Copper deposition on TiO2 from copper(II)hexafluoroacetylacetonate

The authors have studied the adsorption of CuII(hfac)2 on the surface of a model oxide system, TiO2(110), and probed the molecular stability with respect to thermal cycling, using atomic scale imaging by scanning tunneling microscopy supported by x-ray photoemission spectroscopy. They find that at 473 K, the adsorbed metal-organic molecules begin to dissociate and release Cu atoms which aggregate and form Cu nanoparticles. These Cu nanoparticles ripen over time and the size (height) distribution develops into a bimodal distribution. Unlike other organometallic systems, which show a bimodal distribution due to enhanced nucleation or growth at surface step edges, the nanoparticles do not preferentially form at steps. The reduced mobility of the Cu islands may be related to the co-adsorbed ligands that remain in very small clusters on the surface.

Enhanced effects of combined cognitive bias modification and computerised cognitive behaviour therapy on social anxiety

This study examines whether combined cognitive bias modification for interpretative biases (CBM-I) and computerised cognitive behaviour therapy (C-CBT) can produce enhanced positive effects on interpretation biases and social anxiety. Forty socially anxious students were randomly assigned into two conditions, an intervention group (positive CBM-I + C-CBT) or an active control (neutral CBM-I + C-CBT). At pre-test, participants completed measures of social anxiety, interpretative bias, cognitive distortions, and social and work adjustment. They were exposed to 6 × 30 min sessions of web-based interventions including three sessions of either positive or neutral CBM-I and three sessions of C-CBT, one session per day. At post-test and two-week follow-up, participants completed the baseline measures. A combined positive CBM-I + C-CBT produced less negative interpretations of ambiguous situations than neutral CBM-I + C-CBT. The results also showed that both positive CBM-I + C-CBT and neutral CBM-I + C-CBT reduced social anxiety and cognitive distortions as well as improving work and social adjustment. However, greater effect sizes were observed in the positive CBM-I + C-CBT condition than the control. This indicates that adding positive CBM-I to C-CBT enhanced the training effects on social anxiety, cognitive distortions, and social and work adjustment compared to the neutral CBM-I + C-CBT condition.

A demonstration of attentional bias, using a novel dual task paradigm, towards clinically salient material in recovering alcohol abuse patients?

Background. This study examined whether alcohol abuse patients are characterized either by enhanced schematic processing of alcohol related cues or by an attentional bias towards the processing of alcohol cues. Method. Abstinent alcohol abusers (N = 25) and non-clinical control participants (N = 24) performed a dual task paradigm in which they had to make an odd/even decision to a centrally presented number while performing a peripherally presented lexical decision task. Stimuli on the lexical decision task comprised alcohol words, neutral words and non-words. In addition, participants completed an incidental recall task for the words presented in the lexical decision task. Results. It was found that, in the presence of alcohol related words, the performance of patients on the odd/even decision task was poorer than in the presence of other stimului. In addition, patients displayed slower lexical decision times for alcohol related words. Both groups displayed better recall for alcohol words than for other stimuli. Conclusions. These results are interpreted as supporting neither model of drug cravings. Rather, it is proposed that, in the presence of alcohol stimuli, alcohol abuse patients display a breakdown in the ability to focus attention.

Extended optimal estimation techniques for sea surface temperature from the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI)

Sea surface temperature (SST) can be estimated from day and night observations of the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) by optimal estimation (OE). We show that exploiting the 8.7 μm channel, in addition to the “traditional” wavelengths of 10.8 and 12.0 μm, improves OE SST retrieval statistics in validation. However, the main benefit is an improvement in the sensitivity of the SST estimate to variability in true SST. In a fair, single-pixel comparison, the 3-channel OE gives better results than the SST estimation technique presently operational within the Ocean and Sea Ice Satellite Application Facility. This operational technique is to use SST retrieval coefficients, followed by a bias-correction step informed by radiative transfer simulation. However, the operational technique has an additional “atmospheric correction smoothing”, which improves its noise performance, and hitherto had no analogue within the OE framework. Here, we propose an analogue to atmospheric correction smoothing, based on the expectation that atmospheric total column water vapour has a longer spatial correlation length scale than SST features. The approach extends the observations input to the OE to include the averaged brightness temperatures (BTs) of nearby clear-sky pixels, in addition to the BTs of the pixel for which SST is being retrieved. The retrieved quantities are then the single-pixel SST and the clear-sky total column water vapour averaged over the vicinity of the pixel. This reduces the noise in the retrieved SST significantly. The robust standard deviation of the new OE SST compared to matched drifting buoys becomes 0.39 K for all data. The smoothed OE gives SST sensitivity of 98% on average. This means that diurnal temperature variability and ocean frontal gradients are more faithfully estimated, and that the influence of the prior SST used is minimal (2%). This benefit is not available using traditional atmospheric correction smoothing.

Estimation of sea surface temperature from the Spinning Enhanced Visible and Infra Red Imager, improved using numerical weather prediction

Most of the operational Sea Surface Temperature (SST) products derived from satellite infrared radiometry use multi-spectral algorithms. They show, in general, reasonable performances with root mean square (RMS) residuals around 0.5 K when validated against buoy measurements, but have limitations, particularly a component of the retrieval error that relates to such algorithms' limited ability to cope with the full variability of atmospheric absorption and emission. We propose to use forecast atmospheric profiles and a radiative transfer model to simulate the algorithmic errors of multi-spectral algorithms. In the practical case of SST derived from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG), we demonstrate that simulated algorithmic errors do explain a significant component of the actual errors observed for the non linear (NL) split window algorithm in operational use at the Centre de Météorologie Spatiale (CMS). The simulated errors, used as correction terms, reduce significantly the regional biases of the NL algorithm as well as the standard deviation of the differences with drifting buoy measurements. The availability of atmospheric profiles associated with observed satellite-buoy differences allows us to analyze the origins of the main algorithmic errors observed in the SEVIRI field of view: a negative bias in the inter-tropical zone, and a mid-latitude positive bias. We demonstrate how these errors are explained by the sensitivity of observed brightness temperatures to the vertical distribution of water vapour, propagated through the SST retrieval algorithm.

Is it me? Self-recognition bias across sensory modalities and its relationship to autistic traits

Background Atypical self-processing is an emerging theme in autism research, suggested by lower self-reference effect in memory, and atypical neural responses to visual self-representations. Most research on physical self-processing in autism uses visual stimuli. However, the self is a multimodal construct, and therefore, it is essential to test self-recognition in other sensory modalities as well. Self-recognition in the auditory modality remains relatively unexplored and has not been tested in relation to autism and related traits. This study investigates self-recognition in auditory and visual domain in the general population and tests if it is associated with autistic traits. Methods Thirty-nine neurotypical adults participated in a two-part study. In the first session, individual participant’s voice was recorded and face was photographed and morphed respectively with voices and faces from unfamiliar identities. In the second session, participants performed a ‘self-identification’ task, classifying each morph as ‘self’ voice (or face) or an ‘other’ voice (or face). All participants also completed the Autism Spectrum Quotient (AQ). For each sensory modality, slope of the self-recognition curve was used as individual self-recognition metric. These two self-recognition metrics were tested for association between each other, and with autistic traits. Results Fifty percent ‘self’ response was reached for a higher percentage of self in the auditory domain compared to the visual domain (t = 3.142; P < 0.01). No significant correlation was noted between self-recognition bias across sensory modalities (τ = −0.165, P = 0.204). Higher recognition bias for self-voice was observed in individuals higher in autistic traits (τ AQ = 0.301, P = 0.008). No such correlation was observed between recognition bias for self-face and autistic traits (τ AQ = −0.020, P = 0.438). Conclusions Our data shows that recognition bias for physical self-representation is not related across sensory modalities. Further, individuals with higher autistic traits were better able to discriminate self from other voices, but this relation was not observed with self-face. A narrow self-other overlap in the auditory domain seen in individuals with high autistic traits could arise due to enhanced perceptual processing of auditory stimuli often observed in individuals with autism.