21147 research outputs found
Sort by
Investigating the Role of ebf3a in Craniofacial Development
Get PDFProper craniofacial development requires many genes. Single-cell RNA sequencing (scRNA-seq) allows us to identify genes expressed in precursor cells for craniofacial structures, but the function of many of these genes in craniofacial development has yet to be characterized. In our scRNA-seq of cranial neural crest cells (NCCs), which are precursor cells for the craniofacial skeleton, we found a gene called early B-cell factor 3 (ebf3a) that may be involved in craniofacial development. In our scRNA-seq data, ebf3a expression is restricted to cranial NCCs of the dorsal and ventral domains of pharyngeal arches 1 and 2. In humans, damaging variants in EBF3 are associated with facial dysmorphism. Based on these observations, we hypothesize that ebf3a is an important gene in craniofacial development. In this study, we begin to test this hypothesis by characterizing the expression pattern of ebf3a in cranial NCCs in zebrafish. Through fluorescence in situ hybridization, we confirmed that ebf3a is expressed in the cranial NCCs of the dorsal and ventral patterning domains of pharyngeal arches 1 and 2. This result will be a starting point for future experiments that will investigate the function of ebf3a in craniofacial development
From Cop to Clinician: Applying an Autoethnography Methodology to Identify the Effects of Law Enforcement Identity on the Development of Therapeutic Relationships with First Responders
No full textMany graduate academic programs at the master’s and doctoral levels offer generalist mental health training that includes specialization specific to services for military and/or public safety members and their families due to the unique factors that impact that segment of the culture which greatly impact psychotherapy and psychological assessment processes and outcomes. For example, increased emphasis on primary and secondary trauma, substance misuse, access to lethal means, hegemonic masculinity, and emotional compartmentalization (Courpasson & Monties, 2017). This paper explores why those in the law enforcement and first responder (LEFR) community appear primed to work especially well with first responder client populations where a past or present shared occupational identity becomes an implicit and/or explicit part of the therapeutic relationship (TR). While historical and current research has confirmed a strong relational bond between therapist and client is critical to the therapeutic process, to date, there is a paucity of research on the degree to which a shared law enforcement and/or first responder identity impacts the TR, treatment processes, or outcomes. To establish a foundation from which to begin work on articulating and studying this area, this paper provides case examples from the author, a former police officer and crisis negotiator turned clinical psychology doctoral intern, to identify clinical dynamics that ostensibly positively impact the TR when clinician and client share a LEFR identity. Inferences made from each case are then supported by the extant literature culminating in the introduction of several guiding principles for clinical practice with LEFR clients in the development of an optimal TR. Strategies to further build a research foundation related to LEFR identity and beneficial therapeutic relationships is provided
Visual Voice: Integrating Art into Psychotherapy for Military Trauma: A Case Study of a Combat Veteran
No full textThis aims of this paper are twofold: one, to evaluate the effectiveness of an integrative approach to therapy and two, to lend support to the use of art making as a successful adjunct to mental health treatment, particularly for Veterans and military personnel. This paper will examine these aims through the perspective of a case study; a therapy client who sought mental health treatment for over 3 years. Through the course of treatment, the client experienced different symptoms warranting unique approaches. Each modality or intervention was different and catered to the client’s specific needs at the given time. This paper follows his therapeutic journey including hardships, successes, and implications for further study
A Geospatial and Machine Learning Framework for Forecasting Ground Level Ozone Pollution
Get PDFThe major detrimental health effects of ground-level ozone (GLO) pollution make it imperative that both policy makers and ordinary citizens have access to high accuracy, high-resolution forecasts of their local area. Recently, advancements in computing power have made it possible to apply artificial intelligence (AI) techniques to a variety of big data modelling problems, including GLO forecasting and estimation. Of these AI methods, deep neural networks (DNN) have demonstrated the highest accuracy due to their ability extract non-linear relationships from high dimensional, noisy data inputs.
This research effort uses novel data sources, namely NOAA’s High Resolution Rapid Refresh (HRRR) meteorology model, and a long-short-term-memory (LSTM) neural network to forecast and interpolate ozone values at high spatiotemporal resolution of 1 hour and 3 km. The accuracies of the LSTM models are analyzed using lagged ozone at various forecast horizons and across the varying geographies of eleven ground sensors. I use Denver, Colorado as my study area due to its long-standing GLO pollution problem and relatively high density of EPA ozone monitoring stations
