Bayesian meta-analysis for identifying periodically expressed genes in fission yeast cell cycle

The effort to identify genes with periodic expression during the cell cycle from genome-wide microarray time series data has been ongoing for a decade. However, the lack of rigorous modeling of periodic expression as well as the lack of a comprehensive model for integrating information across genes and experiments has impaired the effort for the accurate identification of periodically expressed genes. To address the problem, we introduce a Bayesian model to integrate multiple independent microarray data sets from three recent genome-wide cell cycle studies on fission yeast. A hierarchical model was used for data integration. In order to facilitate an efficient Monte Carlo sampling from the joint posterior distribution, we develop a novel Metropolis–Hastings group move. A surprising finding from our integrated analysis is that more than 40% of the genes in fission yeast are significantly periodically expressed, greatly enhancing the reported 10–15% of the genes in the current literature. It calls for a reconsideration of the periodically expressed gene detection problem.Statistic

Towards self-powered wireless sensor networks

Ubiquitous computing aims at creating smart environments in which computational and communication capabilities permeate the word at all scales, improving the human experience and quality of life in a totally unobtrusive yet completely reliable manner. According to this vision, an huge variety of smart devices and products (e.g., wireless sensor nodes, mobile phones, cameras, sensors, home appliances and industrial machines) are interconnected to realize a network of distributed agents that continuously collect, process, share and transport information. The impact of such technologies in our everyday life is expected to be massive, as it will enable innovative applications that will profoundly change the world around us. Remotely monitoring the conditions of patients and elderly people inside hospitals and at home, preventing catastrophic failures of buildings and critical structures, realizing smart cities with sustainable management of traffic and automatic monitoring of pollution levels, early detecting earthquake and forest fires, monitoring water quality and detecting water leakages, preventing landslides and avalanches are just some examples of life-enhancing applications made possible by smart ubiquitous computing systems. To turn this vision into a reality, however, new raising challenges have to be addressed, overcoming the limits that currently prevent the pervasive deployment of smart devices that are long lasting, trusted, and fully autonomous. In particular, the most critical factor currently limiting the realization of ubiquitous computing is energy provisioning. In fact, embedded devices are typically powered by short-lived batteries that severely affect their lifespan and reliability, often requiring expensive and invasive maintenance. In this PhD thesis, we investigate the use of energy-harvesting techniques to overcome the energy bottleneck problem suffered by embedded devices, particularly focusing on Wireless Sensor Networks (WSNs), which are one of the key enablers of pervasive computing systems. Energy harvesting allows to use energy readily available from the environment (e.g., from solar light, wind, body movements, etc.) to significantly extend the typical lifetime of low-power devices, enabling ubiquitous computing systems that can last virtually forever. However, the design challenges posed both at the hardware and at the software levels by the design of energy-autonomous devices are many. This thesis addresses some of the most challenging problems of this emerging research area, such as devising mechanisms for energy prediction and management, improving the efficiency of the energy scavenging process, developing protocols for harvesting-aware resource allocation, and providing solutions that enable robust and reliable security support. %, including the design of mechanisms for energy prediction and management, improving the efficiency of the energy harvesting process, the develop of protocols for harvesting-aware resource allocation, and providing solutions that enable robust and reliable security support

Multivariate hydrometeorological extreme events and their impacts on vegetation: potential methods and applications

Trockenheiten und Hitzewellen beeinflussen unsere Gesellschaft und die Vegetation. Insbesondere im Zusammenhang mit dem Klimawandel sind die Auswirkungen auf die Vegetation von besonderer Bedeutung. Im globalen Kohlenstoffkreislauf sind terrestrische Ökosysteme normalerweise Senken von Kohlenstoffdioxid, können sich aber während und nach Klimaextremereignissen in Kohlenstoffquellen verwandeln. Ein entscheidender Aspekt hierbei ist die Rolle verschiedener Pflanzenarten und Vegetationstypen auf verschiedenen Skalen, die die Auswirkungen auf den Kohlenstoffkreislauf beeinflussen. Obwohl durch physiologische Unterschiede zwischen verschiedenen Pflanzenarten unterschiedliche Reaktionen auf Extremereignisse naheliegen, sind diese Unterschiede auf globaler Ebene nicht systematisch ausgewertet und vollständig verstanden. Ein weiter Aspekt ist, dass Klimaextremereignissen von Natur aus multivariat sind. Beispielsweise kann heiße Luft mehr Wasser aufnehmen als kalte Luft. Extremereignisse mit starken Auswirkungen waren in der Vergangenheit häufig multivariat, wie beispielsweise in Europa 2003, Russland 2012, oder den USA 2012. Diese multivariate Natur von Klimaextremen erfordert eine multivariate Perspektive auf diese Ereignisse. Bisher werden meistens einzelne Variablen zu Detektion von Extremereignissen genutzt und keine Kovariation oder Nichtlinearitäten berücksichtigt. Neue generische Workflows, die solche multivariaten Strukturen berücksichtigen, müssen erst entwickelt oder aus anderen Disziplinen übertragen werden, um uns eine multivariate Perspektive auf Klimaextreme zu bieten. Das übergeordnete Ziel der Dissertation ist es, die Erkennung und das Verständnis von Klimaextremen und deren Auswirkungen auf die Vegetation zu verbessern, indem eine breitere multivariate Perspektive ermöglicht wird, die bisherige Ansätze zur Erkennung von Extremereignissen ergänzt

Prognostic and pathophysiological features of uraemic cardiomyopathy using cardiovascular magnetic resonance imaging

Premature cardiovascular (CV) death is the commonest cause of death in patients with end stage renal disease (ESRD), which includes those receiving or close to requiring renal replacement therapy. In ESRD patients, CV deaths are most commonly caused by cardiac arrhythmia and sudden cardiac death compared to the general population where myocardial ischaemia and infarction predominate. Higher CV disease burden is due to accumulation of “conventional” risk factors (e.g. hypertension, diabetes mellitus, smoking) and “novel” risk factors (e.g. oxidative stress, proteinuria, anaemia, inflammation) in ESRD patients. In addition, risk factors specific to patients with renal disease have been identified including alteration in left ventricular (LV) structure, called uraemic cardiomyopathy. These structural abnormalities are common in patients with ESRD (between 60-80% of subjects upon initiation of dialysis) and include left ventricular hypertrophy (LVH), systolic dysfunction (LVSD) and dilatation. These changes in LV structure confer adverse CV outcome in ESRD patients and have proven difficult to reverse. Detection of these abnormalities is usually performed using echocardiography, however this technique is inaccurate in ESRD patients due to significant alterations in LV shape and geometric assumptions made during calculation of myocardial mass. Cardiovascular MRI (CMR) negates these assumptions and is the most accurate, reproducible and reliable method of assessing LV dimensions independent of intravascular volume, particularly in patients with altered myocardial architecture. Furthermore, maximal left atrial volume can be measured using CMR. The principle aims of the studies presented in this thesis were to elucidate prognostic and pathophysiological features of uraemic cardiomyopathy using CMR. In a large study (n=246) of haemodialysis patients, the determinants of each LV abnormality of uraemic cardiomyopathy were identified from past clinical history, haemodialysis and blood parameters and other LV measurements. For LV changes, major determinants were clinical features associated with advanced renal disease, namely expansion of intravascular/ extracellular fluid compartment, abnormal bone mineral biochemistry and hypertension. Furthermore, presence of one LV abnormality was one of the strongest predictors of presence of another, perhaps indicating differing stages of uraemic cardiomyopathy development. In a subsequent prognostic study including these patients (n=446), presence of LVSD and LV dilatation on CMR were significantly associated with poorer all cause and CV mortality. Presence of LVH, which is by far the most common structural change, was associated with poorer cardiovascular survival only. In addition, presence of two or three abnormalities (commonly LVH with another abnormality) had a significantly poorer prognosis and independently predicted CV and all cause mortality. This has implications for therapeutic strategies which should aim to slow or reverse cardiac changes of ESRD and prevent progression from one cardiac abnormality to 2 or more. In a further study (n=201) investigating additional prognostic features of ESRD patients with LVH, maximal left atrial volume (LAV) was measured using the bi-plane area length method at end LV systole. Elevated LAV and presence of LVSD were significantly associated with poorer all cause survival and were independent predictors of death. The most likely causes of elevated LAV in ESRD patients are LV diastolic dysfunction and expanded extracellular compartment and may provide a target for therapeutic intervention. The electrophysiological features of uraemic cardiomyopathy were assessed using microvolt T wave alternans (MTWA) which is a novel, non-invasive method of measuring small variations in surface electrocardiogram (ECG) T wave morphology and thus ventricular repolarisation. This technique has been used to stratify other cohorts at elevated risk of sudden cardiac death (such as ischaemic and non ischaemic cardiomyopathy, hypertensive LVH). A study presented in this thesis, compared MTWA results between ESRD (n=200) and hypertensive patients with LVH on echocardiography (n=30). Abnormal MTWA result was significantly more common in ESRD patients compared to hypertensive patients with LVH. Furthermore, abnormal MTWA result was significantly associated with myocardial abnormalities of uraemic cardiomyopathy and a history of macrovascular atheromatous disease in ESRD patients. Despite preservation of LV function on CMR, the frequency of abnormal MTWA result in ESRD patients was similar to previous studies in subjects with heart failure. 31Phosphorus magnetic resonance spectroscopy is a novel, non-invasive technique of estimating cardiac energetic status and high energy phosphate (HEP) metabolism in a myocardial area of interest and has previously been used to assess patients with global myocardial disease (dilated cardiomyopathy, hypertensive LVH). High energy phosphate metabolism was compared between patients with ESRD (n=53) and hypertensive LVH (n=30) and despite similar LV mass between both groups, PCr: ATP (an indicator of HEP metabolism) was significantly reduced in ESRD patients. These findings are most likely due to cardiac interstitial fibrosis and the alteration of tissue composition within the area of interest, and changes in metabolic function within cardiomyocytes of uraemic hearts. Finally, a small study (n=50) investigated the effect of successful renal transplantation on LV mass measured by CMR. On comparison of patients who remained on the renal transplant waiting list, there was no significant difference in LV mass in patients who received a renal transplant. It is likely that previous echocardiography studies that demonstrated significant regression of LVH, measured improvement in fluid control rather that actual reduction in myocardial mass. Future studies investigating benefit of therapeutic intervention may require identification of individuals at higher CV risk and the results of studies presented in this thesis aim to provide information for selecting such ESRD patients. With these results in mind, further prospective studies will be able to carefully select groups of ESRD patients with differing left ventricular, left atrial, electrophysiological and biochemical properties to demonstrate survival benefit with interventional agents. In this way, future therapies for ESRD patients can be tailored to improve cardiovascular survival

Clinical and Mechanistic Insights into Novel Probiotic Functions and Formulations

Using a combination of hypothesis and discovery based approaches, the goal of this thesis was to better describe novel probiotic functions and their mechanisms while striving to better understand the effect of formulation on Bifidobacterium animalis subsp. lactis, Lactobacillus paracasei and L. rhamnosus. Using RNA-Seq, a bacterial metatranscriptome analysis of a commonly consumed probiotic yogurt showed that the organisms adapted to storage time and flavor additions. This led to the discovery that in addition to the probiotic health benefits, members of the L. casei group (L. rhamnosus and L. paracasei) produce volatile sulfur compounds mediated by a novel sulfur/taurine metabolism gene cluster that affect taste and texture. The benefits of selected probiotic strains were tested in a further series of human studies. A systems biology approach was developed and a double-blind, placebo-controlled clinical trial of post-menopausal women showed that vaginally administered probiotics could influence the microbiota and host responses. Changes in the vaginal microbiota were noted in late pregnancy in a rural Tanzanian population, and maternal intake of Moringa supplemented L. rhamnosus GR-1 yogurt appeared to improve the gut microbiota profile of the newborn babies. Having discovered that L. rhamnosus GR-1, and selected other lactobacilli, could sequester heavy metals in vitro, a randomized open-label pilot study was performed and showed a reduction in toxic metal uptake in Tanzanian pregnant women and school children. The latter series of findings led to the discovery, development and characterization of a new strain, L. rhamnosus Lr60, with high potential to reduce toxic metal accumulation in the host. Using a mouse model, strains of L. rhamnosus were tested to better understand mechanisms of protection against mercury as well as to examine potential modulation of host xenobiotic metabolism by probiotics. Data suggest it is possible to sequester mercury and prevent it from entering the bloodstream. Collectively, these studies have increased our knowledge of probiotic mechanisms as well as lead to the development of novel applications of relevance to human health

Advances in Forensic Genetics

The book has 25 articles about the status and new directions in forensic genetics. Approximately half of the articles are invited reviews, and the remaining articles deal with new forensic genetic methods. The articles cover aspects such as sampling DNA evidence at the scene of a crime; DNA transfer when handling evidence material and how to avoid DNA contamination of items, laboratory, etc.; identification of body fluids and tissues with RNA; forensic microbiome analysis with molecular biology methods as a supplement to the examination of human DNA; forensic DNA phenotyping for predicting visible traits such as eye, hair, and skin colour; new ancestry informative DNA markers for estimating ethnic origin; new genetic genealogy methods for identifying distant relatives that cannot be identified with conventional forensic DNA typing; sensitive DNA methods, including single-cell DNA analysis and other highly specialised and sensitive methods to examine ancient DNA from unidentified victims of war; forensic animal genetics; genetics of visible traits in dogs; statistical tools for interpreting forensic DNA analyses, including the most used IT tools for forensic STR-typing and DNA sequencing; haploid markers (Y-chromosome and mitochondria DNA); inference of ethnic origin; a comprehensive logical framework for the interpretation of forensic genetic DNA data; and an overview of the ethical aspects of modern forensic genetics

Emerging Hydro-Climatic Patterns, Teleconnections and Extreme Events in Changing World at Different Timescales

This Special Issue is expected to advance our understanding of these emerging patterns, teleconnections, and extreme events in a changing world for more accurate prediction or projection of their changes especially on different spatial–time scales

Understanding global resource allocation in fission yeast through data analysis and coarse-grained mathematical modelling

Unicellular organisms can grow in a large variety of environments. Even in those supporting robust growth, cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate and the cell size. I have approached this topic from two angles, namely a comprehensive analysis of a gene expression data set and the construction of coarse-grained resource allocation models (C-GRAMs). First, I studied a combined data set of protein and transcript abundances during growth of the fission yeast Schizosaccharomyces pombe on various abundant nitrogen sources. Approximately half of gene expression was significantly correlated with the growth rate, and this came alongside wide-spread nutrient-specific expression. Genes positively correlated with the growth rate participated in protein production, whereas those negatively correlated mainly belonged to the environmental stress response programme. Critically, the expression of metabolic enzymes was mainly condition specific. Second, C-GRAMs are simple models of single cells, where large components of the macromolecular composition are abstracted into single entities. The dynamics and steady-state behaviour of such models can then be easily explored. A minimal C-GRAM with nitrogen and carbon pathways converging on biomass production described the effects of the uptake of sugars, ammonium, and/or compound nutrients such as amino acids on the translational resource allocation towards proteome sectors that maximised the growth rate. Prompted by new observations that the relation between cell volume and the growth rate was identical for both carbon and nitrogen perturbations, but that the surface-to-volume ratio was elevated in low-nitrogen conditions, I extended this to a C-GRAM that additionally accounted for the cell cycle, cell division, cell wall biosynthesis, and the effect of molecular crowding on the ribosomal efficiency.Open Acces