dataesr/scientific-paragraphs-categorization huggingface dataset:
For quantification of testis GFP protein expression patterns, whole testes were imaged at 20x magnification. GFP area was quantified using ImageJ. Threshold was adjusted for the GFP channel (ImageJ function: Image > Adjust > Threshold) to subtract background, then the size of the area above the threshold was considered (ImageJ function: analyze particles) and averaged by testis size. Data was collected from at least 25 testes per genotype, and is displayed as boxplots showing all data points.
Statistics and data presentation All statistical analyses were carried out in GraphPad Prism 7. 04. Comparisons between two genotypes and/or conditions were analyzed with the Mann-Whitney-Wilcoxon rank sum test. The Mann-Whitney-Wilcoxon rank sum test does not require the assumption of normal distributions, so no methods were used to determine whether the data met such assumptions. All graphs were generated using GraphPad Prism 7. 04. All confocal and bright field images belonging to the same experiment and displayed together in our figures were acquired using the exact same settings. For visualization purposes, level, and channel adjustments were applied using ImageJ to the confocal images shown in the figure panels (the same correction was applied to all images belonging to the same experiment), but all quantitative analyses were carried out on unadjusted raw images or maximum projections.
In all figures, n denotes the number of midguts, wings, testes, flies or group of flies that were analyzed for each genotype. Data are presented as boxplots with all data points shown, p values from Mann-Whitney-Wilcoxon test (non-significant (ns): p > 0. 05; *: 0. 05 > p > 0. 01; **: 0. 01 > p > 0. 001; ***p < 0. 001). Asterisks highlighting significant comparisons across sexes are displayed in gray boxes, whereas those highlighting significant comparisons within same-sex datasets are displayed in red boxes (for females) and blue boxes (for males).
(E) RT-qPCR expression analysis of midgut-specific sugar genes in flies with Act5C-Gal4 driven tra KD (Act5C > flp,tra FRT ) in relation to controls. (F) RT-qPCR expression analysis of midgut-specific sugar genes in sterile male flies lacking hub and germline stem cells (esg SHOF ), and in sterile males lacking germline but with intact hub (zpg z-2533/z-5352 ). In all graphs, n = number of fly groups analyzed per genotype (each group, 20 flies), except in (B), where n = number of viscera analyzed. Asterisks highlighting significant comparisons across sexes are displayed in gray boxes at bottom of graphs; those highlighting significant comparisons within female and male datasets are displayed in red and blue boxes, respectively. See TableS4for a list of full genotypes.
(C) Left: quantification of FRET signal of CIT8 citrate sensor in R4 ECs of control midguts and midguts with R2 and R4 EC-specific KD of citrate synthase knockdown (kdn RNAi ). Quantification of FRET signal in R4 ECs expressing CIT8 or CIT0 citrate sensors from control male midguts. In the middle is the quantification and representative images of FRET signal in control male and female R4 ECs expressing CIT8 citrate sensor. On the right is the quantification of FRET signal of CIT8 citrate sensor in R4 ECs of control midguts and midguts with R2 and R4 EC-specific KD of plasma membrane citrate transporter I'm not dead yet (Indy RNAi ). Representative FRET ratio images in R4 are shown. In all graphs, n = number of midguts analyzed per genotype/condition. Scale bars, 5 mm in all images. Asterisks highlighting significant comparisons across sexes are displayed in gray boxes at bottom of boxplots, those highlighting significant comparisons within male datasets are displayed in blue boxes.
ACKNOWLEDGMENTS We thank  Marc Amoyel ,  Erika Bach ,  Halyna Shcherbata , and  Guy Tanentzapf  for providing reagents and advice and  Utpal Banerjee ,  James Castelli-Gair Hombria ,  Leanne Jones ,  Masayasu Kojima ,  Ronald Ku ¨hnlein ,  Paul Schedl , and  Stefanie Schirmeier  for sharing reagents. We are grateful to  Marcos Gonzalez-Gaitan  for advice and  Louise Fets ,  Susumu Hirabayashi , and  Santiago Vernia  for providing comments on an earlier version of this manuscript. This work was funded by an  ERC   Advanced Grant  to  I. M. -A. (ERCAdG 787470 ''IntraGutSex'') , an  EMBO Advanced Fellowship  to B. H. , an  EMBO LTF  ( 63-2017 ) to A. M. , an  ERC Advanced Grant  to T. P. ( ERCAdG 741550 ''En-ergyMemo '') and  MRC  intramural funding. B. H. is currently sponsored by the  CNRS  and supported by an  ATIP-Avenir CNRS  grant and by the  Universite Ćote d'Azur Acade  ´mie (4 IDEX JEDI).
, 2016 GEO: GSE74775 Experimental Models: Organisms/Strains D. melanogaster lines -See Table S4 Various N/A Oligonucleotides RT-qPCR primers -See Table S3 This paper N/A Software and Algorithms Fiji PMID: 22743772 https://fiji. sc/ Adobe Illustrator CC 2018 Adobe. com N/A Prism 7 GraphPad GraphPad Software https://www. graphpad. com/scientific- software/prism/.
Simulation cohort The baseline characteristics of the simulation cohort were taken from a cross-sectional study, whose primary objective was to describe the demographic and clinical characteristics of T1D patients affiliated with IMSS  [8]. Additional unpublished data is presented here, however the detailed methodology is presented elsewhere  [8]. Data was collected from 192 T1D adult patients aged ≥ 18 years who attended follow-up visit(s) at the Endocrinology Department of two IMSS tertiary care hospitals in Mexico City during the year 2016. Specifically, two trained research nurses reviewed all clinical records of the T1D adult patients (n = 192) and conducted telephone interviews to verify and complement the information that was missing in the clinical records. Data on baseline demographics-sex, age, schooling, occupation, smoking and alcohol consumption, risk factors, pre-existing complications, and patient management (e.
This figure included all hypoglycemic events that required visiting emergency services and hospitalizations due to this condition. All hypoglycemic events that occurred in the baseline cohort were attributed to MDI, as this is standard treatment for IMSS T1D adult patients. To calculate the rate ratio of hypoglycemic events for CSII, data calculated by Pickup et al. [22]  were inputted into the following regression formula: Rate ratio of hypoglycemic events = (Coefficient of severe hypoglycemia on MDI) * (Rate IMSS cohort) + (Coefficient mean age) * (Mean age IMSS cohort) -Constant where: Rate ratio of hypoglycemic events = 0. 5 (22. 4) + 0. 016 (32. 3) -1. 18 = 10. 5. As a result, the rate of hypoglycemic events for CSII was = 22. 4/10. 5 = 2. 1 episodes per 100 adult patients/year.
Equally important, the baseline patient characteristics of the cohort under analysis were taken from Mexican T1D patients treated in IMSS. Together with the use of IMSS direct and indirect costs, the results presented in this analysis reflect the reality of an institution that provides care to over 60% of the Mexican population. A potential limitation of this analysis is that several inputs came from sources outside Mexico and Latin America, as these data were not available for Mexican T1D patients. Of importance, data on the dis-utilities of complications was based on a non-Mexican population. As such, the Life-Years Gained results may be of equal importance in the interpretation of results for some audiences. To address this limitation, future studies on diabetes management utilities for T1D patients in Mexico will be relevant.
Cell Migration Assay Using the Agarose Drop Method. HUVEC cells were resuspended in basal medium containing 0. 3% low melting agarose at a density of 40 × 10 6 cells/ml. Droplets (3 μl) of the agarose cell suspension were seeded into 24-well plates coated with polyDL prior to be incubated with conditioned media from hMADS cells cultured during 24 hours in the absence or in the presence of 5%, 10%, and 20% PRP. Following a 24-hour exposure to conditioned media, the droplets were stained using the Diff-Quick kit (Medion Diagnostics AG, Dudingen, Switzerland) and pictures were taken with a microscope. The droplet area and total area (area of the droplet+area of migrating cells) were measured using the ImageJ software, and the cell migration index was determined by the ratio: total area/droplet area.
ClinicalTrials. gov). Although MSC are presently used in a wide array of clinical trials for several degenerative diseases, most of the completed trials have revealed modest efficacy of MSC in promoting regeneration of the damaged tissues including the skin  [22, 38]. These disappointing outcomes are partly explained by a poor survival and retention of the MSC following their delivery into the damaged organs  [23]. Therefore, the optimization of the clinical efficacy of MSC is needed. With this goal, several strategies have been developed to improve the viability, retention, and functionality of MSC based on genetic modifications, pharmacological preconditioning, or on the use of scaffolds or biomaterials  [39] [40] [41]. However, most of these approaches are expensive and not easily translatable to humans.
Acknowledgments We are grateful to  Aurélie Guguin  ( INSERM U955, Créteil, France ) for the helpful technical assistance in flow cytometry experiments and  Cecile Lecointe  ( INSERM U955, Créteil, France ) for mouse wound healing experiments. This work was supported by funding from the  French National Institute of Health and Medical Research (INSERM)  and  Fondation des Gueules Cassées.
The work was supported by  LABEX ECOFECT  ( ANR-11-LABX-0048 ) of  Lyon University  within the " Investissements d'Avenir" Grant information: program  ( ANR-11-IDEX-0007 ) conducted by the  French National Research Agency (NRA)  and by the  Aviesan Sino-French agreement on Nipah virus study. RP is supported by a doctoral fellowship from the  Direction Générale de l'Armement (DGA). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Introduction Alemtuzumab (LEMTRADA ® ; Sanofi Genzyme, Cambridge, MA, USA), a humanized monoclonal antibody, demonstrated efficacy in 2-year, phase 3 clinical trials (Comparison of Alemtuzumab and Rebif ® Efficacy in Multiple Sclerosis (CARE-MS) I (NCT00530348) and II (NCT00548405))  1, 2  by significantly improving clinical and magnetic resonance imaging (MRI) outcomes in patients with relapsing-remitting multiple sclerosis (RRMS) versus subcutaneous interferon beta-1a (SC IFNB-1a; Rebif ® ; EMD Serono Inc. , Rockland, MA, USA). Outcomes were maintained over a 4-year open-label CARE-MS extension study (CAMMS03409 (NCT00930553))  [3] [4] [5]  and continued into the ongoing 5-year long-Term follow-up study for multiple sclerOsis Patients who have completed the AlemtuZumab extension study (TOPAZ; NCT02255656).
Patients and procedures Post hoc analyses of patient subgroups, defined by total number of alemtuzumab courses received (2-courses, 3-courses, 4-courses, and ⩾5-courses), were carried out with alemtuzumab-treated patients from CARE-MS I and II who subsequently entered the extension studies. Analysis included 8 years of follow-up after initiating alemtuzumab (analysis cutoff date: 4 October 2017). Patients who received another disease-modifying therapy (DMT) at any time, or received Courses 3, 4, or 5 after Month 85 (i. e. <12 months of follow-up after the last alemtuzumab course before data cut-off date), were excluded. Data from patients who received ⩾6 alemtuzumab courses were censored at the time of Course 6 administration to assess the efficacy of Course 5. Patients who received SC IFNB-1a in the core studies and switched to alemtuzumab in the CARE-MS extension were not included in this analysis.
Acknowledgements The authors and Sanofi would like to thank the patients for their participation in the  CARE-MS I, CARE-MS II ,  CAMMS03409 , and TOPAZ studies, as well as the Steering Committees and the investigators. Critical review of the manuscript was provided by  Darren P. Baker, PhD ,  Ericka M. Bueno, PhD , and  Colin Mitchell, PhD , of  Sanofi. Editorial and writing assistance was provided by  Rebecca L. Orndorff, PhD , and  Valerie P. Zediak, PhD , of  Eloquent Scientific Solutions  and was funded by  Sanofi. ClinicalTrials. govRegistration Numbers:  CARE-MS I, II , extension, TOPAZ:  NCT00530348 ,  NCT00548405 ,  NCT00930553 ,  NCT02255656.
Data Sharing Qualified researchers may request access to patient level data and related study documents including the clinical study report, study protocol with any amendments, blank case report form, statistical analysis plan, and dataset specifications. Patient level data will be anonymized and study documents will be redacted to protect the privacy of trial participants. Further details on Sanofi's data sharing criteria, eligible studies, and process for requesting access can be found at: https:// www. clinicalstudydatarequest. com.
Overall, the F ST for autosomes was much higher than for chromosome X within taurine breeds and within the indicine cattle (Fig. 5b ; Additional file 1: Figure  S7 ). When compared to publicly available genomes  [24]  (database information in the Materials section) of taurine (European Holstein, Angus and Jersey, and African N'Dama) and African indicine cattle (Ogaden, Kenana and Borana), Iberian breeds are clearly assigned by NGSadmix  [27]  to the European cluster (Fig. 2a ) with a slight suggestion of African taurine admixture at K = 3 for autosomal data. As observed previously  [24] , at K = 3 the clusters observed represent European taurine, African taurine and African indicine ancestries. All analyzed breeds have a positive Tajima's D (Additional file 1: Figure  S4 ). As observed previously  [24] , the commercial European breeds have lower nucleotide diversity (average number of pairwise differences) relative to the African breeds (Fig.
2b ) which had been previously suggested to have occurred  [15, 17, 18]. We explicitly test for differential African cattle introgression into Iberian breeds, using the D-statistics  [30, 31]  and publicly available genomes  [24]  (database information in the Materials section) of taurine (European Holstein, Angus and Jersey, and African N'Dama). We can confirm that there is a significant excess of shared derived alleles in varying amounts between Iberian breeds and the African taurine N'Dama when compared to a panel of European taurine breeds (Fig. 3 ). This was observed both for southern Iberian Brava that had the largest African (N'Dama) influence, but also in breeds from the north of Portugal such as Barrosã. These results are further supported by the occurrence of ~17% of T1-matrilines in the Iberian cattle analyzed here (Fig.
Sampling was done as described in  [19] , briefly 9 ml of whole-blood were collected from each animal by qualified veterinarians during their routine practice in the framework of official health control programs. Berrenda en negro, (Spain), Cardena (Spain), Lidia (Spain), Limia (Spain), Pajuna (Spain), Sayaguesa (Spain). We also included data of an aurochs  [5]  (England; Bioproject ID: PRJNA294709) to test for admixture with domesticated cattle. Furthermore, 149 full mitochondrial genomes from NCBI's PopSets 157,778,019  [7] , 306,977,267  [37] , 355,330,537  [18] , and 946,518,556  [38]  were used together with the mitochondrial consensus sequences obtained from our shotgun data (details below).
Sequencing data pre-processing The 48 samples were sequenced to between 1. 4X and 2. 3X depth of coverage (Additional file 1: Table  S2 ). Methods appropriate for low coverage NGS data  [27, [39] [40] [41]  were used throughout the analyses and applied to all samples. Raw Illumina reads were first processed with Trimmomatic (version 0. 36)  [42]  for removal of adapter sequences and trimming bases with quality < 20 and discarded reads with length < 80. Mapping to cattle genome versions UMD_3. 1. 1 (bosTau8)  [25]  and Btau_4. 6. 1 (bos-Tau7; contains an assembled Y-chromosome)  [25] , and to the outgroup wild yak (Bos mutus; Bioproject ID: PRJNA74739)  [26]  was done with BWA mem (version 0. 7. 12-r1039). Reads showing a mapping hit were further filtered for mapping quality > 25. PCR duplicates were removed with Picard MarkDuplicates (version 1.
Population structure NGSadmix version 32  [27]  was used to detect population structure with autosomal data from samples for which shotgun resequencing data was available. NGSadmix infers population structure from genotype likelihoods (that contain all relevant information on the uncertainty of the underlying genotype  [45] ). NGSadmix was run for K equal 2 to 8 for sites present in a minimum of 10% of the individuals: a total of 951,213 SNP sites for the 48 Iberian samples (Fig. 1b ); 129,829 SNP sites for the data set including all 128 animals (Fig. 2a ); 628,774 for SNP sites for the data set including the 94 female individuals (Fig. 6 ). The program was run with different seed values until convergence was reached. A principal component analysis using the same SNP set for the Iberian breeds was done with PCAngsd  [40]  which estimates the covariance matrix for low depth NGS data in an iterative procedure based on genotype likelihoods.
The software RAxML  [47]  version 8. 1. 7 with 100 rapid bootstrap replicates was used to estimate the phylogenetic trees under the GTR + GAMMA model of sequence evolution for complete mitochondrial sequences from  [7, 18, 37, 38]  together with consensus sequences from the shotgun resequencing data analyzed in this study obtained by choosing the most common base per position (-doFasta 2 in ANGSD  [39] ).
Acknowledgments We thank  Anders Albrechtsen  for stimulating discussion,  Nuno Carolino  ( INIAV, Portugal ) for providing information on the demographic characterization of the breeds Alentejana and  Brava ,  Jolita Dilyté  ( CIBIO-InBIO ) for technical assistance with the Illumina sequencing, and  John Archer  for technical assistance in processing and transferring the Illumina Fastq files (nextgeneration sequencing raw data). We thank breeder associations and breeders for contributing samples for genomic analysis and information on the animals included in our study. The Iberian cattle illustrations were done by biologist Carlos Medeiros for Ruralbit, Portugal (https://ilustracoes. ruralbit. com/). Paulo.
Funding The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors gratefully acknowledge the following for funding their research:  Villum Fonden Young Investigator  Grant  VKR023446  (R. D. F. ); Fundação Nacional para a Ciência e a Tecnologia (FCT),  Portugal, Investigador FCT  Grant  IF/00866/2014  (C. G. ), postdoctorate fellowship  SFRH/BPD/112653/2015  (A. E. P. ) and Project grant  PTDC/ CVTLIV/2827/2014  co-funded by  COMPETE   2020 POCI-01-0145-FEDER-016647  (C. G. ). R. D. F. thanks the  Danish National Research Foundation  for its funding of the  Center for Macroecology, Evolution, and Climate  (grant  DNRF96 ).
They were ad libitum fed with a starting diet (AME n 12. 1 MJ/kg, CP 185 g/kg) from 1 to 28 D and with a growing diet (AME n 12. 6 MJ/kg, CP 160 g/kg) from 29 to 57 D. Then they were fed with a finishing diet (AME n 12. 6 MJ/kg, CP 150 g/kg) from 58 to 80 D. At that period, the access to feeders was controlled to prepare the ducks for overfeeding as previously described by  Arroyo et al. (2014). Feed intake was collectively determined once a week until 56 D. Then it was collectively registered once a day until 80 D. All data were presented as daily feed intake (DFI) and cumulative feed intake (CFI) in defined period. Ducks were individually weighed at the ages of  28, 56, 70, and 80 D (BW). The ADG was computed in those periods. At age 81 D, 180 mule ducks were divided into 2 groups. Both groups were split into 7 pens of 15 ducks in randomized complete blocks.
(1957). Then total lipid content was predicted for all samples by using prediction equation developed on NIRS spectra according to the method described by  Marie-Etancelin et al. (2014). Spectrum data were shortened from 650 to 2,350 nm and transformed with a Standard Normal Variate and Detrend 1, 10, 10, 1 normalization with the Winisi software (version 4. 6. 8, FOSS Analytical A/S, Hilleroed, Denmark). Then the prediction equation was based on a modified Partial Least Square (PLS) analysis. Crude Protein Content Liver total nitrogen matter content was determined on 123 samples (15 samples from D0 and 9 samples from each day and each group). It was measured using a LECO analyzer (FP 428 model, Garges les Gonesse, France) after total combustion. Crude protein content was estimated as total nitrogen content multiplied by 6.
Statistics Statistical analyses were performed with the R software (version 3. 4. 0). As ducks had to be slaughtered for measuring liver characteristics, the data of successive time points came from different animals. So animals were considered to be independent and time sequence analyses were not performed. During the overfeeding period data were individually recorded thus the animal was the statistical unit. First the group effect was analyzed at each time point with non-parametric Wilcoxon tests. As it was almost non-significant for all variables, the study focused on the time effect without considering the group effect. Non-parametric tests of Kruskal-Wallis and Wilcoxon were performed to study the overall effect of time and to compare values 2 by 2, respectively (n = 15 at D0, and n = 30 at the other time-point).
Statistics were considered as significant when P-values < 0. 05. The correlations of LW and TY with duck performances and liver characteristics were computed with data from D6 to D12 with the corrplot R package and their significance was calculated with the cor. mtest function. Models to predict LW and TY were computed with the procedure BestR2 of SAS software (version 9. 4). To select the best model, the Mallows' Cp-statistics C(p) was computed. The best model was the one with the C(P) value closest to the number of variables, with the highest adjusted R2 value, with the smallest Akaike Information Criterion and the smallest Bayesian information criterion.
Duck Performances During the Overfeeding Period At age 80 D before the overfeeding period BW was equivalent in all groups (P > 0. 1). A group of 15 ducks from both groups were slaughtered every other day during the overfeeding period. Comparisons of the Two Overfeeding Programs A total of Two overfeeding programs were performed to analyze their impacts on duck performances and liver characteristics. After the last meal CFI stood at 9,865 g/duck and 9,435 g/duck, in the test and control groups, respectively. However the overfeeding program had little influence on duck performances and liver characteristics (P > 0. 1 for many variables and time points; Supplemental data 1 and 2). In the test group the LW tended to be higher than the control group at D8 (488 vs. 427 g, P = 0. 081) but their quality was degraded at D10 (TY 85% vs.
The highest glycogen content was measured at D2 with 106. 8 mg/g. It was 72. 9 mg/g at D0 and it decreased gradually from 50. 9 mg/g to 16. 7 mg/g between D4 and D12 (Table  2 ). The peak of free glucose was reached at D0, then it decreased strongly at D2 (-40% from D0) increased again at D4 and declined later on (Table  2 ). The evolution of lactate content in the liver was not linear. It oscillated between 0. 7 and 1. 5 mg/g of liver (Table  2 ). The correlations of LW with liver glycogen, free glucose, and lactate contents were -0. 63, -0. 89, and + 0. 61, respectively with data from D6 to D12 (P < 0. 001; Figure  5B ). Thus the biochemical evolution of the liver seemed to highlight a two-step evolution of the liver metabolism with a strong utilization of glucose. First, glucose seemed to be converted mainly into glycogen and in a lesser extent into lipids.