Balancing Selection Pressures, Multiple Objectives, and Neural Modularity to Coevolve Cooperative Agent Behavior

Previous research using evolutionary computation in Multi-Agent Systems indicates that assigning fitness based on team vs.\ individual behavior has a strong impact on the ability of evolved teams of artificial agents to exhibit teamwork in challenging tasks. However, such research only made use of single-objective evolution. In contrast, when a multiobjective evolutionary algorithm is used, populations can be subject to individual-level objectives, team-level objectives, or combinations of the two. This paper explores the performance of cooperatively coevolved teams of agents controlled by artificial neural networks subject to these types of objectives. Specifically, predator agents are evolved to capture scripted prey agents in a torus-shaped grid world. Because of the tension between individual and team behaviors, multiple modes of behavior can be useful, and thus the effect of modular neural networks is also explored. Results demonstrate that fitness rewarding individual behavior is superior to fitness rewarding team behavior, despite being applied to a cooperative task. However, the use of networks with multiple modules allows predators to discover intelligent behavior, regardless of which type of objectives are used

“Expanding our pedagogical imagination”: Faculty Experiences with Scholarly Digital Storytelling across Disciplines

Despite growing pressure to increase technology-enhanced assessment, research has repeatedly demonstrated a lack of widespread meaningful integration of technology in higher education. This is in part due to faculty reluctance which is compounded by lack of training and incentives. This qualitative research grounded in the scholarship of teaching and learning (SoTL) is based on in-depth interviews with 25 faculty internationally, across disciplines and teaching contexts, who integrated scholarly digital storytelling into their pedagogy. It examines their motivations for adopting this technology-enhanced assessment and their experiences implementing it, including increased engagement and student learning. It further explores challenges faced and lessons learned that may prove valuable in supporting the expansion of technology-enhanced assessment across disciplines

Pathways Involved in Recognition and Induction of Trained Innate Immunity by Plasmodium falciparum

Malarial infection in naïve individuals induces a robust innate immune response, but our understanding of the mechanisms by which the innate immune system recognizes malaria and regulates its response remain incomplete. Our group previously showed that stimulation of macrophages with Plasmodium falciparum genomic DNA (gDNA) and AT-rich oligodeoxynucleotides (ODNs) derived from this gDNA induces the production of type I interferons (IFN-I) through a STING/TBK1/IRF3-dependent pathway; however, the identity of the upstream cytosolic DNA receptor remained elusive. Here, we demonstrate that this IFN-I response is dependent on cyclic GMP-AMP synthase (cGAS). cGAS produced the cyclic dinucleotide 2’3’-cGAMP in response to P. falciparum gDNA and AT-rich ODNs, inducing IRF3 phosphorylation and IFNB transcription. In the recently described model of innate immune memory, an initial stimulus primes the innate immune system to either hyperrespond (termed “training”) or hyporespond (“tolerance”) to subsequent immune challenge. Previous work in mice and humans demonstrated that infection with malaria can both serve as a priming stimulus and promote tolerance to subsequent infection. In this study, we demonstrate that initial stimulation with P. falciparum-infected red blood cells (iRBCs) or the malaria crystal hemozoin (Hz) induced human adherent peripheral blood mononuclear cells (PBMCs) to hyperrespond to subsequent Toll-like receptor (TLR) challenge. This hyperresponsiveness correlated with increased H3K4me3 at important immunometabolic promoters, and these epigenetic modifications were also seen in monocytes from Kenyan children naturally infected with malaria. However, the use of epigenetic and metabolic inhibitors indicated that malaria-induced trained immunity may occur via previously unrecognized mechanism(s)

Impact of marine mercury cycling on coastal atmospheric mercury concentrations in the North- and Baltic Sea region

The cycling of mercury between ocean and atmosphere is an important part of the global Hg cycle. Here we study the regional contribution of the air-sea exchange in the North- and Baltic Sea region. We use a newly developed coupled regional chemistry transport modeling (CTM) system to determine the flux between atmosphere and ocean based on the meteorological model COSMO-CLM, the ocean-ecosystem model ECOSMO, the atmospheric CTM CMAQ and a newly developed module for mercury partitioning and speciation in the ocean (MECOSMO). The model was evaluated using atmospheric observations of gaseous elemental mercury (GEM), surface concentrations of dissolved gaseous mercury (DGM), and air-sea flux (ASF) calculations based on observations made on seven cruises in the western and central Baltic Sea and three cruises in the North Sea performed between 1991 and 2006. It was shown that the model is in good agreement with observations: DGM (Normalized Mean Bias NMB=-0.27 N=413), ASF (NMB=-0.32, N=413), GEM (NMB=0.07, N=2359). Generally, the model was able to reproduce the seasonal DGM cycle with the best agreement during winter and autumn (NMBWinter=-0.26, NMBSpring=-0.41, NMBSummer=-0.29, NMBAutumn=-0.03). The modelled mercury evasion from the Baltic Sea ranged from 3400 to 4000 kg/a for the simulation period 1994–2007 which is on the lower end of previous estimates. Modelled atmospheric deposition, river inflow and air-sea exchange lead to an annual net Hg accumulation in the Baltic Sea of 500 to 1000 kg/a. For the North Sea the model calculates an annual mercury flux into the atmosphere between 5700 and 6000 kg/a. The mercury flux from the ocean influenced coastal atmospheric mercury concentrations. Running CMAQ coupled with the ocean model lead to better agreement with GEM observations. Directly at the coast GEM concentrations could be increased by up to 10% on annual average and observed peaks could be reproduced much better. At stations 100km downwind the impact was still observable but reduced to 1–3%