Declines in Wavelength Discrimination and Shifts in Unique Hue with Hypoxia.

INTRODUCTION: Hypoxia can be a problem for warfighters, compromising visual and cognitive performance. One area of study has been hypoxia-induced decrements in color vision.METHODS: The present study examined how hypoxia affected the perception of wavelengths associated with unique green and with unique yellow as well as discriminability by the blue vs. yellow (b-y) and the red vs. green (r-g) spectrally opponent color channels while breathing O2 levels found at sea level and at 5500 m. Measurements of wavelengths producing unique green (minimizing response by the b-y channel) and unique yellow (minimizing response by the r-g channel) preceded measurements of wavelength discriminability near those unique hues.RESULTS: Relative to sea level, unique yellow shifted to shorter wavelengths (0.54 nm) and unique green shifted to longer wavelengths (2.3 nm) under hypoxia. In terms of an equal psychophysical scale, both unique hues shifted by similar magnitudes. Wavelength discriminability of both color channels was compromised by statistically reliable amounts of 16-17% under hypoxia.DISCUSSION: These results were consistent with previous studies and the inference that postreceptor, M-cone neurons were differentially compromised by hypoxia. However, these measurable changes in color vision due to hypoxia were not perceived by the subjects.Bierman A, LaPlumm T, Rea MS. Declines in wavelength discrimination and shifts in unique hue with hypoxia. Aerosp Med Hum Perform. 2020; 91(5):394-402.

A High-Throughput Phenotyping System Using Machine Vision to Quantify Severity of Grapevine Powdery Mildew.

Powdery mildews present specific challenges to phenotyping systems that are based on imaging. Having previously developed low-throughput, quantitative microscopy approaches for phenotyping resistance to Erysiphe necator on thousands of grape leaf disk samples for genetic analysis, here we developed automated imaging and analysis methods for E. necator severity on leaf disks. By pairing a 46-megapixel CMOS sensor camera, a long-working distance lens providing 3.5× magnification, X-Y sample positioning, and Z-axis focusing movement, the system captured 78% of the area of a 1-cm diameter leaf disk in 3 to 10 focus-stacked images within 13.5 to 26 seconds. Each image pixel represented 1.44 µm2 of the leaf disk. A convolutional neural network (CNN) based on GoogLeNet determined the presence or absence of E. necator hyphae in approximately 800 subimages per leaf disk as an assessment of severity, with a training validation accuracy of 94.3%. For an independent image set the CNN was in agreement with human experts for 89.3% to 91.7% of subimages. This live-imaging approach was nondestructive, and a repeated measures time course of infection showed differentiation among susceptible, moderate, and resistant samples. Processing over one thousand samples per day with good accuracy, the system can assess host resistance, chemical or biological efficacy, or other phenotypic responses of grapevine to E. necator. In addition, new CNNs could be readily developed for phenotyping within diverse pathosystems or for diverse traits amenable to leaf disk assays.