Correlation of the in vitro antifungal drug susceptibility with the in vivo activity of fluconazole in a murine model of cerebral infection caused by Cryptococcus gattii

Forty Cryptococcus gattii strains were submitted to antifungal susceptibility testing with fluconazole, itraconazole, amphotericin B and terbinafine. The minimum inhibitory concentration (MIC) ranges were 0.5-64.0 for fluconazole, < 0.015-0.25 for itraconazole, 0.015-0.5 for amphotericin B and 0.062-2.0 for terbinafine. A bioassay for the quantitation of fluconazole in murine brain tissue was developed. Swiss mice received daily injections of the antifungal, and their brains were withdrawn at different times over the 14-day study period. The drug concentrations varied from 12.98 to 44.60 mu g/mL. This assay was used to evaluate the therapy with fluconazole in a model of infection caused by C. gattii. Swiss mice were infected intracranially and treated with fluconazole for 7, 10 or 14 days. The treatment reduced the fungal burden, but an increase in fungal growth was observed on day 14. The MIC for fluconazole against sequential isolates was 16 mu g/mL, except for the isolates obtained from animals treated for 14 days (MIC = 64 mu g/mL). The quantitation of cytokines revealed a predominance of IFN-gamma and IL-12 in the non-treated group and elevation of IL-4 and IL-10 in the treated group. Our data revealed the possibility of acquired resistance during the antifungal drug therapy.

Gamma-linolenic acid inhibits both tumour cell cycle progression and angiogenesis in the orthotopic C6 glioma model through changes in VEGF, Flt1, ERK1/2, MMP2, cyclin D1, pRb, p53 and p27 protein expression

Background: Gamma-linolenic acid is a known inhibitor of tumour cell proliferation and migration in both in vitro and in vivo conditions. The aim of the present study was to determine the mechanisms by which gamma-linolenic acid (GLA) osmotic pump infusion alters glioma cell proliferation, and whether it affects cell cycle control and angiogenesis in the C6 glioma in vivo. Methods: Established C6 rat gliomas were treated for 14 days with 5 mM GLA in CSF or CSF alone. Tumour size was estimated, microvessel density (MVD) counted and protein and mRNA expression measured by immunohistochemistry, western blotting and RT-PCR. Results: GLA caused a significant decrease in tumour size (75 +/- 8.8%) and reduced MVD by 44 +/- 5.4%. These changes were associated with reduced expression of vascular endothelial growth factor (VEGF) (71 +/- 16%) and the VEGF receptor Flt1 (57 +/- 5.8%) but not Flk1. Expression of ERK1/2 was also reduced by 27 +/- 7.7% and 31 +/- 8.7% respectively. mRNA expression of matrix metalloproteinase-2 (MMP2) was reduced by 35 +/- 6.8% and zymography showed MMP2 proteolytic activity was reduced by 32 +/- 8.5%. GLA altered the expression of several proteins involved in cell cycle control. pRb protein expression was decreased (62 +/- 18%) while E2F1 remained unchanged. Cyclin D1 protein expression was increased by 42 +/- 12% in the presence of GLA. The cyclin dependent kinase inhibitors p21 and p27 responded differently to GLA, p27 expression was increased (27 +/- 7.3%) while p21 remained unchanged. The expression of p53 was increased (44 +/- 16%) by GLA. Finally, the BrdU incorporation studies found a significant inhibition (32 +/- 11%) of BrdU incorporation into the tumour in vivo. Conclusion: Overall the findings reported in the present study lend further support to the potential of GLA as an inhibitor of glioma cell proliferation in vivo and show it has direct effects upon cell cycle control and angiogenesis. These effects involve changes in protein expression of VEGF, Flt1, ERK1, ERK2, MMP2, Cyclin D1, pRb, p53 and p27. Combination therapy using drugs with other, complementary targets and GLA could lead to gains in treatment efficacy in this notoriously difficult to treat tumour.

Tight-binding model for carbon nanotubes from ab initio calculations

Here we present a parametrized tight-binding (TB) model to calculate the band structure of single-wall carbon nanotubes (SWNTs). On the basis of ab initio calculations we fit the band structure of nanotubes of different radii with results obtained with an orthogonal TB model to third neighbors, which includes the effects of orbital hybridization by means of a reduced set of parameters. The functional form for the dependence of these parameters on the radius of the tubes can be used to interpolate appropriate TB parameters for different SWNTs and to study the effects of curvature on their electronic properties. Additionally, we have shown that the model gives an appropriate description of the optical spectra of SWNTs, which can be useful for a proper assignation of SWNTs` specific chirality from optical absorption experiments.

Pseudolikelihood equations for Potts MRF model parameter estimation on higher order neighborhood systems

This letter presents pseudolikelihood equations for the estimation of the Potts Markov random field model parameter on higher order neighborhood systems. The derived equation for second-order systems is a significantly reduced version of a recent result found in the literature (from 67 to 22 terms). Also, with the proposed method, a completely original equation for Potts model parameter estimation in third-order systems was obtained. These equations allow the modeling of less restrictive contextual systems for a large number of applications in a computationally feasible way. Experiments with both simulated and real remote sensing images provided good results.

Computational aspects of harmonic wavelet Galerkin methods and an application to a precipitation front propagation model

This article is dedicated to harmonic wavelet Galerkin methods for the solution of partial differential equations. Several variants of the method are proposed and analyzed, using the Burgers equation as a test model. The computational complexity can be reduced when the localization properties of the wavelets and restricted interactions between different scales are exploited. The resulting variants of the method have computational complexities ranging from O(N(3)) to O(N) (N being the space dimension) per time step. A pseudo-spectral wavelet scheme is also described and compared to the methods based on connection coefficients. The harmonic wavelet Galerkin scheme is applied to a nonlinear model for the propagation of precipitation fronts, with the front locations being exposed in the sizes of the localized wavelet coefficients. (C) 2011 Elsevier Ltd. All rights reserved.

Ixolaris, a tissue factor inhibitor, blocks primary tumor growth and angiogenesis in a glioblastoma model

Background: The expression levels of the clotting initiator protein Tissue Factor (TF) correlate with vessel density and the histological malignancy grade of glioma patients. Increased procoagulant tonus in high grade tumors (glioblastomas) also indicates a potential role for TF in progression of this disease, and suggests that anticoagulants could be used as adjuvants for its treatment. Objectives: We hypothesized that blocking of TF activity with the tick anticoagulant Ixolaris might interfere with glioblastoma progression. Methods and results: TF was identified in U87-MG cells by flow-cytometric and functional assays (extrinsic tenase). In addition, flow-cytometric analysis demonstrated the exposure of phosphatidylserine in the surface of U87-MG cells, which supported the assembly of intrinsic tenase (FIXa/FVIIIa/FX) and prothrombinase (FVa/FXa/prothrombin) complexes, accounting for the production of FXa and thrombin, respectively. Ixolaris effectively blocked the in vitro TF-dependent procoagulant activity of the U87-MG human glioblastoma cell line and attenuated multimolecular coagulation complexes assembly. Notably, Ixolaris inhibited the in vivo tumorigenic potential of U87-MG cells in nude mice, without observable bleeding. This inhibitory effect of Ixolaris on tumor growth was associated with downregulation of VEGF and reduced tumor vascularization. Conclusion: Our results suggest that Ixolaris might be a promising agent for anti-tumor therapy in humans.

Classification of Parkinson’s Disease using MultiPass Lvq,Logistic Model Tree,K-Star for Audio Data set : Classification of Parkinson Disease using Audio Dataset

Parkinson's disease (PD) is a degenerative illness whose cardinal symptoms include rigidity, tremor, and slowness of movement. In addition to its widely recognized effects PD can have a profound effect on speech and voice.The speech symptoms most commonly demonstrated by patients with PD are reduced vocal loudness, monopitch, disruptions of voice quality, and abnormally fast rate of speech. This cluster of speech symptoms is often termed Hypokinetic Dysarthria.The disease can be difficult to diagnose accurately, especially in its early stages, due to this reason, automatic techniques based on Artificial Intelligence should increase the diagnosing accuracy and to help the doctors make better decisions. The aim of the thesis work is to predict the PD based on the audio files collected from various patients.Audio files are preprocessed in order to attain the features.The preprocessed data contains 23 attributes and 195 instances. On an average there are six voice recordings per person, By using data compression technique such as Discrete Cosine Transform (DCT) number of instances can be minimized, after data compression, attribute selection is done using several WEKA build in methods such as ChiSquared, GainRatio, Infogain after identifying the important attributes, we evaluate attributes one by one by using stepwise regression.Based on the selected attributes we process in WEKA by using cost sensitive classifier with various algorithms like MultiPass LVQ, Logistic Model Tree(LMT), K-Star.The classified results shows on an average 80%.By using this features 95% approximate classification of PD is acheived.This shows that using the audio dataset, PD could be predicted with a higher level of accuracy.

Uncertainty Characterization Of A Conceptual Rainfall-Runoff Model By Using Grey Numbers

A procedure for characterizing global uncertainty of a rainfall-runoff simulation model based on using grey numbers is presented. By using the grey numbers technique the uncertainty is characterized by an interval; once the parameters of the rainfall-runoff model have been properly defined as grey numbers, by using the grey mathematics and functions it is possible to obtain simulated discharges in the form of grey numbers whose envelope defines a band which represents the vagueness/uncertainty associated with the simulated variable. The grey numbers representing the model parameters are estimated in such a way that the band obtained from the envelope of simulated grey discharges includes an assigned percentage of observed discharge values and is at the same time as narrow as possible. The approach is applied to a real case study highlighting that a rigorous application of the procedure for direct simulation through the rainfall-runoff model with grey parameters involves long computational times. However, these times can be significantly reduced using a simplified computing procedure with minimal approximations in the quantification of the grey numbers representing the simulated discharges. Relying on this simplified procedure, the conceptual rainfall-runoff grey model is thus calibrated and the uncertainty bands obtained both downstream of the calibration process and downstream of the validation process are compared with those obtained by using a well-established approach, like the GLUE approach, for characterizing uncertainty. The results of the comparison show that the proposed approach may represent a valid tool for characterizing the global uncertainty associable with the output of a rainfall-runoff simulation model.

Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for large-scale systems. Nonetheless, a critical obstacle, which needs to be overcome in MPC, is the large computational burden when a large-scale system is considered or a long prediction horizon is involved. In order to solve this problem, we use an adaptive prediction accuracy (APA) approach that can reduce the computational burden almost by half. The proposed MPC scheme with this scheme is tested on the northern Dutch water system, which comprises Lake IJssel, Lake Marker, the River IJssel and the North Sea Canal. The simulation results show that by using the MPC-APA scheme, the computational time can be reduced to a large extent and a flood protection problem over longer prediction horizons can be well solved.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties for a lack of parsimony, as well as the traditional ones. We suggest a new procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties. In order to compute the fit of each model, we propose an iterative procedure to compute the maximum likelihood estimates of parameters of a VAR model with short-run and long-run restrictions. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank, relative to the commonly used procedure of selecting the lag-length only and then testing for cointegration.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian inflation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in different measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian in ation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in di¤erent measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. A Monte Carlo study explores the finite sample performance of this procedure and evaluates the forecasting accuracy of models selected by this procedure. Two empirical applications confirm the usefulness of the model selection procedure proposed here for forecasting.

Forecasting Multivariate Time Series under Present-Value-Model Short- and Long-run Co-movement Restrictions

This paper has two original contributions. First, we show that the present value model (PVM hereafter), which has a wide application in macroeconomics and fi nance, entails common cyclical feature restrictions in the dynamics of the vector error-correction representation (Vahid and Engle, 1993); something that has been already investigated in that VECM context by Johansen and Swensen (1999, 2011) but has not been discussed before with this new emphasis. We also provide the present value reduced rank constraints to be tested within the log-linear model. Our second contribution relates to forecasting time series that are subject to those long and short-run reduced rank restrictions. The reason why appropriate common cyclical feature restrictions might improve forecasting is because it finds natural exclusion restrictions preventing the estimation of useless parameters, which would otherwise contribute to the increase of forecast variance with no expected reduction in bias. We applied the techniques discussed in this paper to data known to be subject to present value restrictions, i.e. the online series maintained and up-dated by Shiller. We focus on three different data sets. The fi rst includes the levels of interest rates with long and short maturities, the second includes the level of real price and dividend for the S&P composite index, and the third includes the logarithmic transformation of prices and dividends. Our exhaustive investigation of several different multivariate models reveals that better forecasts can be achieved when restrictions are applied to them. Moreover, imposing short-run restrictions produce forecast winners 70% of the time for target variables of PVMs and 63.33% of the time when all variables in the system are considered.

Influence of laparoscopy and laparotomy on gasometry, leukocytes and cytokines in a rat abdominal sepsis model

Laparoscopic surgery is associated with reduced surgical trauma, and less acute phase response, as compared with open surgery. Cytokines are important regulators of the biological response to surgical and anesthetic stress. The aim of this study was to determine if CO2 pneumoperitoneum would change cytokine expression, gas parameters and leukocyte count in septic rats. Methods: Wistar rats were randomly assigned to five groups: control (anesthesia only), laparotomy, CO2 pneumoperitoneum, cecum ligation and puncture by laparotomy, and laparoscopic cecum ligation and puncture. After 30 min of the procedures, arterial blood samples were obtained to determine leukocytes subpopulations by hemocytometer. TNFα, IL-1β, IL-6 were determined in intraperitoneal fluid (by ELISA). Gas parameters were measured on arterial blood, intraperitoneal and subperitoneal exsudates. Results: Peritoneal TNFα, IL-1β and IL-6 concentrations were lower in pneumoperitoneum rats than in all other groups (p<0.05). TNFα, IL-1β and IL-6 expression was lower in the laparoscopic than in laparotomic sepsis (p<0.05). Rats from laparoscopic cecum ligation and puncture group developed significant hypercarbic acidosis in blood and subperitoneal fluid when compared to open procedure group. Total white blood cells and lymphocytes were significantly lower in laparoscopic cecum ligation and puncture rats than in the laparotomic (p<0.01). Nevertheless, the laparotomic cecum ligation rats had a significant increase in blood neutrophils and eosinophils when compared with controls (p<0.05). Conclusions: This study demonstrates that the CO2 pneumoperitoneum reduced the inflammatory response in an animal model of peritonitis with respect to intraperitoneal cytokines, white blood cell count and clinical correlates of sepsis. The pneumoperitoneum produced hypercarbic acidosis in septic animals