Abstract

Field study of thermal comfort was conducted in six naturally ventilated hostel buildings of composite climate considering Class-II protocol of field measurement during summer 2011. Total 429 survey samples of same age group (average 19.6 years) were collected including objective and subjective measurements. Statistical analysis of student's responses and measured thermal environment variables was performed to determine existing indoor environmental conditions and priority of using behavioural controls. Thermal comfort indices were also calculated and compared to the student's perceptions. Neutral temperature was found to be 30.15 °C through regression analysis, with an average clothing of 0.41 Clo (min. 0.19 Clo, max 0.82 Clo). Results have shown a wide bandwidth of neutral temperatures (25.9–33.8 °C) for the hostel buildings which is higher than national/international standards of thermal comfort …

Abstract

BACKGROUND: Ankyrin repeat is one of the most frequently observed structural motif in proteins across all kingdoms of life. These proteins are involved in diverse set of cellular functions and act as transcriptional initiators, cell-cycle regulators, cytoskeletal, ion transporters and signal transducers, and consequently, defects in ankyrin repeat proteins have been found in a number of human diseases. Identification of these structural repeats at the sequence level is difficult due to low conservation between the repeat copies. Thus, analysis at the structure level is desirable. RESULTS: In this study, we propose a graph based approach in the identification and analysis of ankyrin repeats. The 3-dimensional topology of protein structures has been shown to be well captured by protein contact graphs. The connectivity information of these networks is represented in the adjacency matrix and here we propose the analysis of the eigen spectra of the adjacency matrix in the identification of structural repeats. A clear two-peak pattern corresponding to the helix-turn-helix region of the Ankyrin motif is observed in the principal eigenvector of the adjacency matrix. The length distribution of this repetitive pattern along with the organization of the secondary structure elements is used to design an algorithm to identify the Ankyrin structural motifs. The analysis has been carried out on a non-redundant set of 51 proteins annotated in the UniProt database and a very good agreement is observed. Analysis of all the proteins in the alpha class and alpha+beta class in SCOP database has been performed and a number of novel repeats, not annotated in the database have been identified. This approach is then applied on other structural repeats such as Tetraticopeptide repeat (TPR), Annexin, HEAT, ARM, etc. CONCLUSIONS: The graph based analysis of protein structures, along with domain information such as the organization of the secondary structure architecture provides a computationally efficient approach for the identification of structural repeats.

Abstract

In recent studies it has been shown that graph representation of protein structures is capable of capturing the 3-dimensional fold of the protein very well, thus providing a computationally efficient approach for protein structure analysis. Centrality measures are generally used to identify the relative importance of a node in the network. Here we demonstrate a novel application of centrality analysis: to identify tandemly repeated structural motifs in 3-d protein structures. This is done by analyzing the profile of various centrality measures in the repeat region. The comparative analysis of five centrality measures based on local connectivity, shortest paths, principal eigen spectra and feedback centrality is presented on proteins containing contiguous ankyrin structural motifs to identify which centrality measure best captures the repetitive pattern of ankyrin. We observe that principal eigen spectra of the adjacency matrix and Katz status index, both exhibit a distinct profile for the ankyrin motif capturing its characteristic anti-parallel helix-turn-helix fold. No such conserved pattern was observed in the repeat regions of equivalent random networks, suggesting that the conserved pattern arises from the 3d fold of the structural motif.

Abstract

BACKGROUND: Repetition of super secondary structure is a common phenomenon, especially in higher eukaryotic organism. The copy number and assembly of these repeating units are responsible for diverse protein-protein interactions, and consequently, defects in repeat proteins are linked to many diseases. The variation within the repeat units makes their identification difficult at the sequence level and structure based approaches are desired. Since most structure based methods employ computationally intensive structure-structure alignment, we propose a computationally efficient structure-based approach for the identification of repeats using concepts from graph theory. RESULTS: The three-dimensional topology of protein structures is known to be well captured by protein contact graphs. The connectivity information in a graph is represented in the adjacency matrix and the eigenspectra of the adjacency matrix depicts the topological importance of each node to the connectivity of the graph. In our earlier work, we observed that the principal eigenspectra of the adjacency matrix well captures the tandemly repeated structural motifs. Here we propose an algorithm for the identification of tandemly repeated structural motif using graph properties and secondary structure information from STRIDE database. The algorithm begins by first identifying the length of repeat motif by analyzing the periodicity of peaks in eigenvector centrality. The repeat boundaries are then identified by superposing the contiguous repeats and extending on either side of the peak regions to the start/end of the secondary structure elements and checking for periodicity of the secondary structure architecture in the identified repeat regions. Thus, using the secondary structure annotation helps in refining the boundaries of the repeat regions and to discard false positives. We have tested the algorithm for identifying various structural repeats such as HEAT, WD, Ankyrin (ANK), Tetratricopeptide repeat (TPR), Leucine rich repeat (LRR), etc. with different super secondary structure motif, ranging from all alpha, to all beta to a mixed topology such alpha-turn-alpha, beta-alpha, etc. The predictions are in agreement with annotation in UniProt database. CONCLUSIONS: The graph based analysis of protein structures, along with domain information such as the organization of the secondary structure elements provides a computationally efficient approach for the identification of structural repeats.

Abstract

Reliable message transmission is a fundamental problem in distributed communication networks. Of late, several interesting results have been obtained by modelling the network as a directed graph. An important result among them was due to Bhavani et al. [Unconditionally reliable message transmission in directed networks, 18 in: SODA’08: Proceedings of the Nineteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SIAM, Philadelphia, PA, USA, 2008, pp. 1048–1055], where it was shown that if a negligible probability of error is allowed, the connectivity required in a synchronous network for unconditionally reliable message transmission (URMT) is significantly reduced. We refer to the variant of URMT studied by them as Monte Carlo URMT; here the receiver is allowed to output an incorrect message with small probability. Another interesting variant which has received little attention so far in the literature is the Las Vegas variant, where the receiver is allowed to abort with a small probability, but never to output an incorrect message. We show that the minimum connectivity requirements for the existence of Las Vegas URMT protocols over synchronous networks are the same as that of Monte Carlo URMT protocols over asynchronous networks—a surprising equivalence between two very different models. Furthermore, we show that the higher connectivity requirements of Las Vegas URMT over asynchronous networks match exactly with that of zero-error (perfect) variant over (a)synchronous networks. We also show that there exists a family of graphs where in the number of critical edges for the ‘easier’ randomized variants are asymptotically higher than that for the perfect variants. Hence, our results demonstrate an interesting interplay between (im)perfectness, synchrony and connectivity for the case of reliable message transmission.

Abstract

In this paper, we show that certain phrases although not present in a given question/query, play a very important role in answering the question. Exploring the role of such phrases in answering questions not only reduces the dependency on matching question phrases for extracting answers, but also improves the quality of the extracted answers. Here matching question phrases means phrases which co-occur in given question and candidate answers. To achieve the above discussed goal, we introduce a bigram-based word graph model populated with semantic and topical relatedness of terms in the given document. Next, we apply an improved version of ranking with a prior-based approach, which ranks all words in the candidate document with respect to a set of root words (ie non-stopwords present in the question and in the candidate document). As a result, terms logically related to the root words are scored higher than terms that are not related to the root words. Experimental results show that our devised system performs better than state-of-the-art for the task of answering Why-questions.

Abstract

Summarization mainly provides the major topics or theme of document in limited number of words. However, in extract summary we depend upon extracted sentences, while in abstract summary, each summary sentence may contain concise information from multiple sentences. The major facts which affect the quality of summary are: (1) the way of handling noisy or less important terms in document, (2) utilizing information content of terms in document (as, each term may have different levels of importance in document) and (3) finally, the way to identify the appropriate thematic facts in the form of summary. To reduce the effect of noisy terms and to utilize the information content of terms in the document, we introduce the graph theoretical model populated with semantic and statistical importance of terms. Next, we introduce the concept of weighted minimum vertex cover which helps us in identifying the most representative and thematic facts in the document. Additionally, to generate abstract summary, we introduce the use of vertex constrained shortest path based technique, which uses minimum vertex cover related information as valuable resource. Our experimental results on DUC-2001 and DUC-2002 dataset show that our devised system performs better than baseline systems.

Abstract

In this work, we propose a solution with sub-logarithmic query time for counting the number of maximal points in an axis parallel query rectangle. The problem has been previously studied in [3]and [5]. To the best of our knowledge, this is the first sub-logarithmic query time solution for the problem. Our model of computation is the word RAM with word size of Θ(logn) bits.

Abstract

Fuzzy Association Rule Mining (ARM) has been extensively used in relational or transactional datasets having less-to-medium number of attributes/dimensions. The mined fuzzy association rules (patterns) are not only used for manual analysis by domain experts, but are also leveraged to drive further mining tasks like classification and clustering which automate decision-making. Such fuzzy association rules can also be derived from high-dimensional numerical datasets, like image datasets, in order to train fuzzy associative classifiers or clustering algorithms. Traditional Fuzzy ARM algorithms are not able to mine rules from them efficiently, since such algorithms are meant to deal with datasets with relatively much less number of attributes/dimensions. Hence, in this paper we propose FAR-HD which is a Fuzzy ARM algorithm designed specifically for large high-dimensional datasets. FAR-HD processes fuzzy frequent itemsets in a DFS manner using a two-phased multiple-partition tidlist-based strategy. It also uses a byte-vector representation of tidlists, with the tidlists stored in the main memory in a compressed form (using a fast generic compression method). Additionally, FAR-HD uses Fuzzy Clustering to convert each numerical vector of the original input dataset to a fuzzy-cluster-based representation, which is ultimately used for the actual Fuzzy ARM process. FAR-HD has been compared experimentally with Fuzzy Apriori (7-15 times faster), which is the most popular Fuzzy ARM algorithm, and a Fuzzy ARM algorithm (1.1-4 times faster) which we proposed earlier and which is designed to work with very large but traditional (with fewer attributes) datasets.

Abstract

In this paper, we explore the use of Bessel features derived from speech utterances, to develop Gaussian mixture speaker models for text independent Speaker Identification. The proposed approach to speaker identification is based on existing methods that employ Gaussian mixtures for the modeling of speakers. However, we have developed the speaker models from the Bessel features derived from the speech utterances, as an alternative to Mel-frequency cepstral coefficients for developing the speaker models. The proposed approach is tested on two databases of ten and twenty speakers respectively and their performance is evaluated. Finally, we have made some suggestions for future work involving the use of Bessel features for text independent speaker identification and verification.