79,476 research outputs found
Functional Regression
Functional data analysis (FDA) involves the analysis of data whose ideal
units of observation are functions defined on some continuous domain, and the
observed data consist of a sample of functions taken from some population,
sampled on a discrete grid. Ramsay and Silverman's 1997 textbook sparked the
development of this field, which has accelerated in the past 10 years to become
one of the fastest growing areas of statistics, fueled by the growing number of
applications yielding this type of data. One unique characteristic of FDA is
the need to combine information both across and within functions, which Ramsay
and Silverman called replication and regularization, respectively. This article
will focus on functional regression, the area of FDA that has received the most
attention in applications and methodological development. First will be an
introduction to basis functions, key building blocks for regularization in
functional regression methods, followed by an overview of functional regression
methods, split into three types: [1] functional predictor regression
(scalar-on-function), [2] functional response regression (function-on-scalar)
and [3] function-on-function regression. For each, the role of replication and
regularization will be discussed and the methodological development described
in a roughly chronological manner, at times deviating from the historical
timeline to group together similar methods. The primary focus is on modeling
and methodology, highlighting the modeling structures that have been developed
and the various regularization approaches employed. At the end is a brief
discussion describing potential areas of future development in this field
Genomic and proteomic profiling for cancer diagnosis in dogs
Global gene expression, whereby tumours are classified according to similar gene expression patterns or ‘signatures’ regardless of cell morphology or tissue characteristics, is being increasingly used in both the human and veterinary fields to assist in cancer diagnosis and prognosis. Many studies on canine tumours have focussed on RNA expression using techniques such as microarrays or next generation sequencing. However, proteomic studies combining two-dimensional polyacrylamide gel electrophoresis or two-dimensional differential gel electrophoresis with mass spectrometry have also provided a wealth of data on gene expression in tumour tissues. In addition, proteomics has been instrumental in the search for tumour biomarkers in blood and other body fluids
An Open Mapping Theorem
It is proved that any surjective morphism onto a
locally compact group is open for every cardinal . This answers a
question posed by Karl Heinrich Hofmann and the second author
Scheme Independence to all Loops
The immense freedom in the construction of Exact Renormalization Groups means
that the many non-universal details of the formalism need never be exactly
specified, instead satisfying only general constraints. In the context of a
manifestly gauge invariant Exact Renormalization Group for SU(N) Yang-Mills, we
outline a proof that, to all orders in perturbation theory, all explicit
dependence of beta function coefficients on both the seed action and details of
the covariantization cancels out. Further, we speculate that, within the
infinite number of renormalization schemes implicit within our approach, the
perturbative beta function depends only on the universal details of the setup,
to all orders.Comment: 18 pages, 8 figures; Proceedings of Renormalization Group 2005,
Helsinki, Finland, 30th August - 3 September 2005. v2: Published in jphysa;
minor changes / refinements; refs. adde
String-Like Lagrangians from a Generalized Geometry
This note will use Hitchin's generalized geometry and a model of axionic
gravity developed by Warren Siegel in the mid-nineties to show that the
construction of Lagrangians based on the inner product arising from the pairing
of a vector and its dual can lead naturally to the low-energy Lagrangian of the
bosonic string.Comment: Conclusions basically unchanged, but presentation streamlined
significantly. Published versio
Mobility Measurements Probe Conformational Changes in Membrane Proteins due to Tension
The function of membrane-embedded proteins such as ion channels depends
crucially on their conformation. We demonstrate how conformational changes in
asymmetric membrane proteins may be inferred from measurements of their
diffusion. Such proteins cause local deformations in the membrane, which induce
an extra hydrodynamic drag on the protein. Using membrane tension to control
the magnitude of the deformations and hence the drag, measurements of
diffusivity can be used to infer--- via an elastic model of the protein--- how
conformation is changed by tension. Motivated by recent experimental results
[Quemeneur et al., Proc. Natl. Acad. Sci. USA, 111 5083 (2014)] we focus on
KvAP, a voltage-gated potassium channel. The conformation of KvAP is found to
change considerably due to tension, with its `walls', where the protein meets
the membrane, undergoing significant angular strains. The torsional stiffness
is determined to be 26.8 kT at room temperature. This has implications for both
the structure and function of such proteins in the environment of a
tension-bearing membrane.Comment: Manuscript: 4 pages, 4 figures. Supplementary Material: 8 pages, 1
figur
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