Effects of Prediction Feedback in Multi-Route Intelligent Traffic Systems

We first study the influence of an efficient feedback strategy named prediction feedback strategy (PFS) based on a multi-route scenario in which dynamic information can be generated and displayed on the board to guide road users to make a choice. In this scenario, our model incorporates the effects of adaptability into the cellular automaton models of traffic flow. Simulation results adopting this optimal information feedback strategy have demonstrated high efficiency in controlling spatial distribution of traffic patterns compared with the other three information feedback strategies, i.e., vehicle number and flux. At the end of this paper, we also discuss in what situation PFS will become invalid in multi-route systems.Comment: 15 pages, 15 figures, Physica A (2010), doi:10.1016/j.physa.2010.02.03

Distinct miRNAs associated with various clinical presentations of SARS-CoV-2 infection.

MicroRNAs (miRNAs) have been shown to play important roles in viral infections, but their associations with SARS-CoV-2 infection remain poorly understood. Here, we detected 85 differentially expressed miRNAs (DE-miRNAs) from 2,336 known and 361 novel miRNAs that were identified in 233 plasma samples from 61 healthy controls and 116 patients with COVID-19 using the high-throughput sequencing and computational analysis. These DE-miRNAs were associated with SASR-CoV-2 infection, disease severity, and viral persistence in the patients with COVID-19, respectively. Gene ontology and KEGG pathway analyses of the DE-miRNAs revealed their connections to viral infections, immune responses, and lung diseases. Finally, we established a machine learning model using the DE-miRNAs between various groups for classification of COVID-19 cases with different clinical presentations. Our findings may help understand the contribution of miRNAs to the pathogenesis of COVID-19 and identify potential biomarkers and molecular targets for diagnosis and treatment of SARS-CoV-2 infection

Controllability of Deterministic Networks with the Identical Degree Sequence

<div><p>Controlling complex network is an essential problem in network science and engineering. Recent advances indicate that the controllability of complex network is dependent on the network's topology. Liu and Barabási, et.al speculated that the degree distribution was one of the most important factors affecting controllability for arbitrary complex directed network with random link weights. In this paper, we analysed the effect of degree distribution to the controllability for the deterministic networks with unweighted and undirected. We introduce a class of deterministic networks with identical degree sequence, called (<i>x</i>,<i>y</i>)-flower. We analysed controllability of the two deterministic networks ((1, 3)-flower and (2, 2)-flower) by exact controllability theory in detail and give accurate results of the minimum number of driver nodes for the two networks. In simulation, we compare the controllability of (<i>x</i>,<i>y</i>)-flower networks. Our results show that the family of (<i>x</i>,<i>y</i>)-flower networks have the same degree sequence, but their controllability is totally different. So the degree distribution itself is not sufficient to characterize the controllability of deterministic networks with unweighted and undirected.</p></div

Determination of Picogram Levels of Diacerein in a Pharmaceutical Formulation by Flow-Injection Chemiluminescence

A simple, sensitive and inexpensive method for determination of diacerein by flow-injection chemiluminescence was proposed, based on the quenching effect of diacerein on the luminol-protein (bovine serum albumin, BSA) reaction. It was found that the decrement of CL intensity was linearly proportional to the logarithm of diacerein concentration ranging from 5.0 to 7.0×103 pg·mL-1 (r = 0.9968), with the limit of detection (LOD) of 1.0 pg·mL-1 (3σ). The proposed procedure was successfully applied to the determination of diacerein in pharmaceutical formulation, human saliva, and serum samples without interference from its potential impurities, with the recoveries ranging from 96.4% to 104.0% and the relative standard deviations (RSDs) less than 4.0% (n=6)

Determination of Picogram Levels of Diacerein in a Pharmaceutical Formulation by Flow-Injection Chemiluminescence

A simple, sensitive and inexpensive method for determination of diacerein by flow-injection chemiluminescence was proposed, based on the quenching effect of diacerein on the luminol-protein (bovine serum albumin, BSA) reaction. It was found that the decrement of CL intensity was linearly proportional to the logarithm of diacerein concentration ranging from 5.0 to 7.0×103 pg·mL-1 (r = 0.9968), with the limit of detection (LOD) of 1.0 pg·mL-1 (3σ). The proposed procedure was successfully applied to the determination of diacerein in pharmaceutical formulation, human saliva, and serum samples without interference from its potential impurities, with the recoveries ranging from 96.4% to 104.0% and the relative standard deviations (RSDs) less than 4.0% (n=6)

The degree distribution of <i>F</i>_<i>t</i>(1, 3) and <i>F</i>_<i>t</i>(2, 2).

<p>Two networks have identical degree distribution and their degree distribution obey a power-law degree distribution <i>P</i>(<i>k</i>) ∼ <i>k</i>⁻³.</p

The minimum number of driver nodes <i>N</i>_<i>D</i> of the two networks increased with <i>s</i>.

<p>CSR denotes the results by computer simulation. AR denotes the results predicted by Eqs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127545#pone.0127545.e018" target="_blank">11</a>) and (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0127545#pone.0127545.e023" target="_blank">16</a>) in two networks. Two networks are iterated to step 7. For the two networks, the CSR and AR are exactly same, respectively. But the <i>N</i>_<i>D</i> of the (2, 2)-flower network is more than the (1, 3)-flower network.</p

Controllability measure <i>n</i>_<i>D</i> of the (<i>x</i>, <i>y</i>) flower networks at different iteration <i>s</i>.

<p>Controllability measure <i>n</i>_<i>D</i> is a function of iteration step <i>s</i> for the (<i>x</i>, <i>y</i>) flower networks, where we take <i>x</i>+<i>y</i> = 6, 8, 10 and 12 respectively. These networks are iterated to step 5. The controllability measure are equal of all networks at <i>s</i> = 0, after start to fall at <i>s</i> > 0, and converges a constant lower than 1 at <i>s</i> > 4.</p

Illustration of the growing process for the (1, 3)-flower.

<p>Where, every old edge generates 2 new nodes. All of these 2 new nodes and the old edge form a circle of length 4.</p

Illustration of the growing process for the (2, 2)-flower.

<p>Where, each old edge connecting two old nodes is removed and replaced by two pathes with the two old nodes as the ends; one new node and the two old nodes form one path of length 2, while another new node and the two old nodes constitute the other path of length 2.</p