dataesr/scientific-paragraphs-categorization huggingface dataset:
Parameter and state estimation To perform the state and parameter estimation, we propose the following algorithm. This algorithm has been implemented in C++. It combines the strategy of bisections used in SIVIA and the validated integration used by VNODE. A threshold ε is considered for the bisections in SIVIA. The choice of this threshold depends on the a priori initial box of parameters to be estimated. In this algorithm, the function bisect-Box divides a box into two sub-boxes and the function VNODELP is the call to the software VNODE-LP. while i <= N do 6: x e (i) := V NODELP(x e (i -1)); 7: j := i; 8: i := i + 1; 9: end while P uncertain := P uncertain ∪ [p]; 10: if [h]([x e (1 : j)]) ⊆ [y(1 : j)] 16: else 17: bisectBox([p]) → {[p] 1 , [p] 2 | [p] 1 ∪ [p] 2 = [p]}; 18: P list := P list ∪ [p] 1 , P list := P list ∪ [p] 2 ; 19: end if 20: end while.
Acknowledgements The authors thank the two anonymous reviewers and an associated editor for their useful comments and suggestions. This work has been partially supported by the  French research national agency (ANR)  under the references  ANR-08BLAN-0314-01  and  ANR 2011 BS01 010 01  projet Calibration. The authors thank  Yannick Baraud ,  Roland Diel ,  Christine Tuleau-Malo  and  Patricia Reynaud-Bouret  for fruitful discussions.
v1 v3  m1 0. 8 0 0. 2 m2 0. 1 0. 9 0 m3 0. 4 0 0. 6 We verified by hand and with MatLab the PCR6 fusion of these BBAs and the authors correctly computed PCR6 by combining these three sources altogether, and they got the correct result: m123|PCR6(v1) = 0. 4340, m123|PCR6(v3) = 0. 4437, and m123|PCR6(  ) = 0. 1223. The authors that "the focal element {v3} absorbs almost all of the conflicting mass (the majority)" that we prove it is clearly untrue in the next section.
With MatLab, we got for PCR5 the following result: m123|PCR5(v1) ≈ 0. 3450, m123|PCR5(v3) ≈ 0. 5327, and m123|PCR5(  ) ≈ 0. 1223. Total conflict is naturally the same: 0. 7920. The redistribution by PCR5 of the conflicting mass for v1 is from m123(v1) = 0. 1000 to m123/PCR5(v1) = 0. 3450, or a gain of 0. 2450. The redistribution by PCR5 of the conflicting mass for v3 is passing from m123(v3) = 0. 1080 to m123/PCR5(v3) = 0. 5327, or a gain of 0. 4247. The redistribution by PCR5 of the conflicting mass for  is passing from m123(  ) = 0. 0000 to m123/PCR5(  ) = 0. 1223, or a gain of 0. 1223. With PCR5 one gets 0. 4247/0. 7920 ≈ 53. 62% of the total conflicting mass redistributed to v3, but not "almost all conflicting mass" either.
Participants The experiment took place in June and October 2009 at the Laboratory of Experimental Economics in Paris (LEEP). One hundred and thirteen subjects were recruited using ORSEE (Online Recruitment System for Experimental Economics) and the LEEP's database. They were students from all fields. Participants were tested in groups of 15-20 at once. We ran two sessions for each rule, resulting in 35-40 subjects for each rule  2. In each session, subjects were randomly affected to a workstation and they answer anonymously to several demographic questions (age, sex, domain of study) at the beginning of the experiment. There was no matching between the data collected anonymously during the experiment and the information contained in the LEEP's database. We received from the LEEP only the experimental data.
Stimuli This computer-based experiment uses MATLAB with the Psychophysics Toolbox version 3  (Brainard, 1997)  and was conducted on computers with 1024 × 768 screens. The perceptual task we use is a two-alternative forced choice, which is known to be a convenient paradigm for SDT analysis (see, e. g. ,  Bogacz et al. , 2006). Subjects have to compare the number of dots contained in two circles (see Figure  2A ). First two outline circles are displayed with fixation crosses at their center. The subjects initiate the trial by pressing the "space" key on standard computer keyboard. The two circles (diameter 5. 1 • ) appears immediately at eccentricities of ±8. 9 • with variable number of dots (diameter 0. 4 • ) displayed inside the circles for 700 ms overall. Subjects have to tell which circle contains the higher number of dots without a time limit.
Vector Construct, Plastid Transformation, and Genotyping A synthetic CrNDA2 gene was designed using the chloroplast codon usage of C. reinhardtii chloroplast (Kazusa Codon Usage Database) and integrated in the atpB-int vector  (Michelet et al. , 2011). While the oligonucleotide coding sequence of the native C. reinhardtii CrNDA2 is 64% guanine-cytosine rich, the synthetic gene is 35% guanine-cytosine rich. The pCLE-CrNDA2 plasmid was derived from pCLE  (Michelet et al. , 2011 ) by replacing the aadA coding sequence excised with the synthetic CrNDA2 gene using NcoI and SphI restriction sites engineered in the synthetic DNA. In pCLE-CrNDA2, CrNDA2 expression was placed under the control of the psaA promoter and the rbcL 39 untranslated region (UTR;  Michelet et al. , 2011). For plastid transformation experiments, cells were grown in TAP medium in the dark until reaching a 4 3 10 6 cells mL -1 density and then plated onto minimalmedium  (Harris, 1989)  agar plates.
1 This work was supported by the  French Agence Nationale pour la Recherche  ( ALGOH 2  and  ALGOMICS projects) , the European  FP7-Energy-RTD program  ( SOLAR-H 2  project no. 212508 ), the HélioBiotec platform, funded by the  European Union  ( European Regional Development Fund) , the  Région Provence Alpes Côte d'Azur , the  French Ministry of Research , and the  Commissariat à l'Energie Atomique et aux Energies Alternatives. 2 These authors contributed equally to the article.
5:1. 4:0. 75 water/ methanol/chloroform, respectively. Samples were then centrifuged (4000 ϫ g for 10 min), and 200 l of the upper phase with methanol/water was moved to an Eppendorf tube and dried in a SpeedVac concentrator for 7 h at room temperature. Samples were then stored at Ϫ80 °C until analysis. Derivatization and Analysis of Primary Metabolites Using GC-MS-Extracted material was derivatized by a GC-MS technique according to the protocol described by Lisec et al. (41). Chromatographic separation was carried out on a Thermo Scientific DSQ II gas chromatograph/mass spectrometer using a FactorFour Capillary VF-5ms column, and chromatograms were analyzed using TagFinder04 software version 1. 0  (42). Metabolites were annotated by comparison with mass spectra in the Golm database  (43).
All four biological replicates were analyzed for each condition. Multivariate and Statistical Analyses-Prior to the analysis, data were normalized to the median of each metabolite over the entire sample set and log-transformed. Cluster analysis was performed based on the PAM (partitioning around medoids) algorithm (44) using the "cluster" package in the R statistical computing environment, version 3. 0. 2. The distance matrix was calculated by using 1 Ϫ r 2 , where r is the Pearson correlation coefficient. The number of clusters k was identified as the square root of n/2, where n is the total number of elements used (metabolites and enzymes). The compounds were ordered according to classes: enzymes, sugars and alcohols, amino acids and nitrogen compounds, carboxylic acids, others. Differences between means were tested for significance using Student's t test.
The average molecular weight of the FAs was approximated by using measured ratios of the main FA constituents. A detailed list of the estimated ratios can be found in supplemental Information Sheet 4. These values were then converted to rates by considering the changes between adjacent time points and calculating the mean value for each two adjacent rates. For the first time point, we used the rate determined from the data for the time points zero and one; for the last time point, we used the rate determined from the data for the second to last and last time point. Furthermore, we used the measured enzyme activity data as a proxy for the flux through the respective reaction. InDisMinimizer-The derived time-dependent flux estimations were integrated by minimizing the discrepancy between data and predictions (InDisMinimizer).
Changes in Metabolic Status Caused by Nitrogen Starvation under HL Cultures of 5-day-old vegetative green H. pluvialis cells were inoculated into a nitrogen-free medium under continuous light of 350 E m Ϫ2 s Ϫ1. The combination of nitrogen deprivation and HL exposure was used to facilitate stress response  (10). Cells were harvested every 6 or 12 h throughout the 48 or 96 h of exposure to stress. Metabolite data were only collected throughout the 48-h experiments, and the results of the physiological, metabolite, and enzyme activity data for these experiments are described below. At time 0, the non-flagellated round palmelloid had an average diameter of 24. 7 m. During the 48 h of stress, cells transformed into red cysts, and their average diameter increased to 36. 5 m (p Ͻ 0. 001). Their number remained stable at 2.
pluvialis cells exposed to nitrogen starvation stress under HL. Moreover, we developed a putative model based on genome scale reconstruction of the model green alga Chlamydomonas, integrated two types of experimental data, and applied constraint-based optimization to predict flux distributions that reflect the experimental observations. We then used flux variability analysis to estimate the effects of the network and data constraints. Finally, two biological hypotheses were tested by considering alternative scenarios and comparing the predictive power. During the first 24 h of stress application, the cells responded by largely accumulating CH, accompanied by moderate FA accumulation. However, after 24 h of stress exposure, FA accumulation was accelerated, and TCH content decreased. Similar behavior has recently been shown in five different Chlorellaceae species, where lipid accumulation followed a reduction in stored starch when exposed to different stresses  (13, 14).
The spectra were recorded within the range 230-800 nm. The recorded reflectance was corrected by using Spectralon's (Labsphere) absolute reflectance. LC/MS studies were carried out with a Waters Q-TOF Micro, mass spectrometer equipped with a Waters Alliance HPLC system from CRMP Center (Centre Regionale de Measures Physique) at the University Blaise Pascal. It is equipped with an electrospray ionisation source (ESI) and a Waters photodiode array detector. Each single experiment permitted the simultaneous recording of both UV chromatogram at a preselected wavelength and an ESI-MS full scan. Data acquisition and processing were performed by MassLynx NT 3. 5 system. Chromatography was run using a Nucleosil column 100-5 C18 ec (250 × 4. 6 mm, 5 m). Samples (5-10 L) were injected either directly or after evaporation of the solvent for better detection.
Formation and reduction of sub-networks Metabolic reactions were grouped by metabolic functions, taking into account cell compartments and metabolic pathways. Six sub-networks were obtained (Figure  4 ) corresponding to i) photosynthesis, ii) upper part of glycolysis iii) carbohydrate synthesis iv) lower part of glycolysis, v) lipids synthesis, vi) biomass synthesis. Then, each sub-network was reduced to macroscopic reactions thanks to elementary flux mode analysis  [18]. To compute elementary flux modes (EFMs) the software efmtool was used  [29]. For all six sub-networks, the EFM could be computed easily, and their number was low (less than 30). It should be noted that an EFM analysis of the full network leads to 18776 modes (see File S1 section 4 for more details). In the following sections, the formation and reduction of each sub-network is developed.
This can be performed automatically using softwares like efmtool  [29]  to compute the EFMs and a small script to deduce the macroscopic reactions from the EFMs obtained. However there is an exponential explosion of the number of Elementary Flux Modes (EFMs) when the number of reactions increases, which implies an exponential explosion of the kinetics parameters to estimate. This could make the approach intractable and annihilate the advantage of DRUM compared to a full kinetics model when using large sub-networks resulting for example from the splitting of a genome-scale metabolic network. To overcome this difficulty, small sub-networks should be favored and there are available methods to reduce the number of EFMs such as the use of experimental data  [7] , a projection of the EFMs space into the yield space  [47]  or the clustering of EFMs into phenotypic families  [48].
In all, 500-20,000 reads per sample were analysed. The processing of NGS (454 Roche) data was done according to the following procedure: we filtered out sequences that (i) were too short and did not cover the target locus (sequences must have at least 20 bp after the end of the target locus), (ii) had too many off-target site indels (more than 20% of the total sequence length) or (iii) had indels that were likely to be 454 sequencing errors. For this purpose, we built a statistical model that detected sequencing errors based on the following indel features: short indel size (1 bp, indels are likely to be sequencing errors), mononucleic stretches and sequence quality around the indel region. An example of filtering is presented in Supplementary Fig. 6. For TGI experiments, the detection of targeted integration was performed via specific PCR amplification using one primer located within the heterologous insert of the DNA repair matrix and another located on the genomic sequence outside the matrix homology arms (Screen left and Screen right).
The apparent K d for MF2 was calculated by determining the fractional saturation, r, and free nickel concentration, [Ni(II)] free , by using Equations 1 and 2, respectively. where n is the Hill coefficient, which in this case was set to 1. The affinity of MF2 for nickel under our buffer conditions was determined to be 150 Ϯ 20 nM in agreement with the previously published value  (30). Competition experiments were prepared by incubating 10 M WT or mutant HpHypB together with 10 M MF2 and varying amounts of NiCl 2 in protein buffer. The samples were incubated overnight at 4 °C in the glove box. The absorbance of MF2 at 366 nm was monitored using electronic absorption spectroscopy, and the data were analyzed by using DYNAFIT (37) using a custom DYNAFIT script that describes the competition between the protein and MF2 for the metal.
WT HpHypB Ni(II) Affinity in the Presence of Magnesium and Nucleotide-The affinity of WT HpHypB for Ni(II) in the presence of MgCl 2 and GDP or GDPNP was also determined using MF2. Although MF2 was originally developed as a magnesiumsensitive fluorescent dye, the MF2-Mg(II) emission spectrum at 505 nm upon excitation at 335 nm decreases as the Mg(II) is displaced by the tighter binding Ni(II) (see Fig. 1C, inset ). This differential response to Ni(II) in the presence of Mg(II) allows for the use of MF2 as a competitor. The affinity of MF2 for Ni(II) in the presence of 5 mM MgCl 2 , 500 M GDP, or 500 M GDPNP was determined by incubating 50 nM MF2 with the corresponding ligand and varying NiCl 2 concentrations for at least 2 h. The decrease of the emission spectrum at 505 nm upon excitation at 335 nm was monitored via fluorescence, and the data were analyzed as described above for MF2 in the absence of magnesium or nucleotide.
In the presence of 5 mM Mg(II), the MF2 Ni(II) apparent K d was determined to be (7 Ϯ 2) ϫ 10 Ϫ7 M. Similarly, control titrations yielded apparent MF2 Ni(II) K d values of 1. 5 Ϯ 0. 3 and 1. 8 Ϯ 0. 3 M in the presence of GDPNP and GDP, respectively. Competition experiments were prepared by incubating 10 M WT apo-HpHypB together with 5 M MF2 and varying amounts of Ni(II) in the presence of 5 mM MgCl 2 and 500 M GDP or GDPNP. The samples were incubated overnight at 4 °C in the glove box, the fluorescence decrease at 505 nm upon excitation at 335 nm was monitored, and the data were analyzed using DYNAFIT (37) using a custom DYNAFIT script that describes the competition between the dimeric protein and MF2 for the metal. Mutant HpHypB Nickel Affinity-Weakened metal binding by the H107A and C142S HpHypB mutants permitted the use of the Ni(II)-protein electronic absorption signal to measure the nickel affinities.
The resulting values were plotted as r versus [Ni(II)] free , and the data were fit to the Hill equation (Equation  3 ) as described above except that n was allowed to vary. HpHypB Zincon Competition for Zinc-To estimate the Zn(II) K d of WT and mutant HpHypB, the competitor zincon was selected because of its ability to form a 1:1 complex with Zn(II) with a reported K d of 12. 7 M  (38, 39). Stocks of zincon were prepared in Milli-Q water. The affinity of zincon for Zn(II) was verified under our experimental conditions by titrating 400 nM zincon in protein buffer with increasing amounts of ZnSO 4. The absorbance at 620 nm was monitored using a 10-cm-path length cuvette and a GBC Cintra 404 spectrophotometer. The data were analyzed by using Equations 4 and 5 and fit to the Hill equation (Equation  3 ) with n ϭ 1.
r ϭ ͓MF-Zn͑II͔͒ ͓MF͔ total ϭ Fl obs Ϫ Fl min Fl max Ϫ Fl min (Eq. 6) where in this case Fl obs is the fluorescence intensity at 505 nm for a given Zn(II) concentration, Fl max is the fluorescence at 505 nm upon saturation of MF2 with Zn(II), and Fl min is the fluorescence at 505 nm of apo-MF2. In the presence of 5 mM Mg(II), the MF2 Zn(II) apparent K d was determined to be (2. 7 Ϯ 0. 7) ϫ 10 Ϫ7 M. Similarly, control titrations yielded apparent MF2 Zn(II) K d values of (5. 9 Ϯ 0. 9) ϫ 10 Ϫ7 and (3. 8 Ϯ 0. 7) ϫ 10 Ϫ7 M in the presence of GDPNP and GDP, respectively. Competition experiments were prepared by incubating 5 M WT apo-HpHypB together with 5 M MF2 and varying amounts of Zn(II) in the presence of 5 mM MgCl 2 and 500 M GDP or GDPNP. The samples were incubated overnight at 4 °C in the glove box, and the decrease of the fluorescence upon excitation at 321 nm was monitored at 505 nm, and the data were analyzed by using DYNAFIT (37) using a custom DYNAFIT script that describes the competition between the protein and MF2 for the metal.
Controls containing only buffer, 5 mM MgCl 2 , and the corresponding GTP concentrations were prepared alongside the protein samples and received the same treatment. After incubation, 100 l of the phosphate detection reagent (2. 6 mM malachite green, 1. 5% ammonium molybdate, and 0. 2% Tween 20) was added to each sample. The samples were mixed by vortexing and incubated at room temperature for 3 min after which sodium citrate was added to a final concentration of 3. 5%. The samples were then mixed again, the color was allowed to develop for 30 min before plating onto a 96-well plate, and the absorbance was measured at 630 nm with a Tecan Safire2 microplate reader. The amount of phosphate released was determined via a standard curve from a phosphate standard (Molecular Probes). The data were analyzed by fitting to the Michaelis-Menten equation using OriginPro 8.
0 M malonate, pH 7. 0, or after several weeks in the presence of 4 M NaCl and 0. 1 M Tris, pH 8. 0. All the crystals were cryoprotected using a solution obtained by adding 15% (v/v) glycerol to the reservoir solution and flash cooled in liquid nitrogen. The x-ray fluorescence from HpHypB crystals was monitored with a solid state Ro ¨ntec XFlash detector operating at the BM30A beamline at the European Synchrotron Radiation Facility; the presence of nickel in the crystals was corroborated. Data Collection and Structure Determination-Data were collected at beamline BM30A of the European Synchrotron Radiation Facility in Grenoble, France. Data reduction was carried out using XDS  (43). To identify the nature of the metal bound to HpHypB, five x-ray diffraction data sets were subsequently collected from one monoclinic crystal at the following x-ray wavelengths: Se ϭ 0.
97969 Å (maximum fЉ for selenium), Zn1 ϭ 1. 28212 Å, Zn2 ϭ 1. 28616 Å, Ni1 ϭ 1. 48627 Å, Ni2 ϭ 1. 49162 Å (corresponding to both sides of maximum fЉ for zinc and nickel, respectively). The structure was solved with Phaser (44) by the molecular replacement method using as a starting model the atomic coordinates of the HypB x-ray model from M. jannaschii (32) (Protein Data Bank ID code 2HF9). Crystallographic refinements were conducted using PHENIX  (45) , and the three-dimensional models were examined and modified using the graphics program Coot  (46). Crystallographic statistics are summarized in supplemental Table S1. Superimpositions of models and root mean square deviation (r. m. s. d. ) values were calculated using the secondary structure matching tool  (47). Fig. 2  was prepared with PyMOL (The PyMOL Molecular Graphics System, Version 1.
HpHypB variant Condition  a To determine the affinity of WT HpHypB for nickel, a competition between WT HpHypB and Mag-fura-2 (MF2) was established. In the case of WT protein without Mg(II) or nucleotide, the competition was monitored via electronic absorption spectroscopy. For all other WT samples, fluorescence spectroscopy was used. In both cases, the resulting data were fit by using DYNAFIT with a model of one nickel binding per protein monomer (in the absence of nucleotide) or dimer (with GDPNP or GDP). b Nickel binding to C142S and H107A HpHypB was monitored by electronic absorption spectroscopy. The data were fit to the Hill equation to calculate the K d as well as the Hill coefficient n. c A Ni(II) titration of 10 M H107A in the presence of GDPNP revealed binding curves similar to that of WT protein ϩ GDPNP, so Ni(II) binding was too tight to extract a K d , and an upper limit of 10 M was estimated.
The nickel stoichiometry measured in solution is consistent with that observed in the crystal structure of nucleotide-loaded HpHypB, which revealed a single nickel ion shared at a dimer interface. A similar decrease in metal stoichiometry was observed for E. coli YjiA  (35) , a member of the same G3E family of GTPases to which HpHypB belongs  (54) , suggesting that this impact on metal binding by nucleotide loading may be a family-wide trait. However, it is unclear whether HpHypB exists in the nucleotide-free state in the H. pylori cytoplasm based on the reported concentration of nucleotides in other Gram-negative bacteria  (64). The change in the nickel-binding site of HpHypB that is induced by nucleotide includes the loss of His-107 coordination. Previous biochemical analysis of an HpHypB double All sequences were retrieved from the NCBI database and aligned by using ClustalW  (68).
C, superimposition of HpHypB (in cyan and purple) and MjHypB (in gray; Protein Data Bank code 2HF8) crystal structures. The GDP ϩ P i of HpHypB and GTP␥S of MjHypB are colored in orange and black, respectively. Residues 188 -203 are depicted in magenta, and residues 216 -223 are depicted in yellow. These two groups of residues adopt different conformations in the Ni(II)-bound HpHypB structure versus that of the Zn(II)-bound MjHypB. D, superimposition of HpHypB (monomer A in cyan and monomer B in purple) and MjHypB (in gray) metal-binding sites. E, superimposition of HpHypB (monomer A in cyan and monomer B in purple) and MjHypB (in gray) GTP-binding sites: the main chains of residues 189 and 190 of HpHypB interact with the guanosine base through intermediate water molecules, whereas the corresponding region of MjHypB interacts directly.
Acknowledgments-We thank  Prof. Andrew Woolley  for the use of the spectropolarimeter;  Prof. Mark Nitz  for use of the spectrofluorometer;  Prof. Cynthia Goh  for the use of the microplate reader;  Colin Douglas  for review of this manuscript; the staff from the BM30A beamline of the  European Synchrotron Radiation Facility in Grenoble, France ; and the  IBS platform AnoXtal of the Partnership for Structural Biology in Grenoble  for anaerobic crystallization assay experiments.
* This work was supported by funding from the  Canadian Institutes of Health Research , the  Natural Sciences and Engineering Research Council of Canada , Commissariat al' Energie Atomique  for institutional support, and  University Joseph Fourier for Ph. D. funding  (to H. L. ). □ S This article contains supplemental Table S1. The atomic coordinates and structure factors (code 4LPS) have been deposited in the Protein Data Bank (http://wwpdb. org/).