dataesr/scientific-paragraphs-categorization huggingface dataset:
Results: The finished product is CRIS, a web-based software for survivors of bladder cancer and their caregivers to serve as a health management and lifestyle resource after surgery. Overarching themes from phase IA (participant feedback) included how to use new medical equipment, tips and tricks for easier living with new medical equipment, questions about health maintenance, and questions about lifestyle modifications. To accommodate our target population, we also incorporated recommendations from the Americans with Disabilities Act for website design, such as large text size, large paragraph spacing, highly contrasting text and background colors, use of headings and labels to describe the purpose of the content, portrait orientation without the need for horizontal scrolling, multiple ways to access a web page within a set of pages, ability to navigate web pages in sequential order, and in-text links that are descriptive.
Overall, software-based patient education is effective in increasing disease-related knowledge and improving health behaviors and self-management skills  [13] [14] [15] [16]. However, there is no existing research on web-based tools to specifically address the unmet needs of patients and caregivers diagnosed with bladder cancer during the cystectomy process. To overcome these gaps and improve the health-related quality of life and postsurgical outcomes of both bladder cancer survivors and their caregivers without further taxing the time of health care providers, the team developed the Cancer Resource and Information Support (CRIS) software. CRIS is an interactive web-based program developed for bladder cancer survivors who will undergo surgical treatment and their caregivers. It is unique in its design to be used before and after surgery; it includes caregivers in all intervention components; and all materials can be accessed on demand.
Guiding Theory: Obesity-Related Behavioral Intervention Trials (ORBIT) Model The development of the CRIS software was guided by the Obesity-Related Behavioral Intervention Trials (ORBIT)  [17]  model for developing behavioral treatments for chronic diseases. The ORBIT model delineates the development, refinement, and testing processes into various phases. We focus on the first phases (phases IA and IB) of the model in this manuscript. During phase IA, we developed and refined solutions for our stated clinical problem: adjustment to life post cystectomy. During phase IB, we refined the intervention content to best fit the needs of our target population. A key feature of the ORBIT model is the emphasis on the early stages of program intervention development through an iterative process of development, feedback, and refinement sprints.
Usability Testing After the first version of the website was completed, we conducted usability testing to explore our prototype CRIS website. Guidelines suggest that the more severe usability problems are most often detected by the first few participants, and more than 80% of usability issues are discovered with just 4-5 users  [19] [20] [21]. Our sample for usability testing consisted of 7 participants (5 survivors and 2 caregivers). Of this sample, 4 participants were from our PAB. Three of the survivors had an ileal conduit, 1 had an Indiana pouch, and 1 had a neobladder. Following informed consent, participants were given a tablet or laptop with the prototype website and were asked to "think aloud" as they navigated through the pages. Four of the participants viewed the program on a tablet (a Samsung Galaxy Tab A 8"), and 3 viewed it on a laptop.
Participants involved in this phase freely explored CRIS for 15-20 minutes; their narrative thoughts and observations were audio-recorded, and a research assistant took extensive notes. Study staff would note any general remarks that participants made, including any difficulties with the program or elements of the program that the participants liked. Study staff helped participants if necessary. After they were finished exploring, survivors were asked to complete specific tasks to simulate real-life situations and assess how intuitive and user-friendly the website was. Examples of these discreet tasks include "navigate to the home page" and "find and click on the section about traveling with your medical equipment. " At the end of the usability session, participants completed the brief, 10-item System Usability Scale, the industry standard for measuring usability  [22].
Participant 4 stated, "I don't remember anyone coming to tell me about what (I) might experience emotionally. " Some of the participants also gave specific feedback on areas for improvement. Participant 3 noted that some of the images of pouch systems were outdated and that some of the languages regarding a description of the pouch system could be clearer. At the end of the usability testing, all participants (N=7) completed the System Usability Scale (see Table  1 ). Overall, survivors evaluated CRIS very positively, indicating that they would use it frequently if it were available, that it was easy to use, and that the functions were well integrated. They also indicated that CRIS was easy to learn and that they felt confident using it.
Principal Findings Existing web-based tools for bladder cancer survivors are prognostic tools that serve to facilitate treatment decision-making  [24] [25] [26]. CRIS is unique because it is the first web-based software developed for bladder cancer survivors and their caregivers to assist in their adjustment to life following cystectomy. Our multidisciplinary team followed an iterative and theory-based approach using the ORBIT model to create the CRIS software. With input from stakeholders and providers, CRIS' intuitive and user-friendly design can easily be adapted to other cancer types and other chronic diseases characterized by significant posttreatment challenges. We undertook the development of CRIS because we identified an urgent need to improve survivors' and caregivers' knowledge and skills for postcystectomy care.
Limitations There are some limitations to note in relation to the development of CRIS. First, our sample size for the usability testing is limited; however, we did include survivors who engaged in all possible treatment options. Second, at this time, we only have the resources to develop CRIS in the English language; therefore, we were limited to obtaining feedback from English-speaking participants on the development and usability of the program. Third, some of the same participants were used in the initial define phase and again in the usability or refine phase, giving these individuals knowledge about the tool that could put them at an advantage during usability testing, affecting usability outcomes. However, the participants who only participated in the usability phase had the same feedback as those who participated in both phases.
Future Research Directions The next steps in this program of research include an evaluation of the efficacy of CRIS in improving quality of life in survivors of bladder cancer and their caregivers through a 2-site randomized controlled trial. The trial will compare CRIS to a time and attention comparison condition that incorporates a standard of care discharge instructions and modules focusing on wellness. While both patients' and caregivers' quality of life is the primary outcome, secondary outcomes are fewer infections and nurse-emergency room visits for patients randomized into CRIS. We will conduct moderator (ie, age, gender, and surgical diversion type) and mediator (ie, patient activation and distress) analyses of intervention efficacy, as well as examine the costs and potential savings associated with developing and implementing the CRIS intervention.
Analyzing diversification dynamics is key to understanding the past evolutionary history of clades that led to present-day biodiversity patterns. While such analyses are widespread in well-characterized groups of species, they are much more challenging in groups which diversity is mostly known through molecular techniques. Here, we use the largest global database on the small subunit (SSU) rRNA gene of Glomeromycotina, a subphylum of microscopic arbuscular mycorrhizal fungi that provide mineral nutrients to most land plants by forming one of the oldest terrestrial symbioses, to analyze the diversification dynamics of this clade in the past 500 million years (Myr). We perform a range of sensitivity analyses and simulations to control for potential biases linked to the nature of the data. We find that Glomeromycotina tend to have low speciation rates compared to other eukaryotes.
Material & methods: Virtual taxa phylogenetic reconstruction: We downloaded the Glomeromycotina SSU rRNA gene sequences from MaarjAM, the largest global database of Glomeromycotina gene sequences updated in June 2019  (Öpik et al. , 2010). We reconstructed several Bayesian phylogenetic trees of the 384 virtual taxa (VT) from the corresponding representative sequences available in the MaarjAM database (Supplementary Methods 1). We used the full length (1,700 base pairs) SSU rRNA gene sequences from  (Rimington et al. , 2018)  to better align the VT sequences using MAFFT  (Katoh & Standley, 2013). We selected the 520 base pair central variable region of the VT aligned sequences and performed a Bayesian phylogenetic reconstruction using BEAST2  (Bouckaert et al. , 2014). We set the crown root age at 505 Myr  (Davison et al.
Delineation into Evolutionary Units (EUs): We considered several ways to delineate Glomeromycotina species based on the SSU rRNA gene. In addition to the VT species proxy, we delineated Glomeromycotina de novo into evolutionary units (EUs) using a monophyly criterion and 5 different thresholds of sequence similarity ranging from 97 to 99%. We gathered Glomeromycotina sequences of the SSU rRNA gene from MaarjAM, mainly amplified by the primer pair NS31-AML2 (variable region)  (Lee, Lee, & Young, 2008; Simon, Lalonde, & Bruns, 1992)  (dataset 1, Supplementary Table  1 ). There were 36,411 sequences corresponding to 27,728 haplotypes. We first built a phylogenetic tree of these haplotypes and then applied to this tree our own algorithm of EU delineation (R-package RPANDA  (Morlon et al. , 2016; R Core Team, 2020) ) that traverses the tree from the root to the tips, at every node computes the average similarity of all sequences descending from the node, and collapses the sequences into a single EU if their sequence dissimilarity is lower than a given threshold (Supplementary Methods 2).
, 2018)  and (ii) evaluate the biological relevance of the VT and various EUs delineations. For each clade, we reconstructed Bayesian phylogenetic trees of haplotypes (Supplementary Methods 1). We then ran GMYC analyses (splits R-package  (Ezard, Fujisawa, & Barraclough, 2009) ) on each of these trees and evaluated the support of the GMYC model compared to a null model in which all tips are assumed to be different species, using a likelihood ratio test (LRT). If the LRT supports the GMYC model, different SSU haplotypes belong to the same Glomeromycotina species, i. e. the SSU rRNA gene has time to accumulate substitutions between Glomeromycotina speciation events.
Total diversity estimates: We evaluated how thoroughly sampled our species-level Glomeromycotina phylogenetic trees are by estimating the total number of VT and EUs using rarefaction curves and the Bayesian Diversity Estimation Software (BDES  (Quince, Curtis, & Sloan, 2008 )) (Supplementary Methods 3). The BDES estimates the total number of species by extrapolating a sampled taxa abundance distribution at global scale  (Quince et al. , 2008).
Additional molecular markers: We explored the possibility to carry some of our analyses using two other molecular markers: the large subunit (LSU) rRNA gene and the ITS2 region. We downloaded the Glomeromycotina LSU database of  Delavaux et al. (2020)  as well as the LSU sequences available in MaarjAM. We obtained a total 2,044 sequences that we aligned using MAFFT and TrimAl. We retained the 1,760 unique haplotypes, reconstructed the phylogenetic tree of the LSU sequences using BEAST2 and used the resulting calibrated tree to delineate Glomeromycotina LSU units with the GMYC model (same pipeline as above). We similarly downloaded the Glomeromycotina ITS dataset of  Lekberg et al. (2018). We tried to align them but confirmed that the ITS sequences of Glomeromycotina are very difficult to align, making them unsuitable for phylogenetic reconstruction and subsequent diversification analyses (Supplementary Fig.
, 2019). We replicated these analyses using the LSU gene. In addition, we applied CoMET (TESS R-package  (Höhna, May, & Moore, 2016; May, Höhna, & Moore, 2016) ), another diversification approach that does not consider rate variation across lineages, but models temporal shifts in speciation and extinction rates affecting all lineages simultaneously. CoMET is a piecewise-constant model in a Bayesian framework. We chose the Bayesian priors according to maximum likelihood estimates from TreePar  (Stadler, 2011) , disallowed or not mass extinction events, and ran the MCMC chains until convergence (minimum effective sample sizes of 500). We also fitted a series of time-dependent and environment-dependent birth-death diversification models using RPANDA  (Condamine, Rolland, & Morlon, 2013; Morlon et al.
Estimates of land plant diversity were obtained using all available Embryophyta fossils from the Paleobiology database (https://paleobiodb. org) and using the shareholder quorum subsampling method (Supplementary Methods 6;  (Alroy, 2010) ). We considered missing species in all our diversification analyses by imputing sampling fractions, computed as the number of observed VT or EUs divided by the corresponding BDES estimates of global Glomeromycotina diversity (Table  1 ). We used a global sampling fraction for all Glomeromycotina, as the main Glomeromycotina clades had a similar sampling fraction (Supplementary Table  2 ). To assess the robustness of our results to global diversity estimates, we replicated all diversification analyses using a range of lower sampling fractions (from 90% to 50%, i.
Testing for correlates of present-day Glomeromycotina speciation rates: To further investigate the potential factors correlating with Glomeromycotina speciation rates, we assessed the relationship between lineage-specific estimates of present-day speciation rates (obtained with the ClaDS analyses) and characteristics of each Glomeromycotina taxonomic unit, i. e. VT or EUs. First, to assess the effect of specialization on speciation rates, we characterized Glomeromycotina relative niche width using a set of 10 abiotic and biotic variables recorded in MaarjAM database for each Glomeromycotina unit. In short, among a curated dataset containing Glomeromycotina sequences occurring only in natural ecosystems (dataset 2; Supplementary Table  2 ; Perez-Lamarque et al. , 2020), for each Glomeromycotina unit, we reported the number of continents, ecosystems, climatic zones, biogeographic realms, habitats, and biomes where it was sampled, as well as its number of plant partners, their phylogenetic diversity, and its centrality in the plant-fungus bipartite network, and performed a principal component analysis (PCA; Supplementary Methods 7).
To account for inhomogeneous sampling along the latitudinal gradient, we re-ran the model on jackknifed datasets (we re-sampled 1,000 interactions per slice of latitude of twenty degrees). Similarly, we tested the effect of climatic zone and habitat on speciation rates. Then, to assess the effect of dispersal capacity on speciation rates, we evaluated the relationship between spore size and speciation rate for the few (n=32) VT that contain sequences of morphologically characterized Glomeromycotina isolates  (Davison et al. , 2018). We gathered measures of their average spore length  (Davison et al. , 2018)  and tested their relationship with speciation rate by using a phylogenetic generalized least square regression (PGLS). Finally, as a first attempt at connecting Glomeromycotina macroevolutionary diversification to microevolutionary processes, we measured intraspecific genetic diversities across Glomeromycotina units.
We used the functions pbtree and rcoal from the R-packages phytools and ape  (Paradis, Claude, & Strimmer, 2004; Revell, 2012)  to simulate the species phylogenies and the intraspecific coalescences respectively. We used two net diversification rates (r=0. 010 and r=0. 015) for simulating the species phylogenies, in order to reach a total number of species similar to that obtained with our empirical data when using the VT and EU99 delineations, respectively. Next, we simulated the evolution of short 520 bp DNA sequences on the obtained trees, using the function simulate_alignment (R-package HOME; Perez-Lamarque &  Morlon, 2019). We used a substitution rate of 0. 001 event per Myr and only 25% of variable sites, which resulted in an alignment that mimicked the Glomeromycotina SSU rDNA alignment.
In comparison, the same pipeline carried on the LSU database delineated only 181 GMYC units, suggesting that it was much less complete than the SSU database. We replicated the subsequent diversification analyses using the LSU region, even though we put more trust in our results using the SSU database given the incompleteness of the LSU database. Rarefaction curves as well as BDES (Bayesian Diversity Estimation Software) and Chao2 estimates of diversity suggested that more than 90% of the total Glomeromycotina diversity is represented in our SSU dataset regardless of the delineation threshold (Fig. 1 , Table  1 , Supplementary Tables  5  & 6 ), which is consistent with the proportion of new Glomeromycotina units detected in recent studies  (Sepp et al. , 2019). The reconstructed Bayesian phylogenetic trees based on VT and EU delineations did not yield high support for the nodes separating the main Glomeromycotina orders; yet, the trees had no significantly-supported conflicts either, and similar branching times of the internal nodes overall (Fig.
Tables: Table  1 : Estimation of the total diversity of Glomeromycotina: Estimated sampling fraction using the Bayesian Diversity Estimation Software (BDES;  Quince et al. , 2008)  for the different species delineations (VT or EU) assuming a Sichel species abundance distribution. The estimated number of units corresponds to the median value and we indicated the 95% confidence interval. We indicated the sampling fractions for each delineation, computed as the number of observed VT or EUs divided by the corresponding BDES estimates of global Glomeromycotina diversity. The Sichel distribution was selected compared to other distributions (log-normal, log-Student, and inverse gaussian) based on lowest deviance information criterion (DIC). We compared the virtual taxa (VT) delineation from  (Öpik et al.
discussions and C. Fruciano and E. Lewitus for providing codes. This work was supported by a doctoral fellowship from the École Normale Supérieure de Paris attributed to BPL and the École Doctorale FIRE -Programme Bettencourt. MÖ was supported by the European Regional Development Fund (Centre of Excellence EcolChange) and University of Tartu (PLTOM20903). Funding of the research of FM was from the Agence Nationale de la Recherche (ANR-19-CE02-0002). HM acknowledges support from the European Research Council (grant CoG-PANDA).
Acknowledgment: The authors acknowledge  C. Strullu-Derrien ,  M. Elias ,  D. de Vienne ,  A. Vogler ,  J. -Y. Dubuisson ,  C. Quince ,  S. -K. Sepp , and  M. Chase  for helpful discussions. They also thank  L. Aristide ,  S. Lambert ,  J. Clavel ,  I. Quintero ,  I. Overcast , and  G. Sommeria  for comments on the manuscript,  D. Marsh  for English editing, and the Editor and three reviewers for improvements of an earlier version of this manuscript. BPL acknowledges  B. Robira ,  F. Foutel-Rodier ,  F. Duchenne ,  E. Faure ,  E. Kerdoncuff ,  R. Petrolli , and  G. Collobert  for useful.
Data Accessibility: All of the data used in this study are available in the open-access MaarjAM database (https://maarjam. botany. ut. ee). Spore lengths of Glomeromycotina were collected in the supplementary data of  Davison et al. (2018). New scripts for delineating evolutionary units (EU) are available in the R-package RPANDA (through this GitHub branch for the moment: https://github. com/hmorlon/PANDA/tree/Benoit_phylosignal). The alignment of all the SSU rRNA sequences and their associated metadata publicly accessible through the Open Science Framework (osf) portal: osf. io/y2ts5.
2020) , suggesting that the activation of these genes could be a primary response to the hybridization stress. Discrepancies found between RT-qPCR and RNAseq results highlight the different sensibilities of these two techniques. In addition, we found that the chromatin mark levels of the piRNA pathway genes was similar to the parental species, with only differences in a few genes, which is consistent to the general trend observed in other genes. In addition, we found that the piRNA amount changes were not associated with TE expression changes in hybrids, because we found an intermediate inheritance of piRNAs between the parental species in the hybrid, being D. koepferae the parental species with the highest amount, as observed in our previous work  (Romero-Soriano et al. 2017). We suggest that changes in transcript amounts in hybrids are either the result of the enrichment/impoverishment of a specific mark or the disproportion in the active/repressive mark content, together with other mechanisms such as cistrans compensatory regulation.
ChIPseq Visualization Patterns ChIPseq sequenced reads were trimmed using Trimmomatic software v0. 39  (Bolger et al. 2014 ) and aligned to the D. buzzatii genome downloaded from the Drosophila buzzatii Genome Project web page (http://dbuz. uab. cat, last accessed January 7, 2015) using Bowtie2 v2. 3. 5. 1  (Langmead and Salzberg 2012). For the alignment, the default parameters of the -very-sensitive-local mode with two extramodifications to increase the sensibility (-local -D 20 -R 3 -L 20 -N 1 -p 12 -gbar 3 -mp 5,1 -rdg 4,2 -rfg 4,2) were used to reach the highest percentage of aligned reads with both parental species and their F 1 hybrids. Reads with map quality score lower than 30 and unmapped reads were filtered using Samtools v1. 10  (Li et al. 2009 ) and excluded from further analysis.
9  (Grabherr et al. 2011 ) and the corresponding RNAseq. Drosophila buzzatii and D. koepferae transcriptomes were then aligned against all the ChIPseq inputs (supplementary file 4: Alignment_inputChIPSeq, Supplementary Material online). We also randomly selected 40 genes amongst Trinity outputs, and computed nucleotide divergence between parental species using the Mega software  (Tamura et al. 2021 ) and the Jukes-Cantor model  (Jukes and Cantor 1969)  (supplementary file 4: Nucleotide_Divergence, Supplementary Material online). Both, the similar alignment percentages and the low average divergence, indicate that bias of using D. buzzatii as reference genome, if any, is marginal. Nevertheless, we decided to be conservative and use only the protein coding genes. Trimmed RNAseq and ChIPseq reads were aligned to the D.
TE Alignment TE RNAseq  (Romero-Soriano et al. 2017)  and ChIPseq read counts were analyzed using the TEcount module of the TEtools pipeline  (Lerat et al. 2017 ) (available at https:// github-com/l-modolo/TEtools). First, the manually constructed TE library containing consensus TEs from both D. buzzatii and D. koepferae, described in a previous work  (Romero-Soriano et al. 2017) , was aligned with the RNAseq and ChIPseq reads, using Bowtie 2 v2. 2. 4 (Langmead and Salzberg 2012) with the most sensitive option and keeping a single alignment for reads mapping to multiple positions (-very-sensitive). Read counts were computed per TE family, adding all reads mapped on copies from the same family. Count tables corresponding to genes and TEs were concatenated and were used for the differential expression and enrichment analyses.