1,336 research outputs found
Ultra-high Dimensional Multiple Output Learning With Simultaneous Orthogonal Matching Pursuit: A Sure Screening Approach
We propose a novel application of the Simultaneous Orthogonal Matching
Pursuit (S-OMP) procedure for sparsistant variable selection in ultra-high
dimensional multi-task regression problems. Screening of variables, as
introduced in \cite{fan08sis}, is an efficient and highly scalable way to
remove many irrelevant variables from the set of all variables, while retaining
all the relevant variables. S-OMP can be applied to problems with hundreds of
thousands of variables and once the number of variables is reduced to a
manageable size, a more computationally demanding procedure can be used to
identify the relevant variables for each of the regression outputs. To our
knowledge, this is the first attempt to utilize relatedness of multiple outputs
to perform fast screening of relevant variables. As our main theoretical
contribution, we prove that, asymptotically, S-OMP is guaranteed to reduce an
ultra-high number of variables to below the sample size without losing true
relevant variables. We also provide formal evidence that a modified Bayesian
information criterion (BIC) can be used to efficiently determine the number of
iterations in S-OMP. We further provide empirical evidence on the benefit of
variable selection using multiple regression outputs jointly, as opposed to
performing variable selection for each output separately. The finite sample
performance of S-OMP is demonstrated on extensive simulation studies, and on a
genetic association mapping problem. Adaptive Lasso; Greedy forward
regression; Orthogonal matching pursuit; Multi-output regression; Multi-task
learning; Simultaneous orthogonal matching pursuit; Sure screening; Variable
selectio
Extracellular volume regulation and growth
We have formalized extracellular and intracellular volume interaction with each other and the influence of these processes on the type of cell growth. The linearized model was verified by stereo metric solution and the results were compared with experimental data. Two theoretical solutions were found: Solution 1, extracellular volume (ECV) was calculated to be about 23% of total body volume (TV). Stereo metric solution suggested the cubic cell cluster formed by 8-cells. This hypothesis (Solution l) explains the ECV to be compatible with the widely accepted value (about 23% of TV). In addition, the 8-cell cluster hypothesis explains the existence of ECV oscillation with the period of about seven days. This hypothesis probably describes the dominant type of growth in humans. Solution 2, in this type of growth, ECV fills about 77% per cent of TV. Instead of the 8-cell cube, in this type of proliferation 4-cells could form a tetrahedron. This type of growth could be beneficial in processes where free space in tissue or organ must be filled for example in peptic ulcer healing and namely in repopulating of free space in a bone after high dose chemotherapy
Graph Estimation From Multi-attribute Data
Many real world network problems often concern multivariate nodal attributes
such as image, textual, and multi-view feature vectors on nodes, rather than
simple univariate nodal attributes. The existing graph estimation methods built
on Gaussian graphical models and covariance selection algorithms can not handle
such data, neither can the theories developed around such methods be directly
applied. In this paper, we propose a new principled framework for estimating
graphs from multi-attribute data. Instead of estimating the partial correlation
as in current literature, our method estimates the partial canonical
correlations that naturally accommodate complex nodal features.
Computationally, we provide an efficient algorithm which utilizes the
multi-attribute structure. Theoretically, we provide sufficient conditions
which guarantee consistent graph recovery. Extensive simulation studies
demonstrate performance of our method under various conditions. Furthermore, we
provide illustrative applications to uncovering gene regulatory networks from
gene and protein profiles, and uncovering brain connectivity graph from
functional magnetic resonance imaging data.Comment: Extended simulation study. Added an application to a new data se
Optical Coherence Tomography for Examination of Parchment Degradation
A novel application of Optical Coherence Tomography utilizing infrared light of 830 nm central wavelength for non invasive examination of the structure of parchment, some covered with iron gall ink, is presented. It is shown that both the parchment and the ink applied are sufficiently transparent to light of this wavelength. In the study, Spectral OCT (SOCT) as well as Polarisation Sensitive OCT (PS-OCT) techniques were used to obtain cross-sectional images of samples of parchment based on scattering properties. The second technique was additionally employed to recover the birefringence properties and the optical axis orientations of the sample. It was shown that freshly produced parchment exhibits a degree of birefringence. However, this property declines with ageing, and samples of old parchment completely depolarise the incident light
TREX1 is expressed by microglia in normal human brain and increases in regions affected by ischemia
BACKGROUND: Mutations in the three-prime repair exonuclease 1 (TREX1) gene have been associated with neurological diseases, including Retinal Vasculopathy with Cerebral Leukoencephalopathy (RVCL). However, the endogenous expression of TREX1 in human brain has not been studied.
METHODS: We produced a rabbit polyclonal antibody (pAb) to TREX1 to characterize TREX1 by Western blotting (WB) of cell lysates from normal controls and subjects carrying an RVCL frame-shift mutation. Dual staining was performed to determine cell types expressing TREX1 in human brain tissue. TREX1 distribution in human brain was further evaluated by immunohistochemical analyses of formalin-fixed, paraffin-embedded samples from normal controls and patients with RVCL and ischemic stroke.
RESULTS: After validating the specificity of our anti-TREX1 rabbit pAb, WB analysis was utilized to detect the endogenous wild-type and frame-shift mutant of TREX1 in cell lysates. Dual staining in human brain tissues from patients with RVCL and normal controls localized TREX1 to a subset of microglia and macrophages. Quantification of immunohistochemical staining of the cerebral cortex revealed that TREX1
CONCLUSIONS: TREX1 is expressed by a subset of microglia in normal human brain, often in close proximity to the microvasculature, and increases in the setting of ischemic lesions. These findings suggest a role for TREX
Estimating time-varying networks
Stochastic networks are a plausible representation of the relational
information among entities in dynamic systems such as living cells or social
communities. While there is a rich literature in estimating a static or
temporally invariant network from observation data, little has been done toward
estimating time-varying networks from time series of entity attributes. In this
paper we present two new machine learning methods for estimating time-varying
networks, which both build on a temporally smoothed -regularized logistic
regression formalism that can be cast as a standard convex-optimization problem
and solved efficiently using generic solvers scalable to large networks. We
report promising results on recovering simulated time-varying networks. For
real data sets, we reverse engineer the latent sequence of temporally rewiring
political networks between Senators from the US Senate voting records and the
latent evolving regulatory networks underlying 588 genes across the life cycle
of Drosophila melanogaster from the microarray time course.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS308 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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