Physical model study of beach profile evolution by sea level rise in the presence of seawalls

Persistent and accelerating sea level rise (SLR) may have a significant impact on the evolution of sandy coastlines this Century. The response of natural sandy beaches to SLR has been much discussed in the literature, however there is a lack of knowledge about the impact of SLR on engineered coasts. Laboratory experiments comprising over 320 h of testing were conducted in a 44 m (L) x 1.2 m (W) x 1.6 m (D) wave flume to investigate the influence of coastal armouring in the form of seawalls on coastal response to SLR. The study was designed to investigate the effects of contrasting types of seawalls (reflective-impermeable versus dissipative-permeable) on beach profile response to increased water levels, in the presence of both erosive and accretionary wave conditions. The results obtained showed that seawalls alter the evolution of the equilibrium profile with rising water level, causing increased lowering of the profile adjacent to the structure. Under erosive wave conditions, modelled profiles both with and without seawall structures in place were observed to translate landward in response to SLR and erode the upper profile. It was found that the erosion demand at the upper beach due to a rise in water level remains similar whether a structure is present or not, but that a seawall concentrates the erosion in the area adjacent to the seawall, resulting in enhanced and localised profile lowering. The type of structure present (dissipative-permeable versus reflective-impermeable) was not observed to have a significant influence on this response. Under accretive conditions, the preservation of a large shoreface and berm resulted in no wave-structure interaction occurring, with the result that the presence of a seawall had no impact on profile evolution. A potential two-step method for estimating the observed profile response to water level rise in the presence of seawalls is proposed, whereby a simple profile translation model is used to provide a first estimate of the erosion demand, and then this eroded volume is redistributed in front of the seawall out to the position of the offshore bar

DNA index determination with Automated Cellular Imaging System (ACIS) in Barrett's esophagus: Comparison with CAS 200

BACKGROUND: For solid tumors, image cytometry has been shown to be more sensitive for diagnosing DNA content abnormalities (aneuploidy) than flow cytometry. Image cytometry has often been performed using the semi-automated CAS 200 system. Recently, an Automated Cellular Imaging System (ACIS) was introduced to determine DNA content (DNA index), but it has not been validated. METHODS: Using the CAS 200 system and ACIS, we compared the DNA index (DI) obtained from the same archived formalin-fixed and paraffin embedded tissue samples from Barrett's esophagus related lesions, including samples with specialized intestinal metaplasia without dysplasia, low-grade dysplasia, high-grade dysplasia and adenocarcinoma. RESULTS: Although there was a very good correlation between the DI values determined by ACIS and CAS 200, the former was 25% more sensitive in detecting aneuploidy. ACIS yielded a mean DI value 18% higher than that obtained by CAS 200 (p < 0.001; paired t test). In addition, the average time required to perform a DNA ploidy analysis was shorter with the ACIS (30–40 min) than with the CAS 200 (40–70 min). Results obtained by ACIS gave excellent inter-and intra-observer variability (coefficient of correlation >0.9 for both, p < 0.0001). CONCLUSION: Compared with the CAS 200, the ACIS is a more sensitive and less time consuming technique for determining DNA ploidy. Results obtained by ACIS are also highly reproducible

High-resolution, large-scale laboratory measurements of a sandy beach and dynamic cobble berm revetment

AbstractHigh quality laboratory measurements of nearshore waves and morphology change at, or near prototype-scale are essential to support new understanding of coastal processes and enable the development and validation of predictive models. The DynaRev experiment was completed at the GWK large wave flume over 8 weeks during 2017 to investigate the response of a sandy beach to water level rise and varying wave conditions with and without a dynamic cobble berm revetment, as well as the resilience of the revetment itself. A large array of instrumentation was used throughout the experiment to capture: (1) wave transformation from intermediate water depths to the runup limit at high spatio-temporal resolution, (2) beach profile change including wave-by-wave changes in the swash zone, (3) detailed hydro and morphodynamic measurements around a developing and a translating sandbar.</jats:p

Fluorescence in situ hybridisation analysis of chromosomal aberrations in gastric tissue: the potential involvement of Helicobacter pylori

In this series of experiments, a novel protocol was developed whereby gastric cells were collected using endoscopic cytology brush techniques, and prepared, such that interphase fluorescence in situ hybridization (FISH) could be performed. In total, 80 distinct histological samples from 37 patients were studied using four chromosome probes (over 32 000 cells analysed). Studies have previously identified abnormalities of these four chromosomes in upper GI tumours. Using premalignant tissues, we aimed to determine how early in Correa's pathway to gastric cancer these chromosome abnormalities occurred. Aneuploidy of chromosomes 4, 8, 20 and 17(p53) was detected in histologically normal gastric mucosa, as well as in gastritis, intestinal metaplasia, dysplasia and cancer samples. The levels of aneuploidy increased as disease severity increased. Amplification of chromosome 4 and chromosome 20, and deletion of chromosome 17(p53) were the more common findings. Hence, a role for these abnormalities may exist in the initiation of, and the progression to, gastric cancer. Helicobactor pylori infection was determined in premalignant tissue using histological analysis and PCR technology. Detection rates were comparable. PCR was used to subtype H. pylori for CagA status. The amplification of chromosome 4 in gastric tissue was significantly more prevalent in H. pylori-positive patients (n=7) compared to H. pylori-negative patients (n=11), possibly reflecting a role for chromosome 4 amplification in H. pylori-induced gastric cancer. The more virulent CagA strain of H. pylori was associated with increased disease pathology and chromosomal abnormalities, although numbers were small (CagA+ n=3, CagA− n=4). Finally, in vitro work demonstrated that the aneuploidy induced in a human cell line after exposure to the reactive oxygen species (ROS) hydrogen peroxide was similar to that already shown in the gastric cancer pathway, and may further strengthen the hypothesis that H. pylori causes gastric cancer progression via an ROS-mediated mechanism

Blind testing of shoreline evolution models

International audienceBeaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer timescales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for tairua beach, new Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. in general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999-2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014-2017), both approaches showed a decrease in models' capability to predict the shoreline position. this was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models. Quantitative prediction of beach erosion and recovery is essential to planning resilient coastal communities with robust strategies to adapt to erosion hazards. Over the last decades, research efforts to understand and predict shoreline evolution have intensified as coastal erosion is likely to be exacerbated by climatic changes 1-5. The social and economic burden of changes in shoreline position are vast, which has inspired development of a growing variety of models based on different approaches and techniques; yet current models can fail (e.g. predicting erosion in accreting conditions). The challenge for shoreline models is, therefore, to provide reliable, robust and realistic predictions of change, with a reasonable computational cost, applicability to a broad variety of systems, and some quantifiable assessment of the uncertainties

Machine learning and coastal processes

This chapter introduces the reader to the basic concepts of machine learning (ML). Popular ML methods, such as neural networks, support vector machines, Bayesian networks, decision trees and the k-nearest neighbours algorithm are described along with simple examples applied to coastal processes. The reader is then guided through the steps required to develop ML algorithms from the first steps of data selection and preparation, to model selection, development and evaluation. A case study worked example is also included for the reader as a step-by-step guide to setting up a simple ML model and is provided as a supplementary online workbook for this chapter. The chapter concludes with a brief discussion on the future of ML in coastal processes research

A comparison of methods for discretizing continuous variables in Bayesian Networks.

Bayesian Networks (BNs) are an increasingly popular method for modelling environmental systems. The discretization of continuous variables is often required to use BNs. There are three main methods of discretization; manual, unsupervised, and supervised. Here, we compare and demonstrate each approach with a BN that predicts coastal erosion. Results reveal that supervised discretization methods produced BNs of the highest average predictive skill (73.8%), followed by manual discretization (69.0%) and unsupervised discretization (64.8%). However, each method has specific advantages that may make them more suitable for particular applications. Manual methods can produce physical meaningful BNs, which is favorable in environmental modelling. Supervised methods can autonomously and optimally discretize variables and may be preferred when predictive skill is a modelling priority. Unsupervised methods are computationally simple and versatile. The optimal discretization scheme should consider both the performance and practicality of the scheme

The first evidence of Middle Palaeolithic Nubian technology in north-central Oman

International audienc