Multi-modality image simulation with the Virtual Imaging Platform: Illustration on cardiac echography and MRI

International audienceMedical image simulation is useful for biological modeling, image analysis, and designing new imaging devices but it is not widely available due to the complexity of simulators, the scarcity of object models, and the heaviness of the associated computations. This paper presents the Virtual Imaging Platform, an openly-accessible web platform for multi-modality image simulation. The integration of simulators and models is described and exemplified on simulated cardiac MRIs and ultrasonic images

Genetic Diversity among Ancient Nordic Populations

Using established criteria for work with fossil DNA we have analysed mitochondrial DNA from 92 individuals from 18 locations in Denmark ranging in time from the Mesolithic to the Medieval Age. Unequivocal assignment of mtDNA haplotypes was possible for 56 of the ancient individuals; however, the success rate varied substantially between sites; the highest rates were obtained with untouched, freshly excavated material, whereas heavy handling, archeological preservation and storage for many years influenced the ability to obtain authentic endogenic DNA. While the nucleotide diversity at two locations was similar to that among extant Danes, the diversity at four sites was considerably higher. This supports previous observations for ancient Britons. The overall occurrence of haplogroups did not deviate from extant Scandinavians, however, haplogroup I was significantly more frequent among the ancient Danes (average 13%) than among extant Danes and Scandinavians (∼2.5%) as well as among other ancient population samples reported. Haplogroup I could therefore have been an ancient Southern Scandinavian type “diluted” by later immigration events. Interestingly, the two Neolithic samples (4,200 YBP, Bell Beaker culture) that were typed were haplogroup U4 and U5a, respectively, and the single Bronze Age sample (3,300–3,500 YBP) was haplogroup U4. These two haplogroups have been associated with the Mesolithic populations of Central and Northern Europe. Therefore, at least for Southern Scandinavia, our findings do not support a possible replacement of a haplogroup U dominated hunter-gatherer population by a more haplogroup diverse Neolithic Culture

A Cell-Free Microtiter Plate Screen for Improved [FeFe] Hydrogenases

, a potential renewable fuel. Attempts to exploit these catalysts in engineered systems have been hindered by the biotechnologically inconvenient properties of the natural enzymes, including their extreme oxygen sensitivity. Directed evolution has been used to improve the characteristics of a range of natural catalysts, but has been largely unsuccessful for [FeFe] hydrogenases because of a lack of convenient screening platforms. [FeFe] hydrogenase HydA1 with a specific activity ∼4 times that of the wild-type enzyme. cell extracts, which allows unhindered access to the protein maturation and assay environment

Enhanced oxygen-tolerance of the full heterotrimeric membrane-bound [NiFe]-hydrogenase of ralstonia eutropha.

Hydrogenases are oxygen-sensitive enzymes that catalyze the conversion between protons and hydrogen. Water-soluble subcomplexes of membrane-bound [NiFe]-hydrogenases (MBH) have been extensively studied for applications in hydrogen-oxygen fuel cells as they are relatively tolerant to oxygen, although even these catalysts are still inactivated in oxidative conditions. Here, the full heterotrimeric MBH of Ralstonia eutropha, including the membrane-integral cytochrome b subunit, was investigated electrochemically using electrodes modified with planar tethered bilayer lipid membranes (tBLM). Cyclic voltammetry and chronoamperometry experiments show that MBH, in equilibrium with the quinone pool in the tBLM, does not anaerobically inactivate under oxidative redox conditions. In aerobic environments, the MBH is reversibly inactivated by O2, but reactivation was found to be fast even under oxidative redox conditions. This enhanced resistance to inactivation is ascribed to the oligomeric state of MBH in the lipid membrane

A Comparative Study on the Potential of Unsupervised Deep Learning-based Feature Selection in Radiomics.

In Radiomics, deep learning-based systems for medical image analysis play an increasing role. However, due to the better explainability, feature-based systems are still preferred, especially by physicians. Often, high-dimensional data and low sample size pose different challenges (e.g. increased risk of overfitting) to machine learning systems. By removing irrelevant and redundant features from the data, feature selection is an effective way of pre-processing. The research in this study is focused on unsupervised deep learning-based methods for feature selection. Five recently proposed algorithms are compared regarding their applicability and efficiency on seven data sets in three different sample applications. It was found that deep learning-based feature selection leads to improved classification results compared to conventional methods, especially for small feature subsets. Clinical Relevance - The exploration of distinctive features and the ability to rank their importance without the need for outcome information is a potential field of application for unsupervised feature selection methods. Especially in multiparametric radiology, the number of features is increasing. The identification of new potential biomarkers is important both for treatment and prevention

2D matrix array optimization by simulated annealing for 3D hepatic imaging

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Non-grid based elements positioning for optimal 2D array beams

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Design of optimal 2-D non-grid sparse arrays for medical ultrasound

International audienceThree-dimensional imaging with 2-D matrix probes is one of the most exciting recent ultrasound innovations. Unfortunately, the number of elements of a 2-D matrix probe is often very high, and reducing this number deteriorates the beam properties. In this paper, we propose a new sparse-array design technique with irregular element positioning, which significantly reduces the number of active elements as well as the grating-lobe level. In particular, we introduce a new cost function for optimizing the weighting coefficients of the elements and a new annealing-based algorithm to compute the lowest-cost solutions. Numerical simulations show substantial improvements over standard sparse arrays