Spectrophotometry of Epsilon Aur, 3295-8880 A

Spectrophotometric scans were obtained at 8 A resolution from 3295 to 8880 A on twenty nights before, during, and after the recent eclipse of epsilon Aurigae, beginning with a pre-eclipse observation on 5 March 1982 U.T. The observations were reduced to absolute flux using the standard stars 109 Vir or xi(2) Ceti. The data confirm that the eclipse is essentially gray over the entire visible spectrum, as others have noted from broadband photometry. High resolution echellograms (450 to 6700 A) made through mid-eclipse and the scans show changes in the equivalent widths of H alpha, Na D, and O I as large as a factor of two

Peanut stripe virus - a new seed-borne potyvirus from China infecting groundnut (Arachis hypogaea)

A new virus, peanut stripe (PStV), isolated from groundnut (Arachis hypogaea) in the USA, induced characteristic striping, discontinuous vein banding along the lateral veins, and oakleaf mosaic in groundnut. The virus was also isolated from germplasm lines introduced from the People's Republic of China. PStV was transmitted by inoculation of sap to nine species of the Chenopodiaceae, Leguminosae, and Solanaceae; Chenopodium amaranticolor was a good local lesion host. PStV was also transmitted by Aphis craccivora in a non-persistent manner and through seed of groundnut up to 37%. The virus remained infective in buffered plant extracts after diluting to 10-3, storage for 3 days at 20°C, and heating for 10 min at 60°C but not 65°C. Purified virus preparations contained flexuous filamentous particles c. 752 nm long, which contained a major polypeptide of 33 500 daltons and one nucleic acid species of 3·1 × 106 daltons. In ELISA, PStV was serologically related to blackeye cowpea mosaic, soybean mosaic, clover yellow vein, and pepper veinal mottle viruses but not to peanut mottle, potato Y, tobacco etch, and peanut green mosaic viruses. On the basis of these properties PStV is identified as a new potyvirus in groundnut

Tensor Regression with Applications in Neuroimaging Data Analysis

Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex form such as multidimensional arrays (tensors). Traditional statistical and computational methods are proving insufficient for analysis of these high-throughput data due to their ultrahigh dimensionality as well as complex structure. In this article, we propose a new family of tensor regression models that efficiently exploit the special structure of tensor covariates. Under this framework, ultrahigh dimensionality is reduced to a manageable level, resulting in efficient estimation and prediction. A fast and highly scalable estimation algorithm is proposed for maximum likelihood estimation and its associated asymptotic properties are studied. Effectiveness of the new methods is demonstrated on both synthetic and real MRI imaging data.Comment: 27 pages, 4 figure

Disclosure of Maternal HIV Status to Children: To Tell or Not To Tell . . . That Is the Question

HIV-infected mothers face the challenging decision of whether to disclose their serostatus to their children. From the perspective of both mother and child, we explored the process of disclosure, providing descriptive information and examining the relationships among disclosure, demographic variables, and child adjustment. Participants were 23 mothers and one of their noninfected children (9 to 16 years of age). Sixty-one percent of mothers disclosed. Consistent with previous research, disclosure was not related to child functioning. However, children sworn to secrecy demonstrated lower social competence and more externalizing problems. Differential disclosure, which occurred in one-third of the families, was associated with higher levels of depressive and anxiety symptoms. Finally, knowing more than mothers had themselves disclosed was related to child maladjustment across multiple domains. Clinical implications and the need for future research are considered

Age-related changes in global motion coherence: conflicting haemodynamic and perceptual responses

Our aim was to use both behavioural and neuroimaging data to identify indicators of perceptual decline in motion processing. We employed a global motion coherence task and functional Near Infrared Spectroscopy (fNIRS). Healthy adults (n = 72, 18-85) were recruited into the following groups: young (n = 28, mean age = 28), middle-aged (n = 22, mean age = 50), and older adults (n = 23, mean age = 70). Participants were assessed on their motion coherence thresholds at 3 different speeds using a psychophysical design. As expected, we report age group differences in motion processing as demonstrated by higher motion coherence thresholds in older adults. Crucially, we add correlational data showing that global motion perception declines linearly as a function of age. The associated fNIRS recordings provide a clear physiological correlate of global motion perception. The crux of this study lies in the robust linear correlation between age and haemodynamic response for both measures of oxygenation. We hypothesise that there is an increase in neural recruitment, necessitating an increase in metabolic need and blood flow, which presents as a higher oxygenated haemoglobin response. We report age-related changes in motion perception with poorer behavioural performance (high motion coherence thresholds) associated with an increased haemodynamic response

Providing social support for underrepresented racial and ethnic minority phd students in the biomedical sciences:a career coaching model

Improvement in the proportion of underrepresented racial and ethnic minorities (URMs) in academic positions has been unsatisfactory. Although this is a complex problem, one key issue is that graduate students often rely on research mentors for career-related support, the effectiveness of which can be variable. We present results from a novel academic career “coaching” intervention, one aim of which was to provide supplementary social support for PhD students, particularly those from underrepresented backgrounds. Coaching was de­livered both within small groups and on an individual basis, with a diverse group of coach­es and students coming from many universities. Coaches were provided with additional diversity training. Ninety-six semistructured interviews with 33 URM students over 3 years were analyzed using a qualitative framework approach. For most of the URM PhD students, coaching provided social support in the form of emotional, informational, and appraisal support. Coaching groups provided a noncompetitive environment and “community of support” within which students were able to learn from one another’s experiences and discuss negative and stressful experiences related to their graduate school, lab, or career plans. This coached peer group model is capable of providing the social support that many URM students do not find at their home universities

Forecasting Daily Variability of the S and P 100 Stock Index using Historical, Realised and Implied Volatility Measurements

The increasing availability of financial market data at intraday frequencies has not only led to the development of improved volatility measurements but has also inspired research into their potential value as an information source for volatility forecasting. In this paper we explore the forecasting value of historical volatility (extracted from daily return series), of implied volatility (extracted from option pricing data) and of realised volatility (computed as the sum of squared high frequency returns within a day). First we consider unobserved components and long memory models for realised volatility which is regarded as an accurate estimator of volatility. The predictive abilities of realised volatility models are compared with those of stochastic volatility models and generalised autoregressive conditional heteroskedasticity models for daily return series. These historical volatility models are extended to include realised and implied volatility measures as explanatory variables for volatility. The main focus is on forecasting the daily variability of the Standard and Poor's 100 stock index series for which trading data (tick by tick) of almost seven years is analysed. The forecast assessment is based on the hypothesis of whether a forecast model is outperformed by alternative models. In particular, we will use superior predictive ability tests to investigate the relative forecast performances of some models. Since volatilities are not observed, realised volatility is taken as a proxy for actual volatility and is used for computing the forecast error. A stationary bootstrap procedure is required for computing the test statistic and its $p$-value. The empirical results show convincingly that realised volatility models produce far more accurate volatility forecasts compared to models based on daily returns. Long memory models seem to provide the most accurate forecasts

Neural correlates of sexual cue reactivity in individuals with and without compulsive sexual behaviours

Although compulsive sexual behaviour (CSB) has been conceptualized as a "behavioural" addiction and common or overlapping neural circuits may govern the processing of natural and drug rewards, little is known regarding the responses to sexually explicit materials in individuals with and without CSB. Here, the processing of cues of varying sexual content was assessed in individuals with and without CSB, focusing on neural regions identified in prior studies of drug-cue reactivity. 19 CSB subjects and 19 healthy volunteers were assessed using functional MRI comparing sexually explicit videos with non-sexual exciting videos. Ratings of sexual desire and liking were obtained. Relative to healthy volunteers, CSB subjects had greater desire but similar liking scores in response to the sexually explicit videos. Exposure to sexually explicit cues in CSB compared to non-CSB subjects was associated with activation of the dorsal anterior cingulate, ventral striatum and amygdala. Functional connectivity of the dorsal anterior cingulate-ventral striatum-amygdala network was associated with subjective sexual desire (but not liking) to a greater degree in CSB relative to non-CSB subjects. The dissociation between desire or wanting and liking is consistent with theories of incentive motivation underlying CSB as in drug addictions. Neural differences in the processing of sexual-cue reactivity were identified in CSB subjects in regions previously implicated in drug-cue reactivity studies. The greater engagement of corticostriatal limbic circuitry in CSB following exposure to sexual cues suggests neural mechanisms underlying CSB and potential biological targets for interventions