Assessment of bioprocess shear stress as a tool to enhance osteogenic induction of mesenchymal cells

Shear stress is an unavoidable bioprocess force encountered during routine cell culture and large scale cell manufacture. It is generally considered as harmful for cells in bio-manufacturing, as it affects cell viability and function. Thus, reducing cell loss, maintaining cell integrity and function during processing are important for cell therapy. Also, based on the fact that mechanical cues can increase bone formation in vivo, we hypothesized that the capillary shear stress could enhance osteogenic differentiation and maturation of cells. This study assessed the effect of capillary shear stress on survival and osteogenic differentiation using rat bone marrow derived mesenchymal stromal cells (MSCs), human MG63 cells and human MSCs. Cells were exposed to defined shear stress by passing through a capillary device. Three key parameters were tested: capillary internal diameters (e.g. 0.254 mm, 0.203 mm), flow rates (e.g. 13, 20, 28 ml/min) and number of passes (e.g. 10, 20, 40 passes). Cell recovery and viability were measured immediately after exposure to shear stress and after 24 and 72 hours cell culture. A small decline in immediate recovery and viability at 24 hrs was evident for MSCs passed through capillaries compared to controls. High flow rates and hence higher shear stress (e.g. 258 Pa) resulted in greater cell loss and reduced cell viability after culture for 24 hrs (p < 0.05). Using the capillary diameters and flow rates we reported here, cell growth is permissible in spite of an initial reduction in viable cell growth, but cells recover from this rapidly in culture. Although longer exposure durations to shear stress lead to more osteogenic differentiation, the increasing trend of mineralization was not linear to the increase of shear stress exposure time. Alizarin red S staining of both MSCs and MG63 cells revealed that appropriate capillary wall shear stress has potential to enhance osteogenic differentiation. In conclusion, sub-lethal fluid shear stress that cells experience during bioprocessing can be used as a mechanical cue for osteogenesis

Unsupervised Feature Selection with Adaptive Structure Learning

The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data. However, the estimated intrinsic structures are unreliable/inaccurate when the redundant and noisy features are not removed. Therefore, we face a dilemma here: one need the true structures of data to identify the informative features, and one need the informative features to accurately estimate the true structures of data. To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously. The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data. By leveraging the interactions between these two essential tasks, we are able to capture accurate structures and select more informative features. Experimental results on many benchmark data sets demonstrate that the proposed method outperforms many state of the art unsupervised feature selection methods

Differential quadrature method for space-fractional diffusion equations on 2D irregular domains

In mathematical physics, the space-fractional diffusion equations are of particular interest in the studies of physical phenomena modelled by L\'{e}vy processes, which are sometimes called super-diffusion equations. In this article, we develop the differential quadrature (DQ) methods for solving the 2D space-fractional diffusion equations on irregular domains. The methods in presence reduce the original equation into a set of ordinary differential equations (ODEs) by introducing valid DQ formulations to fractional directional derivatives based on the functional values at scattered nodal points on problem domain. The required weighted coefficients are calculated by using radial basis functions (RBFs) as trial functions, and the resultant ODEs are discretized by the Crank-Nicolson scheme. The main advantages of our methods lie in their flexibility and applicability to arbitrary domains. A series of illustrated examples are finally provided to support these points.Comment: 25 pages, 25 figures, 7 table

Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI

Salvia miltiorrhiza treatment during early reperfusion reduced postischemic myocardial injury in the rat

Oxidative stress may play a causative role in myocardial ischemia-reperfusion injury. However, it is a relatively understudied aspect regarding an optimal timing of antioxidant intervention during ischemia-reperfusion. The present study investigates the effect of different treatment regimens of Salvia miltiorrhiza (SM) herb extracts containing phenolic compounds that possess potent antioxidant properties on postischemic myocardial functional recovery in the setting of global myocardial ischemia and reperfusion. Langendorff-perfused rat hearts were subjected to 40 min of global ischemia at 37°C followed by 60 min of reperfusion, and were randomly assigned into the untreated control and 2 SM-treated groups (n = 7 per group). In treatment 1 (SM1), 3 mg/mL of water soluble extract of SM was given for 10 min before ischemia and continued during ischemia through the aorta at a reduced flow rate of 60 μL/min, but not during reperfusion. In treatment 2 (SM2), SM (3 mg/mL) was given during the first 15 min of reperfusion. During ischemia, hearts in the control and SM2 groups were given physiological saline at 60 μL/min. The SM1 treatment reduced the production of 15-F2t- isoprostane, a specific index of oxidative stress-induced lipid peroxidation, during ischemia (94 ± 20, 43 ± 6, and 95 ± 15 pg/mL in the coronary effluent in control, SM1, and SM2 groups, respectively; p < 0.05, SM1 vs. control or SM2) and post-poned the onset of ischemic contracture. However, SM2, but not the SM1 regimen, significantly reduced 15-F 2t-isoprostane production during early reperfusion and led to optimal postischemic myocardial functional recovery (left ventricular developed pressure 51 ± 4, 46 ± 4, and 60 ± 6 mmHg in the control, SM1, and SM2 groups, respectively, at 60 min of reperfusion; p < 0.05, SM2 vs. control or SM1) and reduced myocardial infarct size as measured by triphenyltetrazolium chloride staining (26% ± 2%, 22% ± 2%, and 20% ± 2% of the total area in the control, SM1, and SM2 groups, respectively, p < 0.05, SM2 vs. control). It is concluded that S. miltiorrhiza could be beneficial in the treatment of myocardial ischemic injury and the timing of administration seems important. © 2007 NRC.published_or_final_versio

Spatio-Temporal Kronecker Compressive Sensing for Traffic Matrix Recovery

A traffic matrix is generally used by several network management tasks in a data center network, such as traffic engineering and anomaly detection. It gives a flow-level view of the network traffic volume. Despite the explicit importance of the traffic matrix, it is significantly difficult to implement a large-scale measurement to build an absolute traffic matrix. Generally, the traffic matrix obtained by the operators is imperfect, i.e., some traffic data may be lost. Hence, we focus on the problems of recovering these missing traffic data in this paper. To recover these missing traffic data, we propose the spatio-temporal Kronecker compressive sensing method, which draws on Kronecker compressive sensing. In our method, we account for the spatial and temporal properties of the traffic matrix to construct a sparsifying basis that can sparsely represent the traffic matrix. Simultaneously, we consider the low-rank property of the traffic matrix and propose a novel recovery model. We finally assess the estimation error of the proposed method by recovering real traffic