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El Lenguaje de las Cosas: El Sonido y la Voz en el Arte y Literatura de Bolivia (2000-2019)
No full textThis dissertation delves into the work of five contemporary Bolivian artists and writers from the perspective of sound and voice. It specifically explores the continuity between the human and non-human through voice and sound. Focusing on cultural objects from 2000-2019, it examines works that accompany political processes in the era of Evo Morales and conflictive environmental policies in the era of the Anthropocene. The dissertation principally aims to inaugurate a methodology grounded in sound and voice studies that is attentive to Bolivia's fraught literacy history stemming from colonialism, as well as the region's profound cultural heterogeneity. Sound and voice as an epistemology could allow perceptions beyond those relegated by an ocularcentric perspective. They may also enable a less violent transition between orality and writing, by realizing that sound is a universal generator of meaning that transcends language and media. Sound allows for a radical continuity and an exploration of vibrational ontology (Goodman). The cultural products analyzed through a sonic perspective in this dissertation reveal the non-human voice in the work of Andres Bedoya, a utopia of coexistence in a radically heterogeneous environment in the work of Elysia Crampton, the fragmentation of reality through violent procedures of silencing and silence in the writings of Liliana Colanzi and Giovanna Rivero, as well as the absence and desensitization of the “Other’s” voice exposed by Ozzo Ukumari’s sonic-haptic sculptures
Anxiety, worry, and subjective difficulty concentrating: Examining concurrent and prospective symptom relationships in the context of the COVID-19 pandemic
No full textDifficulty concentrating is an understudied cognitive phenomenon, despite its status as a diagnostic criterion for generalized anxiety disorder and contributor to clinically significant distress and impairment. Existing theoretical accounts of the observed relationship between worry and subjective difficulty concentrating rely on a deficit model, in which impairments in trait attentional control render anxious individuals vulnerable to pathological worry. However, worse attentional task performance is not reliably associated with subjective difficulty concentrating in daily life. Alternatively, anxiety could be framed as a source of attentional interference via its primary cognitive manifestation, worry. The present study examined concurrent and prospective associations between anxiety (Depression Anxiety Stress Scales-21), worry (Penn State Worry Questionnaire), and subjective difficulty concentrating (Attentional Control Scale) in an adult sample (N = 723, 70.86% female) aged 19-81 years (M = 34, SD = 14.70). Data were drawn from a larger study of psychopathology during the COVID-19 pandemic. Online surveys were administered at three timepoints: April/May 2020, July/August 2020, and September/October 2020. Two linear mixed-effects models were constructed to examine within- and between-person effects of anxiety on worry, and worry on difficulty concentrating, while controlling for clustering within individuals over time. Anxiety was associated with worry both between (b = 1.81, SE = 0.13, = 0.65, p < .001) and within (b = 0.84, SE = 0.11, = 0.12, p < .001) participants. Difficulty concentrating was associated with both between-person differences in average worry (b = 0.19, SE = 0.03, = 0.38, p < .001) and within-person variation in worry (b = 0.12, SE = 0.02, = 0.09, p < .001). A path analysis using structural equation modeling found that worry was a partial mediator of the longitudinal association between anxiety and difficulty concentrating, though this effect did not survive the addition of covariates. These preliminary findings support theoretical accounts of worry as a cognitive mechanism linking anxiety with subjective attentional problems. Future research should continue to examine mechanistic pathways by which worry may interfere with attention, with the goal of developing more effective interventions for anxiety-related difficulty concentrating and associated functional impairment
Impacts of Metal Contamination and Reactive Nitrogen on Sediment Microbial Communities in Roadside Freshwater Ponds
No full textHuman activities such as industry, agriculture, and road construction alter not only the landscape, but also ecosystem geochemistry and microbiology. In particular, metallurgical activities and fossil fuel combustion redistribute trace metals across these landscapes. Additionally, agriculture has increased environmental reactive nitrogen loadings. However, the impacts arising from this combination of pollutants on microbial communities and ecosystem services are largely unknown. The nitrogen cycle is mediated by microbial activities, and influx of reactive nitrogen can alter microbial nitrogen cycling activities. Such alterations can disrupt nutrient cycles and trophic webs. Further, microbially mediated nitrogen cycling is driven by enzymes dependent on metal cofactors. Therefore, changes in metal mixes can interfere with assimilation of these cofactors, and thus degrade enzyme functions.
Roadside ponds are ideal systems for studying the impacts of metal and nitrogen contamination mixes on both microbial community structures and nitrogen cycling functions. As road networks grow and evolve, and environmental regulations and new technologies are implemented, the source mixes of trace metal and reactive nitrogen contamination change, further complicating ecosystem impacts. Sediment records reveal details throughout road lifecycles and reflect technological changes. Microbial responses to anthropogenic nitrogen and metals are strongly influenced by variability in microbial community composition. This dissertation examines and compares nitrogen, metals, and microbes in sediment cores from two roadside ponds in contrasting environments.
Harmar Pond is an urban, temperate pond in Pennsylvania. Poudre Lake is a remote, subalpine/alpine pond in Colorado. Metal and nitrogen inputs increase in both ponds following road construction. Endmember mixing with metal-to-metal ratios confirms a shift in metal sources from industry to road runoff in both ponds, more clearly in Harmar Pond. In addition, shifts in microbial community composition are apparent in both sediment records. These shifts occur at distinct times, suggesting that community structure drivers vary. Moreover, shifts in microbial community composition and potential nitrogen cycling functions correspond to a rapid increase in nitrogen and metal fluxes coincident with road construction. Continued reconstruction of multi-proxy histories from sediment records is fundamental to comprehensive clarification of human impacts
Telehealth and Contraceptive Accessibility among Black Women: A Literature Review
No full textRecent public health research has focused on reproductive health disparities among Black women. These studies highlighted Black women's experiences of adverse health outcomes, particularly in terms of contraceptive care access compared to White women. The COVID-19 pandemic not only heightened the focus on reproductive health disparities among Black women but also revealed the potential of telehealth medicine to enhance access to care for a broader population than previously hypothesized before the pandemic. As telehealth medicine grows in popularity and Black women continue to lack access to contraceptive care, it is vital to examine the emerging literature on this relationship. An Ovid-Medline literature review search produced seven articles that asses the experiences of Black women accessing contraceptives and examines their preferences toward telehealth services within the healthcare system. Many of the articles concluded that Black women have higher odds of having decreased access to contraceptives in comparison to White women across many geographic regions in the United States. Concerning telehealth preferences, Black women are more likely to prefer and schedule telehealth services than White women. Future research should investigate the lasting impact of ongoing access to telehealth services. Additionally, future studies should incorporate health equity and reproductive justice principles, taking an intersectional approach to address this public health concern
Explainable Course Recommendation: Connecting College Education to Knowledge and Careers Through Skills
No full textAcademic choice and exploration are essential aspects of undergraduate education in the United States, allowing students to select courses with minimal restrictions.
However, students often face challenges in navigating the complex academic landscape, hindered by limited information, insufficient guidance, and an overwhelming number of choices. Time constraints from the academic calendar and high demand for popular courses make a thorough evaluation of options difficult. Although academic institutions provide career guidance counselors or advisers, the number of advisers is still limited. Course recommendation systems aim to offer personalized suggestions based on students' academic backgrounds, preferences, skills, and career goals. However, there is a lack of research on students' perceptions of recommendations and the provision of explanations to help them evaluate course relevance. Moreover, the majority of course recommender systems concentrate only on the context of learning in higher education. Despite the importance of career goals, none have attempted to establish a connection between learning and work by incorporating job information into course recommendation and explanation.
This dissertation explores the development of an advanced course recommendation system in higher education, linking academic courses to career paths using deep learning and natural language processing techniques. It begins by examining various methods for representing and recommending courses, utilizing institutional big data and combining content-based and collaborative models for better performance. A central aspect of this dissertation is the development of skill-based explanations for course recommendations. This is achieved by employing a deep concept extraction model that utilizes BERT and BI-LSTM-CRF architectures. This model effectively extracts concepts from course descriptions, enhancing the recommendation process. Using this concept extraction model, I investigate the impact of skill-based explanations in a serendipitous course recommendation system, which was tested using the AskOski system at the University of California, Berkeley. The findings indicate that these explanations not only increase user interest, particularly in courses with high unexpectedness, but also bolster decision-making confidence. To achieve greater personalization in course recommendations, the future of this field extends beyond academics by incorporating insights from the job market to align with students' career aspirations. This dissertation introduces an innovative approach for integrating skill-related data into course recommendation systems, effectively bridging the gap between academic pursuits and career aspirations. Finally, I develop an explainable, personalized course recommendation system that incorporates insights from the job market. This system tailors course suggestions based on students' academic histories and career preferences. Its objective is to enhance course exploration in higher education, assisting students in navigating their educational paths and acquiring the essential skills required for their chosen majors and future careers. A user study conducted at the University of Pittsburgh demonstrates that the recommendations were generally perceived as valuable, with explanations playing a pivotal role in aiding students to assess their interest in the recommended courses. This underscores the significance of integrating skill-related data and explanations into educational recommendation systems
Algorithmically mediating communication to enhance collective decision-making in online social networks
Get PDF Digitally enabled means for judgment aggregation have renewed interest in “wisdom of the crowd” effects and kick-started collective intelligence design as an emerging field in the cognitive and computational sciences. A keenly debated question here is whether social influence helps or hinders collective accuracy on estimation tasks, with recent results on the role of network structure hinting at a reconciliation of seemingly contradictory past results. Yet, despite a growing body of literature linking social network structure and collective accuracy, strategies for exploiting network structure to harness crowd wisdom are underexplored. We introduce one such strategy: rewiring algorithms that dynamically manipulate the structure of communicating social networks. Through agent-based simulations and an online multiplayer experiment, we provide a proof of concept showing how rewiring algorithms can increase the accuracy of collective estimations—even in the absence of knowledge of the ground truth. However, we also find that the algorithms’ effects are contingent on the distribution of estimates initially held by individuals before communication occurs. CCS Concepts •Human-centered computing → Collaborative and social computing• Applied computing → Psychology. </jats:sec
The Impact of Canine and Feline Companionship on Loneliness Among Adults Aged 60 Years and Older in the United States and Western Europe: A Literature Review
No full textLoneliness among adults 60 years and older is an important issue that impacts various aspects of well-being, including mental and physical health, quality of life, and aging experiences. With the aging population growing, understanding factors influencing mental health outcomes is crucial. One intriguing dimension is the role of pet ownership, particularly canine and feline companionship, in mitigating loneliness. This literature review explored the impact of canine and feline companionship on loneliness among adults aged > 60 in the United States and Western Europe. A comprehensive search across Medline, APA PsycInfo, and Embase yielded 194 records, with 6 papers meeting inclusion criteria, all cross-sectional studies. Loneliness assessment methods predominantly involved questionnaires, including variations of the UCLA Loneliness Scale. Mixed findings emerged regarding the association between animal companionship and loneliness, with some studies reporting no significant association while others observed positive correlations. Notably, one study identified a slight decrease in loneliness among older adults with feline companionship after 4 months, although long-term effects were not seen at 12 months. Strengths of this literature review included the inclusion of studies published in 2011 or later, detailed explanation of loneliness scales, and specific focus on canine and feline companionship, and focus on older adults aged 60 and over. Limitations include reliance on a small number of papers and limited geographic scope. More rigorous studies are needed to further explore strategies to alleviate loneliness, particularly with animal companionships, to improve the quality of life of older individuals in these regions. Given the potential harmful effects of loneliness on the mental (and physical) well-being of older adults, addressing this issue remains of significant public health importance
Causal Graph Methods for Heterogeneous Data and Application to MUC1 Cancer Vaccine Response
No full textThe rapid advancement of biomedical technology has ushered in a new era of research that integrates powerful analytical approaches with traditional experimental lab work. To tackle the increasingly complex multi-modal datasets, numerous innovative new computational algorithms have been developed. These algorithms can accurately perform a range of tasks, from providing comprehensive insights and building complex models of biological systems to designing detailed personalized treatment plans and streamlining clinician workflow. However, despite their success, many of these algorithms are limited in their ability to infer causal relationships, a core component of scientific research. To bridge this gap, researchers have started to build new or adapt existing algorithms to incorporate causal strategies. In this dissertation, I will present several strategies to improve causal search approaches and demonstrate how we can use these and other ML approaches on the MUC1 cancer vaccine data. First, I will present how to use dimensionality reduction and latent factor models to improve the performance of causal search algorithms. Building upon this idea, we propose several ways to incorporate longitudinal data into existing causal models using time aggregation metrics. Next, I will introduce two new approaches to edge selection that improve efficiency of constraint-based causal search algorithms. After the computational work, the dissertation presents experimental work with the MUC1 cancer vaccine. I showcase how we can use causal search and machine learning algorithms to facilitate the experimental process and final data analysis
Ultrasound-targeted microbubble cavitation facilitates drug delivery across the blood-brain barrier
No full textAlzheimer’s disease (AD) is a devastating neurodegenerative disease that affects millions of Americans. To date, there is no cure for AD, and the treatment options are limited. One of the challenges in developing new treatments for AD and other neurological disorders is the restrictive blood-brain barrier (BBB). The BBB protects the central nervous system but as a consequence also limits the permeability of most drugs. Therefore, new methods are needed to open the BBB safely and transiently to allow therapeutics to enter the brain. One technique that is being explored to reversibly open the BBB is ultrasound-targeted microbubble cavitation (UTMC). In this approach, external ultrasound is applied to intravenously injected contrast agents (microbubbles [MBs]) as they transit through the microcirculation of target tissue. MBs are gas-filled lipid microspheres which cavitate under ultrasound exposure, causing shear stress to the microvascular endothelium, which temporarily increases endothelial barrier permeability. When UTMC is applied to the brain, it induces transient BBB hyperpermeability. This dissertation studies UTMC for BBB opening in an in vitro model of the BBB and the 5XFAD mouse model, an in vivo system that recapitulates aspects of AD pathology. We develop a contact co-culture transwell model of the BBB to study the mechanisms of UTMC-induced BBB hyperpermeability. We show that UTMC increases transcellular and paracellular permeability. Moreover, UTMC-induced BBB hyperpermeability is calcium-dependent and occurs, at least in part, due to a Ras homolog gene family, member A (RhoA)-dependent mechanism. In our in vivo studies, we develop a protocol for opening the BBB in the right hemisphere of 5XFAD mice. UTMC concentrates LY2886721 (LY, a beta-site amyloid precursor protein cleaving enzyme 1 [BACE1] inhibitor) in the brain and decreases beta-amyloid levels compared to non-insonified brain. Taken together, these research projects provide mechanistic insight into UTMC-mediated BBB opening and use this drug delivery strategy to increase the efficacy of a small molecule developed for the treatment of AD. Ultimately, these findings should help facilitate the translation of UTMC-mediated BBB opening to the clinic
Determinants and Associations of 24-hour Movement Behaviors in Desk Workers who Work from Home
No full textWork from Home (WFH) has become a viable and common work modality. Scarce evidence exists assessing 24-hour movement behaviors in those who WFH. The goals of this project were to compare behaviors of WFH desk workers to office-based desk workers and to identify determinants and gain perspectives of those who WFH in relation to their 24-hour movement patterns.
Methods: The first project was a secondary data analysis of baseline data from a randomized clinical trial among desk workers (n=275). Device-measured and self-reported 24-hour movement behaviors were compared between in-office vs. WFH worksite locations using linear regression. Odds of meeting 24-hour guidelines were also compared by worksite location, using the Canadian 24-hour movement guidelines as a framework. The second project used a mixed methods study design in WFH desk workers (n=27) to assess device-measured and self-reported 24-hour movement behavior data and gather qualitative data using focus groups. Focus group discussions intended to identify determinants and desired supports for healthy 24-hour movement behaviors in those who WFH. Following descriptive analysis of 24-hour behavior data, transcript analysis of five focus groups used Atlas.ti coding software, with two study coders, and identified salient themes.
Results: For aim 1, WFH workers accumulated significantly less light physical activity (LPA) [-0.51±0.20 hours/day , p=0.01] and higher prolonged sedentary behavior (SB) [0.54±0.25 hours/day, p=0.03] compared to in-office workers. WFH workers were less likely to meet MVPA guidelines (OR: 0.52 [95% CI: 0.28, 0.98)]) and more likely to meet the sleep quality (OR: 2.23 [95% CI: 1.10, 4.51]), waketime consistency (OR: 2.63 [95% CI: 1.27, 5.48)]), and overall sleep guidelines (OR: 2.42 [95% CI: 1.25, 4.67]). For aim 2, focus groups in WFH individuals revealed three key themes that impacted their 24-hour movement behaviors: 1) workspace characteristics such as physical environment characteristics, 2) support for healthy living such as employer support, and 3) non-work-related influences including family obligations and self-motivation.
Conclusions: WFH desk workers had different 24-hour behavior profiles to those who worked in office settings. Unique determinants of these behaviors for WFH contexts suggest future studies should incorporate WFH-specific determinants when designing interventions for this population