An fMRI Normative Database for Connectivity Networks Using One-Class Support Vector Machines

The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal pattern, and intersubject variability-two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one-class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy Subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored. Hum Brain Mapp 30:1068-1076, 2009. (C) 2008 Wiley-Liss. Inc.

Approximate polytope ensemble for one-class classification

In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, the decision whether a point belongs to the convex hull model in high dimensional spaces is approximated by means of random projections and an ensemble decision process. Finally, a tiling strategy is proposed in order to model non-convex structures. Experimental results show that the proposed strategy is significantly better than state of the art one-class classification methods on over 200 datasets.

Spontaneous Speech and Emotional Response modeling based on One-class classifier oriented to Alzheimer Disease diagnosis

The purpose of our project is to contribute to earlier diagnosis of AD and better estimates of its severity by using automatic analysis performed through new biomarkers extracted from non-invasive intelligent methods. The methods selected in this case are speech biomarkers oriented to Sponta-neous Speech and Emotional Response Analysis. Thus the main goal of the present work is feature search in Spontaneous Speech oriented to pre-clinical evaluation for the definition of test for AD diagnosis by One-class classifier. One-class classifi-cation problem differs from multi-class classifier in one essen-tial aspect. In one-class classification it is assumed that only information of one of the classes, the target class, is available. In this work we explore the problem of imbalanced datasets that is particularly crucial in applications where the goal is to maximize recognition of the minority class as in medical diag-nosis. The use of information about outlier and Fractal Dimen-sion features improves the system performance.

Comparison of Lagrangian Bounds for One Class of Generalized Assignment Problems

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Discernment of bee pollen loads using computer vision and one-class classification techniques

In this paper, we propose a system for authenticating local bee pollen against fraudulent samples using image processing and classification techniques. Our system is based on the colour properties of bee pollen loads and the use of one-class classifiers to reject unknown pollen samples. The latter classification techniques allow us to tackle the major difficulty of the problem, the existence of many possible fraudulent pollen types. Also presented is a multi-classifier model with an ambiguity discovery process to fuse the output of the one-class classifiers. The method is validated by authenticating Spanish bee pollen types, the overall accuracy of the final system of being 94%. Therefore, the system is able to rapidly reject the non-local pollen samples with inexpensive hardware and without the need to send the product to the laboratory.

One Class of Stochastic Local Search Algorithms

Accelerated probabilistic modeling algorithms, presenting stochastic local search (SLS) technique, are considered. General algorithm scheme and specific combinatorial optimization method, using “golden section” rule (GS-method), are given. Convergence rates using Markov chains are received. An overview of current combinatorial optimization techniques is presented.

Labeling large scale social media data using budget-driven One-class SVM classification

The social media classification problems draw more and more attention in the past few years. With the rapid development of Internet and the popularity of computers, there is astronomical amount of information in the social network (social media platforms). The datasets are generally large scale and are often corrupted by noise. The presence of noise in training set has strong impact on the performance of supervised learning (classification) techniques. A budget-driven One-class SVM approach is presented in this thesis that is suitable for large scale social media data classification. Our approach is based on an existing online One-class SVM learning algorithm, referred as STOCS (Self-Tuning One-Class SVM) algorithm. To justify our choice, we first analyze the noise-resilient ability of STOCS using synthetic data. The experiments suggest that STOCS is more robust against label noise than several other existing approaches. Next, to handle big data classification problem for social media data, we introduce several budget driven features, which allow the algorithm to be trained within limited time and under limited memory requirement. Besides, the resulting algorithm can be easily adapted to changes in dynamic data with minimal computational cost. Compared with two state-of-the-art approaches, Lib-Linear and kNN, our approach is shown to be competitive with lower requirements of memory and time.

Evaluation of Fly Ash Concrete Durability Containing Class II Durability Aggregates, July 1986

Fly ash was used in this evaluation study to replace 15% of the cement in Class C-3 concrete paving mixes. One Class "c" ash from Iowa approved sources was examined in each mix. Substitution rate was based on 1 to 1 basis, for each pound of cement removed 1.0 pound of ash was added. The freeze/thaw durability of the concrete studied was not adversely affected by the presence of fly ash. This study reveals that the durability of the concrete test specimens made with Class II durability aggregates was slightly increased in all cases by the substitution of cement with 15% Class "c" fly ash. In all cases durability factors either remained the same or slightly improved except for one case where the durability factor decreased from 36 to 34. The expansion decreased in all cases.

Evaluation of Fly Ash Concrete Durability Containing Class II Durability Aggregates, July 1986

Fly ash was used in this evaluation study to replace 15% of the cement in Class C-3 concrete paving mixes. One Class "c" ash from Iowa approved sources was examined in each mix. Substitution rate was based on 1 to 1 basis, for each pound of cement removed 1.0 pound of ash was added. The freeze/thaw durability of the concrete studied was not adversely affected by the presence of fly ash. This study reveals that the durability of the concrete test specimens made with Class II durability aggregates was slightly increased in all cases by the substitution of cement with 15% Class "c" fly ash. In all cases durability factors either remained the same or slightly improved except for one case where the durability factor decreased from 36 to 34. The expansion decreased in all cases.

Morphometric analysis of treatment effects of the Balters bionator in growing Class II patients

Objective: To investigate the effects of the standard (Class II) Balters bionator in growing patients with Class II malocclusion with mandibular retrusion by using morphometrics (thin-plate spline [TPS] analysis). Materials and Methods: Thirty-one Class II patients (17 male and 14 female) were treated with the Balters bionator (bionator group). Mean age at the start of treatment (T0) was 10.3 years, while it was 13 years at the end of treatment (T1). Mean treatment time was 2 years and 2 months. The control group consisted of 22 subjects (14 male and 8 female) with untreated Class II malocclusion. Mean age at T0 was 10.2 years, while it was 12.2 years at T1. The observation period lasted 2 years on average. TPS analysis evaluated statistical (permutation tests) differences in the craniofacial shape and size between the bionator and control groups. Results: Through TPS analysis (deformation grids) the bionator group showed significant shape changes in the mandible that could be described as a mandibular forward and downward displacement. The control group showed no statistically significant differences in the correction of Class II malocclusion. Conclusions: Bionator appliance is able to induce significant mandibular shape changes that lead to the correction of Class II dentoskeletal disharmony. © 2013 by The EH Angle Education and Research Foundation, Inc.

Enamel matrix protein derivative plus synthetic bone substitute for the treatment of mandibular Class II furcation defects: a case series

Objectives: The aim of this study is to report on the treatment of mandibular Class II furcation defects with enamel matrix protein derivative (EMD) combined with a beta TCP/HA (beta-tricalcium phosphate/hydroxyapatite) alloplastic material. Method and Materials: Thirteen patients were selected. All patients were nonsmokers, systemically healthy, and diagnosed with chronic periodontitis; had not taken medications known to interfere with periodontal tissue health and healing; presented one Class II mandibular furcation defect with horizontal probing equal to or greater than 4 mm at buccal site. The clinical parameters evaluated were probing depth (PD), relative gingival margin position (RGMP), relative vertical clinical attachment level (RVCAL), and relative horizontal clinical attachment level (RHCAL). A paired Student t test was used to detect differences between the baseline and 6-month measurements, with the level of significance of .05. Results: After 6 months, the treatment produced a statistically significant reduction in PD and a significant gain in RVCAL and RHCAL, but no observable change in RGMP. RVCAL ranged from 13.77 (+/- 1.31) at baseline to 12.15 (+/- 1.29) after 6 months, with a mean change of -1.62 +/- 1.00 mm (P<.05). RHCAL ranged from 5.54 (+/- 0.75) to 2.92 (+/- 0.92), with a mean change of -2.62 +/- 0.63 mm (P<.05). After 6 months, 76.92% of the patients improved their diagnosis to Class I furcation defects while 23.08% remained as Class II. Conclusion: The present study has shown that positive clinical results may be expected from the combined treatment of Class II furcation defects with EMD and beta TCP/HA, especially considering the gain of horizontal attachment level. Despite this result, controlled clinical studies are needed to confirm our outcomes.

Mining for Class-Specific Motifs in Protein Sequence Classification

Background: In protein sequence classification, identification of the sequence motifs or n-grams that can precisely discriminate between classes is a more interesting scientific question than the classification itself. A number of classification methods aim at accurate classification but fail to explain which sequence features indeed contribute to the accuracy. We hypothesize that sequences in lower denominations (n-grams) can be used to explore the sequence landscape and to identify class-specific motifs that discriminate between classes during classification. Discriminative n-grams are short peptide sequences that are highly frequent in one class but are either minimally present or absent in other classes. In this study, we present a new substitution-based scoring function for identifying discriminative n-grams that are highly specific to a class. Results: We present a scoring function based on discriminative n-grams that can effectively discriminate between classes. The scoring function, initially, harvests the entire set of 4- to 8-grams from the protein sequences of different classes in the dataset. Similar n-grams of the same size are combined to form new n-grams, where the similarity is defined by positive amino acid substitution scores in the BLOSUM62 matrix. Substitution has resulted in a large increase in the number of discriminatory n-grams harvested. Due to the unbalanced nature of the dataset, the frequencies of the n-grams are normalized using a dampening factor, which gives more weightage to the n-grams that appear in fewer classes and vice-versa. After the n-grams are normalized, the scoring function identifies discriminative 4- to 8-grams for each class that are frequent enough to be above a selection threshold. By mapping these discriminative n-grams back to the protein sequences, we obtained contiguous n-grams that represent short class-specific motifs in protein sequences. Our method fared well compared to an existing motif finding method known as Wordspy. We have validated our enriched set of class-specific motifs against the functionally important motifs obtained from the NLSdb, Prosite and ELM databases. We demonstrate that this method is very generic; thus can be widely applied to detect class-specific motifs in many protein sequence classification tasks. Conclusion: The proposed scoring function and methodology is able to identify class-specific motifs using discriminative n-grams derived from the protein sequences. The implementation of amino acid substitution scores for similarity detection, and the dampening factor to normalize the unbalanced datasets have significant effect on the performance of the scoring function. Our multipronged validation tests demonstrate that this method can detect class-specific motifs from a wide variety of protein sequence classes with a potential application to detecting proteome-specific motifs of different organisms.

Ctp1 and the MRN-complex are required for endonucleolytic Rec12 removal with release of a single class of oligonucleotides in fission yeast

DNA double-strand breaks (DSBs) are formed during meiosis by the action of the topoisomerase-like Spo11/Rec12 protein, which remains covalently bound to the 5' ends of the broken DNA. Spo11/Rec12 removal is required for resection and initiation of strand invasion for DSB repair. It was previously shown that budding yeast Spo11, the homolog of fission yeast Rec12, is removed from DNA by endonucleolytic cleavage. The release of two Spo11 bound oligonucleotide classes, heterogeneous in length, led to the conjecture of asymmetric cleavage. In fission yeast, we found only one class of oligonucleotides bound to Rec12 ranging in length from 17 to 27 nucleotides. Ctp1, Rad50, and the nuclease activity of Rad32, the fission yeast homolog of Mre11, are required for endonucleolytic Rec12 removal. Further, we detected no Rec12 removal in a rad50S mutant. However, strains with additional loss of components localizing to the linear elements, Hop1 or Mek1, showed some Rec12 removal, a restoration depending on Ctp1 and Rad32 nuclease activity. But, deletion of hop1 or mek1 did not suppress the phenotypes of ctp1Delta and the nuclease dead mutant (rad32-D65N). We discuss what consequences for subsequent repair a single class of Rec12-oligonucleotides may have during meiotic recombination in fission yeast in comparison to two classes of Spo11-oligonucleotides in budding yeast. Furthermore, we hypothesize on the participation of Hop1 and Mek1 in Rec12 removal.

Three essays on dual-class stock structure

Dual-class stock structure is characterized by the separation of voting rights and cash flow rights. The departure from a common "one share-one vote" configuration creates ideal conditions for conflicts of interest and agency problems between controlling insiders (the holders of voting rights) and remaining shareholders. The owners of voting rights have the opportunity to extract private benefits and act in their personal interest; as a result, dual-class firms are often perceived to have low transparency and high information asymmetry. This dissertation investigates the quality of information and the information environment of firms with two classes of stock. The first essay examines the quality of information by studying accruals in dual-class firms in comparison to firms with only one class of stock. The results suggest that the quality of accruals is better in dual-class firms than in single-class firms. In addition, the difference in the quality of accruals between firms that abolish their dual-class share structure by unification and singe-class firms disappears in the post-unification period. The second essay investigates the earnings informativeness of dual-class firms by examining the explanatory power of earnings for returns. The results indicate that the earnings informativeness is lower for dual-class firms as compared to single-class firms. Earnings informativeness improves in firms that unify their shares. The third essay compares the level of information asymmetry between dual-class firms and single-class firms. It is documented that the information environment for dual-class firms is worse than for single-class firms. Also, the finding suggests that the difference in information environment between dual-class firms and single-class firms disappears after dual-class stock unification.