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Xiao-Er-An-Shen Decoction (XEASD) has been used clinically for the treatment of Tourette syndrome (TS) in children for more than 20 years in mainland China. The biochemical mechanism underlying the therapeutic action produced by XEASD treatment against TS remains unknown. However, a previous study has shown that pre-incubation of PC12 neuronal cells with XEASD can induce neurite outgrowth and protect against oxidative stress. In the present study, using a mouse model of TS induced by 3,3’-iminodipropionitrile (IDPN), stereotypy scoring, and locomotor activity were assessed. Levels of neurotransmitters including glutamate, aspartate, and gamma-aminobutyric acid (GABA) in brain tissue as well as plasma cyclic adenosine monophosphate (cAMP) were measured using assay kits. The ratio of reduced glutathione (GSH)/oxidized glutathione (GSSG) and Mn-superoxide dismutase (MnSOD) activity in brain mitochondrial fractions as well as mitochondrial glutathione reductase and cytosolic γ-glutamylcysteine activities were also examined. The phosphorylation of cAMP-responsive element binding protein (CREB) in brain tissue was measured by Western blot analysis. XEASD treatment was found to significantly ameliorate the severity of behavioral symptoms in affected mice, as evidenced by decreases in the stereotypy score and locomotor activity. The beneficial effect of XEASD was accompanied by the reversal of abnormal levels of GABA, glutamate, and aspartate, in brain tissue of IDPN-challenged mice. In addition, XEASD treatment increased plasma cyclic adenosine monophosphate (cAMP) levels and activated the phosphorylation of CREB in brain tissue of TS mice. Furthermore, XEASD treatment was found to enhance the antioxidant status of brain tissue in affected mice, as evidenced by increases in the GSH/GSSG ratio and the activity of MnSOD in brain mitochondrial fractions. Taken together, these experimental results will hopefully provide insight into the pharmacological basis for the beneficial effects of XEASD in children suffering from TS.

The outbreak of atypical pneumonia caused by the novel coronavirus (COVID-19) has currently become a global concern. The generations of the epidemic spread are not well known, yet these are critical parameters to facilitate an understanding of the epidemic. A seafood wholesale market and Wuhan city, China, were recognized as the primary and secondary epidemic sources. Human movements nationwide from the two epidemic sources revealed the characteristics of the first-generation and second-generation spreads of the COVID-19 epidemic, as well as the potential third-generation spread. Methods: We used spatiotemporal data of COVID-19 cases in mainland China and two categories of location-based service (LBS) data of mobile devices from the primary and secondary epidemic sources to calculate Pearson correlation coefficient,r, and spatial stratified heterogeneity, q, statistics. Results: Two categories of device trajectories had generally significant correlations and determinant powers of the epidemic spread. Bothr and q statistics decreased with distance from the epidemic sources and their associations changed with time. At the beginning of the epidemic, the mixed first-generation and second-generation spreads appeared in most cities with confirmed cases. They strongly interacted to enhance the epidemic in Hubei province and the trend was also significant in the provinces adjacent to Hubei. The third-generation spread started in Wuhan from January 17-20, 2020, and in Hubei from January 23-24. No obvious third-generation spread was detected outside Hubei. Conclusions: The findings provide important foundations to quantify the effect of human movement on epidemic spread and inform ongoing control strategies. The spatiotemporal association between the epidemic spread and human movements from the primary and secondary epidemic sources indicates a transfer from second to third generations of the infection. Urgent control measures include preventing the potential third-generation spread in mainland China, eliminating it in Hubei, and reducing the interaction influence of first-generation and second-generation spreads.

The goal of admission control is to support the quality-of-service demands of real-time applications via resource reservation. In this paper, we introduce a new approach to measurement-based admission control for multiclass networks with link sharing. We employ adaptive and measurement-based maximal rate envelopes of the aggregate traffic flow to provide a general and accurate traffic characterization that captures its temporal correlation as well as the available statistical multiplexing gain. In estimating applications' future performance, we introduce the notion of a schedulability confidence level which describes the uncertainty of the measurement-based "prediction" and reflects temporal variations in the measured envelope. We then devise techniques to control loss probability for a buffered multiplexer servicing heterogeneous and bursty traffic flows, even in the regime of a moderate number of traffic flows, which is important in link-sharing environments. Finally, we have developed an implementation of the scheme on a prototype router and performed a testbed measurement study, which together with extensive trace-driven simulations illustrates the effectiveness of the approach in practical scenarios. Index Terms-Admission control, quality of service, traffic envelopes, real-time flows. I. INTRODUCTION E NSURING minimum quality-of-service (QoS) levels to traffic flows and groups of flows is an important challenge for future packet networks, and resource provisioning via admission control is a key mechanism for achieving this. Consequently, a number of schemes have been devised which provide statistical services [19]. Here, a primary goal has been to admit the maximum number of flows possible (thereby efficiently utilizing system resources) subject to user requirements on throughput, loss probability, and delay. Extant algorithms achieve this goal by employing user-specified traffic parameters to estimate aggregate resource demands after accounting for the effects of statistical multiplexing. Unfortunately, this acute reliance on each flow's traffic parameters renders statistical services difficult to deploy for 1) applications that cannot accurately estimate their traffic parameters when the flow is first Manuscript