Understanding Neural Pathways in Zebrafish through Deep Learning and High Resolution Electron Microscope Data

The tracing of neural pathways through large volumes of image data is an incredibly tedious and time-consuming process that significantly encumbers progress in neuroscience. We are exploring deep learning's potential to automate segmentation of high-resolution scanning electron microscope (SEM) image data to remove that barrier. We have started with neural pathway tracing through 5.1GB of whole-brain serial-section slices from larval zebrafish collected by the Center for Brain Science at Harvard University. This kind of manual image segmentation requires years of careful work to properly trace the neural pathways in an organism as small as a zebrafish larva (approximately 5mm in total body length). In automating this process, we would vastly improve productivity, leading to faster data analysis and breakthroughs in understanding the complexity of the brain. We will build upon prior attempts to employ deep learning for automatic image segmentation extending methods for unconventional deep learning data.Comment: 8 pages, 5 figures (1a to 5c), PEARC '18: Practice and Experience in Advanced Research Computing, July 22--26, 2018, Pittsburgh, PA, US

Anatomy of extraordinary rainfall and flash flood in a Dutch lowland catchment

On 26 August 2010 the eastern part of The Netherlands and the bordering part of Germany were struck by a series of rainfall events lasting for more than a day. Over an area of 740 km2 more than 120 mm of rainfall were observed in 24 h. This extreme event resulted in local flooding of city centres, highways and agricultural fields, and considerable financial loss. In this paper we report on the unprecedented flash flood triggered by this exceptionally heavy rainfall event in the 6.5 km2 Hupsel Brook catchment, which has been the experimental watershed employed by Wageningen University since the 1960s. This study aims to improve our understanding of the dynamics of such lowland flash floods. We present a detailed hydrometeorological analysis of this extreme event, focusing on its synoptic meteorological characteristics, its space-time rainfall dynamics as observed with rain gauges, weather radar and a microwave link, as well as the measured soil moisture, groundwater and discharge response of the catchment. At the Hupsel Brook catchment 160 mm of rainfall was observed in 24 h, corresponding to an estimated return period of well over 1000 years. As a result, discharge at the catchment outlet increased from 4.4 × 10-3 to nearly 5 m3 s-1. Within 7 h discharge rose from 5 × 10-2 to 4.5 m3 s-1. The catchment response can be divided into four phases: (1) soil moisture reservoir filling, (2) groundwater response, (3) surface depression filling and surface runoff and (4) backwater feedback. The first 35 mm of rainfall were stored in the soil without a significant increase in discharge. Relatively dry initial conditions (in comparison to those for past discharge extremes) prevented an even faster and more extreme hydrological response

Characteristic analysis of a flash flood-affected creek catchment using LiDAR-derived DEM

Flooding occurred across a large area of southern and central Queensland in December 2010 and January 2011. Intense rainfall over the Gowrie Creek catchment caused severe flash flooding through the Toowoomba CBD (Central Business District) on the afternoon of Monday, 10 January 2011, taking lives and damaging the community. Flash floods are sudden and unexpected floods that arise from intense rainfall, generally over a small, steep catchment area. Smaller and steeper catchments have shorter critical storm duration, and they respond more quickly to rainfall events. The resulting flood wave is characterized by very high water flows and velocities and abrupt water level rises, leading to extremely hazardous conditions. Effective flash flood forecasting for specific locations is a big challenge because of the behaviour of intense thunderstorms. A flash flood forecasting and warning system calls for accurate spatial information on catchment characteristics. A high-resolution DEM is a key spatial dataset for the characterization of a catchment to design possible flood mitigation measures. The characteristics of a catchment have a strong influence on its hydrological response. The nature of floods is dependent on both the intensity and duration of the rainfall and the catchment characteristics such as catchment area, drainage patterns and waterway steepness. Therefore, analysis of catchment characteristics is critical for hydrologic modelling and planning for flood risk mitigation. The analysis of catchment characteristics can support hydrological modelling and planning for flood risk mitigation. For example, the shape indices of sub-catchments can be used to compare the hydrological behaviour of different subcatchments. The longitudinal profiles of the creeks illustrate the slope gradients of the waterways. A hypsometric curve for each sub-catchment provides an overall view of the slope of a catchment and is closely related to ground slope characteristics of a catchment. Airborne light detection and ranging (LiDAR), also referred to as airborne laser scanning (ALS), is one of the most effective means of terrain data collection. Using LiDAR data for generation of DEMs is becoming a standard practice in the spatial science community. This study used airborne LiDAR data to generate a high-resolution DEM for characteristic analysis of Gowrie Creek catchment in Toowoomba, Queensland, Australia, which was affected by a flash flood in January 2011. Drainage networks and sub-catchment boundaries were extracted from LiDAR-derived DEM. Catchment characteristics including sub-catchment areas and shape indices, longitudinal profiles of creeks and hypsometric curves of sub-catchments were calculated and analysed