Closed-loop stable model predictive control of integrating systems with dead time

Model predictive control (MPC) applications in the process industry usually deal with process systems that show time delays (dead times) between the system inputs and outputs. Also, in many industrial applications of MPC, integrating outputs resulting from liquid level control or recycle streams need to be considered as controlled outputs. Conventional MPC packages can be applied to time-delay systems but stability of the closed loop system will depend on the tuning parameters of the controller and cannot be guaranteed even in the nominal case. In this work, a state space model based on the analytical step response model is extended to the case of integrating time systems with time delays. This model is applied to the development of two versions of a nominally stable MPC, which is designed to the practical scenario in which one has targets for some of the inputs and/or outputs that may be unreachable and zone control (or interval tracking) for the remaining outputs. The controller is tested through simulation of a multivariable industrial reactor system. (C) 2012 Elsevier Ltd. All rights reserved.

OPTIMIZATION AND CONTROL OF A CONTINUOUS POLYMERIZATION REACTOR

This work studies the optimization and control of a styrene polymerization reactor. The proposed strategy deals with the case where, because of market conditions and equipment deterioration, the optimal operating point of the continuous reactor is modified significantly along the operation time and the control system has to search for this optimum point, besides keeping the reactor system stable at any possible point. The approach considered here consists of three layers: the Real Time Optimization (RTO), the Model Predictive Control (MPC) and a Target Calculation (TC) that coordinates the communication between the two other layers and guarantees the stability of the whole structure. The proposed algorithm is simulated with the phenomenological model of a styrene polymerization reactor, which has been widely used as a benchmark for process control. The complete optimization structure for the styrene process including disturbances rejection is developed. The simulation results show the robustness of the proposed strategy and the capability to deal with disturbances while the economic objective is optimized.

A Bayesian destructive weighted Poisson cure rate model and an application to a cutaneous melanoma data

In this article, we propose a new Bayesian flexible cure rate survival model, which generalises the stochastic model of Klebanov et al. [Klebanov LB, Rachev ST and Yakovlev AY. A stochastic-model of radiation carcinogenesis - latent time distributions and their properties. Math Biosci 1993; 113: 51-75], and has much in common with the destructive model formulated by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)]. In our approach, the accumulated number of lesions or altered cells follows a compound weighted Poisson distribution. This model is more flexible than the promotion time cure model in terms of dispersion. Moreover, it possesses an interesting and realistic interpretation of the biological mechanism of the occurrence of the event of interest as it includes a destructive process of tumour cells after an initial treatment or the capacity of an individual exposed to irradiation to repair altered cells that results in cancer induction. In other words, what is recorded is only the damaged portion of the original number of altered cells not eliminated by the treatment or repaired by the repair system of an individual. Markov Chain Monte Carlo (MCMC) methods are then used to develop Bayesian inference for the proposed model. Also, some discussions on the model selection and an illustration with a cutaneous melanoma data set analysed by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)] are presented.

Modeling gene expression regulatory networks with the sparse vector autoregressive model

Abstract Background To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems. Results We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets. Conclusion The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.

Optimization and control of a continuous polymerization reactor

This work studies the optimization and control of a styrene polymerization reactor. The proposed strategy deals with the case where, because of market conditions and equipment deterioration, the optimal operating point of the continuous reactor is modified significantly along the operation time and the control system has to search for this optimum point, besides keeping the reactor system stable at any possible point. The approach considered here consists of three layers: the Real Time Optimization (RTO), the Model Predictive Control (MPC) and a Target Calculation (TC) that coordinates the communication between the two other layers and guarantees the stability of the whole structure. The proposed algorithm is simulated with the phenomenological model of a styrene polymerization reactor, which has been widely used as a benchmark for process control. The complete optimization structure for the styrene process including disturbances rejection is developed. The simulation results show the robustness of the proposed strategy and the capability to deal with disturbances while the economic objective is optimized.

Analytical methods for evaluating the quality and the genuineness of olive oils

This Ph.D. thesis focuses on the investigation of some chemical and sensorial analytical parameters linked to the quality and purity of different categories of oils obtained by olives: extra virgin olive oils, both those that are sold in the large retail trade (supermarkets and discounts) and those directly collected at some Italian mills, and lower-quality oils (refined, lampante and “repaso”). Concurrently with the adoption of traditional and well-known analytical procedures such as gas chromatography and high-performance liquid chromatography, I carried out a set-up of innovative, fast and environmentally-friend methods. For example, I developed some analytical approaches based on Fourier transform medium infrared spectroscopy (FT-MIR) and time domain reflectometry (TDR), coupled with a robust chemometric elaboration of the results. I investigated some other freshness and quality markers that are not included in official parameters (in Italian and European regulations): the adoption of such a full chemical and sensorial analytical plan allowed me to obtain interesting information about the degree of quality of the EVOOs, mostly within the Italian market. Here the range of quality of EVOOs resulted very wide, in terms of sensory attributes, price classes and chemical parameters. Thanks to the collaboration with other Italian and foreign research groups, I carried out several applicative studies, especially focusing on the shelf-life of oils obtained by olives and on the effects of thermal stresses on the quality of the products. I also studied some innovative technological treatments, such as the clarification by using inert gases, as an alternative to the traditional filtration. Moreover, during a three-and-a-half months research stay at the University of Applied Sciences in Zurich, I also carried out a study related to the application of statistical methods for the elaboration of sensory results, obtained thanks to the official Swiss Panel and to some consumer tests.

High-throughput procedure for tick surveys of tick-borne encephalitis virus and its application in a national surveillance study in Switzerland

Tick-borne encephalitis (TBE), a viral infection of the central nervous system, is endemic in many Eurasian countries. In Switzerland, TBE risk areas have been characterized by geographic mapping of clinical cases. Since mass vaccination should significantly decrease the number of TBE cases, alternative methods for exposure risk assessment are required. We established a new PCR-based test for the detection of TBE virus (TBEV) in ticks. The protocol involves an automated, high-throughput nucleic acid extraction method (QIAsymphony SP system) and a one-step duplex real-time reverse transcription-PCR (RT-PCR) assay for the detection of European subtype TBEV, including an internal process control. High usability, reproducibility, and equivalent performance for virus concentrations down to 5 x 10(3) viral genome equivalents/microl favor the automated protocol compared to the modified guanidinium thiocyanate-phenol-chloroform extraction procedure. The real-time RT-PCR allows fast, sensitive (limit of detection, 10 RNA copies/microl), and specific (no false-positive test results for other TBEV subtypes, other flaviviruses, or other tick-transmitted pathogens) detection of European subtype TBEV. The new detection method was applied in a national surveillance study, in which 62,343 Ixodes ricinus ticks were screened for the presence of TBE virus. A total of 38 foci of endemicity could be identified, with a mean virus prevalence of 0.46%. The foci do not fully agree with those defined by disease mapping. Therefore, the proposed molecular test procedure constitutes a prerequisite for an appropriate TBE surveillance. Our data are a unique complement of human TBE disease case mapping in Switzerland.

Nonparametric Estimation of the Bivariate Recurrence Time Distribution

This paper considers statistical models in which two different types of events, such as the diagnosis of a disease and the remission of the disease, occur alternately over time and are observed subject to right censoring. We propose nonparametric estimators for the joint distribution of bivariate recurrence times and the marginal distribution of the first recurrence time. In general, the marginal distribution of the second recurrence time cannot be estimated due to an identifiability problem, but a conditional distribution of the second recurrence time can be estimated non-parametrically. In literature, statistical methods have been developed to estimate the joint distribution of bivariate recurrence times based on data of the first pair of censored bivariate recurrence times. These methods are efficient in the current model because recurrence times of higher orders are not used. Asymptotic properties of the estimators are established. Numerical studies demonstrate the estimator performs well with practical sample sizes. We apply the proposed method to a Denmark psychiatric case register data set for illustration of the methods and theory.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.

No surgical innovation without evaluation: the IDEAL recommendations

Surgery and other invasive therapies are complex interventions, the assessment of which is challenged by factors that depend on operator, team, and setting, such as learning curves, quality variations, and perception of equipoise. We propose recommendations for the assessment of surgery based on a five-stage description of the surgical development process. We also encourage the widespread use of prospective databases and registries. Reports of new techniques should be registered as a professional duty, anonymously if necessary when outcomes are adverse. Case series studies should be replaced by prospective development studies for early technical modifications and by prospective research databases for later pre-trial evaluation. Protocols for these studies should be registered publicly. Statistical process control techniques can be useful in both early and late assessment. Randomised trials should be used whenever possible to investigate efficacy, but adequate pre-trial data are essential to allow power calculations, clarify the definition and indications of the intervention, and develop quality measures. Difficulties in doing randomised clinical trials should be addressed by measures to evaluate learning curves and alleviate equipoise problems. Alternative prospective designs, such as interrupted time series studies, should be used when randomised trials are not feasible. Established procedures should be monitored with prospective databases to analyse outcome variations and to identify late and rare events. Achievement of improved design, conduct, and reporting of surgical research will need concerted action by editors, funders of health care and research, regulatory bodies, and professional societies.

How nice is good for patients and for therapy outcome? The role of confrontation in the process of psychotherapy

It is well established that the therapeutic relationship contributes about as much to therapy outcome as 'technical' intervention. Furthermore, it follows clear prescriptive concepts in the same manner as technical interventions do. 'Motive Oriented Therapeutic Relationship' is such a concept for establishing a solid basis for whatever therapeutic work the patients' problems require (Grawe, 1980, 1992; Caspar, 1996). Yet, the therapeutic relationship doesn't explain everything because other factors play a significant role too. Previous studies showed that outcome is clearly better when therapists achieved a generally high quality of a therapeutic relationship when they did not shy away from possibly threatening interventions such as confrontations. This ratio of a fruitful alliance and marginally present confrontations in the same session also showed significant correlations with patient's assessment of alliance and progress in therapy (Figlioli et al., 2009).Aim: The current state of research in the field does not give any answers to questions like how good and bad confrontations can be characterized or what role does the intensity, respectively frequency of confrontations play in the process of psychotherapy. Methods: A sample of 80 therapies of 3 sessions each representing either good or bad outcome was judged moment by moment by independent raters if and how therapists used confrontative interventions. Results: Preliminary analyses show that successful confrontations are explicitly uttered, short but intense, related to important patients goals in therapy and embedded in prior complementarity. Discussion: The results will be discussed in terms of their implications for the clinical daily work.

How nice is good for patients and for therapy outcome? The role of confrontation in the process of psychotherapy

It is well established that the therapeutic relationship contributes about as much to therapy outcome as ‘technical’ intervention. Furthermore, it follows clear prescriptive concepts in the same manner as technical interventions do. ‘Motive Oriented Therapeutic Relationship’ is such a concept for establishing a solid basis for whatever therapeutic work the patients’ problems require (Grawe, 1980, 1992; Caspar, 1996). Yet, the therapeutic relationship doesn’t explain everything because other factors play a significant role too. Previous studies showed that outcome is clearly better when therapists achieved a generally high quality of a therapeutic relationship when they did not shy away from possibly threatening interventions such as confrontations. This ratio of a fruitful alliance and marginally present confrontations in the same session also showed significant correlations with patient’s assessment of alliance and progress in therapy (Figlioli et al., 2009). These findings are also very much in line with Sachse’s metaphor of accumulating, but then also using ‘relationship credits’ and Farrelly’s ‘Provocative Therapy’ (1986), as well as the ‘Intensive Short-Term Dynamic Psychotherapy’ by Davanloo (1980).Aim: The current state of research in the field does not give any answers to questions like how good and bad confrontations can be characterized or what role does the intensity, respectively frequency of confrontations play in the process of psychotherapy.Methods: A sample of 80 therapies of 3 sessions each representing either good or bad outcome was judged moment by moment by independent raters if and how therapists used confrontative interventions. Results / Discussion: The results will be discussed in terms of their implications for the clinical daily work. Preliminary analyses show that successful confrontations are explicitly uttered, short but intense, related to important patients goals in therapy and embedded in prior complementarity.

Urinary leukotriene E4 concentrations as a potential marker of inflammation in dogs with inflammatory bowel disease

BACKGROUND: Inflammatory bowel disease (IBD) and food-responsive diarrhea (FRD) are chronic enteropathies of dogs (CCE) that currently can only be differentiated by their response to treatment after exclusion of other diseases. In humans, increased urinary concentrations of leukotriene E4 (LTE4) have been associated with active IBD. OBJECTIVES: To evaluate urinary LTE4 concentrations in dogs with IBD, FRD, and healthy controls, and to assess correlation of urinary LTE4 concentrations with the canine IBD activity index (CIBDAI) scores. ANIMALS: Eighteen dogs with IBD, 19 dogs with FRD, and 23 healthy control dogs. METHODS: In this prospective study, urine was collected and CIBDAI scores were calculated in client-owned dogs with IBD and those with FRD. Quantification of LTE4 in urine was performed by liquid chromatography-tandem mass spectrometry and corrected to creatinine. RESULTS: Urinary LTE4 concentrations were highest in dogs with IBD (median 85.2 pg/mg creatinine [10th-90th percentiles 10.9-372.6]) followed by those with FRD (median 31.2 pg/mg creatinine [10th-90th percentiles 6.2-114.5]) and control dogs (median 21.1 pg/mg creatinine [10th-90th percentiles 9.1-86.5]). Urinary LTE4 concentrations were higher in dogs with IBD than in control dogs (P = .011), but no significant difference between IBD and FRD was found. No correlation was found between urinary LTE4 concentrations and CIBDAI. CONCLUSIONS AND CLINICAL IMPORTANCE: The higher urinary LTE4 concentrations in dogs with IBD suggest that cysteinyl leukotriene pathway activation might be a component of the inflammatory process in canine IBD. Furthermore, urinary LTE4 concentrations are of potential use as a marker of inflammation in dogs with CCE.

Genital chlamydia prevalence in Europe and in non-European high income countries: systematic review and meta-analysis

Background: Accurate information about the prevalence of Chlamydia trachomatis is needed to assess national prevention and control measures. Methods: We systematically reviewed population-based cross-sectional studies that estimated chlamydia prevalence in European Union/European Economic Area (EU/EEA) Member States and non-European high income countries from January 1990 to August 2012. We examined results in forest plots, explored heterogeneity using the I2 statistic, and conducted random effects meta-analysis if appropriate. Metaregression was used to examine the relationship between study characteristics and chlamydia prevalence estimates. Results: We included 25 population-based studies from 11 EU/EEA countries and 14 studies from five other high income countries. Four EU/EEA Member States reported on nationally representative surveys of sexually experienced adults aged 18-26 years (response rates 52-71%). In women, chlamydia point prevalence estimates ranged from 3.0-5.3%; the pooled average of these estimates was 3.6% (95% CI 2.4, 4.8, I2 0%). In men, estimates ranged from 2.4-7.3% (pooled average 3.5%; 95% CI 1.9, 5.2, I2 27%). Estimates in EU/EEA Member States were statistically consistent with those in other high income countries (I2 0% for women, 6% for men). There was statistical evidence of an association between survey response rate and estimated chlamydia prevalence; estimates were higher in surveys with lower response rates, (p=0.003 in women, 0.018 in men). Conclusions: Population-based surveys that estimate chlamydia prevalence are at risk of participation bias owing to low response rates. Estimates obtained in nationally representative samples of the general population of EU/EEA Member States are similar to estimates from other high income countries.

A full pedigree based method for the statistical assessment of genetic anticipation

Genetic anticipation is defined as a decrease in age of onset or increase in severity as the disorder is transmitted through subsequent generations. Anticipation has been noted in the literature for over a century. Recently, anticipation in several diseases including Huntington's Disease, Myotonic Dystrophy and Fragile X Syndrome were shown to be caused by expansion of triplet repeats. Anticipation effects have also been observed in numerous mental disorders (e.g. Schizophrenia, Bipolar Disorder), cancers (Li-Fraumeni Syndrome, Leukemia) and other complex diseases. ^ Several statistical methods have been applied to determine whether anticipation is a true phenomenon in a particular disorder, including standard statistical tests and newly developed affected parent/affected child pair methods. These methods have been shown to be inappropriate for assessing anticipation for a variety of reasons, including familial correlation and low power. Therefore, we have developed family-based likelihood modeling approaches to model the underlying transmission of the disease gene and penetrance function and hence detect anticipation. These methods can be applied in extended families, thus improving the power to detect anticipation compared with existing methods based only upon parents and children. The first method we have proposed is based on the regressive logistic hazard model. This approach models anticipation by a generational covariate. The second method allows alleles to mutate as they are transmitted from parents to offspring and is appropriate for modeling the known triplet repeat diseases in which the disease alleles can become more deleterious as they are transmitted across generations. ^ To evaluate the new methods, we performed extensive simulation studies for data simulated under different conditions to evaluate the effectiveness of the algorithms to detect genetic anticipation. Results from analysis by the first method yielded empirical power greater than 87% based on the 5% type I error critical value identified in each simulation depending on the method of data generation and current age criteria. Analysis by the second method was not possible due to the current formulation of the software. The application of this method to Huntington's Disease and Li-Fraumeni Syndrome data sets revealed evidence for a generation effect in both cases. ^