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

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

Highly Sensitive Flow Cytometry Allows Monitoring of Changes in Circulating Immune Cells in Blood After Tdap Booster Vaccination

© 2021 Diks, Khatri, Oosten, de Mooij, Groenland, Teodosio, Perez-Andres, Orfao, Berbers, Zwaginga, van Dongen and Berkowska.Antigen-specific serum immunoglobulin (Ag-specific Ig) levels are broadly used as correlates of protection. However, in several disease and vaccination models these fail to predict immunity. In these models, in-depth knowledge of cellular processes associated with protective versus poor responses may bring added value. We applied high-throughput multicolor flow cytometry to track over-time changes in circulating immune cells in 10 individuals following pertussis booster vaccination (Tdap, Boostrix®, GlaxoSmithKline). Next, we applied correlation network analysis to extensively investigate how changes in individual cell populations correlate with each other and with Ag-specific Ig levels. We further determined the most informative cell subsets and analysis time points for future studies. Expansion and maturation of total IgG1 plasma cells, which peaked at day 7 post-vaccination, was the most prominent cellular change. Although these cells preceded the increase in Ag-specific serum Ig levels, they did not correlate with the increase of Ig levels. In contrast, strong correlation was observed between Ag-specific IgGs and maximum expansion of total IgG1 and IgA1 memory B cells at days 7 to 28. Changes in circulating T cells were limited, implying the need for a more sensitive approach. Early changes in innate immune cells, i.e. expansion of neutrophils, and expansion and maturation of monocytes up to day 5, most likely reflected their responses to local damage and adjuvant. Here we show that simultaneous monitoring of multiple circulating immune subsets in blood by flow cytometry is feasible. B cells seem to be the best candidates for vaccine monitoring.K is supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 707404. The here presented study is a pilot study for the Innovative Medicines Initiative (IMI) PERISCOPE program, a Joint Undertaking under grant agreement No 115910. This Joint Undertaking receives support from the European Union’s Horizon 2020 Research and Innovation Programme, the European Federation of Pharmaceutical Industries and Associations (EFPIA), and the Bill and Melinda Gates Foundation (BMGF). The flow cytometric studies in this study were supported by the EuroFlow Consortium. The EuroFlow Consortium received support from the FP6-2004-LIFESCIHEALTH-5 program of the European Commission (grant LSHB-CT-2006-018708) as Specific Targeted Research Project (STREP)

Robot-assisted laparoscopic surgery of the infrarenal aorta: The early learning curve

Background Recently introduced robot-assisted laparoscopic surgery (RALS) facilitates endoscopic surgical manipulation and thereby reduces the learning curve for (advanced) laparoscopic surgery. We present our learning curve with RALS for aortobifemoral bypass grafting as a treatment for aortoiliac occlusive disease. Methods Between February 2002 and May 2005, 17 patients were treated in our institution with robot-assisted laparoscopic aorto-bifemoral bypasses. Dissection was performed laparoscopically and the robot was used to make the aortic anastomosis. Operative time, clamping time, and anastomosis time, as well as blood loss and hospital stay, were used as parameters to evaluate the results and to compare the first eight (group 1) and the last nine patients (group2). Results Total median operative, clamping, and anastomosis times were 365 min (range: 225–589 min), 86 min (range: 25–205 min), and 41 min (range: 22–110 min), respectively. Total median blood loss was 1,000 ml (range: 100–5,800 ml). Median hospital stay was 4 days (range: 3–57 days). In this series 16/18 anastomoses were completed with the use of the robotic system. Three patients were converted (two in group 1, one in group 2), and one patient died postoperatively (group 1). Median clamping and anastomosis times were significantly different between groups 1 and 2 (111 min [range: 85–205 min] versus 57.5 min [range: 25–130 min], p < 0.01 and 74 min [range: 40–110 min] versus 36 min [range: 22–69 min], p < 0.01, respectively) Total operative time, blood loss, and hospital stay showed no significant difference between groups 1 and 2. Conclusions Robot-assisted aortic anastomosis was shown to have a steep learning curve with considerable reduction of clamping and anastomosis times. However, due to a longer learning curve for laparoscopic dissection of the abdominal aorta, operation times were not significantly shortened. Even with robotic assistance, laparoscopic aortoiliac surgery remains a complex procedure

Staphylococcal PknB as the First Prokaryotic Representative of the Proline-Directed Kinases

In eukaryotic cell types, virtually all cellular processes are under control of proline-directed kinases and especially MAP kinases. Serine/threonine kinases in general were originally considered as a eukaryote-specific enzyme family. However, recent studies have revealed that orthologues of eukaryotic serine/threonine kinases exist in bacteria. Moreover, various pathogenic species, such as Yersinia and Mycobacterium, require serine/threonine kinases for successful invasion of human host cells. The substrates targeted by bacterial serine/threonine kinases have remained largely unknown. Here we report that the serine/threonine kinase PknB from the important pathogen Staphylococcus aureus is released into the external milieu, which opens up the possibility that PknB does not only phosphorylate bacterial proteins but also proteins of the human host. To identify possible human targets of purified PknB, we studied in vitro phosphorylation of peptide microarrays and detected 68 possible human targets for phosphorylation. These results show that PknB is a proline-directed kinase with MAP kinase-like enzymatic activity. As the potential cellular targets for PknB are involved in apoptosis, immune responses, transport, and metabolism, PknB secretion may help the bacterium to evade intracellular killing and facilitate its growth. In apparent agreement with this notion, phosphorylation of the host-cell response coordinating transcription factor ATF-2 by PknB was confirmed by mass spectrometry. Taken together, our results identify PknB as the first prokaryotic representative of the proline-directed kinase/MAP kinase family of enzymes

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

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

Ordering heuristics for parallel graph coloring

This paper introduces the largest-log-degree-first (LLF) and smallest-log-degree-last (SLL) ordering heuristics for paral-lel greedy graph-coloring algorithms, which are inspired by the largest-degree-first (LF) and smallest-degree-last (SL) serial heuristics, respectively. We show that although LF and SL, in prac-tice, generate colorings with relatively small numbers of colors, they are vulnerable to adversarial inputs for which any paralleliza-tion yields a poor parallel speedup. In contrast, LLF and SLL allow for provably good speedups on arbitrary inputs while, in practice, producing colorings of competitive quality to their serial analogs. We applied LLF and SLL to the parallel greedy coloring algo-rithm introduced by Jones and Plassmann, referred to here as JP. Jones and Plassman analyze the variant of JP that processes the ver-tices of a graph in a random order, and show that on an O(1)-degree graph G = (V,E), this JP-R variant has an expected parallel run-ning time of O(lgV / lg lgV) in a PRAM model. We improve this bound to show, using work-span analysis, that JP-R, augmented to handle arbitrary-degree graphs, colors a graph G = (V,E) with degree ∆ using Θ(V +E) work and O(lgV + lg ∆ ·min{√E,∆+ lg ∆ lgV / lg lgV}) expected span. We prove that JP-LLF and JP-SLL — JP using the LLF and SLL heuristics, respectively — execute with the same asymptotic work as JP-R and only logarith-mically more span while producing higher-quality colorings than JP-R in practice. We engineered an efficient implementation of JP for modern shared-memory multicore computers and evaluated its performance on a machine with 12 Intel Core-i7 (Nehalem) processor cores. Our implementation of JP-LLF achieves a geometric-mean speedup of 7.83 on eight real-world graphs and a geometric-mean speedup of 8.08 on ten synthetic graphs, while our implementation using SLL achieves a geometric-mean speedup of 5.36 on these real-world graphs and a geometric-mean speedup of 7.02 on these synthetic graphs. Furthermore, on one processor, JP-LLF is slightly faster than a well-engineered serial greedy algorithm using LF, and like-wise, JP-SLL is slightly faster than the greedy algorithm using SL