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Biology subjects

Maureira, F.

Publications and source records attributed to Maureira, F..

2 recordsLinked to original sources

A Multi-Model Ensemble Reveals Soil Carbon Gains from Regenerative Practices in the U.S. Midwest Cropland

Process-based cropping systems models (CSMs) are key components of measurement, monitoring, reporting, and verification (MMRV) frameworks of carbon markets, but their application suffers from model-specific differences that keep any one model from working well across all combinations of soils, climates, crops, and agronomic practices at varying scales. Multi-model ensemble (MME), successfully used to quantify soil, management and climate impact on crop productivity, provide an opportunity to better estimate changes in soil organic carbon (SOC) outcomes for agronomic practices that have the potential to mitigate SOC loss at scale. We used an MME across 46 million hectares of US Midwest cropland at a resolution of 4- km2 to assess the aggregate ability of different regenerative practices to sequester SOC at this scale compared to their dynamic baselines. MME was validated with long-term experimental data and compared to its constituent CSMs, showing greater accuracy and lower uncertainty. The results show that adopting no-till combined with cover crops increased SOC stocks by 0.36 {+/-} 0.12 Mg ha-1 yr-1 aggregated across the entire U.S. Midwest cropland. At the regional scale, this corresponds to a net SOC gain of 16.4 Tg C yr-1 compared to business-as-usual baselines. These benefits are approximately halved when each management change is practiced individually, and the modest gains are only fully realized when continued over the long-term in soils with low initial carbon stock. Results demonstrate the power of MMEs run at high resolution for providing robust estimates of environmental outcomes following agricultural practice change, and for pinpointing locations for most effective intervention. This approach can alleviate many producer carbon market participation barriers and help address market issues while ultimately supporting large-scale regenerative agriculture initiatives.

ecology↗

SimpylCellCounter: An Automated Solution for Quantifying Cells in Brain Tissue

Rationale & ObjectiveManual quantification of activated cells can provide valuable information about stimuli-induced changes within brain regions; however, this analysis remains time intensive. Therefore, we created SimpylCellCounter (SCC), an automated method to quantify cells that express Cfos protein, an index of neuronal activity, in brain tissue and benchmarked it against two widely-used methods: OpenColonyFormingUnit (OCFU) and ImageJ Edge Detection Macro (IMJM). MethodsIn Experiment 1, manually-obtained counts were compared to those detected via OCFU, IMJM and SCC. The absolute error in counts (manual versus automated method) was calculated, and error types were categorized as false positives or negatives. In Experiment 2, performance analytics of OCFU, IMJM and SCC were compared. In Experiment 3, SCC performed analysis on images it was not trained on, to assess its general utility. Results & ConclusionsWe found SCC to be highly accurate and efficient in quantifying both cells with circular morphologies and those expressing Cfos. Additionally, SCC utilizes a new approach for counting overlapping cells with a pretrained convolutional neural network classifier. The current study demonstrates that SCC is a novel, automated tool to quantify cells in brain tissue, complementing current, open-sourced quantification methods designed to detect cells in vitro.

neuroscience↗