Clustering of Th cell epitopes on exposed regions of HIV envelope despite defects in antibody activity

A long-standing question in the field of immunology concerns the factors that contribute to Th cell epitope immunodominance. For a number of viral membrane proteins, Th cell epitopes are localized to exposed protein surfaces, often overlapping with Ab binding sites. It has therefore been proposed that Abs on B cell surfaces selectively bind and protect exposed protein fragments during Ag processing, and that this interaction helps to shape the Th cell repertoire. While attractive in concept, this hypothesis has not been thoroughly tested. To test this hypothesis, we have compared Th cell peptide immunodominance in normal C57BL/6 mice with that in C57BL/6MT/MT mice (lacking normal B cell activity). Animals were first vaccinated with DNA constructs expressing one of three different HIV envelope proteins, after which the CD4 T cell response profiles were characterized toward overlapping peptides using an IFN- ELISPOT assay. We found a striking similarity between the peptide response profiles in the two mouse strains. Profiles also matched those of previous experiments in which different envelope vaccination regimens were used. Our results clearly demonstrate that normal Ab activity is not required for the establishment or maintenance of Th peptide immunodominance in the HIV envelope response. To explain the clustering of Th cell epitopes, we propose that localization of peptide on exposed envelope surfaces facilitates proteolytic activity and preferential peptide shuttling through the Ag processing pathway.

Minimizing the limitations of GL analyser of fusion based email classification

In the last decade, the Internet email has become one of the primary method of communication used by everyone for the exchange of ideas and information. However, in recent years, along with the rapid growth of the Internet and email, there has been a dramatic growth in spam. Classifications algorithms have been successfully used to filter spam, but with a certain amount of false positive trade-offs. This problem is mainly caused by the dynamic nature of spam content, spam delivery strategies, as well as the diversification of the classification algorithms. This paper presents an approach of email classification to overcome the burden of analyzing technique of GL (grey list) analyser as further refinements of our previous multi-classifier based email classification [10]. In this approach, we introduce a “majority voting grey list (MVGL)” analyzing technique with two different variations which will analyze only the product of GL emails. Our empirical evidence proofs the improvements of this approach, in terms of complexity and cost, compared to existing GL analyser. This approach also overcomes the limitation of human interaction of existing analyzing technique.

Multi-tier phishing email classification with an impact of classifier rescheduling

Protecting user's mailbox from infiltration of phishing email is a significant research issue now a day. Many researches are going on filtering phishing using classification based algorithms and achieve substantial performance. It has been studied and investigated with different classification algorithms and observed that the outputs of the classifiers vary from one another with same corpora. This paper presents the impact of classifier rescheduling of multi-tier classification of phishing email to observe the best scheduling in the classification process. In our method, the features of phishing email will be extracted and classified in a sequential fashion by using the multi-tier classification and the outputs will be sent to the decision fusion process. Empirical evidence proofs that the impact of rescheduling of classifiers among the tiers gives diverse outcomes in terms of accuracy as well as number of false positive instances.

Formation of defects in boron nitride by low energy ion bombardment

Formation of defects in hexagonal and cubic boron nitride (h -BN and c -BN, respectively) under low-energy argon or nitrogen ion-bombardment has been studied by near-edge x-ray absorption fine structure (NEXAFS) around boron and nitrogen K -edges. Breaking of B-N bonds for both argon and nitrogen bombardment and formation of nitrogen vacancies, VN, has been identified from the B K -edge of both h -BN and c -BN, followed by the formation of molecular nitrogen, N2, at interstitial positions. The presence of N 2 produces an additional peak in photoemission spectra around N 1s core level and a sharp resonance in the low-resolution NEXAFS spectra around N K -edge, showing the characteristic vibrational fine structure in high-resolution measurements. In addition, several new peaks within the energy gap of BN, identified by NEXAFS around B and N K -edges, have been assigned to boron or nitrogen interstitials, in good agreement with theoretical predictions. Ion bombardment destroys the cubic phase of c -BN and produces a phase similar to a damaged hexagonal phase. © 2009 American Institute of Physics.

Email classification using data reduction method

Classifying user emails correctly from penetration of spam is an important research issue for anti-spam researchers. This paper has presented an effective and efficient email classification technique based on data filtering method. In our testing we have introduced an innovative filtering technique using instance selection method (ISM) to reduce the pointless data instances from training model and then classify the test data. The objective of ISM is to identify which instances (examples, patterns) in email corpora should be selected as representatives of the entire dataset, without significant loss of information. We have used WEKA interface in our integrated classification model and tested diverse classification algorithms. Our empirical studies show significant performance in terms of classification accuracy with reduction of false positive instances.

Phishing email feature selection approach

Phishing emails are more dynamic and cause high risk of significant data, brand and financial loss to average computer user and organizations. To address this problem, we propose a hybrid feature selection approach based on combination of content-based and behavior-based. Our proposed hybrid features selections are able to achieve 93% accuracy rate as compared to other approaches. In addition, we successfully tested the quality of our proposed behavior-based feature using the Information Gain, Gain Ratio and Symmetrical Uncertainty.

Hybrid feature selection for phishing email detection

Phishing emails are more active than ever before and putting the average computer user and organizations at risk of significant data, brand and financial loss. Through an analysis of a number of phishing and ham email collected, this paper focused on fundamental attacker behavior which could be extracted from email header. It also put forward a hybrid feature selection approach based on combination of content-based and behavior-based. The approach could mine the attacker behavior based on email header. On a publicly available test corpus, our hybrid features selections are able to achieve 96% accuracy rate. In addition, we successfully tested the quality of our proposed behavior-based feature using the information gain.

Detecting unwanted email using VAT

Spam or unwanted email is one of the potential issues of Internet security and classifying user emails correctly from penetration of spam is an important research issue for anti-spam researchers. In this paper we present an effective and efficient spam classification technique using clustering approach to categorize the features. In our clustering technique we use VAT (Visual Assessment and clustering Tendency) approach into our training model to categorize the extracted features and then pass the information into classification engine. We have used WEKA (www.cs.waikato.ac.nz/ml/weka/) interface to classify the data using different classification algorithms, including tree-based classifiers, nearest neighbor algorithms, statistical algorithms and AdaBoosts. Our empirical performance shows that we can achieve detection rate over 97%.

An analytical model on the propagation of modern email worms

Email worms propagate across networks by taking advantage of email relationships. Modeling the propagation of email worms can help predict their potential damages and develop countermeasures. We propose a novel analytical model on the propagation process of modern reinfection email worms. It relies on probabilistic analysis, and thus can provide a steady and reliable assessment on the propagation dynamics. Additionally, by introducing virtual users to represent the repetitious spreading process, the proposed model overcomes the computational challenge caused by reinfection processes. To demonstrate the benefits of our model, we conduct a series of experimental evaluation. The results show that our novel approach achieves a greater accuracy and is more suitable for modeling modern email worms than previous models.

A multi-tier ensemble construction of classifiers for phishing email detection and filtering

This paper is devoted to multi-tier ensemble classifiers for the detection and filtering of phishing emails. We introduce a new construction of ensemble classifiers, based on the well known and productive multi-tier approach. Our experiments evaluate their performance for the detection and filtering of phishing emails. The multi-tier constructions are well known and have been used to design effective classifiers for email classification and other applications previously. We investigate new multi-tier ensemble classifiers, where diverse ensemble methods are combined in a unified system by incorporating different ensembles at a lower tier as an integral part of another ensemble at the top tier. Our novel contribution is to investigate the possibility and effectiveness of combining diverse ensemble methods into one large multi-tier ensemble for the example of detection and filtering of phishing emails. Our study handled a few essential ensemble methods and more recent approaches incorporated into a combined multi-tier ensemble classifier. The results show that new large multi-tier ensemble classifiers achieved better performance compared with the outcomes of the base classifiers and ensemble classifiers incorporated in the multi-tier system. This demonstrates that the new method of combining diverse ensembles into one unified multi-tier ensemble can be applied to increase the performance of classifiers if diverse ensembles are incorporated in the system.