Implementation of an interior point source in the ultra weak variational formulation through source extraction

The Ultra Weak Variational Formulation (UWVF) is a powerful numerical method for the approximation of acoustic, elastic and electromagnetic waves in the time-harmonic regime. The use of Trefftz-type basis functions incorporates the known wave-like behaviour of the solution in the discrete space, allowing large reductions in the required number of degrees of freedom for a given accuracy, when compared to standard finite element methods. However, the UWVF is not well disposed to the accurate approximation of singular sources in the interior of the computational domain. We propose an adjustment to the UWVF for seismic imaging applications, which we call the Source Extraction UWVF. Differing fields are solved for in subdomains around the source, and matched on the inter-domain boundaries. Numerical results are presented for a domain of constant wavenumber and for a domain of varying sound speed in a model used for seismic imaging.

Distributed audio network for speech enhancement in challenging noise backgrounds

This paper presents a new approach to enhance speech based on a distributed microphone network. Each microphone is used to simultaneously classify the input into either one of the noise types or as speech. For enhancing the speech signal a modified spectral subtraction approach is used that utilise the sound information of the entire network to update the noise model even during speech. This improves the reduction of the ambient noise, especially for non-stationary noise types such as street or beach noise. Experiments demonstrate the effectiveness of the proposed system.

Um método primal-dual aplicado na resolução do problema de fluxo de potência ótimo

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A Convex Solution to Disparity Estimation from Light Fields via the Primal-Dual Method

We present a novel approach to the reconstruction of depth from light field data. Our method uses dictionary representations and group sparsity constraints to derive a convex formulation. Although our solution results in an increase of the problem dimensionality, we keep numerical complexity at bay by restricting the space of solutions and by exploiting an efficient Primal-Dual formulation. Comparisons with state of the art techniques, on both synthetic and real data, show promising performances.

Using interior point algorithms for the solution of linear programs with special structural features

Linear Programming (LP) is a powerful decision making tool extensively used in various economic and engineering activities. In the early stages the success of LP was mainly due to the efficiency of the simplex method. After the appearance of Karmarkar's paper, the focus of most research was shifted to the field of interior point methods. The present work is concerned with investigating and efficiently implementing the latest techniques in this field taking sparsity into account. The performance of these implementations on different classes of LP problems is reported here. The preconditional conjugate gradient method is one of the most powerful tools for the solution of the least square problem, present in every iteration of all interior point methods. The effect of using different preconditioners on a range of problems with various condition numbers is presented. Decomposition algorithms has been one of the main fields of research in linear programming over the last few years. After reviewing the latest decomposition techniques, three promising methods were chosen the implemented. Sparsity is again a consideration and suggestions have been included to allow improvements when solving problems with these methods. Finally, experimental results on randomly generated data are reported and compared with an interior point method. The efficient implementation of the decomposition methods considered in this study requires the solution of quadratic subproblems. A review of recent work on algorithms for convex quadratic was performed. The most promising algorithms are discussed and implemented taking sparsity into account. The related performance of these algorithms on randomly generated separable and non-separable problems is also reported.

A general collaborative framework for modeling and perceiving distributed network behavior

Collaborative Anomaly Detection (CAD) is an emerging field of network security in both academia and industry. It has attracted a lot of attention, due to the limitations of traditional fortress-style defense modes. Even though a number of pioneer studies have been conducted in this area, few of them concern about the universality issue. This work focuses on two aspects of it. First, a unified collaborative detection framework is developed based on network virtualization technology. Its purpose is to provide a generic approach that can be applied to designing specific schemes for various application scenarios and objectives. Second, a general behavior perception model is proposed for the unified framework based on hidden Markov random field. Spatial Markovianity is introduced to model the spatial context of distributed network behavior and stochastic interaction among interconnected nodes. Algorithms are derived for parameter estimation, forward prediction, backward smooth, and the normality evaluation of both global network situation and local behavior. Numerical experiments using extensive simulations and several real datasets are presented to validate the proposed solution. Performance-related issues and comparison with related works are discussed.

Inhibition of the JAK2/STAT3 pathway in ovarian cancer results in the loss of cancer stem cell-like characteristics and a reduced tumor burden

Background Current treatment of ovarian cancer patients with chemotherapy leaves behind a residual tumor which results in recurrent ovarian cancer within a short time frame. We have previously demonstrated that a single short-term treatment of ovarian cancer cells with chemotherapy in vitro resulted in a cancer stem cell (CSC)-like enriched residual population which generated significantly greater tumor burden compared to the tumor burden generated by control untreated cells. In this report we looked at the mechanisms of the enrichment of CSC-like residual cells in response to paclitaxel treatment. Methods The mechanism of survival of paclitaxel-treated residual cells at a growth inhibitory concentration of 50% (GI50) was determined on isolated tumor cells from the ascites of recurrent ovarian cancer patients and HEY ovarian cancer cell line by in vitro assays and in a mouse xenograft model. Results Treatment of isolated tumor cells from the ascites of ovarian cancer patients and HEY ovarian cancer cell line with paclitaxel resulted in a CSC-like residual population which coincided with the activation of Janus activated kinase 2 (JAK2) and signal transducer and activation of transcription 3 (STAT3) pathway in paclitaxel surviving cells. Both paclitaxel-induced JAK2/STAT3 activation and CSC-like characteristics were inhibited by a low dose JAK2-specific small molecule inhibitor CYT387 (1 μM) in vitro. Subsequent, in vivo transplantation of paclitaxel and CYT387-treated HEY cells in mice resulted in a significantly reduced tumor burden compared to that seen with paclitaxel only-treated transplanted cells. In vitro analysis of tumor xenografts at protein and mRNA levels demonstrated a loss of CSC-like markers and CA125 expression in paclitaxel and CYT387-treated cell-derived xenografts, compared to paclitaxel only-treated cell-derived xenografts. These results were consistent with significantly reduced activation of JAK2 and STAT3 in paclitaxel and CYT387-treated cell-derived xenografts compared to paclitaxel only-treated cell derived xenografts. Conclusions This proof of principle study demonstrates that inhibition of the JAK2/STAT3 pathway by the addition of CYT387 suppresses the ‘stemness’ profile in chemotherapy-treated residual cells in vitro, which is replicated in vivo, leading to a reduced tumor burden. These findings have important implications for ovarian cancer patients who are treated with taxane and/or platinum-based therapies. Keywords: Ovarian carcinoma, Cancer stem cell, Metastasis, Ascites, Chemoresistance, Recurrence, JAK2/STAT3 pathway

A Neural Network Model of Spatio-Temporal Pattern Recognition, Recall and Timing

This paper describes the design of a self~organizing, hierarchical neural network model of unsupervised serial learning. The model learns to recognize, store, and recall sequences of unitized patterns, using either short-term memory (STM) or both STM and long-term memory (LTM) mechanisms. Timing information is learned and recall {both from STM and from LTM) is performed with a learned rhythmical structure. The network, bearing similarities with ART (Carpenter & Grossberg 1987a), learns to map temporal sequences to unitized patterns, which makes it suitable for hierarchical operation. It is therefore capable of self-organizing codes for sequences of sequences. The capacity is only limited by the number of nodes provided. Selected simulation results are reported to illustrate system properties.

Rod photoreceptor loss in Rho(-/-) mice reduces retinal hypoxia and hypoxia-regulated gene expression

PURPOSE. This study was conducted to evaluate whether regions of the retinal neuropile become hypoxic during periods of high oxygen consumption and whether depletion of the outer retina reduces hypoxia and related changes in gene expression.

METHODS. Retinas from rhodopsin knockout (Rho(-/-)) mice were evaluated along with those of wild-type (WT) control animals. Retinas were also examined at the end of 12-hour dark or light periods, and a separate group was treated with L-cis-diltiazem at the beginning of a 12-hour dark period. Hypoxia was assessed by deposition of hypoxyprobe (HP) and HP-protein adducts were localized by immunohistochemistry and quantified using ELISA. Also, hypoxia-regulated gene expression and transcriptional activity were assessed alongside vascular density.

RESULTS. Hypoxia was observed in the inner nuclear and ganglion cell layers in WT retina and was significantly reduced in Rho (-/-) mice (P < 0.05). Retinal hypoxia was significantly increased during dark adaptation in WT mice (P < 0.05), whereas no change was observed in Rho(-/-) or with L-cis-diltiazem-treated WT mice. Hypoxia-inducible factor (HIF)-1 alpha DNA-binding and VEGF mRNA expression in Rho(-/-) retina was significantly reduced in unison with outer retinal depletion (P < 0.05). Retina from the Rho(-/-) mice displayed an extensive intraretinal vascular network after 6 months, although there was evidence that capillary density was depleted in comparison with that in WT retinas.

CONCLUSIONS. Relative hypoxia occurs in the inner retina especially during dark adaptation. Photoreceptor loss reduces retinal oxygen usage and hypoxia which corresponds with attenuation of the retinal microvasculature. These studies suggest that in normal physiological conditions and diurnal cycles the adult retina exists in a state of borderline hypoxia, making this tissue particularly susceptible to even subtle reductions in perfusion.

Loss of Par3 leads to aberrant divisions and faster mitotic progression in differentiating primary murine keratinocytes

Dissertação de Mestrado, Oncobiologia: Mecanismos Moleculares do Cancro, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

Dual studies on autonomic function and cerebral autoregulation in ischemic stroke patients

Tese de mestrado, Neurociências, Faculdade de Medicina, Universidade de Lisboa, 2015

The effect of perturbation warning on attention switching abilities during dual-task performance in young and older adults

This study examined whether providing an auditory warning would facilitate attention switching abilities in older adults during dual-tasking. Fifteen young and 16 older adults performed a tracking task while recovering their balance from a support surface translation. For half of the trials, an auditory warning was presented to inform participants of the upcoming translation. Performance was quantified through electromyographic (EMG) recordings of the lower limb muscles, while the ability to switch attention between tasks was determined by tracking task error. Providing warning of an upcoming loss of balance resulted in both young and older adults increasing their leg EMG activity by 10-165% (p<0.05) in preparation for the upcoming translation. However, no differences in the timing of attention switching were observed with or without the warning (p=0.424). Together, these findings suggest that providing a perturbation warning has minimal benefits in improving attention switching abilities for balance recovery in healthy older adults.

Distribution network reconfiguration using an efficient evolutionary algorithm

Network reconfiguration is an important tool to optimize the operating conditions of a distribution system. This is accomplished modifying the network structure of distribution feeders by changing the open/close status of sectionalizing switches. This not only reduces the power losses, but also relieves the overloading of the network components. Network reconfiguration belongs to a complex family of problems because of their combinatorial nature and multiple constraints. This paper proposes a solution to this problem, using a specialized evolutionary algorithm, with a novel codification, and a brand new way of implement the genetic operators considering the problem characteristics. The algorithm is presented and tested in a real distribution system, showing excellent results and computational efficiency. © 2007 IEEE.