10,176 research outputs found
Intestinal macromolecular transport supporting adaptive immunity
The gastrointestinal tract performs opposing functions of nutrient absorption, barrier maintenance, and the delivery of luminal substances for the appropriate induction of tolerogenic or protective adaptive immunity. The single-layer epithelium lining the gastrointestinal tract is central to each of these functions by facilitating the uptake and processing of nutrients, providing a physical and chemical barrier to potential pathogens, and delivering macromolecular substances to the immune system to initiate adaptive immune responses. Specific transport mechanisms allow nutrient uptake and the delivery of macromolecules to the immune system while maintaining the epithelial barrier. This review examines historical observations supporting macromolecular transport by the intestinal epithelium, recent insights into the transport of luminal macromolecules to promote adaptive immunity, and how this process is regulated to promote appropriate immune responses. Understanding how luminal macromolecules are delivered to the immune system and how this is regulated may provide insight into the pathophysiology of inflammatory diseases of the gastrointestinal tract and potential preventative or therapeutic strategies. Keywords: Antigen Transport, Mucosal Tolerance, Goblet Cell
The (Double) White Dwarf Binary SDSS 1257+5428
SDSS 1257+5428 is a white dwarf in a close orbit with a companion that has
been suggested to be a neutron star. If so, it hosts the closest known neutron
star, and its existence implies a great abundance of similar systems and a rate
of white-dwarf neutron-star mergers similar to that of the type Ia supernova
rate. Here, we present high signal-to-noise spectra of SDSS 1257+5428, which
confirm an independent finding that the system is in fact composed of two white
dwarfs, one relatively cool and with low mass, and the other hotter and more
massive. With this, the demographics and merger rate are no longer puzzling
(various factors combine to lower the latter by more than two orders of
magnitude). We show that the spectra are fit well with a combination of two
hydrogen model atmospheres, as long as the lines of the higher-gravity
component are broadened significantly relative to what is expected from just
pressure broadening. Interpreting this additional broadening as due to
rotation, the inferred spin period is short, about 1 minute. Similarly rapid
rotation is only seen in accreting white dwarfs that are magnetic; empirically,
it appears that in non-magnetized white dwarfs, accreted angular momentum is
lost by nova explosions before it can be transferred to the white dwarf. This
suggests that the massive white dwarf in SDSS 1257+5428 is magnetic as well,
with B~10^5 G. Alternatively, the broadening seen in the spectral lines could
be due to a stronger magnetic field, of ~10^6 G. The two models could be
distinguished by further observations.Comment: 9 pages, 3 figures, submitted to Ap
Collaborative Training in Sensor Networks: A graphical model approach
Graphical models have been widely applied in solving distributed inference
problems in sensor networks. In this paper, the problem of coordinating a
network of sensors to train a unique ensemble estimator under communication
constraints is discussed. The information structure of graphical models with
specific potential functions is employed, and this thus converts the
collaborative training task into a problem of local training plus global
inference. Two important classes of algorithms of graphical model inference,
message-passing algorithm and sampling algorithm, are employed to tackle
low-dimensional, parametrized and high-dimensional, non-parametrized problems
respectively. The efficacy of this approach is demonstrated by concrete
examples
Consistency in Models for Distributed Learning under Communication Constraints
Motivated by sensor networks and other distributed settings, several models
for distributed learning are presented. The models differ from classical works
in statistical pattern recognition by allocating observations of an independent
and identically distributed (i.i.d.) sampling process amongst members of a
network of simple learning agents. The agents are limited in their ability to
communicate to a central fusion center and thus, the amount of information
available for use in classification or regression is constrained. For several
basic communication models in both the binary classification and regression
frameworks, we question the existence of agent decision rules and fusion rules
that result in a universally consistent ensemble. The answers to this question
present new issues to consider with regard to universal consistency. Insofar as
these models present a useful picture of distributed scenarios, this paper
addresses the issue of whether or not the guarantees provided by Stone's
Theorem in centralized environments hold in distributed settings.Comment: To appear in the IEEE Transactions on Information Theor
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