Getting to the heart of cardiac remodeling; how collagen subtypes may contribute to phenotype

The objective of this study was to investigate the nature and biomechanical properties of collagen fibers within the human myocardium. Targeting cardiac interstitial abnormalities will likely become a major focus of future preventative strategies with regard to the management of cardiac dysfunction. Current knowledge regarding the component structures of myocardial collagen networks is limited, further delineation of which will require application of more innovative technologies. We applied a novel methodology involving combined confocal laser scanning and atomic force microscopy to investigate myocardial collagen within ex-vivo right atrial tissue from 10 patients undergoing elective coronary bypass surgery. Immuno-fluorescent co-staining revealed discrete collagen I and III fibers. During single fiber deformation, overall median values of stiffness recorded in collagen III were 37±16% lower than in collagen I [p<0.001]. On fiber retraction, collagen I exhibited greater degrees of elastic recoil [p<0.001; relative percentage increase in elastic recoil 7±3%] and less energy dissipation than collagen III [p<0.001; relative percentage increase in work recovered 7±2%]. In atrial biopsies taken from patients in permanent atrial fibrillation (n=5) versus sinus rhythm (n=5), stiffness of both collagen fiber subtypes was augmented (p<0.008). Myocardial fibrillar collagen fibers organize in a discrete manner and possess distinct biomechanical differences; specifically, collagen I fibers exhibit relatively higher stiffness, contrasting with higher susceptibility to plastic deformation and less energy efficiency on deformation with collagen III fibers. Augmented stiffness of both collagen fiber subtypes in tissue samples from patients with atrial fibrillation compared to those in sinus rhythm are consistent with recent published findings of increased collagen cross-linking in this setting.

Macroscopic Quantum Resonators (MAQRO): 2015 update

Do the laws of quantum physics still hold for macroscopic objects - this is at the heart of Schrödinger’s cat paradox - or do gravitation or yet unknown effects set a limit for massive particles? What is the fundamental relation between quantum physics and gravity? Ground-based experiments addressing these questions may soon face limitations due to limited free-fall times and the quality of vacuum and microgravity. The proposed mission Macroscopic Quantum Resonators (MAQRO) may overcome these limitations and allow addressing such fundamental questions. MAQRO harnesses recent developments in quantum optomechanics, high-mass matter-wave interferometry as well as state-of-the-art space technology to push macroscopic quantum experiments towards their ultimate performance limits and to open new horizons for applying quantum technology in space. The main scientific goal is to probe the vastly unexplored ‘quantum-classical’ transition for increasingly massive objects, testing the predictions of quantum theory for objects in a size and mass regime unachievable in ground-based experiments. The hardware will largely be based on available space technology. Here, we present the MAQRO proposal submitted in response to the 4th Cosmic Vision call for a medium-sized mission (M4) in 2014 of the European Space Agency (ESA) with a possible launch in 2025, and we review the progress with respect to the original MAQRO proposal for the 3rd Cosmic Vision call for a medium-sized mission (M3) in 2010. In particular, the updated proposal overcomes several critical issues of the original proposal by relying on established experimental techniques from high-mass matter-wave interferometry and by introducing novel ideas for particle loading and manipulation. Moreover, the mission design was improved to better fulfill the stringent environmental requirements for macroscopic quantum experiments.

Morality, Power and the Planning Subject

Ethical issues are at the heart of planning. Thus, planning theory has long displayed an interest in debating both the ethical justification for planning and how the activity of planning can be rendered more ethically sensitive. However, comparatively little attention has been shown to how the very constitution of the planner as a ‘moral subject’ may be ethically problematic for planning practice. This article addresses this lacuna through an engagement with the philosophy of Michel Foucault. In contrast to how his work is normally applied, this article accords with Foucault’s own direction that his later examination of ethics be used as a lens through which to read his earlier analysis of power and knowledge. Accordingly, the article first outlines Foucault’s innovative reinterpretation of how power and knowledge operate in society before setting this within his novel reconception of ethics. This theoretical exposition is then employed to interpret the material drawn from in-depth qualitative interviews with 20 planning officers working in a range of different contexts. The article subsequently employs Foucault’s ethically informed reading of power and knowledge to identify ethical issues arising from the approaches used by practitioners to justify their planning activities. The article concludes by suggesting how such issues can be resolved.

Person Re-Identification using Deep Convnets with Multi-task Learning

Person re-identification involves recognizing a person across non-overlapping camera views, with different pose, illumination, and camera characteristics. We propose to tackle this problem by training a deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to common appearance variations encountered in the re-identification problem. Specifically, a Siamese-network architecture is used to train a feature extraction network using pairs of similar and dissimilar images. We show that use of a novel multi-task learning objective is crucial for regularizing the network parameters in order to prevent over-fitting due to the small size the training dataset. We complement the verification task, which is at the heart of re-identification, by training the network to jointly perform verification, identification, and to recognise attributes related to the clothing and pose of the person in each image. Additionally, we show that our proposed approach performs well even in the challenging cross-dataset scenario, which may better reflect real-world expected performance.