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Demarcating geographic regions using community detection in commuting networks with significant self-loops
We develop a method to identify statistically significant communities in a weighted network with a high proportion of self-looping weights. We use this method to find overlapping agglomerations of U.S. counties by representing inter-county commuting as a weighted network. We identify three types of communities; non-nodal, nodal and monads, which correspond to different types of regions. The results suggest that traditional regional delineations that rely on ad hoc thresholds do not account for important and pervasive connections that extend far beyond expected metropolitan boundaries or megaregions.
Journal Article
Defining comprehensive models of care for NAFLD
Non- alcoholic fatty liver disease (NAFLD) is now the leading cause of chronic liver disease globally. Despite the increased demand placed on health- care systems, little attention has been given to the design and implementation of efficient and effective models of care for patients with NAFLD. In many health- care settings, no formal pathways exist and, where pathways are in place, they are often not standardized according to good practices. We systematically searched the peer- reviewed literature with the aim of identifying published examples of comprehensive models of care that answered four key questions: what services are provided? Where are they provided? Who is offering them? How are they coordinated and integrated within health- care systems? We identified seven models of care and synthesized the findings into eight recommendations nested within the ‘what, where, who and how’ of care models. These recommendations, aimed at policy- makers and practitioners designing and implementing models of care, can help to address the increasing need for the provision of good practice care for patients with NAFLD.
Journal Article
Basic Science and Pathogenesis
Cardiovascular risk factors are among the most significant contributors to dementia, yet the biological mediators involved in this relationship need to be clarified. Prior research shows that elevated platelet aggregation is associated with an increased risk of dementia. However, comorbidities that influence platelet activity and serve as risk factors for Alzheimer's disease (AD) and related dementias (AD/ADRD) complicate this association, particularly in individuals with high vascular burden who do not have dementia. To address this, we leveraged the NIH-funded Platelet Activity and Cardiovascular Events (PACE) in patients with peripheral artery disease (PAD) and examined the relationship between platelet aggregation and AD biomarkers.
We assessed associations between platelet aggregation measured by light transmission aggregometry (LTA) and concentrations of phosphorylated tau (p-tau181), total tau, neurofilament light (NfL), and glial fibrillary acidic protein (GFAP) in serum samples using SIMOA (Quanterix). Quantile regression models, using median cutoffs (τ=0.5), were employed to evaluate the relationships across three levels of adjustment: (1) univariate, (2) LASSO-selected covariates, and (3) adjusted by age, sex, race, and the use of platelet modifying treatments (PMT), such as aspirin or clopidogrel.
Of the 125 participants included in the analysis, 101 had complete measures of LTA exposures and all AD sera biomarkers. The median age of participants was 70 [Q1=64, Q3=77], 69.3% were male, 63.4% had poly-vascular disease (PVD), and 87.1% were on PMT (Table 1). Elevated platelet aggregation in response to submaximal doses of ADP and Epinephrine (Epi) associated with increased p-tau181 (Table 2) and NfL levels (Table 3). Associations between platelet aggregation and tau and GFAP were not observed.
Although limited in sample size, our study identified associations of platelet aggregation with markers of AD pathology (p-tau181) and neurodegeneration (NfL) in PAD patients without dementia. Future studies in a larger cohort are needed to further investigate this relationship and the potential role of platelet aggregation as a mediator of AD pathology and neuroinflammatio.
Journal Article
Neutrophil inflammation metrics are associated with the risk of future dementia in large‐scale electronic health record data from hospital systems
Background Neutrophils play a role in Alzheimer's disease (AD) pathology and AD‐related dementias (AD/ADRD), and prior research has shown that the neutrophil to lymphocyte ratio (NLR), a marker of neutrophil‐mediated inflammation, is associated with the risk of future dementia. To date, studies looking at this relationship have used small cohorts. We address whether this is generalizable to larger populations using electronic health records (EHR) data from all six sites of NYU Langone Hospitals Method Our study window ranged from 2011‐2023. NLR values were obtained from laboratory test results. For each patient, the first NLR obtained was used; the associated date was taken to be the time origin. The outcome was AD/ADRD incidence, defined using ICD‐codes over the same study window at least 6 months post‐baseline. Cause‐specific Cox regression was used to determine the independent association of log‐transformed NLR values with the risk of future AD/ADRD adjusting for demographic and clinical confounders and with death as a censoring event. To account for confounding effects of covariates, the Cox model was weighted by the predicted probabilities of all adjusting covariates on binarized NLR (“high” if >median, “low” if
Journal Article
Elevated Platelet Aggregation Associates with Increased Alzheimer's Disease Blood Biomarker Concentrations in Patients with Peripheral Artery Disease
Background Cardiovascular risk factors are among the most significant contributors to dementia, yet the biological mediators involved in this relationship need to be clarified. Prior research shows that elevated platelet aggregation is associated with an increased risk of dementia. However, comorbidities that influence platelet activity and serve as risk factors for Alzheimer's disease (AD) and related dementias (AD/ADRD) complicate this association, particularly in individuals with high vascular burden who do not have dementia. To address this, we leveraged the NIH‐funded Platelet Activity and Cardiovascular Events (PACE) in patients with peripheral artery disease (PAD) and examined the relationship between platelet aggregation and AD biomarkers. Method We assessed associations between platelet aggregation measured by light transmission aggregometry (LTA) and concentrations of phosphorylated tau (p‐tau181), total tau, neurofilament light (NfL), and glial fibrillary acidic protein (GFAP) in serum samples using SIMOA (Quanterix). Quantile regression models, using median cutoffs (τ=0.5), were employed to evaluate the relationships across three levels of adjustment: (1) univariate, (2) LASSO‐selected covariates, and (3) adjusted by age, sex, race, and the use of platelet modifying treatments (PMT), such as aspirin or clopidogrel. Result Of the 125 participants included in the analysis, 101 had complete measures of LTA exposures and all AD sera biomarkers. The median age of participants was 70 [Q1=64, Q3=77], 69.3% were male, 63.4% had poly‐vascular disease (PVD), and 87.1% were on PMT (Table 1). Elevated platelet aggregation in response to submaximal doses of ADP and Epinephrine (Epi) associated with increased p‐tau181 (Table 2) and NfL levels (Table 3). Associations between platelet aggregation and tau and GFAP were not observed. Conclusion Although limited in sample size, our study identified associations of platelet aggregation with markers of AD pathology (p‐tau181) and neurodegeneration (NfL) in PAD patients without dementia. Future studies in a larger cohort are needed to further investigate this relationship and the potential role of platelet aggregation as a mediator of AD pathology and neuroinflammatio
Journal Article
Basic Science and Pathogenesis
Neutrophils play a role in Alzheimer's disease (AD) pathology and AD-related dementias (AD/ADRD), and prior research has shown that the neutrophil to lymphocyte ratio (NLR), a marker of neutrophil-mediated inflammation, is associated with the risk of future dementia. To date, studies looking at this relationship have used small cohorts. We address whether this is generalizable to larger populations using electronic health records (EHR) data from all six sites of NYU Langone Hospitals METHOD: Our study window ranged from 2011-2023. NLR values were obtained from laboratory test results. For each patient, the first NLR obtained was used; the associated date was taken to be the time origin. The outcome was AD/ADRD incidence, defined using ICD-codes over the same study window at least 6 months post-baseline. Cause-specific Cox regression was used to determine the independent association of log-transformed NLR values with the risk of future AD/ADRD adjusting for demographic and clinical confounders and with death as a censoring event. To account for confounding effects of covariates, the Cox model was weighted by the predicted probabilities of all adjusting covariates on binarized NLR (\"high\" if >median, \"low\" if
Journal Article
Community Detection in Multimodal Networks
Community detection on networks is a basic, yet powerful and ever-expanding set of methodologies that is useful in a variety of settings. This dissertation discusses a range of different community detection on networks with multiple and non-standard modalities. A major focus of analysis is on the study of networks spanning several layers, which represent relationships such as interactions over time, different facets of high-dimensional data. These networks may be represented by several different ways; namely the few-layer (i.e. longitudinal) case as well as the many-layer (time-series cases). In the first case, we develop a novel application of variational expectation maximization as an example of the top-down mode of simultaneous community detection and parameter estimation. In the second case, we use a bottom-up strategy of iterative nodal discovery for these longer time-series, abetted with the assumption of their structural properties. In addition, we explore significantly self-looping networks, whose features are inseparable from the inherent construction of spatial networks whose weights are reflective of distance information. These types of networks are used to model and demarcate geographical regions. We also describe some theoretical properties and applications of a method for finding communities in bipartite networks that are weighted by correlations between samples. We discuss different strategies for community detection in each of these different types of networks, as well as their implications for the broader contributions to the literature. In addition to the methodologies, we also highlight the types of data wherein these ``non-standard\" network structures arise and how they are fitting for the applications of the proposed methodologies: particularly spatial networks and multilayer networks. We apply the top-down and bottom-up community detection algorithms to data in the domains of demography, human mobility, genomics, climate science, psychiatry, politics, and neuroimaging. The expansiveness and diversity of these data speak to the flexibility and ubiquity of our proposed methods to all forms of relational data.
Dissertation
Essays in Asset Pricing
In chapter 1, we investigate the cross-sectional spillovers of earnings surprises and their implications for anomaly returns. By forming quarterly text embeddings derived from earnings call transcripts, we capture multifaceted relationships among public firms and effectively reflect shifts in their business focus. Employing these embeddings to project earnings surprises, we identify significant contemporaneous spillovers and a medium-term drift that persists for up to 20 days. One standard deviation increase in the projected earnings surprises induces a 3 basis points increase in the same-day return and about 2 basis points in the next day. Utilizing the predicted earnings surprise, we construct an aggregate factor capable of pricing a broad spectrum of market anomalies and demonstrating substantial forward-looking predictive power for prominent factors, including value and size.Chapter 2 introduces an innovative asset-pricing model designed to analyze the co-movement between stock and bitcoin returns within a dual-agent equilibrium framework. By weaving habit formation and fluctuating risk aversion into the fabric of this model, we enable an exploration of dynamic risk-sharing strategies between equity and cryptocurrency markets. Such an approach underscores the model's capacity to elucidate the empirical phenomena characterizing cryptocurrency markets, with a particular focus on the time-varying correlation with stock returns. Additionally, our model innovatively connects both the spot and futures prices of cryptocurrencies to these dynamic risk-sharing mechanisms, guided by crucial state variables that influence consumption patterns. Furthermore, the model delves into the covariance of returns and their association with both external and internal habit formation preferences, thereby offering new insights into the complexities of interactions within and between traditional and digital asset markets.
Dissertation
Placing Names
Well before the innovation of maps, gazetteers served as the main geographic referencing system for hundreds of years. Consisting of a specialized index of place names, gazetteers traditionally linked descriptive elements with topographic features and coordinates. Placing Names is inspired by that tradition of discursive place-making and by contemporary approaches to digital data management that have revived the gazetteer and guided its development in recent decades. Adopted by researchers in the Digital Humanities and Spatial Sciences, gazetteers provide a way to model the kind of complex cultural, vernacular, and perspectival ideas of place that can be located in texts and expanded into an interconnected framework of naming history. This volume brings together leading and emergent scholars to examine the history of the gazetteer, its important role in geographic information science, and its use to further the reach and impact of spatial reasoning into the digital age.
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