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Preliminary psychometric validation of a novel patient-based measure evaluating outcomes following treatment for functional seizures
No full textBackground: A consistent measure of patient reported outcomes following treatment for functional seizures is lacking. This study presents evidence regarding preliminary validation of a novel brief questionnaire, the Functional Seizures Pre-Post Intervention Comparison Survey (FSPICS) designed to measure patient relevant metrics following treatment. Methods: Exploratory and confirmatory factor analyses were conducted to assess the latent structure of the FSPICS. Spearman's correlation coefficients were used to estimate the association between relevant FSPICS questionnaire items and changes in monthly seizure count, Short Form-12 (SF-12) physical and mental quality of life component summaries, as well as the Work and Social Adjustment Scale (WSAS) function measure. Results: A total of 56 participants enrolled in the active treatment arms of a Randomised Controlled Trial who completed the FSPICS were included. The final 19-item FSPICS assessed perceived treatment effectiveness across two highly correlated latent factors (r = 0.880), “Seizure Control” and “Quality of Life”. The final model exhibited overall very good fit (CFI = 0.999, TLI = 0.999, SRMR = 0.085, RMSEA = 0.034) and had strong internal consistency and reliability for both latent factors (“Seizure Control”: Ordinal Cronbach's α = 0.957, McDonald's ω = 0.929; “Quality of Life”: α = 0.913, ω = 0.867). Q12 of the FSPICS, “seizure occurrence”, demonstrated significant moderate-to-strong correlations with both absolute change in monthly seizure count (Spearman's ρ = -0.514, p < 0.001) and seizure count ratio (Spearman's ρ = −0.644, p < 0.001). Q9, “the strategies I use to manage my seizures help me function better in everyday life” and Q13, “quality of life” showed significant weak correlations (Spearman's ρ = 0.362, p = 0.006 and Spearman's ρ = 0.286, p = 0.032 respectively) with the change in the WSAS functional impairment score. Conclusion: This study provides encouraging evidence of the FSPIC's capacity to assess patient-reported outcomes and experiences of functional seizure treatment within a single brief questionnaire. Future studies with larger external sample sizes are required to further validate these findings
Master protocol for a series of cohort-based randomized controlled trials to test tools to communicate research results to study participants and others with relevant lived experience: the SPIN-CLEAR Trials
No full textBACKGROUND: Research results are often not communicated to study participants or others with relevant lived experience. Effective communication of research results would help study participants understand their contribution to research and could improve trust in research and likelihood of research participation. Few randomized controlled trials (RCTs), however, have compared the effectiveness of research communication tools, and it is not known which tools work best for different people. We will conduct the Scleroderma Patient-centered Intervention Network-Communicating Latest Evidence and Results (SPIN-CLEAR) trial series via the multi-national SPIN Cohort to compare tool effectiveness. Primary objectives of each RCT will be to compare tools based on (1) information completeness, (2) understandability, and (3) ease of use. We will additionally evaluate comprehension of key aspects of disseminated research; likelihood that participants would enroll in a similar future study; and, for all primary and secondary outcomes, outcomes by participant characteristics (gender, age, race or ethnicity, country, language, education level, health literacy). METHODS: An advisory team of people with systemic sclerosis (SSc, also known as scleroderma) participated in developing research questions, selecting outcomes, and designing the series of parallel-arm RCTs that will each compare two or more tools or tool variations to a plain-language summary comparator; the common comparator will facilitate across-trial comparisons. In each RCT, people with SSc and researchers will select a recent SSc research study to disseminate. Tools will be developed by experienced tool developers and people with SSc. SPIN Cohort participants (current N eligible = 1522 from 50 SPIN sites in Australia, Canada, France, UK, USA) and additional participants recruited via social media and patient organization partners who consent to participate will be randomized to a dissemination tool or plain-language summary comparator and complete outcomes. Analyses will be intent-to-treat and use linear regression models. DISCUSSION: Each trial in the planned series of trials will build upon knowledge from previous trials. Results will contribute to the evidence base on how to best disseminate results to study participants and others with relevant lived experience. TRIAL REGISTRATION: ClinicalTrials.gov NCT06373263. Registered on April 17, 2024 (first trial in series)
How resource acquisition influences the detection of trade-offs
No full textTrade-offs should be ubiquitous in nature. Yet, direct trade-offs between traits essential for fitness are challenging to detect. Recent theory suggests that population-level variation in resource acquisition could play an important role in our ability to detect trade-offs. Here, we test experimentally the hypothesis that the detection of trade-offs depends on the underlying distribution of individuals with different resource acquisition in a population. Specifically, we resampled ecologically and experimentally relevant resource acquisition distributions from a population of male Australian field crickets (Teleogryllus oceanicus) subjected to a continuous range of diet manipulation. While we found evidence for trade-offs between different male fitness traits, the distribution of resource acquisition in the population had no systematic effect on the strength of these trade-offs. Interestingly, trade-offs were most pronounced between postcopulatory traits and immune function, but trade-offs involving precopulatory traits were relatively weak. Overall, our findings question the hypothesis that resource acquisition may influence our ability to detect trade-offs and instead suggest that other factors, like the hierarchical complexity of resource allocation, make detecting trade-offs so elusive
A 10-year journey towards clinical translation of an implantable endovascular BCI a keynote lecture given at the BCI society meeting in Brussels
No full textIn the rapidly evolving field of brain-computer interfaces (BCIs), a novel modality for recording electrical brain signals has quietly emerged over the past decade. The technology is endovascular electrocorticography (ECoG), an innovation that stands alongside well-established methods such as electroencephalography, traditional ECoG, and single/multi-unit activity recording. This system was inspired by advancements in interventional cardiology, particularly the integration of electronics into various medical interventions. The breakthrough led to the development of the Stentrode system, which employs stent-mounted electrodes to record electrical brain activity for applications in a motor neuroprosthesis. This perspective explores four key areas in our quest to bring the Stentrode BCI to market: the critical patient need for autonomy driving our efforts, the hurdles and achievements in assessing BCI performance, the compelling advantages of our unique endovascular approach, and the essential steps for clinical translation and product commercialization
LADs Making Mischief at Home and Abroad: Should the Penalties Doctrine Apply to Whether Low-Value Liquidated Damages Are an Exclusive Remedy for Delay?
No full textThis article examines an under-considered aspect of the “mischief” which characterises the principles and practice of liquidated damages law in Australia and across the common law world. This is whether low-value liquidated damages – especially, those where the parties have specified a value of “nil” or “$1” – override the non-defaulting party’s right to recover general damages for its loss in breach of contract. The article regards the case law on this point – illustrated by the recent New South Wales Court of Appeal decision in Carbone v Fowler Homes Pty Ltd – as tending to promote uncertainty due to its roots in contract interpretation principles. As a way of alleviating this uncertainty, the article poses the question: if the doctrine of penalties can intervene when it comes to a maximum rate of liquidated damages, why should it not also act to regulate a minimum rate of liquidated damages
Inhibition of RIPK1 or RIPK3 kinase activity post ischemia-reperfusion reduces the development of chronic kidney injury
No full textIschemia-reperfusion injury (IRI) occurs when the blood supply to an organ is temporarily reduced and then restored. Kidney IRI is a form of acute kidney injury (AKI), which often progresses to kidney fibrosis. Necroptosis is a regulated necrosis pathway that has been implicated in kidney IRI. Necroptotic cell death involves the recruitment of the RIPK1 and RIPK3 kinases and the activation of the terminal effector, the mixed lineage kinase domain-like (MLKL) pseudokinase. Phosphorylated MLKL causes cell death by plasma membrane rupture, driving 'necroinflammation'. Owing to their apical role in the pathway, RIPK1 and RIPK3 have been implicated in the development of kidney fibrosis. Here, we used a mouse model of unilateral kidney IRI to assess whether the inhibition of RIPK1 or RIPK3 kinase activity reduces AKI and the progression to kidney fibrosis. Mice treated with the RIPK1 inhibitor Nec-1s, either before or after IR, showed reduced kidney injury at 24 hr compared with controls, whereas no protection was offered by the RIPK3 inhibitor GSK´872. In contrast, treatment with either inhibitor from days 3 to 9 post-IR reduced the degree of kidney fibrosis at day 28. These findings further support the role of necroptosis in IRI and provide important validation for the contribution of both RIPK1 and RIPK3 catalytic activities in the progression of kidney fibrosis. Targeting the necroptosis pathway could be a promising therapeutic strategy to mitigate kidney disease following IR
Expanding the parameter space of 2002es-like type Ia supernovae: On the underluminous ASASSN-20jq/SN 2020qxp
No full textWe present optical photometric and spectroscopic observations of the peculiar Type Ia supernovae (SNe Ia) ASASSN-20jq/SN 2020qxp. It is a low-luminosity object, with a peak absolute magnitude of MB = −17.1 ± 0.5 mag, while its post-peak light-curve decline rate of Δm15(B) = 1.35 ± 0.09 mag and color-stretch parameter of sBV ⪆ 0.82 is similar to that of normal luminosity SNe Ia. That makes it a prevalent outlier in both the SN Ia luminosity-width and the luminosity-color-stretch relations. The analysis of the early light curves indicates a possible “bump” during the first ≈1.4 days of explosion. ASASSN-20jq synthesized a low radioactive 56Ni mass of 0.09 ± 0.01 M⊙. The near-maximum light spectra of the supernova show strong Si II absorption lines, indicating a cooler photosphere than normal SNe Ia; however, it lacks Ti II absorption lines. Additionally, it shows unusually strong absorption features of O Iλ7773 and the Ca II near-infrared triplet. The nebular spectra of ASASSN-20jq show a remarkably strong but narrow forbidden [Ca II] λλ7291, 7324 doublet emission that has not been seen in SNe Ia except for a handful of Type Iax events. There is also a marginal detection of the [O I] λλ6300, 6364 doublet emission in nebular spectra, which is extremely rare. Both the [Ca II] and [O I] lines are redshifted by roughly 2000 km s−1. ASASSN-20jq also exhibits a strong [Fe II] λ7155 emission line with a tilted-top line profile, which is identical to the [Fe II] λ16433 line profile. The asymmetric [Fe II] line profiles, along with the redshifted [Ca II] and emission lines, suggest a high central density white dwarf progenitor that underwent an off-center delayed-detonation explosion mechanism, synthesizing roughly equal amounts of 56Ni during the deflagration and detonation burning phases. The equal production of 56Ni in both burning phases distinguishes ASASSN-20jq from normal bright and subluminous SNe Ia. Assuming this scenario, we simultaneously modeled the optical and near-infrared nebular spectra, achieving a good agreement with the observations. The light curve and spectroscopic features of ASASSN-20jq do not align with any single sub-class of SNe Ia. However, the significant deviation from the luminosity versus light-curve shape relations (along with several light-curve and spectroscopic features) exhibits similarities to some 2002es-like objects. Therefore, we have identified ASASSN-20jq as an extreme candidate within the broad and heterogeneous parameter space of 2002es-like SNe Ia
Speaking to everyone about Crystallography – The Bragg Your Pattern Project
No full textMany programs for science communication are targeted towards secondary-school ages (11 +) and for good reason, as this is when students make choices on subjects to study further. It is vital that these students are supported in their continuing science education. But are we missing out on inspiring them in the first place? Can we help students to see the bigger picture of science, beyond grades and textbooks? What if we run programs that target younger students, as well as their families? For younger students, it is vital to have strong visual and hands-on components to science communication activities. Crystallography lends itself extremely well to visual science communication – we have a great history of leveraging that. But do we have enough hands-activities that are suitable for under 11s, can be undertaken cheaply, and are linked to big crystallographic science ideas?
The Australian and New Zealand crystallographic community leveraged hosting the IUCr2023 meeting to launch a program of events and initiatives to communicate crystallography to those under 11 years old and their families. We undertook a range of events and activities, from pattern competitions, to a crystallographic science festival, [1] to even attempting to break a world record. For this we used existing ideas on 3D printing structures [2] (but upsized it), modified established hands-on activities [3] [4] [5], and developed more. In this contribution I’ll review what we carried out, what did (and didn't) work and how we are planning to continue the momentum into the future which could be applied at IUCr2026
Assessing Susceptibility Factors of Confirmation Bias in News Feed Reading
No full textIndividuals tend to apply preferences and beliefs as heuristics to effectively sift through the sheer amount of information available online. Such tendencies, however, often result in cognitive biases, which can skew judgment and open doors for manipulation. In this work, we investigate how individual and contextual factors lead to instances of confirmation bias when seeking, evaluating, and recalling polarising information. We conducted a lab study, in which we exposed participants to opinions on controversial issues through a Twitter-like news feed. We found that low-effortful thinking, strong political beliefs, and content conveying a strong issue amplify the occurrences of confirmation bias, leading to skewed information processing and recall. We discuss how the adverse effects of confirmation bias can be mitigated by taking bias-susceptibility into account. Specifically, social media platforms could aim to reduce strong expressions and integrate media literacy-building mechanisms, as low-effortful thinking styles and strong political beliefs render individuals especially susceptible to cognitive biases
Accurate Spatial-Temporal Forecasting with Noisy and Incomplete Data
No full text© 2025 Xinyu SuUrbanisation has brought increasing pressures on infrastructure, mobility, and sustainability, prompting the rise of smart cities that utilise technology to enhance urban efficiency and quality of life. A key component of these systems is the widespread deployment of sensors and IoT devices, which continuously collect large volumes of spatial-temporal data covering traffic flow, air pollution, weather conditions, and more. Effectively forecasting such data is essential for real-time decision-making and long-term planning in many real-world applications.
In recent years, data-driven deep learning models have achieved remarkable success in spatial-temporal forecasting, particularly in capturing regular patterns. However, these models typically rely on clean, complete datasets, and their performance often deteriorates under real-world conditions, where data may be noisy (e.g., road accidents) or missing (e.g., sensor failure or transmission errors). Such complexities are not only unavoidable but may also carry valuable information that models should be able to recognise and respond to. Moreover, the inherent complexity of urban systems further compounds this challenge.
This thesis aims to address these challenges by focusing on accurate spatial-temporal forecasting under noisy and incomplete data, and investigates four core problems arising from this setting.
The first problem focuses on aperiodic events in traffic series and encourages the model to identify and respond to such events promptly. Most existing spatial-temporal forecasting models are optimised to minimise average forecasting errors, making them well-suited for capturing regular, periodic patterns. However, this often results in poor generalisation to aperiodic events, where model performance tends to degrade significantly. To address this issue, we propose a generic framework, DualCast, which disentangles intrinsic (periodic) and external environmental (aperiodic) signals from input sequences using three tailored loss functions. The separated components are then fused to generate more robust forecasting results. Furthermore, DualCast adopts a rooted sub-tree-based cross-time attention mechanism to capture high-order spatial-temporal dependencies from both periodic and aperiodic patterns. Extensive experiments show that integrating DualCast into existing forecasting models consistently improves their performance under multiple real-world traffic datasets.
The second problem introduces a novel spatial-temporal forecasting task: forecasting for regions without observations, a challenging yet essential problem commonly encountered in real-world applications. While existing approaches to incomplete data scenarios often assume missing values at scattered spatial or temporal points, they overlook the case of continuous missing data, frequently seen in practice due to unbalanced regional development or progressive sensor deployment. When existing models are adapted to this setting, their performance degrades significantly due to the lack of observed signals. To address this, we propose a model named STSM, which enhances generalisability to unobserved regions. The key insight of STSM is to learn from the locations that resemble those in the region of interest, and we propose a selective masking strategy to enable the learning.
Furthermore, STSM use pseudo-observations, temporal-based relational matrices, and contrastive learning to effectively capture transferable spatial-temporal patterns and improve forecasting in regions without direct observations. As a result, our proposed model outperforms adapted state-of-the-art models, reducing errors consistently over both traffic and air pollutant forecasting tasks.
The third problem addresses the inherent limitations of data-driven models under incomplete data and seeks to use generalisable prior knowledge to support forecasting in unobserved regions. To this end, we propose GenCast, a physics-informed model that uses physical constraints with multimodal spatial-temporal data, enhancing robustness and interpretability. Furthermore, to mitigate the negative impact of localised aperiodic events, which are often difficult to generalise, we introduce a spatial grouping module. This module filters out local signals that are unlikely to transfer across regions, allowing the model to focus on broadly applicable learning patterns. Extensive experiments verify the effectiveness of our proposed model.
The fourth problem addresses the challenge of managing large-scale spatial-temporal data, a growing concern driven by the development of smart cities and the increasing use of multimodal information such as points of interest (POIs) and environmental attributes. As models incorporate more auxiliary data to enhance generalisability, efficiently organising and matching these heterogeneous sources within a single region becomes critical. Learned spatial indices offer a promising solution for efficient spatial data access. However, despite claims of external memory support, few existing methods are effectively implemented on secondary storage. To bridge this gap, we propose FHSIE, an external-memory-friendly spatial index that hierarchically groups spatial objects using lightweight unsupervised models. The resulting structure is integrated with a grid-based framework, enabling fast and scalable retrieval based on spatial coordinates