Influence of fluctuating supply on the emplacement dynamics of channelized lava flows

The evolution of lava flows emplaced on Mount Etna (Italy) in September 2004 is examined in detail through the analysis ofmorphometricmeasurements of flow units. The growth of the main channelized flow is consistent with a layering of lava blankets, which maintains the initial geometry of the channel (although levees are widened and raised), and is here explicitly related to the repeated overflow of lava pulses. A simple analytical model is introduced describing the evolution of the flow level in a channelized flow unit fed by a fluctuating supply. The model, named FLOWPULSE, shows that a fluctuation in the velocity of lava extrusion at the vent triggers the formation of pulses, which become increasingly high the farther they are from the vent, and are invariably destined to overflow within a given distance. The FLOWPULSE simulations are in accordance with the observed morphology, characterized by a very flat initial profile followed by a massive increase in flow unit cross-section area between 600 and 700 m downflow. The modeled emplacement dynamics provides also an explanation for the observed substantial “loss” of the original flowing mass with increasing distance from the vent

Unsupervised domain adaptation for mobile semantic segmentation based on cycle consistency and feature alignment

The supervised training of deep networks for semantic segmentation requires a huge amount of labeled real world data. To solve this issue, a commonly exploited workaround is to use synthetic data for training, but deep networks show a critical performance drop when analyzing data with slightly different statistical properties with respect to the training set. In this work, we propose a novel Unsupervised Domain Adaptation (UDA) strategy to address the domain shift issue between real world and synthetic representations. An adversarial model, based on the cycle consistency framework, performs the mapping between the synthetic and real domain. The data is then fed to a MobileNet-v2 architecture that performs the semantic segmentation task. An additional couple of discriminators, working at the feature level of the MobileNet-v2, allows to better align the features of the two domain distributions and to further improve the performance. Finally, the consistency of the semantic maps is exploited. After an initial supervised training on synthetic data, the whole UDA architecture is trained end-to-end considering all its components at once. Experimental results show how the proposed strategy is able to obtain impressive performance in adapting a segmentation network trained on synthetic data to real world scenarios. The usage of the lightweight MobileNet-v2 architecture allows its deployment on devices with limited computational resources as the ones employed in autonomous vehicles

Mutations in the met Oncogene Unveil a "Dual Switch" Mechanism Controlling Tyrosine Kinase Activity *

The met oncogene, encoding the high affinity hepatocyte growth factor receptor, is the only known gene inherited in human cancer that is invariably associated with somatic duplication of the mutant locus. Intriguingly, mutated Met requires ligand stimulation in order to unleash its transforming potential. Furthermore, individuals bearing a germ line met mutation develop cancer only late in life and with incomplete penetrance. To date, there is no molecular explanation for this unique behavior, which is unusual for a dominant oncogene. Here we investigate the molecular mechanisms underlying met oncogenic conversion by generating antibodies specific for the differently phosphorylated forms of the Met protein. Using these antibodies, we show that activation of wild-type Met is achieved through sequential phosphorylation of Tyr1235 and Tyr1234 in the activation loop and that mutagenesis of either tyrosine dramatically impairs kinase function. Surprisingly, oncogenic Met mutants never become phosphorylated on Tyr1234 despite their high enzymatic activity, and mutagenesis of Tyr1234 does not affect their biochemical or biological function. By analyzing the enzymatic properties of the mutant proteins in different conditions, we demonstrate that oncogenic mutations do not elicit constitutive kinase activation but simply overcome the requirement for the second phosphorylation step, thus reducing the threshold for activation. In the presence of activating signals, these mutations result therefore in a dynamic imbalance toward the active conformation of the kinase. This explains why mutant met provides an oncogenic predisposition but needs a second activating "hit," provided by sustained ligand stimulation or receptor overexpression, to achieve a fully transformed phenotype

Uncertainty quantification and sensitivity analysis of volcanic columns models: results from the integral model PLUME-MoM

The behavior of plumes associated with explosive volcanic eruptions is complex and dependent on eruptive source parameters (e.g. exit velocity, gas fraction, temperature and grain-size distribution). It is also well known that the atmospheric environment interacts with volcanic plumes produced by explosive eruptions in a number of ways. The wind field can bend the plume but also affect atmospheric air entrainment into the column, enhancing its buoyancy and in some cases, preventing column collapse. In recent years, several numerical simulation tools and observational systems have investigated the action of eruption parameters and wind field on volcanic column height and column trajectory, revealing an important influence of these variables on plume behavior. In this study, we assess these dependencies using the integral model PLUME-MoM, whereby the continuous polydispersity of pyroclastic particles is described using a quadrature-based moment method, an innovative approach in volcanology well-suited for the description of the multiphase nature of magmatic mixtures. Application of formalized uncertainty quantification and sensitivity analysis techniques enables statistical exploration of the model, providing information on the extent to which uncertainty in the input or model parameters propagates to model output uncertainty. In particular, in the framework of the IAVCEI Commission on tephra hazard modeling inter-comparison study, PLUME-MoM is used to investigate the parameters exerting a major control on plume height, applying it to a weak plume scenario based on 26 January 2011 Shinmoe-dake eruptive conditions and a strong plume scenario based on the climatic phase of the 15 June 1991 Pinatubo eruption

Insights into the formation and dynamics of coignimbrite plumes from one-dimensional models

Coignimbrite plumes provide a common and effective mechanism by which large volumes of fine-grained ash are injected into the atmosphere. Nevertheless, controls on formation of these plumes as a function of eruptive conditions are still poorly constrained. Herein, two 1-D axysymmetric steady state models were coupled, the first describing the parent pyroclastic density current and the second describing plume rise. Global sensitivity analysis is applied to investigate controls on coignimbrite plume formation and describe coignimbrite source and the maximum plume height attained. For a range of initial mass flow rates between 108 and 1010 kg/s, modeled liftoff distance (the distance at which neutral buoyancy is attained), assuming radial supercritical flow, is controlled by the initial flow radius, gas mass fraction, flow thickness, and temperature. The predicted decrease in median grain size between flow initiation and plume liftoff is negligible. Calculated initial plume vertical velocities, assuming uniform liftoff velocity over the pyroclastic density current invasion area, are much greater (several tens of m/s) than those previously used in modeling coignimbrite plumes (1 m/s). Such velocities are inconsistent with the fine grain size of particles lofted into coignimbrite plumes, highlighting an unavailability of large clasts, possibly due to particle segregation within the flow, prior to plume formation. Source radius and initial vertical velocity have the largest effect on maximum plume height, closely followed by initial temperature. Modeled plume heights are between 25 and 47 km, comparable with Plinian eruption columns, highlighting the potential of such events for distributing fine-grained ash over significant areas