Finding banded patternsin large data set using segmentation

No Abstrac

Structural RNA Homology Search and Alignment Using Covariance Models

Functional RNA elements do not encode proteins, but rather function directly as RNAs. Many different types of RNAs play important roles in a wide range of cellular processes, including protein synthesis, gene regulation, protein transport, splicing, and more. Because important sequence and structural features tend to be evolutionarily conserved, one way to learn about functional RNAs is through comparative sequence analysis - by collecting and aligning examples of homologous RNAs and comparing them. Covariance models: CMs) are powerful computational tools for homology search and alignment that score both the conserved sequence and secondary structure of an RNA family. However, due to the high computational complexity of their search and alignment algorithms, searches against large databases and alignment of large RNAs like small subunit ribosomal RNA: SSU rRNA) are prohibitively slow. Large-scale alignment of SSU rRNA is of particular utility for environmental survey studies of microbial diversity which often use the rRNA as a phylogenetic marker of microorganisms. In this work, we improve CM methods by making them faster and more sensitive to remote homology. To accelerate searches, we introduce a query-dependent banding: QDB) technique that makes scoring sequences more efficient by restricting the possible lengths of structural elements based on their probability given the model. We combine QDB with a complementary filtering method that quickly prunes away database subsequences deemed unlikely to receive high CM scores based on sequence conservation alone. To increase search sensitivity, we apply two model parameterization strategies from protein homology search tools to CMs. As judged by our benchmark, these combined approaches yield about a 250-fold speedup and significant increase in search sensitivity compared with previous implementations. To accelerate alignment, we apply a method that uses a fast sequence-based alignment of a target sequence to determine constraints for the more expensive CM sequence- and structure-based alignment. This technique reduces the time required to align one SSU rRNA sequence from about 15 minutes to 1 second with a negligible effect on alignment accuracy. Collectively, these improvements make CMs more powerful and practical tools for RNA homology search and alignment

Effects of Variable and Changing Environments on Demography: Inferences from a Lesser Snow Goose Colony

Anthropogenic pressures have caused changes in both the mean and variance of environmental conditions, with associated effects on the demography of natural populations. The demographic effects of environmental change can manifest through direct (i.e., physiological) or indirect pathways (i.e., through shifts in species interactions). For many populations, environmental change will affect multiple life cycle stages simultaneously, thereby altering vital rate correlation structures with potentially important impacts on evolutionary fitness. The effects of environmental change will also often be habitat-specific, particularly when species interactions modify demographic sensitivity to climate. As a result, the effects of climate change are likely to vary across a species range, with important implications for range expansion and population viability. In chapter 2, I examine the effects of joint vital rate responses to environmental drivers on the evolution of life histories in variable environments. I show that vital rate covariation, generated when multiple vital rates respond to a shared environmental driver, can fundamentally alter evolutionary selection pressures. Negative vital rate covariation promotes the evolution of demographic lability (stronger demographic responsiveness), while positive covariation promotes buffering (weaker demographic responsiveness), altering the range of life histories over which the evolution of buffered and labile vital rates are a predicted evolutionary outcome. By identifying the life histories for which selection pressures are most sensitive to environmentally-driven vital rate covariation, this study provides a richer understanding of both life history evolution and the capacity of species to cope with ongoing changes to contemporary environments. In chapter 3, I use a long-term study of lesser snow geese to test the hypothesis that demographic and developmental responses to climate will be weakest in habitats where resource diversity is greatest. I find support for this hypothesis, and my results indicate that gosling demography is much more responsive to climate in recently colonized, freshwater habitats where landscape diversity and gosling diet diversity is low. These results underscore the potential importance of accounting for biotic interactions when predicting spatio-temporal responses to climate. In chapter 4, I quantify the consequences of observed climate change for lesser snow goose population dynamics across habitats. I find that climate change increases population growth in all habitats, but that such increases are disproportionately large in novel inland freshwater habitats. These results suggest that in a warmer and more variable climate, the breeding range and population growth of lesser snow geese is likely to increase, counteracting current management efforts to reduce overabundant populations

Demographic consequences of agricultural practices on a long-lived avian predator

Includes bibliographical references.2022 Fall.To view the abstract, please see the full text of the document

How do Households Value the Future? Evidence from Property Taxes

Despite the near ubiquity of inter-temporal choice, there is little consensus on the rate at which individuals trade present and future costs and benefits. We contribute to this debate by estimating discount rates from extensive data on housing transactions and spatio-temporal variation in property taxes in England. Our findings imply longterm average discount rates that are between 3 and 4%. The close correspondence to prevailing market interest rates gives little reason to suggest that households misoptimise by materially undervaluing very long term financial flows in this high stakes context

Analysis of invasive Aedes japonicus populations and bloodmeals in rural, suburban, and urban land-use conditions

Adult female mosquitoes were collected at six sites with differing land-use and livestock characteristics to characterize populations and bloodmeal habits of the invasive vector mosquito species, Aedes japonicus in Southern Maine. Mosquitoes were collected and DNA was extracted for PCR amplification of cytochrome C oxidase I (COi) mitochondrial DNA for barcoding analysis of vertebrate bloodmeals. A total of 7460 adult female mosquitoes were collected, with 444 being Ae. japonicus (5.6%). This indicates an established breeding population of Ae. japonicus in Southern Maine. The rural site adjacent to livestock had the highest yield of total mosquitoes as well as the catch rate (indiv./day) for both total female mosquitoes and Ae. japonicus. Following PCR amplification, 192 samples resulted in sequence alignments. Hits from Mammalia, Amphibia, Actinopterygii, Aves, and Reptilia were identified, with the most abundant taxa belonging to Mammalia and Amphibia. Avian bloodmeals were identified, including a sample with a high likelihood of identity as Gallus gallus (Domestic chicken). Bloodmeal information is important for characterizing the zoonotic epidemiology of invasive vector mosquito species such as Ae. japonicus

Colour polymorphism in the terrestrial snail Cepaea nemoralis: from genetics and genomics to spectroscopy and deep learning

Colour variation in the animal kingdom has been important in science to determine the principles of biology, especially in genetics and evolution. In the past decades, much effort has been targeted at the evolutionary, ecological and genetic basis of colour variation. Although land snails have been relatively neglected, especially in latter years, a comprehension of genetics and the evolution is important to understand colour variation precisely because snails may be representative of many species. When studying colour polymorphism, one of the remaining challenges is to describe colour. Generally, colour is described manually, relying on the judgment of human perception, classifying them into a discrete types. The main issue, then, is that human perception is subjective and colour is continuous. Fortunately, technology has enabled new techniques to score colour, which may help to investigate colour polymorphism. This thesis aims to contribute to the knowledge of the maintenance of colour polymorphism by firstly, understanding the genetics and genomics and secondly, developing new methods for the scoring of colour. To achieve this, the grove snail Cepaea nemoralis was selected as a model species. Cepaea nemoralis was chosen due to their highly polymorphic shell, its easy collection, is widely distributed in all variety of habitats and the colour and banding morphs showing Mendelian inheritance (Cain & Sheppard, 1950, Cain & Sheppard, 1952, Cain & Sheppard, 1954, Lamotte, 1959, Jones et al., 1977). In the first part, I aimed for a better understanding of the inheritance of colour. Hence, new crosses of C. nemoralis were used, with flanking restriction site–associated DNA sequencing (RAD-seq) markers used to identify putative instances of recombination with the supergene that determines colour and banding. No evidence of the predicted recombinants was found. Instead, a better explanation could involve incomplete penetrance and epistasis (Gonzalez et al., 2019). The findings therefore challenge the previous assumption of the supergene architecture and provides a new resource for the future creation of a fine mapping of the supergene (Gonzalez et al., 2019). In the second part, I aimed to understand the evolutionary history of C. nemoralis, by investigating the relationship of the genomic and supergene variation with the geographic distribution over Europe. High-throughput genome-wide genotyping was achieved via a double digest restriction-site associated DNA sequencing (ddRADseq) method. A broad phylogenomic relationship showed geographic structure. However, no relationship between the geographical distribution and colour variation was found. Furthermore, possible genomic regions under selection, which may be driving the genomic variation, were identified. In addition, the phylogeny described the evolution of C. nemoralis and indicated how the Pyrenean lineages colonised Europe after the Pleistocene. The results suggest new roads of research into the evolutionary and genomic mechanisms that have led the geographical genomic and supergene variation of C. nemoralis. In the third part, colour manual scoring was tested using new quantitative methods to describe colour to better understand colour variation. Therefore, a comparative study with historical and present shell colour patterns of C. nemoralis in the Pyrenees was used. Prior studies manually scored shell ground colour into three discrete colours; yellow, pink or brown. However, colour is continuous and the description of discrete colours may incur potential error and biased results. Thus, a quantitative method to score shell colour and to test manual scoring, comparing patterns of C. nemoralis shell colour polymorphism was used. Similar altitudinal trends irrespective of the method were found, even though quantitative measures of shell colour reduced the possibility of error. Moreover, a remarkable stability in the local shell patterns over five decades were found. This study determined that both methods remains valuable illustrating several advantages and disadvantages. In the future, a combination of both methods may be a possible solution. Finally, and as continuation of the third part, a new visual recognition and classification method for C. nemoralis based on spectrophotometry and deep learning was created. Firstly, colour of the shells were quantified by spectrometry, and secondly, pictures were taken of the measured shells, in different backgrounds. Those pictures were used to train and test a Region-based Fully Convolutional Networks (R-FCN). Furthermore, public domain pictures were collected from iNaturalist database (https://www.inaturalist.org/), to validate the model. The results illustrate that this method can achieve high accuracy of detection and classification of snails into the right morph. This work may facilitate the way of how colour polymorphism was investigated, illustrating new avenues for future research. In conclusion, this thesis evaluates the limitations found in prior studies and generates new data for the genetic and genomic understanding of C. nemoralis colour polymorphism. It also produced viable solutions, using new technologies, to score the diverse colour morphs. I also contributed to the geographic evolutionary genomic diversity knowledge

Fitting Physical Models to Spatiotemporal Observations: Discovering Developmental Regulatory Networks of Drosophila

Deep learning continues to solve significant scientific and engineering problems, but the solutions found are neural networks with thousands of parameters that provide no scientific or engineering insights. A solution to this problem, explored in this work, is to learn mathematical models that represent mechanisms that can be interpreted by scientists and engineers. A challenging learning problem is to discover the genetic regulatory mechanisms that drive pattern formation during early biological development. Using known mathematical models of these processes, consisting of coupled ordinary differential and partial differential equations, we aim to identify the model parameters that describe the biological mechanisms at play. To guide learning, we use raw gene expression data sampled from the model organism Drosophila melanogaster, a fruit fly, which is normalized through a series of processing steps before learning. Our learning method applies the powerful techniques of algorithmic differentiation and gradient descent that underlie deep-learning advances. The results of this study reveal multiple genetic regulatory solutions capable of producing genetic expression patterns that match those observed in the fruit fly embryo. Cluster analysis of these solutions identifies a set of discrete genetic regulatory networks that more closely match those that function in the actual embryo

Colour polymorphism in the terrestrial snail Cepaea nemoralis: from genetics and genomics to spectroscopy and deep learning

Colour variation in the animal kingdom has been important in science to determine the principles of biology, especially in genetics and evolution. In the past decades, much effort has been targeted at the evolutionary, ecological and genetic basis of colour variation. Although land snails have been relatively neglected, especially in latter years, a comprehension of genetics and the evolution is important to understand colour variation precisely because snails may be representative of many species. When studying colour polymorphism, one of the remaining challenges is to describe colour. Generally, colour is described manually, relying on the judgment of human perception, classifying them into a discrete types. The main issue, then, is that human perception is subjective and colour is continuous. Fortunately, technology has enabled new techniques to score colour, which may help to investigate colour polymorphism. This thesis aims to contribute to the knowledge of the maintenance of colour polymorphism by firstly, understanding the genetics and genomics and secondly, developing new methods for the scoring of colour. To achieve this, the grove snail Cepaea nemoralis was selected as a model species. Cepaea nemoralis was chosen due to their highly polymorphic shell, its easy collection, is widely distributed in all variety of habitats and the colour and banding morphs showing Mendelian inheritance (Cain & Sheppard, 1950, Cain & Sheppard, 1952, Cain & Sheppard, 1954, Lamotte, 1959, Jones et al., 1977). In the first part, I aimed for a better understanding of the inheritance of colour. Hence, new crosses of C. nemoralis were used, with flanking restriction site–associated DNA sequencing (RAD-seq) markers used to identify putative instances of recombination with the supergene that determines colour and banding. No evidence of the predicted recombinants was found. Instead, a better explanation could involve incomplete penetrance and epistasis (Gonzalez et al., 2019). The findings therefore challenge the previous assumption of the supergene architecture and provides a new resource for the future creation of a fine mapping of the supergene (Gonzalez et al., 2019). In the second part, I aimed to understand the evolutionary history of C. nemoralis, by investigating the relationship of the genomic and supergene variation with the geographic distribution over Europe. High-throughput genome-wide genotyping was achieved via a double digest restriction-site associated DNA sequencing (ddRADseq) method. A broad phylogenomic relationship showed geographic structure. However, no relationship between the geographical distribution and colour variation was found. Furthermore, possible genomic regions under selection, which may be driving the genomic variation, were identified. In addition, the phylogeny described the evolution of C. nemoralis and indicated how the Pyrenean lineages colonised Europe after the Pleistocene. The results suggest new roads of research into the evolutionary and genomic mechanisms that have led the geographical genomic and supergene variation of C. nemoralis. In the third part, colour manual scoring was tested using new quantitative methods to describe colour to better understand colour variation. Therefore, a comparative study with historical and present shell colour patterns of C. nemoralis in the Pyrenees was used. Prior studies manually scored shell ground colour into three discrete colours; yellow, pink or brown. However, colour is continuous and the description of discrete colours may incur potential error and biased results. Thus, a quantitative method to score shell colour and to test manual scoring, comparing patterns of C. nemoralis shell colour polymorphism was used. Similar altitudinal trends irrespective of the method were found, even though quantitative measures of shell colour reduced the possibility of error. Moreover, a remarkable stability in the local shell patterns over five decades were found. This study determined that both methods remains valuable illustrating several advantages and disadvantages. In the future, a combination of both methods may be a possible solution. Finally, and as continuation of the third part, a new visual recognition and classification method for C. nemoralis based on spectrophotometry and deep learning was created. Firstly, colour of the shells were quantified by spectrometry, and secondly, pictures were taken of the measured shells, in different backgrounds. Those pictures were used to train and test a Region-based Fully Convolutional Networks (R-FCN). Furthermore, public domain pictures were collected from iNaturalist database (https://www.inaturalist.org/), to validate the model. The results illustrate that this method can achieve high accuracy of detection and classification of snails into the right morph. This work may facilitate the way of how colour polymorphism was investigated, illustrating new avenues for future research. In conclusion, this thesis evaluates the limitations found in prior studies and generates new data for the genetic and genomic understanding of C. nemoralis colour polymorphism. It also produced viable solutions, using new technologies, to score the diverse colour morphs. I also contributed to the geographic evolutionary genomic diversity knowledge