Inferring intra-motif dependencies of DNA binding sites from ChIP-seq data Background: Statistical modeling of transcription factor binding sites is one of the classical fields in bioinformatics. The position weight matrix (PWM) model, which assumes statistical independence among all nucleotides in a binding site, used to be the standard model for this task for more than three decades but its simple assumptions are increasingly put into question. Recent high-throughput sequencing methods have provided data sets of sufficient size and quality for studying the benefits of more complex models. However, learning more complex models typically entails the danger of overfitting, and while model classes that dynamically adapt the model complexity to data have been developed, effective model selection is to date only possible for fully observable data, but not, e.g., within de novo motif discovery. Results: To address this issue, we propose a stochastic algorithm for performing robust model selection in a latent variable setting. This algorithm yields a solution without relying on hyperparameter-tuning via massive cross-validation or other computationally expensive resampling techniques. Using this algorithm for learning inhomogeneous parsimonious Markov models, we study the degree of putative higher-order intra-motif dependencies for transcription factor binding sites inferred via de novo motif discovery from ChIP-seq data. We find that intra-motif dependencies are prevalent and not limited to first-order dependencies among directly adjacent nucleotides, but that second-order models appear to be the significantly better choice. Conclusions: The traditional PWM model appears to be indeed insufficient to infer realistic sequence motifs, as it is on average outperformed by more complex models that take into account intra-motif dependencies. Moreover, using such models together with an appropriate model selection procedure does not lead to a significant performance loss in comparison with the PWM model for any of the studied transcription factors. Hence, we find it worthwhile to recommend that any modern motif discovery algorithm should attempt to take into account intra-motif dependencies. In this work, we have studied intra-motif dependencies within transcription factor binding sites based on ChIPseq data. Due to the danger of overfitting, such a study requires (i) a model that is capable of adapting its complexity dynamically to a given ChIP-seq data set and (ii) an effective learning algorithm that allows a model selection even within de novo motif discovery, i.e., in the presence of latent variables. Both is given by inhomogeneous parsimonious Markov models and a stochastic algorithm for model selection in the presence of latent variables as presented in this work. In the empirical part, we have seen that a certain caution in interpreting results is warranted, as ChIP-seq data sets can contain secondary motifs that can lead to an overestimation of motif complexity. We have corrected against this effect and observed that intra-motif dependencies remain prevalent and that attempting to model second-order dependencies appears to be the best choice on average. Hence, we suggest that any modern motif discovery algorithm should attempt to take into account additional statistical features beyond position-specific mononucleotide frequencies to some degree. We have investigated the prevalence of intra-motif dependencies in transcription factor binding sites as well as the task of utilizing them for improving de novo motif discovery from ChIP-seq data. However, we have observed in the previous sections that a certain caution in drawing conclusions from such studies is warranted. In several cases, an improved classification of ChIP-positives to background can be caused by the presence of multiple motifs in the data, which may be explained more appropriately by two independent sequence motifs instead of one motif with weak consensus but high dependencies. Being aware of this problem, we devised a threshold-based method for distinguishing multi-motif occurrences from "true" intramotif dependencies.. It should be noted that some cases are at the borderline and no threshold can give a perfect separation between both cases. For instance, the data sets JUN and JUND yield a high Jensen-Shannon divergence because roughly half of the binding sites show a deletion of single nucleotide in the motif center. Such a feature yields strong dependencies and also a high evidence for multiple motifs, even though we still may consider all binding sites to be bound by the same protein.. But even for those datasets that appear to contain predominantly binding sites corresponding to one motif of a single TF, we find that de novo motif discovery can be substantially improved by modeling intra-motif dependencies. Taking into account dependencies with PMMs does not only significantly outperform the PWM model on average, but there is not a single case where using a PWM model would have been a significantly better choice than any PMM, all of which are capable of adapting their model structure to data. These findings indicate that intra-motif dependencies are widely present and not an exception that only occurs for special TFs such as CTCF [36] .. An interesting question concerns the biophysical cause of the observed intra-motif dependencies. One hypothesis is that transcription factors mainly detect the shape of the DNA and not precise nucleotides [17] . On the other hand, we have also observed a case like YY1 where a dependency actually reveals the presence or absence of a specific dinucleotide that is present in some, but not all, binding sites. Since YY1 is a zinc-finger protein, this feature might be explained by a somewhat optional contact of one of the zinc fingers to DNA, as proposed for another zinc finger protein [41] . Hence, there may be not one single, but rather multiple biophysical reasons for observing intra-motif dependencies, but in all cases we are capable of exploiting these additional statistical features to obtain a more realistic representation of binding sites in terms of a more accurate statistical model.. We must be cautious with extreme conclusions and stop short of declaring the ultimate death of the PWM model. For estimating a sequence motif from limited training data, e.g., a few experimentally verified binding sites, the PWM model will certainly remain the optimal choice. For fully observable data, more than 10 2 training sequences are typically required before any model with more parameters than a PWM model can effectively utilize additional information in the data [34, 42] . Moreover, due to its small parameter space, the PWM model is more robust to lowquality data, where models that attempt to infer dependencies may be more prone to adapting to noise. For inferring sequence motifs from high quality ChIP-seq data, however, neglecting intra-motif dependencies yieldson average -a significantly worse motif, so modern motif discovery algorithms should attempt to take into account intra-motif dependencies to some degree.. In addition to the general presence of intra-motif dependencies, we have investigated their degree by comparing different models that are capable of taking into account dependencies up to a certain maximal order. We find that for about a third of the relevant data sets intra-motif dependencies of higher order yield an improved classification performance and thus an improved de novo motif discovery. The degree of dependency that continues to increase classification varies among data sets. We find the second-order PMM to be the best choice on average, closely followed the third-and fourth-order PMMs with a non-significant difference. There are noteworthy cases, though, in which third-and fourth-order models are helpful for enhancing motif discovery, but this kind of higher-order dependency appears to be the exception.. It may be possible that the data quality resulting from current ChIP-seq experiments is just not high enough to effectively utilize all intra-motif dependencies that protein-DNA binding truly involves. Whereas a decade ago data quality prohibited effectively utilizing dependencies at all, it is nowadays apparently possible to reliably take into account first-and second-order dependencies when present in data and to choose for a simple model otherwise. We may thus speculate that advances in experimental technologies will enable to effectively learn even more sophisticated models that are capable of robustly taking into account additional features.. De novo motif discovery remains interesting in the sense that it is not well understood what exactly a sequence motif truly is. For more than two decades, the sequence logo, which corresponds to the sufficient statistics or to the parameters of the PWM model, used to be the common perception of a sequence motif of a TF. While this notion is gradually shifting towards first-order dependency models, the results from this work indicate that at least second-order dependencies should be considered, and we believe that this is not the end of the story. Perhaps it may be more appropriate to generally define a sequence motif as the set of all statistical features describing the set of binding sites of the TF of interest. Attempting to unveil which statistical features are actually relevant for the process of protein-DNA binding and which are not will certainly remain an ambitious goal for years to come. In this work, we investigate 50 different human ChIP-seq data sets from the Uniform TFBS track of the ENCODE project [37, 38] ('Methods'). First, we infer an overrepresented sequence motif in each of these data sets by using models that take into account different degrees of intramotif dependencies within motif discovery and we discuss several obstacles that arise due to the nature of ChIP-seq data. We then evaluate the classification performance of the underlying models on previously unseen test data and investigate how widespread intra-motif dependencies in putative binding sites of human transcription factors are. Finally, we conduct a qualitative analysis of the resulting sequence motifs for several transcription factors in order to investigate statistical features that enable an improved motif discovery. In the first two sections, we discuss the model and the robust learning approach that allows PMM model selection without relying on hyperparameter-tuning via crossvalidation. Afterwards, we describe the procedures for predicting binding sites from learned motif models, the test for multiple motif occurrence, and the classification approach used for comparing different models. Finally, we provide information regarding the implementation and the data sets used in the study. All models and learning algorithms are implemented in Java and based on the Jstacs library [49] . 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O B I O O O O O B I O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I I I O O B I I I O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B I O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I O O B I I I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I I I O O O O O O O O O O B I I I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Intoxicated speech detection: A fusion framework with speaker-normalized hierarchical functionals and GMM supervectors Segmental and suprasegmental speech signal modulations offer information about paralinguistic content such as affect, age and gender, pathology, and speaker state. Speaker state encompasses medium-term, temporary physiological phenomena influenced by internal or external bio-chemical actions (e.g., sleepiness, alcohol intoxication). Perceptual and computational research indicates that detecting speaker state from speech is a challenging task. In this paper, we present a system constructed with multiple representations of prosodic and spectral features that provided the best result at the Intoxication Subchallenge of Interspeech 2011 on the Alcohol Language Corpus. We discuss the details of each classifier and show that fusion improves performance. We additionally address the question of how best to construct a speaker state detection system in terms of robust and practical marginalization of associated variability such as through modeling speakers, utterance type, gender, and utterance length. As is the case in human perception, speaker normalization provides significant improvements to our system. We show that a held-out set of baseline (sober) data can be used to achieve comparable gains to other speaker normalization techniques. Our fused frame-level statistic-functional systems, fused GMM systems, and final combined system achieve unweighted average recalls (UARs) of 69.7%, 65.1%, and 68.8%, respectively, on the test set. More consistent numbers compared to development set results occur with matched-prompt training, where the UARs are 70.4%, 66.2%, and 71.4%, respectively. The combined system improves over the Challenge baseline by 5.5% absolute (8.4% relative), also improving upon our previously best result. Understanding and modeling variations in prosody and articulation are at the core of spoken language study. Increasingly, researchers are interested in computing the information carried in a person's speech beyond the linguistic content. Notably, the study of paralinguistic aspects of speech has recently included modeling and estimating accuracy was worst near the cutoff, and improved with increased intoxication or sobriety (Levit et al., 2001 ). In our previous work at the Interspeech 2011 Speaker State Intoxication Subchallenge (Schuller et al., 2011) , our proposed approach yielded the top unweighted average recall of 70.5% on the Alcohol Language Corpus test set by fusing scores from various feature-representation models, utilizing hierarchical feature extraction, and applying two types of speaker normalization (Bone et al., 2011) . Corresponding to speaker normalization benefits, differences at the phoneme level have been discovered in single-vowel experiments, where speaker-specific models for voice-excitation features achieved an accuracy of 70% (Sigmund and Zelinka, 2011) .. The present work focuses on two questions. Firstly, we investigate speaker state classification with our general model, focusing on improving performance on the Intoxication Subchallenge test set. Our approach is detailed, and the benefits of individual components are thoroughly evaluated. Since previous studies have noted perceptual differences in prosodic expression between genders during intoxicated speech, we also investigate gender-dependent models.. Our complete fusion model is comprised of: (i) hierarchical contour functionals; (ii) alternative feature representation with Gaussian mixture model (GMM) mean supervectors, Universal Background Model (UBM) weight posterior probability (UWPP) supervectors, and latent factor analysis (LFA) supervectors; and (iii) global and iterative speaker normalization on the feature vectors generated by each of these representations.. Secondly, we focus our intoxicated speech detection analysis toward a system that can marginalize certain sources of variability, in turn gaining understanding about the perceptibility of intoxicated speech under different conditions. Experimental results are presented on the development dataset for prompt-type specific models, experiments using matched prompts that occur in both the sober and intoxicated condition, and various speaker normalization techniques. Prompt-type specific models may indicate that certain types of prompts are more or less difficult to model due to increasing cognitive load. Matched-prompt data reduces the tendency of a system to model lexical content. One of the proposed speaker normalizations that is suitable for live applications is referred to as background normalization in this paper. This type of normalization makes a practical assumption that each speaker is enrolled with sober intoxication data. Having such held-out enrollment data from unseen prompts leads to accuracies comparable to other speaker normalization techniques that may be dependent on the speaker's class-label distribution.. The rest of this article is organized as follows: Section 2 describes the ALC corpus and experimental setup; Section 3 details the feature extraction, modeling, and classification methods utilized; general speaker state experimental results are discussed in Section 4; Section 5 presents results of the final, fused model; error analysis is conducted in Section 6; and conclusions and ideas for future research are presented in Section 7. Speaker states are medium-term phenomena that are reflected, at least in part, in the speech signal patterns. We have presented a fused system composed of five classification subsystems that provides robustness and improvements when evaluated on the test set. The final unweighted accuracy on the test set was 71.4% when training with only matched-prompts (approximately matched lexical content) and 68.8% when modeling with the official prompt set. If optimized to improve UWA directly for the test set (assuming a 1-1 reference label distribution), we were able to achieve 70.5% performance (Bone et al., 2011) . While such a result from models trained using the non-matched prompts was better than our results without assuming the test set label distribution (68.8%), training with matched prompts avoided modeling of extraneous lexical content and improved our final prediction (71.4%). We demonstrated that global and iterative speaker normalizations are both useful tools, although global normalization generally obtained higher results. In a potentially more realistic experiment, we have shown that with background speaker normalization, in which only baseline data is required for each speaker, we were able to achieve higher results than with no speaker normalization, and very similar numbers to what the related iterative speaker normalization produced. Importantly, we performed all classifications in this paper without making assumptions about the test set label distribution, showing some robustness in our system design.. Individual prompt-types were classified and the results were analyzed -the observations were congruous with psychological theory. It was demonstrated that modeling each prompt separately may provide higher recalls, but we did not explore this on the test set since we did not have a prompt recognition system and this information may be known in live systems. We corroborated perceptual studies on increasing intoxication perception given either amplified cognitive load or higher BAC. We demonstrated that longer utterances increased detection performance. It was also shown that gender-dependent models did not improve upon combined-gender models in our hierarchical functional subsystem.. Lastly, we showed through recall analysis that our system could detect 'alcoholized' versus 'non-alcoholized' speech, but could not do so explicitly at the threshold of 0.5 g/L. An important finding was that when using background speaker normalization on the matched-prompt development set, the number of predicted drunk utterance increases with BAC, ρ = 0.35 (p < 0.05) -an indication that this alternative speaker normalization framework will make even less implicit assumptions about the distribution of evaluation set class-labels. Unweighted average recall improved for higher BAC and for longer utterance lengths. These results should inform design of real-world speaker state detection systems.. Our model is general, and may be readily applied to other speaker state investigations, such as sleepiness detection, or it can be fused with additional, orthogonal subsystems. Future research into intoxicated speech detection may focus on further feature optimization, such that more complex modeling may be successful on a reduced set of features. Phonetic-based prosodic systems appear promising and intuitive (Honig et al., 2011) . Dynamical modeling of prosody may also prove beneficial. Since we have demonstrated that recall increases with increasing BAC, ordinal techniques to create soft-labels may be useful for improved modeling. Our framework consists of five subsystems. Two are built on static-and hierarchical-functionals and are different only in the adopted speaker normalization method -global versus iterative. The remaining three are GMM supervector constructs that incorporate global speaker normalization. The static/hierarchical functional systems are described in the first subsection, followed by details of the GMM systems. Next, speaker normalization techniques are discussed, followed by description of a feature selection technique with which we have experimented. Lastly, score-level fusion is described. All classification is performed using LIBLINEAR (Fan et al., 2008; Chang and Lin, 2011) , except for RBF-kernel SVM in association with feature selection which uses LIBSVM (Chang and Lin, 2011) .

O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B I B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Spatial transformation of motion and deformation fields using nonrigid registration Abstract-In this paper, we present a technique that can be used to transform the motion or deformation fields defined in the coordinate system of one subject into the coordinate system of another subject. Such a transformation accounts for the differences in the coordinate systems of the two subjects due to misalignment and size/shape variation, enabling the motion or deformation of each of the subjects to be directly quantitatively and qualitatively compared. The field transformation is performed by using a nonrigid registration algorithm to determine the intersubject coordinate system mapping from the first subject to the second subject. This fixes the relationship between the coordinate systems of the two subjects, and allows us to recover the deformation/motion vectors of the second subject for each corresponding point in the first subject. Since these vectors are still aligned with the coordinate system of the second subject, the inverse of the intersubject coordinate mapping is required to transform these vectors into the coordinate system of the first subject, and we approximate this inverse using a numerical line integral method. The accuracy of our numerical inversion technique is demonstrated using a synthetic example, after which we present applications of our method to sequences of cardiac and brain images.

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Theoretical study on novel nitrogen-rich energetic compounds of bis(amino)-azobis(azoles) with tetrazene unit Abstract Six stereoisomers of 5,5'-bis(amino)-1,1'-azobis (tetrazoles) and 30 other structures, including all possible bis(amino)-azobis(azoles) with an N−N0N−N unit, were designed. The molecular geometries were fully optimized at the DFT-B3LYP level with the 6-31++g (d, p) basis set. From the absence of any imaginary frequency in the infrared vibration frequency spectrum, it is predicted that all these studied structures may exist in stable forms. The results of the total energies of the stereoisomers of 5,5'-bis(amino)-1,1'-azobis(tetrazoles) indicate that the two symmetric trans-form structures are more likely to exist than the other four. The pyrolysis process, chemical stability and molecular electrostatic potential were studied via the investigation of their electronic structure. Heats of formation (HOFs) were calculated using the atomization energy method based on the results of the harmonic vibration frequencies, and a linear relationship was found between the HOF and nitrogen chain or nitrogen content. Densities of the title compounds were predicted with the Monte Carlo method. Finally, according to the results of the calculated HOFs and densities, the explosive parameters of these compounds were calculated using the Kamlet−Jacobs formula. 5,5'-Bis(amino)-1,1'-azobis(tetrazoles) and its isomer 5,5'-bis (amino)-2,2'-azobis(tetrazoles) may have potential for use as energetic compounds. High nitrogen-contented heterocyclic compounds have attracted increasing attention in the field of energetic materials due to their excellent thermal stability, high heats of formation (HOFs) caused by the high strain energies and the many N−N single or double bonds, and their environmentally friendly properties [1] [2] [3] [4] [5] [6] [7] [8] [9] . Azoles and their derivatives are part of these high nitrogen-contented heterocyclic compounds. With fascinating structures and excellent properties, azole-based derivatives, polycyclic compounds, complexes and salts have been intensively studied in an effort to meet the increased demand for new energetic materials [5, [10] [11] [12] [13] [14] [15] [16] [17] [18] . Klapötke and his co-workers [19] [20] [21] [22] [23] and Shreeve's [17, 24, 25] group have performed lots of studies of nitrogen-rich energetic compounds on the basis of triazole, tetrazole and their derivatives. Talawar et al. [3, 26, 27] and Huynh et al. [28, 29] investigated the coordination complexes of the 5-nitro-tetrazolate anion as potential primary explosives. 1,5-diaminotetrazole has a nitrogen content of 84.8 %, good thermal stability and abundant coordination atoms, and thus has been widely utilized to synthesize complexes. Zhang et al. synthesized a series of coordination compounds using 1,5-diaminotetrazole as the ligand, transition metal ions as the central metal cation, and perchlorate, picrate, styphnate, nitrate, or chloride as the outer ion [30] [31] [32] [33] [34] . All of these complexes have good properties and offer the potential for application as energetic materials.. With the development of calculation chemistry, theoretical calculation has evolved into a useful method for better understanding structure-property relationships and has been widely used to explore new energetic compounds [35] [36] [37] [38] [39] [40] [41] [42] . Zhang et al. [43] studied the effect of the π-stack structure in explosives in reducing sensitivity toward external mechanical stimuli. Li et al. [44] investigated substituted pyrazole derivatives with density functional theory (DFT) and predicted that the HOF of 3,4,5-triazidopyrozole was as high as 1240.6 kJmol -1 . Zhang et al. [45] reported their theoretical investigation of the electronic structure and the explosive properties of diazidotetrazole and its isomers. Politzer et al. [46, 47] have done many works on the method of calculation for the performances of energetic compounds, such as to quantitatively analyze the molecular surfaces, an improved crystal density prediction method. With its high efficiency and safety, theoretical calculation can play an increasingly important role when seeking new energetic compounds.. Coupling two molecules of heterocyclic compounds together provides a good way of obtaining explosives with higher nitrogen content and better properties. It was reported [48] that introducing the azo (−N0N−) linkage into nitrogen heterocyclics can not only improve their HOF but also their stability. In 2010, Li et al. [49] synthesized 1,1′-azobis-1,2,3-triazole with a stable N8 chain by coupling two 1-amino-1,2,3-triazole molecules together, and investigated its thermal stability and photochromic properties. 1,1′-azobis-1,2,3-triazole has a higher HOF than 4,4′-azobis-1,3,4-triazole which has an N−N0N−N structure and this is thought to result from the former having more N−N bonds. Later, 1,10-azobis(tetrazole) was synthesized as a highly energetic nitrogen-rich compound with an N10 chain by Klapötke et al. [19] . A considerable number of studies have confirmed that high nitrogen compounds with N chain perform well as energetic compounds.. Based on the previous studies, we investigated theoretically all the bis(amino)-azobis(azoles) with an N−N0N−N structure. These compounds can be formed when two molecules of bis(amino) azoles (Fig. 1) are coupled together. Since six stereoisomers are thought to exist for each compound, we took 5,5'-bis(amino)-1,1'-azobis(tetrazoles) as an example to calculate the total energies of its six stereoisomers and the results showed that two types of configuration possibly exist; hence, we fully optimized these two configuration type of all the bis(amino)-azobis(azoles) (shown in Fig. 2 ) and tried to determine the most stable configuration of each compound. The fact that no imaginary frequencies were found in the infrared vibration spectra proved that all the investigated structures corresponded to a minimum point on the potential energy surface. Natural bond orbital analyses were undertaken to explore the electronic structures of these compounds. The HOF, density and explosive parameters were calculated to evaluate their potential for use as novel energetic compounds. In this study, 36 structures of all bis(amino)-azobis(azoles) were designed and their stable configurations were obtained. The investigation of their molecular geometries, electronic structures, heats of formation and explosive properties proved:. (1) The two symmetrical trans-form structures are more stable than the other four stereoisomers. When the two −NH 2 groups of the bis(amino) azoles molecule, which form the bis(amino)-azobis(azoles) compound, substitute in the adjacent atoms of the five-membered ring, the C i trans-form structure is the most stable; otherwise the C 2 trans-form structure is the most stable. All the structures are quite close to planar configurations. (2) With the increase in the number of nitrogen atoms, the nitrogen atoms in the five-membered ring are more and more likely to be coordinated atoms than N1(N1') and N7(N7') when the compounds coordinate with cations. All of the studied compounds may start to decompose with the rupture of the two azole rings. N atom in metaposition of the linked C atom may contribute to the thermal stability of the compounds.

O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B I I O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Validating the semantics of a medical iconic language using ontological reasoning a b s t r a c t 22To help clinicians read medical texts such as clinical practice guidelines or drug monographs, we pro-23 posed an iconic language called VCM. This language can use icons to represent the main medical con-24 cepts, including diseases, symptoms, treatments and follow-up procedures, by combining various 25 pictograms, shapes and colors. However, the semantics of this language have not been formalized, and 26 users may create inconsistent icons, e.g. by combining the ''tumor'' shape and the ''sleeping'' pictograms 27 into a ''tumor of sleeping'' icon. This work aims to represent the VCM language using DLs and OWL for 28 evaluating its semantics by reasoners, and in particular for determining inconsistent icons. 29 We designed an ontology for formalized the semantics of VCM icons using the Protégé editor and 30 scripts for translating the VCM lexicon in OWL. We evaluated the ability of the ontology to determine 31 icon consistency for a set of 100 random icons. The evaluation showed good results for determining icon 32 consistency, with a high sensitivity. The ontology may also be useful for the design of mapping between 33 VCM and other medical terminologies, for generating textual labels for icons, and for developing user 34 interfaces for creating VCM icons. 35

O O B O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


PHORMA: Perfectly Hashable Order Restricted Multidimensional Arrays In this paper we propose a simple and efficient data structure yielding a perfect hashing of quite general arrays. The data structure is named phorma, which is an acronym for perfectly hashable order restricted multidimensional array. We have defined a data structure generator which permits the perfect hash of order restricted multidimensional arrays A(a, B, C). The restrictions accord a general type which might be itself a boolean function on the δ's, we use the NW-family R ∞ in a C-restricted implicit enumeration search. The whole scheme is summarized by two facts: (i) an α ∈ A(a, B, C) induces three pieces of information, ⌊α⌋, α ց and ⌈α⌉ a and is recoverable from the first two, α ≡ (⌊α⌋, α ց ); (ii) this decomposition reflects in the rank formula for a perfect hashing of A(a, B, C): h(α) = ||⌊α⌋|| + h ⌈α⌉ a (α ց ). This encoding scheme has the power of perfectly addressing huge and quite intricate arrays A(a, B, C) by means of the logarithmically smaller NW-family G(a, B, C). This general type of perfect hash scheme does not seem to have been treated before in the literature. In particular, its use in database systems is a possible source of relevant applications and remains to be investigated. Let a = a 1 a 2 . . . a n and α = α 1 α 2 . . . a n be n-sequences of positive integers, α ≤ a, meaning α i ≤ a i , i = 1, 2, . . . , n. Suppose that f (α) is a symmetric function on the variables α i , that is, the value of f (α) does not change if the coordinates of α are permuted in an arbitrary way. To store the function f , it is enough to allocate space for the values of f (α), where α i ≥ α i+1 , 1 ≤ i ≤ n − 1. Thus, we need to enumerate the α's satisfying α ≤ a and the boolean function The motivation for this work is to enumerate and give a perfect hash function [2, 4] for multidimensional arrays which have order restrictions on their entries. The simplest example of this situation is when the restrictions are given by B n≥ sym . We show that quite general boolean functions can take the place of B n≥ sym and that the large class of enumerative/perfect hash associated problems can be put under a common framework. To exemplify the appearance of a more complex boolean function, consider the problem of efficiently enumerate all the L-shaped pieces with vertices which fit in a (p × q) integer grid. This is a typical situation treated in [7] . An L-shaped piece is a rectangle R from which we have removed a smaller rectangle r ⊆ R. Moreover R and r have a corner in common. By effecting rotations, translations and reflections we may suppose that our L-shaped piece has a corner in the origin and the common vertex to r and R is the vertex opposite to the origin in rectangle R. Positioned in this way, the L-piece is represented by a quadruple of positive integers (X, Y, x, y) = α 1 α 2 α 3 α 4 ≤ a 1 a 2 a 3 a 4 = (p, q, p, q), as in Figure 1 .. The geometry imposes the restrictions: (1) X ≥ x; (2) Y ≥ y. Symmetry considerations enable us to partition the set of a-bounded L-pieces into equivalent classes and to distinguish a set A of representatives for these classes. For the occupancy purposes in [7] , the L-pieces (X, Y, x, y) and (Y, X, y, x) must be considered equivalent. This implies the restrictions: (3) X ≥ Y and (4) X = Y ⇒ x ≥ y. In terms of occupancy, (X, Y, X, y) with y < X, which is a degenerated L, can (and must) be replaced by the rectangle (X, Y, X, Y ). Analogously, (X, Y, x, Y ) with x < X must be replaced by (X, Y, X, Y ). In this way, the equivalence (X = x) ⇔ (Y = y) holds. The equivalence is rewritten as two opposite implications in the disguised form: (5) (X = x) ∨ (Y = y) and (6) (Y = y) ∨ (X = x). The restrictions (1) to (6) are gathered in a boolean expression B L in terms of the α i 's:. So, we want to enumerate the 4-sequences α = α 1 α 2 α 3 α 4 of positive integers α ≤ a and satisfying B L . If, as it is typically needed in packing problems, a is of order (120, 100, 120, 100) = (120, 100) 2 then we have 23, 094, 225 α's that satisfies B L in a total of 144, 000, 000 possibilities. If a = (7, 5) 2 , then there is a total of 190 α's in 1225 possibilities. The valid 190 α's are in 1 − 1 correspondence with the st-paths in the digraph of Figure 5 .

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Negatively correlated firing: the functional meaning of lateral inhibition within cortical columns Abstract Lateral inhibition is a well documented aspect of neural architecture in the main sensory systems. Existing accounts of lateral inhibition focus on its role in sharpening distinctions between inputs that are closely related. However, these accounts fail to explain the functional role of inhibition in cortical columns, such as those in V1, where neurons have similar response properties. In this paper, we outline a model of position tracking using cortical columns of integrate-and-fire and Hodgkin-Huxley-type neurons which respond optimally to a particular location, to show that negatively correlated firing patterns arise from lateral inhibition in cortical columns and that this provides a clear benefit for population coding in terms of stability, accuracy, estimation time and neural resources. OverviewUnderstanding the functional meaning of particular aspects of neural architecture is a central objective of neuroscience. Inhibitory interneurons are very common in the neocortex, and lateral inhibition has been shown to play an important role in sharpening the distinctions between similar inputs, where such inputs would otherwise invoke nearly the same response in neurons that have only slightly different response properties. However, it is our belief Tate et al. 2005) that inhibition also plays an important role in population coding, stabilising the mean field potential (Eq. 1) and greatly improving the ability of a group of neurons to accurately represent a given stimulus, by creating negatively correlated firing patterns. In this paper, we will describe the principle on which this improvement is based, showing how it extends the benefit of population coding. We will then present a model and a set of experiments using this model, which demonstrates that pools of neurons operating with inhibitory connections perform better on a stimulus-tracking task than the same model with no inhibitory connections. The model is designed to show how this can work in principle in neural sensory coding, incorporating a number of realistic aspects such as the use of spiking neurons, online estimation and a simple filtering mechanism. To extend our results to a biophysically realistic model is straightforward, and we will include a brief discussion on how our model is related to the circuits found in the primary visual cortex. Furthermore, we want to point out here that negatively correlated firing is opposite to the scenario of synchronised firing, which is still a hot Understanding the functional meaning of particular aspects of neural architecture is a central objective of neuroscience. Inhibitory interneurons are very common in the neocortex, and lateral inhibition has been shown to play an important role in sharpening the distinctions between similar inputs, where such inputs would otherwise invoke nearly the same response in neurons that have only slightly different response properties. However, it is our belief Tate et al. 2005) that inhibition also plays an important role in population coding, stabilising the mean field potential (Eq. 1) and greatly improving the ability of a group of neurons to accurately represent a given stimulus, by creating negatively correlated firing patterns. In this paper, we will describe the principle on which this improvement is based, showing how it extends the benefit of population coding. We will then present a model and a set of experiments using this model, which demonstrates that pools of neurons operating with inhibitory connections perform better on a stimulus-tracking task than the same model with no inhibitory connections. The model is designed to show how this can work in principle in neural sensory coding, incorporating a number of realistic aspects such as the use of spiking neurons, online estimation and a simple filtering mechanism. To extend our results to a biophysically realistic model is straightforward, and we will include a brief discussion on how our model is related to the circuits found in the primary visual cortex. Furthermore, we want to point out here that negatively correlated firing is opposite to the scenario of synchronised firing, which is still a hot topic in the literature (Gray et al. 1989; Palanca and DeAngelis 2005) . A number of experiments have been conducted to examine the effect of inhibitory connections in the network on performance on the stimulus-tracking task. Basic performance was measured using the mean squared error (MSE), of the position estimate and the actual stimulus position, at the end of each estimation window period. Although a sliding estimation window was used to produce a position estimate at the end of every simulation step, in calculating the MSE we used only the position at the end of a stimulus period (the period of time during which the stimulus has been in one location) in order to ensure that there were no contamination effects from previous stimulus position adversely affecting the current position estimate. In addition to examining basic performance, correlation measures (both across and within columns and estimate autocorrelation) and firing rates have been shown where appropriate. We also vary a number of other parameters in the experiments, including:. -Stimulus type: both constant and steplike signals are used.. -Estimation window size: values from 100 ms down to just 10 ms are tested. -Number of neurons in each column: set to 100 by default, but varied in one experiment to examine the effect on central limit convergence of neuron numbers. -Firing threshold V thre : set to 5 by default for the network with lateral inhibitory input and 20 for the network without to ensure similar firing rates and therefore a balanced input for both, but varied in one experiment to have the same threshold value of 20 for each. -Inhibitory connection strength w (i,k) (set to −1 by default, but all values from 0 to −1 tested in one experiment).. The results of all of these experiments are presented in the next section.

O O O O O O O O B O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Searching Appropriate Mother Wavelets for Hyperanalytic Denoising Abstract- During acquisition and transmission, images are often corrupted by additive noise. The aim of an image denoising algorithm is then to reduce the noise level, while preserving the image features. The acquired image is expressed as: n s f + = , where s represents the noiseless input image and n the noise. Generally n is considered as AWGN. Computing a WT of both members of the last equation, its equivalent form is obtained:. y w n = + , where y=WT{f} and n=WT{n}, because the WT is linear and the WT of AWGN is also AWGN, [1] . One may classify the denoising systems in two categories: those directly applied to the signal [2] and those who use a wavelet transform before processing. The multi-resolution analysis performed by the WT is a powerful tool to achieve good denoising. In the wavelet domain, the noise is uniformly spread throughout the coefficients, while most of the image information is concentrated in the few largest ones (sparsity of the wavelet representation). Softthresholding filter (stf) is the most popular strategy to denoise in the wavelets domain and has been theoretically justified by Donoho and Johnstone [1] . They propose a three steps denoising algorithm:. 1) the computation of the forward WT, 2) the filtering of the wavelet coefficients, 3) the computation of the inverse WT (IWT) of the result obtained.. They use the Discrete Wavelet Transform (DWT) and the stf. This method can be considered non-parametric. Consequently, regarding the three steps denoising algorithm, there are two tools to be chosen: the WT and the filter. In what concerns the first choice, in this paper we will use a new implementation of the HWT [3] . In [4] was used the 2D Undecimated Wavelet Transform (2D UDWT), in [5] the 1 This work was supported in the framework of a grant funded by the Romanian research council (CNCSIS) with the title "Using wavelets theory for decision making" no. 349/13.01.09. 2D Double Tree Complex Wavelet Transform (2D DTCWT), and in [6] the 2D DWT. Concerning the second choice, numerous non-linear filter types can be used in the WT domain. Basically, there are two categories of estimators: non-parametric and parametric. From the first category can be mentioned: the hard-thresholding filter, [1] , the stf [1] , the Efficient SURE-Based Inter-scales Point-wise Thresholding Filter [6] , and the filter proposed in [7] . To the second category belong filters obtained by minimizing a Bayesian risk under a cost function, typically a delta cost function (MAP estimation [4, 5] ) or the minimum mean squared error (MMSE estimation [6] ). We have already associated the proposed implementation of HWT with a marginal MAP filter in [3] . The aim of this paper is to improve the results in [3] by the substitution of the marginal MAP filter with the bishrink filter [5] .. The structure of the paper is the following. In the second section we present the new version of the HWT. In the third section we recall the bishrink filter. The fourth section is dedicated to simulation results. Finally, the last section summarizes our conclusions. The simulation results reported in this paper are better than the results presented in [3] proving that the bishrink filter is one of the best MAP filters. The proposed variant of HWT outperforms the 2D DWT in denoising applications, due to its quasi shift-invariance and better directional selectivity. The results reported in this paper are inferior with 0.3 dB in comparison with the results reported in [5] , where the 2D DTCWT [11] was associated with the bishrink filter. But the architecture in Fig. 1 is simpler than the architecture of the 2D DTCWT, and our algorithm is faster.. The superiority of 2D DTCWT versus the HWT in denoising applications, already mentioned, disappears when the noise is multiplicative as in the case of despecklisation systems. Recent simulations, reported in the PhD Thesis of the first author, prove that the 2D DTCWT and the HWT can be considered equivalent in despecklisation applications.. The blurring effect introduced by the HWT can be reduced by substituting the 2D DWTs in Fig. 1 , by other 2D WTs with better frequency localization like for example the 2D Wavelet Packets Transform (2D WPT) [14] . This flexibility is another very fine feature of the HWT. The four 2D DWTs in figure 1 can be substituted directly by other WTs, like the 2D WPT or the 2D M-band WT. The HWT can be regarded as a Gabor's filter bank [15] .. We have searched here the best mother wavelets following a classical approach based on trials. We have found results compatible with the whole noiseless image. This strategy could be applied for other image processing methods as well. For example we have applied it for the images watermarking in [16] .. The searching strategy can be enhanced considering the variability of natural images. They are composed by different structures: homogeneous regions, textured regions and contours. Each type of structure has a set of features and can be associated with best mother wavelets. So, a refined strategy for the search of the best mother wavelets segments first a partial denoising result (obtained using the mother wavelets Daubechies 9/7 for example) to identify the structures composing the noiseless component of the image under processing. Next the features of each structure are identified and the corresponding mother wavelets are searched. Finally each corresponding structure of the noisy image is denoised using its corresponding best mother wavelets. The identification of the structures and of their features can be done using the algorithm proposed in [17] . The searching of the best mother wavelets for textured regions can be done following the method proposed in [18] . The searching of the best mother wavelets for homogeneous regions and for contours can be done on the basis of their time-frequency localization. The best mother wavelets for the processing of such a structure could be mother wavelets with the same time-frequency localization. In [19] are estimated the time-frequency localizations of the elements of the Daubechies family denoted in the present paper by Dau. Based on this classification in [20] is proposed an enhancement of the proposed denoising method by diversification followed by fusion.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling Background: The genome-scale metabolic model of Saccharomyces cerevisiae, first presented in 2003, was the first genome-scale network reconstruction for a eukaryotic organism. Since then continuous efforts have been made in order to improve and expand the yeast metabolic network. Results: Here we present iTO977, a comprehensive genome-scale metabolic model that contains more reactions, metabolites and genes than previous models. The model was constructed based on two earlier reconstructions, namely iIN800 and the consensus network, and then improved and expanded using gap-filling methods and by introducing new reactions and pathways based on studies of the literature and databases. The model was shown to perform well both for growth simulations in different media and gene essentiality analysis for single and double knock-outs. Further, the model was used as a scaffold for integrating transcriptomics, and flux data from four different conditions in order to identify transcriptionally controlled reactions, i.e. reactions that change both in flux and transcription between the compared conditions. Conclusion: We present a new yeast model that represents a comprehensive up-to-date collection of knowledge on yeast metabolism. The model was used for simulating the yeast metabolism under four different growth conditions and experimental data from these four conditions was integrated to the model. The model together with experimental data is a useful tool to identify condition-dependent changes of metabolism between different environmental conditions. In this paper we present iTO977, a new updated genomescale metabolic model for yeast. The model is bigger in scope than previous models and can be used both for simulations as well as a scaffold for integrated analysis. The model follows the standards that were introduced in the consensus reconstruction regarding annotation of metabolites and reactions and can therefore be seen as a well-annotated database representing the current knowledge of the yeast metabolism. By using the model to simulate different growth conditions and compare the simulated fluxes from different conditions using the random sampling algorithm we investigate the yeast metabolism in carbon limited, nitrogen limited, aerobic and anaerobic conditions. The change in predicted flux for each reaction in the model is compared with the changes in expression of the involved enzymes and reactions that are directly transcriptionally controlled are identified as well as transcription factors controlling these reactions. The genome-scale metabolic model together with experimental data is a valuable tool for data integration in order to find condition-dependent differences in yeast metabolism. The number of metabolic network reconstructions for microorganisms has increased rapidly during the past decade following the genome revolution and the rapid increase of genome sequencing projects. The yeast Saccharomyces cerevisiae was the first eukaryote to have its metabolic network reconstructed and its metabolism is very well-studied. During the last 10 years the yeast metabolic network has been further updated and further reconstructed leading to several genome-scale models [1] [2] [3] [4] [5] [6] [7] [8] [9] . Yeast genome-scale modeling have many different applications, which have been reviewed lately [2] . Yeast has for example been used extensively in biotechnology as a workhorse for production of a wide range of chemicals, and metabolic modeling has helped in many cases to guide the strain construction by finding strategies for improved chemical production [10] . Recently the S. cerevisiae metabolic network has also been used as the basis for constructing metabolic models for other yeasts, e.g. Schizosaccharomyces pombe [11] , Yarrowia lipolytica [12] , Pichia pastoris and Pichia stipitis [13] .. The first genome-scale metabolic model for S. cerevisiae was presented in 2003 [1] and since then no less than 11 genome-scale reconstructions have been released [2] . To overcome the problem with different models having different metabolite names, different scopes and representing parts of the metabolism in slightly different ways a consensus metabolic network was constructed as the result of a community jamboree effort [3] . The consensus network introduced a nomenclature standard for metabolites following MIRIAM, using ChEBI annotations and InChI codes. The consensus network can be viewed as a genome-scale reconstruction (GENRE) of the yeast metabolism, but not as a genome-scale model (GEM) ready for simulations using e.g. constraint-based methods such as flux balance analysis (FBA), due to missing information about reversibility of some reactions and missing biomass equations. The consensus network was further developed and improved and the updated version called Yeast 4 [4] is a GEM ready for simulations. It was further expanded and updated leading to another version called Yeast 5 [5] . The relationship between the yeast genome-scale models is shown in the pedigree in Figure 1A . The first model that included a detailed description of the lipid metabolism, namely iIN800 [8] , was not included in the consensus reconstruction.. A genome-scale metabolic model can be thought of 1) being a biochemically and genetically structured database of the metabolism linking enzymes, metabolites and reactions together, and 2) a predictive model that can estimate the fluxes of intracellular reactions as well as predicting the growth of the cell when constraining the extracellular (measured) fluxes to represent the growth conditions. The first part corresponds to the GENRE and the second part corresponds to the GEM concept. The genome-scale metabolic model can be applied as an in silico hypothesis testing tool to gain insight into the operations of metabolism, leading to identification of targets for superior strain development.. Genome-scale metabolic model reconstructions can also be used as a scaffold for integrated data analysis, where the network structure of the model is the basis for the integration of omics data in order to gain more mechanistic knowledge of the cell's behavior in different environments or under different conditions. The reporter metabolite algorithm [14] is a useful tool for integration of transcriptomics data into the metabolic network. The algorithm identifies key metabolites whose neighboring genes in the metabolic network are transcriptionally changed between two conditions. However, the regulation of the cellular response to different perturbations such as gene deletion or changes of environmental conditions may occur on many different levels in the cell. By using the genome-scale model to predict changes in metabolic flux between different conditions and correlate these changes with the change in expression of the genes involved, reactions that are directly controlled by transcription can be identified. The random sampling algorithm presented by Bordel et al. [15] is a framework for identification of this kind of trancriptionally controlled reactions using the genome-scale metabolic model.. Here we present an updated genome-scale model (GEM) that is based on the consensus network and the iIN800 model. The goal of the model is to provide an up-to-date and comprehensive description of the yeast metabolism, so the new model contains more genes, reactions and metabolites than previous models. We show that the model performs well in simulating the growth under different environmental conditions. The model was also used as a tool for integrating a large amount of omics data from different conditions. It was used together with the random sampling algorithm to correlate the changes in fluxes between aerobic and anaerobic conditions as well as carbon limited and nitrogen limited conditions with changes in gene expression in order to identify condition-dependent transcriptionally controlled reactions.

O O O O O O O O O O O O B I I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I


Optimization of lipid production with a genome-scale model of Yarrowia lipolytica Background: Yarrowia lipolytica is a non-conventional yeast that is extensively investigated for its ability to excrete citrate or to accumulate large amounts of storage lipids, which is of great significance for single cell oil production. Both traits are thus of interest for basic research as well as for biotechnological applications but they typically occur simultaneously thus lowering the respective yields. Therefore, engineering of strains with high lipid content relies on novel concepts such as computational simulation to better understand the two competing processes and to eliminate citrate excretion. As an alternative approach to available genome scale reconstructions of Y. lipolytica, which were assembled by fully or partly automated reconstruction procedures [10, 11] , we transformed a functional and widely used scaffold of S. cerevisiae into the new reconstruction iMK735 by manually changing gene annotations, evaluating reversibilities of reactions and their compartmentalization and by adding or deleting species-specific reactions. This procedure resulted in a GSM that accurately predicts growth behavior of Y. lipolytica and can be used to simulate processes that are of importance for this yeast, like lipid production. However, further efforts regarding Genome scale models can be used for the optimization of production processes by analyzing the impact of mutations or of environmental conditions on the performance of the organism of interest. Here, we presented approaches for the latter in the context of lipid accumulation in this oleaginous yeast, which is typically accompanied by excretion of considerable amounts of citric acid. Indeed, Y. lipolytica under standard nitrogen-limiting conditions might rather be regarded as a good host for citrate production than for lipid accumulation (Fig. 2a) . We have shown that accumulation of lipid can not only be induced by depletion of nitrogen or one of the other 'typical' essential nutrients that are part of the medium, but also by reduction of oxygen supply. Furthermore, we showed that the simultaneous depletion of nitrogen and oxygen had additive effects on lipid accumulation and productivity. Since Y. lipolytica, like other oleaginous yeasts, is investigated for large scale production of TAG as feedstock for the biodiesel industry, this might be a promising approach because the reduction of aeration results in reduction of costs. Yeasts, especially Saccharomyces cerevisiae, have been used for centuries for biotechnological applications [1] . In recent years, so-called 'non-conventional' yeasts have gained considerable interest for several reasons. First, S. cerevisiae is a Crabtree positive yeast that covers most of its ATP requirement from substrate-level phosphorylation and fermentative metabolism. In contrast, most of the non-conventional yeasts, such as Yarrowia lipolytica, Kluyveromyces lactis or Pichia pastoris, have a respiratory metabolism, resulting in significantly higher biomass yields and no loss of carbon due to ethanol or acetate excretion. Second, S. cerevisiae is highly specialized and evolutionary optimized for the uptake of glucose, but performs poorly on most other carbon sources. Several nonconventional yeasts, on the other hand, are able to grow at high growth rates on alternative carbon sources, like pentoses, C1 carbon sources or glycerol, which may be available as cheap feedstock. Third, non-conventional yeasts are extensively exploited for production processes, for which the productivity of S. cerevisiae is rather low. Prominent examples are the use of P. pastoris for highlevel protein expression [2] and oleaginous yeasts for the production of single cell oils [3] .. Despite this growing interest in the development of biotechnological processes in other yeast species, the development of tools for the investigation and manipulation of these organisms still lags behind the advances in S. cerevisiae for which the broadest spectrum of methods for the engineering of production strains and the best knowledge about manipulation and cultivation are available. One such tool is the use of reconstructed metabolic networks for the computational analysis and optimization of pathways and production processes. These genomescale models (GSM) are becoming increasingly important as whole genome sequences and deduced pathways are available for many different organisms. In combination with mathematical algorithms like flux balance analysis (FBA) and variants thereof, GSMs have the potential to predict and guide metabolic engineering strategies and significantly improve their success rates [4] . FBA quantitatively simulates cellular metabolism using physicochemical constraints such as mass balance, energy balance, flux limitations and assuming a steady state [5, 6] . A major advantage of FBA is that no knowledge about kinetic enzyme constants and intracellular metabolite or protein concentrations is required. This makes FBA a widely applicable tool for the simulation of metabolic processes.. Whereas the yeast community provides continuous updates for the reconstruction of the S. cerevisiae model [7] , hardly any GSM for non-conventional yeasts are currently available. Recent attempts in this direction are the reconstructions for P. pastoris and P. stipitis [8, 9] and for the oleaginous yeast Yarrowia lipolytica, for which two GSMs have been published [10, 11] . Y. lipolytica is considered to be an excellent candidate for single-cell oil production as it is able to accumulate high amounts of neutral lipids. Furthermore, Y.lipolytica production strains efficiently excrete proteins and organic acids, like the intermediates of the tricarboxylic acid (TCA) cycle citrate, α-ketoglutarate and succinic acid [3, [12] [13] [14] . This yeast is also known to metabolize a broad range of substrates, such as glycerol, alkanes, fatty acids, fats and oils [15] [16] [17] ; the efficient utilization of glycerol as a carbon and energy source provides a major economic advantage for making high value products from cheap raw glycerol, which is available in large quantities from the biodiesel industry. Additionally, its high quality manually curated genome sequence is publicly available [18, 19] , making altogether Y. lipolytica a promising host for the biotech industry.. Y. lipolytica is known for both efficient citrate excretion and high lipid productivity under stress conditions such as nitrogen limitation. However, due to the undesired by-product citrate, processes aiming at high lipid content suffer from low yields with regard to the carbon conversion, despite the use of mutant strains with increased lipid storage properties. In this study, we reconstructed a new GSM of Y. lipolytica to analyze the physiology of this yeast and to design fermentation strategies towards optimizing the productivity for neutral lipid accumulation by simultaneously reducing the excretion of citrate. These predictions were experimentally confirmed, demonstrating that precisely defined fed batch strategies and oxygen limitation can be used to channel carbon fluxes preferentially towards lipid production.

O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B I O O O O O O O O O O O