A general lower bound for collaborative tree exploration

We consider collaborative graph exploration with a set of $k$ agents. All agents start at a common vertex of an initially unknown graph and need to collectively visit all other vertices. We assume agents are deterministic, vertices are distinguishable, moves are simultaneous, and we allow agents to communicate globally. For this setting, we give the first non-trivial lower bounds that bridge the gap between small ($k \leq \sqrt n$) and large ($k \geq n$) teams of agents. Remarkably, our bounds tightly connect to existing results in both domains. First, we significantly extend a lower bound of $\Omega(\log k / \log\log k)$ by Dynia et al. on the competitive ratio of a collaborative tree exploration strategy to the range $k \leq n \log^c n$ for any $c \in \mathbb{N}$. Second, we provide a tight lower bound on the number of agents needed for any competitive exploration algorithm. In particular, we show that any collaborative tree exploration algorithm with $k = Dn^{1+o(1)}$ agents has a competitive ratio of $\omega(1)$, while Dereniowski et al. gave an algorithm with $k = Dn^{1+\varepsilon}$ agents and competitive ratio $O(1)$, for any $\varepsilon > 0$ and with $D$ denoting the diameter of the graph. Lastly, we show that, for any exploration algorithm using $k = n$ agents, there exist trees of arbitrarily large height $D$ that require $\Omega(D^2)$ rounds, and we provide a simple algorithm that matches this bound for all trees

Divergence Measure Between Chaotic Attractors

We propose a measure of divergence of probability distributions for quantifying the dissimilarity of two chaotic attractors. This measure is defined in terms of a generalized entropy. We illustrate our procedure by considering the effect of additive noise in the well known H\'enon attractor. Comparison of two H\'enon attractors for slighly different parameter values, has shown that the divergence has complex scaling structure. Finally, we show how our approach allows to detect non-stationary events in a time series.Comment: 9 pages, 6 figure

Time series irreversibility: a visibility graph approach

We propose a method to measure real-valued time series irreversibility which combines two differ- ent tools: the horizontal visibility algorithm and the Kullback-Leibler divergence. This method maps a time series to a directed network according to a geometric criterion. The degree of irreversibility of the series is then estimated by the Kullback-Leibler divergence (i.e. the distinguishability) between the in and out degree distributions of the associated graph. The method is computationally effi- cient, does not require any ad hoc symbolization process, and naturally takes into account multiple scales. We find that the method correctly distinguishes between reversible and irreversible station- ary time series, including analytical and numerical studies of its performance for: (i) reversible stochastic processes (uncorrelated and Gaussian linearly correlated), (ii) irreversible stochastic pro- cesses (a discrete flashing ratchet in an asymmetric potential), (iii) reversible (conservative) and irreversible (dissipative) chaotic maps, and (iv) dissipative chaotic maps in the presence of noise. Two alternative graph functionals, the degree and the degree-degree distributions, can be used as the Kullback-Leibler divergence argument. The former is simpler and more intuitive and can be used as a benchmark, but in the case of an irreversible process with null net current, the degree-degree distribution has to be considered to identifiy the irreversible nature of the series.Comment: submitted for publicatio

Anonymous Graph Exploration with Binoculars

International audienceWe investigate the exploration of networks by a mobile agent. It is long known that, without global information about the graph, it is not possible to make the agent halts after the exploration except if the graph is a tree. We therefore endow the agent with binoculars, a sensing device that can show the local structure of the environment at a constant distance of the agent current location.We show that, with binoculars, it is possible to explore and halt in a large class of non-tree networks. We give a complete characterization of the class of networks that can be explored using binoculars using standard notions of discrete topology. This class is much larger than the class of trees: it contains in particular chordal graphs, plane triangulations and triangulations of the projective plane. Our characterization is constructive, we present an Exploration algorithm that is universal; this algorithm explores any network explorable with binoculars, and never halts in non-explorable networks

Violacein Induces Death of Resistant Leukaemia Cells via Kinome Reprogramming, Endoplasmic Reticulum Stress and Golgi Apparatus Collapse

It is now generally recognised that different modes of programmed cell death (PCD) are intimately linked to the cancerous process. However, the mechanism of PCD involved in cancer chemoprevention is much less clear and may be different between types of chemopreventive agents and tumour cell types involved. Therefore, from a pharmacological view, it is crucial during the earlier steps of drug development to define the cellular specificity of the candidate as well as its capacity to bypass dysfunctional tumoral signalling pathways providing insensitivity to death stimuli. Studying the cytotoxic effects of violacein, an antibiotic dihydro-indolone synthesised by an Amazon river Chromobacterium, we observed that death induced in CD34(+)/c-Kit(+)/P-glycoprotein(+)/MRP1(+) TF1 leukaemia progenitor cells is not mediated by apoptosis and/or autophagy, since biomarkers of both types of cell death were not significantly affected by this compound. To clarify the working mechanism of violacein, we performed kinome profiling using peptide arrays to yield comprehensive descriptions of cellular kinase activities. Pro-death activity of violacein is actually carried out by inhibition of calpain and DAPK1 and activation of PKA, AKT and PDK, followed by structural changes caused by endoplasmic reticulum stress and Golgi apparatus collapse, leading to cellular demise. Our results demonstrate that violacein induces kinome reprogramming, overcoming death signaling dysfunctions of intrinsically resistant human leukaemia cells.TopInstitute pharma (The Netherlands)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Dutch Cancer SocietyErasmus MC Univ Med Ctr, Dept Gastroenterol & Hepatol, Rotterdam, NetherlandsUniv Amsterdam, Acad Med Ctr, Ctr Expt & Mol Med, NL-1105 AZ Amsterdam, NetherlandsUniv Estadual Campinas, Brazil UNICAMP, Dept Biochem, Inst Biol, São Paulo, BrazilFed Univ São Paulo UNIFESP, Dept Biochem, São Paulo, BrazilFed Univ São Paulo UNIFESP, Dept Cell Biol, São Paulo, BrazilUniv Grande Rio UNIGRANRIO, Heath Sci Sch, Multidisciplinary Lab Dent Res, Rio de Janeiro, BrazilNatl Inst Metrol Qual & Technol Inmetro, Biotechnol Lab, Bioengn Sect, Rio de Janeiro, BrazilUniv Campinas UNICAMP, Inst Chem, Biol Chem Lab, Rio de Janeiro, BrazilUniv Groningen, Univ Med Ctr Groningen, Dept Pediat Oncol, Beatrix Childrens Hosp, Groningen, NetherlandsFed Univ São Paulo UNIFESP, Dept Biochem, São Paulo, BrazilFed Univ São Paulo UNIFESP, Dept Cell Biol, São Paulo, BrazilDutch Cancer Society: EMCR 2010-4737Web of Scienc

International longitudinal registry of patients with atrial fibrillation and treated with rivaroxaban: RIVaroxaban Evaluation in Real life setting (RIVER)

Background Real-world data on non-vitamin K oral anticoagulants (NOACs) are essential in determining whether evidence from randomised controlled clinical trials translate into meaningful clinical benefits for patients in everyday practice. RIVER (RIVaroxaban Evaluation in Real life setting) is an ongoing international, prospective registry of patients with newly diagnosed non-valvular atrial fibrillation (NVAF) and at least one investigator-determined risk factor for stroke who received rivaroxaban as an initial treatment for the prevention of thromboembolic stroke. The aim of this paper is to describe the design of the RIVER registry and baseline characteristics of patients with newly diagnosed NVAF who received rivaroxaban as an initial treatment. Methods and results Between January 2014 and June 2017, RIVER investigators recruited 5072 patients at 309 centres in 17 countries. The aim was to enroll consecutive patients at sites where rivaroxaban was already routinely prescribed for stroke prevention. Each patient is being followed up prospectively for a minimum of 2-years. The registry will capture data on the rate and nature of all thromboembolic events (stroke / systemic embolism), bleeding complications, all-cause mortality and other major cardiovascular events as they occur. Data quality is assured through a combination of remote electronic monitoring and onsite monitoring (including source data verification in 10% of cases). Patients were mostly enrolled by cardiologists (n = 3776, 74.6%), by internal medicine specialists 14.2% (n = 718) and by primary care/general practice physicians 8.2% (n = 417). The mean (SD) age of the population was 69.5 (11.0) years, 44.3% were women. Mean (SD) CHADS2 score was 1.9 (1.2) and CHA2DS2-VASc scores was 3.2 (1.6). Almost all patients (98.5%) were prescribed with once daily dose of rivaroxaban, most commonly 20 mg (76.5%) and 15 mg (20.0%) as their initial treatment; 17.9% of patients received concomitant antiplatelet therapy. Most patients enrolled in RIVER met the recommended threshold for AC therapy (86.6% for 2012 ESC Guidelines, and 79.8% of patients according to 2016 ESC Guidelines). Conclusions The RIVER prospective registry will expand our knowledge of how rivaroxaban is prescribed in everyday practice and whether evidence from clinical trials can be translated to the broader cross-section of patients in the real world

Predicting S&P 500 based on its constituents and their social media derived sentiment

Collective intelligence, represented as sentiment extracted from social media mining, is encountered in various applications. Numerous studies involving machine learning modelling have demonstrated that such sentiment information may or may not have predictive power on the stock market trend, depending on the application and the data used. This work proposes, for the first time, an approach to predicting S&P 500 based on the closing stock prices and sentiment data of the S&P 500 constituents. One of the significant complexities of our framework is due to the high dimensionality of the dataset to analyse, which is based on a large number of constituents and their sentiments, and their lagging. Another significant complexity is due to the fact that the relationship between the response and the explanatory variables is time-varying in the highly volatile stock market data, and it is difficult to capture. We propose a predictive modelling approach based on a methodology specifically designed to effectively address the above challenges and to devise efficient predictive models based on Jordan and Elman recurrent neural networks. We further propose a hybrid trading model that incorporates a technical analysis, and the application of machine learning and evolutionary optimisation techniques. We prove that our unprecedented and innovative constituent and sentiment based approach is efficient in predicting S&P 500, and thus may be used to maximise investment portfolios regardless of whether the market is bullish or bearish

Kinase Activity Profiling of Pneumococcal Pneumonia

Background: Pneumonia represents a major health burden. Previous work demonstrated that although the induction of inflammation is important for adequate host defense against pneumonia, an inability to regulate the host's inflammatory response within the lung later during infection can be detrimental. Intracellular signaling pathways commonly rely on activation of kinases, and kinases play an essential role in the regulation of the inflammatory response of immune cells. Methodology/Principal Findings: Pneumonia was induced in mice via intranasal instillation of Streptococcus (S.) pneumoniae. Kinomics peptide arrays, exhibiting 1024 specific consensus sequences for protein kinases, were used to produce a systems biology analysis of cellular kinase activity during the course of pneumonia. Several differences in kinase activity revealed by the arrays were validated in lung homogenates of individual mice using western blot. We identified cascades of activated kinases showing that chemotoxic stress and a T helper 1 response were induced during the course of pneumococcal pneumonia. In addition, our data point to a reduction in WNT activity in lungs of S. pneumoniae infected mice. Moreover, this study demonstrated a reduction in overall CDK activity implying alterations in cell cycle biology. Conclusions/Significance: This s

Are Small GTPases Signal Hubs in Sugar-Mediated Induction of Fructan Biosynthesis?

External sugar initiates biosynthesis of the reserve carbohydrate fructan, but the molecular processes mediating this response remain obscure. Previously it was shown that a phosphatase and a general kinase inhibitor hamper fructan accumulation. We use various phosphorylation inhibitors both in barley and in Arabidopsis and show that the expression of fructan biosynthetic genes is dependent on PP2A and different kinases such as Tyr-kinases and PI3-kinases. To further characterize the phosphorylation events involved, comprehensive analysis of kinase activities in the cell was performed using a PepChip, an array of >1000 kinase consensus substrate peptide substrates spotted on a chip. Comparison of kinase activities in sugar-stimulated and mock(sorbitol)-treated Arabidopsis demonstrates the altered phosphorylation of many consensus substrates and documents the differences in plant kinase activity upon sucrose feeding. The different phosphorylation profiles obtained are consistent with sugar-mediated alterations in Tyr phosphorylation, cell cycling, and phosphoinositide signaling, and indicate cytoskeletal rearrangements. The results lead us to infer a central role for small GTPases in sugar signaling