The hologenome concept of evolution: a philosophical and biological study

The hologenome concept of evolution is a hypothesis about the evolution of animals and plants. It asserts that the evolution of animals and plants was partially triggered by their interactions with their symbiotic microbiomes. In that vein, the hologenome concept posits that the holobiont (animal host + symbionts of the microbiome) is a unit of selection. The hologenome concept has been severely criticized on the basis that selection on holobionts would only be possible if there were a tight transgenerational host-genotype-to-symbiont-genotype connection. As our current evidence suggests that this is not the case for most of the symbiont species that compose the microbiome of animals and plants, the opportunity for holobiont selection is very low in relation to the opportunity for selection on each of the species that compose the host microbiome. Therefore, holobiont selection will always be disrupted ‘from below’, by selection on each of the species that compose the microbiome. This thesis constitutes a conceptual effort to defend philosophically the hologenome concept. I argue that the criticism according to which holobiont selection requires tight transgenerational host-genotype-to-symbiont-genotype connection is grounded on a metaphysical view of the world according to which the biological hierarchy needs to be nested, such that each new level of selection includes every entity from below. Applied to hologenomes, it entails that the hologenome is a collection of genomes, and selection of hologenomes is assumed to entail cospeciation of the host with the species that constitute its microbiome. Against that interpretation, I propose the ‘stability of traits’ account, according to which hologenome evolution is the result of the action of natural selection in a non-nested hierarchical world. In that vein, hologenome evolution does not entail cospeciation, and thus it does not require tight transgenerational host-genotype-to-symbiont-genotype connection. By embracing a multilevel selection perspective, I argue that hologenome evolution results from the simultaneous action of natural selection on each of the lineages that compose the microbiome, and on the assemblage composed by the host genome plus the functional traits of its microbiome. Hologenome selection occurs when the evolution of the traits of the microbiome result from their effects on the fitness of the host, and it can take the form of multilevel selection 1, or multilevel selection 2. In both cases, hologenome selection entails the evolution of microbiome traits, as well as evolution of the host genome, rather than cospeciation of lineages

Analysis Challenges for High Dimensional Data

In this thesis, we propose new methodologies targeting the areas of high-dimensional variable screening, influence measure and post-selection inference. We propose a new estimator for the correlation between the response and high-dimensional predictor variables, and based on the estimator we develop a new screening technique termed Dynamic Tilted Current Correlation Screening (DTCCS) for high dimensional variables screening. DTCCS is capable of picking up the relevant predictor variables within a finite number of steps. The DTCCS method takes the popular used sure independent screening (SIS) method and the high-dimensional ordinary least squares projection (HOLP) approach as its special cases. Two methods of high-dimensional influence measure have also been emplored. They are from the perspective of the extreme value distribution (EVD) and the robustness of design respectively. For the first method, EVD-type statistics have been shown to be powerful in measuring high-dimensional influence theoretically and numerically. From the second method, we propose Hellinger distance for high-dimensional influence measure (HD-HIM). Inner product of two transformed influence function is used to measure the Hellinger distance of two discrete distribution function from the whole and deleted dataset. This construction gives detecting power to flag the influence observations. Lastly, we propose a new numerically feasible post-selection inference method termed Cosine PoSI in high-dimensional framework. Cosine PoSI focus on the geometric aspect of Least Angle Regression (LARS). LARS efficiently provide a solution path along which the entered predictors always have the same absolute correlation with the current residual. At each step of the LARS algorithm, the proposed Cosine PoSI method employs an angle from the correlation between the entering variable and current residual and considers this angle as a random variable from the cosine distribution. The post-selection inference is then conducted based on the order statistics of this cosine distribution. Given the collection of the possible angles, hypothesis tests are performed on the limiting distribution of the maximum angle. To confirm the effectiveness of the proposed method, we conduct simulation studies and a real-life data analysis to illustrate the usefulness of this post-selection method

Keeping Up with the Joneses: New Models to Support Developing Needs

The purpose of this paper is to explore models that may improve interdisciplinary collection strategies. Practical alternatives and expansions to existing services that can be explored without the burden of irreversible consequences will be discussed. This paper is intended more so as a conversation starter about altering our thought processes in regards to how librarians carry out their work to meet new demands. It is not intended to be a guide with proven methods that will work universally. These proposals are set within the context of a library that is part of a large research institution.International Federation of Library AssociationsUniversity of Toronto, LibraryUniversity of Toronto, Faculty of InformationUniversity of Illinois, LibraryTitle VI National Resource Center Grant (P015A060066)unpublishednot peer reviewe

Introduction to Library Trends 48 (4) 2000: Collection Development in an Electronic Environment

published or submitted for publicatio

Optical tomography: Image improvement using mixed projection of parallel and fan beam modes

Mixed parallel and fan beam projection is a technique used to increase the quality images. This research focuses on enhancing the image quality in optical tomography. Image quality can be defined by measuring the Peak Signal to Noise Ratio (PSNR) and Normalized Mean Square Error (NMSE) parameters. The findings of this research prove that by combining parallel and fan beam projection, the image quality can be increased by more than 10%in terms of its PSNR value and more than 100% in terms of its NMSE value compared to a single parallel beam