Studies of somatic mitochondrial DNA mutations have become an important aspect of cancer research because these mutations might have functional significance and/or serve as a biosensor for tumor detection. Here we report somatic mitochondrial DNA mutations from three specific tissue types (tumor, adjacent benign, and distant benign) recovered from 24 prostatectomy samples. Needle biopsy tissue from 12 individuals referred for prostate biopsy, yet histologically benign (symptomatic benign), were used as among individual control samples. We also sampled blood (germplasm tissue) from each patient to serve as within individual controls relative to the somatic tissues sampled (malignant, adjacent, and distant benign). Complete mitochondrial genome sequencing was attempted on each sample. In contrast to both control groups [within patient (blood) and among patient (symptomatic benign)], all of the tissue types recovered from the malignant group harbored significantly different mitochondrial DNA (mtDNA) mutations. We conclude that mitochondrial genome mutations are an early indicator of malignant transformation in prostate tissue. These mutations occur well before changes in tissue histo-pathology, indicative of prostate cancer, are evident to the pathologist.
We report the usefulness of a 3.4-kb mitochondrial genome deletion (3.4 mtdelta) for molecular definition of benign, malignant, and proximal to malignant (PTM) prostate needle biopsy specimens. The 3.4 mtdelta was identified through long-extension polymerase chain reaction (PCR) analysis of frozen prostate cancer samples. A quantitative PCR assay was developed to measure the levels of the 3.4 mtdelta in clinical samples. For normalization, amplifications of a nuclear target and total mitochondrial DNA were included. Cycle threshold data from these targets were used to calculate a score for each biopsy sample. In a pilot study of 38 benign, 29 malignant, and 41 PTM biopsy specimens, the difference between benign and malignant core biopsy specimens was well differentiated (P & .0001), with PTM indistinguishable from malignant samples (P = .833). Results of a larger study were identical. In comparison with histopathologic examination for benign and malignant samples, the sensitivity and specificity were 80% and 71%, respectively, and the area under a receiver operating characteristic (ROC) curve was 0.83 for the deletion. In a blinded external validation study, the sensitivity and specificity were 83% and 79%, and the area under the ROC curve was 0.87. The 3.4 mtdelta may be useful in defining malignant, benign, and PTM prostate tissues.
In Australia, the Grassland Fire Danger Index is determined by several inputs including an essential component, the degree of grassland curing, defined as the proportion of senescent material. In the state of Victoria (south-eastern Australia), techniques used for curing assessment have included the use of ground-based observations and the use of satellite imagery. Both techniques alone have inherent limitations. An improved technique has been developed for estimating the degree of curing that entails the use of satellite observations adjusted by observations from the ground. First, a satellite model was developed, named MapVictoria, based on historical satellite and ground-based observations. Second, with use of the new (MapVictoria) satellite model, an integrated model was developed, named the Victorian Improved Satellite Curing Algorithm, combining near-real-time satellite data with weekly observations of curing from the ground. This integrated model was deployed in operations supporting accurate fire danger calculations for grasslands in Victoria in 2013.
The ready availability of codes such as LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) for molecular dynamics simulations has opened up the realm of atomistic modelling to novice code users with an interest in computational materials modelling but who lack the appropriate theoretical or computational background. As such, there is significant risk of the “user effect” having a negative impact on the quality of results obtained using such codes. Here, we present a “how-to” procedure for equilibrium molecular dynamics-based nuclear fuel thermal conductivity calculations using the Green–Kubo method with an interatomic potential developed by Cooper et al. [ 1 ]. The various steps of the simulation are identified and explained, along with criteria to assess the quality of the intermediate and final results, discussion of some problems that can arise during a simulation, and some inherent limitations of the method. Calculated thermal conductivities for UO2 and ThO2 will be compared with the available experimental data and also with similar thermal conductivity calculations using nonequilibrium molecular dynamics, reported in the open literature.
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