Profile control of the specific growth rate in fed-batch experiments

Summary The aim of this paper is to apply a computer control scheme to a laboratory scale fermentor so that the specific growth rate in a baker's yeast fed-batch culture, which cannot be measured directly, will follow as accurately as possible the desired profile specified in advance. Using an extended Kalman filter and programmed controller/feedback compensator (PF) system proposed previously, profile control of the specific growth rate () was achieved experimentally in a baker's yeast fed-batch culture. Also, bang-bang type profile control of minimized the proportion of budding cells, which have a strong correlation with the fermentative activity in bread-making.     

Noise effects on robust synchronization of a small pacemaker neuronal ensemble via nonlinear controller: electronic circuit design

In this paper, we report on the synchronization of a pacemaker neuronal ensemble constituted of an AB neuron electrically coupled to two PD neurons. By the virtue of this electrical coupling, they can fire synchronous bursts of action potential. An external master neuron is used to induce to the whole system the desired dynamics, via a nonlinear controller. Such controller is obtained by a combination of sliding mode and feedback control. The proposed controller is able to offset uncertainties in the synchronized systems. We show how noise affects the synchronization of the pacemaker neuronal ensemble, and briefly discuss its potential benefits in our synchronization scheme. An extended Hindmarsh–Rose neuronal model is used to represent a single cell dynamic of the network. Numerical simulations and Pspice implementation of the synchronization scheme are presented. We found that, the proposed controller reduces the stochastic resonance of the network when its gain increases.     

Influences of the recombinant artificial cell adhesive proteins on the behavior of human umbilical vein endothelial cells in serum-free culture

To improve the safety of cellular therapy products, it is necessary to establish a serum-free cell culture method that can exclude animal-derived materials in order to avoid contamination with transmissible agents. It would be optimal if the proteins necessary to a serum-free culture could be provided as recombinant proteins. In this study, the influences of recombinant artificial cell adhesive proteins on the behavior of human umbilical vein endothelial cells (HUVECs) in serum-free culture were examined in comparison with the influence of plasma fibronectin (FN). The recombinant proteins used were Pronectin F (PF), Pronectin F PLUS (PFP), Pronectin L (PL), Retronectin (RN), and Attachin (AN). HUVECs adhered more efficiently on PF or PFP than on FN. No cells adhered on PL. Regarding the VEGF or bFGF-induced cell growth, the cells on PF and PFP proliferated at a similar rate to the cells on FN. RN and AN were less effective in supporting cell growth. Since cell adhesion on PF and PFP induced phosphorylation of focal adhesion kinase, they are thought to activate integrin-mediated intracellular signaling. The cells cultured on PF or PFP were able to produce prostaglandin I(2) or tissue-plasminogen activator in response to thrombin. However, thrombin caused detachment of the cells from PF but not from PFP or FN, meaning that the cells were able to adhere more tightly on PFP or FN than on PF. These data indicate that PFP could be applicable as a substitute for plasma FN.     

Hardware (DNA) circuits

A scheme is presented whereby a new genetic control circuit can be introduced into an organism, permitting the experimenter to turn the expression of a given gene (or set of genes) on or off at will. The proposed scheme involves a positive feedback loop--here, a positive regulator, the CII protein of phage lambda, with its structural gene engineered so as to require CII for its expression. This feedback loop creates the possibility of two stable steady states, with gene cII ON or OFF. Genes added downstream of cII and lacking a promoter will follow the same expression as cII. Two additional circuits allow the experimenter to switch at will between the ON and OFF states.     

Optimal feedback control to describe multiple representations of primary motor cortex neurons

Primary motor cortex (M1) neurons are tuned in response to several parameters related to motor control, and it was recently reported that M1 is important in feedback control. However, it remains unclear how M1 neurons encode information to control the musculoskeletal system. In this study, we examined the underlying computational mechanisms of M1 based on optimal feedback control (OFC) theory, which is a plausible hypothesis for neuromotor control. We modelled an isometric torque production task that required joint torque to be regulated and maintained at desired levels in a musculoskeletal system physically constrained by muscles, which act by pulling rather than pushing. Then, a feedback controller was computed using an optimisation approach under the constraint. In the presence of neuromotor noise, known as signal-dependent noise, the sensory feedback gain is tuned to an extrinsic motor output, such as the hand force, like a population response of M1 neurons. Moreover, a distribution of the preferred directions (PDs) of M1 neurons can be predicted via feedback gain. Therefore, we suggest that neural activity in M1 is optimised for the musculoskeletal system. Furthermore, if the feedback controller is represented in M1, OFC can describe multiple representations of M1, including not only the distribution of PDs but also the response of the neuronal population.     

On the sample size requirement in genetic association tests when the proportion of false positives is controlled

With respect to the multiple-tests problem, recently an increasing amount of attention has been paid to control the false discovery rate (FDR), the positive false discovery rate (pFDR), and the proportion of false positives (PFP). The new approaches are generally believed to be more powerful than the classical Bonferroni one. This article focuses on the PFP approach. It demonstrates via examples in genetic association studies that the Bonferroni procedure can be more powerful than the PFP-control one and also shows the intrinsic connection between controlling the PFP and controlling the overall type I error rate. Since controlling the PFP does not necessarily lead to a desired power level, this article addresses the design issue and recommends the sample sizes that can attain the desired power levels when the PFP is controlled. The results in this article also provide rough guidance for the sample sizes to achieve the desired power levels when the FDR and especially the pFDR are controlled.     

Real-time spectrum estimation–based dual-channel speech-enhancement algorithm for cochlear implant

Background

Improvement of the cochlear implant (CI) front-end signal acquisition is needed to increase speech recognition in noisy environments. To suppress the directional noise, we introduce a speech-enhancement algorithm based on microphone array beamforming and spectral estimation. The experimental results indicate that this method is robust to directional mobile noise and strongly enhances the desired speech, thereby improving the performance of CI devices in a noisy environment.

Methods

The spectrum estimation and the array beamforming methods were combined to suppress the ambient noise. The directivity coefficient was estimated in the noise-only intervals, and was updated to fit for the mobile noise.

Results

The proposed algorithm was realized in the CI speech strategy. For actual parameters, we use Maxflat filter to obtain fractional sampling points and cepstrum method to differentiate the desired speech frame and the noise frame. The broadband adjustment coefficients were added to compensate the energy loss in the low frequency band.

Discussions

The approximation of the directivity coefficient is tested and the errors are discussed. We also analyze the algorithm constraint for noise estimation and distortion in CI processing. The performance of the proposed algorithm is analyzed and further be compared with other prevalent methods.

Conclusions

The hardware platform was constructed for the experiments. The speech-enhancement results showed that our algorithm can suppresses the non-stationary noise with high SNR. Excellent performance of the proposed algorithm was obtained in the speech enhancement experiments and mobile testing. And signal distortion results indicate that this algorithm is robust with high SNR improvement and low speech distortion.     

Adaptive computer control of anesthesia in humans

This paper presents an efficient computer control technique for regulation of anesthesia in humans. The anesthetic used is propofol and the objective is to control the degree of hypnosis of the patient. The paper describes the basic hardware/software setup of the system and the closed-loop methodologies. The bispectral index (BIS) is considered as the feedback signal. The control methods proposed here are based in the use of proportional integral controllers with dead-time compensation to avoid undesirable oscillations in the BIS signal during the process. The compensation is based on the Smith predictor. To guarantee the applicability of the method to different patients, an adaptive module to tune the compensator is developed. Some real and simulated results are presented in this work to attest the efficiency of the methods used.     

Optimal control mode of a biochemical feedback system

An optimal feedback system for constant-value control of biochemical reaction system was investigated by computer simulations. A feedback system containing a cyclic enzyme system where two enzyme types share a substrate in a cyclic manner, was found to be the most reliable one. This feedback system has a capability to keep the stationary value of the end product at a desired level against not only exogenous substrate supply but also endogenous parametric disturbances. The cyclic enzyme system installed as a control element of this feedback system played the role of comparator in this feedback system. The control mode of this feedback system was in good agreement with that of a system established by means of optimization technique based on the maximum principle. Also bang - bang control could be performed in this biochemical feedback system as well as in electrical one.     

Adaptive control for mimo uncertain nonlinear systems using recurrent wavelet neural network

Recurrent wavelet neural network (RWNN) has the advantages such as fast learning property, good generalization capability and information storing ability. With these advantages, this paper proposes an RWNN-based adaptive control (RBAC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The RBAC system is composed of a neural controller and a bounding compensator. The neural controller uses an RWNN to online mimic an ideal controller, and the bounding compensator can provide smooth and chattering-free stability compensation. From the Lyapunov stability analysis, it is shown that all signals in the closed-loop RBAC system are uniformly ultimately bounded. Finally, the proposed RBAC system is applied to the MIMO uncertain nonlinear systems such as a mass-spring-damper mechanical system and a two-link robotic manipulator system. Simulation results verify that the proposed RBAC system can achieve favorable tracking performance with desired robustness without any chattering phenomenon in the control effort.     

Robust compensator control of continuous fermentation processes

The paper deals with the robust compensator control of continuous fermentation processes described by a set of three non-linear differential equations. For the design purposes the non-linear model is transformed into linear one with interval parameters. Robust state space compensator is designed by the internal model principle, which ensures robust step-wise set points asymptotic tracking and external disturbances rejection in the wide working range. The effectiveness of the algorithm designed is performed by computer simulation experiments. An important feature of the proposed algorithms is their robustness over parameter uncertainties in the process models.     

Pulsed-field electrophoretic fingerprinting of Salmonella indiana and its epidemiological applicability

Eight Xba I-generated pulsed-field profile (PFP) types and four subtypes within one of the most common PFP types have been identified in Salmonella indiana from patients, poultry and human food in England and Wales in the three-year period from January 1994 to December 1996. Two PFP types have predominated, PFP X1 and PFP X2. Although the PFP X1 type was identified throughout the study period, the PFP X2 type was not identified until late 1995, subsequently becoming the most common PFP type in humans in the first six months of 1996 with a significant distribution in elderly patients. It is concluded that PFGE can be used in support of epidemiological investigations for the subdivision of Salm. indiana. Furthermore, as both conditions and interpretation criteria can be easily standardized, it is suggested that for many salmonella serotypes, PFGE can provide the basis for a definitive scheme of genotypic subtyping suitable for epidemiological investigations at both a national and international level.     

Pulsed-field electrophoretic fingerprinting of Salmonella indiana and its epidemiological applicability

Eight Xba I-generated pulsed-field profile (PFP) types and four subtypes within one of the most common PFP types have been identified in Salmonella indiana from patients, poultry and human food in England and Wales in the three-year period from January 1994 to December 1996. Two PFP types have predominated, PFP X1 and PFP X2. Although the PFP X1 type was identified throughout the study period, the PFP X2 type was not identified until late 1995, subsequently becoming the most common PFP type in humans in the first six months of 1996 with a significant distribution in elderly patients. It is concluded that PFGE can be used in support of epidemiological investigations for the subdivision of Salm. indiana . Furthermore, as both conditions and interpretation criteria can be easily standardized, it is suggested that for many salmonella serotypes, PFGE can provide the basis for a definitive scheme of genotypic subtyping suitable for epidemiological investigations at both a national and international level.     

Adjusting phenotypes by noise control

Genetically identical cells can show phenotypic variability. This is often caused by stochastic events that originate from randomness in biochemical processes involving in gene expression and other extrinsic cellular processes. From an engineering perspective, there have been efforts focused on theory and experiments to control noise levels by perturbing and replacing gene network components. However, systematic methods for noise control are lacking mainly due to the intractable mathematical structure of noise propagation through reaction networks. Here, we provide a numerical analysis method by quantifying the parametric sensitivity of noise characteristics at the level of the linear noise approximation. Our analysis is readily applicable to various types of noise control and to different types of system; for example, we can orthogonally control the mean and noise levels and can control system dynamics such as noisy oscillations. As an illustration we applied our method to HIV and yeast gene expression systems and metabolic networks. The oscillatory signal control was applied to p53 oscillations from DNA damage. Furthermore, we showed that the efficiency of orthogonal control can be enhanced by applying extrinsic noise and feedback. Our noise control analysis can be applied to any stochastic model belonging to continuous time Markovian systems such as biological and chemical reaction systems, and even computer and social networks. We anticipate the proposed analysis to be a useful tool for designing and controlling synthetic gene networks.     

A Markovian approach to the control of genetic regulatory networks

This paper presents an approach for controlling gene networks based on a Markov chain model, where the state of a gene network is represented as a probability distribution, while state transitions are considered to be probabilistic. An algorithm is proposed to determine a sequence of control actions that drives (without state feedback) the state of a given network to within a desired state set with a prescribed minimum or maximum probability. A heuristic is proposed and shown to improve the efficiency of the algorithm for a class of genetic networks.     

Mixed immunotherapy and chemotherapy of tumors: feedback design and model updating schemes

In this paper, a recently developed model governing the cancer growth on a cell population level with combination of immune and chemotherapy is used to develop a reactive (feedback) mixed treatment strategy. The feedback design proposed here is based on nonlinear constrained model predictive control together with an adaptation scheme that enables the effects of unavoidable modeling uncertainties to be compensated. The effectiveness of the proposed strategy is shown under realistic human data showing the advantage of treatment in feedback form as well as the relevance of the adaptation strategy in handling uncertainties and modeling errors. A new treatment strategy defined by an original optimal control problem formulation is also proposed. This new formulation shows particularly interesting possibilities since it may lead to tumor regression under better health indicator profile.     

Capsaicin formation in p-fluorophenylalanine resistant and normal cell cultures ofCapsicum frutescens and activity of phenylalanine ammonia lyase

Callus cultures ofCapsicum frutescens capable of producing a maximum of 53 μg capsaicin/g FW were exposed to various levels of p-fluorophenyialanine (PFP) at 100, 400, 1000 and 2000 μM to develop a resistant cell line that over produces capsaicin. After 15 days of culturing on media lacking PFP, cell lines resistant to 100, 400 and 1000 μM registered 18%, 34.5% and 45% increase in capsaicin content over normal cell line (cells not exposed to PFP). Capsaicin accumulation was inhibited in 2000 μM PFP resistant cell line. The profile of phenylalanine ammonia lyase (PAL), the key enzyme in pheny1propanoid pathway in resistant cell cultures was studied and compared with normal cell cultures to understand its role in capsaicin formation. Importantly increased production of capsaicin was obtained using PFP resistant cell lines. The activity profile of PAL had no correlation with capsaicin content in both control and PFP resistant cells.     

Artificial Neural Networks Based Controller for Glucose Monitoring during Clamp Test

Insulin resistance (IR) is one of the most widespread health problems in modern times. The gold standard for quantification of IR is the hyperinsulinemic-euglycemic glucose clamp technique. During the test, a regulated glucose infusion is delivered intravenously to maintain a constant blood glucose concentration. Current control algorithms for regulating this glucose infusion are based on feedback control. These models require frequent sampling of blood, and can only partly capture the complexity associated with regulation of glucose. Here we present an improved clamp control algorithm which is motivated by the stochastic nature of glucose kinetics, while using the minimal need in blood samples required for evaluation of IR. A glucose pump control algorithm, based on artificial neural networks model was developed. The system was trained with a data base collected from 62 rat model experiments, using a back-propagation Levenberg-Marquardt optimization. Genetic algorithm was used to optimize network topology and learning features. The predictive value of the proposed algorithm during the temporal period of interest was significantly improved relative to a feedback control applied at an equivalent low sampling interval. Robustness to noise analysis demonstrates the applicability of the algorithm in realistic situations.     

Adaptive filter model of the cerebellum

The Marr-Albus model of the cerebellum has been reformulated with linear system analysis. This adaptive linear filter model of the cerebellum performs a filtering action of a phase lead-lag compensator with learning capability, and will give an account for the phenomena which have been termed cerebellar compensation. It is postulated that a Golgi cell may act as a phase lag element; for example, as a leaky integrator with time constant about several seconds. Under this assumption, a mossy fiber-granule cell-Golgi cell input network functions as a phase lead-lag compensator. Output signals from Golgi-granule cell systems, namely, parallel fiber signals, are gathered together through variable synaptic connections to form a Purkinje cell output. From a general theory of adaptive linear filters, learning principles for these modifiable connections are derived. By these learning principles, a Purkinje cell output converges to the desired response to minimize the mean square error of the performance. In a more general sense, a Purkinje cell acquires a filtering function on the basis of multiple pairs of input signals and corresponding desired output signals. The mode of convergence of the output signal is described when the input signal is sinusoidal.