Opportunity café: a community-based intervention to promote employability and self-care independence for transition-aged students with intellectual and developmental disabilities

The Individual with Disabilities Education Act (IDEA) mandates that a transition plan be in place for students with disabilities by the time they turn 16. This plan aims to facilitate the child’s movement from high school “to post-school activities, including postsecondary education, vocational education, integrated employment (including supported employment), and continuing and adult education, adult services, independent living, or community participation,” (20 U.S. C. 1401 (34)). Despite these mandates, studies have shown that youth with disabilities are having poor post-school outcomes when compared to their peers (Lindsay at el., 2019; Lipscomb et al., 2018; Rowe et al., 2021; Test, Mazzotti et al., 2009). Occupational therapy practitioners (OTP) are well situated to collaboratively work as part of the Individualized Education Plan (IEP) team with transition planning (Kardos & White, 2005). The OTP is distinctly qualified to assist the IEP team with developing goals, improving activities of daily living, assisting with staff and student training, and determining student occupational interests. Transition interventions are a widely variable and unregulated area of practice for school-based OTPs. Opportunity Café represents a solution to the problem of poor post-school outcomes for students with intellectual and developmental disabilities (IDD). This transition intervention applies evidenced based practices to guide education teams, students, and families through the transition planning process. It fulfills a need mandated by the IDEA for IEP teams to support the transition needs of students with IDD and provides an inclusive workplace to facilitate growth. Opportunity Café is a dynamic community-based replicable program that can impact student success. Program guidelines, methods for program dissemination, evaluation, and funding are discussed.

Investigating the neural activity elicited by induced memory recall

As the population ages, memory dysfunction is an increasingly prevalent issue resulting from neurodegenerative diseases such as Alzheimer’s disease and dementia. Despite the resulting decline in quality of life, current treatment options remain limited for this patient population. These treatment options do not target specific neural circuits, remaining agnostic to the cognitive processes underlying memory. To increase therapeutic specificity for neurodegenerative diseases, a fundamental understanding of memory processes is required. Memory recall is traditionally induced by external stimuli, though recent advances have allowed for memory reactivation through neural stimulation. The network effects of stimulation induced memory reactivation remain unknown because the dynamics resultant of such stimulation have not been well studied. In this work I first increase the fidelity of recording from multiple neuronal populations before developing novel methods for stimulating memory associated neurons and apply these techniques to investigate the downstream dynamics during induced memory reactivation. Investigation of expressing multiple fluorophores resulted in significantly improved co-expression over previously established methods, thus enabling high fidelity dual color imaging. After optimizing fluorophore expression for imaging, I next developed a novel method to conduct induced memory recall. Previously, only the blue-light activated channelrhodopsin had been used for induced memory reactivation, thus limiting experimental designs to those compatible with blue light stimulation. This work demonstrates the potential of ChrimsonR, a red-shifted opsin, to induce memory recall and shows that ChrimsonR induced memory recall can take place in both open field and head-fixed experimental paradigms. These results enable calcium imaging during induced memory recall, allowing for all-optical stimulation and recording of memory-associated neuron activity. Having developed the tools for observing neural activity during induced memory recall, I then applied this approach to observe the downstream network dynamics during upstream memory-associated neuron stimulation. This investigation uncovered the inhibitory mechanism responsible for reducing the firing of downstream non memory-associated cells during stimulation. This mechanism provides novel insight into the neural basis of induced memory recall. Additionally, this demonstration of calcium imaging during induced memory recall provides a proof of concept for future investigation of the dynamics of individual memories throughout the brain.2025-01-17T00:00:00

Methods for drawing causal inference from electronic health record data

Data collected in routine clinical practice, such as electronic health records (EHRs), enable clinical researchers to answer questions that cannot be addressed by randomized controlled trials (RCTs) and complement safety and efficacy data from RCTs in the post-approval phase. The use of EHRs in this way has grown tremendously over the past two decades. Although EHRs contain valuable information, there are several complexities inherent to these data because they are not collected specifically for research purposes. Robins’ generalized methods (g-methods), such as the parametric g-formula and inverse probability weighting (IPW), can estimate causal effects from EHR data while addressing time-varying confounding and treatment-confounder feedback. However, further investigation into the additional impact of informed presence, which occurs when the timing and frequency of clinic visits depends on an individual’s prognosis, on causal inference is warranted. Therefore, the overarching goal of this dissertation is to address methodological issues when applying g-methods to draw causal inference from EHR data. We first utilized real EHR data to answer a novel clinical question: What is the causal effect of hepatitis C virus (HCV) treatment on renal function? We used the target trial approach to design the study and the parametric g-formula to estimate the causal effect. We combined multiple analytical approaches to address time-varying confounding, treatment-confounder feedback and informed presence. To expand on these challenges, we next investigated how discretization of a continuous time scale, which is required to implement g-methods, impacts causal effect estimation with the parametric g-formula. We designed a rigorous simulation study to quantify properties of discretized estimators. In addition, we proposed two data adaptive methods to reduce bias due to discretization, and we applied these methods to a real EHR database. Lastly, we focused on confidence interval (CI) estimation for causal parameters in the context of EHR data. Typically, CIs around causal parameters are generated with the percentile bootstrap. This can be computationally expensive and vary in accuracy. We compared candidate bootstrap CI methods in the context of an IPW causal estimator and proposed practical, evidence-based guidance for selecting an appropriate bootstrap CI method. Considering the expanded application of causal inference methods to EHR data sources, this dissertation provides an essential perspective and functional solutions to problems that arise from the complexities innate to this source of data.2026-09-10T00:00:00

Thulium-doped ultrafast fiber laser system designs and dynamics

Thulium (Tm)-doped ultrafast fiber lasers with emission wavelengths around 2 μm are desirable sources for scientific, industrial, medical, and environmental applications and flexible testbeds for investigating nonlinear pulse dynamics. Although exceptional research attention has been drawn by Tm-doped ultrafast fiber lasers in recent years, their designs and dynamics are significantly less explored compared to other fiber laser systems. Despite the broad emission spectrum of Tm-doped fibers, power scaling of Tm-doped ultrafast fiber lasers has been limited at shorter wavelengths of their emission spectrum (<1920 nm) due to challenges including signal re-absorption. However, compact, high-energy ultrafast sources at these less-exploited wavelengths can enable various applications including nonlinear microscopy. Further, due to the challenges of implementing real-time characterization around 2 μm, transient nonlinear pulse dynamics have rarely been reported from Tm-doped ultrafast fiber lasers. Resolving these dynamics can not only provide insights into new laser designs but also guide the generation of novel pulse profiles which can benefit a wide range of applications depending on their parameters. This dissertation focuses on developing various novel Tm-doped ultrafast fiber laser systems with unprecedented performance: High-energy operation is demonstrated at less-exploited wavelengths and unique waveforms are generated with their nonlinear dynamics investigated in real-time. First, a high-energy (394-nJ) Tm-doped chirped-pulse-amplification fiber laser system is designed and optimized for operation at the wavelength of 1900 nm and supports the generation of 950-nm ultrashort (390-fs) pulses via frequency-doubling. The system represents the highest pulse-energy (138 nJ) in the femtosecond regime for any fiber-based systems around this wavelength to date, which can be highly attractive for two-photon microscopy with spatiotemporal-multiplexing. To gain deeper insights into the operation of ultrafast Tm-doped fiber lasers, various new nonlinear dynamics are investigated by a home-built real-time characterization setup based on dispersive Fourier transform for 2 μm pulses: A new mode-locking regime is demonstrated which can deliver both up-chirped and close-to-chirp-free dissipative pulses with a 10-fold difference in their pulse energies/durations, providing a versatile source that can switch between different pulse profiles. Following that, soliton molecules with unique partial spectral modulation patterns are synthesized based on two dissimilar pulses from the same cavity, which represent an interesting analogy to ‘heteronuclear’ chemical molecules and hold great potential for optical information processing. Further, mode-locking evolution between dissimilar coherent pulses are studied in Tm-doped ultrafast fiber lasers. Finally, combining both high-energy operation and novel waveform-generation, we present a Tm-doped fiber laser source delivering amplified (~ 200 nJ) noise-like pulses without requiring any feedback mechanism.2025-09-10T00:00:00

Comparative effectiveness on various Graves’ Disease treatment options

Graves’ Disease is an autoimmune disorder represented by the overproduction of thyroid hormones (hyperthyroidism). Graves’ Disease is more common among women of reproductive age, and genetic, endogenous, and environmental factors influence the pathogenesis of Graves’ Disease. Graves’ Disease presents with many clinical manifestations, such as tachycardia, fatigue, heat intolerance, palpitations, weight loss, muscle weakness, alterations in menstrual cycles, insomnia, hair loss, goiter, and others. Currently, there are three main treatment routes for Graves’ Disease: antithyroid drugs, radioactive iodine therapy, and thyroidectomy. Antithyroid drug therapy has a high relapse rate. At the same time, both radioactive iodine and thyroidectomy eradicate or surgically remove the tissue of the thyroid and lead to the consequence of developing another disease, hyperthyroidism, that requires a life-long supplementation of the thyroid replacement hormone, levothyroxine. Presently, investigations are focused on finding new therapeutics that can supplement existing treatments as a combination therapy that can lengthen the remission period after cessation of ATDs or conduction of RAI therapy. Future research is exploring treatment options that target different components of the immune system response pathway, the thyroid stimulating hormone receptor or thyrotropin receptor autoantibodies, that have the potential to cure Graves’ Disease

Cold-deciduous broadleaf phenology: monitoring using a geostationary satellite and predicting using trigger-less dynamic models

Vegetation phenology serves as a primary ecological indicator of climate change and has numerous ecosystem and climate impacts including nutrient cycling, energy budgets, and annual primary productivity. Phenology models, especially ones of autumnal processes like senescence, are typically based on correlations between environmental threshold triggers and transition dates and less is known about the specific mechanisms behind phenological events. Higher temporal resolution satellite data is needed to continue to identify the mechanisms at larger scales. It is unclear if a start of senescence (SOS) trigger is needed in mechanistic models and if decreased photosynthesis drives senescence. In this dissertation, I have two main themes: the first (Chapters 2 and 3) is to investigate the potential of the Geostationary Operational Environmental Satellite (GOES) to track changes to the phenology-sensitive Normalized Difference Vegetation Index (NDVI) and the second (Chapters 4 and 5) is to develop dynamic mechanistic models to predict senescence in cold-deciduous broadleaf forests. In Chapter 2, I created a novel statistical model to estimate daily NDVI with uncertainty from high temporal resolution (five - ten minutes) GOES-16 and -17 data. In Chapter 3, I used this data to track forest phenology by fitting double-logistic Bayesian models and comparing transition dates to those obtained from PhenoCams (digital cameras) and the Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to MODIS, GOES was more correlated with PhenoCam at the start and middle of spring. In Chapter 4, I developed a dynamic Bayesian model based on the physiological process of chlorophyll cycling that assumes a constant chlorophyll breakdown rate and synthesis dependent on temperature and photoperiod to predict senescence without including a SOS trigger or degree-day memory. I fit the model to greenness time series from 24 PhenoCam sites and found that for 49% of the site-years the model could predict SOS using only pre-SOS data. Furthermore, the model could regularly predict greenness at other sites better than their climatologies. In Chapter 5, I investigated if including photosynthetic feedbacks could improve the chlorophyll synthesis model at the canopy and leaf-levels. Testing this against leaf-level measurements of photosynthetic capacity and changes in chlorophyll concentrations of Fagus grandifolia and Quercus rubra demonstrated that the model fit improved at the canopy level, but not at the leaf-level. This dissertation illustrates that GOES can track phenology and that senescence in cold-deciduous broadleaf forests might not be initiated with a threshold-based trigger

Journal of African Christian Biography: v. 9, no. 1 (Jan. 2024) A quaterly publication of the Dictionary of African Christian Biography (www.DACB.org)

[African Christians from the DACB collection are showcased here as well. We also included a short section on the Gospel writer John Mark later in the issue. This excerpt from Oden’s 2011 book the African Memory of Mark: Reassessing Early Church Tradition (InterVarsity Press.) also includes an important historiographical concept—that of African memory and how it contrasts with Western memory. This volume includes three resources made available by CEAC to JACB readers: the abovementioned lecture by Andrews Walls, a transcript of an interview with Lamin Sanneh, and a selection from We Believe, an Early African commentary on the Nicene Creed. This work, written by Christopher Hall and commissioned by Oden, illustrates the intellectual and spiritual wisdom of the early African church. These resources affirm and complement Oden’s historiographical legacy.

A paradigm for exploring the impact of social isolation on olfactory sensitivity in mice

BACKGROUND: Mice have millions of olfactory sensory neurons that express one out of about 1,200 odorant receptor genes, giving them the ability to detect over 100,000 odorants. The activation of the sensory neurons is based on the different structural features of odor stimuli that each type of receptor has been genetically programmed to respond to. The activation at the level of the receptors corresponds to specific combinatorial codes for each odorant. Information from the receptors is sent to the olfactory bulb - where there is also a specific glomerular activation pattern for each odorant - and then to the olfactory tubercle, which plays a role in goal-directed behaviors and receives input from other parts of the brain that are essential for motivated behaviors. As a result of chronic social isolation, mice have been found to have impaired neurogenesis in their olfactory bulb, increased Tac2 expression, and decreased prefrontal cortex and hippocampal volumes. Since these neurological deficits alter the processing of olfactory information, using social isolation as a way to induce depression-like phenotypes in mice may provide insight into how changes in mental states are reflected in mouse behavior. OBJECTIVE: To determine the relationship between odor concentration and olfactory sensitivity in mice, and how the relationship is impacted by social isolation. METHODS: A total of 7 mice of either the C57BL/6J or tac1-cre strain aged 3-4 months were used. They underwent headplate surgery before going through habituation, after which they went through go/no-go task training. A custom 8-slot olfactometer and a behavioral box were used to run behavioral experiments, where up to 8 odorant tubes were placed in the olfactometer and mice were head-fixed in the behavioral box. Odorants were either blank odors made of only deionized water or different concentrations of n-butanol diluted in deionized water. With the blank odors as the “go” stimulus, the n-butanol odors as the “no-go” stimulus, and another blank odor as the “cheat” stimulus, mice went through go/no-go/cheat sessions over decreasing n-butanol concentrations. Python scripts were used to run experiments and collect data regarding the responses of the mice during each trial. RESULTS: By the end of the training period, mice were able to achieve an accuracy of at least 85% during go/no-go tasks. There is an overall downward trend in the performances of mice over decreasing n-butanol concentrations, but there were also large and unexpected improvements in performance at lower concentrations before and after isolation. There were many fluctuations in the average latencies to odor on incorrect no-go trials over decreasing n-butanol concentrations before and after the isolation period. Although sample sizes for each sex were too low for statistical analyses, preliminary data suggests that at low odor concentrations, social isolation might lead to enhanced performance in males and decreased performance in females. CONCLUSIONS: Mice can learn to associate novel odors with a water reward. Using social isolation as a way to induce depression in mice does not hinder mice from performing odor discrimination tasks. Conclusions cannot be made regarding the effect of social isolation on mouse olfactory sensitivity. Although there appears to be an improvement in performance as a result of isolation in male mice and a dampening of performance in female mice, further research will need to be conducted using larger sample sizes across both sexes

Evaluating the efficacy of a smartphone mental health app, mindLAMP, in reducing anxiety and depression symptoms

BACKGROUND: Despite the growing popularity and widespread adoption of mobile mental health apps, there is still insufficient high-quality evidence demonstrating their safety and efficacy. Aims: This exploratory analysis investigates the potential effect size of mindLAMP, a smartphone mental health app, on reducing symptoms of depression and anxiety by comparing the results of using mindLAMP in a control implementation and in a intervention implementation. METHODS: A total of 238 participants were eligible and finished the study in the control implementation, while 156 participants completed the study in the intervention implementation of the mindLAMP app. All participants (both groups) had access to the same in-app activities, including self-assessments and therapeutic interventions. RESULTS: After multiple imputation, analysis revealed significant minor effect sizes of Hedge’s g = 0.21 and Hedge’s g = 0.34 in the reduction of depression and anxiety symptoms respectively. CONCLUSIONS: MindLAMP demonstrates a promising potential in reducing symptoms of depression and anxiety. Additionally, this study underscores the adaptability, reusability, and scalability of smartphone apps, as they can be implemented in diverse settings. These results serve as a basis for further research to examine the effectiveness of not only mindLAMP but also other mental health apps in addressing symptoms of depression and anxiety

BlackWomen: keepers of the Democratic Party, democratic process and democracy

https://www.cambridge.org/core/journals/politics-and-gender/article/black-women-keepers-of-democracy-the-democratic-process-and-the-democratic-party/8372A741075812D0AC7ACDDA5C871E1