Exploring temporal information in neonatal seizures using a dynamic time warping based SVM kernel

Seizure events in newborns change in frequency, morphology, and propagation. This contextual information is explored at the classifier level in the proposed patient-independent neonatal seizure detection system. The system is based on the combination of a static and a sequential SVM classifier. A Gaussian dynamic time warping based kernel is used in the sequential classifier. The system is validated on a large dataset of EEG recordings from 17 neonates. The obtained results show an increase in the detection rate at very low false detections per hour, particularly achieving a 12% improvement in the detection of short seizure events over the static RBF kernel based system

Toward a personalized real-time diagnosis in neonatal seizure detection

The problem of creating a personalized seizure detection algorithm for newborns is tackled in this paper. A probabilistic framework for semi-supervised adaptation of a generic patient-independent neonatal seizure detector is proposed. A system that is based on a combination of patient-adaptive (generative) and patient-independent (discriminative) classifiers is designed and evaluated on a large database of unedited continuous multichannel neonatal EEG recordings of over 800 h in duration. It is shown that an improvement in the detection of neonatal seizures over the course of long EEG recordings is achievable with on-the-fly incorporation of patient-specific EEG characteristics. In the clinical setting, the employment of the developed system will maintain a seizure detection rate at 70% while halving the number of false detections per hour, from 0.4 to 0.2 FD/h. This is the first study to propose the use of online adaptation without clinical labels, to build a personalized diagnostic system for the detection of neonatal seizures

Trauma exposure, PTSD and psychotic-like symptoms in post-conflict Timor Leste: an epidemiological survey.

BACKGROUND: Studies in developed countries indicate that psychotic-like symptoms are prevalent in the community and are related to trauma exposure and PTSD. No comparable studies have been undertaken in low-income, post-conflict countries. This study aimed to assess the prevalence of psychotic-like symptoms in conflict-affected Timor Leste and to examine whether symptoms were associated with trauma and PTSD. METHODS: The Psychosis Screening Questionnaire and the Harvard Trauma Questionnaire (assessing trauma exposure and PTSD) were administered in an epidemiological survey of 1245 adults (response rate 80.6%) in a rural and an urban setting in Timor Leste. We defined PSQ screen-positive cases as those people reporting at least one psychotic-like symptom (paranoia, hallucinations, strange experiences, thought interference, hypomania). RESULTS: The prevalence of PSQ screen-positive cases was 12 percent and these persons were more disabled. PSQ cases were more likely to reside in the urban area, experienced higher levels of trauma exposure and a greater prevalence of PTSD. PTSD only partially mediated the relationship between trauma exposure and psychotic-like symptoms. CONCLUSIONS: Psychotic-like symptoms may be prevalent in countries exposed to mass conflict. The cultural and contextual meaning of psychotic-like symptoms requires further inquiry in low-income, post-conflict settings such as Timor Leste.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

When women lead, workplaces should listen

“Las organizaciones reflexivas pueden transformarse en lugares de trabajo verdaderamente inclusivos, aprovechando al máximo los beneficios significativos de los equipos diversos que funcionan de la mejor manera”. Las organizaciones inteligentes se están moviendo más rápidamente para afinar las habilidades de escucha y reforzar una gama más amplia de estilos de liderazgo. Este es un gran resumen de esta tendencia, con datos concretos que lo respaldanThe most valuable lessons of women’s leadership programs are those that show organizations where to improve. For years, female executives have come away from women-only leadership programs empowered to do—and ask for—more, valuing the opportunity to examine their strengths and shortcomings in the psychological safety of their peers and to use the experience as a springboard for personal development

Natural Sovereignty on the High Seas

The purpose of this paper is to examine a current dispute involving resources in the sea, determine the issues involved, and hypothesize a solution. Specifically, the dispute chosen involves Latin American claims to extensive sovereign fishery rights in adjacent coastal waters and objections to these claims. The United States and the parties to a declaration made in Santiago, Chile, in 1882, Chile, Ecuador, and Peru, have been chosen as most representative of the opposing positions. The International Court of Justice, primarily utilizing as a framework for analysis the dicta, findings, and opinions in their February 1969 Judgment in the North Sea Cases, has been chosen as the arena within which to hypothesize a solution

Critical role of the disintegrin metalloprotease ADAM-like Decysin-1 (ADAMDEC1) for intestinal immunity and inflammation

BACKGROUND AND AIMS: ADAM (A Disintegrin And Metalloproteinase) is a family of peptidase proteins, which have diverse roles in tissue homeostasis and immunity. Here, we study ADAM-like Decysin-1 (ADAMDEC1) a unique member of the ADAM family. ADAMDEC1 expression is restricted to the macrophage/dendritic cell populations of the gastrointestinal tract and secondary lymphoid tissue. The biological function of ADAMDEC1 is unknown but it has been hypothesised to play a role in immunity. The identification of reduced ADAMDEC1 expression in Crohn's disease patients has provided evidence of a potential role in bowel inflammation. METHODS: Adamdec1(-/-) mice were exposed to dextran sodium sulphate or infected orally with Citrobacter rodentium or Salmonella typhimurium The clinical response was monitored. RESULTS: The loss of Adamdec1 rendered mice more susceptible to the induction of bacterial and chemical induced colitis, as evidenced by increased neutrophil infiltration, greater IL-6 and IL-1β secretion, more weight loss and increased mortality. In the absence of Adamdec1, greater numbers of Citrobacter rodentium were found in the spleen, suggestive of a breakdown in mucosal immunity which resulted in bacteraemia. CONCLUSION: In summary, Adamdec1 protects the bowel from chemical and bacterial insults, failure of which may predispose to Crohn's disease

Evaluating similarity network construction in biomedical data: implications for community detection performance

Similarity network construction is a fundamental step in many approaches to community detection in biomedical analysis. It is used both in the creation of network structures from non-relational data and as a processing step in clustering pipelines. The foundation of any network analysis hinges on the quality of the underlying network. With the rising popularity of network learning and network-based clustering, the importance of correctly constructing these networks is vital. However, the implications of key choices in similarity network construction — specifically in sparsification methods and multi-modal integration — remain poorly explored. Similarity network construction involves several critical stages: computing pairwise similarities using an appropriate metric, sparsifying these similarities to define edges, and, in the case of multi-modal data, integrating the modalities. This thesis evaluates two key components within this pipeline — similarity sparsification and multi-modal integration — by measuring their impact on community detection performance in the final network. To this end, I developed a flexible network generation framework and used it to create a suite of simulated datasets with known embedded cluster structures. These networks, with ground-truth communities, were evaluated using a novel analytic framework focused on the community detection performance of diverse clustering algorithms — Stochastic Block Modelling, Leiden clustering, and Spectral clustering. A comprehensive set of metrics, including ground-truth cluster modularity, Adjusted Rand Index (ARI), Adjusted Mutual Information (AMI), and network statistics such as density, were employed to evaluate the quality of the constructed networks. Firstly, I assess the quality of single-modality networks generated using common sparsification methods by evaluating the community detection performance of the clustering algorithms. The key sparsification approaches studied include K-Nearest Neighbour and ε-Thresholding. The analysis reveals a critical limitation of ε-Thresholding, which fails to account for variations in cluster density, resulting in networks of poor quality. The thesis then extends the analysis to evaluate the effectiveness of popular multi-modal similarity integration techniques, such as Similarity Network Fusion (SNF) and Neighborhood-based Multi-Omics clustering (NEMO), across various multi-modal data scenarios. By applying transformations to ground-truth clusters, a range of modalities with differing embedded cluster information and noise levels were generated to stress-test the integration techniques. These scenarios included adjustments such as merging ground-truth clusters, increasing the presence of outliers and noise, and adding uninformative modalities. Notably, SNF and NEMO fail to outperform simpler techniques, such as mean similarity aggregation, when incorporating modalities with inconsistently embedded clusters. I demonstrate how integration methods can be used to incorporate partial modalities — datasets where not all individuals have a full set of measurements in all modalities. SNF shows significant sensitivity to incomplete modalities while NEMO and mean aggregation are more resilient. Finally, I validate the findings of our synthetic data scenarios using two biomedical datasets; one for discerning cancer subtypes using data from The Cancer Genome Atlas (TCGA) and the second for differentiating individuals with Autism Spectrum Disorder (ASD) using data from the Simons Simplex Collection (SSC). Both datasets exemplify common challenges encountered with biomedical data; high dimensionality, unbalanced class membership and partial modalities

The functional diversity of fish assemblages in the vicinity of oil and gas pipelines compared to nearby natural reef and soft sediment habitats

We would like to thank skippers John Totterdell and Kylie Skipper who assisted and made data collection possible. We acknowledge David Whillas and Kevin Holden who operated the stereo-ROV on the pipelines. The contributions of Laura Fullwood and Damon Driessen both in the field and with image analysis are gratefully acknowledged, as is Jack Park for his assistance with image analysis. This research project was funded by Chevron through its Anchor Partnership with the UK National Decommissioning Centre. We also acknowledge in-kind support from Net Zero Technology Centre and the University of Aberdeen through their partnership in the UK National Decommissioning Centre.Peer reviewedPublisher PD