Biology subjects

Carlson, T.

Publications and source records attributed to Carlson, T..

4 recordsLinked to original sources

bioRxiv2018

Open-Source Food: Nutrition, Toxicology, and Availability of Wild Edible Greens in the East Bay

SignificanceForaged leafy greens are consumed around the globe, including in urban areas, and may play a larger role when food is scarce or expensive. It is thus important to assess the safety and nutritional value of wild greens foraged in urban environments.\n\nMethodsField observations, soil tests, and nutritional and toxicology tests on plant tissue were conducted for three sites, each roughly 9 square blocks, in disadvantaged neighborhoods in the East San Francisco Bay Area in 2014-2015. The sites included mixed-use areas and areas with high vehicle traffic.\n\nResultsEdible wild greens were abundant, even during record droughts. Soil at some survey sites had elevated concentrations of lead and cadmium, but tissue tests suggest that rinsed greens of the tested species are safe to eat. Daily consumption of standard servings comprise less than the EPA reference doses of lead, cadmium, and other heavy metals. Pesticides, glyphosate, and PCBs were below detection limits.\n\nThe nutrient density of 6 abundant species compared favorably to that of the most nutritious domesticated leafy greens.\n\nConclusionsWild edible greens harvested in industrial, mixed-use, and high-traffic urban areas in the San Francisco East Bay area are abundant and highly nutritious. Even grown in soils with elevated levels of heavy metals, tested species were safe to eat after rinsing in tap water. This does not mean that all edible greens growing in contaminated soil are safe to eat--tests on more species, in more locations, and over a broader range of soil chemistry are needed to determine what is generally safe and what is not. But it does suggest that wild greens could contribute to nutrition, food security, and sustainability in urban ecosystems. Current laws, regulations, and public-health guidance that forbid or discourage foraging on public lands, including urban areas, should be revisited.

ecology↗

bioRxiv2018

Seeing versus Knowing: The Temporal Dynamics of Real and Implied Colour Processing in the Human Brain

Colour is a defining feature of many objects, playing a crucial role in our ability to rapidly recognise things in the world around us and make categorical distinctions. For example, colour is a useful cue when distinguishing lemons from limes or blackberries from raspberries. That means our representation of many objects includes key colour-related information. The question addressed here is whether the neural representation activated by knowing that something is red is the same as that activated when we actually see something red, particularly in regard to timing. We addressed this question using neural timeseries (magnetoencephalography, MEG) data to contrast real colour perception and implied object colour activation. We applied multivariate pattern analysis (MVPA) to analyse the brain activation patterns evoked by colour accessed via real colour perception and implied colour activation. Applying MVPA to MEG data allows us here to focus on the temporal dynamics of these processes. Male and female human participants (N=18) viewed isoluminant red and green shapes and grey-scale, luminance-matched pictures of fruits and vegetables that are red (e.g., tomato) or green (e.g., kiwifruit) in nature. We show that the brain activation pattern evoked by real colour perception is similar to implied colour activation, but that this pattern is instantiated at a later time. These results suggest that a common colour representation can be triggered by activating object representations from memory and perceiving colours.

neuroscience↗

bioRxiv2018

Decoding digits and dice with Magnetoencephalography: Evidence for a shared representation of magnitude

Numerical format describes the way magnitude is conveyed, for example as a digit ( 3) or Roman Numeral ( III). In the field of numerical cognition, there is an ongoing debate of whether magnitude representation is independent of numerical format. Here, we examine the time course of magnitude processing when using different symbolic formats. We presented participants with a series of digits and dice patterns corresponding to the magnitudes of 1 to 6 while performing a 1-back task on magnitude. Magnetoencephalography (MEG) offers an opportunity to record brain activity with high temporal resolution. Multivariate Pattern Analysis (MVPA) applied to MEG data allows us to draw conclusions about brain activation patterns underlying information processing over time. The results show that we can crossdecode magnitude when training the classifier on magnitude presented in one symbolic format and testing the classifier on the other symbolic format. This suggests similar representation of these numerical symbols. Additionally, results from a time-generalisation analysis show that digits were accessed slightly earlier than dice, demonstrating temporal asynchronies in their shared representation of magnitude. Together, our methods allow a distinction between format-specific signals and format-independent representations of magnitude showing evidence that there is a shared representation of magnitude accessed via different symbols.

neuroscience↗

bioRxiv2018

Finding decodable information that is read out in behaviour

Multivariate decoding methods applied to neuroimaging data have become the standard in cognitive neuroscience for unravelling statistical dependencies between brain activation patterns and experimental conditions. The current challenge is to demonstrate that information decoded as such by the experimenter is in fact used by the brain itself to guide behaviour. Here we demonstrate a promising approach to do so in the context of neural activation during object perception and categorisation behaviour. We first localised decodable information about visual objects in the human brain using a spatially-unbiased multivariate decoding analysis. We then related brain activation patterns to behaviour using a machine-learning based extension of signal detection theory. We show that while there is decodable information about visual category throughout the visual brain, only a subset of those representations predicted categorisation behaviour, located mainly in anterior ventral temporal cortex. Our results have important implications for the interpretation of neuroimaging studies, highlight the importance of relating decoding results to behaviour, and suggest a suitable methodology towards this aim.

neuroscience↗