Mitochondrial DNA deletions are abundant and cause functional impairment in aged human substantia nigra neurons

Using a novel single-molecule PCR approach to quantify the total burden of mitochondrial DNA (mtDNA) molecules with deletions, we show that a high proportion of individual pigmented neurons in the aged human substantia nigra contain very high levels of mtDNA deletions. Molecules with deletions are largely clonal within each neuron; that is, they originate from a single deleted mtDNA molecule that has expanded clonally. The fraction of mtDNA deletions is significantly higher in cytochrome c oxidase (COX)-deficient neurons than in COX-positive neurons, suggesting that mtDNA deletions may be directly responsible for impaired cellular respiration.     

Dopamine and octopamine in whole body extracts of the bulb mite

Summary Using high performance liquid chromatography with electrochemical detection, whole body extracts of the bulb mite,Rhizoglyphus echinopus (Fumouze and Robin), were found to contain the biogenic amines dopamine and octopamine at concentrations of 4.3±0.6 and 2.3±1.4 ng g^–1 wet weight, respectively. Adrenaline, noradrenaline, tyramine,N-methyldopamine,N-acetyldopamine, and 5-hydroxytryptamine, if present, were below the limits of detectability. This is the initial demonstration of the presence of octopamine in a mite species.     

A disjunct ponderosa pine stand in southeastern Oregon

An isolated stand of ponderosa pine ( Pinus ponderosa ) is surviving on an extremely harsh site in southeastern Oregon. Seed production is low because of insects, primarily pine coneworm ( Dioryctria auranticella ), feeding in developing cones. Seedling establishment is infrequent and difficult because of drought and coarse, rocky soils. A rock-mulch soil surface probably reduces interspecific competition. Because stand size is small (     

Rough sets bankruptcy prediction models versus auditor signalling rates

Both international and US auditing standards require auditors to evaluate the risk of bankruptcy when planning an audit and to modify their audit report if the bankruptcy risk remains high at the conclusion of the audit. Bankruptcy prediction is a problematic issue for auditors as the development of a cause–effect relationship between attributes that may cause or be related to bankruptcy and the actual occurrence of bankruptcy is difficult. Recent research indicates that auditors only signal bankruptcy in about 50% of the cases where companies subsequently declare bankruptcy. Rough sets theory is a new approach for dealing with the problem of apparent indiscernibility between objects in a set that has had a reported bankruptcy prediction accuracy ranging from 76% to 88% in two recent studies. These accuracy levels appear to be superior to auditor signalling rates, however, the two prior rough sets studies made no direct comparisons to auditor signalling rates and either employed small sample sizes or non‐current data. This study advances research in this area by comparing rough set prediction capability with actual auditor signalling rates for a large sample of United States companies from the 1991 to 1997 time period. Prior bankruptcy prediction research was carefully reviewed to identify 11 possible predictive factors which had both significant theoretical support and were present in multiple studies. These factors were expressed as variables and data for 11 variables was then obtained for 146 bankrupt United States public companies during the years 1991–1997. This sample was then matched in terms of size and industry to 145 non‐bankrupt companies from the same time period. The overall sample of 291 companies was divided into development and validation subsamples. Rough sets theory was then used to develop two different bankruptcy prediction models, each containing four variables from the 11 possible predictive variables. The rough sets theory based models achieved 61% and 68% classification accuracy on the validation sample using a progressive classification procedure involving three classification strategies. By comparison, auditors directly signalled going concern problems via opinion modifications for only 54% of the bankrupt companies. However, the auditor signalling rate for bankrupt companies increased to 66% when other opinion modifications related to going concern issues were included. In contrast with prior rough sets theory research which suggested that rough sets theory offered significant bankruptcy predictive improvements for auditors, the rough sets models developed in this research did not provide any significant comparative advantage with regard to prediction accuracy over the actual auditors' methodologies. The current research results should be fairly robust since this rough sets theory based research employed (1) a comparison of the rough sets model results to actual auditor decisions for the same companies, (2) recent data, (3) a relatively large sample size, (4) real world bankruptcy/non‐bankruptcy frequencies to develop the variable classifications, and (5) a wide range of industries and company sizes. Copyright © 2003 John Wiley & Sons, Ltd.     

Predicting bankruptcy using recursive partitioning and a realistically proportioned data set

Auditors must assess their clients' ability to function as a going concern for at least the year following the financial statement date. The audit profession has been severely criticized for failure to ‘blow the whistle’ in numerous highly visible bankruptcies that occurred shortly after unmodified audit opinions were issued. Financial distress indicators examined in this study are one mechanism for making such assessments. This study measures and compares the predictive accuracy of an easily implemented two‐variable bankruptcy model originally developed using recursive partitioning on an equally proportioned data set of 202 firms. In this study, we test the predictive accuracy of this model, as well as previously developed logit and neural network models, using a realistically proportioned set of 14,212 firms' financial data covering the period 1981–1990. The previously developed recursive partitioning model had an overall accuracy for all firms ranging from 95 to 97% which outperformed both the logit model at 93 to 94% and the neural network model at 86 to 91%. The recursive partitioning model predicted the bankrupt firms with 33–58% accuracy. A sensitivity analysis of recursive partitioning cutting points indicated that a newly specified model could achieve an all firm and a bankrupt firm predictive accuracy of approximately 85%. Auditors will be interested in the Type I and Type II error tradeoffs revealed in a detailed sensitivity table for this easily implemented model. Copyright © 2000 John Wiley & Sons, Ltd.     

A minimum column density of 1 g cm(-2) for massive star formation

Massive stars are very rare, but their extreme luminosities make them both the only type of young star we can observe in distant galaxies and the dominant energy sources in the Universe today. They form rarely because efficient radiative cooling keeps most star--forming gas clouds close to isothermal as they collapse, and this favours fragmentation into stars of one solar mass or lower. Heating of a cloud by accreting low-mass stars within it can prevent fragmentation and allow formation of massive stars, but the necessary properties for a cloud to form massive stars-and therefore where massive stars form in a galaxy--have not yet been determined. Here we show that only clouds with column densities of at least 1 g cm(-2) can avoid fragmentation and form massive stars. This threshold, and the environmental variation of the stellar initial mass function that it implies, naturally explain the characteristic column densities associated with massive star clusters and the difference between the radial profiles of Halpha and ultraviolet emission in galactic disks. The existence of a threshold also implies that the initial mass function should show detectable variation with environment within the Galaxy, that the characteristic column densities of clusters containing massive stars should vary between galaxies, and that star formation rates in some galactic environments may have been systematically underestimated.     

The transmembrane protein meckelin (MKS3) is mutated in Meckel-Gruber syndrome and the wpk rat

Meckel-Gruber syndrome is a severe autosomal, recessively inherited disorder characterized by bilateral renal cystic dysplasia, developmental defects of the central nervous system (most commonly occipital encephalocele), hepatic ductal dysplasia and cysts and polydactyly. MKS is genetically heterogeneous, with three loci mapped: MKS1, 17q21-24 (ref. 4); MKS2, 11q13 (ref. 5) and MKS3 (ref. 6). We have refined MKS3 mapping to a 12.67-Mb interval (8q21.13-q22.1) that is syntenic to the Wpk locus in rat, which is a model with polycystic kidney disease, agenesis of the corpus callosum and hydrocephalus. Positional cloning of the Wpk gene suggested a MKS3 candidate gene, TMEM67, for which we identified pathogenic mutations for five MKS3-linked consanguineous families. MKS3 is a previously uncharacterized, evolutionarily conserved gene that is expressed at moderate levels in fetal brain, liver and kidney but has widespread, low levels of expression. It encodes a 995-amino acid seven-transmembrane receptor protein of unknown function that we have called meckelin.