dataesr/scientific-paragraphs-categorization huggingface dataset:
Effect of body mass on laying dates and breeding success Both members of a pair were not always recorded by the penguin monitoring system platform in every year of the study. Rather than removing individuals from the data analysis in any given year when their partner was not recorded in that year (n = 111 out of 519 breeding attempts), all individuals were retained and male and female effects were assessed separately. This allowed identification of sex-specific carry-over effects. This method assumes any potential carry-over effects of a parent's winter body mass on laying date and breeding success are independent of their partner's winter body mass. Annual differences in the level of synchrony in laying date were quantified using a Levene's test, and then we test for the effect of body mass on subsequent laying date or breeding success.
For that, all records of body mass within the winter month within a year were averaged (mean), so that a single but robust mean value for each individual body mass could be related to its laying date or breeding success. This prevented inflating degrees of freedom and reduced the chance of type II error if we had retained all records of body mass in the analysis. We used data in two different formats in our body mass analysis: standardized and normal (unstandardized) to address the inter-individual verses inter-annual questions. We standardized individual's body mass data against its 'long-term' mean (i. e. body mass over the whole study period) to look at mass change across all years, i. e. whether individual body condition and breeding success (for that individual) was related to a good or bad breeding season  [33].
Variables were considered significant for p < 0. 05, and means are presented with standard errors. Table  2. Annual data from monitoring individual little penguins (IDs) crossing an automated penguin monitoring system throughout the inter-breeding season and the associated breeding chronology: length of the laying period (LLP in days), mean laying date (MLD in Julian days), and breeding success (CPP, chicks per pair). (The number of individuals that were recaptured from a previous year/s are indicated in parentheses. The number of body mass records collected by the penguin monitoring system within an inter-breeding season is divided between the moult-recovery (MR), winter (July; W) and pre-breeding (PB) periods. ) The number of penguins crossing the penguin monitoring system varied throughout the year.
Acknowledgements. We are grateful for all people who assisted on the data collection, in particular  P. Wasiak ,  P. Fallow ,  T. Daniel  and  S. Robinson. We thank the  Australian Antarctic Division  for kindly providing the automated penguin monitoring system (APMS), in particular Knowles Kerry and Kym Newbery. We thank the continued support of the  Phillip Island Nature Parks. We also thank the two anonymous reviewers for their constructive feedback that have improved the quality of the manuscript.
Contents lists available at ScienceDirect Genomics Data j o u r n a l h o m e p a g e : h t t p : / / w w w. j o u r n a l s. e l s e v i e r. c o m / g e n o m i c s -d a t a / genome with 6020 predicted genes  [7]  and a circular genome of 29 kbp  [8]  located within mitochondria-like organelles (MLO). Other MLO genomes with conserved gene synteny have also been sequenced from Blastocystis ST1, ST3 and ST4 isolates  [9, 10]. Here we report the sequencing of the Blastocystis ST4-WR1 genome from an isolate of a laboratory rodent and cultured axenically  [11]. Genomic DNA was isolated using a Qiagen DNeasy blood and tissue kit and sequencing was performed with the Illumina HiSeq 2000 system (Genoscreen, Lille, France). A total of 43. 855. 085 of 100-bp high quality paired-end reads were generated and were de novo assembled using the IDBA-ud algorithm  [12].
The output was then scaffolded using SSPACE  [13]  and gaps were filled by Gapfiller software  [14]. In total, 1301 scaffolds from 494 bp to 133,271 bp were obtained, with a scaffold N 50 of 29,931 bp. The draft genome sequence of Blastocystis ST4 has a deduced total length of 12. 91 Mbp and a G + C content of 39. 7%. Assembly also provided a circular DNA molecule of 27,717 bp in size with a G + C content of 21. 9% corresponding to the whole MLO genome sequence. Genes were carried out using the Maker gene annotation pipeline  [15]. The Maker pipeline was set with the results of ab initio gene prediction algorithms Augustus  [16]  and SNAP  [17] , the 6020 protein-coding genes of Blastocystis ST7  [5] , ESTs of both Blastocystis ST7  [5]  and ST1  [18]  and 414 manually-designed genes of the ST4-WR1 isolate.
Basic information about the assembled genome and predicted genes are shown in Table  1. Gene functions were annotated by BLAST2GO  [19]  and BLAST analyses with NCBI (http://www. ncbi. nlm. nih. gov/). 183 tRNA were predicted using tRNAscan-SE 1. 21  [20]. The preliminary annotation data revealed that Blastocystis ST4-WR1 nuclear genome harbors 5713 protein-coding genes. The presence of proteases was determined using BLAST against MEROPS database  [21] , and secreted proteases were identified using SIGNALP 3. 0  [22]  and WoLF PSORT  [23]. Finally, OrthoMCL  [24]  was applied to compare both ST4 and ST7 genomes. This comparative analysis revealed that the ST4 genome contains less duplicated genes than ST7 and that more than 30% of ST4 genes have no ortholog in the ST7 genome at an E value cutoff of 10 -5.
Acknowledgments This work was funded by grants from the  French National Center for Scientific Research (CNRS) , the  INSERM , the  Programme Orientations Stratégiques  from the  University of Lille  2 and the  Institut Pasteur of Lille. MO was supported by a PhD fellowship from the  Conseil National de la Recherche Scientifique  and the  Azm & Saade Association from Lebanon  and AC by a PhD fellowship from the  Pasteur Institute of Lille  and the  University of Lille 2.
Hence, their implementation is not very widespread. On the contrary, elastic backscatter lidar systems offer an easier set-up and maintenance and larger signal-to-noise ratio during daytime. Their operation is much more extended worldwide due to implementation in networks such as EARLINET (European Aerosol Research Lidar Network)  (Pappalardo et al. , 2014)  and  MPLNET (Micro Pulse Lidar Network). In this context, the LIRIC (Lidar Radiometer Inversion Code) code was developed by  Chaikovsky et al. (2012)  to obtain vertically-resolved profiles of aerosol microphysical properties by combining elastic lidar measurements and column-integrated sun-photometer microphysical properties retrieved from AERONET. The first results obtained with LIRIC have already been presented by  Wagner et al. (2013)  and  Granados-Muñoz et al.
, 2011; Valenzuela et al. , 2012c) , as indicated by the backward trajectories analysis performed with HYSPLIT model using GDAS meteorological database (http://ready. arl. noaa. gov/HYSPLIT_traj. php, figure not shown). Another possible explanation is that it could be an artifact introduced by LIRIC due to problems with the incomplete overlap or in cases of non-homogeneous layering The layering observed in the optical properties profiles (Fig. 3 ) indicated the presence of mineral dust in the upper layer between 3000 and 5000 m a. s. l. and the presence of anthropogenic particles in the lower part of the troposphere that was not detected by LIRIC. This can be explained by the fact that LIRIC assumes several AERONET retrieved properties (i. e. , refractive index, size distribution, sphericity, etc.
These results underscore the need for vertically resolved measurements to adequately monitor the evolution of the aerosol properties. The discrepancies between regularization and LIRIC results are mainly explained by the natural variability of the aerosol during the night. Because of this temporal variability, results obtained from the two different approaches are not comparable in absolute terms. However, from our analysis we can conclude that the combined use of LIRIC and the regularization technique improves our capability for evaluating the evolution of microphysical properties profiles during night-and day-time. For a direct intercomparison of LIRIC with the regularization technique more simultaneous datasets are required. Advances in vibrational Raman will allow measurement of the extinction coefficients during the daytime and measurements from HSRL systems could also help in this aspect.
The volume concentrations from the CAS-POL are likely overestimates due to the asphericity of the dust particles but the LIRIC derived values are underestimates because of the presence of particles with equivalent optical diameters larger than 20 µm. The synergy of instruments and different techniques presented in this study reveals the potential to obtain a complete characterization of the aerosol properties using a combination of the measurements. However, future efforts should aim to reduce the uncertainty of the measured and retrieved aerosol properties in order to obtain more reliable and accurate aerosol properties databases.
1. 16-262254) ; and by the University of Granada through the contract "Plan Propio. Programa 9. Convocatoria 2013". CIMEL Calibration was performed at the AERONET-EUROPE calibration center, supported by ACTRIS  (European Union Seventh Framework Program, (FP7/2007 -2013)  under grant agreement no. 262254). Granados-Muñoz was funded under grant AP2009-0552. The authors thankfully acknowledge the computer resources, technical expertise, and assistance provided by the Barcelona Supercomputing Center for the BSC-DREAM8b model dust data. The authors express gratitude to the NOAA Air Resources Laboratory for the HYSPLIT transport and dispersion model and those at the NRL-Monterey that helped in the development of the NAAPS model. We also express our gratitude to the developers of LIRIC algorithm and software.
aeruginosa strains from CF patients. 12 hile the first multicenter randomized controlled study (NCT02116010) evaluating the efficacy and safety of phages against P. aeruginosa wound infections in burn patients will soon begin, several steps remain before bacteriophage-based therapies can be applied to CF patients. After a brief reminder of the history of bacteriophage use, this review summarizes the current data available and the perspectives regarding bacteriophage-based therapy in the treatment of P. aeruginosa pneumonia in CF patients. In the absence of consistent clinical data in this field, we will enlarge the topic to in vitro and animal studies focusing on CF bacterial flora.
° Figure  2  example of Pseudomonas aeruginosa-specific high-titer phage-production protocol. Target strains of P. aeruginosa are incubated separately for 24 hours in the early exponential phase (OD 600 nm =0. 1) with a known phage cocktail at a 0. 035 multiplicity of infection (= volume of phage suspension/volume of bacterial suspension). After 24 hours, the obtained lysates are centrifuged at 5,000× g for 30 minutes at 4°C. Supernatant is filtered through a 0. 2 μm membrane and stored at 4°C. Abbreviations: CFU, colony-forming units; OD, optical density. P. aeruginosa to compare phage therapy with silver sulfadiazine (NCT02116010).
This first step was recently supported in the Evaluation of Phage Therapy for the Treatment of Escherichia Coli and Pseudomonas Aeruginosa Wound Infections in Burned Patients -"Phagoburn" -study by the European Ethics Committees, and its funding was provided by the European Commission. Other controlled studies using bacteriophages in the treatment of infected wounds  14  and chronic otitis  16  demonstrated encouraging results and showed the safety of the bacteriophages.
Acknowledgements. GIADA was built by a consortium led by the  Universitá degli Studi di Napoli "Parthenope " and  INAF -Osservatorio Astronomico di Capodimonte , in collaboration with the  Instituto de Astrofisica de Andalucia, Selex-ES, FI , and  SENER. GIADA is currently managed and operated by the  Istituto di Astrofisica e Planetologia Spaziali-INAF, Italy. GIADA was funded and managed by the  Agenzia Spaziale Italiana , with the support of the  Spanish Ministry of Education and Science Ministerio de Educacion y Ciencias (MEC). GIADA was developed from a  Principal Investigator proposal  from the  University of Kent ; science and technology contributions were provided by  CISAS, Italy; Laboratoire d'Astrophysique Spatiale, France , and institutions from the  UK, Italy, France ,  Germany , and the  USA.
Bathymetry is derived from the US National Geophysical Data Center (NGDC) two-minute global relief data set (ETOPO2), which uses the International Bathymetric Chart of the Arctic Ocean (IBCAO) product for Arctic bathymetry  (Jakobsson et al. , 2008). The ETOPO2 data are smoothed to the model's horizontal mesh and mapped to the ocean's vertical levels using a "lopped cell" strategy  (Adcroft et al. , 1997) , which permits an accurate representation of the ocean bottom boundary. The ocean model's hydrography is initialized with observations taken from the Polar Science Center Hydrographic Climatology (PHC) 3. 0 database  (Steele et al. , 2001). Initial sea-ice distributions are taken from the pan-Arctic Ice-Ocean Modeling and Assimilation System data sets  (Zhang and Rothrock, 2003). Atmospheric forcings (10 m surface winds, 2 m air temperature and humidity, and downward longwave and shortwave radiation) are taken from the 6-hourly data sets of the Japanese 25-year ReAnalysis (JRA-25;  Onogi et al.
, 2007). Monthly mean estuarine fluxes of freshwater are based on the Arctic Runoff database  (Lammers et al. , 2001; Shiklomanov et al. , 2000). The sea-ice component of the coupled model follows the viscous-plastic rheology formulation of  Hibler (1979)  with momentum equations solved implicitly on a C-grid  (Arakawa and Lamb, 1977)  using a procedure based on  Zhang and Hibler (1997). Fluxes of momentum into ice due to the overlying atmospheric winds and momentum fluxes between sea ice and the ocean are calculated by solving for the momentum balance at each surface grid column  (Hibler and Bryan, 1987). This model configuration was previously used to study the Arctic freshwater budget  (Condron et al. , 2009). Modelling studies of  Condron et al. (2009)  compared to observations by  Serreze et al.
(2001)  discharge estimates into estimates of RDOC export. Estimates of RDOC export for December-March, April-July, and August-November were divided into monthly bins according to measured distributions of RDOC export for those months in Arctic rivers. For each season, [RDOC]-discharge relationships were developed. North American and Eurasian rivers were considered separately. Data from the Yukon, Mackenzie, and Kuparuk rivers were used to define a runoff-[RDOC] relationship for drainage areas in North America, and data from the Ob', Yenisey, and Lena rivers were used to define a runoff-[RDOC] relationship for drainage areas in Eurasia. RDOC for the Yenisey, Ob', Lena, and Mackenzie were collected as part of the Pan-Arctic River Transport of Nutrients, Organic Matter, and Suspended Sediments (PARTNERS) project  (McClelland et al.
, 2008). RDOC concentrations for the Kuparuk River were collected as part of the NSF Study of the Northern Alaska Coastal System (SNACS, http://www. arcus. org/arcss/snacs/ index. php). In all cases, discharge data were acquired from ArcticRIMS (http://rims. unh. edu/). Recent sampling efforts on these rivers have provided exceptional seasonal coverage  (McClelland et al. , 2008)  and the total annual discharge of RDOC in the model is 37. 7 TgC yr -1 , which is consistent with the estimate of  Raymond et al. (2007). To initialize the model, we used the three-dimensional RDOC field obtained from the 3-decade integration of the model by  Manizza et al. (2009). After that time, RDOC distributions are relatively steady, because the flushing time for tracers through the surface waters of the basin is of the order of a decade.
Acknowledgements. V. Le Fouest acknowledges support from the  European Space Agency  and the  Centre national d'études spatiales (CNES)  as part of the  MALINA  project, funded by the  Institut national des sciences de l'univers -Centre national de la recherche scientifique (CYBER/LEFE  and  PICS programmes) , the  Agence nationale de la recherche  and the  CNES. M. Babin  is supported by the  Canada Excellence Chair in Remote sensing of Canada 's new Arctic frontier. The authors wish to thank  O. Jahn  from  MIT  for having kindly provided MITgcm biogeochemical boundary data. We also thank  D. Christiansen-Stowe  for English proofreading. Edited by:  A. Bracher.
TssF and TssG are components of the T6SS baseplate Bioinformatic analyses: TssF and TssG are homologues to phage tail proteins. To gain further insights onto TssF and TssG we performed a bioinformatic analysis using HHPred (homology detection and structure prediction,  [41] ). The Sci-1 EAEC TssF (accession number: EC042_4542; gene ID: 387609963) and TssG (accession number: EC042_4543; gene ID: 387609964) protein sequences were used as baits to identify homologues in bacteriophages. HHpred analyses with TssF reported that the fragment comprising residues 7-144 (over 587) resembles region 9-132 of the phage tail-like protein TIGR02243 (PFAM04865) that has for prototype the protein J of phage P2. The segment encompassing residues 86-179 shares also secondary structures with residues 89-178 of gp6, a baseplate wedge component of bacteriophage T4 (Fig 2B  and 2C ).
To avoid false positive, each event was manually controlled in the original raw data. Box-and-whisker representations of the number of foci per cell were made with R software. For sub-pixel resolution tracking, fluorescent foci were detected using a local and sub-pixel resolution maxima detection algorithm and tracked over time with a specifically-developed plug-in for ImageJ  [56]. The x and y coordinates were obtained for each fluorescent focus on each frame. The mean square displacement was calculated as the distance of the foci from its location at t 0 at each time using R software and plotted over time. For each strain tested, the mean square displacement of at least ten individual focus trajectories was calculated. Kymographs were obtained after background fluorescence substraction and sectioning using the Kymoreslicewide plug-in under Fiji  [56].
Acknowledgments We dedicate this work to  Arlette Darfeuille-Michaud  in loving memory [58]. We thank the members of the  Cascales, Cambillau, Lloubès, Sturgis and Bouveret research groups  for helpful discussions,  Emmanuelle Bouveret  for bacterial two-hybrid strains, vectors, protocols and helpful discussions,  Tâm Mignot  for kymograph analyses and discussions on fluorescence microscopy,  Laure Journet ,  Erwan Gueguen ,  Eric Durand ,  Roland Lloubès ,  Mireille Ansaldi ,  Bérengère Ize  and  Brigitte Meunier  for fruitful discussions and support,  Marthe Gavioli ,  Olivier Uderso ,  Isabelle Bringer  and  Annick Brun  for technical assistance, and  Thomas Toquette-Schoepp  for encouragements.
This work was supported by the  Centre National de la Recherche Scientifique , the  Aix-Marseille Université  and grants from the  Agence Nationale de la Recherche  ( ANR-10-JCJC-1303-03  and  ANR-14-CE14-0006-02 ) to EC. YRB and AZ were supported by doctoral fellowships from the  French Ministry of Research. AZ was supported by a fellowship from the  Fondation pour la Recherche Médicale  ( FDT20140931060 ). The funders had no.
Samples were fixed in 95% ethanol for 1 week, placed in acetone for 2 days and incubated with 0. 015% alcian blue 8GS, 0. 005% alizarin red S and 5% acetic acid in 70% ethanol for 3 days. After washing in distilled water, the samples were cleared in 1% KOH for several days and in 1% KOH glycerol series until the surrounding tissues turned transparent. The specimens were stored in glycerol. In situ hybridization. Digoxigenin (DIG)-labelled riboprobes for mouse Bapx1, Pax1, Aggrecan, chicken Dlx5, Dlx6, Bapx1, Gsc, shark Bapx1 and gecko Gsc were generated by DIG RNA labelling kit (Roche) based on the nucleotide sequences NM_007524, NM_008780, L07049, NM_204159, AY_640308, NM_204137, NM_205331, AB293587 and AB985798 deposited in GenBank, respectively. The nucleotide sequence of mouse Gsc, chicken Aggrecan, and quail Pax1 were gifts from Gen Yamada (Kumamoto University), Atsushi Kuroiwa (Nagoya University) and Hirohiko Aoyama (Hiroshima University), respectively.
Acknowledgements We express our gratitude to  M. Depew ,  E. Allin  and  J. Meng  for their helpful discussion. We are also grateful to  A. Kuroiwa ,  H. Aoyama  and  G. Yamada  for giving us materials for in situ hybridization,  H. Nagashima ,  F. Sugahara ,  F. Nagao ,  Y. Fujisawa  and  S. Kushiyama  for technical assistance, and  R. Ladher  and  D. Sipp  for critical reading of this manuscript. We also thank the  Animal Resource Unit of, RIKEN CDB , for the gift of gecko embryos, and  Katsumi Shirato  for his help in catching adult sharks. Our gratitude is extended to  Actelion Pharmaceuticals Ltd  for the bosentan usage. T. K. is a Research Fellow of the  JSPS (PD). This work was supported in part by  Global COE Program (Integrative Life Science  based on the  Study of Biosignaling Mechanisms ),  KAKENHI  and  Platform for Dynamic Approaches  to Living System from the  MEXT, Japan ;  grantsin-aid for scientific research  from the  Ministry of Health, Labour and Welfare, Japan ;  CREST  from  JST, Japan.
Only few data are available concerning rivers' histories, which are often traced using phylogeographical patterns  [25]. From a forest origin to a wide ecological success throughout Africa Origin of the C. olivieri complex in central/eastern Africa and first radiation in the Congo Basin Animals occurring within clades V to VIII, the most basal, are endemic either to eastern or central Africa, but the topology of the phylogenetic tree does not provide a precise signal as to the origin of the C. olivieri complex. Individuals associated with clades V and VI were captured in lowland rain forests of the Congo Basin, so the most likely scenario is that early members of the complex colonized this watershed. A radiation then occurred within forest ecosystems, probably correlated with increase in body size.
olivieri and one C. fulvastra from Chad, three C. viaria from Senegal). We only succeeded in amplifying the two mitochondrial markers 16S and COI using specific internal primers. PCR conditions were initial denaturation of 3 min at 95°C, then 45 cycles of 95°C 10 s, 60°C 10 s, 72°C 20 s, and a final extension step at 72°C for 7 min. To avoid contaminations with modern DNA, all these steps were performed on separate days and in an isolated room exclusively devoted to manipulations of ancient DNA. Purification of PCR products and cycle-sequencing reactions of both DNA strands were performed at the Génoscope (Evry, France). Sequences were edited and assembled using Sequencher 4. 9 (Gene Codes Corporation, Ann Arbor, MI, USA) and deposited in GenBank (see Additional file 6).
We first tested for signs of positive selection affecting the coding nuclear marker BRCA using Datamonkey server and the SLAC method  [44]. We also used the GARD method  [45]  to detect potential signs of recombination in each nuclear alignment. Neither positive selection nor recombination was detected in our dataset (significance level of 0. 01). Phylogenetic relationships were inferred for each marker separately using Bayesian Inference (MrBayes version 3. 1  [46] ) following procedures described by Jacquet et al. [9]. No well-supported conflict was observed between topologies, so we built a restricted data set, corresponding to the seven markers combined (5305 bp) by retaining only one specimen per locality and per clade (156 specimens, see Additional file 6). A partition for each marker was defined.