Using molecular diet analysis to inform invasive species management: A case study of introduced rats consuming endemic New Zealand frogs

The decline of amphibians has been of international concern for more than two decades, and the global spread of introduced fauna is a major factor in this decline. Conservation management decisions to implement control of introduced fauna are often based on diet studies. One of the most common metrics to report in diet studies is Frequency of Occurrence (FO), but this can be difficult to interpret, as it does not include a temporal perspective. Here, we examine the potential for FO data derived from molecular diet analysis to inform invasive species management, using invasive ship rats (Rattus rattus) and endemic frogs (Leiopelma spp.) in New Zealand as a case study. Only two endemic frog species persist on the mainland. One of these, Leiopelma archeyi, is Critically Endangered (IUCN 2017) and ranked as the world\u27s most evolutionarily distinct and globally endangered amphibian (EDGE, 2018). Ship rat stomach contents were collected by kill-trapping and subjected to three methods of diet analysis (one morphological and two DNA-based). A new primer pair was developed targeting all anuran species that exhibits good coverage, high taxonomic resolution, and reasonable specificity. Incorporating a temporal parameter allowed us to calculate the minimum number of ingestion events per rat per night, providing a more intuitive metric than the more commonly reported FO. We are not aware of other DNA-based diet studies that have incorporated a temporal parameter into FO data. The usefulness of such a metric will depend on the study system, in particular the feeding ecology of the predator. Ship rats are consuming both species of native frogs present on mainland New Zealand, and this study provides the first detections of remains of these species in mammalian stomach contents

Quantitative promoter methylation analysis of multiple cancer-related genes in renal cell tumors

<p>Abstract</p> <p>Background</p> <p>Aberrant promoter hypermethylation of cancer-associated genes occurs frequently during carcinogenesis and may serve as a cancer biomarker. In this study we aimed at defining a quantitative gene promoter methylation panel that might identify the most prevalent types of renal cell tumors.</p> <p>Methods</p> <p>A panel of 18 gene promoters was assessed by quantitative methylation-specific PCR (QMSP) in 85 primarily resected renal tumors representing the four major histologic subtypes (52 clear cell (ccRCC), 13 papillary (pRCC), 10 chromophobe (chRCC), and 10 oncocytomas) and 62 paired normal tissue samples. After genomic DNA isolation and sodium bisulfite modification, methylation levels were determined and correlated with standard clinicopathological parameters.</p> <p>Results</p> <p>Significant differences in methylation levels among the four subtypes of renal tumors were found for <it>CDH1 </it>(<it>p </it>= 0.0007), <it>PTGS2 </it>(<it>p </it>= 0.002), and <it>RASSF1A </it>(<it>p </it>= 0.0001). <it>CDH1 </it>hypermethylation levels were significantly higher in ccRCC compared to chRCC and oncocytoma (<it>p </it>= 0.00016 and <it>p </it>= 0.0034, respectively), whereas <it>PTGS2 </it>methylation levels were significantly higher in ccRCC compared to pRCC (<it>p </it>= 0.004). <it>RASSF1A </it>methylation levels were significantly higher in pRCC than in normal tissue (<it>p </it>= 0.035). In pRCC, <it>CDH1 </it>and <it>RASSF1A </it>methylation levels were inversely correlated with tumor stage (<it>p </it>= 0.031) and nuclear grade (<it>p </it>= 0.022), respectively.</p> <p>Conclusion</p> <p>The major subtypes of renal epithelial neoplasms display differential aberrant <it>CDH1</it>, <it>PTGS2</it>, and <it>RASSF1A </it>promoter methylation levels. This gene panel might contribute to a more accurate discrimination among common renal tumors, improving preoperative assessment and therapeutic decision-making in patients harboring suspicious renal masses.</p

Rivalry and uncertainty in complementary investments with dynamic market sharing

We study the effects of revenue and investment cost uncertainty, as well non- preemption duopoly competition, on the timing of investments in two complementary inputs, where either spillover-knowledge is allowed or proprietary-knowledge holds. We find that the ex-ante and ex-post revenue market shares play a very important role in firms’ behavior. When competition is considered, the leader’s behavior departs from that of the monopolist firm of Smith (Ind Corp Change 14:639–650, 2005). The leader is justified in following the conventional wisdom (i.e., synchronous investments are more likely), whereas, the follower’s behavior departs from that of the conventional wisdom (i.e., asynchronous investments are more likely)

The stellar wind cycles and planetary radio emission of the Tau Boo system

Tau Boo is an intriguing planet-host star that is believed to undergo magnetic cycles similar to the Sun, but with a duration that is about one order of magnitude smaller than that of the solar cycle. With the use of observationally derived surface magnetic field maps, we simulate the magnetic stellar wind of Tau Boo by means of three-dimensional MHD numerical simulations. As the properties of the stellar wind depend on the particular characteristics of the stellar magnetic field, we show that the wind varies during the observed epochs of the cycle. Although the mass loss-rates we find (~2.7e-12 Msun/yr) vary less than 3 per cent during the observed epochs of the cycle, our derived angular momentum loss-rates vary from 1.1 to 2.2e32erg. The spin-down times associated to magnetic braking range between 39 and 78Gyr. We also compute the emission measure from the (quiescent) closed corona and show that it remains approximately constant through these epochs at a value of ~10^{50.6} cm^{-3}. This suggests that a magnetic cycle of Tau Boo may not be detected by X-ray observations. We further investigate the interaction between the stellar wind and the planet by estimating radio emission from the hot-Jupiter that orbits at 0.0462 au from Tau Boo. By adopting reasonable hypotheses, we show that, for a planet with a magnetic field similar to Jupiter (~14G at the pole), the radio flux is estimated to be about 0.5-1 mJy, occurring at a frequency of 34MHz. If the planet is less magnetised (field strengths roughly <4G), detection of radio emission from the ground is unfeasible due to the Earth's ionospheric cutoff. According to our estimates, if the planet is more magnetised than that and provided the emission beam crosses the observer line-of-sight, detection of radio emission from Tau Boo b is only possible by ground-based instruments with a noise level of < 1 mJy, operating at low frequencies.Comment: 15 pages, 10 figures, MNRAS accepte

Photovoltaic power plants: a multicriteria approach to investment decisions and a case study in western Spain

his paper proposes a compromise programming (CP) model to help investors decide whether to construct photovoltaic power plants with government financial support. For this purpose, we simulate an agreement between the government, who pursues political prices (guaranteed prices) as low as possible, and the project sponsor who wants returns (stochastic cash flows) as high as possible. The sponsor s decision depends on the positive or negative result of this simulation, the resulting simulated price being compared to the effective guaranteed price established by the country legislation for photovoltaic energy. To undertake the simulation, the CP model articulates variables such as ranges of guaranteed prices, tech- nical characteristics of the plant, expected energy to be generated over the investment life, investment cost, cash flow probabilities, and others. To determine the CP metric, risk aver- sion is assumed. As an actual application, a case study on photovoltaic power investment in Extremadura, western Spain, is developed in detail.Garcia-Bernabeu, A.; Benito Benito, A.; Bravo Selles, M.; Pla Santamaría, D. (2015). Photovoltaic power plants: a multicriteria approach to investment decisions and a case study in western Spain. Annals of Operations Research. 1-12. doi:10.1007/s10479-015-1836-2S112Andrews, R. W., Pollard, A., & Pearce, J. M. (2012). Improved parametric empirical determination of module short circuit current for modelling and optimization of solar photovoltaic systems. Solar Energy, 86(9), 2240–2254.Anwar, Y., & Mulyadi, M. S. (2011). Income tax incentives on renewable energy industry: Case of geothermal industry in USA and Indonesia. African Journal of Business Management, 5(31), 12264–12270.Aouni, B., & Kettani, O. (2001). Goal programming model: A glorious history and a promising future. European Journal of Operational Research, 133(2), 225–231.Ballestero, E. (1997). Selecting the CP metric: A risk aversion approach. European Journal of Operational Research, 97(3), 593–596.Ballestero, E. (2000). Project finance: A multicriteria approach to arbitration. Journal of Operational Research Society, 51, 183–197.Ballestero, E. (2007). Compromise programming: A utility-based linear-quadratic composite metric from the trade-off between achievement and balanced (non-corner) solutions. European Journal of Operational Research, 182(3), 1369–1382.Ballestero, E., Pérez-Gladish, B., Arenas-Parra, M., & BilbaoTerol, A. (2009). Selecting portfolios given multiple Eurostoxx-based uncertainty scenarios: A stochastic goal programming approach from fuzzy betas. INFOR: Information Systems and Operational Research, 47(1), 59–70.Ballestero, E., & Plà-Santamaría, D. (2003). Portfolio selection on the Madrid exchange: A compromise programming model. International Transactions in Operational Research, 10(1), 33–51.Ballestero, E., & Pla-Santamaria, D. (2004). Selecting portfolios for mutual funds. Omega, 32(5), 385–394.Ballestero, E., & Pla-Santamaria, D. (2005). Grading the performance of market indicators with utility benchmarks selected from Footsie: A 2000 case study. Applied Economics, 37(18), 2147–2160.Ballestero, E., & Romero, C. (1996). Portfolio selection: A compromise programming solution. Journal of the Operational Research Society, 47, 1377–1386.Bastian-Pinto, C., Brandão, L., & de Lemos Alves, M. (2010). Valuing the switching flexibility of the ethanol–gas flex fuel car. Annals of Operations Research, 176(1), 333–348.Branker, K., Pathak, M., & Pearce, J. M. (2011). A review of solar photovoltaic levelized cost of electricity. Renewable and Sustainable Energy Reviews, 15(9), 4470–4482.Casares, F., Lopez-Luque, R., Posadillo, R., & Varo-Martinez, M. (2014). Mathematical approach to the characterization of daily energy balance in autonomous photovoltaic solar systems. Energy, 72, 393–404.Chatterji, A. K., Levine, D. I., & Toffel, M. W. (2009). How well do social ratings actually measure corporate social responsibility? Journal of Economics & Management Strategy, 18(1), 125–169.Copeland, T. E., & Weston, J. (1988). Financial theory and corporate policy. Reading, Massachusetts: Addison-Wesley.Gallagher, K. S. (2013). Why & how governments support renewable energy. Daedalus, 142(1), 59–77.García-Cascales, M. S., Lamata, M. T., & Sánchez-Lozano, J. M. (2012). Evaluation of photovoltaic cells in a multi-criteria decision making process. Annals of Operations Research, 199(1), 373–391.Gupta, S. (2012). Financing renewable energy. In F. L. Toth (Ed.), Energy for development (pp. 171–186). Springer.Karaarslan, A. (2012). Obtaining renewable energy from piezoelectric ceramics using Sheppard–Taylor converter. International Review of Electrical Engineering, 7(2), 3949–3956.Koellner, T., Weber, O., Fenchel, M., & Scholz, R. (2005). Principles for sustainability rating of investment funds. Business Strategy and the Environment, 14(1), 54–70.Lorenzo, E., & Navarte, L. (2000). On the usefulness of stand-alone PV sizing methods. Progress in Photovoltaics: Research and Applications, 8(4), 391–409.Lüdeke-Freund, F., & Loock, M. (2011). Debt for brands: Tracking down a bias in financing photovoltaic projects in Germany. Journal of Cleaner Production, 19(12), 1356–1364.Mavrotas, G., Diakoulaki, D., & Capros, P. (2003). Combined MCDA-IP approach for project selection in the electricity market. Annals of Operations Research, 120(1–4), 159–170.Mendez-Rodriguez, P., Garcia Bernabeu, A., Hilario, A., & Perez-Gladish, B. (2013). Some effects on the efficient frontier of the investment strategy: A preliminary approach. Recta, 14, 131–144.Michelson, G., Wailes, N., Van Der Laan, S., & Frost, G. (2004). Ethical investment processes and outcomes. Journal of Business Ethics, 52(1), 1–10.Mills, S. J. (1994). Project finance for renewable energy. Renewable energy, 5(1–4), 700–708.ORourke, A. (2003). The message and methods of ethical investment. Journal of Cleaner Production, 11(6), 683–693.Pla-Santamaria, D., & Bravo, M. (2013). Portfolio optimization based on downside risk: A mean-semivariance efficient frontier from Dow Jones blue chips. Annals of Operations Research, 205(1), 189–201.Richter, N. (2009). Renewable project finance options: ITC, PTC, or cash grant? Power, 153(5), 90–92.Schrader, U. (2006). Ignorant advice-customer advisory service for ethical investment funds. Business Strategy and the Environment, 15(3), 200–214.Sitarz, S. (2013). Compromise programming with tehebycheff norm for discrete stochastic orders. Annals of Operations Research, 211(1), 433–446.van de Kaa, G., Rezaei, J., Kamp, L., & de Winter, A. (2014). Photovoltaic technology selection: A fuzzy MCDM approach. Renewable and Sustainable Energy Reviews, 32, 662–670.Yaqub, M., Shahram Sarkni, P., & Mazzuchi, T. (2012). Feasibility analysis of solar photovoltaic commercial power generation in California. Engineering Management Journal, 24(4), 36–49.Yazdani-Chamzini, A., Fouladgar, M. M., Zavadskas, E. K., & Moini, S. H. H. (2013). Selecting the optimal renewable energy using multi criteria decision making. Journal of Business Economics and Management, 14(5), 957–978.Yu, P. (1985). Multiple criteria decision making: Concepts, techniques and extensions. New York: Springer.Zeleny, M. (1982). Multiple criteria decision making (Vol. 25). New York: McGraw-Hill.Zhao, R., Shi, G., Chen, H., Ren, A., & Finlow, D. (2011). Present status and prospects of photovoltaic market in China. Energy Policy, 39(4), 2204–2207

Origins of the Ambient Solar Wind: Implications for Space Weather

The Sun's outer atmosphere is heated to temperatures of millions of degrees, and solar plasma flows out into interplanetary space at supersonic speeds. This paper reviews our current understanding of these interrelated problems: coronal heating and the acceleration of the ambient solar wind. We also discuss where the community stands in its ability to forecast how variations in the solar wind (i.e., fast and slow wind streams) impact the Earth. Although the last few decades have seen significant progress in observations and modeling, we still do not have a complete understanding of the relevant physical processes, nor do we have a quantitatively precise census of which coronal structures contribute to specific types of solar wind. Fast streams are known to be connected to the central regions of large coronal holes. Slow streams, however, appear to come from a wide range of sources, including streamers, pseudostreamers, coronal loops, active regions, and coronal hole boundaries. Complicating our understanding even more is the fact that processes such as turbulence, stream-stream interactions, and Coulomb collisions can make it difficult to unambiguously map a parcel measured at 1 AU back down to its coronal source. We also review recent progress -- in theoretical modeling, observational data analysis, and forecasting techniques that sit at the interface between data and theory -- that gives us hope that the above problems are indeed solvable.Comment: Accepted for publication in Space Science Reviews. Special issue connected with a 2016 ISSI workshop on "The Scientific Foundations of Space Weather." 44 pages, 9 figure

An insight to HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) pathogenesis; evidence from high-throughput data integration and meta-analysis

Background Human T-lymphotropic virus 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a progressive disease of the central nervous system that significantly affected spinal cord, nevertheless, the pathogenesis pathway and reliable biomarkers have not been well determined. This study aimed to employ high throughput meta-analysis to find major genes that are possibly involved in the pathogenesis of HAM/TSP. Results High-throughput statistical analyses identified 832, 49, and 22 differentially expressed genes for normal vs. ACs, normal vs. HAM/TSP, and ACs vs. HAM/TSP groups, respectively. The protein-protein interactions between DEGs were identified in STRING and further network analyses highlighted 24 and 6 hub genes for normal vs. HAM/TSP and ACs vs. HAM/TSP groups, respectively. Moreover, four biologically meaningful modules including 251 genes were identified for normal vs. ACs. Biological network analyses indicated the involvement of hub genes in many vital pathways like JAK-STAT signaling pathway, interferon, Interleukins, and immune pathways in the normal vs. HAM/TSP group and Metabolism of RNA, Viral mRNA Translation, Human T cell leukemia virus 1 infection, and Cell cycle in the normal vs. ACs group. Moreover, three major genes including STAT1, TAP1, and PSMB8 were identified by network analysis. Real-time PCR revealed the meaningful down-regulation of STAT1 in HAM/TSP samples than AC and normal samples (P = 0.01 and P = 0.02, respectively), up-regulation of PSMB8 in HAM/TSP samples than AC and normal samples (P = 0.04 and P = 0.01, respectively), and down-regulation of TAP1 in HAM/TSP samples than those in AC and normal samples (P = 0.008 and P = 0.02, respectively). No significant difference was found among three groups in terms of the percentage of T helper and cytotoxic T lymphocytes (P = 0.55 and P = 0.12). Conclusions High-throughput data integration disclosed novel hub genes involved in important pathways in virus infection and immune systems. The comprehensive studies are needed to improve our knowledge about the pathogenesis pathways and also biomarkers of complex diseases.Peer reviewe