Deriving genetic programming fitness properties by static analysis

Deriving Genetic Programming Fitness Properties by Static Analysis Colin G. Johnson The aim of this paper is to introduce the idea of using static analysis of computer programs as a way of measuring fitness in genetic programming. Such techniques extract information about the programs without explicitly running them, and in particular they infer properties which hold across the whole of the input space of a program. This can be applied to measure fitness, and has a number of advantages over measuring fitness by running members of the population on test cases. The most important advantage is that if a solution is found then it is possible to formally trust that solution to be correct across all inputs. This paper introduces these ideas, discusses various ways in which they could be applied, discusses the type of problems for which they are appropriate, and ends by giving a simple test example and some questions for future research

Low-Grade Systemic Inflammation Profile, Unrelated to Homocysteinemia, in Obese Children

To investigate in prepubertal obese children (POC) the profile of chronic low-grade systemic inflammation (CLGSI) and its relation to homocysteinemia, 72 POC were evaluated for serum C-reactive protein (CRP) and amyloid A (SAA) levels, both markers of CLGSI, and plasma levels of total homocysteine (tHcy), an independent risk factor for adult atherosclerosis, in comparison to 42 prepubertal lean children (PLC). The main observations in POC were higher CRP levels compared to PLC, positive association of SAA levels to CRP levels, no association of CRP or SAA levels to tHcy levels. Thus, in POC, positively interrelated to each other, elevated CRP and unaltered SAA levels reveal a unique profile of the CLGSI, not explaining homocysteinemia-induced risk for future atherosclerosis

Small whole heart volume predicts cardiovascular events in patients with stable chest pain: insights from the PROMISE trial

Objectives The size of the heart may predict major cardiovascular events (MACE) in patients with stable chest pain. We aimed to evaluate the prognostic value of 3D whole heart volume (WHV) derived from non-contrast cardiac computed tomography (CT). Methods Among participants randomized to the CT arm of the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE), we used deep learning to extract WHV, defined as the volume of the pericardial sac. We compared the WHV across categories of cardiovascular risk factors and coronary artery disease (CAD) characteristics and determined the association of WHV with MACE (all-cause death, myocardial infarction, unstable angina; median follow-up: 26 months). Results In the 3798 included patients (60.5 +/- 8.2 years; 51.5% women), the WHV was 351.9 +/- 57.6 cm(3)/m(2). We found smaller WHV in no- or non-obstructive CAD, women, people with diabetes, sedentary lifestyle, and metabolic syndrome. Larger WHV was found in obstructive CAD, men, and increased atherosclerosis cardiovascular disease (ASCVD) risk score (p < 0.05). In a time-to-event analysis, small WHV was associated with over 4.4-fold risk of MACE (HR (per one standard deviation) = 0.221; 95% CI: 0.068-0.721; p = 0.012) independent of ASCVD risk score and CT-derived CAD characteristics. In patients with non-obstructive CAD, but not in those with no- or obstructive CAD, WHV increased the discriminatory capacity of ASCVD and CT-derived CAD characteristics significantly. Conclusions Small WHV may represent a novel imaging marker of MACE in stable chest pain. In particular, WHV may improve risk stratification in patients with non-obstructive CAD, a cohort with an unmet need for better risk stratification

Thai visitors’ expectations and experiences of explainer interaction within a science museum context

© The Author(s) 2015. In Western literature, there is evidence that museum explainers offer significant potential for enhancing visitors’ learning through influencing their knowledge, content, action, behaviour and attitudes. However, little research has focused on the role of explainers in other cultural contexts. This study explored interactions between visitors and museum explainers within the setting of Thailand. Two questionnaires were distributed to 600 visitors and 41 museum explainers. The results demonstrated both potential similarities and differences with Western contexts. Explainers appeared to prefer didactic approaches, focussing on factual knowledge rather than encouraging deep learning. Two-way communication, however, appeared to be enhanced by the use of a ‘pseudo-sibling relationship’ by explainers. Traditional Thai social reserve was reduced through such approaches, with visitors taking on active learning roles. These findings have implications for training museum explainers in non-Western cultures, as well as museum communication practice more generally

Efficient unfolding pattern recognition in single molecule force spectroscopy data

BackgroundSingle-molecule force spectroscopy (SMFS) is a technique that measures the force necessary to unfold a protein. SMFS experiments generate Force-Distance (F-D) curves. A statistical analysis of a set of F-D curves reveals different unfolding pathways. Information on protein structure, conformation, functional states, and inter- and intra-molecular interactions can be derived.ResultsIn the present work, we propose a pattern recognition algorithm and apply our algorithm to datasets from SMFS experiments on the membrane protein bacterioRhodopsin (bR). We discuss the unfolding pathways found in bR, which are characterised by main peaks and side peaks. A main peak is the result of the pairwise unfolding of the transmembrane helices. In contrast, a side peak is an unfolding event in the alpha-helix or other secondary structural element. The algorithm is capable of detecting side peaks along with main peaks.Therefore, we can detect the individual unfolding pathway as the sequence of events labeled with their occurrences and co-occurrences special to bR\u27s unfolding pathway. We find that side peaks do not co-occur with one another in curves as frequently as main peaks do, which may imply a synergistic effect occurring between helices. While main peaks co-occur as pairs in at least 50% of curves, the side peaks co-occur with one another in less than 10% of curves. Moreover, the algorithm runtime scales well as the dataset size increases.ConclusionsOur algorithm satisfies the requirements of an automated methodology that combines high accuracy with efficiency in analyzing SMFS datasets. The algorithm tackles the force spectroscopy analysis bottleneck leading to more consistent and reproducible results

The effect of iterative model reconstruction on coronary artery calcium quantification

Coronary artery calcium (CAC) scoring with computed tomography (CT) is an established tool for quantifying calcified atherosclerotic plaque burden. Despite the widespread use of novel image reconstruction techniques in CT, the effect of iterative model reconstruction on CAC score remains unclear. We sought to assess the impact of iterative model based reconstruction (IMR) on coronary artery calcium quantification as compared to the standard filtered back projection (FBP) algorithm and hybrid iterative reconstruction (HIR). In addition, we aimed to simulate the impact of iterative reconstruction techniques on calcium scoring based risk stratification of a larger asymptomatic population. We studied 63 individuals who underwent CAC scoring. Images were reconstructed with FBP, HIR and IMR and CAC scores were measured. We estimated the cardiovascular risk reclassification rate of IMR versus HIR and FBP in a larger asymptomatic population (n = 504). The median CAC scores were 147.7 (IQR 9.6-582.9), 107.0 (IQR 5.9-526.6) and 115.1 (IQR 9.3-508.3) for FBP, HIR and IMR, respectively. The HIR and IMR resulted in lower CAC scores as compared to FBP (both p < 0.001), however there was no difference between HIR and IMR (p = 0.855). The CAC score decreased by 7.2 % in HIR and 7.3 % in IMR as compared to FBP, resulting in a risk reclassification rate of 2.4 % for both HIR and IMR. The utilization of IMR for CAC scoring reduces the measured calcium quantity. However, the CAC score based risk stratification demonstrated modest reclassification in IMR and HIR versus FBP

Conformation-regulated mechanosensory control via titin domains in cardiac muscle

The giant filamentous protein titin is ideally positioned in the muscle sarcomere to sense mechanical stimuli and transform them into biochemical signals, such as those triggering cardiac hypertrophy. In this review, we ponder the evidence for signaling hotspots along the titin filament involved in mechanosensory control mechanisms. On the way, we distinguish between stress and strain as triggers of mechanical signaling events at the cardiac sarcomere. Whereas the Z-disk and M-band regions of titin may be prominently involved in sensing mechanical stress, signaling hotspots within the elastic I-band titin segment may respond primarily to mechanical strain. Common to both stress and strain sensor elements is their regulation by conformational changes in protein domains

Genetics of Mechanosensation in the Heart

Mechanosensation (the ultimate conversion of a mechanical stimulus into a biochemical signal) as well as mechanotransduction (transmission of mechanically induced signals) belong to the most fundamental processes in biology. These effects, because of their dynamic nature, are particularly important for the cardiovascular system. Therefore, it is not surprising that defects in cardiac mechanosensation, are associated with various types of cardiomyopathy and heart failure. However, our current knowledge regarding the genetic basis of impaired mechanosensation in the cardiovascular system is beginning to shed light on this subject and is at the centre of this brief review