dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , Microcystis, Woronichinia, etc. ) and smaller species associated with larger cell densities (e. g. , Aphanocapsa, Aphanothece, etc. ). As biomass is a consequence of nutrient availability  [3] , all biovolumes were converted to percentage (cyanobacteria) biovolume in order to reduce any inter-site bias related to different trophic status. Generic biovolume contributions were mapped with ESRI ArcGis Desktop. Microcystins (MCs) were analyzed either with LC/MS, HPLC or ELISA immunoassays depending of the laboratory. All toxin analyses involved a preliminary cyanobacteria cell lysis step with methanol  [44]  to account for total (dissolved and intra-cell) toxin concentrations. These methods quantification limits range from 0. 05 to 0. 2 µg/L. As no interlaboratory calibration data was available, all quantitative results were sorted as lower than (MCs < 0.
Acknowledgments This work is part of the  ORACLE  project funded by the  French National Agency for Sanitary Security (ANSES)  under the national program  PNRSET 2012  (project  11-115 ). The authors sincerely thank the  French Agence Régionale de Santé (ARS) of Brittany  and the  French Agence de l'Eau Loire-Bretagne (AELB)  for the data provided and are grateful to  Caroline Farenc , student at the  School of Environmental Engineering (EME) , for data collection and exploitation.
, 2013). -At site 2, eddy-covariance stations sampled three contiguous large areas (about 1-2 km long) with relatively homogeneous vegetation: forest (ss5) (Fig. 3c ), maize (ss6) and moor (ss7). The site was specifically devoted to the study of the role of surface heterogeneity. The turbulence characteristics and decay over the different vegetation covers will be compared taking into account the local circulations which may develop between the fields during this phase of the day. For consistency, uniform data processing was carried out for all eddy-covariance stations mentioned above. In addition to the previous measurements, three scintillometers were used. They measured the structure parameter of refractive index and temperature averaged along the path between the transmitter and the receiver  (Moene et al.
Data set During the field experiment, a field catalog (http://boc. sedoo. fr) supplied quick looks of the continuous measurements and IOP observations, satellite images, reports, model forecasts and analyses, which are still available. The BLLAST web site (http://bllast. sedoo. fr) describes the project and contains the documentation, presentations and field catalog, and also gives access to the observational and modelling data and metadata. The data set was reserved for BLLAST participants until 2014, and has been opened to the scientific community since then. We encourage people to contact instrument principal investigators whenever using one of the BLLAST data sets.
Turbulence decay Turbulence decay is one of the main foci of the analyses of BLLAST data. Here we give examples of this decay observed at the surface and in the boundary layer above and illustrate how BLLAST data set enables to address the questions raised in Sect. 2. 2. 1. Figure  12a  presents the turbulent kinetic energy decay observed from surface stations over five different surfaces and from aircraft, on all IOP days. RPASs and the turbulence probe carried by the tethered balloon at site 1 also measured turbulence at heights that are complementary to those probed by the aircraft and by the instrumented towers (not shown). The BLLAST data set therefore provides a diverse combination of estimates for the study of turbulence decay. Figure  12a  shows the two regimes of the decay that were presented by  Nadeau et al.
Preliminary analysis of the decay observed as a function of the synoptic conditions reveals the role that wind shear might play in delaying the abrupt decay phase (not shown), which supports the results found by  Pino et al. (2006)  with LES and by  Goulart et al. (2010)  with a theoretical model. The diversity of the conditions observed during BLLAST, together with measurements at different heights during the AT, will allow a sensitivity analysis of the TKE decay with respect to the various forcing influences. Further analysis of BLLAST data set should also allow us to determine for how long the CBL remains quasi-steady in its whole depth, and whether unsteadiness and departure from usual scaling occur simultaneously with the decoupling of the upper and lower part of the CBL.
Conclusions One of the main strengths of BLLAST project and field campaign is its focus on a well-defined issue: turbulence decay during the afternoon over land. Added to this, the large collaborative efforts that enabled assembling almost all the observational platforms that are useful for probing the PBL, as well as a complete hierarchy of modelling tools have resulted in a rich data set for the study of the changing characteristics of turbulence during the AT and ET, throughout 12 IOP days. The field campaign took place in an environment of complex and heterogeneous terrain, which is both a challenge and an opportunity to link the AT and ET processes with mountainplain flow reversal and surface heterogeneity. The combination of manned and unmanned aircraft, together with numerous remote-sensing systems and in situ techniques, each one with different capabilities, enable the interested community to (i) test and validate new sensors and techniques, (ii) gain a critical insight into (old and new) techniques through redundancy and (iii) participate in the process studies of the AT and ET.
BLLAST will thus contribute to the design of advanced high-resolution numerical simulations, by providing complementary data and allowing both more realistic simulations and a means to evaluate them. Table  A2. List of the variables measured at the surface sites (first column), instruments used (second column) and their acquisition frequency (third column). The abbreviation for the measured variables are T : temperature, WS: wind speed, WD: wind direction, H 2 O: humidity, CO 2 : carbon dioxide concentration, P: pressure, Rad: radiative budget terms, ST: soil temperature, SM: soil moisture, G: ground heat flux. For each surface station, the number of specific instruments installed is indicated (columns 4 to 12). Note that the lines in italic correspond to high frequency instruments.
Acknowledgements. The BLLAST field experiment was made possible thanks to the contribution of several institutions and supports:  INSU-CNRS (Institut National des Sciences de l'Univers ,  Centre national de la Recherche Scientifique, LEFE-IMAGO program ),  Météo-France ,  Observatoire Midi-Pyrénées  ( University of Toulouse ),  EUFAR (EUropean Facility for Airborne Research)   BLLATE-1&2 ,  COST ES0802  (European Cooperation in the field of Scientific and Technical) and the  Spanish MINECO  projects  CGL2009-08609 ,  CGL2012-37416-C04-03 ,  CGL2012-37416-C04-02  and  CGL2011-13477-E. The field experiment would not have occurred without the contribution of all participating European and American research groups, which all have contributed in a significant amount. The Piper Aztec research airplane is operated by  SAFIRE , which is a unit supported by  INSU-CNRS ,  Météo-France  and the  French Spatial Agency (CNES).
From these longitudinal cross-sections also the distributions of eutectic and dendrite arm spacing (DAS) were analyzed  [17]. To identify the grain structure qualitatively the cross-sections were etched electrolytically and analyzed in a polarized light microscope. Then, different colors represent different orientations of the dendritic grains  [18]. For quantitative determination of crystallographic orientations of the grains, Electron BackScattered Diffraction (EBSD) patterns were made from selected longitudinal cross-sections. These data were used to evaluate the 3D-crystallographic orientations of the grains with respect to the solidification direction and to evaluate the distributions of sizes and shapes of the grains in the 2D sections.
Conclusion This paper gives an overview of the experiments performed so far on-board the International Space Station (ISS) by the CETSOL team. In the Materials Science Laboratory, Al-7 wt% Si alloys with and without grain refiners were solidified in diffusive conditions. Detailed grain structure analysis showed columnar growth in case of non-refined alloy, but the existence of a columnar to equiaxed transition in refined alloy. Main result is a sharp CET when increasing the solidification velocity and a progressive CET for lowering the temperature gradient. Applying a front tracking model this behavior was confirmed numerically. Using a CAFE model both segregation and grain structures, as well as CET, were numerically modeled and show a fair agreement with the experimental findings. In future, more experiments on-board the ISS will be performed with different process parameters to broaden the data basis.
These genomic islands were suggested to represent a gene repository that could play a role in adaptation to different environments, such as compost or a living host  (Fedorova et al. , 2008). Transcriptome studies based on RNA-Seq and proteome studies are routinely carried out with A. fumigatus  (Horn et al. , 2012; Kroll et al. , 2014). The updated features of the A. fumigatus genome are available at the Aspergillus Genome Database website (Table  3 ).
oxysporum. The genome sequence of the tomato pathogenic isolate F. oxysporum f. sp. lycopersici was published in 2010  (Ma et al. , 2010). Since then, eleven additional F. oxysporum strains have been sequenced by the Broad Institute. The availability of the complete genome sequences (accesible at the Fusarium Comparative Database, Table  3 ) as well as of molecular tools and well-established pathogenicity assays has allowed to address the genetic bases and evolutionary origins of pathogenicity and host range in F. oxysporum.
Acknowledgments The authors would like to thank  Ashley Bird  from the  Medical Illustration (MedIT) unit at the University of Aberdeen  for her assistance with digitization and edition of the life cycle figures. This review was carried out by members of the  EU-Initial Training Network Ariadne  ( PITN-GA-2009-237936 ), which provided financial support for C. B. , S. D. , M. E. G. , E. G. , E. M. , P. V. M. , M. M. , V. N. , M. F. A. N. , T. O. , M. O. R. , K. S. and L. W.
Complete genome-wise gene knock-out libraries would facilitate screening efforts such as analysis of the mode of action of anti-ATCC10895 CBS102347 and derivatives fungal compounds, but are not yet available for A. fumigatus. In parallel, huge efforts are being made to gain insight into virulence mechanisms by analyzing the interaction of A. fumigatus with dif-ferent cellular systems such as neutrophils, macrophages, epithe-lial cells and dendritic cells with increasing complexity, such as Ashbya Genome Database combining different cellular systems. FB1 (a1b1), FB2 (a2b2), FB6a (a2b1), FB6b (a1b2), SG200 (a1mfa2 bE1/bW2), FBD11 (a1a2 b1b2), AB31 (a2 P crg1 :bE1 P crg1 :bW2) AB33 (a2 P nar1 :bE1 P nar1 :bW2) Ustilago maydis Database IPO323 Mycosphaerella graminicola (anamorph: Septoria tritici) F.
oxysporum f. sp. lycopersici wild type 4287 (FGSC 9935) Fusarium Comparative Database SC5314, NGY152, CAI4, Guy11, P1-2, 70-15, PH14, RM1000, WP17, SN87, TH3, FR13, BR88 SN95, SN152 Candida Genome Database Magnaporthe comparative Database CEA10 CEA17 akuB Aspergillus Genome Database http://www. aspgd. org/ Strains Bioinformatic/ genome databases.
Section 4. 4 has been rewritten to discuss the large differences between flux estimates at 200 m and 300 m by our sediment trap method, but indeed those of independent estimates. We have attempted to constrain the "trapping efficiency" of our sediment trap deployment by comparison to 234 Th estimates of POC export, although it should be noted that the latter technique is not considered to be an independent reference. Nevertheless, we find smaller fluxes measured by the sediment trap relative to those obtained with the thorium technique. Consequently we proceed to discuss the potential biases that might have impacted the collection efficiency of the moored trap, and finally a make direct comparisons with independent datasets at depths > 300 m that also report low POC fluxes. In spite of the calculated trapping efficiency there are still very large differences in flux estimates at 200 m and 300 m that are evident from multiple independent approaches and are consistent with attenuation reported in other areas of the Southern Ocean.
R3-C7. The authors then appeal to a direct comparison with KEOPS1 results. (p17059 lines 2 -5) "Even if we assume that our A3 sediment trap did undersample the particle flux, it seems unlikely that this in itself could explain the significant reduction in POC flux observed between 200 and 300m (Table  3 ). " The case for comparison to a different season in an earlier year is not well made. This notwithstanding, one of the 4 comparators in Table  3  actually indicates a 15% flux increase at 330 m relative to 220 m. R3-R7. We deleted the direct comparison with previous measurements as inter annual variability might play an important role in the difference we observe. We removed Fig. 8  and placed the POC export data from KEOPS2 into Table  4. Instead of a quantitative comparison with these estimates, we choose the general statement that the fluxes we report are very low compared to previous estimates at 200 m and that it raises questions about the trapping efficiency.
R3-C8. Next there is a comparison with other KEOPS2 studies using contrasting techniques: short deployments of free drifting conventional and gel traps (Laurenceau et al. ); 234Th method  (Planchon et al. )  and underwater vision profiler (UVP)  (Jouandet et al. , 2014). The only one of these to sample at a similar depth is the UVP that measures particle size and concentration. This has been related to flux, but this single point from a method that does not collect settling particles is not compelling. As the authors correctly state: "The diversity of approaches prevents absolute comparison of the fluxes. " R3-R8. We removed direct comparison with previous estimates as well as flux attenuation calculation including different methods. Instead we used data from two independent dataset from KEOPS1  (Ebersbach and Trull, 2008)  and KEOPS2  (Jouandet et al.
Concluding response: As suggested by reviewers we have removed the quantitative representation of flux attenuation obtained from different methods and rather present a more qualitative view. We now further acknowledge the potential biases with upper ocean sediment trap deployments and estimate a probable trapping efficiency from thorium data. We feel that our revised paper is an important contribution to the Biogeosciences KEOPS2 special issue because: (i) Our paper serves as an important companion dataset for the detailed biological and geochemical analyses presented in part 2 (ii) The paper is significant in its own right despite over 8 papers on export processes from KEOPS 1 and 2, our dataset serves as the only annual record of flux from this important iron-fertilized site. This has allowed us to uniquely identify the significant temporal lags between the accumulation of surface biomass and export out of the mixed layer.
(iii) Together with our own data we synthesize the various POC flux estimates obtained from the iron-fertilized Kerguelen bloom to bring together numerous lines of independent evidence supporting a scenario of significant flux attenuation between 200-300 m. We acknowledge however that our paper should represent these arguments in a more qualitative manner and more dedicated techniques would be required to quantitatively constrain attenuation. Our findings are thus in line with an emerging biogeochemical paradigm in the Southern Ocean that high biomass blooms fertilized by iron do not necessarily lead to significant export into the bathypelagic ocean. We hope that following our careful attention to the reviewers comments and significant modifications for the manuscript our paper is now considered acceptable for publication in the Biogeosciences KEOPS2 special issue.
Nevertheless, this unrealistic behavior of the timer mechanism is based on a biologically meaningless assumption, namely the exponential growth of cells of infinitely small or large size  [23, 24]. Cells of size zero or infinity do not exist and particularly small or large cells are likely to exhibit abnormal growth behavior. In conclusion, the mathematical arguments that were previously developed are insufficient to rule out a size-independent, timer model of bacterial division: quantitative comparisons between models and data are needed. In the present study, we test whether age (i. e. the time elapsed since birth) or size is a determinant of cell division in E. coli. To do so, we analyzed two datasets derived from two major single-cell experimental studies on E. coli growth, performed by Stewart et al.
[25]  and Wang et al. [26]. Our analysis is based on division rate estimation by state-of-the-art nonparametric inference methods that we recently developed  [27, 28]. The two datasets correspond to different experimental setups and image analysis methods but lead to similar conclusions. We show that even though a model with a simple timer triggering division is sufficient to maintain cell size homeostasis, such a model is not compatible with the data. In addition, our analysis of the timer model shows that this model is very sensitive to hypotheses regarding the growth law of rare cells of very small or large size. This lack of robustness argues against a timer mechanism for division control in E. coli as well as in other exponentially growing organisms. In contrast, a model where cell size determines the probability of division is in good agreement with experimental data.
Age and size distribution of the bacterial population The results reported in this study were obtained from the analysis of two different datasets, obtained through microscopic time-lapse imaging of single E. coli cells growing in a rich medium, by Stewart et al. [25]  and Wang et al. [26]. Stewart et al. followed single E. coli cells growing into microcolonies on LB-agarose pads at 30°C. The length of each cell in the microcolony was measured every 2 min. Wang et al. grew cells in LB: Luria Bertani medium at 37°C in a microfluidic setup  [26]  and the length of the cells was measured every minute. Due to the microfluidic device structure, at each division only one daughter cell could be followed (data s i : sparse tree), in contrast to the experiment of  Stewart et al. where all the individuals of a genealogical tree were followed (data f i : full tree).
It is worth noting that the different structures of the data f i and s i lead to different PDE models, and the statistical analysis was adapted to each situation (see below and Additional file 1). From each dataset (f i and s i ) we extracted the results of three experiments (experiments f 1 , f 2 and f 3 and s 1 , s 2 and s 3 ). Each experiment f i corresponds to the growth of approximately six microcolonies of up to approximately 600 cells and each experiment s i to the growth of bacteria in 100 microchannels for approximately 40 generations. Given the accuracy of image analysis, we do not take into account variations of cell width within the population, which are negligible compared to cell-cycle-induced length variations. Thus, in the present study we do not distinguish between length, volume and mass and use the term cell size as a catch-all descriptor.
Cell age and cell size distributions of a representative experiment from each dataset are shown in Figure  1. These distributions are estimated from the age and size measurements of every cell at every time step of a given experiment f i or s i , by using a simple kernel density estimation method (kernel estimation is closely related to histogram construction but gives smooth estimates of distributions, as shown in Figure  1 , for instance; for details see the Methods and Additional file 1). As expected for the different data structures (full tree f i or sparse tree s i ) and different experimental conditions, the distributions for the two datasets are not identical. The age distribution is decreasing with a maximum for age zero and the size distribution is wide and positively skewed, in agreement with previous results using various bacterial models  [29] [30] [31].
Testing the Age Model (timer) and the Size Model (sizer) with experimental data In this study we tested the hypothesis of an age-dependent versus size-dependent division rate by comparing the ability of the Age Model and Size Model to describe experimental data. The PDE given by Equations (  1 ) and (  2 ) can be embedded into a two-dimensional age-and-sizestructured equation (Age & Size Model), describing the temporal evolution of the density n(t, a, x) of cells of age a and size x at time t, with a division rate B a,s a priori depending on both age and size: ∂ ∂t + ∂ ∂a n(t, a, x) + ∂ ∂x (v(x) n(t, a, x)) = -B a,s (a, x)n(t, a, x) (3) with the boundary condition n(t, a = 0, x) = 4 ∞ 0 B a,s (a, 2x)n(t, a, 2x)da In this augmented setting, the Age Model governed by the PDE (1) and the Size Model governed by (  2 ) are restrictions to the hypotheses of an age-dependent or size-dependent division rate, respectively (B a,s = B a or B a,s = B s ).
Black dots are the average length increase for every cell of a given experiment f 1 , as a function of cell length; error bars are the average +/ -2 SEM (standard error of the mean). The red line is a linear fit for lengths between 2. 5 µm and 4. 5 µm. Inset: For each single cell of f 1 , the evolution of cell length with age was fitted with a linear or an exponential function (as shown in panel A). We thus obtain a distribution of R 2 coefficients corresponding to the linear (green) and exponential (red) fits. approximates the data (a perfect fit would give R 2 = 1 and lower values indicate a poorer fit). The inset of Figure  2B  shows the distribution of the R 2 coefficient for all single cells for exponential (red) and linear (green) regressions, demonstrating that the exponential growth model fits the data very well and outperforms the linear growth model.
The age-size joint distribution of E. coli corresponds to a size-dependent division rate We used both the Age Model and Size Model to fit the experimental age-size distributions, following the approach described above. The growth law below x min and above x max is unknown. Therefore, to test the Age Model, growth was assumed to be exponential between x min and x max and we tested several growth functions v(x) for x < x min and x > x max , such as constant (i. e. linear growth) and polynomial functions. Figure  3  shows the best fit we could obtain. Comparing the experimental data f 1 shown in Figure  3A  (Figure  3B  for s 1 data) with the reconstructed distribution shown in Figure  3C  (Figure  3D  for s 1 data) we can see that the Age Model fails to reconstruct the experimental age-size distribution and produces a distribution with a different shape.
5 both for s i and f i data (P < 10 -16 ; see Additional file 1: Figure  S7 ). We used various growth functions for x < x min and x > x max but a satisfying fit could not be obtained with the Age Model. In addition, we found that the results of the Age Model are very sensitive to the assumptions made for the growth law of rare cells of very small and large size (see Additional file 1: Figure  S3 ). This ultrasensitivity to hypotheses regarding rare cells makes the timer model unrealistic generally for any exponentially growing organisms. In contrast, the Size Model is in good agreement with the data (Figure  3 : A compared to E and B compared to F) and allows a satisfactory reconstruction of the age-size structure of the population. The shape of the experimental and fitted distributions as well as their localization along the y-axis and x-axis are similar (size distributions and age distributions, i.