The model is systematically tested on both simulated and real data. The simulation results reflect that our model can be robust to diverse scenarios, while the real data results demonstrate the wet-lab applicability of our model for high-throughput screening experiments. Lastly, we attribute the model success to its exploration ability by revealing the related matrix ranks and distinct experiment coverage comparisons.Utilizing cell culture medium to grow cells in vitro has been widely studied in the past decades and has been recognized as an acknowledged way for investigating cell activities. However, due to the lack of adequate observation tools, the detailed mechanisms regulating cell growth in cell culture medium are still not fully understood. In this work, atomic force microscopy (AFM), a powerful tool for observing native biological systems under near-physiological conditions with high resolution, was applied to reveal the nanogranular surfaces formed in cell culture medium in situ for promoting cell growth. First, AFM imaging of glass slides (glass slides were previously incubated in cell culture medium) in aqueous environment clearly visualized the cell culture medium-forming nanogranular surfaces on glass slides. By altering the incubation time of glass slides in cell culture medium, the dynamic formation of nanogranular surfaces was remarkably observed. Next, fluorescent labeling experiments of the cell culture medium-treated glass slides showed that bovine serum proteins were contained in the nanogranular surfaces. Further, the adhesive interactions between cells and nanogranular surfaces probed by AFM force spectroscopy and the cell growth experiments showed that cell culture medium-forming nanogranular surfaces promote cell attachment and growth. The study provides novel insights into nanotopography-regulated molecular mechanisms in cell growth and demonstrates the outstanding capabilities of AFM in addressing biological issues with unprecedented spatial resolution under aqueous conditions, which will have potential impacts on the studies of cell behaviors and cell functions.We evaluated different muscle excitation estimation techniques, and their sensitivity to Motor Unit (MU) distribution in muscle tissue. For this purpose, the Convolution Kernel Compensation (CKC) method was used to identify the MU spike trains from High-Density ElectroMyoGrams (HDEMG). Afterwards, Cumulative MU Spike Train (CST) was calculated by summing up the identified MU spike trains. Muscle excitation estimation from CST was compared to the recently introduced Cumulative Motor Unit Activity Index (CAI) and classically used Root-Mean-Square (RMS) amplitude envelop of EMG. To emphasize their dependence on the MU distribution further, all three muscle excitation estimates were used to calculate the agonist-antagonist co-activation index. We showed on synthetic HDEMG that RMS envelopes are the most sensitive to MU distribution (10 % dispersion around the real value), followed by the CST (7 % dispersion) and CAI (5 % dispersion). In experimental HDEMG from wrist extensors and flexors of post-stroke subjects, RMS envelopes yielded significantly smaller excitations of antagonistic muscles than CST and CAI. As a result, RMS-based co-activation estimates differed significantly from the ones produced by CST and CAI, illuminating the problem of large diversity of muscle excitation estimates when multiple muscles are studied in pathological conditions. Similar results were also observed in experimental HDEMG of six intact young males.Efficient and accurate segmentation of full 4D light fields is an important task in computer vision and computer graphics. The massive volume and the redundancy of light fields make it an open challenge. In this paper, we propose a novel light field hypergraph (LFHG) representation using the light field super-pixel (LFSP) for interactive light field segmentation. The LFSPs not only maintain the light field spatio-angular consistency, but also greatly contribute to the hypergraph coarsening. These advantages make LFSPs useful to improve segmentation performance. Based on the LFHG representation, we present an efficient light field segmentation algorithm via graph-cut optimization. Experimental results on both synthetic and real scene data demonstrate that our method outperforms state-of-the-art methods on the light field segmentation task with respect to both accuracy and efficiency.Mesh color edit propagation aims to propagate the color from a few color strokes to the whole mesh, which is useful for mesh colorization, color enhancement and color editing, etc. Compared with image edit propagation, luminance information is not available for 3D mesh data, so the color edit propagation is more difficult on 3D meshes than images, with far less research carried out. This paper proposes a novel solution based on sparse graph regularization. Firstly, a few color strokes are interactively drawn by the user, and then the color will be propagated to the whole mesh by minimizing a sparse graph regularized nonlinear energy function.  https://www.selleckchem.com/products/azd9291.html  The proposed method effectively measures geometric similarity over shapes by using a set of complementary multiscale feature descriptors, and effectively controls color bleeding via a sparse ℓ1 optimization rather than quadratic minimization used in existing work. The proposed framework can be applied for the task of interactive mesh colorization, mesh color enhancement and mesh color editing. Extensive qualitative and quantitative experiments show that the proposed method outperforms the state-of-the-art methods.Recent works on adaptive sparse and on low-rank signal modeling have demonstrated their usefulness in various image/video processing applications. Patch-based methods exploit local patch sparsity, whereas other works apply low-rankness of grouped patches to exploit image non-local structures. However, using either approach alone usually limits performance in image reconstruction or recovery applications. In this work, we propose a simultaneous sparsity and low-rank model, dubbed STROLLR, to better represent natural images. In order to fully utilize both the local and non-local image properties, we develop an image restoration framework using a transform learning scheme with joint low-rank regularization. The approach owes some of its computational efficiency and good performance to the use of transform learning for adaptive sparse representation rather than the popular synthesis dictionary learning algorithms, which involve approximation of NP-hard sparse coding and expensive learning steps. We demonstrate the proposed framework in various applications to image denoising, inpainting, and compressed sensing based magnetic resonance imaging.