dataesr/scientific-paragraphs-categorization huggingface dataset:
The wider credibility intervals in our study are due to the use of the more conservative uniform prior on the calibration point as compared to the previous phylogeny of Solanaceae, which implemented a log-normal prior that tends to drive ages towards the mode of the prior distribution  43. Consequently, the ages of the lineages used for calibrating the phylogeny of Pteronymia are older than those inferred previously  43  (Table  2 ). Dated combined Pteronymia phylogeny. We combined morphological and molecular data to generate a species-level phylogeny that was calibrated using secondary calibrations from a published Nymphalidae phylogeny  39  , and from Solanaceae lineage ages estimated in this study, using BEAST 1. 7. 5  45. This resulted in 901 trees (after burnin), from which we extracted the maximum clade credibility tree with median branch length (hereafter MCC tree).
The host-plant ages used in this study as maximum constraints are based on one of the 'youngest' hypotheses for Angiosperm diversification and recent studies suggest older ages of Solanaceae. A phylogenetic inference using genomic data found a much older origin of Angiosperms  55  , but this had a moderate impact on the stem age of the order containing Solanaceae (Solanales: ca. 92 mya 55 versus 85. 9 mya  44  , with overlapping 95% confidence ranges  55  ) and presumably on that of Solanaceae (not inferred in that study). The recent description of a 52. 2 my old Physalis fossil  56  , a solanaceous genus that is inferred to be 9. 1 [5. 9-12. 9] my old in our study (stem age), is much more challenging for the ages of Solanaceae and Angiosperms as a whole. It is possible that this recently described Physalis fossil represents an earlier lineage of Solanaceae.
1 ). Sequences of all Pteronymia specimens were combined into a consensus sequence for each species to maximize sequence coverage for each species  35. We used PartitionFinder 1. 1. 1  84 to select the best partition scheme applying to this new dataset, where only the models implemented in BEAST were tested (see Supplementary Table  S4 ). A time-tree was generated in BEAST 1. 7. 5  45  under an uncorrelated lognormal relaxed clock using the same outgroups as previously, and the dating procedure described above. To select the tree prior (Yule versus Birth-Death), we ran analyses with each type of prior and used Stepping Stone sampling  88  to estimate marginal likelihood (MLE). This method is not implemented in BEAST 1. 7. 5, thus we performed this analysis on BEAST 1. 8. 2 on the molecular dataset only (BEAST 1.
Acknowledgements We thank authorities of  Peru, Ecuador, Colombia, Panama and Brazil (ICMBio, SISBIO  no. 10802-5 ) for issuing research and collection permits, as well as many assistants for their help in the field and in curating and databasing museum specimens. We are grateful to  Haydon Warren-Gash  and  Niklas Wahlberg  for providing specimens. We thank  Christine Bacon  and  Fabien Condamine  for providing constructive comments on this manuscript. ME acknowledges financial support from  ANR SPECREP  and  CNRS (France)  and the  Leverhulme trust (UK). LDS's postdoc was funded by an  ATIP (CNRS, France)  grant awarded to ME. NC was funded by a doctoral grant from the  Doctoral School 227  ( Sciences de la Nature et de l'Homme: Evolution et Ecologie, France ). KW acknowledges funding from  NSF  ( DEB-0639861 ,  DEB-0103746 ), the  National Geographic Society , the  Darwin Initiative  and the  Leverhulme Trust.
Species coverage was 98% for the mitochondrial fragment, 73% for tektin and 63% for EF1A. Primers and PCR conditions followed previously described conditions  6. In addition, 52 ithomiine and danaine outgroup species were selected  35  (see Supplementary Table  S1 ). The dataset was then partitioned by gene and codon positions and the best models of substitution for optimized sets of nucleotides were selected over all models implemented in (1) RAxML 82 and (2) MrBayes 83 , using the 'greedy' algorithm and linked rates implemented in PartitionFinder 1. 1. 1  84 (see Supplementary Table  S3 ).
We implemented three time slices (11-8, 8-5, 5-0 million years ago, Fig. 1 ) with different dispersal probabilities, which took into consideration major geological events through time  9. Ancestral state reconstruction and evolution of elevational range. Elevational range (elevation range, i. e. , boundaries of the elevational interval containing 95% of the records and mean elevation) for all extant species were extracted from the distributional database computed above (Table  1 ). Evolution of elevational range was investigated on both the MCC tree and the subset of 100 trees as follows. The phylogenetic signal and the tempo of evolution of the mean elevation and the boundaries of the elevational range were assessed by estimating simultaneously the values of the λ and δ scaling parameters  47  that maximized the likelihood of the data using BayesTraits v2  90.
Sampling methods for selection of districts, villages, and people The cases are the geographic entities that represent the four selected districts: Biankouma, Danané, Odienné and Touba. Data was collected in three villages in each of the four target districts. In total, 12 villages were selected. In each selected village, data was collected through individual in-depth interviews with members of monitoring committees, ordinary community members, and CHW supervisors (i. e. , qualified health workers). Data was also collected through focus group discussions with ordinary community members (4-8 participants, all members of the same community). The selection of participants matched the aim of the research projectto collect information and experiences from the various actors involved in behavior change as a result of the Ebola epidemic.
Diversification of data sources was sought: data was extracted from i) different geographical areas (according to their distance from the border), and ii) a variety of participants' profiles. Based on the lists of villages covered by the communityled approach, villages were selected using stratified randomization based on geographical parameters. The latter are relevant in the case of Ebola (i. e. , distance to the border of highly-affected countries). Stratification was done according to the location of each village from the border with Guinea and Liberia. For each district, a first sub-list of all villages between 0 and 30 km from a border, a second sub-list of all villages located between 30 and 50 km from a border, and a third sub-list of all villages located more than 50 km from a border (up to 80 km) were put together.
Data collection methods Introduced by IRC's community mobilization assistants, the research team went to health centers or "talking trees" to meet with the CHWs and village authorities. After presenting the study, participants were recruited based on suggestions made by monitoring committee membersindependently of any influence by IRC. Three pilot interviews were conducted in Biankouma district prior to regular data collection. The first data collection was carried out in Biankouma and Danané districts from September 9th to 19th, 2015. Two research assistants pursued data collection in the districts of Odienné and Touba from September 24th to October 3rd, and then transcribed all the data. In total, data was collected from 62 participants through 46 individual interviews and three focus group discussions.
Data analysis Data was coded by the first author using the qualitative data processing software "TAMS Analyzer". Based on standard coding techniques  [8] , the code book was revised by another person (the second author). The coded data was analyzed using thematic analysis  [9]. Reliability of the results was ensured by researchers' compliance with a transparent and systematic methodology, and the existence of a pre-test for interview guides in order to test participants' understanding of issues. Data was collected by an independent research team (first author and two research assistants) in order to maintain constant neutrality during interviews, focus groups and participatory workshops. The team made sure that no undue influence from IRC was interfering with data collection. First, stratified randomization prevented any sort of influence in village selection.
Second, IRC staff was absent from any direct interaction between participants and investigators. The internal validity of the data collected was tested during participatory workshops. The participatory approach of this study enabled workshop participants to provide highly relevant contributions to this research. Their views and interpretation of the results is therefore considered in this report. Results' confirmability was tested during a predissemination workshop held in the capital of Tonkpi region on September 22nd 2015. The preliminary results of data collected in Biankouma and Danané districts were presented to a wider audience (including regional and district-levels authorities, healthcare workers in charge of the selected health areas, and CHWs supervisors who were interviewed). Participants' inputs during discussions were included in the analysis.
Acknowledgments The authors would like to thank the two research assistants that greatly contributed to this study:  Bilé  and  Konan N'Guessan. We would also like to thank the 62 participants for their time and for accepting to answer researchers' questions. IRC's team based in Côte d'Ivoire, and in particular, the IRC office in Man, kindly facilitated the operationalization of this study. Finally, our thanks go to  Heather Hickey  for proofreading the article, and to four anonymous reviewers for their useful comments. Acknowledgments go to donors, i. E. the  Government of Côte d'Ivoire's Contract of Debt Reduction  and  Development C2D  (funded by the  European Commission  humanitarian office with funds from  Agence Française de Développement)  and  USAID  who funded the  IRC Ebola Programme.
Florescence image acquisition and analysis Preparations were imaged either with a wide field fluorescent microscope (Olympus IX81) equipped of 60x oil immersion objective and a Coolsnap HQ CCD camera, or with a Leica Sp5-II confocal microscope with a 63 X oil immersion objective with a distance of 0. 5 µm between each plane in the z-stacks. Protein recruitments at cell-cell contacts not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. were determined on z-stack confocal images encompassing the apical cell domain of the monolayer (3 most apical stacks) analyzed the "surfaces" module of Imaris software, applying background subtraction, thresholding of junction area and removal objects outside junctions. Then the mean intensities of the different staining were measured in the volume of junctions.
, 2006). The mobile fraction and the t 1/2 were determined by fitting the normalized recovery curves using onephase decay non-linear regression function of the GraphPad Prism 5. 01 software. Ecad-Fc-coated bead doublets attached to the surface of transfected cells were submitted to magnetic twisting using an in-house built magnetic tweezers. Bead doublets were used instead of individual beads to increase the applied torque and facilitate subsequent image analysis. The bead doublets were made by mixing antibody immobilized Ecad-Fc-coated beads with Protein A coated beads therefore only one bead of the doublet binds to the cell through E-cadherin interaction. Bead doublet rotation was captured while the rotation of the magnet was simultaneously recorded. The fluctuation angle of the bead doublet relative to its original direction was calculated to obtain its response to magnet rotation, and the standard deviation of the fluctuation angle was extracted.
Bead displacement was captured with an inverted microscope equipped with a 50X long working distance objective lens and a camera (Pike from Allied vision) with 100 Hz sampling frequency while the rotation of the magnet was recorded directly. The image sequence of the bead not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. doublets was loaded into imageJ. The image stack was thresholded to only highlight the contours of the bead doublet and other features of the image like cell contours were removed manually. The 'Directionality' plugin of the Fiji package was used to calculate the principle orientations of the bead doublet. The fluctuation angle of the bead was matched to the direction of the magnet to obtain its response to magnet rotation.
PIV analysis PIV analysis of monolayer movement was performed as described in  (Petitjean et al. , 2010)  using MatPIV v. 1. 6. 1 package and implemented in MATLAB MatPIVthe PIV toolbox for MATLAB-available at http://folk. uio. no/jks/matpiv/Download/index2. html). Cross correlation techniques were performed to compute the displacement vectors at each sub-window by finding their best match at the successive time frame. The analysis was done with 32 X 32 pixels (19 X 19 μm) interrogation windows with an overlap of 50%. The correlation length was then calculated using the formula previously described  (Petitjean et al. , 2010). Velocity values obtained were the mean of the norm of all velocity vectors extracted from each image. For this study, we consider the mean velocities over a time interval of 6 hours to minimize effects of cell division, time zero being the moment at which cells reach confluency.
NUS - SPC joined program  (RMM),  Fondation ARC (RMM) ,  Human Frontier Science Program (HFSP)  grant  RPG0040/2012  (B. L. , R. M. M. ),  Agence Nationale de la Recherche  ( ANR 2010 Blan1515 ). BL is supported by the  European Research Council  under the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013/ERC  grant agreement no. 617233 ) and the  Mechanobiology Institute. R. S. has been supported by a  C'nano program   Région Ile de France   doctoral fellowship  and FRM ( FDT20140930851 ). The authors acknowledge the  IJM Imagoseine Imaging Facility , member of the  France BioImaging  infrastructure supported by the  French National Research Agency  ( ANR-10-INSB-04 ,  « Investments  of the future »).
Study design The study is a three-group, parallel, superiority, randomized controlled trial (Fig. 1 ). Participants will be postoperative adult inpatients. The tested intervention will be PC6, LI4 and HT7 acupressure therapy. The main outcome will be quality of recovery with secondary outcome assessments of patient satisfaction, PONV and pain by the third postoperative day. The use of two comparator groups, i. e. a sham (light touch) group and a no intervention group (control group), will allow the assessment of different aspects of the placebo effect, including behavioural aspects (visiting and interacting with the patient)  [14]. Research team members obtaining survey and other outcome data will not be told the allocated group identities. The study design aims at fulfilling the Standard Protocol Items: Recommendations for Interventional Trials  [15]  (Additional file 1: SPIRIT 2013 checklist) and the Revised Standards for Reporting Interventions in Clinical Trials of Acupuncture (STRICTA): extending the CONSORT Statement  [16].
Sample size Sample size calculations were carried out using SAS© 9. 4 software, SAS, Cary, NC, USA. Based on results from a study examining the psychometric properties of the quality of recovery (QoR-15) evaluation  [6, 17] , we estimate the overall change from baseline to follow-up in untreated patients will be approximately 5% of the instrument total, or +7. 5 points. We powered the study to be able to detect a clinically relevant change of 10% (+15 points) in the treatment group, with a standard deviation of 12 points. The primary hypothesis is a better quality of recovery in patients randomized to the active acupressure intervention group. A sample size of 50 subjects per arm (150 total) was calculated based on a twosample t test for mean difference, with power = 0. 85, alpha = 0. 05. We have requested a total of 200 patients consented in order to allow for some screen failures that will inevitably occur, e.
Outcome measures Data will be collected using an electronic case report form and a few written forms created to maintain blinding of the assessors. The primary endpoint will be the change in the established quality of recovery (QoR)-15 score  [6]. The QoR-15 is a 15-question survey measuring patient satisfaction/quality of recovery including pain, nausea, sleep and well-being. Each question is rated on a Likert scale from 0 to 10. The maximum score of the QoR-15 is 150 points, indicating ideal health status. This score will be assessed by a research team member on study day 0 between 7:30 and 8:30 am prior to first intervention and on study day 3 between 7:00 and 10:00 am. If the patient is discharged from the hospital before study day 3, the QoR-15 score will be completed by telephone. Secondary endpoints include individual measures of pain, nausea and patient satisfaction, using a numeric rating scale, and vomiting that will be assessed on study days 0 (between 7:30 and 8:30 am, i.
e. before the first intervention), 1 and 2 (between 3:00 and 4:00 pm). Patient opinion about acupressure effect will be assessed on study day 0 between 7:30 and 8:30 am prior to the first intervention and on study day 2 between 7:00 and 10:00 am. Other secondary endpoints will be recorded daily from the patient medical record, including sleep aids, antiemetic administration, opioid and non-opioid consumption, episodes of delirium and hospital length of stay. A research coordinator will monitor accuracy of the data in randomly sampled case report forms by comparing hospital source documents with data in the electronic case report form. QoR-15 scores will be recorded on paper case report forms and later entered into an MS Access© database. Study documents will be stored in a locked cabinet. Study team members will have access to the final trial dataset.
Data analysis Analysis plan This is a randomized, single-blinded controlled trial. The study will be analysed in four ways. The primary method of analysis will use the modified intention to treat (mITT) approach, with the treatment group incurring at least one treatment session. Patients who obtain the baseline measurement and are randomized to the treatment group but do not receive at least one treatment will be excluded from the analysis. Otherwise, missing data will be accounted for using a multiple imputations method  [20]. In addition, if data are missing at random, a secondary sensitivity analysis will be conducted that excludes patients with missing follow-up data, but includes patients who did not complete the entire treatment regimen (follow-up complete, FC). A tertiary adherers-only (AO) analysis may be included to examine the subset of patients who fully received the treatment to which they were randomized and completed both baseline and follow-up assessments.
using Illumina technology techniques  (12). A standard Illumina shotgun library was constructed and sequenced using the Illumina HiSeq2500 platform, which generated 21,371,196 reads (260-bp insert size) totaling 5,129 Mb. The Illumina sequence data were trimmed by Trimmonatic version 0. 32  (13)  and assembled using Spades version 3. 5  (14)  and ALLPaths-LG version r52488  (15). The final draft assembly for Ensifer sp. strain LCM 4579 consisted of 61 contigs with an N 50 contig size of 247. 7 kb and 63. 8ϫ coverage of the genome. The final assembled genome contained a total sequence length of 6,137,409 bp with a GϩC content of 62. 4%. The assembled Ensifer sp. strain LCM 4579 genome was annotated via the NCBI Prokaryotic Genome Annotation Pipeline (PGAP) and resulted in 5,613 candidate protein-encoding genes, 47 tRNAs, and four rRNAs.
ACKNOWLEDGMENTS Partial funding was provided by the  New Hampshire Agricultural Experiment Station. This is Scientific Contribution Number  2706. This work was supported by the  USDA National Institute of Food and Agriculture Hatch 022821  (L. S. T. ),  USDA Foreign Agricultural Services Borlaug Fellowship Program   BF-CR-16-004  (N. D. ), and the  College of Life Science and Agriculture at the University of New Hampshire-Durham. Sequencing was performed on an Illumina HiSeq2500 purchased with  NSF   MRI grant   DBI-1229361 (W. K. T. ).
Safety was evaluated in patients who received ≥1 treatment; efficacy was evaluated in the intention-to-treat population (patients who received ≥1 dose of study treatment and were positive for either ALK or ROS1 rearrangement). The primary endpoint was dose-limiting toxicities (DLTs) during cycle 1; secondary endpoints included safety, pharmacokinetics, and overall response rate (ORR). This study is registered with ClinicalTrials. gov, NCT01970865. Findings-Fifty-four patients were treated, including 41 with ALK-positive and 12 with ROS1positive NSCLC. Twenty-eight patients had received ≥2 TKIs, and 39 patients had CNS metastases. The most common treatment-related adverse events among the 54 patients were hypercholesterolemia (39 [72%] of 54 patients), hypertriglyceridemia (21 [39%] of 54 patients), peripheral neuropathy (21 [39%] of 54 patients), and peripheral edema (21 [39%] of 54 patients).
20 Lorlatinib has also demonstrated antitumor activity across a variety of different subcutaneous xenograft tumor models of ALK-positive NSCLC and in intracranial ALK-positive tumor models. In addition, lorlatinib potently inhibits ROS1 and retains activity against ROS1 G2032R in vitro and in vivo. 21 Taken together, these preclinical studies suggest that lorlatinib may represent an effective therapeutic strategy for ALK-and ROS1-positive patients who have relapsed on currently available TKIs. Here, we report results from an ongoing, first-in-human phase 1 study of lorlatinib (NCT01970865) to determine its safety, maximum tolerated dose (MTD), and antitumor activity in patients with advanced ALK-or ROS1-positive NSCLC.
Dose holds and dose reductions were permitted per investigator discretion and recommended in the event of specific treatment-related hematologic and nonhematologic toxicities, which included pancreatitis, pneumonitis, QTc prolongation, lipid elevations, CNS effects, and LVEF dysfunction. Patients who did not recover from treatment-related toxicity within 42 days were discontinued. Once a dose was reduced for a given patient, all subsequent doses were administered at that dose level, unless further dose reduction was required. Dose reescalation was allowed per investigator discretion for appropriate patient management. Pharmacokinetic (PK) samples were collected for lorlatinib after single-and multiple-dose administration for all dosing cohorts evaluated in the study. The lorlatinib PK parameters were calculated using nominal collection times and quality-controlled, non-quality-assured bioanalytical data and estimated using noncompartmental analysis (Phoenix WinNonlin version 6•3; Pharsight Corporation, Mountain View, CA, USA).
Statistical Analysis As a dose-finding study, no hypothesis was pre-established and no power calculation was done; due to the dynamic nature of dose escalation schema, the sample size was not defined prior to study initiation. Safety data were summarized for all patients who received at least one dose of lorlatinib. Efficacy data are reported for the ITT population, which was initially defined as patients who had received at least one dose of lorlatinib; following the inclusion of one patient with unconfirmed ALK/ROS1 status, the definition was amended to include that patients were also positive for ALK or ROS1 gene rearrangements; all amendments to the statistical analysis plan are included on pp 501-504 of the appendix. Intracranial efficacy was determined for patients in the ITT population with brain metastases.