Application of the log-conformation tensor to three-dimensional time-dependent free surface flows

The numerical simulation of flows of highly elastic fluids has been the subject of intense research over the past decades with important industrial applications. Therefore, many efforts have been made to improve the convergence capabilities of the numerical methods employed to simulate viscoelastic fluid flows. An important contribution for the solution of the High-Weissenberg Number Problem has been presented by Fattal and Kupferman [J. Non-Newton. Fluid. Mech. 123 (2004) 281-285] who developed the matrix-logarithm of the conformation tensor technique, henceforth called log-conformation tensor. Its advantage is a better approximation of the large growth of the stress tensor that occur in some regions of the flow and it is doubly beneficial in that it ensures physically correct stress fields, allowing converged computations at high Weissenberg number flows. In this work we investigate the application of the log-conformation tensor to three-dimensional unsteady free surface flows. The log-conformation tensor formulation was applied to solve the Upper-Convected Maxwell (UCM) constitutive equation while the momentum equation was solved using a finite difference Marker-and-Cell type method. The resulting developed code is validated by comparing the log-conformation results with the analytic solution for fully developed pipe flows. To illustrate the stability of the log-conformation tensor approach in solving three-dimensional free surface flows, results from the simulation of the extrudate swell and jet buckling phenomena of UCM fluids at high Weissenberg numbers are presented. (C) 2012 Elsevier B.V. All rights reserved.

Heterogeneous time-dependent response of adipose tissue during the development of cancer cachexia

Cancer cachexia induces loss of fat mass that accounts for a large part of the dramatic weight loss observed both in humans and in animal models; however, the literature does not provide consistent information regarding the set point of weight loss and how the different visceral adipose tissue depots contribute to this symptom. To evaluate that, 8-week-old male Wistar rats were subcutaneously inoculated with 1 ml (2 x 10(7)) of tumour cells (Walker 256). Samples of different visceral white adipose tissue (WAT) depots were collected at days 0, 4, 7 and 14 and stored at -80 degrees C (seven to ten animals/each day per group). Mesenteric and retroperitoneal depot mass was decreased to the greatest extent on day 14 compared with day 0. Gene and protein expression of PPAR gamma(2) (PPARG) fell significantly following tumour implantation in all three adipose tissue depots while C/EBP alpha (CEBPA) and SREBP-1c (SREBF1) expression decreased over time only in epididymal and retroperitoneal depots. Decreased adipogenic gene expression and morphological disruption of visceral WAT are further supported by the dramatic reduction in mRNA and protein levels of perilipin. Classical markers of inflammation and macrophage infiltration (f4/80, CD68 and MIF-1 alpha) in WAT were significantly increased in the later stage of cachexia (although showing a incremental pattern along the course of cachexia) and presented a depot-specific regulation. These results indicate that impairment in the lipid-storing function of adipose tissue occurs at different times and that the mesenteric adipose tissue is more resistant to the 'fat-reducing effect' than the other visceral depots during cancer cachexia progression. Journal of Endocrinology (2012) 215, 363-373

Generalized Metropolis dynamics with a generalized master equation: An approach for time-independent and time-dependent Monte Carlo simulations of generalized spin systems

The extension of Boltzmann-Gibbs thermostatistics, proposed by Tsallis, introduces an additional parameter q to the inverse temperature beta. Here, we show that a previously introduced generalized Metropolis dynamics to evolve spin models is not local and does not obey the detailed energy balance. In this dynamics, locality is only retrieved for q = 1, which corresponds to the standard Metropolis algorithm. Nonlocality implies very time-consuming computer calculations, since the energy of the whole system must be reevaluated when a single spin is flipped. To circumvent this costly calculation, we propose a generalized master equation, which gives rise to a local generalized Metropolis dynamics that obeys the detailed energy balance. To compare the different critical values obtained with other generalized dynamics, we perform Monte Carlo simulations in equilibrium for the Ising model. By using short-time nonequilibrium numerical simulations, we also calculate for this model the critical temperature and the static and dynamical critical exponents as functions of q. Even for q not equal 1, we show that suitable time-evolving power laws can be found for each initial condition. Our numerical experiments corroborate the literature results when we use nonlocal dynamics, showing that short-time parameter determination works also in this case. However, the dynamics governed by the new master equation leads to different results for critical temperatures and also the critical exponents affecting universality classes. We further propose a simple algorithm to optimize modeling the time evolution with a power law, considering in a log-log plot two successive refinements.

Constraint-based analysis of gene interactions using restricted boolean networks and time-series data

Abstract Background A popular model for gene regulatory networks is the Boolean network model. In this paper, we propose an algorithm to perform an analysis of gene regulatory interactions using the Boolean network model and time-series data. Actually, the Boolean network is restricted in the sense that only a subset of all possible Boolean functions are considered. We explore some mathematical properties of the restricted Boolean networks in order to avoid the full search approach. The problem is modeled as a Constraint Satisfaction Problem (CSP) and CSP techniques are used to solve it. Results We applied the proposed algorithm in two data sets. First, we used an artificial dataset obtained from a model for the budding yeast cell cycle. The second data set is derived from experiments performed using HeLa cells. The results show that some interactions can be fully or, at least, partially determined under the Boolean model considered. Conclusions The algorithm proposed can be used as a first step for detection of gene/protein interactions. It is able to infer gene relationships from time-series data of gene expression, and this inference process can be aided by a priori knowledge available.

Time-dependent effects of chitosan on dentin structures

Complete debridement with smear layer removal are essential measures for achieving a successful outcome of root canal treatment. The aim of this study was to evaluate the effects of chitosan at different concentrations on the removal of the smear layer and on dentin structure after 3 and 5 min of application. Twelve recently extracted maxillary canine teeth were instrumented using the crown-down technique and irrigated with 1% sodium hypochlorite. The specimens were distributed according to the time and concentration of the final irrigating solution: G1: 0.1% chitosan for 3 min; G2: 0.2% chitosan for 3 min; G3: 0.37% chitosan for 3 min; G4: 0.1% chitosan for 5 min; G5: 0.2% chitosan for 5 min; G6: 0.37% chitosan for 5 min. All samples were prepared for SEM analysis. G1 exhibited removal of the smear layer, but not the smear plugs. G2 showed visible and open tubules with slight erosion of the peritubular dentin. Cleaning in G3 was similar to that in G2, however, the erosive effect was greater. There was expansion of the diameter of the tubules in G4; and in G5 and G6, there was severe erosion with deterioration of dentin surface. In conclusion, 0.2% chitosan for 3 min appeared to be efficient for removing the smear layer, causing little erosion of dentin.

A system for classification of time-series data from industrial non-destructive device

This work proposes a system for classification of industrial steel pieces by means of magnetic nondestructive device. The proposed classification system presents two main stages, online system stage and off-line system stage. In online stage, the system classifies inputs and saves misclassification information in order to perform posterior analyses. In the off-line optimization stage, the topology of a Probabilistic Neural Network is optimized by a Feature Selection algorithm combined with the Probabilistic Neural Network to increase the classification rate. The proposed Feature Selection algorithm searches for the signal spectrogram by combining three basic elements: a Sequential Forward Selection algorithm, a Feature Cluster Grow algorithm with classification rate gradient analysis and a Sequential Backward Selection. Also, a trash-data recycling algorithm is proposed to obtain the optimal feedback samples selected from the misclassified ones.

Time-dependent behaviour of reinforced concrete slabs

In this thesis is studied the long-term behaviour of steel reinforced slabs paying particular attention to the effects due to shrinkage and creep. Despite the universal popularity of using this kind of slabs for simply construction floors, the major world codes focus their attention in a design based on the ultimate limit state, restraining the exercise limit state to a simply verification after the design. For Australia, on the contrary, this is not true. In fact, since this country is not subjected to seismic effects, the main concern is related to the long-term behaviour of the structure. Even if there are a lot of studies about long-term effects of shrinkage and creep, up to date, there are not so many studies concerning the behaviour of slabs with a cracked cross section and how shrinkage and creep influence it. For this reason, a series of ten full scale reinforced slabs was prepared and monitored under laboratory conditions to investigate this behaviour. A wide range of situations is studied in order to cover as many cases as possible, as for example the use of a fog room able to reproduce an environment of 100% humidity. The results show how there is a huge difference in terms of deflections between the case of slabs which are subjected to both shrinkage and creep effects soon after the partial cracking of the cross section, and the case of slabs which have already experienced shrinkage effects for several weeks, when the section has not still cracked, and creep effects only after the cracking.

Blood component ratios in massively transfused, blunt trauma patients--a time-dependent covariate analysis

This study evaluated critical thresholds for fresh frozen plasma (FFP) and platelet (PLT) to packed red blood cell (PRBC) ratios and determined the impact of high FFP:PRBC and PLT:PRBC ratios on outcomes in patients requiring massive transfusion (MT).

THE RESONATE-AND-FIRE NEURON: TIME DEPENDENT AND FREQUENCY SELECTIVE NEURONS IN NEURAL NETWORKS

The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.

Prognostic impact of lymph node ratio outperforms positive lymph nodes and lymph nodes harvested: a time-dependent analysis in mismatch repair-proficient and –deficient colrectal cancers

Objective: We compare the prognostic strength of the lymph node ratio (LNR), positive lymph nodes (+LNs) and collected lymph nodes (LNcoll) using a time-dependent analysis in colorectal cancer patients stratified by mismatch repair (MMR) status. Method: 580 stage III-IV patients were included. Multivariable Cox regression analysis and time-dependent receiver operating characteristic (tROC) curve analysis were performed. The Area under the Curve (AUC) over time was compared for the three features. Results were validated on a second cohort of 105 stage III-IV patients. Results: The AUC for the LNR was 0.71 and outperformed + LNs and LNcoll by 10–15 % in both MMR-proficient and deficient cancers. LNR and + LNs were both significant (p<0.0001) in multivariable analysis but the effect was considerably stronger for the LNR [LNR: HR=5.18 (95 % CI: 3.5–7.6); +LNs=1.06 (95 % CI: 1.04–1.08)]. Similar results were obtained for patients with >12 LNcoll. An optimal cut off score for LNR=0.231 was validated on the second cohort (p<0.001). Conclusion: The LNR outperforms the + LNs and LNcoll even in patients with >12 LNcoll. Its clinical value is not confounded by MMR status. A cut-of score of 0.231 may best stratify patients into prognostic subgroups and could be a basis for the future prospective analysis of the LNR.

Increased sensitivity in mapping task demand in visuospatial processing using reaction-time-dependent hemodynamic response predictors in rapid event-related fMRI

Searching for the neural correlates of visuospatial processing using functional magnetic resonance imaging (fMRI) is usually done in an event-related framework of cognitive subtraction, applying a paradigm comprising visuospatial cognitive components and a corresponding control task. Besides methodological caveats of the cognitive subtraction approach, the standard general linear model with fixed hemodynamic response predictors bears the risk of being underspecified. It does not take into account the variability of the blood oxygen level-dependent signal response due to variable task demand and performance on the level of each single trial. This underspecification may result in reduced sensitivity regarding the identification of task-related brain regions. In a rapid event-related fMRI study, we used an extended general linear model including single-trial reaction-time-dependent hemodynamic response predictors for the analysis of an angle discrimination task. In addition to the already known regions in superior and inferior parietal lobule, mapping the reaction-time-dependent hemodynamic response predictor revealed a more specific network including task demand-dependent regions not being detectable using the cognitive subtraction method, such as bilateral caudate nucleus and insula, right inferior frontal gyrus and left precentral gyrus.