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Alfonso Rodríguez-Patón

Bio: Alfonso Rodríguez-Patón is an academic researcher from Technical University of Madrid. The author has contributed to research in topics: Membrane computing & P system. The author has an hindex of 21, co-authored 108 publications receiving 2022 citations. Previous affiliations of Alfonso Rodríguez-Patón include Complutense University of Madrid.


Papers
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Journal ArticleDOI
08 Mar 2003
TL;DR: A computing model called a tissue P system is proposed, which processes symbols in a multiset rewriting sense, in a net of cells, which can simulate a Turing machine even when using a small number of cells.
Abstract: Starting from the way the inter-cellular communication takes place by means of protein channels (and also from the standard knowledge about neuron functioning), we propose a computing model called a tissue P system, which processes symbols in a multiset rewriting sense, in a net of cells. Each cell has a finite state memory, processes multisets of symbol-impulses, and can send impulses (“excitations”) to the neighboring cells. Such cell nets are shown to be rather powerful: they can simulate a Turing machine even when using a small number of cells, each of them having a small number of states. Moreover, in the case when each cell works in the maximal manner and it can excite all the cells to which it can send impulses, then one can easily solve the Hamiltonian Path Problem in linear time. A new characterization of the Parikh images of ET0L languages is also obtained in this framework. Besides such basic results, the paper provides a series of suggestions for further research.

412 citations

Book ChapterDOI
15 Aug 2002
TL;DR: In this article, a tissue P system is proposed, which processes symbols in a multiset rewriting sense, in a net of cells similar to a neural net, each cell has a finite state memory, processes multisets of symbol-impulses, and can send impulses ("excitations") to the neighboring cells.
Abstract: Starting from the way the inter-cellular communication takes place by means of protein channels and also from the standard knowledge about neuron functioning, we propose a computing model called a tissue P system, which processes symbols in a multiset rewriting sense, in a net of cells similar to a neural net. Each cell has a finite state memory, processes multisets of symbol-impulses, and can send impulses ("excitations") to the neighboring cells. Such cell nets are shown to be rather powerful: they can simulate a Turing machine even when using a small number of cells, each of them having a small number of states. Moreover, in the case when each cell works in the maximal manner and it can excite all the cells to which it can send impulses, then one can easily solve the Hamiltonian Path Problem in linear time. A new characterization of the Parikh images of ET0L languages are also obtained in this framework.

129 citations

Journal ArticleDOI
TL;DR: This work reconstructs a miRNA functional similarity network using the following biological information: the miRNA family information, miRNA cluster information, experimentally valid miRNA—target association and disease—miRNA information, and reconstructing a disease similarity networks using disease functional information and disease semantic information.
Abstract: MicroRNAs (miRNAs) play critical roles in regulating gene expression at post-transcriptional levels. Numerous experimental studies indicate that alterations and dysregulations in miRNAs are associated with important complex diseases, especially cancers. Predicting potential miRNA—disease association is beneficial not only to explore the pathogenesis of diseases, but also to understand biological processes. In this work, we propose two methods that can effectively predict potential miRNA—disease associations using our reconstructed miRNA and disease similarity networks, which are based on the latest experimental data. We reconstruct a miRNA functional similarity network using the following biological information: the miRNA family information, miRNA cluster information, experimentally valid miRNA—target association and disease—miRNA information. We also reconstruct a disease similarity network using disease functional information and disease semantic information. We present Katz with specific weights and Katz with machine learning, on the comprehensive heterogeneous network. These methods, which achieve corresponding AUC values of 0.897 and 0.919, exhibit performance superior to the existing methods. Comprehensive data networks and reasonable considerations guarantee the high performance of our methods. Contrary to several methods, which cannot work in such situations, the proposed methods also predict associations for diseases without any known related miRNAs. A web service for the download and prediction of relationships between diseases and miRNAs is available at http://lab.malab.cn/soft/MDPredict/ .

114 citations

Journal ArticleDOI
TL;DR: It is shown that SN P systems with colored spikes having three neurons are sufficient to compute Turing computable sets of numbers, and such system having two neurons is able to compute the family of recursive functions.
Abstract: Spiking neural P systems (SN P systems) are bio-inspired neural-like computing models, which are obtained by abstracting the way of biological neurons’ spiking and communication by means of spikes in central nervous systems. SN P systems performed well in describing and modeling behaviors that occur simultaneously, yet weak at modeling complex systems with the limits of using a single spike. In this paper, drawing on the idea from colored petri nets, SN P systems with colored spikes are proposed, where a finite set of colors is introduced to mark the spikes such that each spike is associated with a unique color. The updated spiking rule is applied by consuming and emitting a number of colored spikes (with the same or different colors). The computation power of the systems is investigated. Specifically, it is shown that SN P systems with colored spikes having three neurons are sufficient to compute Turing computable sets of numbers, and such system having two neurons is able to compute the family of recursive functions. These results improved the corresponding ones on the number of neurons needed to construct universal SN P systems recently appeared in [Neurocomputing, 2016, 193(12): 193–200]. To our best knowledge, this is the smallest number of neurons used to construct Turing universal SN P systems as number generator and function computing device by far.

104 citations

Journal ArticleDOI
TL;DR: It is shown that conjugation and specific killing of targeted bacteria occurs in the microbiota of zebrafish and crustacean larvae, which are natural hosts for Vibrio spp.
Abstract: Targeted killing of pathogenic bacteria without harming beneficial members of host microbiota holds promise as a strategy to cure disease and limit both antimicrobial-related dysbiosis and development of antimicrobial resistance. We engineer toxins that are split by inteins and deliver them by conjugation into a mixed population of bacteria. Our toxin-intein antimicrobial is only activated in bacteria that harbor specific transcription factors. We apply our antimicrobial to specifically target and kill antibiotic-resistant Vibrio cholerae present in mixed populations. We find that 100% of antibiotic-resistant V. cholerae receiving the plasmid are killed. Escape mutants were extremely rare (10-6-10-8). We show that conjugation and specific killing of targeted bacteria occurs in the microbiota of zebrafish and crustacean larvae, which are natural hosts for Vibrio spp. Toxins split with inteins could form the basis of precision antimicrobials to target pathogens that are antibiotic resistant.

93 citations


Cited by
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Proceedings ArticleDOI
22 Jan 2006
TL;DR: Some of the major results in random graphs and some of the more challenging open problems are reviewed, including those related to the WWW.
Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

7,116 citations

Journal ArticleDOI
TL;DR: UNet++ as mentioned in this paper proposes an efficient ensemble of U-Nets of varying depths, which partially share an encoder and co-learn simultaneously using deep supervision, leading to a highly flexible feature fusion scheme.
Abstract: The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN). Despite their success, these models have two limitations: (1) their optimal depth is apriori unknown, requiring extensive architecture search or inefficient ensemble of models of varying depths; and (2) their skip connections impose an unnecessarily restrictive fusion scheme, forcing aggregation only at the same-scale feature maps of the encoder and decoder sub-networks. To overcome these two limitations, we propose UNet++, a new neural architecture for semantic and instance segmentation, by (1) alleviating the unknown network depth with an efficient ensemble of U-Nets of varying depths, which partially share an encoder and co-learn simultaneously using deep supervision; (2) redesigning skip connections to aggregate features of varying semantic scales at the decoder sub-networks, leading to a highly flexible feature fusion scheme; and (3) devising a pruning scheme to accelerate the inference speed of UNet++. We have evaluated UNet++ using six different medical image segmentation datasets, covering multiple imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), and electron microscopy (EM), and demonstrating that (1) UNet++ consistently outperforms the baseline models for the task of semantic segmentation across different datasets and backbone architectures; (2) UNet++ enhances segmentation quality of varying-size objects—an improvement over the fixed-depth U-Net; (3) Mask RCNN++ (Mask R-CNN with UNet++ design) outperforms the original Mask R-CNN for the task of instance segmentation; and (4) pruned UNet++ models achieve significant speedup while showing only modest performance degradation. Our implementation and pre-trained models are available at https://github.com/MrGiovanni/UNetPlusPlus .

1,487 citations

Journal Article
TL;DR: In this article, the authors introduce a class of neural-like P systems which they call spiking neural P systems (in short, SN P systems), in which the result of a computation is the time between the moments when a specified neuron spikes.
Abstract: This paper proposes a way to incorporate the idea of spiking neurons into the area of membrane computing, and to this aim we introduce a class of neural-like P systems which we call spiking neural P systems (in short, SN P systems). In these devices, the time (when the neurons fire and/or spike) plays an essential role. For instance, the result of a computation is the time between the moments when a specified neuron spikes. Seen as number computing devices, SN P systems are shown to be computationally complete (both in the generating and accepting modes, in the latter case also when restricting to deterministic systems). If the number of spikes present in the system is bounded, then the power of SN P systems falls drastically, and we get a characterization of semilinear sets. A series of research topics and open problems are formulated.

589 citations

Journal ArticleDOI
TL;DR: The extraordinary dependence of HIV on human host proteins for efficient transmission and replication provides many new potential targets for antiretroviral therapy.
Abstract: Brass AL, Dykxhoorn DM, Benita Y, et al. Science . 2008;319(5865):921–926 PURPOSE OF THE STUDY. The HIV genome encodes only 15 proteins and, therefore, must use multiple host-cell collaborators for successful replication and transmission. Required host-derived proteins include CD4 as the primary virus receptor and chemokine receptors as coreceptors. This study identified multiple other host proteins required for HIV activity. METHODS. Human cells known to be susceptible to HIV were exposed in vitro to HIV. Using small interfering RNAs able to inhibit each known gene in the human genome 1 at a time, the investigators tested whether HIV could establish an infection and copy itself. HIV dependence on >21 000 human genes was examined. RESULTS. More than 250 human genes were identified to be required for efficient HIV replication. Termed “HIV-dependency factors,” the products of these genes are known to participate in a broad array of cellular functions and implicate unsuspected pathways in the virus life cycle. CONCLUSIONS. The extraordinary dependence of HIV on human host proteins for efficient transmission and replication provides many new potential targets for antiretroviral therapy. REVIEWER COMMENTS. An example of targeting host proteins is the use of chemokine receptor 5 (CCR5) inhibitors. Many people with CCR5 deficiency are very resistant to HIV infection yet have limited if any clinical consequences. Maraviroc CCR5 inhibitor is approved for treatment for HIV infection. This study identified many more such potential targets.

552 citations