Tuomas Turpeinen

Effects of diet-induced obesity and voluntary wheel running on the microstructure of the murine distal femur

BackgroundObesity and osteoporosis, two possibly related conditions, are rapidly expanding health concerns in modern society. Both of them are associated with sedentary life style and nutrition. To investigate the effects of diet-induced obesity and voluntary physical activity we used high resolution micro-computed tomography (μCT) together with peripheral quantitative computed tomography (pQCT) to examine the microstructure of the distal femoral metaphysis in mice.MethodsForty 7-week-old male C57BL/6J mice were assigned to 4 groups: control (C), control + running (CR), high-fat diet (HF), and high-fat diet + running (HFR). After a 21-week intervention, all the mice were sacrificed and the left femur dissected for pQCT and μCT measurements.ResultsThe mice fed the high-fat diet showed a significant weight gain (over 70% for HF and 60% for HFR), with increased epididymal fat pad mass and impaired insulin sensitivity. These obese mice had significantly higher trabecular connectivity density, volume, number, thickness, area and mass, and smaller trabecular separation. At the whole bone level, they had larger bone circumference and cross-sectional area and higher density-weighted maximal, minimal, and polar moments of inertia. Voluntary wheel running decreased all the cortical bone parameters, but increased the trabecular mineral density, and decreased the pattern factor and structure model index towards a more plate-like structure.ConclusionsThe results suggest that in mice the femur adapts to obesity by improving bone strength both at the whole bone and micro-structural level. Adaptation to running exercise manifests itself in increased trabecular density and improved 3D structure, but in a limited overall bone growth

Dynamics and interactions of parvoviral NS1 protein in the nucleus

Nuclear positioning and dynamic interactions of viral proteins with nuclear substructures play essential roles during infection with DNA viruses. Visualization of the intranuclear interactions and motility of the parvovirus replication protein (NS1) in living cells gives insight into specific parvovirus protein–cellular structure interactions. Confocal analysis of highly synchronized infected Norden Laboratory Feline Kidney cells showed accumulation of nuclear NS1 in discrete interchromosomal foci. NS1 fused with enhanced yellow fluorescence protein (NS1‐EYFP) provided a marker in live cells for dynamics of NS1 traced by photobleaching techniques. Fluorescence Recovery after Photobleaching suggested that the NS1 protein is not freely diffusing but undergoes transient interactions with nuclear compartments. Fluorescence Loss in Photobleaching demonstrated for the first time the shuttling of a parvoviral protein between the nucleus and the cytoplasm as assayed with NS1‐EYFP. Finally, time‐lapse imaging of infected cells revealed that the intranuclear distribution of NS1‐EYFP evolves dramatically starting from the formation of NS1 foci and proceeding to a homogenous distribution extending throughout the nucleus.

Evaluating the performance of artificial neural networks for the classification of freshwater benthic macroinvertebrates

Abstract Macroinvertebrates form an important functional component of aquatic ecosystems. Their ability to indicate various types of anthropogenic stressors is widely recognized which has made them an integral component of freshwater biomonitoring. The use of macroinvertebrates in biomonitoring is dependent on manual taxa identification which is currently a time-consuming and cost-intensive process conducted by highly trained taxonomical experts. Automated taxa identification of macroinvertebrates is a relatively recent research development. Previous studies have displayed great potential for solutions to this demanding data mining application. In this research we have a collection of 1350 images from eight different macroinvertebrate taxa and the aim is to examine the suitability of artificial neural networks (ANNs) for automated taxa identification of macroinvertebrates. More specifically, the focus is drawn on different training algorithms of Multi-Layer Perceptron (MLP), probabilistic neural network (PNN) and Radial Basis Function network (RBFN). We performed thorough experimental tests and we tested altogether 13 training algorithms for MLPs. The best classification accuracy of MLPs, 95.3%, was obtained by two conjugate gradient backpropagation variations and scaled conjugate gradient backpropagation. For PNN 92.8% and for RBFN 95.7% accuracies were achieved. The results show how important a proper choice of ANN is in order to obtain high accuracy in the automated taxa identification of macroinvertebrates and the obtained model can outperform the level of identification which is made by a taxonomist.

A prospect for computing in porous materials research: Very large fluid flow simulations

Abstract Properties of porous materials, abundant both in nature and industry, have broad influences on societies via, e.g. oil recovery, erosion, and propagation of pollutants. The internal structure of many porous materials involves multiple scales which hinders research on the relation between structure and transport properties: typically laboratory experiments cannot distinguish contributions from individual scales while computer simulations cannot capture multiple scales due to limited capabilities. Thus the question arises how large domain sizes can in fact be simulated with modern computers. This question is here addressed using a realistic test case; it is demonstrated that current computing capabilities allow the direct pore-scale simulation of fluid flow in porous materials using system sizes far beyond what has been previously reported. The achieved system sizes allow the closing of some particular scale gaps in, e.g. soil and petroleum rock research. Specifically, a full steady-state fluid flow simulation in a porous material, represented with an unprecedented resolution for the given sample size, is reported: the simulation is executed on a CPU-based supercomputer and the 3D geometry involves 16,384 3 lattice cells (around 590 billion of them are pore sites). Using half of this sample in a benchmark simulation on a GPU-based system, a sustained computational performance of 1.77 PFLOPS is observed. These advances expose new opportunities in porous materials research. The implementation techniques here utilized are standard except for the tailored high-performance data layouts as well as the indirect addressing scheme with a low memory overhead and the truly asynchronous data communication scheme in the case of CPU and GPU code versions, respectively.

Statistical classification and proportion estimation - an application to a macroinvertebrate image database

We apply and compare a random Bayes forest classifier and three traditional classification methods to a dataset of complex benthic macroinvertebrate images of known taxonomical identity. Since in biomonitoring changes in benthic macroinvertebrate taxa proportions correspond to changes in water quality, their correct estimation is pivotal. As classification errors are passed on to the allocated proportions, we explore a correction method known as a confusion matrix correction. Classification methods were compared using the misclassification error and the χ2 distance measures of the true proportions to the allocated and to the corrected proportions. Using low misclassification error and smallest χ2 distance measures as performance criteria the classical Bayes classifier performed best followed closely by the random Bayes forest.

Dependence of thermal conductivity on structural parameters in porous samples

The in-plane thermal conductivity of porous sintered bronze plates was studied both experimentally and numerically. We developed and validated an experimental setup, where the sample was placed in vacuum and heated while its time-dependent temperature field was measured with an infrared camera. The porosity and detailed three-dimensional structure of the samples were determined by X-ray microtomography. Lattice-Boltzmann simulations of thermal conductivity in the tomographic reconstructions of the samples were used to correct the contact area between bronze particles as determined by image analysis from the tomographic reconstructions. Small openings in the apparent contacts could not be detected with the imaging resolution used, and they caused an apparent thermal contact resistance between particles. With this correction included, the behavior of the measured thermal conductivity was successfully explained by an analytical expression, originally derived for regular structures, which involves three struc...

Interface Detection Using a Quenched-Noise Version of the Edwards–Wilkinson Equation

We report here a multipurpose dynamic-interface-based segmentation tool, suitable for segmenting planar, cylindrical, and spherical surfaces in 3D. The method is fast enough to be used conveniently even for large images. Its implementation is straightforward and can be easily realized in many environments. Its memory consumption is low, and the set of parameters is small and easy to understand. The method is based on the Edwards-Wilkinson equation, which is traditionally used to model the equilibrium fluctuations of a propagating interface under the influence of temporally and spatially varying noise. We report here an adaptation of this equation into multidimensional image segmentation, and its efficient discretization.

Characterizing Low-Permeable Granitic Rock From Micrometer to Centimeter Scale: X-ray Micro-computed Tomography, Confocal Laser Scanning Microscopy and {sup 14}C-PMMA Method

First results of combining X-ray micro-computed tomography ({mu}CT), confocal laser-scanning microscopy (CLSM) and {sup 14}C-poly-methyl-methacrylate ({sup 14}C-PMMA) impregnation techniques in the study of granitic rock samples are reported. Combining results of {mu}CT and CLSM with those of the {sup 14}C-PMMA technique, the mineral-specific porosity and morphology of the open pore space, as well as its connectivity, could be analyzed from a micrometer up to a decimeter scale. Three different types of granite were studied. In two cases part of the micro-fissure and pore apertures were found to be in a micrometer scale, but in one case all grain-boundary openings were below the detection limit. Micrometer-scale apertures could be analyzed by CLSM and {mu}CT. The benefit of {mu}CT is that it can also provide the heterogeneous distribution of minerals in 3D. The 2D porosity distributions in the mineral phases, consisting of nanometer-scale pores, could be measured by the {sup 14}C-PMMA method together with the micro-fissures. This method does not, however, give the exact pore apertures. The limitations and applicability of the methods are discussed. (authors)