Multiprobabilistic prediction in early medical diagnoses

This paper describes the methodology of providing multiprobability predictions for proteomic mass spectrometry data. The methodology is based on a newly developed machine learning framework called Venn machines. Is allows to output a valid probability interval. The methodology is designed for mass spectrometry data. For demonstrative purposes, we applied this methodology to MALDI-TOF data sets in order to predict the diagnosis of heart disease and early diagnoses of ovarian cancer and breast cancer. The experiments showed that probability intervals are narrow, that is, the output of the multiprobability predictor is similar to a single probability distribution. In addition, probability intervals produced for heart disease and ovarian cancer data were more accurate than the output of corresponding probability predictor. When Venn machines were forced to make point predictions, the accuracy of such predictions is for the most data better than the accuracy of the underlying algorithm that outputs single probability distribution of a label. Application of this methodology to MALDI-TOF data sets empirically demonstrates the validity. The accuracy of the proposed method on ovarian cancer data rises from 66.7 % 11 months in advance of the moment of diagnosis to up to 90.2 % at the moment of diagnosis. The same approach has been applied to heart disease data without time dependency, although the achieved accuracy was not as high (up to 69.9 %). The methodology allowed us to confirm mass spectrometry peaks previously identified as carrying statistically significant information for discrimination between controls and cases.

Humans and fire: changing relations in early agricultural and built environments in the Zagros, Iran, Iraq

Fire-centred studies have recently been highlighted as powerful avenues for investigation of energy flows and relations between humans, materials, environments and other species. The aim in this paper is to evaluate this potential first by reviewing the diverse theories and methods that can be applied to investigate the ecological and social significance of anthropogenic fire, and second by applying these to new and existing data sets in archaeology. This paper examines how fire-centred approaches can inform on one of the most significant step-changes in human lifeways and inter-relations with environment and other species – the transition from mobile hunting-gathering to more sedentary agriculture in a key heartland of change, the Zagros region of Iraq and Iran, c. 12,000–8,000 BP. In the review and case studies multiple links are investigated between human fire use and environment, ecology, energy use, technology, the built environment, health, social roles and relations, cultural practices and catastrophic events