No abstract

Feature selection has been applied to the analysis of complex structured data, such as functional connectivity networks (FCNs) constructed on resting-state functional magnetic resonance imaging (rs-fMRI), for removing redundant/noisy information. Previous studies usually first extract topological measures (e.g., clustering coefficients) from FCNs as feature vectors, and then perform vector-based algorithms (e.g., t-test) for feature selection. However, due to the use of vector-based representations, these methods simply ignore important local-to-global structural information of connectivity networks, while such structural information could be used as prior knowledge of networks to improve the learning performance. To this end, we propose a graph kernel-based structured feature selection (gk-SFS) method for brain disease classification with connectivity networks. Different from previous studies, our proposed gk-SFS method uses the graph kernel technique to calculate the similarity of networks and thus can explicitly take advantage of the structural information of connectivity networks. Specifically, we first develop a new graph kernel-based Laplacian regularizer in our gk-SFS model to preserve the structural information of connectivity networks. We also employ an l 1 -norm based sparsity regularizer to select a small number of discriminative features for brain disease analysis (classification). The experimental results on both ADNI and ADHD-200 datasets with rs-fMRI data demonstrate that the proposed gk-SFS method can further improve the classification performance compared with the state-of-the-art methods.

Fingerprint orientation field (FOF) estimation plays a key role in enhancing the performance of the automated fingerprint identification system (AFIS): accurate estimation of FOF can evidently improve the performance of AFIS. However, despite the enormous attention on the FOF estimation research in the past decades, the accurate estimation of FOFs, especially for poor-quality fingerprints, still remains a challenging task. In this paper, we devote to review and categorization of the large number of FOF estimation methods proposed in the specialized literature, with particular attention to the most recent work in this area. Broadly speaking, the existing FOF estimation methods can be grouped into three categories: gradient-based methods, mathematical models-based methods, and learning-based methods. Identifying and explaining the advantages and limitations of these FOF estimation methods is of fundamental importance for fingerprint identification, because only a full understanding of the nature of these methods can shed light on the most essential issues for FOF estimation. In this paper, we make a comprehensive discussion and analysis of these methods concerning their advantages and limitations. We have also conducted experiments using publically available competition dataset to effectively compare the performance of the most relevant algorithms and methods.

Summary Understanding how broadly neutralizing antibodies (bnAbs) to influenza hemagglutinin (HA) naturally develop in humans is critical to the design of universal influenza vaccines. Several classes of bnAbs directed to the conserved HA stem were found in multiple individuals, including one encoded by heavy-chain variable domain V H 6-1. We describe two genetically similar V H 6-1 bnAb clonotypes from the same individual that exhibit different developmental paths toward broad neutralization activity. One clonotype evolved from a germline precursor recognizing influenza group 1 subtypes to gain breadth to group 2 subtypes. The other clonotype recognized group 2 subtypes and developed binding to group 1 subtypes through somatic hypermutation. Crystal structures reveal that the specificity differences are primarily mediated by complementarity-determining region H3 (CDR H3). Thus, while V H 6-1 provides a framework for development of HA stem-directed bnAbs, sequence differences in CDR H3 junctional regions during VDJ recombination can alter reactivity and evolutionary pathways toward increased breadth.