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Two PIEZO mechanosensitive cation channels, PIEZO1 and PIEZO2, have been identified in mammals, where they are involved in numerous sensory processes. While structurally similar, PIEZO channels are expressed in distinct tissues and exhibit unique properties. How different PIEZOs transduce force, how their transduction mechanism varies, and how their unique properties match the functional needs of the distinct tissues where they are expressed remain all-important unanswered questions. The nematode Caenorhabditis elegans has a single PIEZO ortholog (pezo-1) predicted to have twelve isoforms. These isoforms share many transmembrane domains, but differ in those that distinguish PIEZO1 and PIEZO2 in mammals. Here we use translational and transcriptional reporters to show that long pezo-1 isoforms are selectively expressed in mesodermally derived tissues (such as muscle and glands). In contrast, shorter pezo-1 isoforms are primarily expressed in neurons. In the digestive system, different pezo-1 isoforms appear to be expressed in different cells of the same organ. We show that pharyngeal muscles, glands, and valve rely on long pezo-1 isoforms to respond appropriately to the presence of food. The unique pattern of complementary expression of pezo-1 isoforms suggest that different isoforms possess distinct functions. The number of pezo-1 isoforms in C. elegans, their differential pattern of expression, and their roles in experimentally tractable processes make this an attractive system to investigate the molecular basis for functional differences between members of the PIEZO family of mechanoreceptors. 

The morphology of deltas is determined by the spatial extent and variability of the geomorphic processes that shape them. While in some cases resilient, deltas are increasingly threatened by natural and anthropogenic forces, such as sea level rise and land use change, which can drastically alter the rates and patterns of sediment transport. Quantifying process patterns can improve our predictive understanding of how different zones within delta systems will respond to future change. Available remotely sensed imagery can help but appropriate tools are needed for pattern extraction and analysis. We present a method for extracting information about the nature and spatial extent of active geomorphic processes across deltas from ten parameters quantifying the geometry of each of 1239 islands and the channels around them using machine learning. The method consists of a two-step unsupervised machine learning algorithm that clusters islands into spatially continuous zones based on the ten morphological metrics extracted from remotely sensed imagery. By applying this method to the Ganges–Brahmaputra–Meghna Delta, we find that the system can be divided into six major zones. Classification results show that active fluvial island construction and bar migration processes are limited to relatively narrow zones along the main Ganges River and Brahmaputra and Meghna corridors, whereas zones in the mature upper delta plain, with smaller fluvial distributary channels stand out as their own morphometric class. The classification also shows good correspondence with known gradients in the influence of tidal energy with distinct classes for islands in the backwater zone and in the purely tidally-controlled region of the delta. Islands at the delta front, under the mixed influence of tides, fluvial-estuarine construction, and local wave reworking have their own characteristic shape and channel configuration. The method does not distinguish between islands with embankments (polders) and natural islands in the nearby mangrove forest (Sundarbans), suggesting that human modifications have not yet altered the gross geometry of the islands beyond their previous natural morphology. These results demonstrate that machine learning and remotely sensed imagery are useful tools for identifying the spatial patterns of geomorphic processes across delta systems. 

Forest restoration occupies centre stage in global conversations about carbon removal and biodiversity conservation, but recent research rarely acknowledges social dimensions or environmental justice implications related to its implementation. We find that 294.5 million people live on tropical forest restoration opportunity land in the Global South, including 12% of the total population in low-income countries. Forest landscape restoration that prioritizes local communities by affording them rights to manage and restore forests provides a promising option to align global agendas for climate miti-gation, conservation, environmental justice and sustainable development. 

Numerical simulation of the form and characteristics of Earth’s surface provides insight into its evolution. Landlab is an Open Source Python package that contains modularized elements of numerical models for Earth’s surface, thus reducing time required for researchers to create new or reimplement existing models. Landlab contains a gridding engine which represents the model domain as a dual graph of structured quadrilaterals (e.g., raster) or irregular Voronoi polygon-Delaunay triangle mesh (e.g., regular hexagons, radially symmetric meshes, fully irregular meshes). Landlab also contains components— modular implementations of single physical processes—and a suite of utilities which support numerical methods, input/output, and visualization. This contribution describes package development since version 1.0 and backward-compatibility breaking changes which necessitates the new major release, version 2.0. Substantial changes include refactoring the grid, improving the component standard interface, dropping Python 2 support, and creating 30 new components—for a total of 57 components in the Landlab package. We describe reasons why many changes were made in order to provide insight to designers of future packages. We conclude by discussing lessons about the dynamics of scientific software development gained from the experience of using, developing, maintaining, and teaching with Landlab.