Conservation and diversification in homeodomain-DNA interactions: a comparative genetic analysis.

Nearly all metazoan homeodomains (HDs) possess DNA binding targets that are related by the presence of a TAAT sequence. We use an in vitro genetic DNA binding site selection assay to refine our understanding of the amino acid determinants for the recognition of the TAAT site. Superimposed upon the conserved ability of metazoan HDs to recognize a TAAT core is a difference in their preference for the bases that lie immediately 3' to it. Amino acid position 50 of the HD has been shown to discriminate among these base pairs, and structural studies have suggested that water-mediated hydrogen bonds and van der Waals contacts underlie for this ability. Here, we show that each of six amino acids tested at position 50 can confer a distinct DNA binding specificity.

Structure of the gene for porcine peptide antibiotic PR-39, a cathelin gene family member: comparative mapping of the locus for the human peptide antibiotic FALL-39.

PR-39 is a porcine 39-aa peptide antibiotic composed of 49% proline and 24% arginine, with an activity against Gram-negative bacteria comparable to that of tetracycline. In Escherichia coli, it inhibits DNA and protein synthesis. PR-39 was originally isolated from pig small intestine, but subsequent cDNA cloning showed that the gene is expressed in the bone marrow. The open reading frame of the clone showed that PR-39 is made as 173-aa precursor whose proregion belongs to the cathelin family. The PR39 gene, which is rather compact and spans only 1784 bp has now been sequenced. The coding information is split into four exons. The first exon contains the signal sequence of 29 residues and the first 37 residues of the cathelin propart. Exons 2 and 3 contain only cathelin information, while exon 4 codes for the four C-terminal cathelin residues and the mature PR-39 peptide extended by three residues. The sequenced upstream region (1183 bp) contains four potential recognition sites for NF-IL6 and three for APRF, transcription factors known to regulate genes for both cytokines and acute phase response factors. Genomic hybridizations revealed a fairly high level of restriction fragment length polymorphism and indicated that there are at least two copies of the PR39 gene in the pig genome. PR39 was mapped to pig chromosome 13 by linkage and in situ hybridization mapping. The gene for the human peptide antibiotic FALL-39 (also a member of the cathelin family) was mapped to human chromosome 3, which is homologous to pig chromosome 13.

The International Human Rights Policies of New Democracies: Brazil and Chile in Comparative Perspective

Since the beginning of the 1990s, the majority of Latin American states have attempted to incorporate in some way or another human rights concern into their respective foreign policies, highlighting a history of human rights abuses and the return of democratic political rule as a trigger for galvanizing a commitment to assist in preventing such violations in other countries. Yet, while human rights have come to play a non-trivial role in the contemporary foreign policy of many Latin American states, there is great diversity in the ways and the extent to which they go about incorporating human rights concerns into their foreign policies. Explaining the diversity of human rights foreign policies of new Latin American democracies is at the heat of this project. The main research questions are the following: Why do new democracies incorporate human rights into their foreign policies? And what explains the different international human rights policies of new democracies? To answer these questions, this research compares the human rights foreign policies of Chile and Brazil for over two decades starting from their respective transitions to democracy. The study argues that states commitment to international human rights is the result of the intersection of domestic and international influences. At the international level, the search for international legitimacy and the desire for recognition and credibility affected the adoption of international human rights in both cases but with different degrees of impact. International values and pressures by themselves, while necessary, are an insufficient condition for human rights initiatives perceived to have not insubstantial political, economic or strategic costs. New democracies will be more or less likely to actively include human rights in their international policies depending on the following four domestic conditions: political leadership legitimizing the inclusion of human rights into a state's policies, civil society groups connected to international human rights advocacy networks with a capacity to influencing the foreign policy decisions of their government, and the Foreign Ministry's attitudes towards international human rights and the degree of influence it exercises over the outcome of the foreign policy process.

Recognition of online handwritten music symbols

Paper submitted to MML 2013, 6th International Workshop on Machine Learning and Music, Prague, September 23, 2013.

A Comparative Study of Registration Methods for RGB-D Video of Static Scenes

The use of RGB-D sensors for mapping and recognition tasks in robotics or, in general, for virtual reconstruction has increased in recent years. The key aspect of these kinds of sensors is that they provide both depth and color information using the same device. In this paper, we present a comparative analysis of the most important methods used in the literature for the registration of subsequent RGB-D video frames in static scenarios. The analysis begins by explaining the characteristics of the registration problem, dividing it into two representative applications: scene modeling and object reconstruction. Then, a detailed experimentation is carried out to determine the behavior of the different methods depending on the application. For both applications, we used standard datasets and a new one built for object reconstruction.

Comparative regional perspectives: the Bologna process and higher education attainment. Jean Monnet/Robert Schumann Paper Series Vol 13 No. 11, August 2013

Since 1999, countries have voluntarily chosen to reform their higher education systems to join the European Higher Education Area. This paper compares Bologna Process implementation across four regions within the European Union. While there are 47 countries participating in the Bologna Process, this paper uses statistical analysis to consider 25 of the 28 EU Member States. The time period of analysis is 2000-2011, prior to Croatia’s accession to the EU on 1 July 2013. Across Europe there are inter-regional differences in how the Bologna Process has been implemented and in the political economy contexts that influence higher education reform for policy convergence. There are three explanatory variables in the political economy context: 1. competitive economic pressures and globalization 2. domestic politics at the national level 3. leadership from the supranational European Union that socially constructs regional norms Tertiary education attainment is the dependent variable of interest in this research. The objective of 40%, for 30-34 year olds, is Europe 2020 benchmark target. There are additional higher education reform criteria encompassed in the Bologna Process. These criteria concern Credit and Degree Structure, Quality Assurance, and Recognition of academic degrees among countries in the EHEA. This tertiary education attainment variable, which is of interest in this paper, does not capture the entire implementation process. Nevertheless, it is a measure of one important indicator of success in providing higher education access to populations within the context of democratic governance. This research finds that statistically GDP Per Capita is the most significant variable in relationship to tertiary education attainment across four regional areas in the European Union.

Modeling the peptide-T cell receptor interaction by the comparative molecular similarity indices analysis-soft independent modeling of class analogy technique

A set of 38 epitopes and 183 non-epitopes, which bind to alleles of the HLA-A3 supertype, was subjected to a combination of comparative molecular similarity indices analysis (CoMSIA) and soft independent modeling of class analogy (SIMCA). During the process of T cell recognition, T cell receptors (TCR) interact with the central section of the bound nonamer peptide; thus only positions 4−8 were considered in the study. The derived model distinguished 82% of the epitopes and 73% of the non-epitopes after cross-validation in five groups. The overall preference from the model is for polar amino acids with high electron density and the ability to form hydrogen bonds. These so-called “aggressive” amino acids are flanked by small-sized residues, which enable such residues to protrude from the binding cleft and take an active role in TCR-mediated T cell recognition. Combinations of “aggressive” and “passive” amino acids in the middle part of epitopes constitute a putative TCR binding motif

A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data

This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space (p) is much larger than the number of observations (n). Specifically, we evaluate massive gene expression microarray cancer data where the ratio κ is less than one. We explore the statistical and computational challenges inherent in these high dimensional low sample size (HDLSS) problems and present statistical machine learning methods used to tackle and circumvent these difficulties. Regularization and kernel algorithms were explored in this research using seven datasets where κ < 1. These techniques require special attention to tuning necessitating several extensions of cross-validation to be investigated to support better predictive performance. While no single algorithm was universally the best predictor, the regularization technique produced lower test errors in five of the seven datasets studied.

An incremental multilinear system for human face learning and recognition

This dissertation establishes a novel system for human face learning and recognition based on incremental multilinear Principal Component Analysis (PCA). Most of the existing face recognition systems need training data during the learning process. The system as proposed in this dissertation utilizes an unsupervised or weakly supervised learning approach, in which the learning phase requires a minimal amount of training data. It also overcomes the inability of traditional systems to adapt to the testing phase as the decision process for the newly acquired images continues to rely on that same old training data set. Consequently when a new training set is to be used, the traditional approach will require that the entire eigensystem will have to be generated again. However, as a means to speed up this computational process, the proposed method uses the eigensystem generated from the old training set together with the new images to generate more effectively the new eigensystem in a so-called incremental learning process. In the empirical evaluation phase, there are two key factors that are essential in evaluating the performance of the proposed method: (1) recognition accuracy and (2) computational complexity. In order to establish the most suitable algorithm for this research, a comparative analysis of the best performing methods has been carried out first. The results of the comparative analysis advocated for the initial utilization of the multilinear PCA in our research. As for the consideration of the issue of computational complexity for the subspace update procedure, a novel incremental algorithm, which combines the traditional sequential Karhunen-Loeve (SKL) algorithm with the newly developed incremental modified fast PCA algorithm, was established. In order to utilize the multilinear PCA in the incremental process, a new unfolding method was developed to affix the newly added data at the end of the previous data. The results of the incremental process based on these two methods were obtained to bear out these new theoretical improvements. Some object tracking results using video images are also provided as another challenging task to prove the soundness of this incremental multilinear learning method.

Datasets, features, learning, and models in visual recognition

Visual recognition is a fundamental research topic in computer vision. This dissertation explores datasets, features, learning, and models used for visual recognition. In order to train visual models and evaluate different recognition algorithms, this dissertation develops an approach to collect object image datasets on web pages using an analysis of text around the image and of image appearance. This method exploits established online knowledge resources (Wikipedia pages for text; Flickr and Caltech data sets for images). The resources provide rich text and object appearance information. This dissertation describes results on two datasets. The first is Berg’s collection of 10 animal categories; on this dataset, we significantly outperform previous approaches. On an additional set of 5 categories, experimental results show the effectiveness of the method. Images are represented as features for visual recognition. This dissertation introduces a text-based image feature and demonstrates that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the Internet. Image tags are noisy. The method obtains the text features of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say, a new viewing direction) must rely on its visual examples. This text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy, they are more stable when appearance changes. The performance of this feature is tested using PASCAL VOC 2006 and 2007 datasets. This feature performs well; it consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small. With more and more collected training data, computational cost becomes a bottleneck, especially when training sophisticated classifiers such as kernelized SVM. This dissertation proposes a fast training algorithm called Stochastic Intersection Kernel Machine (SIKMA). This proposed training method will be useful for many vision problems, as it can produce a kernel classifier that is more accurate than a linear classifier, and can be trained on tens of thousands of examples in two minutes. It processes training examples one by one in a sequence, so memory cost is no longer the bottleneck to process large scale datasets. This dissertation applies this approach to train classifiers of Flickr groups with many group training examples. The resulting Flickr group prediction scores can be used to measure image similarity between two images. Experimental results on the Corel dataset and a PASCAL VOC dataset show the learned Flickr features perform better on image matching, retrieval, and classification than conventional visual features. Visual models are usually trained to best separate positive and negative training examples. However, when recognizing a large number of object categories, there may not be enough training examples for most objects, due to the intrinsic long-tailed distribution of objects in the real world. This dissertation proposes an approach to use comparative object similarity. The key insight is that, given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. This dissertation develops a regularized kernel machine algorithm to use this category dependent similarity regularization. Experiments on hundreds of categories show that our method can make significant improvement for categories with few or even no positive examples.

Comparative study of human age estimation based on hand-crafted and deep face features

In the past few years, human facial age estimation has drawn a lot of attention in the computer vision and pattern recognition communities because of its important applications in age-based image retrieval, security control and surveillance, biomet- rics, human-computer interaction (HCI) and social robotics. In connection with these investigations, estimating the age of a person from the numerical analysis of his/her face image is a relatively new topic. Also, in problems such as Image Classification the Deep Neural Networks have given the best results in some areas including age estimation. In this work we use three hand-crafted features as well as five deep features that can be obtained from pre-trained deep convolutional neural networks. We do a comparative study of the obtained age estimation results with these features.

Comparative Electrochemical Study of Unsubstituted and Substituted Bis(phthalocyaninato) Rare Earth(III) Complexes

The electrochemistry of homoleptic substituted phthalocyaninato rare earth double-decker complexes M(TBPc)2 and M(OOPc)2 [M = Y, La...Lu except Pm; H2TBPc = 3(4),12(13),21(22),30(31)-tetra-tert-butylphthalocyanine, H2OOPc = 3,4,12,13,21,22,30,31-octakis(octyloxy)phthalocyanine] has been comparatively studied by cyclic voltammetry (CV) and differential pulse voltammetry (DPV) in CH2Cl2 containing 0.1 M tetra-n-butylammonium perchlorate (TBAP). Two quasi-reversible one-electron oxidations and three or four quasi-reversible one-electron reductions have been revealed for these neutral double-deckers of two series of substituted complexes, respectively. For comparison, unsubstituted bis(phthalocyaninato) rare earth analogues M(Pc)2 (M = Y, La...Lu except Pm; H2Pc = phthalocyanine) have also been electrochemically investigated. Two quasi-reversible one-electron oxidations and up to five quasi-reversible one-electron reductions have been revealed for these neutral double-decker compounds. The three bis(phthalocyaninato)cerium compounds display one cerium-centered redox wave between the first ligand-based oxidation and reduction. The half-wave potentials of the first and second oxidations and first reduction for double-deckers of the tervalent rare earths depend on the size of the metal center. The difference between the redox potentials of the second and third reductions for MIII(Pc)2, which represents the potential difference between the first oxidation and first reduction of [MIII(Pc)2]−, lies in the range 1.08−1.37 V and also gradually diminishes along with the lanthanide contraction, indicating enhanced π−π interactions in the double-deckers connected by the smaller, lanthanides. This corresponds well with the red-shift of the lowest energy band observed in the electronic absorption spectra of reduced double-decker [MIII(Pc′)2]− (Pc′ = Pc, TBPc, OOPc).