Luteinizing hormone regulates inhibin-alpha subunit expression through multiple signaling pathways involving steroidogenic factor-1 and beta-catenin in the macaque corpus luteum

We employed different experimental model systems to define the role of GATA4, beta-catenin, and steroidogenic factor (SF-1) transcriptional factors in the regulation of monkey luteal inhibin secretion. Reverse transcription polymerase chain reactions and western blotting analyses show high expression of inhibin-alpha, GATA4, and beta-catenin in corpus luteum (CL) of the mid-luteal phase. Gonadotropin-releasing hormone receptor antagonist-induced luteolysis model suggested the significance of luteinizing hormone (LH) in regulating these transcriptional factors. Inducible cyclic AMP early repressor mRNA expression was detected in the CL and no change was observed in different stages of CL. Following amino acid sequence analysis, interaction between SF-1 and beta-catenin in mid-stage CL was verified by reciprocal co-immunoprecipitation experiments coupled to immunoblot analysis. Electrophoretic mobility shift analysis support the role of SF-1 in regulating luteal inhibin-alpha expression. Our results suggest a possible multiple crosstalk of Wnt, cAMP, and SF-1 in the regulation of luteal inhibin secretion.

Graphene oxide based multilayer capsules with unique permeability properties: facile encapsulation of multiple drugs

Novel composite graphene oxide (GO)/poly(allylamine hydrochloride) (PAH) multilayer capsules have been fabricated by layer-by-layer (LbL) assembly. They were found to possess unique permeability properties compared to traditional LbL capsules. These hybrid capsules showed special ``core-shell'' loading property for encapsulation of dual drugs simultaneously into the core and shell of the capsules respectively.

Aggregate download throughput for TCP-controlled long file transfers in a WLAN with multiple STA-AP association rates

We consider several WLAN stations associated at rates r(1), r(2), ... r(k) with an Access Point. Each station (STA) is downloading a long file from a local server, located on the LAN to which the Access Point (AP) is attached, using TCP. We assume that a TCP ACK will be produced after the reception of d packets at an STA. We model these simultaneous TCP-controlled transfers using a semi-Markov process. Our analytical approach leads to a procedure to compute aggregate download, as well as per-STA throughputs, numerically, and the results match simulations very well. (C) 2012 Elsevier B.V. All rights reserved.

The multiple and enigmatic roles of guanylyl cyclase C in intestinal homeostasis

Guanylyl cyclase C (GC-C) is predominantly expressed in intestinal epithelial cells and serves as the receptor for the gastrointestinal hormones guanylin and uroguanylin, and the heat-stable enterotoxin, the causative agent for Travellers' Diarrhea. Activation of GC-C results in an increase in intracellular levels of cGMP, which can regulate fluid and ion secretion, colon cell proliferation, and the gut immune system. This review highlights recent findings arising from studies in the GC-C knockout mouse, along with enigmatic results obtained from the first descriptions of human disease caused by mutations in the GC-C gene. We provide some insight into these new findings and comment on areas of future study, which may enhance our knowledge of this evolutionarily conserved receptor and signaling system. (C) 2012 Federation of European Biochemical Societies. Published by Elsevier B. V. All rights reserved.

Ultrasound modulation of coherent light in a multiple-scattering medium: experimental verification of nonzero average phase carried by light

We demonstrate the phase fluctuation introduced by oscillation of scattering centers in the focal volume of an ultrasound transducer in an optical tomography experiment has a nonzero mean. The conditions to be met for the above are: (i) the frequency of the ultrasound should be in the vicinity of the most dominant natural frequency of vibration of the ultrasound focal volume, (ii) the corresponding acoustic wavelength should be much larger than l(n)*, a modified transport mean-free-path applicable for phase decorrelation and (iii) the focal volume of the ultrasound transducer should not be larger than 4 - 5 times (l(n)*)(3). We demonstrate through simulations that as the ratio of the ultrasound focal volume to (l(n)*)(3) increases, the average of the phase fluctuation decreases and becomes zero when the focal volume becomes greater than around 4(l(n)*)(3); and through simulations and experiments that as the acoustic frequency increases from 100 Hz to 1 MHz, the average phase decreases to zero. Through experiments done in chicken breast we show that the average phase increases from around 110 degrees to 130 degrees when the background medium is changed from water to glycerol, indicating that the average of the phase fluctuation can be used to sense changes in refractive index deep within tissue.

An approach to multi-temporal MODIS image analysis using image classification and segmentation

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation for extracting water-covered regions. Analysis of MODIS satellite images is applied in three stages: before flood, during flood and after flood. Water regions are extracted from the MODIS images using image classification (based on spectral information) and image segmentation (based on spatial information). Multi-temporal MODIS images from ``normal'' (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification (SVM and ANN) and region-based image segmentation is an accurate and reliable approach for the extraction of water-covered regions. (c) 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.

Completely random nanoporous Cu4O3-CuO-C composite thin films for potential application as multiple channel photonic band gap based filter in the telecommunication wavelengths

In 2003, Babin et al. theoretically predicted (J. Appl. Phys. 94:4244, 2003) that fabrication of organic-inorganic hybrid materials would probably be required to implement structures with multiple photonic band gaps. In tune with their prediction, we report synthesis of such an inorganic-organic nanocomposite, comprising Cu4O3-CuO-C thin films that experimentally exhibit the highest (of any known material) number (as many as eleven) of photonic band gaps in the near infrared. On contrary to the report by Wang et al. (Appl. Phys. Lett. 84:1629, 2004) that photonic crystals with multiple stop gaps require highly correlated structural arrangement such as multilayers of variable thicknesses, we demonstrate experimental realization of multiple stop gaps in completely randomized structures comprising inorganic oxide nanocrystals (Cu4O3 and CuO) randomly embedded in a randomly porous carbonaceous matrix. We report one step synthesis of such nanostructured films through the metalorganic chemical vapor deposition technique using a single source metalorganic precursor, Cu-4(deaH)(dea)(oAc)(5) a <...aEuro parts per thousand(CH3)(2)CO. The films displaying multiple (4/9/11) photonic band gaps with equal transmission losses in the infrared are promising materials to find applications as multiple channel photonic band gap based filter for WDM technology.

Combining heterogeneous classifiers for relational databases

Practical usage of machine learning is gaining strategic importance in enterprises looking for business intelligence. However, most enterprise data is distributed in multiple relational databases with expert-designed schema. Using traditional single-table machine learning techniques over such data not only incur a computational penalty for converting to a flat form (mega-join), even the human-specified semantic information present in the relations is lost. In this paper, we present a practical, two-phase hierarchical meta-classification algorithm for relational databases with a semantic divide and conquer approach. We propose a recursive, prediction aggregation technique over heterogeneous classifiers applied on individual database tables. The proposed algorithm was evaluated on three diverse datasets. namely TPCH, PKDD and UCI benchmarks and showed considerable reduction in classification time without any loss of prediction accuracy. (C) 2012 Elsevier Ltd. All rights reserved.

Conformational dynamics of HIV-1 protease: a comparative molecular dynamics simulation study with multiple amber force fields

Flap dynamics of HIV-1 protease (HIV-pr) controls the entry of inhibitors and substrates to the active site. Dynamical models from previous simulations are not all consistent with each other and not all are supported by the NMR results. In the present work, the er effect of force field on the dynamics of HIV-pr is investigated by MD simulations using three AMBER force fields ff99, ff99SB, and ff03. The generalized order parameters for amide backbone are calculated from the three force fields and compared with the NMR S2 values. We found that the ff99SB and ff03 force field calculated order parameters agree reasonably well with the NMR S2 values, whereas ff99 calculated values deviate most from the NMR order parameters. Stereochemical geometry of protein models from each force field also agrees well with the remarks from NMR S2 values. However, between ff99SB and ff03, there are several differences, most notably in the loop regions. It is found that these loops are, in general, more flexible in the ff03 force field. This results in a larger active site cavity in the simulation with the ff03 force field. The effect of this difference in computer-aided drug design against flexible receptors is discussed.

Acoustical behavior of single inlet and multiple outlet elliptical cylindrical chamber muffler

Transmission loss (TL) of an elliptical cylindrical chamber muffler having a single side/end inlet and multiple side/end outlet is analyzed by means of the 3-D semi-analytical formulation based upon the modal expansion (in terms of the angular and radial Mathieu functions) and the Green's function. The acoustic pressure response obtained in terms of Green's function is integrated over surface area of the side/end ports (modeled as rigid pistons) and upon subsequent division by the port area, yields the acoustic pressure response or impedance Z] matrix parameters due to the uniform piston-driven model. The 3-D semi-analytical results are found to be in excellent agreement with the results obtained by means of 3-D FEA (SYSNOISE) simulations, thereby validating the semi-analytical procedure suggested in this work. Parametric studies such as the effect of chamber length (L), angular and axial locations of the ports, interchanging the locations of inlet and outlet ports as well as the addition of an outlet port for double outlet mufflers on the TL performance are reported, thereby leading to the formulation of design guidelines for obtaining muffler configurations exhibiting a broad-band TL spectrum. One such configuration is an axially long chamber having side-inlet and side-outlet ports such that one of the side ports is located at half the axial length on themajor/minor axis and the other side port is located at three-quarters (or one-quarter) of the axial length on the minor/major axis. (C) 2012 Institute of Noise Control Engineering.

Rendezvous of Multiple UAVs with Collision Avoidance Using Consensus

This paper addresses the problem of multiple unmanned aerial vehicle (UAV) rendezvous when the UAVs have to perform maneuvers to avoid collisions with other UAVs. The proposed solution consists of using velocity control and a wandering maneuver, if needed, of the UAVs based on a consensus among them on the estimated time of arrival at the point of the rendezvous. This algorithm, with a slight modification is shown to be useful in tracking stationary or slowly moving targets with a standoff distance. The proposed algorithm is simple and computationally efficient. The simulation results demonstrate the efficacy of the proposed approach. DOI: 10.1061/(ASCE)AS.1943-5525.0000145. (C) 2012 American Society of Civil Engineers.

Efficient Methods for Robust Classification Under Uncertainty in Kernel Matrices

In this paper we study the problem of designing SVM classifiers when the kernel matrix, K, is affected by uncertainty. Specifically K is modeled as a positive affine combination of given positive semi definite kernels, with the coefficients ranging in a norm-bounded uncertainty set. We treat the problem using the Robust Optimization methodology. This reduces the uncertain SVM problem into a deterministic conic quadratic problem which can be solved in principle by a polynomial time Interior Point (IP) algorithm. However, for large-scale classification problems, IP methods become intractable and one has to resort to first-order gradient type methods. The strategy we use here is to reformulate the robust counterpart of the uncertain SVM problem as a saddle point problem and employ a special gradient scheme which works directly on the convex-concave saddle function. The algorithm is a simplified version of a general scheme due to Juditski and Nemirovski (2011). It achieves an O(1/T-2) reduction of the initial error after T iterations. A comprehensive empirical study on both synthetic data and real-world protein structure data sets show that the proposed formulations achieve the desired robustness, and the saddle point based algorithm outperforms the IP method significantly.

Semi-supervised SVMs for classification with unknown class proportions and a small labeled dataset

In the design of practical web page classification systems one often encounters a situation in which the labeled training set is created by choosing some examples from each class; but, the class proportions in this set are not the same as those in the test distribution to which the classifier will be actually applied. The problem is made worse when the amount of training data is also small. In this paper we explore and adapt binary SVM methods that make use of unlabeled data from the test distribution, viz., Transductive SVMs (TSVMs) and expectation regularization/constraint (ER/EC) methods to deal with this situation. We empirically show that when the labeled training data is small, TSVM designed using the class ratio tuned by minimizing the loss on the labeled set yields the best performance; its performance is good even when the deviation between the class ratios of the labeled training set and the test set is quite large. When the labeled training data is sufficiently large, an unsupervised Gaussian mixture model can be used to get a very good estimate of the class ratio in the test set; also, when this estimate is used, both TSVM and EC/ER give their best possible performance, with TSVM coming out superior. The ideas in the paper can be easily extended to multi-class SVMs and MaxEnt models.

A non-syntactic approach for text sentiment classification with stopwords

The present approach uses stopwords and the gaps that oc- cur between successive stopwords –formed by contentwords– as features for sentiment classification.

Combination of similarity measures for time series classification using genetic algorithms

Time series classification deals with the problem of classification of data that is multivariate in nature. This means that one or more of the attributes is in the form of a sequence. The notion of similarity or distance, used in time series data, is significant and affects the accuracy, time, and space complexity of the classification algorithm. There exist numerous similarity measures for time series data, but each of them has its own disadvantages. Instead of relying upon a single similarity measure, our aim is to find the near optimal solution to the classification problem by combining different similarity measures. In this work, we use genetic algorithms to combine the similarity measures so as to get the best performance. The weightage given to different similarity measures evolves over a number of generations so as to get the best combination. We test our approach on a number of benchmark time series datasets and present promising results.