Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development

Unmanned Aerial Vehicles and Systems (UAV or UAS) have become increasingly popular in recent years for agricultural research applications. UAS are capable of acquiring images with high spatial and temporal resolutions that are ideal for applications in agriculture. The objective of this study was to evaluate the performance of a UAS-based remote sensing system for quantification of crop growth parameters of sorghum (Sorghum bicolor L.) including leaf area index (LAI), fractional vegetation cover (fc) and yield. The study was conducted at the Texas A&M Research Farm near College Station, Texas, United States. A fixed-wing UAS equipped with a multispectral sensor was used to collect image data during the 2016 growing season (April±October). Flight missions were successfully carried out at 50 days after planting (DAP; 25 May), 66 DAP (10 June) and 74 DAP (18 June). These flight missions provided image data covering the middle growth period of sorghum with a spatial resolution of approximately 6.5 cm. Field measurements of LAI and fc were also collected. Four vegetation indices were calculated using the UAS images. Among those indices, the normalized difference vegetation index (NDVI) showed the highest correlation with LAI, fc and yield with R2 values of 0.91, 0.89 and 0.58 respectively. Empirical relationships between NDVI and LAI and between NDVI and fc were validated and proved to be accurate for estimating LAI and fc from UAS-derived NDVI values. NDVI determined from UAS imagery acquired during the flowering stage (74 DAP) was found to be the most highly correlated with final grain yield. The observed high correlations between UAS-derived NDVI and the crop growth parameters (fc, LAI and grain yield) suggests the applicability of UAS for withinseason data collection of agricultural crops such as sorghum

The human Na⁺/H⁺ exchanger 1 is a membrane scaffold protein for extracellular signal-regulated kinase 2

Background Extracellular signal-regulated kinase 2 (ERK2) is an S/T kinase with more than 200 known substrates, and with critical roles in regulation of cell growth and differentiation and currently no membrane proteins have been linked to ERK2 scaffolding. Methods and results Here, we identify the human Na+/H+ exchanger 1 (hNHE1) as a membrane scaffold protein for ERK2 and show direct hNHE1-ERK1/2 interaction in cellular contexts. Using nuclear magnetic resonance (NMR) spectroscopy and immunofluorescence analysis we demonstrate that ERK2 scaffolding by hNHE1 occurs by one of three D-domains and by two non-canonical F-sites located in the disordered intracellular tail of hNHE1, mutation of which reduced cellular hNHE1-ERK1/2 co-localization, as well as reduced cellular ERK1/2 activation. Time-resolved NMR spectroscopy revealed that ERK2 phosphorylated the disordered tail of hNHE1 at six sites in vitro, in a distinct temporal order, with the phosphorylation rates at the individual sites being modulated by the docking sites in a distant dependent manner. Conclusions This work characterizes a new type of scaffolding complex, which we term a “shuffle complex”, between the disordered hNHE1-tail and ERK2, and provides a molecular mechanism for the important ERK2 scaffolding function of the membrane protein hNHE1, which regulates the phosphorylation of both hNHE1 and ERK2

Dual modification of Alzheimer’s disease PHF-tau protein by lysine methylation and ubiquitylation: a mass spectrometry approach

In sporadic Alzheimer’s disease (AD), neurofibrillary lesion formation is preceded by extensive post-translational modification of the microtubule associated protein tau. To identify the modification signature associated with tau lesion formation at single amino acid resolution, immunopurified paired helical filaments were isolated from AD brain and subjected to nanoflow liquid chromatography–tandem mass spectrometry analysis. The resulting spectra identified monomethylation of lysine residues as a new tau modification. The methyl-lysine was distributed among seven residues located in the projection and microtubule binding repeat regions of tau protein, with one site, K254, being a substrate for a competing lysine modification, ubiquitylation. To characterize methyl lysine content in intact tissue, hippocampal sections prepared from post mortem late-stage AD cases were subjected to double-label confocal fluorescence microscopy using anti-tau and anti-methyl lysine antibodies. Anti-methyl lysine immunoreactivity colocalized with 78 ± 13% of neurofibrillary tangles in these specimens. Together these data provide the first evidence that tau in neurofibrillary lesions is post-translationally modified by lysine methylation

Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development

Unmanned Aerial Vehicles and Systems (UAV or UAS) have become increasingly popular in recent years for agricultural research applications. UAS are capable of acquiring images with high spatial and temporal resolutions that are ideal for applications in agriculture. The objective of this study was to evaluate the performance of a UAS-based remote sensing system for quantification of crop growth parameters of sorghum (Sorghum bicolor L.) including leaf area index (LAI), fractional vegetation cover (fc) and yield. The study was conducted at the Texas A&M Research Farm near College Station, Texas, United States. A fixed-wing UAS equipped with a multispectral sensor was used to collect image data during the 2016 growing season (April±October). Flight missions were successfully carried out at 50 days after planting (DAP; 25 May), 66 DAP (10 June) and 74 DAP (18 June). These flight missions provided image data covering the middle growth period of sorghum with a spatial resolution of approximately 6.5 cm. Field measurements of LAI and fc were also collected. Four vegetation indices were calculated using the UAS images. Among those indices, the normalized difference vegetation index (NDVI) showed the highest correlation with LAI, fc and yield with R2 values of 0.91, 0.89 and 0.58 respectively. Empirical relationships between NDVI and LAI and between NDVI and fc were validated and proved to be accurate for estimating LAI and fc from UAS-derived NDVI values. NDVI determined from UAS imagery acquired during the flowering stage (74 DAP) was found to be the most highly correlated with final grain yield. The observed high correlations between UAS-derived NDVI and the crop growth parameters (fc, LAI and grain yield) suggests the applicability of UAS for withinseason data collection of agricultural crops such as sorghum

Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development - Fig 3

<p>(a) Relationship between normalized difference vegetation index (NDVI) and fraction cover (f_c); (b) Measured f_c vs. corresponding f_c values predicted using empirical equation in Fig 3A. The solid black diagonal line in the graph is the 1:1 line. The dashed black line is the least-squares linear regression between the measured and predicted values.</p

Unmanned aerial systems-based remote sensing for monitoring sorghum growth and development - Fig 3

<p>(a) Relationship between normalized difference vegetation index (NDVI) and fraction cover (f_c); (b) Measured f_c vs. corresponding f_c values predicted using empirical equation in Fig 3A. The solid black diagonal line in the graph is the 1:1 line. The dashed black line is the least-squares linear regression between the measured and predicted values.</p

Fractional vegetation cover (f_c) map of the sorghum field derived from UAS imagery acquired on 10 June 2016.

<p>Fractional vegetation cover (f_c) map of the sorghum field derived from UAS imagery acquired on 10 June 2016.</p

Relationship between leaf area index (LAI) and fraction cover (f_c) of sorghum.

<p>Relationship between leaf area index (LAI) and fraction cover (f_c) of sorghum.</p

Relationships between normalized difference vegetation index (NDVI) and seeding rates for six different sorghum hybrids at 50, 66 and 74 days after planting (DAP) in 2016.

<p>Each data point represents the mean of three replicates and was regressed against seeding rate.</p