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We describe the Sketch-and-Stitch method for bringing together a cognitive model and EEG to reconstruct the cognition of a subject. The method was tested in the context of a video game where the actions are highly interdependent and variable: simply changing whether a key was pressed or not for a 30th of a second can lead to a very different outcome. The Sketch level identifies the critical events in the game and the Stitch level fills in the detailed actions between these events. The critical events tend to produce robust EEG signals and the cognitive model provides probabilities of various transitions between critical events and the distribution of intervals between these events. This information can be combined in a hidden semi-Markov model that identifies the most probable sequence of critical events and when they happened. The Stitch level selects detailed actions from an extensive library of model games to produce these critical events. The decision about which sequence of actions to select from the library is made on the basis of how well they would produce weaker aspects of the EEG signal. The resulting approach can produce quite compelling replays of actual games from the EEG of a subject., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

A theory is presented about how instruction and experience combine to produce human fluency in a complex skill. The theory depends critically on 4 aspects of the ACT-R architecture. The first is the timing of various modules, particularly motor timing, which results in behavior that closely matches human behavior. The second is the ability to interpret declarative representations of instruction so that they lead to action. The third aspect concerns how practice converts this declarative knowledge into a procedural form so that appropriate actions can be quickly executed. The fourth component, newly added to the architecture, is a Controller module that learns the setting of control variables for actions. The overall theory is implemented in a computational model that is capable of simulating human learning. Its predictions are confirmed in a first experiment involving 2 games derived from the experimental video game Space Fortress. The second experiment tests predictions from the Controller module about lack of transfer between video games. Across the 2 experiments a single model, with the same parameter settings, is shown to simulate human learning of 3 video games. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Background: The fungal genus Aspergillus is of critical importance to humankind. Species include those with industrial applications, important pathogens of humans, animals and crops, a source of potent carcinogenic contaminants of food, and an important genetic model. The genome sequences of eight aspergilli have already been explored to investigate aspects of fungal biology, raising questions about evolution and specialization within this genus., Results: We have generated genome sequences for ten novel, highly diverse Aspergillus species and compared these in detail to sister and more distant genera. Comparative studies of key aspects of fungal biology, including primary and secondary metabolism, stress response, biomass degradation, and signal transduction, revealed both conservation and diversity among the species. Observed genomic differences were validated with experimental studies. This revealed several highlights, such as the potential for sex in asexual species, organic acid production genes being a key feature of black aspergilli, alternative approaches for degrading plant biomass, and indications for the genetic basis of stress response. A genome-wide phylogenetic analysis demonstrated in detail the relationship of the newly genome sequenced species with other aspergilli., Conclusions: Many aspects of biological differences between fungal species cannot be explained by current knowledge obtained from genome sequences. The comparative genomics and experimental study, presented here, allows for the first time a genus-wide view of the biological diversity of the aspergilli and in many, but not all, cases linked genome differences to phenotype. Insights gained could be exploited for biotechnological and medical applications of fungi.

Process models of cognition, written in architectures such as ACT-R and EPIC, should be able to interact with the same software with which human subjects interact. By eliminating the need to simulate the experiment, this approach would simplify the modeler's effort, while ensuring that all steps required of the human are also required by the model. In practice, the difficulties of allowing one software system to interact with another present a significant barrier to any modeler who is not also skilled at this type of programming. The barrier increases if the programming language used by the modeling software differs from that used by the experimental software. The JSON Network Interface simplifies this problem for ACT-R modelers, and potentially, modelers using other systems.

Most of the current EEG-based workload classifiers are subject-specific; that is, a new classifier is built and trained for each human subject. In this paper we introduce a cross-subject workload classifier based on a hierarchical Bayes model. The cross-subject classifier is trained and tested with data from a group of subjects. In our work, it was trained and tested on EEG data collected from 8 subjects as they performed the Multi-Attribute Task Battery across three levels of difficulty. The accuracy of this cross-subject classifier is stable across the three levels of workload and comparable to a benchmark subject-specific neural network classifier., (Copyright © 2011 Elsevier Inc. All rights reserved.)

EEG data has been used to discriminate levels of mental workload when classifiers are created for each subject, but the reliability of classifiers trained on multiple subjects has yet to be investigated. Artificial neural network and naive Bayesian classifiers were trained with data from single and multiple subjects and their ability to discriminate among three difficulty conditions was tested. When trained on data from multiple subjects, both types of classifiers poorly discriminated between the three levels. However, a novel model, the naive Bayesian classifier with a hidden node, performed nearly as well as the models trained and tested on individuals. In addition, a hierarchical Bayes model with a higher level constraint on the hidden node can further improve its performance.

Helicobacter pylori is a gram-negative pathogenic microaerophile with a particular tropism for the mucosal surface of the gastric epithelium. Despite its obligatory microaerophilic character, it can metabolize D-glucose and/or D-galactose in both oxidative and fermentative pathways via a Na(+)-dependent secondary active transport, a glucokinase and enzymes of the pentose phosphate pathway. We have assigned the Na(+)-dependent transport of glucose to the protein product of the H. pylori 1174 gene. The gene was heterologously expressed in a glucose transport-deficient Escherichia coli strain, where transport activities of radiolabelled D-glucose, D-galactose and 2-deoxy-D-glucose were restored, consistent with the expected specificity of the hexose uptake system in H. pylori. D-mannose was also identified as a substrate. The HP1174 transport protein was purified and reconstituted into proteoliposomes, where sodium dependence of sugar transport activity was demonstrated. Additionally the tryptophan/tyrosine fluorescence of the purified protein showed quenching by 2-deoxy-D-glucose, D-mannose, D-glucose or D-galactose in the presence of sodium ions. This is the first reported purification and characterization of an active glucose transport protein member of the TC 2.1.7 subgroup of the Major Facilitator Superfamily, constituting the route for entry of sugar nutrients into H. pylori. A model is derived of its three-dimensional structure as a paradigm of the family.

The nucleobase-cation-symport-1 (NCS1) transporters are essential components of salvage pathways for nucleobases and related metabolites. Here, we report the 2.85-angstrom resolution structure of the NCS1 benzyl-hydantoin transporter, Mhp1, from Microbacterium liquefaciens. Mhp1 contains 12 transmembrane helices, 10 of which are arranged in two inverted repeats of five helices. The structures of the outward-facing open and substrate-bound occluded conformations were solved, showing how the outward-facing cavity closes upon binding of substrate. Comparisons with the leucine transporter LeuT(Aa) and the galactose transporter vSGLT reveal that the outward- and inward-facing cavities are symmetrically arranged on opposite sides of the membrane. The reciprocal opening and closing of these cavities is synchronized by the inverted repeat helices 3 and 8, providing the structural basis of the alternating access model for membrane transport.

A genomic strategy for the overexpression of bacterial multidrug and antibiotic resistance membrane efflux proteins in Escherichia coli is described. Expression is amplified so that the encoded proteins from a range of Gram-positive and Gram-negative bacteria comprise 5% to 35% of E. coli inner membrane protein. Depending upon their topology, proteins are produced with RGS(His)(6)-tag or a Strep-tag at the C terminus. These tags facilitate the purification of the overexpressed proteins in milligram quantities for structural studies. The strategy is illustrated for the bicyclomycin resistance efflux protein, Bcr, of E. coli.