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The Levi Perryman Collection, 1873-1921
No full textTranscript of a letter from W.O. Davis, Lindsay, Davis & Garnett Attorney's at Law to Misters Stephens and Matlock concerning case being handled in Cooke County rather than Montague County. The letter tells the sheriff the author will "see him out" if there is any trouble over it
The Levi Perryman Collection, 1873-1921
No full textTranscript of a letter from W.O. Davis, Lindsay, Davis & Garnett Attorney's at Law to Misters Stephens and Matlock concerning case being handled in Cooke County rather than Montague County. The letter tells the sheriff the author will "see him out" if there is any trouble over it
Nurture-U student mental health longitudinal survey: a study protocol
No full textUniversity life represents a critical period for young adults, providing opportunities for personal growth and development of coping skills but also posing significant mental health challenges. Recent trends indicate rising mental health concerns among university students, exacerbated by the COVID-19 pandemic and its aftermath. This study aims to address gaps in longitudinal data on student mental health in the UK and to identify risk and protective factors across diverse student populations. The current Nurture-U survey is developed from the U-Flourish biannual survey study piloted at Queen's and Oxford universities in Canada and the UK, respectively. Nurture-U is a longitudinal survey study conducted at five UK universities, aiming to create a comprehensive data set from over 5000 students. The study will collect data at the start and completion of each academic year, using validated measures to assess well-being, mental health symptoms, lifestyle factors and access to support. Recruitment will target all students, with an emphasis on first-year students, to track their mental health trajectory from university entry through subsequent years. Ethical approval has been obtained from relevant committees at each participating university. Students will provide informed consent prior to participation, with risk messages and support information provided for those indicating self-harm or suicidal thoughts. Data will be de-identified and securely stored, with results disseminated through academic publications, social media and student engagement activities. [Abstract copyright: © Author(s) (or their employer(s)) 2025. Re-use permitted under CC BY. Published by BMJ Group.
Buffelgrass Forecast Shiny App
No full textThis map was created by Travis Matlock as a product of USA National Phenology Network. It is a Raster plus Spatial Point map of Arizona that calculates the number of distinct precipiation "events" over a 30-day rolling period prior to the forecast date. The number of these events, in turn, is used to forecast areas where buffelgrass will be "greening up" in the following 1-2 weeks.A rainfall event is counted as any calendar day with precipitation over 0.25 in. In order to distinguish separate events, a "buffer" of three days with little or no rainfall must occur following an event in order for the next day with rainfall over 0.25 in to be counted as a new event. The first day of the buffer must witness 0" of precipitation. This sequence is calculated separately for each Raster cell and for each Spatial Point to determine its event value.Raster data is obtained from the PRISM climate group at Oregon State University. Spatial point data represents individual precipitation gauges and is obtained from both RainLog and NOAA's RCC-ACIS precipitation data (labels are given by clicking on points in the interactive map).This iteration of this map was entered into the 2024 UA Libraries Data Visualization Challenge.For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to [email protected] item is part of the University of Arizona Libraries 2024 Data Visualization Challenge</i
Discrimination, Depression, and Anxiety as Predictors of Well-Being in a Sample of Latinx Patients with Type 2 Diabetes
No full textBackground: Latinxs—those who identify as Hispanic or Latino/Latina—are currently the largest ethnic minority in the US. Compared to European Americans, Latinxs are at a greater risk for developing diabetes and anxiety disorders. They also experience significant stress due to discrimination, and are more likely to develop poor mental health as a result. Aim: Given the challenges faced by Latinxs, this study seeks to assess discrimination, depression, and anxiety as predictors of well-being in Latinx patients with type 2 diabetes. Methods: Secondary analysis was conducted using data from the Community Health Educators Assisting Latinos Manage Stress and Diabetes (CALMS-D) study. In CALMS-D, participants who self-identified as Latino/Latina or Hispanic and had been diagnosed with type 2 diabetes for at least 6 months (n = 121) were recruited from a clinic in Hartford, Connecticut, US. Participants were predominately women (74%) with ages ranging from 21 to 86 years old (M = 61). Interviews were conducted in Spanish or English based on the participant’s preference, and responses were collected using REDCap, an internet-based survey tool. Interviews included items on exercise habits and perceived health, and scales measuring chronic discrimination (Everyday Discrimination Scale; EDS), depression (Patient Health Questionnaire; PHQ-8), anxiety (Patient-Reported Outcomes Measurement Information System; PROMIS), and well-being (World Health Organisation Well-Being Index; WHO-5). Results: A four-step hierarchical regression model was employed with well-being as the dependent variable. After controlling for gender, age, and income in Model 1, results from Model 2 revealed more frequent exercise (β = .10, p = .01) and better perceived health (β = .20, p < .01) were each associated with greater well-being. These effects remained significant after the addition of discrimination in Model 3, which itself demonstrated a unique effect (β = -.22, p = .02), as more frequent discrimination was associated with reduced well-being. Finally, depression and anxiety were introduced in Model 4, R2 = .62, F(8, 109) = 22.4, p < .01. Taken as a set, the eight predictors in Model 4 explained significantly more variance in well-being than the six predictors in Model 3, R2change = .28, Fchange (2, 109) = 39.8, p < .01. Interestingly, Model 4 revealed not only that elevated symptoms of depression (β = -.27, p < .01) and anxiety (β = -.39, p < .01) were strongly and uniquely associated with reduced well-being when controlling for all other predictors, but the effects for perceived health (β = .06, p = .33) and discrimination (β = .04, p = .58) were substantially reduced and no longer significant. Conclusions: More frequent exercise, greater perceived health, and fewer instances of chronic discrimination were associated with greater well-being in a Latinx sample with type 2 diabetes. However, further analysis revealed that elevated depression and anxiety were not only the strongest unique predictors of diminished well-being, but may also account for the relationship between perceived health and discrimination and well-being
Discrimination, Depression, and Anxiety as Predictors of Well-Being in a Sample of Latinx Patients with Type 2 Diabetes
No full textBackground: Latinxs—those who identify as Hispanic or Latino/Latina—are currently the largest ethnic minority in the US. Compared to European Americans, Latinxs are at a greater risk for developing diabetes and anxiety disorders. They also experience significant stress due to discrimination, and are more likely to develop poor mental health as a result. Aim: Given the challenges faced by Latinxs, this study seeks to assess discrimination, depression, and anxiety as predictors of well-being in Latinx patients with type 2 diabetes. Methods: Secondary analysis was conducted using data from the Community Health Educators Assisting Latinos Manage Stress and Diabetes (CALMS-D) study. In CALMS-D, participants who self-identified as Latino/Latina or Hispanic and had been diagnosed with type 2 diabetes for at least 6 months (n = 121) were recruited from a clinic in Hartford, Connecticut, US. Participants were predominately women (74%) with ages ranging from 21 to 86 years old (M = 61). Interviews were conducted in Spanish or English based on the participant’s preference, and responses were collected using REDCap, an internet-based survey tool. Interviews included items on exercise habits and perceived health, and scales measuring chronic discrimination (Everyday Discrimination Scale; EDS), depression (Patient Health Questionnaire; PHQ-8), anxiety (Patient-Reported Outcomes Measurement Information System; PROMIS), and well-being (World Health Organisation Well-Being Index; WHO-5). Results: A four-step hierarchical regression model was employed with well-being as the dependent variable. After controlling for gender, age, and income in Model 1, results from Model 2 revealed more frequent exercise (β = .10, p = .01) and better perceived health (β = .20, p < .01) were each associated with greater well-being. These effects remained significant after the addition of discrimination in Model 3, which itself demonstrated a unique effect (β = -.22, p = .02), as more frequent discrimination was associated with reduced well-being. Finally, depression and anxiety were introduced in Model 4, R2 = .62, F(8, 109) = 22.4, p < .01. Taken as a set, the eight predictors in Model 4 explained significantly more variance in well-being than the six predictors in Model 3, R2change = .28, Fchange (2, 109) = 39.8, p < .01. Interestingly, Model 4 revealed not only that elevated symptoms of depression (β = -.27, p < .01) and anxiety (β = -.39, p < .01) were strongly and uniquely associated with reduced well-being when controlling for all other predictors, but the effects for perceived health (β = .06, p = .33) and discrimination (β = .04, p = .58) were substantially reduced and no longer significant. Conclusions: More frequent exercise, greater perceived health, and fewer instances of chronic discrimination were associated with greater well-being in a Latinx sample with type 2 diabetes. However, further analysis revealed that elevated depression and anxiety were not only the strongest unique predictors of diminished well-being, but may also account for the relationship between perceived health and discrimination and well-being
Metafiction As Anti-Genre Across Narrative Mediums
No full textThis thesis examines the various functions of self-conscious and self-reflexive narrative such as authorial control over meaning, mutability of history, and ontological exploration/experimentation such as the relationship between fiction and reality. It examines texts from multiple narrative genres and media (novels, comic books/graphic novels, film, and theater) as well as established critical work on metafiction, such as Patricia Waugh’s Metafiction: The Theory and Practice of Self-Conscious Fiction, Robert Alter’s Partial Magic: The Novel as a Self-Conscious Genre, and Linda Hutcheon’s “Historiographic Metafiction” to establish the prototypical characteristics of metafiction. It also discusses the specific ways in which these metafictional practices are manifested within various narrative mediums. Current scholarship fails to address the active role of audiences in metafiction. This thesis concentrates on the ways in which audiences are required to actively engage in narrative construction of metafictional texts in order to interpret the text or derive meaning from it. As a consequence, it also addresses the question of with whom textual authority resides, citing Michel Foucault’s “What Is an Author?” and Stanley Fish’s Is There a Text in This Class?: The Authority of Interpretive Communities alongside examples of metafictional practices to argue that audiences are the ultimate interpretive authority
Discrimination, Depression, and Anxiety as Predictors of Well-Being in a Sample of Latinx Patients with Type 2 Diabetes
No full textBackground: Latinxs—those who identify as Hispanic or Latino/Latina—are currently the largest ethnic minority in the US. Compared to European Americans, Latinxs are at a greater risk for developing diabetes and anxiety disorders. They also experience significant stress due to discrimination, and are more likely to develop poor mental health as a result. Aim: Given the challenges faced by Latinxs, this study seeks to assess discrimination, depression, and anxiety as predictors of well-being in Latinx patients with type 2 diabetes. Methods: Secondary analysis was conducted using data from the Community Health Educators Assisting Latinos Manage Stress and Diabetes (CALMS-D) study. In CALMS-D, participants who self-identified as Latino/Latina or Hispanic and had been diagnosed with type 2 diabetes for at least 6 months (n = 121) were recruited from a clinic in Hartford, Connecticut, US. Participants were predominately women (74%) with ages ranging from 21 to 86 years old (M = 61). Interviews were conducted in Spanish or English based on the participant’s preference, and responses were collected using REDCap, an internet-based survey tool. Interviews included items on exercise habits and perceived health, and scales measuring chronic discrimination (Everyday Discrimination Scale; EDS), depression (Patient Health Questionnaire; PHQ-8), anxiety (Patient-Reported Outcomes Measurement Information System; PROMIS), and well-being (World Health Organisation Well-Being Index; WHO-5). Results: A four-step hierarchical regression model was employed with well-being as the dependent variable. After controlling for gender, age, and income in Model 1, results from Model 2 revealed more frequent exercise (β = .10, p = .01) and better perceived health (β = .20, p < .01) were each associated with greater well-being. These effects remained significant after the addition of discrimination in Model 3, which itself demonstrated a unique effect (β = -.22, p = .02), as more frequent discrimination was associated with reduced well-being. Finally, depression and anxiety were introduced in Model 4, R2 = .62, F(8, 109) = 22.4, p < .01. Taken as a set, the eight predictors in Model 4 explained significantly more variance in well-being than the six predictors in Model 3, R2change = .28, Fchange (2, 109) = 39.8, p < .01. Interestingly, Model 4 revealed not only that elevated symptoms of depression (β = -.27, p < .01) and anxiety (β = -.39, p < .01) were strongly and uniquely associated with reduced well-being when controlling for all other predictors, but the effects for perceived health (β = .06, p = .33) and discrimination (β = .04, p = .58) were substantially reduced and no longer significant. Conclusions: More frequent exercise, greater perceived health, and fewer instances of chronic discrimination were associated with greater well-being in a Latinx sample with type 2 diabetes. However, further analysis revealed that elevated depression and anxiety were not only the strongest unique predictors of diminished well-being, but may also account for the relationship between perceived health and discrimination and well-being
Algorithms addressing heterogeneity in anti-cancer drug sensitivity prediction studies
No full textThis manuscript is the accumulation of several years of knowledge gained by studying computational models for improving the efficacy of personalized medicine. There are an
untold number of issues encounter by scientist working the field of computation biology
but of particular interest here is in how to deal with heterogeneity in multiple forms. A
brief summary of main topics covered are as follows.
Combination therapy design for maximizing sensitivity and minimizing toxicity:
The design of personalized targeted therapies involve modeling of patient sensitivity to
various drugs and drug combinations. Majority of studies evaluate the sensitivity of tumor
cells to targeted drugs without modeling the effect of the drugs on normal cells. By
considering the individual modeling of drug responses to tumor and normal cells targeted
combination therapies that maximize sensitivity over tumor cells and minimize toxicity
over normal cells were developed. By utilizing the constrained structure of tumor
proliferation models an accelerated lexicographic search algorithm was developed for
generating the optimal solution. For comparison purposes, two suboptimal search
algorithms based on evolutionary algorithms and hill-climbing based techniques were also
designed and tested. Results over synthetic models and models generated from Genomics
of Drug Sensitivity in Cancer database shows the ability of the proposed algorithms to
arrive at optimal or close to optimal solutions in significantly lower number of steps as
compared to exhaustive search. Also present is the theoretical analysis of the expected
number of comparisons required for the proposed Lexicographic search that compare
favorably with the observed number of computations. The proposed algorithms provide a
framework for design of combination therapy that tackles tumor heterogeneity while
satisfying toxicity constraints.
Heterogeneity Aware Random Forest for Drug Sensitivity Prediction: Samples
collected in pharmacogenomics databases typically belong to various cancer types. For
designing a drug sensitivity predictive model from such a database, a natural question
arises whether a model trained on diverse inter-tumor heterogeneous samples will perform
similar to a predictive model that takes into consideration the heterogeneity of the samples
in model training and prediction. We explore this hypothesis and observe that ensemble
model predictions obtained when cancer type is known out-perform predictions when that information is withheld even when the samples sizes for the former is considerably lower
than the combined sample size. To incorporate the heterogeneity idea in the commonly
used ensemble based predictive model of Random Forests, we propose Heterogeneity
Aware Random Forests (HARF) that assigns weights to the trees based on the category of
the sample. We treat heterogeneity as a latent class allocation problem and present a
covariate free class allocation approach based on the distribution of leaf nodes of the
model ensemble. Applications on CCLE and GDSC databases show that HARF
outperforms traditional Random Forest when the average drug responses of cancer types
are different.
Investigation of model stacking for drug sensitivity prediction: A significant
problem in precision medicine is the prediction of drug sensitivity for individual cancer
cell lines. Predictive models such as Random Forests have shown promising performance
while predicting from individual genomic features such as gene expressions. However,
accessibility of various other forms of data types including information on multiple tested
drugs necessitates the examination of designing predictive models incorporating the
various data types. An exploration the predictive performance of model stacking and the
effect of stacking on the predictive bias and squared error has been performed. In addition,
the analytical underpinnings supporting the advantages of stacking in reducing squared
error and inherent bias of random forests in prediction of outliers is discussed. The
framework is tested on a setup including gene expression, drug target, physical properties
and drug response information for a set of drugs and cell lines.The performance of
individual and stacked models are compared. It has been noted that stacking models built
on two heterogeneous datasets provide superior performance to stacking different models
built on the same dataset. It is also noted that stacking provides a noticeable reduction in
the bias of our predictors when the dominant eigenvalue of the principle axis of variation
in the residuals is significantly higher than the remaining eigenvalues.
Sstack: An R Package for Stacking with Applications to Scenarios Involving
Sequential Addition of Samples and Features: Biological processes are characterized
by a variety of different genomic feature sets. However, often times when building models
portions of these features are missing for a subset of the dataset. A modeling framework
has been provided to effectively integrate this type of heterogeneous data to improve
prediction accuracy. To test the methodology, data from the Cancer Cell Line Encyclopedia and the MD Anderson Cell Lines Project has been stacked to reduce the
mean absolute error of drug sensitivity prediction by more than 5%. The package
addresses the dynamic regime of information integration involving sequential addition of
features and samples
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