Gaussian mixture model based switched split vector quantization of LSF parameters

We address the issue of rate-distortion (R/D) performance optimality of the recently proposed switched split vector quantization (SSVQ) method. The distribution of the source is modeled using Gaussian mixture density and thus, the non-parametric SSVQ is analyzed in a parametric model based framework for achieving optimum R/D performance. Using high rate quantization theory, we derive the optimum bit allocation formulae for the intra-cluster split vector quantizer (SVQ) and the inter-cluster switching. For the wide-band speech line spectrum frequency (LSF) parameter quantization, it is shown that the Gaussian mixture model (GMM) based parametric SSVQ method provides 1 bit/vector advantage over the non-parametric SSVQ method.

Computationally efficient optimum weighting function for vector quantization of LSF parameters

We propose a new weighting function which is computationally simple and an approximation to the theoretically derived optimum weighting function shown in the literature. The proposed weighting function is perceptually motivated and provides improved vector quantization performance compared to several weighting functions proposed so far, for line spectrum frequency (LSF) parameter quantization of both clean and noisy speech data.

Cluster based training for scaling non-linear support vector machines

Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points. In this paper we propose a novel kernel based incremental data clustering approach and its use for scaling Non-linear Support Vector Machines to handle large data sets. The clustering method introduced can find cluster abstractions of the training data in a kernel induced feature space. These cluster abstractions are then used for selective sampling based training of Support Vector Machines to reduce the training time without compromising the generalization performance. Experiments done with real world datasets show that this approach gives good generalization performance at reasonable computational expense.

Gene expression profiling during Forskolin induced differentiation of BeWo cells by differential display RT-PCR

The differentiation of cytotrophoblasts into syncytiotrophoblasts in the placenta has been employed as a model to investigate stage specific expression as well as regulation of genes during this process. While the cytotrophoblasts are highly invasive and proliferative with relatively less capacity to synthesize pregnancy related proteins, the multinucleated syncytiotrophoblasts are non-proliferative and non-invasive. However, syncytiotrophoblasts are the site of synthesis of a variety of protein, peptide and steroid hormones as well as several growth factors. Both the freshly isolated cytotrophoblasts from human placenta as well as the BeWo cell, a choriocarcinoma cell line model which retain several characteristic of cytotrophoblasts has been employed by us to study regulation of differentiation. In the present study, we have employed the differential display RT-PCR analysis (DD-RT-PCR) to evaluate gene expression changes during Forskolin induced in vitro differentiation of BeWo cells. We have identified several genes which are differentially expressed during differentiation and the differential expression of 10 transcripts was confirmed by Northern blot analysis. Based on the identity of the transcripts an attempt has been made to relate the known function of the gene products, to changes observed during differentiation. Of the several transcripts, one of the transcripts, namely Secretory Leukocyte Protease Inhibitor (SLPI) which is known to have multiple functions was found to increase 15-fold in the syntiotrophoblast.

Internet privatization, WikiLeaks, and free expression

In late 2010, the online nonprofit media organization WikiLeaks published classified documents detailing correspondence between the U.S. State Department and its diplomatic missions around the world, numbering around 250,000 cables. These diplomatic cables contained classified information with comments on world leaders, foreign states, and various international and domestic issues. Negative reactions to the publication of these cables came from both the U.S. political class (which was generally condemnatory of WikiLeaks, invoking national security concerns and the jeopardizing of U.S. interests abroad) and the corporate world, with various companies ceasing to continue to provide services to WikiLeaks despite no legal measure (e.g., a court injunction) forcing them to do so. This article focuses on the legal remedies available to WikiLeaks against this corporate suppression of its speech in the U.S. and Europe since these are the two principle arenas in which the actors concerned are operating. The transatlantic legal protection of free expression will be considered, yet, as will be explained in greater detail, the legal conception of this constitutional and fundamental right comes from a time when the state posed the greater threat to freedom. As a result, it is not generally enforceable against private, non-state entities interfering with speech and expression which is the case here. Other areas of law, namely antitrust/competition, contract and tort will then be examined to determine whether WikiLeaks and its partners can attempt to enforce their right indirectly through these other means. Finally, there will be some concluding thoughts about the implications of the corporate response to the WikiLeaks embassy cables leak for freedom of expression online.

Expression profiling of lymph nodes in tuberculosis patients reveal inflammatory milieu at site of infection

Extrapulmonary manifestations constitute 15 to 20% of tuberculosis cases, with lymph node tuberculosis (LNTB) as the most common form of infection. However, diagnosis and treatment advances are hindered by lack of understanding of LNTB biology. To identify host response, Mycobacterium tuberculosis infected lymph nodes from LNTB patients were studied by means of transcriptomics and quantitative proteomics analyses. The selected targets obtained by comparative analyses were validated by quantitative PCR and immunohistochemistry. This approach provided expression data for 8,728 transcripts and 102 proteins, differentially regulated in the infected human lymph node. Enhanced inflammation with upregulation of T-helper1-related genes, combined with marked dysregulation of matrix metalloproteinases, indicates tissue damage due to high immunoactivity at infected niche. This expression signature was accompanied by significant upregulation of an immunoregulatory gene, leukotriene A4 hydrolase, at both transcript and protein levels. Comparative transcriptional analyses revealed LNTB-specific perturbations. In contrast to pulmonary TB-associated increase in lipid metabolism, genes involved in fatty-acid metabolism were found to be downregulated in LNTB suggesting differential lipid metabolic signature. This study investigates the tissue molecular signature of LNTB patients for the first time and presents findings that indicate the possible mechanism of disease pathology through dysregulation of inflammatory and tissue-repair processes.

High-level expression of biologically active glycoprotein hormones in Pichia pastoris strains—selection of strain GS115, and not X-33, for the production of biologically active N-glycosylated 15N-labeled phCG

The methylotrophic yeast Pichia pastoris is widely used for the production of recombinant glycoproteins. With the aim to generate biologically active 15N-labeled glycohormones for conformational studies focused on the unravelling of the NMR structures in solution, the P. pastoris strains GS115 and X-33 were explored for the expression of human chorionic gonadotropin (phCG) and human follicle-stimulating hormone (phFSH). In agreement with recent investigations on the N-glycosylation of phCG, produced in P. pastoris GS115, using ammonia/glycerol-methanol as nitrogen/carbon sources, the N-glycosylation pattern of phCG, synthesized using NH4Cl/glucose–glycerol–methanol, comprised neutral and charged, phosphorylated high-mannose-type N-glycans (Man8–15GlcNAc2). However, the changed culturing protocol led to much higher amounts of glycoprotein material, which is of importance for an economical realistic approach of the aimed NMR research. In the context of these studies, attention was also paid to the site specific N-glycosylation in phCG produced in P. pastoris GS115. In contrast to the rather simple N-glycosylation pattern of phCG expressed in the GS115 strain, phCG and phFSH expressed in the X-33 strain revealed, besides neutral high-mannose-type N-glycans, also high concentrations of neutral hypermannose-type N-glycans (Manup-to-30GlcNAc2). The latter finding made the X-33 strain not very suitable for generating 15N-labeled material. Therefore, 15N-phCG was expressed in the GS115 strain using the new optimized protocol. The 15N-enrichment was evaluated by 15N-HSQC NMR spectroscopy and GLC-EI/MS. Circular dichroism studies indicated that 15N-phCG/GS115 had the same folding as urinary hCG. Furthermore, 15N-phCG/GS115 was found to be similar to the unlabeled protein in every respect as judged by radioimmunoassay, radioreceptor assays, and in vitro bioassays.

OCR Prediction Using Support Vector Machine Based on Piezocone Data

The determination of the overconsolidation ratio (OCR) of clay deposits is an important task in geotechnical engineering practice. This paper examines the potential of a support vector machine (SVM) for predicting the OCR of clays from piezocone penetration test data. SVM is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. The five input variables used for the SVM model for prediction of OCR are the corrected cone resistance (qt), vertical total stress (sigmav), hydrostatic pore pressure (u0), pore pressure at the cone tip (u1), and the pore pressure just above the cone base (u2). Sensitivity analysis has been performed to investigate the relative importance of each of the input parameters. From the sensitivity analysis, it is clear that qt=primary in situ data influenced by OCR followed by sigmav, u0, u2, and u1. Comparison between SVM and some of the traditional interpretation methods is also presented. The results of this study have shown that the SVM approach has the potential to be a practical tool for determination of OCR.

Novel flutamide regulated genes in the rat ventral prostate: differential modulation of their expression by castration and flutamide treatments

Aim: To identify flutamide regulated genes in the rat ventral prostate. Methods: Total RNA from ventral prostates control and flutamide treated rats were isolated. Differentially expressed transcripts were identified using display reverse transcriptase polymerase chain reaction. The effect of castration on the expression of regulated transcripts was studied. Results: We have identified beta 2-microglobulin, cytoplasmic FMR1 protein 2 and pumilio 1 as flutamide induced and spermine binding protein and ribophorin II as flutamide targets in the rat ventral prostate. Although flutamide treatment caused an induction of pumilio I mRNA, had no effect. Conclusion: Castration and flutamide treatments exert differential effects on gene expression. might also have direct AR independent effects, which might have implications in the emergence of androgen dent prostate cancer and the failure of flutamide therapy.

Multilevel Dodecagonal Space Vector Generation for Open-end Winding Induction Motor Drive Using Conventional Three Level Inverters

A novel dodecagonal space vector structure for induction motor drive is presented in this paper. It consists of two dodecagons, with the radius of the outer one twice the inner one. Compared to existing dodecagonal space vector structures, to achieve the same PWM output voltage quality, the proposed topology lowers the switching frequency of the inverters and reduces the device ratings to half. At the same time, other benefits obtained from existing dodecagonal space vector structure are retained here. This includes the extension of the linear modulation range and elimination of all 6+/-1 harmonics (n=odd) from the phase voltage. The proposed structure is realized by feeding an open-end winding induction motor with two conventional three level inverters. A detailed calculation of the PWM timings for switching the space vector points is also presented. Simulation and experimental results indicate the possible application of the proposed idea for high power drives.

Support vector machine applied to settlement of shallow foundations on cohesionless soils

The determination of settlement of shallow foundations on cohesionless soil is an important task in geotechnical engineering. Available methods for the determination of settlement are not reliable. In this study, the support vector machine (SVM), a novel type of learning algorithm based on statistical theory, has been used to predict the settlement of shallow foundations on cohesionless soil. SVM uses a regression technique by introducing an ε – insensitive loss function. A thorough sensitive analysis has been made to ascertain which parameters are having maximum influence on settlement. The study shows that SVM has the potential to be a useful and practical tool for prediction of settlement of shallow foundation on cohesionless soil.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.