dataesr/scientific-paragraphs-categorization huggingface dataset:
Allele distribution and isolation by distance evaluation Allele distribution and allelic introgression among the 56 strains were evaluated on concatenated sequences from the seven loci included in the MLSA scheme plus hsp70 and got genes. MHOM/FR/78/LEM75 (BCN 527) was used as a reference strain for subsequent analysis. Being non-contributory for genetic exchange analysis, conserved positions or singletons were not included in the analysis. Genetic isolation by distance was assessed through a Mantel test using the ade4 package of R software version 3. 6. 0 (R Core Team, www. R-project. org). The matrix of pairwise genetic distances, previously calculated using MEGA-X software with the p-distance method, was compared with a matrix of geographical distances obtained with the Geographic Distance Matrix Generator version 1.
MALDI-TOF analysis Sample preparation was done as described by  Lachaud et al. (2017). Strain spectra were acquired with an AutoFlex II MALDI TOF/TOF instrument from Bruker Daltonics (USA). Ten replicates of each strain were obtained using Flex Control version 3. 4 (Bruker Daltonics) software. Spectra were converted to mzML format using the MSConvertGUI tool (ProteoWizad  (Chambers et al. , 2012) ) and processed and analysed using Bionumerics software version 7. 6. 3 (Applied Maths). Baseline background noise was initially removed from raw spectra using the rolling disk tool (100 points size). Curves were smoothed with a Kaiser window operator (window size of 20 points and beta of 10) and noise was again removed with a second rolling disk (200 points) after being computed with a continuous wavelet transformation (CWT) algorithm (scale 1, noise percentile at 50, window size 501 points and minimum noise value of 0.
Shimodaira-Hasegawa test The Shimodaira-Hasegawa (SH) test was used to evaluate the congruence between phylogenetic trees constructed from genetic (hsp70, got, MLSA), biochemical (MLEE) and mass spectrometry (MALDI-TOF) data. Maximum likelihood (ML) trees were reconstructed with genetic data using the GTR + I + G model with a bootstrap of 100 repetitions using PhyML software version 3. 1  (Guindon et al. , 2010). The SH test was conducted using PAUP* Phylogenetic analysis using parsimony (*and other methods) version 4. 0a165 software (Swofford, D. L. , 2003. Sinauer Associates, Sunderland, Massachusetts) for each ML tree versus the trees obtained using the other approaches and 100 additional random trees, to avoid bias in the results. Values obtained were P values for a null hypothesis of no difference between trees.
Average nucleotide identity calculation Available and comparable genomes from the L. donovani complex (L. donovani and L. infantum, n = 12), L. tropica complex (L. tropica and L. aethiopica, n = 5) and L. major complex (L. major, L. turanica, L. arabica and L. gerbilli, n = 6) were downloaded from GenBank (see Supplementary Table  S4 ). Average nucleotide identity (ANI) values were obtained by pairwise comparison of genomes inside each complex using the MUMmer at JSpeciesWS online service (Ribocon GmbH) available at http://jspecies. ribohost. com/jspeciesws/ (Richter et al. , 2016).
Comparative evaluation of genetic distances within different Leishmania complexes: low genetic variability of the L. donovani complex Leishmania donovani complex genetic distances were assessed relative to other Leishmania complexes, i. e. those of L. major and L. tropica, by both NN analysis and systematic pairwise comparison of whole-genome sequences. Whatever the dataset used (i. e. an MLSA scheme based on seven genes, partial hsp70 gene or complete got gene sequence), the three NN networks showed comparable structures, with the three complexes clearly separated (Fig. 2 ). When complex structures were compared, those of L. major and L. tropica were clearly more diversified (long branches) and structured (less boxes) compared with the L. donovani complex. Low genetic variability in the L.
g. allelic positions 258 and 4287 in the MLSA analysis or position 370 in hsp70). However, the most striking fact revealed by these data is a genetic mixing at most of the allelic positions. For example, allele C at position 1479 was present in strains isolated from China, India, Israel, Yemen, Turkey, Ukraine, eastern Africa and countries around the Mediterranean basin, whereas allele T was present in eastern Africa and countries around the Mediterranean basin. In addition, heterozygous alleles (i. e. C/T) were detected in strains from the Mediterranean basin and eastern Africa. Similarly, differences in the spectra obtained by MALDI-TOF analysis were correlated with geographic distances; however, no isolated groups of strains were identifiable. Although no convincing chronology for the evolution is available, this genetic patchwork is probably the result of intensive genetic exchange over time.
donovani complex over 150 years to be proposed. For example,  Imamura et al. (2016)  reported a divergence of the Indian population in the mid-19th century, which matches historical large-scale outbreaks, while another divergence coincides with the end of the dichlorodiphenyltrichloroethane (DDT) spraying campaign in the 1960 s  (Imamura et al. , 2016). However, on a shorter time-scale, the detection of rapid population change might be more challenging. Baleela et al. (2014)  analysed 124 Leishmania strains isolated between 1954 and 2006 in Sudan and their dataset revealed multiple examples of temporally separated strains which were closely related genetically, at least on a time-scale of a few dozen years. As noted above, in our data there are no signs of a recent evolution of the population structure pattern across the L.
Acknowledgements The ISGlobal team is supported by the  Agència de Gestió d'Ajuts Universitaris i de Recerca  ( AGAUR, Catalonia, Spain ) ( 2017 SGR 00924 ), the  Tropical Disease Cooperative Research Network (RICET, Spain)  ( RD12/0018/0010 ), and the  Spanish Ministry of Science, Innovation and Universities  through the  ''Centro de Excelencia Severo Ochoa 2019-2023" Program  ( CEX2018-000806-S ). ISGlobal is a member of the  Centres de Recerca de Catalunya (CERCA) Programme, Government of Catalonia (Spain). This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Perspectives Diagnostic radioentomology is a powerful tool to monitor otherwise difficult to observe processes. However, this recently developed method could be further developed to increase its usefulness. Scans performed immediately after behavioral observations in hives with transparent sides can help relate density patterns with the behavior that generated them. Better labeling techniques (e. g. , avoiding sedimentation of contrast agent) of cell content would provide further opportunities to monitor processes involved in nectar storage and honey production with an even higher resolution. Scanning of storage combs should also be performed more frequently as done to date, aiming at investigating the processes of honey production with higher time resolution. Further, generating a database of densities of nectar and honey of specific origins (with various sugar compositions) or at different ripening stages could improve the sets of tools available and might relieve the need for calibration before each experiment.
The European Union (EU) requires that honey be tested for pesticide residues within the framework of the monitoring program covered by the Council Directive 96/23/EC  (European Communities, 1996). Maximum residue limits (MRLs) are defined for pesticides in honey (EU Pesticides Database) which mostly are between 0. 01 and 0. 05 mg/kg. For acaricides used in beekeeping, two MRLs were defined in the Commission Regulation (EU) No 37/2010 (2009) for amitraz (and its metabolite, 2,4dimethylaniline) and coumaphos: 0. 2 and 0. 1 mg/kg, respectively.
Acknowledgments The following authors were responsible for the respective manuscript sections: Ligia Bicudo de Almeida-Muradian , (Introduction);  Ortrud Monika Barth  and  Alex da Silva de Freitas  (Honey authenticity-botanical, geographical, and entomological origin);  Ligia Bicudo de Almeida-Muradian ,  Maria Teresa Sancho ,  Ana Pascual-Mat e, Jos e Augusto Gasparotto Sattler  (Honey physicochemical analysis for nutritional, chemical composition, and antioxidant activity);  Michael Eyer  and  Vincent Dietemann  (Investigating honey production with diagnostic radioentomology);  Anne-Claire Martel  (Pesticide residues in honey);  Wim Reybroeck (Antibiotics and chemotherapeutics in honey) ;  Gian Luigi Marcazzan ,  Carla Mucignat ,  Carla Marina Marchese  (Sensory analysis). The authors thanks  Lucia Piana  for participation in the Sensory Analysis section.
Daratumumab in combination with lenalidomide-dexamethasone (D-Rd) recently received FDA approval for the treatment of transplant-ineligible patients with newly diagnosed multiple myeloma (NDMM). The present PEGASUS study compared progression-free survival (PFS) in patients treated with D-Rd in the MAIA trial and patients treated with common standard-of-care regimens from the Flatiron Health electronic health record-derived deidentified database, which has data from patients treated primarily at community-based oncology practices in the United States. Individual-level patient data from both data sources were used to perform an anchored indirect treatment comparison (ITC) of D-Rd to bortezomib-lenalidomide-dexamethasone (VRd) and bortezomib-dexamethasone (Vd); lenalidomide-dexamethasone (Rd) was the common anchor for the ITC.
| Study population and design The present study (PEGASUS) used individual-level patient data from the global MAIA phase III randomized controlled trial and the US Flatiron Health electronic health record (EHR)-derived deidentified database (Figure  S1  in Appendix S1). An anchored indirect treatment comparison (ITC) study design was used to compare relative treatment effects across the two data sources (Figure  1 ). 6, 7 Details on the data sources and methodology are provided below.
| Flatiron health database Records for patients with NDMM were extracted from the Flatiron Health (FH) EHR-derived deidentified database. The FH database is a nationwide, longitudinal, demographically and geographically diverse database derived from EHR data. The database includes deidentified data from over 280 cancer clinics, primarily community-based oncology practices, visited by more than 2. 4 million US cancer patients. Curated via technology-enabled abstraction, the FH database includes both structured data (eg, patient demographics, laboratory results, and coded diagnoses) and unstructured data (eg, free text from clinician notes and laboratory reports) from the EHR to define clinical measures that are often unavailable in real-world data sources, including International Staging System (ISS) stage, ECOG performance status, and disease progression.
| Outcomes The primary outcome was PFS, defined as the time interval in months between LOT1 initiation and disease progression or death. In the MAIA cohort, the definition of disease progression was based on the International Myeloma Working Group (IMWG) criteria  2, 8, 9  for progressive disease. In the FH cohort, a derived progression measure based on the application of IMWG criteria to the setting of real-world data was used to ascertain progression events. Details on how disease progression and mortality are ascertained in FH are provided in Appendix S1. Patients were followed until their first PFS event, or were censored due to loss of follow-up or reaching the maximum follow-up time of 48. 5 months (the maximum available for MAIA participants). This analysis includes an additional 9 months of patient follow-up after the cut-off date for the first prespecified MAIA interim analysis; 2,10 details may be found in Appendix S1.
| Statistical methods The present study used an anchored ITC design (Figure  1 ), in which relative treatment effects were compared across the MAIA and FH study populations. The methodology follows published guidelines for an anchored matching-adjusted indirect comparison, a preferred approach for formally comparing results from two trials that share a common comparator or anchor when individual-level patient data are available from at least one of the trials. 6, 7  anchored ITC across two distinct study populations requires that the two populations be balanced on treatment effect modifiers. To satisfy this requirement, two steps were taken. First, a common set of eligibility criteria was applied to both the MAIA and FH cohorts. Second, FH patients treated with VRd, Vd, or Rd as LOT1 were weighted to resemble the MAIA trial population on measured baseline characteristics using propensity-score (PS) methods.
12 The purpose of the PS weighting was to ensure that (a) patients treated with VRd, Vd, or Rd in FH were balanced on measured baseline covariates, allowing for unconfounded comparisons of outcomes across the FH treatment groups, and (b) to ensure that the FH cohort resembled the MAIA trial population on possible treatment effect modifiers, allowing for a valid ITC to be performed across the two data sources. Standardized differences were used to assess covariate balance between each of the FH treatment groups and the MAIA trial population after PS weighting. An absolute standardized difference > 0. 1 was interpreted as a meaningful covariate imbalance  13  and was addressed by using both PS weighting and outcome model covariate adjustment (ie, doubly robust estimation). 14 r the ITC, Rd was used as the common anchor across the MAIA and FH data sources.
Within each data source, Cox proportional hazards regression was used to estimate HRs reflecting differences in PFS between treatment groups relative to the common Rd anchor (D-Rd vs Rd in MAIA; VRd vs Rd and Vd vs Rd in FH). The HRs reflecting direct treatment comparisons within each data source were then used to produce indirect estimates of the HRs for D-Rd vs VRd and D-Rd vs Vd. 6, 7  The proportional hazards assumption was evaluated by inspecting the Kaplan-Meier survival plots and testing the significance of interactions between treatment and follow-up time. Multivariate imputation by chained equations was used to impute missing baseline covariate data, and was repeated to create 10 complete datasets. 15, 16 After imputation, analyses were performed separately in each of the 10 complete datasets; the resulting parameter estimates and SEs were pooled according to Rubin's rules to obtain a summary parameter estimate and SE.
In this analysis, patients were analyzed according to their initial treatment regimen regardless of subsequent treatment discontinuation. An additional sensitivity analysis was performed to evaluate the possible impact of multiple imputation of missing baseline covariate data on our results. In this analysis, the FH patients were PS-weighted based on demographic characteristics alone (age, gender, and race), and clinical characteristics with higher rates of missing data (eg, ISS stage, ECOG performance status) were omitted from the PS model. T A B L E 1.
| Imputation of missing baseline covariate data In FH, the percentage of patients with missing data was low for race (8%), ranged from 13%-27% for the lab results with the exception of neutrophil count (55%), and was 30%, 40%, and 47% for cytogenetic risk, ECOG PS, and ISS stage, respectively. Data were complete for other baseline covariates in FH. In MAIA, data were missing on cytogenetic risk for 13% of patients; data were complete for other baseline covariates. For all covariates with missing values, covariate distributions were similar before and after multiple imputation (Tables  S2  and S3  in Appendix S1).
Patient management is also likely to differ in trial and real-world settings. 11 Patients in MAIA were treated with D-Rd or Rd until disease progression or unacceptable toxicity. Patients identified from the FH database were treated in real-world clinical settings according to the judgment of their treating physician rather than a protocol-driven standardized treatment plan. In addition, medication management to maximize treatment persistence may be more optimal in clinical trial settings. To account for this inconsistency, the primary analyses in this study were on-treatment analyses, with patients censored at treatment discontinuation for reasons other than disease progression or death. The on-treatment analysis and the anchored ITC study design help to minimize the impact of differences in patient management and treatment duration in the MAIA trial vs routine clinical practice, where treatment discontinuation in the absence of disease progression or death may be more common.
Sensitivity analyses that explored the possible impact of treatment discontinuation and loss to follow-up among FH patients resulted in findings that were similar to the principal study findings (Table  2 ). In addition, dosing and treatment schedules may vary in real-world settings compared with clinical trials. However, data on dosing were limited in FH. The anchored ITC study design would mitigate against bias due to systemic differences in dose intensity in real-world vs clinical trial settings. In addition, methods for assessing progression varied for the study populations, with MAIA trial patients having disease progression assessed at prespecified time intervals based on IMWG criteria. Due to these differences in patient populations, care settings, and methods for outcome ascertainment across MAIA and FH, an anchored ITC study design was chosen.
There were missing data on key patient clinical characteristics among the study groups identified from the FH database. An explicit determination of transplant eligibility status was unavailable in the FH database; therefore, age ≥ 65 years and no receipt of transplant during LOT1 were used as a proxy for transplant ineligibility. For consistency, the age ≥ 65 restriction was also applied to the MAIA trial population. Multiple imputation was employed to account for other missing baseline covariate data; however, the validity of multiple imputation relies on the assumption that the data are missing at random (MAR)-ie, that the probability of a data point being missing is unrelated to the true value of that missing data point after conditioning on the variables included in the imputation models.
74; 96% CI 0. 59, 0. 93; P =. 003) based on the most recent dataset update. 20 The difference in the HR estimates for VRd vs Rd in the present study and vs SWOG S0777 may be attributable to sampling variation (the CIs for the HRs have considerable overlap) and differences in the patient populations. In SWOG S0777, 69% of participants had intent for an eventual transplant at disease progression, and the median age of patients was 63 vs 74 years in the PEGASUS study. To our knowledge, PFS HRs for VRd vs Rd in SWOG S0777 have not been published for subgroups of patients aged <65 and ≥ 65 years. However, in subgroup analyses of OS based on age presented in the most recent SWOG S0777 update, 20  HRs for VRd vs Rd were 0. 64 (P =. 028) and 0. 77 (P =. 168) for patients aged <65 and ≥ 65 years, respectively, suggesting that there may be clinically meaningful treatment heterogeneity based on age and fitness, with a smaller relative treatment effect for VRd vs Rd observed in patients ≥65 years old.
ACKNOWLEDGMENTS The authors thank  Yu-Ning Wong, MD, MSCE , for providing insights on study design, and  Wenze Tang, MPH , for providing insights on study design and assistance with data management and statistical programming. Both were employed by  Janssen  during the conduct of the study. The authors thank  Jay Lin  and  Melissa Lingohr-Smith  of  Novosys Health, Greenbrook, NJ, USA , for providing medical writing support, which was funded by  Janssen Scientific Affairs, LLC. Editorial support was provided by  Eloquent Scientific Services , which was funded by  Janssen Global Services, LLC.
DATA AVAILABILITY Requests for access to the MAIA trial study data may be submitted through the Yale Open Data Access (YODA) Project site at http:// yoda. yale. edu. The data sharing policy of Janssen Pharmaceutical Companies is available at https://www. janssen. com/clinical-trials/ transparency. The deidentified data originating from Flatiron Health, Inc. , may be made available upon request, and are subject to a license agreement with Flatiron Health; interested researchers should contact DataAccess@FH. com to determine licensing terms.
Study Design and Data Source The OPTIMISMM study was a phase 3, multicenter, randomized, open-label, active-controlled trial that compared PVd with Vd in patients with RRMM. Details of the study design and primary results, have been previously described  [30]. Key enrolment and treatment information is outlined below. The study enrolled adult patients with RRMM who had received 1 to 3 prior anti-myeloma regimens and had previously been treated with a lenalidomide-containing regimen. Patients were randomly assigned to receive PVd (n = 281) or Vd (n = 278) in 21-day treatment cycles until disease progression or treatment discontinuation. In cycles 1 to 8, bortezomib (1. 3 mg/m 2 ) was administered intravenously or subcutaneously on days 1, 4, 8, and 11, and oral dexamethasone (20 mg/day for patients aged ≤ 75 years, 10 mg/day for patients > 75 years) was administered on days 1, 2, 4, 5, 8, 9, 11, and 12.
Statistical Analysis Data from the OPTIMISMM study obtained up to October 26, 2017 were used for the present HRQoL analyses. All statistical analyses were performed using SAS version 9. 4 or higher. A detailed HRQoL analysis plan was signed on January 25, 2018, before the study database lock. Adherence rates for completion of the HRQoL assessments were assessed at each visit. For QLQ-C30 and QLQ-MY20, adherence was defined as completion of ≥ 50% of the items. For the EQ-5D-3L, adherence was defined as completion of the 5 dimensions. A 2-sided Fisher exact test was used to compare the proportion of adherent patients at each visit between treatment groups. The intent-to-treat (ITT) population comprised all patients randomized in the study. The primary analyses of HRQoL data were based on the HRQoL-evaluable population: all randomized patients who completed the QLQ-C30 at baseline and ≥ 1 post-baseline assessment.
The least-square (LS) mean treatment group difference (PVd -Vd) and corresponding 95% CI were calculated. Hedges g effect size and corresponding 95% CI were also calculated. A mixed-model repeated measure (MMRM) analysis was used to estimate the treatment effects over time for each QLQ-C30 and QLQ-MY20 domain (longitudinal analysis) and the EQ-5D-3L health utility index and to assess the differences in treatment effects between groups. Withingroup and between-group differences were assessed at each visit by calculating the LS mean change from baseline and 95% CI. Instrument scoring and missing items were handled in accordance with the developer's instructions  [34, 35]. Missing domain scores for a given assessment visit were not imputed in the primary HRQoL analyses. However, post hoc analyses were conducted to investigate missing data/dropout patterns and examine whether imputation of missing data using pattern-mixture models altered the findings of the primary analyses.
Figures Figure 1 Time to the first clinically meaningful worsening for the HRQoL-evaluable population was calculated as the time between randomization and the first time a patient's change in a given domain was, at a minimum, 10 points worse than at baseline. In Cox proportional hazard regression analyses, time to first clinically meaningful worsening for all exploratory domains was similar between the 2 treatment groups (data not shown). Within-group LS mean changes and differences between groups were estimated based on a random intercept/slope model under the assumption of unstructured covariance and with the following covariates as fixed effects: baseline domain score, age, number of prior anti-myeloma regimens, β2-microglobulin, treatment, visit, treatment-by-visit interaction, and baseline domain score-by-visit interaction.