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An Exploration of Connection, Engagement, and Belonging in Environmental Education
No full textAddressing environmental challenges—such as global warming, climate change, and environmental justice—requires diverse perspectives and active participation across multicultural communities. Despite this, environmental organizations often struggle to attract and retain a diverse and engaged membership base. This study examined the challenges faced by the Pennsylvania Association of Environmental Educators (PAEE) in engaging its members and addressing declining participation. Guided by my theory of improvement aimed at understanding a sense of belonging among members, the study employed a mixed-methods approach, including semi-structured interviews with eight participants and a survey of 341 members, yielding a 24% response rate. Findings revealed that members valued connection and communication through in-person events but identified a need for targeted outreach to specific groups, such as formal educators and young people. The study highlights the importance of cultivating a sense of belonging to enhance meaningful engagement, build stronger community connections, and increase membership participation and retention. These insights provide a framework for environmental educational organizations to build inclusive and active networks
Quantifying Free-Living Physical Activity in People with Spinal Cord Injury or Disorder Using Wearable Devices
No full textPeople with Spinal Cord Injury/Disorder (SCI/D) have a significantly higher obesity prevalence, which elevates their risk of developing secondary conditions. Accurate objective measurements of energy expenditure (EE) and physical activity (PA) are essential for obesity prevention and weight management. While wearable devices have been widely validated in the general population, and sophisticated PA pattern analysis using wearable device data has been proposed, relative validation studies and the use of wearable devices to analyze PA patterns in people with SCI/D are lacking. This dissertation focuses on improving the use of wearable devices, specifically the ActiGraph wearable accelerometer, to assess PA and EE in two specific SCI/D groups: adults with spinal cord injury (SCI) and children with Spina Bifida (SB). For adults with SCI, we developed a custom raw acceleration signal-based algorithm to predict total daily energy expenditure (TDEE). We evaluated its field validity against the criterion measure of doubly labeled water (DLW) and compared it with other existing ActiGraph Count-based custom prediction algorithms in this population. In children with SB, we utilized data from an ongoing study on body composition and EE. We evaluated existing TDEE prediction equations in this population and improved prediction accuracy by developing custom equations that use demographic variables alone or in combination with ActiGraph outputs. We also developed new sets of custom ActiGraph PA intensity classification cut-points for children with SB. Using these cut-points and free-living PA data, we conducted an exploratory analysis to understand PA patterns and their preliminary relationship with obesity in children with SB. In summary, our validated custom TDEE prediction algorithms for adults with SCI and children with SB provide new methods for accurately assessing free-living TDEE in these groups. Additionally, the custom ActiGraph PA intensity cut-points and our analytical approach to PA patterns provide a solid foundation for future studies that further investigate the links between free-living PA and health outcomes in these populations
A Description of Symptoms and Physical Function among Participants of a Randomized Trial of a Telerehabilitation Exercise Intervention after Lung Transplantation
No full textPurpose: The aim of this dissertation was to determine the difference in lung transplant recipients participating in LTGO telerehabilitation intervention versus enhanced usual care (EUC) intervention in symptoms and physical function improvement at three months. The second aim was to assess if the relationship between symptoms and physical function improvement was moderated by group assignment (LTGO telerehabilitation intervention vs. EUC)
Participants: Sixty-three participants were eligible from lung transplant recipients participating in the Lung Transplant Go randomized trial study. Participants' mean age was 57 years, ranging from 27 to 70 years old; 41% were female, 90% were white, and all participants had undergone a double lung transplant.
Methods: This study involved secondary analysis of baseline and three-month data. For physical function assessment, the 5 Times Sit-to-Stand test (5STS) and Sit-to-Stand in 30 seconds (STS-30) were used. The Borg RPE scale was used post each physical function assessment to measure leg exertion. For symptoms, the Questionnaire for Lung Transplant Patients (QLTP), was used to measure the number of self-reported adverse symptoms. Pre-selected symptoms from the QLTP (muscle pain, fatigue, shortness of breath, depression, problems with activity due to breathlessness, sleep problems, and muscle weakness) were also examined.
Results: At three months, the LTGO group showed trends towards improvement in the QLTP total score and across all QLTP subscales, while the EUC group exhibited increased symptoms. However, no statistically significant between-group differences were observed (p>.05). Both groups showed improvement in physical measures, but these improvements were not statistically significant. The LTGO group showed a greater decrease in preselected symptoms than the EUC, but these differences were not statistically insignificant. Regression analysis indicated potential moderation effects by group assignment on the relationship between symptom and physical function improvement. For the LTGO group, increased activity symptom scores were associated with better STS-30 performance.
Conclusion: Our study found no significant difference between telerehabilitation and EUC in terms of symptom improvement and physical function. Future research with larger and more diverse samples is needed to confirm these findings and explore interactions between symptoms and physical function
Feeding Challenges in Infants with Bronchopulmonary Dysplasia: A Comprehensive Exploration of Interventions, Outcomes, and Maternal Perspective Post NICU-Discharge
No full textPremature infants have underdeveloped, possibly impaired, physical and/or neurophysiologic functioning required for oral feeding. A scoping review of infant feeding intervention research (Study 1) determined that there is a gap in feeding research for infants with bronchopulmonary dysplasia (BPD). BPD is a chronic respiratory disorder characterized by abnormal lung growth and/or underdevelopment, exacerbated by cycles of injury and recovery. BPD affects the mechanics of feeding, putting the infant at risk for short and long-term adverse events. Up to 50% of infants with BPD are likely to be re-hospitalized for respiratory infections within the first year, likely due to feeding and swallowing difficulties. It is crucial to determine which factors contribute to future feeding difficulties, to avoid the financial, social, and psychological stress associated with hospital encounters and readmissions. Consequently, Study 2 aimed to determine which factors lead to unplanned, feeding-related healthcare encounters within one year of discharge. When infants are discharged, their caregivers become the sole providers of safe and consistent feeding; however, infants with BPD often have feeding difficulties and growth delays, which can be emotionally and psychologically taxing for caregivers. Research suggests that parents with high levels of perceived self-efficacy are more adept at coping with challenges related to newborn care. Therefore, Study 3 aimed to explore the experience of a caregiver feeding her infant with BPD post discharge, the impact of the severity of the feeding problem on the caregiver, and the role of self-efficacy in mitigating that impact
Nitroalkenes Exploit Dependence on Autophagy-Lysosome Pathway in PARPi-Resistant Triple Negative Breast Cancer
No full textTriple negative breast cancer (TNBC) currently lacks targeted therapy despite being the most aggressive breast subtype. However, patients with a mutation in the BRCA1/2 genes can receive treatment that exploits a vulnerability in DNA double-strand break (DSB) repair. Loss of BRCA1/2 function critically sensitizes tumors to poly-ADP ribose polymerase inhibitor (PARPi) treatment through prolonged DNA damage. Unfortunately, resistance to PARPi is an insurmountable problem for patients. Mechanisms conferring insensitivity to therapy include increased DNA damage repair and autophagy.
Electrophilic nitroalkenes (NFA) have emerged as potent anticancer treatments and sensitize TNBC cells to PARPi and other DNA-damaging therapies. NFAs post-translationally modify reactive protein cysteine thiols through a Michael addition reaction. To better understand the mechanism of action of NFA in cancer, we applied click chemistry pulldown approaches that identified NFA targets in the autophagy pathway, a PARPi resistance mechanism in TNBC. Thus, we hypothesize that NFAs sensitize PARPi-resistant TNBC cells to PARPi. To test this hypothesis, we generated three PARPi-resistant TNBC cell lines. RNA-seq analysis and proteomic approaches revealed upregulation in autophagy and lysosomal pathways in PARPi-resistant TNBC cell lines. Using click-chemistry pulldown approaches, we confirmed the autophagy regulator SQSTM1/p62 as an NFA target. p62 has redox-sensitive cysteine residues, Cys105 and Cys113, needed for its oligomerization during autophagy. We confirmed Cys105 and Cys113 as NFA targets, and NFA treatment phenocopied Cys105.113Ala mutants, including impaired p62 oligomerization, degradation, and inhibition of autophagy. Importantly, like chloroquine (autophagy inhibitor), treatment of PARPi-resistant TNBC with NFA resensitized TNBC to PARPi therapy by inhibiting autophagy and decreasing lysosomal biomass
Scratching that Itch: When, how, and why scratching makes your rash worse
No full textItch is a dominant symptom in dermatitis and scratching promotes cutaneous inflammation thereby worsening disease. However, the mechanisms through which scratching exacerbates inflammation and whether scratching provides benefit to the host are largely unknown. We now provide a mechanistic underpinning and propose a functional benefit of scratching. While itch-sensing neurons alone are not sufficient to trigger inflammation, we found that itch-sensing neurons and scratching were required for inflammation, mast cell degranulation, and neutrophilic infiltration in the FITC and oxazolone models of CHS, which are Th2 models dependent on IgE/FcεRI. In contrast, itch-sensing neurons and scratching were not required for DNFB-mediated inflammation, a Th1/17 CHS model with no IgE component. Scratching was also required for edematous inflammation in a model of IgE/FcεRI-mediated mast cell activation. In this model, scratching-induced inflammation also required pain-sensing nociceptors, Substance P and MrgprB2. Moreover, direct nociceptor activation rescued scratching-dependent inflammation in collared mice that could not scratch. Scratching also increased mast cell-dependent inflammation and augmented host defense against superficial S. aureus infection. Furthermore, in the context of skin inflammation, scratching also reduced the diversity of the skin microbiome and number of viable skin bacteria. Thus, through activation of nociceptor driven neuroinflammation, scratching both exacerbates allergic skin disease and provides protection from S. aureus, reconciling the seemingly paradoxical role of scratching as a pathological process and evolutionary adaptation
Mechanisms of Spinal Cord Stimulation for the Recovery of Voluntary Motor Control after Paralysis
No full textEvery year more than 4.5 million people in the world suffer from spinal cord injury and stroke. These diseases severely impact the quality of life. The most devastating impairment is some form of motor paralysis, with the restoration of walking and arm/hand movement being the main priorities for these people. While no clinical therapies currently exist, new neurostimulation treatments are emerging, offering hope for reversing the seemingly permanent condition of paralysis. Recently, epidural spinal cord stimulation (SCS) recovered the ability to regain motor control in patients with spinal cord injury and stroke. This exciting clinical evidence results from decades of scientific studies exploring the underlying mechanisms of SCS. However, these studies never considered the contribution of residual supraspinal inputs, thereby failing to explain the facilitation of voluntary movements. Indeed, residual supraspinal fibers are rarely completely abolished after a lesion and remain crucial in conveying voluntary commands. We believe that understating the transformation of artificial sensory inputs into voluntary motor function is the only approach to improve the design of stimulation protocols targeted to maximize residual volitional input, supporting the transition of SCS to all lesion severities and upper limb paralysis. In this thesis, we studied the role of residual supraspinal inputs in the recovery of voluntary motor control enabled by SCS. To do so, we inspected neural structures at postsynaptic, presynaptic and population levels. First, we investigated the integration of supraspinal and sensory postsynaptic potentials in the motoneuron membrane. By combining biophysical modelling, monkey and human experiments, we found that supraspinal inputs control motoneurons during specific combinations of SCS parameters. Second, we explored the effects of presynaptic mechanisms in the facilitation of supraspinal input during SCS. In anesthetized monkeys, we demonstrated that this facilitation is strongly contingent on presynaptic GABA. Third, we analyzed the impact of SCS on intraspinal population activity in monkeys. We showed that artificial pulsed stimulation impairs neural activity of functions unrelated to the stimulation target. Our results have direct clinical implications that can enhance SCS efficacy, thereby accelerating the transition of this technology into a clinical therapy
Employing Optimization and Hierarchical Decision Making for Modeling and Solving Practical Problems
No full textMulti-level optimization serves as a highly effective framework for modeling hierarchical decision-making processes in real-world applications. By integrating the strengths of this approach with data-driven insights, we can achieve impactful decisions. This dissertation delves into the modeling capabilities and development of solution methodologies for multi-level optimization and leveraging data to enhance decision-making across various domains.
In the first study, we propose a general framework that integrates both optimistic and pessimistic optimization approaches in solving the regression problem to address outlier cleaning and robustification in a unified fashion. This multi-level optimization scheme ensures constructing robust models trained on data sets with outliers and adversarial points.
In the second study, we develop a solution methodology for bilevel mixed integer linear programs, using Lagrangian relaxations and column and constraint generation algorithm framework. By using branch and bound method, the algorithm guarantees optimal solution for this challenging problem.
In our next study, we develop a new scheme to handle missing values in a data set, moving beyond the common "impute first, predict after" approach. Our strategy directly incorporates incomplete data points into building a predictive model, utilizing the available data from the data with missing features. In a robust optimization framework, we define local uncertainty sets leveraging projection.
In our final project, we predict length of stay of an inpatient patient, exploring the effect of nursing care and the patient characteristics, via interpretable machine learning models. We work on a novel data set with shift-level granularity to develop models that can help clinicians and hospital administrators in capacity planning
Predicting Atomic Structure of Multi-metallic Nanoparticles with Physics-based Machine Learning
No full textMetal nanoparticles (NPs) find tremendous application in various fields, including
catalysis, biomedicine, and electronics, due to their unique physicochemical properties arising
from their morphology (i.e., size and shape) and composition. The chemical ordering of NPs,
consisting of more than one metal, is crucial for optimizing their application performance,
including stability. Traditionally, Density Functional Theory (DFT) is used to investigate NP
stability, but it is computationally expensive, limited to small systems and cannot be applied to
multi-metallic NPs where the materials space is enormous. To address this, recent efforts coupled
a physics-based model (Bond-Centric Model, BCM) with a developed genetic algorithm (GA) to
optimize the chemical ordering of NPs leading to minimum (most exothermic) cohesive energies
(CEs). Central to this approach is the calculation of weighting factors that scale the monometallic
bond strength to describe that of the bimetallic bond. Herein, we perform a critical analysis and
set some rules on how to apply these methods for rapid and accurate nanomaterials predictions.
Specifically, we optimized the chemical ordering of 2869-atom cuboctahedron NPs across 15
different bimetallic combinations. In comparison with both experimental and computational
results, our findings indicate that the use of small metal dimers for the calculation of the weighting
factors leads to accurate and computationally efficient chemical ordering and stability predictions
for a wide range of NP compositions. We further extended our investigation to 6 trimetallic NPs
with a tremendously large materials space, testing our model’s capability to predict chemical
ordering patterns in multi-metallic systems and demonstrating its power as a rapid and accurate computational method. This methodology can facilitate the design of thermodynamically stable
multi-metallic NPs and predict the distribution of different metal atoms from the core to the
surface, which is central to any nanotechnological application
Developmental changes in mouse motor by skill learning and cortical circuitry
No full textLearning motor skills requires plasticity in the primary motor cortex (M1), including changes in inhibitory circuitry. But how inhibitory synaptic connections change during skill acquisition and whether this varies over development is not fully understood. This study assesses the normal developmental trajectory of motor learning and then addresses inhibitory connectivity changes after motor learning. We trained mice of both sexes to run on a custom accelerating rotarod at ages from postnatal day (P) 20 to P120, tracking paw position and quantifying time to fall and changes in gait pattern. Performance improved most rapidly between P30-60, while paw position and gait patterns change with learning, though differently between age groups. To address circuit changes, we labeled task-active and task-inactive pyramidal cells with CaMPARI2, a genetically encoded activity marker. We then evoked inhibitory responses (IPSCs) from two major interneuron types: parvalbumin-expressing (PV+) interneurons and somatostatin-expressing (SST+) interneurons. After one training day, PV-mediated inhibition is greater in the active cells, while SST-mediated inhibition is not different. These results suggest early changes in PV-mediated inhibition may support motor skill acquisition in mice. Whether PV-mediated inhibitory changes persist or changes in SST+ interneuron connections arise later in training remains to be tested