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Real-world systems interact with one another via dependency connectivities. Dependency connectivities make systems less robust because failures may spread iteratively among systems via dependency links. Most previous studies have assumed that two nodes connected by a dependency link are strongly dependent on each other; that is, if one node fails, its dependent partner would also immediately fail. However, in many real scenarios, nodes from different networks may be weakly dependent, and links may fail instead of nodes. How interdependent networks with weak dependency react to link failures remains unknown. In this paper, we build a model of fully interdependent networks with weak dependency and define a parameter α in order to describe the node-coupling strength. If a node fails, its dependent partner has a probability of failing of 1−α. Then, we develop an analytical tool for analyzing the robustness of interdependent networks with weak dependency under link failures, with which we can accurately predict the system robustness when 1−p fractions of links are randomly removed. We find that as the node coupling strength increases, interdependent networks show a discontinuous phase transition when α<αc and a continuous phase transition when α>αc. Compared to site percolation with nodes being attacked, the crossover points αc are larger in the bond percolation with links being attacked. This finding can give us some suggestions for designing and protecting systems in which link failures can happen.

Over the last few years, a new methodology to address the P versus NP problem has been developed, based on searching for borderlines between the nonefficiency of computing models (only problems in class P can be solved in polynomial time) and the presumed efficiency (ability to solve NP-complete problems in polynomial time). These borderlines can be seen as frontiers of efficiency, which are crucial in this methodology. “Translating,” in some sense, an efficient solution in a presumably efficient model to an efficient solution in a nonefficient model would give an affirmative answer to problem P versus NP. In the framework of Membrane Computing, the key of this approach is to detect the syntactic or semantic ingredients that are needed to pass from a nonefficient class of membrane systems to a presumably efficient one. This paper deals with tissue P systems with communication rules of type symport/antiport allowing the evolution of the objects triggering the rules. In previous works, frontiers of efficiency were found in these kinds of membrane systems both with division rules and with separation rules. However, since they were not optimal, it is interesting to refine these frontiers. In this work, optimal frontiers of the efficiency are obtained in terms of the total number of objects involved in the communication rules used for that kind of membrane systems. These optimizations could be easier to translate, if possible, to efficient solutions in a nonefficient model.

A word is a finite sequence of symbols. Parikh matrix of a word is an upper triangular matrix with ones in the main diagonal and nonnegative integers above the main diagonal which are counts of certain scattered subwords in the word. On the other hand, a picture array, which is a rectangular arrangement of symbols, is an extension of the notion of a word to two dimensions. Parikh matrices associated with a picture array have been introduced, and their properties have been studied. Here, we obtain certain algebraic properties of Parikh matrices of binary picture arrays based on the notions of power, fairness, and a restricted shuffle operator extending the corresponding notions studied in the case of words. We also obtain properties of Parikh matrices of arrays formed by certain geometric operations.

Tissue P systems with evolutional communication (symport/antiport) rules are computational models inspired by biochemical systems consisting of multiple individuals living and cooperating in a certain environment, where objects can be modified when moving from one region to another region. In this work, cell separation, inspired from membrane fission process, is introduced in the framework of tissue P systems with evolutional communication rules. The computational complexity of this kind of P systems is investigated. It is proved that only problems in class P can be efficiently solved by tissue P systems with cell separation with evolutional communication rules of length at most (n,1), for each natural number n≥1. In the case where that length is upper bounded by (3,2), a polynomial time solution to the SAT problem is provided, hence, assuming that P≠NP a new boundary between tractability and NP-hardness on the basis of the length of evolutional communication rules is provided. Finally, a new simulator for tissue P systems with evolutional communication rules is designed and is used to check the correctness of the solution to the SAT problem.

DNA strand displacement technology performs well in sensing and programming DNA segments. In this work, we construct DNA molecular systems based on DNA strand displacement performing computation of logic gates. Specifically, a class of so-called "DNA neurons" are achieved, in which a "smart" way inspired by biological neurons encoding information is developed to encode and deliver information using DNA molecules. The "DNA neuron" is bistable, that is, it can sense DNA molecules as input signals, and release "negative" or "positive" signals DNA molecules. We design intelligent DNA molecular systems that are constructed by cascading some particularly organized "DNA neurons", which could perform logic computation, including AND, OR, XOR logic gates, automatically. Both simulation results using visual DSD (DNA strand displacement) software and experimental results are obtained, which shows that the proposed systems can detect DNA signals with high sensitivity and accretion; moreover, the systems can process input signals automatically with complex nonlinear logic. The method proposed in this work may provide a new way to construct a sensitive molecular signal detection system with neurons spiking behavior in vitro, and can be used to develop intelligent molecular processing systems in vivo.

Non-small cell lung cancer (NSCLC) has two major subtypes: adenocarcinoma (AC) and squamous cell carcinoma (SCC). The diagnosis and treatment of NSCLC are hindered by the limited knowledge about the pathogenesis mechanisms of subtypes of NSCLC. It is necessary to research the molecular mechanisms related with AC and SCC. In this work, we improved the logic analysis algorithm to mine the sufficient and necessary conditions for the presence states (presence or absence) of phenotypes. We applied our method to AC and SCC specimens, and identified [Formula: see text] lower and [Formula: see text] higher logic relationships between genes and two subtypes of NSCLC. The discovered relationships were independent of specimens selected, and their significance was validated by statistic test. Compared with the two earlier methods (the non-negative matrix factorization method and the relevance analysis method), the current method outperformed these methods in the recall rate and classification accuracy on NSCLC and normal specimens. We obtained [Formula: see text] biomarkers. Among [Formula: see text] biomarkers, [Formula: see text] genes have been used to distinguish AC from SCC in practice, and other six genes were newly discovered biomarkers for distinguishing subtypes. Furthermore, NKX2-1 has been considered as a molecular target for the targeted therapy of AC, and [Formula: see text] other genes may be novel molecular targets. By gene ontology analysis, we found that two biological processes ('epidermis development' and 'cell adhesion') were closely related with the tumorigenesis of subtypes of NSCLC. More generally, the current method could be extended to other complex diseases for distinguishing subtypes and detecting the molecular targets for targeted therapy.