Integrated Numerical Modelling System for Extreme Wave Events at the Wave Hub Site

This paper examines an extreme wave event which occurred during a storm at the Wave Hub site in 2012. The extreme wave of 9.57 m height was identified from a time series of the heave data collected by an Oceanor Seawatch Mini II Buoy deployed at the site. An energy density spectrum was derived from this time series and then used to drive a physical model, which represents the extreme wave at 1:20 scale in Plymouth University’s new COAST Lab. The NewWave technique was used to define the input to the physical model. The experiment is reproduced in a numerical wave tank using the fully nonlinear CFD library OpenFOAM® and the wave generation toolbox waves2Foam. Results are evaluated, and issues regarding the predictions of a numerical model that is driven by the NewWave input signal are discussed. This study sets the basis for further research in coupling field data, physical modelling and numerical modelling in a more efficient and balanced way. This will lead to the new approach of composite modelling that will be implemented in future work

Data centers with quantum random access memory and quantum networks

In this paper, we propose the Quantum Data Center (QDC), an architecture combining Quantum Random Access Memory (QRAM) and quantum networks. We give a precise definition of QDC, and discuss its possible realizations and extensions. We discuss applications of QDC in quantum computation, quantum communication, and quantum sensing, with a primary focus on QDC for $T$-gate resources, QDC for multi-party private quantum communication, and QDC for distributed sensing through data compression. We show that QDC will provide efficient, private, and fast services as a future version of data centers.Comment: 23 pages, many figure

Minimal Elastographic Modeling of Breast Cancer for Model Based Tumour Detection in a Digital Image Elasto Tomography (DIET) System

Digital Image Elasto Tomography (DIET) is a non-invasive breast cancer screening technology that images the surface motion of a breast under harmonic mechanical actuation. A new approach capturing the dynamics and characteristics of tumor behavior is presented. A simple mechanical model of the breast is used to identify a transfer function relating the input harmonic actuation to the output surface displacements using imaging data of a silicone phantom. Areas of higher stiffness cause significant changes of damping and resonant frequencies as seen in the resulting Bode plots. A case study on a healthy and tumor silicone breast phantom shows the potential for this model-based method to clearly distinguish cancerous and healthy tissue as well as correctly predicting the tumor position

Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non-small-cell lung cancer treated with stereotactic body radiation therapy.

Background and purposeChest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We utilize machine learning algorithms to identify toxicity predictors to develop dose-volume constraints.Materials and methodsTwenty-five patient, tumor, and dosimetric features were recorded for 197 consecutive patients with Stage I NSCLC treated with SBRT, 11 of whom (5.6%) developed CTCAEv4 grade ≥2 chest wall pain. Decision tree modeling was used to determine chest wall syndrome (CWS) thresholds for individual features. Significant features were determined using independent multivariate methods. These methods incorporate out-of-bag estimation using Random forests (RF) and bootstrapping (100 iterations) using decision trees.ResultsUnivariate analysis identified rib dose to 1 cc < 4000 cGy (P = 0.01), chest wall dose to 30 cc < 1900 cGy (P = 0.035), rib Dmax < 5100 cGy (P = 0.05) and lung dose to 1000 cc < 70 cGy (P = 0.039) to be statistically significant thresholds for avoiding CWS. Subsequent multivariate analysis confirmed the importance of rib dose to 1 cc, chest wall dose to 30 cc, and rib Dmax. Using learning-curve experiments, the dataset proved to be self-consistent and provides a realistic model for CWS analysis.ConclusionsUsing machine learning algorithms in this first of its kind study, we identify robust features and cutoffs predictive for the rare clinical event of CWS. Additional data in planned subsequent multicenter studies will help increase the accuracy of multivariate analysis

The HSV-1 Latency-Associated Transcript Functions to Repress Latent Phase Lytic Gene Expression and Suppress Virus Reactivation from Latently Infected Neurons

open access articleHerpes simplex virus 1 (HSV-1) establishes life-long latent infection within sensory neurons, during which viral lytic gene expression is silenced. The only highly expressed viral gene product during latent infection is the latency-associated transcript (LAT), a non-protein coding RNA that has been strongly implicated in the epigenetic regulation of HSV-1 gene expression. We have investigated LAT-mediated control of latent gene expression using chromatin immunoprecipitation analyses and LAT-negative viruses engineered to express firefly luciferase or β-galactosidase from a heterologous lytic promoter. Whilst we were unable to determine a significant effect of LAT expression upon heterochromatin enrichment on latent HSV-1 genomes, we show that reporter gene expression from latent HSV-1 genomes occurs at a greater frequency in the absence of LAT. Furthermore, using luciferase reporter viruses we have observed that HSV-1 gene expression decreases during long-term latent infection, with a most marked effect during LAT-negative virus infection. Finally, using a fluorescent mouse model of infection to isolate and culture single latently infected neurons, we also show that reactivation occurs at a greater frequency from cultures harbouring LAT-negative HSV-1. Together, our data suggest that the HSV-1 LAT RNA represses HSV-1 gene expression in small populations of neurons within the mouse TG, a phenomenon that directly impacts upon the frequency of reactivation and the maintenance of the transcriptionally active latent reservoir

A Minimal C-Peptide Sampling Method to Capture Peak and Total Pre-Hepatic Insulin Secretion in Model-Based Experimental Insulin Sensitivity Studies

Aims and Background: Model-based insulin sensitivity testing via the intravenous glucose tolerance test (IVGTT) or similar is clinically very intensive due to the need for frequent sampling to accurately capture the dynamics of insulin secretion and clearance. The goal of this study was to significantly reduce the number of samples required in intravenous glucose tolerance test protocols to accurately identify C-peptide and insulin secretion characteristics. Methods: Frequently sampled IVGTT data from 12 subjects [5 normal glucose-tolerant (NGT) and 7 type 2 diabetes mellitus (T2DM)] were analyzed to calculate insulin and C-peptide secretion using a well-accepted C-peptide model. Samples were reduced in a series of steps based on the critical IVGTT profile points required for the accurate estimation of C-peptide secretion. The full data set of 23 measurements was reduced to sets with six or four measurements. The peak secretion rate and total secreted C-peptide during 10 and 20 minutes postglucose input and during the total test time were calculated. Results were compared to those from the full data set using the Wilcoxon rank sum to assess any differences. Results: In each case, the calculated secretion metrics were largely unchanged, within expected assay variation, and not significantly different from results obtained using the full 23 measurement data set (P < 0.05). Conclusions: Peak and total C-peptide and insulin secretory characteristics can be estimated accurately in an IVGTT from as few as four systematically chosen samples, providing an opportunity to minimize sampling, cost, and burden

Insulin + nutrition control for tight critical care glycaemic regulation

A new insulin and nutrition control method for tight glycaemic control in critical care is presented from concept to clinical trials to clinical practice change. The primary results show that the method can provide very tight glycaemic control in critical care for a very critically ill cohort. More specifically, the final clinical practice change protocol provided 2100 hours of control with average blood glucose of 5.8 +/- 0.9 mmol/L for an initial 10 patient pilot study. It also used less insulin, while providing the same or greater nutritional input, as compared to retrospective hospital control for a relatively very critically ill cohort with high insulin resistance

Long term verification of glucose-insulin regulatory system model dynamics

doi: 10.1109/IEMBS.2004.1403269Hyperglycaemia in critically ill patients increases the risk of further complications and mortality. A long-term verification of a model that captures the essential glucose- and insulin-kinetics is presented, using retrospective data gathered in an Intensive Care Unit (ICU). The model uses only two patient specific parameters, for glucose clearance and insulin sensitivity. The optimization of these parameters is accomplished through a novel integration-based fitting approach, and a piecewise linearization of the parameters. This approach reduces the non-linear, non-convex optimization problem to a simple linear equation system. The method was tested on long-term blood glucose recordings from 17 ICU-patients, resulting in an average error of 7%, which is in the range of the sensor error. One-hour predictions of blood glucose data proved acceptable with an error range between 7- 11%. These results verify the model’s ability to capture longterm observed glucose-insulin dynamics in hyperglycaemic ICU patients