dataesr/scientific-paragraphs-categorization huggingface dataset:
This little preference of F-ZF for Lys-63 and linear di-ubiquitin compared with Lys-48 di-Ub was confirmed by fluorescence polarization data, as similar K D (38 and 39 M, respectively) were obtained for Lys-63 and linear diubiquitin, whereas the K D for Lys-48 di-ubiquitin was 113 Ϯ 11 M. More importantly, when we compared the WT F-ZF with its D406V mutant upon gel filtration, an impairment of the binding to Lys-63 and linear di-ubiquitin was observed with the mutant. By contrast, no detectable effect on the binding to the Lys-48 di-ubiquitin was observed as similar amounts of complex with the WT and mutant were detected. All these gel filtration data were further confirmed by a more quantitative binding assay using fluorescence polarization. Indeed, the mutation preferentially alters the Lys-63 and linear binding as their K D values are 3.
4-and 2. 8-fold increased, respectively, as compared with the WT, and this effect appears to be specific as similar K D values of 113 Ϯ 11 and 90 Ϯ 8 M for Lys-48-diubiquitin were observed with the WT and the mutant. Our in vitro data with the isolated ZF hence correlate with those with the NEMO full length. This provides an explanation of why the D406V mutation induces such an alteration of the NF-B pathway, causing the EDA-ID pathology. The functional defect is not due to an incapacity to fold but rather is due to a specific binding defect with Lys-63 and linear di-ubiquitins but not with mono-or Lys-48 di-ubiquitins. Structure-guided Mapping and Mutagenesis to Elucidate the Second Ubiquitin Binding Site in NEMO ZF-Complete characterization of the EDA-ID-related D406V mutant prompted us to pursue the exact localization of the second ubiquitin binding site within the ZF domain.
Moreover, these data provide strong evidences that the Phe to Trp substitution at position 395 can act as a sensitive probe for monitoring the folding or ubiquitin binding by fluorescence (Fig. 2A ) as the F395W mutant is able to restore the NF-B activation as efficiently as WT NEMO. Finally, our thorough analysis of the NEMO ZF confirms that it individually forms a functional UBD domain. Some linkage preference with Lys-63 and linear di-Ub over Lys-48 di-Ub was observed by the use of three quantitative biochemical methods such as SV-AUC, gel filtration, and fluorescent polarization. These latter methods appear to be more appropriate to detect linkage preference of any UBD for poly-Ub because they do not generate linkage-preference artifacts such as the one produced in GST pulldown assays or SPR assays when a bivalent antibody is used  (43).
Microsatellite data analysis GIMLET software  [53]  was used to quantify genotyping errors for the 10 microsatellites by repeat-genotyping. We randomly selected approximately 25% of all samples (n = 47 individuals) and independently repeat-genotyped these four times for all loci. Across the four genotypings, averaged across loci and across samples, we detected low error values: 1. 2% of dropout, 1,1% of false allele, 0. 4%, 0. 4%, 0. 5%, 0. 4% and 0. 2% of type 1, type 2, type 3, type 4, and type 5 errors, respectively. We checked for occurrence of linkage disequilibrium among the 10 microsatellite loci with GENEPOP 1. 2  [54]  and verified any presence of null alleles with MICROCHECKER 2. 2. 3  [55]  and INEst 1. 0  [56] , the latter also making it possible to adjust genotype frequencies using the PIM estimator  [57].
The best K value was defined using the log probability of the data Pr(X | K) for each value of K  [63]. We also used a multivariate method to make assumptions regarding data structure. Unlike STRUCTURE, multivariate models do not assume that populations are in Hardy-Weinberg equilibrium. Accordingly, a Discriminant Analysis of Principal Components (DAPC,  [39] ) was performed with the package adegenet in R 2. 13. 0  [64]  in order to identify and describe sequence clusters. The DAPC relies on data transformation using Principal Component Analysis (PCA) as a prior step to Discriminant Analysis (DA), which maximizes the separation between groups. The optimal number of clusters was predicted using the sequential K-means clustering method, and the Bayesian Information Criterion (BIC) was used to choose the best number of groups (K) from 1 to 10.
Microsatellite data Since leatherbacks from AY had been sampled over a long period, two preliminary approaches were implemented in order to control a putative bias resulting from genetic drift during this period: (i) differentiation among rookeries was calculated using RST and FST indexes between 2 periods: samples collected in 1990-2000 vs. those collected in 2001-2010, and no structuration was evidenced; (ii) STRUCTURE was used to investigate the number of ancestral stocks within this sample. It revealed that a single stock (K = 1) was the most probable solution. Consequently, all the samples from AY were considered as a single rookery. All the 10 microsatellite loci were polymorphic and linkage disequilibria were not significant (p. 0. 05) after Bonferroni correction. Regarding the 4 new microsatellite markers developed specifically for this study (Dc003, Dc005, Dc008 and Dc013), Dc008 presented the highest allelic richness (AR = 10.
Therefore DAPC also suggested a single ancestral stock. When analyzing all ten loci with model genotypes that were either original or adjusted with PIM model  [64] , RST was only significant between CAY and AY (RST = 0. 0289, p,0. 05). However when excluding the three loci with null alleles, RST was significant between AY and FWI (RST = 0. 0106, p,0. 05), and between CAY and FWI (RST = 0. 0211, p,0. 05). FST provided a stronger structuration signal and was significant between the 3 rookeries with the 10 loci dataset, but not significant between CAY and AY only with the 7 loci (Table  3 ). According to AMOVA more than 98% of the genetic variation was within populations, while less than 2% was between populations. Observed and expected heterozygosities, and Inbreeding coefficients, are shown in Table  2  for each locus and each population.
Methodological issues This work puts forward the complexity of analytic choices with concurrent approaches. We used three methods based on Bayesian inference, namely MSVAR, BEAST and MIGRATE, to explore recent demographic history and changes in the evolution of effective population size in distant rookeries with contrasted numbers of nesting females. MSVAR has been shown to be a relevant tool to detect expansions and declines in different species  [34] , including sea turtles  [17]. Bottlenecks of variable extent and dates were detected by MSVAR, but not by the EBSP method. However, to our knowledge this is the first time the EBSP method has been used with data from natural populations, and thus precludes further analysis of the comparative sensitivity of these methods. Interestingly, all three approaches identified low and congruent values of current effective population sizes, and the very recent expansion signal detected by MIGRATE is indicative of populations recovering after bottlenecks.
One other key point in our study was a high estimated occurrence of null alleles in our dataset. Null alleles result in lower heterozygosity and consequently impact the structuration signal among populations, overestimating the distances among clades  [76]. The recent adaptation of the PIM model  [57]  makes it possible to assess inbreeding coefficients and allele frequencies with a high level of confidence  [64]. In our study however, the use of our full dataset, including loci without null alleles and loci with corrected frequencies, resulted in lower structuration among rookeries than when only loci without null alleles were used. Consequently, as observed in other endangered species  [77]  and confirmed by the low rate of genotyping errors, we conclude that a true homozygote excess has resulted from low population sizes, inbreeding and genetic drift  [78] , rather than a high occurrence of null alleles.
Acknowledgments We are grateful to the volunteers and employees of  Kwata NGO  (www. kwata. net). We thank the  laboratoire de parasitologie, Institut Pasteur de la Guyane , for supplying the necessary facilities to conduct our laboratory work. We thank  Andrea Benazzo  for his help with data analysis. We would also like to thank the two captains of Awala and Yalimapo, M. Therese and  D. William , and the inhabitants of  Awala-Yalimapo  for their hospitality. We are also grateful to the IPHC students who participated in the field sessions at  Awala-Yalimapo , and to  J. Lignot  for providing detailed English language editing of the final text.
Part of the laboratory work (processing of West Indies, Cayenne and Awala samples) was funded by  DEAL Guyane and Kwata NGO  (www. kwata. net). The authors thank  CNPq  and  Fapemig  for fundings for the part of the research realized in Brazil (microsatellite databank). EM, FRS and STV were supported by  CNPq (Brazil). The authors' thanks go to CNPq and Fapemig for their funding of the research carried out in Brazil (microsatellite databank). EM, FRS and STV were supported by  CNPq (Brazil)  and to  Agence Nationale pour la Recherche  through the  MIRETTE  project (http://projetmirette. fr, ANR-07-JCJC-0122 ) led by  JYG. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Input Our plugin operates on target gene set provided by a user. The list of gene names can be given directly or imported from an existing network created in Cytoscape (many gene identifier formats are accepted, e. g. Ensembl, HGNC, Entrez, UniProt). The other main parameters are the reference annotation database (KEGG, BioCarta, GO, InterPro, etc. ) and the molecular interaction network (STRING or a user-defined network).
Processing There are two types of analysis available in JEPETTO based on: (1) enrichment or (2) topology. For the enrichment analysis, EnrichNet and PathExpand web servers are used and the topological analysis is performed with TopoGSA. The plugin communicates with the web servers as shown in Figure  1A , and integrates the results within the Cytoscape environment. This single-point integration eliminates the need to use the web servers individually and simplifies repeated analysis on previously obtained results. EnrichNet  (Glaab et al. , 2012)  maps the input gene set onto a molecular interaction network and, using a random walk, scores distances between the genes and pathways/processes in a reference database. This network-based association score (XD-score) is relative to the average distance to all pathways and represents a deviation (positive or negative) from the average distance.
As an option, EnrichNet provides XD-scores for 60 human tissues, derived from tissue-specific gene expression data. PathExpand  (Glaab et al. , 2010a)  maps the input pathway/process onto the human protein-protein interaction network and extends it with proteins that (1) are strongly associated with the pathway nodes; and (2) increase the pathway compactness by connecting its disconnected members. The exact expansion acceptance criteria can be modified in the plugin advanced options. TopoGSA  (Glaab et al. , 2010b)  maps the input gene set on an interaction network, computes its topological signature and compares it against signatures of pathways/processes in a reference database. The topological signature is built from five distinct *To whom correspondence should be addressed.
This article, the sixth in the ACP journal series, presents data evaluated by the IUPAC Task Group on Atmospheric Chemical Kinetic Data Evaluation. It covers the heterogeneous processes involving liquid particles present in the atmosphere with an emphasis on those relevant for the upper troposphere/lower stratosphere and the marine boundary layer, for which uptake coefficients and adsorption parameters have been presented on the IUPAC website since 2009. The article consists of an introduction and guide to the evaluation, giving a unifying framework for parameterisation of atmospheric heterogeneous processes. We provide summary sheets containing the recommended uptake parameters for the evaluated processes. The experimental data on which the recommendations are based are provided in data sheets in separate appendices for the four surfaces considered: liquid water, deliquesced halide salts, other aqueous electrolytes and sulfuric acid.
Introduction Since 2005 the IUPAC Task Group on Atmospheric Chemical Kinetic Data Evaluation has extended its evaluation work to produce recommendations for essential parameters, which can be used to calculate the rates of heterogeneous reactions of trace gases in the atmosphere. The results of this work have been added to the IUPAC website over the last 5 years, together with the updated kinetic data for gas phase reactions. Following our policy of publication of our updated evaluations in Atmospheric Chemistry and Physics, we first presented evaluations of heterogeneous processes involving solid particles in Volume V  (Crowley et al. , 2010). Volume VI of the series extends our work to cover heterogeneous processes involving liquid particles present in the atmosphere. This is done with a view to widening the dissemination and enhancing the accessibility of this evaluated material to the scientific community.
Comments on preferred values We adopt the single dataset of  Worsnop et al. (1989)  as the basis of our recommendation. The strong negative temperature dependence of α b was suggested to derive from an activation energy necessary to overcome a barrier between a precursor state and the solvated state of H 2 O 2. The title reaction therefore represents a complex reaction involving a preequilibrium whose temperature dependence affects the overall T-dependence of the uptake.
Comments (a) Interaction of HNO 2 (60 ppm) with a liquid jet of pure water. The kinetics were studied by ion chromatographic analysis of the liquid jet nitrite content. The gas phase diffusion coefficient value used was 0. 13 cm 2 s -1 , the value used for the solubility of HNO 2 in water was 60 M atm -1. A kinetic model taking into account diffusion in the gas phase as well as the radially resolved flow velocity within the jet was used to simulate the rate of appearance of nitrite in the aqueous phase. Agreement with the data was not very good likely due to uncertainties in the velocity distribution of the liquid jet. Taking into account dissociation of HNO 2 in the aqueous phase did not improve the fits. Thus, the bulk accommodation coefficient is regarded as a lower limit. (b) A flow of 6 l per min of air at 97 % relative humidity containing about 0.
Comments (a) Experiments performed at room temperature flow with the water vapor pressure adjusted to the vapour pressure of water at the experimental temperature. HNO 3 was detected downstream of the flow tube using IR absorption. The uptake coefficients were corrected for gas phase diffusion. The data show significant negative temperature dependence within the range indicated in the table. Given that under the experimental conditions the near surface region of the droplet does not saturate, it is argued that the uptake coefficients derived are actually bulk accommodation coefficients equivalent to the rate limiting step being solvation. (b) Interaction of HNO 3 (60 ppm) in 1 bar of synthetic air with a liquid jet of pure water. The kinetics were studied by ion chromatographic analysis of the liquid jet nitrate content.
(1990)  has recently been challenged  (Morita et al. , 2003; Hanson et al. , 2004; Garrett et al. , 2006; Davidovits et al. , 2006). Van Doren's experiments at high temperature were measured at higher total pressure than those at lower temperature. The two droplet train studies, in spite of determining uptake via loss of gas phase HNO 3 in one case  (Van Doren et al. , 1990)  and the appearance of nitrate in the aqueous phase in the other  (Ponche et al. , 1993) , but both using the same method to correct for diffusion, agree fairly well with each other. The recommended expression for α b is a least square fit to the data by  Ponche et al. and Van Doren et al. in   Hanson (2005)  AFT-CIMS (c).
Comments on preferred values Due to the very low reactivity of O 3 with chloride, uptake of O 3 to aqueous sea salt solutions is dominated by the reactions with bromide and iodide  (Haag and Hoigné, 1983). We thus only provide kinetic parameters for the reactive processes with bromide and iodide, treated separately in this discussion. We assume that the solubility and the bulk accommodation coefficient for a pure NaCl solution would be the same as that preferred for the iodide solution. O 3 + I - The datasets of  Hu et al. (1995) ,  Magi et al. (1997) ,  Rouvière et al. (2010)  concur in that the reaction proceeds without a significant surface component and can be described by the following resistor expression: 1 γ = 1 α b + c 4H RT k II a(I -)D l These studies indicate that α b > 0. 1. The absence of a surface component is not consistent with experimental and theoretical evidence that indicates a high surface propensity for the iodide ion  (Jungwirth and Tobias, 2006 , and references therein).
The accommodation coefficient chosen is slightly lower than the lower limit given by Rudich et al. , as this better represents their dataset within the parameterisation listed below. The rate coefficient for the aqueous phase reaction of NO 3 with Cl -was taken from  Exner et al. (1992) , although there is some disagreement concerning this number  (Herrmann, 2003). We adopt this rate coefficient and the solubility derived by  Rudich et al. (1996b)  as it allows us to present an internally consistent set of parameters based on observations of the composite H D 0. 5 l k 0. 5 term. The rate coefficient listed for the reaction of NO 3 with Br -was taken from the ratio k Cl /k Br = 37 ± 4 as reported by  Rudich et al. (1996b). The reliability of the parameters listed above cannot be given individually, but an assessment of the overall uncertainty in γ (calculated as below) is given.
Within experimental scatter and the range covered there is also no dependence of γ on the temperature, with datasets on synthetic salt surfaces at 295 K giving the same uptake coefficient as NaCl and NaCl/NaBr containing aqueous solutions at ∼270 K. The observation that the uptake coefficient is insensitive to the aqueous composition (content of chloride, bromide or iodide) and that the yield of ClNO 2 following uptake of N 2 O 5 to chloride solutions of concentration ≥1 M approaches unity  (Behnke et al. , 1997; Schweitzer et al. , 1998; Roberts et al. , 2009)  has led to the following mechanism being proposed, with dissociation of N 2 O 5 (Reaction R1) the rate limiting step. N 2 O 5 + H 2 O ⇔ H 2 NO + 3 + NO - 3 (R1) H 2 NO + 3 + H 2 O → H 3 O + + HNO 3 (R2) H 2 NO + 3 + Cl - → ClNO 2 + H 2 O (R3) From the reaction scheme above, the yield of ClNO 2 is defined by competition between hydrolysis (with rate coefficient k 2 ) and reaction with chloride anions (rate coefficient k 3 ) so that: [ClNO 2 ] [N 2 O 5 ] = k 2 [H 2 O] k 3 Cl -+ 1 -1 Schweitzer  et al.
They calculated k 3 /k 2 (at 297 K) = 450 ± 100. Bertram and Thornton (2009)  examined the effect of chloride and nitrate concentrations on the ClNO 2 yield from the uptake of N 2 O 5 to mixed nitrate/chloride particles and derived k 3 /k 2 (at 298 K) = 483 ± 175. Our preferred value for the value of k 3 /k 2 (at 298 K) is based on the most detailed study  (Roberts et al. , 2009). Roberts et al. (2009)  suggest that k 2 (but not the ion recombination reaction, k 3 ) is likely to have a large barrier and thus be slower at lower temperatures, which would result in a larger k 3 /k 2 and thus larger ClNO 2 yield for a given Cl -concentration. The experimental datasets are however not precise enough to clarify if the larger k 3 /k 2 value of  Behnke et al. (1997)  is due to use of slightly lower temperatures in their experiments or if it is related to use of various ionic strength solutions and thus Cl - activities.
The depth through which the surface reaction is considered to take place is integrated in the values of k s • K LangC • N max , which were derived by fitting to experimental data. 1 γ = 1 + The temperature dependence of s can arise from both the partition coefficient of Cl 2 to the surface (K LangC ) or via the surface rate coefficient (k s ), though the former is expected to dominate. The parameterisation gives values of γ that are consistent with those presented by  Hu et al. (1995)  at all temperatures and bromide activities covered in their experiments. Diffusive effects were also taken into account when fitting to the experimental datasets by using 1 γ = 1 + 1 diff + 1 s + b and effective gas-phase diffusion coefficients for Cl 2 reported by  Hu et al. (1995). The tabulated, temperature dependent expressions for aqueous phase rate coefficients, solubilities and aqueous phase diffusion coefficients listed for calculation of b were calculated from values given by  Hu et al.
, 2004)  and H 2 SO 4 droplets. The recommended liquid phase rate constant k I (= k II × [H 2 O] aq ), calculated using k II = 1 × 10 5 M -1 s -1 , which is intermediate between the values derived by  Thornton et al. (2003)  from uptake of N 2 O 5 on malonic acid aerosol (2. 5 × 10 4 M -1 s -1 ) and by  Mentel et al. (1999)  from uptake on NaNO 3 aerosols (1. 5 × 10 5 M -1 s -1 ). The RH dependence of γ can be calculated using water mass fractions taken from the AIM database and using D l = 1 × 10 -5 cm 2 s -1 and 2 M atm -1 , values used in the analyses for k II. The expression fits the >50 % RH data well but overestimates uptake rates at low RH. The reacto-diffusive parameter (l = [D l /k II [H 2 O]] 0. 5 ) predicts a significant size dependence of γ for r < 100 nm. The temperature dependence of N 2 O 5 uptake reported earlier by  Hallquist et al.
, 2004)  and H 2 SO 4 droplets  (Hallquist et al. , 2003). k I is calculated using the recommended liquid phase rate constant for N 2 O 5 + [H 2 O] aq : k II = 1. 5×10 5 M -1 s -1 , which is based on the values derived by  Mentel et al. (1999)  from uptake on NaNO 3 aerosols (1. 5 × 10 5 M -1 s -1 ), and on mixed NH 4 HSO 4 /dicarboxylic acid aerosols by  Griffiths et al. (2009). The RH dependence of γ can be calculated using water mass fractions taken from the AIM database. At >50 % RH this expression provides a good compromise between the Bertram and Thornton data and the main body of the experimental data. Observed uptake rates at low RH are underestimated with this formula, due to the nitrate effect and volume limited uptake, as discussed above. The size dependence of γ can be evaluated by Eq.
79 × 10 4 M -1 s -1 ; for malonic acid: 1. 8 × 10 4 M -1 s -1 ; and for glutaric acid: 1. 2 × 10 4 M -1 s -1 are also likely to be underestimated. The recommended expression for RH dependence uses a size dependent resistance-model formulation: γ = 1 α b + c 4H RT (D l k I ) 0. 5 coth r l - l r -1 The recommended value of α b = 0. 035 is based on γ observed for uptake on malonic acid. k I is calculated using the recommended liquid phase rate constant for N 2 O 5 + [H 2 O] aq : k II = 1 × 10 5 M -1 s -1 , which is intermediate between the values derived by  Thornton et al. (2003)  from hydrolysis of N 2 O 5 on malonic acid aerosol (2. 5 × 10 4 M -1 s -1 ) and by  Mentel et al. (1999)  from uptake on NaNO 3 aerosols (1. 5 × 10 5 M -1 s -1 ). The RH dependence of γ can be calculated using water mass fractions taken from the AIM database, and using H (D l ) 0.