5,358 research outputs found
SLIQ: Simple Linear Inequalities for Efficient Contig Scaffolding
Scaffolding is an important subproblem in "de novo" genome assembly in which
mate pair data are used to construct a linear sequence of contigs separated by
gaps. Here we present SLIQ, a set of simple linear inequalities derived from
the geometry of contigs on the line that can be used to predict the relative
positions and orientations of contigs from individual mate pair reads and thus
produce a contig digraph. The SLIQ inequalities can also filter out unreliable
mate pairs and can be used as a preprocessing step for any scaffolding
algorithm. We tested the SLIQ inequalities on five real data sets ranging in
complexity from simple bacterial genomes to complex mammalian genomes and
compared the results to the majority voting procedure used by many other
scaffolding algorithms. SLIQ predicted the relative positions and orientations
of the contigs with high accuracy in all cases and gave more accurate position
predictions than majority voting for complex genomes, in particular the human
genome. Finally, we present a simple scaffolding algorithm that produces linear
scaffolds given a contig digraph. We show that our algorithm is very efficient
compared to other scaffolding algorithms while maintaining high accuracy in
predicting both contig positions and orientations for real data sets.Comment: 16 pages, 6 figures, 7 table
Write-limited sorts and joins for persistent memory
To mitigate the impact of the widening gap between the memory needs of CPUs and what standard memory technology can deliver, system architects have introduced a new class of memory technology termed persistent memory. Persistent memory is byteaddressable, but exhibits asymmetric I/O: writes are typically one order of magnitude more expensive than reads. Byte addressability combined with I/O asymmetry render the performance profile of persistent memory unique. Thus, it becomes imperative to find new ways to seamlessly incorporate it into database systems. We do so in the context of query processing. We focus on the fundamental operations of sort and join processing. We introduce the notion of write-limited algorithms that effectively minimize the I/O cost. We give a high-level API that enables the system to dynamically optimize the workflow of the algorithms; or, alternatively, allows the developer to tune the write profile of the algorithms. We present four different techniques to incorporate persistent memory into the database processing stack in light of this API. We have implemented and extensively evaluated all our proposals. Our results show that the algorithms deliver on their promise of I/O-minimality and tunable performance. We showcase the merits and deficiencies of each implementation technique, thus taking a solid first step towards incorporating persistent memory into query processing. 1
Automatic Liver Segmentation Using an Adversarial Image-to-Image Network
Automatic liver segmentation in 3D medical images is essential in many
clinical applications, such as pathological diagnosis of hepatic diseases,
surgical planning, and postoperative assessment. However, it is still a very
challenging task due to the complex background, fuzzy boundary, and various
appearance of liver. In this paper, we propose an automatic and efficient
algorithm to segment liver from 3D CT volumes. A deep image-to-image network
(DI2IN) is first deployed to generate the liver segmentation, employing a
convolutional encoder-decoder architecture combined with multi-level feature
concatenation and deep supervision. Then an adversarial network is utilized
during training process to discriminate the output of DI2IN from ground truth,
which further boosts the performance of DI2IN. The proposed method is trained
on an annotated dataset of 1000 CT volumes with various different scanning
protocols (e.g., contrast and non-contrast, various resolution and position)
and large variations in populations (e.g., ages and pathology). Our approach
outperforms the state-of-the-art solutions in terms of segmentation accuracy
and computing efficiency.Comment: Accepted by MICCAI 201
Olfactory learning alters navigation strategies and behavioral variability in C. elegans
Animals adjust their behavioral response to sensory input adaptively
depending on past experiences. The flexible brain computation is crucial for
survival and is of great interest in neuroscience. The nematode C. elegans
modulates its navigation behavior depending on the association of odor butanone
with food (appetitive training) or starvation (aversive training), and will
then climb up the butanone gradient or ignore it, respectively. However, the
exact change in navigation strategy in response to learning is still unknown.
Here we study the learned odor navigation in worms by combining precise
experimental measurement and a novel descriptive model of navigation. Our model
consists of two known navigation strategies in worms: biased random walk and
weathervaning. We infer weights on these strategies by applying the model to
worm navigation trajectories and the exact odor concentration it experiences.
Compared to naive worms, appetitive trained worms up-regulate the biased random
walk strategy, and aversive trained worms down-regulate the weathervaning
strategy. The statistical model provides prediction with accuracy of
the past training condition given navigation data, which outperforms the
classical chemotaxis metric. We find that the behavioral variability is altered
by learning, such that worms are less variable after training compared to naive
ones. The model further predicts the learning-dependent response and
variability under optogenetic perturbation of the olfactory neuron
AWC. Lastly, we investigate neural circuits downstream from
AWC that are differentially recruited for learned odor-guided
navigation. Together, we provide a new paradigm to quantify flexible navigation
algorithms and pinpoint the underlying neural substrates
A Tale of Two Narrow-Line Regions: Ionization, Kinematics, and Spectral Energy Distributions for a Local Pair of Merging Obscured Active Galaxies
We explore the gas ionization and kinematics, as well as the optical--IR
spectral energy distributions for UGC 11185, a nearby pair of merging galaxies
hosting obscured active galactic nuclei (AGNs), also known as SDSS
J181611.72+423941.6 and J181609.37+423923.0 (J1816NE and J1816SW, ). Due to the wide separation between these interacting galaxies ( kpc), observations of these objects provide a rare glimpse of the
concurrent growth of supermassive black holes at an early merger stage. We use
BPT line diagnostics to show that the full extent of the narrow line emission
in both galaxies is photoionized by an AGN and confirm the existence of a
10-kpc-scale ionization cone in J1816NE, while in J1816SW the AGN narrow-line
region is much more compact (1--2 kpc) and relatively undisturbed. Our
observations also reveal the presence of ionized gas that nearly spans the
entire distance between the galaxies which is likely in a merger-induced tidal
stream. In addition, we carry out a spectral analysis of the X-ray emission
using data from {\em XMM-Newton}. These galaxies represent a useful pair to
explore how the [\ion{O}{3}] luminosity of an AGN is dependent on the size of
the region used to explore the extended emission. Given the growing evidence
for AGN "flickering" over short timescales, we speculate that the appearances
and impact of these AGNs may change multiple times over the course of the
galaxy merger, which is especially important given that these objects are
likely the progenitors of the types of systems commonly classified as "dual
AGNs."Comment: 15 pages, 10 figures, accepted by the Astrophysical Journa
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