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.

Effects of volume resuscitation on splanchnic perfusion in canine model of severe sepsis induced by live Escherichia coli infusion

Abstract Introduction We conducted the present study to investigate whether early large-volume crystalloid infusion can restore gut mucosal blood flow and mesenteric oxygen metabolism in severe sepsis. Methods Anesthetized and mechanically ventilated male mongrel dogs were challenged with intravenous injection of live Escherichia coli (6 × 109 colony-forming units/ml per kg over 15 min). After 90 min they were randomly assigned to one of two groups – control (no fluids; n = 13) or lactated Ringer's solution (32 ml/kg per hour; n = 14) – and followed for 60 min. Cardiac index, mesenteric blood flow, mean arterial pressure, systemic and mesenteric oxygen-derived variables, blood lactate and gastric carbon dioxide tension (PCO2; by gas tonometry) were assessed throughout the study. Results E. coli infusion significantly decreased arterial pressure, cardiac index, mesenteric blood flow, and systemic and mesenteric oxygen delivery, and increased arterial and portal lactate, intramucosal PCO2, PCO2 gap (the difference between gastric mucosal and arterial PCO2), and systemic and mesenteric oxygen extraction ratio in both groups. The Ringer's solution group had significantly higher cardiac index and systemic oxygen delivery, and lower oxygen extraction ratio and PCO2 gap at 165 min as compared with control animals. However, infusion of lactated Ringer's solution was unable to restore the PCO2 gap. There were no significant differences between groups in mesenteric oxygen delivery, oxygen extraction ratio, or portal lactate at the end of study. Conclusion Significant disturbances occur in the systemic and mesenteric beds during bacteremic severe sepsis. Although large-volume infusion of lactated Ringer's solution restored systemic hemodynamic parameters, it was unable to correct gut mucosal PCO2 gap.

Metabolic, hemodynamic and structural adjustments to low intensity exercise training in a metabolic syndrome model

Background The increase in fructose consumption is paralleled by a higher incidence of metabolic syndrome, and consequently, cardiovascular disease mortality. We examined the effects of 8 weeks of low intensity exercise training (LET) on metabolic, hemodynamic, ventricular and vascular morphological changes induced by fructose drinking in male rats. Methods Male Wistar rats were divided into (n = 8 each) control (C), sedentary fructose (F) and ET fructose (FT) groups. Fructose-drinking rats received D-fructose (100 g/l). FT rats were assigned to a treadmill training protocol at low intensity (30% of maximal running speed) during 1 h/day, 5 days/week for 8 weeks. Measurements of triglyceride concentrations, white adipose tissue (WAT) and glycemia were carried out together with insulin tolerance test to evaluate metabolic profile. Arterial pressure (AP) signals were directly recorded. Baroreflex sensitivity (BS) was evaluated by the tachycardic and bradycardic responses. Right atria, left ventricle (LV) and ascending aorta were prepared to morphoquantitative analysis. Results LET reduced WAT (−37.7%), triglyceride levels (−33%), systolic AP (−6%), heart weight/body weight (−20.5%), LV (−36%) and aortic (−76%) collagen fibers, aortic intima-media thickness and circumferential wall tension in FT when compared to F rats. Additionally, FT group presented improve of BS, numerical density of atrial natriuretic peptide granules (+42%) and LV capillaries (+25%), as well as the number of elastic lamellae in aorta compared with F group. Conclusions Our data suggest that LET, a widely recommended practice, seems to be particularly effective for preventing metabolic, hemodynamic and morphological disorders triggered by MS.

Adhesion molecules of detrusor muscle cells are influenced by a hypercholesterolemic diet or bladder outlet obstruction in a wistar rat model

Abstract Background Cell adhesion molecules (CAMs) are essential for maintaining tissue integrity by regulating intercellular and cell to extracellular matrix interactions. Cadherins and catenins are CAMs that are located on the cell membrane and are important for adherens junction (AJ) function. This study aims to verify if hypercholesterolemic diet (HCD) or bladder outlet obstruction (BOO) promotes structural bladder wall modifications specific to alterations in the expression of cadherins and catenins in detrusor muscle cells. Methods Forty-five 4-week-old female Wistar rats were divided into the following three groups: group 1 was a control group that was fed a normal diet (ND); group 2 was the BOO model and was fed a ND; and group 3 was a control group that was fed a HCD (1.25% cholesterol). Initially, serum cholesterol, LDL cholesterol and body weight were determined. Four weeks later, groups 1 and 3 underwent a sham operation; whereas group 2 underwent a partial BOO procedure that included a suture tied around the urethra. Six weeks later, all rats had their bladders removed, and previous exams were repeated. The expression levels of N-, P-, and E-cadherin, cadherin-11 and alpha-, beta- and gamma-catenins were evaluated by immunohistochemistry with a semiquantitative analysis. Results Wistar rats fed a HCD (group 3) exhibited a significant increase in LDL cholesterol levels (p=0.041) and body weight (p=0.017) when compared to both groups that were fed a normal diet in a ten-week period. We found higher β- and γ-catenin expression in groups 2 and 3 when compared to group 1 (p = 0.042 and p = 0.044, respectively). We also observed Cadherin-11 overexpression in group 3 when compared to groups 1 and 2 (p = 0.002). Conclusions A HCD in Wistar rats promoted, in addition to higher body weight gain and increased serum LDL cholesterol levels, overexpression of β- and γ-catenin in the detrusor muscle cells. Similar finding was observed in the BOO group. Higher Cadherin-11 expression was observed only in the HCD-treated rats. These findings may be associated with bladder dysfunctions that occur under such situations.

Model and control of tendon actuated robots

The use of tendons for the transmission of the forces and the movements in robotic devices has been investigated from several researchers all over the world. The interest in this kind of actuation modality is based on the possibility of optimizing the position of the actuators with respect to the moving part of the robot, in the reduced weight, high reliability, simplicity in the mechanic design and, finally, in the reduced cost of the resulting kinematic chain. After a brief discussion about the benefits that the use of tendons can introduce in the motion control of a robotic device, the design and control aspects of the UB Hand 3 anthropomorphic robotic hand are presented. In particular, the tendon-sheaths transmission system adopted in the UB Hand 3 is analyzed and the problem of force control and friction compensation is taken into account. The implementation of a tendon based antagonistic actuated robotic arm is then investigated. With this kind of actuation modality, and by using transmission elements with nonlinear force/compression characteristic, it is possible to achieve simultaneous stiffness and position control, improving in this way the safety of the device during the operation in unknown environments and in the case of interaction with other robots or with humans. The problem of modeling and control of this type of robotic devices is then considered and the stability analysis of proposed controller is reported. At the end, some tools for the realtime simulation of dynamic systems are presented. This realtime simulation environment has been developed with the aim of improving the reliability of the realtime control applications both for rapid prototyping of controllers and as teaching tools for the automatic control courses.

Model predictive flight control systems for rotorcraft UAS

Constraints are widely present in the flight control problems: actuators saturations or flight envelope limitations are only some examples of that. The ability of Model Predictive Control (MPC) of dealing with the constraints joined with the increased computational power of modern calculators makes this approach attractive also for fast dynamics systems such as agile air vehicles. This PhD thesis presents the results, achieved at the Aerospace Engineering Department of the University of Bologna in collaboration with the Dutch National Aerospace Laboratories (NLR), concerning the development of a model predictive control system for small scale rotorcraft UAS. Several different predictive architectures have been evaluated and tested by means of simulation, as a result of this analysis the most promising one has been used to implement three different control systems: a Stability and Control Augmentation System, a trajectory tracking and a path following system. The systems have been compared with a corresponding baseline controller and showed several advantages in terms of performance, stability and robustness.

Model predictive control in thermal management of multiprocessor systems-on-chip

MultiProcessor Systems-on-Chip (MPSoC) are the core of nowadays and next generation computing platforms. Their relevance in the global market continuously increase, occupying an important role both in everydaylife products (e.g. smartphones, tablets, laptops, cars) and in strategical market sectors as aviation, defense, robotics, medicine. Despite of the incredible performance improvements in the recent years processors manufacturers have had to deal with issues, commonly called “Walls”, that have hindered the processors development. After the famous “Power Wall”, that limited the maximum frequency of a single core and marked the birth of the modern multiprocessors system-on-chip, the “Thermal Wall” and the “Utilization Wall” are the actual key limiter for performance improvements. The former concerns the damaging effects of the high temperature on the chip caused by the large power densities dissipation, whereas the second refers to the impossibility of fully exploiting the computing power of the processor due to the limitations on power and temperature budgets. In this thesis we faced these challenges by developing efficient and reliable solutions able to maximize performance while limiting the maximum temperature below a fixed critical threshold and saving energy. This has been possible by exploiting the Model Predictive Controller (MPC) paradigm that solves an optimization problem subject to constraints in order to find the optimal control decisions for the future interval. A fully-distributedMPC-based thermal controller with a far lower complexity respect to a centralized one has been developed. The control feasibility and interesting properties for the simplification of the control design has been proved by studying a partial differential equation thermal model. Finally, the controller has been efficiently included in more complex control schemes able to minimize energy consumption and deal with mixed-criticalities tasks

An insulin infusion advisory system based on autotuning nonlinear model-predictive control

This paper aims at the development and evaluation of a personalized insulin infusion advisory system (IIAS), able to provide real-time estimations of the appropriate insulin infusion rate for type 1 diabetes mellitus (T1DM) patients using continuous glucose monitors and insulin pumps. The system is based on a nonlinear model-predictive controller (NMPC) that uses a personalized glucose-insulin metabolism model, consisting of two compartmental models and a recurrent neural network. The model takes as input patient's information regarding meal intake, glucose measurements, and insulin infusion rates, and provides glucose predictions. The predictions are fed to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. An algorithm based on fuzzy logic has been developed for the on-line adaptation of the NMPC control parameters. The IIAS has been in silico evaluated using an appropriate simulation environment (UVa T1DM simulator). The IIAS was able to handle various meal profiles, fasting conditions, interpatient variability, intraday variation in physiological parameters, and errors in meal amount estimations.

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 1: data requirements, processing, and analysis

This is the first part of a study investigating a model-based transient calibration process for diesel engines. The motivation is to populate hundreds of parameters (which can be calibrated) in a methodical and optimum manner by using model-based optimization in conjunction with the manual process so that, relative to the manual process used by itself, a significant improvement in transient emissions and fuel consumption and a sizable reduction in calibration time and test cell requirements is achieved. Empirical transient modelling and optimization has been addressed in the second part of this work, while the required data for model training and generalization are the focus of the current work. Transient and steady-state data from a turbocharged multicylinder diesel engine have been examined from a model training perspective. A single-cylinder engine with external air-handling has been used to expand the steady-state data to encompass transient parameter space. Based on comparative model performance and differences in the non-parametric space, primarily driven by a high engine difference between exhaust and intake manifold pressures (ΔP) during transients, it has been recommended that transient emission models should be trained with transient training data. It has been shown that electronic control module (ECM) estimates of transient charge flow and the exhaust gas recirculation (EGR) fraction cannot be accurate at the high engine ΔP frequently encountered during transient operation, and that such estimates do not account for cylinder-to-cylinder variation. The effects of high engine ΔP must therefore be incorporated empirically by using transient data generated from a spectrum of transient calibrations. Specific recommendations on how to choose such calibrations, how many data to acquire, and how to specify transient segments for data acquisition have been made. Methods to process transient data to account for transport delays and sensor lags have been developed. The processed data have then been visualized using statistical means to understand transient emission formation. Two modes of transient opacity formation have been observed and described. The first mode is driven by high engine ΔP and low fresh air flowrates, while the second mode is driven by high engine ΔP and high EGR flowrates. The EGR fraction is inaccurately estimated at both modes, while EGR distribution has been shown to be present but unaccounted for by the ECM. The two modes and associated phenomena are essential to understanding why transient emission models are calibration dependent and furthermore how to choose training data that will result in good model generalization.

Development of a model-based transient calibration process for diesel engine electronic control module tables – Part 2: modelling and optimization

This is the second part of a study investigating a model-based transient calibration process for diesel engines. The first part addressed the data requirements and data processing required for empirical transient emission and torque models. The current work focuses on modelling and optimization. The unexpected result of this investigation is that when trained on transient data, simple regression models perform better than more powerful methods such as neural networks or localized regression. This result has been attributed to extrapolation over data that have estimated rather than measured transient air-handling parameters. The challenges of detecting and preventing extrapolation using statistical methods that work well with steady-state data have been explained. The concept of constraining the distribution of statistical leverage relative to the distribution of the starting solution to prevent extrapolation during the optimization process has been proposed and demonstrated. Separate from the issue of extrapolation is preventing the search from being quasi-static. Second-order linear dynamic constraint models have been proposed to prevent the search from returning solutions that are feasible if each point were run at steady state, but which are unrealistic in a transient sense. Dynamic constraint models translate commanded parameters to actually achieved parameters that then feed into the transient emission and torque models. Combined model inaccuracies have been used to adjust the optimized solutions. To frame the optimization problem within reasonable dimensionality, the coefficients of commanded surfaces that approximate engine tables are adjusted during search iterations, each of which involves simulating the entire transient cycle. The resulting strategy, different from the corresponding manual calibration strategy and resulting in lower emissions and efficiency, is intended to improve rather than replace the manual calibration process.

The Dual Ownership Structure: a Model to Connect the Cooperative Model With Financial Markets

In business literature, the conflicts among workers, shareholders and the management have been studied mostly in the frame of stakeholder theory. The stakeholder theory recognizes this issue as an agency problem, and tries to solve the problem by establishing a contractual relationship between the agent and principals. However, as Marcoux pointed out, the appropriateness of the contract as a medium to reduce the agency problem should be questioned. As an alternative, the cooperative model minimizes the agency costs by integrating the concept of workers, owners and management. Mondragon Corporation is a successful example of the cooperative model which grew into the sixth largest corporation in Spain. However, the cooperative model has long been ignored in discussions of corporate governance, mainly because the success of the cooperative model is extremely difficult to duplicate in reality. This thesis hopes to revitalize the scholarly examination of cooperatives by developing a new model that overcomes the fundamental problem in the cooperative model: the limited access to capital markets. By dividing the ownership interest into financial and control interest, the dual ownership structure allows cooperatives to issue stock in the capital market by making a financial product out of financial interest.

Closed-loop control of mean arterial blood pressure during surgery with alfentanil: clinical evaluation of a novel model-based predictive controller

BACKGROUND: In contrast to hypnosis, there is no surrogate parameter for analgesia in anesthetized patients. Opioids are titrated to suppress blood pressure response to noxious stimulation. The authors evaluated a novel model predictive controller for closed-loop administration of alfentanil using mean arterial blood pressure and predicted plasma alfentanil concentration (Cp Alf) as input parameters. METHODS: The authors studied 13 healthy patients scheduled to undergo minor lumbar and cervical spine surgery. After induction with propofol, alfentanil, and mivacurium and tracheal intubation, isoflurane was titrated to maintain the Bispectral Index at 55 (+/- 5), and the alfentanil administration was switched from manual to closed-loop control. The controller adjusted the alfentanil infusion rate to maintain the mean arterial blood pressure near the set-point (70 mmHg) while minimizing the Cp Alf toward the set-point plasma alfentanil concentration (Cp Alfref) (100 ng/ml). RESULTS: Two patients were excluded because of loss of arterial pressure signal and protocol violation. The alfentanil infusion was closed-loop controlled for a mean (SD) of 98.9 (1.5)% of presurgery time and 95.5 (4.3)% of surgery time. The mean (SD) end-tidal isoflurane concentrations were 0.78 (0.1) and 0.86 (0.1) vol%, the Cp Alf values were 122 (35) and 181 (58) ng/ml, and the Bispectral Index values were 51 (9) and 52 (4) before surgery and during surgery, respectively. The mean (SD) absolute deviations of mean arterial blood pressure were 7.6 (2.6) and 10.0 (4.2) mmHg (P = 0.262), and the median performance error, median absolute performance error, and wobble were 4.2 (6.2) and 8.8 (9.4)% (P = 0.002), 7.9 (3.8) and 11.8 (6.3)% (P = 0.129), and 14.5 (8.4) and 5.7 (1.2)% (P = 0.002) before surgery and during surgery, respectively. A post hoc simulation showed that the Cp Alfref decreased the predicted Cp Alf compared with mean arterial blood pressure alone. CONCLUSION: The authors' controller has a similar set-point precision as previous hypnotic controllers and provides adequate alfentanil dosing during surgery. It may help to standardize opioid dosing in research and may be a further step toward a multiple input-multiple output controller.

Model-based control of neuromuscular block using mivacurium: design and clinical verification

BACKGROUND: Short-acting agents for neuromuscular block (NMB) require frequent dosing adjustments for individual patient's needs. In this study, we verified a new closed-loop controller for mivacurium dosing in clinical trials. METHODS: Fifteen patients were studied. T1% measured with electromyography was used as input signal for the model-based controller. After induction of propofol/opiate anaesthesia, stabilization of baseline electromyography signal was awaited and a bolus of 0.3 mg kg-1 mivacurium was then administered to facilitate endotracheal intubation. Closed-loop infusion was started thereafter, targeting a neuromuscular block of 90%. Setpoint deviation, the number of manual interventions and surgeon's complaints were recorded. Drug use and its variability between and within patients were evaluated. RESULTS: Median time of closed-loop control for the 11 patients included in the data processing was 135 [89-336] min (median [range]). Four patients had to be excluded because of sensor problems. Mean absolute deviation from setpoint was 1.8 +/- 0.9 T1%. Neither manual interventions nor complaints from the surgeons were recorded. Mean necessary mivacurium infusion rate was 7.0 +/- 2.2 microg kg-1 min-1. Intrapatient variability of mean infusion rates over 30-min interval showed high differences up to a factor of 1.8 between highest and lowest requirement in the same patient. CONCLUSIONS: Neuromuscular block can precisely be controlled with mivacurium using our model-based controller. The amount of mivacurium needed to maintain T1% at defined constant levels differed largely between and within patients. Closed-loop control seems therefore advantageous to automatically maintain neuromuscular block at constant levels.

Predicting asthma control and exacerbations: chronic asthma as a complex dynamic model

PURPOSE OF REVIEW: Predicting asthma episodes is notoriously difficult but has potentially significant consequences for the individual, as well as for healthcare services. The purpose of this review is to describe recent insights into the prediction of acute asthma episodes in relation to classical clinical, functional or inflammatory variables, as well as present a new concept for evaluating asthma as a dynamically regulated homeokinetic system. RECENT FINDINGS: Risk prediction for asthma episodes or relapse has been attempted using clinical scoring systems, considerations of environmental factors and lung function, as well as inflammatory and immunological markers in induced sputum or exhaled air, and these are summarized here. We have recently proposed that newer mathematical methods derived from statistical physics may be used to understand the complexity of asthma as a homeokinetic, dynamic system consisting of a network comprising multiple components, and also to assess the risk for future asthma episodes based on fluctuation analysis of long time series of lung function. SUMMARY: Apart from the classical analysis of risk factor and functional parameters, this new approach may be used to assess asthma control and treatment effects in the individual as well as in future research trials.