Small universal spiking neural P systems

In search for small universal computing devices of various types, we consider here the case of spiking neural P systems (SN P systems), in two variants: as devices that compute functions and as devices that generate sets of numbers. We start with the first case and we produce a universal spiking neural P system with 84 neurons. If a slight generalization of the used rules is adopted, namely, we allow rules for producing simultaneously several spikes, then a considerable reduction, to 49 neurons, is obtained. For SN P systems used as generators of sets of numbers, we find a universal system with restricted rules having 76 neurons and one with extended rules having 50 neurons.     

Sequential spiking neural P systems with exhaustive use of rules

Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes, where neurons work in parallel in the sense that each neuron that can fire should fire, but the work in each neuron is sequential in the sense that at most one rule can be applied at each computation step. In this work, we consider SN P systems with the restriction that at most one neuron can fire at each step, and each neuron works in an exhaustive manner (a kind of local parallelism - an applicable rule in a neuron is used as many times as possible). Such SN P systems are called sequential SN P systems with exhaustive use of rules. The computation power of sequential SN P systems with exhaustive use of rules is investigated. Specifically, characterizations of Turing computability and of semilinear sets of numbers are obtained, as well as a strict superclass of semilinear sets is generated. The results show that the computation power of sequential SN P systems with exhaustive use of rules is closely related with the types of spiking rules in neurons.     

Multiset splicing systems

We consider splicing systems reflecting two important aspects of the behaviour of DNA molecules in nature or in laboratory experiments which so far have not been studied in the literature. We examine the effect of splicing rules applied to finite multisets of words using sequential and different types of parallel derivation strategies and compare the sets of words or sets of multisets which can be obtained.     

Network of evolutionary processors with splicing rules and permitting context

In this paper we consider networks of evolutionary processors with splicing rules and permitting context (NEPPS) as language generating and computational devices. Such a network consists of several processors placed on the nodes of a virtual graph and are able to perform splicing (which is a biologically motivated operation) on the words present in that node, according to the splicing rules present there. Before applying the splicing operation on words, we check for the presence of certain symbols (permitting context) in the strings on which the rule is applied. Each node is associated with an input and output filter. When the filters are based on random context conditions, one gets the computational power of Turing machines with networks of size two. We also show how these networks can be used to solve NP-complete problems in linear time.     

Association pattern mining of intron retention events in human based on hybrid learning machine

Alternative splicing is a main component of protein diversity, and aberrant splicing is known to be one of the main causes of genetic disorders such as cancer. Many statistical and computational approaches have identified several major factors that determine the splicing event, such as exon/intron length, splice site strength, and density of splicing enhancers or silencers. These factors may be correlated with one another and thus result in a specific type of splicing, but there has not been a systematic approach to extracting comprehensible association patterns. Here, we attempted to understand the decision making process of the learning machine on intron retention event. We adopted a hybrid learning machine approach using a random forest and association rule mining algorithm to determine the governing factors of intron retention events and their combined effect on decision-making processes. By quantifying all candidate features into five category values, we enhanced the understandability of generated rules. The interesting features found by the random forest algorithm are that only the adenine- and thymine-based triplets such as ATA, TTA, and ATT, but not the known intronic splicing enhancer GGG triplet is shown the significant features. The rules generated by the association rule mining algorithm also show that constitutive introns are generally characterized by high adenine- and thymine-based triplet frequency (level 3 and above), 3' and 5' splice site scores, exonic splicing silencer scores, and intron length, whereas retained introns are characterized by low-level counterpart scores.     

Diagnostic Value of Run Chart Analysis: Using Likelihood Ratios to Compare Run Chart Rules on Simulated Data Series

Run charts are widely used in healthcare improvement, but there is little consensus on how to interpret them. The primary aim of this study was to evaluate and compare the diagnostic properties of different sets of run chart rules. A run chart is a line graph of a quality measure over time. The main purpose of the run chart is to detect process improvement or process degradation, which will turn up as non-random patterns in the distribution of data points around the median. Non-random variation may be identified by simple statistical tests including the presence of unusually long runs of data points on one side of the median or if the graph crosses the median unusually few times. However, there is no general agreement on what defines “unusually long” or “unusually few”. Other tests of questionable value are frequently used as well. Three sets of run chart rules (Anhoej, Perla, and Carey rules) have been published in peer reviewed healthcare journals, but these sets differ significantly in their sensitivity and specificity to non-random variation. In this study I investigate the diagnostic values expressed by likelihood ratios of three sets of run chart rules for detection of shifts in process performance using random data series. The study concludes that the Anhoej rules have good diagnostic properties and are superior to the Perla and the Carey rules.     

Controlled synthesis of target strings in a class of splicing systems

This article presents an approach for synthesizing target strings in a class of computational models of DNA recombination. The computational models are formalized as splicing systems in the context of formal languages. Given a splicing system (of a restricted type) and a target string to be synthesized, we construct (i) a rule-embedded splicing automaton that recognizes languages containing strings embedded with symbols representing splicing rules, and (ii) an automaton that implicitly recognizes the target string. By manipulating these two automata, we extract all rule sequences that lead to the production of the target string (if that string belongs to the splicing language). An algorithm for synthesizing a certain type of target strings based on such rule sequences is presented.     

On visual orientation of dot patterns

Two-dimensional normally distributed random dot patterns were used in two experiments on visual orientation estimation. In the first experiment the patterns differed in their sample correlation and in dot number. In the second one the number of dots was maintained constant but the patterns were generated as a superposition of two normally distributed orthogonal sets composed of different number of dots. In both experiments the estimated orientation depended on stimuli correlation-with increasing correlation the estimated orientation gets closer to the orientation of the least square distance axis of the pattern. Even at very low unsignificant correlations there still remained a hint about stimulus orientation which was not estimated at random. Equalizing consecutively the number of dots in the two orthogonal dot patterns during the second experiment did not result in chance performance either. The bimodal angular distributions of the obtained responses permitted to approach the problem of orientation ambiguity. The results are discussed in terms of optimization processes taking place in the visual system.