Haptic Exploratory Behavior During Object Discrimination: A Novel Automatic Annotation Method

In order to acquire information concerning the geometry and material of handheld objects, people tend to execute stereotypical hand movement patterns called haptic Exploratory Procedures (EPs). Manual annotation of haptic exploration trials with these EPs is a laborious task that is affected by subjectivity, attentional lapses, and viewing angle limitations. In this paper we propose an automatic EP annotation method based on position and orientation data from motion tracking sensors placed on both hands and inside a stimulus. A set of kinematic variables is computed from these data and compared to sets of predefined criteria for each of four EPs. Whenever all criteria for a specific EP are met, it is assumed that that particular hand movement pattern was performed. This method is applied to data from an experiment where blindfolded participants haptically discriminated between objects differing in hardness, roughness, volume, and weight. In order to validate the method, its output is compared to manual annotation based on video recordings of the same trials. Although mean pairwise agreement is less between human-automatic pairs than between human-human pairs (55.7% vs 74.5%), the proposed method performs much better than random annotation (2.4%). Furthermore, each EP is linked to a specific object property for which it is optimal (e.g., Lateral Motion for roughness). We found that the percentage of trials where the expected EP was found does not differ between manual and automatic annotation. For now, this method cannot yet completely replace a manual annotation procedure. However, it could be used as a starting point that can be supplemented by manual annotation.     

A Markov-blanket-based model for gene regulatory network inference

An efficient two-step Markov blanket method for modeling and inferring complex regulatory networks from large-scale microarray data sets is presented. The inferred gene regulatory network (GRN) is based on the time series gene expression data capturing the underlying gene interactions. For constructing a highly accurate GRN, the proposed method performs: 1) discovery of a gene's Markov Blanket (MB), 2) formulation of a flexible measure to determine the network's quality, 3) efficient searching with the aid of a guided genetic algorithm, and 4) pruning to obtain a minimal set of correct interactions. Investigations are carried out using both synthetic as well as yeast cell cycle gene expression data sets. The realistic synthetic data sets validate the robustness of the method by varying topology, sample size, time delay, noise, vertex in-degree, and the presence of hidden nodes. It is shown that the proposed approach has excellent inferential capabilities and high accuracy even in the presence of noise. The gene network inferred from yeast cell cycle data is investigated for its biological relevance using well-known interactions, sequence analysis, motif patterns, and GO data. Further, novel interactions are predicted for the unknown genes of the network and their influence on other genes is also discussed.     

Estimating dataset size requirements for classifying DNA microarray data.

A statistical methodology for estimating dataset size requirements for classifying microarray data using learning curves is introduced. The goal is to use existing classification results to estimate dataset size requirements for future classification experiments and to evaluate the gain in accuracy and significance of classifiers built with additional data. The method is based on fitting inverse power-law models to construct empirical learning curves. It also includes a permutation test procedure to assess the statistical significance of classification performance for a given dataset size. This procedure is applied to several molecular classification problems representing a broad spectrum of levels of complexity.     

Optimal approach for classification of acute leukemia subtypes based on gene expression data

The classification of cancer subtypes, which is critical for successful treatment, has been studied extensively with the use of gene expression profiles from oligonucleotide chips or cDNA microarrays. Various pattern recognition methods have been successfully applied to gene expression data. However, these methods are not optimal, rather they are high-performance classifiers that emphasize only classification accuracy. In this paper, we propose an approach for the construction of the optimal linear classifier using gene expression data. Two linear classification methods, linear discriminant analysis (LDA) and discriminant partial least-squares (DPLS), are applied to distinguish acute leukemia subtypes. These methods are shown to give satisfactory accuracy. Moreover, we determined optimally the number of genes participating in the classification (a remarkably small number compared to previous results) on the basis of the statistical significance test. Thus, the proposed method constructs the optimal classifier that is composed of a small size predictor and provides high accuracy.     

Adenovirus type 2 early polypeptides immunoprecipitated by antisera to five lines of adenovirus-transformed rat cells.

We have identified adenovirus type 2 (Ad2)-induced early polypeptides (EPs) and have attempted to determine which EPs are coded by each of the four early gene blocks. [35S]methionine-labeled EPs were resolved by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Cycloheximide pretreatment followed by labeling in hypertonic medium (210 to 250 mM NaCl) facilitated the detection of EPs. Seven major (reproducible bands in autoradiograms) EPs were detected with molecular weights of 74,000 (74K), 21K, 19K, 15K, 13.5K, 11.5K, and 11K. Minor (weaker bands) EPs of 55K, 52K, 42K, 18K, 12K, 8.8K, and 8.3K were also often seen. To identify and map the genes for virus-coded EPs, we prepared antisera against five lines of adenovirus-transformed cells that retain different fractions of the viral genome. The lines were F17, 8617, F4, and T2C4 transformed by Ad2 virions and 5RK (clone I) transformed by transfection with the Ad5 HsuI-G fragment (map position 0 to 8). The early gene blocks retained and expressed (in part) as RNA in these cells were as follows: 5RK(I), block 1 (70% of left 8% of genome); F17, block 1; 8617, blocks 1 and 4; F4 blocks 1, 2, and 4; T2C4, blocks 1, 2, 3, and 4. The following major EPs were immunoprecipitated: 15K by all antisera; 53K and 14.5K by F17, T2C4, 8617, and F4 antisera; 11.5K by T2C4, 8617, and F4 antisera; 44K, 42K, 19K, and 13.5K by T2C4 antisera; 11K by 8617 antisera. Minor EPs of 28K, 18K, and 12K were precipitated by all antisera except 5RK(I). The 53K and 15K EPs were precipitated also from Ad2 early infected monkey cells by the F17 antiserum and by sera from hamsters bearing tumors induced by Ad1-simian virus 40. The relationships between some of the immunoprecipitated EPs were investigated by the partial proteolysis procedure. All 53K EPs are the "same" (i.e., highly related), all 15K EPs are the "same," and all 11.5K EPs are the "same." The 15K EP is highly related to the 14.5 K EP. Although less certain, all 28K EPs appeared related, as did all 18K EPs. The T2C4-specific 44K EP is probably a dimer of the 21K glycopolypeptide. The T2C4-specific 13.5K EP and the 8617-specific 11K EP appear unrelated to any other polypeptides. These immunoprecipitation data provide evidence that early gene block I (map position 1 to 11) may encode major 53K, 15K, and 14.5K polypeptides, and minor 28K, 18K, and 12K polypeptides, and that all or some of the gene for 15K and 14.5K lies within map position 1 to 8. The surprisingly complex pattern of polypeptides coded by early gene block I raises the possibility that some polypeptides may be coded by overlapping "spliced" mRNA's. The possible block locations of the genes for the 21K, 13.5K, and 11.5K polypeptides are discussed.     

GOurmet: A tool for quantitative comparison and visualization of gene expression profiles based on gene ontology (GO) distributions

Background  

The ever-expanding population of gene expression profiles (EPs) from specified cells and tissues under a variety of experimental conditions is an important but difficult resource for investigators to utilize effectively. Software tools have been recently developed to use the distribution of gene ontology (GO) terms associated with the genes in an EP to identify specific biological functions or processes that are over- or under-represented in that EP relative to other EPs. Additionally, it is possible to use the distribution of GO terms inherent to each EP to relate that EP as a whole to other EPs. Because GO term annotation is organized in a tree-like cascade of variable granularity, this approach allows the user to relate (e.g., by hierarchical clustering) EPs of varying length and from different platforms (e.g., GeneChip, SAGE, EST library).     

Partial muting leads to age-dependent modification of motor patterns underlying crystallized zebra finch song

The crystallized structure of adult zebra finch (Taeniopygia guttata) song is modifiable if sensory feedback is altered during sound production. Such song plasticity has been studied by examining acoustic modifications to the motif; however, the underlying changes to the vocal motor patterns of these acoustic modifications have not been addressed. Adult birds in two age categories (young=90-120 days or middle aged 150-250 days) that sang crystallized song were used in the experiment. Vocal motor patterns were monitored by recording respiratory air sac pressure before, during, and after song plasticity was induced by partial or complete reduction of phonation (i.e., "partial muting"). Birds were recorded until changes in air sac pressure patterns underlying the song structure were observed (up to 160 days). Young adult birds were more likely to insert shorter duration (<125 ms) expiratory pulses (EPs) into the motif than middle-aged adults. These shorter duration EPs were produced with a unique pressure pattern relative to the intact song, and therefore appeared to be generated by novel motor gestures. Stuttering (atypical repetition of an EP) was observed when these novel EPs were inserted into the motif, regardless of age. The EP of the distance call, which is also a learned vocalization in zebra finches, showed a similar reduction in duration if EPs were also shortened in the song. The emergence of shorter duration EPs was not related to sound production, or nonspecific effects of the surgical procedure, which suggests an age-dependent neural process for song plasticity.     

Hotelling's T2 multivariate profiling for detecting differential expression in microarrays

The most widely used statistical methods for finding differentially expressed genes (DEGs) are essentially univariate. In this study, we present a new T(2) statistic for analyzing microarray data. We implemented our method using a multiple forward search (MFS) algorithm that is designed for selecting a subset of feature vectors in high-dimensional microarray datasets. The proposed T2 statistic is a corollary to that originally developed for multivariate analyses and possesses two prominent statistical properties. First, our method takes into account multidimensional structure of microarray data. The utilization of the information hidden in gene interactions allows for finding genes whose differential expressions are not marginally detectable in univariate testing methods. Second, the statistic has a close relationship to discriminant analyses for classification of gene expression patterns. Our search algorithm sequentially maximizes gene expression difference/distance between two groups of genes. Including such a set of DEGs into initial feature variables may increase the power of classification rules. We validated our method by using a spike-in HGU95 dataset from Affymetrix. The utility of the new method was demonstrated by application to the analyses of gene expression patterns in human liver cancers and breast cancers. Extensive bioinformatics analyses and cross-validation of DEGs identified in the application datasets showed the significant advantages of our new algorithm.     

Visual evoked potentials to structured stimuli in children with cataracts

EPs elicited by diffuse light flashes and check patterns, were studied in children and juveniles with congenital and traumatic cataract, during different periods and various durations of deprivation, as well as in a control group of children and juveniles with normal vision. Specific age characteristics were found in the EPs to check patterns in seven years-old children. In children with congenital cataracts significant changes in the shape and amplitude-temporal EP parameters were recorded. In children with traumatic cataracts, presumably developed after the end of the sensitive period, the changes in EPs were much less pronounced. The obtained data are discussed from the point of view of the duration of the sensitive period of the formation of the visual function in humans and of informative value of different EP components.     

Partial muting leads to age‐dependent modification of motor patterns underlying crystallized zebra finch song

The crystallized structure of adult zebra finch (Taeniopygia guttata) song is modifiable if sensory feedback is altered during sound production. Such song plasticity has been studied by examining acoustic modifications to the motif; however, the underlying changes to the vocal motor patterns of these acoustic modifications have not been addressed. Adult birds in two age categories (young = 90–120 days or middle aged 150–250 days) that sang crystallized song were used in the experiment. Vocal motor patterns were monitored by recording respiratory air sac pressure before, during, and after song plasticity was induced by partial or complete reduction of phonation (i.e., “partial muting”). Birds were recorded until changes in air sac pressure patterns underlying the song structure were observed (up to 160 days). Young adult birds were more likely to insert shorter duration (<125 ms) expiratory pulses (EPs) into the motif than middle‐aged adults. These shorter duration EPs were produced with a unique pressure pattern relative to the intact song, and therefore appeared to be generated by novel motor gestures. Stuttering (atypical repetition of an EP) was observed when these novel EPs were inserted into the motif, regardless of age. The EP of the distance call, which is also a learned vocalization in zebra finches, showed a similar reduction in duration if EPs were also shortened in the song. The emergence of shorter duration EPs was not related to sound production, or nonspecific effects of the surgical procedure, which suggests an age‐dependent neural process for song plasticity. © 2004 Wiley Periodicals, Inc. J Neurobiol, 2004     

Gene selection for sample classifications in microarray experiments

DNA microarray technology provides useful tools for profiling global gene expression patterns in different cell/tissue samples. One major challenge is the large number of genes relative to the number of samples. The use of all genes can suppress or reduce the performance of a classification rule due to the noise of nondiscriminatory genes. Selection of an optimal subset from the original gene set becomes an important prestep in sample classification. In this study, we propose a family-wise error (FWE) rate approach to selection of discriminatory genes for two-sample or multiple-sample classification. The FWE approach controls the probability of the number of one or more false positives at a prespecified level. A public colon cancer data set is used to evaluate the performance of the proposed approach for the two classification methods: k nearest neighbors (k-NN) and support vector machine (SVM). The selected gene sets from the proposed procedure appears to perform better than or comparable to several results reported in the literature using the univariate analysis without performing multivariate search. In addition, we apply the FWE approach to a toxicogenomic data set with nine treatments (a control and eight metals, As, Cd, Ni, Cr, Sb, Pb, Cu, and AsV) for a total of 55 samples for a multisample classification. Two gene sets are considered: the gene set omegaF formed by the ANOVA F-test, and a gene set omegaT formed by the union of one-versus-all t-tests. The predicted accuracies are evaluated using the internal and external crossvalidation. Using the SVM classification, the overall accuracies to predict 55 samples into one of the nine treatments are above 80% for internal crossvalidation. OmegaF has slightly higher accuracy rates than omegaT. The overall predicted accuracies are above 70% for the external crossvalidation; the two gene sets omegaT and omegaF performed equally well.     

CT-FOCS: a novel method for inferring cell type-specific enhancer–promoter maps

Spatiotemporal gene expression patterns are governed to a large extent by the activity of enhancer elements, which engage in physical contacts with their target genes. Identification of enhancer–promoter (EP) links that are functional only in a specific subset of cell types is a key challenge in understanding gene regulation. We introduce CT-FOCS (cell type FOCS), a statistical inference method that uses linear mixed effect models to infer EP links that show marked activity only in a single or a small subset of cell types out of a large panel of probed cell types. Analyzing 808 samples from FANTOM5, covering 472 cell lines, primary cells and tissues, CT-FOCS inferred such EP links more accurately than recent state-of-the-art methods. Furthermore, we show that strictly cell type-specific EP links are very uncommon in the human genome.     

Exploring the predictive value of the evoked potentials score in MS within an appropriate patient population: a hint for an early identification of benign MS?

ABSTRACT: BACKGROUND: The prognostic value of evoked potentials (EPs) in multiple sclerosis (MS) has not been fully established. The correlations between the Expanded Disability Status Scale (EDSS) at First Neurological Evaluation (FNE) and the duration of the disease, as well as between EDSS and EPs, have influenced the outcome of most previous studies. To overcome this confounding relations, we propose to test the prognostic value of EPs within an appropriate patient population which should be based on patients with low EDSS at FNE and short disease duration. METHODS: We retrospectively selected a sample of 143 early relapsing remitting MS (RRMS) patients with an EDSS < 3.5 from a larger database spanning 20 years. By means of bivariate logistic regressions, the best predictors of worsening were selected among several demographic and clinical variables. The best multivariate logistic model was statistically validated and prospectively applied to 50 patients examined during 2009--2011. RESULTS: The Evoked Potentials score (EP score) and the Time to EDSS 2.0 (TT2) were the best predictors of worsening in our sample (Odds Ratio 1.10 and 0.82 respectively, p=0.001). Low EP score (below 15--20 points), short TT2 (lower than 3--5 years) and their interaction resulted to be the most useful for the identification of worsening patterns. Moreover, in patients with an EP score at FNE below 6 points and a TT2 greater than 3 years the probability of worsening was 10% after 4--5 years and rapidly decreased thereafter. CONCLUSIONS: In an appropriate population of early RRMS patients, the EP score at FNE is a good predictor of disability at low values as well as in combination with a rapid buildup of disability. Interestingly, an EP score at FNE under the median together with a clinical stability lasting more than 3 years turned out to be a protective pattern. This finding may contribute to an early identification of benign patients, well before the term required to diagnose Benign MS (BMS).     

Inferring gene regulatory relationships by combining target-target pattern recognition and regulator-specific motif examination

Although microarray data have been successfully used for gene clustering and classification, the use of time series microarray data for constructing gene regulatory networks remains a particularly difficult task. The challenge lies in reliably inferring regulatory relationships from datasets that normally possess a large number of genes and a limited number of time points. In addition to the numerical challenge, the enormous complexity and dynamic properties of gene expression regulation also impede the progress of inferring gene regulatory relationships. Based on the accepted model of the relationship between regulator and target genes, we developed a new approach for inferring gene regulatory relationships by combining target-target pattern recognition and examination of regulator-specific binding sites in the promoter regions of putative target genes. Pattern recognition was accomplished in two steps: A first algorithm was used to search for the genes that share expression profile similarities with known target genes (KTGs) of each investigated regulator. The selected genes were further filtered by examining for the presence of regulator-specific binding sites in their promoter regions. As we implemented our approach to 18 yeast regulator genes and their known target genes, we discovered 267 new regulatory relationships, among which 15% are rediscovered, experimentally validated ones. Of the discovered target genes, 36.1% have the same or similar functions to a KTG of the regulator. An even larger number of inferred genes fall in the biological context and regulatory scope of their regulators. Since the regulatory relationships are inferred from pattern recognition between target-target genes, the method we present is especially suitable for inferring gene regulatory relationships in which there is a time delay between the expression of regulating and target genes.     

Functional sites in protein families uncovered via an objective and automated graph theoretic approach

We report a method for detection of recurring side-chain patterns (DRESPAT) using an unbiased and automated graph theoretic approach. We first list all structural patterns as sub-graphs where the protein is represented as a graph. The patterns from proteins are compared pair-wise to detect patterns common to a protein pair based on content and geometry criteria. The recurring pattern is then detected using an automated search algorithm from the all-against-all pair-wise comparison data of proteins. Intra-protein pattern comparison data are used to enable detection of patterns recurring within a protein. A method has been proposed for empirical calculation of statistical significance of recurring pattern. The method was tested on 17 protein sets of varying size, composed of non-redundant representatives from SCOP superfamilies. Recurring patterns in serine proteases, cysteine proteases, lipases, cupredoxin, ferredoxin, ferritin, cytochrome c, aspartoyl proteases, peroxidases, phospholipase A2, endonuclease, SH3 domain, EF-hand and lectins show additional residues conserved in the vicinity of the known functional sites. On the basis of the recurring patterns in ferritin, EF-hand and lectins, we could separate proteins or domains that are structurally similar yet different in metal ion-binding characteristics. In addition, novel recurring patterns were observed in glutathione-S-transferase, phospholipase A2 and ferredoxin with potential structural/functional roles. The results are discussed in relation to the known functional sites in each family. Between 2000 and 50,000 patterns were enumerated from each protein with between ten and 500 patterns detected as common to an evolutionarily related protein pair. Our results show that unbiased extraction of functional site pattern is not feasible from an evolutionarily related protein pair but is feasible from protein sets comprising five or more proteins. The DRESPAT method does not require a user-defined pattern, size or location of the pattern and therefore, has the potential to uncover new functional sites in protein families.     

A multivariate approach for integrating genome-wide expression data and biological knowledge

MOTIVATION: Several statistical methods that combine analysis of differential gene expression with biological knowledge databases have been proposed for a more rapid interpretation of expression data. However, most such methods are based on a series of univariate statistical tests and do not properly account for the complex structure of gene interactions. RESULTS: We present a simple yet effective multivariate statistical procedure for assessing the correlation between a subspace defined by a group of genes and a binary phenotype. A subspace is deemed significant if the samples corresponding to different phenotypes are well separated in that subspace. The separation is measured using Hotelling's T(2) statistic, which captures the covariance structure of the subspace. When the dimension of the subspace is larger than that of the sample space, we project the original data to a smaller orthonormal subspace. We use this method to search through functional pathway subspaces defined by Reactome, KEGG, BioCarta and Gene Ontology. To demonstrate its performance, we apply this method to the data from two published studies, and visualize the results in the principal component space.     

Testing the link between species interactions and species co‐occurrence in a trophic network

Species interactions are dynamic processes that vary across environmental and ecological contexts, and operate across scale boundaries, making them difficult to quantify. Nevertheless, ecologists are increasingly interested in inferring species interactions from observational data using statistical analyses of their spatial co‐occurrence patterns. Trophic interactions present a particular challenge, as predators and prey may frequently or rarely co‐occur, depending on the spatial or temporal scale of observation. In this study, we investigate the accuracy of inferred interactions among species that both compete and trophically interact. We utilized a long‐term dataset of pond‐breeding amphibian co‐occurrences from Mt Rainier National Park (Washington, USA) and compiled a new dataset of their empirical interactions from the literature. We compared the accuracy of four statistical methods in inferring these known species interactions from spatial associations. We then used the best performing statistical method, the Markov network, to further investigate the sensitivity of interaction inference to spatial scale‐dependence and the presence of predators. We show that co‐occurrence methods are generally inaccurate when estimating trophic interactions. Further the strength and sign of inferred interactions were dependent upon the spatial scale of observation and predator presence influenced the detectability of competitive interactions among prey species. However, co‐occurrence analysis revealed new patterns of spatial association among pairs of species with known interactions. Overall, our study highlights a limiting frontier in co‐occurrence theory and the disconnect between widely implemented methodologies and their ability to accurately infer interactions in trophically‐structured communities.