Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models

Peanut (Arachis hypogaea L.) is an important crop for United States agriculture and worldwide. Low soil moisture is a major constraint for production in all peanut growing regions with negative effects on yield quantity and quality. Leaf wilting is a visual symptom of low moisture stress used in breeding to improve stress tolerance, but visual rating is slow when thousands of breeding lines are evaluated and can be subject to personnel scoring bias. Photogrammetry might be used instead. The objective of this article is to determine if color space indices derived from red-green-blue (RGB) images can accurately estimate leaf wilting for breeding selection and irrigation triggering in peanut production. RGB images were collected with a digital camera proximally and aerially by a unmanned aerial vehicle during 2018 and 2019. Visual rating was performed on the same days as image collection. Vegetation indices were intensity, hue, saturation, lightness, a∗, b∗, u∗, v∗, green area (GA), greener area (GGA), and crop senescence index (CSI). In particular, hue, a∗, u∗, GA, GGA, and CSI were significantly (p ≤ 0.0001) associated with leaf wilting. These indices were further used to train an ordinal logistic regression model for wilting estimation. This model had 90% accuracy when images were taken aerially and 99% when images were taken proximally. This article reports on a simple yet key aspect of peanut screening for tolerance to low soil moisture stress and uses novel, fast, cost-effective, and accurate RGB-derived models to estimate leaf wilting

Smart Phone, Smart Science: How the Use of Smartphones Can Revolutionize Research in Cognitive Science

Investigating human cognitive faculties such as language, attention, and memory most often relies on testing small and homogeneous groups of volunteers coming to research facilities where they are asked to participate in behavioral experiments. We show that this limitation and sampling bias can be overcome by using smartphone technology to collect data in cognitive science experiments from thousands of subjects from all over the world. This mass coordinated use of smartphones creates a novel and powerful scientific “instrument” that yields the data necessary to test universal theories of cognition. This increase in power represents a potential revolution in cognitive science

Association between Age and the 7 Repeat Allele of the Dopamine D4 Receptor Gene

Longevity is in part (25%) inherited, and genetic studies aim to uncover allelic variants that play an important role in prolonging life span. Results to date confirm only a few gene variants associated with longevity, while others show inconsistent results. However, GWAS studies concentrate on single nucleotide polymorphisms, and there are only a handful of studies investigating variable number of tandem repeat variations related to longevity. Recently, Grady and colleagues (2013) reported a remarkable (66%) accumulation of those carrying the 7 repeat allele of the dopamine D4 receptor gene in a large population of 90-109 years old Californian centenarians, as compared to an ancestry-matched young population. In the present study we demonstrate the same association using continuous age groups in an 18-97 years old Caucasian sample (N = 1801, p = 0.007). We found a continuous pattern of increase from 18-75, however frequency of allele 7 carriers decreased in our oldest age groups. Possible role of gene-environment interaction effects driven by historical events are discussed. In accordance with previous findings, we observed association preferentially in females (p = 0.003). Our results underlie the importance of investigating non-disease related genetic variants as inherited components of longevity, and confirm, that the 7-repeat allele of the dopamine D4 receptor gene is a longevity enabling genetic factor, accumulating in the elderly female population