Time-of-day dependent performance efficiency in student nurses

A psychological test battery comprising reasoning ability, name and number checking, letter cancellation and associative recall was administered to a group of 70 student nurses at 2-hr intervals from 0800 to 2000 to determine if their performance varied at different times of day. Simultaneously, their oral temperature was recorded. A 7 x 7 latin square design was utilized, and latin square repeated measure analysis of variance was employed. The results indicated variations in oral temperature as the day advanced. While accuracy of performance in cognitive tasks was found to be superior during the early morning, noon or early evening hours, the speed of performance was highest during the period from late-morning to late-evening.     

Seasonal Abundance of Acanthamoeba rhysodes (Singh, 1952) (Protozoa: Gymnamoebia) in a Mangrove Litter-Soil Ecosystem of Gangetic-Estuary,India1,2

Acanthamoeba rhysodes has been found to be a predominant intertidal benthic gymnamoeba in the mangrove ecosystem of Sundarbans of lower deltaic Bengal, facing the Bay. The sampling zones under study were the highest high tide regions, with characteristic mangrove litter-soil, inundated twice per month during the highest ebb of spring tide. Population abundance of this species, both in its trophic and cystic forms in the three distinct seasonal periods of pre-monsoon (March to June), monsoon (July to October), and post-monsoon (November to February) has been surveyed for over two years. These seasonal periods affect the physico-chemical parameters of the habitat substrata, including temperature, pH, and salinity. It has been found that the overall number of organisms per gram of soil attains peak value during the monsoon period. This value comes down in post-monsoon samples and is the least in pre-monsoon ones.     

Can We Accurately Characterize Wildlife Resource Use When Telemetry Data Are Imprecise?

ABSTRACT Telemetry data have been widely used to quantify wildlife habitat relationships despite the fact that these data are inherently imprecise. All telemetry data have positional error, and failure to account for that error can lead to incorrect predictions of wildlife resource use. Several techniques have been used to account for positional error in wildlife studies. These techniques have been described in the literature, but their ability to accurately characterize wildlife resource use has never been tested. We evaluated the performance of techniques commonly used for incorporating telemetry error into studies of wildlife resource use. Our evaluation was based on imprecise telemetry data (mean telemetry error = 174 m, SD = 130 m) typical of field-based studies. We tested 5 techniques in 10 virtual environments and in one real-world environment for categorical (i.e., habitat types) and continuous (i.e., distances or elevations) rasters. Technique accuracy varied by patch size for the categorical rasters, with higher accuracy as patch size increased. At the smallest patch size (1 ha), the technique that ignores error performed best on categorical data (0.31 and 0.30 accuracy for virtual and real data, respectively); however, as patch size increased the bivariate-weighted technique performed better (0.56 accuracy at patch sizes >31 ha) and achieved complete accuracy (i.e., 1.00 accuracy) at smaller patch sizes (472 ha and 1,522 ha for virtual and real data, respectively) than any other technique. We quantified the accuracy of the continuous covariates using the mean absolute difference (MAD) in covariate value between true and estimated locations. We found that average MAD varied between 104 m (ignore telemetry error) and 140 m (rescale the covariate data) for our continuous covariate surfaces across virtual and real data sets. Techniques that rescale continuous covariate data or use a zonal mean on values within a telemetry error polygon were significantly less accurate than other techniques. Although the technique that ignored telemetry error performed best on categorical rasters with smaller average patch sizes (i.e., ≤31 ha) and on continuous rasters in our study, accuracy was so low that the utility of using point-based approaches for quantifying resource use is questionable when telemetry data are imprecise, particularly for small-patch habitat relationships.     

Localization of the glucose phosphotransferase to a cytoplasmically accessible site on intracellular membranes

UDP-glucose:glycoprotein glucose-1-phosphotransferase (Glc-phosphotransferase) catalyzes the transfer of alpha Glc-1-P from UDP-Glc to mannose residues on acceptor glycoproteins. The predominant acceptor for this transfer in rat liver is a glycoprotein of 62 kDa. This acceptor was labeled in liver homogenates through incubation with the 35S-labeled phosphorothioate analogue of UDP-Glc, and its distribution following differential centrifugation was compared to that of the glycoproteins labeled by CMP-[3H]N-acetylneuraminic acid. Whereas 94% of the 3H-labeled macromolecules fractionated to the microsomal pellet, 85% of the 35S-labeled 62-kDa glycoprotein was found in the high-speed supernatant. The distribution of the Glc-phosphotransferase was also examined following differential centrifugation, and the bulk of the activity was found in the 100,000 x g pellet. In contrast to results obtained with the lumenal microsomal markers 4 beta-galactosyltransferase and mannose-6-phosphatase, however, optimal activity of the Glc-phosphotransferase was not dependent on the disruption of microsomal vesicles by detergent. In addition, Glc-phosphotransferase was degraded by exogenous proteases in the absence of detergent, whereas the lumenal markers were not. We conclude, therefore, that the 62-kDa acceptor glycoprotein is cytoplasmic and is glycosylated by the Glc-phosphotransferase at a site accessible to the cytoplasm. This may prove to be a model for the topography of glycosylation of other cytoplasmic glycoproteins as well.