3,734 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Improving diagnostic procedures for epilepsy through automated recording and analysis of patients’ history

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    Transient loss of consciousness (TLOC) is a time-limited state of profound cognitive impairment characterised by amnesia, abnormal motor control, loss of responsiveness, a short duration and complete recovery. Most instances of TLOC are caused by one of three health conditions: epilepsy, functional (dissociative) seizures (FDS), or syncope. There is often a delay before the correct diagnosis is made and 10-20% of individuals initially receive an incorrect diagnosis. Clinical decision tools based on the endorsement of TLOC symptom lists have been limited to distinguishing between two causes of TLOC. The Initial Paroxysmal Event Profile (iPEP) has shown promise but was demonstrated to have greater accuracy in distinguishing between syncope and epilepsy or FDS than between epilepsy and FDS. The objective of this thesis was to investigate whether interactional, linguistic, and communicative differences in how people with epilepsy and people with FDS describe their experiences of TLOC can improve the predictive performance of the iPEP. An online web application was designed that collected information about TLOC symptoms and medical history from patients and witnesses using a binary questionnaire and verbal interaction with a virtual agent. We explored potential methods of automatically detecting these communicative differences, whether the differences were present during an interaction with a VA, to what extent these automatically detectable communicative differences improve the performance of the iPEP, and the acceptability of the application from the perspective of patients and witnesses. The two feature sets that were applied to previous doctor-patient interactions, features designed to measure formulation effort or detect semantic differences between the two groups, were able to predict the diagnosis with an accuracy of 71% and 81%, respectively. Individuals with epilepsy or FDS provided descriptions of TLOC to the VA that were qualitatively like those observed in previous research. Both feature sets were effective predictors of the diagnosis when applied to the web application recordings (85.7% and 85.7%). Overall, the accuracy of machine learning models trained for the threeway classification between epilepsy, FDS, and syncope using the iPEP responses from patients that were collected through the web application was worse than the performance observed in previous research (65.8% vs 78.3%), but the performance was increased by the inclusion of features extracted from the spoken descriptions on TLOC (85.5%). Finally, most participants who provided feedback reported that the online application was acceptable. These findings suggest that it is feasible to differentiate between people with epilepsy and people with FDS using an automated analysis of spoken seizure descriptions. Furthermore, incorporating these features into a clinical decision tool for TLOC can improve the predictive performance by improving the differential diagnosis between these two health conditions. Future research should use the feedback to improve the design of the application and increase perceived acceptability of the approach

    Using machine learning to predict pathogenicity of genomic variants throughout the human genome

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    GeschĂ€tzt mehr als 6.000 Erkrankungen werden durch VerĂ€nderungen 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 begĂŒnstigen. All diese Prozesse mĂŒssen ĂŒberprĂŒft werden, um die zum beschriebenen PhĂ€notyp 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 PathogenitĂ€t. Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier prĂ€sentiere 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 fĂŒr 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 AllelhĂ€ufigkeit 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 verfĂŒgbar.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

    Complexity Science in Human Change

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    This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

    Science and Innovations for Food Systems Transformation

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    This Open Access book compiles the findings of the Scientific Group of the United Nations Food Systems Summit 2021 and its research partners. The Scientific Group was an independent group of 28 food systems scientists from all over the world with a mandate from the Deputy Secretary-General of the United Nations. The chapters provide science- and research-based, state-of-the-art, solution-oriented knowledge and evidence to inform the transformation of contemporary food systems in order to achieve more sustainable, equitable and resilient systems

    Weather or not? The role of international sanctions and climate on food prices in Iran

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    IntroductionThe scarcity of resources have affected food production, which has challenged the ability of Iran to provide adequate food for the population. Iterative and mounting sanctions on Iran by the international community have seriously eroded Iran's access to agricultural technology and resources to support a growing population. Limited moisture availability also affects Iran's agricultural production. The aim of this study was to analyze the influence of inflation, international sanctions, weather disturbances, and domestic crop production on the price of rice, wheat and lentils from 2010 to 2021 in Iran.MethodData were obtained from the statistical yearbooks of the Ministry of Agriculture in Iran, Statistical Center of Iran, and the Central Bank of Iran. We analyzed econometric measures of food prices, including CPI, food inflation, subsidy reform plan and sanctions to estimate economic relationships. After deflating the food prices through CPI and detrending the time series to resolve the non-linear issue, we used monthly Climate Hazards group Infrared Precipitation with Stations (CHIRPS) precipitation data to analyze the influence of weather disturbances on food prices.Results and discussionThe price of goods not only provides an important indicator of the balance between agricultural production and market demand, but also has strong impacts on food affordability and food security. This novel study used a combination of economic and climate factors to analyze the food prices in Iran. Our statistical modeling framework found that the monthly precipitation on domestic food prices, and ultimately food access, in the country is much less important than the international sanctions, lowering Iran's productive capability and negatively impacting its food security

    Role of Community Pharmacists in Optimizing Opioid Therapy for Chronic Non-malignant Pain Patients in Pakistan

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    Background Chronic Non-Malignant Pain (CNMP) is one of the most common conditions in both high-income countries (HICs) and low middle-income countries (LMICs). CNMP can have a substantial impact on people, communities and puts an economic burden on the society. Opioids are commonly used worldwide for CNMP management. However, their use might have contributed to opioid use-related harm and increased mortality. There have been substantial reports of opioid diversion and misuse in Pakistan. Community pharmacists (CPs) might be able to help optimise the use of opioids in CNMP management but there is no regularised CP-based patient-centred services in Pakistan. Aim and objectives The aim of the study was to explore the potential role of CPs in opioid optimisation in people with CNMP in Pakistan. Objectives of this study included systematically exploring the role of CPs in opioid optimisation in CNMP management, exploring the current use of opioids in Pakistan and identify the role CPs can play to optimise the use of opioids in people with CNMP and explore factors that might influence the development and delivery of role of CPs in an opioid optimisation service. Methodology Conceptual guiding framework The UK Medical Research Council (MRC) guidelines for complex interventions was used as conceptual guiding framework for exploring the aim of this study. The data was collected in two phases: Phase 1: Systematic review The systematic review followed the 27-item PRISMA guidelines and studies between January 1990-June 2020 were included. All studies where pharmacists in ambulatory care settings helped in optimisation of opioids in the treatment of CNMP, as individuals or as part of a team were included and were descriptively synthesized. Phase 2: In-depth qualitative methods (Interviews, focus groups and case studies) Two studies were conducted to collect the data. The first study constituted of semi-structured interviews and focus groups from four stakeholder groups: pharmacy policy makers, people with CNMP, doctors and CPs. The second study included non-participant multiple case study observations in six community pharmacies. The data in phase two was collected from November 2019–December 2020. Data analysis Interviews and focus groups with all stakeholders in phase two were inductively analysed using reflexive thematic analysis using N-Vivo 12. For case studies, reflexive thematic analysis as well a cross case synthesis method using explanation building technique was used to analyse the data across six cases. Data triangulation Findings from both studies in phase two were triangulated using two steps; comparing, and categorising. Any code or subtheme about a particular phenomenon or a theme across both studies were brought together using one sheet one paper data visualisation technique. Diagrammatic model development Schematic diagrammatic models were developed in this thesis usual process mapping data visualisation technique. This was done selecting and representing events and situating data in time/process meaningfully. Results In this study 98 stakeholders participated (38 females). A total of 240 hours (40 hours/case) were observed during a six-week period of non-participant observational case studies in six community pharmacies. Phase 1: Systematic review In the systematic review 14 studies were included in the final data synthesis (total number of participants n=1175). Interventions by pharmacists decreased opioid dose in four studies and improved patient opioid safety in five studies. Qualitative studies showed positive perception of stakeholders for the development of CP role in optimisation of opioid therapy for people with CNMP. No actual interventions involving CPs or studies form LMICs were identified. Phase 2: Focus groups, interviews and case studies These studies were able to identify reasons contributing towards the non-availability of opioids, factors contributing towards the unsafe use of opioids and certain actions that can be taken by CPs to overcome existing barriers contributing to the unsafe use of opioids and help optimise their use. These studies also highlight advantages and benefits of developing the role of CPs in optimising opioid use in people with CNMP. In addition, these studies identified multiple level barriers and facilitators for the development and delivery of CP opioid service. They also helped identify strategies to overcome the perceived barriers and to leverage the facilitators in order to develop and deliver an opioid service. Data visualisation helped develop diagrammatic models after triangulation. Firstly, a logic model was developed that identifies the possible actions that can be undertaken by CPs to help overcome the barriers causing/contributing towards unsafe use of opioids. Secondly a CP proposed opioid service model was developed, tailored to the health system of Pakistan, that is anticipated to help optimise the use of opioids in people with CNMP. Finally, a CP opioid service logic model was developed that shows strategies perceived to develop and improve the capability of CPs to deliver the opioid service and help optimise the use of opioids. Conclusion This thesis explored the process, the need and service delivery of CP role in opioid optimisation. This thesis identified factors contributing towards unsafe use of opioids (logic model), what can be done by CPs to help people use opioids in an optimised manner (CP proposed service model), what challenges might CPs face while delivering the service and what can be done to improve the development and delivery of a CP opioid service for people with CNMP using opioids (CP service logic model). The findings provide policy makers with possible steps and actions that may be followed to facilitate the development and delivery of a CP service for opioid optimisation in Pakistan

    Blockchain Technology: Disruptor or Enhnancer to the Accounting and Auditing Profession

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    The unique features of blockchain technology (BCT) - peer-to-peer network, distribution ledger, consensus decision-making, transparency, immutability, auditability, and cryptographic security - coupled with the success enjoyed by Bitcoin and other cryptocurrencies have encouraged many to assume that the technology would revolutionise virtually all aspects of business. A growing body of scholarship suggests that BCT would disrupt the accounting and auditing fields by changing accounting practices, disintermediating auditors, and eliminating financial fraud. BCT disrupts audits (Lombard et al.,2021), reduces the role of audit firms (Yermack 2017), undermines accountants' roles with software developers and miners (Fortin & Pimentel 2022); eliminates many management functions, transforms businesses (Tapscott & Tapscott, 2017), facilitates a triple-entry accounting system (Cai, 2021), and prevents fraudulent transactions (Dai, et al., 2017; Rakshit et al., 2022). Despite these speculations, scholars have acknowledged that the application of BCT in the accounting and assurance industry is underexplored and many existing studies are said to lack engagement with practitioners (Dai & Vasarhelyi, 2017; Lombardi et al., 2021; Schmitz & Leoni, 2019). This study empirically explored whether BCT disrupts or enhances accounting and auditing fields. It also explored the relevance of audit in a BCT environment and the effectiveness of the BCT mechanism for fraud prevention and detection. The study further examined which technical skillsets accountants and auditors require in a BCT environment, and explored the incentives, barriers, and unintended consequences of the adoption of BCT in the accounting and auditing professions. The current COVID-19 environment was also investigated in terms of whether the pandemic has improved BCT adoption or not. A qualitative exploratory study used semi-structured interviews to engage practitioners from blockchain start-ups, IT experts, financial analysts, accountants, auditors, academics, organisational leaders, consultants, and editors who understood the technology. With the aid of NVIVO qualitative analysis software, the views of 44 participants from 13 countries: New Zealand, Australia, United States, United Kingdom, Canada, Germany, Italy, Ireland, Hong Kong, India, Pakistan, United Arab Emirates, and South Africa were analysed. The Technological, Organisational, and Environmental (TOE) framework with consequences of innovation context was adopted for this study. This expanded TOE framework was used as the theoretical lens to understand the disruption of BCT and its adoption in the accounting and auditing fields. Four clear patterns emerged. First, BCT is an emerging tool that accountants and auditors use mainly to analyse financial records because technology cannot disintermediate auditors from the financial system. Second, the technology can detect anomalies but cannot prevent financial fraud. Third, BCT has not been adopted by any organisation for financial reporting and accounting purposes, and accountants and auditors do not require new skillsets or an understanding of the BCT programming language to be able to operate in a BCT domain. Fourth, the advent of COVID-19 has not substantially enhanced the adoption of BCT. Additionally, this study highlights the incentives, barriers, and unintended consequences of adopting BCT as financial technology (FinTech). These findings shed light on important questions about BCT disrupting and disintermediating auditors, the extent of adoption in the accounting industry, preventing fraud and anomalies, and underscores the notion that blockchain, as an emerging technology, currently does not appear to be substantially disrupting the accounting and auditing profession. This study makes methodological, theoretical, and practical contributions. At the methodological level, the study adopted the social constructivist-interpretivism paradigm with an exploratory qualitative method to engage and understand BCT as a disruptive innovation in the accounting industry. The engagement with practitioners from diverse fields, professions, and different countries provides a distinctive and innovative contribution to methodological and practical knowledge. At the theoretical level, the findings contribute to the literature by offering an integrated conceptual TOE framework. The framework offers a reference for practitioners, academics and policymakers seeking to appraise comprehensive factors influencing BCT adoption and its likely unintended consequences. The findings suggest that, at present, no organisations are using BCT for financial reporting and accounting systems. This study contributes to practice by highlighting the differences between initial expectations and practical applications of what BCT can do in the accounting and auditing fields. The study could not find any empirical evidence that BCT will disrupt audits, eliminate the roles of auditors in a financial system, and prevent and detect financial fraud. Also, there was no significant evidence that accountants and auditors required higher-level skillsets and an understanding of BCT programming language to be able to use the technology. Future research should consider the implications of an external audit firm as a node in a BCT network on the internal audit functions. It is equally important to critically examine the relevance of including programming languages or codes in the curriculum of undergraduate accounting students. Future research could also empirically evaluate if a BCT-enabled triple-entry system could prevent financial statements and management fraud

    Examining the Link between Personality Traits, Cognitive Performance, and Consecutive Interpreting

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    Interpreting is a highly complex activity that not only demands proficient linguistic expertise, but also non-linguistic abilities such as non-linguistic cognitive performance (Macnamara, 2012; Riesbeck et al., 1978; Wang, 2004). In addition to this, individual differences in personality may also play a potential role in the interpreter's ability to perform their job (Barrick & Mount, 1991; Rothmann & Coetzer, 2003). The current study sought to examine whether there is a relationship between personality traits, cognitive ability, and consecutive interpreting. The five-factor model of personality (Costa & McCrae, 1988) was used to examine the personality of participants with its five categories of personality type (Openness to Experience; Conscientiousness; Extraversion; Agreeableness; and Neuroticism), and five cognitive ability tasks (Working Memory; Attentional Control; Multi-tasking; Speed of Information Processing; and Psychological Endurance) were chosen to examine their potential relationship with interpreting ability. To fulfill this goal, an empirical study was conducted, collecting data from 80 participants in total (40 with consecutive interpreting backgrounds in the experimental group and 40 without interpreting foundations as a control group). Data was collected using online questionnaires and a set of cognitive tasks. The three online questionnaires, the Big Five (Goldberg, 1992), Attentional Control Scale (Derryberry & Reed, 2002) and Psychological Endurance Scale (Hamby et al., 2015) were used to examine participants’ personality, Attentional Control and Psychological Endurance respectively, whilst the objective cognitive tasks were designed to measure participant Working Memory, Multi-tasking ability and Speed of Information Processing using the Listening Span Test (Liu et al., 2004), Digits Symbol Substitution Test (Kaufman & Lichtenberger, 2006; Wechsler, 1939) and Linguistic Dual Task (Stachowiak, 2015; Meyer & Kieras, 1997) respectively. The main findings of the current results were: firstly, a significant difference was found in cognitive abilities between experimental and control group in the areas of Working Memory, Attentional Control, Multi-tasking and Psychological Endurance. Secondly, several personality traits correlated with scores on some cognitive abilities. For example, Openness to Experience positively correlated with Attentional Control and Psychological Endurance; Conscientiousness positively correlated with Working Memory, Attentional Control and Psychological Endurance; Extraversion positively correlated with Attentional Control and Psychological Endurance; whilst Neuroticism negatively correlated with Attentional Control and Psychological Endurance. Thirdly, several personality traits (Openness to Experience, Conscientiousness and Extraversion) appear to be significantly related more to the experimental group than the control group. Finally, mediation analysis appears to show that interpreting training has a mediating effect on the relationship between certain types of personality traits and cognitive abilities. In some cases, interpreting training and personality traits appear to exert an interacting effect and have a combining influence on some cognitive abilities. These findings can hopefully provide a foundation for future study and be applied in practice to help interpreting training projects and cognitive ability improvement
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