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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
A Phenomenological Study of How Active Engagement in Black Greek Letter Sororities Influences Christian Members\u27 Spiritual Growth
This phenomenological study explored how being part of a Black Greek Letter. Organization (BGLO) sorority impacts the spiritual growth of its Christian members. One of the issues explored was the influence relationships within these sororities have on members striving to be like Christ. There is a dichotomy of perspectives regarding Black Greek Letter Organizations (BGLOs). They have a significant role in the Black community as organizations that foster leadership, philanthropy, and sisterhood and promote education. They are admired on and off college campuses and in the broader community in graduate chapters. The objective of phenomenology is to describe phenomena of spiritual growth among Christian sorority members from the life experiences of those who live them; that premise guided the interviews conducted for this study. The results found that active engagement in a BGLO sorority positively impacts its members\u27 spiritual growth. From the emotional stories of sisterhood, service, and devotion to prayer, their experiences evidenced strengthened walks of faith. This study contrasts the Anti-BGLO narrative as a testament to these organizations\u27 legacy and practices deeply grounded in the church
MUFFLE: Multi-Modal Fake News Influence Estimator on Twitter
To alleviate the impact of fake news on our society, predicting the popularity of fake news posts on social media is a crucial problem worthy of study. However, most related studies on fake news emphasize detection only. In this paper, we focus on the issue of fake news influence prediction, i.e., inferring how popular a fake news post might become on social platforms. To achieve our goal, we propose a comprehensive framework, MUFFLE, which captures multi-modal dynamics by encoding the representation of news-related social networks, user characteristics, and content in text. The attention mechanism developed in the model can provide explainability for social or psychological analysis. To examine the effectiveness of MUFFLE, we conducted extensive experiments on real-world datasets. The experimental results show that our proposed method outperforms both state-of-the-art methods of popularity prediction and machine-based baselines in top-k NDCG and hit rate. Through the experiments, we also analyze the feature importance for predicting fake news influence via the explainability provided by MUFFLE
Early Sibling Play Interactions as a Source of Developmental Support for Toddlers: Observation of Young Children\u27s Developmental Support During Play with Toddler Siblings
The sibling relationship is a unique and important context for infant and early child development. Despite the important role of siblings and the unique aspects of the sibling relationship, sibling interactions are largely overlooked by scholars as a resource of potential developmental support. Identifying and fostering developmentally supportive interaction (DSI) behaviors in sibling relationships may expand available supports for children’s early development and may also support family well-being.
This study used a sample of 15 child-toddler sibling pairs to identify DSI behaviors in interactions between young children and their toddler-aged siblings, determine if and how well DSI behaviors could be observed, determine the similarities and differences between DSIs in child-toddler and caregiver-child interactions, and identify child factors that were associated with DSI behaviors. Caregivers completed a questionnaire online in Qualtrics, answering questions about their children and family, their children’s sibling relationship, and their children’s play skills. Caregivers then recorded and submitted 10-minute videos of their young children playing together, these videos were coded by research assistants who were trained to identify DSI behaviors using an established measure of caregiver-child interaction quality, the Parenting Interactions with Children: Checklist of Observations Linked to Outcomes (PICCOLO). Older siblings across the 15 sibling pairs were observed engaging in each DSI behavior and research assistants were able to reliably code videos for behaviors in the Affection, Responsiveness, and Encouragement domains. When compared to an adult comparison sample, DSI behaviors in young sibling interactions were less frequent, less complex, and lower quality than in adult-child interactions. Younger brothers received more encouragement support from older siblings than younger sisters. Older children who were older siblings provided more developmental support than younger children who were older siblings. Older siblings interacted with more warmth when the age gap was larger than when it was smaller. Older siblings reported by their caregivers to have higher levels of empathy/concern engaged in fewer DSI behaviors and older siblings reported by their caregivers to have higher levels of conflict/aggression engaged in more DSI behaviors. These results may provide guidance for supporting developmentally supportive sibling interactions at home and in intervention
Socio-endocrinology revisited: New tools to tackle old questions
Animals’ social environments impact their health and survival, but the proximate links between sociality and fitness are still not fully understood. In this thesis, I develop and apply new approaches to address an outstanding question within this sociality-fitness link: does grooming (a widely studied, positive social interaction) directly affect glucocorticoid concentrations (GCs; a group of steroid hormones indicating physiological stress) in a wild primate? To date, negative, long-term correlations between grooming and GCs have been found, but the logistical difficulties of studying proximate mechanisms in the wild leave knowledge gaps regarding the short-term, causal mechanisms that underpin this relationship. New technologies, such as collar-mounted tri-axial accelerometers, can provide the continuous behavioural data required to match grooming to non-invasive GC measures (Chapter 1). Using Chacma baboons (Papio ursinus) living on the Cape Peninsula, South Africa as a model system, I identify giving and receiving grooming using tri-axial accelerometers and supervised machine learning methods, with high overall accuracy (~80%) (Chapter 2). I then test what socio-ecological variables predict variation in faecal and urinary GCs (fGCs and uGCs) (Chapter 3). Shorter and rainy days are associated with higher fGCs and uGCs, respectively, suggesting that environmental conditions may impose stressors in the form of temporal bottlenecks. Indeed, I find that short days and days with more rain-hours are associated with reduced giving grooming (Chapter 4), and that this reduction is characterised by fewer and shorter grooming bouts. Finally, I test whether grooming predicts GCs, and find that while there is a long-term negative correlation between grooming and GCs, grooming in the short-term, in particular giving grooming, is associated with higher fGCs and uGCs (Chapter 5). I end with a discussion on how the new tools I applied have enabled me to advance our understanding of sociality and stress in primate social systems (Chapter 6)
International services marketing : an integrative assessment of the literature 国际化服务行业市场营销 : 综合文献分析
We are tremendously grateful to Professor S. Tamer Cavusgil for his valuable support and guidance. We cordially thank the Editor Professor Levent Altinay and three anonymous reviewers for their constructive comments and feedback. We also thank Christian Sturmaier for providing assistance.Peer reviewedPublisher PD
Network analysis of the human structural connectome including the brainstem: a new perspective on consciousness
The underlying anatomical structure is fundamental to the study of brain
networks and is likely to play a key role in the generation of conscious
experience. We conduct a computational and graph-theoretical study of the human
structural connectome incorporating a variety of subcortical structures
including the brainstem, which is typically not considered in similar studies.
Our computational scheme involves the use of Python DIPY and Nibabel libraries
to develop an averaged structural connectome comprised of 100 healthy adult
subjects. We then compute degree, eigenvector, and betweenness centralities to
identify several highly connected structures and find that the brainstem ranks
highest across all examined metrics. Our results highlight the importance of
including the brainstem in structural network analyses. We suggest that
structural network-based methods can inform theories of consciousness, such as
global workspace theory (GWT), integrated information theory (IIT), and the
thalamocortical loop theory.Comment: 23 pages, 5 figure
Linguistic- and Acoustic-based Automatic Dementia Detection using Deep Learning Methods
Dementia can affect a person's speech and language abilities, even in the early stages. Dementia is incurable, but early detection can enable treatment that can slow down and maintain mental function. Therefore, early diagnosis of dementia is of great importance. However, current dementia detection procedures in clinical practice are expensive, invasive, and sometimes inaccurate. In comparison, computational tools based on the automatic analysis of spoken language have the potential to be applied as a cheap, easy-to-use, and objective clinical assistance tool for dementia detection.
In recent years, several studies have shown promise in this area. However, most studies focus heavily on the machine learning aspects and, as a consequence, often lack sufficient incorporation of clinical knowledge. Many studies also concentrate on clinically less relevant tasks such as the distinction between HC and people with AD which is relatively easy and therefore less interesting both in terms of the machine learning and the clinical application.
The studies in this thesis concentrate on automatically identifying signs of neurodegenerative dementia in the early stages and distinguishing them from other clinical, diagnostic categories related to memory problems: (FMD, MCI, and HC). A key focus, when designing the proposed systems has been to better consider (and incorporate) currently used clinical knowledge and also to bear in mind how these machine-learning based systems could be translated for use in real clinical settings.
Firstly, a state-of-the-art end-to-end system is constructed for extracting linguistic information from automatically transcribed spontaneous speech. The system's architecture is based on hierarchical principles thereby mimicking those used in clinical practice where information at both word-, sentence- and paragraph-level is used when extracting information to be used for diagnosis. Secondly, hand-crafted features are designed that are based on clinical knowledge of the importance of pausing and rhythm. These are successfully joined with features extracted from the end-to-end system. Thirdly, different classification tasks are explored, each set up so as to represent the types of diagnostic decision-making that is relevant in clinical practice. Finally, experiments are conducted to explore how to better deal with the known problem of confounding and overlapping symptoms on speech and language from age and cognitive decline. A multi-task system is constructed that takes age into account while predicting cognitive decline. The studies use the publicly available DementiaBank dataset as well as the IVA dataset, which has been collected by our collaborators at the Royal Hallamshire Hospital, UK. In conclusion, this thesis proposes multiple methods of using speech and language information for dementia detection with state-of-the-art deep learning technologies, confirming the automatic system's potential for dementia detection
The Role of the Metabolome in the Development of Gestational Diabetes Mellitus in High-Risk Minority Women: A Causal Investigation
Gestational Diabetes Mellitus (GDM) is the most common pregnancy complication worldwide. However, GDM prevalence is substantially lower in white Europeans (WEs) compared to other ethnicities, especially South Asians (SAs) who experience the highest risk. Globally, healthy diet promotion is the mainstay in GDM prevention, however current guidelines are predominantly based on evidence from WEs. Furthermore, metabolic factors responsible for the disparities in prevalence are unknown but may offer guidance for improved prevention and management.
This project aimed to (i) assess the association between diet and GDM across ethnic groups, (ii) determine if distinct metabolic profiles characterise GDM in SAs and WEs, and (iii) evaluate the presence of ethnic-specific causal associations between metabolites and gestational dysglycemia. Aims (ii) and (iii) utilised data from the Born in Bradford cohort (mean gestational age 26.1 weeks).
First, through a systematic review of observational and randomised studies, pre-pregnancy diet was found to associate with GDM in WEs, but not in Asians. Secondly, the multivariate analyses of metabolites identified 7 metabolites that were characteristic of GDM in both ethnicities, with an additional 6 characteristic in WEs only. Finally, through Mendelian Randomisation (MR) analyses, 14 metabolites associated with pregnancy dysglycemia in WEs and 11 in SAs. No metabolites were identified in both ethnicities. Cholesterols and fatty acids were the most commonly identified classes identified in WEs and SAs, respectively.
This project demonstrated (i) inconsistencies in the association between diet and GDM across ethnicities (ii) distinct metabolic profiles that associate with GDM in WEs and SAs and offers and supports the need for ethnic-specific manage GDM management strategies. In high-risk SAs, fatty acids may be the most important predictors of GDM. Future work should evaluate the role of pre-pregnancy fatty acid intake in GDM development in SAs to aid in the development of culturally tailored dietary interventions
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