Calibration and Validation of the Hybrid-Maize Crop Model for Regional Analysis and Application over the U.S. Corn Belt

Detailed parameter sensitivity, model validation, and regional calibration of the Hybrid-Maize crop model were undertaken for the purpose of regional agroclimatic assessments. The model was run at both field scale and county scale. The county-scale study was based on 30-yr daily weather data and corn yield data from the National Agricultural Statistics Service survey for 24 locations across the Corn Belt of the United States. The field-scale study was based on AmeriFlux sites at Bondville, Illinois, andMead, Nebraska. By using the one-at-a-time and interaction-explicit factorial design approaches for sensitivity analysis, the study found that the five most sensitive parameters of the model were potential number of kernels per ear, potential kernel filling rate, initial light use efficiency, upper temperature cutoff for growing degree-days’ accumulation, and the grain growth respiration coefficient. Model validation results show that the Hybrid-Maize model performed satisfactorily for field-scale simulations with a mean absolute error (MAE) of 10 bu acre-1 despite the difficulties of obtaining hybrid-specific information. At the county scale, the simulated results, assuming optimal crop management, overpredicted the yields but captured the variability well. A simple regional adjustment factor of 0.6 rescaled the potential yield to actual yield well. These results highlight the uncertainties that exist in applying crop models at regional scales because of the limitations in accessing cropspecific information. This study also provides confidence that uncertainties can potentially be eliminated via simple adjustment factor and that a simple crop model can be adequately useful for regional-scale agroclimatic studies

A Test Stand System for High-Energy Physics Applications

The Front-End R&D group at Fermilab has been developing pixel hybridized modules and silicon strip detectors for the past decade for high-energy physics experiments. To accomplish this goal, one of the activities the group has been working on includes the development of a flexible high-speed and high-bandwidth data acquisition and test system to characterize front-end electronics. In this paper, we present a general purpose PCI-based test stand system developed to meet the stringent requirements of testing silicon strip and pixel detectors. The test stand is based on a platform that is flexible enough to be adapted to different types of front-end electronics. This system has been used to test the performance of the electronics for different experiments such as BTeV, CDF, CMS, and Phenix. The paper presents the capabilities of the system and how it can be adapted to meet the testing requirements of different applications

Crop models capture the impacts of climate variability on corn yield

We investigate the ability of three different crop models of varying complexity for capturing El Niño–Southern Oscillation-based climate variability impacts on the U.S. Corn Belt (1981–2010). Results indicate that crop models, irrespective of their complexity, are able to capture the impacts of climate variability on yield. Multiple-model ensemble analysis provides best results. There was no significant difference between using on-site and gridded meteorological data sets to drive the models. These results highlight the ability of using simpler crop models and gridded regional data sets for crop-climate assessments

An application using micro TCA for real-time event assembly

Abstract Not Provide

From Too Much to Too Little: How the central U.S. drought of 2012 evolved out of one of the most devastating floods on record in 2011

Table of Contents Section 1: Introduction....................................................................... 1 Section 2: Regional Drought Perspective................................. 2 Section 3: State Drought Perspectives........................................ 3 Section 3.1: Colorado........................................................................... 20 Section 3.2: Illinois.................................................................. 25 Section 3.3: Indiana................................................. 29 Section 3.4: Iowa...................... 36 Section 3.5: Kansas............................................................... 42 Section 3.6: Kentucky............................................................................ 46 Section 3.7: Michigan.............................. 52 Section 3.8: Minnesota............................................................ 58 Section 3.9: Missouri..................................................... 63 Section 3.10: Nebraska................................................. 67 Section 3.11: North Dakota............................................ 73 Section 3.12: Ohio................................................... 79 Section 3.13: South Dakota..................................... 85 Section 3.14: Wyoming........................................... 96 Section 4: Conclusions.............................................................. 9

The Splicing Efficiency of Activating HRAS Mutations Can Determine Costello Syndrome Phenotype and Frequency in Cancer

Costello syndrome (CS) may be caused by activating mutations in codon 12/13 of the HRAS proto-oncogene. HRAS p.Gly12Val mutations have the highest transforming activity, are very frequent in cancers, but very rare in CS, where they are reported to cause a severe, early lethal, phenotype. We identified an unusual, new germline p.Gly12Val mutation, c.35_36GC>TG, in a 12-year-old boy with attenuated CS. Analysis of his HRAS cDNA showed high levels of exon 2 skipping. Using wild type and mutant HRAS minigenes, we confirmed that c.35_36GC>TG results in exon 2 skipping by simultaneously disrupting the function of a critical Exonic Splicing Enhancer (ESE) and creation of an Exonic Splicing Silencer (ESS). We show that this vulnerability of HRAS exon 2 is caused by a weak 3' splice site, which makes exon 2 inclusion dependent on binding of splicing stimulatory proteins, like SRSF2, to the critical ESE. Because the majority of cancer- and CS- causing mutations are located here, they affect splicing differently. Therefore, our results also demonstrate that the phenotype in CS and somatic cancers is not only determined by the different transforming potentials of mutant HRAS proteins, but also by the efficiency of exon 2 inclusion resulting from the different HRAS mutations. Finally, we show that a splice switching oligonucleotide (SSO) that blocks access to the critical ESE causes exon 2 skipping and halts proliferation of cancer cells. This unravels a potential for development of new anti-cancer therapies based on SSO-mediated HRAS exon 2 skipping

Heurísticas para balanceamento de carga de máquinas em infraestruturas de nuvem.

Em ambientes de Computação na Nuvem, principalmente os que utilizam o modelo de infraestrutura como um serviço, a característica de elasticidade no provisionamento de recursos traz consigo a necessidade de gerenciar os recursos físicos de forma apropriada para preservar a qualidade de serviço aos seus usuários, e o bom desempenho da infraestrutura. Este trabalho propõe heurísticas que são capazes de auxiliar no balanceamento de carga dos servidores em uma infraestrutura de nuvem, propondo migrações para diminuir a sobrecarga nos servidores que foram identificados como sobrecarregados,visto que, como passar do tempo há uma variação natural na quantidade de recursos em uso. Esta variação é uma consequência da remoção ou adição de aplicações, ou até mesmo de tentativas de melhoramento do desempenho das aplicações através do provisionamento vertical. Uma ferramenta foi implementada para fazer uso dos algoritmos das heurísticas e assim auxiliar nos experimentos para a validação das mesmas. As métricas utilizadas vem diretamente de servidores heterogêneos da nuvem OpenStack do Laboratório de Sistemas Distribuídos. Os resultados obtidos mostram que além da diminuição no consumo de CPU dos servidores dos quais que estavam sobrecarregados, também é possível melhorar o desempenho destes servidores em alguns casos.In CloudComputingenvironments,especiallythoseusingtheinfrastructureasaservice model, theelasticitycharacteristicinresourceprovisioningcomeswiththeneedtomanage resources sothequalityofservicecancontinuetobeguaranteedtousersandalsoto maintain agoodperformanceoftheinfrastructure.Thisworkproposesheuristicsthat are abletoassistintheloadbalancingoftheserversinaCloudinfrastructure,proposing migrations toreducetheoverheadintheserversthatwereidentifiedasoverloaded,since with thepassageoftimethereisanaturalvariationintheamountofresourcesinuse.This variationinaconsequenceofremovaloradditionofapplicationsandevenoftheusageof verticalscalingtoimproveapplication’sperformance.Atoolwasimplementedtomake use oftheheuristicalgorithmsandthustoaidintheexperimentsandtheirvalidation,the metrics usedcomedirectlyfromheterogeneousserversoftheOpenStackCloudofthe DistributedSystemsLaboratory.TheresultsshowthatinadditiontothedecreaseinCPU consumption ofserversthatwereoverloaded,itisalsopossibletoimprovetheperformance of theseserversinsomecases

Calibration and Validation of the Hybrid-Maize Crop Model for Regional Analysis and Application over the U.S. Corn Belt

Detailed parameter sensitivity, model validation, and regional calibration of the Hybrid-Maize crop model were undertaken for the purpose of regional agroclimatic assessments. The model was run at both field scale and county scale. The county-scale study was based on 30-yr daily weather data and corn yield data from the National Agricultural Statistics Service survey for 24 locations across the Corn Belt of the United States. The field-scale study was based on AmeriFlux sites at Bondville, Illinois, andMead, Nebraska. By using the one-at-a-time and interaction-explicit factorial design approaches for sensitivity analysis, the study found that the five most sensitive parameters of the model were potential number of kernels per ear, potential kernel filling rate, initial light use efficiency, upper temperature cutoff for growing degree-days’ accumulation, and the grain growth respiration coefficient. Model validation results show that the Hybrid-Maize model performed satisfactorily for field-scale simulations with a mean absolute error (MAE) of 10 bu acre-1 despite the difficulties of obtaining hybrid-specific information. At the county scale, the simulated results, assuming optimal crop management, overpredicted the yields but captured the variability well. A simple regional adjustment factor of 0.6 rescaled the potential yield to actual yield well. These results highlight the uncertainties that exist in applying crop models at regional scales because of the limitations in accessing cropspecific information. This study also provides confidence that uncertainties can potentially be eliminated via simple adjustment factor and that a simple crop model can be adequately useful for regional-scale agroclimatic studies