Compaction control of topography and fault network structure along strike-slip faults in sedimentary basins

Strike-slip faults commonly display structurally complex areas of positive or negative topography. Understanding the development of such areas has important implications for earthquake studies and hydrocarbon exploration. Previous workers identified the key factors controlling the occurrence of both topographic modes and the related structural styles. Kinematic and stress boundary conditions are of first-order relevance. Surface mass transport and material properties affect fault network structure. Experiments demonstrate that dilatancy can generate positive topography even under simple-shear boundary conditions. Here, we use physical models with sand to show that the degree of compaction of the deformed rocks alone can determine the type of topography and related surface fault network structure in simple-shear settings. In our experiments, volume changes of ∼5% are sufficient to generate localized uplift or subsidence. We discuss scalability of model volume changes and fault network structure and show that our model fault zones satisfy geometrical similarity with natural flower structures. Our results imply that compaction may be an important factor in the development of topography and fault network structure along strike-slip faults in sedimentary basins.

The rise of birds and mammals: are microevolutionary processes sufficient for macroevolution?

It is a basis of darwinian evolution that the microevolutionary mechanisms that can be studied in the present are sufficient to account for macroevolution. However, this idea needs to be tested explicitly, as highlighted here by the example of the superceding of dinosaurs and pterosaurs by birds and placental mammals that occurred near the Cretaceous/Tertiary boundary approximately 65 million years ago. A major problem for testing the sufficiency of microevolutionary processes is that independent ideas (such as the existence of an extraterrestrial impact, and the extinction of dinosaurs) were linked without the evidence for each idea being evaluated separately. Here, we suggest and discuss five testable models for the times and divergences of modern mammals and birds. Determination of the model that best represents these events will enable the role of microevolutionary mechanisms to be evaluated. The question of the sufficiency of microevolutionary processes for macroevolution is solvable, and available evidence supports an important role for biological processes in the initial decline of dinosaurs and pterosaurs.

Genome-scale phylogeny and the detection of systematic biases

Phylogenetic inference from sequences can be misled by both sampling (stochastic) error and systematic error (nonhistorical signals where reality differs from our simplified models). A recent study of eight yeast species using 106 concatenated genes from complete genomes showed that even small internal edges of a tree received 100% bootstrap support. This effective negation of stochastic error from large data sets is important, but longer sequences exacerbate the potential for biases (systematic error) to be positively misleading. Indeed, when we analyzed the same data set using minimum evolution optimality criteria, an alternative tree received 100% bootstrap support. We identified a compositional bias as responsible for this inconsistency and showed that it is reduced effectively by coding the nucleotides as purines and pyrimidines (RY-coding), reinforcing the original tree. Thus, a comprehensive exploration of potential systematic biases is still required, even though genome-scale data sets greatly reduce sampling error.

A description of the Mei2-like protein family; structure, phylogenetic distribution and biological context

The Schizosaccharomyces pombe Mei2 gene encodes an RNA recognition motif (RRM) protein that stimulates meiosis upon binding a specific non-coding RNA and subsequent accumulation in a “mei2-dot” in the nucleus. We present here the first systematic characterization of the family of proteins with characteristic Mei2-like amino acid sequences. Mei2-like proteins are an ancient eukaryotic protein family with three identifiable RRMs. The C-terminal RRM (RRM3) is unique to Mei2-like proteins and is the most highly conserved of the three RRMs. RRM3 also contains conserved sequence elements at its C-terminus not found in other RRM domains. Single copy Mei2-like genes are present in some fungi, in alveolates such as Paramecium and in the early branching eukaryote Entamoeba histolytica, while plants contain small families of Mei2-like genes. While the C-terminal RRM is highly conserved between plants and fungi, indicating conservation of molecular mechanisms, plant Mei2-like genes have changed biological context to regulate various aspects of developmental pattern formation.

Exact and approximate Bayesian inference for low count time series models with intractable likelihoods

In this paper we present a new simulation methodology in order to obtain exact or approximate Bayesian inference for models for low-valued count time series data that have computationally demanding likelihood functions. The algorithm fits within the framework of particle Markov chain Monte Carlo (PMCMC) methods. The particle filter requires only model simulations and, in this regard, our approach has connections with approximate Bayesian computation (ABC). However, an advantage of using the PMCMC approach in this setting is that simulated data can be matched with data observed one-at-a-time, rather than attempting to match on the full dataset simultaneously or on a low-dimensional non-sufficient summary statistic, which is common practice in ABC. For low-valued count time series data we find that it is often computationally feasible to match simulated data with observed data exactly. Our particle filter maintains $N$ particles by repeating the simulation until $N+1$ exact matches are obtained. Our algorithm creates an unbiased estimate of the likelihood, resulting in exact posterior inferences when included in an MCMC algorithm. In cases where exact matching is computationally prohibitive, a tolerance is introduced as per ABC. A novel aspect of our approach is that we introduce auxiliary variables into our particle filter so that partially observed and/or non-Markovian models can be accommodated. We demonstrate that Bayesian model choice problems can be easily handled in this framework.

Improved estimates of percolation and anisotropic permeability from 3-D X-ray microtomography using stochastic analyses and visualization

X-ray microtomography (micro-CT) with micron resolution enables new ways of characterizing microstructures and opens pathways for forward calculations of multiscale rock properties. A quantitative characterization of the microstructure is the first step in this challenge. We developed a new approach to extract scale-dependent characteristics of porosity, percolation, and anisotropic permeability from 3-D microstructural models of rocks. The Hoshen-Kopelman algorithm of percolation theory is employed for a standard percolation analysis. The anisotropy of permeability is calculated by means of the star volume distribution approach. The local porosity distribution and local percolation probability are obtained by using the local porosity theory. Additionally, the local anisotropy distribution is defined and analyzed through two empirical probability density functions, the isotropy index and the elongation index. For such a high-resolution data set, the typical data sizes of the CT images are on the order of gigabytes to tens of gigabytes; thus an extremely large number of calculations are required. To resolve this large memory problem parallelization in OpenMP was used to optimally harness the shared memory infrastructure on cache coherent Non-Uniform Memory Access architecture machines such as the iVEC SGI Altix 3700Bx2 Supercomputer. We see adequate visualization of the results as an important element in this first pioneering study.

Anisotropic damage mechanics as a novel approach to improve pre-and post-failure borehole stability analysis

Anisotropic damage distribution and evolution have a profound effect on borehole stress concentrations. Damage evolution is an irreversible process that is not adequately described within classical equilibrium thermodynamics. Therefore, we propose a constitutive model, based on non-equilibrium thermodynamics, that accounts for anisotropic damage distribution, anisotropic damage threshold and anisotropic damage evolution. We implemented this constitutive model numerically, using the finite element method, to calculate stress–strain curves and borehole stresses. The resulting stress–strain curves are distinctively different from linear elastic-brittle and linear elastic-ideal plastic constitutive models and realistically model experimental responses of brittle rocks. We show that the onset of damage evolution leads to an inhomogeneous redistribution of material properties and stresses along the borehole wall. The classical linear elastic-brittle approach to borehole stability analysis systematically overestimates the stress concentrations on the borehole wall, because dissipative strain-softening is underestimated. The proposed damage mechanics approach explicitly models dissipative behaviour and leads to non-conservative mud window estimations. Furthermore, anisotropic rocks with preferential planes of failure, like shales, can be addressed with our model.

A two-stage approach for generating topic models

Topic modeling has been widely utilized in the fields of information retrieval, text mining, text classification etc. Most existing statistical topic modeling methods such as LDA and pLSA generate a term based representation to represent a topic by selecting single words from multinomial word distribution over this topic. There are two main shortcomings: firstly, popular or common words occur very often across different topics that bring ambiguity to understand topics; secondly, single words lack coherent semantic meaning to accurately represent topics. In order to overcome these problems, in this paper, we propose a two-stage model that combines text mining and pattern mining with statistical modeling to generate more discriminative and semantic rich topic representations. Experiments show that the optimized topic representations generated by the proposed methods outperform the typical statistical topic modeling method LDA in terms of accuracy and certainty.

Biological performance of a polycaprolactone-based scaffold plus recombinant human morphogenetic protein-2 (rhBMP-2) in an ovine thoracic interbody fusion model

Introduction. We develop a sheep thoracic spine interbody fusion model to study the suitability of polycaprolactone-based scaffold and recombinant human bone morphogenetic protein-2 (rhBMP-2) as a bone graft substitute within the thoracic spine. The surgical approach is a mini- open thoracotomy with relevance to minimally invasive deformity correction surgery for adolescent idiopathic scoliosis. To date there are no studies examining the use of this biodegradable implant in combination with biologics in a sheep thoracic spine model. Methods. In the present study, six sheep underwent a 3-level (T6/7, T8/9 and T10/11) discectomy with randomly allocated implantation of a different graft substitute at each of the three levels; (i) calcium phosphate (CaP) coated polycaprolactone based scaffold plus 0.54µg rhBMP-2, (ii) CaP coated PCL- based scaffold alone or (iii) autograft (mulched rib head). Fusion was assessed at six months post-surgery. Results. Computed Tomographic scanning demonstrated higher fusion grades in the rhBMP-2 plus PCL- based scaffold group in comparison to either PCL-based scaffold alone or autograft. These results were supported by histological evaluations of the respective groups. Biomechanical testing revealed significantly higher stiffness for the rhBMP-2 plus PCL- based scaffold group in all loading directions in comparison to the other two groups. Conclusions. The results of this study demonstrate that rhBMP-2 plus PCL-based scaffold is a viable bone graft substitute, providing an optimal environment for thoracic interbody spinal fusion in a large animal model.

Privacy of RFID - models and protocols

Radio Frequency Identification is a wireless identification method that utilizes the reception of electromagnetic radio waves. This research has proposed a novel model to allow for an in-depth security analysis of current protocols and developed new flexible protocols that can be adapted to offer either stronger security or better efficiency.

How host plant variability influences the advantages to learning : a theoretical model for oviposition behaviour in Lepidoptera

Learning can allow individuals to increase their fitness in particular environments. The advantage to learning depends on the predictability of the environment and the extent to which animals can adjust their behaviour. Earlier general models have investigated when environmental predictability might favour the evolution of learning in foraging animals. Here, we construct a theoretical model that predicts the advantages to learning using a specific biological example: oviposition in the Lepidoptera. Our model includes environmental and behavioural complexities relevant to host selection in these insects and tests whether the predictions of the general models still hold. Our results demonstrate how the advantage of learning is maximised when within-generation variability is minimised (the local environment consists mainly of a single host plant species) and between-generation variability is maximised (different host plant species are the most common in different generations). We discuss how our results: (a) can be applied to recent empirical work in different lepidopteran species and (b) predict an important role of learning in lepidopteran agricultural pests.

Investigating epidermogenesis in a human skin equivalent model

Skin is the largest, and arguably, the most important organ of the body. It is a complex and multi-dimensional tissue, thus making it essentially impossible to fully model in vitro in conventional 2-dimensional culture systems. In view of this, rodents or pigs are utilised to study wound healing therapeutics or to investigate the biological effects of treatments on skin. However, there are many differences between the wound healing processes in rodents compared to humans (contraction vs. re-epithelialisation) and there are also ethical issues associated with animal testing for scientific research. Therefore, the development of skin equivalent (HSE) models from surgical discard human skin has become an important area of research. The studies in this thesis compare, for the first time, native human skin and the epidermogenesis process in a HSE model. The HSE was reported to be a comparable model for human skin in terms of expression and localisation of key epidermal cell markers. This validated HSE model was utilised to study the potential wound healing therapeutic, hyperbaric oxygen (HBO) therapy. There is a significant body of evidence suggesting that lack of cutaneous oxygen results in and potentiates the chronic, non-healing wound environment. Although the evidence is anecdotal, HBO therapy has displayed positive effects on re-oxygenation of chronic wounds and the clinical outcomes suggest that HBO treatment may be beneficial. Therefore, the HSE was subjected to a daily clinical HBO regime and assessed in terms of keratinocyte migration, proliferation, differentiation and epidermal thickening. HBO treatment was observed to increase epidermal thickness, in particular stratum corneum thickening, but it did not alter the expression or localisation of standard epidermal cell markers. In order to elucidate the mechanistic changes occurring in response to HBO treatment in the HSE model, gene microarrays were performed, followed by qRT-PCR of select genes which were differentially regulated in response to HBO treatment. The biological diversity of the HSEs created from individual skin donors, however, overrode the differences in gene expression between treatment groups. Network analysis of functional changes in the HSE model revealed general trends consistent with normal skin growth and maturation. As a more robust and longer term study of these molecular changes, protein localisation and expression was investigated in sections from the HSEs undergoing epidermogenesis in response to HBO treatment. These proteins were CDCP1, Metallothionein, Kallikrein (KLK) 1 and KLK7 and early growth response 1. While the protein expression within the HSE models exposed to HBO treatment were not consistent in all HSEs derived from all skin donors, this is the first study to detect and compare both KLK1 and CDCP1 protein expression in both a HSE model and native human skin. Furthermore, this is the first study to provide such an in depth analysis of the effect of HBO treatment on a HSE model. The data presented in this thesis, demonstrates high levels of variation between individuals and their response to HBO treatment, consistent with the clinical variation that is currently observed.

Cell migration and proliferation on homogeneous and non-homogeneous domains : modelling on the scale of individuals and populations

Cell migration is a behaviour critical to many key biological effects, including wound healing, cancerous cell invasion and morphogenesis, the development of an organism from an embryo. However, given that each of these situations is distinctly different and cells are extremely complicated biological objects, interest lies in more basic experiments which seek to remove conflating factors and present a less complex environment within which cell migration can be experimentally examined. These include in vitro studies like the scratch assay or circle migration assay, and ex vivo studies like the colonisation of the hindgut by neural crest cells. The reduced complexity of these experiments also makes them much more enticing as problems to mathematically model, like done here. The primary goal of the mathematical models used in this thesis is to shed light on which cellular behaviours work to generate the travelling waves of invasion observed in these experiments, and to explore how variations in these behaviours can potentially predict differences in this invasive pattern which are experimentally observed when cell types or chemical environment are changed. Relevant literature has already identified the difficulty of distinguishing between these behaviours when using traditional mathematical biology techniques operating on a macroscopic scale, and so here a sophisticated individual-cell-level model, an extension of the Cellular Potts Model (CPM), is been constructed and used to model a scratch assay experiment. This model includes a novel mechanism for dealing with cell proliferations that allowed for the differing properties of quiescent and proliferative cells to be implemented into their behaviour. This model is considered both for its predictive power and used to make comparisons with the travelling waves which result in more traditional macroscopic simulations. These comparisons demonstrate a surprising amount of agreement between the two modelling frameworks, and suggest further novel modifications to the CPM that would allow it to better model cell migration. Considerations of the model’s behaviour are used to argue that the dominant effect governing cell migration (random motility or signal-driven taxis) likely depends on the sort of invasion demonstrated by cells, as easily seen by microscopic photography. Additionally, a scratch assay simulated on a non-homogeneous domain consisting of a ’fast’ and ’slow’ region is also used to further differentiate between these different potential cell motility behaviours. A heterogeneous domain is a novel situation which has not been considered mathematically in this context, nor has it been constructed experimentally to the best of the candidate’s knowledge. Thus this problem serves as a thought experiment used to test the conclusions arising from the simulations on homogeneous domains, and to suggest what might be observed should this non-homogeneous assay situation be experimentally realised. Non-intuitive cell invasion patterns are predicted for diffusely-invading cells which respond to a cell-consumed signal or nutrient, contrasted with rather expected behaviour in the case of random-motility-driven invasion. The potential experimental observation of these behaviours is demonstrated by the individual-cell-level model used in this thesis, which does agree with the PDE model in predicting these unexpected invasion patterns. In the interest of examining such a case of a non-homogeneous domain experimentally, some brief suggestion is made as to how this could be achieved.