Assessment of the Hemispheric Lateralization of Grapheme-Color Synesthesia with Stroop-Type Tests

Grapheme-color synesthesia, the idiosyncratic, arbitrary association of colors to letters or numbers, develops in childhood once reading is mastered. Because language processing is strongly left-lateralized in most individuals, we hypothesized that grapheme-color synesthesia could be left-lateralized as well. We used synesthetic versions of the Stroop test with colored letters and numbers presented either in the right or the left visual field of thirty-four synesthetes. Interference by synesthetic colors was stronger for stimuli in the right hemifield (first experiment, color naming task). Synesthetes were also faster in the right hemifield when naming the synesthetic color of graphemes (second experiment). Overall, the lateralization effect was 7 ms (the 95% confidence interval was [1.5 12] ms), a delay compatible with an additional callosal transfer for stimuli presented in the left hemifield. Though weak, this effect suggests that the association of synesthetic colors to graphemes may be preferentially processed in the left hemisphere. We speculate that this left-lateralization could be a landmark of synesthetic grapheme-color associations, if not found for color associations learnt by non-synesthete adults.     

Sensory Perception: Lessons from Synesthesia: Using Synesthesia to Inform the Understanding of Sensory Perception

Synesthesia, the conscious, idiosyncratic, repeatable, and involuntary sensationof one sensory modality in response to another, is a condition that has puzzledboth researchers and philosophers for centuries. Much time has been spentproving the condition’s existence as well as investigating its etiology, butwhat can be learned from synesthesia remains a poorly discussed topic. Here,synaesthesia is presented as a possible answer rather than a question to thecurrent gaps in our understanding of sensory perception. By first appreciatingthe similarities between normal sensory perception and synesthesia, one can usewhat is known about synaesthesia, from behavioral and imaging studies, to informour understanding of “normal” sensory perception. In particular, in consideringsynesthesia, one can better understand how and where the different sensorymodalities interact in the brain, how different sensory modalities can interactwithout confusion ― the binding problem ― as well as how sensory perceptiondevelops.     

Synesthesia and Migraine: Case Report

Background  

Synesthesia is, as visual migraine aura, a common and fascinating perceptual phenomenon. Here we present a unique case with synesthesias exclusively during visual migraine auras.     

A Dynamic Analysis of IRS-PKR Signaling in Liver Cells: A Discrete Modeling Approach

A major challenge in systems biology is to develop a detailed dynamic understanding of the functions and behaviors in a particular cellular system, which depends on the elements and their inter-relationships in a specific network. Computational modeling plays an integral part in the study of network dynamics and uncovering the underlying mechanisms. Here we proposed a systematic approach that incorporates discrete dynamic modeling and experimental data to reconstruct a phenotype-specific network of cell signaling. A dynamic analysis of the insulin signaling system in liver cells provides a proof-of-concept application of the proposed methodology. Our group recently identified that double-stranded RNA-dependent protein kinase (PKR) plays an important role in the insulin signaling network. The dynamic behavior of the insulin signaling network is tuned by a variety of feedback pathways, many of which have the potential to cross talk with PKR. Given the complexity of insulin signaling, it is inefficient to experimentally test all possible interactions in the network to determine which pathways are functioning in our cell system. Our discrete dynamic model provides an in silico model framework that integrates potential interactions and assesses the contributions of the various interactions on the dynamic behavior of the signaling network. Simulations with the model generated testable hypothesis on the response of the network upon perturbation, which were experimentally evaluated to identify the pathways that function in our particular liver cell system. The modeling in combination with the experimental results enhanced our understanding of the insulin signaling dynamics and aided in generating a context-specific signaling network.     

Environmentally Conscious Product Design: A Collaborative Internet-based Modeling Approach

This paper proposes a computer-based method for providing product designers with real-time environmental impact assessment. In this concurrent modeling approach, environmental experts build life-cycle models, define their interfaces, and publish them as distributed objects on the Internet. Traditional designers integrating these objects into their design models have access to the impact assessment methods provided by the environmental expert. In this paradigm, the focus shifts from providing techniques that let non-expert designers perform life-cycle impact assessments to tools that facilitate timely communication and information transfer between designers and appropriate environmental experts. Establishing real-time communication between the product design models and the environmental life-cycle models is the primary focus of this paper. Methods for establishing and maintaining the interaction between life-cycle and product design models are described. A beverage container design example illustrates how this collaborative approach can use environmental and traditional design goals to determine effective tradeoffs between design alternatives.     

Multiple Human Taste Receptor Sites: A Molecular Modeling Approach

Numerous experimental data on the human peripheral taste systemsuggest the existence of multiple low-affinity and low-specificityreceptor sites which are responsible for the detection and thecomplete discrimination of a very large number of organic molecules.According to this hypothesis, a given molecule interacts withnumerous taste receptors and vice versa. Statistical analysisof taste intensities estimated by 58 human subjects for variousmolecules enables the calculation of taste intermolecular distances.For the present modeling study, we hypothesized that a shorttaste distance (i.e. taste similarity) between two distinctmolecules indicates that they bind with similar distributionsof affinities to the taste receptors, and hence display similarbinding motifs. In order to find common molecular binding motifsamong 14 selected organic tastants, hydrogen-bonding and hydrophobicinteraction properties were mapped onto their molecular surfaces.The 14 surfaces were then cut in 240 fragments, most of whichwere made up of 2–4 potentially interacting zones. A correspondenceindex was defined to measure the analogy between two optimallysuperimposed fragments. The 75 most representative fragmentswere all matched pairwise. Twelve distinct clusters of fragmentswere isolated from the 2775 calculated comparisons. These 12fragment types were used to calculate structural similaritydistances. We then performed a combinatorial analysis to identifywhich fragment combination best reconciled structural and tastedistances. We finally identified an optimal subset of sevenfragment types out of the 12, which significantly and best accountedfor the 91 pairwise taste distances between all 14 modeled tastants.These seven validated fragment types are therefore presentedas good candidates to be recognized by the same number of distincttaste receptor sites. Potential applications of these identifiedbinding motifs to tastant design are suggested. Chem. Senses21: 425–445, 1996.     

Landscape-Level Phenology of a Threatened Butterfly: A GIS-Based Modeling Approach

Phenology of organismal development varies between growing seasons according to the weather and also varies within growing seasons across topoclimatic gradients. Combining these factors is necessary to predict landscape-level patterns of phenology and their consequences for population dynamics. We developed a model on a Geographic Information System (GIS) that predicts phenology of adult emergence of the threatened Bay checkerspot butterfly across complex terrain under variable weather. Physiological time was modeled by accumulated slope-specific direct insolation. Insolation sums through growing seasons were calculated for each cell of a digital terrain model (skipping over cloudy days) until a threshold for adult emergence was reached. Emergence times of adult butterflies for a given year were then mapped out across a 100-ha area. To generate predicted emergence curves for the population in a given year, these maps ofemergence times were then modified by incorporating microdistributions of postdiapause larvae. Different larval microdistributions changed both the magnitude and shape of emergence curves under the same yearly weather and could change mean population-wide emergence dates by 11 days. Reproductive success in this butterfly is strongly dependent on the timing of adult emergence, and these models provide insights into the effects of weather, topography, and population history on population dynamics. Because adult emergence phenology is often a key component of reproductive success for insects, understanding the components of phenological variation in space and time in complex terrain may provide insights into population dynamics for management of pests and conservation of rare species. Received 2 December 1997; accepted 24 March 1998.     

Pediatric Health-Related Quality of Life: A Structural Equation Modeling Approach

Objectives

One of the most referenced theoretical frameworks to measure Health Related Quality of Life (HRQoL) is the Wilson and Cleary framework. With some adaptions this framework has been validated in the adult population, but has not been tested in pediatric populations. Our goal was to empirically investigate it in children.

Methods

The contributory factors to Health Related Quality of Life that we included were symptom status (presence of chronic disease or hospitalizations), functional status (developmental status), developmental aspects of the individual (social-emotional) behavior, and characteristics of the social environment (socioeconomic status and area of education). Structural equation modeling was used to assess the measurement structure of the model in 214 German children (3–5 years old) participating in a follow-up study that investigates pediatric health outcomes.

Results

Model fit was χ2 = 5.5; df = 6; p = 0.48; SRMR  = 0.01. The variance explained of Health Related Quality of Life was 15%. Health Related Quality of Life was affected by the area education (i.e. where kindergartens were located) and development status. Developmental status was affected by the area of education, socioeconomic status and individual behavior. Symptoms did not affect the model.

Conclusions

The goodness of fit and the overall variance explained were good. However, the results between children'' and adults'' tests differed and denote a conceptual gap between adult and children measures. Indeed, there is a lot of variety in pediatric Health Related Quality of Life measures, which represents a lack of a common definition of pediatric Health Related Quality of Life. We recommend that researchers invest time in the development of pediatric Health Related Quality of Life theory and theory based evaluations.     

Phenotypic Variance Predicts Symbiont Population Densities in Corals: A Modeling Approach

Background

We test whether the phenotypic variance of symbionts (Symbiodinium) in corals is closely related with the capacity of corals to acclimatize to increasing seawater temperatures. Moreover, we assess whether more specialist symbionts will increase within coral hosts under ocean warming. The present study is only applicable to those corals that naturally have the capacity to support more than one type of Symbiodinium within the lifetime of a colony; for example, Montastraea annularis and Montastraea faveolata.

Methodology/Principal Findings

The population dynamics of competing Symbiodinium symbiont populations were projected through time in coral hosts using a novel, discrete time optimal–resource model. Models were run for two Atlantic Ocean localities. Four symbiont populations, with different environmental optima and phenotypic variances, were modeled to grow, divide, and compete in the corals under seasonal fluctuations in solar insolation and seawater temperature. Elevated seawater temperatures were input into the model 1.5°C above the seasonal summer average, and the symbiont population response was observed for each location. The models showed dynamic fluctuations in Symbiodinium populations densities within corals. Population density predictions for Lee Stocking Island, the Bahamas, where temperatures were relatively homogenous throughout the year, showed a dominance of both type 2, with high phenotypic variance, and type 1, a high-temperature and high-insolation specialist. Whereas the densities of Symbiodinium types 3 and 4, a high-temperature, low-insolation specialist, and a high-temperature, low-insolation generalist, remained consistently low. Predictions for Key Largo, Florida, where environmental conditions were more seasonally variable, showed the coexistence of generalists (types 2 and 4) and low densities of specialists (types 1 and 3). When elevated temperatures were input into the model, population densities in corals at Lee Stocking Island showed an emergence of high-temperature specialists. However, even under high temperatures, corals in the Florida Keys were dominated by generalists.

Conclusions/Significance

Predictions at higher seawater temperatures showed endogenous shuffling and an emergence of the high-temperature Symbiodinium specialists, even though their phenotypic variance was low. The model shows that sustaining these “hidden” specialists becomes advantageous under thermal stress conditions, and shuffling symbionts may increase the corals'' capacity to acclimatize but not adapt to climatechange–induced ocean warming.     

Modeling Brain Energy Metabolism and Function: A Multiparametric Monitoring Approach

Mathematical modeling of brain function is an important tool needed for a better understanding of experimental results and clinical situations. In the present study, we are constructing and testing a mathematical model capable of simulating changes in brain energy metabolism that develop in real time under various pathophysiological conditions. The model incorporates the following parameters: cerebral blood flow, partial oxygen pressure, mitochondrial NADH redox state, and extracellular potassium. Accordingly, all the model variables are only time dependent (`point-model' approach). Numerical runs demonstrate the ability of the model to mimic pathological conditions, such as complete and partial ischemia, cortical spreading depression under normoxic and partial ischemic conditions. They also show that, when properly tuned, a model of this type permits the monitoring of only one or two crucial variables and the computation of the remaining variables in real time during clinical or experimental procedures.     

Mathematical Modeling of Learning from an Inconsistent Source: A Nonlinear Approach

Continuing the discussion of how children can modify and regularize linguistic inputs from adults, we present a new interpretation of existing algorithms to model and investigate the process of a learner learning from an inconsistent source. On the basis of this approach is a (possibly nonlinear) function (the update function) that relates the current state of the learner with an increment that it receives upon processing the source’s input, in a sequence of updates. The model can be considered a nonlinear generalization of the classic Bush–Mosteller algorithm. Our model allows us to analyze and present a theoretical explanation of a frequency boosting property, whereby the learner surpasses the fluency of the source by increasing the frequency of the most common input. We derive analytical expressions for the frequency of the learner, and also identify a class of update functions that exhibit frequency boosting. Applications to the Feature-Label-Order effect in learning are presented.     

Functional Proteomics: A Promising Approach to Find Novel Components of the Circadian System

In the postgenome era, the analysis of entire subproteomes in correlation with their function has emerged due to high throughput technologies. Early approaches have been initiated to identify novel components of the circadian system. For example, in the marine dinoflagellate Lingulodinium polyedra, a chronobiological proteome assay was performed, which resulted in the identification of already known circadian expressed proteins as well as novel temporal controlled proteins involved in metabolic pathways. In the green alga Chlamydomonas reinhardtii, two circadian expressed proteins (a protein disulfide isomerase and a tetratricopeptide repeat protein) were identified by functional proteomics. Also, the first hints of temporal control within chloroplast proteins of Arabidopsis thaliana were identified by proteome analysis.     

Properties of Polycrystalline Diamond: Multiscale Modeling Approach

Abstract

Two modeling techniques to characterize fracture behavior of polycrystalline diamond films are discussed. The first technique is a multiscale modeling method in which first-principles local density approximation calculations on selected structures are combined with an analytic mesoscale model to obtain energies and cleavage fracture energies for symmetric ?001? tilt grain boundaries (GBs) over the entire misorientation range. The second technique is large-scale atomistic simulation of the dynamics of failure in notched polycrystalline diamond samples under an applied strain. Electronic characteristics of selected ?001? symmetrical tilt GBs calculated with a semiempirical tight-binding Hamiltonian are also presented, and the possible role of graphitic defects on field emission from polycrystalline diamond is briefly discussed.     

RFMix: A Discriminative Modeling Approach for Rapid and Robust Local-Ancestry Inference

Local-ancestry inference is an important step in the genetic analysis of fully sequenced human genomes. Current methods can only detect continental-level ancestry (i.e., European versus African versus Asian) accurately even when using millions of markers. Here, we present RFMix, a powerful discriminative modeling approach that is faster (∼30×) and more accurate than existing methods. We accomplish this by using a conditional random field parameterized by random forests trained on reference panels. RFMix is capable of learning from the admixed samples themselves to boost performance and autocorrect phasing errors. RFMix shows high sensitivity and specificity in simulated Hispanics/Latinos and African Americans and admixed Europeans, Africans, and Asians. Finally, we demonstrate that African Americans in HapMap contain modest (but nonzero) levels of Native American ancestry (∼0.4%).     

Beyond GLMs: A Generative Mixture Modeling Approach to Neural System Identification

Generalized linear models (GLMs) represent a popular choice for the probabilistic characterization of neural spike responses. While GLMs are attractive for their computational tractability, they also impose strong assumptions and thus only allow for a limited range of stimulus-response relationships to be discovered. Alternative approaches exist that make only very weak assumptions but scale poorly to high-dimensional stimulus spaces. Here we seek an approach which can gracefully interpolate between the two extremes. We extend two frequently used special cases of the GLM—a linear and a quadratic model—by assuming that the spike-triggered and non-spike-triggered distributions can be adequately represented using Gaussian mixtures. Because we derive the model from a generative perspective, its components are easy to interpret as they correspond to, for example, the spike-triggered distribution and the interspike interval distribution. The model is able to capture complex dependencies on high-dimensional stimuli with far fewer parameters than other approaches such as histogram-based methods. The added flexibility comes at the cost of a non-concave log-likelihood. We show that in practice this does not have to be an issue and the mixture-based model is able to outperform generalized linear and quadratic models.     

t-LSE: A Novel Robust Geometric Approach for Modeling Protein-Protein Interaction Networks

Protein-protein interaction (PPI) networks provide insights into understanding of biological processes, function and the underlying complex evolutionary mechanisms of the cell. Modeling PPI network is an important and fundamental problem in system biology, where it is still of major concern to find a better fitting model that requires less structural assumptions and is more robust against the large fraction of noisy PPIs. In this paper, we propose a new approach called t-logistic semantic embedding (t-LSE) to model PPI networks. t-LSE tries to adaptively learn a metric embedding under the simple geometric assumption of PPI networks, and a non-convex cost function was adopted to deal with the noise in PPI networks. The experimental results show the superiority of the fit of t-LSE over other network models to PPI data. Furthermore, the robust loss function adopted here leads to big improvements for dealing with the noise in PPI network. The proposed model could thus facilitate further graph-based studies of PPIs and may help infer the hidden underlying biological knowledge. The Matlab code implementing the proposed method is freely available from the web site: http://home.ustc.edu.cn/~yzh33108/PPIModel.htm.     

Plant-Herbivore Interaction and Its Consequences for Succession in Wetland Ecosystems: A Modeling Approach

Herbivore grazing is increasingly used as a management tool to prevent the dominance of vegetation by tall grasses or trees. In this report, a model is described that is used to analyze plant-herbivore interactions and their scaling up to landscape scale. The model can be used to predict effects of herbivory on vegetation development. The model is an ecosystem model including modules for carbon and nitrogen cycling through plants, soil organic matter, and atmosphere. Plants compete for light and nitrogen. An herbivory module is included that implements selective foraging by a herbivore in a spatially heterogeneous area. Simulations were done to analyze the effects of herbivore density on vegetation dynamics, to analyze the impact of soil fertility on maximum herbivore density, and to analyze effects of herbivore density on landscapes. Two important points come forward from the model. Maximum herbivore abundance shows a hump-shaped curve along a soil fertility gradient. At higher soil fertility, light competition becomes more important. Herbivory interferes with plant competition, giving the tall, less palatable species a competitive advantage and thereby reducing the food quality and availability and hence the carrying capacity of the area. At a landscape scale, herbivory leads to increased heterogeneity. This increased heterogeneity may increase carrying capacity. The implications of these points for nature management are discussed. Received 13 May 1998; accepted 23 November 1998.     

Neurocognition,Insight and Medication Nonadherence in Schizophrenia: A Structural Equation Modeling Approach

Objective

The aim of this study was to examine the complex relationships among neurocognition, insight and nonadherence in patients with schizophrenia.

Methods

Design: Cross-sectional study. Inclusion criteria: Diagnosis of schizophrenia according to the DSM-IV-TR criteria. Data collection: Neurocognition was assessed using a global approach that addressed memory, attention, and executive functions; insight was analyzed using the multidimensional ‘Scale to assess Unawareness of Mental Disorder;’ and nonadherence was measured using the multidimensional ‘Medication Adherence Rating Scale.’ Analysis: Structural equation modeling (SEM) was applied to examine the non-straightforward relationships among the following latent variables: neurocognition, ‘awareness of positive symptoms’ and ‘negative symptoms’, ‘awareness of mental disorder’ and nonadherence.

Results

One hundred and sixty-nine patients were enrolled. The final testing model showed good fit, with normed χ² = 1.67, RMSEA = 0.063, CFI = 0.94, and SRMR = 0.092. The SEM revealed significant associations between (1) neurocognition and ‘awareness of symptoms,’ (2) ‘awareness of symptoms’ and ‘awareness of mental disorder’ and (3) ‘awareness of mental disorder’ and nonadherence, mainly in the ‘attitude toward taking medication’ dimension. In contrast, there were no significant links between neurocognition and nonadherence, neurocognition and ‘awareness of mental disorder,’ and ‘awareness of symptoms’ and nonadherence.

Conclusions

Our findings support the hypothesis that neurocognition influences ‘awareness of symptoms,’ which must be integrated into a higher level of insight (i.e., the ‘awareness of mental disorder’) to have an impact on nonadherence. These findings have important implications for the development of effective strategies to enhance medication adherence.