484 research outputs found
Emotional and Behavioural Profiles, Parenting Factors and Empathy in Young Children with Callous- Unemotional Traits and/or Autism Spectrum Disorder
Callous-Unemotional (CU) traits are associated with more severe behavioural challenges and poorer
long-term outcomes. In recent years, research into CU traits has extended into earlier developmental
periods. The literature has also taken a neurodevelopmental and transdiagnostic shift into
considering CU traits profiles within not only disruptive behaviour disorders, but also Autism
Spectrum Disorder. This thesis aims to determine whether CU traits can be observed as a distinct cooccurring
feature in young autistic children, and whether high CU traits present with similar profiles of
behaviour, regulation, parent-related factors and empathy development across non-autistic and
likely-autistic young children. The thesis comprises three empirical studies that involved intensive
data collection with families of clinic-referred children. CU traits were measured using the Inventory of
Callous-Unemotional Traits, while likely-autistic children were identified using a combination of clinical interview, Social Communication Questionnaire, and ADOS-2 administration. Profiles of child
behaviour, regulation, parent-related factors, and empathy were measured using both parent-report
questionnaires and in-vivo observational tasks. Results supported the view that CU traits can be
observed and measured within early childhood and can co-occur but with a distinct profile within
likely-autistic children. Findings also demonstrated that CU traits appear to be characterised by
distinct correlates when occurring across non-autistic and likely-autistic young children with conduct
problems. Further, CU traits may be more strongly associated than ASD features with various
empathy deficits. This research highlights the importance of considering dysregulation within the
context of empathy behaviours in young children. We also highlight a lack of convergence between
parent-report measures and observational-coded measures of empathy and discuss the implications
of this for future research
AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale
BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project
Letting the Narrative Unfold: Black Female Storytellers of the 21st Century
The important aspects of film and television are the stories that are portrayed. Everyone has a story to tell. However, who tells the story is equally important as who portrays the story. This thesis analyzes three Black female auteurs and the work they have created with Black women at the center of those narratives. Shonda Rhimes, Ava DuVernay, and Issa Rae are Black female auteurs because they are Black female storytellers each with their own story to tell. They each have created and produced content that portrays Black women in a three-dimensional light
Motoric cognitive risk: epidemiology of a walking speed-based syndrome to predict dementia
Dementia is a huge global health challenge without a cure. Identifying the early stages enables the implementation of risk-modifying interventions when they may be most effective. Slow gait speed and self-reported cognitive complaints are among the earliest findings reported in the preclinical stage of dementia. The Motoric Cognitive Risk (MCR) syndrome is a high-risk predementia state combining objective slow gait speed and subjective cognitive complaint in independent, dementia-free individuals. This thesis investigates the association between MCR and dementia using meta-analysis and several epidemiological approaches in a Scottish cohort of community-dwelling older adults.
The first study presents a systematic review and meta-analysis of the prognostic ability of MCR. This review also outlined hypotheses regarding the underlying mechanisms of MCR, areas that are explored further in the final chapter of the thesis. It examined longitudinal cohort studies that compared an MCR group to a non-MCR group for any health outcome. A thorough search returned 705 records with 15 cohorts eligible for meta-analysis. The meta-analysis included only health outcomes reported from at least three cohorts and judged satisfactory by our clinical content experts. When a study reported an incompatible effect measure, I contacted authors to request data to allow for our own calculation, or I converted the effect measure where possible and appropriate. The meta-analysis found that participants with MCR were at an increased risk of cognitive impairment (adjusted Hazard Ratio [aHR] 1.76, 95% CI 1.49–2.08; I2 = 24.9%), dementia (aHR 2.12, 1.85–2.42; 33.1%), falls (adjusted relative risk 1.38, 1.15–1.66; 62.1%), and mortality (aHR 1.49, 1.16–1.91; 79.2%). There was considerable heterogeneity in how studies diagnosed MCR, cognitive impairment, and dementia. Our review of the underlying mechanisms of MCR suggested that interactions between MCR, poor brain health, falls, and increased mortality are likely due to a range of biological, psychological, and social mechanisms. A major strength of this systematic review and meta-analysis is the thoroughness of its methodology.
The second study of the thesis described the prevalence of MCR and associated factors in the Lothian Birth Cohort 1936 (LBC1936). It was the first time MCR had been derived in a Scottish cohort, so it detailed how MCR was coded and implemented. This study also reported slow gait speed cut-offs for the first time in an older Scottish population. It also assessed the overlap of MCR with three other high-risk states of ageing - Mild Cognitive Impairment (MCI), Prefrailty, and Frailty, thus clarifying the degree of cross-over between these related states. MCR was derived in three waves of the cohort at mean ages of 76.3 years (n = 690), 79.3 years (n = 543) and 82 years (n = 425). MCR prevalence rate ranged from 5.3% to 5.7% across the three waves, a little lower than the global average. Factors associated with MCR in this cohort included age, socioeconomic status, and tests of executive function. There was partial overlap between individuals with MCR and MCI, indicating that these concepts, although derived using similar criteria, capture different cohorts of people. This supports the conceptualisation of MCR as complementary to MCI rather than an alternative. The study highlights the need to explore further the strong association between lower socioeconomic status in early and mid-life with MCR later in life.
Building on a key finding from the second study of the thesis, the third study focused on socioeconomic status as a risk factor for MCR. This longitudinal observational study used logistic regression analysis adjusting for important demographic, lifestyle, and health covariates to explore the association between MCR at age 76 years, and years of education and occupational social class, categorised into manual versus non-manual occupations. The final model included 671 participants. Results show that lower socioeconomic status as defined by non-manual versus manual occupation (and not years of education) is associated with a greater than three-fold risk of having MCR later in life (adjusted odds ratio 3.55, 95% CI 1.46–8.74; p = 0.005). Putting this study in context with the literature is difficult as there is a paucity of work focussing on socioeconomic status as a risk factor for MCR. However, having a low socioeconomic status is a widely accepted predictor of ill health generally, and dementia more specifically. Therefore, it is no surprise that it was strongly associated with MCR, which is a high-risk state for dementia. This study highlights a novel risk factor for MCR and offers a hypothesis on underlying mechanisms but concludes by recommending further work to unravel the relationship between lower socioeconomic status and MCR.
The fourth study shifted temporarily to focus on identifying dementia in LBC1936, an essential piece of work to allow for the later study of MCR as a predictor of dementia. Previously, the LBC1936 cohort lacked a clinically diagnosed dementia outcome. Our study introduced a novel approach to identifying dementia in cohort studies and reported for the first time the incidence and prevalence of all-cause dementia and its subtypes in the LBC1936. We comprehensively evaluated all participants' electronic health records to identify any indications of cognitive impairment. In addition, we performed in-person clinician assessments whenever a participant's cognition was in doubt. Clinical dementia specialists from Old Age Psychiatry, Neurology, and Geriatrics agreed on a diagnosis of probable dementia, possible dementia, or the absence of dementia, and determined the subtype whenever possible. Of the 865 LBC1936 participants included, 118 (13.6%) had dementia by an average age of approximately 86 years. Dementia was more common with increasing age and in women, and the most common type of dementia was due to Alzheimer disease (49.2%). Self-reported dementia diagnoses were positive in only 17.8% of clinically identified dementia diagnoses. This illustrates the importance of a robust clinical dementia diagnosis instead of relying on self-reported diagnoses. Our work will enable researchers to explore the extensive LBC936 data accumulated over a 16-year period for signals that differentiate participants currently living with dementia from those who are not. This includes my newly derived MCR measure, which brings us to the final study of the thesis.
The fifth and final study provided a time-to-event analysis with MCR as the predictor variable and dementia as the outcome of interest. It also explored the various trajectories of participants diagnosed with MCR. It classified a total of 680 community-dwelling participants (mean [SD] age 76.3 [0.8] years) free from dementia into non-MCR or MCR groups. It used Cox proportional hazards methods and competing risk regression to evaluate the risk of developing all-cause dementia in the years following MCR diagnosis. The final model adjusted for potential confounders. Results show that, after 10 years of follow-up, 79 of 680 (11.6%) participants developed dementia. The presence of MCR increased the risk of dementia (aHR 2.34 [1.14 to 4.78, p=0.020]) in this Scottish cohort to a similar extent as in other populations. Individuals with MCR follow similar trajectories to the related predementia syndrome, MCI. This study reinforces that MCR could potentially identify a target group for early interventions of modifiable risk factors for dementia. However, it illustrates the heterogeneous nature of MCR progression and highlights that not all older adults with MCR will follow a similar path.
This thesis explores the predementia syndrome MCR through meta-analysis and several epidemiological approaches in the Lothian Birth Cohort 1936. The findings represent a significant advancement in our understanding of MCR prevalence, risk factors, predictive ability, and trajectories. Since there are no effective treatments for dementia, prevention is paramount. By improving our understanding of this high-risk predementia state, this thesis brings us closer to the ultimate goal of intervening early in the lifecourse to reduce the number of people living with dementia
A Logical Account of Subtyping for Session Types
We study the notion of subtyping for session types in a logical setting,
where session types are propositions of multiplicative/additive linear logic
extended with least and greatest fixed points. The resulting subtyping relation
admits a simple characterization that can be roughly spelled out as the
following lapalissade: every session type is larger than the smallest session
type and smaller than the largest session type. At the same time, we observe
that this subtyping, unlike traditional ones, preserves termination in addition
to the usual safety properties of sessions. We present a calculus of sessions
that adopts this subtyping relation and we show that subtyping, while useful in
practice, is superfluous in the theory: every use of subtyping can be "compiled
away" via a coercion semantics.Comment: In Proceedings PLACES 2023, arXiv:2304.0543
Programming languages and tools with multiparty session
Distributed software systems are used in a wide variety of applications, including health care, telecommunications, finance, and entertainment. These systems typically consist of multiple software components, each with its own local memory, that are deployed across networks of hosts and communicate by passing messages in order to achieve a common goal. Distributed systems offer several benefits, including scalability — since computation happens independently on each component, it is easy and generally inexpensive to add additional components and functionality as necessary; reliability—since systems can be made up of hundreds of components working together, there is little disruption if a single component fails; performance—since work loads can be broken up and sent to multiple components, distributed systems tend to be very efficient. However, they can also be difficult to implement and analyze due to the need for heterogeneous software components to communicate and synchronize correctly and the potential for hardware or software failures.
Distributed and concurrent programming is challenging due to the complexity of coordinating the communication and interactions between the various components of a system that may be running on different machines or different threads. Behavioural types can help to address some of these difficulties by providing a way to formally specify the communication between components of a distributed system. This specification can then be used to verify the correctness of the communication between these components using static typechecking, dynamic monitoring, or a combination of the two. Perhaps the most well-known form of behavioural types are session types. They define the sequences of messages that are exchanged between two or more parties in a communication protocol, as well as the order in which these messages are exchanged. More generally, behavioural types include typestate systems, which specify the state-dependent availability of operations, choreographies, which specify collective communication behaviour, and behavioural contracts that specify the expected behaviour of a system. By using behavioural types, it is possible to ensure that the communication between components of a distributed system is well-defined and follows a set of predefined rules, which can help to prevent errors and ensure that the system behaves correctly.
The focus of this thesis is on using session type systems to provide static guarantees about the runtime behaviour of concurrent programs. We investigate two strands of work in this context. The first strand focuses on the relationship between session types and linearity. Linearity is a property of certain resources, in this case communication channels, that can only be used once. For instance a linear variable can only be assigned once, after which it cannot be changed. This property is useful for session types because it helps to prevent race conditions and guarantees that no messages are lost or duplicated. We look at relaxing the standard access control in multiparty session types systems. This is typically based on linear or affine types, that offer strong guarantees of communication safety and session. However, these exclude many naturally occurring scenarios that make use of shared channels or need to store channels in shared data structures. We introduce a new and more flexible session type system, which allows channel references to be shared and stored in persistent data structures. We prove that the resulting language satisfies type safety, and we illustrate our type system through examples.
The second strand of research in this thesis looks at the expressive power of session types, and their connection to typestate for safe distributed programming in the Java language. Typestates are a way of annotating objects with a set of operations that are valid to perform on them at a given state. We expand the expressive power of two existing tools, use them to represent real-world case studies, and end by considering language usability and human factors
Functional activity level reported by an informant is an early predictor of Alzheimer’s disease
Background Loss of autonomy in day-to-day functioning is one of the feared outcomes of Alzheimer’s disease (AD), and relatives may have been worried by subtle behavioral changes in ordinary life situations long before these changes are given medical attention. In the present study, we ask if such subtle changes should be given weight as an early predictor of a future AD diagnosis. Methods Longitudinal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were used to define a group of adults with a mild cognitive impairment (MCI) diagnosis remaining stable across several visits (sMCI, n=360; 55-91 years at baseline), and a group of adults who over time converted from having an MCI diagnosis to an AD diagnosis (cAD, n=320; 55-88 years at baseline). Eleven features were used as input in a Random Forest (RF) binary classifier (sMCI vs. cAD) model. This model was tested on an unseen holdout part of the dataset, and further explored by three different permutation-driven importance estimates and a comprehensive post hoc machine learning exploration. Results The results consistently showed that measures of daily life functioning, verbal memory function, and a volume measure of hippocampus were the most important predictors of conversion from an MCI to an AD diagnosis. Results from the RF classification model showed a prediction accuracy of around 70% in the test set. Importantly, the post hoc analyses showed that even subtle changes in everyday functioning noticed by a close informant put MCI patients at increased risk for being on a path toward the major cognitive impairment of an AD diagnosis. Conclusion The results showed that even subtle changes in everyday functioning should be noticed when reported by relatives in a clinical evaluation of patients with MCI. Information of these changes should also be included in future longitudinal studies to investigate different pathways from normal cognitive aging to the cognitive decline characterizing different stages of AD and other neurodegenerative disorders.publishedVersio
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