An exploration of apatheia and the definition of apathy: Understanding people’s experience of apathy in Huntington’s disease

Apathy is broadly defined as a loss of motivation and seems to be a relatively common clinical problem in neurodegenerative disorders such as Huntington’s disease. The definition and conceptualisation of apathy, however, is unstandardised, which leads to confusion about what precisely apathy is and how to identify it. This thesis sought to clarify the concept of apathy.First, an etymological exploration of the concept of apatheia, including comparing it to its modern derivative, apathy, helped to give context to what apathy may be. Building on this, a systematic review looked at how apathy is defined and measured in clinical literature, finding a lack of standardisation but some common ground in terms of how recent authors have thought about apathy. Semi-structured interviews with people with apathy in Huntington’s disease, alongside measures of apathy, explored what it is like to experience apathy and found that people struggle with their identity following an experience of apathy. This led to the uncovering of two types of apathy; bewildered and empty apathy. These terms were discussed in relation to the work conducted in the previous chapters and compared with some of the conceptualisations of apathy in the literature. Directions for future research were discussed, with emphasis on identifying different apathy phenomena and using the positive elements of apatheia in helping to realign people’s identity. This would enable future work to concentrate on identifying appropriate treatment and management techniques to alleviate the burden of apathy in chronic illness

Task Relation-aware Continual User Representation Learning

User modeling, which learns to represent users into a low-dimensional representation space based on their past behaviors, got a surge of interest from the industry for providing personalized services to users. Previous efforts in user modeling mainly focus on learning a task-specific user representation that is designed for a single task. However, since learning task-specific user representations for every task is infeasible, recent studies introduce the concept of universal user representation, which is a more generalized representation of a user that is relevant to a variety of tasks. Despite their effectiveness, existing approaches for learning universal user representations are impractical in real-world applications due to the data requirement, catastrophic forgetting and the limited learning capability for continually added tasks. In this paper, we propose a novel continual user representation learning method, called TERACON, whose learning capability is not limited as the number of learned tasks increases while capturing the relationship between the tasks. The main idea is to introduce an embedding for each task, i.e., task embedding, which is utilized to generate task-specific soft masks that not only allow the entire model parameters to be updated until the end of training sequence, but also facilitate the relationship between the tasks to be captured. Moreover, we introduce a novel knowledge retention module with pseudo-labeling strategy that successfully alleviates the long-standing problem of continual learning, i.e., catastrophic forgetting. Extensive experiments on public and proprietary real-world datasets demonstrate the superiority and practicality of TERACON. Our code is available at https://github.com/Sein-Kim/TERACON.Comment: KDD 202

Heterogeneous Graph Learning for Multi-modal Medical Data Analysis

Routine clinical visits of a patient produce not only image data, but also non-image data containing clinical information regarding the patient, i.e., medical data is multi-modal in nature. Such heterogeneous modalities offer different and complementary perspectives on the same patient, resulting in more accurate clinical decisions when they are properly combined. However, despite its significance, how to effectively fuse the multi-modal medical data into a unified framework has received relatively little attention. In this paper, we propose an effective graph-based framework called HetMed (Heterogeneous Graph Learning for Multi-modal Medical Data Analysis) for fusing the multi-modal medical data. Specifically, we construct a multiplex network that incorporates multiple types of non-image features of patients to capture the complex relationship between patients in a systematic way, which leads to more accurate clinical decisions. Extensive experiments on various real-world datasets demonstrate the superiority and practicality of HetMed. The source code for HetMed is available at https://github.com/Sein-Kim/Multimodal-Medical.Comment: AAAI 202

Shift-Robust Molecular Relational Learning with Causal Substructure

Recently, molecular relational learning, whose goal is to predict the interaction behavior between molecular pairs, got a surge of interest in molecular sciences due to its wide range of applications. In this work, we propose CMRL that is robust to the distributional shift in molecular relational learning by detecting the core substructure that is causally related to chemical reactions. To do so, we first assume a causal relationship based on the domain knowledge of molecular sciences and construct a structural causal model (SCM) that reveals the relationship between variables. Based on the SCM, we introduce a novel conditional intervention framework whose intervention is conditioned on the paired molecule. With the conditional intervention framework, our model successfully learns from the causal substructure and alleviates the confounding effect of shortcut substructures that are spuriously correlated to chemical reactions. Extensive experiments on various tasks with real-world and synthetic datasets demonstrate the superiority of CMRL over state-of-the-art baseline models. Our code is available at https://github.com/Namkyeong/CMRL.Comment: KDD 202

The prevalence of mild to moderate distress in patients with end stage renal disease:results from a patient survey using the emotion thermometers in four hospital Trusts in the West Midlands, UK

Objectives To assess the prevalence of mild-To-moderate distress in patients with end-stage renal disease (ESRD) and determine the association between distress and patient characteristics. Design Cross-sectional survey using emotion thermometer and distress thermometer problem list. Setting Renal units in four hospital Trusts in the West Midlands, UK. Participants Adult patients with stage 5 chronic kidney disease who were: (1) On prerenal replacement therapy. (2) On dialysis for less than 2 years. (3) On dialysis for 2 years or more (4) With a functioning transplant. Outcomes The prevalence of mild-To-moderate distress, and the incidence of distress thermometer problems and patient support needs. Results In total, 1040/3730 surveys were returned (27.9%). A third of survey respondents met the criteria for mild-To-moderate distress (n=346; 33.3%). Prevalence was highest in patients on dialysis for 2 years or more (n=109/300; 36.3%) and lowest in transplant patients (n=118/404; 29.2%). Prevalence was significantly higher in younger versus older patients (χ 2 =14.33; p=0.0008), in women versus men (χ 2 =6.63; p=0.01) and in black and minority ethnic patients versus patients of white ethnicity (χ 2 =10.36; p=0.013). Over 40% of patients (n=141) reported needing support. More than 95% of patients reported physical problems and 91.9% reported at least one emotional problem. Conclusions Mild-To-moderate distress is common in patients with ESRD, and there may be substantial unmet support needs. Regular screening could help identify patients whose distress may otherwise remain undetected. Further research into differences in distress prevalence over time and at specific transitional points across the renal disease pathway is needed, as is work to determine how best to support patients requiring help.</p

How does pre-dialysis education need to change?:Findings from a qualitative study with staff and patients

Abstract Background Pre-dialysis education (PDE) is provided to thousands of patients every year, helping them decide which renal replacement therapy (RRT) to choose. However, its effectiveness is largely unknown, with relatively little previous research into patients’ views about PDE, and no research into staff views. This study reports findings relevant to PDE from a larger mixed methods study, providing insights into what staff and patients think needs to improve. Methods Semi-structured interviews in four hospitals with 96 clinical and managerial staff and 93 dialysis patients, exploring experiences of and views about PDE, and analysed using thematic framework analysis. Results Most patients found PDE helpful and staff valued its role in supporting patient decision-making. However, patients wanted to see teaching methods and materials improve and biases eliminated. Staff were less aware than patients of how informal staff-patient conversations can influence patients’ treatment decision-making. Many staff felt ill equipped to talk about all treatment options in a balanced and unbiased way. Patient decision-making was found to be complex and patients’ abilities to make treatment decisions were adversely affected in the pre-dialysis period by emotional distress. Conclusions Suggested improvements to teaching methods and educational materials are in line with previous studies and current clinical guidelines. All staff, irrespective of their role, need to be trained about all treatment options so that informal conversations with patients are not biased. The study argues for a more individualised approach to PDE which is more like counselling than education and would demand a higher level of skill and training for specialist PDE staff. The study concludes that even if these improvements are made to PDE, not all patients will benefit, because some find decision-making in the pre-dialysis period too complex or are unable to engage with education due to illness or emotional distress. It is therefore recommended that pre-dialysis treatment decisions are temporary, and that PDE is replaced with on-going RRT education which provides opportunities for personalised education and on-going review of patients’ treatment choices. Emotional support to help overcome the distress of the transition to end-stage renal disease will also be essential to ensure all patients can benefit from RRT education

mGovernment Services and Adoption: Current Research and Future Direction

Part 5: Research in ProgressInternational audienceWith the unprecedented growth of mobile technologies, governments of both developed and developing countries have started adopting mobile services in the form of m-government. While the vendors and practitioners are heavily engaged in this transformation, the scholarly world is lagging to keep pace with the progress and to provide clear theoretical guidance for successful adoption. This paper takes a stock of scholarly publications on m-government adoption since the year 2000 and reports findings and future directions based on meta-analysis of secondary data. The articles were classified into research themes, delivery mode, theory and methods. The paper identifies the dearth of scholarly work and calls for more in-depth work to make important contribution in this area

Water-Soluble Epitaxial NaCl Thin Film for Fabrication of Flexible Devices

We studied growth mechanisms of water-soluble NaCl thin films on single crystal substrates. Epitaxial growth of NaCl(100) on Si(100) and domain-matched growth of NaCl(111) on c-sapphire were obtained at thicknesses below 100 nm even at room temperature from low lattice mismatches in both cases. NaCl thin film, which demonstrates high solubility selectivity for water, was successfully applied as a water-soluble sacrificial layer for fabrication of several functional materials, such as WO3 nano-helix and Sn doped In2O3 nano-branches.111Ysciescopu