Specific neuroprotective effects of manual stimulation of real acupoints versus non-acupoints in rats after middle cerebral artery occlusion

The objective of this study was to investigate the effectiveness and specific effects of acupuncture on ischemic-induced damage in rats after permanent middle cerebral artery occlusion. Cerebral ischemia was induced by middle cerebral artery occlusion in male Wistar rats. The rats were divided into the following 4 groups: normal controls, ischemic, real acupuncture-treated (Shuigou, DU26), and non-acupoint-treated groups. On the third postoperative day, neurological deficit scores, cerebral blood flow, infarction volume, and neuronal cell death counts were measured. In the real acupuncture-treated group, the neurological deficit scores and cerebral blood flow were improved (p < 0.05) and the infarction volume and neuronal cell death counts were reduced (p < 0.01) compared to the ischemic and non-acupoint-treated groups. The present study demonstrated that real acupuncture was effective against focal ischemia-induced damage in rats after middle cerebral artery occlusion, and the effects were specifically related to the right needling location.Key words: specificity, real acupoint, non-acupoint, middle cerebral artery occlusion, animal experimentatio

Bayesian estimates of astronomical time delays between gravitationally lensed stochastic light curves

The gravitational field of a galaxy can act as a lens and deflect the light emitted by a more distant object such as a quasar. Strong gravitational lensing causes multiple images of the same quasar to ap- pear in the sky. Since the light in each gravitationally lensed image traverses a different path length from the quasar to the Earth, fluc- tuations in the source brightness are observed in the several images at different times. The time delay between these fluctuations can be used to constrain cosmological parameters and can be inferred from the time series of brightness data or light curves of each image. To estimate the time delay, we construct a model based on a state- space representation for irregularly observed time series generated by a latent continuous-time Ornstein-Uhlenbeck process. We account for microlensing, an additional source of independent long-term ex- trinsic variability, via a polynomial regression. Our Bayesian strategy adopts a Metropolis-Hastings within Gibbs sampler. We improve the sampler by using an ancillarity-sufficiency interweaving strategy and adaptive Markov chain Monte Carlo. We introduce a profile likeli- hood of the time delay as an approximation of its marginal posterior distribution. The Bayesian and profile likelihood approaches comple- ment each other, producing almost identical results; the Bayesian method is more principled but the profile likelihood is simpler to implement. We demonstrate our estimation strategy using simulated data of doubly- and quadruply-lensed quasars, and observed data from quasars Q0957+561 and J1029+2623

Estimating soil salinity in different landscapes of the Yellow River Delta through Landsat OLI/TIRS and ETM plus Data

Soil salinization has increasingly become a serious issue in coastal zone due to global climate changes and human disturbances. Assessment of soil salinity, especially at the landscape scale, is critical to coastal management and restoration. Two data from OLI/TIRS and ETM+ sensors of Landsat satellite were used to compare their ability to invert the spatial pattern of soil salinity in both farmland and salt marsh landscapes in the Yellow River Delta, China, respectively. The results showed that the in situ electrical conductivity (EC (a) ) of soil, representing soil salinity, were closely related with spectral parameters and salinity indices calculated by the remote sensing data. The results of multiple regression models have showed that nearly all the spectral parameters and salinity indices calculated by OLI/TRIS data were more sensitive to soil salinity than those by ETM+ data. Therefore, the models based on OLI/TIRS data are superior to those on ETM+ data in estimating the spatial pattern of soil salinity in farmland and salt marsh landscapes. Our results were very helpful to evaluate the levels of soil salinization in the Yellow River Delta

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

Exploratory spatial data analysis for the identification of risk factors to birth defects

BACKGROUND: Birth defects, which are the major cause of infant mortality and a leading cause of disability, refer to "Any anomaly, functional or structural, that presents in infancy or later in life and is caused by events preceding birth, whether inherited, or acquired (ICBDMS)". However, the risk factors associated with heredity and/or environment are very difficult to filter out accurately. This study selected an area with the highest ratio of neural-tube birth defect (NTBD) occurrences worldwide to identify the scale of environmental risk factors for birth defects using exploratory spatial data analysis methods. METHODS: By birth defect registers based on hospital records and investigation in villages, the number of birth defects cases within a four-year period was acquired and classified by organ system. The neural-tube birth defect ratio was calculated according to the number of births planned for each village in the study area, as the family planning policy is strictly adhered to in China. The Bayesian modeling method was used to estimate the ratio in order to remove the dependence of variance caused by different populations in each village. A recently developed statistical spatial method for detecting hotspots, Getis's [Image: see text] [7], was used to detect the high-risk regions for neural-tube birth defects in the study area. RESULTS: After the Bayesian modeling method was used to calculate the ratio of neural-tube birth defects occurrences, Getis's [Image: see text] statistics method was used in different distance scales. Two typical clustering phenomena were present in the study area. One was related to socioeconomic activities, and the other was related to soil type distributions. CONCLUSION: The fact that there were two typical hotspot clustering phenomena provides evidence that the risk for neural-tube birth defect exists on two different scales (a socioeconomic scale at 6.84 km and a soil type scale at 22.8 km) for the area studied. Although our study has limited spatial exploratory data for the analysis of the neural-tube birth defect occurrence ratio and for finding clues to risk factors, this result provides effective clues for further physical, chemical and even more molecular laboratory testing according to these two spatial scales

Transmission characteristics of EM wave in a finite thickness plasma

One of the key factors for solving the problems of re-entry communication interruption is electromagnetic (EM) wave transmission characteristics in a plasma. Theoretical and experimental studies were carried out on specific transmission characteristics for different plasma sheath characteristic under thin sheath condition in re-entry state. The paper presents systematic studies on the variations of wave attenuation characteristics versus plasma sheath thickness L, collision frequency ν, electron density ne and wave working frequency f in a φ 800mm high temperature shock tube. In experiments, L is set to 4 cm and 38 cm. ν is 2 GHz and 15 GHz. ne is from 1×10^10 cm−3 to 1×10^13 cm−3, and f is set to 2, 5, 10, 14.6 GHz, respectively. Meanwhile, Wentzel–Kramers–Brillouin (WKB) and finite-difference time-domain (FDTD) methods are adopted to carry out theoretical simulation for comparison with experimental results. It is found that when L is much larger than EM wavelength λ (thick sheath) and ν is large, the theoretical result is in good agreement with experimental one, when sheath thickness L is much larger than λ, while ν is relatively small, two theoretical results are obviously different from the experimental ones. It means that the existing theoretical model can not fully describe the contribution of ν. Furthermore, when L and λ are of the same order of magnitude (thin sheath), the experimental result is much smaller than the theoretical values, which indicates that the current model can not properly describe the thin sheath effect on EM attenuation characteristics

Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines

Background: Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to obtain the estimates of interest. The estimates from each imputed dataset are then combined into one overall estimate and variance, incorporating both the within and between imputation variability. Rubin's rules for combining these multiply imputed estimates are based on asymptotic theory. The resulting combined estimates may be more accurate if the posterior distribution of the population parameter of interest is better approximated by the normal distribution. However, the normality assumption may not be appropriate for all the parameters of interest when analysing prognostic modelling studies, such as predicted survival probabilities and model performance measures. Methods: Guidelines for combining the estimates of interest when analysing prognostic modelling studies are provided. A literature review is performed to identify current practice for combining such estimates in prognostic modelling studies. Results: Methods for combining all reported estimates after MI were not well reported in the current literature. Rubin's rules without applying any transformations were the standard approach used, when any method was stated. Conclusion: The proposed simple guidelines for combining estimates after MI may lead to a wider and more appropriate use of MI in future prognostic modelling studies

Scalable solar thermoelectrics and photovoltaics (SUNTRAP)

This is the final version of the article. Available from AIP Publishing via the DOI in this record.This paper presents the design, manufacture and electrical test of a novel integrated III:V low concentrator photovoltaic and thermoelectric device for enhanced solar energy harvesting efficiency. The PCB-based platform is a highly reliable means of controlling CPV cell operational temperature under a range of irradiance conditions. The design enables reproducible data acquisition from CPV solar cells whilst minimizing transient time for solid state cooling capability.The authors would like to acknowledge the Sêr Cymru National Research Network and EPSRC for financial support

Nucleolar protein CSIG is required for p33ING1 function in UV-induced apoptosis

Cellular senescence-inhibited gene (CSIG) protein, a nucleolar protein with a ribosomal L1 domain in its N-terminus, can exert non-ribosomal functions to regulate biological processes, such as cellular senescence. Here, we describe a previously unknown function for CSIG: promotion of apoptosis in response to ultraviolet (UV) irradiation-induced CSIG upregulation. We identified p33ING1 as a binding partner that interacts with CSIG. After UV irradiation, p33ING1 increases its protein expression, translocates into the nucleolus and binds CSIG. p33ING1 requires its nucleolar targeting sequence region to interact with CSIG and enhance CSIG protein stability, which is essential for activation of downstream effectors, Bcl-2-associated X protein, to promote apoptosis. Thus, our data imply that p33ING1–CSIG axis functions as a novel pro-apoptotic regulator in response to DNA damage

Large-scale Synthesis of β-SiC Nanochains and Their Raman/Photoluminescence Properties

Although the SiC/SiO2 nanochain heterojunction has been synthesized, the chained homogeneous nanostructure of SiC has not been reported before. Herein, the novel β-SiC nanochains are synthesized assisted by the AAO template. The characterized results demonstrate that the nanostructures are constructed by spheres of 25–30 nm and conjoint wires of 15–20 nm in diameters. Raman and photoluminescence measurements are used to explore the unique optical properties. A speed-alternating vapor–solid (SA-VS) growth mechanism is proposed to interpret the formation of this typical nanochains. The achieved nanochains enrich the species of one-dimensional (1D) nanostructures and may hold great potential applications in nanotechnology