Thermo-Mechanical Structural Optimisation of a Chemical Propulsion Satellite Thruster Using Lattice Structures

Small satellite space thrusters are designed to provide force for short time periods. • New generation of thrusters will be made with High Entropy Alloy (HEA) materials. • Predicting damage initiation for this kind of metallic structure subjected to thermal shock is of fundamental importance. • The preliminary thermal-stress analysis is mandatory in order to understand the complex failure mechanism of the space thruster. • An optimisation analysis of the material distribution along the combustion chamber thickness can lead to an improvement of the thermal-stress response

Extreme Antimicrobial Peptide and Polymyxin B Resistance in the Genus Burkholderia

Cationic antimicrobial peptides and polymyxins are a group of naturally occurring antibiotics that can also possess immunomodulatory activities. They are considered a new source of antibiotics for treating infections by bacteria that are resistant to conventional antibiotics. Members of the genus Burkholderia, which includes various human pathogens, are inherently resistant to antimicrobial peptides. The resistance is several orders of magnitude higher than that of other Gram-negative bacteria such as Escherichia coli, Salmonella enterica, or Pseudomonas aeruginosa. This review summarizes our current understanding of antimicrobial peptide and polymyxin B resistance in the genus Burkholderia. These bacteria possess major and minor resistance mechanisms that will be described in detail. Recent studies have revealed that many other emerging Gram-negative opportunistic pathogens may also be inherently resistant to antimicrobial peptides and polymyxins and we propose that Burkholderia sp. are a model system to investigate the molecular basis of the resistance in extremely resistant bacteria. Understanding resistance in these types of bacteria will be important if antimicrobial peptides come to be used regularly for the treatment of infections by susceptible bacteria because this may lead to increased resistance in the species that are currently susceptible and may also open up new niches for opportunistic pathogens with high inherent resistance

Antimicrobial Heteroresistance: an Emerging Field in Need of Clarity

“Heteroresistance” describes a phenomenon where subpopulations of seemingly isogenic bacteria exhibit a range of susceptibilities to a particular antibiotic. Unfortunately, a lack of standard methods to determine heteroresistance has led to inappropriate use of this term. Heteroresistance has been recognized since at least 1947 and occurs in Gram-positive and Gram-negative bacteria. Its clinical relevance may be considerable, since more resistant subpopulations may be selected during antimicrobial therapy. However, the use of nonstandard methods to define heteroresistance, which are costly and involve considerable labor and resources, precludes evaluating the clinical magnitude and severity of this phenomenon. We review the available literature on antibiotic heteroresistance and propose recommendations for definitions and determination criteria for heteroresistant bacteria. This will help in assessing the global clinical impact of heteroresistance and developing uniform guidelines for improved therapeutic outcomes

Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates

Large, fine-grained image segmentation datasets, annotated at pixel-level, are difficult to obtain, particularly in medical imaging, where annotations also require expert knowledge. Weakly-supervised learning can train models by relying on weaker forms of annotation, such as scribbles. Here, we learn to segment using scribble annotations in an adversarial game. With unpaired segmentation masks, we train a multi-scale GAN to generate realistic segmentation masks at multiple resolutions, while we use scribbles to learn their correct position in the image. Central to the model's success is a novel attention gating mechanism, which we condition with adversarial signals to act as a shape prior, resulting in better object localization at multiple scales. Subject to adversarial conditioning, the segmentor learns attention maps that are semantic, suppress the noisy activations outside the objects, and reduce the vanishing gradient problem in the deeper layers of the segmentor. We evaluated our model on several medical (ACDC, LVSC, CHAOS) and non-medical (PPSS) datasets, and we report performance levels matching those achieved by models trained with fully annotated segmentation masks. We also demonstrate extensions in a variety of settings: semi-supervised learning; combining multiple scribble sources (a crowdsourcing scenario) and multi-task learning (combining scribble and mask supervision). We release expert-made scribble annotations for the ACDC dataset, and the code used for the experiments, at https://vios-s.github.io/multiscale-adversarial-attention-gatesComment: Paper accepted for publication at: IEEE Transaction on Medical Imaging - Project page: https://vios-s.github.io/multiscale-adversarial-attention-gate

An automatic deep learning approach for coronary artery calcium segmentation

Coronary artery calcium (CAC) is a significant marker of atherosclerosis and cardiovascular events. In this work we present a system for the automatic quantification of calcium score in ECG-triggered non-contrast enhanced cardiac computed tomography (CT) images. The proposed system uses a supervised deep learning algorithm, i.e. convolutional neural network (CNN) for the segmentation and classification of candidate lesions as coronary or not, previously extracted in the region of the heart using a cardiac atlas. We trained our network with 45 CT volumes; 18 volumes were used to validate the model and 56 to test it. Individual lesions were detected with a sensitivity of 91.24%, a specificity of 95.37% and a positive predicted value (PPV) of 90.5%; comparing calcium score obtained by the system and calcium score manually evaluated by an expert operator, a Pearson coefficient of 0.983 was obtained. A high agreement (Cohen's k = 0.879) between manual and automatic risk prediction was also observed. These results demonstrated that convolutional neural networks can be effectively applied for the automatic segmentation and classification of coronary calcifications

Low cycle fatigue predictions of a space thruster built with a new refractory high entropy alloy

Directional thrusters are designed to provide force for short time periods. • New generation of thrusters could be made using a High Entropy Alloy (HEA). • Predicting fatigue damage initiation for this kind of metallic structure subjected to cyclic loads is important. • Design for fatigue resistance relies on empirical with high financial costs. • Unified Mechanics Theory (UMT) will be used to predict fatigue damage initiation of a material system without having experimental fatigue dat