dataesr/scientific-paragraphs-categorization huggingface dataset:
html or with DEN-DROSCOPE  [14] , available from http://dendroscope. org/. We will refer to these algorithms as the exact algorithms. Each instance has been run on a single core of an Intel Xeon E5506 processor. Each run that took more than one hour was aborted. For each instance, we ran our program with the option 2-approx, and, in case the latter did not finish within one hour, we ran it again, this time using the option 4-approx, always with a one-hour limit (see Section "New algorithms: CYCLEKILLER and NONBINARYCYCLEKILLER"). We used the program RSPR v1. 03  [22, 23]  to solve or approximate MAF and GLPK v4. 47 (http://www. gnu. org/software/glpk/) to solve the following simple polynomial-size ILP formulation of DFVS: min v∈V x v s. t. 0 ≤ v ≤ |V | -1 f o r a l lv ∈ V v ≥ u + 1 -|V |x u -|V |x v for all e = (u, v) ∈ E v ∈ Z for all v ∈ V x v ∈ {0, 1} for all v ∈ V Given a directed graph D = (V , E), the binary variables x v model whether a vertex is in the feedback vertex set, and the integer variables v model the positions of the surviving vertices in the induced topological order.
The average approximation ratio (ninth column) is taken over all instances for which at least one exact method finished. The last column indicates the percentage of those instances for which CYCLEKILLER found an optimal solution. rithm for two nonbinary trees. When RSPR is used in exact mode, NONBINARYCYCLEKILLER yields a theoretical worst-case approximation ratio of 4. When RSPR is used in its 3-approximation mode, NONBINARYCY-CLEKILLER yields a theoretical worst-case approximation ratio of 6 (see Section "New algorithms: CYCLEKILLER and NONBINARYCYCLEKILLER"). The results of this run are summarized in Table  2. For the experiments with two nonbinary trees, the RSPR software can no longer be used, and instead we used the exact and 4-approximate MAF algorithm described in  [21]. This makes NONBINARYCYCLEKILLER behave as a 4-approximation and 7-approximation respectively (see Section "New algorithms: CYCLEKILLER and NONBINARYCYCLEKILLER").
Note that the exact algorithm  [21]  is considerably slower than RSPR, meaning that in practice NONBINARYCYCLEKILLER struggles with two nonbinary trees more than when at most one of the trees is nonbinary. The results for this run are summarized in Table  3. The exact hybridization number in both runs was computed by TERMINUSEST  [27]. Each instance that took longer than 10 minutes to compute was aborted and the running time was set to 600 seconds. The averages of the running-times are taken over all instances, with running-time taken to be 600 if the program timed out for that instance. (We used a shorter time-out than in the binary experiments because of the observation that, in the nonbinary case, exact algorithms running longer than 10 minutes almost always took longer than 60 minutes too.
Practical experiments on biologically relevant trees Finally, we tested our methods on phylogenetic trees obtained from GreenPhylDB  [30]  -version 3, a database containing twenty-two full genomes of members of the plantae kingdom, ranging from algae to angiosperms. We were able to retrieve from the database the 9903 rooted phylogenetic trees associated to the gene families contained in the database (the gene trees), along with the rooted phylogenetic tree describing the history of the twenty-two species contained in GreenPhylDB (the species tree). Note that the species tree for these species is not completely resolved, i. e. it is nonbinary. Among the gene trees, 2769 contain less than 3 species and they were discarded. Of the remaining 7134 trees, only 204 were directly usable for testing our methods.
Indeed, because of gene duplication events arising in genomes, some species host several copies of the same gene, hence individual gene trees usually have several leaves labeled with identical species names. Unfortunately, our methods do not handle such multi-labeled gene trees (MUL trees). We thus transformed the MUL trees into trees containing single copies of labels, applying the tools described in  [31, 32]  to the forest F of 7134 trees. As in Section 4. 1 G ∈ (F 1 ∪ F 2 ∪ F p 3 ∪ F s 3 ), we restricted the species tree S (containing 22 taxa) to the leaves of G and we applied our methods to all so obtained pairs (restricted S, G). The results are presented in Tables 4, 5, 6 and 7. For all four datasets both TER-MINUSEST and NONBINARYCYCLEKILLER ran extremely quickly, rarely taking more than a couple of seconds for each species-gene tree pair.
Moreover, the clear conclusion with this dataset is that, although the species-gene pairs are often incompatible, there are rarely many cycles to kill and optimum solutions to the hybridization number problem are generally extremely close to optimal solutions to MAF. In 5552 instances TERMINUSEST returned the same size solution as RSPR, in 5415 cases TERMINUSEST returned the same size solution as NONBINARYCYCLEKILLER (running in 4-approx mode), and in 5209 cases NONBINARYCYCLEKILLER (running in 4-approx mode) returned the same size solution as MAF. In this dataset the gene trees were also nonbinary, meaning that NONBINARYCYCLEKILLER had to use the MAF algorithm described in  [21]  instead of RSPR.
Conclusions Our experiments with binary trees show that CYCLEKILLER is much faster than available exact methods once the input trees become sufficiently large and/or discordant. In over 96% of the cases CYCLEKILLER finds the optimal solution and in the remaining cases it finds a solution very close to the optimum. We have shown that the most accurate mode of the program produces solutions that are at most a factor 2 from the optimum. In practice, the average-case approximation ratio that we observed was 1. 003. The fastest mode of the algorithm can be used on trees with thousands of leaves and provably constructs networks that are at most a factor of 4 from the optimum. Our experiments with nonbinary trees highlight once again that the cycle-breaking technique described in this article is intrinsically linked to the current state-of-theart in MAF algorithms.
Acknowledgements A preliminary version of this paper (restricted to the binary case) appeared in the proceedings of the 12th  Workshop on Algorithms in Bioinformatics  ( WABI 2012 ) [28]. We thank  Simone Linz  and  Leen Stougie  for fruitful discussions. This publication is the contribution no. 2014-040  of the  Institut des Sciences de l'Evolution de Montpellier (ISE-M ,  UMR 5554 ). This work has been partially funded by the  French Agence Nationale de la Recherche , Investissements d'avenir /Bioinformatique  ( ANR-10-BINF-01-02 , Ancestrome), and it has benefited from the  ISE -M computing facilities.
Reproductive schedules The individual reproductive schedule measurements are summarized in Additional file 1: Figure  S1  and Additional file 1: Table  S1  (full dataset before quality control can be found in  [49] ). Breeding mode influences the lifetime production of embryo and adult progeny. Selfing hermaphrodites start reproducing between day 3 and day 4 of the life-cycle and by day 7 have mostly ceased to reproduce. Outcrossed hermaphrodites and females on the other hand start reproducing between day 3 and 4, but only cease reproduction by day 9. As a consequence, outcrossed individuals have higher total lifetime reproductive output. These reproduction patterns are explained by the fact that C. elegans hermaphrodites are self sperm limited. Also as expected, mating with males early in life greatly decreases the average longevity of hermaphrodites and females, when compared with hermaphrodites undergo selfing.
Reproductive schedule quality control As an initial screen, we eliminated individual observations where the count of adult offspring was higher than the count of embryos, as that would imply progeny viabilities higher than 100% (eliminated n = 43). To properly estimate progeny sex ratios, a high number of individuals needed to be counted, so as a second step, we eliminated individuals with less than 30 viable adult progeny (n = 31). We further eliminated all infertile and low fertility individuals. In the case of females, this step allowed us to prevent the inclusion of observations where there was poor tester male sperm insemination. Specifically for the outcrossed hermaphrodites, we next eliminated data where fewer than 10% male offspring were observed in order to match the minimum sex ratio observed in the outcrossed females (n = 4).
Outcrossed treatments were checked for GFP-positive broods indicative of successful cross-fertilization, and therefore hermaphrodites with mixed (non-GFP containing) broods were also eliminated. Finally, we eliminated observations from selfed hermaphrodites with more than 1% in their male progeny. This was done because males can only arise in the progeny of selfed hermaphrodites from the rare (10 -3 -10 -4 ) non-disjunction of the X-chromosome during gametogenesis  [38] , and thus we prevent the inclusion of hermaphrodites that may have been outcrossed to males from their own population, prior to the assay setup. The sample size, mean and standard deviation of the quality controlled data are presented in Additional file 1: Table  S1  and plotted in Additional file 1: Figure  S1.
Data were first Anscombe-transformed [x' = √(x + 3/8)] and linear mixed effect models (LMM) performed (with fixed generation and random block as independent variables) using restricted maximum likelihood methods, as implemented in the lme4 package of R  [60]. Leastsquare estimates and significance of effects were obtained against t-distributions using the lsmeans package of R  [61]. Degrees of freedom for these t-tests were asymptotically determined, c. f. [61]. When significant generation effects were inferred at α ≤ 0. 05, the residuals of the models were tested for normality with Shapiro-Wilk tests and for homocedasticity with Bartlett's tests  [62]. In case either of these two LMM assumptions were not followed we defined outlier observations as those whose residuals fell outside the 95% confidence limits of the normal cumulative distribution function, as inspected with QQ plots in the car package of R  [63].
Acknowledgements We thank  R. Azevedo ,  C. Braendle ,  I. M. Chelo ,  S. Estes ,  M. Matos , and  P. Sandner  for discussion. The Editor and anonymous referees for suggestions on the manuscript. S. C. was supported by a PhD fellowship from  Fundação para a Ciência e a Tecnologia (FCT ;  SFRH/BD/36726/2007 ). Funding provided by the  National Science Foundation  ( DEB-1120417 ) to P. C. P. , and the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013 Grant Agreement no. 243285 ") to H. T.
Safran analyses are now available beginning in 1958  (Vidal et al. , 2010a). The water balance was computed using the Isba atmospheric surface scheme  (Noilhan and Planton, 1989) , for which the parameters were derived from a physiographic database  (Masson et al. , 2003). Isba is coupled to the Modcou hydrological model  (Ledoux et al. , 2007) , for which hydrogeological parameters were calibrated in the Seine and Rhone aquifers. However, since the region of interest is not deeply affected by aquifers, groundwater modelling was not used, and only surface water routing was used. The river flow was simulated using the RAPID (Routing Application for Parallel computatIon of Discharge) routing scheme  (David et al. , 2011a, b)  for which the parameters were calibrated over the whole of France and not calibrated over a single basin.
( 1 ) The small farm dam fraction can be estimated using the present day irrigation water volume in the Pays de la Loire region, by considering that all the irrigation water taken from the surface water comes from small farm dams, as is the case in the Layon Basin  (SAGE, 2002a). To do this, we used the pumping volume (V P in m 3 ) provided by the basin water agency. We then needed to estimate an average water depth, d w , of small farm dams. An average value of d w = 3 m was chosen because it is the average depth of a small database referencing 171 small farm dams in south-western France, and is below the 5 m depth threshold for which an annual survey of the dam structure is required. Then the small farm dam fraction can be expressed as D = V P /d w /A basin × 100. ( 2 ) Table  1  gives an estimate of this percentage for seven basins of Pays de la Loire.
Acknowledgements. This study was supported by the  DREAL Pays de la Loire. We would like to thank  Stéphanie Poligot-Pitsch  for her assistance, as well as  Michel Déqué  from  CNRM-GAME  and  Christian Pagé  and  Laurent Terray from Cerfacs  for providing the downscaled climate projections. We are grateful to  Patrice Dumas  and the anonymous reviewer for their comments that helped to improve this article. Edited by:  N. Basu.
GROWTH AND NITROGEN METABOLISM IN TOMATO PLANTS In Experiment 4, all measured tomato growth parameters were significantly influenced by the plant-derived protein hydrolysate applications. Our data indicated that the two concentrations (5 or 10 ml/L) tested enhanced growth of tomato transplants as evidenced by the shoot (+19. 5%), root (+27. 5%), and total dry biomass (+20. 5%, Table  2 ). In addition, increasing the concentration of the plant-derived protein hydrolysate from 0 to 10 ml/L increased chlorophyll content (SPAD index) and leaf nitrogen content by 15 and 21. 5%, respectively. The positive effect exerted by plant-derived protein hydrolysate "Trainer" on plant growth parameters occurred through stimulation of nitrogen uptake and assimilation. Increased leaf nitrogen content may account for enhanced photosynthesis and improved translocation of photosynthates to the sinks that contribute to the greater plant biomass of plants treated with the protein hydrolysate.
A 2-step thermocycling protocol was used: initial denaturation at 98°C for 1 min, followed by 30 cycles of 98°C for 15 s and 72°C for 30 s, then 72°C for 10 min, and then a final step at 12°C for 4 min. Amplification products were purified as described above and sequenced (GATC Biotech AG, Mulhouse, France). The sequences of the pf279 and pf774 genes were analyzed using Vector NTI software (Invitrogen) and aligned at the gene and protein levels using ClustalW 2. 1.
R-project. org) to determine the effect of the strains on the concentrations of FFA and on their net production. Differences between the treatment means were compared at the 5% level of significance by using the Fisher's least significance difference (LSD) test. Nucleotide sequence accession numbers. The sequences of the pf279 and pf774 genes have been deposited in the EMBL data library, and the accession numbers are given in Table  3.
ACKNOWLEDGMENTS  M. C. Abeijón Mukdsi  benefited from a grant from  ECOS-MINCyT  (Action no. A08B02 ) and a postdoctoral  Bernardo Houssay grant. The sequencing was supported by  INRA   AIP Bioressource PropioDive 2009. Valentin Loux  and  Julien Buratti  created databases and implemented the AGMIAL tool. We are grateful to  Alyson Yee  for English language corrections. We thank  Pascal Pachot ,  Stat-Plan , for support concerning statistical analyses. We also thank  Y. Murooka ,  Osaka University , for providing the pPK705 vector used in this study.
Data available in the dossier used for this evaluation The studies submitted for evaluation followed the EFSA guidelines on submission of a dossier for safety evaluation by the EFSA of active or intelligent substances present in active and intelligent materials and articles intended to come into contact with food (EFSA, 2009). Non-toxicity data: -Data on identity -Data on physical and chemical properties -Data on manufacturing process -Data on function, intended use and authorisation -Data on overall and specific migrations Toxicity data: -.
This confirms that melanization is not required for encapsulation by lamellocytes consistent with a previous report  [31]. We next focused our attention on the role of PPO1 and PPO2 in adults. As observed in larvae, a reduced melanization spot was observed in PPO1 D flies while PPO2 D flies displayed a more intense melanization at the wound site compared to wild-type. No melanization spot was observed in injured PPO1 D , PPO2 D flies whatever the microbe used (Fig S2A, S2B, data not shown). We next measured enzymatic PO activity with an L-DOPA assay in adult hemolymph samples from wild-type and PPO mutants. In these experiments, we also included as controls hemolymph samples from Black cells mutant flies which carry an uncharacterized mutation affecting PO activity  [5] , and from Spn27A 1 deficient flies that exhibit constitutive PO activity  [14, 15].
Acknowledgments We thank our colleagues  Barbara Lecrinier  and  Laurent Alter  for technical support and  Dani Osman  for stimulating discussions;  Won-Jae Lee  for  Drosophila PPO1  and  PPO2  antibodies;  Pascale Serror  for the E. faecalis strain;  Marie Meister  for comments on the manuscript; the  Bloomington Drosophila Stock Center  for fly stocks.
Results: Manual scoring following TC staining revealed a significantly higher frequency of dicentrics (p < 10 -3 ) (up to 30%) and estimated DSB (p < 10 -4 ) compared to uniform staining due to improved detection of dicentrics with centromeres juxtaposed with other centromeres or telomeres. This improvement permitted the development of the software, TCScore, that detected 95% of manually scored dicentrics compared to 50% for the best currently available software (DCScore TM ).
The slides with the probe solution were denatured on a hot plate at 80 • C for 3 min and incubated in the dark for 1 h at room temperature. After hybridization, the slides were washed with 70% formamide/10 mM Tris pH 7. 2 3 times during 15 min and then in 50 mM Tris pH 7. 2/150 mM NaCl pH 7. 5/0. 05% Tween 20 (3× 5 min). After washing with PBS, the slides were counterstained with DAPI and mounted with PPD. Quantification image acquisition and analysis were performed using Isissoftware (version 3. 9. 1, MetaSystems, Newton, MA).
Slide scanning and metaphase acquisition Images of metaphase cells were acquired using automated acquisition module Autocapt software (MetaSystems, version 3. 9. 1) using a ZEISS Plan-Apochromat 63×/1. 40 oil and CoolCube 1 Digital High Resolution CCD Camera. For manual scoring after uniform or TC staining, the analysis was carried out using Isis software (MetaSystems, version 5. 5). For automatic scoring of dicentrics after uniform staining, DCScore software (version 3. 8. 101 Meta-Systems) was used to detect dicentrics and an operator validated and excluded the false dicentrics  [15]. Automatic scoring following TC staining was performed using TSCsore (CEA) as described in supplementary data, Fig. 3.
Development of new software to score CA after TC staining Given the better detection of dicentrics, centric rings and acentric fragments, we have developed a new software called TCScore ®. Fig. 4A  shows a simplified version of the workflow used by TCScore. The three channel image contains the DAPI information in the blue channel and the telomere and centromere information in the red and green channels respectively. The chromosomes are first segmented using the DAPI information and then the centromeres and telomeres are detected. The chromosomes are then defined by the number of telomeres and centromeres and finally classified according to the aberration class (supplementary data Fig. 1 ). This processing scheme allows for batch processing and report generation using a highly intuitive and interactive user interface designed for rapid review and correction by the user (Fig.
4B ). The computer requirements for the use of this software are minimal allowing its use on any computer, and images can be treated regardless of the type of image capture platform used. Using TCScore, we were able to detect 95% of dicentrics scored manually. In addition, 85% of all unstable CA that were scored manually were detected by TCScore, leading to a better estimation of DSB. No significant difference was detected between the manual and automatic dose-response curves for either dicentrics alone (Fig. 5A ) or for the estimated DSB based on all CA (Fig. 5B ). In addition, the analysis using TCScore, including operator correction, was more straightforward and less time consuming (1/10 of the time compared to manual scoring). For example, the scoring of 200 metaphases exposure to 2 Gy requires 20 min including operator validation.
Importantly, a significant difference was observed between the frequency of dicentrics scored using the commercial image analysis software DCScore TM (MetaSystems) following uniform staining Fig. 1. In cytogenetic biological dosimetry, only the first metaphase is scored. Fluorescence plus Giemsa staining makes it possible to distinguish the 1st metaphase with homogeneous coloration (A) and the second metaphase after fluorescence plus Giesma staining (FPG), due to the incorporation of BrdU in the second chromatin strand (B). This step adds approximately 2 h to the protocol. PNA probe staining distinguishes the 1st metaphase (C) from the 2nd due to the cross hybridization of the FITC signal in the 2nd generation (D). For men, the centromere of chromosome Y had a very weak signal (E) and for this reason we used the Dapi image (strong heterochromatin) or inverse DAPI to identify this chromosome (F).
and TCScore (CEA) software following TC staining (p < 10 -3 ) (Fig. 5 ). This difference between the two softwares is essentially due to the improvement in the detection of the CA after TC staining, leading to a more than threefold improvement for dicentric detection and a fivefold improvement for the estimation of DSB based on all CA as it is not possible to calculate the DSB following scoring by DCScore based on CA other than dicentrics.
The dose-response curves obtained after the scoring of dicentrics as well as the estimated DSB following TC staining are the first to be published. Using this improvement for the detection of CA, we have successfully automated the scoring of CA that is as efficient as manual scoring. This software is operational and allows for batch processing and report generation using a highly intuitive user interface designed for fast review and correction. The application of TCScore software is independent of the image analysis system and can be applied to all types of images. In this study, we used the commercial software DCScore by MetaSystems  [26]  to automatically score the frequency of DC following uniform staining. Using DCScore, half of the dicentrics were detected compared to manual scoring and the lowest detectable dose was 0.
5 Gy  [15]. These data are comparable to the best published results using DCScore  [15, 23]. The efficiency of this software depended highly on the quality of the cytogenetic preparation and the major problem was the detection of dicentrics with corresponding acrocentric chromosomes and the rejection of metaphases with overlapping chromosomes. A significant difference was observed between the frequency of dicentrics scored using DCScore (MetaSystems) and TCScore (CEA) software (p < 10 -3 ). The difference between the performance of the two softwares is essentially due to the improvement in the detection of CA following TC staining, leading to a threefold improvement for dicentric detection and a fivefold improvement for estimated DSB based on all CA. The need to improve the automatic detection of dicentrics has been driven by the need to transfer this technique, which is used in only a few laboratories, to all cytogenetics laboratories to achieve high sample throughput for the processing of large cohorts of exposed populations and their follow-up.