Next-Generation Intensity-Duration-Frequency Curves for Diverse Land across the Continental United States.

The current methods for designing hydrological infrastructure rely on precipitation-based intensity-duration-frequency curves. However, they cannot accurately predict flooding caused by snowmelt or rain-on-snow events, potentially leading to underdesigned infrastructure and property damage. To address these issues, next-generation intensity-duration-frequency (NG-IDF) curves have been developed for the open condition, characterizing water available for runoff from rainfall, snowmelt, and rain-on-snow. However, they lack consideration of land use land cover (LULC) factors, which can significantly affect runoff processes. We address this limitation by expanding open area NG-IDF dataset to include eight vegetated LULCs over the continental United States, including forest (deciduous, evergreen, mixed), shrub, grass, pasture, crop, and wetland. This NG-IDF 2.0 dataset offers a comprehensive analysis of hydrological extreme events and their associated drivers under different LULCs at a continental scale. It will serve as a useful resource for improving standard design practices and aiding in the assessment of infrastructure design risks. Additionally, it provides useful insights into how changes in LULC impact flooding magnitude, mechanisms, timing, and snow water supply.

Computational cytometer based on magnetically modulated coherent imaging and deep learning.

Detecting rare cells within blood has numerous applications in disease diagnostics. Existing rare cell detection techniques are typically hindered by their high cost and low throughput. Here, we present a computational cytometer based on magnetically modulated lensless speckle imaging, which introduces oscillatory motion to the magnetic-bead-conjugated rare cells of interest through a periodic magnetic force and uses lensless time-resolved holographic speckle imaging to rapidly detect the target cells in three dimensions (3D). In addition to using cell-specific antibodies to magnetically label target cells, detection specificity is further enhanced through a deep-learning-based classifier that is based on a densely connected pseudo-3D convolutional neural network (P3D CNN), which automatically detects rare cells of interest based on their spatio-temporal features under a controlled magnetic force. To demonstrate the performance of this technique, we built a high-throughput, compact and cost-effective prototype for detecting MCF7 cancer cells spiked in whole blood samples. Through serial dilution experiments, we quantified the limit of detection (LoD) as 10 cells per millilitre of whole blood, which could be further improved through multiplexing parallel imaging channels within the same instrument. This compact, cost-effective and high-throughput computational cytometer can potentially be used for rare cell detection and quantification in bodily fluids for a variety of biomedical applications.