dataesr/scientific-paragraphs-categorization huggingface dataset:
The values were then normalized to similar background measurements for each sample. In Figure  8  the number of transcriptional zfh1-short dots was counted manually with Image J and normalized to the total number of nuclei (Zfh1 staining), which was determined by a Matlab homemade script. Graphs and statistical analysis were performed with Prism seven using unpaired t-test. Error bars indicate standard error of the mean. Further statistical details of experiments can be found in the figure legends. Quantitative RT PCR 30 Wing Imaginal discs from each genotype were dissected and RNA isolated using TRIzol (Life technologies). Quantitative PCR were performed as described  (Krejcı ´and Bray, 2007). Values were normalized to the level of Rpl32. The following primers were used. Rpl32, Fwd 5'-ATGCTAAGCTG TCGCACAAATG-3' and Rev 5'-GTTCGATCCGTAACCGATGT-3'.
Acknowledgements We acknowledge the  Bloomington Stock Center , the  VDRC Stock Center  and the  Developmental Studies Hybridoma Bank  for Drosophila strains and antibodies. We thank  Ruth Lehmann ,  Renate Renkawitz-Pohl ,  Eileen Furlong  and  Istva  ´n Andofor antibodies. We thank  Eva Zacharioudaki ,  Alain Vincent  and  Michalis Averof  for critical reading of the manuscript and other members of  SJB lab  for valuable discussion; and we are grateful to  Hannah Green  and  Juanjo Perez-Moreno  for advice on muscle preparations. This work was funded by a program grant from the  MRC  to SJB and by an  EMBO   Long Term Fellowship  for HB.
OCEAN code LiH XRS spectra were also calculated through the OCEAN code  [29, 30] , which provides a package to numerically solve the Bethe-Salpeter equation (BSE) for core-level excitations. This ab initio code takes into account explicitly the screened interaction between the core-hole and the photo-electron in a two-particle formalism. Its primary focus is on the near-edge region of the core excitation spectrum. Orbitals from the plane-wave DFT package QUANTUM ESPRESSO  [31]  were used. These calculations were performed using norm-conserving pseudopotentials and therefore both an auxiliary atomic code and a pseudopotential inversion scheme are necessary for core-level spectroscopy  [29]. We employed the local-density approximation to the exchangecorrelation functional and used a 100 Ry energy cut-off for the plane-wave basis.
Data analysis Usual energy dependent corrections due to absorption of the incident beam in beam paths, windows and sample were applied. The measured intensity of each spectrum was normalised to the monitor intensity. The extraction of the XRS spectrum from the whole electron excitation spectrum needs some care because the core spectrum is usually superimposed on a background due to the valence electron contribution, which can strongly depend on the momentum transfer. An extraction algorithm for the core electron contribution in IXS experiments was proposed by Sternemann et al  [33]. In this work, we have followed a similar procedure for low and high momentum transfers, as discussed below; whereas for medium q-values, some modifications to that algorithm were needed in order to obtain a satisfactory extraction.
Conclusions We reported an experimental and theoretical study of the Li 1s core excitation spectra in LiH using XRS spectroscopy and calculations based on the RSMS and BSE methods, as implemented in the FEFF and OCEAN codes, respectively. By introducing some modifications to a previously reported data processing algorithm, originally applied to the regime of low and high momentum transfer, we were able to successfully extend its application to the intermediate q range. An analysis based on a fitting model, supported by BSE calculations, indicates that the lowest energy structure observed in the measured core excitation spectra is not a pure excitonic feature, as proposed by previous spectroscopic studies. This feature is consistent with a double-peak structure, with a peak associated with transitions from the core level to the lowest states of the conduction band and a pre-edge peak of p-type excitonic character.
Acknowledgments Research was supported by  SeCyT-UNC  ( Universidad Nacional de Córdoba, Argentina ),  LNLS  ( Brazilian Synchrotron Light Laboratory ),  CNPEM/MCTI  and the  Academy of Finland  (grant No. 1295696 ). Computing time at the  Finnish Grid and Cloud Infrastructure  (persistent identifier urn:nbn:fi:researchinfras-2016072533) is gratefully acknowledged. O A P M acknowledges  CONICET  for a graduate fellowship and the  Finnish National Agency for Education  for a CIMO fellowship. Assistance from  F Gennari  is gratefully acknowledged.
OPA1 is a multi-isoform dynamin GTPase, mainly regulated post-translationally, that plays a central role in processing the mitochondrial inner membrane and in mitochondrial dynamics. Through its role in mitochondrial fusion, OPA1 intervenes in pleiotropic functions such as maintaining the integrity of mitochondrial cristae, apoptosis  1, 2  , oxidative phosphorylation and energy production  3  , calcium fluxes  4  , mitochondrial DNA maintenance  [5] [6] [7]  , mitochondrial autophagic flux and renewal  [8] [9] [10]  , oxidative stress  11  , inflammation  12  , ageing  9, 12  , neurogenesis  13, 14  , and more generally, in mitochondrial plasticity and quality control  15. More than three hundred pathogenic variants have been reported in the OPA1 LOVD (Leiden Open Variation Database) database  16  since the first reports in 2000  17, 18.
22 (±0. 0019) vs. 0. 60 (±0. 12), p = 0. 0048 for R/F and 0. 48 (±0. 08) vs. 0. 18 (±0. 07), p = 0. 0029 for R-O/F). The leak control ratio (O/F) was also significantly greater in Opa1 -/-MEFs than in Opa1 +/+ MEFs, accounting for 13% of the maximal uncoupled stimulated respiration compared to 4% in Opa1 +/+ MEFs (p = 0. 0364). Thus, consistently with earlier studies  25, 26  , our findings show that the complete loss of OPA1 leads to mitochondrial fragmentation and mitochondrial respiration failure. Other mitochondrial functional parameters such as in NAD/NADH and ATP were also assessed to evaluate the bioenergetics dysfunction. Our data shows an absence of alteration of NAD/NADH ratio (0. 15 (±0. 02) in Opa1 +/+ vs 0. 09 (±0. 019) in Opa1 -/-(p = 0. 0903)). Surprisingly, ATP content was not reduced in Opa1 -/-cells compared to Opa1 +/+ (respectively, 0.
The metabolomic signature of Opa1 -/-MEFs. To explore the global structure of the data, we first carried out the principal component analysis (PCA) (Fig. 4a ). This unsupervised approach highlights any spontaneous clustering or separation of samples according to their global metabolite profile, distributed in the first two most discriminant components (the t [1] and t [2]), explaining the largest variations of the dataset. The PCA model (Fig. 4a ) revealed an important difference between the Opa1 -/-and Opa1 +/+ MEF cell lines with respect to their genotype, delimited by the t [2] axis (R2X [2]: 37. 2%). The PCA model showed two clusters within the Opa1 +/+ MEF group, probably representing sub-populations of the controls, whereas the Opa1 -/-MEFs were well grouped together. To further the investigation, we used the supervised OPLS-DA (Orthogonal Partial Least Squares-Discriminant Analysis) approach, retaining only the metabolites with the most robust ability of discrimination.
1% formic acid and a methanolic mobile phase B with 0. 1% of formic acid, was employed with a flow rate maintained at 0. 3 ml/min during a total runtime of 22. 5 min. The UHPLC autosampler temperature was set at 4 °C. Metabolite identification was facilitated using the MSMLS TM (Mass Spectrometry Metabolite Library of Standards) molecule library (IROA Technologies, Bolton, MA, U. S. A. ) for mass spectrometry metabolomics. These commercial standards were injected in the same analytical conditions in order to create an in-house database (a final collection of 500 accurately identified carboxylic acids, amino acids, nucleotides, saccharides, fatty acids, lipids and hormones). Then, a TraceFinder 4. 1 processing method was designed to allow a comparison of the Retention Times (RT), the MS/MS fragmentation spectra, the m/z and the isotopic patterns of metabolites.
Statistical analyses. Before performing statistical analyses, data were normalised by the total ion current (TIC) of each sample using Microsoft Excel software. In addition, the dataset was log 10 transformed, mean-centred and scaled by the square root of the standard deviation of each variable (Pareto scaling) to reduce the contribution of the most intense ions. Statistical analyses were performed following the workflow chart shown in Fig. 3. Multivariate analysis was carried out with Simca-P + v 14. 0 (Umetrics, Umea, Sweden). Principal Component Analysis (PCA), an unsupervised method, was used to investigate the population structure and to emphasize spontaneous clustering or separation of samples on the basis of their global metabolite profiles. To highlight molecules implicated in the metabolomic signature, Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), a supervised analysis, was carried out, retaining only the metabolites that showed a strong power of discrimination and a high statistical reliability in the model.
Finally, only metabolites with a VIP value greater than 1 were considered as "highly relevant" for the metabolomic foot-printing. Univariate analysis was performed with MetaboAnalyst 3. 5  32  using the Volcano plot module. Consequently, only metabolites with an FC greater than 1. 2 and a Wilcoxon test at a 0. 05 threshold were considered. The Benjamini-Hochberg correction was applied to minimize the error rate, and only the molecules that remained significant were retained. Statistical analysis of the western blots, mitochondrial respiration, and quantitative mitochondrial imaging was done with GraphPad statistical software (San Diego, CA, USA). The non-parametric Mann-Whitney test was used for western blot analysis and Student's unpaired t-test was used for imaging and respiration analysis.
Acknowledgements We are grateful to  Kanaya Malkani  for his critical reading and comments on the manuscript. Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Centre National de la Recherche Scientifique (CNRS) ,  University of Angers, University Hospital of Angers. This work was also supported by the following patients' associations:  Kjer France ,  Fondation VISIO, Ouvrir les Yeux, Union Nationale des Aveugles et Déficients Visuels , Association contre les  Maladies Mitochondriales ,  Retina France , and  Association Point de Mire.
Jet reconstruction and background subtraction Jet reconstruction for both the p-Pb and Pb-Pb analysis was performed with the k T  [36]  and anti-k T  [37]  sequential recombination jet algorithms as implemented in the FastJet package  [38]. The anti-k T algorithm was used for the signal jets while clusters reconstructed with the k T algorithm were used to estimate the background density of the events. Jets were reconstructed using charged tracks detected in the Time Projection Chamber (TPC)  [39]  and the Inner Tracking System (ITS)  [40]  which cover the full azimuthal angle and pseudorapidity |η| < 0. 9. Jets were reconstructed using the E-scheme to recombine the four-vectors of the constituents, assigning the charged-pion mass for each particle. A resolution parameter, R, of 0. 4 was used, and the jet area was calculated by the FastJet algorithm using essentially zero momentum particles, called ghosts, with area 0.
Detector effects on the jet energy and mass were investigated by matching detector level jets and particle level jets from a Pythia simulation and comparing their properties (including Pb-Pb background). The jets were matched based on distance, in a way that guarantees a one-to-one match. The constituents of the detector level jets are all assigned the pion mass, as is done for the data analysis, while the the particle mass is used for the particle level jet reconstruction. A comparison between the jet mass response due to background fluctuations and the full response, which also contains detector effects, is shown in Fig. 1. While background fluctuations induce a positive shift of the reconstructed jet mass, detector effects, which are dominated by the finite tracking efficiency and the mass assumption of the jet constituents, reduce the reconstructed jet mass.
Acknowledgements The ALICE Collaboration would like to thank all its engineers and technicians for their invaluable contributions to the construction of the experiment and the CERN accelerator teams for the outstanding performance of the LHC complex. The ALICE Collaboration gratefully acknowledges the resources and support provided by all Grid centres and the  Worldwide LHC Computing Grid  (WLCG) collaboration. The ALICE Collaboration acknowledges the following funding agencies for their support in building and running the  ALICE detector: A. I. Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation (ANSL) ,  State Committee of Science and World Federation of Scientists  (WFS), Armenia;  Austrian Academy of Sciences and Nationalstiftung für Forschung ,  Technologie und Entwicklung, Austria; Ministry of Communications and High Technologies, National Nuclear Research Center, Azerbaijan ;  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Universidade istry of Education and Scientific Research, Institute of Atomic Physics and Romanian National Agency for Science, Technology and Innovation ,  Romania ;  Joint Institute for Nuclear Research (JINR) ,  Ministry of Education and Science of the Russian Federation and National Research Centre Kurchatov Institute, Russia; Ministry of Education, Science, Research and Sport of the Slovak Republic , Slovakia;  National Research Foundation of South Africa ,  South Africa ;  Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN) ,  Cubaenergía ,  Cuba, Ministerio de Ciencia e Innovacion and Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Spain; Swedish Research Council (VR)  and  Knut & Alice Wallenberg Foundation (KAW), Sweden ;  European Organization for Nuclear Research, Switzerland ;  National Science and Technology Development Agency (NSDTA) ,  Suranaree University of Technology (SUT)  and  Office of the Higher Education Commission  under  NRU  project of Thailand, Thailand;  Turkish Atomic Energy Agency (TAEK) ,  Turkey ;  National Academy of Sciences of Ukraine, Ukraine ;  Science and Technology Facilities Council (STFC) ,  United Kingdom ;  National Science Foundation  of the  United States of America (NSF)  and  United States Department of Energy, Office of Nuclear Physics (DOE NP) ,  United States of America.
Because the monitoring did not include parental captures during the first two years and because all females were not captured (when abandoning before chicks reached 9 days old, death or failure to capture), female identity was unknown for 54% of broods (342 out of 739). To avoid the loss of half of our dataset, we attributed a fictitious ring number to each non-captured female. For each trait, the best model was selected using a backward stepwise procedure starting from a full model, and the significance of each fixed effect and interaction was tested using a F-test (R function anova()). This procedure was used to eliminate non-significant variables: at each step, the effect presenting the highest p-value was removed and the model run again without it. While we present only ANOVA of the best-fitting models below, summaries of full and best-fitting models are provided for each trait in the electronic supplementary material (tables S2 -S5).
Also, because of the short time scale of our dataset and following arguments that natural selection is a within-generation process  [32] , we chose the number of fledglings as a fitness measure rather than the number of recruits. We first estimated standardized univariate selection differentials (S) on all traits individually  [33, 34]  in each habitat separately. The linear selection differential for a trait is estimated by the slope of the regression between a measure of relative fitness and standardized values of the phenotypic trait. Selection differentials measure the total force of selection affecting a trait, including both direct and indirect selection  [32]. As all four morphological traits were correlated with body size (electronic supplementary material, table  S1 ), estimations of selection differentials were performed on measures corrected for size by using residuals of a linear regression between the trait (body mass, wing or tail length) and tarsus length run on the pooled dataset with both urban and forest birds.
g. [36] ). First, estimates of selection differentials and gradients were obtained from linear models as described by equations (2,1) and (2. 2). Second, significance (i. e. p-values) of these selection differentials and gradients were estimated from linear mixed models including year and individual ring number (only female identity for life-history traits) as random effects to control for variation across years and repeated measurements on the same individuals. Then, to test for a difference in selection operating in the two habitats, urban and forest datasets were combined and habitat (forest versus urban) and habitat Â trait were added as fixed effects in the models described above. Significance of the interaction terms was tested using F-tests (R function anova()). Finally, because variance can provide insight on past selection as well as relaxed selection, a comparison of variance was made between habitats for each trait with a Fisher variance ratio test (var.
(b) Selection on morphology Only selection gradients are presented in the main results (see electronic supplementary material, table S6 and figure  S5  for selection differentials). Regarding females, no linear selection was detected on morphological traits, but we found stabilizing selection on tarsus length in the city (g ¼ 20. 166, p ¼ 0. 036; electronic supplementary material, table  S7 ). However, pooling the urban and forest datasets revealed that this quadratic selection was not significantly different between habitats (non-significant tarsus 2 Â habitat interaction, p ¼ 0. 632; electronic supplementary material, table  S7 ). Concerning males, we detected linear selection for larger tarsi in the forest ( p ¼ 0. 01; table  3 ), but once again the difference in selection between habitats was non-significant (non-significant tarsus Â habitat interaction, p ¼ 0.
2012167-0003). Data accessibility. Dryad (http://dx. doi. org/10. 5061/dryad. tv45802) [56]. Authors' contributions. A. E. C. carried out the statistical analyses and participated in data collection. A. G. and A. C. conceived and funded the study and collected the data. All the authors contributed to the writing of the manuscript and gave the final approval for publication. Competing interests. We declare we have no competing interests. Funding. This project was funded by the European Research Council (starting grant no. ERC-2013-StG-337365-SHE to A. C. ) and the OSU-OREME.
Acknowledgements. We would like to thank everyone who helped with the fieldwork since 2011, especially  Samuel Perret ,  Virginie Demeyrier ,  Marcel Lambrechts ,  Baptiste Pepin ,  Arnaud Borne , the  City Council of Montpellier  and the  Zoo de Lunaret ;  Carolyn Hall  for correcting the English;  Suzanne Bonamour ,  Charles Perrier ,  Celine Teplitsky  and  Marcel Lambrechts  for helpful discussions; and the anonymous referees as well as the editors of this special issue for helpful comments and corrections.
Patients who were negative for methylation defects within the imprinted domains controlling IGF2/H19, DLK1/GTL2, and MEST/ GRB10 assessed by allele-specific methylated multiplex realtime quantitative polymerase chain reaction, as previously described,  2  were retained for further molecular analysis. Whole-exome sequencing and targeted HMGA2-PLAG1-IGF2 sequencing Library preparation, exome capture/gene enrichment, sequencing, and data analysis were performed by IntegraGen SA (Evry, France). The sequencing methods and bioinformatics analysis are described in detail in the Supplementary Methods online. Sanger sequencing and short tandem repeat typing HMGA2, PLAG1, and IGF2 mutations identified with wholeexome sequencing and targeted sequencing were verified by standard methods of Sanger sequencing, using the ABI PRISM Big Dye Terminator v3.
ACKNOWLEDGMENTS This work was supported by the  Institut National de la Santé et de la Recherche Médicale , funding from the  Université Pierre et Marie Curie  ( UPMC-Paris6 ), the  Agence Nationale de la Recherche  ( ANR EPIFEGRO 2010 ) and from the " Association Françaises des Familles ayant un enfant atteint du Syndrome Silver Russell ou né Petit pour l'Age Gestationnel (AFIF/PAG )". W. A. H. was supported by the  People Programme Marie Curie Actions (MCA)  of the European Union's  Seventh Framework Programme FP7 /ITN Ingenium 2007-2013/ under REA grant agreement no. 290123  and by the  Société Française d'Endocrinologie et Diabétologie Pédiatrique. F. B. was supported by a  Novonordisk "Growth Hormone , Growth and Metabolism" grant. J. T. B. is supported by a  Pediatric Early Research Career Award  from  Seattle Children's Research Institute.
Results and Discussion Temporal Characterization of the DNA Damage Response Reveals Coexpressed Gene Sets with Distinct Biological Functions and Regulatory Features. To obtain a temporal view of the expression networks underpinning the DNA damage response in Arabidopsis, mRNA sequencing (mRNA-seq) experiments were conducted at six time points from 20 min to 24 h after either mock or γ-IR treatments in wild-type plants (SI Appendix, Fig. S1A  and Dataset S1). Furthermore, as the SOG1 TF is known to regulate many genes induced by DNA damage (13), a γ-IR time course experiment was also conducted in the sog1 mutant (SI Appendix, Fig. S1A  and Dataset S1). Consistent with having selected a suitable time scale to capture the dynamics of the DNA damage response, identification of DE genes during the wild-type γ-IR time course (Dataset S2 A and C) [fold-change (FC) ≥ 2, false-discovery rate (FDR) ≤ 0.
01] revealed that of the ∼2,400 DE genes identified, 320 were already observed after just 20 min, while only 141 remained by 24 h (SI Appendix, Fig. S1B ). To determine whether the gene-expression changes observed in our γ-IR time course are consistent with previous assessments of the DNA damage response, the DE genes identified in at least one time point from the wild-type time course were compared with four previously published DE gene sets identified using similar γ-IR treatments and seedling stages, but at single time points  (13, 32, 33, 38)  (SI Appendix, Fig. S1 C  and D ). These analyses revealed that our γ-IR time course captures the vast majority of previously identified up-regulated genes, 65. 2-91. 4%, with the three datasets based on the most similar conditions and stages  (13, 32, 33)  showing the greatest overlaps (SI Appendix, Fig.
mock-treated) of the   (45) (46) (47)  and DAP-seq databases  (48). As these data represent a mixture of in vitro-and in vivo-derived interactions, the DREM model was generated based solely on the expression data, with the TF predictions determined subsequently. The resulting wild-type DREM model revealed 11 groups of coexpressed genes with distinct biological functions and regulatory features (Fig. 1 ). At the level of gene expression, investigation of the DREM network confirmed that the 11 coexpressed groups, designated as paths W1-W11 (Fig. 1A  and Dataset S3A), display highly correlated expression profiles across the entire γ-IR time course (SI Appendix, Fig. S2A ) and capture a wide range of expression dynamics, as exemplified by the representative gene-expression patterns shown in Fig.