Pose-Normalized Image Generation for Person Re-identification

Person Re-identification (re-id) faces two major challenges: the lack of cross-view paired training data and learning discriminative identity-sensitive and view-invariant features in the presence of large pose variations. In this work, we address both problems by proposing a novel deep person image generation model for synthesizing realistic person images conditional on the pose. The model is based on a generative adversarial network (GAN) designed specifically for pose normalization in re-id, thus termed pose-normalization GAN (PN-GAN). With the synthesized images, we can learn a new type of deep re-id feature free of the influence of pose variations. We show that this feature is strong on its own and complementary to features learned with the original images. Importantly, under the transfer learning setting, we show that our model generalizes well to any new re-id dataset without the need for collecting any training data for model fine-tuning. The model thus has the potential to make re-id model truly scalable.Comment: 10 pages, 5 figure

A complex pattern of post‐divergence expansion, contraction, introgression and asynchronous responses to Pleistocene climate changes in two Dipelta sister species from western China

The well-known vicariance and dispersal models dominate in understanding the allopatric pattern for related species and presume the simultaneous occurrence of speciation and biogeographic events. However, the formation of allopatry may postdate the species divergence. We examined this hypothesis using DNA sequence data from 3 chloroplast fragments and 5 nuclear loci of Dipelta floribunda and D. yunnanensis, two shrub species with the circum Sichuan Basin distribution, combining the climatic niche modeling approach. The best-fit model supported by the approximate Bayesian computation (ABC) analysis indicated that, D. floribunda and D. yunnanensis diverged during the mid-Pleistocene period, consistent with the largest glacial period in the Qinghai-Tibet Plateau (QTP). The historically inter-specific gene flow was identified but seemed to have ceased after the last interglacial period (LIG), when the range of D. floribunda moved northward from the south of the Sichuan Basin. Further, populations of D. floribunda had expanded obviously in the north of the Sichuan Basin after the last glacial maximum (LGM). Relatively, the range of D. yunnanensis expanded before the LGM, reduced during the post-LGM especially in the north of the Sichuan Basin, reflecting the asynchronous responses of related species to the contemporary climate changes. Our results suggested that complex topography should be considered in understanding the distributional patterns even for closely related species and their demographic responses

Semantic distillation: a method for clustering objects by their contextual specificity

Techniques for data-mining, latent semantic analysis, contextual search of databases, etc. have long ago been developed by computer scientists working on information retrieval (IR). Experimental scientists, from all disciplines, having to analyse large collections of raw experimental data (astronomical, physical, biological, etc.) have developed powerful methods for their statistical analysis and for clustering, categorising, and classifying objects. Finally, physicists have developed a theory of quantum measurement, unifying the logical, algebraic, and probabilistic aspects of queries into a single formalism. The purpose of this paper is twofold: first to show that when formulated at an abstract level, problems from IR, from statistical data analysis, and from physical measurement theories are very similar and hence can profitably be cross-fertilised, and, secondly, to propose a novel method of fuzzy hierarchical clustering, termed \textit{semantic distillation} -- strongly inspired from the theory of quantum measurement --, we developed to analyse raw data coming from various types of experiments on DNA arrays. We illustrate the method by analysing DNA arrays experiments and clustering the genes of the array according to their specificity.Comment: Accepted for publication in Studies in Computational Intelligence, Springer-Verla

Proceedings of the Second Annual Conference of the MidSouth Computational Biology and Bioinformatics Society

The MCBIOS 2004 conference brought together regional researchers and students in biology, computer science and bioinformatics on October 7th-9th 2004 to present their latest work. This editorial describes the conference itself and introduces the twelve peer-reviewed manuscripts accepted for publication in the Proceedings of the MCBIOS 2004 Conference. These manuscripts included new methods for analysis of high-throughput gene expression experiments, EST clustering, analysis of mass spectrometry data and genomic analysi

Membranes, molecules and biophysics: enhancing monocyte derived dendritic cell (MDDC) immunogenicity for improved anti-cancer therapy

Despite great medical advancement in the treatment of cancer, cancer remains a disease of global significance. Chemotherapeutics can be very expensive and drain medical resources at a national level and in some cases the cost of treatment is so great that it prohibits their use by local health authorities. Drug resistance is also a major limiting factor to the successful treatment of cancer with many patients initially responding well but then becoming refractory to treatment with the same drug and in some case may become multi-drug resistant. The immune system is known to be important in the prevention of tumors by eliminating pre-cancerous or cancerous cells. This concept of immune surveillance has largely been super-ceded by the concept of immunoediting whereby the immune system imposes a selective pressure on tumor cells which may either control tumor growth or inadvertently select for tumor cells which have evolved to escape the immune response and which may induce tumor development. Stimulation of the immune system by vaccination offers many benefits in the treatment of cancer. It is highly cost effective and vaccines can be manipulated to include multi-antigens which in some cases may overcome equilibrium (and selective pressure) while also preventing the establishment of reactivated cancer cells, since cancer antigen-specific memory would be induced following the initial vaccination/booster phase. To date studies using vaccination as a treatment for cancer have been a little disappointing, probably due to insufficient level of immunogenicity. In this review we will discuss methods of manipulation of the immune system to increase the anti-cancer activity of dendritic cells in vivo and how monocyte derived dendritic cells may be manipulated ex vivo to provide more robust, patient-specific treatments

Multivariate analyses for biomarkers hunting and validation through on-tissue bottom-up or in-source decay in MALDI-MSI: application to prostate cancer.

peer reviewe

The catalytic subunit of the system L1 amino acid transporter (S<i>lc7a5</i>) facilitates nutrient signalling in mouse skeletal muscle

The System L1-type amino acid transporter mediates transport of large neutral amino acids (LNAA) in many mammalian cell-types. LNAA such as leucine are required for full activation of the mTOR-S6K signalling pathway promoting protein synthesis and cell growth. The SLC7A5 (LAT1) catalytic subunit of high-affinity System L1 functions as a glycoprotein-associated heterodimer with the multifunctional protein SLC3A2 (CD98). We generated a floxed Slc7a5 mouse strain which, when crossed with mice expressing Cre driven by a global promoter, produced Slc7a5 heterozygous knockout (Slc7a5+/-) animals with no overt phenotype, although homozygous global knockout of Slc7a5 was embryonically lethal. Muscle-specific (MCK Cre-mediated) Slc7a5 knockout (MS-Slc7a5-KO) mice were used to study the role of intracellular LNAA delivery by the SLC7A5 transporter for mTOR-S6K pathway activation in skeletal muscle. Activation of muscle mTOR-S6K (Thr389 phosphorylation) in vivo by intraperitoneal leucine injection was blunted in homozygous MS-Slc7a5-KO mice relative to wild-type animals. Dietary intake and growth rate were similar for MS-Slc7a5-KO mice and wild-type littermates fed for 10 weeks (to age 120 days) with diets containing 10%, 20% or 30% of protein. In MS-Slc7a5-KO mice, Leu and Ile concentrations in gastrocnemius muscle were reduced by ∼40% as dietary protein content was reduced from 30 to 10%. These changes were associated with >50% decrease in S6K Thr389 phosphorylation in muscles from MS-Slc7a5-KO mice, indicating reduced mTOR-S6K pathway activation, despite no significant differences in lean tissue mass between groups on the same diet. MS-Slc7a5-KO mice on 30% protein diet exhibited mild insulin resistance (e.g. reduced glucose clearance, larger gonadal adipose depots) relative to control animals. Thus, SLC7A5 modulates LNAA-dependent muscle mTOR-S6K signalling in mice, although it appears non-essential (or is sufficiently compensated by e.g. SLC7A8 (LAT2)) for maintenance of normal muscle mass

Modulated Instability in Five-Dimensional U(1) Charged AdS Black Hole with R**2-term

We study the effect of R**2 term to the modulated instability in the U(1) charged black hole in five-dimensional Anti-de Sitter space-time. We consider the first-order corrections of R**2 term to the background and the linear order perturbations in the equations of motion. From the analysis, we clarify the effect of R**2 term in the modulated instability, and conclude that fluctuations are stable in the whole bulk in the range of values the coefficient of R**2 term can take.Comment: 19 pages, 1 figures; (v4) Published version in JHE

The EDKB: an established knowledge base for endocrine disrupting chemicals

<p>Abstract</p> <p>Background</p> <p>Endocrine disruptors (EDs) and their broad range of potential adverse effects in humans and other animals have been a concern for nearly two decades. Many putative EDs are widely used in commercial products regulated by the Food and Drug Administration (FDA) such as food packaging materials, ingredients of cosmetics, medical and dental devices, and drugs. The Endocrine Disruptor Knowledge Base (EDKB) project was initiated in the mid 1990’s by the FDA as a resource for the study of EDs. The EDKB database, a component of the project, contains data across multiple assay types for chemicals across a broad structural diversity. This paper demonstrates the utility of EDKB database, an integral part of the EDKB project, for understanding and prioritizing EDs for testing.</p> <p>Results</p> <p>The EDKB database currently contains 3,257 records of over 1,800 EDs from different assays including estrogen receptor binding, androgen receptor binding, uterotropic activity, cell proliferation, and reporter gene assays. Information for each compound such as chemical structure, assay type, potency, etc. is organized to enable efficient searching. A user-friendly interface provides rapid navigation, Boolean searches on EDs, and both spreadsheet and graphical displays for viewing results. The search engine implemented in the EDKB database enables searching by one or more of the following fields: chemical structure (including exact search and similarity search), name, molecular formula, CAS registration number, experiment source, molecular weight, etc. The data can be cross-linked to other publicly available and related databases including TOXNET, Cactus, ChemIDplus, ChemACX, Chem Finder, and NCI DTP. </p> <p>Conclusion</p> <p>The EDKB database enables scientists and regulatory reviewers to quickly access ED data from multiple assays for specific or similar compounds. The data have been used to categorize chemicals according to potential risks for endocrine activity, thus providing a basis for prioritizing chemicals for more definitive but expensive testing. The EDKB database is publicly available and can be found online at <url>http://edkb.fda.gov/webstart/edkb/index.html</url>.</p> <p><b>Disclaimer:</b><it>The views presented in this article do not necessarily reflect those of the US Food and Drug Administration.</it></p

An effective non-parametric method for globally clustering genes from expression profiles

Clustering is widely used in bioinformatics to find gene correlation patterns. Although many algorithms have been proposed, these are usually confronted with difficulties in meeting the requirements of both automation and high quality. In this paper, we propose a novel algorithm for clustering genes from their expression profiles. The unique features of the proposed algorithm are twofold: it takes into consideration global, rather than local, gene correlation information in clustering processes; and it incorporates clustering quality measurement into the clustering processes to implement non-parametric, automatic and global optimal gene clustering. The evaluation on simulated and real gene data sets demonstrates the effectiveness of the algorithm. <br /