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A critical approach to online, English-language representation of Korean linguistic variation
Get PDFZacrosTools: A Python Library for Automated Preparation, Analysis, and Visualization of Kinetic Monte Carlo Simulations with Zacros
Get PDFThis paper presents ZacrosTools, a free and open-source Python library designed to simplify and automate the preparation and analysis of kinetic Monte Carlo (KMC) simulations with the widely used Zacros package. ZacrosTools provides a user-friendly and robust interface for building KMC models, automating the generation of Zacros input files, extracting and processing simulation data, and visualizing results through multiple plotting functionalities. The library benefits new users by simplifying model preparation and helping them avoid common mistakes while also being suitable to advanced users who wish to fine-tune complex KMC models and conduct comprehensive analyses. ZacrosTools is extensively documented with numerous examples available on ReadTheDocs and is publicly accessible on GitHub under the MIT license. Furthermore, it integrates continuous integration via GitHub Actions to facilitate seamless contributions from the user community
Changes in food quality and habits in urban Ghana: evidence from a mixed-methods study
Get PDFBackground: Globally, diets are changing from good quality to limited nutrition. However, an in-depth analysis of the nature of the changes is under-researched. This study examined past and current food consumption, acquisition, and preparation habits of urban poor residents in Accra, Ghana. Methods: Data from the Contextual Awareness Response and Evaluation: Diabetes in Ghana project was used. The Food Group Diversity Score, NCD-Risk and NCD-Protect scores were calculated using the Diet Quality Questionnaire and analysed using means and crosstabulations with the estimation of the 95% confidence intervals (n = 854). Focus group discussions were held to discuss current and past food habits, and data were analysed thematically (n = 30). The qualitative and quantitative data were integrated during the analysis. Results: From the early 1950s to the 1980s, the community consumed more traditional homemade meals made from cassava, corn and plantains (such as fufu, kenkey, kokonte and ampesi). Currently, the community consume these traditional meals in addition to foods considered modern, such as instant noodles (6%), milk (19%), rice (67%), sugar-sweetened beverages (21%), and Milo (21%). Respondents, on average, ate four food groups (x̄=3.8 ± 1.5) and about half were food insecure (47%). The most frequently consumed NCD-protect foods were whole grains (63%) and other vegetables (69%). The NCD-risk items commonly consumed were deep-fried foods (23%), unprocessed red meat (22%) and sugar-sweetened beverages (21%). Conclusion: Respondents reported a shift from home cooking and communal meals toward eating out-of-home meals. The current dietary habits reflect a hybrid of modern foods with traditional foods. Food insecurity is high, and their diets provide little protection against chronic non-communicable diseases. This limits opportunities to move towards healthy diets and improved health outcomes as envisioned in the Agenda 2030
A qualitative analysis of young adults’ beliefs about bullying: exploring associations with social anxiety and post-traumatic stress
No full textBackground: Bullying can be associated with emotional and social difficulties, but not all individuals experience enduring negative effects. Objective: This study aimed to explore beliefs about bullying, self, and other people among young adults who were bullied that may be associated with ongoing anxiety and distress related to those experiences. Method: Semi-structured interviews with 20 people, aged 18–29 years, who had experienced bullying were analysed using thematic analysis. The sample was split, by current symptoms of social anxiety and post-traumatic stress related to bullying, into a lower symptoms group (n = 12) and a higher symptoms group (n = 8). Results: Participants reported multiple types of bullying, including online. Four superordinate themes were identified in negative beliefs related to bullying experiences: personal deficiency (i.e. victimization was due to own low value or undesirable traits), social threat (i.e. wariness of others due to their negative motives or traits), acceptance is fragile (i.e. being accepted by others is transient and requires effort), and minimizing (i.e. downplaying severity and impact of past experiences). These were evident in both groups but were more frequently endorsed in the higher symptoms group. Conclusion: Negative appraisals related to bullying can persist into young adulthood and may influence social interactions and mental health. Interventions targeting these beliefs could mitigate negative outcomes and bolster resilience among individuals affected by bullying. Further research should explore these themes to inform effective therapeutic strategies for young adults who have been bullied
Distinctive but not unique: the risks of psychedelic ethical exceptionalism
No full textWhen used clinically, psychedelics may appear unusual or even unique when compared to many more familiar or long-standing medical interventions, prompting some to suggest that the ethical issues raised may likewise be exceptional. If that is correct, then perhaps psychedelics should be treated differently from other substances used within medicine: for example, by being subjected to different ethical or evidentiary standards. Alternatively, it may be that psychedelics have more in common with various existing medical interventions than first meets the eye. We argue in favor of the latter position, drawing on parallels from earlier debates around genetic exceptionalism in bioethics. We suggest there are risks to adopting a stance of “psychedelic ethical exceptionalism,” and propose that consistent ethical rules and evidentiary standards should be applied across all relevant areas of clinical medicine. Importantly, this does not preclude the possibility that changes to existing standards should be made; but if so, this should not be justified by appealing to the alleged uniqueness of psychedelics
Here’s Charlie! Realising the semantic web vision of agents in the age of LLMs
No full textThis paper presents our research towards a near-term future in which legal entities, such as individuals and organisations can entrust semi-autonomous AI-driven agents to carry out online interactions on their behalf. The author’s research concerns the development of semi-autonomous Web agents, which consult users if and only if the system does not have sufficient context or confidence to proceed working autonomously. This creates a user-agent dialogue that allows the user to teach the agent about the information sources they trust, their data-sharing preferences, and their decision-making preferences. Ultimately, this enables the user to maximise control over their data and decisions while retaining the convenience of using agents, including those driven by LLMs. In view of developing near-term solutions, the research seeks to answer the question: “How do we build a trustworthy and reliable network of semi-autonomous agents which represent individuals and organisations on the Web?”. After identifying key requirements, the paper presents a demo for a sample use case of a generic personal assistant. This is implemented using (Notation3) rules to enforce safety guarantees around belief, data sharing and data usage and LLMs to allow natural language interaction with users and serendipitous dialogues between software agents
A comparative machine learning study of schizophrenia biomarkers derived from functional connectivity
Get PDFFunctional connectivity holds promise as a biomarker of schizophrenia. Yet, the high dimensionality of predictive models trained on functional connectomes, combined with small sample sizes in clinical research, increases the risk of overfitting. Recently, low-dimensional representations of the connectome such as macroscale cortical gradients and gradient dispersion have been proposed, with studies noting consistent gradient and dispersion differences in psychiatric conditions. However, it is unknown which of these derived measures has the highest predictive capacity and how they compare to raw functional connectivity specifically in the case of schizophrenia. Our study evaluates which connectome features derived from resting state functional MRI — functional connectivity, gradients, or gradient dispersion — best identify schizophrenia. To this end, we leveraged data of 936 individuals from three large open-access datasets: COBRE, LA5c, and SRPBS-1600. We developed a pipeline which allows us to aggregate over a million different features and assess their predictive potential in a single, computationally efficient experiment. We selected top 1% of features with the largest permutation feature importance and trained 13 classifiers on them using 10-fold cross-validation. Our findings indicate that functional connectivity outperforms its low-dimensional derivatives such as cortical gradients and gradient dispersion in identifying schizophrenia (Mann–Whitney test conducted on test accuracy: connectivity vs. 1st gradient: U = 142, p < 0.003; connectivity vs. neighborhood dispersion: U = 141, p = 0.004). Additionally, we demonstrated that the edges which contribute the most to classification performance are the ones connecting primary sensory regions. Functional connectivity within the primary sensory regions showed the highest discrimination capabilities between subjects with schizophrenia and neurotypical controls. These findings along with the feature selection pipeline proposed here will facilitate future inquiries into the prediction of schizophrenia subtypes and transdiagnostic phenomena
Negotiating power: examining the role of procedural elements in international peace talks
Get PDFPeace agreements don’t write themselves. They are negotiated — in complex, iterative sessions with highly contingent results. Diplomats take pains to establish a negotiation’s procedural elements (e.g., timing, venue, and rules of engagement) in advance; they often contest or adapt these elements throughout the negotiation process. This area of diplomatic practice is under-examined in International Relations (IR). Accordingly, this research explores the following questions: Do the procedural elements of negotiation impact the dynamics and outcomes of international peace processes? If so, how?
This thesis argues that procedural elements have meaningful effects on negotiation dynamics and subsequent foreign policy outcomes; understanding these mechanisms is key to understanding ‘power’ in the international system. After critically examining existing theories of power in IR and identifying a crucial area of underdevelopment in the field’s construction of ‘agenda-setting power,’ this thesis advances the novel ‘Cubist concept of power.’ Inspired by the Cubist art movement, this conceptual framework highlights the multi-dimensionality, fragmentation, and perceptual relativity of power in negotiation contexts.
This article-based thesis presents three discrete studies of illustrative procedural elements, adopting a pragmatic approach to research aims and methodologies. The first article leverages historical sources to assess US mediators’ use of time pressure tactics; the second uses a web-based survey to investigate the impact of ‘virtual venue’ selection during the COVID-19 pandemic; and the third draws on interviews with seasoned practitioners to explore the effects of notetaking practices. All three studies validate the influence of procedural elements in shaping negotiation dynamics and outcomes. Cross-cutting findings highlight the role of technological affordances, perceptions of procedural justice, and interpersonal relationship-building. This thesis complicates existing theories of power in IR while offering policy-relevant findings to support practitioners in designing more effective peace negotiations in the future
Function and regulation of the ADP-ribosylhydrolase TARG1
No full textThe timely removal of ADP-ribosylation is essential for DNA repair, yet much remains to be learnt about ADP-ribosylhydrolases. The previously proposed role of the ADP-ribosylhydrolase TARG1 in the DNA damage response, along with its link to neurodegeneration, underscored the crucial activity of this hydrolytic enzyme in maintaining cellular homeostasis. Despite these considerations, the cellular function of TARG1, together with its physiological substrates, remained enigmatic. The work presented in this thesis aimed to validate the DNA repair function of TARG1. Moreover, I sought to translate the prior in vitro findings to a more physiological context by directly investigating TARG1’s glutamate/aspartate-linked ADPr reversal activity in cells. To do so, I examined TARG1’s interplay with the main cellular ADP-ribosylhydrolase, PARG. An additional research goal emerged from studying the synergistic function of TARG1 and PARG, namely revealing the molecular mechanisms that regulate the degradation of TARG1 upon PARG inhibition
A novel machine learning based framework for developing composite digital biomarkers of disease progression
Get PDFBackground: Current methods of measuring disease progression of neurodegenerative disorders, including Parkinson's disease (PD), largely rely on composite clinical rating scales, which are prone to subjective biases and lack the sensitivity to detect progression signals in a timely manner. Digital health technology (DHT)-derived measures offer potential solutions to provide objective, precise, and sensitive measures that address these limitations. However, the complexity of DHT datasets and the potential to derive numerous digital features that were not previously possible to measure pose challenges, including in selection of the most important digital features and construction of composite digital biomarkers. Methods: We present a comprehensive machine learning based framework to construct composite digital biomarkers for progression tracking. This framework consists of a marginal (univariate) digital feature screening, a univariate association test, digital feature selection, and subsequent construction of composite (multivariate) digital disease progression biomarkers using Penalized Generalized Estimating Equations (PGEE). As an illustrative example, we applied this framework to data collected from a PD longitudinal observational study. The data consisted of Opal™ sensor-based movement measurements and MDS-UPDRS Part III scores collected at 3-month intervals for 2 years in 30 PD and 10 healthy control participants. Results: In our illustrative example, 77 out of 235 digital features from the study passed univariate feature screening, with 11 features selected by PGEE to include in construction of the composite digital measure. Compared to MDS-UPDRS Part III, the composite digital measure exhibited a smoother and more significant increasing trend over time in PD groups with less variability, indicating improved ability for tracking disease progression. This composite digital measure also demonstrated the ability to classify between de novo PD and healthy control groups. Conclusion: Measures from DHTs show promise in tracking neurodegenerative disease progression with increased sensitivity and reduced variability as compared to traditional clinical scores. Herein, we present a novel framework and methodology to construct composite digital measure of disease progression from high-dimensional DHT datasets, which may have utility in accelerating the development and application of composite digital biomarkers in drug development