Experimental taxonomy of Stellaria species

Imperial Users onl

Prediction of DNA-Binding Proteins and their Binding Sites

DNA-binding proteins play an important role in various essential biological processes such as DNA replication, recombination, repair, gene transcription, and expression. The identification of DNA-binding proteins and the residues involved in the contacts is important for understanding the DNA-binding mechanism in proteins. Moreover, it has been reported in the literature that the mutations of some DNA-binding residues on proteins are associated with some diseases. The identification of these proteins and their binding mechanism generally require experimental techniques, which makes large scale study extremely difficult. Thus, the prediction of DNA-binding proteins and their binding sites from sequences alone is one of the most challenging problems in the field of genome annotation. Since the start of the human genome project, many attempts have been made to solve the problem with different approaches, but the accuracy of these methods is still not suitable to do large scale annotation of proteins. Rather than relying solely on the existing machine learning techniques, I sought to combine those using novel “stacking technique” and used the problem-specific architectures to solve the problem with better accuracy than the existing methods. This thesis presents a possible solution to the DNA-binding proteins prediction problem which performs better than the state-of-the-art approaches

Prediction of DNA-Binding Proteins and their Binding Sites

DNA-binding proteins play an important role in various essential biological processes such as DNA replication, recombination, repair, gene transcription, and expression. The identification of DNA-binding proteins and the residues involved in the contacts is important for understanding the DNA-binding mechanism in proteins. Moreover, it has been reported in the literature that the mutations of some DNA-binding residues on proteins are associated with some diseases. The identification of these proteins and their binding mechanism generally require experimental techniques, which makes large scale study extremely difficult. Thus, the prediction of DNA-binding proteins and their binding sites from sequences alone is one of the most challenging problems in the field of genome annotation. Since the start of the human genome project, many attempts have been made to solve the problem with different approaches, but the accuracy of these methods is still not suitable to do large scale annotation of proteins. Rather than relying solely on the existing machine learning techniques, I sought to combine those using novel “stacking technique” and used the problem-specific architectures to solve the problem with better accuracy than the existing methods. This thesis presents a possible solution to the DNA-binding proteins prediction problem which performs better than the state-of-the-art approaches

DESIGN OF CMOS COMPRESSIVE SENSING IMAGE SENSORS

This work investigates the optimal measurement matrices that can be used in compressive sensing (CS) image sensors. It also optimizes CMOS current-model pixel cell circuits for CS image sensors. Based on the outcomes from these optimization studies, three CS image senor circuits with compression ratios of 4, 6, and 8 are designed with using a 130 nm CMOS technology. The pixel arrays used in the image sensors has a size of 256X256. Circuit simulations with benchmark image Lenna show that the three images sensors can achieve peak signal to noise ratio (PSNR) values of 37.64, 33.29, and 32.44 dB respectively

General Stochastic Calculus and Applications

In 1942, K. Itô published his pioneering paper on stochastic integration with respect to Brownian motion. This work led to the framework for Itô calculus. Note that, Itô calculus is limited in working with knowledge from the future. There have been many generalizations of the stochastic integral in being able to do so. In 2008, W. Ayed and H.-H. Kuo introduced a new stochastic integral by splitting the integrand into the adaptive part and the counterpart called instantly independent. In this doctoral work, we conduct deeper research into the Ayed–Kuo stochastic integral and corresponding anticipating stochastic calculus. We provide a new proof for the extension of Itô isometry for the Ayed–Kuo stochastic integral which clearly demonstrates the intrinsic nature of the construction of the general integral. Furthermore, we extend classical It\^o theory results for martingales to their Ayed–Kuo stochastic integral analogue, near-martingale. We show the near-martingale property of Ayed–Kuo stochastic integral and optional stopping theorem for near-martingales with bounded stopping times. Using the general Itô formula for the Ayed–Kuo stochastic integral, we find explicit solutions for linear stochastic differential equations with anticipation. We show existence of solutions for certain classes linear stochastic differential equations with anticipation coming from initial condition as well as from the drift. We present a Trotter inspired product formula to construct the solution. In the process, we also show the uniqueness of the solution. While we mainly rely on the Ayed–Kuo formalism, other theories are used minimally and out of necessity. Using the explicit solution, we show the relation between a solution of an anticipating stochastic differential equations and its Itô projection. Furthermore, we establish Wentzell–Friedlin type large deviation principle for the solution of a class of linear stochastic differential equation with an anticipating drift and non-adapted initial condition

Ocean Wave Prediction and Characterization for Intelligent Maritime Transportation

The national Earth System Prediction (ESPC) initiative aims to develop the predictionsfor the next generation predictions of atmosphere, ocean, and sea-ice interactions in the scale of days to decades. This dissertation seeks to demonstrate the methods we can use to improve the ESPC models, especially the ocean prediction model. In the application side of the weather forecasts, this dissertation explores imitation learning with constraints to solve combinatorial optimization problems, focusing on the weather routing of surface vessels. Prediction of ocean waves is essential for various purposes, including vessel routing, ocean energy harvesting, agriculture, etc. Since the machine learning approaches cannot forecast ocean waves with sufficient accuracy for longer forecast horizons and the numerical methods are not flexible due to being expert-designed, there is a need to study both methods to improve forecasts. One popular way to improve forecasts is to perform data assimilation, which fails to improve the numerical model in the model space. In this dissertation, we explore different ways to improve wave forecasts. We combine data assimilation and machine learning methods to improve predictions from the numerical model WaveWatch III. We have also explored rogue ocean waves, which are not predicted using traditional numerical methods. Moreover, using imitation learning to guide combinatorial optimization problems should allow fast training of reinforcement learning algorithms while satisfying the constraints

IMPROVING CORROSION RESISTANCE OF MAGNESIUM NANOCOMPOSITES BY USING ELECTROLESS NICKEL COATINGS

The present study aims at improving corrosion resistance of magnesium nanocomposites through autocatalytic Ni-P coating. Electroless Ni-P coatings with different concentration of sodium hypophosphite are deposited on 2% WC incorporated magnesium nanocomposites (AZ31-2WC) and the coated samples are further heat-treated. Basic characterizations and compositional analyses are done by using scanning electron microscope (SEM), energy dispersive x-ray analysis (EDAX), and X-ray diffraction analysis (XRD). Microhardness values of the developed materials are also evaluated. The attempt is made to improve corrosion resistance of AZ31-2WC by modifying surface roughness. Corrosion characteristics of Ni-P coated AZ31-2WC nanocomposites are examined by performing potentiodynamic polarization test and electrochemical impedance spectroscopy (EIS). Corrosion resistance improves with enhancement of surface quality. Corrosion resistance of AZ31-2WC nanocomposite also improves due to application of Ni-P coating. Finally, corrosion morphologies are scrutinized by SEM micrographs of corroded surface

Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis

Pancreatic cancer has the poorest prognosis among all cancer types. Intraductal Papillary Mucinous Neoplasms (IPMNs) are radiographically identifiable precursors to pancreatic cancer; hence, early detection and precise risk assessment of IPMN are vital. In this work, we propose a Convolutional Neural Network (CNN) based computer aided diagnosis (CAD) system to perform IPMN diagnosis and risk assessment by utilizing multi-modal MRI. In our proposed approach, we use minimum and maximum intensity projections to ease the annotation variations among different slices and type of MRIs. Then, we present a CNN to obtain deep feature representation corresponding to each MRI modality (T1-weighted and T2-weighted). At the final step, we employ canonical correlation analysis (CCA) to perform a fusion operation at the feature level, leading to discriminative canonical correlation features. Extracted features are used for classification. Our results indicate significant improvements over other potential approaches to solve this important problem. The proposed approach doesn't require explicit sample balancing in cases of imbalance between positive and negative examples. To the best of our knowledge, our study is the first to automatically diagnose IPMN using multi-modal MRI.Comment: Accepted for publication in IEEE International Symposium on Biomedical Imaging (ISBI) 201