994 resultados para Simulator FTE DATCOM damage tolerance inspections algorithms
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MicroRNAs (miRs) are involved in the pathogenesis of several neoplasms; however, there are no data on their expression patterns and possible roles in adrenocortical tumors. Our objective was to study adrenocortical tumors by an integrative bioinformatics analysis involving miR and transcriptomics profiling, pathway analysis, and a novel, tissue-specific miR target prediction approach. Thirty-six tissue samples including normal adrenocortical tissues, benign adenomas, and adrenocortical carcinomas (ACC) were studied by simultaneous miR and mRNA profiling. A novel data-processing software was used to identify all predicted miR-mRNA interactions retrieved from PicTar, TargetScan, and miRBase. Tissue-specific target prediction was achieved by filtering out mRNAs with undetectable expression and searching for mRNA targets with inverse expression alterations as their regulatory miRs. Target sets and significant microarray data were subjected to Ingenuity Pathway Analysis. Six miRs with significantly different expression were found. miR-184 and miR-503 showed significantly higher, whereas miR-511 and miR-214 showed significantly lower expression in ACCs than in other groups. Expression of miR-210 was significantly lower in cortisol-secreting adenomas than in ACCs. By calculating the difference between dCT(miR-511) and dCT(miR-503) (delta cycle threshold), ACCs could be distinguished from benign adenomas with high sensitivity and specificity. Pathway analysis revealed the possible involvement of G2/M checkpoint damage in ACC pathogenesis. To our knowledge, this is the first report describing miR expression patterns and pathway analysis in sporadic adrenocortical tumors. miR biomarkers may be helpful for the diagnosis of adrenocortical malignancy. This tissue-specific target prediction approach may be used in other tumors too.
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Report on the Iowa Department of Inspections and Appeals for the year ended June 30, 2012
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The 2011 Missouri River flooding caused significant damage to many geo-infrastructure systems including levees, bridge abutments/foundations, paved and unpaved roadways, culverts, and embankment slopes in western Iowa. The flooding resulted in closures of several interchanges along Interstate 29 and of more than 100 miles of secondary roads in western Iowa, causing severe inconvenience to residents and losses to local businesses. The main goals of this research project were to assist county and city engineers by deploying and using advanced technologies to rapidly assess the damage to geo-infrastructure and develop effective repair and mitigation strategies and solutions for use during future flood events in Iowa. The research team visited selected sites in western Iowa to conduct field reconnaissance, in situ testing on bridge abutment backfills that were affected by floods, flooded and non-flooded secondary roadways, and culverts. In situ testing was conducted shortly after the flood waters receded, and several months after flooding to evaluate recovery and performance. Tests included falling weight deflectometer, dynamic cone penetrometer, three-dimensional (3D) laser scanning, ground penetrating radar, and hand auger soil sampling. Field results indicated significant differences in roadway support characteristics between flooded and non-flooded areas. Support characteristics in some flooded areas recovered over time, while others did not. Voids were detected in culvert and bridge abutment backfill materials shortly after flooding and several months after flooding. A catalog of field assessment techniques and 20 potential repair/mitigation solutions are provided in this report. A flow chart relating the damages observed, assessment techniques, and potential repair/mitigation solutions is provided. These options are discussed for paved/unpaved roads, culverts, and bridge abutments, and are applicable for both primary and secondary roadways.
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This work is divided into three volumes: Volume I: Strain-Based Damage Detection; Volume II: Acceleration-Based Damage Detection; Volume III: Wireless Bridge Monitoring Hardware. Volume I: In this work, a previously-developed structural health monitoring (SHM) system was advanced toward a ready-for-implementation system. Improvements were made with respect to automated data reduction/analysis, data acquisition hardware, sensor types, and communication network architecture. The statistical damage-detection tool, control-chart-based damage-detection methodologies, were further investigated and advanced. For the validation of the damage-detection approaches, strain data were obtained from a sacrificial specimen attached to the previously-utilized US 30 Bridge over the South Skunk River (in Ames, Iowa), which had simulated damage,. To provide for an enhanced ability to detect changes in the behavior of the structural system, various control chart rules were evaluated. False indications and true indications were studied to compare the damage detection ability in regard to each methodology and each control chart rule. An autonomous software program called Bridge Engineering Center Assessment Software (BECAS) was developed to control all aspects of the damage detection processes. BECAS requires no user intervention after initial configuration and training. Volume II: In this work, a previously developed structural health monitoring (SHM) system was advanced toward a ready-for-implementation system. Improvements were made with respect to automated data reduction/analysis, data acquisition hardware, sensor types, and communication network architecture. The objective of this part of the project was to validate/integrate a vibration-based damage-detection algorithm with the strain-based methodology formulated by the Iowa State University Bridge Engineering Center. This report volume (Volume II) presents the use of vibration-based damage-detection approaches as local methods to quantify damage at critical areas in structures. Acceleration data were collected and analyzed to evaluate the relationships between sensors and with changes in environmental conditions. A sacrificial specimen was investigated to verify the damage-detection capabilities and this volume presents a transmissibility concept and damage-detection algorithm that show potential to sense local changes in the dynamic stiffness between points across a joint of a real structure. The validation and integration of the vibration-based and strain-based damage-detection methodologies will add significant value to Iowa’s current and future bridge maintenance, planning, and management Volume III: In this work, a previously developed structural health monitoring (SHM) system was advanced toward a ready-for-implementation system. Improvements were made with respect to automated data reduction/analysis, data acquisition hardware, sensor types, and communication network architecture. This report volume (Volume III) summarizes the energy harvesting techniques and prototype development for a bridge monitoring system that uses wireless sensors. The wireless sensor nodes are used to collect strain measurements at critical locations on a bridge. The bridge monitoring hardware system consists of a base station and multiple self-powered wireless sensor nodes. The base station is responsible for the synchronization of data sampling on all nodes and data aggregation. Each wireless sensor node include a sensing element, a processing and wireless communication module, and an energy harvesting module. The hardware prototype for a wireless bridge monitoring system was developed and tested on the US 30 Bridge over the South Skunk River in Ames, Iowa. The functions and performance of the developed system, including strain data, energy harvesting capacity, and wireless transmission quality, were studied and are covered in this volume.
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We previously showed that exposure of 3D organotypic rat brain cell cultures to 1mM 2-methylcitrate (2-MCA) or 3-hydroxyglutarate (3- OHGA) every 12h over three days (DIV11-DIV14) results in ammonium accumulation and cell death. The aim of this study was to define the time course (every 24h) of the observed effects. Ammonium in culture medium already increased at DIV12 staying stable on the following days under 3-OHGA exposure, while it increased consecutively up to much higher levels under 2-MCA exposure. Lactate increase and glucose decrease were observed from DIV13 and DIV14, respectively. We conclude that ammonium accumulation precedes alterations of energy metabolism. As observed by immunohistochemistry glial cells were the predominant dying cells. Immunoblotting and immunohistochemistry with cell death specific markers (caspase-3, alpha-fodrin, LC3) showed that 2-MCA exposure significantly increased apoptosis on DIV14, but did not alter autophagy or necrosis. In contrast, 3-OHGA exposure substantially increased necrosis already from DIV13, while no change was observed for apoptosis and autophagy. In conclusion, ammonium accumulation, secondary disturbance of energy metabolism and glial cell death are involved in the neuropathogenesis ofmethylmalonic aciduria and glutaric aciduria type I. Interestingly, brain cells are dying by necrosis under 3-OHGA exposure and by apoptosis under 2-MCA exposure.
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We have studied ischemic tolerance induced by the serine protease thrombin in two different models of experimental ischemia. In organotypic hippocampal slice cultures, we demonstrate that incubation with low doses of thrombin protects neurons against a subsequent severe oxygen and glucose deprivation. L-JNKI1, a highly specific c-jun N-terminal kinase (JNK) inhibitor, and a second specific JNK inhibitor, SP600125, prevented thrombin preconditioning (TPC). We also show that the exposure to thrombin increases the level of phosphorylated c-jun, the major substrate of JNK. TPC, in vivo, leads to significantly smaller lesion sizes after a 30-min middle cerebral artery occlusion (MCAo), and the preconditioned mice were better off in the three tests used to evaluate functional recovery. In accordance with in vitro results, TPC in vivo was prevented by administration of L-JNKI1, supporting a role for JNK in TPC. These results, from two different TPC models and with two distinct JNK inhibitors, show that JNK is likely to be involved in TPC.
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Agency Performance Report
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Agency Performance Report
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Agency Performance Report
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Agency Performance Report