statistical software (R) and other web tools that we have developed (available online). For instance, when the data consists of a mix of technical and biological replicates, this can be visualized as a superplot  (Lord et al. , 2020)  by uploading the data in the SuperPlotsOfData app  (Goedhart, 2020). Figure  3. GloMax data conversion to tidy format. PlotXpress allows uploading of GloMax spreadsheet input. An additional experimental design file in tidy format should be provided to connect the reads with the conditions. After supplying the design file, the GloMax data is converted to a table in tidy format, where each row contains a single observation and the experimental conditions.