dataesr/scientific-paragraphs-categorization huggingface dataset:
We used spectrophotometry to measure specular transmittance of the transparent patches, which is a    quantitative measurement of transparency. As birds are assumed to be the main predators of butterflies  (Brower, 1984) , we applied bird perceptual vision modelling on the resulting spectra to calculate the chromatic and achromatic contrasts (respectively dS and dL) for each pair of species from our dataset. If transparent patches among co-mimetic species are more similar than expected at random or given the phylogeny, contrasts between pairs of co-mimetic species are expected to be smaller than predicted at random and given the phylogeny, respectively. We only compared analogous spots (i. e. occupying a similar position on the forewing) between species. The results presented in Figure  1  show that for three spots out of five and across all mimicry rings the difference in achromatic contrast (dL) between co-mimetic species is significantly smaller than expected both at random and given the phylogenetic distances between species (Figure  1B ), irrespective of the illuminating light or the visual system considered (see Supplementary file 1a and b for results under the full range of conditions).
A lack of statistical power may also explain why we do not find any convergence or similarity for some mimicry rings as many of these mimicry rings only comprises two (e. g 'blue' mimicry ring) to three (e. g. 'hewitsoni' mimicry ring) species in our dataset. Achromatic aspects (achromatic contrast dL) appear more significant than chromatic aspects (chromatic contrast dS) (Figure  1 , Supplementary file 1a,b), suggesting that selection may act more on broadband transmittance (which is related to the degree of transparency) than on colour in transparent patches.
Scales could be lamellar (55 % of species), or piliform (45%). In our dataset, piliform scales (mainly bifid) appeared to be almost exclusively found in the Ithomiini tribe, although one erebid species also harboured monofid piliform scales (Figure  3 ). We also revealed an unexpected diversity of the nanostructures that cover the wing membrane (Figure  2B ). In our sample, we found five types of nanostructures: absent (10 % of species), maze (3%), nipple arrays (55%), pillars (21%), and sponge-like (11%). Phylogenetic signal tests show that both micro-and nanostructure features are highly conserved in the phylogeny (Figure  3 , Supplementary file 1c), suggesting the existence of constraints in the developmental pathways underlying micro-and nanostructures. However, the value of δ, the metric   used to quantify phylogenetic signal of traits with discrete states  (Borges et al.
, 2019) , is higher for scale type than for nanostructures. This means that the phylogenetic signal is stronger for scale type than for nanostructures. Moreover, in the nymphalid tribe Ithomiini, which is highly represented in our dataset, microstructures seem to be more conserved (all species but the basal species M. curvifascia have piliform scales in transparent patches) than nanostructures (all five types of nanostructures, mixed in the Ithomiini clade, Figure  3 ). We then investigated the convergence of structures among co-mimetic species by testing whether co-mimetic species shared structures more often than expected at random and given the phylogeny (see Materials and methods for details). We show that, across all mimicry rings, co-mimetic species share structural features (either scale type, nanostructure type, nanostructure density or structural syndrome, defined as the association of scale type and nanostructure type) more than expected at random and given the phylogeny (Figure  4 ).
Phylogeny We used both published and de novo (see 'Phylogeny' section in SI for detailed protocol) sequences from one mitochondrial gene and seven nuclear genes, representing a total length of 7433 bp to infer a molecular phylogeny (knowing that for many taxa there are missing data, see Supplementary file 2a). To improve the phylogeny topology, we added 35 species representing eight additional families to the dataset (see Supplementary file 2a). We performed a Bayesian inference of the phylogeny using BEAST 1. 8. 3  (Baele et al. , 2017). We forced the monophyly of some groups and we added eleven secondary calibration points (see Supplementary file 2b) following  Kawahara et al. , 2019.
We used the receptor-noise limited model of  Vorobyev and Osorio, 1998  with neural noise and with the following relative cone densities 1:1. 9:2. 7:2. 7 (for UVS:S:M:L,  Hart et al. , 2000)  and 1:0. 7:1:1. 4 (for VS:S:M:L,  Hart, 2004)  for UVS and VS vision respectively, and a Weber fraction of 0. 1 for chromatic vision  (Lind et al. , 2013a; Maier and Bowmaker, 1993)  and 0. 2 for achromatic vision (average of the two species studied in  Lind et al. , 2013b)  for both visual systems to compute chromatic and achromatic contrasts. In total we calculated four different vision models, using the R package pavo  (Maia et al. , 2019) , representing all combinations of bird visual systems and light environments. We extracted the chromatic and achromatic contrasts between each pair of species in the dataset, comparing only analogous spots (i.
Statistical analyses All statistical analyses were performed with the software R version 3. 6. 2 and 4. 0. 3 (R Development Core  Team, 2019). All scripts and data used to produce the results of statistical analyses are available at Pinna, 2021, https:// github. com/ ChPinna/ Lepidoptera_ Transparency-mimicry; copy archived at swh:1:rev:fb5017880f034cfd818d7f5f5f4acc51530680fb.
Phylogenetic signal To assess whether transmission properties and structural features were conserved in the phylogeny, we estimated the phylogenetic signal of each variable. For quantitative variable (mean transmittance, scale density, scale length, scale width, nanostructure density, and membrane thickness), we calculated both Pagel's λ  (Pagel, 1999 ) and Blomberg's K  (Blomberg et al. , 2003)  implemented in the R package 'phytools'  (Revell, 2012). For multicategorical variables (scale type and nanostructure type), we used the δ-statistic  (Borges et al. , 2019)  and we compared it to the distribution of values of δ when the trait is randomised along the phylogeny to estimate whether the trait is randomly distributed along the phylogeny. Finally, for binary variables (scale colour), we used Fritz and Purvis' D  (Fritz and Purvis, 2010)  implemented in the R package 'caper'  (Orme et al.
Convergence on structures We tested whether structural features (microstructures, i. e. scales, nanostructures, and nanostructure density but also structural syndrome, that is the association between microstructures and nanostructures) are more similar between co-mimetic species than expected at random. To do so, we considered every pair of species in our dataset and we calculated the number of co-mimetic species sharing the same structural features. We compared this number to the null distribution of the number of species sharing the same structural features where the structural feature has been randomised 10,000 times, a method similar to that used in  Willmott and Mallet, 2004. We calculated the p-value as the proportion of randomisations where the number of species sharing structures is higher than the observed number.
Link between transparency (mean transmittance) and structures To assess the link between structural features and the degree of transparency we only used the spectrophotometric data of the points that correspond to the location of the SEM images (between 1 and 3 points per species) and we calculated the average of mean transmittance over 300-700 nm for each specimen (see Supplementary file 1d). We tested the link between this average mean transmittance and all the structural features we measured (scale type, scale colour, scale density, scale length, scale width, nanostructure type, nanostructure density, membrane thickness and the following interactions: interaction between scale type and scale density, interaction between scale density and nanostructure density and the triple interaction between scale density, scale length and scale width), while controlling for phylogenetic relationships by performing Phylogenetic Generalised Least Square regression (PGLS) implemented in the R package 'caper'  (Orme et al.
, 2018). We compared all possible models with all the structural variables, but we prevented some variables from being in the same model because they were highly correlated, using the R package 'MuMIn'  (Barton, 2019). Among the 308 models, we selected the best models (difference in AICc inferior to 2). Eight such models were retained. achromatic contrasts and phylogenetic distances to account for the effect of phylogeny on the achromatic contrast and we considered the mean of residuals for co-mimetic species. If co-mimetic species are more similar than expected according to their phylogenetic relationship, the mean of residuals should be negative. To test whether the mean of residuals is smaller than expected according to the phylogeny, we randomised residuals over all pair of species and we calculated the mean of residuals for co-mimetic species.
Acknowledgements We thank  Jonathan Pairraire  and  Céline Houssin  for the help with photonic imaging and  Josquin Gerber  and  Edgar Attivissimo  for helping with spectroscopic measurements. We thank  Benoit Vincent  for the identification of some Arctiini specimens. We thank  Keith Willmott ,  Raúl Aldaz ,  Paola Santacruz Endara ,  José Simbaña ,  Ramón Mamallacta ,  Alexandre Toporov ,  Stéphanie Galusser ,  Jay, Bruno  and  César Ramirez Garcia  for companionship and help in the field over the last 15 years, and  Gerardo Lamas ,  Luis Figueroa  and  Santiago Villamarín  for assistance with research permits. We are thankful to the  Institut de Physique du Globe de Paris (IPGP)  for giving us access to SEM and to the Peruvian and Ecuadorian governmental authorities for collection permits (021 C⁄C-2005-INRENA-IANP, 002-2015-SERFOR-DGGSPFFS, 373-2017-SERFOR-DGGSPFFS,  005-IC-FAU-DNBAPVS/MA ,  019-IC-FAU-DNBAPVS/MA ).
Funding Funder Grant reference number  Author Human Frontier Science Program   RGP0014/2016   Nipam H Patel Serge Berthier Marianne  Elias  Agence Nationale de la Recherche   ANR-16-CE02-0012   Serge Berthier Christine Andraud Doris Gomez Marianne Elias France-Berkeley  fund grant FBF # 2015-58   Nipam H Patel Marianne  Elias The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Data availability All the data needed for computing the analyses are provided in the supplementary material, Dryad (for phylogenetic tree and gene aligment, accessible here: 10. 5061/dryad. c2fqz617s) and GitHub repository (https:// github. com/ ChPinna/ Lepidoptera_ Transparency-mimicry copy archived at https:// archive. softwareheritage. org/ swh: 1: rev: fb50 1788 0f03 4cfd 818d 7f5f 5f4a cc51 530680fb). The sequences are submitted to GenBank and the accession numbers are provided in the Supplementary file 2a. Those sequences can also be seen in the alignment deposited in Dryad. The following dataset was generated: Author(s) Year Dataset title Dataset URL Database and Identifier Pinna CS, Piron-Prunier F, Elias M 2021 Data from: Alignement and phylogenetic tree of 106 Lepidoptera http:// dx.
Quantitative data Cohort study Women were recruited while attending one of the participating facilities for CC screening and were followed up until completion of the screening sequence. The inclusion and exclusion criteria in the cohort were similar to those considered for screening eligibility. Data were collected through questionnaires at each step of the screening sequence: after women returned their sample to the laboratory, after they received their HPV test result and after they underwent VIA and treatment if needed. When women did not return to the healthcare center over the 30 days following the sampling, they were contacted by phone. The questionnaires collected demographic information (age, screening history, living area, and socioeconomic level), CC literacy, intervention delivery data (choice of the sampling method, understanding of test results and recommendations (see Additional file 1: Table  S2 ), date and time for testing, result and postresult management, triage and treatment), and satisfaction at each step of the screening sequence.
An in-depth search was made by looking for data related to the implementation process and indicators. Facility routine health information system Each participating facility has a health information system that collects routine data. We retrieved data derived from two registries: CC screening process (clinical data) and HPV testing (laboratory data) for the first implementation semester. Collected clinical data consisted of women's screening history and description of screening steps (date and time, sampling method, HPV results, VIA results, treatment performed and referral when needed). Laboratory data consisted of day and time for sampling reception, sampling validity, HPV results and genotyping, and time and date for results transferred to midwives.
Qualitative data Direct observations Direct observations were performed at each participating facility. The anthropologist performed participant observations during the first 2 months of the project implementation. He repeatedly observed various screening activities that took place in the waiting room, in the cervical screening room (sampling performance, the results communication, VIA triage and treatment) and in the laboratory room until saturation was obtained. Realized at various times of the day and of the week over 2 months, the observations covered 90 medical visits and 30 laboratory procedures. Semistructured in-depth interviews Maximum variation sampling was used to achieve a diverse sample of providers of various qualifications, sexes and seniorities (n = 08 per facility) for individual in-depth interviews.
Fidelity The included individual measures of fidelity were derived from the cohort study and were related to the completion of the different steps of the screening and facility-based measures of fidelity that arose from the weekly supervision reports. Fidelity was considered optimal when the elements listed above were successfully completed and the expected rate was 100%. Reach Reach was measured at the facility level and was defined by the daily number of women screened per center. We assumed 20 working days per month with a level of desired achievement set at 4 women screened per day and per center. Data were extracted from the facilities' routine health information systems. Dose Indicators of dose were assessed at the individual level using data from the cohort study.
Acknowledgements We thank all the participants of our study: the women who agreed to participate and spent their time with us; the caregivers who allowed us to observe and collect data from them; our research assistants-Fatoumata KARAMBIRI,  Daraja KORASSOUM ,  Dorothée Salimata SALAMAMBÉRÉ  and  Alice TIENDREBEOGO. Mr. Koné Issiaka -for cleaning and managing the data; the local DOTW team based in Ouagadougou who hosted us and implemented the project, especially  Dr. Linda TRAORÉ ; and the lead DOTW team that supported us throughout the study. We also thank  Sidaction  for funding our research through the  Young researcher grant.
COVID-19 is associated with high mortality in patients with haematological malignancies (HM) and rate of seroconversion is unknown. The ITA-HEMA-COV project (NCT04352556) investigated patterns of seroconversion for SARS-CoV-2 IgG in patients with HMs. A total of 237 patients, SARS-CoV-2 PCR-positive with at least one SARS-CoV-2 IgG test performed during their care, entered the analysis. Among these, 62 (26Á2%) had myeloid, 121 (51Á1%) lymphoid and 54 (22Á8%) plasma cell neoplasms. Overall, 69% of patients (164 of 237) had detectable IgG SARS-CoV-2 serum antibodies. Serologically negative patients (31%, 73 of 237) were evenly distributed across patients with myeloid, lymphoid and plasma cell neoplasms. In the multivariable logistic regression, chemoimmunotherapy [odds ratio (OR), 3Á42; 95% confidence interval (CI), 1Á04-11Á21; P = 0Á04] was associated with a lower rate of seroconversion.
A low rate (67%) of anti-SARS-CoV-2 IgG development has been described in 21 patients with CLL (67%);  9  a slightly better rate (88%) was found in eight patient with acute myeloid leukaemia. 10 his study was registered in the ClinicalTrials. gov Identifier, NCT04352556. We investigated patterns of seroconversion for SARS-CoV-2 IgG in a broad range of patients with HMs. The is a retrospective study and the cohort came from the Italian Hematology Alliance on COVID-19 (ITA-HEMA-COV) project, that already provided information on mortality in HM with COVID-19. 1  Addressing the rate of humoral immune response to SARS-CoV-2 in HM patients who have been exposed to SARS-CoV-2 is a critical step to properly inform and develop vaccination strategies and monitoring in this vulnerable patient population.
Methods To investigate the presence of SARS-CoV-2 antibodies, we analyzed serology reports within the ITA-HEMA-COV database, starting 23 February 2020, until 17 February 2021 (first and last observed HM patient with COVID-19). The study was approved by the institutional review board of each centre. A total of 2 463 patients (735 myeloid, 1 214 lymphoid, 514 plasma cell neoplasms) were identified with a real-time polymerase chain reaction (RT-PCR) positive for SARS-CoV-2. We excluded 577 cases who died without performing serology and 1 649 coming from centres without test availability. Eventually, 237 patients with HM, SARS-CoV-2 PCR-positive and at least one SARS-CoV-2 IgG test performed during their standard care, were included. For SARS-CoV-2 serological testing we used commercial, widely available high-throughput immunoassays available at the centres such as the Abbott (SARS-CoV-2 nucleocapsid IgG, Chicago, IL, USA), the DiaSorin (SARS-CoV-2 Spike-1 and Spike-2 IgG, Saluggia, Italy), the Roche [SARS-CoV-2 nucleocapsid and Spike protein RBD (receptor-binding-domain) IgG, Basel, Switzerland] or the Siemens (Atellica, SARS-CoV-2 Spike protein RBD IgG, Munich, Germany) tests.
A multivariable logistic regression model, adjusted for the time from COVID-19 to first serology evaluation, was implemented using parameters of interest. In addition, we did this latter analysis on the subgroup on active therapy. The odds ratio (OR) together with the 95% confidence interval (CI) and Wald chi-square P-value were calculated. All statistical analyses were done with SAS version 9. 4 and R statistical software version 3. 2. 0 (Cary, NC, USA).
Acknowledgements We thank  Dr Roberta Mattarucchi  and  Dr Alessia Ingrassia  from the  Clinical Trial Center  of the A. S. S. T. and  Sette Laghi of Varese for managing the study protocol and procedures across many Institutional Review Boards. The list of ITA-HEMA-COV members is reported below: all provided and analyzed data. Open Access Funding provided by  Universita degli Studi dell'Insubria  within the  CRUI-CARE Agreement. [Correction added on 26 May 2022, after first online publication: CRUI funding statement has been added. ].
Behavioral analysis in rodents Male and female C57/BL6 (originally from The Jackson Laboratory) and global CD300f knockout (CD300fÀ/À in C57/BL6 background,  Xi et al. , 2010)  mice (4-5-months-old) were obtained from the SPF animal facility of the Institute Pasteur de Montevideo-Uruguay. Animals were housed under a controlled environment (20 AE 1 C, 12 h-light/dark cycle, free access to food and water). All behavioral analyses were videotaped and scored using the Any Maze software. Mice were handled according to the guidelines of the Institut Pasteur de Montevideo Animal Care Committee (protocols #004-16 and #014-16) and following the guidelines of the European Commission on Animal Care and Uruguayan national law and ethical guidelines on animal care.
Statistical analysis Differences between wild-type (WT) and CD300fÀ/À mice were determined by unpaired Students' t-test using Graph Pad Prism 6. 0 (GraphPad Software Inc. , San Diego, USA). The Kolmogorov-Smirnov test assessed data normality, and outlier values were identified using Grubbs' test. Data are presented as mean AE SEM. Human data were analyzed by SPSS 22. 0 (IBM Corporation, Armonk, NY). Variables are presented as number and percentage (%), or mean and standard deviation (SD). The sociodemographic characteristics, according to the genotypes, were compared using Chi-squared or One-Way ANOVA followed by Bonferroni's post-hoc test. The allele and genotype frequencies were evaluated by direct counting, and the Hardy-Weinberg equilibrium was calculated using the Chi-squared test. The magnitude of the association between different genotypes and GAD was estimated using odds ratio (OR) with 95% confidence intervals (CI), adjusted for age, sex, and ethnicity in the logistic regression analysis.
I). Data are represented as mean AE S. E. M. * represents p < 0. 05, and ** represents p < 0. 01 compared to WT. Abbreviations: OF: Open Field; EPM: Elevated Plus Maze; NSF: Novelty Suppressed Feeding; WT: Wild-type mouse; CD300fÀ/À: CD300f knockout mouse. time in the closed arms [t(15) ¼ 2. 18, p ¼ 0. 045, Supplementary Fig. 1F ] when compared to WT, while no differences were observed in the number of entries in the closed arms [t(15) ¼ 0. 04, p ¼ 0. 962, Fig. 1H ]. Next, we assessed behavior in a new environment in a fasting-state using the NSF test. CD300fÀ/À mice had a decreased latency to eat compared to WT mice [t(27) ¼ 2. 22, p ¼ 0. 035, Fig. 1I ], but no differences were observed in the time to approach the food pellet [t(26) ¼ 0. 37, p ¼ 0. 714, Supplementary  Fig 1G]. Altogether, these results demonstrate that, when placed in a new environment, CD300fÀ/À mice present lower anxiety-like behaviors, better habituation, and better emotional state in different behavioral paradigms compared to WT mice.
Acknowledgements This work has been supported by grants from  Conselho Nacional de Desenvolvimento Científico e Tecnol ogico (CNPq) ,  Coordenação de Aperfeiçoamento de Pessoal de Ensino Superior (CAPES)  and  PRONEX-FAPERGS (08/2009 -Pronex 10/0055-0 ),  International Brain Research Organization (IBRO)-LARC   PROLAB grant. Comisi on Sectorial de Investigaci on Científica (CSIC) ,  Uruguay ;  Programa de Desarrollo de las Ciencias B asicas (PEDECIBA) ,  Uruguay ;  Fondo de Convergencia Estructural del MERCOSUR  ( COF 03/1111 );  Banco de Seguros del Estado, Uruguay ;  Agencia Nacional de Investigaci on e Innovaci on (ANII) ;  International Center for Genetic Engineering and Biotechnology  ( ICGEB-CRP/ URY19-01 ). FNK received  International Society for Neurochemistry (ISN)  and  IBRO  awards, and a  CAPES fellowship  to perform this project.
Leaf Gas Exchanges Assimilation rate (Amax), transpiration rate (Tr), stomatal conductance (gsmax), and internal CO 2 concentration (Ci) were measured on both non-infested and leaves infested by the green aphid within the period June 2, 2013 to July 12, 2013. Leaf gas exchanges were measured with an infrared gas analyzer equipped with a 2 × 3 cm leaf chamber system (LI-6400, Li-Cor Inc. , Lincoln, NE, United States) and an external light source (6400-02B, Li-Cor Inc. ). Gas exchange was measured under optimal conditions for apple leaves  (Pincebourde and Casas, 2006; Pincebourde et al. , 2006; Massonnet et al. , 2007) : irradiance 1500 µmol/m 2 /s, leaf temperature 25 • C, leaf water vapor deficit 1 kPa and CO 2 concentration of 390 ppm in air. Leaves were allowed to equilibrate for 20-30 min before any measurements were taken, and data were discarded if stomatal conductance was not stable after 45 min.