NK1 receptor antagonism and the neural processing of emotional information in healthy volunteers

The neuropeptide substance P and its receptor NK1 have been implicated in emotion, anxiety and stress in preclinical studies. However, the role of NK1 receptors in human brain function is less clear and there have been inconsistent reports of the value of NK1 receptor antagonists in the treatment of clinical depression. The present study therefore aimed to investigate effects of NK1 antagonism on the neural processing of emotional information in healthy volunteers. Twenty-four participants were randomized to receive a single dose of aprepitant (125 mg) or placebo. Approximately 4 h later, neural responses during facial expression processing and an emotional counting Stroop word task were assessed using fMRI. Mood and subjective experience were also measured using self-report scales. As expected a single dose of aprepitant did not affect mood and subjective state in the healthy volunteers. However, NK1 antagonism increased responses specifically during the presentation of happy facial expressions in both the rostral anterior cingulate and the right amygdala. In the emotional counting Stroop task the aprepitant group had increased activation in both the medial orbitofrontal cortex and the precuneus cortex to positive vs. neutral words. These results suggest consistent effects of NK1 antagonism on neural responses to positive affective information in two different paradigms. Such findings confirm animal studies which support a role for NK1 receptors in emotion. Such an approach may be useful in understanding the effects of novel drug treatments prior to full-scale clinical trials.

Market research and the ethics of big data

The term 'big data' has recently emerged to describe a range of technological and commercial trends enabling the storage and analysis of huge amounts of customer data, such as that generated by social networks and mobile devices. Much of the commercial promise of big data is in the ability to generate valuable insights from collecting new types and volumes of data in ways that were not previously economically viable. At the same time a number of questions have been raised about the implications for individual privacy. This paper explores key perspectives underlying the emergence of big data, and considers both the opportunities and ethical challenges raised for market research.

Sources, sinks, and transatlantic transport of North African dust aerosol: a multimodel analysis and comparison with remote sensing data

This study evaluates model-simulated dust aerosols over North Africa and the North Atlantic from five global models that participated in the Aerosol Comparison between Observations and Models phase II model experiments. The model results are compared with satellite aerosol optical depth (AOD) data from Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), and Sea-viewing Wide Field-of-view Sensor, dust optical depth (DOD) derived from MODIS and MISR, AOD and coarse-mode AOD (as a proxy of DOD) from ground-based Aerosol Robotic Network Sun photometer measurements, and dust vertical distributions/centroid height from Cloud Aerosol Lidar with Orthogonal Polarization and Atmospheric Infrared Sounder satellite AOD retrievals. We examine the following quantities of AOD and DOD: (1) the magnitudes over land and over ocean in our study domain, (2) the longitudinal gradient from the dust source region over North Africa to the western North Atlantic, (3) seasonal variations at different locations, and (4) the dust vertical profile shape and the AOD centroid height (altitude above or below which half of the AOD is located). The different satellite data show consistent features in most of these aspects; however, the models display large diversity in all of them, with significant differences among the models and between models and observations. By examining dust emission, removal, and mass extinction efficiency in the five models, we also find remarkable differences among the models that all contribute to the discrepancies of model-simulated dust amount and distribution. This study highlights the challenges in simulating the dust physical and optical processes, even in the best known dust environment, and stresses the need for observable quantities to constrain the model processes.

Towards a parallel computationally efficient approach to scaling up data stream classification

Advances in hardware technologies allow to capture and process data in real-time and the resulting high throughput data streams require novel data mining approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in real-time. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current classifiers for streaming data address this problem by using incremental learning algorithms. However, even so these algorithms are fast, they are challenged by high velocity data streams, where data instances are incoming at a fast rate. This is problematic if the applications desire that there is no or only a very little delay between changes in the patterns of the stream and absorption of these patterns by the classifier. Problems of scalability to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Neighbours (KNN) as the basis for a real-time adaptive and parallel methodology for scalable data stream classification tasks.

Research data management and openness: the role of data sharing in developing institutional policies and practices

Purpose: To investigate the relationship between research data management (RDM) and data sharing in the formulation of RDM policies and development of practices in higher education institutions (HEIs). Design/methodology/approach: Two strands of work were undertaken sequentially: firstly, content analysis of 37 RDM policies from UK HEIs; secondly, two detailed case studies of institutions with different approaches to RDM based on semi-structured interviews with staff involved in the development of RDM policy and services. The data are interpreted using insights from Actor Network Theory. Findings: RDM policy formation and service development has created a complex set of networks within and beyond institutions involving different professional groups with widely varying priorities shaping activities. Data sharing is considered an important activity in the policies and services of HEIs studied, but its prominence can in most cases be attributed to the positions adopted by large research funders. Research limitations/implications: The case studies, as research based on qualitative data, cannot be assumed to be universally applicable but do illustrate a variety of issues and challenges experienced more generally, particularly in the UK. Practical implications: The research may help to inform development of policy and practice in RDM in HEIs and funder organisations. Originality/value: This paper makes an early contribution to the RDM literature on the specific topic of the relationship between RDM policy and services, and openness ��� a topic which to date has received limited attention.

When enough is not enough: information overload and metacognitive decisions to stop studying information

People are often exposed to more information than they can actually remember. Despite this frequent form of information overload, little is known about how much information people choose to remember. Using a novel ���stop��� paradigm, the current research examined whether and how people choose to stop receiving new���possibly overwhelming���information with the intent to maximize memory performance. Participants were presented with a long list of items and were rewarded for the number of correctly remembered words in a following free recall test. Critically, participants in a stop condition were provided with the option to stop the presentation of the remaining words at any time during the list, whereas participants in a control condition were presented with all items. Across five experiments, we found that participants tended to stop the presentation of the items to maximize the number of recalled items, but this decision ironically led to decreased memory performance relative to the control group. This pattern was consistent even after controlling for possible confounding factors (e.g., task demands). The results indicated a general, false belief that we can remember a larger number of items if we restrict the quantity of learning materials. These findings suggest people have an incomplete understanding of how we remember excessive amounts of information.

Investigating the relationship between Eurasian snow and the Arctic Oscillation with data and models

Recent research suggests Eurasian snow-covered area (SCA) influences the Arctic Oscillation (AO) via the polar vortex. This could be important for Northern Hemisphere winter season forecasting. A fairly strong negative correlation between October SCA and the AO, based on both monthly and daily observational data, has been noted in the literature. While reproducing these previous links when using the same data, we find no further evidence of the link when using an independent satellite data source, or when using a climate model.

Do jumps matter for volatility forecasting? Evidence from energy markets

This paper characterizes the dynamics of jumps and analyzes their importance for volatility forecasting. Using high-frequency data on four prominent energy markets, we perform a model-free decomposition of realized variance into its continuous and discontinuous components. We find strong evidence of jumps in energy markets between 2007 and 2012. We then investigate the importance of jumps for volatility forecasting. To this end, we estimate and analyze the predictive ability of several Heterogenous Autoregressive (HAR) models that explicitly capture the dynamics of jumps. Conducting extensive in-sample and out-of-sample analyses, we establish that explicitly modeling jumps does not significantly improve forecast accuracy. Our results are broadly consistent across our four energy markets, forecasting horizons, and loss functions

Diel surface temperature range scales with lake size

Ecological and biogeochemical processes in lakes are strongly dependent upon water temperature. Long-term surface warming of many lakes is unequivocal, but little is known about the comparative magnitude of temperature variation at diel timescales, due to a lack of appropriately resolved data. Here we quantify the pattern and magnitude of diel temperature variability of surface waters using high-frequency data from 100 lakes. We show that the near-surface diel temperature range can be substantial in summer relative to long-term change and, for lakes smaller than 3 km2, increases sharply and predictably with decreasing lake area. Most small lakes included in this study experience average summer diel ranges in their near-surface temperatures of between 4 and 7��C. Large diel temperature fluctuations in the majority of lakes undoubtedly influence their structure, function and role in biogeochemical cycles, but the full implications remain largely unexplored.

Analysis of the genome of Spodoptera frugiperda nucleopolyhedrovirus (SfMNPV-19) and of the high genomic heterogeneity in group II nucleopolyhedroviruses

The genome of the most virulent among 22 Brazilian geographical isolates of Spodoptera frugiperda nucleopolyhedrovirus, isolate 19 (SfMNPV-1 9), was completely sequenced and shown to comprise 132 565 bp and 141 open reading frames (ORFs). A total of 11 ORFs with no homology to genes in the GenBank database were found. Of those, four had typical baculovirus; promoter motifs and polyadenylation sites. Computer-simulated restriction enzyme cleavage patterns of SfMNPV-1 9 were compared with published physical maps of other SfMNPV isolates. Differences were observed in terms of the restriction profiles and genome size. Comparison of SfMNPV-1 9 with the sequence of the SfMNPV isolate 3AP2 indicated that they differed due to a 1427 bp deletion, as well as by a series of smaller deletions and point mutations. The majority of genes of SfMNPV-1 9 were conserved in the closely related Spodoptera exigua NPV (SeMNPV) and Agrotis segetum NPV (AgseMNPV-A), but a few regions experienced major changes and rearrangements. Synthenic maps for the genomes of group 11 NPVs revealed that gene collinearity was observed only within certain clusters. Analysis of the dynamics of gene gain and loss along the phylogenetic tree of the NPVs showed that group 11 had only five defining genes and supported the hypothesis that these viruses form ten highly divergent ancient lineages. Crucially, more than 60% of the gene gain events followed a power-law relation to genetic distance among baculoviruses, indicative of temporal organization in the gene accretion process.

Structural and spectroscopic properties of the diazocarbene radical (CNN) and its ions CNN(+) and CNN(-): a high-level theoretical investigation

The diazocarbene radical, CNN, and the ions CNN(+) and CNN(-) were investigated at a high level of theory. Very accurate structural parameters for the states X(3)Sigma(-) and A(3)Pi of CNN, and X(2)Pi of both CNN(+) and CNN(-) were obtained with the UCCSD(T) method using correlated-consistent basis functions with extrapolations to the complete basis set limit, with valence only and also with all electrons correlated. Harmonic and anharmonic frequencies were obtained for all species and the Renner parameter and average frequencies evaluated for the Pi states. At the UCCSD(T)/CBS(T-5) level of theory, Delta(f)H(0 K) = 138.89 kcal/mol and Delta(f)H(298 K) = 139.65 kcal/mol were obtained for diazocarbene; for the ionization potential and the electron affinity of CNN, 10.969 eV (252.95 kcal/mol), and 1.743 eV (40.19 kcal/mol), respectively, are predicted. Geometry optimization was also carried out with the CASSCF/MRCI/CBS(T-5) approach for the states X(3)Sigma(-) A(3)Pi, and a(1)Delta of CNN, and with the CASSCF/MRSDCI/aug-cc-pVTZ approach for the states b(1)Sigma(+), c(1)Pi, d(1)Sigma(-), and B(3)Sigma(-), and excitation energies (T(e)) evaluated. Vertical energies were calculated for 15 electronic states, thus improving on the accuracy of the five transitions already described, and allowing for a reliable overview of a manifold of other states, which is expected to guide future spectroscopic experiments. This study corroborates the experimental assignment for the vertical transition X (3)Sigma(-) <- E (3)Pi.

Are price limits on futures markets that cool? evidence from the brazilian merchantile & futures exchange

This paper investigates the impact of price limits on the Brazil- ian future markets using high frequency data. The aim is to identify whether there is a cool-off or a magnet effect. For that purpose, we examine a tick-by-tick data set that includes all contracts on the S��o Paulo stock index futures traded on the Brazilian Mercantile and Futures Exchange from January 1997 to December 1999. Our main finding is that price limits drive back prices as they approach the lower limit. There is a strong cool-off effect of the lower limit on the conditional mean, whereas the upper limit seems to entail a weak magnet effect on the conditional variance. We then build a trading strategy that accounts for the cool-off effect so as to demonstrate that the latter has not only statistical, but also economic signifi- cance. The resulting Sharpe ratio indeed is way superior to the buy-and-hold benchmarks we consider.