Channel characterisation and modelling for transcranial Doppler ultrasound.

The detection of micro-embolic signals (MES) is a mature application of transcranial Doppler (TCD) ultrasound. It involves the identification of abnormally highpitched signals within the arterial waveform as a method of diagnosis and prediction of embolic complications in stroke patients. More recently, algorithms have been developed to help characterise and classify MES using advanced signal processing techniques. These advances aim to improve our understanding of the causes of cereberovascular disease, helping to target the most appropriate interventions and quantifying the risk to patients of further stroke events. However, there are a number of limitations with current TCD systems which reduce their effectiveness. In particular, improvements in our understanding of the scattering effects in TCD ultrasound propagation channels will benefit our ability to develop algorithms that more robustly and reliably identify the consistency and material make-up of MES. This thesis explores TCD propagation channels in three related research areas. Firstly, a method of characterising TCD ultrasound propagation channels is proposed. Isotropic and non-isotropic three dimensional space (3-D) spherical scattering channel models are described in terms of theoretical reference models, simulation models, and sum of sinusoids (SoS) simulators, allowing the statistical properties to be analysed and reported. Secondly, a TCD ultrasound medical blood flow phantom is described. The phantom, designed to replicate blood flow in the middle cerebral arteries (MCA) for TCD ultrasound studies, is discussed in terms of material selection, physical construction and acoustic characteristics, including acoustic velocity, attenuation and backscatter coefficients. Finally, verification analysis is performed on the non-isotropic models against firstly, the blood flow phantom, and secondly, a patient recordings database. This analysis expands on areas of agreement and disagreement before assessing the usefulness of the models and describing their potential to improve signal processing approaches for detection of MES. The proposed non-isotropic channel reference model, simulation model, SoS simulator, and blood flow phantom are expected to contribute to improvements in the design, testing, and performance evaluation of future TCD ultrasound systems

Location of Mach Discs and Diamonds Supersonic Air Jets

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77542/1/AIAA-3788-127.pd

Light-touch Interventions to Improve Software Development Security

Many software developers still have little interest in software security. To change this, we need ‘interventions’ to development teams to motivate and help them towards security improvement. An intervention costing less than two days’ effort from a facilitator plus half a day of team effort can significantly improve that team’s software security. This case study describes how this approach was used with one commercial team, and identifies its impact using Participative Action Research. With suitable improvements, the approach has the potential to help many other development teams

Use of “Hidden in Plain Sight” de-identification methodology in electronic healthcare data provides minimal risk of misidentification: Results from the iCAIRD Safe Haven Artificial Intelligence Platform.

Objectives To determine the risk of misidentification when using a “Hidden In Plain Sight (HIPS)” Named Entity Recognition (NER) de-identification methodology applied to Scottish healthcare data within The Industrial Centre for Artificial Intelligence Research in Digital Diagnostics (iCAIRD) Safe Haven Artificial Intelligence Platform (SHAIP). Approach Rather than the traditional redaction of potential identifiable information in routinely collected healthcare data, our HIPS methodology utilises an NER “find and replace” approach to de-identification that keeps the structure of text intact. This ensures that context is maintained, key to the interpretation of free text information and potential Artificial Intelligence applications. To our knowledge these methods have been previously untested on Scottish healthcare data. We therefore performed assessment of this approach in terms of potential risk of misidentification using HIPS on structured Scottish data deployed in SHAIP as part of the iCAIRD programme. Results Five individual cohorts, with a total of 169,964 patients were included. For each cohort the HIPS approach was applied, and then compared to actual patient information from within the same region, in order to determine the risk of misidentification. The following fields were included: Forename, Surname, Previous Name, Gender, Date of Birth (DOB), and Postcode. Across the five cohorts and varying combinations of identifiable data fields there were a total of 94 instances of potential misidentification (0.06%). 85/94 (90.4%) of these were for the combination of Gender, Date of Birth and Postcode. Across the five cohorts there were only 3 instances (0.002%) of Forename/Surname/DOB, and 5 instances (0.003%) of Forename/Surname/Postcode potential misidentification amongst the 169,964 patients. Conclusions The iCAIRD NER HIPS Methodology provides an acceptably low misidentification rate. Further work is now required to determine the recall and precision rates. Benefits of this approach include retaining the structure of free text, as well as reducing the ability to detect any potential leaked identifiable data

Acute stroke CDS: automatic retrieval of thrombolysis contraindications from unstructured clinical letters

Introduction: Thrombolysis treatment for acute ischaemic stroke can lead to better outcomes if administered early enough. However, contraindications exist which put the patient at greater risk of a bleed (e.g. recent major surgery, anticoagulant medication). Therefore, clinicians must check a patient's past medical history before proceeding with treatment. In this work we present a machine learning approach for accurate automatic detection of this information in unstructured text documents such as discharge letters or referral letters, to support the clinician in making a decision about whether to administer thrombolysis. Methods: We consulted local and national guidelines for thrombolysis eligibility, identifying 86 entities which are relevant to the thrombolysis decision. A total of 8,067 documents from 2,912 patients were manually annotated with these entities by medical students and clinicians. Using this data, we trained and validated several transformer-based named entity recognition (NER) models, focusing on transformer models which have been pre-trained on a biomedical corpus as these have shown most promise in the biomedical NER literature. Results: Our best model was a PubMedBERT-based approach, which obtained a lenient micro/macro F1 score of 0.829/0.723. Ensembling 5 variants of this model gave a significant boost to precision, obtaining micro/macro F1 of 0.846/0.734 which approaches the human annotator performance of 0.847/0.839. We further propose numeric definitions for the concepts of name regularity (similarity of all spans which refer to an entity) and context regularity (similarity of all context surrounding mentions of an entity), using these to analyse the types of errors made by the system and finding that the name regularity of an entity is a stronger predictor of model performance than raw training set frequency. Discussion: Overall, this work shows the potential of machine learning to provide clinical decision support (CDS) for the time-critical decision of thrombolysis administration in ischaemic stroke by quickly surfacing relevant information, leading to prompt treatment and hence to better patient outcomes

Detection of early-universe gravitational-wave signatures and fundamental physics

Detection of a gravitational-wave signal of non-astrophysical origin would be a landmark discovery, potentially providing a significant clue to some of our most basic, big-picture scientific questions about the Universe. In this white paper, we survey the leading early-Universe mechanisms that may produce a detectable signal—including inflation, phase transitions, topological defects, as well as primordial black holes—and highlight the connections to fundamental physics. We review the complementarity with collider searches for new physics, and multimessenger probes of the large-scale structure of the Universe.Peer reviewe

Detection of early-universe gravitational-wave signatures and fundamental physics

Detection of a gravitational-wave signal of non-astrophysical origin would be a landmark discovery, potentially providing a significant clue to some of our most basic, big-picture scientific questions about the Universe. In this white paper, we survey the leading early-Universe mechanisms that may produce a detectable signal—including inflation, phase transitions, topological defects, as well as primordial black holes—and highlight the connections to fundamental physics. We review the complementarity with collider searches for new physics, and multimessenger probes of the large-scale structure of the Universe.Peer reviewe

The collagen prolyl hydroxylases are bifunctional growth regulators in melanoma

Appropriate post-translational processing of collagen requires prolyl hydroxylation, catalyzed by the prolyl 3- (C-P3H) and prolyl 4- (C-P4H) hydroxylases is essential for normal cell function. Here we have investigated the expression, transcriptional regulation and function of the C-P3H and C-P4H families in melanoma. We show that the CP3H family exemplified by Leprel1 and Leprel2 are subject to methylation-dependent transcriptional silencing in primary and metastatic melanoma consistent with a tumour suppressor function. In contrast, although there is transcriptional silencing of P4HA3 in a sub-set of melanomas, the CP4H family members P4HA1, P4HA2 and P4HA3 are often over-expressed in melanoma, expression being prognostic of worse clinical outcomes. Consistent with tumour suppressor function, ectopic expression of Leprel1 and Leprel2 inhibits melanoma proliferation, whereas P4HA2 and P4HA3 increase proliferation and particularly invasiveness of melanoma cells. Pharmacological inhibition with multiple selective C-P4H inhibitors reduces proliferation and inhibits invasiveness of melanoma cells. Together, our data identify the C-P3H and C-P4H families as potentially important regulators of melanoma growth and invasiveness and suggest that selective inhibition of C-P4H is an attractive strategy to reduce the invasive properties of melanoma cells

p53 and ovarian carcinoma survival: an Ovarian Tumor Tissue Analysis consortium study

Our objective was to test whether p53 expression status is associated with survival for women diagnosed with the most common ovarian carcinoma histotypes (high-grade serous carcinoma [HGSC], endometrioid carcinoma [EC], and clear cell carcinoma [CCC]) using a large multi-institutional cohort from the Ovarian Tumor Tissue Analysis (OTTA) consortium. p53 expression was assessed on 6,678 cases represented on tissue microarrays from 25 participating OTTA study sites using a previously validated immunohistochemical (IHC) assay as a surrogate for the presence and functional effect of TP53 mutations. Three abnormal expression patterns (overexpression, complete absence, and cytoplasmic) and the normal (wild type) pattern were recorded. Survival analyses were performed by histotype. The frequency of abnormal p53 expression was 93.4% (4,630/4,957) in HGSC compared to 11.9% (116/973) in EC and 11.5% (86/748) in CCC. In HGSC, there were no differences in overall survival across the abnormal p53 expression patterns. However, in EC and CCC, abnormal p53 expression was associated with an increased risk of death for women diagnosed with EC in multivariate analysis compared to normal p53 as the reference (hazard ratio [HR] = 2.18, 95% confidence interval [CI] 1.36-3.47, p = 0.0011) and with CCC (HR = 1.57, 95% CI 1.11-2.22, p = 0.012). Abnormal p53 was also associated with shorter overall survival in The International Federation of Gynecology and Obstetrics stage I/II EC and CCC. Our study provides further evidence that functional groups of TP53 mutations assessed by abnormal surrogate p53 IHC patterns are not associated with survival in HGSC. In contrast, we validate that abnormal p53 IHC is a strong independent prognostic marker for EC and demonstrate for the first time an independent prognostic association of abnormal p53 IHC with overall survival in patients with CCC

An Analysis of Two Genome-wide Association Meta-analyses Identifies a New Locus for Broad Depression Phenotype

AbstractBackgroundThe genetics of depression has been explored in genome-wide association studies that focused on either major depressive disorder or depressive symptoms with mostly negative findings. A broad depression phenotype including both phenotypes has not been tested previously using a genome-wide association approach. We aimed to identify genetic polymorphisms significantly associated with a broad phenotype from depressive symptoms to major depressive disorder.MethodsWe analyzed two prior studies of 70,017 participants of European ancestry from general and clinical populations in the discovery stage. We performed a replication meta-analysis of 28,328 participants. Single nucleotide polymorphism (SNP)-based heritability and genetic correlations were calculated using linkage disequilibrium score regression. Discovery and replication analyses were performed using a p-value-based meta-analysis. Lifetime major depressive disorder and depressive symptom scores were used as the outcome measures.ResultsThe SNP-based heritability of major depressive disorder was 0.21 (SE = 0.02), the SNP-based heritability of depressive symptoms was 0.04 (SE = 0.01), and their genetic correlation was 1.001 (SE = 0.2). We found one genome-wide significant locus related to the broad depression phenotype (rs9825823, chromosome 3: 61,082,153, p = 8.2 × 10–9) located in an intron of the FHIT gene. We replicated this SNP in independent samples (p = .02) and the overall meta-analysis of the discovery and replication cohorts (1.0 × 10–9).ConclusionsThis large study identified a new locus for depression. Our results support a continuum between depressive symptoms and major depressive disorder. A phenotypically more inclusive approach may help to achieve the large sample sizes needed to detect susceptibility loci for depression