A Fast Algorithm for Robust Regression with Penalised Trimmed Squares

The presence of groups containing high leverage outliers makes linear regression a difficult problem due to the masking effect. The available high breakdown estimators based on Least Trimmed Squares often do not succeed in detecting masked high leverage outliers in finite samples. An alternative to the LTS estimator, called Penalised Trimmed Squares (PTS) estimator, was introduced by the authors in \cite{ZiouAv:05,ZiAvPi:07} and it appears to be less sensitive to the masking problem. This estimator is defined by a Quadratic Mixed Integer Programming (QMIP) problem, where in the objective function a penalty cost for each observation is included which serves as an upper bound on the residual error for any feasible regression line. Since the PTS does not require presetting the number of outliers to delete from the data set, it has better efficiency with respect to other estimators. However, due to the high computational complexity of the resulting QMIP problem, exact solutions for moderately large regression problems is infeasible. In this paper we further establish the theoretical properties of the PTS estimator, such as high breakdown and efficiency, and propose an approximate algorithm called Fast-PTS to compute the PTS estimator for large data sets efficiently. Extensive computational experiments on sets of benchmark instances with varying degrees of outlier contamination, indicate that the proposed algorithm performs well in identifying groups of high leverage outliers in reasonable computational time.Comment: 27 page

A pivotal role for NF-κB in the macrophage inflammatory response to the myeloperoxidase oxidant hypothiocyanous acid

© 2018 Elsevier Inc. Atherosclerosis is characterised by the infiltration of macrophages at sites of inflammation within the vessel wall and the release of myeloperoxidase (MPO), which forms hypochlorous acid (HOCl) and hypothiocyanous acid (HOSCN). HOCl is a damaging oxidant implicated in the development of atherosclerosis. Preferential formation of HOSCN occurs under conditions where thiocyanate ions are elevated, as is the case in smokers. HOSCN reacts selectively with thiols, which can result in more enzyme inactivation and damage than HOCl at susceptible sites, which may contribute to atherosclerosis in smokers. In this study, we show that exposure of macrophages to HOSCN results in a time- and dose-dependent increase in the mRNA expression and release of pro-inflammatory cytokines and chemokines, including monocyte chemotactic protein 1, tumour necrosis factor alpha, and interleukins 6, 8 and 1β. At high oxidant concentrations (>200 μM), a significant loss of cellular thiols and increased cell death is observed. HOSCN-induced cytokine/chemokine expression and cell death were decreased on pharmacological inhibition of nuclear factor kappa B. These data highlight a pathway by which HOSCN could promote inflammation and the development of atherosclerosis, in the presence of supra-physiological levels of the precursor thiocyanate, which are achievable by cigarette smoking

Novel critical point drying (CPD) based preparation and transmission electron microscopy (TEM) imaging of protein specific molecularly imprinted polymers (HydroMIPs)

We report the transmission electron microscopy (TEM) imaging of a hydrogel-based molecularly imprinted polymer (HydroMIP) specific to the template molecule bovine haemoglobin (BHb). A novel critical point drying based sample preparation technique was employed to prepare the molecularly imprinted polymer (MIP) samples in a manner that would facilitate the use of TEM to image the imprinted cavities, and provide an appropriate degree of both magnification and resolution to image polymer architecture in the <10 nm range. For the first time, polymer structure has been detailed that clearly displays molecularly imprinted cavities, ranging from 5-50 nm in size, that correlate (in terms of size) with the protein molecule employed as the imprinting template. The modified critical point drying sample preparation technique used may potentially play a key role in the imaging of all molecularly imprinted polymers, particularly those prepared in the aqueous phase

Comparative reactivity of the myeloperoxidase-derived oxidants HOCl and HOSCN with low-density lipoprotein (LDL): Implications for foam cell formation in atherosclerosis

© 2015 Elsevier Inc. All rights reserved. Abstract Atherosclerosis is characterised by the accumulation of lipids within macrophages in the artery wall. Low-density lipoprotein (LDL) is the source of this lipid, owing to the uptake of oxidised LDL by scavenger receptors. Myeloperoxidase (MPO) released by leukocytes during inflammation produces oxidants that are implicated in atherosclerosis. Modification of LDL by the MPO oxidant hypochlorous acid (HOCl), results in extensive lipid accumulation by macrophages. However, the reactivity of the other major MPO oxidant, hypothiocyanous acid (HOSCN) with LDL is poorly characterised, which is significant given that thiocyanate is the favoured substrate for MPO. In this study, we comprehensively compare the reactivity of HOCl and HOSCN with LDL, and show key differences in the profile of oxidative damage observed. HOSCN selectively modifies Cys residues on apolipoprotein B100, and oxidises cholesteryl esters resulting in formation of lipid hydroperoxides, 9-hydroxy-10,12-octadecadienoic acid (9-HODE) and F2-isoprostanes. The modification of LDL by HOSCN results macrophage lipid accumulation, though generally to a lesser extent than HOCl-modified LDL. This suggests that a change in the ratio of HOSCN:HOCl formation by MPO from variations in plasma thiocyanate levels, will influence the nature of LDL oxidation in vivo, and has implications for the progression of atherosclerosis

Quantification and confocal imaging of protein specific molecularly imprinted polymers

We have employed FITC-albumin as the protein template molecule in an aqueous phase molecular imprinted polymer (HydroMIP) strategy. For the first time, the use of a fluorescently labelled template is reported, with subsequent characterisation of the smart material to show that the HydroMIP possess a significant molecular memory in comparison to that of the nonimprinted control polymer (HydroNIP). The imaging of the FITC-albumin imprinted HydroMIP using confocal microscopy is described, with the in situ removal of imprinted protein displayed in terms of observed changes in the fluorescence of the imprinted polymer, both before and after template elution (using a 10% SDS/10% AcOH (w/v) solution). We also report the imaging of a bovine haemoglobin (BHb) imprinted HydroMIP using two-photon confocal microscopy, and describe the effects of template elution upon protein autofluorescence. The findings further contribute to the understanding of aqueous phase molecular imprinting protocols, and document the use of fluorescence as a useful tool in template labelling/detection and novel imaging strategies

Preference for different relaxation techniques by COPD patients: comparison between six techniques.

BACKGROUND: A review of the effectiveness of relaxation techniques for chronic obstructive pulmonary disease patients has shown inconsistent results, but studies have varied in terms of technique and outcome measures. AIM: To determine patient preference for different relaxation techniques. METHODS: Chronic obstructive pulmonary disease patients were presented with six techniques via a DVD and asked to rate the techniques in terms of effectiveness, rank in order of likely use, and comment. RESULTS: Patients differed in the technique preferred and reason for that preference, but the most commonly preferred technique both for effectiveness and ease of use was "thinking of a nice place" followed by progressive relaxation and counting. Familiarity and ease of activity were commonly given reasons for preference. CONCLUSION: Rather than providing patients with a single technique that they might find difficult to implement, these results suggest that it would be better to give a choice. "Thinking of a nice place" is a popular but under-investigated technique

A procedure for the change point problem in parametric models based on phi-divergence test-statistics

This paper studies the change point problem for a general parametric, univariate or multivariate family of distributions. An information theoretic procedure is developed which is based on general divergence measures for testing the hypothesis of the existence of a change. For comparing the accuracy of the new test-statistic a simulation study is performed for the special case of a univariate discrete model. Finally, the procedure proposed in this paper is illustrated through a classical change-point example

learning and adaptation to detect changes and anomalies in high dimensional data

The problem of monitoring a datastream and detecting whether the data generating process changes from normal to novel and possibly anomalous conditions has relevant applications in many real scenarios, such as health monitoring and quality inspection of industrial processes. A general approach often adopted in the literature is to learn a model to describe normal data and detect as anomalous those data that do not conform to the learned model. However, several challenges have to be addressed to make this approach effective in real world scenarios, where acquired data are often characterized by high dimension and feature complex structures (such as signals and images). We address this problem from two perspectives corresponding to different modeling assumptions on the data-generating process. At first, we model data as realization of random vectors, as it is customary in the statistical literature. In this settings we focus on the change detection problem, where the goal is to detect whether the datastream permanently departs from normal conditions. We theoretically prove the intrinsic difficulty of this problem when the data dimension increases and propose a novel non-parametric and multivariate change-detection algorithm. In the second part, we focus on data having complex structure and we adopt dictionaries yielding sparse representations to model normal data. We propose novel algorithms to detect anomalies in such datastreams and to adapt the learned model when the process generating normal data changes

Irreducible uncertainty in near-term climate projections

Model simulations of the next few decades are widely used in assessments of climate change impacts and as guidance for adaptation. Their non-linear nature reveals a level of irreducible uncertainty which it is important to understand and quantify, especially for projections of near-term regional climate. Here we use large idealised initial condition ensembles of the FAMOUS global climate model with a 1 %/year compound increase in CO2 levels to quantify the range of future temperatures in model-based projections. These simulations explore the role of both atmospheric and oceanic initial conditions and are the largest such ensembles to date. Short-term simulated trends in global temperature are diverse, and cooling periods are more likely to be followed by larger warming rates. The spatial pattern of near-term temperature change varies considerably, but the proportion of the surface showing a warming is more consistent. In addition, ensemble spread in inter-annual temperature declines as the climate warms, especially in the North Atlantic. Over Europe, atmospheric initial condition uncertainty can, for certain ocean initial conditions, lead to 20 year trends in winter and summer in which every location can exhibit either strong cooling or rapid warming. However, the details of the distribution are highly sensitive to the ocean initial condition chosen and particularly the state of the Atlantic meridional overturning circulation. On longer timescales, the warming signal becomes more clear and consistent amongst different initial condition ensembles. An ensemble using a range of different oceanic initial conditions produces a larger spread in temperature trends than ensembles using a single ocean initial condition for all lead times. This highlights the potential benefits from initialising climate predictions from ocean states informed by observations. These results suggest that climate projections need to be performed with many more ensemble members than at present, using a range of ocean initial conditions, if the uncertainty in near-term regional climate is to be adequately quantified

Offspring sex ratio and gonadal irradiation in the British Childhood Cancer Survivor Study

We investigated offspring sex ratio among 6232 offspring born to 3218 survivors of childhood cancer in relation to therapeutic irradiation, and pooled our data with those from two other large-scale studies giving a total of 9685 offspring. Exposure to high-dose gonadal irradiation was not associated with a significant alteration in offspring sex ratio compared to low doses (men: P=0.58, women: P=0.66). There was also no evidence that the ratio varied with time since cancer diagnosis when comparing survivors treated with radiotherapy vs those without (men: P=0.51; women: P=0.46). This, the largest study to date, finds no evidence that exposure to radiation affects the offspring sex ratio among survivors of childhood cancer