Centrality Dependence Of The Pseudorapidity Density Distribution For Charged Particles In Pb-pb Collisions At √snn=2.76tev

7264/Mai61062

Long-range Angular Correlations On The Near And Away Side In P-pb Collisions At √snn=5.02 Tev

7191/Mar294

The Amino Acid Sequence Of Ribitol Dehydrogenase-f, A Mutant Enzyme With Improved Xylitol Dehydrogenase Activity

A mutant ribitol dehydrogenase (RDH-F) was purified from Klebsiella aerogenes strain F which evolved from the wild-type strain A under selective pressure to improve growth on xylitol, a poor substrate used as sole carbon source. The ratio of activities on xylitol (500 mM) and ribitol (50 mM) was 0.154 for RDH-F compared to 0.033 for the wild-type (RDH-A) enzyme. The complete amino acid sequence of RDH-F showed the mutations. Q60 for E60 and V215 for L215 in the single polypeptide chain of 249 amino acid residues. Structural modeling based on homologies with two other microbial dehydrogenases suggests that E60 → Q60 is a neutral mutation, since it lies in a region far from the catalytic site and should not cause structural perturbations. In contrast, L215 → V215 lies in variable region II and would shift a loop that interacts with the NADH cofactor. Another improved ribitol dehydrogenase, RDH-D, contains an Al96 → P196 mutation that would disrupt a surface α-helix in region II. Hence conformational changes in this region appear to be responsible for the improved xylitol specificity. © 1999 Plenum Publishing Corporation.184489495Burleigh, B.D., Rigby, P.W.J., Hartley, B.S., (1974) Biochem. J., 143, pp. 341-352Butler, P.J.G., Hartley, B.S., (1972) Methods in Enzymology, 25, pp. 191-199. , (Hirs, C. H., and Timasheff, S. N., eds:), Academic Press, New YorkCintra, A.C.O., Vieira, C.A., Giglio, J.R., (1990) J. Protein Chem., 9, pp. 221-227Cintra, A.C.O., Marangoni, S., Oliveira, B., Giglio, J.R., (1993) J. Protein Chem., 12, pp. 57-64Dothie, J.M., Giglio, J.R., Moore, C.B., Taylor, S.S., Hartley, B.S., (1985) Biochem, J., 230, pp. 569-578Ghosh, D., Weeks, C.M., Grochulski, P., Duax, W.L., Erman, M., Rimsay, R.L., Orr, J.C., (1991) Proc. Natl. Acad. Sci. USA, 88, pp. 10064-10068Giglio, J.R., (1977) Anal. Biochem., 82, pp. 262-264Gray, W.R., (1972) Methods in Enzymology, 25, pp. 333-344. , (Hirs, C. H., and Timasheff, S. N., eds.), Academic Press, New YorkGuex, N., Peitsch, M.C., (1997) Electrophoresis, 18, pp. 2714-2723Hartley, B.S., (1984) Microorganisms As Model Systems for Studying Evolution, pp. 23-54. , (Mortlock, R. P., ed.), Plenum Press, New YorkHartley, B.S., (1984) Microorganisms As Model Systems for Studying Evolution, pp. 55-108. , (Mortlock, R. P., ed.), Plenum Press, New YorkHartley, B.S., Altosaar, I., Dothie, J.W., Neuberger, M.S., (1976) Structure-Function Relationship of Proteins, pp. 191-200. , (Markham, R., and Horn, R. W., eds.), Elsevier/North-Holland, AmsterdamHulsmeyer, M., Hecht, H.J., Niefeld, K., Hofer, B., Eltis, L.D., Timmis, K.N., Schomberg, D., (1998) Protein Sci., 7, pp. 1286-1293Itzaki, R.F., Gill, D.M., (1964) Analyt. Biochem., 9, pp. 401-410Laemmli, U.K., (1970) Nature, 227, pp. 680-685Lerner, S.A., Wu, T.T., Lin, E.C.C., (1964) Science, 146, pp. 1313-1315Loviny, T., Norton, P.M., Hartley, B.S., (1985) Biochem. J., 230, pp. 579-585Marangoni, S., Ghiso, J., Sampaio, S.V., Arantes, E.C., Giglio, J.R., Oliveira, B., Frangione, B., (1990) J. Protein Chem., 9, pp. 595-601Mortlock, R.P., Fossit, D.D., Wood, W.A., (1965) Proc. Natl. Acad. Sci. USA, 54, pp. 572-579Offord, R.E., (1966) Nature, 211, pp. 591-593Rigby, W.J., Burleigh, B.D., Hartley, B.S., (1974) Nature, 251, pp. 200-204Ryle, A.P., Sanger, F., Smith, L.F., Kitai, R., (1955) Biochem. J., 60, pp. 541-556Skoog, B., Wichman, A., (1986) Trends Anal. Chem., 5, pp. 82-93Taylor, S.S., Rigby, P.W.J., Hartley, B.S., (1974) Biochem. J., 141, pp. 693-700Wu, T.T., Lin, E.C.C., Tanaka, S., (1968) J. Bact., 96, pp. 447-45

Postwar wildlife recovery in an African savanna: evaluating patterns and drivers of species occupancy and richness

As local and global disturbances reshape African savannas, an understanding of how animal communities recover and respond to landscape features can inform conservation and restoration. Here, we explored the spatial ecology of a wildlife community in Gorongosa National Park, Mozambique, where conservation efforts have fostered the recovery of large mammal populations after their near-extirpation during Mozambique’s civil war. We deployed a grid of 60 camera traps and used a hierarchical, multi-species occupancy modeling approach to examine patterns of occupancy and its environmental and anthropogenic correlates for different functional groups and species. Our survey provides strong evidence that wildlife in Gorongosa is recovering. Throughout the study area, modeled species richness was comparable to richness in less-disturbed savanna systems in Tanzania and Botswana, and exceeded estimates of richness from a mixed-use landscape outside the park and from postwar (1997–2002) aerial surveys. However, the mammal community in Gorongosa differs from prewar conditions and from those of more intact systems, with few large carnivores, low occupancy probabilities for large ungulate species that were dominant prior to the war, and high occupancy for other ungulates that are now ubiquitous. Associations with tree cover varied among species and guilds. Contrary to our expectation, there was no effect of lake proximity on community and group-level occupancy, and previously dominant floodplain ungulate species now occupy more wooded areas. Mammals were more likely to occupy areas that burned frequently, as post-fire vegetation regrowth provides high-quality forage, highlighting the importance of Gorongosa’s fire regime. Occupancy was lower in areas with more illegal hunting, and higher closer to roads, potentially because roads were established in areas of high wildlife density and facilitate animal movement. Continued multi-species monitoring in Gorongosa can shed light on the different recovery trajectories of ungulate species and the consequences of ongoing large carnivore restoration, guiding conservation interventions

The Use of Fire Radiative Power to Estimate the Biomass Consumption Coefficient for Temperate Grasslands in the Atlantic Forest Biome

Every year, many active fire spots are identified in the satellite images of the southern Brazilian grasslands in the Atlantic Forest biome and Pampa biome. Fire Radiative Power (FRP) is a technique that uses remotely sensed data to quantify burned biomass. FRP measures the radiant energy released per time unit by burning vegetation. This study aims to use satellite and field data to estimate the biomass consumption rate and the biomass consumption coefficient for the southern Brazilian grasslands. Three fire points were identified in satellite FRP products. These data were combined with field data, collected through literature review, to calculate the biomass consumption coefficient. The type of vegetation is an important variable in the estimation of the biomass consumption coefficient. The biomass consumption rate was estimated to be 2.237 kg s-1 for the southern Brazilian grasslands in Atlantic Forest biome, and the biomass consumption coefficient was estimated to be 0.242 kg MJ-1.Todos os anos muitos focos de incêndio são identificados pelas imagens de satélite sobre a vegetação campestre natural dos biomas Pampa e Mata Atlântica. A Energia Radiativa do Fogo (FRP) é uma técnica para quantificar a biomassa queimada usando dados de sensoriamento remoto. A FRP mede a energia radiante emitida por unidade de tempo pela vegetação queimada. O objetivo deste estudo foi estimar o coeficiente de consumo de biomassa para a vegetação campestre natural do sul do Brasil. Três focos de incêndio foram identificados em produtos de satélite que quantificam a FRP. Esses dados foram utilizados em conjunto com os dados de campo, obtidos por revisão de literatura, para calcular o coeficiente de consumo de biomassa. O tipo de vegetação é uma variável importante para estimar esse coeficiente, assim, para a vegetação campestre natural do bioma Mata Atlântica no sul do Brasil a taxa de consumo de biomassa foi estimada em 2,237 kg s-1 e o coeficiente de consumo de biomassa foi estimado em 0,242 kg MJ-1