Genetics of rheumatoid arthritis contributes to biology and drug discovery

A major challenge in human genetics is to devise a systematic strategy to integrate disease-associated variants with diverse genomic and biological data sets to provide insight into disease pathogenesis and guide drug discovery for complex traits such as rheumatoid arthritis (RA)1. Here we performed a genome-wide association study meta-analysis in a total of >100,000 subjects of European and Asian ancestries (29,880 RA cases and 73,758 controls), by evaluating ~10 million single-nucleotide polymorphisms. We discovered 42 novel RA risk loci at a genome-wide level of significance, bringing the total to 101 (refs 2, 3, 4). We devised an in silico pipeline using established bioinformatics methods based on functional annotation5, cis-acting expression quantitative trait loci6 and pathway analyses7, 8, 9—as well as novel methods based on genetic overlap with human primary immunodeficiency, haematological cancer somatic mutations and knockout mouse phenotypes—to identify 98 biological candidate genes at these 101 risk loci. We demonstrate that these genes are the targets of approved therapies for RA, and further suggest that drugs approved for other indications may be repurposed for the treatment of RA. Together, this comprehensive genetic study sheds light on fundamental genes, pathways and cell types that contribute to RA pathogenesis, and provides empirical evidence that the genetics of RA can provide important information for drug discovery.

ASEAN and ADB implementing new approaches to building road safety capacity

Following the Association of Southeast Asian Nations (ASEAN) senior transport officials meeting in May 2011, the Secretariat requested the Asian Development Bank (ADB) to provide assistance to improve road safety in ASEAN. In response, ADB, funded by the Japan Fund for Poverty Reduction, has begun an innovative approach to capacity building that has already been adapted and replicated in other sub-regions. This paper will discuss the model central to the project. The Road Safety Capacity Building for ASEAN Project commenced in May 2013. Each country has appointed a National Focal Point (NFP) to identify and coordinate information. A team of International Experts were appointed to develop materials and present a comprehensive train the trainer program focused on five key areas. Thirty eight senior Government officers from across ASEAN attended a two week program at ADB headquarters in Manila and will arrange and deliver specific training and associated activities to other colleagues within their country. ADB has appointed a National Consultant to work in partnership with the trainees on a range of activities including development of “pipeline project proposals” for funding consideration investors and donors. As part of the project, a draft ASEAN Regional Road Safety Strategy document has been prepared and consultation will further refine its directions and contents. The project will reach its conclusion in 2015 and a follow up phase three project is being considered.

Depositional model for the early Miocene turbidite sequence, the Whakataki Formation, NZ

This research examined the influence of tectonic activity on submarine sedimentation processes, through a deposit-based analysis of turbidites in outcrop. A comprehensive field study of the Miocene Whakataki Formation yielded significant data that was analysed using methods of process-sedimentology, stratigraphy, and ichnology. Signatures of the tectonically active depositional environment were identifiable at very high resolution, from grain composition and texture to trace-fossil assemblages, as well as on a broader-scale in stratigraphic stacking patterns and structural deformation. From these results and environmental interpretations, an original facies characterisation and conceptual depositional model have been established.

Why are students choosing STEM and when do they make their choice?

BACKGROUND OR CONTEXT The higher education sector plays an important role in encouraging students into the STEM pipeline through fostering partnerships with schools, building on universities long tradition in engagement and outreach to secondary schools. Numerous activities focus on integrated STEM learning experiences aimed at developing conceptual scientific and mathematical knowledge with opportunities for students to show and develop skills in working with each other and actively engaging in discussion, decision making and collaborative problem solving. (NAS, 2013; AIG, 2015; OCS, 2014). This highlights the importance of the development and delivery of engaging integrated STEM activities connected to the curriculum to inspire the next generation of scientists and engineers and generally preparing students for post-secondary success. The broad research objective is to gain insight into which engagement activities and to what level they influence secondary school students’ selection of STEM-related career choices at universities. PURPOSE OR GOAL To evaluate and determine the effectiveness of STEM engagement activities impacting student decision making in choosing a STEM-related degree choice at university. APPROACH A survey was conducted with first-year domestic students studying STEM-related fieldswithin the Science and Engineering Faculty at Queensland University of Technology. Of the domestic students commencing in 2015, 29% responded to the survey. The survey was conducted using Survey Monkey and included a variety of questions ranging from academic performance at school to inspiration for choosing a STEM degree. Responses were analysed on a range of factors to evaluate the influence on students’ decisions to study STEM and whether STEM high school engagement activities impacted these decisions. To achieve this the timing of decision making for students choice in study area, degree, and university is compared with the timing of STEM engagement activities. DISCUSSION Statistical analysis using SPSS was carried out on survey data looking at reasons for choosing STEM degrees in terms of gender, academic performance and major influencers in their decision making. It was found that students choose their university courses based on what subjects they enjoyed and exceled at in school. These results found a high correlation between enjoyment of a school subject and their interest in pursuing this subject at university and beyond. Survey results indicated students are heavily influenced by their subject teachers and parents in their choice of STEM-related disciplines. In terms of career choice and when students make their decision, 60% have decided on a broad area of study by year 10, whilst only 15% had decided on a specific course and 10% had decided on which university. The timing of secondary STEM engagement activities is seen as a critical influence on choosing STEM disciplines or selection of senior school subjects with 80% deciding on specific degree between year 11 and 12 and 73% making a decision on which university in year 12. RECOMMENDATIONS/IMPLICATIONS/CONCLUSION Although the data does not support that STEM engagement activities increase the likelihood of STEM-related degree choice, the evidence suggests the students who have participated in STEM activities associate their experiences with their choice to pursue a STEM-related course. It is important for universities to continue to provide quality engaging and inspirational learning experiences in STEM, to identify and build on students’ early interest and engagement, increase STEM knowledge and awareness, engage them in interdisciplinary project-based STEM practices, and provide them with real-world application experiences to sustain their interest.

Uncanny nature: Hybrid stop motion using Blender

This talk gives an overview of the project "Uncanny Nature", which incoporates a style of animation called Hybrid Stop Motion, that combines physical object armatures with virtual copies. The development of the production pipeline (using a mix of Blender, Dragonframe, Photoscan and Arduino) is discussed, as well as the way that Blender was used throughout the production to visualise, model, animate and composite the elements together.

Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

Field performance of in-service cast iron gas reticulation pipe buried in reactive clay

Field instrumentation of an in-service cast iron gas pipe buried in a residential area is detailed in this paper. The aim of the study was to monitor the long-term pipe behavior to understand the mechanisms of pipe bending in relation to ground movement as a result of seasonal fluctuation of soil moisture content. Field data showed that variation of soil temperature, suction, and moisture content are closely related to the prevailing climate. Change of soil temperature is generally related to the ambient air temperature, with a variation of approximately −3°C −3°C per meter depth from the ground surface in summer (decrease with depth) and winter (increase with depth). Seasonal cyclic variation in moisture content was observed with maxima in February and March, and a minimum around September. The pipe top was under tensile strain during summer and subsequently subjected to compressive strain as soil swelling occurred as a result of increase in moisture content. The study suggests that downward pipe bending occurs in summer because of soil shrinkage, while upward pipe bending occurs in winter when the soil swells.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.