Application of Earth Observation Data and Standardized Precipitation Index Based Approach for Meteorological Drought Monitoring, Assessment and Prediction Over Kutch, Gujarat, India

Drought is a natural phenomenon which differs from other natural hazards by its slow accumulating process and its indefinite commencement and termination. The present study addresses water deficiency and drought occurrence over Kutch district, Gujarat, because nearly 45% of the whole Kutch district is severely suffering by deficiency of water. Earth observation data (LANDSAT ETM+) and Standardized Precipitation Index were used to analyze drought severity. Daily rainfall data over the study area were obtained from Indian Meteorological Department (IMD) for the period of study (1990-2014) and geo-referenced for further analyses. Using Remote Sensing and GIS techniques, rainfall variability map over the period of study has been prepared to show rainfall distribution and land use and land cover map is prepared to show the area under different land use classes and impacts of drought over land uses. Standardized Precipitation Index (SPI) was generated for each block wise and scenario of drought development has been analyzed using decadal data set for the study period (1990-2014). The present study suggests method and techniques for continuous drought monitoring by linking temporal earth observation and rainfall data. The methodology will be very useful for the development of a regional drought monitoring syste

Superhuman cell death detection with biomarker-optimized neural networks.

Cellular events underlying neurodegenerative disease may be captured by longitudinal live microscopy of neurons. While the advent of robot-assisted microscopy has helped scale such efforts to high-throughput regimes with the statistical power to detect transient events, time-intensive human annotation is required. We addressed this fundamental limitation with biomarker-optimized convolutional neural networks (BO-CNNs): interpretable computer vision models trained directly on biosensor activity. We demonstrate the ability of BO-CNNs to detect cell death, which is typically measured by trained annotators. BO-CNNs detected cell death with superhuman accuracy and speed by learning to identify subcellular morphology associated with cell vitality, despite receiving no explicit supervision to rely on these features. These models also revealed an intranuclear morphology signal that is difficult to spot by eye and had not previously been linked to cell death, but that reliably indicates death. BO-CNNs are broadly useful for analyzing live microscopy and essential for interpreting high-throughput experiments