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When movement ecology of target species is coupled with spatial management approaches, such as marine protected areas (MPAs), the results can establish effective conservation outcomes. Nevertheless, a knowledge gap persists regarding how many marine organisms use specific environments over long, continuous periods of time. Acoustic telemetry arrays and fine-scale positioning systems are quickly pervading the marine environment as they can monitor animal movements on a near continuous basis, filling in many previous unknowns on spatial use patterns. Further, coupling fine-scale movement patterns and benthic habitat data provides a spatial framework foundation essential to understanding the intricacies of how habitats can drive movement ecology, and how organisms might link adjacent habitats and resources through movement. The first chapter of this thesis quantified both the broad- and fine-scale movement patterns of yellowtail snapper Ocyurus chrysurus (n = 8) around Buck Island Reef National Monument (BIRNM), St. Croix, U.S. Virgin Islands, an MPA managed by the National Park Service. High site fidelity and a clear affinity to the western shelf break characterized common broad-scale movements observed for this species. Two distinct contingents were detected by the positioning system suggesting individuals were using habitats in two unique, highly structured ways, however, this result requires further validation through an increased sample size. For the second chapter, I characterized the broad-scale movement ecology of horse-eye jack Caranx latus (n = 7), an understudied, but common predatory reef fish. Horse-eye jack are wide ranging, with most individuals visiting almost all receivers (n = 78) in the BIRNM array network. Comparatively, horse-eye jack made more frequent BIRNM boundary crossings into adjacent MPAs harboring various levels of protection. Taken together, these two case studies highlight how sympatric reef species differentially use space within BIRNM and highlight the necessity of evaluating MPA efficacy across species and over longer time scales. Constructing single species movement assessments is essential information, yet there is now a demonstrated need for community movement studies. The final chapter of this thesis highlights promising next steps for this project, including the proposal of a new hourly or sub hourly movement trajectory analysis, potentially capable of elucidating species interactions in near real-time. Together, this thesis not only fills data gaps on species deficient in ecological studies (horse-eye jack) but illuminates individuality in habitat and space use (yellowtail snapper), and how these analyses can be tied back in to developing stronger holistic community population assessments. With continued exploitation of marine environments and increasing anthropogenic demand of marine resources, the need for understanding processes driving species movements is essential in developing successful spatial management plans.

Objectives: The aim of this study was to gain a thorough understanding of parents’ perceptions of and experiences with the care networks surrounding young children (<=12 years) with inherited metabolic diseases (IMDs). Methods: In this mixed methods study, parent participants created a ‘care map’ depicting their child’s network of care providers. We analyzed care maps using social network analysis. A subset of parents participated in a semi-structured interview. We analyzed interviews thematically and integrated quantitative and qualitative results narratively. Results: Sixty parents contributed care maps and 10 participated in interviews. Parent-drawn care networks were large with few connections between providers. Parents felt responsible for creating and maintaining care networks and for coordinating care. They valued providers who trusted them as part of their child’s health care team. Conclusions: Our findings highlight the complexity of care for children with IMDs and can inform the design of interventions to improve care.

Genom en systematisk litteraturöversikt i kombination med en intervju är syftet med denna uppsats att försöka beskriva varför den sociala nätverksanalysen är lämplig i brottsutredningssammanhang samt hur den sociala nätverksanalysen används i brottsutredningar. Tanken är också att översikten ska kunna bidra till att se vilka möjligheter det finns att praktiskt utveckla metoden. Det finns en hel del forskning kring både social nätverksanalys (SNA) som metod och som teori och det används idag inom en mängd olika områden. Som teori handlar SNA om hur vi människor är sociala varelser som påverkar varandra i de tankar vi har och i de val som vi gör. Som metod är SNA istället olika matematiska uträkningar som kan användas för att beskriva mänskliga relationer. Inom kriminologin är SNA relativt nytt trots att brott i sig ofta är ett ”nätverksfenomen”. Flera kriminologiska teorier trycker också på betydelsen av att den egna brottsligheten har ett samband med de personer som vi umgås med. Resultatet visar att det finns klara fördelar med att använda sig av SNA i en brottsutredning, strukturer och nyckelpersoner kan identifieras, något som inte alltid hade kunnat ske utan teknikens hjälp. Den data som i utredningssammanhang används till nätverksanalyser är vanligtvis kvantitativa data, exempelvis telefontrafik. Olika typer av data kan ge väldigt olika resultat och blir det fel i datainsamling kan det sabotera för hela analysen. Det behövs mer teoretisk forskning kring SNA för att den som metod ska kunna appliceras på kriminologisk teori och på sikt även kunna användas bättre i utredningssammanhang. Ett stort problem med att forska om metoden är att den kvantitativa datan kan vara svår att få tag på, det finns därför väldigt lite litteratur om hur social nätverksanalys kan användas i brottsutredningar. / With a systematic literature review and an interview, the aim of this essay is to try to describe how the social network analysis (SNA) is used in criminal investigations. Hopefully, the essay can also help in pointing out why future research is needed and in what direction that research should go. As a theory, SNA focuses on man as a social being and how we affect each other in the way we think and act. As a method SNA is a number of mathematical computations that aims to explain relationships. There is a large amount of research about social network analysis, both as a theory and as a method but in the criminological field SNA is still relatively new. That is surprising as many criminological theories focuses on the importance of the people we engage with and our own delinquency. The result in this essay shows that there are many advantages with using SNA in a criminal investigation, structures and key-persons becomes more visible which sometimes is hard without technology. Different types of data can generate very different results and if something goes wrong in the collection of data it can sabotage the entire analysis. There is a need for more theoretical research on SNA so that it, as a method, can be applied to criminological theory and later to criminal investigations. There is a big problem when doing research about social networks, the access to network-data. It is very hard to collect and is usually only available to police-officers or other qualified groups. Therefore the amount of literature in the subject is limited.

Currently the network centric operation and network centric warfare have generated a new area of research focused on determining how hierarchical organizations composed by human beings and machines make decisions over collaborative environments. One of the most stressful scenarios for these kinds of organizations is the so-called extreme events. This dissertation provides a hybrid simulation methodology based on classical simulation paradigms combined with social network analysis for evaluating and improving the organizational structures and procedures, mainly the incident command systems and plans for facing those extreme events. According to this, we provide a methodology for generating hypotheses and afterwards testing organizational procedures either in real training systems or simulation models with validated data. As long as the organization changes their dyadic relationships dynamically over time, we propose to capture the longitudinal digraph in time and analyze it by means of its adjacency matrix. Thus, by using an object oriented approach, three domains are proposed for better understanding the performance and the surrounding environment of an emergency management organization. System dynamics is used for modeling the critical infrastructure linked to the warning alerts of a given organization at federal, state and local levels. Discrete simulations based on the defined concept of "community of state" enables us to control the complete model. Discrete event simulation allows us to create entities that represent the data and resource flows within the organization. We propose that cognitive models might well be suited in our methodology. For instance, we show how the team performance decays in time, according to the Yerkes-Dodson curve, affecting the measures of performance of the whole organizational system. Accordingly we suggest that the hybrid model could be applied to other types of organizations, such as military peacekeeping operations and joint task forces. Along with providing insight about organizations, the methodology supports the analysis of the "after action review" (AAR), based on collection of data obtained from the command and control systems or the so-called training scenarios. Furthermore, a rich set of mathematical measures arises from the hybrid models such as triad census, dyad census, eigenvalues, utilization, feedback loops, etc., which provides a strong foundation for studying an emergency management organization. Future research will be necessary for analyzing real data and validating the proposed methodology.

Insight into opportunities for process improvement provides a competitive advantage through increases in organizational effectiveness and innovation As a result, it is important to understand the conditions under which employees are willing to communicate this information. This study examined the relationship between trust and psychological safety on the willingness to report errors in a medical setting. Trust and psychological safety were measured at the team and leader level. In addition, the moderating effect of a learning orientation climate at three levels of the organization (i.e., team members, team leaders, organizational) was examined on the relationship between trust and psychological safety on willingness to report errors. Traditional surveys and social network analysis were employed to test the research hypotheses. Findings indicate that team trust, when examined using traditional surveys, is not significantly associated with informally reporting errors. However, when the social networks within the team were examined, evidence that team trust is associated with informally discussing errors was found. Results also indicate that trust in leadership is associated with informally discussing errors, especially severe errors. These findings were supported and expanded to include a willingness to report all severity of errors when social network data was explored. Psychological safety, whether within the team or fostered by leadership, was not found to be associated with a willingness to informally report errors. Finally, learning orientation was not found to be a moderating variable between trust and psychological safety on a willingness to report errors. Instead, organizational learning orientation was found to have a main effect on formally reporting errors to risk management and documenting errors in patient charts. Theoretical and practical implications of the study are offered.

Advanced molecular profiling technologies, utilizing the entire human genome, have opened new avenues to study biological systems. In recent decades, the generation of vast volumes of multi-omics data, spanning a broad range of phenotypes. Development of advanced bioinformatics tools to identify informative biomarkers from these data becomes increasingly important. These tools are crucial to extract meaningful biomarkers from this data, especially for understanding the biological pathways responsible for disease development. The identification of signature genes and the analysis of differentially networked genes are two fundamental and critically important tasks. However, many current methodologies employ test statistics that don't align perfectly with the signature definition, potentially leading to the identification of imprecise signatures. It may be challenging because the test statistics employed by many prevailing methods fall short of fulfilling the exact definition of a marker genes, inherently leaving them susceptible to deriving inaccurate features. The problem is further compounded when attempting to identify marker genes across biologically diverse samples, especially when comparing more than two biological conditions. Additionally, traditional differential group analysis or co-expression analysis under singular conditions often falls short in certain scenarios. For instance, the subtle expression levels of transcription factors (TFs) make their detection daunting, despite their pivotal role in guiding gene expression. Pinpointing the intricate network landscape of complex ailments and isolating core genes for subsequent analysis are challenging tasks. Yet, these marker genes are instrumental in identifing potential pivotal pathways. Multi-omics data, with its inherent complexity and diversity, presents unique challenges that traditional methods might struggle to address effectively. Recognizing this, our team sought to introduce new and innovative techniques specifically designed to handle this intricate dataset. To overcome these challenges, it is vital to develop and adopt innovative methods tailored to handle the complexity and diversity inherent in multi-omics data. In response to these challenges, we have pioneered the Cosine-based One-sample Test (COT), a method meticulously crafted for the analysis of biologically diverse samples. Tailored to discern marker genes across a spectrum of subtypes using their expression profiles, COT employs a one-sample test framework. The test statistic within COT utilizes cosine similarity, comparing a molecule's expression profile across various subtypes with the precise mathematical representation of ideal marker genes. To ensure ease of application and accessibility, we've encapsulated the COT workflow within a Python package. To assess its effectiveness, we undertook an exhaustive evaluation, juxtaposing the marker genes detection capabilities of COT against its contemporaries. This evaluation employed realistic simulation data. Our findings indicated that COT was not only adept at handling gene expression data but was also proficient with proteomics data. This data, sourced from enriched tissue or cell subtype samples, further accentuated COT's superior performance. We demonstrated the heightened effectiveness of COT when applied to gene expression and proteomics data originating from distinct tissue or cell subtypes. This led to innovative findings and hypotheses in several biomedical case studies. Additionally, we have enhanced the Differential Dependency Network (DDN) framework to detect network rewiring between different conditions where significantly rewired network modes serve as informative biomarkers. Using cross-condition data and a block-wise Lasso network model, DDN detects significant network rewiring together with a subnetwork of hub molecular entities. In DDN 3.0, we took the imbalanced sample size into the consideration, integrated several acceleration strategies to enable it to handle large datasets, and enhanced the network presentation for more informative network displays including color-coded differential dependency network and gradient heatmap. We applied it to the simulated data and real data to detect critical changes in molecular network topology. The current tool stands as a valuable blueprint for the development and validation of mechanistic disease models. This foundation aids in offering a coherent interpretation of data, deepening our understanding of disease biology, and sparking new hypotheses ripe for subsequent validation and exploration. As we chart our future course, our vision is to expand the scope of tools like COT and DDN 3.0, explore the vast realm of multi-omics data, including those from longitudinal studies or clinical trials. We're looking at incorporating datasets from longitudinal studies and clinical trials – domains where data complexity scales to new heights. We believe that these tools can facilitate more nuanced and comprehensive understanding of disease development and progression. Furthermore, by integrating these methods with other advanced bioinformatics and machine learning tools, we aim to create a holistic pipeline that will allow for seamless extraction of significant biomarkers and actionable insights from multi-omics data. This is a promising step towards precision medicine, where individual genomic information can guide personalized treatment strategies. / Doctor of Philosophy / Recent advances in technology have allowed us to study human biology on a much larger scale than ever before. These technologies have produced a lot of data on many different types of traits. As a result, it's becoming increasingly important to develop tools that can sift through this data and find meaningful biomarkers – essentially, indicators that can help us understand what causes diseases. Two key parts of this process are identifying 'signature genes' and analyzing groups of genes that work together differently depending on the circumstances. But, current methods have their drawbacks – they don't always pick out the right genes and can struggle when comparing more than two groups at once. There are also other challenges when it comes to identifying groups of genes that express differently or work together under one set of conditions. For instance, some important genes – known as transcription factors (TFs) – control the activity of other genes. But because TFs are often expressed at low levels, they're hard to detect, even though they play a key role in controlling gene activity. And, it can be tough to identify 'hub' genes, which are central to gene networks and can help us understand the potential key pathways in diseases. To address these challenges, we introduced the Cosine based One-sample Test (COT), a novel approach to identify pivotal genes across diverse samples. COT gauges the alignment of a gene's expression profile with the quintessential marker genes' definition. Our evaluations underscore COT's robust performance, paving the way for deeper disease understanding. Further enhancing our toolkit, we've refined the Differential Dependency Network (DDN), a method to unravel the dynamic interplay of genes under diverse conditions. DDN 3.0 is a more robust iteration, adept at accommodating varied sample sizes, efficiently processing vast datasets, and offering richer visualizations of gene networks. Its prowess in pinpointing crucial alterations in gene networks is noteworthy. The Cosine based One-sample Test (COT) and the Differential Dependency Network (DDN) are revolutionary tools, poised to significantly elevate genomics research. COT, with its precision in gauging the alignment of a gene's expression pattern with predefined ideal gene markers, emerges as an invaluable asset in the hunt for marker genes. It acts as a fine-tuned sieve, meticulously screening vast datasets to unveil these crucial genetic signposts. On the other hand, DDN offers a comprehensive framework to decipher the intricate web of gene interactions under diverse conditions. It meticulously analyzes the interplay between genes, spotlighting potential 'hub' genes and highlighting shifts in their dynamic relationships. Together, COT and DDN not only pave the way for the identification of pivotal marker genes but also furnish a richer, more nuanced understanding of the genomic landscape. By leveraging these tools, researchers are empowered to unravel the intricate tapestry of genes, laying the foundation for groundbreaking discoveries in genomics. Looking to the future, we plan to apply COT and DDN 3.0 to more complex datasets. We believe these tools will give us a better understanding of how diseases develop and progress. By integrating these methods with other advanced tools, we're aiming to create a complete system for extracting important biomarkers and insights from this complex data. This is a big step towards precision medicine, where a person's unique genetic information could guide their treatment strategy.

Through methods in network theory and time-series analysis, we will analyze the spread of COVID-19 in the United States by determining trends in state-by-state daily cases through a network construction. Previous researchers have found frameworks for approximating the spread of the COVID-19 pandemic and identifying potential rises in cases by a network construction based on correlation of cases between regions [1]. Applying this network construction we determine how this network and its structure act as a predictor for overall COVID-19 cases in the United States by preforming a trend analysis on a variety of network statistics and US COVID-19 cases.

The aim of this PhD thesis is to investigate the political role of Adelheid of Burgundy in tenth-century Europe. Adelheid was certainly one of the central figures of the Ottonian dynasty during her years as empress and during her widowhood. The systematic study of the diplomas in which she acted as mediator alongside Otto I, Otto II and Otto III was an attempt to understand the basis of her political relevance. The result of the diplomatic research was analyzed through the method of social network analysis, which offered a new and global point of view on the issue and allowed to better focus on the various actors that composed the network of relationships of Adelheid during her life. / Lo scopo della presente tesi di dottorato è l’analisi del ruolo politico di Adelaide di Borgogna nell’Europa del secolo X. Adelaide fu certamente una figura di spicco all’interno della dinastia ottoniana sia in qualità di imperatrice al fianco di Ottone I sia negli anni della vedovanza. Lo studio sistematico dei diplomi in cui la sovrana venne indicata come mediatrice presso il marito, il figlio e il nipote ha rappresentato il punto di partenza per indagare le basi e le motivazioni della sua rilevanza politica. In particolare, il risultato della ricerca diplomatica è stato esaminato attraverso la metodologia della social network analysis che ha offerto un punto di vista nuovo e globale sulla questione e ha permesso di individuare più chiaramente i vari attori che composero la rete di relazioni dell’imperatrice nell’intero corso della sua vita.