1,432 research outputs found
Emergent dynamic chirality in a thermally driven artificial spin ratchet
Modern nanofabrication techniques have opened the possibility to create novel functional materials, whose properties transcend those of their constituent elements. In particular, tuning the magnetostatic interactions in geometrically frustrated arrangements of nanoelements called artificial spin ice1, 2 can lead to specific collective behaviour3, including emergent magnetic monopoles4, 5, charge screening6, 7 and transport8, 9, as well as magnonic response10, 11, 12. Here, we demonstrate a spin-ice-based active material in which energy is converted into unidirectional dynamics. Using X-ray photoemission electron microscopy we show that the collective rotation of the average magnetization proceeds in a unique sense during thermal relaxation. Our simulations demonstrate that this emergent chiral behaviour is driven by the topology of the magnetostatic field at the edges of the nanomagnet array, resulting in an asymmetric energy landscape. In addition, a bias field can be used to modify the sense of rotation of the average magnetization. This opens the possibility of implementing a magnetic Brownian ratchet13, 14, which may find applications in novel nanoscale devices, such as magnetic nanomotors, actuators, sensors or memory cells
Surface electrons at plasma walls
In this chapter we introduce a microscopic modelling of the surplus electrons
on the plasma wall which complements the classical description of the plasma
sheath. First we introduce a model for the electron surface layer to study the
quasistationary electron distribution and the potential at an unbiased plasma
wall. Then we calculate sticking coefficients and desorption times for electron
trapping in the image states. Finally we study how surplus electrons affect
light scattering and how charge signatures offer the possibility of a novel
charge measurement for dust grains.Comment: To appear in Complex Plasmas: Scientific Challenges and Technological
Opportunities, Editors: M. Bonitz, K. Becker, J. Lopez and H. Thomse
Enhancing gold recovery from electronic waste via lixiviant metabolic engineering in Chromobacterium violaceum
10.1038/srep02236Scientific Reports3
Deep Decomposition Learning for Inverse Imaging Problems
Deep learning is emerging as a new paradigm for solving inverse imaging
problems. However, the deep learning methods often lack the assurance of
traditional physics-based methods due to the lack of physical information
considerations in neural network training and deploying. The appropriate
supervision and explicit calibration by the information of the physic model can
enhance the neural network learning and its practical performance. In this
paper, inspired by the geometry that data can be decomposed by two components
from the null-space of the forward operator and the range space of its
pseudo-inverse, we train neural networks to learn the two components and
therefore learn the decomposition, i.e. we explicitly reformulate the neural
network layers as learning range-nullspace decomposition functions with
reference to the layer inputs, instead of learning unreferenced functions. We
empirically show that the proposed framework demonstrates superior performance
over recent deep residual learning, unrolled learning and nullspace learning on
tasks including compressive sensing medical imaging and natural image
super-resolution. Our code is available at
https://github.com/edongdongchen/DDN.Comment: To appear in ECCV 202
Hypoxia inducible factor HIF-1 promotes myeloid-derived suppressor cells accumulation through ENTPD2/CD39L1 in hepatocellular carcinoma
published_or_final_versio
Path and Ridge Regression Analysis of Seed Yield and Seed Yield Components of Russian Wildrye (Psathyrostachys juncea Nevski) under Field Conditions
The correlations among seed yield components, and their direct and indirect
effects on the seed yield (Z) of Russina wildrye (Psathyrostachys
juncea Nevski) were investigated. The seed yield components:
fertile tillers m-2 (Y1), spikelets per fertile tillers
(Y2), florets per spikelet- (Y3), seed
numbers per spikelet (Y4) and seed weight (Y5) were
counted and the Z were determined in field experiments from 2003 to 2006 via big
sample size. Y1 was the most important seed yield component
describing the Z and Y2 was the least. The total direct effects of
the Y1, Y3 and Y5 to the Z were positive while
Y4 and Y2 were weakly negative. The total effects
(directs plus indirects) of the components were positively contributed to the Z
by path analyses. The seed yield components Y1, Y2,
Y4 and Y5 were significantly (P<0.001) correlated
with the Z for 4 years totally, while in the individual years, Y2
were not significant correlated with Y3, Y4 and
Y5 by Peason correlation analyses in the five components in the
plant seed production. Therefore, selection for high seed yield through direct
selection for large Y1, Y2 and Y3 would be
effective for breeding programs in grasses. Furthermore, it is the most
important that, via ridge regression, a steady algorithm model between Z and the
five yield components was founded, which can be closely estimated the seed yield
via the components
SProtP: A Web Server to Recognize Those Short-Lived Proteins Based on Sequence-Derived Features in Human Cells
Protein turnover metabolism plays important roles in cell cycle progression, signal transduction, and differentiation. Those proteins with short half-lives are involved in various regulatory processes. To better understand the regulation of cell process, it is important to study the key sequence-derived factors affecting short-lived protein degradation. Until now, most of protein half-lives are still unknown due to the difficulties of traditional experimental methods in measuring protein half-lives in human cells. To investigate the molecular determinants that affect short-lived proteins, a computational method was proposed in this work to recognize short-lived proteins based on sequence-derived features in human cells. In this study, we have systematically analyzed many features that perhaps correlated with short-lived protein degradation. It is found that a large fraction of proteins with signal peptides and transmembrane regions in human cells are of short half-lives. We have constructed an SVM-based classifier to recognize short-lived proteins, due to the fact that short-lived proteins play pivotal roles in the control of various cellular processes. By employing the SVM model on human dataset, we achieved 80.8% average sensitivity and 79.8% average specificity, respectively, on ten testing dataset (TE1-TE10). We also obtained 89.9%, 99% and 83.9% of average accuracy on an independent validation datasets iTE1, iTE2 and iTE3 respectively. The approach proposed in this paper provides a valuable alternative for recognizing the short-lived proteins in human cells, and is more accurate than the traditional N-end rule. Furthermore, the web server SProtP (http://reprod.njmu.edu.cn/sprotp) has been developed and is freely available for users
Identifying novel genetic determinants of hemostatic balance
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73734/1/j.1538-7836.2005.01461.x.pd
Effect of reducing groundwater on the retardation of redox-sensitive radionuclides
Laboratory batch sorption experiments were used to investigate variations in the retardation behavior of redox-sensitive radionuclides. Water-rock compositions were designed to simulate subsurface conditions at the Nevada Test Site (NTS), where a suite of radionuclides were deposited as a result of underground nuclear testing. Experimental redox conditions were controlled by varying the oxygen content inside an enclosed glove box and by adding reductants into the testing solutions
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