Biostimulatory effects of laser irradiation on cell proliferation and wound healing has been reported. However, little is known about the molecular basis of the mechanism. Interleukin 1beta (IL-1beta), tumor necrotic factor-alpha (TNF-alpha), and interferon-gamma (IFN-gamma) play an important role in inflammation, while platelet-derived growth factor (PDGF), transforming growth factor-beta (TGF-beta) and blood-derived fibroblast growth factor (bFGF) are the most important growth factors of periodontal tissues. The aim of this study was to investigate the effect of low-level He-Ne laser on the gene expression of these mediators in rats' gingiva and mucosal tissues. Twenty male Wistar rats were randomly assigned into four groups (A(24), A(48), B(24), B(48)) in which A(24) and A(48) were cases and B(24), B(48) were controls. An incision was made on gingiva and mucosa of the labial surface of the rats' mandibular incisors. Group A(24) was irradiated twice with 24 hours interval, while the inflamed tissues of group A(48) was irradiated three times with continuous He-Ne laser (632.8 nm) at a dose of 7.5 J/cm2 for 300 s. An energy of 5.1 J was given to the 68 mm(2) irradiation zone. Rats were killed 30 min after the last irradiation of case and control groups, then excisional biopsy was performed. Gene expression of the cytokines was measured using reverse transcriptase-polymerase chain reaction (RT-PCR) technique. Results were analyzed with Kruskal-Wallis and Mann-Whitney U tests. The gene expression of IL-1beta and IFN-gamma was significantly inhibited in the test groups (P < 0.05), while the gene expression of PDGF and TGF-beta were significantly increased (P < 0.05). The case and control groups did not have a significant difference in the gene expression of TNF-alpha and bFGF (P > 0.05). These findings suggest that low-level He-Ne laser irradiation decreases the amount of inflammation and accelerates the wound healing process by changing the expression of genes responsible for the production of inflammatory cytokines.

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In this paper, we study the Besov regularity of a general d-dimensional Lévy white noise. More precisely, we describe new sample paths properties of a given noise in terms of weighted Besov spaces. In particular, we characterize the smoothness and integrability properties of the noise using the indices introduced by Blumenthal, Getoor, and Pruitt. Our techniques rely on wavelet methods and generalized moments estimates for Lévy noises.

We study distributed stochastic gradient (D-SG) method and its accelerated variant (D-ASG) for solving decentralized strongly convex stochastic optimization problems where the objective function is distributed over several computational units, lying on a fixed but arbitrary connected communication graph, subject to local communication constraints where noisy estimates of the gradients are available. We develop a framework which allows to choose the stepsize and the momentum parameters of these algorithms in a way to optimize performance by systematically trading off the bias, variance and dependence to network effects. When gradients do not contain noise, we also prove that D-ASG can achieve acceleration, in the sense that it requires O( √ κ log(1/ε)) gradient evaluations and O( √ κ log(1/ε)) communications to converge to the same fixed point with the non-accelerated variant where κ is the condition number and ε is the target accuracy. For quadratic functions, we also provide finer performance bounds that are tight with respect to bias and variance terms. Finally, we study a multistage version of D-ASG with parameters carefully varied over stages to ensure exact convergence to the optimal solution. It achieves optimal and accelerated O(−k/ √ κ) linear decay in the bias term as well as optimal O(σ 2 /k) in the variance 1

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