IDyOMpy: A new Python-based model for the statistical analysis of musical expectations.

Background: IDyOM (Information Dynamics of Music) is the statistical model of music the most used in the community of neuroscience of music. It has been shown to allow for significant correlations with EEG (Marion, 2021), ECoG (Di Liberto, 2020) and fMRI (Cheung, 2019) recordings of human music listening. The language used for IDyOM -Lisp- is not very familiar to the neuroscience community and makes this model hard to use and more importantly to modify.

Ontology-based integration and querying of heterogeneous rare disease data sources - POLVAS perspective.

The integration of rare disease medical databases belonging to different countries is an important problem, as a large number of observations are required for reliable statistical inference of patient data in order to facilitate clinical research. Such integration of national registry data, which requires harmonization of the heterogeneous data sets into a unified view, is facilitated in the European FAIRVASC project by developing a domain-specific ontology. The FAIRVASC project is dedicated to the rare disease of anti-neutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV).

Cortical encoding of phonetic onsets of both attended and ignored speech in hearing impaired individuals.

Hearing impairment alters the sound input received by the human auditory system, reducing speech comprehension in noisy multi-talker auditory scenes. Despite such difficulties, neural signals were shown to encode the attended speech envelope more reliably than the envelope of ignored sounds, reflecting the intention of listeners with hearing impairment (HI). This result raises an important question: What speech-processing stage could reflect the difficulty in attentional selection, if not envelope tracking? Here, we use scalp electroencephalography (EEG) to test the hypothesis that the neural encoding of phonological information (i.

Forecasting Patient Early Readmission from Irish Hospital Discharge Records Using Conventional Machine Learning Models.

Background/objectives: Predicting patient readmission is an important task for healthcare risk management, as it can help prevent adverse events, reduce costs, and improve patient outcomes. In this paper, we compare various conventional machine learning models and deep learning models on a multimodal dataset of electronic discharge records from an Irish acute hospital.

Methods: We evaluate the effectiveness of several widely used machine learning models that leverage patient demographics, historical hospitalization records, and clinical diagnosis codes to forecast future clinical risks.

Translating the Behaviour Change Technique Taxonomy v.1 to other languages: the approach used in European Portuguese (BCTTv1-PT).

Background: The Behaviour Change Techniques Taxonomy v1 (BCTTv1) is the most widely used classification of behaviour change techniques (BCTs), contributing to the accurate report and evaluation of behaviour change interventions and accumulation of evidence. This study reports a structured approach to adapt the BCTTv1 into European Portuguese (BCTTv1-PT).

Methods: A collaborative and iterative approach was used.

Autoantibodies towards HFE and SYT5 in anti-neutrophil cytoplasm antibody-associated vasculitis relapse.

Objective: Identification of those at high and low risk of disease relapse is a major unmet need in the management of patients with ANCA-associated vasculitis (AAV). Precise stratification would allow tailoring of immunosuppressive medication. We profiled the autoantibody repertoire of AAV patients in remission to identify novel autoantibodies associated with relapse risk.

DERCo: A Dataset for Human Behaviour in Reading Comprehension Using EEG.

This paper introduces the DERCo (Dublin EEG-based Reading Experiment Corpus), a language resource combining electroencephalography (EEG) and next-word prediction data obtained from participants reading narrative texts. The dataset comprises behavioral data collected from 500 participants recruited through the Amazon Mechanical Turk online crowd-sourcing platform, along with EEG recordings from 22 healthy adult native English speakers. The online experiment was designed to examine the context-based word prediction by a large sample of participants, while the EEG-based experiment was developed to extend the validation of behavioral next-word predictability.

Enabling data linkages for rare diseases in a resilient environment with the SERDIF framework.

Environmental factors amplified by climate change contribute significantly to the global burden of disease, disproportionately impacting vulnerable populations, such as individuals with rare diseases. Researchers require innovative, dynamic data linkage methods to enable the development of risk prediction models, particularly for diseases like vasculitis with unknown aetiology but potential environmental triggers. In response, we present the Semantic Environmental and Rare Disease Data Integration Framework (SERDIF).

Mining impactful discoveries from the biomedical literature.

Background: Literature-based discovery (LBD) aims to help researchers to identify relations between concepts which are worthy of further investigation by text-mining the biomedical literature. While the LBD literature is rich and the field is considered mature, standard practice in the evaluation of LBD methods is methodologically poor and has not progressed on par with the domain. The lack of properly designed and decent-sized benchmark dataset hinders the progress of the field and its development into applications usable by biomedical experts.

Data-driven subclassification of ANCA-associated vasculitis: model-based clustering of a federated international cohort.

Background: Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis is a heterogenous autoimmune disease. While traditionally stratified into two conditions, granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA), the subclassification of ANCA-associated vasculitis is subject to continued debate. Here we aim to identify phenotypically distinct subgroups and develop a data-driven subclassification of ANCA-associated vasculitis, using a large real-world dataset.

Data and Process Harmonisation of Multi-National, Multi-Site Research Data.

To achieve a single fully harmonised research data set suitable for analysis from data collected at multiple sites requires not only semantic integration of collection concepts and convergence onto single collection units, but harmonisation of data collection processes. We describe our experience of identifying harmonisation challenges in the Precision ALS project, with particular focus on process alignment challenges in a multi-site multi-national research data collection project.

Correction: Flynn et al. Introducing and Familiarising Older Adults Living with Dementia and Their Caregivers to Virtual Reality. 2022, , 16343.

The authors would like to make the following corrections to the published article [...

Reliable, rapid, and remote measurement of metacognitive bias.

Metacognitive biases have been repeatedly associated with transdiagnostic psychiatric dimensions of 'anxious-depression' and 'compulsivity and intrusive thought', cross-sectionally. To progress our understanding of the underlying neurocognitive mechanisms, new methods are required to measure metacognition remotely, within individuals over time. We developed a gamified smartphone task designed to measure visuo-perceptual metacognitive (confidence) bias and investigated its psychometric properties across two studies (N = 3410 unpaid citizen scientists, N = 52 paid participants).

Atypical low-frequency cortical encoding of speech identifies children with developmental dyslexia.

Slow cortical oscillations play a crucial role in processing the speech amplitude envelope, which is perceived atypically by children with developmental dyslexia. Here we use electroencephalography (EEG) recorded during natural speech listening to identify neural processing patterns involving slow oscillations that may characterize children with dyslexia. In a story listening paradigm, we find that atypical power dynamics and phase-amplitude coupling between delta and theta oscillations characterize dyslexic versus other child control groups (typically-developing controls, other language disorder controls).

AiCarePWP: Deep learning-based novel research for Freezing of Gait forecasting in Parkinson.

Background And Objectives: Episodes of Freezing of Gait (FoG) are among the most debilitating motor symptoms of Parkinson's Disease (PD), leading to falls and significantly impacting patients' quality of life. Accurate assessment of FoG by neurologists provides crucial insights into patients' conditions and disease symptoms. This proposed strategy involves utilizing a Weighted Fuzzy Logic Controller, Kalman Filter, and Kaiser-Meyer-Olkin test to detect the gait parameters while walking, resting, and standing phases.

Conversational artificial intelligence: the interface with the patient concerns inventory.

The patient concerns inventory (PCI) allows patients to highlight the issues they would like to discuss at their outpatient consultation. It improves patient-clinician communication and has proven benefits. While the PCI is effective, patient experiences could be improved with better access to it and the ability to more easily and frequently express their concerns.

Social connectedness and the role of virtual reality: experiences and perceptions of people living with dementia and their caregivers.

Purpose: People living with dementia are often at increased risk of becoming socially disconnected due to dementia-related challenges. In recent years, digital technology has been designed to help address the social health of people living with dementia and provide opportunities to promote or maintain their social connectedness. This paper presents the findings from phase two of a participatory action research project, which explored people living with dementia and their caregiver's experiences and perceptions of social connectedness and the potential role of Virtual Reality (VR) in promoting or maintaining same.

Emergence of the cortical encoding of phonetic features in the first year of life.

Even prior to producing their first words, infants are developing a sophisticated speech processing system, with robust word recognition present by 4-6 months of age. These emergent linguistic skills, observed with behavioural investigations, are likely to rely on increasingly sophisticated neural underpinnings. The infant brain is known to robustly track the speech envelope, however previous cortical tracking studies were unable to demonstrate the presence of phonetic feature encoding.

Memory of Fictional Information: A Theoretical Framework.

Much of the information people encounter in everyday life is not factual; it originates from fictional sources, such as movies, novels, and video games, and from direct experience such as pretense, role-playing, and everyday conversation. Despite the recent increase in research on fiction, there is no theoretical account of how memory of fictional information is related to other types of memory or of which mechanisms allow people to separate fact and fiction in memory. We present a theoretical framework that places memory of fiction in relation to other cognitive phenomena as a distinct construct and argue that it is an essential component for any general theory of human memory.

Taking the unreal seriously: enriching cognitive science with the notion of fictionality.

Fictionality and fictional experiences are ubiquitous in people's everyday lives in the forms of movies, novels, video games, pretense and role playing, and digital technology use. Despite this ubiquity, though, the field of cognitive science has traditionally been dominated by a focus on the real world. Based on the limited understanding from previous research on questions regarding fictional information and the cognitive processes for distinguishing reality from fiction, we argue for the need for a comprehensive and systematic account that reflects on related phenomena, such as narrative comprehension or imagination embedded into general theories of cognition.

DECENT: A sociotechnical approach for developing mobile health apps in underserved settings.

Objective: Despite the fact that user engagement is critical to the efficacy of mobile health (mHealth) interventions in the Global South, many of these interventions lack user engagement features. This is because sociotechnical aspects of such initiatives are frequently ignored during the design, development, and implementation stages. This research highlighted the importance of considering sociotechnical factors when developing mHealth apps.