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Abstract Earthquakes present severe hazards for people and economies and can be primary drivers of landscape change yet their impact to river-channel networks remains poorly known. Here we show evidence for an abrupt earthquake-triggered avulsion of the Ganges River at ~2.5 ka leading to relocation of the mainstem channel belt in the Bengal delta. This is recorded in freshly discovered sedimentary archives of an immense relict channel and a paleo-earthquake of sufficient magnitude to cause major liquefaction and generate large, decimeter-scale sand dikes >180 km from the nearest seismogenic source region. Precise luminescence ages of channel sand, channel fill, and breached and partially liquefied floodplain deposits support coeval timing of the avulsion and earthquake. Evidence for reorganization of the river-channel network in the world’s largest delta broadens the risk posed by seismic events in the region and their recognition as geomorphic agents in this and other tectonically active lowlands. The recurrence of comparable earthquake-triggered ground liquefaction and a channel avulsion would be catastrophic for any of the heavily populated, large river basins and deltas along the Himalayan arc (e.g., Indus, Ganges, Brahmaputra, Ayeyarwady). The compounding effects of climate change and human impacts heighten and extend the vulnerability of many lowlands worldwide to such cascading hazards. 

Abstract CRISPR‐Cas9 screens facilitate the discovery of gene functional relationships and phenotype‐specific dependencies. The Cancer Dependency Map (DepMap) is the largest compendium of whole‐genome CRISPR screens aimed at identifying cancer‐specific genetic dependencies across human cell lines. A mitochondria‐associated bias has been previously reported to mask signals for genes involved in other functions, and thus, methods for normalizing this dominant signal to improve co‐essentiality networks are of interest. In this study, we explore three unsupervised dimensionality reduction methods—autoencoders, robust, and classical principal component analyses (PCA)—for normalizing the DepMap to improve functional networks extracted from these data. We propose a novel “onion” normalization technique to combine several normalized data layers into a single network. Benchmarking analyses reveal that robust PCA combined with onion normalization outperforms existing methods for normalizing the DepMap. Our work demonstrates the value of removing low‐dimensional signals from the DepMap before constructing functional gene networks and provides generalizable dimensionality reduction‐based normalization tools.