2,829 research outputs found
Three Dimensional Electrical Impedance Tomography
The electrical resistivity of mammalian tissues varies widely and is correlated with physiological
function. Electrical impedance tomography (EIT) can be used to probe such variations in vivo, and offers a
non-invasive means of imaging the internal conductivity distribution of the human body. But the
computational complexity of EIT has severe practical limitations, and previous work has been restricted to
considering image reconstruction as an essentially two-dimensional problem. This simplification can limit
significantly the imaging capabilities of EIT, as the electric currents used to determine the conductivity variations will not in general be confined to a two-dimensional plane. A few studies have attempted three-dimensional EIT image reconstruction, but have not yet succeeded in generating images of a quality suitable for clinical applications. Here we report the development of a three-dimensional EIT system with greatly improved imaging capabilities, which combines our 64-electrode data-collection apparatus with customized matrix inversion techniques. Our results demonstrate the practical potential of EIT for clinical applications, such as lung or brain imaging and diagnostic screening
smt: a Matlab structured matrices toolbox
We introduce the smt toolbox for Matlab. It implements optimized storage and
fast arithmetics for circulant and Toeplitz matrices, and is intended to be
transparent to the user and easily extensible. It also provides a set of test
matrices, computation of circulant preconditioners, and two fast algorithms for
Toeplitz linear systems.Comment: 19 pages, 1 figure, 1 typo corrected in the abstrac
The structure of iterative methods for symmetric linear discrete ill-posed problems
The iterative solution of large linear discrete ill-posed problems with an error contaminated data vector requires the use of specially designed methods in order to avoid severe error propagation. Range restricted minimal residual methods have been found to be well suited for the solution of many such problems. This paper discusses the structure of matrices that arise in a range restricted minimal residual method for the solution of large linear discrete ill-posed problems with a symmetric matrix. The exploitation of the structure results in a method that is competitive with respect to computer storage, number of iterations, and accuracy.Acknowledgments We would like to thank the referees for comments. The work of F. M. was supported
by Dirección General de Investigación CientÃfica y Técnica, Ministerio de EconomÃa y Competitividad of
Spain under grant MTM2012-36732-C03-01. Work of L. R. was supported by Universidad Carlos III de
Madrid in the Department of Mathematics during the academic year 2010-2011 within the framework of
the Chair of Excellence Program and by NSF grant DMS-1115385
Climate Policy Under Fat-Tailed Risk: An Application of Dice
Uncertainty plays a significant role in evaluating climate policy, and fat-tailed uncertainty may dominate policy advice. Should we make our utmost effort to prevent the arbitrarily large impacts of climate change under deep uncertainty? In order to answer to this question, we propose a new way of investigating the impact of (fat-tailed) uncertainty on optimal climate policy: the curvature of the optimal carbon tax against the uncertainty. We find that the optimal carbon tax increases as the uncertainty about climate sensitivity increases, but it does not accelerate as implied by Weitzman's Dismal Theorem. We find the same result in a wide variety of sensitivity analyses. These results emphasize the importance of balancing the costs of climate change against its benefits, also under deep uncertainty. © 2013 Springer Science+Business Media Dordrecht
Multidirectional Subspace Expansion for One-Parameter and Multiparameter Tikhonov Regularization
Tikhonov regularization is a popular method to approximate solutions of linear discrete ill-posed problems when the observed or measured data is contaminated by noise. Multiparameter Tikhonov regularization may improve the quality of the computed approximate solutions. We propose a new iterative method for large-scale multiparameter Tikhonov regularization with general regularization operators based on a multidirectional subspace expansion. The multidirectional subspace expansion may be combined with subspace truncation to avoid excessive growth of the search space. Furthermore, we introduce a simple and effective parameter selection strategy based on the discrepancy principle and related to perturbation results
Paleoclimate Implications for Human-Made Climate Change
Paleoclimate data help us assess climate sensitivity and potential human-made
climate effects. We conclude that Earth in the warmest interglacial periods of
the past million years was less than 1{\deg}C warmer than in the Holocene.
Polar warmth in these interglacials and in the Pliocene does not imply that a
substantial cushion remains between today's climate and dangerous warming, but
rather that Earth is poised to experience strong amplifying polar feedbacks in
response to moderate global warming. Thus goals to limit human-made warming to
2{\deg}C are not sufficient - they are prescriptions for disaster. Ice sheet
disintegration is nonlinear, spurred by amplifying feedbacks. We suggest that
ice sheet mass loss, if warming continues unabated, will be characterized
better by a doubling time for mass loss rate than by a linear trend. Satellite
gravity data, though too brief to be conclusive, are consistent with a doubling
time of 10 years or less, implying the possibility of multi-meter sea level
rise this century. Observed accelerating ice sheet mass loss supports our
conclusion that Earth's temperature now exceeds the mean Holocene value. Rapid
reduction of fossil fuel emissions is required for humanity to succeed in
preserving a planet resembling the one on which civilization developed.Comment: 32 pages, 9 figures; final version accepted for publication in
"Climate Change at the Eve of the Second Decade of the Century: Inferences
from Paleoclimate and Regional Aspects: Proceedings of Milutin Milankovitch
130th Anniversary Symposium" (eds. Berger, Mesinger and Sijaci
Chemotactic response and adaptation dynamics in Escherichia coli
Adaptation of the chemotaxis sensory pathway of the bacterium Escherichia
coli is integral for detecting chemicals over a wide range of background
concentrations, ultimately allowing cells to swim towards sources of attractant
and away from repellents. Its biochemical mechanism based on methylation and
demethylation of chemoreceptors has long been known. Despite the importance of
adaptation for cell memory and behavior, the dynamics of adaptation are
difficult to reconcile with current models of precise adaptation. Here, we
follow time courses of signaling in response to concentration step changes of
attractant using in vivo fluorescence resonance energy transfer measurements.
Specifically, we use a condensed representation of adaptation time courses for
efficient evaluation of different adaptation models. To quantitatively explain
the data, we finally develop a dynamic model for signaling and adaptation based
on the attractant flow in the experiment, signaling by cooperative receptor
complexes, and multiple layers of feedback regulation for adaptation. We
experimentally confirm the predicted effects of changing the enzyme-expression
level and bypassing the negative feedback for demethylation. Our data analysis
suggests significant imprecision in adaptation for large additions.
Furthermore, our model predicts highly regulated, ultrafast adaptation in
response to removal of attractant, which may be useful for fast reorientation
of the cell and noise reduction in adaptation.Comment: accepted for publication in PLoS Computational Biology; manuscript
(19 pages, 5 figures) and supplementary information; added additional
clarification on alternative adaptation models in supplementary informatio
Elongation, rooting and acclimatization of micropropagated shoots from mature material of hybrid larch
Factors were defined for elongation, rooting and acclimatization of micropropagated shoots of Larix x eurolepis Henry initiated from short shoot buds of plagiotropic stecklings serially propagated for 9 years from an 8-year-old tree. Initiation and multiplication were on Schenk and Hildebrandt (SH) medium supplemented with 5 μM 6-benzyladenine (BA) and 1 μM indole-butyric acid (IBA). Stem elongation was obtained in 36% of the shoots on SH medium containing 0.5 μM BA and 63% of the remaining non-elongated shoots initiated stem elongation after transfer on SH medium devoid of growth regulators. Rooting involved 2 steps: root induction on Campbell and Durzan mineral salts and Murashige and Skoog organic elements, both half-strength (CD-MS/2), supplemented with 1 μM of both naphthaleneacetic acid (NAA) and IBA, and root elongation following transfer to CD-MS/2 medium devoid of growth regulators. Repeating this 2-step sequence yielded up to 67% rooted shoots. Acclimatization of plantlets ranged from 83% to 100%. Over 300 plants were transferred to the greenhouse; some showed plagiotropic growth
Linear, Deterministic, and Order-Invariant Initialization Methods for the K-Means Clustering Algorithm
Over the past five decades, k-means has become the clustering algorithm of
choice in many application domains primarily due to its simplicity, time/space
efficiency, and invariance to the ordering of the data points. Unfortunately,
the algorithm's sensitivity to the initial selection of the cluster centers
remains to be its most serious drawback. Numerous initialization methods have
been proposed to address this drawback. Many of these methods, however, have
time complexity superlinear in the number of data points, which makes them
impractical for large data sets. On the other hand, linear methods are often
random and/or sensitive to the ordering of the data points. These methods are
generally unreliable in that the quality of their results is unpredictable.
Therefore, it is common practice to perform multiple runs of such methods and
take the output of the run that produces the best results. Such a practice,
however, greatly increases the computational requirements of the otherwise
highly efficient k-means algorithm. In this chapter, we investigate the
empirical performance of six linear, deterministic (non-random), and
order-invariant k-means initialization methods on a large and diverse
collection of data sets from the UCI Machine Learning Repository. The results
demonstrate that two relatively unknown hierarchical initialization methods due
to Su and Dy outperform the remaining four methods with respect to two
objective effectiveness criteria. In addition, a recent method due to Erisoglu
et al. performs surprisingly poorly.Comment: 21 pages, 2 figures, 5 tables, Partitional Clustering Algorithms
(Springer, 2014). arXiv admin note: substantial text overlap with
arXiv:1304.7465, arXiv:1209.196
Two novel human cytomegalovirus NK cell evasion functions target MICA for lysosomal degradation
NKG2D plays a major role in controlling immune responses through the regulation of natural killer (NK) cells, αβ and γδ T-cell function. This activating receptor recognizes eight distinct ligands (the MHC Class I polypeptide-related sequences (MIC) A andB, and UL16-binding proteins (ULBP)1–6) induced by cellular stress to promote recognition cells perturbed by malignant transformation or microbial infection. Studies into human cytomegalovirus (HCMV) have aided both the identification and characterization of NKG2D ligands (NKG2DLs). HCMV immediate early (IE) gene up regulates NKGDLs, and we now describe the differential activation of ULBP2 and MICA/B by IE1 and IE2 respectively. Despite activation by IE functions, HCMV effectively suppressed cell surface expression of NKGDLs through both the early and late phases of infection. The immune evasion functions UL16, UL142, and microRNA(miR)-UL112 are known to target NKG2DLs. While infection with a UL16 deletion mutant caused the expected increase in MICB and ULBP2 cell surface expression, deletion of UL142 did not have a similar impact on its target, MICA. We therefore performed a systematic screen of the viral genome to search of addition functions that targeted MICA. US18 and US20 were identified as novel NK cell evasion functions capable of acting independently to promote MICA degradation by lysosomal degradation. The most dramatic effect on MICA expression was achieved when US18 and US20 acted in concert. US18 and US20 are the first members of the US12 gene family to have been assigned a function. The US12 family has 10 members encoded sequentially through US12–US21; a genetic arrangement, which is suggestive of an ‘accordion’ expansion of an ancestral gene in response to a selective pressure. This expansion must have be an ancient event as the whole family is conserved across simian cytomegaloviruses from old world monkeys. The evolutionary benefit bestowed by the combinatorial effect of US18 and US20 on MICA may have contributed to sustaining the US12 gene family
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