Structured model-following neuro-adaptive design for attitude maneuver of rigid bodies

A new structured model-following adaptive approach is presented in this paper to achieve large attitude maneuvers of rigid bodies. First, a nominal controller is designed using the dynamic inversion philosophy. Next, a neuro- adaptive design is proposed to augment the nominal design in order to assure robust performance in the presence of parameter inaccuracies as well as unknown constant external disturbances. The structured approach proposed in this paper (where kinematic and dynamic equations are handled separately), reduces the complexity of the controller structure. From simulation studies, this adaptive controller is found to be very effective in assuring robust performance.

An optimal dynamic inversion-based neuro-adaptive approach for treatment of chronic myelogenous leukemia

Combining the advanced techniques of optimal dynamic inversion and model-following neuro-adaptive control design, an innovative technique is presented to design an automatic drug administration strategy for effective treatment of chronic myelogenous leukemia (CML). A recently developed nonlinear mathematical model for cell dynamics is used to design the controller (medication dosage). First, a nominal controller is designed based on the principle of optimal dynamic inversion. This controller can treat the nominal model patients (patients who can be described by the mathematical model used here with the nominal parameter values) effectively. However, since the system parameters for a realistic model patient can be different from that of the nominal model patients, simulation studies for such patients indicate that the nominal controller is either inefficient or, worse, ineffective; i.e. the trajectory of the number of cancer cells either shows non-satisfactory transient behavior or it grows in an unstable manner. Hence, to make the drug dosage history more realistic and patient-specific, a model-following neuro-adaptive controller is augmented to the nominal controller. In this adaptive approach, a neural network trained online facilitates a new adaptive controller. The training process of the neural network is based on Lyapunov stability theory, which guarantees both stability of the cancer cell dynamics as well as boundedness of the network weights. From simulation studies, this adaptive control design approach is found to be very effective to treat the CML disease for realistic patients. Sufficient generality is retained in the mathematical developments so that the technique can be applied to other similar nonlinear control design problems as well.

A neuro-adaptive augmented optimal dynamic inversion approach for effective and efficient treatment of chronic myelogenous leukemia

Combining the advanced techniques of optimal dynamic inversion and model-following neuro-adaptive control design, an efficient technique is presented for effective treatment of chronic myelogenous leukemia (CML). A recently developed nonlinear mathematical model for cell dynamics is used for the control (medication) synthesis. First, taking a set of nominal parameters, a nominal controller is designed based on the principle of optimal dynamic inversion. This controller can treat nominal patients (patients having same nominal parameters as used for the control design) effectively. However, since the parameters of an actual patient can be different from that of the ideal patient, to make the treatment strategy more effective and efficient, a model-following neuro-adaptive controller is augmented to the nominal controller. In this approach, a neural network trained online (based on Lyapunov stability theory) facilitates a new adaptive controller, computed online. From the simulation studies, this adaptive control design approach (treatment strategy) is found to be very effective to treat the CML disease for actual patients. Sufficient generality is retained in the theoretical developments in this paper, so that the techniques presented can be applied to other similar problem as well. Note that the technique presented is computationally non-intensive and all computations can be carried out online.

A Neuro-Adaptive Approach for Robust Control of High Performance Aircrafts with Parameter Inaccuracies

For high performance aircrafts, the flight control system needs to be quite effective in both assuring accurate tracking of pilot commands, while simultaneously assuring overall stability of the aircraft. In addition, the control system must also be sufficiently robust to cater to possible parameter variations. The primary aim of this paper is to enhance the robustness of the controller for a HPA using neuro-adaptive control design. Here the architecture employs a network of Gaussian Radial basis functions to adaptively compensate for the ignored system dynamics. A stable weight mechanism is determined using Lyapunov theory. The network construction and performance of the resulting controller are illustrated through simulations with a low-fidelity six –DOF model of F16 that is available in open literature.

Model-following Neuro-adaptive approach for robust control of high performance aircrafts

Based on dynamic inversion, a relatively straightforward approach is presented in this paper for nonlinear flight control design of high performance aircrafts, which does not require the normal and lateral acceleration commands to be first transferred to body rates before computing the required control inputs. This leads to substantial improvement of the tracking response. Promising results are obtained from six degree-offreedom simulation studies of F-16 aircraft, which are found to be superior as compared to an existing approach (which is also based on dynamic inversion). The new approach has two potential benefits, namely reduced oscillatory response (including elimination of non-minimum phase behavior) and reduced control magnitude. Next, a model-following neuron-adaptive design is augmented the nominal design in order to assure robust performance in the presence of parameter inaccuracies in the model. Note that in the approach the model update takes place adaptively online and hence it is philosophically similar to indirect adaptive control. However, unlike a typical indirect adaptive control approach, there is no need to update the individual parameters explicitly. Instead the inaccuracy in the system output dynamics is captured directly and then used in modifying the control. This leads to faster adaptation, which helps in stabilizing the unstable plant quicker. The robustness study from a large number of simulations shows that the adaptive design has good amount of robustness with respect to the expected parameter inaccuracies in the model.

Fast Robust Pattern Classification Algorithms for Real Time Neuro-Motor Prosthetic Applications

In this paper, we give a brief review of pattern classification algorithms based on discriminant analysis. We then apply these algorithms to classify movement direction based on multivariate local field potentials recorded from a microelectrode array in the primary motor cortex of a monkey performing a reaching task. We obtain prediction accuracies between 55% and 90% using different methods which are significantly above the chance level of 12.5%.

A Novel Anticancer Agent, 8-Methoxypyrimido4 `,5 `: 4,5]thieno(2,3-b) Quinoline-4(3H)-One Induces Neuro 2a Neuroblastoma Cell Death through p53-Dependent, Caspase-Dependent and Independent Apoptotic Pathways

Neuroblastoma is the most common cancer in infants and fourth most common cancer in children. Despite recent advances in cancer treatments, the prognosis of stage-IV neuroblastoma patients continues to be dismal which warrant new pharmacotherapy. A novel tetracyclic condensed quinoline compound, 8-methoxypyrimido 4 `,5 `: 4,5] thieno(2,3-b) quinoline-4(3H)-one (MPTQ) is a structural analogue of an anticancer drug ellipticine and has been reported to posses anticancer property. Study on MPTQ on neuroblastoma cells is very limited and mechanisms related to its cytotoxicity on neuroblastoma cells are completely unknown. Here, we evaluated the anticancer property of MPTQ on mouse neuro 2a and human SH-SY5Y neuroblastoma cells and investigated the mechanisms underlying MPTQ-mediated neuro 2a cell death. MPTQ-mediated neuro 2a and SH-SY5Y cell deaths were found to be dose and time dependent. Moreover, MPTQ induced cell death reached approximately 99.8% and 90% in neuro 2a and SH-SY5Y cells respectively. Nuclear oligonucleosomal DNA fragmentation and Terminal dUTP Nick End Labelling assays indicated MPTQ-mediated neuro 2a cell death involved apoptosis. MPTQ-mediated apoptosis is associated with increased phosphorylation of p53 at Ser15 and Ser20 which correlates with the hyperphosphorylation of Ataxia-Telangiectasia mutated protein (ATM). Immunocytochemical analysis demonstrated the increased level of Bax protein in MPTQ treated neuro 2a cells. MPTQ-mediated apoptosis is also associated with increased activation of caspase-9, -3 and -7 but not caspase-2 and -8. Furthermore, increased level of caspase-3 and cleaved Poly ( ADP Ribose) polymerase were observed in the nucleus of MPTQ treated neuro 2a cells, suggesting the involvement of caspase-dependent intrinsic but not extrinsic apoptotic pathway. Increased nuclear translocation of apoptosis inducing factor suggests additional involvement of caspase-independent apoptosis pathway in MPTQ treated neuro 2a cells. Collectively, MPTQ-induced neuro 2a cell death is mediated by ATM and p53 activation, and Bax-mediated activation of caspase-dependent and caspase-independent mitochondrial apoptosis pathways.

Database Independent Human Emotion Recognition with Meta-Cognitive Neuro-Fuzzy Inference System

Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition. The emotion recognition system employed in this work is a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). McFIS has two components, a neuro-fuzzy inference system, which is the cognitive component and a self-regulatory learning mechanism, which is the meta-cognitive component. The meta-cognitive component, monitors the knowledge in the neuro-fuzzy inference system and decides on what-to-learn, when-to-learn and how-to-learn the training samples, efficiently. For each sample, the McFIS decides whether to delete the sample without being learnt, use it to add/prune or update the network parameter or reserve it for future use. This helps the network avoid over-training and as a result improve its generalization performance over untrained databases. In this study, we extract pixel based emotion features from well-known (Japanese Female Facial Expression) JAFFE and (Taiwanese Female Expression Image) TFEID database. Two sets of experiment are conducted. First, we study the individual performance of both databases on McFIS based on 5-fold cross validation study. Next, in order to study the generalization performance, McFIS trained on JAFFE database is tested on TFEID and vice-versa. The performance The performance comparison in both experiments against SVNI classifier gives promising results.

A fuzzy dynamic flood routing model for natural channels

A fuzzy dynamic flood routing model (FDFRM) for natural channels is presented, wherein the flood wave can be approximated to a monoclinal wave. This study is based on modification of an earlier published work by the same authors, where the nature of the wave was of gravity type. Momentum equation of the dynamic wave model is replaced by a fuzzy rule based model, while retaining the continuity equation in its complete form. Hence, the FDFRM gets rid of the assumptions associated with the momentum equation. Also, it overcomes the necessity of calculating friction slope (S-f) in flood routing and hence the associated uncertainties are eliminated. The fuzzy rule based model is developed on an equation for wave velocity, which is obtained in terms of discontinuities in the gradient of flow parameters. The channel reach is divided into a number of approximately uniform sub-reaches. Training set required for development of the fuzzy rule based model for each sub-reach is obtained from discharge-area relationship at its mean section. For highly heterogeneous sub-reaches, optimized fuzzy rule based models are obtained by means of a neuro-fuzzy algorithm. For demonstration, the FDFRM is applied to flood routing problems in a fictitious channel with single uniform reach, in a fictitious channel with two uniform sub-reaches and also in a natural channel with a number of approximately uniform sub-reaches. It is observed that in cases of the fictitious channels, the FDFRM outputs match well with those of an implicit numerical model (INM), which solves the dynamic wave equations using an implicit numerical scheme. For the natural channel, the FDFRM Outputs are comparable to those of the HEC-RAS model.

Effective treatment of infectious diseases: A nonlinear adaptive control theoretic approach

A nonlinear adaptive system theoretic approach is presented in this paper for effective treatment of infectious diseases that affect various organs of the human body. The generic model used does not represent any specific disease. However, it mimics the generic immunological dynamics of the human body under pathological attack, including the response to external drugs. From a system theoretic point of view, drugs can be interpreted as control inputs. Assuming a set of nominal parameters in the mathematical model, first a nonlinear controller is designed based on the principle of dynamic inversion. This treatment strategy was found to be effective in completely curing "nominal patients". However, in some cases it is ineffective in curing "realistic patients". This leads to serious (sometimes fatal) damage to the affected organ. To make the drug dosage design more effective, a model-following neuro-adaptive control design is carried out using neural networks, which are trained (adapted) online. From simulation studies, this adaptive controller is found to be effective in killing the invading microbes and healing the damaged organ even in the presence of parameter uncertainties and continuing pathogen attack.

Cytotoxic T lymphocytes raised against Japanese encephalitis virus: effector cell phenotype, target specificity and in vitro virus clearance.

Several H-2 defined cell lines were examined for their ability to support infection and replication of Japanese encephalitis virus (JEV) before their use in in vitro and in vivo stimulation protocols for generating cytotoxic T lymphocytes (CTLs) against JEV. Among II different cell lines tested, two H-2(d) macrophage tumour lines (P388D1, RAW 264.7), an H-2(d) hybridoma (Sp2/0), an H-2K(k)D(d) neuroblastoma (Neuro 2a), and H-2(k) fibroblast cell line (L929) were found to support JEV infection and replication. These cell lines were used to generate anti-JEV CTLs by using in vivo immunization followed by in vitro stimulation of BALB/c mice. We observed that not only syngeneic and allogeneic infected cells but also JEV-infected xenogeneic cells could prime BALB/c mice for the generation of JEV-specific CTLs upon subsequent in vitro stimulation of splenocytes with JEV-infected syngeneic cells. Although infected xenogeneic cells were used for immunization, the anti-JEV effecters that were generated lysed infected syngeneic targets but not JEV-infected xenogeneic or allogeneic target cells in a 5h Cr-51 release assay. These anti-JEV effecters recognized syngeneic target cells infected with West Nile virus to a lesser extent and were shown to be Lyt-2.2(+) T cells. The results of unlabelled cold target competition studies suggested alterations in the cell surface expression of viral antigenic determinants recognized by these CTLs. We further demonstrate that the JEV-specific CTLs generated could virtually block the release of infectious virus particles from infected P388D1 and Neuro 2a cells in vitro.

Japanese encephalitis virus infection of mouse cell lines: ability to prime mice for generation of virus specific cytotoxic T lymphocytes and differences in CTL recognisable viral determinants

Ten different mouse cell lines were examined for Japanese encephalitis virus (JEV) infection in vitro and then tested for their ability to generate virus specific cytotoxic T lymphocytes (CTL). Among all cell lines examined, Neuro La (a neuroblastoma) was readily infected with JEV as examined by immunofluorescence and viral replication. Among other cells, P388D1, RAW 264.7 (Macrophage origin), Sp2/0 (B-cell Hybridoma), YAC-1 (T-cell lymphoma), and L929 (Fibroblast) were semipermissive to JEV infection. The cytopathic effects caused by progressive JEV infection varied from cell line to cell line. In the case of YAC-1 cells long-term viral antigen expression was observed without significant alterations in cell viability. Intermediate degrees of cytopathicity are seen in RAW 264.7 and L929 cells while infection of PS, Neuro 2a, P388D1 and Sp2/0 caused major viability losses. All infected cell lines were able to prime adult BALB/c (H-2(d)) mice for the generation of secondary JEV specific CTL. In contrast to YAC-1, the permissive neuroblastoma cell line Neuro 2a (H-2K(k)D(d)) was found to be least efficient in its ability to stimulate anti-viral CTL generation. Cold target competition studies demonstrated that both Neuro 2a and YAC-1 (H-2K(k)D(d)) cells expressed similar viral determinants that are recognised by CTL, suggesting that the reason for the lower ability of Neuro 2a to stimulate anti-viral CTL was not due to lack of viral CTL determinants. These findings demonstrate that a variety of mouse cell lines can be infected with Japanese encephalitis virus, and that these infected cells could be utilised to generate virus specific CTL in BALB/c mice.

Subclinical autonomic neuropathy in alcoholics: Decreased heart rate variability

Cardiac autonomic neuropathy is known to occur in alcoholics but the extent of its subclinical form is not usually recognized, Heart Rate Variability (HRV) analysis can detect subclinical autonomic neuropathy. In this study the HRV parameters were compared in 20 neurologically asymptomatic alcoholics, 20 age-matched normals and 16 depressives. All were males, ECG was recorded in a quiet room for four minutes in supine position. Time and Frequency domain parameters of HRV were computed by a researcher blind to clinical details. Alcoholics had significantly smaller Coefficient of Variation of R-R intervals (CVR-R) on time domain analysis and smaller HF band (0.15-0.5 Hz) power on spectral analysis. The decreased Heart Rate Variability indicates cardiac autonomic dysfunction.

A smart approach to precision attitude maneuvers of spacecrafts

A nonlinear adaptive approach is presented to achieve rest-to-rest attitude maneuvers for spacecrafts in the presence of parameter uncertainties and unknown disturbances. A nonlinear controller, designed on the principle of dynamic inversion achieves the goals for the nominal model but suffers performance degradation in the presence of off-nominal parameter values and unwanted inputs. To address this issue, a model-following neuro-adaptive control design is carried out by taking the help of neural networks. Due to the structured approach followed here, the adaptation is restricted to the momentum level equations.The adaptive technique presented is computationally nonintensive and hence can be implemented in real-time. Because of these features, this new approach is named as structured model-following adaptive real-time technique (SMART). From simulation studies, this SMART approach is found to be very effective in achieving precision attitude maneuvers in the presence of parameter uncertainties and unknown disturbances.

Molecular profile of oligodendrogliomas in young patients

Several studies on molecular profiling of oligodendrogliomas (OGs) in adults have shown a distinctive genetic pattern characterized by combined deletions of chromosome arms 1 p and 19q, O6-methylguanine-methyltransferase (MGMT) methylation, and isocitrate dehydrogenase 1 (IDH1) mutation, which have potential diagnostic, prognostic, and even therapeutic relevance. OGs in pediatric and young adult patients are rare and have been poorly characterized on a molecular and biological basis, and it remains uncertain whether markers with prognostic significance in adults also have predictive value in these patients. Fourteen cases of OGs in young patients (age, <= 25 years) who received a diagnosis over 7 years were selected (7 pediatric patients age <= 18 years and 7 young adults aged 19-25 years). The cases were evaluated for 1p/19q status, MGMT promoter methylation, p53 mutation, and IDH1 mutation. None of the pediatric cases showed 1p/19q deletion. In young adults, combined 1p/19q loss was observed in 57% and isolated 1p loss in 14% of cases. The majority of cases in both subgroups (71% in each) harbored MGMT gene promoter methylation. TP53 and IDH1 mutations were not seen in any of the cases in both the groups. To our knowledge, this is the first study to show that molecular profile of OGs in pediatric and young adult patients is distinct. Further large-scale studies are required to identify additional clinically relevant genetic alterations in this group of patients.