Spiritual aspects of living with infertility: synthesis of qualitative studies.

AIM: To identify the spiritual aspects of patients experiencing infertility and seek a deeper and broader meaning of the involuntary childlessness experience. BACKGROUND: Infertility can be the cause for a spiritual crisis among some couples. Those who endure this involuntary childlessness condition frequently experience contradictory feelings and needs. In this context, core aspects of spirituality such as meaning and purpose in life are often questioned. DESIGN: A review and synthesis of qualitative empirical research was undertaken in order to seek a deeper understanding of the spiritual aspects of patients' experiences of infertility. METHODS: An aggregative synthesis was conducted according to Saini & Shlonsky (2012), using thematic analysis. RESULTS: A total of 26 studies included female, male and couples. Settings revealed interviewees in different infertility phases such as diagnosis, Assisted Reproductive Technologies (ARTs) and following fertility treatments. Two main themes emerged: spiritual needs and spirituality as a coping resource for infertility. CONCLUSION: Infertility affects the holistic existence of the couples. This adversity awakens spiritual needs along with unmet needs of parenthood. Coping strategies incorporating spirituality can enhance the ability of couples to overcome childlessness and suffering. RELEVANCE TO CLINICAL PRACTICE: Infertile couples' experiences of infertility may offer an opportunity for spiritual care particularly related to the assessment of spiritual needs and the promotion of spiritual coping strategies. Effective holistic care should support couples in overcoming and finding meaning in this life and health condition

Development of a Novel Gene Therapy & Investigation of Synthetic Gene Therapy Delivery Systems

Thesis (Ph.D.)--University of Washington, 2023Dystroglycanopathies are a family of neuromuscular disorders, in which enzymes that glycosylate the protein dystroglycan and therefore play a key role in muscle structure, have reduced or nonexistent activity. For example, Limb-girdle muscular dystrophy type R9 is caused by a mutation in the FKRP gene, that encodes one of various enzymes that glycosylates the muscle membrane protein dystroglycan. The result is muscle degeneration and weakness, and palliative care is presently the only available treatment for dystroglycanopathy patients. We approached the need for treatment from a gene therapy perspective, focusing on two main ideas: 1) the development of a novel AAV gene therapy with which to treat limb-girdle muscular dystrophy type R9, and 2) the evolution of a synthetic nanoparticle with a long-range goal of improving tissue targeting and therapeutic gene delivery. Our research into AAV gene therapy led us to determine that removal of the untranslated regions of the FKRP gene increases protein expression. Following these in vitro results, we further verified the restoration of muscle strength and health in a 10-month-old LGMDR9 mouse model. Additionally, potential deleterious effects of AAV-FKRP gene therapy has created controversy in the field, and our data suggest that this is not an issue at the doses and vectors tested, as treated WT mice show no physiological evidence of harmful effects. However, AAVs are unavailable as a treatment for patients with preexisting immunity to the vector, such that alternative gene therapy delivery systems must be considered. Using customizable synthetic nanoparticles bearing a library of surface miniproteins that encapsulate their own mRNA, we selected for desired characteristics (i.e. tissue tropism) over multiple rounds of selection in vivo. This genetically coded library consisted of millions of nanoparticles, which we injected into mice for two rounds of in vivo selection for binding to specific cell types, such as skeletal muscle. Following each round, we sequenced nanoparticle mRNA in desired tissues, from which we then created a new library to be evaluated in vivo again. The results of this suggest common binding moieties in mini-protein binders on the surface of the nanoparticles. The goal is to identify synthetic particles bearing surface proteins that have high affinity for selected tissues that could eventually be used as gene therapy delivery mechanisms for neuromuscular disorders

Exploring AI Moral Reasoning

Artificial intelligence is being deployed in increasingly autonomous systems where it will have to make moral decisions. However, the rapid growth in artificial intelligence is outpacing the research in building explainable systems. In this paper, a number of problems around one facet of explainable artificial intelligence, training data, is explored. Possible solutions to these problems are presented. Additionally, the human decision-making process in unavoidable accident scenarios is explored through qualitative analysis of survey results

Electronic and Structural Properties of Silicene and Graphene Layered Structures

Graphene is a two-dimensional nanomaterial with useful and novel properties, but it is a material that does not integrate well with the current silicon microchip infrastructure. Silicene could solve this problem, as it is made of silicon yet retains the novel properties that make graphene desirable. This thesis will outline density functional calculations of a newly proposed structure involving the combination of these two materials. The structure includes silicene layered on graphene in such a manner that it composes a superlattice. It will be examined using the ab-initio density functional theory software Quantum Espresso. This superlattice structure is proposed to have an increase in electronic transport as well as higher binding energy versus standard graphene. Examination of the proposed superlattice is accomplished by using PBE-GGA functionals versus a previous LDA methodology. In conclusion, the results confirm the pattern of increased binding energy in the superlattice as well as increased electron transport, but the amount of increase in the electron transport is not the same as the accepted results. The desirable structural effects of graphene are maintained by the data

INNDiE: An Integrated Neural Network Development Environment

Modern machine learning methods are capable of tackling problems that are traditionally difficult for computers to solve. These methods present a steep learning curve with a wall of information and concepts for novices and often requires expensive computing resources to implement. INNDiE solves these problems by helping the user configure, train, and test neural networks with limited programming and machine learning background knowledge via a graphical user interface. INNDiE also supports training neural networks in the cloud which reduces amount of personal computing resources required for training these networks

A Study of Deep Neural Networks Transfer Learning For Fault Diagnosis Applications

Intelligent fault diagnosis utilizing deep learning algorithms has been widely investigated recently. Although previous results demonstrated excellent performance, features learned by Deep Neural Networks (DNN) are part of a large black box. Consequently, lack of understanding of underlying physical meanings embedded within the features can lead to poor performance when applied to different but related datasets i.e. transfer learning applications. This study will investigate the transfer learning performance of a Convolution Neural Network (CNN) considering 4 different operating conditions. Utilizing the Case Western Reserve University (CWRU) bearing dataset, the CNN will be trained to classify 12 classes. Each class represents a unique differentfault scenario with varying severity i.e. inner race fault of 0.007”, 0.014” diameter. Initially, zero load data will be utilized for model training and the model will be tuned until testing accuracy above 99% is obtained. The model performance will be evaluated by feeding vibration data collected when the load is varied to 1, 2 and 3 HP. Initial results indicated that the classification accuracy will degrade substantially. Hence, this paper will visualize convolution kernels in time and frequency domains and will investigate the influence of changing loads on fault characteristics, network classification mechanism and activation strength.</jats:p

Adaptive Programming Improves Outcomes in Drug Court: An Experimental Trial

Prior studies in drug courts have reported improved outcomes when participants were matched to schedules of judicial status hearings based on their criminological risk level. The current experiment determined whether incremental efficacy could be gained by periodically adjusting the schedule of status hearings and clinical case management sessions in response to participants\u27 ensuing performance in the program. The adjustments were made pursuant to a priori criteria specified in an adaptive algorithm. Results confirmed that participants in the full adaptive condition (n = 62) were more than twice as likely as those assigned to baseline matching only (n = 63) to be drug abstinent during the first 18 weeks of the program; however, graduation rates and the average time to case resolution were not significantly different. The positive effects of the adaptive program appear to have stemmed from holding noncompliant participants more accountable for meeting their attendance obligations in the program. Directions for future research and practice implications are discussed. © 2012 International Association for Correctional and Forensic Psychology