Supplementation of mesenchymal stem cells (MSCs) at sites of bone resorption is required for bone homeostasis because of the non-proliferation and short lifespan properties of the osteoblasts. Calcium ions (Ca2+) are released from the bone surfaces during osteoclast-mediated bone resorption. However, how elevated extracellular Ca2+ concentrations would alter MSCs behavior in the proximal sites of bone resorption is largely unknown. In this study, we investigated the effect of extracellular Ca2+ on MSCs phenotype depending on Ca2+ concentrations. We found that the elevated extracellular Ca2+ promoted cell proliferation and matrix mineralization of MSCs. In addition, MSCs induced the expression and secretion of osteopontin (OPN), which enhanced MSCs migration under the elevated extracellular Ca2+ conditions. We developed in vitro osteoclast-mediated bone resorption conditions using mouse calvaria bone slices and demonstrated Ca2+ is released from bone resorption surfaces. We also showed that the MSCs phenotype, including cell proliferation and migration, changed when the cells were treated with a bone resorption-conditioned medium. These findings suggest that the dynamic changes in Ca2+ concentrations in the microenvironments of bone remodeling surfaces modulate MSCs phenotype and thereby contribute to bone regeneration.

In this paper, we present techniques for automatically classifying players and tracking ball movements in basketball game video clips under poor conditions, where the camera angle dynamically shifts and changes. In the core of our system lies Yolo, a realtime object detection system. Given the ground truth boxes collected by our data specialists, Yolo is trained to detect the presence of objects in every video frame. In addition, Yolo uses Darknet that implements convolution neural networks to classify a detected object to a player and to recognize its jersey numbers of specific movements. By identifying players and ball possessions, we can automatically compute ball distributions that are reflected on complex networks. With original Yolo system, player movement can be interrupted, when the players move out of the frame due to camera shift and when players overlap each other on a two-dimensional frame. We have adapted Yolo to keep track of players even under such poor condition by considering contextual information available from the framework preceding and/or succeeding problematic video frames. In addition to the novel movement inference method, we provide a framework for analyzing the pass networks in various perspectives to help the managing staff to reveal critical determinants of team performance and to design better game strategies. We assess the performance of our system in terms of accuracy by making a comparison with the analytical reports generated by human experts.INDEX TERMS Sports analytics, object detection, complex networks, deep learning, video processing.

Maritime logistics operations are full of uncertainty such as severe weather, mechanical problems, strikes, and fluctuating freight rates. Traditional ship-scheduling models ignore uncertainty, even in highly volatile markets. Although ship operators can increase revenue by delivering many spot cargoes, they have to embrace the risk of the fluctuation of spot rates. We present a set-packing model that limits risk using a quadratic variance constraint. We use a traditional Kelley's cutting plane algorithm and a delayed column-and-cut generation (branch-and-price-and-cut) algorithm on medium-sized ship-scheduling problems with restricted variance. We develop a second set of cuts that are more restrictive under certain conditions. Computational results show that the variance of profit can be significantly reduced with a reasonable increase in cost.

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Define the ECHO sequence {bn} recursively by (b 0 , b 1 , b 2 , b 3 ) = (1, 1, 2, 1) and for n ≥ 4,We relate this sequence {bn} to the coordinates of points on the elliptic curve E : y 2 + y = x 3 − 3x + 4. We use Galois representations attached to E to prove that the density of primes dividing a term in this sequence is equal to 179 336 . Furthermore, we describe an infinite family of elliptic curves whose Galois images match those of E.