Algorithms for Exact Structure Discovery in Bayesian Networks

Bayesian networks are compact, flexible, and interpretable representations of a joint distribution. When the network structure is unknown but there are observational data at hand, one can try to learn the network structure. This is called structure discovery. This thesis contributes to two areas of structure discovery in Bayesian networks: space--time tradeoffs and learning ancestor relations. The fastest exact algorithms for structure discovery in Bayesian networks are based on dynamic programming and use excessive amounts of space. Motivated by the space usage, several schemes for trading space against time are presented. These schemes are presented in a general setting for a class of computational problems called permutation problems; structure discovery in Bayesian networks is seen as a challenging variant of the permutation problems. The main contribution in the area of the space--time tradeoffs is the partial order approach, in which the standard dynamic programming algorithm is extended to run over partial orders. In particular, a certain family of partial orders called parallel bucket orders is considered. A partial order scheme that provably yields an optimal space--time tradeoff within parallel bucket orders is presented. Also practical issues concerning parallel bucket orders are discussed. Learning ancestor relations, that is, directed paths between nodes, is motivated by the need for robust summaries of the network structures when there are unobserved nodes at work. Ancestor relations are nonmodular features and hence learning them is more difficult than modular features. A dynamic programming algorithm is presented for computing posterior probabilities of ancestor relations exactly. Empirical tests suggest that ancestor relations can be learned from observational data almost as accurately as arcs even in the presence of unobserved nodes.

Ring Resonators and Micro Fluidics: Novel Design for Drug/Disease Detection

MEMS systems are technologically developed from integrated circuit industry to create miniature sensors and actuators. Originally these semiconductor processes and materials were used to build electrical and mechanical systems, but expanded to include biological, optical fluidic magnetic and other systems 12]. Here a novel approach is suggested where in two different fields are integrated via moems, micro fluidics and ring resonators. It is well known at any preliminary stage of disease onset, many physiological changes occur in the body fluids like saliva, blood, urine etc. The drawback till now was that current calibrations are not sensitive enough to detect the minor physiological changes. This is overcome using optical detector techniques 1]. The basic concepts of ring resonators, with slight variations can be used for optical detection of these minute disease markers. A well known fact of ring resonators is that a change in refractive index will trigger a shift in the resonant wavelength 5]. The trigger for the wavelength shift in the case discussed will be the presence of disease agents. To trap the disease agents specific antibody has to be used (e. g. BSA).

Effective drug dosage design for treatment of infectious diseases using dynamic inversion

A generic nonlinear mathematical model describing the human immunological dynamics is used to design an effective automatic drug administration scheme. Even though the model describes the effects of various drugs on the dynamic system, this work is confined to the drugs that kill the invading pathogen and heal the affected organ. From a system theoretic point of view, the drug inputs can be interpreted as control inputs, which can be designed based on control theoretic concepts. The controller is designed based on the principle of dynamic inversion and is found to be effective in curing the �nominal model patient� by killing the invading microbes and healing the damaged organ. A major advantage of this technique is that it leads to a closed-form state feedback form of control. It is also proved from a rigorous mathematical analysis that the internal dynamics of the system remains stable when the proposed controller is applied. A robustness study is also carried out for testing the effectiveness of the drug administration scheme for parameter uncertainties. It is observed from simulation studies that the technique has adequate robustness for many �realistic model patients� having off-nominal parameter values as well.

Drug-drug co-crystals: Temperature-dependent proton mobility in the molecular complex of isoniazid with 4-aminosalicylic acid

Two drug-drug co-crystals of the anti-tuberculosis drugs isoniazid (INH), pyrazinamide (PYR) and 4-aminosalicylic acid (PAS) are reported. The first is the 1 : 1 molecular complex of INH and PAS. The second is the monohydrate of the 1 : 1 complex of PYR and PAS. The crystal structures of both co-crystals are characterized by a number of hydrogen bonded synthons. Hydrogen bonding of the COOH center dot center dot center dot N-pyridine type is found in both cases. In the INH : PAS co-crystal, there are two symmetry independent COOH center dot center dot center dot center dot N-pyridine hydrogen bonds. In one of these, the H-atom is located on the carboxylic group and is indicative of a co-crystal. In the second case, partial proton transfer occurs across the hydrogen bond, and the extent of proton transfer depends on the temperature. This is more indicative of a salt. Drug-drug co-crystals may have some bearing in the treatment of tuberculosis.

Context based service discovery for ubiquitous applications

Service discovery is vital in ubiquitous applications, where a large number of devices and software components collaborate unobtrusively and provide numerous services without user intervention. Existing service discovery schemes use a service matching process in order to offer services of interest to the users. Potentially, the context information of the users and surrounding environment can be used to improve the quality of service matching. To make use of context information in service matching, a service discovery technique needs to address certain challenges. Firstly, it is required that the context information shall have unambiguous representation. Secondly, the devices in the environment shall be able to disseminate high level and low level context information seamlessly in the different networks. And thirdly, dynamic nature of the context information be taken into account. We propose a C-IOB(Context-Information, Observation and Belief) based service discovery model which deals with the above challenges by processing the context information and by formulating the beliefs based on the observations. With these formulated beliefs the required services will be provided to the users. The method has been tested with a typical ubiquitous museum guide application over different cases. The simulation results are time efficient and quite encouraging.

Discovery of Application Workloads from Network File Traces

An understanding of application I/O access patterns is useful in several situations. First, gaining insight into what applications are doing with their data at a semantic level helps in designing efficient storage systems. Second, it helps create benchmarks that mimic realistic application behavior closely. Third, it enables autonomic systems as the information obtained can be used to adapt the system in a closed loop.All these use cases require the ability to extract the application-level semantics of I/O operations. Methods such as modifying application code to associate I/O operations with semantic tags are intrusive. It is well known that network file system traces are an important source of information that can be obtained non-intrusively and analyzed either online or offline. These traces are a sequence of primitive file system operations and their parameters. Simple counting, statistical analysis or deterministic search techniques are inadequate for discovering application-level semantics in the general case, because of the inherent variation and noise in realistic traces.In this paper, we describe a trace analysis methodology based on Profile Hidden Markov Models. We show that the methodology has powerful discriminatory capabilities that enable it to recognize applications based on the patterns in the traces, and to mark out regions in a long trace that encapsulate sets of primitive operations that represent higher-level application actions. It is robust enough that it can work around discrepancies between training and target traces such as in length and interleaving with other operations. We demonstrate the feasibility of recognizing patterns based on a small sampling of the trace, enabling faster trace analysis. Preliminary experiments show that the method is capable of learning accurate profile models on live traces in an online setting. We present a detailed evaluation of this methodology in a UNIX environment using NFS traces of selected commonly used applications such as compilations as well as on industrial strength benchmarks such as TPC-C and Postmark, and discuss its capabilities and limitations in the context of the use cases mentioned above.

DNA topoisomerase I from mycobacteria - A potential drug target

DNA topoisomerases are ubiquitous group of enzymes altering the topology of DNA by concerted breakage and rejoining of the phosphodiester backbone of DNA. The enzymes are classified based on the pattern of DNA cleavage. Type IA enzymes found in all bacteria nick the DNA and attach themselves covalently to the 5' side of the nick during the first transesterification reaction. Most of the information on this group of enzymes comes from studies with E. coli topoisomerase I and III. Members of type IA group are single subunit Zn++ metalloenzymes recognizing single stranded DNA without high degree of sequence specificity during relaxation reaction of negatively super coiled DNA. So far no inhibitors are known for this group of enzymes inspite of their important role in maintaining homeostasis of DNA topology. Molecular characterization of DNA topoisomerase I from mycobacteria has revealed some of the important features of type IA enzymes hitherto unknown and provide scope for identifying novel inhibitors. The present review describes the recent developments in the area summarizing the distinctive features of mycobacterial topoisomerase I. The enzyme has several properties not shared by either type IA or 113 enzymes with respect to DNA binding, recognition, sequence specificity and interaction pattern. The physiological basis of the unusual features is discussed. The unique properties described would aid in developing the enzyme as a target molecule in pharmaceutical design. In addition, the findings lead to address some fundamental questions on the intracellular role of topoisomerase I in the biology of mycobacteria which are one of the most formidable group of pathogenic organisms.

Online Unsupervised Pattern Discovery in Speech Using Parallelization

Segmental dynamic time warping (DTW) has been demonstrated to be a useful technique for finding acoustic similarity scores between segments of two speech utterances. Due to its high computational requirements, it had to be computed in an offline manner, limiting the applications of the technique. In this paper, we present results of parallelization of this task by distributing the workload in either a static or dynamic way on an 8-processor cluster and discuss the trade-offs among different distribution schemes. We show that online unsupervised pattern discovery using segmental DTW is plausible with as low as 8 processors. This brings the task within reach of today's general purpose multi-core servers. We also show results on a 32-processor system, and discuss factors affecting scalability of our methods.

Biologically triggered exploding protein based microcapsules for drug delivery

Biologically triggered exploding microcapsules were synthesized by layer-by-layer assembly of biopolymers. The microcapsules showed controlled rupturing behaviour upon exposure to a pathologically relevant biomolecule, trypsin. These microcapsules offer significant potential for clinical applications.

Investigation of biodegradable and biocompatible castor oil poly(mannitol-citric-sebacate) polyester as a drug carrier

Castor oil-based poly(mannitol-citric sebacate) was synthesized by simple, catalyst-free melt condensation process using monomers having potential to be metabolized in vivo. The polymer was characterized using various techniques and the tensile and hydration properties of the polymers were also determined. The biocompatibility of the polymer was tested using human foreskin fibroblasts cells. The in vitro degradation studies show that the time for complete degradation of the polymer was more than 21 days. The usage of castor oil polyester as a drug carrier was analysed by doping the polymer with 5-fluorouracil model drug and the release rate was studied by varying the percentage loading of drugs and the pH of the PBS solution medium. The cumulative drug-release profiles exhibited a biphasic release with an initial burst release and cumulative 100% release within 42 h. To understand the role of the polymer as a drug carrier in the release behaviour, drug-release studies were conducted with another drug, isoniazid. The release behaviour of isoniazid drug from the same polymer matrix followed an nth order kinetic model and 100% cumulative release was achieved after 12 days. The variation in the release behaviour for two model drugs from the same polymer matrix suggests a strong interaction between the polymer and the drug molecule.