Variable-length Sequences Research Articles

Highly Accurate Structure-Based Prediction of HIV-1 Coreceptor Usage Suggests Intermolecular Interactions Driving Tropism.

HIV-1 entry into host cells is mediated by interactions between the V3-loop of viral glycoprotein gp120 and chemokine receptor CCR5 or CXCR4, collectively known as HIV-1 coreceptors. Accurate genotypic prediction of coreceptor usage is of significant clinical interest and determination of the factors driving tropism has been the focus of extensive study. We have developed a method based on nonlinear support vector machines to elucidate the interacting residue pairs driving coreceptor usage and provide highly accurate coreceptor usage predictions. Our models utilize centroid-centroid interaction energies from computationally derived structures of the V3-loop:coreceptor complexes as primary features, while additional features based on established rules regarding V3-loop sequences are also investigated. We tested our method on 2455 V3-loop sequences of various lengths and subtypes, and produce a median area under the receiver operator curve of 0.977 based on 500 runs of 10-fold cross validation. Our study is the first to elucidate a small set of specific interacting residue pairs between the V3-loop and coreceptors capable of predicting coreceptor usage with high accuracy across major HIV-1 subtypes. The developed method has been implemented as a web tool named CRUSH, CoReceptor USage prediction for HIV-1, which is available at http://ares.tamu.edu/CRUSH/.

Realizing the Now-or-Never bottleneck and Chunk-and-Pass processing with Item-Order-Rank working memories and masking field chunking networks.

Christiansen & Chater's (C&C's) key goals for a language system have been realized by neural models for short-term storage of linguistic items in an Item-Order-Rank working memory, which inputs to Masking Fields that rapidly learn to categorize, or chunk, variable-length linguistic sequences, and choose the contextually most predictive list chunks while linguistic inputs are stored in the working memory.

A repetitive sequence assembler based on next-generation sequencing.

Repetitive sequences of variable length are common in almost all eukaryotic genomes, and most of them are presumed to have important biomedical functions and can cause genomic instability. Next-generation sequencing (NGS) technologies provide the possibility of identifying capturing these repetitive sequences directly from the NGS data. In this study, we assessed the performances in identifying capturing repeats of leading assemblers, such as Velvet, SOAPdenovo, SGA, MSR-CA, Bambus2, ALLPATHS-LG, and AByss using three real NGS datasets. Our results indicated that most of them performed poorly in capturing the repeats. Consequently, we proposed a repetitive sequence assembler, named NGSReper, for capturing repeats from NGS data. Simulated datasets were used to validate the feasibility of NGSReper. The results indicate that the completeness of capturing repeat is up to 99%. Cross validation was performed in three real NGS datasets, and extensive comparisons indicate that NGSReper performed best in terms of completeness and accuracy in capturing repeats. In conclusion, NGSReper is an appropriate and suitable tool for capturing repeats directly from NGS data.

Exploitation of Phase-Based Features for Whispered Speech Emotion Recognition

Features for speech emotion recognition are usually dominated by the spectral magnitude information while they ignore the use of the phase spectrum because of the difficulty of properly interpreting it. Motivated by recent successes of phase-based features for speech processing, this paper investigates the effectiveness of phase information for whispered speech emotion recognition. We select two types of phase-based features (i.e., modified group delay features and all-pole group delay features), both which have shown wide applicability to all sorts of different speech analysis and are now studied in whispered speech emotion recognition. When exploiting these features, we propose a new speech emotion recognition framework, employing outer product in combination with power and L2 normalization. The according technique encodes any variable length sequence of the phase-based features into a fixed dimension vector regardless of the length of the input sequence. The resulting representation is fed to train a classification model with a linear kernel classifier. Experimental results on the Geneva Whispered Emotion Corpus database, including normal and whispered phonation, demonstrate the effectiveness of the proposed method when compared with other modern systems. It is also shown that, combining phase information with magnitude information could significantly improve performance over the common systems solely adopting magnitude information.

BLSTM Recurrent Neural Network for Object Recognition

Multi-object relationship information can help eliminate some incorrect combinations or locations of objects. Moreover, it is favorable to extract scene information for object recognition. In this paper, we introduce a new way to generate image representation and propose a deep learning framework to fuse the contextual dependencies among objects and scene information in an image. It adopts a bidirectional long short-term memory recurrent neural network (BLSTM-RNN) to deal with the problem of variable-length sequence produced by local detectors in different images. Then it is applied to the existing tree context model for further recognition. Experimental results on SUN09 dataset show that our model outperforms the state-of the-art object localization methods.

Single Strand Annealing Plays a Major Role in RecA-Independent Recombination between Repeated Sequences in the Radioresistant Deinococcus radiodurans Bacterium.

The bacterium Deinococcus radiodurans is one of the most radioresistant organisms known. It is able to reconstruct a functional genome from hundreds of radiation-induced chromosomal fragments. Our work aims to highlight the genes involved in recombination between 438 bp direct repeats separated by intervening sequences of various lengths ranging from 1,479 bp to 10,500 bp to restore a functional tetA gene in the presence or absence of radiation-induced DNA double strand breaks. The frequency of spontaneous deletion events between the chromosomal direct repeats were the same in recA+ and in ΔrecA, ΔrecF, and ΔrecO bacteria, whereas recombination between chromosomal and plasmid DNA was shown to be strictly dependent on the RecA and RecF proteins. The presence of mutations in one of the repeated sequence reduced, in a MutS-dependent manner, the frequency of the deletion events. The distance between the repeats did not influence the frequencies of deletion events in recA + as well in ΔrecA bacteria. The absence of the UvrD protein stimulated the recombination between the direct repeats whereas the absence of the DdrB protein, previously shown to be involved in DNA double strand break repair through a single strand annealing (SSA) pathway, strongly reduces the frequency of RecA- (and RecO-) independent deletions events. The absence of the DdrB protein also increased the lethal sectoring of cells devoid of RecA or RecO protein. γ-irradiation of recA + cells increased about 10-fold the frequencies of the deletion events, but at a lesser extend in cells devoid of the DdrB protein. Altogether, our results suggest a major role of single strand annealing in DNA repeat deletion events in bacteria devoid of the RecA protein, and also in recA + bacteria exposed to ionizing radiation.

Neuropeptidomics Mass Spectrometry Reveals Signaling Networks Generated by Distinct Protease Pathways in Human Systems.

Neuropeptides regulate intercellular signaling as neurotransmitters of the central and peripheral nervous systems, and as peptide hormones in the endocrine system. Diverse neuropeptides of distinct primary sequences of various lengths, often with post-translational modifications, coordinate and integrate regulation of physiological functions. Mass spectrometry-based analysis of the diverse neuropeptide structures in neuropeptidomics research is necessary to define the full complement of neuropeptide signaling molecules. Human neuropeptidomics has notable importance in defining normal and dysfunctional neuropeptide signaling in human health and disease. Neuropeptidomics has great potential for expansion in translational research opportunities for defining neuropeptide mechanisms of human diseases, providing novel neuropeptide drug targets for drug discovery, and monitoring neuropeptides as biomarkers of drug responses. In consideration of the high impact of human neuropeptidomics for health, an observed gap in this discipline is the few published articles in human neuropeptidomics compared with, for example, human proteomics and related mass spectrometry disciplines. Focus on human neuropeptidomics will advance new knowledge of the complex neuropeptide signaling networks participating in the fine control of neuroendocrine systems. This commentary review article discusses several human neuropeptidomics accomplishments that illustrate the rapidly expanding diversity of neuropeptides generated by protease processing of pro-neuropeptide precursors occurring within the secretory vesicle proteome. Of particular interest is the finding that human-specific cathepsin V participates in producing enkephalin and likely other neuropeptides, indicating unique proteolytic mechanisms for generating human neuropeptides. The field of human neuropeptidomics has great promise to solve new mechanisms in disease conditions, leading to new drug targets and therapeutic agents for human diseases. Graphical Abstract ᅟ.

Estimating Protistan Diversity Using High‐Throughput Sequencing

Sequencing hypervariable regions from the 18S rRNA gene is commonly employed to characterize protistan biodiversity, yet there are concerns that short reads do not provide the same taxonomic resolution as full-length sequences. A total of 7,432 full-length sequences were used to perform an in silico analysis of how sequences of various lengths and target regions impact downstream ecological interpretations. Sequences that were longer than 400 nucleotides and included the V4 hypervariable region generated results similar to those derived from full-length 18S rRNA gene sequences. Present high-throughput sequencing capabilities are approaching protistan diversity estimation comparable to whole gene sequences.

Manifold Learning for Multivariate Variable-Length Sequences With an Application to Similarity Search.

Multivariate variable-length sequence data are becoming ubiquitous with the technological advancement in mobile devices and sensor networks. Such data are difficult to compare, visualize, and analyze due to the nonmetric nature of data sequence similarity measures. In this paper, we propose a general manifold learning framework for arbitrary-length multivariate data sequences driven by similarity/distance (parameter) learning in both the original data sequence space and the learned manifold. Our proposed algorithm transforms the data sequences in a nonmetric data sequence space into feature vectors in a manifold that preserves the data sequence space structure. In particular, the feature vectors in the manifold representing similar data sequences remain close to one another and far from the feature points corresponding to dissimilar data sequences. To achieve this objective, we assume a semisupervised setting where we have knowledge about whether some of data sequences are similar or dissimilar, called the instance-level constraints. Using this information, one learns the similarity measure for the data sequence space and the distance measures for the manifold. Moreover, we describe an approach to handle the similarity search problem given user-defined instance level constraints in the learned manifold using a consensus voting scheme. Experimental results on both synthetic data and real tropical cyclone sequence data are presented to demonstrate the feasibility of our manifold learning framework and the robustness of performing similarity search in the learned manifold.

Sequence Characteristics and Phylogenetic Implications of the nrDNA Internal Transcribed Spacers (ITS) in Protospecies and Landraces of Sugarcane (Saccharum officinarum L.)

Internal transcribed spacers (ITS) of nuclear ribosomal DNA (nrDNA) cistrons were studied in 19 protospecies and 11 landraces of Saccharum spp. The purpose of the present study was to investigate genetic variability and genetic divergence among the 30 genotypes of Saccharum officinarum L. for exploitation of potential parent resources in sugarcane breeding. ITS region analysis revealed the length of the ITS sequences from 589 to 590 bp, G+C content from 64.01 to 65.03 % (ITS1: 207 bp, G+C: 62.8–65.22 %; ITS2: 218–219 bp, G+C: 69.27–70.78 %; and 5.8S: 164 bp, G+C: 57.32–57.93 %). The ITS1 and ITS2 regions showed variable sequence lengths and G+C content. The 5.8S region was found to be more conserved (99.39 %). ITS1 recorded highest percentage of variation sites (90.48 %), high number of transitions and transversions. The neighbor-joining (NJ) method was used to assess the phylogenetic relationship based on the combined nucleotide sequence data of ITS1, 5.8S and ITS2, and the 30 accessions were divided into two clades. Clade I included all the 11 S. spontaneum accessions, and clade II consisted of S. officinarum, S. barberi, S. sinense, S. robustum and endemic species. Eleven accessions of S. spontaneum were clustered into several subclasses, indicating that the genetic diversity of S. spontaneum genotypes is abundant. Six landraces namely Hongpi S17, Tuojianghong, Loethers, Guzhizhe, Mango, Guilin Zhuzhe in the present study were closely related to S. spontaneum species, which may be used as available resources. This study validates the utility of rDNA-ITS region as a reliable indicator of phylogenetic relationships, especially ITS1 as probable DNA barcode at higher levels and can serve as an additional approach for studying genetic diversity in sugarcane and related relatives. The analysis on ITS sequences could provide a reference for exploitation of potential parent resources in sugarcane breeding.

Excision of unstable artificial gene-specific inverted repeats mediates scar-free gene deletions in Escherichia coli.

Inverted repeat and palindromic sequences have the propensity to form non-beta cruciform structures during DNA replication, leading to perturbations within the genome or plasmid replicon. In this study, the tolerance of the Escherichia coli genome to inverted repeat sequences from 25 to 1200 bp was investigated. Genomic inverted repeats were readily created via the homologous insertion of an overlap extension PCR product containing a gene-specific region of the genome together with thyA coding sequence, creating inverted repeat sequences of various lengths flanking the thyA selection marker in the resulting genome. Inverted repeat sequences below 100 bp were stably propagated, while those above and up to 1200 bp were found to be transiently unstable under auxotrophic thymine selection. Excision efficiency improves with increases of the inverted repeat until 600-800 bp, indicating that the genomic stability of inverted repeat sequences is due to secondary structure formation. Its effectiveness of creating precise and scar-free gene deletions was further demonstrated by deleting a number of genes in E. coli. The procedure can be readily adapted for sequence integration and point mutations in E. coli genome. It also has the potential for applications on other bacteria for efficient gene deletions.

Emotion recognition from speech signals via a probabilistic echo-state network

The paper presents a probabilistic echo-state network (π-ESN) for density estimation over variable-length sequences of multivariate random vectors. The π-ESN stems from the combination of the reservoir of an ESN and a parametric density model based on radial basis functions. A constrained maximum likelihood training algorithm is introduced, suitable for sequence classification. Extensions of the algorithm to unsupervised clustering and semi-supervised learning (SSL) of sequences are proposed. Experiments in emotion recognition from speech signals are conducted on the WaSeP© dataset. Compared with established techniques, the π-ESN yields the highest recognition accuracies, and shows interesting clustering and SSL capabilities.

Real-time learning of predictive recognition categories that chunk sequences of items stored in working memory.

How are sequences of events that are temporarily stored in a cognitive working memory unitized, or chunked, through learning? Such sequential learning is needed by the brain in order to enable language, spatial understanding, and motor skills to develop. In particular, how does the brain learn categories, or list chunks, that become selectively tuned to different temporal sequences of items in lists of variable length as they are stored in working memory, and how does this learning process occur in real time? The present article introduces a neural model that simulates learning of such list chunks. In this model, sequences of items are temporarily stored in an Item-and-Order, or competitive queuing, working memory before learning categorizes them using a categorization network, called a Masking Field, which is a self-similar, multiple-scale, recurrent on-center off-surround network that can weigh the evidence for variable-length sequences of items as they are stored in the working memory through time. A Masking Field hereby activates the learned list chunks that represent the most predictive item groupings at any time, while suppressing less predictive chunks. In a network with a given number of input items, all possible ordered sets of these item sequences, up to a fixed length, can be learned with unsupervised or supervised learning. The self-similar multiple-scale properties of Masking Fields interacting with an Item-and-Order working memory provide a natural explanation of George Miller's Magical Number Seven and Nelson Cowan's Magical Number Four. The article explains why linguistic, spatial, and action event sequences may all be stored by Item-and-Order working memories that obey similar design principles, and thus how the current results may apply across modalities. Item-and-Order properties may readily be extended to Item-Order-Rank working memories in which the same item can be stored in multiple list positions, or ranks, as in the list ABADBD. Comparisons with other models, including TRACE, MERGE, and TISK, are made.

CRISPRstrand: predicting repeat orientations to determine the crRNA-encoding strand at CRISPR loci.

Motivation: The discovery of CRISPR-Cas systems almost 20 years ago rapidly changed our perception of the bacterial and archaeal immune systems. CRISPR loci consist of several repetitive DNA sequences called repeats, inter-spaced by stretches of variable length sequences called spacers. This CRISPR array is transcribed and processed into multiple mature RNA species (crRNAs). A single crRNA is integrated into an interference complex, together with CRISPR-associated (Cas) proteins, to bind and degrade invading nucleic acids. Although existing bioinformatics tools can recognize CRISPR loci by their characteristic repeat-spacer architecture, they generally output CRISPR arrays of ambiguous orientation and thus do not determine the strand from which crRNAs are processed. Knowledge of the correct orientation is crucial for many tasks, including the classification of CRISPR conservation, the detection of leader regions, the identification of target sites (protospacers) on invading genetic elements and the characterization of protospacer-adjacent motifs.Results: We present a fast and accurate tool to determine the crRNA-encoding strand at CRISPR loci by predicting the correct orientation of repeats based on an advanced machine learning approach. Both the repeat sequence and mutation information were encoded and processed by an efficient graph kernel to learn higher-order correlations. The model was trained and tested on curated data comprising >4500 CRISPRs and yielded a remarkable performance of 0.95 AUC ROC (area under the curve of the receiver operator characteristic). In addition, we show that accurate orientation information greatly improved detection of conserved repeat sequence families and structure motifs. We integrated CRISPRstrand predictions into our CRISPRmap web server of CRISPR conservation and updated the latter to version 2.0.Availability: CRISPRmap and CRISPRstrand are available at http://rna.informatik.uni-freiburg.de/CRISPRmap.Contact: backofen@informatik.uni-freiburg.deSupplementary information: Supplementary data are available at Bioinformatics online.

Cross-Modal Decoding of Neural Patterns Associated with Working Memory: Evidence for Attention-Based Accounts of Working Memory.

Recent studies suggest common neural substrates involved in verbal and visual working memory (WM), interpreted as reflecting shared attention-based, short-term retention mechanisms. We used a machine-learning approach to determine more directly the extent to which common neural patterns characterize retention in verbal WM and visual WM. Verbal WM was assessed via a standard delayed probe recognition task for letter sequences of variable length. Visual WM was assessed via a visual array WM task involving the maintenance of variable amounts of visual information in the focus of attention. We trained a classifier to distinguish neural activation patterns associated with high- and low-visual WM load and tested the ability of this classifier to predict verbal WM load (high-low) from their associated neural activation patterns, and vice versa. We observed significant between-task prediction of load effects during WM maintenance, in posterior parietal and superior frontal regions of the dorsal attention network; in contrast, between-task prediction in sensory processing cortices was restricted to the encoding stage. Furthermore, between-task prediction of load effects was strongest in those participants presenting the highest capacity for the visual WM task. This study provides novel evidence for common, attention-based neural patterns supporting verbal and visual WM.

Effective combination of xenoprotective transgenes

Abundant expression of a series of xenoprotective transgenes is vital for clinically useful xeno‐donor pigs. Breeding of such multi‐transgenic animals requires that xeno‐transgenes be placed at a single, or a small number of loci in the pig genome to avoid or minimise separation by genetic segregation. We are investigating methods that support transgene expression and also enable transgenes to be “stacked” at a single locus. These include the use of BAC based multi‐transgene vectors, and very recently serial transgene placement at a permissive locus by homologous recombination supported by TALENs [1].BAC vector constructs containing genomic sequences for the complement activation inhibitors CD55, CD46 and CD59 were assessed to determine the minimum size without reduction of expression level. These constructs enable us to express all biologically active CD55 splice variants (membrane bound and soluble forms). Effective xenoprotection also requires ubiquitous expression. We are thus examining endogenous promoter sequences of various lengths as well as the widely used CAGGs (CMV enhancer/chicken β actin) promoter to direct complement inhibitory gene expression levels in all porcine tissues.To investigate means of coexpressing groups of transgenes that provide xeno‐protection at different levels, we developed a series of BAC vector constructs containing the CD55 genomic sequence plus two additional xeno‐transgene cDNAs, including A20 [2], HO1 [3], thrombomodulin [4] and CTLA4‐Ig (LEA29Y) [5].Analysis of porcine adipose MSC stably transfected cell clones revealed expression of all inserted xeno‐transgenes most importantly high expression of complement inhibitory genes (Fig. 1). This demonstrates that xeno‐transgenes can be effectively combined in a single construct, thus minimising the number of transgene loci in the final donor pigs. The huge carrying capacity of BAC vectors enables the addition of further xeno‐transgenes in a single vector construct.We have further examined the system in vivo. Primary cells transfected with up to three BAC constructs were used for nuclear transfer, fetuses explanted and examined for expression with very promising results.

Average Utility Maximization: A Preference Foundation

This paper provides necessary and sufficient preference conditions for average utility maximization over sequences of variable length. We obtain full generality by using a new algebraic technique that exploits the richness structure naturally provided by the variable length of the sequences. Thus we generalize many preceding results in the literature. For example, continuity in outcomes, a condition needed in other approaches, now is an option rather than a requirement. Applications to expected utility, decisions under ambiguity, welfare evaluations for variable population size, discounted utility, and quasilinear means in functional analysis are presented.

SCALED SELF-ORGANIZING MAP – HIDDEN MARKOV MODEL ARCHITECTURE FOR BIOLOGICAL SEQUENCE CLUSTERING

The self-organizing hidden Markov model map (SOHMMM) introduces a hybrid integration of the self-organizing map (SOM) and the hidden Markov model (HMM). Its scaled, online gradient descent unsupervised learning algorithm is an amalgam of the SOM unsupervised training and the HMM reparameterized forward-backward techniques. In essence, with each neuron of the SOHMMM lattice, an HMM is associated. The image of an input sequence on the SOHMMM mesh is defined as the location of the best matching reference HMM. Model tuning and adaptation can take place directly from raw data, within an automated context. The SOHMMM can accommodate and analyze deoxyribonucleic acid, ribonucleic acid, protein chain molecules, and generic sequences of high dimensionality and variable lengths encoded directly in nonnumerical/symbolic alphabets. Furthermore, the SOHMMM is capable of integrating and exploiting latent information hidden in the spatiotemporal dependencies/correlations of sequences’ elements.

Link monitoring mathod based on satellite communication network

Based on the statistical characteristics of satellite link error rate,this paper proposed a new link directly monitoring technology using variable length sequence.Using the analysis of bit error rate segmentation strategy and statistical principles of statistical confidence,the monitoring method determined the sequence of the link selection criteria through the training sequence length,the error simulation accuracy and reliability of monitoring and statistical analysis.Experimental results show that link directly monitoring technology using variable length sequence is effective to improve the range of link monitoring and reduce the computational complexity under a certain premise of the channel resources.It has certain advantages in the satellite to link monitoring.

Runtime Application Behavior Prediction Using a Statistical Metric Model

Adaptive computing systems rely on accurate predictions of application behavior to understand and respond to the dynamically varying characteristics. In this study, we present a Statistical Metric Model (SMM) that is system- and metric-independent for predicting application behavior. SMM is a probability distribution over application patterns of varying length and it models how likely a specific behavior occurs. Maximum Likelihood Estimation (MLE) criterion is used to estimate the parameters of SMM. The parameters are further refined with a smoothing method to improve prediction robustness. We also propose an extension to SMM (i.e., SMM-Interp) to handle sudden short-term changes in application behavior. SMM learns the application patterns during runtime, and at the same time predicts the upcoming application phases based on what it has learned up to that point. We demonstrate several key features of SMM: (1) adaptation, (2) variable length sequence modeling, and (3) long-term memory. An extensive and rigorous series of prediction experiments show the superior performance of the SMM predictor over existing predictors on a wide range of benchmarks. For some of the benchmarks, SMM reduces the prediction error rate by 10X and 3X, compared to last value and table-based prediction approaches, respectively. SMM's improved prediction accuracy results in superior power-performance tradeoffs when it is applied to an adaptive dynamic power management scheme.