Food price and affordability update

This presentation outlines recent achievements in development of tools, protocols and methods to monitoring and benchmark food prices and affordability globally under International Network for Food and Obesity/non-communicable diseases Research, Monitoring and Action Support(INFORMAS)

Next-generation sequencing: A frameshift in skeletal dysplasia gene discovery

In the last decade, huge breakthroughs in genetics - driven by new technology and different statistical approaches - have resulted in a plethora of new disease genes identified for both common and rare diseases. Massive parallel sequencing, commonly known as next-generation sequencing, is the latest advance in genetics, and has already facilitated the discovery of the molecular cause of many monogenic disorders. This article describes this new technology and reviews how this approach has been used successfully in patients with skeletal dysplasias. Moreover, this article illustrates how the study of rare diseases can inform understanding and therapeutic developments for common diseases such as osteoporosis. © International Osteoporosis Foundation and National Osteoporosis Foundation 2013.

Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure

This paper addresses the problem of discovering business process models from event logs. Existing approaches to this problem strike various tradeoffs between accuracy and understandability of the discovered models. With respect to the second criterion, empirical studies have shown that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several automated process discovery methods generate block-structured models by construction. These approaches however intertwine the concern of producing accurate models with that of ensuring their structuredness, sometimes sacrificing the former to ensure the latter. In this paper we propose an alternative approach that separates these two concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic technique that discovers more accurate but sometimes unstructured (and even unsound) process models, and then transform the resulting model into a structured one. An experimental evaluation shows that our “discover and structure” approach outperforms traditional “discover structured” approaches with respect to a range of accuracy and complexity measures.