Neuroprotective effects of α-lipoic acid against hypoxic– ischemic brain injury in neonatal rats

Purpose: To explore the neuroprotective efficacy of α-lipoic acid (ALA) against hypoxic-ischemic encephalopathy (HIE) in neonatal rats.Methods: Forty-eight rats (P7-pups) were randomly assigned to one of four groups: group I received saline; group II (HI) underwent unilateral carotid artery ligation and hypoxia (92 % N2 and 8 % O2) for 2.5 h; and groups III and IV (ALA 50 and 100) were treated with 50 or 100 mg ALA/kg for 7 days prior to against hypoxic-ischemic (HI) insult. Cerebral antioxidant status, edema, and the levels of inflammatory markers were determined.Results: ALA administration substantially (p < 0.01) attenuated both cerebral infarct area and degree of edema while decreasing the levels of several inflammatory markers (TNF-α, NF-p65, IL-1β, IL-6). In addition, in the ALA groups, antioxidant enzyme (SOD, CAT, GSH) activities were significantly elevated,while the expressions of TNF-α and IL-1β protein were significantly (p < 0.01) down-regulated.Conclusion: The neuroprotective efficacy of ALA in HIE can be attributed to its suppression of both oxidative stress and the levels of inflammatory markers.Keywords: Hypoxic–ischemic brain injury, α-Lipoic acid, Cerebral infarct area, Edema, Antioxidants, Inflammatory marker

Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition

Sparse representation based classification (SRC) methods have achieved remarkable results. SRC, however, still suffer from requiring enough training samples, insufficient use of test samples and instability of representation. In this paper, a stable inverse projection representation based classification (IPRC) is presented to tackle these problems by effectively using test samples. An IPR is firstly proposed and its feasibility and stability are analyzed. A classification criterion named category contribution rate is constructed to match the IPR and complete classification. Moreover, a statistical measure is introduced to quantify the stability of representation-based classification methods. Based on the IPRC technique, a robust tumor recognition framework is presented by interpreting microarray gene expression data, where a two-stage hybrid gene selection method is introduced to select informative genes. Finally, the functional analysis of candidate's pathogenicity-related genes is given. Extensive experiments on six public tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods.Comment: 14 pages, 19 figures, 10 table

Light anti-nuclei production in pp collisions at s\sqrt{s}=7 and 14 TeV

A dynamically constrained coalescence model based on the phase space quantization and classical limit method was proposed to investigate the production of light nuclei (anti-nuclei) in non-single diffractive (NSD) pp collisions at $\sqrt{s}$=7 and 14 TeV. This calculation was based on the final hadronic state in the PYTHIA and PACIAE model simulations, the event sample consisted of 1.2$\times 10^8$ events in both simulations. The PACIAE model calculated $\bar D$ yield of 6.247$\times 10^{-5}$ in NSD pp collisions at $\sqrt{s}$=7 TeV is well comparing with the ALICE rough datum of 5.456$\times 10^{-5}$. It indicated the reliability of proposed method in some extent. The yield, transverse momentum distribution, and rapidity distribution of the $\bar D$, $^3{\bar{He}}$, and $_{\bar\Lambda} ^3{\bar H}$ in NSD pp collisions at $\sqrt{s}$=7 and 14 TeV were predicted by PACIAE and PYTHIA model simulations. The yield resulted from PACIAE model simulations is larger than the one from PYTHIA model. This might reflect the role played by the parton and hadron rescatterings.Comment: 5 pages, 2 figure

Waiting Endurance Time Estimation of Electric Two-Wheelers at Signalized Intersections

The paper proposed a model for estimating waiting endurance times of electric two-wheelers at signalized intersections using survival analysis method. Waiting duration times were collected by video cameras and they were assigned as censored and uncensored data to distinguish between normal crossing and red-light running behavior. A Cox proportional hazard model was introduced, and variables revealing personal characteristics and traffic conditions were defined as covariates to describe the effects of internal and external factors. Empirical results show that riders do not want to wait too long to cross intersections. As signal waiting time increases, electric two-wheelers get impatient and violate the traffic signal. There are 12.8% of electric two-wheelers with negligible wait time. 25.0% of electric two-wheelers are generally nonrisk takers who can obey the traffic rules after waiting for 100 seconds. Half of electric two-wheelers cannot endure 49.0 seconds or longer at red-light phase. Red phase time, motor vehicle volume, and conformity behavior have important effects on riders’ waiting times. Waiting endurance times would decrease with the longer red-phase time, the lower traffic volume, or the bigger number of other riders who run against the red light. The proposed model may be applicable in the design, management and control of signalized intersections in other developing cities