As adults, we represent and think about number, space, and time in at least two ways: our intuitive-but imprecise-perceptual representations, and the slowly learned-but precise-number words. With development, these representational formats interface, allowing us to use precise number words to estimate imprecise perceptual experiences. We test two accounts of this developmental milestone. Either slowly learned associations are required for the interface to form, predicting that deviations from typical experiences (e.g., presentation of a novel unit or unpracticed dimension) will disrupt children\'s ability to map number words to their perceptual experiences or children\'s understanding of the logical similarity between number words and perceptual representations allows them to flexibly extend this interface to novel experiences (e.g., units and dimensions they have not yet learned how to formally measure). 5-11-year-olds completed verbal estimation and perceptual sensitivity tasks across three dimensions: Number, Length, and Area. For verbal estimation, they were given novel units (i.e., a three-dot unit called one \"toma\" for Number, a 44 px long line called one \"blicket\" for Length, a 111 px2 blob called one \"modi\" for Area) and asked to estimate how many tomas/blickets/modies they saw when shown a larger set of dots, lines, and blobs. Children could flexibly link number words to novel units across dimensions, demonstrating positive estimation slopes, even for Length and Area, which younger children had limited experience with. This suggests that the logic of structure mapping can be dynamically utilized across perceptual dimensions, even without extensive experience.

Low-abundance short-lived non-native conformations referred to as excited states (ESs) are increasingly observed in vitro and implicated in the folding and biological activities of regulatory RNAs. We developed an approach for assessing the relative abundance of RNA ESs within the functional cellular context. Nuclear magnetic resonance (NMR) spectroscopy was used to estimate the degree to which substitution mutations bias conformational equilibria toward the inactive ES in vitro. The cellular activity of the ES-stabilizing mutants was used as an indirect measure of the conformational equilibria within the functional cellular context. Compensatory mutations that restore the ground-state conformation were used to control for changes in sequence. Using this approach, we show that the ESs of two regulatory RNAs from HIV-1, the transactivation response element (TAR) and the Rev response element (RRE), likely form in cells with abundances comparable to those measured in vitro, and their targeted stabilization may provide an avenue for developing anti-HIV therapeutics.

Creative thought is conventionally believed to involve searching memory and generating multiple independent candidate ideas followed by selection and refinement of the most promising. Honing theory, which grew out of the quantum approach to describing how concepts interact, posits that what appears to be discrete, separate ideas are actually different projections of the same underlying mental representation, which can be described as a superposition state, and which may take different outward forms when reflected upon from different perspectives. As creative thought proceeds, this representation loses potentiality to be viewed from different perspectives and manifest as different outcomes. Honing theory yields different predictions from conventional theories about the mental representation of an idea midway through the creative process. These predictions were pitted against one another in two studies: one closed-ended and one open-ended. In the first study, participants were interrupted midway through solving an analogy problem and wrote down what they were thinking in terms of a solution. In the second, participants were instructed to create a painting that expressed their true essence and describe how they conceived of the painting. For both studies, naïve judges categorized these responses as supportive of either the conventional view or the honing theory view. The results of both studies were significantly more consistent with the predictions of honing theory. Some implications for creative cognition, and cognition in general, are discussed.

Current healthcare services promise improved life-quality and care. Nevertheless, most of these entities operate independently due to the ingested data\' diversity, volume, and distribution, maximizing the challenge of data processing and exchange. Multi-site clinical healthcare organizations today, request for healthcare data to be transformed into a common format and through standardized terminologies to enable data exchange. Consequently, interoperability constraints highlight the need of a holistic solution, as current techniques are tailored to specific scenarios, without meeting the corresponding standards\' requirements. This manuscript focuses on a data transformation mechanism that can take full advantage of a data intensive environment without losing the realistic complexity of health, confronting the challenges of heterogeneous data. The developed mechanism involves running ontology alignment and transformation operations in healthcare datasets, stored into a triple-based data store, and restructuring it according to specified criteria, discovering the correspondence and possible transformations between the ingested data and specific Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) through semantic and ontology alignment techniques. The evaluation of this mechanism results into the fact that it should be used in scenarios where real-time healthcare data streams emerge, and thus their exploitation is critical in real-time, since it performs better and more efficient in comparison with a different data transformation mechanism.

Analogical reasoning was investigated among children with autism spectrum disorders (ASD) without intellectual disabilities and typical development (TD). Children were asked to select one of two targets in two conditions: (1) with and without spatial structure similarity; (2) with and without a perceptual distractor. Results demonstrate that children with ASD were able to select targets based on structural similarity, but this ability decreased to chance level when presented with a perceptual distractor. Everyday executive functions were positively correlated with structural selections among children with ASD. Results suggest that although children with ASD were able to select based on systematicity principle, perceptual distractor decreased their selection so that their cognitive system produced less structure similarities, that negatively affects spatial analogical reasoning.

While extremes tend to capture attention, the ordinary is often most of the story. So it may be with the structure-mapping process. The structure-mapping process can account for such pinnacles of thinking as analogy and metaphor, which can lead to overlooking the mundane, incremental use of structure mapping. Consequently, the current discussion shifts focus to the value of close comparisons between literally similar items for the development of knowledge. The intent is to foster greater integration between process and content as well as between individuals and collectives. The payoff is identifying some undue simplifications and some promising new directions.

Two decades of research into the ligand-dependent modulation of the activity of the peroxisome proliferator-activated receptor γ (PPARγ) have demonstrated the heterogeneous modes of action of PPARγ ligands, in terms of their interaction surfaces in the ligand-binding pocket, binding stoichiometry and ability to interact with functionally important parts of the receptor, through both direct and allosteric mechanisms. These findings signal the complex mechanistic bases of the distinct biological effects of different classes of PPARγ ligands. Today, the development of PPARγ ligands focuses on partial- and non-agonists as opposed to classical agonists, due to the severe side effects observed with PPARγ classical agonists as therapeutic agents. To aid this development, we performed principal component analyses of the atomic (Cartesian) coordinates (cPCA) and dihedral angles (dPCA) of the structures of human PPARγ from X-ray crystallography, available in the public domain, seeking to reveal ligand-induced trends. In the cPCA, projections of the structures along the principal components (PCs) demonstrated a moderate correlation between cPC1 and structural parameters related to the stabilization of helix 12, which is central to the transcriptional activation by PPARγ classical agonists. Consequently, the presented cPCA mapping of the PPARγ-ligand complexes may guide in silico drug discovery programs seeking to avoid stabilization of helix 12 in their development of partial- and non-agonistic PPARγ ligands. Notably, while the dPCA could identify key regions of dihedral fluctuation in the structural ensemble, the distributions along dPC1 - 2 could not be classified according to the same parameters as the distribution along cPC1. Proteins 2017; 85:1684-1698. © 2017 Wiley Periodicals, Inc.

Reliable modeling of RNA tertiary structures is key to both understanding these structures\' roles in complex biological machines and to eventually facilitating their design for molecular computing and robotics. In recent years, a concerted effort to improve computational prediction of RNA structure through the RNA-Puzzles blind prediction trials has accelerated advances in the field. Among other approaches, the versatile and expanding Rosetta molecular modeling software now permits modeling of RNAs in the 100-300 nucleotide size range at consistent subhelical (~1 nm) resolution. Our laboratory\'s current state-of-the-art methods for RNAs in this size range involve Fragment Assembly of RNA with Full-Atom Refinement (FARFAR), which optimizes RNA conformations in the context of a physically realistic energy function, as well as hybrid techniques that leverage experimental data to inform computational modeling. In this chapter, we give a practical guide to our current workflow for modeling RNA three-dimensional structures using FARFAR, including strategies for using data from multidimensional chemical mapping experiments to focus sampling and select accurate conformations.

We present a web toolkit STructure mapper and Online Coarse-graining Kit for setting up coarse-grained molecular simulations. The kit consists of two tools: structure mapping and Boltzmann inversion tools. The aim of the first tool is to define a molecular mapping from high, for example, all-atom, to low, that is, coarse-grained, resolution. Using a graphical user interface it generates input files, which are compatible with standard coarse-graining packages, for example, Versatile Object-oriented Toolkit for Coarse-graining Applications and DL_CGMAP. Our second tool generates effective potentials for coarse-grained simulations preserving the structural properties, for example, radial distribution functions, of the underlying higher resolution model. The required distribution functions can be provided by any simulation package. Simulations are performed on a local machine and only the distributions are uploaded to the server. The applicability of the toolkit is validated by mapping atomistic pentane and polyalanine molecules to a coarse-grained representation. Effective potentials are derived for systems of TIP3P (transferable intermolecular potential 3 point) water molecules and salt solution. The presented coarse-graining web toolkit is available at http://stock.cmm.ki.si.

Structure mapping is a classic experimental approach for determining nucleic acid structure that has gained renewed interest in recent years following advances in chemistry, genomics, and informatics. The approach encompasses numerous techniques that use different means to introduce nucleotide-level modifications in a structure-dependent manner. Modifications are assayed via cDNA fragment analysis, using electrophoresis or next-generation sequencing (NGS). The recent advent of NGS has dramatically increased the throughput, multiplexing capacity, and scope of RNA structure mapping assays, thereby opening new possibilities for genome-scale, de novo, and in vivo studies. From an informatics standpoint, NGS is more informative than prior technologies by virtue of delivering direct molecular measurements in the form of digital sequence counts. Motivated by these new capabilities, we introduce a novel model-based in silico approach for quantitative design of large-scale multiplexed NGS structure mapping assays, which takes advantage of the direct and digital nature of NGS readouts. We use it to characterize the relationship between controllable experimental parameters and the precision of mapping measurements. Our results highlight the complexity of these dependencies and shed light on relevant tradeoffs and pitfalls, which can be difficult to discern by intuition alone. We demonstrate our approach by quantitatively assessing the robustness of SHAPE-Seq measurements, obtained by multiplexing SHAPE (selective 2\'-hydroxyl acylation analyzed by primer extension) chemistry in conjunction with NGS. We then utilize it to elucidate design considerations in advanced genome-wide approaches for probing the transcriptome, which recently obtained in vivo information using dimethyl sulfate (DMS) chemistry.