A machine learning approach to differentiating bacterial from viral meningitis

Clinical reports indicate that differentiating bacterial from viral (aseptic) meningitis is still a difficult issue, compounded by factors such as age and time of presentation. Clinicians routinely rely on the results from blood and cerebrospinal fluid (CSF) to discriminate bacterial from viral meningitis. Tests such as the CSF Gram stain performed prior to broad-spectrum antibiotic treatment yield sensitivities between 60 and 92%. Sensitivity can be increased by performing additional laboratory testing, but the results are never completely accurate and are not cost effective in many cases. In this study, we wished to determine if a machine learning approach, based on rough sets and a probabilistic neural network could be used to differentiate between viral and bacterial meningitis. We analysed a clinical dataset containing records for 581 cases of acute bacterial or viral meningitis. The rough sets approach was used to perform dimensionality reduction in addition to classification. The results were validated using a probabilistic neural network. With an overall accuracy of 98%, these results indicate rough sets is a useful approach to differentiating bacterial from viral meningitis

Koszul binomial edge ideals

It is shown that if the binomial edge ideal of a graph $G$ defines a Koszul algebra, then $G$ must be chordal and claw free. A converse of this statement is proved for a class of chordal and claw free graphs

Finite Density QCD in the Chiral Limit

We present the first results of an exact simulation of full QCD at finite density in the chiral limit. We have used a MFA (Microcanonical Fermionic Average) inspired approach for the reconstruction of the Grand Canonical Partition Function of the theory; using the fugacity expansion of the fermionic determinant we are able to move continuously in the ($\beta -\mu$) plane with $m=0$.Comment: 3 pages, LaTeX, 3 figures, uses espcrc2.sty, psfig. Talk presented by A. Galante at Lattice 97. Correction of some reference

A breast cancer diagnosis system: a combined approach using rough sets and probabilistic neural networks

In this paper, we present a medical decision support system based on a hybrid approach utilising rough sets and a probabilistic neural network. We utilised the ability of rough sets to perform dimensionality reduction to eliminate redundant attributes from a biomedical dataset. We then utilised a probabilistic neural network to perform supervised classification. Our results indicate that rough sets was able to reduce the number of attributes in the dataset by 67% without sacrificing classification accuracy. Our classification accuracy results yielded results on the order of 93%

Host carbon sources modulate cell wall architecture, drug resistance and virulence in a fungal pathogen

The survival of all microbes depends upon their ability to respond to environmental challenges. To establish infection, pathogens such as Candida albicans must mount effective stress responses to counter host defences while adapting to dynamic changes in nutrient status within host niches. Studies of C. albicans stress adaptation have generally been performed on glucose-grown cells, leaving the effects of alternative carbon sources upon stress resistance largely unexplored. We have shown that growth on alternative carbon sources, such as lactate, strongly influence the resistance of C. albicans to antifungal drugs, osmotic and cell wall stresses. Similar trends were observed in clinical isolates and other pathogenic Candida species. The increased stress resistance of C. albicans was not dependent on key stress (Hog1) and cell integrity (Mkc1) signalling pathways. Instead, increased stress resistance was promoted by major changes in the architecture and biophysical properties of the cell wall. Glucose- and lactate-grown cells displayed significant differences in cell wall mass, ultrastructure, elasticity and adhesion. Changes in carbon source also altered the virulence of C. albicans in models of systemic candidiasis and vaginitis, confirming the importance of alternative carbon sources within host niches during C. albicans infection

Lactate signalling regulates fungal β-glucan masking and immune evasion

AJPB: This work was supported by the European Research Council (STRIFE, ERC- 2009-AdG-249793), The UK Medical Research Council (MR/M026663/1), the UK Biotechnology and Biological Research Council (BB/K017365/1), the Wellcome Trust (080088; 097377). ERB: This work was supported by the UK Biotechnology and Biological Research Council (BB/M014525/1). GMA: Supported by the CNPq-Brazil (Science without Borders fellowship 202976/2014-9). GDB: Wellcome Trust (102705). CAM: This work was supported by the UK Medical Research Council (G0400284). DMM: This work was supported by UK National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC/K000306/1). NARG/JW: Wellcome Trust (086827, 075470,101873) and Wellcome Trust Strategic Award in Medical Mycology and Fungal Immunology (097377). ALL: This work was supported by the MRC Centre for Medical Mycology and the University of Aberdeen (MR/N006364/1).Peer reviewedPostprin

The Role of Threonine Deaminase/Dehydratase in Winter Dormancy in Sweet Cherry Buds

The determination of the endodormancy release and the beginning of ontogenetic development is a challenge, because these are non-observable stages. Changes in protein activity are important aspects of signal transduction. The conversion of threonine to 2-oxobutanoate is the first step towards isoleucine (Ile) biosynthesis, which promote growth and development. The reaction is catalyzed by threonine deaminase/dehydratase (TD). This study on TD activity was conducted at the experimental sweet cherry orchard at Berlin-Dahlem. Fresh (FW), dry weight (DW), water content (WC), and the specific TD activity for the cherry cultivars Summit, Karina and Regina were conducted from flower bud samples between October and April. The content of asparagine (Asn), aspartic acid (Asp), Ile, and valine (Val) were exemplarily shown for Summit. In buds of Summit and Karina, the TD activity was one week after the beginning of the ontogenetic development (t1*), significantly higher compared to samplings during endo- and ecodormancy. Such “peak” activity did not occur in the buds of Regina; TD tended for a longer time (day of year, DOY 6–48) to a higher activity, compared to the time DOY 287–350. For the date “one week after t1*”, the upregulation of TD, the markedly increase of the Ile and Val content, and the increase of the water content in the buds, all this enzymatically confirms the estimated start of the ontogenetic development (t1*) in sweet cherry buds.Peer Reviewe