dataesr/scientific-paragraphs-categorization huggingface dataset:
Of course, the sender's computational cost also highly depends on the number of random OT outputs that are desired. Implementation. We evaluated our protocol on two machines running over a 1Gbps local network, and also simulated a WAN environment with 50Mbps bandwidth and 100ms round-trip latency to model a real-life scenario over the Internet. All benchmarks have been run on modern Core i7 machines at 2-3 GHz. Our implementation is in plain C, and uses the SimpleOT [CO15] oblivious transfer software 4  to run the base OTs, and blake2  [ANWOW13]  for hashing, as this provides fast hashing on short inputs. Otherwise, it does not rely on any other software and is available in the public domain. The executable occupies 280K. The core protocol covers roughly 200 lines of C code. It mostly runs on single thread, except we use OpenMP to parallelize the encodings and hash function evaluations, which are the computational bottlenecks of the protocol.
Acknowledgements We thank  Ranjit Kumaresan  for providing us with an extended version of [KK13]. The work in this paper has been partially supported by the  ERC via Advanced  Grant  ERC-2010-AdG-267188-CRIPTO  and the  Defense Advanced Research Projects Agency (DARPA) and Space and Naval Warfare Systems Center, Pacific (SSC Pacific)  under contract No. N66001-15-C-4070.
Iron isotope analyses Iron isotopic analyses were performed on the bulk soil samples. Sample preparations were carried out in a laminar flow box, at INRA-GSE Aix-en-Provence. Approximately 250 mg of sample powder was first treated with 30% H2O2 in order to eliminate organic matter. After H2O2 treatment, the solution was evaporated to dryness and the residue dissolved using a mixture of concentrated HF-HNO3, followed by concentrated HCl acid, at ~130 °C. We verified that the digestion data were equivalent, within analytical error, to the total Fe concentration data obtained after fusion in Li-metaborate + Li-tetraborate at 1000°C. The Cu-Fe fraction was isolated using the AG1X8, 200-400 mesh resin (e. g. ,  Moynier et al. , 2006). Iron was further separated and purified by anion exchange chromatography using the AG MP1, 100-200 mesh, chloride form  (Maréchal et al.
Weathering vs parent material source change To assess these variations, we performed principal component analysis (PCA) considering the sand, the total Fe, Zr, Ti concentrations and the Si/Al ratios, which can be considered as a proxy for the quartz content (as quartz does not contain Al). We included in the analysis all the horizons of the studied soil profiles that experienced neither weathering nor podzolization, i. e. , beach sand (BS), BC and Bw horizons (Figure  3 ). The first two principal components of the PCA explained 94% of the variance of the dataset. The first principal component explained 79% of the variance. This component opposed two groups of variables: the Si/Al ratios representing the quartz fraction with a loading of -0. 931 and total Zr, Ti and Fe with loadings higher than 0.
Dynamics of Fe mobilization The major Fe transfers evidenced by decreasing FeDCB concentration in the E horizon and accumulation of FeDCB in the Bhs horizon started at 270 years (pit 3, Figure  4c ). This evolution, synchronized with an increase in OC in the B horizon, marks the beginning of podzolization from 270 to 335 years as demonstrated by the strong correlation between elementary OC and FeDCB fluxes in the B horizons (R 2 =0. 99, n=3, correlation significant at 1% confidence level). This correlation was already observed in other Podzols (e. g. ,  Eusterhues et al. , 2005). Once the required pH has been reached, podzolization starts within 50 years. Sauer et al. (2008)  compiled data on incipient podzolization and showed that it is generally recorded within 200-500 years. However, this duration includes the initial acidification phase.
, 2016). Accumulation of FeDCB in the B podzolic horizons (Figure  4c ) can be explained by a positive flux of OM-bound and colloidal Fe in the Bhs horizon at 335 (pit 4) and 530 years (pit 5) and a small positive flux in crystalline Fe oxides at 530 years (pit 5) in the same horizon (Figure  5b ). Likewise, a loss of Fe is recorded at both 335 and 530 years. These results are in agreement with  Caner et al. (2010)  who observed changes in silicate mineralogy, in both E and B horizons, and an increase in both oxalate and DCB Fe concentrations in the B horizon on the same time step. Rapid silicate transformations in Podzols were also recorded elsewhere (see the compilation by  Cornu et al. , 2012; Cornelis et al. , 2014). 5). These data are in agreement with the interpretation of  Wiederhold et al.
Acknowledgments We thank  Bernard Angeletti  from  CEREGE (UMR 7330 Aix Marseille Université -CNRS ) for his help with the ICP MS,  Emmanuel Ponzevera  from the  Pole Spectrometrie Ocean, Ifremer, Brest  for his help with the Neptune MC-ICP-MS analyses and  INRA  for financial support for this project. We also thank  Anne Iserentant  and the  Soil Science and Environment Geochemistry lab of the Earth and Life institute (Université catholique de Louvain, Belgium)  for the analysis with the  ICP-AES. Financial support was also provided by the  Fonds de la Recherche Scientifique (F. R. S. -FNRS, Belgium) , through a  FRFC project.
Acknowledgements This work was supported in part by the  Spanish MINECO  through Projects No. MAT2012-39308 ,  FIS2015-67287-P  and  PCIN-2015-111 , and by the  Comunidad de Madrid  through Project  NANOFRONTMAG CM. P. P. acknowledges support through the  Marie Curie AMAROUT EU Programme  and  JCI-2011-09602. The authors acknowledge support from the  CNRS  through  PICS2012   MRLSMO  project. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 737116  (ByAXON).
These properties were not reflected in the previous models  [33]. The present model was calibrated to represent the metabolic state of E. coli cultivated under carbon limitation, a condition frequently experienced by this bacterium in laboratories, in industrial bioprocesses, and likely in its natural environment. To the extent possible, values of the biochemical parameters were taken from experimental determinations available in the literature. Parameters not available in the literature were estimated to reproduce steady-state and time-course experimental data obtained from a unique E. coli strain (the model strain K-12 MG1655) grown under a unique reference condition (M9 minimal medium with glucose as sole carbon source, dilution rate = 0. 1 h -1 , temperature = 37˚C, pH = 7. 0, pO 2 > 20%)  [13, [34] [35] [36] [37].
This step is critical since both metabolite concentrations and fluxes depend on environmental conditions and differ between strains  [38] [39] [40]. While results described below are largely in agreement with other experimental observations, the model was not forced to reproduce them, providing an important validation of the model. Detailed information on the construction and validation of the model is given in the Methods section and Supporting Information (S1 Text). The model is included in Supporting Information (S1 Model) formatted in SBML  [41]  and COPASI  [42]  formats, and is available from the BioModels database  [43]  with accession number MODEL1505110000.
Interestingly, the opposite picture is observed when metabolic regulation is applied on this network (Fig 4B ): a smaller region of the solution space is reached, where the growth rate is now coupled to the glucose uptake rate. To evaluate this prediction quantitatively, we gathered from the literature experimental data obtained from 254 growth experiments carried out under similar environmental conditions (glucose as sole carbon source in aerobic conditions)  [5, 27, 34, [53] [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [66] [67]  (S2 Dataset). These data were collected from "wild-type" and mutant strains obtained by deletion or overexpression of central metabolic enzyme genes or global regulators of gene expression, and cultivated under a wide range of experimental conditions.
92) and positively correlated with energy yields (ρ > 0. 90 for ATP and NADH yields). Thus, the partition of carbon between energy production (ATP and NADPH) and growth (via the synthesis of many anabolic precursors) is predicted to be realized primarily at the level of the TCA cycle and appears to be largely controlled at the metabolic level. To evaluate these model predictions, additional experimental data on extracellular and intracellular fluxes (growth rate, glucose and oxygen uptake rates, and TCA cycle fluxes through the citrate synthase) were collected from the literature [5, 27, 34, 53, 54, 56, 58-60, 62-65, 67] (S2 Dataset). These data, which were not used to calibrate the model, covered the particular regions highlighted by the kinetic model (Fig 5B  -5D ). The excellent agreement between the spread of simulated and experimental data strongly supports the existence of the functional couplings predicted by the model.
Model construction The kinetic model of the central carbon and energy metabolism of Escherichia coli K-12 MG1655 (Fig  1 ) was developed with the software COPASI (build 45)  [42]. This model is briefly described in this section, and additional information can be found in Supporting Information (S1 Text). The model is available in SBML and COPASI formats in Supporting Information (S1 Model), as well as from the Biomodels database  [43]  with identifier MODEL1505110000. Compartments. The model contains three compartments: the environment and the two cellular compartments: periplasm and cytoplasm. The periplasmic volume is set to be 20% of the total cell volume  [76]. Since the calibrated model simulates the metabolic operation of E. coli cultivated under glucose-limited conditions (chemostat), two reactions were included to represent the continuous glucose inflow into the environment and acetate wash out (GLC_FEED and ACE_OUT, respectively).
Parameter estimation Values of 56% of the parameters (253 on a total of 449) were directly taken from the literature. Parameters not available in the literature, which do not have a real biochemical estimate (e. g. Michaelis constants of the biomass function), or for which biochemical measurements are generally not representative of intracellular conditions (e. g. Vmax) were estimated to reproduce in the best possible way 276 experimental data obtained from E. coli K-12 MG1655 grown on glucose, under aerobic condition, at a dilution rate of 0. 1 h -1. These data were steady state fluxes and metabolite concentrations  [13, 34, 36, 37, 82]  and time-course concentrations of intracellular metabolites in response to a glucose pulse  [35]  (S1 Dataset). Parameter estimation was formulated as a constrained optimization problem: minimize f ðpÞ subject to gðpÞ ! c where p is the parameter vector, f is the objective function which evaluates the deviation between the simulated and measured data, g(p) is the constraint function, and c is the constraint vector.
The objective function f was defined as the sum of squared weighted errors: f ðpÞ ¼ X i x i À y i ðpÞ s i 2 where x i is the experimental value of the data point i, with experimental standard deviation σ i , and y i (p) is the corresponding simulated value. Constraints were defined on estimated parameters (10 -4 mM K M 10 3 mM; 10 -2 mM/s Vmax 10 3 mM/s; 10 -4 K eq 10 6 ) to ensure they are kept within a biologically reasonable range. The objective function was minimized with the Particle Swarm Optimization algorithm  [83] , using the Condor-COPASI system  [84]  on a pool of 2500 CPU cores. The experimental and fitted data are provided in Supporting Information (S1 Dataset). Values of all the parameters (and the corresponding references for those values taken from the literature) are given in Supporting Information (S1 Text).
Model analysis and validation Analyses described below were performed using R (v3. 0, www. r-project. org) after converting the model into Fortran. All the scripts are provided in Supporting Information (S1 Code). Simulations. Steady-states were calculated using the runsteady function of the rootSolve R package (v1. 6. 5). Absolute and relative error tolerances were set to 10 -8 and 10 -6 , respectively. Metabolic control analysis. Scaled flux (C J E ) and concentration (C M E ) controls, which represent the fractional change in the steady-state flux J and metabolite M in response to a fractional change in the rate of the step E coefficients, were calculated as follows: C J E ¼ @lnJ @lnE ð8Þ and C M E ¼ @lnM @lnE ð9Þ The overall flux (CJ E ) and concentration (CC E ) controls exerted on the system by the step E were calculated as the L2-norm of all its control coefficients: CJ E ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi X J ðC J E Þ 2 q ð10Þ and CC E ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi X M ðC M E Þ 2 q ð11Þ To evaluate the sensitivity of these coefficients to parameters, this analysis was performed for 100,000 sets of random parameters uniformly sampled within ±20% around their reference values.
Sampling of the solution space. A total of 600,000 flux distributions were uniformly sampled within the solution space of a stoichiometric version of the kinetic model, using the Cobra Toolbox (v2. 0. 5) and Matlab 2013. In parallel, the runif function of R was used to generate 600,000 random sets of enzyme levels, and the corresponding steady-states were simulated under excess glucose (concentration fixed at 10 mM). For each set, enzyme levels were sampled over two orders of magnitude (between 0. 1 and 10 times the Vmax of the initial model) from a log uniform distribution, which ensures each order of magnitude to be sampled in similar proportions. Identification of functional couplings. For each steady state, the following variables were calculated: growth rate, glucose and oxygen uptake rates, ATP, NADH and NADPH production rates and yields, sum of fluxes, cost of enzymes, biomass yield, total intracellular metabolites pool, ATP production rate and enzyme cost per sum of fluxes, and sum of fluxes per glucose consumed.
Enzyme cost, defined as the total amount of amino acids present in central metabolic enzymes, was calculated by summing for each reaction the product of enzyme concentration by the number of amino acids in the corresponding enzyme, similarly to  [85, 86]. Enzyme concentrations were taken from  [63] , those not available were assumed to be the average concentration of other enzymes  (18 μM). Pairwise relationships between steady-state systemic variables and (absolute and relative) intracellular fluxes through the main pathways were evaluated using Spearman correlation test (cor function of R stats package) and mutual information (mutinformation function of infotheo R package v1. 2. 0). Model validation. Exploration of the model solution space suggested the existence of robust couplings between several metabolic processes (carbon uptake, catabolism, energy and redox production, and growth).
We independently validated these model predictions based on a test set of experimentally observed phenotypes that were not used during model construction. We collected from the literature a total of 778 intracellular and extracellular flux data from some 266 experiments where several wild-type and mutant E. coli K-12 strains (MG1655 and its close derivatives JM101, BW25113, W3110 and TG1) were grown in different conditions (deep well plate, bioreactor, shake flask, and chemostat) (S2 Dataset)  [5, 27, 34, [53] [54] [55] [56] [57] [58] [59] [60] [61] [62] [63] [64] [65] [66] [67]. This data set therefore represents a very broad range of the metabolic states that can be expressed by E. coli growing on glucose. The testing data set is very different from the training data set, which contains experimental data from a single wild-type E.
Acknowledgments We thank  Ed Kent  ( MCISB, University of Manchester ) for help with running the Condor-COPASI system, and  Jean-Charles Portais  and  Brice Enjalbert  ( MetaSys team, LISBP, INSA Toulouse ) for helpful comments and discussion on this work. We are grateful to the  IT Research  staff of the  University of Manchester's Faculty of Engineering and Physical Sciences  for help with running the HTCondor pool.
PMi received the support of the  French Institut National de la Recherche Agronomique  ( CJS Program , www. inra. fr)and of the  European Union  in the framework of the  Marie-Curie FP7 COFUND People Programme , through the award of model of the carbon and energy metabolism an  AgreenSkills' fellowship (under grant  agreement  n˚267196 , www. agreenskills. eu). KS and PMe were supported by grants from the  European Union   FP7  ( KBBE grant   289434 -BioPreDyn , ec. europa. eu). PMe was supported from the  UK's Biotechnology and Biological Sciences Research Council  ( BB/K019783/1 ,  BB/J019259/1 , www. bbsrc. ac. uk), and the  National Institute of General Medical Sciences  ( GM080219 , www. nigms. nih. gov). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
A total of 78 pools, each containing up to 30 insects, were analyzed according to location, sex, and date of trapping (Table  1 ). They were tested by real-time RT-qPCR for TOSV RNA  (6). Two pools, C63 and C64, were positive with respective Ct values at 22. 5 and 35. 3. Since sequence analysis of the corresponding PCR product did not allow to identify the lineage, two other PCR assays were used for partial sequencing of the nucleoprotein gene  (7, 8). Colinearization of the two sequences obtained from pool C63 resulted in a 576-nt long sequence (GenBank acc no KY867756)  (9). From pool C64, only the Sandfly Fever Naples Virus (SFNV) nested PCR was positive and resulted in a 320-nt long sequence (GenBank acc no KY867757)  (10). Virus isolation was attempted by inoculating 50 µL of the homogenate supernatant onto Vero cells as previously described  (11).
After six blind passages, TOSV was not isolated. C63 and C64 sequences were aligned using CLUSTAL X (MEGA 6. 06) with homologous sequences of other TOSV strains and selected phleboviruses belonging to the Sandfly fever Naples FIgURe 1 | Phylogenetic analysis of Toscana virus (TOSV) based on a 576-nt long sequences between positions 88 and 663 (numbered after strain IssPhL3, Acc No X53794) located in the nucleoprotein gene. Distances and groupings between the N protein sequences were determined by the p-distance algorithm and the neighbor-joining method with the MEGA 6. 06 software program  (12). Bootstrap values are indicated and correspond to 1,000 pseudoreplications. species obtained from GenBank (see Supplementary Material)  (12). Amino acid and nucleotide identities were calculated with the p-distance algorithm.
Phylogenetic studies were performed using the neighbor-joining method in MEGA6 (Figure  1 ). The robustness of the nodes was tested by 1,000 bootstrap replications. The two TOSV sequences were clearly different from each other with 3. 8 and 16. 5% genetic divergence at amino acid and nucleotide level, respectively. C63 sequence was grouped with the unique Croatian TOSV sequence (corresponding to the CSF sample of a patient presenting with TOSV meningitis in 2008) (4); hence, C63 sequence was identified as belonging to the lineage C. Surprisingly, C64 sequence was most closely related to the sequence of TOSV strain 113/Nice (GenBank acc no KU204981) isolated from sand flies trapped in southeastern France, belonging to the lineage B. To the best of our knowledge, this is the first evidence that TOSV strains belonging to the lineage B are present in Croatia and probably more largely in the Balkan Peninsula.
This work was supported by funds received from (i) VectorNet, a European network for sharing data on the geographic distribution of arthropod vectors, transmitting human and animal disease agents (Contract OC/EFSA/AHAW/2013/02-FWC1) funded by the European Food Safety Authority (EFSA) and the European Centre for Disease Prevention and Control (ECDC) (http://ecdc. europa. eu/en/healthtopics/vectors/VectorNet/Pages/VectorNet. aspx), (ii) the European Virus Archive goes Global (EVAg) project in the European Union's Horizon 2020 research and innovation Toscana virus (TOSV) positive pools information.
ACKNOWLEDGMENTS We thank  Agnes Llored ,  Jordi Tronchoni , and  Martin Zackrisson  for technical help;  Elizabeth Gibson  for support with library preparation and sequencing; and  Erik Garrison ,  Daniel Kunz ,  Leopold Parts ,  David Posada , and  Magda Reis  for critical reading of the manuscript. We also thank participants of the program on Evolution of  Drug Resistance  at the  Kavli Institute for Theoretical Physics (University of California, Santa Barbara ) for discussions. I. V. -G. is a recipient of a  Wellcome Trust Ph. D. fellowship and a  Sanger Early Career Innovation Award. This research was supported by  Wellcome Trust  grants  WT097678  (to I. V. -G. ) and  WT098051  (to V. M. ),  Fundacion Ibercaja  (to I. V. -G. ),  ATIP-Avenir (CNRS/INSERM) ,  Fondation ARC  grant  SFI20111203947 ,  FP7-PEOPLE-2012-CIG grant   322035 ,  French National Research Agency  grant  ANR-13-BSV6-0006-01 ,  Canceropôle PACA (AAP Emergence) , and a  DuPont Young Professor Award  (to G.
1 ), which is the primary egg predator  (Bêty et al. 2002 , Liebezeit and Zack 2008 , McKinnon and Bêty 2009 , Royer-Boutin 2015). In areas where Arctic foxes mainly feed on cyclic small rodents during the summer, geese and shorebirds are typically considered as alternative or incidental prey  (Angelstam et al. 1984 , Bêty et al. 2001 , 2002 , McKinnon et al. 2013). Lemming population cycles induce strong numerical and functional response of both avian and mammalian predators, with cascading effects on breeding geese and shorebirds  (Bêty et al. 2001 , 2002 , Gauthier et al. 2004 , Morrissette et al. 2010 , Nolet et al. 2013). Moreover, breeding Arctic foxes show an aggregative response to the presence of the snow goose colony modulated by lemming density  (Giroux et al. 2012). We used data collected over six years within and near a large greater snow goose (Chen caerulescens atlantica) colony in the Canadian High Arctic (Bylot Island, Nunavut) to test the hypothesis that geese negatively affect arctic-nesting shorebirds by increasing nest predation pressure.
Lemming density Lemmings were live-trapped every summer with Longworth traps to obtain mark-recapture estimates of abundance as described in  Fauteux et al. (2015). The trapping session lasted three days and traps were checked twice a day. We had two 11-ha trapping grids, one in mesic and the other in wetland habitats, each containing 144 traps. The July trapping session took place while transects and artificial nests were performed. Therefore, we pooled data from this session only to have a total of all lemmings of the species present on Bylot Island (collared lemmings [Dicrostonyx groenlandicus] and brown lemmings [Lemmus sibiricus]). We used the average between grids, as a measure of overall lemming density. Considering the distribution of the annual lemming density observed during the study period, we used two categories in our analyses (low density, <2.
To account for a non-linear effect of DIST, a quadratic term was added (DIST2). To better interpret non-linear relationships, we used the lavielle function of the R package adehabitatLT  (Calenge 2006)  to identify segments (thresholds) on the fitted logistic relationships. This function performs a non-parametric segmentation with the penalized contrast method of  Lavielle (1999 Lavielle ( , 2005) )  to find the distance from the centroid of the colony at which the occurrence (or nest predation risk) starts changing. This analysis is usually applied to time series but because transects were well spread around the colony, we calculated the mean occurrence of predators or nesting plovers for each 0. 5 km away from the colony and applied the lavielle function on this dataset. All values are presented with AE95% CI unless specified otherwise.