The International Symposium on Fungal Stress - ISFUS

Fungi play central roles in many biological processes, influencing soil fertility, decomposition, cycling of minerals, and organic matter, plant health, and nutrition. They produce a wide spectrum of molecules, which are exploited in a range of industrial processes to manufacture foods, food preservatives, flavoring agents, and other useful biological products. Fungi can also be used as biological control agents of microbial pathogens, nematodes or insect pests, and affect plant growth, stress tolerance, and nutrient acquisition. Successful exploitation of fungi requires better understanding of the mechanisms that fungi use to cope with stress as well as the way in which they mediate stress tolerance in other organisms. It is against this backdrop that a scientific meeting on fungal stress was held in São José dos Campos, Brazil, in October 2014. The meeting, hosted by Drauzio E. N. Rangel and Alene E. Alder-Rangel, and supported by the São Paulo Research Foundation (FAPESP), brought together more than 30 young, mid-career, and highly accomplished scientists from ten different countries. Here we summarize the highlights of the meeting.

Discrete Conditional Phase-type Model Utilising a Multiclass Support Vector Machine for the Prediction of Retinopathy of Prematurity

Retinopathy of prematurity (ROP) is a rare disease in which retinal blood vessels of premature infants fail to develop normally, and is one of the major causes of childhood blindness throughout the world. The Discrete Conditional Phase-type (DC-Ph) model consists of two components, the conditional component measuring the inter-relationships between covariates and the survival component which models the survival distribution using a Coxian phase-type distribution. This paper expands the DC-Ph models by introducing a support vector machine (SVM), in the role of the conditional component. The SVM is capable of classifying multiple outcomes and is used to identify the infant's risk of developing ROP. Class imbalance makes predicting rare events difficult. A new class decomposition technique, which deals with the problem of multiclass imbalance, is introduced. Based on the SVM classification, the length of stay in the neonatal ward is modelled using a 5, 8 or 9 phase Coxian distribution.