Functions for S. cerevisiae Swd2p in 3' end formation of specific mRNAs and snoRNAs and global histone 3 lysine 4 methylation

The Saccharomyces cerevisiae WD-40 repeat protein Swd2p associates with two functionally distinct multiprotein complexes: the cleavage and polyadenylation factor (CPF) that is involved in pre-mRNA and snoRNA 3′ end formation and the SET1 complex (SET1C) that methylates histone 3 lysine 4. Based on bioinformatic analysis we predict a seven-bladed β-propeller structure for Swd2p proteins. Northern, transcriptional run-on and in vitro 3′ end cleavage analyses suggest that temperature sensitive swd2 strains were defective in 3′ end formation of specific mRNAs and snoRNAs. Protein–protein interaction studies support a role for Swd2p in the assembly of 3′ end formation complexes. Furthermore, histone 3 lysine 4 di-and tri-methylation were adversely affected and telomeres were shortened in swd2 mutants. Underaccumulation of the Set1p methyltransferase accounts for the observed loss of SET1C activity and suggests a requirement for Swd2p for the stability or assembly of this complex. We also provide evidence that the roles of Swd2p as component of CPF and SET1C are functionally independent. Taken together, our results establish a dual requirement for Swd2p in 3′ end formation and histone tail modification.

Penalty functions over a cartesian product of lattices

In this work we present the concept of penalty function over a Cartesian product of lattices. To build these mappings, we make use of restricted dissimilarity functions and distances between fuzzy sets. We also present an algorithm that extends the weighted voting method for a fuzzy preference relation.

Restricted dissimilarity functions and penalty functions

In this work we introduce the definition of restricted dissimilarity functions and we link it with some other notions, such as metrics. In particular, we also show how restricted dissimilarity functions can be used to build penalty functions.

Brain MRI thresholding using incomparability and overlap functions

In this work we present a new image thresholding algorithm for the segmentation of MRI brain images into two classes: gray matter and white matter. The proposed algorithm is based on the concept of incomparability proposed by Fodor and Roubens for fuzzy preference relations. We test our algorithm for local and global segmentation of brain images. We proof that global segmentation performs better results than local segmentation and improves the results obtained by other thresholding algorithm.

Efficient subwindow search with submodular score functions

Subwindow search aims to find the optimal subimage which maximizes the score function of an object to be detected. After the development of the branch and bound (B&B) method called Efficient Subwindow Search (ESS), several algorithms (IESS [2], AESS [2], ARCS [3]) have been proposed to improve the performance of ESS. For nn images, IESS's time complexity is bounded by O(n³) which is better than ESS, but only applicable to linear score functions. Other work shows that Monge properties can hold in subwindow search and can be used to speed up the search to O(n³), but only applies to certain types of score functions. In this paper we explore the connection between submodular functions and the Monge property, and prove that sub-modular score functions can be used to achieve O(n³) time complexity for object detection. The time complexity can be further improved to be sub-cubic by applying B&B methods on row interval only, when the score function has a multivariate submodular bound function. Conditions for sub-modularity of common non-linear score functions and multivariate submodularity of their bound functions are also provided, and experiments are provided to compare the proposed approach against ESS and ARCS for object detection with some nonlinear score functions.

Hierarchical topical segmentation in instructional films based on cinematic expressive functions

In this paper, we propose a novel solution for segmenting an instructional video into hierarchical topical sections. Incorporating the knowledge of education-oriented film theory with our previous study of expressive functions namely the content density and the thematic functions, we develop an algorithm to effectively structuralize an instructional video into a two-tiered hierarchy of topical sections at the main and sub-topic levels. Our experimental results on a set of ten industrial instructional videos demonstrate the validity of the detection scheme.

On the extraction of thematic and dramatic functions of content in educational videos

In this paper, we propose novel computational models for the extraction of high level expressive constructs related to, namely thematic and dramatic functions of the content shown in educational and training videos. Drawing on the existing knowledge of film theory, and media production rules and conventions used by the filmmakers. we hypothesize key aesthetic elements contributing to convey these functions of the content. Computational models to extract them are then formulated and their performance evaluated on a set of ten educational and training videos is presented.

Metadiscourse functions in English and Persian sociology articles : a study in contrastive rhetoric

This study on contrastive rhetoric reports on metadiscourse functions in sociology articles in Persian and English. The results have revealed a higher number of metadiscourse elements in the English texts. Among the different metadiscourse elements used, text connectors are the most frequently employed in both languages. Modality markers are the second most frequent in both languages although the English writers used nearly twice the number of these markers. Overall, it is found that the frequency of textual metadiscourse markers is greater than the interpersonal markers in both language samples. It was further revealed that the Persian writers of sociology texts are less interested in explicitly orienting the readers and some of the main points in an article, especially in the concluding section, are left for the readers to infer. This, we believe, is the result of less reliance on academic writing in the educational system of the country. Instead, the Iranians are largely encouraged to employ a flowery language and rhetoric to decorate their writing in their school years which makes them less attentive of their readers.

Predicting protein functions from PPI networks using functional aggregation

Predicting protein functions computationally from massive protein–protein interaction (PPI) data generated by high-throughput technology is one of the challenges and fundamental problems in the post-genomic era. Although there have been many approaches developed for computationally predicting protein functions, the mutual correlations among proteins in terms of protein functions have not been thoroughly investigated and incorporated into existing prediction methods, especially in voting based prediction methods. In this paper, we propose an innovative method to predict protein functions from PPI data by aggregating the functional correlations among relevant proteins using the Choquet-Integral in fuzzy theory. This functional aggregation measures the real impact of each relevant protein function on the final prediction results, and reduces the impact of repeated functional information on the prediction. Accordingly, a new protein similarity and a new iterative prediction algorithm are proposed in this paper. The experimental evaluations on real PPI datasets demonstrate the effectiveness of our method.