Angelman Syndrome Protein UBE3A Interacts with Primary Microcephaly Protein ASPM, Localizes to Centrosomes and Regulates Chromosome Segregation

Many proteins associated with the phenotype microcephaly have been localized to the centrosome or linked to it functionally. All the seven autosomal recessive primary microcephaly (MCPH) proteins localize at the centrosome. Microcephalic osteodysplastic primordial dwarfism type II protein PCNT and Seckel syndrome (also characterized by severe microcephaly) protein ATR are also centrosomal proteins. All of the above findings show the importance of centrosomal proteins as the key players in neurogenesis and brain development. However, the exact mechanism as to how the loss-of-function of these proteins leads to microcephaly remains to be elucidated. To gain insight into the function of the most commonly mutated MCPH gene ASPM, we used the yeast two-hybrid technique to screen a human fetal brain cDNA library with an ASPM bait. The analysis identified Angelman syndrome gene product UBE3A as an ASPM interactor. Like ASPM, UBE3A also localizes to the centrosome. The identification of UBE3A as an ASPM interactor is not surprising as more than 80% of Angelman syndrome patients have microcephaly. However, unlike in MCPH, microcephaly is postnatal in Angelman syndrome patients. Our results show that UBE3A is a cell cycle regulated protein and its level peaks in mitosis. The shRNA knockdown of UBE3A in HEK293 cells led to many mitotic abnormalities including chromosome missegregation, abnormal cytokinesis and apoptosis. Thus our study links Angelman syndrome protein UBE3A to ASPM, centrosome and mitosis for the first time. We suggest that a defective chromosome segregation mechanism is responsible for the development of microcephaly in Angelman syndrome.

Autocalibration of dynamic bacterial growth model for water distribution system using GA

The presence of residual chlorine and organic matter govern the bacterial regrowth within a water distribution system. The bacterial growth model is essential to predict the spatial and temporal variation of all these substances throughout the system. The parameters governing the bacterial growth and biodegradable dissolved organic carbon (BDOC) utilization are difficult to determine by experimentation. In the present study, the estimation of these parameters is addressed by using simulation-optimization procedure. The optimal solution by genetic algorithm (GA) has indicated that the proper combination of parameter values are significant rather than correct individual values. The applicability of the model is illustrated using synthetic data generated by introducing noise in to the error-free measurements. The GA was found to be a potential tool in estimating the parameters controlling the bacterial growth and BDOC utilization. Further, the GA was also used for evaluating the sensitivity issues relating parameter values and objective function. It was observed that mu and k(cl) are more significant and dominating compared to the other parameters. But the magnitude of the parameters is also an important issue in deciding the dominance of a particular parameter. GA is found to be a useful tool in autocalibration of bacterial growth model and a sensitivity study of parameters.

The inactive X chromosome in the human female is enriched in 5-methylcytosine to an unusual degree and appears to contain more of this modified nucleotide than the remainder of the genome

By employing a procedure that combines ELISA and photoacoustic spectroscopy, we have examined the content of 5-methylcytosine (m(5)C) in DNA of individuals who differed from one another in the number of X chromosomes in their genomes. The results show that the human inactive X chromosome (Xi) contains very high amounts of this modified nucleotide. We estimate that in the 46,XX female there is more m(5)C in Xi (similar to3.6 x 10(7)) than in all the remaining chromosomes put together (similar to2.1 x 10(7)). Our results also suggest that nearly one-fifth of all cytosines in Xi are methylated and that, in addition to CpG methylation, there is extensive non-CpG methylation as well.

Volume Change behaviour in Calcitic Soil influenced with Sulphuric Acid using Artificial Neural Networks

This paper elucidates the methodology of applying artificial neural network model (ANNM) to predict the percent swell of calcitic soil in sulphuric acid solutions, a complex phenomenon involving many parameters. Swell data required for modelling is experimentally obtained using conventional oedometer tests under nominal surcharge. The phases in ANN include optimal design of architecture, operation and training of architecture. The designed optimal neural model (3-5-1) is a fully connected three layer feed forward network with symmetric sigmoid activation function and trained by the back propagation algorithm to minimize a quadratic error criterion.The used model requires parameters such as duration of interaction, calcite mineral content and acid concentration for prediction of swell. The observed strong correlation coefficient (R2 = 0.9979) between the values determined by the experiment and predicted using the developed model demonstrates that the network can provide answers to complex problems in geotechnical engineering.

Stress-Strain Prediction of Jointed Rocks using Artificial Neural Networks

The applicability of Artificial Neural Networks for predicting the stress-strain response of jointed rocks at varied confining pressures, strength properties and joint properties (frequency, orientation and strength of joints) has been studied in the present paper. The database is formed from the triaxial compression tests on different jointed rocks with different confining pressures and different joint properties reported by various researchers. This input data covers a wide range of rock strengths, varying from very soft to very hard. The network was trained using a 3 layered network with feed forward back propagation algorithm. About 85% of the data was used for training and remaining15% for testing the predicting capabilities of the network. Results from the analyses were very encouraging and demonstrated that the neural network approach is efficient in capturing the complex stress-strain behaviour of jointed rocks. A single neural network is demonstrated to be capable of predicting the stress-strain response of different rocks, whose intact strength vary from 11.32 MPa to 123 MPa and spacing of joints vary from 10 cm to 100 cm for confining pressures ranging from 0 to 13.8 MPa.

Three-Dimensional Site Characterization Model Using Artificial Neural Networks

This research is designed to develop a new technique for site characterization in a three-dimensional domain. Site characterization is a fundamental task in geotechnical engineering practice, as well as a very challenging process, with the ultimate goal of estimating soil properties based on limited tests at any half-space subsurface point in a site.In this research, the sandy site at the Texas A&M University's National Geotechnical Experimentation Site is selected as an example to develop the new technique for site characterization, which is based on Artificial Neural Networks (ANN) technology. In this study, a sequential approach is used to demonstrate the applicability of ANN to site characterization. To verify its robustness, the proposed new technique is compared with other commonly used approaches for site characterization. In addition, an artificial site is created, wherein soil property values at any half-space point are assumed, and thus the predicted values can compare directly with their corresponding actual values, as a means of validation. Since the three-dimensional model has the capability of estimating the soil property at any location in a site, it could have many potential applications, especially in such case, wherein the soil properties within a zone are of interest rather than at a single point. Examples of soil properties of zonal interest include soil type classification and liquefaction potential evaluation. In this regard, the present study also addresses this type of applications based on a site located in Taiwan, which experienced liquefaction during the 1999 Chi-Chi, Taiwan, Earthquake.

Bacterial transformation using micro-shock waves

Shock waves are one of the most competent mechanisms of energy dissipation observed in nature. We have developed a novel device to generate controlled micro-shock waves using an explosive-coated polymer tube. In this study, we harnessed these controlled micro-shock waves to develop a unique bacterial transformation method. The conditions were optimized for the maximum transformation efficiency in Escherichia coli. The maximum transformation efficiency was obtained when we used a 30 cm length polymer tube, 100 mu m thick metal foil, 200 mM CaCl(2), 1 ng/mu l plasmid DNA concentration, and 1 x 10(9) cell density. The highest transformation efficiency achieved (1 x 10(-5) transformants/cell) was at least 10 times greater than the previously reported ultrasound-mediated transformation (1 x 10(-6) transformants/cell). This method was also successfully employed for the efficient and reproducible transformation of Pseudomonas aeruginosa and Salmonella typhimurium. This novel method of transformation was shown to be as efficient as electroporation with the added advantage of better recovery of cells, reduced cost (40 times cheaper than a commercial electroporator), and growth phase independent transformation. (C) 2011 Elsevier Inc. All rights reserved.