An adaptive drug delivery design using neural networks for effective treatment of infectious diseases: A simulation study

An adaptive drug delivery design is presented in this paper using neural networks for effective treatment of infectious diseases. The generic mathematical model used describes the coupled evolution of concentration of pathogens, plasma cells, antibodies and a numerical value that indicates the relative characteristic of a damaged organ due to the disease under the influence of external drugs. From a system theoretic point of view, the external drugs can be interpreted as control inputs, which can be designed based on control theoretic concepts. In this study, assuming a set of nominal parameters in the mathematical model, first a nonlinear controller (drug administration) is designed based on the principle of dynamic inversion. This nominal drug administration plan was found to be effective in curing "nominal model patients" (patients whose immunological dynamics conform to the mathematical model used for the control design exactly. However, it was found to be ineffective in curing "realistic model patients" (patients whose immunological dynamics may have off-nominal parameter values and possibly unwanted inputs) in general. Hence, to make the drug delivery dosage design more effective for realistic model patients, a model-following adaptive control design is carried out next by taking the help of neural networks, that are trained online. Simulation studies indicate that the adaptive controller proposed in this paper holds promise in killing the invading pathogens and healing the damaged organ even in the presence of parameter uncertainties and continued pathogen attack. Note that the computational requirements for computing the control are very minimal and all associated computations (including the training of neural networks) can be carried out online. However it assumes that the required diagnosis process can be carried out at a sufficient faster rate so that all the states are available for control computation.

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 self-adaptive migration model genetic algorithm for data mining applications

Data mining involves nontrivial process of extracting knowledge or patterns from large databases. Genetic Algorithms are efficient and robust searching and optimization methods that are used in data mining. In this paper we propose a Self-Adaptive Migration Model GA (SAMGA), where parameters of population size, the number of points of crossover and mutation rate for each population are adaptively fixed. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions and a set of actual classification datamining problems. Michigan style of classifier was used to build the classifier and the system was tested with machine learning databases of Pima Indian Diabetes database, Wisconsin Breast Cancer database and few others. The performance of our algorithm is better than others.

An adaptive decomposition method for video

Scalable video coding (SVC) is an emerging standard built on the success of advanced video coding standard (H.264/AVC) by the Joint video team (JVT). Motion compensated temporal filtering (MCTF) and Closed loop hierarchical B pictures (CHBP) are two important coding methods proposed during initial stages of standardization. Either of the coding methods, MCTF/CHBP performs better depending upon noise content and characteristics of the sequence. This work identifies other characteristics of the sequences for which performance of MCTF is superior to that of CHBP and presents a method to adaptively select either of MCTF and CHBP coding methods at the GOP level. This method, referred as "Adaptive Decomposition" is shown to provide better R-D performance than of that by using MCTF or CRBP only. Further this method is extended to non-scalable coders.

Design, synthesis, and application of novel triclosan prodrugs as potential antimalarial and antibacterial agents

A number of new triclosan-conjugated analogs bearing biodegradable ester linkage have been synthesized, characterized and evaluated for their antimalarial and antibacterial activities. Many of these compounds exhibit good inhibition against Plasmodium falciparum and Escherichia coli. Among them tertiary amine containing triclosan-conjugated prodrug (5) inhibited both P. falciparum (IC50; 0.62 μM) and E. coli (IC50; 0.26 μM) at lower concentrations as compared to triclosan. Owing to the presence of a cleavable ester moiety, these new prodrugs are hydrolyzed under physiological conditions and parent molecule, triclosan, is released. Further, introduction of tertiary/quaternary functionality increases their cellular uptake. These properties impart them with higher potency to their antimalarial as well as antibacterial activities. The best compound among them 5 shows close to four-fold enhanced activities against P. falciparum and E. coli cultures as compared to triclosan.

Computational analysis of proteome of H5N1 avian influenza virus to define T cell epitopes with vaccine potential

The existing vaccines against influenza are based on the generation of neutralizing antibody primarily directed against surface proteins-hernagglutinin and neuraminidase. In this work, we have computationally defined conserved T cell epitopes of proteins of influenza virus H5N1 to help in the design of a vaccine with haplotype specificity for a target population. The peptides from the proteome of H5NI irus which are predicted to bind to different HLAs, do not show similarity with peptides of human proteorne and are also identified to be generated by proteolytic cleavage. These peptides could be made use of in the design of either a DNA vaccine or a subunit vaccine against V influenza. (c) 2007 Elsevier Ltd. All rights reserved.

Pharmacological inhibition of gut-derived serotonin synthesis is a potential bone anabolic treatment for osteoporosis

Osteoporosis is a disease of low bone mass most often caused by an increase in bone resorption that is not sufficiently compensated for by a corresponding increase in bone formation(1). As gut-derived serotonin (GDS) inhibits bone formation(2), we asked whether hampering its biosynthesis could treat osteoporosis through an anabolic mechanism (that is, by increasing bone formation). We synthesized and used LP533401, a small molecule inhibitor of tryptophan hydroxylase-1 (Tph-1), the initial enzyme in GDS biosynthesis. Oral administration of this small molecule once daily for up to six weeks acts prophylactically or therapeutically, in a dose-dependent manner, to treat osteoporosis in ovariectomized rodents because of an isolated increase in bone formation. These results provide a proof of principle that inhibiting GDS biosynthesis could become a new anabolic treatment for osteoporosis.

Nonlinear structural dynamical system identification using adaptive particle filters

The problem of identifying parameters of nonlinear vibrating systems using spatially incomplete, noisy, time-domain measurements is considered. The problem is formulated within the framework of dynamic state estimation formalisms that employ particle filters. The parameters of the system, which are to be identified, are treated as a set of random variables with finite number of discrete states. The study develops a procedure that combines a bank of self-learning particle filters with a global iteration strategy to estimate the probability distribution of the system parameters to be identified. Individual particle filters are based on the sequential importance sampling filter algorithm that is readily available in the existing literature. The paper develops the requisite recursive formulary for evaluating the evolution of weights associated with system parameter states. The correctness of the formulations developed is demonstrated first by applying the proposed procedure to a few linear vibrating systems for which an alternative solution using adaptive Kalman filter method is possible. Subsequently, illustrative examples on three nonlinear vibrating systems, using synthetic vibration data, are presented to reveal the correct functioning of the method. (c) 2007 Elsevier Ltd. All rights reserved.

Unlocking the potential of bile acids in synthesis, supramolecular/materials chemistry and nanoscience

The Maitra group has explored a variety of chemistry with bile acids during the past 15 years and these experiments have covered a wide variety of chemistry - asymmetric synthesis, molecular recognition, ion receptors/sensors, dendrimers, low molecular mass organo and hydrogelators, gel-nanoparticle composites, etc. Some of what excites us in this field is highlighted in this perspective article.

Resource recovery potential from secondary components of segregated municipal solid wastes

Fermentable components of municipal solid wastes (MSW) such as fruit and vegetable wastes (FVW), leaf litter, paddy straw, cane bagasse, cane trash and paper are generated in large quantities at various pockets of the city. These form potential feedstocks for decentralized biogas plants to be operated in the vicinity. We characterized the fermentation potential of six of the above MSW fractions for their suitability to be converted to biogas and anaerobic compost using the solid-state stratified bed (SSB) process in a laboratory study. FVW and leaf litter (papermulberry leaves) decomposed almost completely while paddy straw, sugarcane trash, sugarcane bagasse and photocopying paper decomposed to a lower extent. In the SSB process between 50-60% of the biological methane potential (BMP) could be realized. Observations revealed that the SSB process needs to be adapted differently for each of the feedstocks to obtain a higher gas recovery. Bagasse produced the largest fraction of anaerobic compost (fermentation residue) and has the potential for reuse in many ways.

An agent based adaptive bandwidth allocation scheme for multimedia applications

Bandwidth allocation for multimedia applications in case of network congestion and failure poses technical challenges due to bursty and delay sensitive nature of the applications. The growth of multimedia services on Internet and the development of agent technology have made us to investigate new techniques for resolving the bandwidth issues in multimedia communications. Agent technology is emerging as a flexible promising solution for network resource management and QoS (Quality of Service) control in a distributed environment. In this paper, we propose an adaptive bandwidth allocation scheme for multimedia applications by deploying the static and mobile agents. It is a run-time allocation scheme that functions at the network nodes. This technique adaptively finds an alternate patchup route for every congested/failed link and reallocates the bandwidth for the affected multimedia applications. The designed method has been tested (analytical and simulation)with various network sizes and conditions. The results are presented to assess the performance and effectiveness of the approach. This work also demonstrates some of the benefits of the agent based schemes in providing flexibility, adaptability, software reusability, and maintainability. (C) 2004 Elsevier Inc. All rights reserved.

A Dynamic Migration Model for Self-adaptive Genetic Algorithms

In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).

Metabolic enzymes as potential drug targets in Plasmodium falciparum.

Plasmodium falciparum causes the most severe form of malaria that is fatal in many cases. Emergence of drug resistant strains of P. falciparum requires that new drug targets be-identified. This review considers in detail enzymes of the glycolytic pathway, purine salvage pathway, pyrimidine biosynthesis and proteases involved in catabolism of haemoglobin. Structural features of P. falciparum triosephosphate isomerase which could be exploited for parasite specific drug development have been highlighted. Utility of P. falciparum hypoxanthine-guanine-phosphoribosyltransferase, adenylosuccinate synthase, dihydroorotate dehydrogenase, thymidylate synthase-dihydrofolate reductase, cysteine and aspartic proteases have been elaborated in detail. The review also briefly touches upon other potential targets in P. falciparum

The dynamics of solvation of an electron in the image potential state by a layer of polar adsorbates

Recently, ultrafast two-photon photoemission has been used to study electron solvation at a two-dimensional metal/polar adsorbate interfaces [A. Miller , Science 297, 1163 (2002)]. The electron is bound to the surface by the image interaction. Earlier we have suggested a theoretical description of the states of the electron interacting with a two-dimensional layer of the polar adsorbate [K. L. Sebastian , J. Chem. Phys. 119, 10350 (2003)]. In this paper we have analyzed the dynamics of electron solvation, assuming a trial wave function for the electron and the solvent polarization and then using the Dirac-Frenkel variational method to determine it. The electron is initially photoexcited to a delocalized state, which has a finite but large size, and causes the polar molecules to reorient. This reorientation acts back on the electron and causes its wave function to shrink, which will cause further reorientation of the polar molecules, and the process continues until the electron gets self-trapped. For reasonable values for the parameters, we are able to obtain fair agreement with the experimental observations. (c) 2005 American Institute of Physics.

An agent based adaptive bandwidth allocation scheme for multimedia applications

Bandwidth allocation for multimedia applications in case of network congestion and failure poses technical challenges due to bursty and delay sensitive nature of the applications. The growth of multimedia services on Internet and the development of agent technology have made us to investigate new techniques for resolving the bandwidth issues in multimedia communications. Agent technology is emerging as a flexible promising solution for network resource management and QoS (Quality of Service) control in a distributed environment. In this paper, we propose an adaptive bandwidth allocation scheme for multimedia applications by deploying the static and mobile agents. It is a run-time allocation scheme that functions at the network nodes. This technique adaptively finds an alternate patchup route for every congested/failed link and reallocates the bandwidth for the affected multimedia applications. The designed method has been tested (analytical and simulation)with various network sizes and conditions. The results are presented to assess the performance and effectiveness of the approach. This work also demonstrates some of the benefits of the agent based schemes in providing flexibility, adaptability, software reusability, and maintainability. (C) 2004 Elsevier Inc. All rights reserved.