The purpose of this study was to identify where ultrasmall superparamagnetic particles of iron oxide (USPIO) locate to in myocardium, develop a methodology that differentiates active macrophage uptake of USPIO from passive tissue distribution; and investigate myocardial inflammation in cardiovascular diseases. Myocardial inflammation is hypothesized to be a key pathophysiological mechanism of heart failure (HF), but human evidence is limited, partly because evaluation is challenging. USPIO-magnetic resonance imaging (MRI) potentially allows specific identification of myocardial inflammation but it remains unclear what the USPIO-MRI signal represents. Histological validation was performed using a murine acute myocardial infarction (MI) model. A multiparametric, multi-time-point MRI methodology was developed, which was applied in patients with acute MI (n = 12), chronic ischemic cardiomyopathy (n = 7), myocarditis (n = 6), dilated cardiomyopathy (n = 5), and chronic sarcoidosis (n = 5). USPIO were identified in myocardial macrophages and myocardial interstitium. R1 time-course reflected passive interstitial distribution whereas multi-time-point R2* was also sensitive to active macrophage uptake. R2*/R1 ratio provided a quantitative measurement of myocardial macrophage infiltration. R2* behavior and R2*/R1 ratio were higher in infarcted (p = 0.001) and remote (p = 0.033) myocardium in acute MI and in chronic ischemic cardiomyopathy (infarct: p = 0.008; remote p = 0.010), and were borderline higher in DCM (p = 0.096), in comparison to healthy controls, but were no different in myocarditis or sarcoidosis. An R2*/R1 threshold of 25 had a sensitivity and specificity of 90% and 83%, respectively, for detecting active USPIO uptake. USPIO are phagocytized by cardiac macrophages but are also passively present in myocardial interstitium. A multiparametric multi-time-point MRI methodology specifically identifies active myocardial macrophage infiltration. Persistent active macrophage infiltration is present in infarcted and remote myocardium in chronic ischemic cardiomyopathy, providing a substrate for HF.

Background: Disturbances of copper (Cu) homeostasis can lead to hypertrophic cardiac phenotypes (eg, Wilson’s disease). We previously identified abnormal Cu homeostasis in patients with hypertrophic cardiomyopathy (HCM) and, therefore, hypothesised that Cu²⁺–selective chelation with trientine dihydrochloride may slow or reverse disease progression in HCM. The aim of this study was, therefore to explore the clinical efficacy, safety and tolerability of trientine in HCM. Methods: In this medicines and healthcare products regulatory agency (MHRA) registered open-label pilot study, we treated 20 HCM patients with trientine for 6 months. Patients underwent a comprehensive assessment schedule including separate cardiac magnetic resonance imaging (CMR) and CMR ³¹P-spectroscopy at baseline and end of therapy. Predefined end points included changes in left ventricular mass (LVM), markers of LV fibrosis, markers of LV performance and myocardial energetics. Ten matched patients with HCM were studied as controls. Results: Trientine treatment was safe and tolerated. Trientine caused a substantial increase in urinary copper excretion (0.42±0.2 vs 2.02±1.0, p=0.001) without affecting serum copper concentrations. Treatment was associated with significant improvements in total atrial strain and global longitudinal LV strain using both Echo and CMR. LVM decreased significantly in the treatment arm compared with the control group (−4.2 g v 1.8 g, p=0.03). A strong trend towards an absolute decrease in LVM was observed in the treatment group (p=0.06). These changes were associated with a significant change in total myocardial volume driven by a significant reduction in extracellular matrix (ECM) volume (43.83±18.42 mL vs 41.49±16.89 mL, p=0.04) as opposed to pure cellular mass reduction and occurred against a background of significant ECM volume increase in the control group (44.59±16.50 mL vs 47.48±19.30 mL, p=0.02). A non-significant 10% increase in myocardial phosphocreatine/adenosine triphosphate (PCr/ATP) ratio with trientine therapy (1.27±0.44 vs 1.4±0.39) was noted. Conclusions: Cu²⁺–selective chelation with trientine in a controlled environment is safe and a potential future therapeutic target. A phase 2b trial is now underway.

We propose a human-in-the-loop approach for oracle improvement and analyse whether the proposed oracle improvement process is helping developers to create better oracles. For this, we conducted two human studies with 68 participants overall: an oracle assessment study and an oracle improvement study. Our results show that developers exhibit poor performance (29% accuracy) when manually assessing whether an assertion oracle contains a false positive, a false negative or none of the two. This shows that automated detection of these oracle deficiencies is beneficial for the users. Our tool OASIs (Oracle ASsessment and Improvement) helps developers produce assertions with higher quality. Participants who used OASIs in the improvement study were able to achieve 33% of full and 67% of partial correctness as opposed to participants without the tool who achieved only 21% of full and 43% of partial correctness.

If a software execution is disrupted, witnessing the execution at a later point may see evidence of the disruption or not. If not, we say the disruption failed to propagate. One name for this phenomenon is software robustness but it appears in different contexts in software engineering with different names. Contexts include testing, security, reliability, and automated code improvement or repair. Names include coincidental correctness, correctness attraction, transient error reliability. As witnessed, it is a dynamic phenomenon but any explanation with predictive power must necessarily take a static view. As a dynamic/static phenomenon it is convenient to take a statistical view of it which we do by way of information theory. We theorise that for failed disruption propagation to occur, a necessary condition is that the code region where the disruption occurs is composed with or succeeded by a subsequent code region that suffers entropy loss over all executions. The higher is the entropy loss, the higher the likelihood that disruption in the first region fails to propagate to the downstream observation point. We survey different research silos that address this phenomenon and explain how the theory might be exploited in software engineering.

Malware detection is in a coevolutionary arms race where the attackers and defenders are constantly seeking advantage. This arms race is asymmetric: detection is harder and more expensive than evasion. White hats must be conservative to avoid false positives when searching for malicious behaviour. We seek to redress this imbalance. Most of the time, black hats need only make incremental changes to evade them. On occasion, white hats make a disruptive move and find a new technique that forces black hats to work harder. Examples include system calls, signatures and machine learning. We present a method, called Hothouse, that combines simulation and search to accelerate the white hat’s ability to counter the black hat’s incremental moves, thereby forcing black hats to perform disruptive moves more often. To realise Hothouse, we evolve EEE, an entropy-based polymorphic packer for Windows executables. Playing the role of a black hat, EEE uses evolutionary computation to disrupt the creation of malware signatures. We enter EEE into the detection arms race with VirusTotal, the most prominent cloud service for running anti-virus tools on software. During our 6 month study, we continually improved EEE in response to VirusTotal, eventually learning a packer that produces packed malware whose evasiveness goes from an initial 51.8% median to 19.6%. We report both how well VirusTotal learns to detect EEE-packed binaries and how well VirusTotal forgets in order to reduce false positives. VirusTotal’s tools learn and forget fast, actually in about 3 days. We also show where VirusTotal focuses its detection efforts, by analysing EEE’s variants.

Diverse test sets are able to expose bugs that test sets generated with structural coverage techniques cannot discover. Input-diverse test set generators have been shown to be effective for this, but also have limitations: e.g., they need to be complemented with semantic information derived from the Software Under Test. We demonstrate how to drive the test set generation process with semantic information in the form of output diversity. We present the first totally automatic output sampling for output diversity unit test set generation tool, called OutGen. OutGen transforms a program into an SMT formula in bit-vector arithmetic. It then applies universal hashing in order to generate an output-based diverse set of inputs. The result offers significant diversity improvements when measured as a high output uniqueness count. It achieves this by ensuring that the test set's output probability distribution is uniform, i.e. highly diverse. The use of output sampling, as opposed to any of input sampling, CBMC, CAVM, behaviour diversity or random testing improves mutation score and bug detection by up to 4150% and 963% respectively on programs drawn from three different corpora: the R-project, SIR and CodeFlaws. OutGen test sets achieve an average mutation score of up to 92%, and 70% of the test sets detect the defect. Moreover, OutGen is the only automatic unit test generation tool that is able to detect bugs on the real number C functions from the R-project.