Advances in sequential data assimilation and numerical weather forecasting: an Ensemble Transform Kalman-Bucy Filter, a study on clustering in deterministic Ensemble Square Root Filters, and a test of a new time stepping scheme in an atmospheric model

This dissertation deals with aspects of sequential data assimilation (in particular ensemble Kalman filtering) and numerical weather forecasting. In the first part, the recently formulated Ensemble Kalman-Bucy (EnKBF) filter is revisited. It is shown that the previously used numerical integration scheme fails when the magnitude of the background error covariance grows beyond that of the observational error covariance in the forecast window. Therefore, we present a suitable integration scheme that handles the stiffening of the differential equations involved and doesn’t represent further computational expense. Moreover, a transform-based alternative to the EnKBF is developed: under this scheme, the operations are performed in the ensemble space instead of in the state space. Advantages of this formulation are explained. For the first time, the EnKBF is implemented in an atmospheric model. The second part of this work deals with ensemble clustering, a phenomenon that arises when performing data assimilation using of deterministic ensemble square root filters in highly nonlinear forecast models. Namely, an M-member ensemble detaches into an outlier and a cluster of M-1 members. Previous works may suggest that this issue represents a failure of EnSRFs; this work dispels that notion. It is shown that ensemble clustering can be reverted also due to nonlinear processes, in particular the alternation between nonlinear expansion and compression of the ensemble for different regions of the attractor. Some EnSRFs that use random rotations have been developed to overcome this issue; these formulations are analyzed and their advantages and disadvantages with respect to common EnSRFs are discussed. The third and last part contains the implementation of the Robert-Asselin-Williams (RAW) filter in an atmospheric model. The RAW filter is an improvement to the widely popular Robert-Asselin filter that successfully suppresses spurious computational waves while avoiding any distortion in the mean value of the function. Using statistical significance tests both at the local and field level, it is shown that the climatology of the SPEEDY model is not modified by the changed time stepping scheme; hence, no retuning of the parameterizations is required. It is found the accuracy of the medium-term forecasts is increased by using the RAW filter.

Comparative genomic indexing reveals the phylogenomics of Escherichia coli pathogens

The Escherichia coli O26 serogroup includes important food-borne pathogens associated with human and animal diarrheal disease. Current typing methods have revealed great genetic heterogeneity within the O26 group; the data are often inconsistent and focus only on verotoxin (VT)-positive O26 isolates. To improve current understanding of diversity within this serogroup, the genomic relatedness of VT-positive and -negative O26 strains was assessed by comparative genomic indexing. Our results clearly demonstrate that irrespective of virulence characteristics and pathotype designation, the O26 strains show greater genomic similarity to each other than to any other strain included in this study. Our data suggest that enteropathogenic and VT-expressing E. coli O26 strains represent the same clonal lineage and that W-expressing E. coli O26 strains have gained additional virulence characteristics. Using this approach, we established the core genes which are central to the E. coli species and identified regions of variation from the E. coli K-12 chromosomal backbone.

Grid warehousing of molecular dynamics protein unfolding data

With the increasing awareness of protein folding disorders, the explosion of genomic information, and the need for efficient ways to predict protein structure, protein folding and unfolding has become a central issue in molecular sciences research. Molecular dynamics computer simulations are increasingly employed to understand the folding and unfolding of proteins. Running protein unfolding simulations is computationally expensive and finding ways to enhance performance is a grid issue on its own. However, more and more groups run such simulations and generate a myriad of data, which raises new challenges in managing and analyzing these data. Because the vast range of proteins researchers want to study and simulate, the computational effort needed to generate data, the large data volumes involved, and the different types of analyses scientists need to perform, it is desirable to provide a public repository allowing researchers to pool and share protein unfolding data. This paper describes efforts to provide a grid-enabled data warehouse for protein unfolding data. We outline the challenge and present first results in the design and implementation of the data warehouse.

The integration of proteomics and systems approaches to map regulatory mechanisms underpinning platelet function.

Platelets in the circulation are triggered by vascular damage to activate, aggregate and form a thrombus that prevents excessive blood loss. Platelet activation is stringently regulated by intracellular signalling cascades, which when activated inappropriately lead to myocardial infarction and stroke. Strategies to address platelet dysfunction have included proteomics approaches which have lead to the discovery of a number of novel regulatory proteins of potential therapeutic value. Global analysis of platelet proteomes may enhance the outcome of these studies by arranging this information in a contextual manner that recapitulates established signalling complexes and predicts novel regulatory processes. Platelet signalling networks have already begun to be exploited with interrogation of protein datasets using in silico methodologies that locate functionally feasible protein clusters for subsequent biochemical validation. Characterization of these biological systems through analysis of spatial and temporal organization of component proteins is developing alongside advances in the proteomics field. This focused review highlights advances in platelet proteomics data mining approaches that complement the emerging systems biology field. We have also highlighted nucleated cell types as key examples that can inform platelet research. Therapeutic translation of these modern approaches to understanding platelet regulatory mechanisms will enable the development of novel anti-thrombotic strategies.

Glycogen synthase kinase 3 (GSK3) in the heart: a point of integration in hypertrophic signalling and a therapeutic target? A critical analysis

Glycogen synthase kinase 3 (GSK3, of which there are two isoforms, GSK3alpha and GSK3beta) was originally characterized in the context of regulation of glycogen metabolism, though it is now known to regulate many other cellular processes. Phosphorylation of GSK3alpha(Ser21) and GSK3beta(Ser9) inhibits their activity. In the heart, emphasis has been placed particularly on GSK3beta, rather than GSK3alpha. Importantly, catalytically-active GSK3 generally restrains gene expression and, in the heart, catalytically-active GSK3 has been implicated in anti-hypertrophic signalling. Inhibition of GSK3 results in changes in the activities of transcription and translation factors in the heart and promotes hypertrophic responses, and it is generally assumed that signal transduction from hypertrophic stimuli to GSK3 passes primarily through protein kinase B/Akt (PKB/Akt). However, recent data suggest that the situation is far more complex. We review evidence pertaining to the role of GSK3 in the myocardium and discuss effects of genetic manipulation of GSK3 activity in vivo. We also discuss the signalling pathways potentially regulating GSK3 activity and propose that, depending on the stimulus, phosphorylation of GSK3 is independent of PKB/Akt. Potential GSK3 substrates studied in relation to myocardial hypertrophy include nuclear factors of activated T cells, beta-catenin, GATA4, myocardin, CREB, and eukaryotic initiation factor 2Bvarepsilon. These and other transcription factor substrates putatively important in the heart are considered. We discuss whether cardiac pathologies could be treated by therapeutic intervention at the GSK3 level but conclude that any intervention would be premature without greater understanding of the precise role of GSK3 in cardiac processes.

Connecting mean field models of neural activity to EEG and fMRI data

Progress in functional neuroimaging of the brain increasingly relies on the integration of data from complementary imaging modalities in order to improve spatiotemporal resolution and interpretability. However, the usefulness of merely statistical combinations is limited, since neural signal sources differ between modalities and are related non-trivially. We demonstrate here that a mean field model of brain activity can simultaneously predict EEG and fMRI BOLD with proper signal generation and expression. Simulations are shown using a realistic head model based on structural MRI, which includes both dense short-range background connectivity and long-range specific connectivity between brain regions. The distribution of modeled neural masses is comparable to the spatial resolution of fMRI BOLD, and the temporal resolution of the modeled dynamics, importantly including activity conduction, matches the fastest known EEG phenomena. The creation of a cortical mean field model with anatomically sound geometry, extensive connectivity, and proper signal expression is an important first step towards the model-based integration of multimodal neuroimages.

Integration of Space and In Situ Observations to Study Global Climate Change

The currently available model-based global data sets of atmospheric circulation are a by-product of the daily requirement of producing initial conditions for numerical weather prediction (NWP) models. These data sets have been quite useful for studying fundamental dynamical and physical processes, and for describing the nature of the general circulation of the atmosphere. However, due to limitations in the early data assimilation systems and inconsistencies caused by numerous model changes, the available model-based global data sets may not be suitable for studying global climate change. A comprehensive analysis of global observations based on a four-dimensional data assimilation system with a realistic physical model should be undertaken to integrate space and in situ observations to produce internally consistent, homogeneous, multivariate data sets for the earth's climate system. The concept is equally applicable for producing data sets for the atmosphere, the oceans, and the biosphere, and such data sets will be quite useful for studying global climate change.

Realistic mean field forward predictions for the integration of co-registered EEG/fMRI

Brain activity can be measured non-invasively with functional imaging techniques. Each pixel in such an image represents a neural mass of about 105 to 107 neurons. Mean field models (MFMs) approximate their activity by averaging out neural variability while retaining salient underlying features, like neurotransmitter kinetics. However, MFMs incorporating the regional variability, realistic geometry and connectivity of cortex have so far appeared intractable. This lack of biological realism has led to a focus on gross temporal features of the EEG. We address these impediments and showcase a "proof of principle" forward prediction of co-registered EEG/fMRI for a full-size human cortex in a realistic head model with anatomical connectivity, see figure 1. MFMs usually assume homogeneous neural masses, isotropic long-range connectivity and simplistic signal expression to allow rapid computation with partial differential equations. But these approximations are insufficient in particular for the high spatial resolution obtained with fMRI, since different cortical areas vary in their architectonic and dynamical properties, have complex connectivity, and can contribute non-trivially to the measured signal. Our code instead supports the local variation of model parameters and freely chosen connectivity for many thousand triangulation nodes spanning a cortical surface extracted from structural MRI. This allows the introduction of realistic anatomical and physiological parameters for cortical areas and their connectivity, including both intra- and inter-area connections. Proper cortical folding and conduction through a realistic head model is then added to obtain accurate signal expression for a comparison to experimental data. To showcase the synergy of these computational developments, we predict simultaneously EEG and fMRI BOLD responses by adding an established model for neurovascular coupling and convolving "Balloon-Windkessel" hemodynamics. We also incorporate regional connectivity extracted from the CoCoMac database [1]. Importantly, these extensions can be easily adapted according to future insights and data. Furthermore, while our own simulation is based on one specific MFM [2], the computational framework is general and can be applied to models favored by the user. Finally, we provide a brief outlook on improving the integration of multi-modal imaging data through iterative fits of a single underlying MFM in this realistic simulation framework.

Pragmatic oriented data interoperability for smart healthcare information systems

Smart healthcare is a complex domain for systems integration due to human and technical factors and heterogeneous data sources involved. As a part of smart city, it is such a complex area where clinical functions require smartness of multi-systems collaborations for effective communications among departments, and radiology is one of the areas highly relies on intelligent information integration and communication. Therefore, it faces many challenges regarding integration and its interoperability such as information collision, heterogeneous data sources, policy obstacles, and procedure mismanagement. The purpose of this study is to conduct an analysis of data, semantic, and pragmatic interoperability of systems integration in radiology department, and to develop a pragmatic interoperability framework for guiding the integration. We select an on-going project at a local hospital for undertaking our case study. The project is to achieve data sharing and interoperability among Radiology Information Systems (RIS), Electronic Patient Record (EPR), and Picture Archiving and Communication Systems (PACS). Qualitative data collection and analysis methods are used. The data sources consisted of documentation including publications and internal working papers, one year of non-participant observations and 37 interviews with radiologists, clinicians, directors of IT services, referring clinicians, radiographers, receptionists and secretary. We identified four primary phases of data analysis process for the case study: requirements and barriers identification, integration approach, interoperability measurements, and knowledge foundations. Each phase is discussed and supported by qualitative data. Through the analysis we also develop a pragmatic interoperability framework that summaries the empirical findings and proposes recommendations for guiding the integration in the radiology context.

Big Data and ecosystem research programmes

The size and complexity of data sets generated within ecosystem-level programmes merits their capture, curation, storage and analysis, synthesis and visualisation using Big Data approaches. This review looks at previous attempts to organise and analyse such data through the International Biological Programme and draws on the mistakes made and the lessons learned for effective Big Data approaches to current Research Councils United Kingdom (RCUK) ecosystem-level programmes, using Biodiversity and Ecosystem Service Sustainability (BESS) and Environmental Virtual Observatory Pilot (EVOp) as exemplars. The challenges raised by such data are identified, explored and suggestions are made for the two major issues of extending analyses across different spatio-temporal scales and for the effective integration of quantitative and qualitative data.

Data Assimilation and Inverse Methods in Terms of a Probabilistic Formulation

The weak-constraint inverse for nonlinear dynamical models is discussed and derived in terms of a probabilistic formulation. The well-known result that for Gaussian error statistics the minimum of the weak-constraint inverse is equal to the maximum-likelihood estimate is rederived. Then several methods based on ensemble statistics that can be used to find the smoother (as opposed to the filter) solution are introduced and compared to traditional methods. A strong point of the new methods is that they avoid the integration of adjoint equations, which is a complex task for real oceanographic or atmospheric applications. they also avoid iterative searches in a Hilbert space, and error estimates can be obtained without much additional computational effort. the feasibility of the new methods is illustrated in a two-layer quasigeostrophic model.

Genomic evidence for the Pleistocene and recent population history of Native Americans

How and when the Americas were populated remains contentious. Using ancient and modern genome-wide data, we found that the ancestors of all present-day Native Americans, including Athabascans and Amerindians, entered the Americas as a single migration wave from Siberia no earlier than 23 thousand years ago (ka) and after no more than an 8000-year isolation period in Beringia. After their arrival to the Americas, ancestral Native Americans diversified into two basal genetic branches around 13 ka, one that is now dispersed across North and South America and the other restricted to North America. Subsequent gene flow resulted in some Native Americans sharing ancestry with present-day East Asians (including Siberians) and, more distantly, Australo-Melanesians. Putative “Paleoamerican” relict populations, including the historical Mexican Pericúes and South American Fuego-Patagonians, are not directly related to modern Australo-Melanesians as suggested by the Paleoamerican Model.

Estrogens and thyroid hormone: Non-genomic effects are coupled to transcription.

Estrogens and thyroid hormones are regulators of important diverse physiological processes such as reproduction, thermogenesis, neural development, neural differentiation and cardiovascular functions. Both are ligands for receptors in the nuclear receptor superfamily, which act as ligand-dependent transcription factors, regulating transcription. However, estrogens and thyroid hormones also rapidly (within minutes or seconds) activate kinase cascades and calcium increases, presumably initiated at the cell membrane. We discuss the relevance of both modes of hormone action, including the membrane estrogen receptor, to physiology, with particular reference to lordosis behavior. We first showed that estrogen restricted to the membrane can, in fact, lead to subsequent increases in transcription from a consensus estrogen response element-based reporter in the neuroblastoma cell line, SK-N-BE(2)C. Using a novel hormonal paradigm, we also showed that the activation of protein kinase A, protein kinase C, mitogen activated protein kinase and increases in calcium were important in the ability of the membrane-limited estrogen to potentiate transcription. We discuss the source of calcium important in transcriptional potentiation. Since estrogens and thyroid hormones have common effects on neuroprotection, cognition and mood, we also hypothesized that crosstalk could occur between the rapid actions of thyroid hormones and the genomic actions of estrogens. In neural cells, we showed that triiodothyronine acting rapidly via MAPK can increase transcription by the nuclear estrogen receptor ERa from a consensus estrogen response element, possibly by the phosphorylation of the ERa. Novel mechanisms that link signals initiated by hormones from the membrane to the nucleus are physiologically relevant and can achieve neuroendocrine integration

Proteomic analysis of the medicinal plant Artemisia annua: data from leaf and trichome extracts

This article contains raw and processed data related to research published by Bryant et al. [1]. Data was obtained by MS-based proteomics, analysing trichome-enriched, trichome-depleted and whole leaf samples taken from the medicinal plant Artemisia annua and searching the acquired MS/MS data against a recently published contig database [2] and other genomic and proteomic sequence databases for comparison. The processed data shows that an order-of-magnitude more proteins have been identified from trichome-enriched Artemisia annua samples in comparison to previously published data. Proteins known to have a role in the biosynthesis of artemisinin and other highly abundant proteins were found which imply additional enzymatically driven processes occurring within the trichomes that are significant for the biosynthesis of artemisinin.