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.

An EM-based algorithm for clustering data streams in sliding windows

Cluster analysis has played a key role in data understanding. When such an important data mining task is extended to the context of data streams, it becomes more challenging since the data arrive at a mining system in one-pass manner. The problem is even more difficult when the clustering task is considered in a sliding window model which requiring the elimination of outdated data must be dealt with properly. We propose SWEM algorithm that exploits the Expectation Maximization technique to address these challenges. SWEM is not only able to process the stream in an incremental manner, but also capable to adapt to changes happened in the underlying stream distribution.

Incremental and adaptive clustering stream data over sliding window

Cluster analysis has played a key role in data stream understanding. The problem is difficult when the clustering task is considered in a sliding window model in which the requirement of outdated data elimination must be dealt with properly. We propose SWEM algorithm that is designed based on the Expectation Maximization technique to address these challenges. Equipped in SWEM is the capability to compute clusters incrementally using a small number of statistics summarized over the stream and the capability to adapt to the stream distribution’s changes. The feasibility of SWEM has been verified via a number of experiments and we show that it is superior than Clustream algorithm, for both synthetic and real datasets.

Computing the numerical scale of the linguistic term set for the 2-tuple fuzzy linguistic representation model

When using linguistic approaches to solve decision problems, we need the techniques for computing with words (CW). Together with the 2-tuple fuzzy linguistic representation models (i.e., the Herrera and Mart´ınez model and the Wang and Hao model), some computational techniques for CW are also developed. In this paper, we define the concept of numerical scale and extend the 2-tuple fuzzy linguistic representation models under the numerical scale.We find that the key of computational techniques
based on linguistic 2-tuples is to set suitable numerical scale with
the purpose of making transformations between linguistic 2-tuples
and numerical values. By defining the concept of the transitive
calibration matrix and its consistent index, this paper develops an optimization model to compute the numerical scale of the linguistic term set. The desired properties of the optimization model are also presented. Furthermore, we discuss how to construct the transitive calibration matrix for decision problems using linguistic preference relations and analyze the linkage between the consistent index of the transitive calibration matrix and one of the linguistic preference relations. The results in this paper are pretty helpful to complete the fuzzy 2-tuple representation models for CW.

Clustering based multi-label classification for image annotation and retrieval

This paper presents a novel multi-label classification framework for domains with large numbers of labels. Automatic image annotation is such a domain, as the available semantic concepts are typically hundreds. The proposed framework comprises an initial clustering phase that breaks the original training set into several disjoint clusters of data. It then trains a multi-label classifier from the data of each cluster. Given a new test instance, the framework first finds the nearest cluster and then applies the corresponding model. Empirical results using two clustering algorithms, four multi-label classification algorithms and three image annotation data sets suggest that the proposed approach can improve the performance and reduce the training time of standard multi-label classification algorithms, particularly in the case of large number of labels.

Pulmonary nodule classification aided by clustering

Lung nodules can be detected through examining CT scans. An automated lung nodule classification system is presented in this paper. The system employs random forests as it base classifier. A unique architecture for classification-aided-by-clustering is presented. Four experiments are conducted to study the performance of the developed system. 5721 CT lung image slices from the LIDC database are employed in the experiments. According to the experimental results, the highest sensitivity of 97.92%, and specificty of 96.28% are achieved by the system. The results demonstrate that the system has improved the performances of its tested counterparts.

Random forest based lung nodule classification aided by clustering

An automated lung nodule detection system can help spot lung abnormalities in CT lung images. Lung nodule detection can be achieved using template-based, segmentation-based, and classification-based methods. The existing systems that include a classification component in their structures have demonstrated better performances than their counterparts. Ensemble learners combine decisions of multiple classifiers to form an integrated output. To improve the performance of automated lung nodule detection, an ensemble classification aided by clustering (CAC) method is proposed. The method takes advantage of the random forest algorithm and offers a structure for a hybrid random forest based lung nodule classification aided by clustering. Several experiments are carried out involving the proposed method as well as two other existing methods. The parameters of the classifiers are varied to identify the best performing classifiers. The experiments are conducted using lung scans of 32 patients including 5721 images within which nodule locations are marked by expert radiologists. Overall, the best sensitivity of 98.33% and specificity of 97.11% have been recorded for proposed system. Also, a high receiver operating characteristic (ROC) A_z of 0.9786 has been achieved.

Ion clustering in molecular dynamics simulations of sodium iodide solutions

Model systems of sodium iodide dissolved in dimethyl ether or 1,2-dimethoxyethane (glyme) were studied in order to investigate the structural and dynamic properties of ionic solutions in small and polymeric ethers. Full molecular dynamics simulations were performed at a range of different salt concentrations. An algorithm was designed which assigns ions to clusters and then calculates all the terms which contribute to ionic conductivity. In dilute solutions, free ions are the most common ionic species, followed by ion pairs. As the concentration increases, pairs become the most common species, with significant concentrations of clusters with 3 through 6 ions. Changing the solvent from dimethyl ether to glyme significantly decreases the ion clustering due to the chelate effect in which the two oxygens on a solvent stabilize an associated cation. The conductivity in stable systems is shown to be primarily the result of the movement of free ions and the relative movement of ions within neutral pairs. The Nernst-Einstein relation, commonly used in the discussion of polymer electrolytes, is shown to be inadequate to quantitatively describe conductivity in the model systems.

Clustering with instance and attribute level side information

Selecting a suitable proximity measure is one of the fundamental tasks in clustering. How to effectively utilize all available side information, including the instance level information in the form of pair-wise constraints, and the attribute level information in the form of attribute order preferences, is an essential problem in metric learning. In this paper, we propose a learning framework in which both the pair-wise constraints and the attribute order preferences can be incorporated simultaneously. The theory behind it and the related parameter adjusting technique have been described in details. Experimental results on benchmark data sets demonstrate the effectiveness of proposed method.

Mining multi-modal crime patterns at different levels of granularity using hierarchical clustering

The appearance of patterns could be found in different modalities of a domain, where the different modalities refer to the data sources that constitute different aspects of a domain. Particularly, the domain of our discussion refers to crime and the different modalities refer to the different data sources such as offender data, weapon data, etc. in crime domain. In addition, patterns also exist in different levels of granularity for each modality. In order to have a thorough understanding a domain, it is important to reveal the hidden patterns through the data explorations at different levels of granularity and for each modality. Therefore, this paper presents a new model for identifying patterns that exist in different levels of granularity for different modes of crime data. A hierarchical clustering approach - growing self organising maps (GSOM) has been deployed. Furthermore, the model is enhanced with experiments that exhibit the significance of exploring data at different granularities.

Corporate commitment to sustainability – is it all hot air? A review of the linkage between executive pay and sustainable performance

Across time, companies are increasingly making public commitments to sustainable development and to reducing their impacts on climate change. Management remuneration plans (MRPs) are a key mechanism to motivate managers to achieve corporate goals. We review the MRPs negotiated with key management personnel in a sample of large Australian carbon-intensive companies. Our results show that, as in past decades, the companies in our sample have MRPs in place that continue to fixate on financial performance. We argue that this provides evidence of a disconnection between the sustainability-related rhetoric of the sample companies, and their ‘real’ organisational priorities.

Optimal radio labellings of complete m-ary trees

A radio labelling of a connected graph G is a mapping f : V (G) → {0, 1, 2, ...} such that | f (u) - f (v) | ≥ diam (G) - d (u, v) + 1 for each pair of distinct vertices u, v ∈ V (G), where diam (G) is the diameter of G and d (u, v) the distance between u and v. The span of f is defined as max_u, _{v ∈ V (G)} | f (u) - f (v) |, and the radio number of G is the minimum span of a radio labelling of G. A complete m-ary tree (m ≥ 2) is a rooted tree such that each vertex of degree greater than one has exactly m children and all degree-one vertices are of equal distance (height) to the root. In this paper we determine the radio number of the complete m-ary tree for any m ≥ 2 with any height and construct explicitly an optimal radio labelling.