There is growing interest in using zebrafish (Danio rerio) as a model of neurodegenerative disorders such as Alzheimer's disease. A zebrafish model of tauopathies has recently been developed and characterized in terms of presence of the pathological hallmarks (i.e., neurofibrillary tangles and cell death). However, it is also necessary to validate these models for function by assessing learning and memory. The majority of tools to assess memory and learning in animal models involve visual stimuli, including color preference. The color preference of zebrafish has received little attention. To validate zebrafish as a model for color-associated-learning and memory, it is necessary to evaluate its natural preferences or any pre-existing biases towards specific colors. In the present study, we have used four different colors (red, yellow, green, and blue) to test natural color preferences of the zebrafish using two procedures: Place preference and T-maze. Results from both experiments indicate a strong aversion toward blue color relative to all other colors (red, yellow, and green) when tested in combinations. No preferences or biases were found among reds, yellows, and greens in the place preference procedure. However, red and green were equally preferred and both were preferred over yellow by zebrafish in the T-maze procedure. The results from the present study show a strong aversion towards blue color compared to red, green, and yellow, with yellow being less preferred relative to red and green. The findings from this study may underpin any further designing of color-based learning and memory paradigms or experiments involving aversion, anxiety, or fear in the zebrafish.

Abstract:The early development of a novel micro-photonic based sensing architecture for use in selective herbicide spraying systems performing noncontact spectral reflectance measurements of plants and soil in real time has been described. A combination module allows three laser diodes of different wavelengths to sequentially emit identically polarized light beams through a common aperture, along one optical path. Each exiting beam enters an optical structure which generates up to 14 parallel laser beams. A pair of combination modules and optical structures generates 28 beams over a 420mm span which illuminates the plants from above. The intensity of the reflected light from each spot is detected by a high speed line scan image sensor. Plant discrimination is based on analyzing the Gaussian profile of reflected laser light at distinguishing wavelengths. Two slopes in the spectral response curves from 635nm to 670nm and 670nm to 785nm are used to discriminate different plants. Furthermore, by using a finely spaced and collimated laser beam array, instead of an un-collimated light source, detection of narrow leaved plants with a width greater than 20mm is achievable.

A bench prototype photonic-based spectral reflectance sensor architecture for use in selective herbicide spraying systems performing noncontact spectral reflectance measurements of plants and soil is described and experimental data obtained with simulated farming vehicle traveling speeds of 7 and 22 km/h is presented. The sensor uses a three-wavelength laser diode module that sequentially emits identically-polarized laser light beams through a common aperture, along one optical path. Each laser beam enters a multi-spot beam generator which produces up to 14 parallel laser beams over a 210mm span. The intensity of the reflected light from each spot is detected by a high-speed line scan image sensor. Plant discrimination is based on calculating the slope of the spectral response between the 635nm to 670nm and 670nm to 785nm laser wavelengths. The use of finely spaced and collimated laser beam array, instead of an un-collimated light source, allows detection of narrow leaved plants with a width as small as 12mm.

A bench prototype photonic-based spectral reflectance sensor architecture for use in selective herbicide spraying systems performing noncontact spectral reflectance measurements of plants and soil is described and experimental data obtained with simulated farming vehicle traveling speeds of 7 and 22 km/h is presented. The sensor uses a three-wavelength laser diode module that sequentially emits identically-polarized laser light beams through a common aperture, along one optical path. Each laser beam enters a multi-spot beam generator which produces up to 14 parallel laser beams over a 210mm span. The intensity of the reflected light from each spot is detected by a high-speed line scan image sensor. Plant discrimination is based on calculating the slope of the spectral response between the 635nm to 670nm and 670nm to 785nm laser wavelengths. The use of finely spaced and collimated laser beam array, instead of an un-collimated light source, allows detection of narrow leaved plants with a width as small as 12mm.

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