Analysis of dynamic models for structural insight and model reduction .2. A multi-stage compressor shutdown case-study

In this second paper, the three structural measures which have been developed are used in the modelling of a three stage centrifugal synthesis gas compressor. The goal of this case study is to determine the essential mathematical structure which must be incorporated into the compressor model to accurately model the shutdown of this system. A simple, accurate and functional model of the system is created via three structural measures. It was found that the model can be correctly reduced into its basic modes and that the order of the differential system can be reduced from 51(st) to 20(th). Of the 31 differential equational 21 reduce to algebraic relations, 8 become constants and 2 can be deleted thereby increasing the algebraic set from 70 to 91 equations. An interpretation is also obtained as to which physical phenomena are dominating the dynamics of the compressor add whether the compressor will enter surge during the shutdown. Comparisons of the reduced model performance against the full model are given, showing the accuracy and applicability of the approach. Copyright (C) 1996 Elsevier Science Ltd

Risk factors for post-stroke depression

Objective. To examine possible risk factors in post-stroke depression (PSD) other than site of lesion in the brain Data sources. 191 first-ever stroke patients were examined physically shortly after their stroke and examined psychiatrically and physically 4 months post-stroke. Setting. A geographically defined segment of the metropolitan area of Perth, Western Australia, from which all strokes over a course of 18 months were examined (the Perth Community Stroke Study). Measures. Psychiatric Assessment Schedule, Mini Mental State Examination, Barthel Index, Frenchay Activities Index, physical illness and sociodemographic data were collected. Post-stroke depression (PSD) included both major depression and minor depression (dysthymia without the 2-year time stipulation) according to DSM-III (American Psychiatric Association) criteria. Patients depressed at the time of the stroke were excluded. Patients. 191 first-ever stroke patients, 111M, 80F, 28% had PSD, 17% major and 11% minor depression. Results. Significant associations with PSD at 4 months were major functional impairment, living in a nursing home, being divorced and having a high pre-stroke alcohol intake (M only). There was no significant association with age, sex, social class, cognitive impairment or pre-stroke physical illness. Conclusion. Results favoured the hypothesis that depression in an unselected group of stroke patients is no more common, and of no more specific aetiology, than it is among elderly patients with other physical illness.

Discrete and continuous models for dry masonry columns

The dynamic response of dry masonry columns can be approximated with finite-difference equations. Continuum models follow by replacing the difference quotients of the discrete model by corresponding differential expressions. The mathematically simplest of these models is a one-dimensional Cosserat theory. Within the presented homogenization context, the Cosserat theory is obtained by making ad hoc assumptions regarding the relative importance of certain terms in the differential expansions. The quality of approximation of the various theories is tested by comparison of the dispersion relations for bending waves with the dispersion relation of the discrete theory. All theories coincide with differences of less than 1% for wave-length-block-height (L/h) ratios bigger than 2 pi. The theory based on systematic differential approximation remains accurate up to L/h = 3 and then diverges rapidly. The Cosserat model becomes increasingly inaccurate for L/h < 2 pi. However, in contrast to the systematic approximation, the wave speed remains finite. In conclusion, considering its relative simplicity, the Cosserat model appears to be the natural starting point for the development of continuum models for blocky structures.

Sertraline vs. ELectrical Current Therapy for Treating Depression Clinical Trial - SELECT TDCS: Design, rationale and objectives

Background: Despite significant advancements in psychopharmacology, treating major depressive disorder (MDD) is still a challenge considering the efficacy, tolerability, safety, and economical costs of most antidepressant drugs. One approach that has been increasingly investigated is modulation of cortical activity with tools of non-invasive brain stimulation - such as transcranial magnetic stimulation and transcranial direct current stimulation (tDCS). Due to its profile, tDCS seems to be a safe and affordable approach. Methods and design: The SELECT TDCS trial aims to compare sertraline vs. tDCS in a double-blinded, randomized, factorial trial enrolling 120 participants to be allocated to four groups to receive sertraline + tDCS, sertraline, tDCS or placebo. Eligibility criteria are moderate-to-severe unipolar depression (Hamilton Depression Rating Scale >17) not currently on sertraline treatment. Treatment will last 6 weeks and the primary outcome is depression change in the Montgomery-Asberg Depression Rating Score (MADRS). Potential biological markers that mediate response, such as BDNF serum levels, Val66Met BDNF polymorphism, and heart rate variability will also be examined. A neuropsychological battery with a focus on executive functioning will be administered. Discussion: With this design we will be able to investigate whether tDCS is more effective than placebo in a sample of patients free of antidepressants and in addition, we will be able to secondarily compare the effect sizes of sertraline vs. tDCS and also the comparison between tDCS and combination of tDCS and sertraline. (C) 2010 Elsevier Inc. All rights reserved.

Depression: a predictor of smoking relapse in a 6-month follow-up after hospitalization for acute coronary syndrome

Objective The objective of the study was to investigate whether depression is a predictor of postdischarge smoking relapse among patients hospitalized for myocardial infarction (MI) or unstable angina (ILIA), in a smoke-free hospital. Methods Current smokers with MI or UA were interviewed while hospitalized; patients classified with major depression (MD) or no humor disorder were reinterviewed 6 months post discharge to ascertain smoking status. Potential predictors of relapse (depression; stress; anxiety; heart disease risk perception; coffee and alcohol consumption; sociodemographic, clinical, and smoking habit characteristics) were compared between those with MD (n = 268) and no humor disorder (n = 135). Results Relapsers (40.4%) were more frequently and more severely depressed, had higher anxiety and lower self-efficacy scale scores, diagnosis of UA, shorter hospitalizations, started smoking younger, made fewer attempts to quit, had a consort less often, and were more frequently at the `precontemplation` stage of change. Multivariate analysis showed relapse-positive predictors to be MD [odds ratio (OR): 2.549; 95% confidence interval (CI): 1.519-4.275] (P<0.001); `precontemplation` stage of change (OR: 7.798; 95% CI: 2.442-24.898) (P<0.001); previous coronary bypass graft surgery (OR: 4.062; 95% CI: 1.356-12.169) (P=0.012); and previous anxiolytic use (OR: 2.365; 95% CI: 1.095-5.107) (P=0.028). Negative predictors were diagnosis of MI (OR: 0.575; 95% CI: 0.361-0.916) (P=0.019); duration of hospitalization (OR: 0.935; 95% CI: 0.898-0.973) (P=0.001); smoking onset age (OR: 0.952; 95% CI: 0.910-0.994) (P=0.028); number of attempts to quit smoking (OR: 0.808; 95% CI: 0.678-0.964) (P=0.018); and `action` stage of change (OR: 0.065; 95% CI: 0.008-0.532) (P= 0.010). Conclusion Depression, no motivation, shorter hospitalization, and severity of illness contributed to postdischarge resumption of smoking by patients with acute coronary syndrome, who underwent hospital-initiated smoking cessation.

Applying linear time-varying constraints to econometric models: With an application to demand systems

When linear equality constraints are invariant through time they can be incorporated into estimation by restricted least squares. If, however, the constraints are time-varying, this standard methodology cannot be applied. In this paper we show how to incorporate linear time-varying constraints into the estimation of econometric models. The method involves the augmentation of the observation equation of a state-space model prior to estimation by the Kalman filter. Numerical optimisation routines are used for the estimation. A simple example drawn from demand analysis is used to illustrate the method and its application.

rTMS treatment for depression in Parkinson`s disease increases BOLD responses in the left prefrontal cortex

The mechanisms underlying the effects of antidepressant treatment in patients with Parkinson`s disease (PD) are unclear. The neural changes after successful therapy investigated by neuroimaging methods can give insights into the mechanisms of action related to a specific treatment choice. To study the mechanisms of neural modulation of repetitive transcranial magnetic Stimulation (rTMS) and fluoxetine, 21 PD depressed patients were randomized into only two active treatment groups for 4 wk: active rTMS over left dorsolateral prefrontal cortex (DLPFC) (5 Hz rTMS; 120% motor threshold) with placebo pill and sham rTMS with fluoxetine 20mg/d. Event-related functional magnetic resonance imaging (fMRI) with emotional stimuli was performed before and after treatment - in two sessions (test and re-test) at each time-point. The two groups of treatment had a significant, similar mood improvement. After rTMS treatment, there were brain activity decreases in left fusiform gyrus, cerebellum and right DLPFC and brain activity increases in left DLPFC and anterior cingulate gyrus compared to baseline. In contrast, after fluoxetine treatment, there were brain activity increases in right premotor and right medial prefrontal cortex. There was a significant interaction effect between groups vs. time in the left medial prefrontal cortex, suggesting that the activity in this area changed differently in the two treatment groups. Our findings show that antidepressant effects of rTMS and fluoxetine in PD are associated with changes in different areas of the depression-related neural network.

Depression in Parkinson`s disease: Convergence from voxel-based morphometry and functional magnetic resonance imaging in the limbic thalamus

Depression is the most frequent psychiatric disorder in Parkinson`s disease (PD). Although evidence Suggests that depression in PD is related to the degenerative process that underlies the disease, further studies are necessary to better understand the neural basis of depression in this population of patients. In order to investigate neuronal alterations underlying the depression in PD, we studied thirty-six patients with idiopathic PD. Twenty of these patients had the diagnosis of major depression disorder and sixteen did not. The two groups were matched for PD motor severity according to Unified Parkinson Disease Rating Scale (UPDRS). First we conducted a functional magnetic resonance imaging (fMRI) using an event-related parametric emotional perception paradigm with test retest design. Our results showed decreased activation in the left mediodorsal (MD) thalamus and in medial prefrontall cortex in PD patients with depression compared to those without depression. Based upon these results and the increased neuron count in MD thalamus found in previous studies, we conducted a region of interest (ROI) guided voxel-based morphometry (VBM) study comparing the thalamic volume. Our results showed an increased volume in mediodorsal thalamic nuclei bilaterally. Converging morphological changes and functional emotional processing in mediodorsal thalamus highlight the importance of limbic thalamus in PD depression. In addition this data supports the link between neurodegenerative alterations and mood regulation. (C) 2009 Elsevier Inc. All rights reserved.

Models of alcohol use by young adults: An examination of various attitude-behavior theories

Objective: The aim of this study was to test the effectiveness of various attitude-behavior theories in explaining alcohol use among young adults. The theory of reasoned action (TRA), the theory of planned behavior and an extension of the TRA that incorporates past behavior were compared by the method of maximum-likelihood estimation, as implemented in LISREL for Windows 8.12. Method: Respondents consisted of 122 university students (82 female) who were questioned about their attitudes, subjective norms, perceived behavioral control, past behavior and intentions relating to drinking behavior. Students received course credit for their participation in the research. Results: Overall, the results suggest that the extension of the theory of reasoned action which incorporates past behavior provides the best fit to the data. For these young adults, their intentions to drink alcohol were predicted by their past behavior as well as their perceptions of what important others think they should do (subjective norm). Conclusions: The main conclusions drawn from the research concern the importance of focusing on normative influences and past behavior in explaining young adult alcohol use. Issues regarding the relative merit of various alternative models and the need for greater clarity in the measure of attitudes are also discussed.

Experimental models of sepsis and their clinical relevance

Sepsis remains a major cause of morbidity and mortality mainly because of sepsis-induced multiple organ dysfunction. In contrast to preclinical studies, most clinical trials of promising new treatment strategies for sepsis have failed to demonstrate efficacy. Although many reasons could account for this discrepancy, the misinterpretation of preclinical data obtained from experimental studies and especially the use of animal models that do not adequately mimic human sepsis may have been contributing factors. In this review, the potentials and limitations of various animal models of sepsis are discussed to clarify to which extent these findings are relevant to human sepsis. Such models include intravascular infusion of endotoxin or live bacteria, bacterial peritonitis, cecal ligation and perforation, soft tissue infection, pneumonia or meningitis models using different animal species including rats, mice, rabbits, dogs, pigs, sheep, and nonhuman primates. Despite several limitations, animal models remain essential in the development of all new therapies for sepsis and septic shock because they provide fundamental information about the pharmacokinetics, toxicity, and mechanism of drug action that cannot be replaced by other methods. New therapeutic agents should be studied in infection models, even after the initiation of the septic process. Furthermore, debility conditions need to be reproduced to avoid the exclusive use of healthy animals, which often do not represent the human septic patient.

Virtual plants - integrating architectural and physiological models

Recent advances in computer technology have made it possible to create virtual plants by simulating the details of structural development of individual plants. Software has been developed that processes plant models expressed in a special purpose mini-language based on the Lindenmayer system formalism. These models can be extended from their architectural basis to capture plant physiology by integrating them with crop models, which estimate biomass production as a consequence of environmental inputs. Through this process, virtual plants will gain the ability to react to broad environmental conditions, while crop models will gain a visualisation component. This integration requires the resolution of the fundamentally different time scales underlying the approaches. Architectural models are usually based on physiological time; each time step encompasses the same amount of development in the plant, without regard to the passage of real time. In contrast, physiological models are based in real time; the amount of development in a time step is dependent on environmental conditions during the period. This paper provides a background on the plant modelling language, then describes how widely-used concepts of thermal time can be implemented to resolve these time scale differences. The process is illustrated using a case study. (C) 1997 Elsevier Science Ltd.

A defense of backwards in time causation models in quantum mechanics

This paper offers a defense of backwards in time causation models in quantum mechanics. Particular attention is given to Cramer's transactional account, which is shown to have the threefold virtue of solving the Bell problem, explaining the complex conjugate aspect of the quantum mechanical formalism, and explaining various quantum mysteries such as Schrodinger's cat. The question is therefore asked, why has this model not received more attention from physicists and philosophers? One objection given by physicists in assessing Cramer's theory was that it is not testable. This paper seeks to answer this concern by utilizing an argument that backwards causation models entail a fork theory of causal direction. From the backwards causation model together with the fork theory one can deduce empirical predictions. Finally, the objection that this strategy is questionable because of its appeal to philosophy is deflected.

Risk Estimates of Dengue in Travelers to Dengue Endemic Areas Using Mathematical Models

Dengue has emerged as a frequent problem in international travelers. The risk depends on destination, duration, and season of travel. However, data to quantify the true risk for travelers to acquire dengue are lacking. We used mathematical models to estimate the risk of nonimmune persons to acquire dengue when traveling to Singapore. From the force of infection, we calculated the risk of dengue dependent on duration of stay and season of arrival. Our data highlight that the risk for nonimmune travelers to acquire dengue in Singapore is substantial but varies greatly with seasons and epidemic cycles. For instance, for a traveler who stays in Singapore for 1 week during the high dengue season in 2005, the risk of acquiring dengue was 0.17%, but it was only 0.00423% during the low season in a nonepidemic year such as 2002. Risk estimates based on mathematical modeling will help the travel medicine provider give better evidence-based advice for travelers to dengue endemic countries.