Protein Expr Purif

E6 is a small oncoprotein involved in tumorigenesis induced by papillomaviruses (PVs). E6 often recognizes its cellular targets by binding to short motifs presenting the consensus LXXLL. E6 proteins have long resisted structural analysis. We found that bovine papillomavirus type 1 (BPV1) E6 binds the N-terminal LXXLL motif of the cellular protein paxillin with significantly higher affinity as compared to other E6/peptide interactions. Although recombinant BPV1 E6 was poorly soluble in the free state, provision of the paxillin LXXLL peptide during BPV1 E6 biosynthesis greatly enhanced the protein's solubility. Expression of BPV1 E6/LXXLL peptide complexes was carried out in bacteria in the form of triple fusion constructs comprising, from N- to C-terminus, the soluble carrier protein maltose binding protein (MBP), the LXXLL motif and the E6 protein. A TEV protease cleavage site was placed either between MBP and LXXLL motif or between LXXLL motif and E6. These constructs allowed us to produce highly concentrated samples of BPV1 E6, either covalently fused to the C-terminus of the LXXLL motif (intra-molecular complex) or non-covalently bound to it (inter-molecular complex). Heteronuclear NMR measurements were performed and showed that the E6 protein was folded with similar conformations in both covalent and non-covalent complexes. These data open the way to novel structural and functional studies of the BPV1 E6 in complex with its preferential target motif

Activity recognition on smartphones using an AKNN based support vectors

Abstract Purpose: Mobile phone-based human activity recognition (HAR) consists of inferring user’s activity type from the analysis of the inertial mobile sensor data. This paper aims to mainly introduce a new classification approach called adaptive k-nearest neighbors (AKNN) for intelligent HAR using smartphone inertial sensors with a potential real-time implementation on smartphone platform. Design/methodology/approach: The proposed method puts forward several modification on AKNN baseline by using kernel discriminant analysis for feature reduction and hybridizing weighted support vector machines and KNN to tackle imbalanced class data set. Findings: Extensive experiments on a five large scale daily activity recognition data set have been performed to demonstrate the effectiveness of the method in terms of error rate, recall, precision, F1-score and computational/memory resources, with several comparison with state-of-the art methods and other hybridization modes. The results showed that the proposed method can achieve more than 50% improvement in error rate metric and up to 5.6% in F1-score. The training phase is also shown to be reduced by a factor of six compared to baseline, which provides solid assets for smartphone implementation. Practical implications: This work builds a bridge to already growing work in machine learning related to learning with small data set. Besides, the availability of systems that are able to perform on flight activity recognition on smartphone will have a significant impact in the field of pervasive health care, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring. Originality/value: The purpose of this study is to build and test an accurate offline model by using only a compact training data that can reduce the computational and memory complexity of the system. This provides grounds for developing new innovative hybridization modes in the context of daily activity recognition and smartphone-based implementation. This study demonstrates that the new AKNN is able to classify the data without any training step because it does not use any model for fitting and only uses memory resources to store the corresponding support vectors

The joint use of sequence features combination and modified weighted SVM for improving daily activity recognition

Abstract Two serious problems affecting the implementation of human activity recognition algorithms have been acknowledged. The first one corresponds to non-informative sequence features. The second is the class imbalance in the training data due to the fact that people do not spend the same amount of time on the different activities. To address these issues, we propose a new scheme based on a combination of principal component analysis, linear discriminant analysis (LDA) and the modified weighted support vector machines. First we added the most significant principal components to the set of features extracted using LDA. This work shows that a suitable sequence feature set combined with the modified WSVM based on our criterion classifier achieves good improvement and efficiency over the traditional used methods

A Novel Trust-Based Authentication Scheme for Low-Resource Devices in Smart Environments

In smart environments, pervasive computing contributes in improving daily life activities for dependent people by providing personalized services. Nevertheless, those environments do not guarantee a satisfactory level for protecting the user privacy and ensuring the trust between communicating entities. In this paper, we propose a trust evaluation model based on user past and present behavior. This model is associated to a lightweight authentication key agreement protocol (EC-SAKA). The aim is to enable the communicating entities to establish a level of trust and then succeed in a mutual authentication using a scheme suitable for low-resource devices in smart environments. Finally, we tested and implemented our scheme on Android mobile phones in a smart environment dedicated for handicapped people. Keywords: devices 1