The genes coding for 4 snRNAs of Drosophila melanogaster: localization and determination of gene numbers.

Four small nuclear RNAs (snRNAs) have been isolated from Drosophila melanogaster flies. They have been characterized by base analysis, fingerprinting, and injection into Axolotl oocytes. The size of the molecules and the modified base composition suggest that the following correlations can be made: snRNA1 approximately U2-snRNA; snRNA2 approximately U3-snRNA; snRNA3 approximately U4-snRNA; snRNA4 approximately U6-snRNA. The snRNAs injected into Axolotl oocytes move into the nuclei, where they are protected from degradation. The genes coding for these snRNAs have been localized by "in situ" hybridization of 125-I-snRNAs to salivary gland chromosomes. Most of the snRNAs hybridize to different regions of the genome: snRNA1 to the cytological regions 39B and 40AB; snRNA2 to 22A, 82E, and 95C; snRNA3 to 14B, 23D, 34A, 35EF, 39B, and 63A; snRNA4 to 96A. The estimated gene numbers (Southern-blot analysis) are: snRNA1:3; snRNA2:7; snRNA3:7; snRNA4:1-3. The gene numbers correspond to the number of sites labeled on the polytene salivary gland chromosomes.     

The nucleotide sequence of glutamate tRNA4 of Drosophila melanogaster.

The nucleotide sequence of Drosophila melanogaster glutamate tRNA4 was determined to be: pU-C-C-C-A-U-A-U-G-G-U-C-psi-A-G-D-G-G-C-D-A-G-G-A-U-A-U-C-U-G-G-C (m) -U-U-U-C-A-C-C-A-G-A-A-G-G-C-C-C-G-G-G-T-psi-U-C-G-A-U-U-C-C-C-G-G-U-A-U-G-G-G-A-A-C-C-AOH. A partial modified C is found at position 32 in the anticodon loop.     

Hydratations- und Permeabilitätsstudien an unbefruchteten Fucus-Eiern (Fucus vesiculosus L.)

Ohne Zusammenfassung     

Addition of Nitrogen Increases Variability of Vegetation Cover in an Arid System with Unpredictable Rainfall

Ecosystems - Addition of nitrogen (N) to rangeland that has been degraded through overgrazing or drought can hasten vegetation recovery. Additional N may influence temporal stability of vegetation...     

Survival synchronicity in two avian insectivore communities

Climate change is forecast to increase climatic variability, in particular the occurrence of extreme events. Consequently, it is imperative to understand how climatic variation influences the dynamics of communities. We investigated synchronicity in survival in response to climatic variation among bird communities occupying habitats that differed in climatic seasonality: a more seasonal wetland and a less seasonal fynbos shrubland in South Africa. We predicted higher synchronicity at the wetland than at the shrubland because there was more potential for weather to induce variation in survival at this climatically more variable site. We estimated survival from ringing data for four wetland species and three fynbos species in hierarchical models with an asynchronous (species-specific) variance component and a synchronous (common) variance component. Comparing models including and excluding a climatic covariate enabled us to estimate the effect of climatic variation as a synchronizing and desynchronizing agent on survival. As hypothesized, synchronicity in survival was substantially greater at the more seasonal wetland than at the climatically more stable fynbos site: 0.50 (95% credible interval 0.01–1.92 on the logit scale) and 0.03 (0.00001–0.19), respectively. Similarly, asynchronicity in survival was greater for wetland species than for fynbos species. However, we found no clear evidence that weather affected survival. We provide the first survival estimates of several African endemic birds and the first estimates of synchronicity and asynchronicity in survival of communities outside the strongly seasonal northern temperate zone. Our results suggest that the relative magnitude of synchronicity and asynchronicity varies among communities and support the idea that environmental variability induces synchronicity.     

The effect of hexose ratios on metabolite production in Saccharomyces cerevisiae strains obtained from the spontaneous fermentation of mezcal

Mezcal from Tamaulipas (México) is produced by spontaneous alcoholic fermentation using Agave spp. musts, which are rich in fructose. In this study eight Saccharomyces cerevisiae isolates obtained at the final stage of fermentation from a traditional mezcal winery were analysed in three semi-synthetic media. Medium M1 had a sugar content of 100 g l^?1 and a glucose/fructose (G/F) of 9:1. Medium M2 had a sugar content of 100 g l^?1 and a G/F of 1:9. Medium M3 had a sugar content of 200 g l^?1 and a G/F of 1:1. In the three types of media tested, the highest ethanol yield was obtained from the glucophilic strain LCBG-3Y5, while strain LCBG-3Y8 was highly resistant to ethanol and the most fructophilic of the mezcal strains. Strain LCBG-3Y5 produced more glycerol (4.4 g l^?1) and acetic acid (1 g l^?1) in M2 than in M1 (1.7 and 0.5 g l^?1, respectively), and the ethanol yields were higher for all strains in M1 except for LCBG-3Y5, -3Y8 and the Fermichamp strain. In medium M3, only the Fermichamp strain was able to fully consume the 100 g of fructose l^?1 but left a residual 32 g of glucose l^?1. Regarding the hexose transporters, a high number of amino acid polymorphisms were found in the Hxt1p sequences. Strain LCBG-3Y8 exhibited eight unique amino acid changes, followed by the Fermichamp strain with three changes. In Hxt3p, we observed nine amino acid polymorphisms unique for the Fermichamp strain and five unique changes for the mezcal strains.     

Dynamic occupancy models for analyzing species' range dynamics across large geographic scales

Large‐scale biodiversity data are needed to predict species' responses to global change and to address basic questions in macroecology. While such data are increasingly becoming available, their analysis is challenging because of the typically large heterogeneity in spatial sampling intensity and the need to account for observation processes. Two further challenges are accounting for spatial effects that are not explained by covariates, and drawing inference on dynamics at these large spatial scales. We developed dynamic occupancy models to analyze large‐scale atlas data. In addition to occupancy, these models estimate local colonization and persistence probabilities. We accounted for spatial autocorrelation using conditional autoregressive models and autologistic models. We fitted the models to detection/nondetection data collected on a quarter‐degree grid across southern Africa during two atlas projects, using the hadeda ibis (Bostrychia hagedash) as an example. The model accurately reproduced the range expansion between the first (SABAP1: 1987–1992) and second (SABAP2: 2007–2012) Southern African Bird Atlas Project into the drier parts of interior South Africa. Grid cells occupied during SABAP1 generally remained occupied, but colonization of unoccupied grid cells was strongly dependent on the number of occupied grid cells in the neighborhood. The detection probability strongly varied across space due to variation in effort, observer identity, seasonality, and unexplained spatial effects. We present a flexible hierarchical approach for analyzing grid‐based atlas data using dynamical occupancy models. Our model is similar to a species' distribution model obtained using generalized additive models but has a number of advantages. Our model accounts for the heterogeneous sampling process, spatial correlation, and perhaps most importantly, allows us to examine dynamic aspects of species ranges.     

An integrated population model sheds light on the complex population dynamics of a unique colonial breeder

Climate models forecast increasing climatic variation and more extreme events, which could increase the variability in animal demographic rates. More variable demographic rates generally lead to lower population growth and can be detrimental to wild populations, especially if the particular demographic rates affected are those to which population growth is most sensitive. We investigated the population dynamics of a metapopulation of 25 colonies of a semi-arid bird species, the sociable weaver Philetairus socius, and how it was influenced by seasonal weather during 1993–2014. We constructed an integrated population model which estimated population sizes similar to observed population counts, and allowed us to estimate annual fecundity and recruitment. Variance in fecundity contributed most to variance in population growth, which showed no trend over time. No weather variables explained overall demographic variation at the population level. However, a separate analysis of the largest colony showed a clear decline with a high extinction probability (0.05 to 0.33) within 5 years after the study period. In this colony, juvenile survival was lower when summers were hot, and adult survival was lower when winters were cold. Rainfall was also negatively correlated with adult survival. These weather effects could be due to increased physiological demands of thermoregulation and rainfall-induced breeding activity. Our results suggest that the dynamics of the population on the whole are buffered against current weather variation, as individual colonies apparently react in different ways. However, if more and increasingly extreme weather events synchronize colony dynamics, they are likely to have negative effects.     

Different antiamebic antibody isotype patterns in the large and small intestine after local and systemic immunization of mice with glutaraldehyde fixed Entamoeba histolytica trophozoites

We have determined the major immunoglobulin isotypes (IgG, IgA, IgM) of antiamebic antibodies induced in the serum and in the large and small intestine after local (oral and rectal) or systemic (intraperitoneal and intramuscular) immunization of mice with glutaraldehyde-fixed Entamoeba histolytica trophozoites (GFT). IgA predominated in the small intestine after immunization through all routes, whereas in the large intestine similar antibody levels of the major isotypes were induced by rectal, intraperitoneal and intramuscular immunization. The intramuscular route elicited intestinal responses lower than those induced by the rectal and intraperitoneal routes, but higher than the slight IgA antibody increase observed after oral immunization. The differences in antiamebic antibody response patterns at the large and small intestine suggest that there are different mucosal effector compartments. They also indicate that isotype analysis of mucosal antibodies from the sites where an infectious agent resides is needed to evaluate whether a vaccine candidate induces responses of higher protective value in the appropriate site, and that the study of antibody responses must not be limited to sampling the serum or mucosal sites distant to the relevant one.