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Institution

University of Nebraska–Lincoln

EducationLincoln, Nebraska, United States
About: University of Nebraska–Lincoln is a education organization based out in Lincoln, Nebraska, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 28059 authors who have published 61544 publications receiving 2139104 citations. The organization is also known as: Nebraska & UNL.


Papers
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Journal ArticleDOI
24 Jul 2015-Science
TL;DR: Evidence is provided for the existence of microbial communities in ~40° to 60°C sediment associated with lignite coal beds at ~1.5 to 2.5 km below the seafloor in the Pacific Ocean off Japan, which suggests that terrigenous sediments retain indigenous community members tens of millions of years after burial in the seabed.
Abstract: Microbial life inhabits deeply buried marine sediments, but the extent of this vast ecosystem remains poorly constrained. Here we provide evidence for the existence of microbial communities in ~40° to 60°C sediment associated with lignite coal beds at ~1.5 to 2.5 km below the seafloor in the Pacific Ocean off Japan. Microbial methanogenesis was indicated by the isotopic compositions of methane and carbon dioxide, biomarkers, cultivation data, and gas compositions. Concentrations of indigenous microbial cells below 1.5 km ranged from <10 to ~10^4 cells cm^(−3). Peak concentrations occurred in lignite layers, where communities differed markedly from shallower subseafloor communities and instead resembled organotrophic communities in forest soils. This suggests that terrigenous sediments retain indigenous community members tens of millions of years after burial in the seabed.

335 citations

Journal ArticleDOI
TL;DR: It is proposed that clique topology is a powerful new tool for matrix analysis in biological settings, where the relationship of observed quantities to more meaningful variables is often nonlinear and unknown.
Abstract: Detecting meaningful structure in neural activity and connectivity data is challenging in the presence of hidden nonlinearities, where traditional eigenvalue-based methods may be misleading. We introduce a novel approach to matrix analysis, called clique topology, that extracts features of the data invariant under nonlinear monotone transformations. These features can be used to detect both random and geometric structure, and depend only on the relative ordering of matrix entries. We then analyzed the activity of pyramidal neurons in rat hippocampus, recorded while the animal was exploring a 2D environment, and confirmed that our method is able to detect geometric organization using only the intrinsic pattern of neural correlations. Remarkably, we found similar results during nonspatial behaviors such as wheel running and rapid eye movement (REM) sleep. This suggests that the geometric structure of correlations is shaped by the underlying hippocampal circuits and is not merely a consequence of position coding. We propose that clique topology is a powerful new tool for matrix analysis in biological settings, where the relationship of observed quantities to more meaningful variables is often nonlinear and unknown.

334 citations

Journal ArticleDOI
TL;DR: The present results showed that the emotional intelligence of the leaders shared significant variance with self-perceptions and rater-perception of transformational leadership, which somewhat support the predictive value of emotional intelligence in antecedent leadership field research.
Abstract: Participants were 80 elected public officials in the United States and 3-6 direct-report staffers for each leader. Together they composed 388 leader-member dyads. The authors surveyed them to explore the relationship between emotional intelligence and transformational leadership. The authors considered the 80 officials as leaders and the staffers as members. The present results showed that the emotional intelligence of the leaders shared significant variance with self-perceptions and rater-perceptions of transformational leadership. The present results also somewhat support the predictive value of emotional intelligence in antecedent leadership field research.

334 citations

Journal ArticleDOI
TL;DR: A facile strategy is reported that can simultaneously enhance the stability and efficiency of p–i–n planar heterojunction-structure perovskite devices and enhances the water and moisture stability of the non-sealed perovkite devices by retaining nearly 90% of their original efficiencies after 30 days' exposure in an ambient environment.
Abstract: The instability of hybrid perovskite materials due to water and moisture arises as one major challenge to be addressed before any practical application of the demonstrated high efficiency perovskite solar cells Here we report a facile strategy that can simultaneously enhance the stability and efficiency of p-i-n planar heterojunction-structure perovskite devices Crosslinkable silane molecules with hydrophobic functional groups are bonded onto fullerene to make the fullerene layer highly water-resistant Methylammonium iodide is introduced in the fullerene layer for n-doping via anion-induced electron transfer, resulting in dramatically increased conductivity over 100-fold With crosslinkable silane-functionalized and doped fullerene electron transport layer, the perovskite devices deliver an efficiency of 195% with a high fill factor of 806% A crosslinked silane-modified fullerene layer also enhances the water and moisture stability of the non-sealed perovskite devices by retaining nearly 90% of their original efficiencies after 30 days' exposure in an ambient environment

333 citations


Authors

Showing all 28272 results

NameH-indexPapersCitations
Donald P. Schneider2421622263641
Suvadeep Bose154960129071
David D'Enterria1501592116210
Aaron Dominguez1471968113224
Gregory R Snow1471704115677
J. S. Keller14498198249
Andrew Askew140149699635
Mitchell Wayne1391810108776
Kenneth Bloom1381958110129
P. de Barbaro1371657102360
Randy Ruchti1371832107846
Ia Iashvili135167699461
Yuichi Kubota133169598570
Ilya Kravchenko132136693639
Andrea Perrotta131138085669
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202393
2022381
20212,809
20202,977
20192,846
20182,854