Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder characterised by the loss of upper and lower motor neurons resulting in paralysis and eventual death. Approximately 10% of ALS cases have a family history of disease, while the remainder present as apparently sporadic cases. Heritability studies suggest a significant genetic component to sporadic ALS, and although most sporadic cases have an unknown genetic aetiology, some familial ALS mutations have also been found in sporadic cases. This suggests that some sporadic cases may be unrecognised familial cases with reduced disease penetrance in their ancestors. A powerful strategy to uncover a familial link is identity-by-descent (IBD) analysis, which detects genomic regions that have been inherited from a common ancestor. IBD analysis was performed on 83 Australian familial ALS cases from 25 families and three sporadic ALS cases, each of whom carried one of three SOD1 mutations (p.I114T, p.V149G and p.E101G). We defined five unique 350-SNP haplotypes that carry these mutations in our cohort, indicative of five founder events. This included two founder haplotypes that carry SOD1 p.I114T; linking familial and sporadic cases. We found that SOD1 p.E101G arose independently in each family that carries this mutation and linked two families that carry SOD1 p.V149G. The age of disease onset varied between cases that carried each SOD1 p.I114T haplotype. Linking families with identical ALS mutations allows for larger sample sizes and increased statistical power to identify putative phenotypic modifiers.

Background Many traits and diseases are thought to be driven by >1 gene (polygenic). Polygenic risk scores (PRS) hence expand on genome-wide association studies by taking multiple genes into account when risk models are built. However, PRS only considers the additive effect of individual genes but not epistatic interactions or the combination of individual and interacting drivers. While evidence of epistatic interactions ais found in small datasets, large datasets have not been processed yet owing to the high computational complexity of the search for epistatic interactions. Findings We have developed VariantSpark, a distributed machine learning framework able to perform association analysis for complex phenotypes that are polygenic and potentially involve a large number of epistatic interactions. Efficient multi-layer parallelization allows VariantSpark to scale to the whole genome of population-scale datasets with 100,000,000 genomic variants and 100,000 samples. Conclusions Compared with traditional monogenic genome-wide association studies, VariantSpark better identifies genomic variants associated with complex phenotypes. VariantSpark is 3.6 times faster than ReForeSt and the only method able to scale to ultra-high-dimensional genomic data in a manageable time.

Recent advances in image classification methods, along with the availability of associated tools, have seen their use become widespread in many domains. This paper presents a novel application of current image classification approaches in the area of Emergency Situation Awareness. We discuss image classification based on low-level features as well as methods built on top of pretrained classifiers. The performance of the classifiers is assessed in terms of accuracy along with consideration to computational aspects given the size of the image database. Specifically, we investigate image classification in the context of a bush fire emergency in the Australian state of NSW, where images associated with Tweets during the emergency were used to train and test classification approaches. Emergency service operators are interested in having images relevant to such fires reported as extra information to help manage evolving emergencies. We show that these methodologies can classify images into fire and not fire-related classes with an accuracy of 86%.Keywords: classification, image processing, emergency response, machine learning, situation awareness InTRoDUcTIonIn times of crisis, it is increasingly common for the public to use social media to broadcast their needs, propagate news, and stay abreast of evolving situations (Landwehr and Carley, 2014). Situation awareness during disaster management and emergency response is an evolving area for research. In this context, situation awareness relates to picking up sensory cues from the environment, interpreting said cues, and forecasting what may occur (Endsley, 1995). The ubiquity of social media platforms presents an opportunity to harness developing information to improve situation awareness for management and response teams.With advances in natural language processing (NLP) technologies, attention has been given to research and development for extracting relevant information from streaming data such as Twitter. For example, Sen (2015) investigates finding tweets that do not reflect user sentiment using NLP. Varga et al. (2013) propose methods for matching problem reports to aid messages while Tweet4Act (Chowdhury et al., 2013) filters for irrelevant tweets. Power et al. (2014) have developed a system for processing large volumes of Twitter data using language models to identify Tweets of interest to emergency managers. An aspect of social media in relation to disaster management, which has so far received little attention, is images. Images have the potential to provide new insights on top of the text-derived intelligence in tweets, giving a rich and contextual information stream in crisis situations. For example, images of fires provide an immediate cue to crisis coordinators about an event allowing them to react appropriately. Images provide a less ambiguous insight into a situation compared to subjective textual descriptions. An image can show the size of the fire and also provide clues to environmental conditions such as weather conditions and the potential fuel load ...