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River relocation, or avulsion, is a fundamental process that fills alluvial basins. However, it is difficult to predict avulsion patterns because the landscape conditions associated with different avulsion styles are currently unknown. Two end-member avulsion styles have been documented in modern rivers: progradational avulsions, during which significant floodplain deposition occurs as a new channel is built, and incisional avulsions, during which floodplain erosion captures flow from a parent channel. Here we propose that avulsion style is related to the tendency for overbank flows to erode or deposit sediment (i.e., floodplain morphodynamics). We present a scaling comparison of floodplain erosion and deposition rates, test it with morphodynamic modeling, and show field data from ancient deposits that are consistent with modeling results. These results demonstrate that floodplain morphodynamics may determine how rivers relocate and that assessing the relative influence of floodplain erosion and deposition may be useful for predicting sedimentation patterns in avulsive systems. [ABSTRACT FROM AUTHOR]

Colliguaya odorifera Mol. is the only species of the Chilean matorral which shows an active dispersion of its seeds (autochory). This mechanism is dependent on daily variation of temperatures. Other climatic variables (air humidity of vapor pressure deficit) appear as not having direct influences on seed dispersal. [ABSTRACT FROM AUTHOR]

Recurrently connected networks of spiking neurons underlie the astounding information processing capabilities of the brain. Yet in spite of extensive research, how they can learn through synaptic plasticity to carry out complex network computations remains unclear. We argue that two pieces of this puzzle were provided by experimental data from neuroscience. A mathematical result tells us how these pieces need to be combined to enable biologically plausible online network learning through gradient descent, in particular deep reinforcement learning. This learning method-called e-prop-approaches the performance of backpropagation through time (BPTT), the best-known method for training recurrent neural networks in machine learning. In addition, it suggests a method for powerful on-chip learning in energy-efficient spike-based hardware for artificial intelligence.

Melanoma is the most dangerous form of skin cancer with a growing incidence over the last decades. Fourty percent of all melanomas harbor a mutation in the signaling adaptor BRAF (V600E) that results in ERK hyperactivity as an oncogenic driver. In these cases, treatment with the BRAF V600E inhibitors Vemurafenib (VEM) or Dabrafenib (DAB) coapplied with the MEK1/2 inhibitors Cobimetinib (COB) or Trametinib (TRA) can result in long-term suppression of tumor growth. Besides direct suppression of ERK activity, these inhibitors have been reported to also modulate tumor immune responses, and exert pro-inflammatory side effects such as fever and rash in some patients. Here we asked for potential effects of BRAF V600E inhibitors on dendritic cells (DC) which are essential for the induction of adaptive anti-tumor responses. Both splenic and bone marrow-derived (BM) mouse dendritic cells (DC) up-regulated costimulator expression (CD80, CD86) in response to DAB but not VEM treatment. Moreover, DAB and to lesser extent VEM enhanced IL-1β (interleukin 1 beta) release by splenic DC, and by LPS-stimulated BMDC. We demonstrate that DAB and VEM activated the NLRC4/Caspase-1 inflammasome. At high concentration, DAB also induced inflammasome activation independent of Caspase-1. TRA and COB elevated MHCII expression on BMDC, and modulated the LPS-induced cytokine pattern. Immunomodulatory activity of DAB and VEM was also observed in human monocyte-derived DC, and DAB induced IL-1β in human primary DC. Altogether, our study shows that BRAF V600E inhibitors upregulate IL-1β release by mouse and human DC which may affect the DC-mediated course of anti-tumor immune responses., Competing Interests: CONFLICTS OF INTEREST The authors have no conflicts of interest to disclose.