Anthropogenic habitat disturbance is the leading cause of global biodiversity decline. Urbanization in particular is one of the most drastic forms of habitat disturbance, and it is associated with a decrease in both plant and animal diversity. Changes in biodiversity can affect the interactions between the remaining species within an ecosystem, which can, in turn, affect the provision of essential ecosystem services such as seed dispersal and pollination. Despite the wealth of studies examining the effects of urbanization on biodiversity, however, relatively few studies have investigated how urbanization impacts the interactions between species and the ecosystem services provided by them. Seed dispersal is one such ecosystem service which provides an ideal study system for investigating these effects. The goal of this study was to assess the impact of urbanization on avian seed dispersal networks in southern Appalachia. Specifically, we investigated the impact on species richness, interaction richness, and several network metrics related to ecosystem function (H2, interaction evenness, and weighted nestedness). The study was conducted across two fall – winter observation periods to coincide with the period of peak fruit production, from September – January in 2021-2022 and 2022-2023. Data was collected from 9 study sites during multiple visits via direct observation of bird-fruit interactions, with a total of 635 interactions recorded from 32 bird species on 18 fruiting plant species. Although data analysis is currently ongoing, initial results seem to indicate that there is no significant difference in species richness or interaction richness between natural and urban sites. This has interesting implications, as it suggests that plant-frugivore networks are relatively robust to disturbance caused by urbanization, which is promising for the continued provision of ecosystem services in urban areas.

Animal pollination is responsible for the majority of the human food supply. Understanding pollination dynamics in agricultural systems is thus essential to help maintain this ecosystem service in the face of human disturbances. Surprisingly, our understanding of plant–pollinator interactions in widely distributed smallholder agricultural systems is still limited. Knowledge of pollination dynamics in these agricultural systems is necessary to fully assess how human disturbances may affect pollination services worldwide. In this study, we describe the structure of a plant–floral visitor network in a smallholder Cucurbitaceae agricultural system. We further identify the main floral visitors of these crops and tested their importance by simulating how their extinction affected network structure and robustness. The observed network was highly connected and generalized but it was neither nested nor compartmentalized. Our results suggest that the structure of agricultural plant–pollinator networks could be inherently different from those in natural communities. These differences in network structure may reflect differences in spatial distribution of floral resources between agricultural and natural systems. We identified Augochlora nigrocyanea and Peponapis limitaris as the two most frequent floral visitors. However, removal of these species did not affect network structure or its robustness, suggesting high levels of interaction rewiring. To our knowledge, this is one of the first studies to describe the structure of a plant–floral visitor network in diverse agricultural systems in the tropics. We emphasize the need for more studies that evaluate network structure in agricultural systems if we want to fully elucidate the impact of human disturbances on this key ecosystem service.

Οι πρωτεϊνικές αλληλεπιδράσεις (PPI) αναφέρονται στην σύνδεση δύο ή περισσοτέρων πρωτεϊνών ώστε να εκτελεστεί μια βιολογική λειτουργία. Την τελευταία δεκαετία, νέες τεχνολογίες υψηλής απόδοσης για τον εντοπισμό αυτών των αλληλεπιδράσεων έχουν παράγει μεγάλης κλίμακας σύνολα δεδομένων τόσο του ανθρώπου όσο και των περισσοτέρων ειδών. Με την αναπαράσταση αυτών των δεδομένων σε δίκτυα, με τους κόμβους να αναπαριστούν τις πρωτεΐνες και τις ακμές τις αλληλεπιδράσεις, μπορούν να εξαχθούν χρήσιμες πληροφορίες σχετικά με τον προσδιορισμό της λειτουργίας των πρωτεϊνών/πρόβλεψη ή σχετικά με το πώς να σχεδιαστούν κατάλληλα φάρμακα που προσδιορίζουν τα νέα γονίδια-στόχους για τον καρκίνο ή τους μηχανισμούς που ελέγχουν (ή ρυθμίζουν) τις βιολογικές αλληλεπιδράσεις που είναι υπεύθυνες για την καλή ή την κακή λειτουργία ενός κυττάρου. Στα πλαίσια της παρούσας διπλωματικής, κληθήκαμε να κάνουμε λειτουργική πρόβλεψη των πρωτεϊνών στο δίκτυο πρωτεϊνικών αλληλεπιδράσεων του ανθρώπου εφαρμόζοντας μια μέθοδο δυναμικής επικάλυψης η οποία βασίζεται στον έλεγχο του πώς οι ταλαντωτές οργανώνονται σε ένα «αρθρωτό»(modular) δίκτυο σχηματίζοντας διεπαφές συγχρονισμού και κοινότητες επικάλυψης. Μελετήσαμε το δίκτυο πρωτεϊνικών αλληλεπιδράσεων του ανθρώπου και τις κλάσεις λειτουργιών θεωρώντας ένα σύνολο ταλαντωτών φάσης (phase oscillators) με μία τοπολογία συνδέσεων που ορίζεται από το δίκτυο πρωτεϊνικών αλληλεπιδράσεων του ανθρώπου. Συγκεκριμένα, αρχίσαμε με μία απλή ομαδοποίηση για κάθε πρωτεΐνη και έπειτα χρησιμοποιήσαμε την μέθοδο δυναμικής επικάλυψης για τον προσδιορισμό των λειτουργιών των πρωτεϊνών του PPI δικτύου. Στην συνέχεια, εντοπίσαμε εκείνες τις πρωτεΐνες οι οποίες δεν είχαν ομαδοποιηθεί σωστά καθώς και τις πρωτεϊνες που ήταν πιθανόν να «συμμετείχαν» σε περισσότερες από μία λειτουργικές κλάσεις (πολυλειτουργικές πρωτεΐνες). Με κατάλληλο έλεγχο των αλληλεπιδράσεων μεσαίας κλίμακας του δικτύου των δυναμικών συστημάτων που δημιουργήθηκε παρήχθησαν χρήσιμες πληροφορίες για τις μικρής και μεγάλης κλίμακας διαδικασίες μέσω των οποίων οι βιολογικές διεργασίες οργανώνονται σε ένα κύτταρο γεγονός που αποκαλύπτει ότι η μέθοδος είναι ικανή όχι μόνο να εντοπίσει τις μη σωστά ομαδοποιημένες πρωτεΐνες αλλά και να αποκαλύψει αυτές που έχουν διπλή λειτουργικότητα (2 λειτουργίες). / Protein-protein interactions (PPI) refer to the binding of two or more proteins to perform a biological function. In the last decade, novel high-throughput technologies for detecting those interactions have produced large-scale data sets across human and most model species. By embedding these data in networks, with nodes representing proteins and edges the detected PPIs, useful information can be extracted regarding protein functional annotation/prediction or on how to design proper drugs, identifying new targets on cancer, or mechanisms to control (or regulate) the biological interactions responsible for the functioning,or malfunctioning, of a cell. Under the framework of my master thesis, I had to apply a method of dynamical overlap based on the inspection of how oscillators organize in a modular network by forming synchronization interfaces and overlapping communities to the human PPI network. I studied the human protein interaction network (PIN) and its functional modules by considering an ensemble of phase oscillators with a topology of connections defined by the human PIN. In particular, I started with a single classification for each protein and I used the dynamical overlap method for identifying/predicting of the proteins function(s) in the PPI network. Then, I identified all those proteins that were misclassified and those proteins that were likely to be involved in more than one of the functional categories in our data(multifunctional proteins). A proper inspection on the meso-scale interactions of the generated network of dynamical systems provided useful information on the micro- and macro- scale processes through which biological processes are organized in a cell, that is, the method is not only able to identify the misclassified proteins but also to unveil those proteins that have double functionality.

Epidemiologists rely on human interaction networks for determining states and dynamics of disease propagations in populations. However, such networks are empirical snapshots of the past. It will greatly benefit if human interaction networks are statistically predicted and dynamically created while an epidemic is in progress. We develop an application framework for the generation of human interaction networks and running epidemiological processes utilizing research on human mobility patterns and agent-based modeling. The interaction networks are dynamically constructed by incorporating different types of Random Walks and human rules of engagements. We explore the characteristics of the created network and compare them with the known theoretical and empirical graphs. The dependencies of epidemic dynamics and their outcomes on patterns and parameters of human motion and motives are encountered and presented through this research. This work specifically describes how the types and parameters of random walks define properties of generated graphs. We show that some configurations of the system of agents in random walk can produce network topologies with properties similar to small-world networks. Our goal is to find sets of mobility patterns that lead to empirical-like networks. The possibility of phase transitions in the graphs due to changes in the parameterization of agent walks is the focus of this research as this knowledge can lead to the possibility of disruptions to disease diffusions in populations. This research shall facilitate work of public health researchers to predict the magnitude of an epidemic and estimate resources required for mitigation.

Orientador: Admilson Írio Ribeiro / Resumo: A demanda por energia elétrica e seus sistemas de produção e distribuição são relevantes para a sociedade, pois está associada ao desenvolvimento das nações. Atualmente muitas empresas do ramo estão surgindo em nosso país, particularmente na região Nordeste, devido às condições ambientais para geração de energia eólica. Assim, como toda ação antrópica, a utilização dos ventos para geração de energia elétrica apresenta impactos positivos e negativos. Nesse sentido, a proposta dessa pesquisa foi realizar uma análise integrada dos aspectos e impactos ambientais na operação do Parque Eólico Complexo Alto Sertão no distrito de Morrinhos - Guanambi\Bahia. A proposta de estudo foi delineada por meio de dois métodos de avaliação de impacto ambiental: rede de interação e matriz de ponderação. Dentre os impactos positivos destacados pode ser citada a melhoria das condições de vida dos proprietários de terra os quais são contratados por arrendamento do uso da área. Outro impacto positivo significativo foi à geração de empregos na instalação e operação do empreendimento, pois sugiram oportunidades de serviços local e regional. Destaca se também como impacto positivo significativo o aumento de recursos econômicos para os municípios da região dado o aumento na arrecadação de impostos e tributos. Assim, a sociedade local entende que produção de energia eólica auxilia desenvolvimento socioeconômico. A produção de energia eólica, mesmo sendo uma fonte renovável, como toda atividade humana pro... (Resumo completo, clicar acesso eletrônico abaixo) / Mestre

Orientador: Felipe Wanderley Amorim / Resumo: As mudanças globais mediadas pela ação antrópica estão alterando a biodiversidade e os ecossistemas em um ritmo acelerado. Um dos principais impulsionadores dessas mudanças é a introdução de espécies exóticas em ecossistemas nativos. Entre os grupos de organismos afetados por este processo, o das plantas é reconhecido um dos mais preocupantes, uma vez que a produção primária limita o tamanho e a composição das comunidades e participa através de efeitos em cascata em interações multi-tróficas. Uma das principais relações ecológicas influenciada por esse efeito é a relação entre plantas e polinizadores, cujo papel é importante para estrutura e funcionamento das comunidades biológicas, não apenas porque as plantas fornecem recursos alimentares essenciais para muitos grupos de animais que visitam flores, mas também porque o sucesso reprodutivo da maioria das plantas com flores depende dos serviços bióticos fornecidos por estes animais. Neste contexto, a introdução de espécies de plantas exóticas invasoras pode ter impactos críticos nas interações planta-polinizador ao nível da comunidade, principalmente através da competição com espécies nativas. Como as interações planta-polinizador são cruciais para determinar a estrutura da comunidade, nesse estudo nós testamos como o crescimento rápido de uma gramínea invasora altera a composição das espécies de plantas nativas em um campo cerrado, juntamente com os impactos deste processo sobre a estrutura das interações planta-polinizador. ... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: The global change mediated by anthropic action is altering biodiversity and ecosystems in a fast pace. One major driver of these changes is the introduction of alien species in native ecosystems. Among the groups of organisms that are affected by this process, plants are recognized to be one of the most concern, since primary production limit global communities’ sizes and composition, and participate through cascade effects on multitrophic interactions. One crucial type of interaction that is influenced by this effect is the plant-pollinator relationship, which have an important role in the structure and functioning of biological communities, not only because plants provide essential food resources for many groups of animals that visit flowers, but also because the reproductive success of most flowering plants depends on the biotic services provided by animals. In this context, the introduction of invasive alien plant species may have critical impacts on plant-pollinator interactions at community level, mainly through competition with native species. Since plantpollinator interactions are determinants of community structure, here we evaluated how the rising of a fast-growing invasive alien grass species changes plant species composition of a Neotropical grassland community along with its impacts on the structure of plant-pollinator interactions. For this, we analyzed the changes in community composition and plantpollinator interactions over time, through the temporal turnover... (Complete abstract click electronic access below) / Mestre

Protein-protein interactions are of vital importance to the cell as they mediate the assembly of protein complexes that carry out diverse biological functions. Many proteins involved in cellular signaling are built by the combinatorial use of peptide recognition modules (PRMs), which are small protein domains that bind to their cognate ligands by recognizing short linear peptide motifs. Thousands of PRMs are found in nature, requiring improved methods to better elucidate their molecular determinants of binding and to allow accurate mapping of their interaction networks. In this thesis, I describe the development and application of phage-displayed peptide libraries to map the binding specificities of two common PRMs. First, I generated specificity profiles for 82 C. elegans and human PDZ domains that could be organized into a specificity map. The map revealed that PDZ domains have far greater substrate sequence specificity than previously believed, providing significant insights into the relationships between PDZ structure and specificity, and allowing specificity prediction for uncharacterized domains. My results were used to predict both endogenous and pathogenic PDZ interactions. This analysis revealed that viruses have evolved ligands that specifically mimic PDZ domains to subvert host cell immunity. Second, I analyzed the binding specificity for the SH3 domain family in S. cerevisae. I found that, like PDZ domains, SH3 domains have binding specificities that are more detailed than the conventional classification system. The phage-derived specificity profiles were combined with data from oriented peptide and yeast two-hybrid screening to generate a highly accurate SH3 domain interaction network. Given the prominent role of SH3 domains in endocytosis, the SH3 domain interaction data was used to predict the dynamic localization of several uncharacterized endocytosis proteins, which was subsequently confirmed by cell-based assays. The application of the techniques described here to other PRM families will significantly improve protein interaction maps for signaling pathways, which will illuminate our understanding of the cell circuitry, allow the use of PRMs as general affinity reagent and detection tools, and guide the development of small molecule inhibitors that mimic their peptide ligands for therapeutic intervention.

Protein-protein interactions are of vital importance to the cell as they mediate the assembly of protein complexes that carry out diverse biological functions. Many proteins involved in cellular signaling are built by the combinatorial use of peptide recognition modules (PRMs), which are small protein domains that bind to their cognate ligands by recognizing short linear peptide motifs. Thousands of PRMs are found in nature, requiring improved methods to better elucidate their molecular determinants of binding and to allow accurate mapping of their interaction networks. In this thesis, I describe the development and application of phage-displayed peptide libraries to map the binding specificities of two common PRMs. First, I generated specificity profiles for 82 C. elegans and human PDZ domains that could be organized into a specificity map. The map revealed that PDZ domains have far greater substrate sequence specificity than previously believed, providing significant insights into the relationships between PDZ structure and specificity, and allowing specificity prediction for uncharacterized domains. My results were used to predict both endogenous and pathogenic PDZ interactions. This analysis revealed that viruses have evolved ligands that specifically mimic PDZ domains to subvert host cell immunity. Second, I analyzed the binding specificity for the SH3 domain family in S. cerevisae. I found that, like PDZ domains, SH3 domains have binding specificities that are more detailed than the conventional classification system. The phage-derived specificity profiles were combined with data from oriented peptide and yeast two-hybrid screening to generate a highly accurate SH3 domain interaction network. Given the prominent role of SH3 domains in endocytosis, the SH3 domain interaction data was used to predict the dynamic localization of several uncharacterized endocytosis proteins, which was subsequently confirmed by cell-based assays. The application of the techniques described here to other PRM families will significantly improve protein interaction maps for signaling pathways, which will illuminate our understanding of the cell circuitry, allow the use of PRMs as general affinity reagent and detection tools, and guide the development of small molecule inhibitors that mimic their peptide ligands for therapeutic intervention.

Genome scale protein-protein interactions (PPIs) are interpreted as networks or graphs with thousands of nodes from the perspective of computer science. PPI networks represent various types of possible interactions among proteins or genes of a genome. PPI data is vital in protein function prediction since functions of the cells are performed by groups of proteins interacting with each other and main complexes of the cell are made of proteins interacting with each other. Recent increase in protein interaction prediction techniques have made great amount of protein-protein interaction data available for genomes. As a consequence, a systematic visualization and analysis technique has become crucial. To the best of our knowledge, no PPI visualization tool consider multi-resolution viewing of PPI network. In this thesis, we implemented a new approach for PPI network visualization which supports multi-resolution viewing of compound graphs. We construct compound nodes and label them by using gene set enrichment methods based on Gene Ontology annotations. This thesis further suggests new methods for PPI network visualization.

Indiana University-Purdue University Indianapolis (IUPUI) / Imaging genetics is an emerging research field in neurodegenerative diseases. It studies the influence of genetic variants on brain structure and function. Genome-wide association studies (GWAS) of brain imaging has identified a few independent risk loci for individual imaging quantitative trait (iQT), which however display only modest effect size and explain limited heritability. This thesis focuses on mining high-level imaging genetic associations and their applications on neurodegenerative diseases. This thesis first presents a novel network-based GWAS framework for identifying functional modules, by employing a two-step strategy in a top-down manner. It first integrates tissue-specific network with GWAS of corresponding phenotype in regression models in addition to classification, to re-prioritize genome-wide associations. Then it detects densely connected and disease-relevant modules based on interactions among top reprioritizations. The discovered modules hold both phenotypical specificity and densely interaction. We applied it to an amygdala imaging genetics analysis in the study of Alzheimer's disease (AD). The proposed framework effectively detects densely interacted modules; and the reprioritizations achieve highest concordance with AD genes. We then present an extension of the above framework, named GWAS top-neighbor-based (tnGWAS); and compare it with previous approaches. This tnGWAS extracts densely connected modules from top GWAS findings, based on the hypothesis that relevant modules consist of top GWAS findings and their close neighbors. It is applied to a hippocampus imaging genetics analysis in AD research, and yields the densest interactions among top candidate genes. Experimental results demonstrate that precise context does help explore collective effects of genes with functional interactions specific to the studied phenotype. In the second part, a novel imaging genetic enrichment analysis (IGEA) paradigm is proposed for discovering complex associations among genetic modules and brain circuits. In addition to genetic modules, brain regions of interest also grouped to play role. We expand the scope of one-dimensional enrichment analysis into imaging genetics. This framework jointly considers meaningful gene sets (GS) and brain circuits (BC), and examines whether given GS-BC module is enriched in gene-iQT findings. We conduct the proof-of-concept study and demonstrate its performance by applying to a brain-wide imaging genetics study of AD.