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

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, $z \approx 0.04$). Due to the wide separation between these interacting galaxies ($\sim 23$ 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

Intraspinal stem cell transplantation for amyotrophic lateral sclerosis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134502/1/ana24584_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134502/2/ana24584.pd