Parameterized Verification of Algorithms for Oblivious Robots on a Ring

We study verification problems for autonomous swarms of mobile robots that self-organize and cooperate to solve global objectives. In particular, we focus in this paper on the model proposed by Suzuki and Yamashita of anonymous robots evolving in a discrete space with a finite number of locations (here, a ring). A large number of algorithms have been proposed working for rings whose size is not a priori fixed and can be hence considered as a parameter. Handmade correctness proofs of these algorithms have been shown to be error-prone, and recent attention had been given to the application of formal methods to automatically prove those. Our work is the first to study the verification problem of such algorithms in the parameter-ized case. We show that safety and reachability problems are undecidable for robots evolving asynchronously. On the positive side, we show that safety properties are decidable in the synchronous case, as well as in the asynchronous case for a particular class of algorithms. Several properties on the protocol can be decided as well. Decision procedures rely on an encoding in Presburger arithmetics formulae that can be verified by an SMT-solver. Feasibility of our approach is demonstrated by the encoding of several case studies

A distributed accelerated gradient algorithm for distributed model predictive control of a hydro power valley

A distributed model predictive control (DMPC) approach based on distributed optimization is applied to the power reference tracking problem of a hydro power valley (HPV) system. The applied optimization algorithm is based on accelerated gradient methods and achieves a convergence rate of O(1/k^2), where k is the iteration number. Major challenges in the control of the HPV include a nonlinear and large-scale model, nonsmoothness in the power-production functions, and a globally coupled cost function that prevents distributed schemes to be applied directly. We propose a linearization and approximation approach that accommodates the proposed the DMPC framework and provides very similar performance compared to a centralized solution in simulations. The provided numerical studies also suggest that for the sparsely interconnected system at hand, the distributed algorithm we propose is faster than a centralized state-of-the-art solver such as CPLEX

Statistical properties of multistep enzyme-mediated reactions

Enzyme-mediated reactions may proceed through multiple intermediate conformational states before creating a final product molecule, and one often wishes to identify such intermediate structures from observations of the product creation. In this paper, we address this problem by solving the chemical master equations for various enzymatic reactions. We devise a perturbation theory analogous to that used in quantum mechanics that allows us to determine the first () and the second (variance) cumulants of the distribution of created product molecules as a function of the substrate concentration and the kinetic rates of the intermediate processes. The mean product flux V=d/dt (or "dose-response" curve) and the Fano factor F=variance/ are both realistically measurable quantities, and while the mean flux can often appear the same for different reaction types, the Fano factor can be quite different. This suggests both qualitative and quantitative ways to discriminate between different reaction schemes, and we explore this possibility in the context of four sample multistep enzymatic reactions. We argue that measuring both the mean flux and the Fano factor can not only discriminate between reaction types, but can also provide some detailed information about the internal, unobserved kinetic rates, and this can be done without measuring single-molecule transition events.Comment: 8 pages, 3 figure

Activation of EGFR, HER2 and HER3 by neurotensin/neurotensin receptor 1 renders breast tumors aggressive yet highly responsive to lapatinib and metformin in mice

A present challenge in breast oncology research is to identify therapeutical targets which could impact tumor progression. Neurotensin (NTS) and its high affinity receptor (NTSR1) are up regulated in 20% of breast cancers, and NTSR1 overexpression was shown to predict a poor prognosis for 5 year overall survival in invasive breast carcinomas. Interactions between NTS and NTSR1 induce pro-oncogenic biological effects associated with neoplastic processes and tumor progression. Here, we depict the cellular mechanisms activated by NTS, and contributing to breast cancer cell aggressiveness. We show that neurotensin (NTS) and its high affinity receptor (NTSR1) contribute to the enhancement of experimental tumor growth and metastasis emergence in an experimental mice model. This effect ensued following EGFR, HER2, and HER3 over-expression and autocrine activation and was associated with an increase of metalloproteinase MMP9, HB-EGF and Neuregulin 2 in the culture media. EGFR over expression ensued in a more intense response to EGF on cellular migration and invasion. Accordingly, lapatinib, an EGFR/HER2 tyrosine kinase inhibitor, as well as metformin, reduced the tumor growth of cells overexpressing NTS and NTSR1. All cellular effects, such as adherence, migration, invasion, altered by NTS/NTSR1 were abolished by a specific NTSR1 antagonist. A strong statistical correlation between NTS-NTSR1-and HER3 (p< 0.0001) as well as NTS-NTSR1-and HER3-HER2 (p< 0.001) expression was found in human breast tumors. Expression of NTS/NTSR1 on breast tumoral cells creates a cellular context associated with cancer aggressiveness by enhancing epidermal growth factor receptor activity. We propose the use of labeled NTS/NTSR1 complexes to enlarge the population eligible for therapy targeting HERs tyrosine kinase inhibitor or HER2 overexpression

Lactobacillus porcinae sp. nov. isolated from traditional Vietnamese nem chua

A species diversity study of lactic acid bacteria occurring in traditional Vietnamese nem chua yielded an isolate, LMG 26767T, that could not be assigned to a validly named species. The isolate was initially investigated by 16S rRNA gene sequence analysis, which revealed that it belonged to the genus Lactobacillus, with Lactobacillus manihotivorans and Lactobacillus camelliae as the closest relatives (98.9% and 96.9% gene sequence similarity towards the type strains, respectively). Comparative (GTG)5-PCR genomic fingerprinting confirmed the unique taxonomic status of the novel strain. DNA-DNA hybridization experiments, DNA G+C content determination, sequence analysis of the phenylalanyl-tRNA synthase (pheS) gene, and physiological and biochemical characterization demonstrated that strain LMG 26767T (= CCUG 62266T) represents a novel species, for which the name Lactobacillus porcinae sp. nov. is proposed. Biochemically, Lb. porcinae can be distinguished from Lb. manihotivorans and Lb. camelliae by its carbohydrate fermentation profile, absence of growth at 45°C, and production of D- and L- lactate as end products of glucose metabolism

LNK suppresses interferon signaling in melanoma.

LNK (SH2B3) is a key negative regulator of JAK-STAT signaling which has been extensively studied in malignant hematopoietic diseases. We found that LNK is significantly elevated in cutaneous melanoma; this elevation is correlated with hyperactive signaling of the RAS-RAF-MEK pathway. Elevated LNK enhances cell growth and survival in adverse conditions. Forced expression of LNK inhibits signaling by interferon-STAT1 and suppresses interferon (IFN) induced cell cycle arrest and cell apoptosis. In contrast, silencing LNK expression by either shRNA or CRISPR-Cas9 potentiates the killing effect of IFN. The IFN-LNK signaling is tightly regulated by a negative feedback mechanism; melanoma cells exposed to IFN upregulate expression of LNK to prevent overactivation of this signaling pathway. Our study reveals an unappreciated function of LNK in melanoma and highlights the critical role of the IFN-STAT1-LNK signaling axis in this potentially devastating disease. LNK may be further explored as a potential therapeutic target for melanoma immunotherapy

Création d'un corpus de traces graphiques de la Langue des Signes Française

International audienceThis work constitutes a contribution to the emergence of a common writing for French Sign Language in a graphical or even a typographical framework. Our hypothesis is as follows : in its execution, the gestural sign contains a readable graphic trace that can be visualized with a photographic device. In order to evaluate this hypothesis, we gather a photographic corpus made of isolated elicited signs.Le projet GestuelScript se présente comme une contribution à l'émergence d'une écriture courante pour la Langue des Signes Française dans un cadre de travail essentiellement graphique, voire typographique. Notre hypothèse : le signe gestuel décrirait, dans sa réalisation, une trace graphique lisible qu'un dispositif photographique permet de visualiser. Pour la tester, nous constituons un corpus de travail photographique fait de signes isolés élicités

Web Data Extraction, Applications and Techniques: A Survey

Web Data Extraction is an important problem that has been studied by means of different scientific tools and in a broad range of applications. Many approaches to extracting data from the Web have been designed to solve specific problems and operate in ad-hoc domains. Other approaches, instead, heavily reuse techniques and algorithms developed in the field of Information Extraction. This survey aims at providing a structured and comprehensive overview of the literature in the field of Web Data Extraction. We provided a simple classification framework in which existing Web Data Extraction applications are grouped into two main classes, namely applications at the Enterprise level and at the Social Web level. At the Enterprise level, Web Data Extraction techniques emerge as a key tool to perform data analysis in Business and Competitive Intelligence systems as well as for business process re-engineering. At the Social Web level, Web Data Extraction techniques allow to gather a large amount of structured data continuously generated and disseminated by Web 2.0, Social Media and Online Social Network users and this offers unprecedented opportunities to analyze human behavior at a very large scale. We discuss also the potential of cross-fertilization, i.e., on the possibility of re-using Web Data Extraction techniques originally designed to work in a given domain, in other domains.Comment: Knowledge-based System