Motor-Incompatibility of Facial Reactions : The influence of valence and stimulus content on voluntary facial reactions

Emotional cues facilitate motor responses that are associated with approach or avoidance. Previous research has shown that evaluative processing of positive and negative facial expression stimuli is also linked to motor schemata of facial muscles. To further investigate the influence of different types of emotional stimuli on facial reactions, we conducted a study with pictures of emotional facial expressions (KDEF) and scenes (IAPS). Healthy participants were asked to respond to the positive or negative facial expressions (KDEF) and scenes (IAPS) with specific facial muscles in a valence-congruent (stimulus valence matches muscle related valence) or a valence-incongruent condition (stimulus valence is contrary to muscle related valence). Additionally, they were asked to rate pictures in terms of valence and arousal. Muscular response latencies were recorded by an electromyogram. Overall, response latencies were shorter in response to facial expressions than to complex pictures of scenes. For both stimulus categories, response latencies with valence-compatible muscles were shorter compared to reactions with incompatible muscles. Moreover, correlations between picture ratings and facial muscle reactions for happy facial expressions as well as positive scenes reflect a direct relationship between perceived intensity of the subjective emotional experience and physiological responding. Results replicate and extend previous research, indicating that incompatibility effects are reliable across different stimulus types and are not limited to facial mimicry

Inhibition of return in fear of spiders: Discrepant eye movement and reaction time data

Inhibition of return (IOR) refers to a bias against returning the attention to a previously attended location. As a foraging facilitator it is thought to facilitate systematic visual search. With respect to neutral stimuli, this is generally thought to be adaptive, but when threatening stimuli appear in our environment, such a bias may be maladaptive. This experiment investigated the influence of phobia-related stimuli on the IOR effect using a discrimination task. A sample of 50 students (25 high, 25 low in spider fear) completed an IOR task including schematic representations of spiders or butterflies as targets. Eye movements were recorded and to assess discrimination among targets, participants indicated with button presses if targets were spiders or butterflies. Reaction time data did not reveal a significant IOR effect but a significant interaction of group and target; spider fearful participants were faster to respond to spider targets than to butterflies. Furthermore, eye-tracking data showed a robust IOR effect independent of stimulus category. These results offer a more comprehensive assessment of the motor and oculomotor factors involved in the IOR effect

Associations of common breast cancer susceptibility alleles with risk of breast cancer subtypes in BRCA1 and BRCA2 mutation carriers

Peer reviewedPublisher PD

Chemical Abundance Analysis of Tucana III, the Second rr-process Enhanced Ultra-Faint Dwarf Galaxy

We present a chemical abundance analysis of four additional confirmed member stars of Tucana III, a Milky Way satellite galaxy candidate in the process of being tidally disrupted as it is accreted by the Galaxy. Two of these stars are centrally located in the core of the galaxy while the other two stars are located in the eastern and western tidal tails. The four stars have chemical abundance patterns consistent with the one previously studied star in Tucana III: they are moderately enhanced in $r$-process elements, i.e. they have $\approx +$0.4 dex. The non-neutron-capture elements generally follow trends seen in other dwarf galaxies, including a metallicity range of 0.44 dex and the expected trend in $\alpha$-elements, i.e., the lower metallicity stars have higher Ca and Ti abundance. Overall, the chemical abundance patterns of these stars suggest that Tucana III was an ultra-faint dwarf galaxy, and not a globular cluster, before being tidally disturbed. As is the case for the one other galaxy dominated by $r$-process enhanced stars, Reticulum II, Tucana III's stellar chemical abundances are consistent with pollution from ejecta produced by a binary neutron star merger, although a different $r$-process element or dilution gas mass is required to explain the abundances in these two galaxies if a neutron star merger is the sole source of $r$-process enhancement.Comment: 18 pages, 10 figures; accepted by Ap

Predicting Distribution of Aedes Aegypti and Culex Pipiens Complex, Potential Vectors of Rift Valley Fever Virus in Relation to Disease Epidemics in East Africa.

The East African region has experienced several Rift Valley fever (RVF) outbreaks since the 1930s. The objective of this study was to identify distributions of potential disease vectors in relation to disease epidemics. Understanding disease vector potential distributions is a major concern for disease transmission dynamics. DIVERSE ECOLOGICAL NICHE MODELLING TECHNIQUES HAVE BEEN DEVELOPED FOR THIS PURPOSE: we present a maximum entropy (Maxent) approach for estimating distributions of potential RVF vectors in un-sampled areas in East Africa. We modelled the distribution of two species of mosquitoes (Aedes aegypti and Culex pipiens complex) responsible for potential maintenance and amplification of the virus, respectively. Predicted distributions of environmentally suitable areas in East Africa were based on the presence-only occurrence data derived from our entomological study in Ngorongoro District in northern Tanzania. Our model predicted potential suitable areas with high success rates of 90.9% for A. aegypti and 91.6% for C. pipiens complex. Model performance was statistically significantly better than random for both species. Most suitable sites for the two vectors were predicted in central and northwestern Tanzania with previous disease epidemics. Other important risk areas include western Lake Victoria, northern parts of Lake Malawi, and the Rift Valley region of Kenya. Findings from this study show distributions of vectors had biological and epidemiological significance in relation to disease outbreak hotspots, and hence provide guidance for the selection of sampling areas for RVF vectors during inter-epidemic periods

Photometric redshifts and clustering of emission line galaxies selected jointly by DES and eBOSS

We present the results of the first test plates of the extended Baryon Oscillation Spectroscopic Survey. This paper focuses on the emission line galaxies (ELG) population targetted from the Dark Energy Survey (DES) photometry. We analyse the success rate, efficiency, redshift distribution, and clustering properties of the targets. From the 9000 spectroscopic redshifts targetted, 4600 have been selected from the DES photometry. The total success rate for redshifts between 0.6 and 1.2 is 71\% and 68\% respectively for a bright and faint, on average more distant, samples including redshifts measured from a single strong emission line. We find a mean redshift of 0.8 and 0.87, with 15 and 13\% of unknown redshifts respectively for the bright and faint samples. In the redshift range 0.6<z<1.2, for the most secure spectroscopic redshifts, the mean redshift for the bright and faint sample is 0.85 and 0.9 respectively. Star contamination is lower than 2\%. We measure a galaxy bias averaged on scales of 1 and 10~Mpc/h of 1.72 \pm 0.1 for the bright sample and of 1.78 \pm 0.12 for the faint sample. The error on the galaxy bias have been obtained propagating the errors in the correlation function to the fitted parameters. This redshift evolution for the galaxy bias is in agreement with theoretical expectations for a galaxy population with MB-5\log h < -21.0. We note that biasing is derived from the galaxy clustering relative to a model for the mass fluctuations. We investigate the quality of the DES photometric redshifts and find that the outlier fraction can be reduced using a comparison between template fitting and neural network, or using a random forest algorithm