Fundamental questions relating to ion conduction in disordered solids

A number of basic scientific questions relating to ion conduction in homogeneously disordered solids are discussed. The questions deal with how to define the mobile ion density, what can be learned from electrode effects, what is the ion transport mechanism, the role of dimensionality, and what are the origins of the mixed-alkali effect, of time-temperature superposition, and of the nearly-constant loss. Answers are suggested to some of these questions, but the main purpose of the paper is to draw attention to the fact that this field of research still presents several fundamental challenges.Comment: Reports on Progress in Physics, to appea

Estrogen treatment following severe burn injury reduces brain inflammation and apoptotic signaling

<p>Abstract</p> <p>Background</p> <p>Patients with severe burn injury experience a rapid elevation in multiple circulating pro-inflammatory cytokines, with the levels correlating with both injury severity and outcome. Accumulations of these cytokines in animal models have been observed in remote organs, however data are lacking regarding early brain cytokine levels following burn injury, and the effects of estradiol on these levels. Using an experimental animal model, we studied the acute effects of a full-thickness third degree burn on brain levels of TNF-α, IL-1β, and IL-6 and the protective effects of acute estrogen treatment on these levels. Additionally, the acute administration of estrogen on regulation of inflammatory and apoptotic events in the brain following severe burn injury were studied through measuring the levels of phospho-ERK, phospho-Akt, active caspase-3, and PARP cleavage in the placebo and estrogen treated groups.</p> <p>Methods</p> <p>In this study, 149 adult Sprague-Dawley male rats received 3rd degree 40% total body surface area (TBSA) burns. Fifteen minutes following burn injury, the animals received a subcutaneous injection of either placebo (n = 72) or 17 beta-estradiol (n = 72). Brains were harvested at 0.5, 1, 2, 4, 6, 8, 12, 18, and 24 hours after injury from the control (n = 5), placebo (n = 8/time point), and estrogen treated animals (n = 8/time point). The brain cytokine levels were measured using the ELISA method. In addition, we assessed the levels of phosphorylated-ERK, phosphorylated-Akt, active caspase-3, and the levels of cleaved PARP at the 24 hour time-point using Western blot analysis.</p> <p>Results</p> <p>In burned rats, 17 beta-estradiol significantly decreased the levels of brain tissue TNF-α (~25%), IL-1β (~60%), and IL-6 (~90%) when compared to the placebo group. In addition, we determined that in the estrogen-treated rats there was an increase in the levels of phospho-ERK (<it>p </it>< 0.01) and Akt (<it>p </it>< 0.05) at the 24 hour time-point, and that 17 beta-estradiol blocked the activation of caspase-3 (<it>p </it>< 0.01) and subsequent cleavage of PARP (<it>p </it>< 0.05).</p> <p>Conclusion</p> <p>Following severe burn injury, estrogens decrease both brain inflammation and the activation of apoptosis, represented by an increase in the levels of phospho-Akt and inhibition of caspase-3 activation and PARP cleavage. Results from these studies will help further our understanding of how estrogens protect the brain following burn injury, and may provide a novel, safe, and effective clinical treatment to combat remote secondary burn injury in the brain and to preserve cognition.</p

Transferring Learning from External to Internal Weights in Echo-State Networks with Sparse Connectivity

Modifying weights within a recurrent network to improve performance on a task has proven to be difficult. Echo-state networks in which modification is restricted to the weights of connections onto network outputs provide an easier alternative, but at the expense of modifying the typically sparse architecture of the network by including feedback from the output back into the network. We derive methods for using the values of the output weights from a trained echo-state network to set recurrent weights within the network. The result of this “transfer of learning” is a recurrent network that performs the task without requiring the output feedback present in the original network. We also discuss a hybrid version in which online learning is applied to both output and recurrent weights. Both approaches provide efficient ways of training recurrent networks to perform complex tasks. Through an analysis of the conditions required to make transfer of learning work, we define the concept of a “self-sensing” network state, and we compare and contrast this with compressed sensing

Interconnected Microphysiological Systems for Quantitative Biology and Pharmacology Studies

Microphysiological systems (MPSs) are in vitro models that capture facets of in vivo organ function through use of specialized culture microenvironments, including 3D matrices and microperfusion. Here, we report an approach to co-culture multiple different MPSs linked together physiologically on re-useable, open-system microfluidic platforms that are compatible with the quantitative study of a range of compounds, including lipophilic drugs. We describe three different platform designs - "4-way", "7-way", and "10-way" - each accommodating a mixing chamber and up to 4, 7, or 10 MPSs. Platforms accommodate multiple different MPS flow configurations, each with internal re-circulation to enhance molecular exchange, and feature on-board pneumatically-driven pumps with independently programmable flow rates to provide precise control over both intra- and inter-MPS flow partitioning and drug distribution. We first developed a 4-MPS system, showing accurate prediction of secreted liver protein distribution and 2-week maintenance of phenotypic markers. We then developed 7-MPS and 10-MPS platforms, demonstrating reliable, robust operation and maintenance of MPS phenotypic function for 3 weeks (7-way) and 4 weeks (10-way) of continuous interaction, as well as PK analysis of diclofenac metabolism. This study illustrates several generalizable design and operational principles for implementing multi-MPS "physiome-on-a-chip" approaches in drug discovery.United States. Army Research Office (Grant W911NF-12-2-0039

A Cognitive Architecture Based on a Learning Classifier System with Spiking Classifiers

© 2015, Springer Science+Business Media New York. Learning classifier systems (LCS) are population-based reinforcement learners that were originally designed to model various cognitive phenomena. This paper presents an explicitly cognitive LCS by using spiking neural networks as classifiers, providing each classifier with a measure of temporal dynamism. We employ a constructivist model of growth of both neurons and synaptic connections, which permits a genetic algorithm to automatically evolve sufficiently-complex neural structures. The spiking classifiers are coupled with a temporally-sensitive reinforcement learning algorithm, which allows the system to perform temporal state decomposition by appropriately rewarding “macro-actions”, created by chaining together multiple atomic actions. The combination of temporal reinforcement learning and neural information processing is shown to outperform benchmark neural classifier systems, and successfully solve a robotic navigation task

Enabling large-scale design, synthesis and validation of small molecule protein-protein antagonists

Although there is no shortage of potential drug targets, there are only a handful known low-molecular-weight inhibitors of protein-protein interactions (PPIs). One problem is that current efforts are dominated by low-yield high-throughput screening, whose rigid framework is not suitable for the diverse chemotypes present in PPIs. Here, we developed a novel pharmacophore-based interactive screening technology that builds on the role anchor residues, or deeply buried hot spots, have in PPIs, and redesigns these entry points with anchor-biased virtual multicomponent reactions, delivering tens of millions of readily synthesizable novel compounds. Application of this approach to the MDM2/p53 cancer target led to high hit rates, resulting in a large and diverse set of confirmed inhibitors, and co-crystal structures validate the designed compounds. Our unique open-access technology promises to expand chemical space and the exploration of the human interactome by leveraging in-house small-scale assays and user-friendly chemistry to rationally design ligands for PPIs with known structure. © 2012 Koes et al

Rac1 Regulates the NLRP3 Inflammasome Which Mediates IL-1beta Production in Chlamydophila pneumoniae Infected Human Mononuclear Cells

Chlamydophila pneumoniae causes acute respiratory tract infections and has been associated with development of asthma and atherosclerosis. The production of IL-1β, a key mediator of acute and chronic inflammation, is regulated on a transcriptional level and additionally on a posttranslational level by inflammasomes. In the present study we show that C. pneumoniae-infected human mononuclear cells produce IL-1β protein depending on an inflammasome consisting of NLRP3, the adapter protein ASC and caspase-1. We further found that the small GTPase Rac1 is activated in C. pneumoniae-infected cells. Importantly, studies with specific inhibitors as well as siRNA show that Rac1 regulates inflammasome activation in C. pneumoniae-infected cells. In conclusion, C. pneumoniae infection of mononuclear cells stimulates IL-1β production dependent on a NLRP3 inflammasome-mediated processing of proIL-1β which is controlled by Rac1

Population genomics applications for conservation: the case of the tropical dry forest dweller Peromyscus melanophrys

Recent advances in genomic sequencing have opened new horizons in the study of population genetics and evolution in non-model organisms. However, very few population genomic studies have been performed on wild mammals to understand how the landscape affects the genetic structure of populations, useful information for the conservation of biodiversity. Here, we applied a genomic approach to evaluate the relationship between habitat features and genetic patterns at spatial and temporal scales in an endangered ecosystem, the Tropical Dry Forest (TDF). We studied populations of the Plateau deer mouse Peromyscus melanophrys to analyse its genomic diversity and structure in a TDF protected area in the Huautla Mountain Range (HMR), Mexico based on 8,209 SNPs obtained through Genotyping-by-Sequencing. At a spatial scale, we found a significant signature of isolation-by-distance, few significant differences in genetic diversity indices among study sites, and no significant differences between habitats with different levels of human perturbation. At a temporal scale, while genetic diversity levels fluctuated significantly over time, neither seasonality nor disturbance levels had a significant effect. Also, outlier analysis revealed loci potentially under selection. Our results suggest that the population genetics of P. melanophrys may be little impacted by anthropogenic disturbances, or by natural spatial and temporal habitat heterogeneity in our study area. The genome-wide approach adopted here provides data of value for conservation planning, and a baseline to be used as a reference for future studies on the effects of habitat fragmentation and seasonality in the HMR and in TDF