Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks

Image orientation detection requires high-level scene understanding. Humans use object recognition and contextual scene information to correctly orient images. In literature, the problem of image orientation detection is mostly confronted by using low-level vision features, while some approaches incorporate few easily detectable semantic cues to gain minor improvements. The vast amount of semantic content in images makes orientation detection challenging, and therefore there is a large semantic gap between existing methods and human behavior. Also, existing methods in literature report highly discrepant detection rates, which is mainly due to large differences in datasets and limited variety of test images used for evaluation. In this work, for the first time, we leverage the power of deep learning and adapt pre-trained convolutional neural networks using largest training dataset to-date for the image orientation detection task. An extensive evaluation of our model on different public datasets shows that it remarkably generalizes to correctly orient a large set of unconstrained images; it also significantly outperforms the state-of-the-art and achieves accuracy very close to that of humans

Discovery of a potent nanoparticle P‐selectin antagonist with anti‐inflammatory effects in allergic airway disease

The severity of allergic asthma is dependent, in part, on the intensity of peribronchial inflammation. P‐selectin is known to play a role in the development of allergen‐induced peribronchial inflammation and airway hyperreactivity. Selective inhibitors of P‐selectin‐ mediated leukocyte endothelial‐cell interactions may therefore attenuate the inflammatory processes associated with allergic airway disease. Novel P‐selectin inhibitors were created using a polyvalent polymer nanoparticle capable of displaying multiple synthetic, low molecular weight ligands. By assembling a particle that presents an array of groups, which as monomers interact with only low affinity, we created a construct that binds extremely efficiently to P‐ selectin. The ligands acted as mimetics of the key binding elements responsible for the high‐ avidity adhesion of P‐selectin to the physiologic ligand, PSGL‐1. The inhibitors were initially evaluated using an in vitro shear assay system in which interactions between circulating cells and P‐selectin‐coated capillary tubes were measured. The nanoparticles were shown to preferentially bind to selectins expressed on activated endothelial cells. We subsequently demonstrated that nanoparticles displaying P‐selectin blocking arrays were functionally active in vivo, significantly reducing allergen‐induced airway hyperreactivity and peribronchial eosinophilic inflammation in a murine model of asthma.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154265/1/fsb2fj030166fje-sup-0001.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154265/2/fsb2fj030166fje.pd

Lipoxin A4 stable analogs reduce allergic airway responses via mechanisms distinct from CysLT1 receptor antagonism

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154665/1/fsb2fj078653com.pd

The Naked Mole Rat Genome Resource: facilitating analyses of cancer and longevity-related adaptations

Motivation: The naked mole rat (Heterocephalus glaber) is an exceptionally long-lived and cancer-resistant rodent native to East Africa. Although its genome was previously sequenced, here we report a new assembly sequenced by us with substantially higher N50 values for scaffolds and contigs. Results: We analyzed the annotation of this new improved assembly and identified candidate genomic adaptations which may have contributed to the evolution of the naked mole rat’s extraordinary traits, including in regions of p53, and the hyaluronan receptors CD44 and HMMR (RHAMM). Furthermore, we developed a freely available web portal, the Naked Mole Rat Genome Resource (http://www.naked-mole-rat.org), featuring the data and results of our analysis, to assist researchers interested in the genome and genes of the naked mole rat, and also to facilitate further studies on this fascinating species. Availability and implementation: The Naked Mole Rat Genome Resource is freely available online at http://www.naked-mole-rat.org. This resource is open source and the source code is available at https://github.com/maglab/naked-mole-rat-portal. Contact: [email protected]

Comprehensive comparative analysis of RNA sequencing methods for degraded or low input samples

RNA-Seq is an effective method to study the transcriptome, but can be difficult to apply to scarce or degraded RNA from fixed clinical samples, rare cell populations, or cadavers. Recent studies have proposed several methods for RNA-Seq of low quality and/or low quantity samples, but their relative merits have not been systematically analyzed. Here, we compare five such methods using metrics relevant to transcriptome annotation, transcript discovery, and gene expression. Using a single human RNA sample, we constructed and sequenced ten libraries with these methods and two control libraries. We find that the RNase H method performed best for low quality RNA, and confirmed this with actual degraded samples. RNase H can even effectively replace oligo (dT) based methods for standard RNA-Seq. SMART and NuGEN had distinct strengths for low quantity RNA. Our analysis allows biologists to select the most suitable methods and provides a benchmark for future method development

A scalable, fully automated process for construction of sequence-ready human exome targeted capture libraries

Genome targeting methods enable cost-effective capture of specific subsets of the genome for sequencing. We present here an automated, highly scalable method for carrying out the Solution Hybrid Selection capture approach that provides a dramatic increase in scale and throughput of sequence-ready libraries produced. Significant process improvements and a series of in-process quality control checkpoints are also added. These process improvements can also be used in a manual version of the protocol

An Improved Canine Genome and a Comprehensive Catalogue of Coding Genes and Non-Coding Transcripts

The domestic dog, Canis familiaris, is a well-established model system for mapping trait and disease loci. While the original draft sequence was of good quality, gaps were abundant particularly in promoter regions of the genome, negatively impacting the annotation and study of candidate genes. Here, we present an improved genome build, canFam3.1, which includes 85 MB of novel sequence and now covers 99.8% of the euchromatic portion of the genome. We also present multiple RNA-Sequencing data sets from 10 different canine tissues to catalog ∼175,000 expressed loci. While about 90% of the coding genes previously annotated by EnsEMBL have measurable expression in at least one sample, the number of transcript isoforms detected by our data expands the EnsEMBL annotations by a factor of four. Syntenic comparison with the human genome revealed an additional ∼3,000 loci that are characterized as protein coding in human and were also expressed in the dog, suggesting that those were previously not annotated in the EnsEMBL canine gene set. In addition to ∼20,700 high-confidence protein coding loci, we found ∼4,600 antisense transcripts overlapping exons of protein coding genes, ∼7,200 intergenic multi-exon transcripts without coding potential, likely candidates for long intergenic non-coding RNAs (lincRNAs) and ∼11,000 transcripts were reported by two different library construction methods but did not fit any of the above categories. Of the lincRNAs, about 6,000 have no annotated orthologs in human or mouse. Functional analysis of two novel transcripts with shRNA in a mouse kidney cell line altered cell morphology and motility. All in all, we provide a much-improved annotation of the canine genome and suggest regulatory functions for several of the novel non-coding transcripts

Enhanced methods for unbiased deep sequencing of Lassa and Ebola RNA viruses from clinical and biological samples

We have developed a robust RNA sequencing method for generating complete de novo assemblies with intra-host variant calls of Lassa and Ebola virus genomes in clinical and biological samples. Our method uses targeted RNase H-based digestion to remove contaminating poly(rA) carrier and ribosomal RNA. This depletion step improves both the quality of data and quantity of informative reads in unbiased total RNA sequencing libraries. We have also developed a hybrid-selection protocol to further enrich the viral content of sequencing libraries. These protocols have enabled rapid deep sequencing of both Lassa and Ebola virus and are broadly applicable to other viral genomics studies. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0519-7) contains supplementary material, which is available to authorized users