Bayesian statistical analysis of bacterial diversity

Bacteria play an important role in many ecological systems. The molecular characterization of bacteria using either cultivation-dependent or cultivation-independent methods reveals the large scale of bacterial diversity in natural communities, and the vastness of subpopulations within a species or genus. Understanding how bacterial diversity varies across different environments and also within populations should provide insights into many important questions of bacterial evolution and population dynamics. This thesis presents novel statistical methods for analyzing bacterial diversity using widely employed molecular fingerprinting techniques. The first objective of this thesis was to develop Bayesian clustering models to identify bacterial population structures. Bacterial isolates were identified using multilous sequence typing (MLST), and Bayesian clustering models were used to explore the evolutionary relationships among isolates. Our method involves the inference of genetic population structures via an unsupervised clustering framework where the dependence between loci is represented using graphical models. The population dynamics that generate such a population stratification were investigated using a stochastic model, in which homologous recombination between subpopulations can be quantified within a gene flow network. The second part of the thesis focuses on cluster analysis of community compositional data produced by two different cultivation-independent analyses: terminal restriction fragment length polymorphism (T-RFLP) analysis, and fatty acid methyl ester (FAME) analysis. The cluster analysis aims to group bacterial communities that are similar in composition, which is an important step for understanding the overall influences of environmental and ecological perturbations on bacterial diversity. A common feature of T-RFLP and FAME data is zero-inflation, which indicates that the observation of a zero value is much more frequent than would be expected, for example, from a Poisson distribution in the discrete case, or a Gaussian distribution in the continuous case. We provided two strategies for modeling zero-inflation in the clustering framework, which were validated by both synthetic and empirical complex data sets. We show in the thesis that our model that takes into account dependencies between loci in MLST data can produce better clustering results than those methods which assume independent loci. Furthermore, computer algorithms that are efficient in analyzing large scale data were adopted for meeting the increasing computational need. Our method that detects homologous recombination in subpopulations may provide a theoretical criterion for defining bacterial species. The clustering of bacterial community data include T-RFLP and FAME provides an initial effort for discovering the evolutionary dynamics that structure and maintain bacterial diversity in the natural environment.

Relaxation Time of Chemical Reactions from Network Thermodynamics

The relationship for the relaxation time(s) of a chemical reaction in terms of concentrations and rate constants has been derived from the network thermodynamic approach developed by Oster, Perelson, and Katchalsky.Generally, it is necessary to draw the bond graph and the “network analogue” of the reaction scheme, followed by loop or nodal analysis of the network and finally solving of the resulting differential equations. In the case of single-step reactions, however, it is possible to obtain an expression for the relaxation time. This approach is simpler and elegant and has certain advantages over the usual kinetic method. The method has been illustrated by taking different reaction schemes as examples.

Statistical and Information-Theoretic Methods for Data-Analysis

In this Thesis, we develop theory and methods for computational data analysis. The problems in data analysis are approached from three perspectives: statistical learning theory, the Bayesian framework, and the information-theoretic minimum description length (MDL) principle. Contributions in statistical learning theory address the possibility of generalization to unseen cases, and regression analysis with partially observed data with an application to mobile device positioning. In the second part of the Thesis, we discuss so called Bayesian network classifiers, and show that they are closely related to logistic regression models. In the final part, we apply the MDL principle to tracing the history of old manuscripts, and to noise reduction in digital signals.

Statistical methods for analysis of multi-harvest data from perennial pasture variety selection trials

Variety selection in perennial pasture crops involves identifying best varieties from data collected from multiple harvest times in field trials. For accurate selection, the statistical methods for analysing such data need to account for the spatial and temporal correlation typically present. This paper provides an approach for analysing multi-harvest data from variety selection trials in which there may be a large number of harvest times. Methods are presented for modelling the variety by harvest effects while accounting for the spatial and temporal correlation between observations. These methods provide an improvement in model fit compared to separate analyses for each harvest, and provide insight into variety by harvest interactions. The approach is illustrated using two traits from a lucerne variety selection trial. The proposed method provides variety predictions allowing for the natural sources of variation and correlation in multi-harvest data.

Statistical analysis of spectral data: A methodology for designing an intelligent monitoring system for the diabetic foot

Early detection of (pre-)signs of ulceration on a diabetic foot is valuable for clinical practice. Hyperspectral imaging is a promising technique for detection and classification of such (pre-)signs. However, the number of the spectral bands should be limited to avoid overfitting, which is critical for pixel classification with hyperspectral image data. The goal was to design a detector/classifier based on spectral imaging (SI) with a small number of optical bandpass filters. The performance and stability of the design were also investigated. The selection of the bandpass filters boils down to a feature selection problem. A dataset was built, containing reflectance spectra of 227 skin spots from 64 patients, measured with a spectrometer. Each skin spot was annotated manually by clinicians as "healthy" or a specific (pre-)sign of ulceration. Statistical analysis on the data set showed the number of required filters is between 3 and 7, depending on additional constraints on the filter set. The stability analysis revealed that shot noise was the most critical factor affecting the classification performance. It indicated that this impact could be avoided in future SI systems with a camera sensor whose saturation level is higher than 106, or by postimage processing.

Second-order statistical structure of geomagnetic field reversals

From the autocorrelation function of geomagnetic polarity intervals, it is shown that the field reversal intervals are not independent but form a process akin to the Markov process, where the random input to the model is itself a moving average process. The input to the moving average model is, however, an independent Gaussian random sequence. All the parameters in this model of the geomagnetic field reversal have been estimated. In physical terms this model implies that the mechanism of reversal possesses a memory.

Conceptual and statistical modelling of environmental effects in population dynamics

Population dynamics are generally viewed as the result of intrinsic (purely density dependent) and extrinsic (environmental) processes. Both components, and potential interactions between those two, have to be modelled in order to understand and predict dynamics of natural populations; a topic that is of great importance in population management and conservation. This thesis focuses on modelling environmental effects in population dynamics and how effects of potentially relevant environmental variables can be statistically identified and quantified from time series data. Chapter I presents some useful models of multiplicative environmental effects for unstructured density dependent populations. The presented models can be written as standard multiple regression models that are easy to fit to data. Chapters II IV constitute empirical studies that statistically model environmental effects on population dynamics of several migratory bird species with different life history characteristics and migration strategies. In Chapter II, spruce cone crops are found to have a strong positive effect on the population growth of the great spotted woodpecker (Dendrocopos major), while cone crops of pine another important food resource for the species do not effectively explain population growth. The study compares rate- and ratio-dependent effects of cone availability, using state-space models that distinguish between process and observation error in the time series data. Chapter III shows how drought, in combination with settling behaviour during migration, produces asymmetric spatially synchronous patterns of population dynamics in North American ducks (genus Anas). Chapter IV investigates the dynamics of a Finnish population of skylark (Alauda arvensis), and point out effects of rainfall and habitat quality on population growth. Because the skylark time series and some of the environmental variables included show strong positive autocorrelation, the statistical significances are calculated using a Monte Carlo method, where random autocorrelated time series are generated. Chapter V is a simulation-based study, showing that ignoring observation error in analyses of population time series data can bias the estimated effects and measures of uncertainty, if the environmental variables are autocorrelated. It is concluded that the use of state-space models is an effective way to reach more accurate results. In summary, there are several biological assumptions and methodological issues that can affect the inferential outcome when estimating environmental effects from time series data, and that therefore need special attention. The functional form of the environmental effects and potential interactions between environment and population density are important to deal with. Other issues that should be considered are assumptions about density dependent regulation, modelling potential observation error, and when needed, accounting for spatial and/or temporal autocorrelation.

Thermodynamics of metal ion binding and denaturation of a calcium binding protein from Entamoeba histolytica

The thermodynamics of tie binding of calcium and magnesium ions to a calcium binding protein from Entamoeba histolytica was investigated by isothermal titration calorimetry (ITC) in 20 mM MOPS buffer (pH 7.0) at 20 degrees C. Enthalpy titration curves of calcium show the presence of four Ca2+ binding sites, There exist two low-affinity sites for Ca2+, both of which are exothermic in nature and with positive cooperative interaction between them. Two other high affinity sites for Ca2+ exist of which one is endothermic and the other exothermic, again with positive cooperative interaction. The binding constants for Ca2+ at the four sites have been verified by a competitive binding assay, where CaBP competes with a chromophoric chelator 5, 5'-Br-2 BAPTA to bind Ca2+ and a Ca2+ titration employing intrinsic tyrosine fluorescence of the protein, The enthalpy of titration of magnesium in the absence of calcium is single site and endothermic in nature. In the case of the titrations performed using protein presaturated with magnesium, the amount of heat produced is altered. Further, the interaction between the high-affinity sites changes to negative cooperativity. No exchange of heat was observed throughout the addition of magnesium in the presence of 1 mM calcium, Titrations performed on a cleaved peptide comprising the N-terminus and the central linker show the existence of two Ca2+ specific sites, These results indicate that this CaBP has one high-affinity Ca-Mg site, one high-affinity Ca-specific site, and two low-affinity Ca-specific sites. The thermodynamic parameters of the binding of these metal ions were used to elucidate the energetics at the individual site(s) and the interactions involved therein at various concentrations of the denaturant, guanidine hydrochloride, ranging from 0.05 to 6.5 M. Unfolding of the protein was also monitored by titration calorimetry as a function of the concentration of the denaturant. These data show that at a GdnHCl concentration of 0.25 M the binding affinity for the Mg2+ ion is lost and there are only two sites which can bind to Ca2+, with substantial loss cooperativity. At concentrations beyond 2.5 M GdnHCl, at which the unfolding of the tertiary structure of this protein is observed by near UV CD spectroscopy, the binding of Ca2+ ions is lost. We thus show that the domain containing the two low-affinity sites is the first to unfold in the presence of GdnHCl. Control experiments with change in ionic strength by addition of KCI in the range 0.25-1 M show the existence of four sites with altered ion binding parameters.

Thermodynamics of Monosaccharide and Disaccharide Binding to Erythrina corallodendron Lectin

Isothermal titration calorimetry measurements of the binding of 2′-fucosyllactose, lactose, N-acetyllactosamine, galactopyranose, 2-acetamido-2-deoxygalactopyranoside, methyl α-N-dansylgalactosaminide (Me-α-DNS-GalN), methyl α-D-galactopyranoside, methyl β-D-galactopyranoside, and fucose to Erythrina corallodendron lectin (ECorL), a dimer with one binding site per subunit, were performed at 283-286 and 297-299 K. The site binding enthalpies, ΔHb, with the exception of Me-α-DNS-GalN, are the same at both temperatures and range from −47.1 ± 1.0 kJ mol−1 for N-acetyllactosamine to −4.4 ± 0.3 kJ mol−1 for fucose, and the site binding constants range from 3.82 ± 0.9 × 105 M−1 for Me-α-DNS-GalN at 283.2 K to 0.46 ± 0.05 × 103 M−1 for fucose at 297.2 K. The binding reactions are mainly enthalpically driven except for fucose and exhibit enthalpy-entropy compensation. The binding enthalpies of the disaccharides are about twice the binding enthalpies of the monosaccharides in contrast to concanavalin A where the binding enthalpies do not double for the disaccharides. Differential scanning calorimetry measurements show that denaturation of the ECorL dimer results in dissociation into its monomer subunits. The binding constants from the increase in denaturation temperature of ECorL in the presence of saccharides are in agreement with values from isothermal titration calorimetry results. The thermal denaturation of ECorL occurs around 333 K, well below the 344-360 K denaturation temperature of other legume lectins of similar size and tertiary structure, undoubtedly due to the difference in its quaternary structure relative to other legume lectins. This is also apparent from the independent unfolding of its two domains.

An update on the thermodynamics of Ta2O5

Using a solid-state electrochemical cell incorporating yttria-doped thoria (YDT) as the electrolyte and a mixture of (Mn + MnO) as the reference electrode, standard Gibbs free energy of formation of beta-Ta2O5 has been determined as a function of temperature in the range (1000 to 1300) K. The solid-state electrochemical cell used can be represented as (-)Pt,Ta +Ta2O5//(Y2O3)ThO2//Mn + MnO, Pt(+) Combining the reversible e.m.f. of the cell with recent data on the free energy of formation of MnO, standard Gibbs free energy of formation of Ta2O5 from Ta metal and diatomic oxygen gas (O-2) in the temperature range (1000 to 1300) K is obtained: Delta fG degrees +/- 0.35/(kJ.mol(-1)) = -2004.376 + 0.40445(T/K). Because of the significant solid solubility of oxygen in tantalum, a small correction for the activity of Ta in the metal phase in equilibrium with Ta2O5 is applied. An analysis of the results obtained in this study and other free energy data reported in the literature by the "third law" method suggests the need for refining data for Ta2O5 reported in thermodynamic compilations. Used in the analysis is a revised value for standard entropy of Ta2O5 based on more recent low-temperature heat capacity measurements. An improved set of thermodynamic properties of ditantalum pentoxide (Ta2O5) are presented in the temperature range (298.15 to 2200) K. (C) 2008 Elsevier Ltd. All rights reserved.

Bond-order wave phase, spin solitons, and thermodynamics of a frustrated linear spin-1/2 Heisenberg antiferromagnet

The linear spin-1/2 Heisenberg antiferromagnet with exchanges J(1) and J(2) between first and second neighbors has a bond-order wave (BOW) phase that starts at the fluid-dimer transition at J(2)/J(1)=0.2411 and is particularly simple at J(2)/J(1)=1/2. The BOW phase has a doubly degenerate singlet ground state, broken inversion symmetry, and a finite-energy gap E-m to the lowest-triplet state. The interval 0.4 < J(2)/J(1) < 1.0 has large E-m and small finite-size corrections. Exact solutions are presented up to N = 28 spins with either periodic or open boundary conditions and for thermodynamics up to N = 18. The elementary excitations of the BOW phase with large E-m are topological spin-1/2 solitons that separate BOWs with opposite phase in a regular array of spins. The molar spin susceptibility chi(M)(T) is exponentially small for T << E-m and increases nearly linearly with T to a broad maximum. J(1) and J(2) spin chains approximate the magnetic properties of the BOW phase of Hubbard-type models and provide a starting point for modeling alkali-tetracyanoquinodimethane salts.

Asteroid identification using statistical orbital inversion methods

An efficient and statistically robust solution for the identification of asteroids among numerous sets of astrometry is presented. In particular, numerical methods have been developed for the short-term identification of asteroids at discovery, and for the long-term identification of scarcely observed asteroids over apparitions, a task which has been lacking a robust method until now. The methods are based on the solid foundation of statistical orbital inversion properly taking into account the observational uncertainties, which allows for the detection of practically all correct identifications. Through the use of dimensionality-reduction techniques and efficient data structures, the exact methods have a loglinear, that is, O(nlog(n)), computational complexity, where n is the number of included observation sets. The methods developed are thus suitable for future large-scale surveys which anticipate a substantial increase in the astrometric data rate. Due to the discontinuous nature of asteroid astrometry, separate sets of astrometry must be linked to a common asteroid from the very first discovery detections onwards. The reason for the discontinuity in the observed positions is the rotation of the observer with the Earth as well as the motion of the asteroid and the observer about the Sun. Therefore, the aim of identification is to find a set of orbital elements that reproduce the observed positions with residuals similar to the inevitable observational uncertainty. Unless the astrometric observation sets are linked, the corresponding asteroid is eventually lost as the uncertainty of the predicted positions grows too large to allow successful follow-up. Whereas the presented identification theory and the numerical comparison algorithm are generally applicable, that is, also in fields other than astronomy (e.g., in the identification of space debris), the numerical methods developed for asteroid identification can immediately be applied to all objects on heliocentric orbits with negligible effects due to non-gravitational forces in the time frame of the analysis. The methods developed have been successfully applied to various identification problems. Simulations have shown that the methods developed are able to find virtually all correct linkages despite challenges such as numerous scarce observation sets, astrometric uncertainty, numerous objects confined to a limited region on the celestial sphere, long linking intervals, and substantial parallaxes. Tens of previously unknown main-belt asteroids have been identified with the short-term method in a preliminary study to locate asteroids among numerous unidentified sets of single-night astrometry of moving objects, and scarce astrometry obtained nearly simultaneously with Earth-based and space-based telescopes has been successfully linked despite a substantial parallax. Using the long-term method, thousands of realistic 3-linkages typically spanning several apparitions have so far been found among designated observation sets each spanning less than 48 hours.

Process variability-aware statistical hybrid modeling of dynamic power dissipation in 65 nm CMOS designs

A generalized technique is proposed for modeling the effects of process variations on dynamic power by directly relating the variations in process parameters to variations in dynamic power of a digital circuit. The dynamic power of a 2-input NAND gate is characterized by mixed-mode simulations, to be used as a library element for 65mn gate length technology. The proposed methodology is demonstrated with a multiplier circuit built using the NAND gate library, by characterizing its dynamic power through Monte Carlo analysis. The statistical technique of Response. Surface Methodology (RSM) using Design of Experiments (DOE) and Least Squares Method (LSM), are employed to generate a "hybrid model" for gate power to account for simultaneous variations in multiple process parameters. We demonstrate that our hybrid model based statistical design approach results in considerable savings in the power budget of low power CMOS designs with an error of less than 1%, with significant reductions in uncertainty by atleast 6X on a normalized basis, against worst case design.