The processing of color preference in the brain

Decades of research has established that humans have preferences for some colors (e.g., blue) and a dislike of others (e.g., dark chartreuse), with preference varying systematically with variation in hue (e.g., Hurlbert & Owen, 2015). Here, we used functional MRI to investigate why humans have likes and dislikes for simple patches of color, and to understand the neural basis of preference, aesthetics and value judgements more generally. We looked for correlations of a behavioural measure of color preference with the blood oxygen level-dependent (BOLD) response when participants performed an irrelevant orientation judgement task on colored squares. A whole brain analysis found a significant correlation between BOLD activity and color preference in the posterior midline cortex (PMC), centred on the precuneus but extending into the adjacent posterior cingulate and cuneus. These results demonstrate that brain activity is modulated by color preference, even when such preferences are irrelevant to the ongoing task the participants are engaged. They also suggest that color preferences automatically influence our processing of the visual world. Interestingly, the effect in the PMC overlaps with regions identified in neuroimaging studies of preference and value judgements of other types of stimuli. Therefore, our findings extends this literature to show that the PMC is related to automatic encoding of subjective value even for basic visual features such as color

Predicting uncertainty and risk in the natural sciences: bridging the gap between academics and industry

The increase in large-scale disasters in recent years, such as the 2007 floods in the UK, has caused disruptions of livelihood, enormous economic losses and increase in fatalities. Losses from natural hazards are only partially derived from the physical event itself but are also caused by society’s vulnerability to it. In the first three months of 2010, an unprecedented US$16 billion in losses occurred from natural hazards caused by events such as the Haiti and Chilean earthquakes, and the European storm Xynthia. This made it the worst ever first quarter for natural hazard losses and left the insurance industry exposed financially to the more loss-prone third and forth quarters. NERC science has a central role to play in the forecasting and mitigation of natural hazards. Research in this area forms the basis for technological solutions to early warning systems, designing mitigation strategies and providing critical information for decision makers to help save lives and avoid economic losses. Understanding uncertainty is essential if reliable forecasting and risk assessments are to be made. However, the quantification and assessment of uncertainty in natural hazards has in general been limited particularly in terms of model limitations and multiplicity. There are several reasons for this; most notably the fragmented nature of natural hazard research which is split both across science areas and between research, risk management and policy. Because of this, each sector has developed its own concepts and language which has acted as a barrier for effective communication and prevented the production of generic methods that have the potential to be used across sectors. It is clear therefore that by bringing the natural hazard community together significant breakthroughs in the visualisation and understanding of risk and uncertainty could be achieved. To accomplish this, this research programme has 4 prime objectives: 1.To improve communication and networking between researchers and risk managers within the financial services sector 2.To provide a platform for the dissemination of information on uncertainty and risk analysis between a range of researchers and practitioners 3.To generate a portfolio of best practice in uncertainty and risk analysis 4.To act as a focal point between the financial sector and natural hazard research in NERC This paper will discuss how the Natural Environmental Research Council, in partnership with other organisations such as TSB, EA and EPSRC etc, is working with academics and industry to bring about a step change in the way that uncertainty and risk assessments are achieved throughout the natural hazard community

Testing an eDNA marker for Common Snapping Turtles

Common snapping turtles (Chelydra serpentina) are a species of concern in southeastern Montana and some southern states; however, they are invasive to the Crown of the Continent ecosystem. Although raccoons and foxes destroy over 90% of the eggs, the few remaining survivors that reach adulthood are enough to raise serious concern as they prey upon many native species and have no natural predators. According to the Montana Natural Heritage Program, there have been only three documented reports of snapping turtles in the Flathead Valley, yet we have observed an additional 19 unreported individuals. We tested a previously developed environmental DNA (eDNA) marker for common snapping turtles to help determine their distribution in the Flathead Valley. We extracted DNA from snapping turtle tissue samples collected in the Flathead Valley to verify marker effectiveness. We hypothesized McGilvray Lake and a nearby small pond would be positive for snapping turtle DNA, while Spencer Lake would be negative. Painted turtles (Chrysemys picta belli) were visually detected in all of the waterbodies while snapping turtles have not been observed in Spencer Lake. We collected eDNA samples via water filtration in December 2016. All of the eDNA samples were negative for snapping turtle DNA. We believe our analysis produced negative results because during the winter the turtles bury themselves in the mud and the DNA can degrade or that we did not capture enough DNA. We plan to sample in the summer when the turtles are more active to increase our probability of detection

Atypical centromeres in plants—what they can tell us

The centromere, visible as the primary constriction of condensed metaphase chromosomes, is a defined chromosomal locus essential for genome stability. It mediates transient assembly of a multi-protein complex, the kinetochore, which enables interaction with spindle fibers and thus faithful segregation of the genetic information during nuclear divisions. Centromeric DNA varies in extent and sequence composition among organisms, but a common feature of almost all active eukaryotic centromeres is the presence of the centromeric histone H3 variant cenH3 (a.k.a. CENP-A).These typical centromere features apply to most studied species. However, a number of species display atypical centromeres, such as holocentromeres (centromere extension along almost the entire chromatid length) or neocentromeres (ectopic centromere activity).In this review, we provide an overview of different atypical centromere types found in plants including holocentromeres, de novo formed centromeres and terminal neocentromeres as well as di-, tri- and metapolycentromeres (more than one centromere per chromosomes). We discuss their specific and common features and compare them to centromere types found in other eukaryotic species. We also highlight new insights into centromere biology gained in plants with atypical centromeres such as distinct mechanisms to define a holocentromere, specific adaptations in species with holocentromeres during meiosis or various scenarios leading to neocentromere formation

Use of Machine Learning Techniques for Iidentification of Robust Teleconnections to East African Rainfall Variability in Observations and Models

Providing advance warning of East African rainfall variations is a particular focus of several groups including those participating in the Famine Early Warming Systems Network. Both seasonal and long-term model projections of climate variability are being used to examine the societal impacts of hydrometeorological variability on seasonal to interannual and longer time scales. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of both seasonal and climate model projections to develop downscaled scenarios for using in impact modeling. The utility of these projections is reliant on the ability of current models to capture the embedded relationships between East African rainfall and evolving forcing within the coupled ocean-atmosphere-land climate system. Previous studies have posited relationships between variations in El Nio, the Walker circulation, Pacific decadal variability (PDV), and anthropogenic forcing. This study applies machine learning methods (e.g. clustering, probabilistic graphical model, nonlinear PCA) to observational datasets in an attempt to expose the importance of local and remote forcing mechanisms of East African rainfall variability. The ability of the NASA Goddard Earth Observing System (GEOS5) coupled model to capture the associated relationships will be evaluated using Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations

Understanding Commuter Patterns and Behavior: An Analysis to Recommend Policies Aimed at Reducing Vehicle Use

This study focused on the use of single occupancy vehicles by employee and student commuters at the University at Albany. The team conducted a review of the existing options for alternative transportation, developed GIS maps of commuting patterns, investigated the on-time performance of mass transit and created a survey to examine perceptions and barriers to using alternative transportation. The report includes a handbook for conducting a similar analysis at other institutions

Protecting disabled children : what the latest research tells us

A recent study of child protection practice in Scotland suggests that disabled children fare less well in child protection services than their non-disabled peers. A group of academics highlight the lessons for social workers from the latest studies of safeguarding disabled children

Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast perio