Institution
State University of New York System
Education•Albany, New York, United States•
About: State University of New York System is a education organization based out in Albany, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 54077 authors who have published 78070 publications receiving 2985160 citations.
Topics: Population, Poison control, RNA, Gene, Receptor
Papers published on a yearly basis
Papers
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TL;DR: Nitrogen fixation rates in the central Atlantic appear to be independent of both dissolved iron levels in sea water and iron content in Trichodesmium colonies, and the structural iron requirement for the growth of nitrogen-fixing organisms is much lower than previously calculated.
Abstract: Marine fixation of atmospheric nitrogen is believed to be an important source of biologically useful nitrogen to ocean surface waters1, stimulating productivity of phytoplankton and so influencing the global carbon cycle2. The majority of nitrogen fixation in tropical waters is carried out by the marine cyanobacterium Trichodesmium3, which supplies more than half of the new nitrogen used for primary production4. Although the factors controlling marine nitrogen fixation remain poorly understood, it has been thought that nitrogen fixation is limited by iron availability in the ocean2,5. This was inferred from the high iron requirement estimated for growth of nitrogen fixing organisms6 and the higher apparent densities of Trichodesmium where aeolian iron inputs are plentiful7. Here we report that nitrogen fixation rates in the central Atlantic appear to be independent of both dissolved iron levels in sea water and iron content in Trichodesmium colonies. Nitrogen fixation was, instead, highly correlated to the phosphorus content of Trichodesmium and was enhanced at higher irradiance. Furthermore, our calculations suggest that the structural iron requirement for the growth of nitrogen-fixing organisms is much lower than previously calculated6. Although iron deficiency could still potentially limit growth of nitrogen-fixing organisms in regions of low iron availability—for example, in the subtropical North Pacific Ocean—our observations suggest that marine nitrogen fixation is not solely regulated by iron supply.
600 citations
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TL;DR: In this article, an efficient up-and down-converted photoluminescence from the rare-earth ions (Er 3+ and Yb 3+ or Eu 3+ ) doped into fluoride nanomatrix allows optical imaging modality for the nanoprobes.
Abstract: Here, novel nanoprobes for combined optical and magnetic resonance (MR) bioimaging are reported. Fluoride (NaYF 4 ) nanocrystals (20-30 nm size) co-doped with the rare earth ions Gd 3+ and Er 3+ /Yb 3+ /Eu 3+ are synthesized and dispersed in water. An efficient up- and downconverted photoluminescence from the rare-earth ions (Er 3+ and Yb 3+ or Eu 3+ ) doped into fluoride nanomatrix allows optical imaging modality for the nanoprobes. Upconversion nanophosphors (UCNPs) show nearly quadratic dependence of the photoluminescence intensity on the excitation light power, confirming a two-photon induced process and allowing two-photon imaging with UCNPs with low power continuous wave laser diodes due to the sequential nature of the two-photon process. Furthermore, both UCNPs and downconversion nanophosphors (DCNPs) are modified with biorecognition biomolecules such as anti-claudin-4 and anti-mesothelin, and show in vitro targeted delivery to cancer cells using confocal microscopy. The possibility of using nanoprobes for optical imaging in vivo is also demonstrated. It is also shown that Gd 3+ co-doped within the nanophosphors imparts strong T1 (Spin-lattice relaxation time) and T2 (spin-spin relaxation time) for high contrast MR imaging. Thus, nanoprobes based on fluoride nanophosphors doped with rare earth ions are shown to provide the dual modality of optical and magnetic resonance imaging.
597 citations
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TL;DR: The state of the art and the major research challenges in architectures, algorithms, and protocols for wireless multimedia sensor networks, including existing solutions at the physical, link, network, transport, and application layers of the communication protocol stack are investigated.
Abstract: In recent years, the growing interest in the wireless sensor network (WSN) has resulted in thousands of peer-reviewed publications. Most of this research is concerned with scalar sensor networks that measure physical phenomena, such as temperature, pressure, humidity, or location of objects that can be conveyed through low-bandwidth and delay-tolerant data streams. Recently, the focus is shifting toward research aimed at revisiting the sensor network paradigm to enable delivery of multimedia content, such as audio and video streams and still images, as well as scalar data. This effort will result in distributed, networked systems, referred to in this paper as wireless multimedia sensor networks (WMSNs). This article discusses the state of the art and the major research challenges in architectures, algorithms, and protocols for wireless multimedia sensor networks. Existing solutions at the physical, link, network, transport, and application layers of the communication protocol stack are investigated. Finally, fundamental open research issues are discussed, and future research trends in this area are outlined.
597 citations
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TL;DR: Compared with the conventional k -nearest-neighbor graph and ¿-ball graph, the ¿1-graph possesses the advantages: greater robustness to data noise, (2) automatic sparsity, and (3) adaptive neighborhood for individual datum.
Abstract: The graph construction procedure essentially determines the potentials of those graph-oriented learning algorithms for image analysis. In this paper, we propose a process to build the so-called directed ?1-graph, in which the vertices involve all the samples and the ingoing edge weights to each vertex describe its ?1-norm driven reconstruction from the remaining samples and the noise. Then, a series of new algorithms for various machine learning tasks, e.g., data clustering, subspace learning, and semi-supervised learning, are derived upon the ?1-graphs. Compared with the conventional k -nearest-neighbor graph and ?-ball graph, the ?1-graph possesses the advantages: (1) greater robustness to data noise, (2) automatic sparsity, and (3) adaptive neighborhood for individual datum. Extensive experiments on three real-world datasets show the consistent superiority of ?1-graph over those classic graphs in data clustering, subspace learning, and semi-supervised learning tasks.
596 citations
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TL;DR: Reaching blood pressure control by 6 months, independent of drug type, was associated with significant benefits for subsequent major outcomes; the blood pressure response after just 1 month of treatment predicted events and survival.
594 citations
Authors
Showing all 54162 results
Name | H-index | Papers | Citations |
---|---|---|---|
Meir J. Stampfer | 277 | 1414 | 283776 |
Bert Vogelstein | 247 | 757 | 332094 |
Zhong Lin Wang | 245 | 2529 | 259003 |
Peter Libby | 211 | 932 | 182724 |
Robert M. Califf | 196 | 1561 | 167961 |
Stephen V. Faraone | 188 | 1427 | 140298 |
David L. Kaplan | 177 | 1944 | 146082 |
David Baker | 173 | 1226 | 109377 |
Nora D. Volkow | 165 | 958 | 107463 |
David R. Holmes | 161 | 1624 | 114187 |
Richard J. Davidson | 156 | 602 | 91414 |
Ronald G. Crystal | 155 | 990 | 86680 |
Jovan Milosevic | 152 | 1433 | 106802 |
James J. Collins | 151 | 669 | 89476 |
Mark A. Rubin | 145 | 699 | 95640 |