COMPARATIVE STUDY OF GENOMIC FEATURES OF EVOLUTIONARILY YOUNG GENE DUPLICATES

Gene duplication is considered a major contributor to genome evolution and functional diversity. Differences in genomic features (such as structural resemblance, transcriptional orientation, and genomic location) between members of a gene duplicate pair may indicate the possible duplication mechanisms, as well as the evolutionary fates the paralogs may experience. In addition to these genomic features, molecular genetic features, such as differences in codon usage and expression levels may provide further insight into functional changes between paralogs. In this dissertation, multiple genomic analyses were conducted in order to evaluate the differences in genomic and genetic properties between duplicate copies in order to understand the effect duplication mechanisms may have on the divergence of duplicate pairs. Chapter Two focuses on differing patterns of sequence asymmetry, codon usage, and gene expression levels between the members of gene duplicate pairs belonging to two different populations of paralogs in Saccharomyces cerevisiae: ohnologs, which arose via a whole genome duplication (WGD), and small segmental duplication (SSD) paralogs. It is shown that ohnologs have more highly conserved gene order (synteny) relative to SSD paralogs, despite their greater evolutionary age. Within SSD pairs, the derived paralog (the copy with lower synteny) seems to evolve faster, simultaneously exhibiting a lower CIA value and lower expression levels relative to the ancestral copy. While synteny and evolutionary rate differences were not coupled in ohnolog pairs, the relationship between evolutionary rate asymmetry, CAI, and expression levels was similar to that observed in SSD pairs. These results indicate that codon usage contributes to rate asymmetry in the evolution of gene duplicates in both, ohnologs and SSD paralogs, while differences in synteny (as experienced by SSD pairs, but not very young ohnologs) only affects rate asymmetry in SSD pairs. This may imply relaxed selection on codon usage and the expression of derived copies, potentially leading to the acquisition of novel functions over time. Chapters Three and Four focus on the effects of structural resemblance and other genomic features on young gene duplicate pairs within the Homo sapiens (human) and Pan troglodytes (chimpanzee) genomes. The results imply that the majority of gene duplicates in both species are structurally complete duplications, encompassing the entire coding region of a gene. The chimpanzee genome additionally contains a large fraction (46%) of retrotransposed young gene duplicates relative to the human genome (13%) which may be due to differences in genome architecture, such as mobile element content between the two genomes. While RNA-mediated processes lead to a majority of inter-chromosomal paralogs, DNA-mediated paralogs reside largely on the same chromosome, in which case inter-paralog distance does not increase over time. These results in conjunction with results of previous studies in nematodes, yeast, and flies, suggest that the structural resemblance types and location of duplicates are closely linked to the duplication mechanism by which paralog pairs arise. This is also true for closely related species, as illustrated by the comparison of the human and chimpanzee genomes. The above studies illustrate the relationship duplication span (as illustrated in Chapter Two) and mechanisms (illustrated in Chapters Three and Four) have on the location, synteny, structural resemblance types, and functionality of gene duplicates in different genomes. The findings imply that differences in mechanisms between species can have significant effects on the genome evolution and divergence between even closely related taxa

The Spatial and Temporal Ecology of Seed Dispersal by Gorillas in Lopé National Park, Gabon: Linking Patterns of Disperser Behavior and Recruitment in an Afrotropical Forest

Western lowland gorillas: Gorilla g. gorilla) consume large quantities of fruit and disperse a great number of seeds. The majority these seeds are dispersed intact and viable in the dung. Dung is often deposited around the rim of a night nest or at a nest-site. Gorillas often construct nests in areas that have a sparse canopy, flattening the ground vegetation. These locations can be beneficial to the growth and survival of the seed species they disperse. Thus, not only are gorillas effective in terms of depositing seeds great distances from parent plants, away from the highest seed rain densities, they are also effective in directing seeds to potentially beneficial microsites. The objective of this research was to develop an understanding of the spatial and temporal patterns in fruit availability, seed deposition, and adult plants, and to test whether these patterns relate to the ecology of seed dispersal by gorillas. Results suggest that gorilla foraging and nesting behavior in particular, impose both spatial and temporal limitations to the distribution of dispersed seeds. In addition, temporal variation in the gorilla diet and factors that affect defecation rates and locations promote variation in the combinations: composition and abundance) of the seed species dispersed to different microsites. The clustered distribution of nest-sites leads to clumped and spatially restricted seed deposition patterns. Recruitment in gorilla-dispersed seed species corresponds with the aggregated: clumped) distribution of nest-sites. Gorillas have a long-lasting effect on the spatial structure and floristic composition of the forests they inhabit, particularly in large-seeded species

LemurFaceID: a face recognition system to facilitate individual identification of lemurs

Background: Long-term research of known individuals is critical for understanding the demographic and evolutionary processes that influence natural populations. Current methods for individual identification of many animals include capture and tagging techniques and/or researcher knowledge of natural variation in individual phenotypes. These methods can be costly, time-consuming, and may be impractical for larger-scale, populationlevel studies. Accordingly, for many animal lineages, long-term research projects are often limited to only a few taxa. Lemurs, a mammalian lineage endemic to Madagascar, are no exception. Long-term data needed to address evolutionary questions are lacking for many species. This is, at least in part, due to difficulties collecting consistent data on known individuals over long periods of time. Here, we present a new method for individual identification of lemurs (LemurFaceID). LemurFaceID is a computer-assisted facial recognition system that can be used to identify individual lemurs based on photographs. Results: LemurFaceID was developed using patch-wise Multiscale Local Binary Pattern features and modified facial image normalization techniques to reduce the effects of facial hair and variation in ambient lighting on identification. We trained and tested our system using images from wild red-bellied lemurs (Eulemur rubriventer) collected in Ranomafana National Park, Madagascar. Across 100 trials, with different partitions of training and test sets, we demonstrate that the LemurFaceID can achieve 98.7% ± 1.81% accuracy (using 2-query image fusion) in correctly identifying individual lemurs. Conclusions: Our results suggest that human facial recognition techniques can be modified for identification of individual lemurs based on variation in facial patterns. LemurFaceID was able to identify individual lemurs based on photographs of wild individuals with a relatively high degree of accuracy. This technology would remove many limitations of traditional methods for individual identification. Once optimized, our system can facilitate long-term research of known individuals by providing a rapid, cost-effective, and accurate method for individual identification

PATTERN DISCOVERY IN DNA USING STOCHASTIC AUTOMATA

We consider the problem of identifying similarities between different species of DNA. To do this we infer a stochastic finite automata from a given training data and compare it with a test data. The training and test data consist of DNA sequence of different species. Our method first identifies sentences in DNA. To identify sentences we read DNA sequence one character at a time, 3 characters form a codon and codons form proteins (also known as amino acid chains).Each amino acid in proteins belongs to a group. In total we have 5 groups’ polar, non-polar, acidic, basic and stop codons. A protein always starts with a start codon ATG that belongs to the group polar and ends with one of the stop codons that belongs to the group stop codon. After identifying sentences our method converts it into a symbolic representation of strings where each number represents the group to which an amino acid belongs to. We then generate a PTA tree and merge equivalent states to produce a Stochastic Finite Automata for a DNA. In addition to producing SFA, we apply secondary storage to handle huge DNA sequences. We also explain some concepts that are necessary to understand our paper

Routing, Localization And Positioning Protocols For Wireless Sensor And Actor Networks

Wireless sensor and actor networks (WSANs) are distributed systems of sensor nodes and actors that are interconnected over the wireless medium. Sensor nodes collect information about the physical world and transmit the data to actors by using one-hop or multi-hop communications. Actors collect information from the sensor nodes, process the information, take decisions and react to the events. This dissertation presents contributions to the methods of routing, localization and positioning in WSANs for practical applications. We first propose a routing protocol with service differentiation for WSANs with stationary nodes. In this setting, we also adapt a sports ranking algorithm to dynamically prioritize the events in the environment depending on the collected data. We extend this routing protocol for an application, in which sensor nodes float in a river to gather observations and actors are deployed at accessible points on the coastline. We develop a method with locally acting adaptive overlay network formation to organize the network with actor areas and to collect data by using locality-preserving communication. We also present a multi-hop localization approach for enriching the information collected from the river with the estimated locations of mobile sensor nodes without using positioning adapters. As an extension to this application, we model the movements of sensor nodes by a subsurface meandering current mobility model with random surface motion. Then we adapt the introduced routing and network organization methods to model a complete primate monitoring system. A novel spatial cut-off preferential attachment model and iii center of mass concept are developed according to the characteristics of the primate groups. We also present a role determination algorithm for primates, which uses the collection of spatial-temporal relationships. We apply a similar approach to human social networks to tackle the problem of automatic generation and organization of social networks by analyzing and assessing interaction data. The introduced routing and localization protocols in this dissertation are also extended with a novel three dimensional actor positioning strategy inspired by the molecular geometry. Extensive simulations are conducted in OPNET simulation tool for the performance evaluation of the proposed protocol

Microbial Ecology of Urban Sewers

Municipal sewage provides a glimpse into the health and activities of a human society. For more than a century, sewage exploration has helped expose the sources of disease outbreaks and track disease progression over time. Recent advancements in wastewater surveillance born from the COVID-19 pandemic have potential to enhance mitigation efforts against the decades-long global health crisis of microbial antibiotic resistance. However, critical knowledge gaps exist in wastewater surveillance, stemming from a lack of understanding in sewer microbial ecology. Ecology reveals trends in how communities respond and adapt to change, which has far-reaching implications for identifying effective strategies for disease control. However, with little knowledge about sewer microbial communities, including its residents, community dynamics, and functions, no baseline picture of the sewer microbiome exists. The goal of this dissertation was to characterize the sewer microbiome using an ecological approach. The specific aims were to determine if (1) microbial communities in urban wastewater exhibit seasonal patterns in assembly; (2) if seasonal community assembly is driven by environmental bacteria responding to changes in water temperature; and (3) if temperature-driven communities modulate the composition and abundance of antibiotic resistance genes in wastewater. Results show that microbes in sewers have seasonal community dynamics akin to other natural environments, and they have adapted to this stressful environment by acquiring and maintaining mechanisms of antibiotic resistance. Using only well-established methods in DNA sequencing and analyzing a wastewater dataset covering expansive temporal and spatial scales, this dissertation builds the foundation of a baseline sewer microbiome in the United States. All data collected and analyses used were made publicly available to aid standardizing methods in global strategy plans. Together, standardizing methods and sharing data related to the sewer microbiome will improve predictive models, guide interventions, and make other public health breakthroughs in wastewater surveillance

Visual Representation Learning with Limited Supervision

The quality of a Computer Vision system is proportional to the rigor of data representation it is built upon. Learning expressive representations of images is therefore the centerpiece to almost every computer vision application, including image search, object detection and classification, human re-identification, object tracking, pose understanding, image-to-image translation, and embodied agent navigation to name a few. Deep Neural Networks are most often seen among the modern methods of representation learning. The limitation is, however, that deep representation learning methods require extremely large amounts of manually labeled data for training. Clearly, annotating vast amounts of images for various environments is infeasible due to cost and time constraints. This requirement of obtaining labeled data is a prime restriction regarding pace of the development of visual recognition systems. In order to cope with the exponentially growing amounts of visual data generated daily, machine learning algorithms have to at least strive to scale at a similar rate. The second challenge consists in the learned representations having to generalize to novel objects, classes, environments and tasks in order to accommodate to the diversity of the visual world. Despite the evergrowing number of recent publications tangentially addressing the topic of learning generalizable representations, efficient generalization is yet to be achieved. This dissertation attempts to tackle the problem of learning visual representations that can generalize to novel settings while requiring few labeled examples. In this research, we study the limitations of the existing supervised representation learning approaches and propose a framework that improves the generalization of learned features by exploiting visual similarities between images which are not captured by provided manual annotations. Furthermore, to mitigate the common requirement of large scale manually annotated datasets, we propose several approaches that can learn expressive representations without human-attributed labels, in a self-supervised fashion, by grouping highly-similar samples into surrogate classes based on progressively learned representations. The development of computer vision as science is preconditioned upon the seamless ability of a machine to record and disentangle pictures' attributes that were expected to only be conceived by humans. As such, particular interest was dedicated to the ability to analyze the means of artistic expression and style which depicts a more complex task than merely breaking an image down to colors and pixels. The ultimate test for this ability is the task of style transfer which involves altering the style of an image while keeping its content. An effective solution of style transfer requires learning such image representation which would allow disentangling image style and its content. Moreover, particular artistic styles come with idiosyncrasies that affect which content details should be preserved and which discarded. Another pitfall here is that it is impossible to get pixel-wise annotations of style and how the style should be altered. We address this problem by proposing an unsupervised approach that enables encoding the image content in such a way that is required by a particular style. The proposed approach exchanges the style of an input image by first extracting the content representation in a style-aware way and then rendering it in a new style using a style-specific decoder network, achieving compelling results in image and video stylization. Finally, we combine supervised and self-supervised representation learning techniques for the task of human and animals pose understanding. The proposed method enables transfer of the representation learned for recognition of human poses to proximal mammal species without using labeled animal images. This approach is not limited to dense pose estimation and could potentially enable autonomous agents from robots to self-driving cars to retrain themselves and adapt to novel environments based on learning from previous experiences

Cortex, countercurrent context, and dimensional integration of lifetime memory

The correlation between relative neocortex size and longevity in mammals encourages a search for a cortical function specifically related to the life-span. A candidate in the domain of permanent and cumulative memory storage is proposed and explored in relation to basic aspects of cortical organization. The pattern of cortico-cortical connectivity between functionally specialized areas and the laminar organization of that connectivity converges on a globally coherent representational space in which contextual embedding of information emerges as an obligatory feature of cortical function. This brings a powerful mode of inductive knowledge within reach of mammalian adaptations, a mode which combines item specificity with classificatory generality. Its neural implementation is proposed to depend on an obligatory interaction between the oppositely directed feedforward and feedback currents of cortical activity, in countercurrent fashion. Direct interaction of the two streams along their cortex-wide local interface supports a scheme of "contextual capture" for information storage responsible for the lifelong cumulative growth of a uniquely cortical form of memory termed "personal history." This approach to cortical function helps elucidate key features of cortical organization as well as cognitive aspects of mammalian life history strategies

Environmental risk factors in infectious diseases: studies in waterborne disease outbreaks, Ebola, and Lyme disease

Thesis (Ph.D.)--Boston UniversityThe resurgence of infectious diseases and global climate change's potential impact on them has refocused public health's attention on the environment's role in infectious disease. The studies in this dissertation utilize the increased availability of satellite image-derived data sets with fine temporal and geographic granularity and the expansion of epidemiologic methods to explore the relationship between the environment and infectious disease in three settings. The first study employed a novel study design and analytic methods to investigate the hypothesis that heavy rainfall is an independent risk factor for waterborne disease outbreaks (WBDOs). We found that a location experiencing a heavy rainfall event had about half the odds of a WBDO two or four weeks later than did a location without a heavy rainfall event. The location-based case-crossover study design utilized in this study may help to expand the research methods available to epidemiologists working in this developing field. The second study employed a location-based case-crossover study design to evaluate standardized differences from historic average of weekly rainfall in locations with a recorded introduction of Ebola into a human. For each 1.0 unit z-score decrease in total rainfall, the odds of an Ebola introduction three weeks later increased by 75%. Given the severity of Ebola outbreaks and the dearth of knowledge about indicators of increased risk, this finding is an important step in advancing our understanding of Ebola ecology. The third study used GIS methods on remote sensing data to estimate the association between peridomestic forest/non-forest interface within 100, 150, 250 meters and Lyme-associated peripheral facial palsy (LAPFP) among pediatric facial palsy patients. After adjustment for sex, age, and socio-economic status, children with the highest level of forest edge in the three radii of analysis had 2.74 (95% CI 1.15, 6.53), 4.58 (1.84, 11.41), and 5.88 (2.11, 16.4) times the odds of LAPFP compared to children with zero forest edge in those radii. This study is the first to examine environmental risk factors for LAPFP. Each of these studies advances the techniques used to investigate environmental risk factors for infectious disease through study design, case definition, data used, or exposure definitions