Publisher Correction: Runx2 stimulates neoangiogenesis through the Runt domain in melanoma.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Runx2 stimulates neoangiogenesis through the Runt domain in melanoma.

Runx2 is a transcription factor involved in melanoma cell migration and proliferation. Here, we extended the analysis of Runt domain of Runx2 in melanoma cells to deepen understanding of the underlying mechanisms. By the CRISPR/Cas9 system we generated the Runt KO melanoma cells 3G8. Interestingly, the proteome analysis showed a specific protein signature of 3G8 cells related to apoptosis and migration, and pointed out the involvement of Runt domain in the neoangiogenesis process. Among the proteins implicated in angiogenesis we identified fatty acid synthase, chloride intracellular channel protein-4, heat shock protein beta-1, Rho guanine nucleotide exchange factor 1, D-3-phosphoglycerate dehydrogenase, myosin-1c and caveolin-1. Upon querying the TCGA provisional database for melanoma, the genes related to these proteins were found altered in 51.36% of total patients. In addition, VEGF gene expression was reduced in 3G8 as compared to A375 cells; and HUVEC co-cultured with 3G8 cells expressed lower levels of CD105 and CD31 neoangiogenetic markers. Furthermore, the tube formation assay revealed down-regulation of capillary-like structures in HUVEC co-cultured with 3G8 in comparison to those with A375 cells. These findings provide new insight into Runx2 molecular details which can be crucial to possibly propose it as an oncotarget of melanoma.

Superstatistical distribution of daily precipitation extremes: A worldwide assessment.

Maximum annual daily precipitation is a fundamental hydrologic variable that does not attain asymptotic conditions. Thus the classical extreme value theory (i.e., the Fisher-Tippett's theorem) does not apply and the recurrent use of the Generalized Extreme Value distribution (GEV) to estimate precipitation quantiles for structural-design purposes could be inappropriate. In order to address this issue, we first determine the exact distribution of maximum annual daily precipitation starting from a Markov chain and in a closed analytical form under the hypothesis of stochastic independence. As a second step, we formulate a superstatistics conjecture of daily precipitation, meaning that we assume that the parameters of this exact distribution vary from a year to another according to probability distributions, which is supported by empirical evidence. We test this conjecture using the world GHCN database to perform a worldwide assessment of this superstatistical distribution of daily precipitation extremes. The performances of the superstatistical distribution and the GEV are tested against data using the Kolmogorov-Smirnov statistic. By considering the issue of model's extrapolation, that is, the evaluation of the estimated model against data not used in calibration, we show that the superstatistical distribution provides more robust estimations than the GEV, which tends to underestimate (7-13%) the quantile associated to the largest cumulative frequency. The superstatistical distribution, on the other hand, tends to overestimate (10-14%) this quantile, which is a safer option for hydraulic design. The parameters of the proposed superstatistical distribution are made available for all 20,561 worldwide sites considered in this work.

Real-Time Alpine Measurement System Using Wireless Sensor Networks.

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra's wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km 2 network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.