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Background: Pediatric gastroenteropancreatic neuroendocrine tumors are exceedingly rare, resulting in most pediatric treatment recommendations being based on data derived from adults. Trametinib is a kinase inhibitor that targets MEK1/2 and has been employed in the treatment of cancers harboring mutations in the Ras pathway. Methods: We utilized an established human pediatric gastroenteropancreatic neuroendocrine-like tumor patient-derived xenograft (PDX) with a known NRAS mutation to study the effects of MEK inhibition. We evaluated the effects of trametinib on proliferation, motility, and tumor growth in vivo. We created an intraperitoneal metastatic model of this PDX, characterized both the phenotype and the genotype of the metastatic PDX and again, investigated the effects of MEK inhibition. Results: We found target engagement with decreased ERK1/2 phosphorylation with trametinib treatment. Trametinib led to decreased in vitro cell growth and motility, and decreased tumor growth and increased animal survival in a murine flank tumor model. Finally, we demonstrated that trametinib was able to significantly decrease gastroenteropancreatic neuroendocrine intraperitoneal tumor metastasis. Conclusions: The results of these studies support the further investigation of MEK inhibition in pediatric NRAS mutated solid tumors.

The Bronze Age was a time of technological, socioeconomic, and political transformation in Europe. Since Bronze Age socioeconomic institutions were rooted in the landscape, they can be investigated using a settlement ecology approach to how people positioned themselves relative to the environment and each other. Transylvania is home to a rare combination of mineral resources, trade infrastructures, and productive agropastoral land, all of which were critical to Bronze Age societies. This study combines size-and rank-size analyses to suggest that there were several shifts in how people positioned themselves across settlements in Transylvania during the Bronze Age. This research contributes to a broader understanding of the factors that inform where people choose to settle down and the consequences those decisions have on the development of social, economic, and political institutions.

Local natural gas distribution companies (LDCs) require accurate demand forecasts across various time periods, geographic regions, and customer class hierarchies. Achieving coherent forecasts across these hierarchies is challenging but crucial for optimal decision making, resource allocation, and operational efficiency. This work introduces a method that structures the gas distribution system into cross-temporal hierarchies to produce accurate and coherent forecasts. We apply our method to a case study involving three operational regions, forecasting at different geographical levels and analyzing both hourly and daily frequencies. Trained on five years of data and tested on one year, our model achieves a 10% reduction in hourly mean absolute scaled error and a 3% reduction in daily mean absolute scaled error.

Increased environmental threats require proper monitoring of animal communities to understand where and when changes occur. Ecoacoustic tools that quantify natural acoustic environments use a combination of biophony (animal sound) and geophony (wind, rain, and other natural phenomena) to represent the natural soundscape and, in comparison to anthropophony (technological human sound) can highlight valuable landscapes to both human and animal communities. However, recording these sounds requires intensive deployment of recording devices and storage and interpretation of large amounts of data, resulting in large data gaps across the landscape and periods in which recordings are absent. Interpolating ecoacoustic metrics like biophony, geophony, anthropophony, and acoustic indices can bridge these gaps in observations and provide insight across larger spatial extents and during periods of interest. Here, we use seven ecoacoustic metrics and acoustically-derived bird species richness across a heterogeneous landscape composed of densely urbanized, suburban, rural, protected, and recently burned lands in Sonoma County, California, U.S.A., to explore spatiotemporal patterns in ecoacoustic measurements. Predictive models of ecoacoustic metrics driven by land-use/land-cover, remotely-sensed vegetation structure, anthropogenic impact, climate, geomorphology, and phenology variables capture landscape and daily differences in ecoacoustic patterns with varying performance (avg. R ^2 = 0.38 ± 0.11) depending on metric and period-of-day and provide interpretable patterns in sound related to human activity, weather phenomena, and animal activity. We also offer a case study on the use of the data-driven prediction of biophony to capture changes in soniferous species activity before (1–2 years prior) and after (1–2 years post) wildfires in our study area and find that biophony may depict the reorganization of acoustic communities following wildfires. This is demonstrated by an upward trend in activity 1–2 years post-wildfire, particularly in more severely burned areas. Overall, we provide evidence of the importance of climate, spaceborne-lidar-derived forest structure, and phenological time series characteristics when modeling ecoacoustic metrics to upscale site observations and map ecoacoustic biodiversity in areas without prior acoustic data collection. Resulting maps can identify areas of attention where changes in animal communities occur at the edge of human and natural disturbances.