Environmental and Occupational Health Sciences Institute
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Real-time substance use urges and suicidal thoughts: predictive utility of average real-time substance use urges, baseline basic psychological needs, and baseline emotion
No full textSubstance use disorders (SUD) are increasingly prevalent among young adults and have been associated with a greater risk of suicidal thoughts and behaviors (STBs). The co-occurrence of SUD and suicidal thoughts is hypothesized to be attributable to them having shared functions, such as avoiding or escaping one’s emotions. It remains largely unknown as to why some individuals with SUD experience STBs while others do not. One possible explanation is that SUD loses its effectiveness in relieving short-term distress, leaving them more susceptible to any cognitive/behavioral approach that provides them with relief, including STBs. Emotion reactivity may play an important role in helping determine who with SUD will go onto experience STBs as strong emotion reactivity can be aversive and can trigger feelings of avoidance or escapism. Moreover, Self-Determination Theory could likely provide helpful insight into SUD and STBs as individuals who experience frustrated basic psychological needs (BPN)—frustrated feelings of autonomy, competency, or relatedness—are at a heightened risk for maladaptive behaviors. Few studies have examined SUD or STBs in the context of emotion reactivity or frustrated BPN, and no studies have assessed emotion reactivity and frustrated BPN in the context of any psychopathology. Therefore, the current study utilized ecological momentary assessment (EMA) to assess whether (1) baseline levels frustrated BPN and/or baseline levels of emotion reactivity, (2) average real-time alcohol use urges or average real-time drug use urge, or (3) any interactions between the baseline variables and average real-time substance use urge variables can help predict who, among those with real-time alcohol and/or drug use urges will endorse real-time suicidal ideation (SI) during the 8-week EMA period. Participants were 295 college students who were recruited from a large university in the Northeast and endorsed at least one instance of real-time alcohol use urge or real-time drug use urge during the EMA period. Participants completed baseline measures, followed by an 8-week EMA period wherein they would receive up to 6 surveys/day that inquired about affect, thoughts, and behaviors. Baseline frustrated BPN (all subscales) and average real-time drug use urge across data collection were significantly predictive of SI endorsement during the EMA period. Furthermore, the interaction between baseline BPN frustrated relatedness and average real-time drug use urge ratings was significantly related to the presence of SI, such that those with high baseline BPN frustrated relatedness and a high average drug use urge rating during the EMA period were most likely to have endorsed SI during that same time period. Experiencing frustrated BPN and/or having an elevated average drug use urge for a period of time are predictive of experiencing SI. Findings from the study have important clinical implications for prevention and treatment efforts related to co-occurring SUD and SI.M.S.Includes bibliographical reference
Inhibition of the guanine deaminase cypin for therapeutic development
No full textHyperuricemia is a condition characterized by an elevated concentration of uric acid in the serum, which may lead to precipitation and deposition of monosodium urate crystals in the joints of patients, causing significant pain. Currently, small molecule inhibitors of uric acid production used in standard urate-lowering therapy (ULT) exclusively target the last step of purine metabolism. Current inhibitors are ineffective and can lead to fatal hypersensitivity and significant kidney and cardiovascular concerns. Moreover, successful attenuation of serum uric acid dissolves tophi and resolves painful flare-ups. Therefore, a more diverse selection of small molecule targets may provide physicians with additional options to improve ULT outcomes. Cypin is a microtubule-binding protein and catalyzes the conversion of guanine to xanthine, the penultimate reaction in the guanine metabolic pathway of purine metabolism. The activity of cypin, the body’s major guanine deaminase, can be inhibited by the small molecule B9; however, the nature of this inhibition is unclear. Cypin also interacts with guanosine analogs, such as valacyclovir; however, whether guanosine analogs can be repurposed into cypin inhibitors is unknown. In this project, we seek to clarify the mechanisms underlying the cypin-tubulin interaction and the inhibition of cypin by B9 using molecular modeling. We also determine whether guanosine analogs act as inhibitors or substrates of cypin. We demonstrate that cypin binds soluble tubulin heterodimers and that the interaction likely relies on certain amino acid clusters on the surface of cypin. We also show that B9 inhibits cypin directly and competitively. While guanosine analogs more resemble substrates of cypin rather than inhibitors, we demonstrated that the affinity of ligands towards cypin is not solely reliant on the guanine motif. Importantly, we combine computational and biochemical approaches to establish a workflow through which to screen novel cypin inhibitors. By combining computational and biochemical approaches for assaying guanine deaminase, we identified three additional cypin inhibitors with comparable efficacy compared to the original hit, B9.Ph.D.Includes bibliographical reference
Cermet wasteforms for immobilization of high-level waste from advanced reactors
No full textCommercial deployment of Generation IV advanced reactors is anticipated by 2030, generating high-level waste streams that require immobilization. High-level waste (HLW), produced at the
back end of advanced reactor (AR) fuel cycles, includes metals, oxides, halide salts, and /or
carbon/graphite (TRISO fuel particles). It is difficult to immobilize these HLWs, especially with
high waste loadings into borosilicate glass. Consequently, the state-of-the-art (SOA) for treating
the HLW from the ARs poses significant challenges, including (1) limited solubility of metallic
waste in glass, (2) poor thermal conductivity (critical for managing high heat decay elements like
Cs and Sr), (3) multi-step, complex processing, (4) multiple waste forms (WFs) with low waste
loadings and significant repository footprint and (5) higher costs. This work aims to address these
challenges by developing a high-density, durable cermet waste form by optimizing ceramic-tometal ratios and sintering parameters to achieve higher densification (> 95%). Cermet waste forms
offer significant advantages over SOA methods, including superior thermal conductivity,
eliminating the need to separate metallic and salt streams, high waste loading, reduced repository
footprint, and lower operational costs.M.E.Includes bibliographical reference
Investigating the synergy between conductivity and molecular signaling of the Integrin-KCNB1-Complex
No full textIon channels are highly conserved throughout phyla reflecting their fundamental role in cellular physiology. From bacteria to humans, ion channels are crucial for maintaining the membrane potential and for many other cellular functions. Structural and functional similarities across species underscore ion channels' deep evolutionary roots and critical importance in diverse biological systems. It is not surprising that mutations in ion channels cause severe conditions collectively known as channelopathies. Neuronal channelopathies include epilepsy, ataxia, migraine, autism spectrum disorder (ASD), developmental epileptic encephalopathies (DEE), and early-onset epileptic encephalopathies (EoEE). The voltage-gated potassium (Kv) channel subfamily B member 1 (KCNB1) is associated with DEE. A missense mutation in the region encoding the voltage sensor of KCNB1, where an arginine is substituted with a histidine at position 312 (KCNB1R312H allele), was found in two unrelated children affected by DEE. One child lost the ability to walk at the age of 5 and never developed language. The other child was unable to articulate speech and walked on tiptoe. To understand the underlying causes of KCNB1-DEE, we developed a novel knockin (KI) mouse bearing the Kcnb1R312H mutated allele. The Kcnb1R312H mouse model is characterized by anxiety-like behaviors, hyperactivity, compulsive and repetitive traits, spontaneous seizures, and depression-like symptoms. To our surprise, electrophysiological analysis of Kcnb1R312H primary neurons reveals no conductivity differences compared to WT neurons. At the anatomical and morphological level, the brain of the Kcnb1R312H mouse presents abnormal neurodevelopment, neuroanatomical deficits, disrupted interregional connectivity, and reduced synaptic integration. We found that the KCNB1 channel forms complexes with integrins named Integrin_K+_Complexes or IKCs. Our results indicate that the IKC is part of the actin machinery, and that the formation of new actin filaments is defective in the presence of IKCs formed with R312H subunits. My studies position KCNB1 as a key contributor to the electrical activity of pyramidal neurons and also a pivotal player in the context of brain development. Furthermore, my studies suggest that KCNB1—through its interactions with integrins--may cause DEE by mechanisms that go beyond defective ionic conductivity. Overall, this work underscores a previously unknown synergy between electrical and non-electrical functions in a single macromolecule.Ph.D.Includes bibliographical reference
Cantor, Nancy Public messages from Rutgers University-Newark Chancellor deposit agreement
No full textAssessing prenatal counseling for the prevention of preterm and low birth weight among African American women.
No full textBACKGROUND: Live birth weighing less than 2500 grams, and a preterm delivery are major causes of morbidity and mortality among infants in the United States. African American women were characterized with increased rate of low birth weight and preterm birth. Previous works on prenatal counseling as a preventive measure in reducing preterm and low birth weight is understudy, further research is required. This study examines correlations between early prenatal counseling and incidence of preterm and low birth weight among African American women.METHOD: A descriptive analysis using chi-square test was conducted on women in the 2018 Natality Public Use data. Using random sampling, ratio 5:1, 7,600 White women and 1400 African American women were randomly selected to be included in the study. Data was analyzed using SPSS version 24. A p value of ≤ 0.05 was considered a significant finding of the study.
RESULTS: African American women who started prenatal care early are more likely to deliver full term baby (p=0.003), African American women with increased number of prenatal visits are less likely to have preterm birth (p=0.001), no significant relationship (p=0.071) between initiation of prenatal care and the birth weight but there were statistical significant relationships (p=0.001) between number of prenatal visits and the birth weight; increase number of prenatal visits correlate positively with birth weight. There is positive correlation between Government Nutrition Program-WIC and the birth weight (p=0.002); women who received WIC benefits during pregnancy are more likely to have higher birth weight. The relationship between mother’s age significantly correlate to birth weight; average birth weight of babies born by African American women increased with increasing age till 25-39 and decline at age 40 and above (p=0.041). Finally, gestational hypertension was found to be statistically significant with obstetric estimates and birth weight (p=0.001). African American women with gestational hypertension were more likely to have very low birth weight.
CONCLUSION: The findings suggest that there is a significant reduced risk of preterm birth and low birth weight among African American women when they start prenatal care early and be receptive to prenatal counseling as early as possible during pregnancy.Ph.D.Includes bibliographical reference
Romina’s Adult Beliefs about Math Teaching and Learning: “Everything Has Romina’s Definition to It”
No full textThis is Analytic 3 (of 3) of "Tracing Romina’s Growth In Reasoning And Sense-Making about Math Problems and Development of Beliefs about Math Teaching/Learning"This analytic explores Romina’s later adult views about learning mathematics in college and post-graduate studies and the development of her beliefs regarding the knowledge, conditions, and processes of mathematical learning. Through clinical and semi-structured interviews from 1999 to 2009 while she was in college, graduate school, and post-graduate work, the video data suggest Romina developed beliefs that knowledge is an active and personal construct, learning environments should foster “comfortable” relationships with teacher-researchers and peers, and learning mathematics involves engagement in “group thinking” where ideas are shared, questioned and argued. As detailed in Steffero (2010), the value of collaborative working environments with personally relevant tasks emerges as a theme of Romina’s descriptions of her experiences in the longitudinal study. References Steffero, M. (2010). Tracing beliefs and behaviors of a participant in a longitudinal study for the development of mathematical ideas and reasoning: A case study. Rutgers The State University of New Jersey, School of Graduate Studies
The impact of the COVID-19 Pandemic on middle level general music: navigating a digital landscape
No full textThe COVID-19 pandemic profoundly disrupted educational systems worldwide, necessitating rapid adaptations and innovations in teaching practices due to quarantine regulations and the sudden shift to the distance learning format. A review of prior literature revealed a need for additional research in the field of middle level general music, various uses for digital technologies in music education, and pre-existing research about the impact of COVID-19 on music education. This dissertation explores the experiences of middle level general music teachers during the pandemic and examines what influence it had on their teaching practices in a post-pandemic world. The purpose of this study was to gain an understanding of how the use of technology in virtual instruction during the COVID-19 pandemic impacted middle level general music instruction during and after mandated lockdown and virtual instruction in one county of New Jersey. The two research questions investigated were: (1) What were the experiences of music teachers (in one NJ county) using digital technologies in middle level general music during mandated COVID-19 virtual instruction? (2) Did music teachers retain digital technologies in their middle level general music classes following the return to in-person learning, and if so, how and why? This study utilized a constructivist worldview to best understand the individual experiences of eight middle level general music teachers during the COVID-19 pandemic. Each participant was interviewed once, and all interviews were transcribed and coded. The themes that emerged from the data included Middle Level General Music Structure and Technology Integration, District Influence on Teacher Experience during Virtual/Hybrid Teaching, and Altered Teacher Understanding of Students/Student Experiences. Data showed that a variety of content was taught at the middle level grades, from instrumental performance to music appreciation and music production. In addition, teachers viewed students from a new lens in post-pandemic education, with a greater awareness to social-emotional wellness in their middle level general music classrooms. This study sought to contribute vital insights to the evolving field of music education, specific to digital technologies, in the wake of unforeseen disruptions such as the COVID-19 pandemic. Implications from the study include teachers’ practical use of digital technologies, their return to in-person instruction in a post-pandemic world, and new views towards students.D.M.A.Includes bibliographical reference
Navigating uncharted waters: relational trust and evidence use among school board members and administrators during the Covid-19 Pandemic
No full textPolitics, context, and culture are critical to the understanding of how education policy reform is influenced by evidence use. Different types of research evidence use occur depending on the specific problems being addressed and on the cultural and organizational context. Relational trust is critically important in the public education policymaking process and plays an important role in how evidence is accessed and used. The COVID-19 pandemic, which began in 2020, was a period of unprecedented crisis and radical change in education. Leaders operated in crisis mode, forced to make rapid decisions about things that were not in their normal “wheelhouse.” These decisions were highly politicized, and local school district leaders were under extreme pressure from people on both sides of the issues. Shortly after the onset of the pandemic, there were protests across the country in the wake of George Floyd’s death. Consequently, school districts were under newly intensified pressure to articulate their stance on anti-racism. The public pressure that local school district leaders encountered during the pandemic provides a unique and intense window into how evidence use and relational trust impact decisions. This study consists of case studies of two purposefully selected K-12 districts in New Jersey that were successful in providing in-person instruction to students during the 2020-2021 school year. Leaders used a wide range of evidence in their decision-making during this crisis. Studying and comparing Suburbantown and Urbantown (pseudonyms used for the districts in this research) over the first year of the COVID-19 pandemic reveals how district context, relational trust, and networks affect evidence use.Ph.D.Includes bibliographical reference
Advancing flood risk estimation in ungauged basins with machine learning and climate data at the global scale
No full textIncreasing flood risk due to urbanization and climate change poses a significant challenge to societies at global scale. Flood prediction across scales and more specifically in ungauged areas remains a great challenge that limits the efficiency of flood risk mitigation strategies and disaster preparedness. Machine learning (ML) based models have demonstrated a great potential for streamflow prediction. ML based procedures are relatively easier to apply and are less computationally demanding, especially for applications at regional scales, than traditional physics-based models. Thus, their application for hydrologic predictions have attracted a lot of interest from stakeholders in academia, industry and federal agencies.For streamflow prediction, these models perform very well at capturing streamflow variability but fail to accurately predict extreme values (i.e. peak flow) of flood events, which are important to be considered in flood design or for flood warning purposes. This work therefore examines an event-based predictive framework that is solely focused on peak flow prediction and considers the characteristics of the flood triggering precipitation, the catchment and antecedent wetness conditions. Analysis for multiple distinct hydroclimatic regions is presented across the contiguous US. Evaluation of the drivers of flood peaks noted distinct dependencies among the dynamic and static predictors considered in the models for flood peaks of different severity. Furthermore, a flood prediction framework is developed and tested in ungauged regions that relies on two fundamental components. First, meteorological data from satellite and reanalysis global datasets (IMERG and ERA5-Land, respectively) provide key input variables and second, ML models trained in the data-rich contiguous US, are applied in climatically similar regions in other parts of the world. Results indicate acceptable performance for both products providing a starting point from which more and improved ML procedures and precipitation datasets can be integrated to potentially address the PUB (Prediction in Ungauged Basins) problem at the global scale. Another remaining and critical need at the global scale is future flood risk estimation. Conventionally, hydrologic models are calibrated and used to facilitate mid- to long-term mitigation strategies. However, the disparity among the vulnerable populations and the resources required for this task calls for approaches that are simple to access and use. The advantageous properties of ML models, motivate an ML-hydrological model comparison to evaluate the capability of ML models to spatially provide information on future flood risk under various climate scenarios. A proposed ML model has been assessed as viable for this purpose; a result that could serve communities at global scale that lack the resources to access, develop and operate hydrologic models.Ph.D.Includes bibliographical reference