Overcoming intuition: Metacognitive difficulty activates analytic reasoning.

Humans appear to reason using two processing styles: System 1 processes that are quick, intuitive, and effortless and System 2 processes that are slow, analytical, and deliberate that occasionally correct the output of System 1. Four experiments suggest that System 2 processes are activated by metacognitive experiences of difficulty or disfluency during the process of reasoning. Incidental experiences of difficulty or disfluency--receiving information in a degraded font (Experiments 1 and 4), in difficult-to-read lettering (Experiment 2), or while furrowing one's brow (Experiment 3)--reduced the impact of heuristics and defaults in judgment (Experiments 1 and 3), reduced reliance on peripheral cues in persuasion (Experiment 2), and improved syllogistic reasoning (Experiment 4). Metacognitive experiences of difficulty or disfluency appear to serve as an alarm that activates analytic forms of reasoning that assess and sometimes correct the output of more intuitive forms of reasoning. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Long-term information collection with energy harvesting wireless sensors: a multi-armed bandit based approach

This paper reports on the development of a multi-agent approach to long-term information collection in networks of energy harvesting wireless sensors. In particular, we focus on developing energy management and data routing policies that adapt their behaviour according to the energy that is harvested, in order to maximise the amount of information collected given the available energy budget. In so doing, we introduce a new energy management technique, based on multi-armed bandit learning, that allows each agent to adaptively allocate its energy budget across the tasks of data sampling, receiving and transmitting. By using this approach, each agent can learn the optimal energy budget settings that give it efficient information collection in the long run. Then, we propose two novel decentralised multi-hop algorithms for data routing. The first proveably maximises the information throughput in the network, but can sometimes involve high communication cost. The second algorithm provides near-optimal performance, but with reduced computational and communication costs. Finally, we demonstrate that, by using our approaches for energy management and routing, we can achieve a 120% improvement in long-term information collection against state-of-the-art benchmarks.     

DDFSGEN

This paper presents a functional compiler for the automatic design of Direct Digital Frequency Synthesizer (DDFS) integrated circuits (ICs) using a ROM based table look-up architecture. The compiler allows the user to specify high-level specifications such as the acceptable spurious response and it generates the IC architecture, floorplan, and layout. To construct the layout for different specifications, a library of parameterized macrocells has been developed in 1.2 m CMOS technology.A test chip with a quadrature DDFS module has been generated, using the compiler, and fabricated. The chip has two input signals: one is for frequency control while the other is for phase initialization. Input and output word lengths are 16 bits and 6 bits respectively. The chip complexity is approximately 12,000 transistors (DDFS core) and the die size is 4.8×2.9mm ². A maximum sample rate of 80 MHz has been attained implying a maximum sine (cosine) output frequency of 40 MHz and a frequency resolution of 1.22 kHz. The maximum spurious level measured is –46 dB.     

The Interaction of Glymes with Surfactant Micelles

The critical micelle concentrations (CMC) values and counterion dissociation (α values) have been determined for a number of mixed micellar systems consisting of two typical ionic surfactants and glycol ethers (glymes) as cosurfactants, namely diethylene glycol dimethyl ether, diethylene glycol diethyl ether, triethylene glycol dimethyl ether, and tetraethylene glycol dimethyl ether. Conductance experiments were used to determine the CMC and α values of the mixed micelles as a function of glyme concentration in the aqueous mixed solvent. Favorable interactions between sodium dodecyl sulfate micelles and glyme cosurfactants were deduced from the decreases in the CMC values and the large increase in the α values of these systems as a function of increasing glyme concentration in the mixed solvents. In contrast to the anionic surfactant/glyme systems, in general, there appeared to be little favorable interactions between the surfactant and glymes when micelles of the cationic surfactant dodecyltrimethylammonium bromide were formed in water/glyme solvent systems containing an increasing amount of the glymes. The interaction of glymes with the surfactant micelles was examined closely via ¹³C nuclear magnetic resonance (NMR) chemical shifts for both surfactant and glyme carbons; these chemical shifts changes were interpreted in terms of the distribution and the localization of the glymes in the aggregates. Finally, partition constants, determined from two-dimensional diffusion-oriented spectroscopy (2D-DOSY) experiments, were used to calculate thermodynamic quantities of transfer of the glymes between the bulk phase and the self-assembled aggregates. All these results are interpreted in terms of the key contributions that both the glyme ethoxylated groups and alkyl endgroups make to the hydrophobic interactions.     

Analysis of Sequence Divergence in Mammalian ABCGs Predicts a Structural Network of Residues That Underlies Functional Divergence

The five members of the mammalian G subfamily of ATP-binding cassette transporters differ greatly in their substrate specificity. Four members of the subfamily are important in lipid transport and the wide substrate specificity of one of the members, ABCG2, is of significance due to its role in multidrug resistance. To explore the origin of substrate selectivity in members 1, 2, 4, 5 and 8 of this subfamily, we have analysed the differences in conservation between members in a multiple sequence alignment of ABCG sequences from mammals. Mapping sets of residues with similar patterns of conservation onto the resolved 3D structure of ABCG2 reveals possible explanations for differences in function, via a connected network of residues from the cytoplasmic to transmembrane domains. In ABCG2, this network of residues may confer extra conformational flexibility, enabling it to transport a wider array of substrates.     

The Role of Innate and Adaptive Immune Cells in Skeletal Muscle Regeneration

Skeletal muscle regeneration is highly dependent on the inflammatory response. A wide variety of innate and adaptive immune cells orchestrate the complex process of muscle repair. This review provides information about the various types of immune cells and biomolecules that have been shown to mediate muscle regeneration following injury and degenerative diseases. Recently developed cell and drug-based immunomodulatory strategies are highlighted. An improved understanding of the immune response to injured and diseased skeletal muscle will be essential for the development of therapeutic strategies.     

Mutant p53 Mediates Sensitivity to Cancer Treatment Agents in Oesophageal Adenocarcinoma Associated with MicroRNA and SLC7A11 Expression

TP53 gene mutations occur in 70% of oesophageal adenocarcinomas (OACs). Given the central role of p53 in controlling cellular response to therapy we investigated the role of mutant (mut-) p53 and SLC7A11 in a CRISPR-mediated JH-EsoAd1 TP53 knockout model. Response to 2 Gy irradiation, cisplatin, 5-FU, 4-hydroxytamoxifen, and endoxifen was assessed, followed by a TaqMan OpenArray qPCR screening for differences in miRNA expression. Knockout of mut-p53 resulted in increased chemo- and radioresistance (2 Gy survival fraction: 38% vs. 56%, p < 0.0001) and in altered miRNA expression levels. Target mRNA pathways analyses indicated several potential mechanisms of treatment resistance. SLC7A11 knockdown restored radiosensitivity (2 Gy SF: 46% vs. 73%; p = 0.0239), possibly via enhanced sensitivity to oxidative stress. Pathway analysis of the mRNA targets of differentially expressed miRNAs indicated potential involvement in several pathways associated with apoptosis, ribosomes, and p53 signaling pathways. The data suggest that mut-p53 in JH-EsoAd1, despite being classified as non-functional, has some function related to radio- and chemoresistance. The results also highlight the important role of SLC7A11 in cancer metabolism and redox balance and the influence of p53 on these processes. Inhibition of the SLC7A11-glutathione axis may represent a promising approach to overcome resistance associated with mut-p53.     

Micron-Scale Soft Actuators Fabricated from Multi-Shell Polystyrene Particle-Gold Nanoparticle Nanohybrids

Actuators made of soft matter are needed for a variety of fields ranging from biomedical devices to soft robotics to microelectromechanical systems. While there are a variety of excellent methods of soft actuation known, the field is still an area of intense research activity as new niches and needs emerge with new technology development. Here, a soft actuation system is described, based on a core-multi-shell particle, which moves via photothermal expansion. The system consists of a novel polystyrene-based thermally expandable microsphere, with a secondary shell of a silicate-silane graft copolymer, to which gold nanoparticles are covalently linked. The gold nanoparticles act as photothermal nano-transducers, converting light energy into the thermal energy necessary for microsphere expansion, which in turn results in material movement. Actuation is shown in isolated particles in thermal and photothermal regimes using metal ceramic heaters or 520 nm laser illumination, respectively. Macroscale actuation is demonstrated by making a composite material of particles suspended in the transparent elastomer polydimethylsiloxane. The sample demonstrates an inchworm-like movement by starting from an arched geometry. Overall, this work describes a new particle-based actuation method for soft materials, and demonstrates its utility in driving the movement of a composite elastomer.     

Predicting and interpreting identification errors in military vehicle training using multidimensional scaling

We compared methods for predicting and understanding the source of confusion errors during military vehicle identification training. Participants completed training to identify main battle tanks. They also completed card-sorting and similarity-rating tasks to express their mental representation of resemblance across the set of training items. We expected participants to selectively attend to a subset of vehicle features during these tasks, and we hypothesised that we could predict identification confusion errors based on the outcomes of the card-sort and similarity-rating tasks. Based on card-sorting results, we were able to predict about 45% of observed identification confusions. Based on multidimensional scaling of the similarity-rating data, we could predict more than 80% of identification confusions. These methods also enabled us to infer the dimensions receiving significant attention from each participant. This understanding of mental representation may be crucial in creating personalised training that directs attention to features that are critical for accurate identification.

Practitioner Summary: Participants completed military vehicle identification training and testing, along with card-sorting and similarity-rating tasks. The data enabled us to predict up to 84% of identification confusion errors and to understand the mental representation underlying these errors. These methods have potential to improve training and reduce identification errors leading to fratricide.     

Learning perceptually grounded word meanings from unaligned parallel data

In order for robots to effectively understand natural language commands, they must be able to acquire meaning representations that can be mapped to perceptual features in the external world. Previous approaches to learning these grounded meaning representations require detailed annotations at training time. In this paper, we present an approach to grounded language acquisition which is capable of jointly learning a policy for following natural language commands such as “Pick up the tire pallet,” as well as a mapping between specific phrases in the language and aspects of the external world; for example the mapping between the words “the tire pallet” and a specific object in the environment. Our approach assumes a parametric form for the policy that the robot uses to choose actions in response to a natural language command that factors based on the structure of the language. We use a gradient method to optimize model parameters. Our evaluation demonstrates the effectiveness of the model on a corpus of commands given to a robotic forklift by untrained users.