Innovative signal processing and data mining techniques for aquatic animal health

Problem: Aquatic animal health data is often stored in unstructured formats like text and medical images, making large-scale analysis challenging due to the complexity of processing such data. Objectives: In this thesis, we aim to develop text mining, signal processing, image processing, and machine learning techniques to analyse unstructured data effectively. These methods will enable the aggregation of information across large datasets of unstructured aquatic animal health data. Methodology: • For text analysis, we have designed an ontology-based framework for extracting and storing information from aquatic animal post-mortem reports, with a focus on gross pathology reports. While we initially applied this framework to marine mammal stranding reports, it can be adapted for various species and report types. • For medical image analysis, we have created methods for identifying and analysing lesions in whole-slide images (WSIs) of Atlantic salmon gills. Our approach includes a novel feature extraction technique utilising the empirical wavelet transform, and we enhance context-awareness by employing a variational autoencoder to identify regions of interest within histology images. Achievements: The research resulted in the development of an ontology-based framework for systematic text extraction and storage from marine mammal gross pathology reports. We showcased our framework’s performance by using it to analyse bottlenose dolphin attacks on harbour porpoises. Additionally, we created innovative methods for lesion detection in Atlantic salmon gill whole-slide images, incorporating advanced techniques such as the empirical wavelet transform, deep learning, and a variational autoencoder for context-awareness. These achievements collectively advance the analysis of unstructured aquatic animal health data, enabling more comprehensive and efficient data processing. At the time of writing, the project is the only one to apply data-driven approaches to marine mammal post-mortem reports and gill WSIs

Undoing a weak quantum measurement of a solid-state qubit

We propose an experiment which demonstrates the undoing of a weak continuous measurement of a solid-state qubit, so that any unknown initial state is fully restored. The undoing procedure has only a finite probability of success because of the non-unitary nature of quantum measurement, though it is accompanied by a clear experimental indication of whether or not the undoing has been successful. The probability of success decreases with increasing strength of the measurement, reaching zero for a traditional projective measurement. Measurement undoing (``quantum un-demolition'') may be interpreted as a kind of a quantum eraser, in which the information obtained from the first measurement is erased by the second measurement, which is an essential part of the undoing procedure. The experiment can be realized using quantum dot (charge) or superconducting (phase) qubits.Comment: 5 page

Quantum efficiency of binary-outcome detectors of solid-state qubits

We discuss definitions of the quantum efficiency for binary-outcome qubit detectors with imperfect fidelity, focusing on the subclass of quantum non-demolition detectors. Quantum efficiency is analyzed for several models of detectors, including indirect projective measurement, linear detector in binary-outcome regime, detector of the superconducting phase qubit, and detector based on tunneling into continuum.Comment: 11 page

Ir-Man: An Information Retrieval Framework for Marine Animal Necropsy Analysis

This paper proposes Ir-Man (Information Retrieval for Marine Animal Necropsies), a framework for retrieving discrete information from marine mammal post-mortem reports for statistical analysis. When a marine mammal is reported dead after stranding in Scotland, the carcass is examined by the Scottish Marine Animal Strandings Scheme (SMASS) to establish the circumstances of the animal's death. This involves the creation of a 'post-mortem' (or necropsy) report , which systematically describes the body. These semi-structured reports record lesions (damage or abnormalities to anatomical regions) as well as other observations. Observations embedded within these texts are used to determine cause of death. While a cause of death is recorded separately, many other descriptions may be of pathological and epidemiological significance when aggregated and analysed collectively. As manual extraction of these descriptions is costly, time consuming and at times erroneous, there is a need for an automated information retrieval mechanism which is a non-trivial task given the wide variety of possible descriptions, pathologies and species. The Ir-Man framework consists of a new ontology, a lexicon of observations and anatomical terms and an entity relation engine for information retrieval and statistics generation from a pool of necropsy reports. We demonstrate the effectiveness of our framework by creating a rule-based binary classifier for identifying bottlenose dolphin attacks (BDA) in harbour porpoise gross pathology reports and achieved an accuracy of 83.4%

Secret Codes: The Hidden Curriculum of Semantic Web Technologies

There is a long tradition in education of examination of the hidden curriculum, those elements which are implicit or tacit to the formal goals of education. This article draws upon that tradition to open up for investigation the hidden curriculum and assumptions about students and knowledge that are embedded in the coding undertaken to facilitate learning through information technologies, and emerging ‘semantic technologies’ in particular. Drawing upon an empirical study of case-based pedagogy in higher education, we examine the ways in which code becomes an actor in both enabling and constraining knowledge, reasoning, representation and students. The article argues that how this occurs, and to what effect, is largely left unexamined and becomes part of the hidden curriculum of electronically mediated learning that can be more explicitly examined by positioning technologies in general, and code in particular, as actors rather than tools. This points to a significant research agenda in technology enhanced learning

The Type 2 Diabetes Knowledge Portal: an Open access Genetic Resource Dedicated to Type 2 Diabetes and Related Traits

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP\u27s comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results

Driving and Driven Architectures of Directed Small-World Human Brain Functional Networks

Recently, increasing attention has been focused on the investigation of the human brain connectome that describes the patterns of structural and functional connectivity networks of the human brain. Many studies of the human connectome have demonstrated that the brain network follows a small-world topology with an intrinsically cohesive modular structure and includes several network hubs in the medial parietal regions. However, most of these studies have only focused on undirected connections between regions in which the directions of information flow are not taken into account. How the brain regions causally influence each other and how the directed network of human brain is topologically organized remain largely unknown. Here, we applied linear multivariate Granger causality analysis (GCA) and graph theoretical approaches to a resting-state functional MRI dataset with a large cohort of young healthy participants (n = 86) to explore connectivity patterns of the population-based whole-brain functional directed network. This directed brain network exhibited prominent small-world properties, which obviously improved previous results of functional MRI studies showing weak small-world properties in the directed brain networks in terms of a kernel-based GCA and individual analysis. This brain network also showed significant modular structures associated with 5 well known subsystems: fronto-parietal, visual, paralimbic/limbic, subcortical and primary systems. Importantly, we identified several driving hubs predominantly located in the components of the attentional network (e.g., the inferior frontal gyrus, supplementary motor area, insula and fusiform gyrus) and several driven hubs predominantly located in the components of the default mode network (e.g., the precuneus, posterior cingulate gyrus, medial prefrontal cortex and inferior parietal lobule). Further split-half analyses indicated that our results were highly reproducible between two independent subgroups. The current study demonstrated the directions of spontaneous information flow and causal influences in the directed brain networks, thus providing new insights into our understanding of human brain functional connectome

Cyanobacterial lipopolysaccharides and human health – a review

Cyanobacterial lipopolysaccharide/s (LPS) are frequently cited in the cyanobacteria literature as toxins responsible for a variety of heath effects in humans, from skin rashes to gastrointestinal, respiratory and allergic reactions. The attribution of toxic properties to cyanobacterial LPS dates from the 1970s, when it was thought that lipid A, the toxic moiety of LPS, was structurally and functionally conserved across all Gram-negative bacteria. However, more recent research has shown that this is not the case, and lipid A structures are now known to be very different, expressing properties ranging from LPS agonists, through weak endotoxicity to LPS antagonists. Although cyanobacterial LPS is widely cited as a putative toxin, most of the small number of formal research reports describe cyanobacterial LPS as weakly toxic compared to LPS from the Enterobacteriaceae. We systematically reviewed the literature on cyanobacterial LPS, and also examined the much lager body of literature relating to heterotrophic bacterial LPS and the atypical lipid A structures of some photosynthetic bacteria. While the literature on the biological activity of heterotrophic bacterial LPS is overwhelmingly large and therefore difficult to review for the purposes of exclusion, we were unable to find a convincing body of evidence to suggest that heterotrophic bacterial LPS, in the absence of other virulence factors, is responsible for acute gastrointestinal, dermatological or allergic reactions via natural exposure routes in humans. There is a danger that initial speculation about cyanobacterial LPS may evolve into orthodoxy without basis in research findings. No cyanobacterial lipid A structures have been described and published to date, so a recommendation is made that cyanobacteriologists should not continue to attribute such a diverse range of clinical symptoms to cyanobacterial LPS without research confirmation