22689 research outputs found
Sort by
Prevalence and Patterns of Adverse Childhood Experiences and Tobacco Cigarette Use Among Black Women and Men
Get PDFBackground
Tobacco cigarette use is the leading cause of preventable morbidity and mortality in the United States. There have been notable declines in tobacco cigarette use among US adults over several decades – indication public health initiatives are working. However, some Americans continue to smoke despite the advances in tobacco control. There is strong evidence that childhood adversity is a determinant of tobacco cigarette use. Black individuals are more likely to experience adverse childhood experiences (ACEs), which may contribute to smoking in adulthood. The study aims to provide a comprehensive understanding of the interplay between early life adversity and tobacco cigarette use behaviors in Black adults.
Methods
This dissertation employs a cross-sectional design using data from the 2019 Behavioral Risk Factor Surveillance System (BRFSS) questionnaire. The analytic sample included Black or African American adults. Indicators of childhood adversity were taken from the BRFSS ACE Module and dichotomized and summed to create an ACE score. We use latent class analysis to identify ACE typologies and regression analyses to examine the association between the ACE classes and smoking status.
Results
Black women were more likely to experience four or more ACEs compared to men and reported a greater average number of ACEs. Individuals reporting four or more childhood adversity exposures were more likely to be a person who smokes compared to people who never smoked. A four-class model had the most superior class fit for the latent class analysis. Current tobacco cigarette use was significantly more prevalent in the high adversity class compared to the other three classes. Latent regression analyses stratified by gender found similar results.
Discussion
This dissertation provides evidence of different constellations of childhood adversity risk, which may lead to tobacco cigarette use behaviors in adulthood. Our study identified that while exposure to a high number of childhood adversities was associated with current tobacco cigarette use, certain combinations of adversities were associated with more harmful tobacco use behaviors than others. Children do not need to experience multiple childhood adversities to be at risk for tobacco use behaviors in adulthood – classes with fewer adversities also conferred risk for tobacco use behaviors in adulthood. The findings have the potential to inform the prevention and tobacco control interventions for Black adults who smoke
CANDIDA ALBICANS METALS AND METALLOPROTEINS IN FUNGAL MORPHOLOGY AND PATHOGENESIS
Get PDFFungal pathogens represent an emerging threat to human health and Candida species alone affect approximately 72 million people per year. The opportunistic fungal pathogen Candida albicans is the number one cause of Candida-driven infection. Certain groups are vulnerable to the life-threatening infection of invasive candidiasis.
This work focuses on the study of C. albicans to reveal pathogenicity mechanisms and virulence traits centered around polarized growth and nutrient acquisition. One virulence trait involves fungal morphogenesis into filaments for invading host tissues. Chapter 2 focuses on the metalloprotein Fre8 that produces reactive oxygen species and promotes C. albicans morphogenesis during infection. Our studies detail the regulation of Fre8 by the Rho GTPase Cdc42. We find that Cdc42 is needed to post-transcriptionally regulate Fre8 activity at the apex of polarized growth.
Nutrient assimilation is also needed for C. albicans virulence, as pathogens must obtain metal micronutrients from the host. These micronutrients are limited within the host in a response to infection called nutritional immunity. Little was previously known about Mn handling at the host-fungal pathogen interface. In Chapter 3, we describe Smf12 and Smf13 as two Mn transporters in C. albicans needed for Mn accumulation, Mn-dependent enzymatic activity, polarized growth, and virulence. C. albicans requires both Smf12 and Smf13 to exhibit polarized growth in
ii
vitro and mutants of these transporters are less virulent in a mouse model of disseminated candidiasis where kidney is the major target organ. We describe for the first time, total loss in kidney Mn during fungal invasion, representing a possible nutritional immunity response.
Total tissue metal analysis does not fully capture the nutritional immunity response and in Chapter 4 we describe changes in local metal availability in the kidney with C. albicans invasion through use of X-ray fluorescence microscopy. Mn, Zn, Fe and Cu exhibit distinct patterns of distribution in healthy tissue that are disrupted during infection. Moreover, these metals are spatially depleted around fungal lesions.
Together these new insights into the role of metals and metalloproteins with infectious C. albicans may apply more broadly to other fungal pathogens that pose a growing threat to human health
Novel Methods and Applications in Analyzing High-dimensional Neuroimaging Data
No full textNeuroimaging is poised to take a substantial leap forward in understanding the neurophysiological underpinnings of human behavior. However, the existing neuroimaging literature stops short in several methodological and application domains. On the methodological front, when modeling multivariate patterns of brain activity, the presence of subjects with unusual characteristics may distort the formulation of brain model and undermine the generalizability of downstream statistical inference. Yet, the ability to identify subjects biasing the selection of statistical submodels, especially in the high-dimensional setting where the number of predictors exceeds the sample size, have been sorely lacking in the neuroimaging literature. On the application front, the close connection between brain health and physical activity is widely recognized. However, the precise quantification of these relationships have not yet been explored. To address the first issue, a diagnostic measure accommodating general model selectors is proposed, followed by the establishment of its distributional properties. This leads to the proposal of parametric and non-parametric approaches to approximate its distribution and derive thresholds for detection purposes. Then, upon incorporating an appropriate high-dimensional clustering procedure, our framework is extended to detect multiple influential observations under both linear and logistic regression models. Simulated data analyses are conducted to assess and compare the strength of different detection procedures, indicating that the proposed metric applied to scaled LASSO as the model selector which is subsequently approximated by a non-parametric method provides overall satisfactory detection performance. Moreover, improved predictive performance is observed upon detecting and removing identified influential points for a neuroimaging dataset in pain study, which is further corroborated by plausible scientific explanation. To address the second issue, bipartial canonical correlation analysis (CCA) is used to assess the association between each of two sets of brain imaging-derived phenotypes (IDPs) and physical activity (PA) in the UK Biobank. Our analysis recognizes that characteristics of the two brain IDPs mostly associated with the PA are linked to the sensorimotor and attention and control networks in the brain. In addition, the importance of individual PA variables in explaining the sample variation in the two IDPs is further ascertained using linear regression models and tree-based machine learning method. Toward that end, a common set of three PA variables measuring the volume of PA and circadian rhythms is determined to exhibit crucial effects
Syzygies of Mori fibre spaces
Get PDFWe introduce homological and homotopical r-syzygies of Mori fibre spaces as a generalization of Sarkisov links and relations of Sarkisov links. For any proper morphism Y/R, we construct a contractible (if not empty) CW complex such that there is a 1-1 correspondence between its cells and the central models of Y/R. We derive from this CW complex a long exact sequence and a spectral sequence converging to the (co)homology of the relative birational automorphism group of Y/R. As an application, we compute the spectral sequence for the second Cremona group and show that its second group (co)homology is non-trivial
OUR BODIES, OUR LAND
Get PDFDid they carve these scars into my body for nothing? Or could their lines become words that help you heal yours—the injuries hidden away in the years of no one believing? Can the shape of my wounds, all jagged and swirled, be a line in the sand between the old world of taking what we get — staying quiet — and a new world, one where we can find health meaning healing and care meaning feeling? For, on the other side of these scabs of mine is a new skin, delicate but strong, ready to bear the weight of its wrongs and say: enough.
Perhaps the knives that carved into me, gloved hands reaching, robots roving, and hormones coursing through this body, left a map that can lead us back to our own terrain. Perhaps the questions I’ve faced have shaped the questions I ask. So, on these pages, I offer up my body—its parts and its pieces, layers and limbs—as a sacrament to your journey, a reckoning of knowing, a revolution through revelation
Characterizing the Post-infarct Ventricular Tachycardia Substrate Using Clinical Data and Personalized Computational Heart Modeling
No full textSudden cardiac death (SCD) remains a leading cause of cardiovascular-related death. Cardiovascular patients who have suffered myocardial infarction (MI) are at increased risk of SCD due primarily to a life-threatening arrhythmia called ventricular tachycardia (VT). Despite numerous advancements in treatment, VT remains challenging to treat clinically, in part due to an incomplete understanding of the underlying arrhythmogenic substrate. This thesis aims to provide a characterization of the patient-specific, post-infarct VT comprehensive substrate and its dynamics using a combination of clinical mapping data, imaging, and personalized 3D computational heart simulations
CONSTRUCTING REDUCED DATA-DRIVEN MODELS FOR DYNAMICAL SYSTEMS USING MANIFOLD- AND DEEP LEARNING
No full textSince Newton’s era we have been constructing mathematical models for dynamical systems from first principles. However, deriving accurate, computationally efficient, and interpretable models for more complex systems (e.g. epidemics or the financial market) still remains a challenging problem. In this dissertation, I will illustrate how the interplay of numerical analysis and machine learning (manifold learning and deep learning) can facilitate the construction of data-driven models; reveal variables or/and parameters that capture the structure of the underlying problem and can possibly alleviate the complexity of the original systems. To construct those data-driven models we will: (a) dis- cover a set of (possibly reduced) variables or parameters from data; (b) build models on those variables/parameters (e.g. identify the dynamics). As case studies, complex dynamical systems governed by effective ordinary (e.g. chem- ical reactions), partial (e.g. Chafee-Infante PDE) and stochastic differential (e.g. Brownian Dynamics) equations will be presented
SHORT AND LONG-TERM CHANGES IN URBAN FOREST SOILS
Get PDFHuman activities influence soil ecosystems, impacting soil properties, such as pH and bulk density, and functions, such as soil respiration. Local indirect human impacts include atmospheric deposition of elements such as nitrogen, calcium, and sulfur, and the introduction of species like jumping worms. Direct impacts include land cover conversion and soil compaction. A global human impact is climate change, which is another indirect factor affecting soil carbon cycling. This dissertation examines how soil properties and functions change over time due to these direct and indirect effects.
The objectives of this study were 1) to characterize changes in soil properties over two decades along an urban-rural gradient in Baltimore, 2) to investigate the relationship between soil properties and the change in earthworm community composition, and 3) to assess soil respiration in response to extreme precipitation events between turf grass and forest cover types over a growing season. This work is necessary, because more accurate projections of changes in soil chemical, physical, and biological properties will enable better urban forest patch management, and understanding how soil respiration relates to land cover and extreme precipitation events improves climate change models.
To achieve these objectives, in Chapter 1, surface soil properties were examined along an urban-rural gradient. Archived soils, collected in Baltimore City and County, were analyzed alongside recently sampled soils. In Chapter 2, earthworms were extracted and identified to determine their effect on soil properties and changes in community composition. Lastly, in Chapter 3, two land cover types, forest and turf grass in a suburban neighborhood, were outfitted with soil CO2, temperature, and moisture sensors at various depths to evaluate the effects of precipitation events on CO2 fluxes from August 2011 to October 2011.
Over two decades soil calcium increased by 35%, pH increased from 4.1 to 4.5, carbon to nitrogen ratios decreased from 17.8 to 16.7, leaf litter decreased by 44%, and bulk density decreased 10% from 1.05 to 0.94 g cm-3. Despite significant correlations between soil properties and earthworm abundance within a given year, changes in soil chemistry were found to be independent of earthworm community shifts. Canonical correspondence analysis revealed that the soil variables pH and Ca explain 69% of the variation of the earthworm species composition for 2020. Earthworm assemblages appeared to be different in urban forests than rural ones, with a higher abundance of jumping worms present in rural forests. In terms of soil respiration, there were differences between turf grass and forest soils, with grass soils showing twice as high CO2 efflux rates (3.04 µmol m-2s-1) than forest (1.55 µmol m-2s-1). Temporal analyses revealed that while turf grass soil was less resistant to precipitation events than forest, both soil types exhibited resilience, returning to baseline CO2 concentrations post-event.
In conclusion, the study demonstrates that human activities influence soil properties and functions even in the absence of significant soil physical alterations. Urban forest soils exhibited significant changes with potential impacts on local ecosystems, such as nutrient availability to seedlings and saplings due to altered soil pH values. Furthermore, the increased abundance of jumping worms in rural forests warrant close monitoring given that this invasive group can fundamentally alter the forest floor. Lastly, modeling of soil respiration in the context of extreme events, which are anticipated to increase with climate change, should incorporate land cover resistance measures of CO2 effluxes to reflect conditions more accurately