dataesr/scientific-paragraphs-categorization huggingface dataset:
The correlation of relative abundance of taxonomic groups across the different analysis treatments was made using Spearman's signed rank tests. Similarly, correlations of the abundances of top bacterial classes and genera were made using separate Spearman's signed rank tests and plotted using 'corrplot' R-package v0. 90  [50]. Detection of ecological signals (effect of community type, river, and sample site) in alpha diversity was conducted with Kruskal-Wallis tests and associated post-hoc pairwise comparisons using 'pgirmess' R-package v1. 7. 0  [51]. Detection of the same ecological effects on the structure of the microbial communities was conducted using separated PERMANOVAs performed on beta-diversity matrices using the 'vegan' R-package (900 permutations). The intensity of the signal was measured using the Rsquared (R 2 ) obtained in PERMANOVA's outcomes.
Such values were compared across treatments to assess the respective impact of ASVs vs. OTUs, rarefaction, and the beta diversity metric used on PERMANOVA's result, using random forest models provided in 'randomForest' R-package v4. 6-14  [52]. These models are based on successive decision trees to allow ranking of the effects of several correlated variables on a given response  [53] , here on the intensity of the detected ecological signal.
Structure and coverage of 16S rRNA datasets ASV and OTU-based analyses resulted in significantly different numbers of operational units in non-rarefied datasets, with fewer ASVs (mean±standard deviation 303±215 per sample) than 97%-OTUs (1,386±1,026), and 99%-OTUs (1,708±1,319) (Wilcoxon signed-rank tests, p<0. 001 in all comparisons), despite a better coverage of ASV-based data based on rarefaction curves (S1-S3 Figs in S1 File). Before rarefaction, Chao's coverage was significantly lower in OTU-based datasets (93. 7±5. 6% and 91. 5±7. 8% respectively in 97%-and 99%-OTUs) than in the ASV-based dataset, where it averaged 100±0% (Wilcoxon signed-rank tests, p<0. 001 between OTUs and ASVs, P>0. 05 between 97%-and 99%-OTUs). Accordingly, after rarefaction, the number of species in each sample was close to the saturation value on rarefaction curves for the ASV datasets (coverage of 98.
0±1. 8% after rarefaction to 2000 sequences), but was well below the saturation point based on 97%-and 99%-OTUs (Coverage of 85. 8±10. 3% and 83. 6±12. 6% after rarefaction to 2000 sequences) (S1-S3 Figs in S1 File). Before rarefaction, the abundance distribution of operational units was more even in the ASV dataset than in the 99%-OTU dataset and 97%-OTU datasets (.
Alpha diversity metrics All alpha diversity metrics were distinct between sequence processing methods (ASVs vs. OTUs), at every rarefaction level (Wilcoxon signed-rank tests p<0. 001 in all comparisons, Fig 2  , S3  Fig in  S1 File ). ASV-derived richness was roughly twice as low as 99%-OTU and 97%-OTU richness, which were closer to each other (99%-OTU richness was 1. 1 times higher than 97%-OTU richness). The difference between ASV-and OTU-derived richness was even higher using iChao1 (4. 8-5. 3 higher in 97%-and 99%-OTUs datasets than in ASVs, respectively) and ACE (3. 5-4. 1 times respectively) (S3 Fig in  S1 File ). However, differences between the approaches were lower in the case of Shannon's alpha diversity index (1. 4 and 1. 7 times respectively,  Fig 2). The ranks of alpha diversity metrics were more congruent across ASV-vs.
The effect of methodological choices on other alpha diversity metrics is available in S3 Fig in  S1   Rarefaction also slightly increased agreement between ASV and OTU-based beta diversity metrics, for both OTU thresholds (Table  2 ). Rarefaction had only a small effect on Bray-Curtis and Jaccard indices, with a correlation >0. 9 between rarefied and unrarefied data and no effect on W-Unifrac (r = 1; S4 and S5 Figs in S1 File). Rarefaction did, however, have a greater influence on U-Unifrac values computed on OTU datasets (r = 0. 82-0. 87, S5 Fig in  S1 File ). Methodological choices had different effects in detecting differences between communities. Random Forests tests on PERMANOVAs based on presence/absence indices (Jaccard and U-Unifrac) revealed that the choice of OTU vs. ASVs had a lower contribution in PERMANO-VA's outcome compared to the other methodological choices (rarefaction level, beta diversity index) (S6 Fig in  S1 File ).
Composition While Gammaproteobacteria (12. 0±9. 0%), Cyanobacteria (11. 1±12. 4%), Alphaproteobacteria (10. 0±8. 0%), and Planctomycetes (15. 7±15. 3%) were the main classes identified in all datasets and rarefaction levels used (Fig 5 ), several major classes of Bacteria had their relative abundance computed on OTUs poorly correlated with abundance computed on ASVs. These classes included Bacilli (Spearman's r = 0. 34±0. 21), Clostridia (r = 0. 66±0. 28), Bacteroidia (0. 75 ±0. 13), and Actinobacteria (0. 81±0. 17). Other major classes presented a higher reproducibility across methods (0. 83±0. 09). The class Verrucomicrobiae, which was dominant in OTU datasets (8. 7±14% of relative abundance), was detected at a much lower level within ASVs, where it wasn't among the most common classes (1.
7±0. 6%) (Fig  5 ). Conversely, the ASV pipeline characterized Mollicutes within the mussel gut microbiome (13. 4±13%), which remained completely undetected in the OTU datasets (Fig  5 ). Differences across methods were more pronounced at the genus level, with only ~50% of the most common 30 genera shared between ASV and OTU-based datasets (excluding genera belonging to unclassified families), and correlations of the relative abundance of such shared genera generally below 0. 9 (Spearman's correlation,  Fig 5; S3    detected genera unique to ASV datasets included Methyloglobus, Escherichia/Shigella, and Clostridium sensus stricto (S3 Table in S2 File). Compared to the differences between ASV and OTU methodologies, rarefaction and OTU identity threshold had only a minor impact on overall class-or genus-level classification, with, for instance, a correlation of >0.
Discussion Using a dataset that contained three types of microbial communities (mussel gut, sediment, and seston), we tested whether alpha and beta diversity metrics and community composition were consistent across sequences clustering methods, in addition to other methodological choices such as rarefaction and index. We detected significant influences of ASVs vs. OTUs on richness estimates, both on their values and the ranking of samples, as reported in previous studies  [15, 19, 23]. Further, these differences resulted in discrepancies in the detection of biological signals. These discrepancies were accentuated by sampling bias corrected richness estimators like iChao1 and ACE, which, of all those tested, were the least correlated across the ASV and OTU datasets (Table  1 ). This discrepancy in diversity estimates was driven by the lower evenness of relative abundances and a much higher number of rare units with OTUs than ASVs (Fig  1 ).
Hence, the large number of OTUs with low abundance likely induced greater variation in such estimators between OTU and ASV-based datasets, as such estimators use low-abundance units to infer the "real" richness within communities  [54, 55]. As a consequence, the impact of ASVs vs. OTUs on the value of all richness measures was greater for  File), due to the typical high microbial richness in environmental samples, especially sediment  [56] , contrary to gut mussel microbiomes which are dominated by fewer operational units  [30, 57]. These findings contrast with Glassman and Martiny  [14] , who did not find a major effect of ASV vs. OTU-based pipelines on richness for bacterial communities sampled on leaf litter, which may be due to the lower richness in leaf litter environments. Purahong et al.
While the use of rarefaction is considered a poor way of normalizing sequence data counts that can sometimes lead to false conclusions  [25] , it is also more likely to remove the rare operational units that could originate from sequencing errors, and may therefore be useful in richness estimation in OTU datasets  [59]. In contrast, the impacts of ASV or OTU selection and rarefaction were much lower on Shannon entropy (with a correlation >0. 9 at every rarefaction level studied, Table  1 ), and were comparable to previous comparisons on natural and mock communities  [23, 60]. Indeed, the Shannon index accounts less for the rarest operational units than species richness metrics and thus is more robust to the difference in the number of rare units obtained with ASVs vs. OTUs clustering. As with previous studies  [14, 22, 23] , we reported more consistency between OTU and ASV-based beta diversity metrics (Table  2 ).
Beta diversity estimates based on 99%-OTUs were slightly more correlated to those based on 97%-OTUs, which is expected given the higher resolution of 99%-compared to 97%-OTUs, making them more comparable to ASVs  [60]. Such correlation was also variable across beta diversity indices and was lower in the case of U-Unifrac, while it was higher in all other indices. This is consistent with previous observations from Nearing et al. [15] , who computed Bray-Curtis, U-Unifrac, and W-Unifrac dissimilarities on host-associated and soil datasets using several ASV-based pipelines and one OTU based pipeline. They also observed that U-Unifrac correlated poorly across pipelines, while the abundance-weighted indices were highly consistent. We further compared the ecological signal raised by PERMANOVAs, testing the community type, river, and site's effect on ASV-and OTU-based datasets (Fig  4 ).
Based on previous observations, distinction across sample types was expected, as these communities are extracted from very distinct environments  [56, 61, 62]. Differences across rivers and sites were also expected in seston and sediment, as is typically the case in similar environmental communities  [62]. Moderate differences across sites in the mussel microbiome were also previously observed by our group based on 97%-OTUs  [30, 57]. According to the higher correlation between beta diversity estimates from ASVs and 99%-OTU datasets, PERMANOVA outcomes were also more similar between these two datasets, 99%-OTUs detecting higher river and site effects in seston and lower detection of the distinction between sample types than 97%-OTUs, consistent with patterns based on ASVs (Fig  4 ). The only exception to an overall high correlation between OTU-and ASV-based datasets was the U-Unifrac index, which showed a poor agreement between OTUs and ASV-based ecological signal (Fig  4 , S7 Fig in  S1 File ).
The choice of beta diversity index is crucial before a PER-MANOVA, as while abundance weighted indices (taxonomic Bray-Curtis or phylogenetic W-Unifrac) are more sensitive to a change in dominant members of the community across contrasted conditions, indices based on presence-absence (taxonomic Jaccard or phylogenetic U-Unifrac) are more sensitive to the rare members of the community, and are therefore generally more useful in detecting subtle changes across near-identical communities  [63]. Surprisingly in our study, the other presence-absence index tested, Jaccard, exhibited a higher correlation across ASVs vs. OTU methodologies (Fig 4, S7 Fig in  S1  File), highlighting that in our dataset, differences across ASVs and OTUs are due to the rarest units, which are phylogenetically distinct from the more dominant ones.
The Jaccard index ignores such phylogenetic information, allowing a more consistent pattern between OTUs and ASVs using this index. Although clustering programs producing OTUs cluster more dissimilar sequences into the same unit than ASVs, previous authors suggested they could also generate a higher number of spurious, low-abundance OTUs that would inflate richness measures  [15, 19]. Similarly, in a shrimp microbiome study, richness measurements were systematically higher in OTUs datasets compared to ASVs  [60]. In our study, OTU-based datasets did generate up to 9 and 12 times (respectively for 97%-and 99%-OTUs) more OTUs than ASVs on the same samples before rarefaction (Fig 1, S1 Fig in  S1 File ). With the present data, we cannot conclude whether the high number of low-abundance, phylogenetically distinct OTUs are indeed erroneous.
However, they certainly impacted the functioning of the U-Unifrac index, which detected lower ecological signals overall. Prodan et al. [19]  reported that applying a cutoff on sequence abundance within Mothur before constructing the OTUs was necessary for reducing the erroneous inflation of observed richness. The removal of singletons (OTUs represented by only one sequence) can drastically reduce the variation across sample replicates within the same sequencing run  [64] , underlining the sensitivity of OTU-based methods to sequencing errors. Including their removal may be important for OTU-based analyses to pre-filter potentially spurious, low abundance sequences that could induce the construction of numerous erroneous OTUs. However, in our study, rarefaction, while improving the agreement of alpha diversity estimates between OTUs and ASVs by removing low-abundance OTUs (Table  1 ), only marginally impacted the signal based on beta diversity, and showed no improvement of the ecological signal based on U-Unifrac in any dataset (Table  2 , S7 Fig in  S1  File).
This suggests that additional filtering of sequences may be needed for performing U-Unifrac on OTU datasets, or at least that patterns should be compared to those obtained from other beta diversity indices to identify potential bias due to rare units. In addition to the influence of the analysis method on patterns in beta diversity, there were significant differences between ASVs and OTUs in terms of community composition, both at the genus and class level, including in the most abundant taxa (  Fig 5, S3 Fig in S1 File). Similar discrepancies were highlighted by Straub et al. [23] , who found that the relative abundance of several dominant taxa was poorly reproduced across both methodologies. We found that the OTU identity threshold and rarefaction level had only a marginal effect on the identity and the relative abundance of major classes and genera.
Regarding the differences across methodologies, ASVs notably detected Mollicutes as preponderant in the mussel gut microbiome, which were absent in the OTU datasets. In contrast, OTUs detected much higher amounts of Verrucomicrobiae in the seston and sediment samples than were detected by the ASV approach. These differences are surprising given that both OTU and ASV approaches classified sequences to the same database. In this specific case, while the cultivation of Mollicutes is difficult  [65] , they have been successfully isolated from the digestive gland of various hosts, including aquatic invertebrates  [66] , making their detection within the gut of the mussels studied here plausible. On the other hand, Verrucomicrobia are also found in soil and water environments, and their presence is not surprising.
Conclusions Using a dataset comprising environmental and host-associated samples, we revealed important differences in results obtained from an ASV-based denoising approach (DADA2) and an OTU-based clustering method (Mothur). We showed that the difference in tests' outcome and the ecological conclusion is predominantly due to rare operational units, which are more prevalent in OTU-based clustering approaches and therefore have more impact on indices computed only on presence/absence of units. However, there are also major differences in the occurrence and relative abundance of major bacterial taxa identified by both approaches. Other methodological choices (rarefaction, OTU identity threshold) had generally lower effects, although rarefaction and a clustering of 99%-identity OTUs tend to reconcile the differences between OTU-and ASV-based approaches.
The reviews presented here have certain limitations. First, they have a linguistic bias because they mainly examined publications written in English. Second, they focused on gender gaps, which leaves out issues related to other categories, such as ethnicity, age, and social class. Third, the quantitative review was limited to two widely used search engines, thus overlooking less popular and independent platforms at the national, regional, or international levels. However, despite these limitations, the qualitative and quantitative data analysis in the present chapter can contribute to migration scholars' reflections on how transnational migration studies can be further developed, notably the gender and migration scholarship. Before presenting the quantitative review, the chapter examines the evolution of gender and migration studies in various social contexts.
Acknowledgements Previous versions of this chapter were presented at two events in Switzerland: the Know the gap Lecture Series focusing on "Talking gender, equality and diversity" at the  World Trade Institute  (wti) at the  University of Bern  on 19 February 2020; and as a keynote lecture on 2 July 2021 during the 4th Neuchâtel Graduate Conference of Migration and Mobility Studies organised by the nccr -On the Move of the  Swiss National Science Foundation. I thank  Elisa  of the wti,  Robin Stünzi  of the nccr -On the Move, and  Janine Dahinden  of  University of Neuchâtel  for giving me the opportunity to share my analysis and reflections regarding gender gaps in migration studies.
Background: Clinical trials allow development of innovative treatments and ameliorate the quality of clinical care in oncology. Data show that only a minority of patients are enrolled in clinical trials. We assessed enrolment in clinical trials and its correlates among women with early breast cancer. Methods: We included 9516 patients with stage I-III breast cancer from the multicenter, prospective CANTO study (NCT01993498), followed-up until year 4 (Y4) post-diagnosis. We assessed factors associated with enrolment using multivariable logistic regression. In exploratory, propensity score matched analyses, we used multiple linear regression to evaluate the relationship of enrolment in clinical trials with the European Organisation for Research and Treatment of Cancer Quality Of Life (QoL) questionnaire (EORTC QLQ-C30) Summary Score and described clinical outcomes (distant disease event, invasive disease event, and death by any cause) according to enrolment.
Data source CANTO (ClinicalTrials. gov/NCT01993498) is a prospective observational cohort study that includes patients with stage I-III breast cancer across 26 participating French cancer centres since 2012. CANTO recruiting centres include 20 comprehensive cancer centres, two university hospitals in Paris, two public non-teaching hospitals and two private hospitals (Supplementary Figure  S1 , available at https://doi. org/10. 1016/j. esmoop. 2022. 100513). UNICANCER, the National Cooperative Group of French Cancer Centers, coordinates CANTO. The CANTO study was approved by the national regulatory authorities on 14 September 2011 (ref 2011-A011095-36) and the central ethical committee for human subjects (CPP -Ile de France 7, on 14 October 2011dref 11-039). All patients were age !18 years old and provided written informed consent at inclusion.
Patients were assessed at diagnosis of breast cancer, and follow-up data were available through year 4 (Y4) after diagnosis at the time of the present analysis. Patients could choose to enrol in concomitant observational or interventional studies at any time during the follow-up period. CANTO study procedures were previously published. 17 We used the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist for observational studies  18  to assess the quality of our report.
Other variables Clinical variables. Clinical, sociodemographic and socioeconomic (educational level, income, and occupation), tumour and treatment-related characteristics, and data regarding cancer centre of care [including geographical region and patient volume (expressed as the number of patients included in the CANTO cohort by centre)] were available at breast cancer diagnosis and categorized as in Table  1. Anxiety and depression were assessed through the Hospital Anxiety and Depression Scale. 20 tient-reported outcomes. Patient-reported outcomes (PROs) were collected using the European Organisation for Research and Treatment of Cancer (EORTC) quality-of-life (QoL) questionnaires QLQ-C30, 21 BR23,  22  and FA12. 23 A higher-order QoL metric, the C30 Summary Score (C30 SumScore), was used to assess overall QoL.
A multiple linear regression model evaluated the association between clinical trials enrolment and C30 SumScore at Y4 after diagnosis, adjusting by C30 SumScore and covariates at baseline. The rates of dDE, iDE, and death by any cause were compared by enrolment in clinical trials using chi-square tests. Sensitivity analyses. To confirm the robustness of our findings, we carried out the analyses of factors associated with enrolment among a subset of patients diagnosed with breast cancer from 2012 to 2015, who had complete followup available 4 years after diagnosis at the time of the most recent database update (n ¼ 7810). All tests were two-sided with a 95% confidence interval (CI) and a P value of <0. 05 was considered statistically significant. Statistical analysis was carried out using SAS statistical software Version 9.
Swimming kinematics In order to characterize their swimming behaviour, 39 swimming trials were recorded (N M = 17, N F 1 = 11, N F 2 = 11) with a high speed camera (Phantom MIRO, 1000fps) by filming snakes swimming in a 4m-long, 40cm wide plexiglass swimming tank. Animals were filmed in ventral view and illuminated using two infrared lights. Kinematic parameters (Fig. 1 ) were extracted by digitizing the snake's midline using an custom-written Matlab routine. We measured the following anatomical and kinematic parameters: the total length, from the tip of the snout to the tip of the tail (L), maximal diameter (d), body mass (w), forward swimming speed (U ), tailbeat frequency (f ), tailbeat amplitude (A) and body wavelength (λ). These data are summarized in Table  1. The Reynolds and Strouhal numbers were also computed as : Re = U L ν (1) St = f A U (2) With ν the water kinematic viscosity.
Base change The vector fields were then imported in an other custom-written MATLAB routine for processing. The swimming trials being in free swimming conditions, the movements of the snake in the measurement volume can be quite different from one sequence to another which makes the comparison challenging. In order to tackle this problem, we changed the regular Cartesian coordinate system (XY Z) to a new rotated coordinate system (X rot Y Z rot ) for each swimming trial. For each recorded sequence the water jet created by the last tailbeat was tracked with a velocity thresholding method. The trajectory of the jet is estimated by a linear regression of the superimposed position of the jet center. A new base is then created by a rotation around the Y-axis so that the new X rot axis is co-linear to the jet's trajectory and the Z rot axis perpendicular to it.
At the tRNA level, 251 D-sites were detected, of which 96% were located on the D-loop between positions 16 and 22  (Figures 3A and 3B)  as expected. We could unambiguously assign 87, 113, 9, and 28 D-sites to Dus1, Dus2, Dus3, and Dus4, respectively. The single-nucleotide distribution of the D-sites matched with the known substrate specificities of the Dus enzymes (Figure  3C ). Intriguingly, the dus dependency of 14 sites could not be statistically determined (Figures  3B  and S5E ), which suggests that the presence of a D-site at a given position may be required for the subsequent modification of another. Alternatively, several Dus enzymes may target the same site in rare cases. In parallel, 104 D-sites were also detected on 92 mRNAs, 90% of which were located within the coding region, similarly to the   (legend continued on next page) previous dataset.