Salt wedges and trapped brines of low-latitude endoreic saline lakes as potential modulators of GHG emission.

Large salt lakes are long-term witnesses to climatic conditions and land use in their basins. The majority are experiencing a drastic drop in water levels due to climate change and human impact. Endoreic Lake Urmia (NW Iran), the sixth largest salt lake worldwide, is a striking example of this decline. Quantification of the relative contributions of natural variability and human impact on the lake's water supply is therefore essential. Here we present isotopic and radiocarbon analyses of surface and groundwater from the Shahr Chay River catchment, entering Lake Urmia on its western shore, and radiocarbon dating of a sedimentary core. Lake Urmia behaves like a large saltwater wedge almost entirely fed by the river and shallow groundwater. This leads to trapping of residual brines and formation of CH4 and secondary CO2 greenhouse gases, impacting sediment geochemical records and corresponding time scales for paleoenvironmental reconstructions. We conclude that (1) salt lakes functioning like a saline wedge, allowing organic matter oxidation, could contribute to increasing methane sources or reducing carbon sinks globally, and (2) endoreic basins worldwide need to be monitored before aridification-related salinization leads to the establishment of a saline wedge precluding any possibility of return to an equilibrium state.

Artificial intelligence workflow quantifying muscle features on Hematoxylin-Eosin stained sections reveals dystrophic phenotype amelioration upon treatment.

Cell segmentation is a key step for a wide variety of biological investigations, especially in the context of muscle science. Currently, automated methods still struggle to perform skeletal muscle fiber quantification on Hematoxylin-Eosin (HE) stained histopathological whole slide images due to low contrast. On the other hand, the Deep Learning algorithm Cellpose offers new perspectives considering its increasing adoption for segmentation of a wide range of cells. Combining two open-source tools, Cellpose and QuPath, we developed MyoSOTHES, an automated Myofibers Segmentation wOrkflow Tuned for HE Staining. MyoSOTHES enables solving segmentation inconsistencies encountered by default Cellpose model in presence of large range size cells and provides information related to muscle Feret's diameter distribution and Centrally Nucleated Fibers, thus depicting muscle health and treatment effects. MyoSOTHES achieves high quality segmentation compared to baseline workflow with a detection F1-score increasing from 0.801 to 0.919 and a Root Mean Square Error (RMSE) on diameter improved by 31%. MyoSOTHES was validated on an animal study featuring gene transfer in [Formula: see text]-Sarcoglycanopathy, for which dose-response effect is visible and conclusions drawn are consistent with those previously published. MyoSOTHES thus paves the way for wide quantification of HE stained muscle sections and retrospective analysis of HE labeled slices used in laboratories for decades.