The Highly Dynamic Behavior of the Innermost Dust and Gas in the Transition Disk Variable LRLL 31

We describe extensive synoptic multi-wavelength observations of the transition disk LRLL 31 in the young cluster IC 348. We combined four epochs of IRS spectra, nine epochs of MIPS photometry, seven epochs of cold-mission IRAC photometry and 36 epochs of warm mission IRAC photometry along with multi-epoch near-infrared spectra, optical spectra and polarimetry to explore the nature of the rapid variability of this object. We find that the inner disk, as traced by the 2-5micron excess stays at the dust sublimation radius while the strength of the excess changes by a factor of 8 on weekly timescales, and the 3.6 and 4.5micron photometry shows a drop of 0.35 magnitudes in one week followed by a slow 0.5 magnitude increase over the next three weeks. The accretion rate, as measured by PaBeta and BrGamma emission lines, varies by a factor of five with evidence for a correlation between the accretion rate and the infrared excess. While the gas and dust in the inner disk are fluctuating the central star stays relatively static. Our observations allow us to put constraints on the physical mechanism responsible for the variability. The variabile accretion, and wind, are unlikely to be causes of the variability, but both are effects of the same physical process that disturbs the disk. The lack of periodicity in our infrared monitoring indicates that it is unlikely that there is a companion within ~0.4 AU that is perturbing the disk. The most likely explanation is either a companion beyond ~0.4 AU or a dynamic interface between the stellar magnetic field and the disk leading to a variable scale height and/or warping of the inner disk.Comment: Accepted to ApJ. 10 pages of text, plus 11 tables and 13 figures at the en

Including Risk in Economic Feasibility Analyses: The Case of Ethanol Production in Texas

The widespread use of personal computers and spreadsheet models for feasibility studies makes risk-based Monte Carlo simulation analysis of proposed investments a relatively simple task. Add-in simulation packages for Microsoft® Excel can be used to make spreadsheet models stochastic. Rather than basing investment decisions on point estimates, investors can easily estimate the implied distributions of returns for uncertain investments and calculate the risk of an investment as well as the probability of success. The benefits of using Monte Carlo simulation to analyze a risky investment are demonstrated using an ethanol plant as an example.economic feasibility analysis, ethanol feasibility, risk management, stochastic simulation, Agribusiness, Research and Development/Tech Change/Emerging Technologies,

Pituitary Adenylate Cyclase-Activating Polypeptide (PACAP) Signaling in The Prefrontal Cortex Modulates Cued Fear Learning, But Not Spatial Working Memory, in Female Rats

A genetic polymorphism within the gene encoding the pituitary adenylate cyclase- activating polypeptide (PACAP) receptor type I (PAC1R) has recently been associated with hyper-reactivity to threat-related cues in women, but not men, with post-traumatic stress disorder (PTSD). PACAP is a highly conserved peptide, whose role in mediating adaptive physiological stress responses is well established. Far less is understood about the contribution of PACAP signaling in emotional learning and memory, particularly the encoding of fear to discrete cues. Moreover, a neurobiological substrate that may account for the observed link between PAC1R and PTSD in women, but not men, has yet to be identified. Sex differences in PACAP signaling during emotional learning could provide novel targets for the treatment of PTSD. Here we investigated the contribution of PAC1R signaling within the prefrontal cortex to the acquisition of cued fear in female and male rats. We used a variant of fear conditioning called trace fear conditioning, which requires sustained attention to fear cues and depends on working-memory like neuronal activity within the prefrontal cortex. We found that cued fear learning, but not spatial working memory, was impaired by administration of a PAC1R antagonist directly into the prelimbic area of the prefrontal cortex. This effect was specific to females. We also found that levels of mRNA for the PAC1R receptor in the prelimbic cortex were greater in females compared with males, and were highest during and immediately following the proestrus stage of the estrous cycle. Together, these results demonstrate a sex-specific role of PAC1R signaling in learning about threat-related cues

Risk Assessment in Economic Feasibility Analysis: The Case of Ethanol Production in Texas

The objective of this study is to demonstrate the benefits of quantifying the economic viability of a proposed agribusiness under risk relative to a feasibility study which ignores risk. To achieve this objective, the economic viability of a 50 MMGPY ethanol facility in Texas is analyzed over a 10-year period in two ways: with no risk and with historical risk for prices and costs.Risk and Uncertainty,

An Economic Examination of Potential Ethanol Production in Texas

Agribusiness, Agricultural and Food Policy, Resource /Energy Economics and Policy,

Representative Farms Economic Outlook for the January 2004 FAPRI/AFPC Baseline

The farm level economic impacts of the Farm Security and Rural Investment Act of 2002 on representative crop and livestock operations are projected in this report. The analysis was conducted over the 2001-2008 planning horizon using FLIPSIM, AFPC’s whole farm simulation model. Data to simulate farming operations in the nation’s major production regions came from two sources: - Producer panel cooperation to develop economic information to describe and simulate representative crop, livestock, and dairy farms, and - Projected prices, policy variables, and input inflation rates from the Food and Agricultural Policy Research Institute (FAPRI) January 2004 Baseline. The FLIPSIM policy simulation model incorporates the historical risk faced by farmers for prices and production. This report presents the results of the January 2004 Baseline in a risk context using selected simulated probabilities and ranges for annual net cash farm income values. The probability of a farm experiencing annual cash flow deficits and the probability of a farm losing real net worth are included as indicators of the cash flow and equity risks facing farms through the year 2008.Agribusiness, Agricultural and Food Policy, Crop Production/Industries,

Representative Farms Economic Outlook for the August 2005 FAPRI/AFPC Baseline

The farm level economic impacts of the Farm Security and Rural Investment Act of 2002 on representative crop and livestock operations are projected in this report. The analysis was conducted over the 2002-2009 planning horizon using FLIPSIM, AFPC’s whole farm simulation model. Data to simulate farming operations in the nation’s major production regions came from two sources: • Producer panel cooperation to develop economic information to describe and simulate representative crop, livestock, and dairy farms, and • Projected prices, policy variables, and input inflation rates from the Food and Agricultural Policy Research Institute (FAPRI) August 2005 Baseline. The FLIPSIM policy simulation model incorporates the historical risk faced by farmers for prices and production. This report presents the results of the August 2005 Baseline in a risk context using selected simulated probabilities and ranges for annual net cash farm income values. The probability of a farm experiencing negative ending cash reserves and the probability of a farm losing real net worth are included as indicators of the cash flow and equity risks facing farms through the year 2009.Agribusiness, Agricultural and Food Policy, Crop Production/Industries, Livestock Production/Industries,

Representative Farms Economic Outlook for the January 2004 FAPRI/AFPC Baseline

The farm level economic impacts of the Farm Security and Rural Investment Act of 2002 on representative crop and livestock operations are projected in this report. The analysis was conducted over the 2001-2008 planning horizon using FLIPSIM, AFPC’s whole farm simulation model. Data to simulate farming and ranching operations in the nation’s major production regions came from two sources: - Producer panel cooperation to develop economic information to describe and simulate representative crop, livestock, and dairy farms. - Projected prices, policy variables, and input inflation rates from the Food and Agricultural Policy Research Institute (FAPRI) January 2004 Baseline. The primary objective of the analysis is to determine the farms’ economic viability by region and commodity through the life of the 2002 Farm Bill. The FLIPSIM policy simulation model incorporates the historical risk faced by farmers and ranchers for prices and production. This report presents the results of the January 2004 Baseline in a risk context using selected simulated probabilities and ranges for annual net cash farm income values. The probability of a farm experiencing annual cash flow deficits and the probability of a farm losing real net worth are included as indicators of the cash flow and equity risks facing farms through the year 2008. This report is organized into ten sections. The first section summarizes the process used to develop the representative farms and the key assumptions utilized for the farm level analysis. The second section summarizes the FAPRI January 2004 Baseline and the policy and price assumptions used for the representative farm analyses. The third through sixth sections present the results of the simulation analyses for feed grain, wheat, cotton, and rice farms. The seventh through ninth sections summarize simulation results for dairy, cattle and hog farms. Two appendices constitute the final section of the report. Appendix A provides tables to summarize the physical and financial characteristics for each of the representative farms. Appendix B provides the names of producers, land grant faculty, and industry leaders who cooperated in the panel interview process to develop the representative farms.Agribusiness, Agricultural and Food Policy, Crop Production/Industries,