On Sensor Coverage by Mobile Sensors

We study the problem of using a small number of mobile sensors to monitor various threats in a geographical area. Using some recent results on stochastic sensor scheduling, we propose a stochastic sensor movement strategy. We present simple conditions under which it is not possible to maintain a bounded estimate error covariance for all the threats. We also study a simple sub-optimal algorithm to generate stochastic trajectories. Simulations are presented to illustrate the results

EEG Biofeedback as a Treatment for Substance Use Disorders: Review, Rating of Efficacy, and Recommendations for Further Research

Electroencephalographic (EEG) biofeedback has been employed in substance use disorder (SUD) over the last three decades. The SUD is a complex series of disorders with frequent comorbidities and EEG abnormalities of several types. EEG biofeedback has been employed in conjunction with other therapies and may be useful in enhancing certain outcomes of therapy. Based on published clinical studies and employing efficacy criteria adapted by the Association for Applied Psychophysiology and Biofeedback and the International Society for Neurofeedback and Research, alpha theta training—either alone for alcoholism or in combination with beta training for stimulant and mixed substance abuse and combined with residential treatment programs, is probably efficacious. Considerations of further research design taking these factors into account are discussed and descriptions of contemporary research are given

Oleic Acid Biosynthesis in Plasmodium falciparum: Characterization of the Stearoyl-CoA Desaturase and Investigation as a Potential Therapeutic Target

BACKGROUND:Plasmodium falciparum parasitization of erythrocytes causes a substantial increase in the levels of intracellular fatty acids, notably oleic acid. How parasites acquire this monounsaturated fatty acid has remained enigmatic. Here, we report on the biochemical and enzymatic characterization of stearoyl-CoA desaturase (SCD) in P. falciparum. METHODOLOGY/PRINCIPAL FINDINGS:Metabolic labeling experiments allowed us to demonstrate the production of oleic acid from stearic acid both in lysates of parasites incubated with [(14)C]-stearoyl-CoA and in parasite-infected erythrocytes labeled with [(14)C]-stearic acid. Optimal SCD activity was detected in schizonts, the stage of maximal membrane synthesis. This activity correlated with a late trophozoite stage-specific induction of PFE0555w transcripts. PFE0555w harbors a typical SCD signature. Similar to mammalian SCDs, this protein was found to be associated with the endoplasmic reticulum, as determined with PFE0555w-GFP tagged transgenic P. falciparum. Importantly, these parasites exhibited increased rates of stearic to oleic acid conversion, providing additional evidence that PFE0555w encodes the plasmodial SCD (PfSCD). These findings prompted us to assess the activity of sterculic acid analogues, known to be specific Delta9-desaturase inhibitors. Methyl sterculate inhibited the synthesis of oleic acid both with parasite lysates and infected erythrocytes, most likely by targeting PfSCD. This compound exhibited significant, rapid and irreversible antimalarial activity against asexual blood stages. This parasiticidal effect was antagonized by oleic acid. CONCLUSION/SIGNIFICANCE:Our study provides evidence that parasite-mediated fatty acid modification is important for blood-stage survival and provides a new strategy to develop a novel antimalarial therapeutic based on the inhibition of PfSCD

Dense percolation in large-scale mean-field random networks is provably "explosive".

Recent reports suggest that evolving large-scale networks exhibit "explosive percolation": a large fraction of nodes suddenly becomes connected when sufficiently many links have formed in a network. This phase transition has been shown to be continuous (second-order) for most random network formation processes, including classical mean-field random networks and their modifications. We study a related yet different phenomenon referred to as dense percolation, which occurs when a network is already connected, but a large group of nodes must be dense enough, i.e., have at least a certain minimum required percentage of possible links, to form a "highly connected" cluster. Such clusters have been considered in various contexts, including the recently introduced network modularity principle in biological networks. We prove that, contrary to the traditionally defined percolation transition, dense percolation transition is discontinuous (first-order) under the classical mean-field network formation process (with no modifications); therefore, there is not only quantitative, but also qualitative difference between regular and dense percolation transitions. Moreover, the size of the largest dense (highly connected) cluster in a mean-field random network is explicitly characterized by rigorously proven tight asymptotic bounds, which turn out to naturally extend the previously derived formula for the size of the largest clique (a cluster with all possible links) in such a network. We also briefly discuss possible implications of the obtained mathematical results on studying first-order phase transitions in real-world linked systems

Small-scale illustration of the concept of dense connected clusters in comparison with traditionally defined connected components.

<p>(<b>A</b>) A connected network with 20 nodes and 22 links. Assuming that the minimum required percentage of links for a group of nodes to form a dense connected (“highly connected”) cluster is  = 70% (this value is chosen arbitrarily for illustrative purposes only), the largest-size dense cluster is 4, and all other dense clusters have only 2 nodes each. (<b>B</b>) The network from (A) after 10 more links have formed (for instance, this may be the result of increasing the value of if one assumes model). Dense clusters with the largest number of nodes are highlighted, with their size still significantly smaller than the size of the whole network. It turns out that if is very large, further increase of in will only produce dense clusters scaling as (no clusters scaling linearly with ) until reaches the critical point , after which the whole network abruptly becomes a dense cluster.</p

Illustration of the behavior of the largest dense cluster size in moderate-size graphs .

<p>(<b>A</b>) Relative size of maximum -quasi-cliques () in random graphs for (this particular value is chosen simply for illustrative purposes: the obtained results hold for any fixed ) and , 1,000, 5,000, 10,000, 20,000. Due to the fact that finding the maximum quasi-clique in a graph is a computationally challenging NP-hard problem (as opposed to finding the largest “regular” connected component, which can be done in polynomial time), the numerical simulations were carried out using GRASP heuristic algorithm <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051883#pone.0051883-Abello1" target="_blank">[14]</a> and plotting the largest relative size found after multiple runs of the algorithm for each . The growth increment of was chosen at in the region where approaches from below (more details on computational experiments are given in Materials and Methods). (<b>B</b>) <i>Theoretical</i> behavior of the relative size of the maximum -quasi-clique in as , which is simply a step function, as indicated by formulas (2a)–(2b).</p