The Pursuit of Hoppiness : Propelling Hop into the Genomic Era

Hop (Humulus lupulus L. var lupulus) is a plant of great cultural significance, used as a medicinal herb for thousands of years, and for flavor and as a preservative in brewing beer. Studies of the medicinal effects of the unique compounds produced by hop have led to interest from the pharmacy and healthcare industries. Although many industries have interest in the plant itself and scientists are interested in the evolution of the Cannabaceae sex chromosomes, little effort has gone into developing genomic resources. H. lupulus is a highly heterozygous, repeat-rich, plant genome with a size of about 2.8 gigabases. This combination presents an immense challenge to studying the genomics of hop. Here we present, a web portal for studying hop genomics, a novel hop genome assembly, gene annotations for both draft genome assemblies, an evolutionary biology study regarding the hop sex chromosomes, and a novel way of modeling transcripts using deep learning. The combination of these manuscripts provides a framework for the future of hop genomics

P-SAMS: a web suite for plant artificial microRNA and synthetic trans-acting small interfering RNA design

[EN] The Plant Small RNA Maker Site (P-SAMS) is a web tool for the simple and automated design of artificial miRNAs (amiRNAs) and synthetic trans-acting small interfering RNAs (syntasiRNAs) for efficient and specific targeted gene silencing in plants. P-SAMS includes two applications, P-SAMS amiRNA Designer and P-SAMS syn-tasiRNA Designer. The navigation through both applications is wizard-assisted, and the job runtime is relatively short. Both applications output the sequence of designed small RNA(s), and the sequence of the two oligonucleotides required for cloning into `B/c¿ compatible vectors.This work was supported by the National Institutes of Health [grant number AI043288 to J.C.C.]; the National Science Foundation [grants numbers MCB-1231726, MCB-1330562 to J.C.C.]; and the United States Department of Agriculture [fellowship number MOW-2012-01361 to N.F.).Fahlgren, N.; Hill, ST.; Carrington, JC.; Carbonell, A. (2016). P-SAMS: a web suite for plant artificial microRNA and synthetic trans-acting small interfering RNA design. Bioinformatics. 32(1):157-158. https://doi.org/10.1093/bioinformatics/btv534S157158321Ahmed, F., Dai, X., & Zhao, P. X. (2015). Bioinformatics Tools for Achieving Better Gene Silencing in Plants. Plant Gene Silencing, 43-60. doi:10.1007/978-1-4939-2453-0_3Carbonell, A., Takeda, A., Fahlgren, N., Johnson, S. C., Cuperus, J. T., & Carrington, J. C. (2014). New Generation of Artificial MicroRNA and Synthetic Trans-Acting Small Interfering RNA Vectors for Efficient Gene Silencing in Arabidopsis. Plant Physiology, 165(1), 15-29. doi:10.1104/pp.113.234989Carbonell, A., Fahlgren, N., Mitchell, S., Cox, K. L., Reilly, K. C., Mockler, T. C., & Carrington, J. C. (2015). Highly specific gene silencing in a monocot species by artificial micro RNA s derived from chimeric mi RNA precursors. The Plant Journal, 82(6), 1061-1075. doi:10.1111/tpj.12835Fahlgren, N., & Carrington, J. C. (2009). miRNA Target Prediction in Plants. Plant MicroRNAs, 51-57. doi:10.1007/978-1-60327-005-2_4Ossowski, S., Schwab, R., & Weigel, D. (2008). Gene silencing in plants using artificial microRNAs and other small RNAs. The Plant Journal, 53(4), 674-690. doi:10.1111/j.1365-313x.2007.03328.xSchwab, R., Ossowski, S., Riester, M., Warthmann, N., & Weigel, D. (2006). Highly Specific Gene Silencing by Artificial MicroRNAs inArabidopsis. The Plant Cell, 18(5), 1121-1133. doi:10.1105/tpc.105.039834Tiwari, M., Sharma, D., & Trivedi, P. K. (2014). Artificial microRNA mediated gene silencing in plants: progress and perspectives. Plant Molecular Biology, 86(1-2), 1-18. doi:10.1007/s11103-014-0224-7Zhang, Z. J. (2014). Artificial trans-acting small interfering RNA: a tool for plant biology study and crop improvements. Planta, 239(6), 1139-1146. doi:10.1007/s00425-014-2054-

Integrating biological knowledge into variable selection : an empirical Bayes approach with an application in cancer biology

Background: An important question in the analysis of biochemical data is that of identifying subsets of molecular variables that may jointly influence a biological response. Statistical variable selection methods have been widely used for this purpose. In many settings, it may be important to incorporate ancillary biological information concerning the variables of interest. Pathway and network maps are one example of a source of such information. However, although ancillary information is increasingly available, it is not always clear how it should be used nor how it should be weighted in relation to primary data. Results: We put forward an approach in which biological knowledge is incorporated using informative prior distributions over variable subsets, with prior information selected and weighted in an automated, objective manner using an empirical Bayes formulation. We employ continuous, linear models with interaction terms and exploit biochemically-motivated sparsity constraints to permit exact inference. We show an example of priors for pathway- and network-based information and illustrate our proposed method on both synthetic response data and by an application to cancer drug response data. Comparisons are also made to alternative Bayesian and frequentist penalised-likelihood methods for incorporating network-based information. Conclusions: The empirical Bayes method proposed here can aid prior elicitation for Bayesian variable selection studies and help to guard against mis-specification of priors. Empirical Bayes, together with the proposed pathway-based priors, results in an approach with a competitive variable selection performance. In addition, the overall procedure is fast, deterministic, and has very few user-set parameters, yet is capable of capturing interplay between molecular players. The approach presented is general and readily applicable in any setting with multiple sources of biological prior knowledge

Self-Doping of Gold Chains on Silicon: A New Structural Model for Si(111)5x2-Au

A new structural model for the Si(111)5x2-Au reconstruction is proposed and analyzed using first-principles calculations. The basic model consists of a "double honeycomb chain" decorated by Si adatoms. The 5x1 periodicity of the honeycomb chains is doubled by the presence of a half-occupied row of Si atoms that partially rebonds the chains. Additional adatoms supply electrons that dope the parent band structure and stabilize the period doubling; the optimal doping corresponds to one adatom per four 5x2 cells, in agreement with experiment. All the main features observed in scanning tunneling microscopy and photoemission are well reproduced.Comment: 4 pages, 4 figures, to appear in Phys. Rev. Lett. (preprint with high quality figures available at http://cst-www.nrl.navy.mil/~erwin/papers/ausi111

A Comparison of Four Models of Delay Discounting in Humans

The present study compared four prominent models of delay discounting: a one-parameter exponential decay, a one-parameter hyperbola (Mazur, 1987), a two-parameter hyperboloid in which the denominator is raised to a power (Green and Myerson, 2004), and a two-parameter hyperbola in which delay is raised to a power (Rachlin, 2006). Sixty-four college undergraduates made choices between hypothetical monetary rewards, one immediate and one delayed, and the fit of the four models to their data was assessed. All four equations accounted for a large proportion of the variance at both the group and the individual levels, but the exponents of both two-parameter models were significantly less than 1.0 at the group level, and frequently so at the individual level. Taken together, these results strongly suggest that more than one parameter is needed to accurately describe delay discounting by humans. Notably, both the Rachlin and the Green and Myerson models accounted for more than 99% of the variance at the group level and for 96% of the variance in the median individual. Because both models provide such good descriptions of the data, model selection will need to be based on other grounds

Perforated gastric corpus in a strangulated paraesophageal hernia: a case report

a case repor

Regulation of L1 expression and retrotransposition by melatonin and its receptor: implications for cancer risk associated with light exposure at night.

Expression of long interspersed element-1 (L1) is upregulated in many human malignancies. L1 can introduce genomic instability via insertional mutagenesis and DNA double-strand breaks, both of which may promote cancer. Light exposure at night, a recently recognized carcinogen, is associated with an increased risk of cancer in shift workers. We report that melatonin receptor 1 inhibits mobilization of L1 in cultured cells through downregulation of L1 mRNA and ORF1 protein. The addition of melatonin receptor antagonists abolishes the MT1 effect on retrotransposition in a dose-dependent manner. Furthermore, melatonin-rich, but not melatonin-poor, human blood collected at different times during the circadian cycle suppresses endogenous L1 mRNA during in situ perfusion of tissue-isolated xenografts of human cancer. Supplementation of human blood with exogenous melatonin or melatonin receptor antagonist during the in situ perfusion establishes a receptor-mediated action of melatonin on L1 expression. Combined tissue culture and in vivo data support that environmental light exposure of the host regulates expression of L1 elements in tumors. Our data imply that light-induced suppression of melatonin production in shift workers may increase L1-induced genomic instability in their genomes and suggest a possible connection between L1 activity and increased incidence of cancer associated with circadian disruption