Performance of single letters and pairs of letters in short-term recognition experiments

Summary The theory of statistical decision is used to describe the recognition of single letters and pairs of letters from verbally presented lists. The results which are explained by means of the memory operating characteristic exhibit that the recognition of homogeneous pairs is predictably better than the recognition of single items.This work was supported by the Deutsche Forschungsgemeinschaft.     

A flexible Bayesian model for studying gene-environment interaction

An important follow-up step after genetic markers are found to be associated with a disease outcome is a more detailed analysis investigating how the implicated gene or chromosomal region and an established environment risk factor interact to influence the disease risk. The standard approach to this study of gene–environment interaction considers one genetic marker at a time and therefore could misrepresent and underestimate the genetic contribution to the joint effect when one or more functional loci, some of which might not be genotyped, exist in the region and interact with the environment risk factor in a complex way. We develop a more global approach based on a Bayesian model that uses a latent genetic profile variable to capture all of the genetic variation in the entire targeted region and allows the environment effect to vary across different genetic profile categories. We also propose a resampling-based test derived from the developed Bayesian model for the detection of gene–environment interaction. Using data collected in the Environment and Genetics in Lung Cancer Etiology (EAGLE) study, we apply the Bayesian model to evaluate the joint effect of smoking intensity and genetic variants in the 15q25.1 region, which contains a cluster of nicotinic acetylcholine receptor genes and has been shown to be associated with both lung cancer and smoking behavior. We find evidence for gene–environment interaction (P-value = 0.016), with the smoking effect appearing to be stronger in subjects with a genetic profile associated with a higher lung cancer risk; the conventional test of gene–environment interaction based on the single-marker approach is far from significant.     

A Bayesian network model for protein fold and remote homologue recognition

MOTIVATION: The Bayesian network approach is a framework which combines graphical representation and probability theory, which includes, as a special case, hidden Markov models. Hidden Markov models trained on amino acid sequence or secondary structure data alone have been shown to have potential for addressing the problem of protein fold and superfamily classification. RESULTS: This paper describes a novel implementation of a Bayesian network which simultaneously learns amino acid sequence, secondary structure and residue accessibility for proteins of known three-dimensional structure. An awareness of the errors inherent in predicted secondary structure may be incorporated into the model by means of a confusion matrix. Training and validation data have been derived for a number of protein superfamilies from the Structural Classification of Proteins (SCOP) database. Cross validation results using posterior probability classification demonstrate that the Bayesian network performs better in classifying proteins of known structural superfamily than a hidden Markov model trained on amino acid sequences alone.     

Bayesian interaction selection model for multimodal neuroimaging data analysis

Multimodality or multiconstruct data arise increasingly in functional neuroimaging studies to characterize brain activity under different cognitive states. Relying on those high-resolution imaging collections, it is of great interest to identify predictive imaging markers and intermodality interactions with respect to behavior outcomes. Currently, most of the existing variable selection models do not consider predictive effects from interactions, and the desired higher-order terms can only be included in the predictive mechanism following a two-step procedure, suffering from potential misspecification. In this paper, we propose a unified Bayesian prior model to simultaneously identify main effect features and intermodality interactions within the same inference platform in the presence of high-dimensional data. To accommodate the brain topological information and correlation between modalities, our prior is designed by compiling the intermediate selection status of sequential partitions in light of the data structure and brain anatomical architecture, so that we can improve posterior inference and enhance biological plausibility. Through extensive simulations, we show the superiority of our approach in main and interaction effects selection, and prediction under multimodality data. Applying the method to the Adolescent Brain Cognitive Development (ABCD) study, we characterize the brain functional underpinnings with respect to general cognitive ability under different memory load conditions.     

A path-based computational model for long non-coding RNA-protein interaction prediction

Recently, lncRNAs have attracted accumulating attentions because more and more experimental researches have shown lncRNA can play critical roles in many biological processes. Predicting potential interactions between lncRNAs and proteins are key to understand the lncRNAs biological functions. But traditional biological experiments are expensive and time-consuming, network similarity methods provide a powerful solution to computationally predict lncRNA-protein interactions. In this work, a novel path-based lncRNA-protein interaction (PBLPI) prediction model is proposed by integrating protein semantic similarity, lncRNA functional similarity, known human lncRNA-protein interactions, and Gaussian interaction profile kernel similarity. PBLPI model utilizes three interlinked sub-graphs to construct a heterogeneous graph, and then infers potential lncRNA-protein interactions through depth-first search algorithm. Consequently, PBLPI achieves reliable performance in the frameworks of 5-fold cross validation (average AUC is 0.9244 and AUPR is 0.6478). In the case study, we use “Mus musculus” data to further validate the reliability of PBLPI method. It is anticipated that PBLPI would become a useful tool to identify potential lncRNA-protein interactions.     

Three-dimensional computational model of left heart diastolic function with fluid-structure interaction

Aided by advancements in computer speed and modeling techniques, computational modeling of cardiac function has continued to develop over the past twenty years. The goal of the current study was to develop a computational model that provides blood-tissue interaction under physiologic flow conditions, and apply it to a thin-walled model of the left heart. To accomplish this goal, the Immersed Boundary Method was used to study the interaction of the tissue and blood in response to fluid forces and changes in tissue pathophysiology. The fluid mass and momentum conservation equations were solved using Patankar's Semi-Implicit Method for Pressure Linked Equations (SIMPLE). A left heart model was developed to examine diastolic function, and consisted of the left ventricle, left atrium, and pulmonary flow. The input functions for the model included the pulmonary driving pressure and time-dependent relationship for changes in chamber tissue properties during the simulation. The results obtained from the left heart model were compared to clinically observed diastolic flow conditions for validation. The inflow velocities through the mitral valve corresponded with clinical values (E-wave = 74.4 cm/s, A-wave = 43 cm/s, and E/A = 1.73). The pressure traces for the atrium and ventricle, and the appearance of the ventricular flow fields throughout filling, agreed with those observed in the heart. In addition, the atrial flow fields could be observed in this model and showed the conduit and pump functions that current theory suggests. The ability to examine atrial function in the present model is something not described previously in computational simulations of cardiac function.     

The lexical categorization model: A computational model of left ventral occipito-temporal cortex activation in visual word recognition

To characterize the functional role of the left-ventral occipito-temporal cortex (lvOT) during reading in a quantitatively explicit and testable manner, we propose the lexical categorization model (LCM). The LCM assumes that lvOT optimizes linguistic processing by allowing fast meaning access when words are familiar and filtering out orthographic strings without meaning. The LCM successfully simulates benchmark results from functional brain imaging described in the literature. In a second evaluation, we empirically demonstrate that quantitative LCM simulations predict lvOT activation better than alternative models across three functional magnetic resonance imaging studies. We found that word-likeness, assumed as input into a lexical categorization process, is represented posteriorly to lvOT, whereas a dichotomous word/non-word output of the LCM could be localized to the downstream frontal brain regions. Finally, training the process of lexical categorization resulted in more efficient reading. In sum, we propose that word recognition in the ventral visual stream involves word-likeness extraction followed by lexical categorization before one can access word meaning.     

Carbon-nitrogen interaction model in field crop production

Two Carbon-nitrogen interaction models were developed, one, expressed as DM=DM ₀ exp(CN ₁ N), was for growth of Gramineae (rice, wheat, and maize) and root crops (potato, sweet potato, and sugar beet), the other DM =DM ₀+CN _I N for Leguminosae (soybean, field bean, and aduki bean), where, DM is dry weight of plant at a given time, N is the amount of nitrogen accumulated in plant at a given time, DM ₀ is the initial amount of dry weight, and CN ₁ or CN ₁ is the carbon-nitrogen index.The CN ₁ value changed with the amount of nitrogen absorbed at the time of harvest (Nh), indicating that the relationship between the CN ₁ value and Nh fitted to a hyperbolic curve as follows: CN ₁= 1/(a Nh + b), where, a and b are the coefficients of the equation. In rice, coefficients a and b were estimated by the Gauss-Newton method.The CN ₁, value of Leguminosae was almost constant regardless of Nh. It is thus concluded that the carbon-nitrogen interaction was significantly different between Leguminosae and other crops (Gramineae and root crops).     

A modified resonant recognition model to predict protein-protein interaction

Proteins are fundamental components of all living cells and the protein-protein interaction plays an important role in vital movement. This paper briefly introduced the original Resonant Recognition Model (RRM), and then modified it by using the wavelet transform to acquire the Modified Resonant Recognition Model (MRRM). The key characteristic of the new model is that it can predict directly the protein-protein interaction from the primary sequence, and the MRRM is more suitable than the RRM for this prediction. The results of numerical experiments show that the MRRM is effective for predicting the protein-protein interaction. Translated from Journal of Shanghai University (Natural Science), 2006, 12(1): 69–73 [译自: 上海大学学报(自然科学版)]     

A modified resonant recognition model to predict protein-protein interaction

Proteins are fundamental components of all living cells and the protein-protein interaction plays an important role in vital movement.This paper briefly introduced the original Resonant Recognition Model (RRM),and then modified it by using the wavelet transform to acquire the Modified Resonant Recognition Model (MRRM).The key characteristic of the new model is that it can predict directly the proteinprotein interaction from the primary sequence,and the MRRM is more suitable than the RRM for this prediction.The results of numerical experiments show that the MRRM is effective for predicting the protein-protein interaction.     

Bayesian methods in bioinformatics and computational systems biology

Bayesian methods are valuable, inter alia, whenever there is a need to extract information from data that are uncertain or subject to any kind of error or noise (including measurement error and experimental error, as well as noise or random variation intrinsic to the process of interest). Bayesian methods offer a number of advantages over more conventional statistical techniques that make them particularly appropriate for complex data. It is therefore no surprise that Bayesian methods are becoming more widely used in the fields of genetics, genomics, bioinformatics and computational systems biology, where making sense of complex noisy data is the norm. This review provides an introduction to the growing literature in this area, with particular emphasis on recent developments in Bayesian bioinformatics relevant to computational systems biology.     

Two models of the recognition and detection of texture-defined letters compared

We quantified texture segregation by measuring psychophysically the percentage correct detection scores for each of a set of 10 texture-defined (TD) letters using the temporal two-alternative forced choice method, and at the same time quantified spatial discrimination of the TD form of measuring psychophysically the percentage correct letter recognition scores for the 10 letters. Ten levels of task difficulty were created by adding noise dots to the texture patterns. The resulting psychophysical data were used to test and compare models of the detection and recognition of texture-defined letters. Each model comprised a sequence of physiologically plausible stages in early visual processing. Each had the same first, second and third stages, namely linear orientation-tuned spatial filters followed by rectification and smoothing. Model 1 had only one non-linear stage. Model 2 had two non-linear stages. In model 2 the second non-linear stage was cross-orientation inhibition. This second non-linear stage enhanced the texture borders by, in effect, comparing textures at different locations in the texture pattern. In both models, the last stage modelled either letter detection or letter recognition. Letter recognition was modelled as follows. We passed a given letter stimulus through the first several stages of a model and, in 10 separate calculations, cross-correlated the output with a template of each of the 10 letters. From these 10 correlations we obtained a predicted percentage correct letter recognition score for the given letter stimulus. The predicted recognition scores closely agreed with the experimental data at all 10 levels of task difficulty for model 2, but not for model 1. We conclude that a borderenhancing algorithm is necessary to model letter recognition. The letter-detection algorithm modelled detection of part of a letter (a single letter stroke) in terms of the signal-to-noise ratio of a letter-segment detector. The predicted letter detection scores fitted the data closely for both models.     

Multi-scale inference of interaction rules in animal groups using Bayesian model selection

Inference of interaction rules of animals moving in groups usually relies on an analysis of large scale system behaviour. Models are tuned through repeated simulation until they match the observed behaviour. More recent work has used the fine scale motions of animals to validate and fit the rules of interaction of animals in groups. Here, we use a Bayesian methodology to compare a variety of models to the collective motion of glass prawns (Paratya australiensis). We show that these exhibit a stereotypical 'phase transition', whereby an increase in density leads to the onset of collective motion in one direction. We fit models to this data, which range from: a mean-field model where all prawns interact globally; to a spatial Markovian model where prawns are self-propelled particles influenced only by the current positions and directions of their neighbours; up to non-Markovian models where prawns have 'memory' of previous interactions, integrating their experiences over time when deciding to change behaviour. We show that the mean-field model fits the large scale behaviour of the system, but does not capture fine scale rules of interaction, which are primarily mediated by physical contact. Conversely, the Markovian self-propelled particle model captures the fine scale rules of interaction but fails to reproduce global dynamics. The most sophisticated model, the non-Markovian model, provides a good match to the data at both the fine scale and in terms of reproducing global dynamics. We conclude that prawns' movements are influenced by not just the current direction of nearby conspecifics, but also those encountered in the recent past. Given the simplicity of prawns as a study system our research suggests that self-propelled particle models of collective motion should, if they are to be realistic at multiple biological scales, include memory of previous interactions and other non-Markovian effects.     

Clustering hybrid regression: a novel computational approach to study and model biohydrogen production through dark fermentation

Clustering hybrid regression (CHR) approach was developed and evaluated using data from H(2)-producing glucose-based, suspended-cell bioreactor operated for 5 months. The aim was to describe the relationship between metabolic end products and H(2)-production rate. Self-organizing maps (SOM) were used to better visualize the dataset and to detect main metabolic patterns in bioprocess data. SOM detected three distinct metabolic patterns with butyrate, acetate and ethanol as dominant metabolites, respectively. Butyrate dominated metabolism was related to high H(2) production, while acetate and ethanol dominated metabolisms resulted in low H(2) production. CHR models performed well [mean square error (MSE) 0.55 and 0.56] in modeling the H(2)-production rate. The results validate the suitability of the CHR approach in describing the bioprocess behavior and in the modeling of H(2) production rate. The developed model can help in discovering key metabolic interactions and suitable process parameters from complex datasets, and increase the understanding of the bioprocesses occurring in engineered and natural environments.     

A computational model for previtamin D(3) production in skin

Low levels of vitamin D have been implicated in a wide variety of health issues from calcemic diseases to cancer, diabetes and cardiovascular disease. For most humans, the majority of vitamin D(3) is derived from sunlight. How much vitamin D is produced under given exposure conditions is still widely discussed. We present a computational model for the production of (pre-)vitamin D within the skin. It accounts for spectral irradiance, optical properties of the skin and concentration profile of provitamin D. Results are computed for various sets of these parameters yielding the distribution of produced previtamin D in the skin.     

Mechanisms of phosphatidylserine influence on viral production: A computational model of Ebola virus matrix protein assembly

Ebola virus (EBOV) infections continue to pose a global public health threat, with high mortality rates and sporadic outbreaks in Central and Western Africa. A quantitative understanding of the key processes driving EBOV assembly and budding could provide valuable insights to inform drug development. Here, we use a computational model to evaluate EBOV matrix assembly. Our model focuses on the assembly kinetics of VP40, the matrix protein in EBOV, and its interaction with phosphatidylserine (PS) in the host cell membrane. It has been shown that mammalian cells transfected with VP40-expressing plasmids are capable of producing virus-like particles (VLPs) that closely resemble EBOV virions. Previous studies have also shown that PS levels in the host cell membrane affects VP40 association with the plasma membrane inner leaflet and that lower membrane PS levels result in lower VLP production. Our computational findings indicate that PS may also have a direct influence on VP40 VLP assembly and budding, where a higher PS level will result in a higher VLP budding rate and filament dissociation rate. Our results further suggest that the assembly of VP40 filaments follow the nucleation-elongation theory, where initialization and oligomerization of VP40 are two distinct steps in the assembly process. Our findings advance the current understanding of VP40 VLP formation by identifying new possible mechanisms of PS influence on VP40 assembly. We propose that these mechanisms could inform treatment strategies targeting PS alone or in combination with other VP40 assembly steps.     

Using fold recognition to search for useful proteins: Bayesian approach to fold recognition

The wealth of protein sequence and structure data is greater than ever, thanks to the ongoing Genomics and Structural Genomics projects. The information available through such efforts needs to be analysed by new methods that combine both databases. One important result of genomic sequence analysis is the inference of functional homology among proteins. Until recently sequence similarity comparison was the only method for homologue inference. The new fold recognition approach reviewed in this paper enhances sequence comparison methods by including structural information in the process of protein comparison. This additional information often allows for the detection of similarities that cannot be found by methods that only use sequence information.