dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to the  UCSD Neuroscience Microscopy Shared Facility  (supported by a grant  P30 NS047101 ) for access to the Spinning disk microscope, to  Jeff Smith  and  Dr Joan Heller Brown  ( UCSD, dept of Pharmacology, UCSD ) for kindly allowing us to use their microinjection set up, and to  Daniel Stockolm  ( Genethon, Evry, France ) for access to the cell imaging facility. We are also thankful to  Dr Valérie Balme  and colleagues at  HALIODX (Marseille)  for helpful discussions and skilful single-cell-seq experiments. We thank the  Leducq Fondation  for supporting  Tui Neri , and funding this research under the framework of the MITRAL network and for generously awarding us for the equipment of our cell imaging facility in the frame of their program " Equipement de Recherche et Plateformes Technologiques " (ERPT to M.
C. Z. ),  NIH :  U54 HL108460  (A. C. Z),  NCATS :  UL1TR000100  (A. C. Z. ); EH was supported by a fellowship of the  Ministere de la recherche et de  l'éducation in France. TM-M was supported by a fellowship from the  Fondation Foulon Delalande  and the  Leducq Foundation. P. v. V. was sponsored by a  UC San   Diego Cardiovascular Scholarship Award  and a  Postdoctoral Fellowship  from the  California Institute for Regenerative Medicine (CIRM)  Interdisciplinary Stem Cell Training Program II. S. M. E. was funded by a grant from the  National Heart, Lung, and Blood Institute  ( HL-117649 ). A. T. is supported by the  National Heart, Lung, and Blood Institute  ( R01-HL134664 ).
D. pachea and D. nannoptera males mate right-sided The position of the male during copulation has not been described for any of the closely related species of D. pachea. In this study, we assessed copulation postures in D. pachea and nine related species: D. acanthoptera and D. nannoptera (sister species of D. pachea), D. machalilla and D. bromeliae (representatives of close outgroup lineages), D. buzzatii and D. mojavensis (members of the repleta species group), as well as representatives of other Drosophila species groups (D. tripunctata, D. willistoni and D. melanogaster). Phylogenetic relationships between the ten studied species were estimated with a Bayesian phylogeny (Additional file 1: Figure  S6 ) based on a previously published sequence dataset  [28] , supplemented with publicly available sequence data (this study) for D.
Conclusion Phallus asymmetries were identified in D. pachea and D. acanthoptera of the nannoptera species group and distinct structures were observed to be asymmetric in both species. An increased copulation duration was found to be specific to nannoptera group species and was not observed in the closely related outgroup species D. machalilla and D. bromeliae. Right-sided mating positions were detected in D. pachea and D. nannoptera and were found to be distinct between them. Our data does not allow us to conclude whether the evolution of the right-sided copulation position may have promoted the evolution of genital asymmetry, or vice versa. Our results nevertheless indicate that asymmetry in genital morphology and in copulation behavior have evolved through multiple evolutionary steps in the nannoptera group, revealing a complex history of sexual trait changes, maybe in relationship with the evolution of giant sperm and unique sperm storage in the nannoptera group.
Phylogenetic analysis We used data of eight species from a multi-locus dataset of  Lang et al. (2014)   [28] , and added corresponding sequences for D. willistoni and D. tripunctata (Additional file 1: Table  S2 , Additional file 3, BEAST input file in DRYAD). For D. tripunctata, only mitochondrial data was available at GenBank (https://www. ncbi. nlm. nih. gov/nucleotide/) and missing data was annotated by '?'. Phylogenetic analysis was performed in BEAST  [62]  according to the settings described in Lang et al. (2014)  [28]. Markov-Chain Monte-Carlo (MCMC) runs were performed with a chain length of 10 7 generations and recorded every 1000 generations. MCMC output analysis was carried out using TreeAnnotator  [62]  and the tree was visualized and edited with FigTree (http://tree. bio. ed.
Movies were recorded in a climate controlled chamber  [26]  at 22 or 25 ± 0. 1 °C and 60% or 85% ± 5% humidity (Additional file 4). Flies were filmed from above using digital microscope cameras 191,251-62 (Conrad), Digi-Micro Profi (DNT) or MIRAZOOM MZ902 (OWL). Movies were recorded with the program GUVCVIEW (version 0. 9. 9) GTK UVC or Cheese (version 3. 18. 1) (https://wiki. gnome. org/Apps/Cheese) at a resolution of 800 X 600 pixels on a Linux Ubuntu operating system. Movies were recorded until copulation ended or for at least 45 min when no copulation was detectable. After movie recording, flies were dissected or stored in ethanol at -20 °C.
Analysis of the D. nannoptera copulation posture Flies were reared and isolated before copulation as described above. One female and one male were CO 2 anesthetized and transferred onto a white plastic support (mating cap) and were caged with a transparent plastic cylindrical 25 mm × 7 mm cap. Once courtship was observed, mating caps were put on a motorized horizontally turning stage (0-30 rpm) (grinding stone 8215, Dremel) in front of a camera MIRAZOOM MZ902 (OWL) and copulation was recorded with the camera being put into an optimized frontal view towards the female head by rotating or turning the mating cap. The transparent cap was optionally removed once copulation had started. The yield of informative experiments with these settings was poor as we performed 167 mating experiments but only 29 experiments were informative for our data analysis (Additional file 7, reasons for discarding the experiments are listed).
Acknowledgements We are grateful to  Sadjo Sidikou  and  Pierre Quevreux  for movie recordings, to  Bertie Joan van Heuven  and  Menno Schilthuizen  for assistance and interpretation of SEM analysis and to  Martina Manns  for discussions of D. nannoptera mating positions. We greatly thank  Álvaro Barragán ,  Violeta Rafael  and  Santiago Palacios  for their help in the field work made in Ecuador (Research Permit No. 003-15 IC-FAU-DNB/MA ),  Therese Markow  and  Alejandro Oceguera-Figueroa  for their help with fly collections in Mexico,  Jean David for Drosophila  laboratory strains and  Alexandre Peluffo  for help with the statistical analysis.
Funding This work was supported by the  CNRS  and by a grant of the  European Research Council  under the European Community's  Seventh Framework Program  (FP7/2007-2013 Grant Agreement no. 337579 ) given to  Virginie Courtier-Orgogozo. FTR was financed by a  Kees Bakker Award , awarded by the  Stichting Professor Dr. K. Bakker Fonds. The funding body had no role in any activities regarding the study including design, sampling procedure, analysis, interpretation of the data and writing the manuscript.
| DNA extraction and microsatellite genotyping Genomic DNA (gDNA) for both species was isolated using the HotShot protocol  (Truett et al. , 2000)  specifically designed for small amount of tissues (method described in Appendix S1). Samples were screened at 10 microsatellite loci per species (Table  S3 ). Details of PCR and profiling are provided in Appendix S1. Fragment analysis was carried on an ABI 3730 DNA Analyzer (Applied Biosystems), and genotype peaks were scored using Peakscanner 2. 0 (Applied Biosystems). Allele calling was performed with FlexiBin v2  (Amos et al. , 2006). Each genotypic dataset was checked for quality in terms of (a) missing data, (b) repeatability, and (c) scoring errors (stuttering bands, allele dropouts, and null alleles). Repeatability was checked by re-calling alleles for a subset of randomly chosen samples (~10% per dataset) and comparing them with previous scoring.
| Estimates of genotypic and genetic diversity The software GenClone2. 0 (Arnaud-Haond & Belkhir, 2007)   The mean number of alleles (N A ), allelic richness (R S ), observed (H O ), expected heterozygosity (H E ), inbreeding coefficient (F IS ), population differentiation (F ST ), and deviation from Hardy-Weinberg equilibrium (HWE) were calculated using FSTAT 2. 9. 4  (Goudet, 2001). Pairwise tests for assessing linkage disequilibrium among loci were carried out using the online version of Genepop 4. 6  (Rousset, 2008 ; http://genep op. curtin. edu. au/).
The MCMC procedure consisted of one long chain with 500,000 recorded genealogies for each locus, with 10,000 genealogies discarded as burn-in. We used the software PAST  (Hammer, Harper, & Ryan, 2001)  to perform one-way ANOVA in order to analyze the relationships of various parameters and hydrological connectivity when data were normally distributed (sometimes requiring logit, mean subtraction, or Box-Cox transformations). When normal distributions could not be achieved, a nonparametric Kruskal-Wallis test was used. Finally, we performed principle components analysis (PCA) on samples within landscapes to examine a potential role of hydrological connectivity in segregating sites. We then employed a discriminant analysis of principle components (DAPC;  Jombart & Ahmed, 2011; Jombart, Devillard, & Balloux, 2010)  to describe the general population structure of both species that accounts for the genetic differences among groups (according to hydrological connectivity) while minimizing the differences within groups using data from principal components analysis (PCA) of genetic variation.
An admixture model and correlated allele frequencies were assumed. Simulations were based on 25,000 discarded iterations (burn-in) and 250,000 MCMC retained replicates; population structures comprised of between 1 and 48 clusters (K) were simulated for C. mucedo and between 1 and 34 clusters (K) for F. sultana. Each K was tested five times. The online software STRUCTURE HARVESTER 0. 6. 92 (Earl & vonHoldt, 2011)  was used to evaluate results under the Evanno method expectations  (Evanno, Regnaut, & Goudet, 2005)  while CLUMPAK server (http://clump ak. tau. ac. il; Kopelman, Mayzel, Jakobsson, Rosenberg, & Mayrose, 2015)  was used to obtain graphical plots.
| Evidence for genetic divergence, selection, and bottlenecks We employed Mantel tests (using Genepop (ISOLDE)  [Rousset, 2008] ; http://genep op. curtin. edu. au/) to gain evidence for genetic divergence by determining whether F ST values were correlated with geographical distance among sampling sites (according to Isolation by Distance theory). For each dataset, a semi-matrix of pairwise genetic distances (F ST ) between samples was created and regressed against the corresponding shortest linear distances between samples with a permutation test of 10,000 iterations. Neutrality of markers was explored using (a) an F ST -based test was carried out in Arlequin  (Excoffier, Hofer, & Foll, 2009; Excoffier & Lischer, 2010)  and (b) a Bayesian method developed in BayeScan2. 1  (Foll & Gaggiotti, 2008).
The F ST -based test involved 20,000 simulations (each composed of 100 demes per group and 10 groups) and was repeated under various sets of combinations to examine associations with hydrological connectivity and regions. A preliminary test was conducted to identify outlier loci using the whole dataset. Pairwise tests were performed between samples collected from different "populations" as revealed by DAPC and STRUCTURE analyses (Norfolk, Cumbria, Greater Glasgow/Northern Ireland; Table  1 ) and between samples collected from different hydrological connectivity regimes within each "population" (Table  1 ). A Bonferroni correction method was applied to increase the level of accuracy in avoiding false-positive tests. The Bayesian approach to test marker neutrality used BayeScan2. 1 with a default setting (20 independent runs with 5,000 iterations each, 50,000 iterations burn-in, sample sizes of 5,000 and thinning of 10).
Non-neutrality in BayeScan2. 1 results was inferred following the Jeffrey's interpretation, as suggested in the software User Guide. We tested for genetic signatures derived from recent population bottlenecks using a method implemented in BOTTLENECK 1. 2  (Piry, Luikart, & Cornuet, 1999). This test is based on a "heterozygosity excess theory" that assumes how the number of alleles declines faster than expected heterozygosity (H E ) during a strong event of reduction in population size. The expected heterozygosity at mutation-drift equilibrium (H eq ) derives from the number of observed alleles. Hence, a rapid decline in alleles results in H E being larger than H eq , producing a heterozygosity excess (H exc  ; Cornuet & Luikart, 1996)  was used to further substantiate the occurrence of a recent bottleneck.
| Dataset and genotyping quality We collected 4,011 statoblasts of C. mucedo from sediments. Of these 1,349 statoblasts were successfully genotyped from 48 sites across Norfolk, Cumbria, Greater Glasgow and Northern Ireland (Table  S1 ). In the case of F. sultana 1,336 colonies were collected and 993 individuals successfully genotyped from 34 sites in Norfolk, Cumbria, and Greater Glasgow (Table  S1 ). Missing genotypes accounted for 6. 5% of the F. sultana dataset and 22. 9% of the C. mucedo dataset. We re-genotyped 96 individuals and 138 individuals of F. sultana and C. mucedo, respectively, and none were dissimilar to the original calling. Allele dropout and stuttering were consistently absent. Null alleles were detected in 45 of 270 tests for F. sultana. After the Brookfield correction, 30 of 270 (11.
11%) remained significant. Null alleles were detected in 15 of 330 tests for C. mucedo with 2 of 330 (0. 6%) remaining significant after the Brookfield correction. Because the Brookfield correction improved both datasets and no loci showed a systematic presence of null alleles, both corrected datasets were used for subsequent statistical analyses. S1 ). For both species, 3 of the 10 microsatellite loci were not in HWE (Cmu5. 5, Cmu1. 1, and Cmu9. 3 in C. mucedo;.
| Overall patterns of genetic diversity Fsu09, Fsu09TKU, and Fsu04 in F. sultana) and the overall test for HWE incorporating all populations was also significant (p <. 001), indicating a general lack of conformation to HWE in both species. For F. sultana, there was strong evidence for linkage disequilibrium (LD) between loci in two cases (Fs04 and Fs12TKU, Fs13 and Fs12TKU; pairwise tests for linkage disequilibrium, p <. 001). Tests were carried out excluding linked loci from the dataset in order to evaluate their impact on characterizing clonal and genetic variation. No impact was observed when excluding the linked loci Fs04, Fs12TKU, and Fs13 and therefore, these loci were retained in the final analysis. There was no evidence for linkage disequilibrium among populations of C. mucedo. S4 ).
| Evidence for genetic divergence, selection and bottlenecks Mantel tests revealed a lack of correlation (p >. 05) between geographic distance and F ST values for both C. mucedo and F. sultana populations within Norfolk, Glasgow, and Northern Ireland (Figure  S2 ). However, in both cases F ST and geographic distance were significantly correlated in Cumbria (C. mucedo: p <. 001, F. sultana: p <. 01; Figure  S2 ). In our analyses for neutrality, 16 of 66 tests to detect outliers in the F. sultana datasets were significant (HIM test in Arlequin, p <. 05;.
Following Bonferroni correction, three tests remained significant (locus Fs14TKUfor overall data; locus Fs14TKU for Norfolk vs. F. sultana and C. mucedo, respectively (Table1). The only locus that was not in HW equilibrium and was identified as a candidate outlier (thus potentially under selection) is Fsu04 in F. sultana. Excluding this locus from the general analysis performed did not change the results obtained. Hence, it was retained in the final dataset here used. In general, loci found in F. sultana as candidate outliers were showing sign of LD that would reinforce the idea that non-neutral processes mediated by local selection are in action for F. sultana populations.
8%) of C. mucedo and 26 of 34 populations (76. 5%) of F. sultana were characterized by a sufficient number of MLGs (≥4) to conduct BOTTLENECK 1. 2 tests. Thirteen of 26 tests (50%) and 20 of 34 tests (58. 8%) provided evidence for recent bottlenecks in F. sultana and C. mucedo, respectively (Table S5). A higher proportion of such bottlenecked populations were from connected sites for C. mucedo (70%; based on 4 DHC, 10 HC and 6 isolated sites) than for F. sultana (33%; based on 4 connected and 8 isolated sites). Cumbria data; locus Fs04 for populations in isolated vs. DHC sites in Cumbria). Six of 63 tests of the C. mucedo datasets (F ST -based test in Arlequin) were significant (p <. 05) but none remained significant after the Bonferroni correction for multiple tests (Table 1). The Jeffrey's.
95 to 9. 56 months. 10 Further improvement of the formulation of decitabine is, thus, desirable to enhance its half-life and efficacy. Various strategies have been proposed to overcome the short half-life of decitabine. For example, guadecitabine, a decitabine prodrug composed of a dinucleotide of decitabine linked to a deoxyguanosine with a phosphodiester bond, is currently being tested in clinical trials (NCT02348489 and NCT02920008). The phosphodiester bond present in guadecitabine is cleaved gradually, resulting in the slow release of decitabine, extending its half-life in vivo. Guadecitabine is also less subject to cytidine deaminase action than decitabine. 11 Loading decitabine in a nanogel formulation has also been tested. The antiproliferative activity of decitabine, evaluated on leukemia cells, was enhanced by.
The (M-H)+m/z transition for decitabine (C12) 2 was 593. 3. 113. 1. The entire system was controlled by Masslynx ® software (Waters). Calibration curves were obtained after solubilizing decitabine (C12) 2 in methanol to obtain a stock solution of 0. 5 mg/mL. A sufficient quantity of the stock solution was then diluted in methanol to obtain a calibration curve between 2. 5 and 500 ng/mL (seven plots were used). A calibration curve was performed on each day of the analysis.
cell-cycle analysis Cells (HEL) were cultured in 12-well plates at 5×10 4 cells/ well. After 24 hours, cells were treated with decitabine in solution (1,000 nM), decitabine (C12) 2 -loaded LNCs (2,500 nM), or blank LNCs with an equivalent excipient concentration as that of the decitabine (C12) 2 -loaded LNCs. After 72 hours, cells were collected and centrifuged at 200×g for 5 minutes. Pellets were then washed in PBS and fixed in 70% cold ethanol. Fixed cells were washed in PBS and incubated for 15 minutes at room temperature in the dark in a PBS staining solution containing 40 µg/mL propidium iodide and 100 µg/mL RNAse A. Samples were then analyzed on a BD FACSCANTO II system (BD Biosciences, Le Pont de Claix, France), and propidium iodide incorporation estimated using BD FACSDiva software (BD Biosciences, Le Pont de Claix, France).
Calibration curves were performed in rat plasma from 25 to 850 ng/mL decitabine or 50 to 1,000 ng/mL decitabine (C12) 2. The main pharmacokinetic parameters of decitabine were determined using Kinetica ® 5. 1 software (Thermo Scientific, Philadelphia, PA, USA). Intravenous non-compartmental analysis was used to evaluate these parameters for decitabine and decitabine (C12) 2 , and extravascular non-compartmental analysis was used to evaluate those of decitabine after decitabine (C12) 2 -loaded LNC injection. The maximal concentration (C max ), time to reach the maximum concentration (T max ), area under the plasma concentration-time curve (AUC), and mean residence time (MRT) were determined.
24 Gemcitabine, another deoxycytidine analog, has been largely chemically modified with numerous different chemical modifications. 25 Among them, esterification of fatty acids on the 5′ position prolonged the cell retention time, rendered cellular internalization independent of the human equilibrative nucleoside transporters (hENT), and improved efficacy in gemcitabine-resistant cell lines. 26, 27 The activity of decitabine inversely correlates with hENT expression, demonstrating its cellular internalization by those transporters. 28 Thus, if the cell penetration pathway of the 5′ fatty acid ester of decitabine designed in this study is hENT independent, as shown for the 5′ fatty acid ester of gemcitabine, then resistance to chemotherapy could be overcome. Note: Data were obtained using an intravascular non-compartmental analysis following IV administration of decitabine (c12) 2 -loaded lNcs, except for decitabine parameters for which an extravascular non-compartmental analysis was performed.
We introduce Disease Knowledge Transfer (DKT), a novel technique for transferring biomarker information between related neurodegenerative diseases. DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets from common neurodegenerative diseases. DKT is a joint-disease generative model of biomarker progressions, which exploits biomarker relationships that are shared across diseases. Our proposed method allows, for the first time, the estimation of plausible multimodal biomarker trajectories in Posterior Cortical Atrophy (PCA), a rare neurodegenerative disease where only unimodal MRI data is available. For this we train DKT on a combined dataset containing subjects with two distinct diseases and sizes of data available: 1) a larger, multimodal typical AD (tAD) dataset from the TADPOLE Challenge, and 2) a smaller unimodal Posterior Cortical Atrophy (PCA) dataset from the Dementia Research Centre (DRC), for which only a limited number of Magnetic Resonance Imaging (MRI) scans are available.
Introduction The estimation of accurate biomarker signatures in Alzheimer's disease (AD) and related neurodegenerative diseases is crucial for understanding underlying arXiv:1901. 03517v2 [cs. LG] 29 Jul 2019 disease mechanisms, predicting subjects' progressions, and enrichment in clinical trials. Recently, data-driven disease progression models were proposed to reconstruct long term biomarker signatures from collections of short term individual measurements  [1, 2]. When applied to large datasets of typical AD, disease progression models have shown important benefits in understanding the earliest events in the AD cascade  [1] , quantifying biomarkers' heterogeneity  [3]  and they showed improved predictions over standard approaches  [1]. However, by necessity these models require large datasets -in addition they should be both multimodal and longitudinal.
Such data is not always available in rare neurodegenerative diseases. In particular, most datasets for rare neurodegenerative diseases come from local clinical centres, are unimodal (e. g. MRI only) and limited both cross-sectionally and longitudinally -this makes the application of disease progression models extremely difficult. Moreover, such a model estimated from common diseases such as typical AD may not generalise to specific variants. For example, in Posterior Cortical Atrophy (PCA) -a neurodegenerative syndrome causing visual disruption -posterior regions such as the occipital lobe are affected early, instead of the hippocampus and temporal regions in typical AD. The problem of limited data in medical imaging has so far been addressed through transfer learning methods. These were successfully used to improve the accuracy of AD diagnosis  [4]  or prediction of MCI conversion  [5] , but have two key limitations.