DNA-dependent Protein Kinase-mediated Phosphorylation of Protein Kinase B Requires a Specific Recognition Sequence in the C-terminal Hydrophobic Motif

DNA-dependent protein kinase (DNA-PK) has been implicated in a variety of nuclear processes including DNA double strand break repair, V(D)J recombination, and transcription. A recent study showed that DNA-PK is responsible for Ser-473 phosphorylation in the hydrophobic motif of protein kinase B (PKB/Akt) in genotoxic-stressed cells, suggesting a novel role for DNA-PK in cell signaling. Here, we report that DNA-PK activity toward PKB peptides is impaired in DNA-PK knock-out mouse embryonic fibroblast cells when compared with wild type. In addition, human glioblastoma cells expressing a mutant form of DNA-PK (M059J) displayed a lower DNA-PK activity when compared with glioblastoma cells expressing wild-type DNA- PK (M059K) when PKB peptide substrates were tested. DNA- PK preferentially phosphorylated PKB on Ser-473 when compared with its known in vitro substrate, p53. A consensus hydrophobic amino acid surrounding the Ser-473 phospho-acceptor site in PKB containing amino acids Phe at position +1 and +4 and Tyr at position -1 are critical for DNA- PK activity. Thus, these data define the specificity of DNA- PK action as a Ser-473 kinase for PKB in DNA repair signaling.

An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.

FPGA Implementation of a Pipelined Gaussian Calculation for HMM-Based Large Vocabulary Speech Recognition

A scalable large vocabulary, speaker independent speech recognition system is being developed using Hidden Markov Models (HMMs) for acoustic modeling and a Weighted Finite State Transducer (WFST) to compile sentence, word, and phoneme models. The system comprises a software backend search and an FPGA-based Gaussian calculation which are covered here. In this paper, we present an efficient pipelined design implemented both as an embedded peripheral and as a scalable, parallel hardware accelerator. Both architectures have been implemented on an Alpha Data XRC-5T1, reconfigurable computer housing a Virtex 5 SX95T FPGA. The core has been tested and is capable of calculating a full set of Gaussian results from 3825 acoustic models in 9.03 ms which coupled with a backend search of 5000 words has provided an accuracy of over 80%. Parallel implementations have been designed with up to 32 cores and have been successfully implemented with a clock frequency of 133?MHz.