A fast algorithm for genome-wide analysis of proteins with repeated sequences.

We present a fast algorithm to search for repeating fragments within protein sequences. The technique is based on an extension of the Smith-Waterman algorithm that allows the calculation of sub-optimal alignments of a sequence against itself. We are able to estimate the statistical significance of all sub-optimal alignment scores. We also rapidly determine the length of the repeating fragment and the number of times it is found in a sequence. The technique is applied to sequences in the Swissprot database, and to 16 complete genomes. We find that eukaryotic proteins contain more internal repeats than those of prokaryotic and archael organisms. The finding that 18% of yeast sequences and 28% of the known human sequences contain detectable repeats emphasizes the importance of internal duplication in protein evolution.     

Expectations of clumpy resources influence predictions of sequential events

When predicting the next outcome in a sequence of events, people often appear to expect streaky patterns, such as that sport players can develop a “hot hand,” even if the sequence is actually random. This expectation, referred to as positive recency, can be adaptive in environments characterized by resources that are clustered across space or time (e.g., expecting to find multiple berries on separate bushes). But how strong is this disposition towards positive recency? If people perceive random sequences as streaky, will there be situations in which they forego a payoff because they prefer an unpredictable random environment over an exploitable but alternating pattern? To find out, 238 participants repeatedly chose to bet on the next outcome of one of two sequences of (binary) events, presented next to each other. One sequence displayed events at random while the other sequence was either more streaky (positively autocorrelated) or more alternating (negatively autocorrelated) than chance. The degree of autocorrelation varied in a between-subject design. Most people preferred to predict purely random sequences over those with moderate negative autocorrelation and thus missed the opportunity for above-chance payoff. Positive recency persisted despite extensive feedback and the opportunity to learn more rewarding behavior over time. Further, most participants' choice strategies were best described by a win-stay/lose-shift strategy, adaptive in clumpy or streaky environments. We discuss the implications regarding an evolved human tendency to expect streaky patterns, even if the sequence is actually random.     

Nucleotide sequence and characterization of a repetitive DNA element from the genome of Bordetella pertussis with characteristics of an insertion sequence

A repeating element of DNA has been isolated and sequenced from the genome of Bordetella pertussis. Restriction map analysis of this element shows single internal ClaI, SphI, BstEII and SalI sites. Over 40 DNA fragments are seen in ClaI digests of B. pertussis genomic DNA to which the repetitive DNA sequence hybridizes. Sequence analysis of the repeat reveals that it has properties consistent with bacterial insertion sequence (IS) elements. These properties include its length of 1053 bp, multiple copy number and presence of 28 bp of near-perfect inverted repeats at its termini. Unlike most IS elements, the presence of this element in the B. pertussis genome is not associated with a short duplication in the target DNA sequence. This repeating element is not found in the genomes of B. parapertussis or B. bronchiseptica. Analysis of a DNA fragment adjacent to one copy of the repetitive DNA sequence has identified a different repeating element which is found in nine copies in B. parapertussis and four copies in B. pertussis, suggesting that there may be other repeating DNA elements in the different Bordetella species. Computer analysis of the B. pertussis repetitive DNA element has revealed no significant nucleotide homology between it and any other bacterial transposable elements, suggesting that this repetitive sequence is specific for B. pertussis.     

Using artificial evolution and selection to model insect navigation.

BACKGROUND: An animal's behavioral strategies are often constrained by its evolutionary history and the resources available to it. Artificial evolution allows one to manipulate such constraints and explore how they influence evolved strategies. Here we compare the navigational strategies of flying insects with those of artificially evolved "animats" endowed with various motor architectures. Using evolutionary algorithms, we generated artificial neural networks that controlled a virtual animat's navigation within a 2D, simulated world. Like a flying insect, the animat possessed motors that generated thrust and torque, a compass, and visual sensors. Some animats were limited to forward motion, while others could also move sideways. Animats were selected for the precision with which they reached a target specified by a visual landmark. RESULTS: Animats given sideways motors could alter flight direction without changing body orientation and evolved strategies similar to those of flying bees or wasps performing the same task. Both animats and insects first aimed at the landmark. In the last phase, both adopted a fixed body orientation and adjusted their position to keep the landmark at a fixed retinal location. Animats unable to uncouple flight direction and body orientation evolved subtly different strategies and performed less robustly. CONCLUSIONS: This convergence between the navigational strategies of animals and animats suggests that the insect's strategies are primarily an adaptation to the demands of using visual information and compass direction to reach a position in space and that they are not significantly compromised by the insect's evolutionary history.     

The cognitive mechanisms of optimal sampling

How can animals learn the prey densities available in an environment that changes unpredictably from day to day, and how much effort should they devote to doing so, rather than exploiting what they already know? Using a two-armed bandit situation, we simulated several processes that might explain the trade-off between exploring and exploiting. They included an optimising model, dynamic backward sampling; a dynamic version of the matching law; the Rescorla-Wagner model; a neural network model; and ?-greedy and rule of thumb models derived from the study of reinforcement learning in artificial intelligence. Under conditions like those used in published studies of birds’ performance under two-armed bandit conditions, all models usually identified the more profitable source of reward, and did so more quickly when the reward probability differential was greater. Only the dynamic programming model switched from exploring to exploiting more quickly when available time in the situation was less. With sessions of equal length presented in blocks, a session-length effect was induced in some of the models by allowing motivational, but not memory, carry-over from one session to the next. The rule of thumb model was the most successful overall, though the neural network model also performed better than the remaining models.     

String matching artificial neural networks.

Three artificial neural networks (ANNs) are proposed for solving a variety of on- and off-line string matching problems. The ANN structure employed as the building block of these ANNs is derived from the harmony theory (HT) ANN, whereby the resulting string matching ANNs are characterized by fast match-mismatch decisions, low computational complexity, and activation values of the ANN output nodes that can be used as indicators of substitution, insertion (addition) and deletion spelling errors.     

Adjacent satellite DNA segments in Drosophila: Structure of junctions

The structure of eight satellite DNA molecules containing a junction between tandem arrays of different repeated sequences is described. In one class of junctions there was an abrupt switch with the juxtaposition of two satellite arrays. These arrays were closely related and the periodicity of repeats was maintained in phase across the junction. These arrays usually showed extreme homogeneity in their repeating sequences. A second class of junctions was more complex, and in two cases may have arisen by the insertion of a mobile element into a satellite array. A novel mechanism of satellite formation is proposed to explain the precision of junctions and sequence similarities of neighboring satellite arrays. Homogeneous satellite arrays would be generated enzymatically by synthesis of a repeat using the preceding repeat as template. Occasional errors in copying of the template, either single base changes or misreading the length of the repeat unit, would lead to abrupt switches in the repeating sequence.     

Characterizing the Impact of Category Uncertainty on Human Auditory Categorization Behavior

Categorization is an important cognitive process. However, the correct categorization of a stimulus is often challenging because categories can have overlapping boundaries. Whereas perceptual categorization has been extensively studied in vision, the analogous phenomenon in audition has yet to be systematically explored. Here, we test whether and how human subjects learn to use category distributions and prior probabilities, as well as whether subjects employ an optimal decision strategy when making auditory-category decisions. We asked subjects to classify the frequency of a tone burst into one of two overlapping, uniform categories according to the perceived tone frequency. We systematically varied the prior probability of presenting a tone burst with a frequency originating from one versus the other category. Most subjects learned these changes in prior probabilities early in testing and used this information to influence categorization. We also measured each subject''s frequency-discrimination thresholds (i.e., their sensory uncertainty levels). We tested each subject''s average behavior against variations of a Bayesian model that either led to optimal or sub-optimal decision behavior (i.e. probability matching). In both predicting and fitting each subject''s average behavior, we found that probability matching provided a better account of human decision behavior. The model fits confirmed that subjects were able to learn category prior probabilities and approximate forms of the category distributions. Finally, we systematically explored the potential ways that additional noise sources could influence categorization behavior. We found that an optimal decision strategy can produce probability-matching behavior if it utilized non-stationary category distributions and prior probabilities formed over a short stimulus history. Our work extends previous findings into the auditory domain and reformulates the issue of categorization in a manner that can help to interpret the results of previous research within a generative framework.     

An artificial hysteresis binary neuron: a model suppressing the oscillatory behaviors of neural dynamics

A hysteresis binary McCulloch-Pitts neuron model is proposed in order to suppress the complicated oscillatory behaviors of neural dynamics. The artificial hysteresis binary neural network is used for scheduling time-multiplex crossbar switches in order to demonstrate the effects of hysteresis. Time-multiplex crossbar switching systems must control traffic on demand such that packet blocking probability and packet waiting time are minimized. The system using n×n processing elements solves an n×n crossbar-control problem with O(1) time, while the best existing parallel algorithm requires O(n) time. The hysteresis binary neural network maximizes the throughput of packets through a crossbar switch. The solution quality of our system does not degrade with the problem size.     

Improvement of protein secondary structure prediction using binary word encoding

We propose a binary word encoding to improve the protein secondary structure prediction. A binary word encoding encodes a local amino acid sequence to a binary word, which consists of 0 or 1. We use an encoding function to map an amino acid to 0 or 1. Using the binary word encoding, we can statistically extract the multiresidue information, which depends on more than one residue. We combine the binary word encoding with the GOR method, its modified version, which shows better accuracy, and the neural network method. The binary word encoding improves the accuracy of GOR by 2.8%. We obtain similar improvement when we combine this with the modified GOR method and the neural network method. When we use multiple sequence alignment data, the binary word encoding similarly improves the accuracy. The accuracy of our best combined method is 68.2%. In this paper, we only show improvement of the GOR and neural network method, we cannot say that the encoding improves the other methods. But the improvement by the encoding suggests that the multiresidue interaction affects the formation of secondary structure. In addition, we find that the optimal encoding function obtained by the simulated annealing method relates to non-polarity. This means that nonpolarity is important to the multiresidue interaction. Proteins 27:36–46 © 1997 Wiley-Liss, Inc.     

A Memory-Efficient Deterministic Finite Automaton-Based Bit-Split String Matching Scheme Using Pattern Uniqueness in Deep Packet Inspection

This paper proposes a memory-efficient bit-split string matching scheme for deep packet inspection (DPI). When the number of target patterns becomes large, the memory requirements of the string matching engine become a critical issue. The proposed string matching scheme reduces the memory requirements using the uniqueness of the target patterns in the deterministic finite automaton (DFA)-based bit-split string matching. The pattern grouping extracts a set of unique patterns from the target patterns. In the set of unique patterns, a pattern is not the suffix of any other patterns. Therefore, in the DFA constructed with the set of unique patterns, when only one pattern can be matched in an output state. In the bit-split string matching, multiple finite-state machine (FSM) tiles with several input bit groups are adopted in order to reduce the number of stored state transitions. However, the memory requirements for storing the matching vectors can be large because each bit in the matching vector is used to identify whether its own pattern is matched or not. In our research, the proposed pattern grouping is applied to the multiple FSM tiles in the bit-split string matching. For the set of unique patterns, the memory-based bit-split string matching engine stores only the pattern match index for each state to indicate the match with its own unique pattern. Therefore, the memory requirements are significantly decreased by not storing the matching vectors in the string matchers for the set of unique patterns. The experimental results show that the proposed string matching scheme can reduce the storage cost significantly compared to the previous bit-split string matching methods.     

An FMRI study of the role of the medial temporal lobe in implicit and explicit sequence learning

fMRI was used to investigate the neural substrates supporting implicit and explicit sequence learning, focusing especially upon the role of the medial temporal lobe. Participants performed a serial reaction time task (SRTT). For implicit learning, they were naive about a repeating pattern, whereas for explicit learning, participants memorized another repeating sequence. fMRI analyses comparing repeating versus random sequence blocks demonstrated activation of frontal, parietal, cingulate, and striatal regions implicated in previous SRTT studies. Importantly, mediotemporal lobe regions were active in both explicit and implicit SRTT learning. Moreover, the results provide evidence of a role for the hippocampus and related cortices in the formation of higher order associations under both implicit and explicit learning conditions, regardless of conscious awareness of sequence knowledge.     

Sequence detectors as a basis of grammar in the brain

Summary Grammar processing may build upon serial-order mechanisms known from non-human species. A circuit similar to that underlying direction-sensitive movement detection in arthropods and vertebrates may become selective for sequences of words, thus yielding grammatical sequence detectors in the human brain. Sensitivity to the order of neuronal events arises from unequal connection strengths between two word specific neural units and a third element, the sequence detector. This mechanism, which critically depends on the dynamics of the neural units, can operate at the single neuron level and may be relevant at the level of neuronal ensembles as well. Due to the repeated occurrence of sequences, for example word strings, the sequence-sensitive elements become more firmly established and, by substitution of elements between strings, a process called auto-associative substitution learning (AASL) is triggered. AASL links the neuronal counterparts of the string elements involved in the substitution process to the sequence detector, thereby providing a brain basis of what can be described linguistically as the generalization of rules of grammar. A network of sequence detectors may constitute grammar circuits in the human cortex on which a separate set of mechanisms establishing temporary binding and recursion can operate.     

Retrieving backbone string neighbors provides insights into structural modeling of membrane proteins

Identification of protein structural neighbors to a query is fundamental in structure and function prediction. Here we present BS-align, a systematic method to retrieve backbone string neighbors from primary sequences as templates for protein modeling. The backbone conformation of a protein is represented by the backbone string, as defined in Ramachandran space. The backbone string of a query can be accurately predicted by two innovative technologies: a knowledge-driven sequence alignment and encoding of a backbone string element profile. Then, the predicted backbone string is employed to align against a backbone string database and retrieve a set of backbone string neighbors. The backbone string neighbors were shown to be close to native structures of query proteins. BS-align was successfully employed to predict models of 10 membrane proteins with lengths ranging between 229 and 595 residues, and whose high-resolution structural determinations were difficult to elucidate both by experiment and prediction. The obtained TM-scores and root mean square deviations of the models confirmed that the models based on the backbone string neighbors retrieved by the BS-align were very close to the native membrane structures although the query and the neighbor shared a very low sequence identity. The backbone string system represents a new road for the prediction of protein structure from sequence, and suggests that the similarity of the backbone string would be more informative than describing a protein as belonging to a fold.     

Visual and buying sequence features-based product image recommendation using optimization based deep residual network

A recommendation system is an imaginative resolution for managing the restrictions in e-commerce services with item details and user details. Also, it is used to determine the user preferences to recommend the items they expected to buy. Several conventional collaborative filtering techniques are devised in the recommender model, but it has some complexities. Hence, an innovative optimization-driven deep residual network is devised in this paper for a product recommendation system. Here, the product of images is used for extracting features where the Convolutional neural network (CNN) features are computed, and then it is given as input to the deep residual network aimed at product recommendation. The deep residual network is trained using developed Elephant Herding Feedback Artificial Optimization (EHFAO), which is obtained by integrating Elephant Herding optimization (EHO) into the Feedback Artificial Tree (FAT). Here, the item grouping is carried out on input data based on K-means clustering. After item grouping, Cosine similarity is used to perform matching of groups, where the best group is acquired among all the available groups. Extraction of list of visitors is done from the best group. Then, the list of items is obtained from the sequence of best visitor. Next, the corresponding binary sequence is obtained for the applicable sequence of visitor. From this sequence of best visitor, the recommended product is acquired. Then, the recommended product is subjected to the sentiment analysis for which the score is determined. Here, the sentiment analysis helps to decide whether the product is recommended or not recommended. If the score is positive, then the same product is recommended; otherwise, the new product is recommended. The proposed EHFAO-based deep residual network attained better performance in comparison to the other techniques with a maximal F-measure at 84.061%, 84.061% precision, 87.845% recall along with minimal Mean Squared Error (MSE) of 0.216.     

Finding significant matches of position weight matrices in linear time

Position weight matrices are an important method for modeling signals or motifs in biological sequences, both in DNA and protein contexts. In this paper, we present fast algorithms for the problem of finding significant matches of such matrices. Our algorithms are of the online type, and they generalize classical multipattern matching, filtering, and superalphabet techniques of combinatorial string matching to the problem of weight matrix matching. Several variants of the algorithms are developed, including multiple matrix extensions that perform the search for several matrices in one scan through the sequence database. Experimental performance evaluation is provided to compare the new techniques against each other as well as against some other online and index-based algorithms proposed in the literature. Compared to the brute-force O(mn) approach, our solutions can be faster by a factor that is proportional to the matrix length m. Our multiple-matrix filtration algorithm had the best performance in the experiments. On a current PC, this algorithm finds significant matches (p = 0.0001) of the 123 JASPAR matrices in the human genome in about 18 minutes.