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Institution

Michigan Technological University

EducationHoughton, Michigan, United States
About: Michigan Technological University is a education organization based out in Houghton, Michigan, United States. It is known for research contribution in the topics: Population & Volcano. The organization has 8023 authors who have published 17422 publications receiving 481780 citations. The organization is also known as: MTU & Michigan Tech.


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Journal ArticleDOI
TL;DR: Creative problem solving is a framework that encourages whole-brain, iterative thinking in the most effective sequence; it is cooperative in nature and is most productive when done as a team effort.
Abstract: Problem solving, as commonly taught in schools, is an analytical or procedural approach. This approach almost exclusively employs left-brain thinking modes, is competitive, and relies on individual effort. However, creative problem solving is a framework that encourages whole-brain, iterative thinking in the most effective sequence; it is cooperative in nature and is most productive when done as a team effort.

442 citations

Journal ArticleDOI
TL;DR: It is suggested that many AONDs can be categorized as dysferopathies, diseases in which alterations in AT represent a critical component in pathogenesis, and Illumination of such mechanisms provides a framework for the development of novel therapeutic strategies aimed to prevent axonal and synaptic dysfunction in several major Aonds.
Abstract: Adult-onset neurodegenerative diseases (AONDs) comprise a heterogeneous group of neurological disorders characterized by a progressive, age-dependent decline in neuronal function and loss of selected neuronal populations. Alterations in synaptic function and axonal connectivity represent early and critical pathogenic events in AONDs, but molecular mechanisms underlying these defects remain elusive. The large size and complex subcellular architecture of neurons render them uniquely vulnerable to alterations in axonal transport (AT). Accordingly, deficits in AT have been documented in most AONDs, suggesting a common defect acquired through different pathogenic pathways. These observations suggest that many AONDs can be categorized as dysferopathies, diseases in which alterations in AT represent a critical component in pathogenesis. Topics here address various molecular mechanisms underlying alterations in AT in several AONDs. Illumination of such mechanisms provides a framework for the development of novel therapeutic strategies aimed to prevent axonal and synaptic dysfunction in several major AONDs.

434 citations

Journal ArticleDOI
TL;DR: In this article, an ensemble of 21 global and hemispheric chemical transport models is used to estimate the spatial average surface ozone (O-3) response over east Asia (EA), Europe (EU), North America (NA), and south Asia (SA) to 20% decreases in anthropogenic emissions of the O-3 precursors, NOx, NMVOC, and CO.
Abstract: Understanding the surface O-3 response over a "receptor" region to emission changes over a foreign "source" region is key to evaluating the potential gains from an international approach to abate ozone (O-3) pollution. We apply an ensemble of 21 global and hemispheric chemical transport models to estimate the spatial average surface O-3 response over east Asia (EA), Europe (EU), North America (NA), and south Asia (SA) to 20% decreases in anthropogenic emissions of the O-3 precursors, NOx, NMVOC, and CO (individually and combined), from each of these regions. We find that the ensemble mean surface O-3 concentrations in the base case (year 2001) simulation matches available observations throughout the year over EU but overestimates them by > 10 ppb during summer and early fall over the eastern United States and Japan. The sum of the O-3 responses to NOx, CO, and NMVOC decreases separately is approximately equal to that from a simultaneous reduction of all precursors. We define a continental-scale "import sensitivity" as the ratio of the O-3 response to the 20% reductions in foreign versus "domestic" (i.e., over the source region itself) emissions. For example, the combined reduction of emissions from the three foreign regions produces an ensemble spatial mean decrease of 0.6 ppb over EU (0.4 ppb from NA), less than the 0.8 ppb from the reduction of EU emissions, leading to an import sensitivity ratio of 0.7. The ensemble mean surface O-3 response to foreign emissions is largest in spring and late fall (0.7-0.9 ppb decrease in all regions from the combined precursor reductions in the three foreign regions), with import sensitivities ranging from 0.5 to 1.1 (responses to domestic emission reductions are 0.8-1.6 ppb). High O-3 values are much more sensitive to domestic emissions than to foreign emissions, as indicated by lower import sensitivities of 0.2 to 0.3 during July in EA, EU, and NA when O-3 levels are typically highest and by the weaker relative response of annual incidences of daily maximum 8-h average O-3 above 60 ppb to emission reductions in a foreign region(< 10-20% of that to domestic) as compared to the annual mean response (up to 50% of that to domestic). Applying the ensemble annual mean results to changes in anthropogenic emissions from 1996 to 2002, we estimate a Northern Hemispheric increase in background surface O-3 of about 0.1 ppb a(-1), at the low end of the 0.1-0.5 ppb a(-1) derived from observations. From an additional simulation in which global atmospheric methane was reduced, we infer that 20% reductions in anthropogenic methane emissions from a foreign source region would yield an O-3 response in a receptor region that roughly equals that produced by combined 20% reductions of anthropogenic NOx, NMVOC, and CO emissions from the foreign source

430 citations

Journal ArticleDOI
TL;DR: A narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends, and discusses the many innovations that have advanced in deep learning and how these tools facilitate U-nets.
Abstract: U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

425 citations

Journal ArticleDOI
02 Dec 1994-Science
TL;DR: Isle Royale's dendrochronology complements a rich literature on food chain control in aquatic systems, which often supports a trophic cascade model, and provides evidence of top-down control in a forested ecosystem.
Abstract: Investigation of tree growth in Isle Royale National Park in Michigan revealed the influence of herbivores and carnivores on plants in an intimately linked food chain. Plant growth rates were regulated by cycles in animal density and responded to annual changes in primary productivity only when released from herbivory by wolf predation. Isle Royale's dendrochronology complements a rich literature on food chain control in aquatic systems, which often supports a trophic cascade model. This study provides evidence of top-down control in a forested ecosystem.

422 citations


Authors

Showing all 8104 results

NameH-indexPapersCitations
Anil K. Jain1831016192151
Marc W. Kirschner162457102145
Yonggang Huang13679769290
Hong Wang110163351811
Fei Wang107182453587
Emanuele Bonamente10521940826
Haoshen Zhou10451937609
Nicholas J. Turro104113153827
Yang Shao-Horn10245849463
Richard P. Novick9929534542
Markus J. Buehler9560933054
Martin L. Yarmush9170234591
Alan Robock9034627022
Patrick M. Schlievert9044432037
Lonnie O. Ingram8831622217
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202349
2022154
2021882
2020891
2019892
2018893