Automatic crater detection using DEM and circular coherency analysis - A case study on South American craters

Accepted versio

Proven, probable, and possible impact craters in South America revealed by ASTER and SRTM data and image processing techniques

Accepted versio

The production rate of small craters on Earth and the expected crater population in South America

Accepted versio

The Rio Cuarto crater field re-visited: remote sensing imagery analysis and new field observations.

Accepted versio

Mortality of Patients with Hematological Malignancy after Admission to the Intensive Care Unit

Background: The admission of patients with malignancies to an intensive care unit (ICU) still remains a matter of substantial controversy. The identification of factors that potentially influence the patient outcome can help ICU professionals make appropriate decisions. Patients and Methods: 90 adult patients with hematological malignancy (leukemia 47.8%, high-grade lymphoma 50%) admitted to the ICU were analyzed retrospectively in this single-center study considering numerous variables with regard to their influence on ICU and day-100 mortality. Results: The median simplified acute physiology score (SAPS) II at ICU admission was 55 (ICU survivors 47 vs. 60.5 for non-survivors). The overall ICU mortality rate was 45.6%. With multivariate regression analysis, patients admitted with sepsis and acute respiratory failure had a significantly increased ICU mortality (sepsis odds ratio (OR) 9.12, 95% confidence interval (CI) 1.1-99.7, p = 0.04; respiratory failure OR 13.72, 95% CI 1.39-136.15, p = 0.025). Additional factors associated with an increased mortality were: high doses of catecholamines (ICU: OR 7.37, p = 0.005; day 100: hazard ratio (HR) 2.96, p < 0.0001), renal replacement therapy (day 100: HR 1.93, p = 0.026), and high SAPS II (ICU: HR 1.05, p = 0.038; day 100: HR 1.2, p = 0.027). Conclusion: The decision for or against ICU admission of patients with hematological diseases should become increasingly independent of the underlying malignant disease

Identification of disease-causing genes using microarray data mining and gene ontology

Background: One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes. Methods: We propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results. Results: The proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth. Conclusions: The proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers

Comparison of prefrontal atrophy and episodic memory performance in dysexecutive Alzheimer’s disease and behavioural-variant frontotemporal dementia

Alzheimer’s disease (AD) sometimes presents with prominent executive dysfunction and associated prefrontal cortex atrophy. The impact of such executive deficits on episodic memory performance as well as their neural correlates in AD, however, remains unclear. The aim of the current study was to investigate episodic memory and brain atrophy in AD patients with relatively spared executive functioning (SEF-AD; n = 12) and AD patients with relatively impaired executive functioning (IEF-AD; n = 23). We also compared the AD subgroups with a group of behavioral-variant frontotemporal dementia patients (bvFTD; n = 22), who typically exhibit significant executive deficits, and age-matched healthy controls (n = 38). On cognitive testing, the three patient groups showed comparable memory profiles on standard episodic memory tests, with significant impairment relative to controls. Voxel-based morphometry analyses revealed extensive prefrontal and medial temporal lobe atrophy in IEF-AD and bvFTD, whereas this was limited to the middle frontal gyrus and hippocampus in SEF-AD. Moreover, the additional prefrontal atrophy in IEF-AD and bvFTD correlated with memory performance, whereas this was not the case for SEF-AD. These findings indicate that IEF-AD patients show prefrontal atrophy in regions similar to bvFTD, and suggest that this contributes to episodic memory performance. This has implications for the differential diagnosis of bvFTD and subtypes of AD

Palynofacies classification of submarine fan depositional environments: Outcrop examples from the Marnoso-Arenacea Formation, Italy

Basin floor fans contain some of the largest deep-water hydrocarbon accumulations discovered, however they also demonstrate extremely complex stratigraphic architecture, understanding of which is crucial for maximum recovery. Here we develop a new method, based upon palynofacies analysis, for the distinction of the different depositional environments that are commonly associated with basin floor fans. Previous studies and our sedimentological analysis allow good confidence in the discrimination of the different depositional environments of the outcropping Marnoso-Arenacea Formation fan system. One hundred and thirty-five samples were collected from mudstones in conjunction with sedimentary logging of 871 m of outcrops. Six lithofacies associations are described and interpreted to represent lobe axis, lobe fringe, fan fringe, contained interlobe, basin plain, and starved high depositional sub-environments. Palynofacies of these elements demonstrate turbidites to be rich in terrestrial organic matter, with sixteen categories of matter recognised. The abundances and proportions of particles varies between sub-environments, with lobe axis deposits containing the largest, densest particles, with a transition to ever smaller and lighter particles moving toward the basin plain. Fuzzy C-means statistical analysis was used to explore this trend. Distribution of organic matter is not random, but is dominated by hydrodynamic sorting and sequential fall-out of particles as turbidity currents passed across the basin. This allows a palynofacies classification scheme to be constructed to assist the identification of depositional environments of submarine fans, which may be combined with subsurface data to assist reservoir characterisation