VDAC PROPERTIES ARE INFLUENCED BY THE SOURCE OF ITS PURIFICATION

Objectives: The Voltage Dependent Anion-Selective Channel (VDAC), the most abundant protein of the outer mitochondrial membrane (OMM), forms the major conduit for metabolite transport across this membrane. It has also been shown to be involved in cell death signalling through interaction with other proteins like Hexokinase and by mediating release of apoptogenic proteins like cyt c from mitochondria. As in case of other channel proteins, functional characterization of purified reconstituted protein by using electrophysiological techniques can be used in development of VDAC targeted drugs. Here we report electrophysiological properties of VDACs (one of the target for cancerous cells) purified from different sources. Methods: Human VDAC1 and rice VDAC4 were heterologously expressed and purified from E. coli BL21 (DE3)-pLysS, while rat and yeast VDACs were purified from mitochondria. Electrophysiological studies of all VDACs were done by using BLM and the data was analysed by using pCLAMP 10 (Axon Instruments). Results: VDACs purified from both the sources showed conserved voltage dependence and channel conductance, however they showed significant difference in dynamics. VDAC purified from mitochondria had relatively short occupancy of each electrophysiological state compared to protein purified from inclusion bodies. Conclusion: Our results suggest that the source of purified protein could be critical for some aspects of channel function

Prevalence and co-infection of Toxoplasma gondii and Neospora caninum in Apodemus sylvaticus in an area relatively free of cats

The protozoan parasite Toxoplasma gondii is prevalent worldwide and can infect a remarkably wide range of hosts despite felids being the only definitive host. As cats play a major role in transmission to secondary mammalian hosts, the interaction between cats and these hosts should be a major factor determining final prevalence in the secondary host. This study investigates the prevalence of T. gondii in a natural population of Apodemus sylvaticus collected from an area with low cat density (<2·5 cats/km2). A surprisingly high prevalence of 40·78% (95% CI: 34·07%–47·79%) was observed despite this. A comparable level of prevalence was observed in a previously published study using the same approaches where a prevalence of 59% (95% CI: 50·13%–67·87%) was observed in a natural population of Mus domesticus from an area with high cat density (>500 cats/km2). Detection of infected foetuses frompregnant dams in both populations suggests that congenital transmission may enable persistence of infection in the absence of cats. The prevalences of the related parasite, Neospora caninum were found to be low in both populations (A. sylvaticus: 3·39% (95% CI: 0·12%–6·66%); M. domesticus: 3·08% (95% CI: 0·11%–6·05%)). These results suggest that cat density may have a lower than expected effect on final prevalence in these ecosystems

Artificial Neural Network-based error compensation procedure for low-cost encoders

An Artificial Neural Network-based error compensation method is proposed for improving the accuracy of resolver-based 16-bit encoders by compensating for their respective systematic error profiles. The error compensation procedure, for a particular encoder, involves obtaining its error profile by calibrating it on a precision rotary table, training the neural network by using a part of this data and then determining the corrected encoder angle by subtracting the ANN-predicted error from the measured value of the encoder angle. Since it is not guaranteed that all the resolvers will have exactly similar error profiles because of the inherent differences in their construction on a micro scale, the ANN has been trained on one error profile at a time and the corresponding weight file is then used only for compensating the systematic error of this particular encoder. The systematic nature of the error profile for each of the encoders has also been validated by repeated calibration of the encoders over a period of time and it was found that the error profiles of a particular encoder recorded at different epochs show near reproducible behavior. The ANN-based error compensation procedure has been implemented for 4 encoders by training the ANN with their respective error profiles and the results indicate that the accuracy of encoders can be improved by nearly an order of magnitude from quoted values of ~6 arc-min to ~0.65 arc-min when their corresponding ANN-generated weight files are used for determining the corrected encoder angle.Comment: 16 pages, 4 figures. Accepted for Publication in Measurement Science and Technology (MST