Moderate consumption of red wine is associated with a decreased incidence of cardiovascular diseases in populations with relatively high amount of fat in the diet. However, the mechanisms involved in this protective effect are not completely understood. Here we show that moderate consumption of red wine (equivalent to 2 glasses/day in humans) but not ethanol only, improves blood flow recovery by 32% after hindlimb ischemia in hypercholesterolemic ApoE-deficient mice. In ischemic tissues, red wine consumption reduces oxidative stress and increases capillary density by 46%. Endothelial progenitor cells (EPCs) have been shown to have an important role in postnatal neovascularization. We found that the number of EPCs is increased by 60% in ApoE mice exposed to red wine. Moreover, the migratory capacity of EPCs is significantly improved in red wine-drinking mice. The wine used in our study is a cabernet sauvignon from Languedoc-Roussillon, France, which contains a relatively high concentration (4-6 mg/L) of the polyphenolic antioxidant resveratrol. We demonstrate that resveratrol can rescue oxidized low-density lipoprotein (oxLDL)-induced impairment of in vitro angiogenic activities in human umbilical vein endothelial cells (HUVECs). Resveratrol exposure is also associated with increased activation of Akt/eNOS together with a restoration of nitric oxide production in HUVECs exposed to oxLDL. Our study suggests that moderate consumption of red wine improves ischemia-induced neovascularization in high-cholesterol conditions by increasing the number and the functional activities of EPCs and by restoring the Akt-eNOS-NO pathway.

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We propose a self-stabilizing algorithm for computing a maximal matching in an anonymous network. The complexity is O(n 3 ) moves with high probability, under the adversarial distributed daemon. In this algorithm, each node can determine whether one of its neighbors points to it or to another node, leading to a contradiction with the anonymous assumption. To solve this problem, we provide under the classical link-register model, a self-stabilizing algorithm that gives a unique name to a link such that this name is shared by both extremities of the link.

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The paper (Compact Routing Messages in Self-Healing Trees, TCS 2017) introduced CompactFTZ, the first self-healing compact routing algorithm that works in a distributed network with each node using only O(log n) words (i.e. O(log 2 n) bits) memory and thus O(log n) sized messages. The routing uses only O(1) and O(log n) words routing table and packet labels respectively on a self-healing tree that also works using only O(1) words repairing the network in face of a strong adversary deleting nodes. This deterministic algorithm sets up its data structures in a preprocessing phase and then updates the required data structures in only O(1) parallel time per healing round during execution of the algorithm.However, CompactFTZ has no constraints in its preprocessing phase which could be done in distributed large memory or even centrally. In this paper, we correct that by developing the algorithms for preprocessing of CompactFTZ in a fully distributed manner using only O(log n) words memory in optimal time. In fact, the preprocessing for the self-healing tree (ForgivingTree) component takes only O(1) memory. We develop a local function which each node invokes to instantly compute and then relay its repair instructions (known as its Will) in only O(1) time.We formalise the low memory CONGEST model setting used in previous low memory algorithms (e.g. [24]); nodes' working memory is restricted to be much smaller (in our case, O(log n)) than the numbers of their neighbours to whom they communicate through their I/O ports. We expand the model to allow for non-contiguous ports (e.g. empty ports or neighbours unmarked or lost in dynamic settings) and adversarial order of inputs from neighbours. Besides the Wills, we set up the tree structures and traversals for the routing scheme using only O(log n) memory and O(D) parallel time, where D is the diameter. Thus, we devise the first self-healing compact routing algorithm that can be fully set up and executed in low memory.

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International audience We investigate the descriptional complexity of basic operations on real-time one-way cellular automata with an unbounded as well well as a fixed number of cells. The size of the automata is measured by their number of states. Most of the bounds shown are tight in the order of magnitude, that is, the sizes resulting from the effective constructions given are optimal with respect to worst case complexity. Conversely, these bounds also show the maximal savings of size that can be achieved when a given minimal real-time OCA is decomposed into smaller ones with respect to a given operation. From this point of view the natural problem of whether a decomposition can algorithmically be solved is studied. It turns out that all decomposition problems considered are algorithmically unsolvable. Therefore, a very restricted cellular model is studied in the second part of the paper, namely, real-time one-way cellular automata with a fixed number of cells. These devices are known to capture the regular languages and, thus, all the problems being undecidable for general one-way cellular automata become decidable. It is shown that these decision problems are $\textsf{NLOGSPACE}$-complete and thus share the attractive computational complexity of deterministic finite automata. Furthermore, the state complexity of basic operations for these devices is studied and upper and lower bounds are given.

This paper seeks to address the question of designing distributed algorithms for the setting of compact memory i.e. sublinear (in n -the number of nodes) bits working memory for connected networks of arbitrary topologies. The nodes in our networks may have much lower internal (working) memory (say, O(poly log n)) as compared to the number of their possible neighbours (O(n)) implying that a node may not be even able to store the IDs of all of its neighbours. These algorithms may be useful for large networks of small devices such as the Internet of Things, for wireless or ad-hoc networks, and, in general, as memory efficient algorithms.More formally, we introduce the Compact Message Passing (CM P ) model -an extension of the standard message passing model considered at a finer granularity where a node can interleave reads and writes with internal computations, using a port only once in a synchronous round. The interleaving is required for meaningful computations due to the low memory requirement and is akin to a distributed network with nodes executing streaming algorithms. Note that the internal memory size upper bounds the message sizes and hence e.g. for O(log n) memory, the model is weaker than the CONGEST model; for such models our algorithms will work directly too.We present some early results in the CMP model for nodes with O(log 2 n) bits working memory. We introduce the concepts of local compact functions and compact protocols and give solutions for some classic distributed problems (leader election, tree constructions and traversals). We build on these to solve the open problem of compact preprocessing for the compact self-healing routing algorithm CompactFTZ posed in Compact Routing Messages in Self-Healing Trees (Theoretical Computer Science 2017) by designing local compact functions for finding particular subtrees of labeled binary trees. Hence, we introduce the first fully compact self-healing routing algorithm. In the process, we also give independent fully compact versions of the Forgiving Tree [PODC 2008] and Thorup-Zwick's tree based compact routing [SPAA 2001].