Performance of an adaptive homodyne receiver in the presence of multipath, Rayleigh-fading and time-varying quadrature errors

In this paper, we carry out a detailed performance analysis of the blind source separation based I/Q corrector operating at the baseband. Performance of the digital I/Q corrector is evaluated not only under time-varying phase and gain errors but also in the presence of multipath and Rayleigh fading channels. Performance under low-SNR and different modulation formats and constellation sizes is also evaluated. What is more, BER improvement after correction is illustrated. The results indicate that the adaptive algorithm offers adequate performance for most communication applications hence, reducing the matching requirements of the analog front-end enabling higher levels of integration.

A review of dynamic Bayesian network techniques with applications in healthcare risk modelling

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.

Do financial distress and liquidity crises affect value and size premiums?

This study investigates the impact of liquidity crises on the relationship between stock (value and size) premiums and default risk in the US market. It first examines whether financial distress can explain value and size premiums, and then, subsequently, aims to determine whether liquidity crises increase the risk of value and size premium investment strategies. The study employs a time-varying approach and a sample of US stock returns for the period between January 1982 and March 2011, a period which includes the current liquidity crisis, so as to examine the relationship between default risk, liquidity crises and value and size premiums. The findings indicate that the default premium has explanatory power for value and size and premiums, which affect firms with different characteristics. We also find that liquidity crises may actually increase the risks related to size and value premium strategies.

Effect of Short-Chain Fatty Acid Acetate on Colon Cancer

Acetate is a short chain fatty acid produced as a result of fermentation of ingested fibers by the gut microbiota. While it has been shown to reduce cell proliferation in some cancer cell lines1,2, more recent studies on liver3 and brain4 tumours suggest that acetate may actually promote tumour growth. Acetate in the cell is normally converted into acetyl-coA by two enzymes and metabolized; mitochondrial (ACSS1) and cytosolic (ACSS2) acetyl-coA synthetase. In the mitochondria acetyl-coA is utilized in the TCA cycle. In the cytosol it is utilized in lipid synthesis. In this study, the effect of acetate treatment on the growth of HT29 colon cancer cell line and its mechanism of action was assessed. HT29 human colorectal adenocarcinoma cells were treated with 10mM NaAc and cell viability, cellular bioenergetics and gene expression were investigated. Cell viability was assessed 24 hours after treatment using an MTT assay (Sigma, UK, n=8). Cellular oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) was measured by XFe Analyzer (Seahorse Bioscience, USA). After a baseline reading cells were treated and OCR and ECAR measurements were observed for 18 hours (n=4). Total mRNA was isolated 24 hours after treatment using RNeasy kit (Qiagen, USA). Quantitative PCR reactions were performed using Taqman gene expression assays and Taqman Universal PCR Master Mix (ThermoFisher Scientific, UK) on Applied Biosystems 7500 Fast Real-Time PCR System (Life Technologies, USA) and analysed using ΔΔCt method (n=3). Acetate treatment led to a significant reduction in cell viability (15.9%, Figure 1). OCR, an indicator of oxidative phosphorylation, was significantly increased (p<0.0001) while ECAR, an indicator of glycolysis, was significantly reduced (p<0.0001, Figure 2). Gene expression of ACSS1 was increased by 1.7 fold of control (p=0.07) and ACSS2 expression was reduced to 0.6 fold of control (p=0.06, Figure 3). In conclusion, in colon cancer cells acetate supplementation induces cell death and increases oxidative capacity. These changes together with the trending decrease in ACSS2 expression suggest suppression of lipid synthesis pathways. We hypothesize that the reduced tumor growth by acetate is a consequence of the suppression of ACSS2 and lipid synthesis, both effects reported previously to reduce tumor growth3–5. These effects clearly warrant further investigation.