4,916 research outputs found
Personality Dysfunction Manifest in Words : Understanding Personality Pathology Using Computational Language Analysis
Personality disorders (PDs) are some of the most prevalent and high-risk mental health conditions, and yet remain poorly understood. Today, the development of new technologies means that there are advanced tools that can be used to improve our understanding and treatment of PD. One promising tool – indeed, the focus of this thesis – is computational language analysis. By looking at patterns in how people with personality pathology use words, it is possible to gain access into their constellation of thinking, feelings, and behaviours. To date, however, there has been little research at the intersection of verbal behaviour and personality pathology. Accordingly, the central goal of this thesis is to demonstrate how PD can be better understood through the analysis of natural language. This thesis presents three research articles, comprising four empirical studies, that each leverage computational language analysis to better understand personality pathology. Each paper focuses on a distinct core feature of PD, while incorporating language analysis methods: Paper 1 (Study 1) focuses on interpersonal dysfunction; Paper 2 (Studies 2 and 3) focuses on emotion dysregulation; and Paper 3 (Study 4) focuses on behavioural dysregulation (i.e., engagement in suicidality and deliberate self-harm). Findings from this research have generated better understanding of fundamental features of PD, including insight into characterising dimensions of social dysfunction (Paper 1), maladaptive emotion processes that may contribute to emotion dysregulation (Paper 2), and psychosocial dynamics relating to suicidality and deliberate self-harm (Paper 3) in PD. Such theoretical knowledge subsequently has important implications for clinical practice, particularly regarding the potential to inform psychological therapy. More broadly, this research highlights how language can provide implicit and unobtrusive insight into the personality and psychological processes that underlie personality pathology at a large-scale, using an individualised, naturalistic approach
NURSING AND MIDWIFERY STUDENTS’ LENS: CONNECTING THEORETICAL KNOWLEDGE WITH CLINICAL PRACTICE: AN INTERPRETATIVE PHENOMENOLOGICAL STUDY
Aim: To explore and critically analyse the strategies employed by final-year BSc pre-registration nursing and midwifery students at an inner London university to connect theoretical knowledge with clinical practice, to promote their learning and professional development. Background: Navigating the theory-practice gap has been a significant challenge for nursing and midwifery students. While there are many perspectives from academics and clinicians, how theoretical knowledge is connected with clinical practice is rarely discussed and studied from the students’ perspectives. Design: Interpretative phenomenological analysis was used to understand nursing and midwifery students' experiences in connecting theoretical knowledge with clinical practice. Rather than attempting to establish objective truth, this thesis focused on participants’ subjective experiences. Method: This study employed a qualitative research design. The data was obtained using semi-structured interviews and analysed using an inductive approach. The study population included (n=12) pre-registration nursing and midwifery students enrolled on a Bachelor of Science programs. Findings: Four themes emerged (1) Complexity of embodied knowledge; (2) Sensing the meaning of personal and professional learning; (3) Demographic attributes and self-understanding; (4) Sense-making of COVID-19. Conclusion: The process by which pre-registration nursing and midwifery students connect theoretical knowledge with clinical practice is complex and multifaceted. It intersects with other factors and cannot be understood in isolation. This interconnectedness necessitates a thorough examination of all the variables involved
Natural and Technological Hazards in Urban Areas
Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events
Combined Nutrition and Exercise Interventions in Community Groups
Diet and physical activity are two key modifiable lifestyle factors that influence health across the lifespan (prevention and management of chronic diseases and reduction of the risk of premature death through several biological mechanisms). Community-based interventions contribute to public health, as they have the potential to reach high population-level impact, through the focus on groups that share a common culture or identity in their natural living environment. While the health benefits of a balanced diet and regular physical activity are commonly studied separately, interventions that combine these two lifestyle factors have the potential to induce greater benefits in community groups rather than strategies focusing only on one or the other. Thus, this Special Issue entitled “Combined Nutrition and Exercise Interventions in Community Groups” is comprised of manuscripts that highlight this combined approach (balanced diet and regular physical activity) in community settings. The contributors to this Special Issue are well-recognized professionals in complementary fields such as education, public health, nutrition, and exercise. This Special Issue highlights the latest research regarding combined nutrition and exercise interventions among different community groups and includes research articles developed through five continents (Africa, Asia, America, Europe and Oceania), as well as reviews and systematic reviews
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Using machine learning to predict pathogenicity of genomic variants throughout the human genome
Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität.
Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores.
Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt.
Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity.
Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants.
The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency.
In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org
Soundscape in Urban Forests
This Special Issue of Forests explores the role of soundscapes in urban forested areas. It is comprised of 11 papers involving soundscape studies conducted in urban forests from Asia and Africa. This collection contains six research fields: (1) the ecological patterns and processes of forest soundscapes; (2) the boundary effects and perceptual topology; (3) natural soundscapes and human health; (4) the experience of multi-sensory interactions; (5) environmental behavior and cognitive disposition; and (6) soundscape resource management in forests
Tradition and Innovation in Construction Project Management
This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings
The Efficacy of Analgesic Subdissociative Dose Ketamine in Trauma Casualties Treated by U.S. Military Special Operations Medical Professionals in a Prehospital Environment
Research Focus. This study’s main objective was to determine the efficacy of sub-dissociative ketamine to reduce the pain of trauma casualties treated by U.S. military medical professionals in a prehospital environment evidenced by the 0–10 numeric rating scale (NRS) for pain. Research Methods. This quantitative study was accomplished using a pragmatic approach integrating social cognitive theory complemented by mixing methods using qualitative phenomenological influence through narrative inquiry. This exploratory retrospective, cross-sectional study, utilizing a quasi-experimental pretest-posttest design, used deidentified sample data (N = 47) for secondary analysis from U.S. Special Operations medical providers and were included in a casualty data collection tool. Quantitative study inclusion criteria were adult casualties treated by U.S. military medical professionals with ketamine in a prehospital environment, had documented injury data, and had both pre- and post-ketamine pain scores. Descriptive statistics, followed by inferential statistical analyses using Shapiro-Wilkes, Wilcoxon Signed Rank, Spearman rho, and Kruskal Wallis tests were used. Additionally, phenomenology guided the analysis of two (n = 2) case studies. In vivo coding was used to develop themes and subthemes. Case studies collected from U.S. military medical professionals provided qualitative insight that reinforced the quantitative data and provided clinical validity to the study. Research Results/Findings. The study showed safe, efficacious use of analgesic sub-disociative ketamine use in prehospital trauma casualties relative to the 0–10 NRS for pain. The median reported pre-ketamine pain scale for casualties was 9.0 (IQR 2). The median post-ketamine pain scale was 0.0 (IQR 3). The mean total dosage of ketamine administered was 98.19 mg (SE = 9.545). There were 6 (12.8%) casualties who experienced side effects from ketamine that were neither permanent nor life-threatening. The case studies provided the human aspect of the study, reinforced the quantitative data, and provided clinical validity. Post-ketamine pain scores were better than pre-ketamine pain scores. Higher dosages of ketamine provided greater pain relief. No life threatening nor adverse drug reactions were found in this study. Conclusions From Research. This study demonstrated a safe, efficacious analgesic ketamine use in prehospital trauma casualties used by U.S. military special operations medical professionals relative to the 0–10 NRS for pain. The results of this study may inform medical practitioners and policymakers regarding the efficacy of analgesic ketamine in a prehospital environment, aid in making informed treatment decisions regarding trauma casualties, and provide facts for updating and improving clinical practice guidelines and policies focused on the U.S. military. Advancing the understanding to promote better prehospital pain management guidelines, procedures, and practices is essential. Education efforts will make medical professionals aware of the importance of analgesic ketamine for trauma casualties in a prehospital environment.
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