From the Schnauzenorgan to the back: Morphological comparison of mormyromast electroreceptor organs at different skin regions of Gnathonemus petersii

The nocturnally active weakly electric fish Gnathonemus petersii is known to employ active electrolocation for the detection of objects and for orientation in its environment. The fish emits pulse‐type electric signals with an electric organ and perceives these signals with more than 3,000 epidermal electroreceptor organs, the mormyromasts, which are distributed over the animal's skin surface. In this study, we measured the metric dimensions of the mormyromasts from different body regions to find structural and functional specialization of the various body parts. We focused on the two foveal regions of G. petersii, which are located at the elongated and movable chin (the Schnauzenorgan; SO) and at the nasal region (NR), the skin region between the mouth and the nares. These two foveal regions were compared to the dorsal part (back) of the fish, which contains typical nonfoveal mormyromasts. While the gross anatomy of the mormyromasts from all skin regions is similar, the metric dimensions of the main substructures differed. The mormyromasts at the SO are the smallest and contain the smallest receptor cells. In addition, the number of receptor cells per organ is lowest at the SO. In contrast, at the back the biggest receptor organs with the highest amount of receptor cells per organ occur. The mormyromasts at the NR are in several respects intermediate between those from the back and the SO. However, mormyromasts at the NR are longer than those at all other skin regions, the canal leading from the receptor pore to the inner chambers were the longest and the overlaying epidermal layers are the thickest. These results show that mormyromasts and the epidermis they are embedded in at both foveal regions differ specifically from those found on the rest of the body. The morphological specializations lead to functional specialization of the two foveae. J. Morphol., 2012. © 2012 Wiley Periodicals, Inc.     

Effect of microwave treatment on the shear bond strength of different types of commercial teeth to acrylic resin

doi:10.1111/j.1741‐2358.2009.00333.x
Effect of microwave treatment on the shear bond strength of different types of commercial teeth to acrylic resin Objective: The purpose of this study was to verify the effect of microwave treatment on the shear bond strength of commercial types of teeth to acrylic resin, when the glossy ridge laps were unmodified (groups 1 and 5), bur abraded (groups 2 and 6), bur grooved (groups 3 and 7) or etched by monomer (groups 4 and 8). Background: Controversial findings have shown that mechanical or chemical changes in ridge‐lap surface of the tooth increase or decrease the bond strength between tooth and acrylic resin, and the microwave disinfection may cause different changes on this bond strength. Materials and methods: Eighty specimens (n = 10) were made with the acrylic resin bonded to tooth glossy ridge lap, polymerised in water at 74°C for 9 h, and deflasked after flask cooling. Specimens of the groups 5, 6, 7 and 8 were individually immersed in 150 ml of water and submitted to microwave treatment in an oven at 650 W for 3 min. Control specimens (groups 1, 2, 3 and 4) were not microwave treated. Shear bond strength test was performed in an Instron machine with a cross‐speed of 1 mm/min. Collected data were submitted to anova and Tukey’s test (α = 0.05). Results: Microwave treatment decreased the shear bond strength values of the tooth/resin bond. In the microwaved and non‐microwaved procedures, mechanical retention improved the shear bond strength when compared with the control and monomer treatments. Conclusion: Shear bond strength of the tooth/resin bond was influenced by the microwave treatment and different commercial teeth association, and was lower for the Biotone tooth.     

A Mathematical Framework for Combining Decisions of Multiple Experts toward Accurate and Remote Diagnosis of Malaria Using Tele-Microscopy

We propose a methodology for digitally fusing diagnostic decisions made by multiple medical experts in order to improve accuracy of diagnosis. Toward this goal, we report an experimental study involving nine experts, where each one was given more than 8,000 digital microscopic images of individual human red blood cells and asked to identify malaria infected cells. The results of this experiment reveal that even highly trained medical experts are not always self-consistent in their diagnostic decisions and that there exists a fair level of disagreement among experts, even for binary decisions (i.e., infected vs. uninfected). To tackle this general medical diagnosis problem, we propose a probabilistic algorithm to fuse the decisions made by trained medical experts to robustly achieve higher levels of accuracy when compared to individual experts making such decisions. By modelling the decisions of experts as a three component mixture model and solving for the underlying parameters using the Expectation Maximisation algorithm, we demonstrate the efficacy of our approach which significantly improves the overall diagnostic accuracy of malaria infected cells. Additionally, we present a mathematical framework for performing ‘slide-level’ diagnosis by using individual ‘cell-level’ diagnosis data, shedding more light on the statistical rules that should govern the routine practice in examination of e.g., thin blood smear samples. This framework could be generalized for various other tele-pathology needs, and can be used by trained experts within an efficient tele-medicine platform.     

Selection for High Oridonin Yield in the Chinese Medicinal Plant Isodon (Lamiaceae) Using a Combined Phylogenetics and Population Genetics Approach

Oridonin is a diterpenoid with anti-cancer activity that occurs in the Chinese medicinal plant Isodon rubescens and some related species. While the bioactivity of oridonin has been well studied, the extent of natural variation in the production of this compound is poorly known. This study characterizes natural variation in oridonin production in order to guide selection of populations of Isodon with highest oridonin yield. Different populations of I. rubescens and related species were collected in China, and their offspring were grown in a greenhouse. Samples were examined for oridonin content, genotyped using 11 microsatellites, and representatives were sequenced for three phylogenetic markers (ITS, rps16, trnL-trnF). Oridonin production was mapped on a molecular phylogeny of the genus Isodon using samples from each population as well as previously published Genbank sequences. Oridonin has been reported in 12 out of 74 species of Isodon examined for diterpenoids, and the phylogeny indicates that oridonin production has arisen at least three times in the genus. Oridonin production was surprisingly consistent between wild-collected parents and greenhouse-grown offspring, despite evidence of gene flow between oridonin-producing and non-producing populations of Isodon. Additionally, microsatellite genetic distance between individuals was significantly correlated with chemical distance in both parents and offspring. Neither heritability nor correlation with genetic distance were significant when the comparison was restricted to only populations of I. rubescens, but this result should be corroborated using additional samples. Based on these results, future screening of Isodon populations for oridonin yield should initially prioritize a broad survey of all species known to produce oridonin, rather than focusing on multiple populations of one species, such as I. rubescens. Of the samples examined here, I. rubescens or I. japonicus from Henan province would provide the best source of oridonin.     

Analysis of a common inheritable idiotype in IgA-deficient sera using monoclonal antibodies

In these studies we describe the production of three mAb raised to an idiotype on an IgG anticasein antibody isolated from the serum of one IgA-deficient blood donor. These are IgM kappa and block the binding of casein Ag to anticasein antibody. Sera of unrelated IgA-deficient donors were tested for the presence of the idiotype; 15 of 56 IgA-deficient sera (25%) contain the anticasein idiotype, whereas 1 of 45 normal sera was positive. Anticasein antibodies as a whole were predominantly of the IgG1 and IgG3 subclass; idiotype-positive anticaseins are predominantly of the IgG1 subclass. For IgA-deficient donors, the relative amount of idiotype-positive anticasein antibody was correlated with the level of anticasein present in the serum. Studies were done to investigate the potential inheritance of the idiotype in families; in three of four families the idiotype was inherited in an apparent autosomal dominant pattern. Our data show that a common cross-reactive idiotype can be detected in the sera of IgA-deficient individuals and their family members. This suggests that V region markers may be conserved in this humoral immunodeficiency disease.     

Predicting Metabolic Pathways of Small Molecules and Enzymes Based on Interaction Information of Chemicals and Proteins

Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.