Stony Brook University

About: Stony Brook University is a education organization based out in Stony Brook, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 32534 authors who have published 68218 publications receiving 3035131 citations. The organization is also known as: State University of New York at Stony Brook & SUNY Stony Brook.

Roles of inflammatory and anabolic cytokines in cartilage metabolism: signals and multiple effectors converge upon MMP-13 regulation in osteoarthritis

Abstract: Human cartilage is a complex tissue of matrix proteins that vary in amount and orientation from superficial to deep layers and from loaded to unloaded zones. A major challenge to efforts to repair cartilage by stem cell-based and other tissue engineering strategies is the inability of the resident chondrocytes to lay down new matrix with the same structural and resilient properties that it had upon its original formation. This is particularly true of the collagen network, which is susceptible to cleavage once proteoglycans are depleted. Thus, a thorough understanding of the similarities and particularly the marked differences in mechanisms of cartilage remodeling during development, osteoarthritis, and aging may lead to more effective strategies for preventing cartilage damage and promoting repair. To identify and characterize effectors or regulators of cartilage remodeling in these processes, we are using culture models of primary human and mouse chondrocytes and cell lines and mouse genetic models to manipulate gene expression programs leading to matrix remodeling and subsequent chondrocyte hypertrophic differentiation, pivotal processes which both go astray in OA disease. Matrix metalloproteinases (MMP)-13, the major type II collagen-degrading collagenase, is regulated by stress-, inflammation-, and differentiation-induced signals that not only contribute to irreversible joint damage (progression) in OA, but importantly, also to the initiation/onset phase, wherein chondrocytes in articular cartilage leave their natural growth- and differentiation-arrested state. Our work points to common mediators of these processes in human OA cartilage and in early through late stages of OA in surgical and genetic mouse models.

Salient Object Detection in the Deep Learning Era: An In-Depth Survey

Abstract: As an essential problem in computer vision, salient object detection (SOD) has attracted an increasing amount of research attention over the years. Recent advances in SOD are predominantly led by deep learning-based solutions (named deep SOD). To enable in-depth understanding of deep SOD, in this paper, we provide a comprehensive survey covering various aspects, ranging from algorithm taxonomy to unsolved issues. In particular, we first review deep SOD algorithms from different perspectives, including network architecture, level of supervision, learning paradigm, and object-/instance-level detection. Following that, we summarize and analyze existing SOD datasets and evaluation metrics. Then, we benchmark a large group of representative SOD models, and provide detailed analyses of the comparison results. Moreover, we study the performance of SOD algorithms under different attribute settings, which has not been thoroughly explored previously, by constructing a novel SOD dataset with rich attribute annotations covering various salient object types, challenging factors, and scene categories. We further analyze, for the first time in the field, the robustness of SOD models to random input perturbations and adversarial attacks. We also look into the generalization and difficulty of existing SOD datasets. Finally, we discuss several open issues of SOD and outline future research directions.

Image thresholding by indicator kriging

Abstract: We consider the problem of segmenting a digitized image consisting of two univariate populations. Assume a priori knowledge allows incomplete assignment of voxels in the image, in the sense that a fraction of the voxels can be identified as belonging to population II/sub 0/, a second fraction to II/sub 1/, and the remaining fraction have no a priori identification. Based upon estimates of the short length scale spatial covariance of the image, we develop a method utilizing indicator kriging to complete the image segmentation.

The Hierarchy of the Sciences

Abstract: For 200 years it has been assumed that the sciences are arranged in a hierarchy, with developed natural sciences like physics at the top and social sciences like sociology at the bottom. Sciences at the top of the hierarchy presumably display higher levels of consensus and more rapid rates of advancement than those at the bottom. A distinction is made between two classes of knowledge: the core, or fully evaluated and universally accepted ideas which serve as the starting points for graduate education, and the research frontier, or all research currently being conducted. Data are presented from a set of empirical studies which show that, at the top and at the bottom of the hierarchy in either cognitive consensus or the rate at which new ideas are incorporated. It is concluded that in all sciences knowledge at the research frontier is a loosely woven web characterized by substantial levels of disagreement and difficulty in determining which contributions will turn out to be significant. Even at the research...

Metal-organic framework with optimally selective xenon adsorption and separation.

Abstract: Nuclear energy is among the most viable alternatives to our current fossil fuel-based energy economy. The mass deployment of nuclear energy as a low-emissions source requires the reprocessing of used nuclear fuel to recover fissile materials and mitigate radioactive waste. A major concern with reprocessing used nuclear fuel is the release of volatile radionuclides such as xenon and krypton that evolve into reprocessing facility off-gas in parts per million concentrations. The existing technology to remove these radioactive noble gases is a costly cryogenic distillation; alternatively, porous materials such as metal-organic frameworks have demonstrated the ability to selectively adsorb xenon and krypton at ambient conditions. Here we carry out a high-throughput computational screening of large databases of metal-organic frameworks and identify SBMOF-1 as the most selective for xenon. We affirm this prediction and report that SBMOF-1 exhibits by far the highest reported xenon adsorption capacity and a remarkable Xe/Kr selectivity under conditions pertinent to nuclear fuel reprocessing.