Thanks to all of our collaborators who have assisted with sweet corn field collections, including Dominic Reisig and Dan Mott at North Carolina State University, and Pat Porter at Texas A&#38; M University in Lubbock, TX. Thanks to Fiona Clissold for helping to optimize the protocols and for providing edits to this manuscript. This work was supported in part by the Texas A&#38; M C. Everette Salyer Fellowship (Department of Entomology) and the Biotechnology Risk Assessment Grant Program competitive grant no. 2015-33522-24099 from the U.S. Department of Agriculture (awarded to GAS and STB).

1. Plant Collection and Processing
<ol>
	<li>Collect and process plant samples
	<ol>
		<li>After collecting plant samples, flash-freeze samples by dipping plant material into liquid nitrogen with forceps and store at -80 &#176;C. If the plant samples collected are too large to flash-freeze, quickly cool the samples using dry ice and transfer to a -80 &#176;C freezer as soon as possible. The macronutrient content of plant material can change after tissues are separated from the plant, so it is important to freeze plant samples as soon as possible after collection. 
		CAUTION: Liquid nitrogen can cause severe frostbite when in contact with skin. Please make sure to transport liquid nitrogen in approved containers and wear proper PPE during its handling, including cryo-gloves, eye googles, face shield, apron, and a lab coat.</li>
		<li>Lyophilize plant material using a freeze-dryer to ensure that the tissues are metabolically inactive while water is being removed. Dry until the mass of the sample stabilizes to ensure all water has been removed.</li>
		<li>Once samples are dry, grind into a fine powder using a grinder or mill. Store samples in a desiccating cabinet until analysis.</li>
	</ol>
	</li>
</ol>
2. Soluble Protein Assay
<ol>
	<li>Sample Preparation
	<ol>
		<li>Weigh out replicate samples of each tissue, approximately 20 mg each, into 1.5 mL polystyrene microcentrifuge tubes and label tubes. These samples will subsequently be referred to as unknown samples. Record the exact mass of each sample, as this information will be required to calculate the %soluble protein in each unknown sample. Use microcentrifuge tubes with locking lids, as non-locking lids may open during the subsequent heating step, resulting in loss of sample solution.</li>
	</ol>
	</li>
	<li>Solubilize and isolate unknown sample proteins
	<ol>
		<li>Using a micro-pipettor, add 500 &#181;L of 0.1 M NaOH to each tube. Close lids tightly and sonicate for 30 minutes. Preheat a hot water bath 90 &#176;C. 
		CAUTION: Sodium hydroxide is a strong base and can cause burns when in contact with skin. Although 0.1 M NaOH represents a low concentration, wear protective gloves to avoid skin irritation.</li>
		<li>Place tubes in a rack in the hot water bath for 15 minutes.</li>
		<li>Centrifuge tubes at 15,000 x g for 10 minutes. Pipette the supernatant liquid into new labeled microcentrifuge tubes, using a new pipette tip for each sample. Add 300 &#181;L of 0.1 M NaOH to the tube containing the pellet and centrifuge again at 15,000 x g for 10 minutes. Again, collect the supernatant liquid and combine it with the other supernatant fluid from the same sample. This is a potential stopping point, and samples can be stored at 4 &#176;C overnight if needed.</li>
	</ol>
	</li>
	<li>Precipitate plant proteins
	<ol>
		<li>Neutralize the pH of the supernatant solution by adding 11 &#181;L of 5.8 M HCl. Confirm a pH of ~7 by dipping litmus paper into each sample solution. 
		CAUTION: Hydrochloric acid is a strong corrosive acid that can cause burns when in contact with skin (skin application or inhalation). Please wear gloves to prevent skin irritation while handling HCl.</li>
		<li>Add 90 &#181;L of 100% trichloroacetic acid (TCA) to each tube and incubate tubes on ice for 30 minutes. The precipitation of proteins will turn the solution from clear to opaque. If this does not occur after 30 minutes, refrigerate the tubes for 1 hour. This is a potential stopping point, and samples can be stored at 4 &#176;C overnight if needed. 
		CAUTION: Trichloroacetic acid is corrosive. Please wear gloves when handling to prevent contact with skin and avoid inhalation.</li>
		<li>Centrifuge samples at 13,000 x g for 10 minutes at 4 &#176;C. Carefully remove TCA supernatant by suctioning it out using a vacuum line attached to a fine glass micropipette tip (the same tip can be used across samples). Do not disturb the protein pellet by avoiding heavy suction and keeping an appropriate distance between pipette tip and the pellet.</li>
		<li>Wash pellet quickly with 100 &#181;L of -20 &#176;C acetone. This step removes any remaining TCA from the pellet, which can interfere with the subsequent Bradford assay; however, acetone will start to dissolve the protein pellet if left in contact too long, so acetone should be added then quickly extracted from the pellet within 5 seconds.</li>
		<li>Allow acetone to evaporate by placing tubes in a fume hood or refrigerator. Then, dissolve the protein pellet by adding 1 mL of 0.1 M NaOH to each tube. Fully dissolving the pellet may require several rounds of heating in a hot water bath, vortexing, and sonicating. 
		Note: The longer the tubes sit in the fume hood or refrigerator and the drier the pellets get, the more difficult they will be to re-solubilize. It is suggested to dry the pellet for 30 minutes, and then check for the presence of acetone every 10-15 minutes until it is clear that the acetone has evaporated (no acetone fumes are detectable).</li>
	</ol>
	</li>
	<li>Mix standard solutions, dilute unknown sample solutions, and quantify the total protein content of unknown samples using the Bradford assay.
	<ol>
		<li>Prepare bovine immunoglobulin G (IgG) standard solutions according to the concentrations listed in Table 1 (lyophilized or IgG stock solution can be mixed with deionized water to achieve each concentration). Store standards at 4 &#176;C. In a 96-well plate, add 160 &#181;L of each IgG standard solution in triplicate starting at the A1 position of the well plate (0 &#181;g/&#181;L in A1, A2, A3 position, 0.0125 &#181;g/&#181;L in B1, B2, B3 positions, etc.).</li>
		<li>Prepare a diluted NaOH solution by adding 50 &#181;L of 0.1 M NaOH to 950 &#181;L of distilled water. As a blank negative control, add 60 &#181;L of this solution to the well plate in triplicate (G1, G2, G3 positions).</li>
		<li>Prepare dilutions of unknown samples by adding 50 &#181;L of each sample solution to 950 &#181;L of distilled water in a new 1.5 mL microcentrifuge tube. Then, add 60 &#181;L of each diluted sample to the well plate in triplicate, starting at the H1 position. A 96-well plate should allow for the analysis of 6 standards, 1 blank, and 25 unknown samples when read in triplicate. Each well plate analyzed should contain its own IgG standard and blank samples.</li>
		<li>Add 100 &#181;L of distilled water to all blank and unknown samples wells. Now each well should contain a total of 160 &#181;L. Using a multi-channel pipette (8 channels), add 40 &#181;L of Coomassie brilliant blue G-250 protein dye to every well in the first column of the well plate. Mix the dye with the samples by plunging several times. Repeat for all other columns, replacing pipette tips to avoid contamination. 
		CAUTION: Coomassie brilliant blue G-250 is an irritant and contact with skin, eyes, or lungs should be avoided. Please wear gloves to prevent contact with skin. If&#160;contact with skin occurs, this product will stain.</li>
		<li>Use a needle to pop any bubbles present in the wells, as they may interfere with the microplate spectrophotometer reading. Clean the needle to avoid contamination between wells. Let the microplate incubate at room temperature for 5 minutes.</li>
		<li>Using a microplate spectrophotometer, record the absorbance values for each well at 595 nm.</li>
		<li>Record the average absorbance for the three blank wells and subtract this value from each of the standard and unknown sample readings. Then, record the average absorbance for each standard and unknown sample. 
		Note: To calculate the amount of protein present in each unknown sample using the standard curve, it is easiest to regress the average absorbance of each standard against the micrograms of protein present in each standard, but one can&#160;plot the protein concentration (&#181;g/&#181;L) of each standard if desirable. The following calculations, however, refer to a standard curve based on micrograms, not concentrations (&#181;g/&#181;L).</li>
		<li>Plot the average absorbance of each standard against the total amount of protein present in each standard well (Table 1). Fit a linear regression line to these data and use the equation to calculate the amount of protein (&#181;g) in each unknown sample well based on the average absorbance (Figure 1a). 
		Note: The correlation coefficient (r value) of this line should be greater than 0.98 and routinely near 0.99. The standard regression will be specific to each plate, hence the unknown sample wells in each plate must be analyzed separately.</li>
		<li>Once the average amount of protein is calculated for each unknown sample well, use the following equation to calculate the total amount of protein (&#181;g) in each original sample: 
		&#160;&#160;&#160;&#160;&#160;Mp = (((Wp/60) x 1000)/50)*1000 
		&#160;&#160;&#160;&#160;&#160;Mp = &#181;g of protein in original sample 
		&#160;&#160;&#160;&#160;&#160;Wp = &#181;g of protein in unknown sample well
		<ol>
			<li>Then, use the following equation to determine the percentage of protein in each sample: 
			&#160;&#160;&#160;&#160;&#160;Pp = ((Mp/1000)/Mi)*100 
			&#160;&#160;&#160;&#160;&#160;Pp =%protein in sample 
			&#160;&#160;&#160;&#160;&#160;Mi = initial mass of sample (mg) 
			Note: In some cases, the sample may surpass the upper absorbance threshold. If so, sample solutions may require further dilution at step 2.4.3. 
			Additional dilutions must then be accounted for in subsequent calculations. Some microplate reader brands also provide computer software that will automatically calculate the average protein concentration of each unknown sample based on the standard curve data.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
3. Digestible Carbohydrate Assay
<ol>
	<li>Sample Preparation
	<ol>
		<li>Weigh out replicate samples of each tissue, approximately 20 mg each, into glass 15 mL tubes with rubber-lined screw caps (100 mm length). These sample will subsequently be referred to as unknown samples. Label tubes with a waterproof marker or with labelling tape on the lids and record the exact mass of each sample, as this information will be required to calculate the %carbohydrates in each unknown sample.</li>
	</ol>
	</li>
	<li>Extract digestible carbohydrates from each unknown sample.
	<ol>
		<li>Add 1 mL of 0.1 M H2SO4 to each tube and screw caps onto tubes tightly. Place in a boiling water bath for 1 hour. 
		CAUTION: Sulfuric acid is very corrosive. Please wear gloves, eye googles, and an apron when handling H2SO4 and perform this step in a fume hood.</li>
		<li>Cool off tubes in a tepid water bath. Pour tube contents into labeled 1.5 mL microcentrifuge tubes (do not be concerned if some plant material remains in the glass tubes).</li>
		<li>Centrifuge tubes at 15,000 x g for 10 minutes. Remove the supernatant liquid with a micro-pipettor and place into new labeled 1.5 mL microcentrifuge tubes. Tubes can be refrigerated overnight at this point. If continuing with assay, refer to step 3.3.2.</li>
	</ol>
	</li>
	<li>Mix standard solutions and quantify the total digestible carbohydrate content of unknown samples.
	<ol>
		<li>Prepare D(+) glucose standard solutions with the concentrations listed in Table 2. Prepare six glass test tubes with 400 &#181;L of each standard solution.</li>
		<li>Pipette 15 &#181;L of each unknown sample into its own test tube and add 385 &#181;L of distilled water to each for a total volume of 400 &#181;L in each test tube. In a fume hood, add 400 &#181;L of 5% phenol to each standard and unknown sample test tube, and then quickly pipette 2 mL of concentrated H2SO4 into each tube. Do not touch the test tube contents with the pipette tip, just add to the solution surface (a repeater pipette works best for this).</li>
		<li>Let tubes incubate for 10 minutes, and then carefully vortex the tubes to mix contents. Incubate for an additional 30 minutes. 
		CAUTION: Phenol and sulfuric acid are corrosive irritants. To protect from exposure to skin, eyes, and inhalation, this step should be performed in a chemical fume hood using the proper PPE, which includes: face shield or eye goggles, rubber gloves, lab apron, and boots. Mixing phenol with sulfuric acid also results in an exothermic reaction, which will result in the tubes becoming hot.</li>
		<li>Pipette 800 &#181;L of sample from each tube into each of 3 polystyrene 1.5 mL semi-micro cuvettes (3 technical replicates per sample). Set the spectrophotometer to read at 490 nm. Calibrate to a blank cuvette containing distilled water before reading standards or unknown samples, and intermittently throughout sample readings. Run each cuvette through a spectrophotometer and record the absorbance. Average across technical replicates for each unknown sample. 
		Note: In some cases, samples may surpass the upper absorbance threshold. If so, samples should be diluted at step 3.2.3. Dilutions must also be accounted for in subsequent calculations.</li>
		<li>Calculate the average absorbance for each standard. 
		Note: To calculate the amount of total digestible carbohydrates present in each unknown sample using the standard curve, it is easiest to regress the average absorbance of each standard against the micrograms of D(+)glucose present in each standard, but one can also&#160;plot the D(+)glucose concentration (&#181;g/&#181;L) of each standard if desirable. The following calculations, however, refer to a standard curve based on micrograms, not concentrations (&#181;g/&#181;L).</li>
		<li>Calculate the standard curve by plotting the average absorbance against the total amount of D(+)glucose (&#181;g) in each standard solution. Fit a linear regression line to these data, and then use the following equation to calculate the total amount of carbohydrates (&#181;g) in each unknown sample: 
		&#160;&#160;&#160;&#160;&#160;Mc = (((Ax &#8211; b)/m)/(15))*1000 
		&#160;&#160;&#160;&#160;&#160;Mc = &#181;g of carbohydrates in sample 
		&#160;&#160;&#160;&#160;&#160;Ax = average absorbance of unknown sample 
		&#160;&#160;&#160;&#160;&#160;b = y-intercept (standard regression line) 
		&#160;&#160;&#160;&#160;&#160;m = slope (standard regression line) 
		The correlation coefficient of this line should be greater than 0.98 and routinely near 0.99. Then, use the following equation to determine the percentage of carbohydrates in each sample: 
		&#160;&#160;&#160;&#160;&#160;Pc = ((Mc/1000)/(Mi))*100 
		&#160;&#160;&#160;&#160;&#160;Pc =%carbohydrates 
		&#160;&#160;&#160;&#160;&#160;Mi = initial mass of sample (mg)</li>
	</ol>
	</li>
</ol>

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J., Sword, G. A., Lorch, P. D., Couzin, I. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cannibal+crickets+on+a+forced+march+for+protein+and+salt.">Cannibal crickets on a forced march for protein and salt.</a> Proceedings of the National Academy of Sciences of the United States of America. 103 (11), 4152-4156 (2006).</li><li>Lihoreau, M., Buhl, J., Sword, G. A., Raubenheimer, D., Simpson, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nutritional+ecology+beyond+the+individual:+a+conceptual+framework+for+integrating+nutrition+and+social+interactions.">Nutritional ecology beyond the individual: a conceptual framework for integrating nutrition and social interactions.</a> Ecology Letters. 18 (3), 273-286 (2015).</li><li>Deans, C. A., Behmer, S. T., Tessnow, A., Tamez-Guerra, P., Pusztai-Carey, M., Sword, G. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nutrition+affects+insect+susceptibility+to+Bt.">Nutrition affects insect susceptibility to Bt.</a> Scientific Reports. 7, 39705 (2017).</li></ol>

The authors have nothing to disclose.

By combining well-established colorimetric assays with effective plant-specific extraction protocols, the assays demonstrated here provide a reasonable and accurate method for measuring plant soluble protein and digestible carbohydrate content. Our results using corn as an exemplar illustrates how these protocols can be used to obtain precise measurements across different biologically-relevant spatial scales. For example, we were able to detect differences in plant soluble protein and digestible carbohydrate content between geographic regions, varieties (or genotypes), tissue types, and even spatially segregated tissues. Both assays can be done using common laboratory equipment and reagents, requiring only basic laboratory skills, and can analyze a relatively large number of samples (50-75) within a short timeframe.
Although relatively easy to perform, some steps are more critical than others, and if done incorrectly can limit the accuracy of the results. For example, it is imperative that plant materials are handled properly during the sampling stage. Even dissected plant tissue will remain metabolically active until exposed to a lethal temperature, and during this period plant macronutrient content can change. As a result, long time periods between plant sampling and freezing (either by liquid N or freezer storage) can increase the likelihood that sample plant macronutrient content may not reflect the content that was present at the time of sampling.
Steps 2.3.3-2.3.5 in the protein protocol are particularly important for a successful outcome, as these steps deal with the precipitated proteins. Care must be taken to avoid losing any of the protein pellet when vacuuming the supernatant TCA from the microcentrifuge tubes, because doing so will result in an underestimation of protein content. It is also important when washing the protein pellet with acetone to do so very quickly. Acetone can degrade the pellet if left in contact for more than several seconds. We suggest limiting contact between acetone and the pellet to less than 5 seconds. Finally, when drying the pellet, it is important to take care to only allow enough time for the acetone to evaporate. If the pellet is left to dry for too long, it becomes very difficult to resuspend in NaOH. We suggest drying the pellet for 30 minutes, and then checking for the presence of acetone, either by visually observing liquid in the tube or by carefully detecting the smell of acetone fumes. Continue checking the pellet every 10-15 minutes until the acetone has evaporated.
As outlined in Bradford 197616, the quantification steps using Coomassie brilliant blue G-250 dye allow for high sensitivity, with a standard deviation of only 5 &#181;g protein/mL, high protein-dye complex stability, and limited interference by non-protein compounds. This assay has a detection range between 1-20 &#181;g total proteins per microplate well (low-concentration assay) or a maximum of 25 &#181;g/mL; however, any concentration that exceeds the highest standard should be diluted and re-analyzed for the most accurate results (the protocol produces an excess of solution in case such dilutions and re-analysis is necessary). There is a bias for the dye to preferentially bind to basic amino acids, such as arginine, and aromatic amino acid residues; however, the Bradford assay remains the most accurate and easy to use method for total protein quantification in mixed samples.
The digestible carbohydrate assay is a fast, cost-effective, and simple method for quantifying plant saccharides while excluding structural carbohydrates (such as cellulose), which are indigestible by most herbivores. The most problematic steps in the carbohydrate assays are steps 3.2.1 and 3.3.2-3.3.4. Here, it is critical to keep tubes upright and to tighten the screwcaps well when boiling, as the introduction of any water will dilute samples and affect accurate quantification. Also, care must be taken when working with phenol and concentrated sulfuric acid, as both are highly corrosive. It should be noted that we advocate recording sample absorbance at 490 nm, which is the maximum absorbance for hexoses, but not other sugars, such as pentoses and uronic acids which have a maximum absorbance at 480 nm20,21. For sugar mixtures in plant samples, 490 nm provides an appropriate wavelength for quantifying overall carbohydrate content22,23,24, but for a more detailed analysis of different sugars see20,21. It should also be noted that Masuko et al.23 provides a streamlined microplate method for phenol-sulfuric acid carbohydrate analysis.
Another notable method for estimating plant nutrient content25,26,27 is near-infrared spectroscopy (NIRS). NIRS technology is widely used in agricultural and food production. This alternative technique is noninvasive, nondestructive, takes a fraction of the time involved in any wet chemistry method, and can be applied at different scales from a landscape down to an individual piece of plant tissue. This technique measures nutrients indirectly and relies on accurate wet chemistry measurements of the plant chemical of interest for calibration. The methods described herein will therefore have a critical place in the future of plant nutrient analysis, ensuring that calibration is based on soluble and digestible macronutrient quantification and not biased by non-nutritive elemental surrogates.
The methods presented for measuring plant soluble protein and digestible carbohydrate content have significant implications for the environmental and biological sciences. Despite a wealth of information on the elemental composition of plant tissues, information on plant macronutrient content is severely lacking. Given the limitations that exist in correlating elemental measures with macronutrient content and acknowledging the strong relationship between plant nutritional content and higher order ecological processes, obtaining this kind of data is essential for advancing the fields of plant physiology, nutritional ecology, plant-herbivore interactions, food-web dynamics11,28,29,30. It is our hope that providing a clear and approachable methodology for measuring plant soluble protein and digestible carbohydrate content will encourage researchers to collect and incorporate this kind of data into future research.

Plant biomass forms the foundation of virtually all terrestrial food-webs. Plants acquire nutritional elements from the soil through their roots systems and utilize sunlight in their foliar tissues to synthesize biomolecules. In particular, carbon and nitrogen are used to create carbohydrates, proteins (comprised of amino acids), and lipids that are needed to build plant biomass (it should be noted that in plant physiology the term &#34;macronutrient&#34; often refers to soil elements, such as N, P, K, and S, however, throughout this paper this term will refer to biomolecules, such as proteins, carbohydrates, and lipids). When herbivores consume plant material, the macronutrients contained in plant tissues are broken down into their constituent parts and then used to drive the physiological processes of the consumer. In this way, plant macronutrients have a strong influence on consumer physiology along with important implications for higher order ecological interactions and food-web dynamics.
Across the animal kingdom, soluble protein and digestible carbohydrates are the macronutrients most closely tied to survival, reproduction, and performance1. Moreover, the majority of animals actively regulate their intake of these two macronutrients to meet their physiological demands1,2. This is particularly true for insect herbivores that detect the concentrations of sugars and amino acids in plant tissues, which in turn directs feeding behavior. As a result, plant soluble protein and digestible carbohydrate content has played a strong role in the evolution of plant-insect interactions.
While data on plant soluble protein and digestible carbohydrate content are relatively rare (but see6,7,8,9,10,11), there is a preponderance of data available on plant elemental content (carbon, nitrogen, and phosphorus). Largely this is because elements play a primary role in plant mineral nutrition3,4,5. Where elements are measured, correlations have been used to extrapolate the amount of soluble protein and digestible carbohydrate, but accurate calculations are often difficult to obtain. For instance, it is impossible to use carbon as an indicator of plant digestible carbohydrate content because carbon is ubiquitously present in all organic compounds. A stronger relationship exists between elemental nitrogen and plant soluble protein content, and generalized nitrogen-to-protein conversion factors are often utilized. However, there is strong evidence that nitrogen-to-protein conversions are highly species-specific12,13,14,15, making the use of generalized conversion likely inaccurate. Because of this, nitrogen-to-protein conversion factors often lack precision, particularly to the extent that is required for nutritional studies on herbivores. Also, the presence of N-containing plant allelochemicals,&#160;such as alkaloids and glucosinolates that are often toxic to herbivores, can confound these conversions.
Here, we offer two chemical assays for measuring the concentration of soluble proteins and digestible carbohydrates in plant tissues. These assays are presented separately, but it is suggested that they be used concurrently to analyze the same plant samples in order to achieve a more comprehensive analysis of plant macronutrients. Both employ similar methodologies, consisting of an extraction step, followed by quantification via absorbance. Plant sample prep is also identical for both protocols, making it easy to run both analyses in tandem. The utility of these assays do not stem from their novelty, as they rely on older, (Bradford, Jones, Dubois) well-established colorimetric assays16,17,18, but here we have organized a clear and easy-to-follow protocol that combines these methods with more obscure plant-specific extraction techniques17,19 in order to make the application of these assays more accessible to those in plant-relevant fields.
For both assays, plant macronutrients are first extracted physically by freezing, lyophilizing, and grinding the plant material. For the soluble protein assay, further chemical extraction is done17,19 through several rounds of vortexing and heating samples in NaOH solution. The well-known Bradford assay, utilizing Coomassie brilliant blue G-250, is then used to quantify soluble proteins and polypeptides between 3,000-5,000 Daltons16,17. This assay has a detection range between 1-20 &#181;g total proteins per microplate well or &#60;25 &#181;g/mL, but does not measure free amino acids. The extraction step of the digestible carbohydrate assay is based on the dilute acid method of Smith et al.20 and allows for the isolation of soluble sugars, starch, and fructosan &#8211; but not structural carbohydrates. A phenol-sulfuric acid quantification method is taken from Dubois et al.18 and measures all mono-, oligo-, and polysaccharides (as well as methyl derivatives). This assay is able to quantify specific sugars, but here we use it as an indicator of total digestible carbohydrate content (see Smith et al.20 for more detailed analysis). Together, these assays measure the two macronutrients that are strongly tied to plant eco-physiology and herbivore performance, providing important data on resource quality at the base of terrestrial food-webs. Presenting these protocols promotes the generation of plant macronutrient datasets in order to obtain a more thorough understanding of plant physiology, herbivore nutritional ecology, and plant-herbivore interactions.

<table border="0" cellpadding="0" cellspacing="0" width="670">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:273px;">microplate reader (spectrophotometer)</td>
			<td style="width:111px;">Bio-Rad</td>
			<td style="width:119px;">Model 680 XR</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:273px;">Bio-Rad Protein Assay Dye Reagent concentrate</td>
			<td style="width:111px;">Bio-Rad</td>
			<td style="width:119px;">#5000006</td>
			<td>450mL</td>
		</tr>
	</tbody>
</table>

quantifying plant soluble protein and digestible carbohydrate content, using corn (zea mays) as an exemplar

Elemental data are commonly used to infer plant quality as a resource to herbivores. However, the ubiquity of carbon in biomolecules, the presence of nitrogen-containing plant defensive compounds, and variation in species-specific correlations between nitrogen and plant protein content all limit the accuracy of these inferences. Additionally, research focused on plant and/or herbivore physiology require a level of accuracy that is not achieved using generalized correlations. The methods presented here offer researchers a clear and rapid protocol for directly measuring plant soluble proteins and digestible carbohydrates, the two plant macronutrients most closely tied to animal physiological performance. The protocols combine well characterized colorimetric assays with optimized plant-specific digestion steps to provide precise and reproducible results. Our analyses of different sweet corn tissues show that these assays have the sensitivity to detect variation in plant soluble protein and digestible carbohydrate content across multiple spatial scales. These include between-plant differences across growing regions and plant species or varieties, as well as within-plant differences in tissue type and even positional differences within the same tissue. Combining soluble protein and digestible carbohydrate content with elemental data also has the potential to provide new opportunities in plant biology to connect plant mineral nutrition with plant physiological processes. These analyses also help generate the soluble protein and digestible carbohydrate data needed to study nutritional ecology, plant-herbivore interactions and food-web dynamics, which will in turn enhance physiology and ecological research.

To show the usefulness of these methods, we analyzed the soluble protein and digestible carbohydrate content of four different field and sweetcorn tissues that serve as distinct potential nutritional resources for insect herbivores. We collected ears of corn from three agricultural regions in the United States (Minnesota, North Carolina, and Texas), encompassing five different varieties of sweet corn (i.e., genotypes) and one variety of field corn as an outgroup. Table 3 shows a summary of these corn samples and where they were collected. All varieties were collected at maturity, but due to developmental differences between varieties, they were not all collected at the same day after planting. We processed the ears into distinct tissues by quickly separating the husks (modified leaves surround the cob), and silks (shiny fibers between the husk and kernels) from the ear before storing all tissues at -80 &#176;C as indicated in the Methods. Each tissue was then freeze-dried. Once dried, we separated the kernels from the base and tip of the ear by shaving off kernels from the top one-third (tip) and bottom one-third (base) of the cob. Next, all tissues were ground into a fine powder. We then analyzed husk, silk, tip kernel, and base kernel tissue samples for soluble protein and digestible carbohydrate content according to the procedures outlined in the Methods above. Given constraints on the amount of tissue available, we analyzed a total of 217 plant samples for both soluble protein and digestible carbohydrate content.
Soluble Protein 
We ran nine sample plates through the spectrophotometer in total, and overall, standard curves had high correlation coefficients (r), with values between 0.985-1.00. Figure 1 shows the standard curve obtained with the highest (A) and lowest (B) r values to exhibit the variability we observed across plates. We calculated the %soluble protein for all samples using initial sample mass (Table 4) and then analyzed the data for statistical differences in soluble protein content between regions, varieties, and tissue types. Data were rank-transformed to meet normality assumptions when necessary.
We observed significant differences in soluble protein content between regions (Welch ANOVA; F(2, 133.5) = 4.303, P = 0.015), as a result, data from each region were subsequently analyzed separately. Minnesota samples showed a significant interaction between variety and tissue type on soluble protein content (two-way ANOVA; F(3, 64) = 16.51, P &#60; 0.001). Most tissues had similar soluble protein content in both varieties, except for base kernels, where the sweet corn variety contained almost 7 times the amount compared to the field corn variety. For the field corn variety (Syngenta/NK-3122A-EZ), husks and silks were similar and had the lowest soluble protein content of all tissues. Kernels from the tip of the ear had the highest soluble protein content, and kernels from the base of the ear were intermediate (Figure 2a). For the sweet corn variety (Providence Bicolor), all tissues were distinct, with the husks and silks containing the lowest soluble protein content, and base kernels contained ~2.5 times more than tip kernels (Figure 2b).
North Carolina samples also showed a significant interaction between variety and tissue type (two-way ANOVA; F(3, 77) = 3.33, P &#60; 0.024). Most tissues showed similar soluble protein content across varieties, except for tip kernels which exhibited a higher content in non-Bt variety (Sweet G90 Hybrid). For the Bt variety (Seedway Bt 1576) husks were the tissue with the lowest soluble protein content, followed by silks and tip kernels, which had a similar content. Base kernels had the highest soluble protein content but were not statistically different from that of tip kernels (Figure 2c). In the non-Bt variety (Sweet G90 Hybrid), all tissues were distinct except for tip and base kernels which were statistically similar. Husks had the lowest soluble protein content, followed by silks, and tip and base kernels which had the highest content (Figure 2d).
Texas samples showed significant differences in soluble protein content between varieties (two-way ANOVA; F(1, 76) = 12.91, P = 0.001) and tissues (two-way ANOVA; F(3, 76) = 21.90, P &#60; 0.001), but no significant interaction (two-way ANOVA; F(3, 76) = 0.436, P = 0.728). Overall, the bicolor variety (Sh2 SS2742 NAT III) showed a lower average soluble protein content than the Silver Queen variety (TRTD F1 (su)). Across both varieties, husks had the lowest protein content, followed by silks, and then tip and base kernels, both of which had similarly high content (Figure 2e-f).
Digestible Carbohydrates 
Because all samples were analyzed at one time, we only ran one standard curve. Figure 1c shows that the correlation coefficient was high, at 0.998. We calculated the %digestible carbohydrate content for all samples (Table 5) and then analyzed data for statistical differences in digestible carbohydrate content between regions, varieties, and tissue types. Data were rank-transformed when necessary to meet normality assumptions.
There were no significant differences in between regions (ANOVA; F(2, 216) = 1.47, P = 0.231), but for the sake of continuity with the protein analyses we again analyzed data from each region separately. Minnesota samples showed no significant differences in digestible carbohydrate content between varieties (two-way ANOVA; F(1, 64) = 0.00014, P = 0.990) or tissue type (two-way ANOVA; F(3, 64) = 0.818, P = 0.489). There was also no significant interaction between variety and tissue (two-way ANOVA; F(3, 64) = 2.26, P = 0.092). Figure 2a-b shows that all tissues exhibited an average content of 36.5% (&#177; 0.53).
North Carolina samples showed a significant effect of tissue type on digestible carbohydrate content (two-way ANOVA; F(3, 77) = 3.99, P = 0.011), but no effect of variety (two-way ANOVA; F(1, 77) = 1.06, P = 0.307) or interaction (two-way ANOVA; F(3, 77) = 0.465, P = 0.708). Figure 2c-d shows that silks had the lowest digestible carbohydrate content, followed by husks, tip kernels, and base kernels. All tissues were statistically similar except for silks and base kernels.
Texas samples showed no significant effect of variety (two-way ANOVA; F(1, 76) = 0.834, P = 0.364) or tissue type (two-way ANOVA; F(3, 76) = 1.03, P = 0.385), but did show a significant interaction (two-way ANOVA; F(3, 76) = 3.34, P = 0.024). This effect was largely because base kernels in the bicolor variety (Sh2 SS2742 NAT III) had a significantly higher digestible carbohydrate content than those in the Silver Queen variety (TRTD F1 (su)). Figure 2e-f shows that there were no statistical differences in digestible carbohydrate content across tissue types in the Silver Queen variety (TRTD F1 (su)), but there was a significant difference between silk and base kernel tissues for the bicolor variety (Sh2 SS2742 NAT III).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58164/58164fig1.jpg" /> 
Figure 1. Standard curves for macronutrient assays. (A) The standard curve for the soluble protein assay showing the plate with the highest correlation coefficient (best curve). (B) The standard curve for the soluble protein assay showing the plate with the lowest correlation coefficient (worst curve). (C) The standard curve for the digestible carbohydrate assay. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58164/58164fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58164/58164fig2.jpg" /> 
Figure 2. Mean %soluble protein and %digestible carbohydrate content for each tissue type. (A) MN- Bt Syngenta/NK-3122A-EZ (field corn), (B) MN- non-Bt Providence Bicolor, (C) NC- Seedway Bt 1576, (D) NC- non-Bt Sweet G90 Hybrid, (E) TX- Sh2 SS2742 F1 NAT III Bicolor, (F) TX- Silver Queen TRTD F1 (su). Circles show raw data, while squares show the mean values. Green (husk), red (silk), yellow (tip kernels), and purple (base kernels). The dotted-lines connecting the origin to each square show the P:C ratio for each tissue (ratio is the slope of the dotted-line). Letters show post hoc results for protein differences along the top and carbohydrate differences along the side, with different letters representing significantly different values across tissues. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/58164/58164fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>IgG Concentration (ug/uL)</td>
			<td>Protein in Standard Samples (ug)</td>
		</tr>
		<tr>
			<td>0.0000</td>
			<td>0</td>
		</tr>
		<tr>
			<td>0.0125</td>
			<td>2</td>
		</tr>
		<tr>
			<td>0.0250</td>
			<td>4</td>
		</tr>
		<tr>
			<td>0.0375</td>
			<td>6</td>
		</tr>
		<tr>
			<td>0.0500</td>
			<td>8</td>
		</tr>
		<tr>
			<td>0.1000</td>
			<td>16</td>
		</tr>
	</tbody>
</table>
Table 1.Standard curve calculations for the soluble protein assay. The amount of protein in each standard is calculated by taking the concentration of each standard and multiplying it by the amount of standard solution in each well (160 &#181;L).
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>D(+)glucose Concentration (ug/uL)</td>
			<td>D(+)glucose in Standard Samples (ug)</td>
		</tr>
		<tr>
			<td>0.0000</td>
			<td>0</td>
		</tr>
		<tr>
			<td>0.0375</td>
			<td>15</td>
		</tr>
		<tr>
			<td>0.0750</td>
			<td>30</td>
		</tr>
		<tr>
			<td>0.1125</td>
			<td>45</td>
		</tr>
		<tr>
			<td>0.1500</td>
			<td>60</td>
		</tr>
		<tr>
			<td>0.1875</td>
			<td>75</td>
		</tr>
	</tbody>
</table>
Table 2. Standard curve calculation for digestible carbohydrate assay. The amount of glucose in each standard is calculated by taking the concentration of each standard and multiplying it by the amount of standard solution in each test tube (400 &#181;L).
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Region</td>
			<td>Variety</td>
			<td>Location</td>
			<td>N</td>
		</tr>
		<tr>
			<td rowspan="2">Minnesota</td>
			<td>Bt Syngenta/NK-3122A-EZ (field corn)</td>
			<td>Rosemount, MN (44.7070, -93.1073)</td>
			<td>10</td>
		</tr>
		<tr>
			<td>non-Bt Providence Bicolor</td>
			<td>Rosemount, MN (44.7070, -93.1073)</td>
			<td>10</td>
		</tr>
		<tr>
			<td rowspan="2">North Carolina</td>
			<td>non-Bt Sweet G90 Hybrid</td>
			<td>Rocky Mount, NC (35.8918, -77.6780)</td>
			<td>10</td>
		</tr>
		<tr>
			<td>Seedway Bt 1576</td>
			<td>Edenton, NC (36.1758, -76.7057)</td>
			<td>10</td>
		</tr>
		<tr>
			<td rowspan="2">Texas</td>
			<td>Sh2 SS2742 F1 NAT III Bicolor</td>
			<td>Lubbock, TX (33.6935, -101.8249)</td>
			<td>10</td>
		</tr>
		<tr>
			<td>Silver Queen TRTD F1 (su)</td>
			<td>Lubbock, TX (33.6935, -101.8249)</td>
			<td>10</td>
		</tr>
	</tbody>
</table>
Table 3. Summary of the corn sampling location and varieties. For each region and variety combination, 10 ears were collected and four tissues were analyzed: husk, silks, tip kernels, and base kernels.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2">Region</td>
			<td rowspan="2">Variety</td>
			<td colspan="4">% soluble protein</td>
		</tr>
		<tr>
			<td>husk</td>
			<td>silks</td>
			<td>tip kernel</td>
			<td>base kernel</td>
		</tr>
		<tr>
			<td rowspan="2">Minnesota</td>
			<td>Bt Syngenta/NK-3122A-EZ (field corn)</td>
			<td>3.29 &#177; 0.38</td>
			<td>4.57 &#177; 1.27</td>
			<td>14.74 &#177; 1.54</td>
			<td>7.07 &#177; 0.84</td>
		</tr>
		<tr>
			<td>non-Bt Providence Bicolor</td>
			<td>3.35 &#177; 0.58</td>
			<td>6.71 &#177; 0.70</td>
			<td>17.87 &#177; 3.85</td>
			<td>48.41 &#177; 2.85</td>
		</tr>
		<tr>
			<td rowspan="2">North Carolina</td>
			<td>non-Bt Sweet G90 Hybrid</td>
			<td>2.90 &#177; 0.60</td>
			<td>6.97 &#177; 0.63</td>
			<td>12.95 &#177; 0.86</td>
			<td>12.23 &#177; 0.83</td>
		</tr>
		<tr>
			<td>Seedway Bt 1576</td>
			<td>5.48 &#177; 0.73</td>
			<td>9.08 &#177; 0.62</td>
			<td>10.75 &#177; 0.93</td>
			<td>12.76 &#177; 1.16</td>
		</tr>
		<tr>
			<td rowspan="2">Texas</td>
			<td>Sh2 SS2742 F1 NAT III Bicolor</td>
			<td>7.74 &#177; 1.03</td>
			<td>12.15 &#177; 0.63</td>
			<td>14.79 &#177; 0.69</td>
			<td>14.88 &#177; 0.48</td>
		</tr>
		<tr>
			<td>Silver Queen TRTD F1 (su)</td>
			<td>6.06 &#177; 1.05</td>
			<td>8.17 &#177; 0.79</td>
			<td>12.95 &#177; 1.19</td>
			<td>12.32 &#177; 1.23</td>
		</tr>
	</tbody>
</table>
Table 4. Mean protein values for each region, variety, and tissue type. Mean percentages (by dry mass) are shown &#177; 1 SE.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td rowspan="2">Region</td>
			<td rowspan="2">Variety</td>
			<td colspan="4">% digestible carbohydrates</td>
		</tr>
		<tr>
			<td>husk</td>
			<td>silks</td>
			<td>tip kernel</td>
			<td>base kernel</td>
		</tr>
		<tr>
			<td rowspan="2">Minnesota</td>
			<td>Bt Syngenta/NK-3122A-EZ (field corn)</td>
			<td>37.55 &#177; 0.88</td>
			<td>32.31 &#177; 1.38</td>
			<td>36.79 &#177; 1.60</td>
			<td>38.66 &#177; 1.57</td>
		</tr>
		<tr>
			<td>non-Bt Providence Bicolor</td>
			<td>37.30 &#177; 0.82</td>
			<td>37.31 &#177; 2.30</td>
			<td>35.80 &#177; 1.77</td>
			<td>34.83 &#177; 1.37</td>
		</tr>
		<tr>
			<td rowspan="2">North Carolina</td>
			<td>non-Bt Sweet G90 Hybrid</td>
			<td>35.79 &#177; 0.81</td>
			<td>35.28 &#177; 1.17</td>
			<td>36.13 &#177; 0.91</td>
			<td>39.47 &#177; 1.18</td>
		</tr>
		<tr>
			<td>Seedway Bt 1576</td>
			<td>35.75 &#177; 0.58</td>
			<td>34.91 &#177; 0.76</td>
			<td>35.73 &#177; 1.35</td>
			<td>37.29 &#177; 1.13</td>
		</tr>
		<tr>
			<td rowspan="2">Texas</td>
			<td>Sh2 SS2742 F1 NAT III Bicolor</td>
			<td>35.55 &#177; 0.87</td>
			<td>33.40 &#177; 1.10</td>
			<td>34.85 &#177; 1.01</td>
			<td>39.14 &#177; 1.22</td>
		</tr>
		<tr>
			<td>Silver Queen TRTD F1 (su)</td>
			<td>34.63 &#177; 1.13</td>
			<td>33.42 &#177; 2.33</td>
			<td>35.16 &#177; 1.16</td>
			<td>33.93 &#177; 1.03</td>
		</tr>
	</tbody>
</table>
Table 5. Mean carbohydrate values for each region, variety, and tissue type. Mean percentages (by dry mass) are shown &#177; 1 SE.

Watch this Scientific Journal Video about Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn Zea mays As an Exemplar at JoVE.com

Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn Zea mays As an Exemplar

The protocols described herein provide a clear and approachable methodology for measuring soluble protein and digestible (non-structural) carbohydrate content in plant tissues. The ability to quantify these two plant macronutrients has significant implications for advancing the fields of plant physiology, nutritional ecology, plant-herbivore interactions and food-web ecology.

Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn (Zea mays) As an Exemplar

Elemental data are commonly used to infer plant quality as a resource to herbivores. However, the ubiquity of carbon in biomolecules, the presence of ...

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19.7K Views.  Texas A&M University. The protocols described herein provide a clear and approachable methodology for measuring soluble protein and digestible (non-structural) carbohydrate content in plant tissues. The ability to quantify these two plant macronutrients has significant implications for advancing the fields of plant physiology, nutritional ecology, plant-herbivore interactions and food-web ecology.

Video: Quantifying Plant Soluble Protein and Digestible Carbohydrate Content, Using Corn Zea mays As an Exemplar

Analysis of Fatty Acid Content and Composition in Microalgae

A method to determine the content and composition of total fatty acids present in microalgae is described. Fatty acids are a major constituent of microalgal biomass. These fatty acids can be present in different acyl-lipid classes. Especially the fatty acids present in triacylglycerol (TAG) are of commercial interest, because they can be used for production of transportation fuels, bulk chemicals, nutraceuticals (&omega;-3 fatty acids), and food commodities. To develop commercial applications, reliable analytical methods for quantification of fatty acid content and composition are needed. Microalgae are single cells surrounded by a rigid cell wall. A fatty acid analysis method should provide sufficient cell disruption to liberate all acyl lipids and the extraction procedure used should be able to extract all acyl lipid classes.
With the method presented here all fatty acids present in microalgae can be accurately and reproducibly identified and quantified using small amounts of sample (5 mg) independent of their chain length, degree of unsaturation, or the lipid class they are part of.
This method does not provide information about the relative abundance of different lipid classes, but can be extended to separate lipid classes from each other.
The method is based on a sequence of mechanical cell disruption, solvent based lipid extraction, transesterification of fatty acids to fatty acid methyl esters (FAMEs), and quantification and identification of FAMEs using gas chromatography (GC-FID). A TAG internal standard (tripentadecanoin) is added prior to the analytical procedure to correct for losses during extraction and incomplete transesterification.

A method for the determination of fatty acid content and composition in microalgae based on mechanical cell disruption, solvent based lipid extraction, transesterification, and quantification and identification of fatty acids using gas chromatography is described. A tripentadecanoin internal standard is used to compensate for the possible losses during extraction and incomplete transesterification.

A method to determine the content and composition of total fatty acids present in microalgae is described. Fatty acids are a major constituent of ...

Transcript and Metabolite Profiling for the Evaluation of Tobacco Tree and Poplar as Feedstock for the Bio-based Industry

The global demand for food, feed, energy, and water poses extraordinary challenges for future generations. It is evident that robust platforms for the exploration of renewable resources are necessary to overcome these challenges. Within the multinational framework MultiBioPro we are developing biorefinery pipelines to maximize the use of plant biomass. More specifically, we use poplar and tobacco tree (Nicotiana glauca) as target crop species for improving saccharification, isoprenoid, long chain hydrocarbon contents, fiber quality, and suberin and lignin contents. The methods used to obtain these outputs include GC-MS, LC-MS and RNA sequencing platforms. The metabolite pipelines are well established tools to generate these types of data, but also have the limitations in that only well characterized metabolites can be used. The deep sequencing will allow us to include all transcripts present during the developmental stages of the tobacco tree leaf, but has to be mapped back to the sequence of Nicotiana tabacum. With these set-ups, we aim at a basic understanding for underlying processes and at establishing an industrial framework to exploit the outcomes. In a more long term perspective, we believe that data generated here will provide means for a sustainable biorefinery process using poplar and tobacco tree as raw material. To date the basal level of metabolites in the samples have been analyzed and the protocols utilized are provided in this article.

Plant biomass offers a renewable resource for multiple products, including fuel, feed, food, and a variety of materials. In this paper we investigate the properties of tobacco tree (Nicotiana glauca) and poplar as suitable sources for a biorefinery pipeline.

The global demand for food, feed, energy, and water poses extraordinary challenges for future generations. It is evident that robust platforms for the ...

Air-sampled Filter Analysis for Endotoxins and DNA Content

Outdoor aerosol research commonly uses particulate matter sampled on filters. This procedure enables various characterizations of the collected particles to be performed in parallel. The purpose of the method presented here is to obtain a highly accurate and reliable analysis of the endotoxin and DNA content of bio-aerosols extracted from filters. The extraction of high molecular weight organic molecules, such as lipopolysaccharides, from sampled filters involves shaking the sample in a pyrogen-free water-based medium. The subsequent analysis is based on an enzymatic reaction that can be detected using a turbidimetric measurement. As a result of the high organic content on the sampled filters, the extraction of DNA from the samples is performed using a commercial DNA extraction kit that was originally designed for soils and modified to improve the DNA yield. The detection and quantification of specific microbial species using quantitative polymerase chain reaction (q-PCR) analysis are described and compared with other available methods.

Two complementary analyses of atmospheric biological particles from air sampled filters are described herein: the extraction and detection of endotoxin, and of DNA.

Outdoor aerosol research commonly uses particulate matter sampled on filters. This procedure enables various characterizations of the collected particles ...

Method to Produce Durable Pellets at Lower Energy Consumption Using High Moisture Corn Stover and a Corn Starch Binder in a Flat Die Pellet Mill

A major challenge in the production of pellets is the high cost associated with drying biomass from 30 to 10% (w.b.) moisture content. At Idaho National Laboratory, a high-moisture pelleting process was developed to reduce the drying cost. In this process the biomass pellets are produced at higher feedstock moisture contents than conventional methods, and the high moisture pellets produced are further dried in energy efficient dryers. This process helps to reduce the feedstock moisture content by about 5-10% during pelleting, which is mainly due to frictional heat developed in the die. The objective of this research was to explore how binder addition influences the pellet quality and energy consumption of the high-moisture pelleting process in a flat die pellet mill. In the present study, raw corn stover was pelleted at moistures of 33, 36, and 39% (w.b.) by addition of 0, 2, and 4% pure corn starch. The partially dried pellets produced were further dried in a laboratory oven at 70 &#176;C for 3-4 hr to lower the pellet moisture to less than 9% (w.b.). The high moisture and dried pellets were evaluated for their physical properties, such as bulk density and durability. The results indicated that increasing the binder percentage to 4% improved pellet durability and reduced the specific energy consumption by 20-40% compared to pellets with no binder. At higher binder addition (4%), the reduction in feedstock moisture during pelleting was &#60;4%, whereas the reduction was about 7-8% without the binder. With 4% binder and 33% (w.b.) feedstock moisture content, the bulk density and durability values observed of the dried pellets were &#62;510 kg/m3 and &#62;98%, respectively, and the percent fine particles generated was&#160;reduced to &#60;3%.

In this study, a protocol was developed to produce good quality pellets using a flat die pellet mill at reduced specific energy consumption testing high-moisture corn stover and a starch based binder. The results indicated that adding a corn starch binder improved the pellet durability, reduced percent fines and decreased specific energy consumption.

A major challenge in the production of pellets is the high cost associated with drying biomass from 30 to 10% (w.b.) moisture content. At Idaho National ...

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize (Zea mays L.)

Maize is an important grain crop in the United States and worldwide. However, maize grain must be processed prior to human consumption. Furthermore, whole grain composition and processing characteristics vary among maize hybrids and can impact the quality of the final processed product. Therefore, in order to produce healthier processed food products from maize, it is necessary to know how to optimize processing parameters for particular sets of germplasm to account for these differences in grain composition and processing characteristics. This includes a better understanding of how current processing techniques impact the nutritional quality of the final processed food product. Here, we describe a microscale protocol that both simulates the processing pipeline to produce cornflakes from large flaking grits and allows for the processing of multiple grain samples simultaneously. The flaking grits, the intermediate processed products, or final processed product, as well as the corn grain itself, can be analyzed for nutritional content as part of a high-throughput analytical pipeline. This procedure was developed specifically for incorporation into a maize breeding research program, and it can be modified for other grain crops. We provide an example of the analysis of insoluble-bound ferulic acid and p-coumaric acid content in maize. Samples were taken at five different processing stages. We demonstrate that sampling can take place at multiple stages during microscale processing, that the processing technique can be utilized in the context of a specialized maize breeding program, and that, in our example, most of the nutritional content was lost during food product processing.

High-throughput, Microscale Protocol for the Analysis of Processing Parameters and Nutritional Qualities in Maize Zea mays L.

Here, we present a microscale protocol for processing grain samples and for incorporating this microscale approach into a high-throughput analytical pipeline. This is a higher throughput adaptation of currently available protocols.

Maize is an important grain crop in the United States and worldwide. However, maize grain must be processed prior to human consumption. Furthermore, whole ...

Design and Use of an Apparatus for Quantifying Bivalve Suspension Feeding at Sea

As shellfish aquaculture moves from coastal embayments and estuaries to offshore locations, the need to quantify ecosystem interactions of farmed bivalves (i.e., mussels, oysters, and clams) presents new challenges. Quantitative data on the feeding behavior of suspension-feeding mollusks is necessary to determine important ecosystem interactions of offshore shellfish farms, including their carrying capacity, the competition with the zooplankton community, the availability of trophic resources at different depths, and the deposition to the benthos. The biodeposition method is used to quantify feeding variables in suspension-feeding bivalves in a natural setting and represents a more realistic proxy than laboratory experiments. This method, however, relies upon a stable platform to satisfy the requirements that water flow rates supplied to the shellfish remain constant and the bivalves are undisturbed. A flow-through device and process for using the biodeposition method to quantify the feeding of bivalve mollusks were modified from a land-based format for shipboard use by building a two-dimensional gimbal table around the device. Planimeter data reveal a minimal pitch and yaw of the chambers containing the test shellfish despite boat motion, the flow rates within the chambers remain constant, and operators are able to collect the biodeposits (feces and pseudofeces) with sufficient consistency to obtain accurate measurements of the bivalve clearance, filtration, selection, ingestion, rejection, and absorption at offshore shellfish aquaculture sites.

A flow-through device for using the biodeposition method to quantify filtration and feeding behavior of bivalve mollusks was modified for shipboard use. A two-dimensional gimbal table built around the device isolates the apparatus from boat motion, thereby allowing the accurate quantification of bivalve filtration variables at offshore shellfish aquaculture sites.

As shellfish aquaculture moves from coastal embayments and estuaries to offshore locations, the need to quantify ecosystem interactions of farmed bivalves ...

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

As individuals increasingly live in cities, methods to study their everyday movements and the data that can be collected becomes important and valuable. Eye-tracking informatics are known to connect to a range of feelings, health conditions, mental states and actions. But because vision is the result of constant eye-movements, teasing out what is important from what is noise is complex and data intensive. Furthermore, a significant challenge is controlling for what people look at compared to what is presented to them.
The following presents a methodology for combining and analyzing eye-tracking on a video of a natural and complex scene with a machine learning technique for analyzing the content of the video. In the protocol we focus on analyzing data from filmed videos, how a video can be best used to record participants&#39; eye-tracking data, and importantly how the content of the video can be analyzed and combined with the eye-tracking data. We present a brief summary of the results and a discussion of the potential of the method for further studies in complex environments.

The objective of the protocol is to detail how to collect video data for use in the laboratory; how to record eye-tracking data of participants looking at the data and how to efficiently analyze the content of the videos that they were looking at using a machine learning technique.

As individuals increasingly live in cities, methods to study their everyday movements and the data that can be collected becomes important and valuable. ...

Separation of Spinach Thylakoid Protein Complexes by Native Green Gel Electrophoresis and Band Characterization using Time-Correlated Single Photon Counting

The light reactions of photosynthesis are carried out by a series of pigmented protein complexes in the thylakoid membranes. The stoichiometry and organization of these complexes is highly dynamic on both long and short time scales due to processes that adapt photosynthesis to changing environmental conditions (i.e., non-photochemical quenching, state transitions, and the long-term response). Historically, these processes have been described spectroscopically in terms of changes in chlorophyll fluorescence, and spectroscopy remains a vital method for monitoring photosynthetic parameters. There are a limited number of ways in which the underlying protein complex dynamics can be visualized. Here we describe a fast and simple method for the high-resolution separation and visualization of thylakoid complexes, native green gel electrophoresis. This method is coupled with time-correlated single photon counting for detailed characterization of the chlorophyll fluorescence properties of bands separated on the green gel.

Here we present a protocol to separate solubilized thylakoid complexes by Native Green Gel electrophoresis. Green gel bands are subsequently characterized by Time Correlated Single Photon Counting (TCSPC) and basic steps for data analysis are provided.

The light reactions of photosynthesis are carried out by a series of pigmented protein complexes in the thylakoid membranes. The stoichiometry and ...

An Optimized Rhizobox Protocol to Visualize Root Growth and Responsiveness to Localized Nutrients

Roots are notoriously difficult to study. Soil is both a visual and mechanical barrier, making it difficult to track roots in situ without destructive harvest or expensive equipment. We present a customizable and affordable rhizobox method that allows the non-destructive visualization of root growth over time and is particularly well-suited to studying root plasticity in response to localized resource patches. The method was validated by assessing maize genotypic variation in plasticity responses to patches containing 15N-labeled legume residue. Methods are described to obtain representative developmental measurements over time, measure root length density in resource-containing and control patches, calculate root growth rates, and determine 15N recovery by plant roots and shoots. Advantages, caveats, and potential future applications of the method are also discussed. Although care must be taken to ensure that experimental conditions do not bias root growth data, the rhizobox protocol presented here yields reliable results if carried out with sufficient attention to detail.

Visualizing and measuring root growth in situ is extremely challenging. We present a customizable rhizobox method to track root development and proliferation over time in response to nutrient enrichment. This method is used to analyze maize genotypic differences in root plasticity in response to an organic nitrogen source.

Roots are notoriously difficult to study. Soil is both a visual and mechanical barrier, making it difficult to track roots in situ without destructive ...

Manufacturing Simple and Inexpensive Soil Surface Temperature and Gravimetric Water Content Sensors

Quantifying temperature and moisture at the soil surface is essential for understanding how soil surface biota respond to changes in the environment. However, at the soil surface these variables are highly dynamic and standard sensors do not explicitly measure temperature or moisture in the upper few millimeters of the soil profile. This paper describes methods for manufacturing simple, inexpensive sensors that simultaneously measure the temperature and moisture of the upper 5 mm of the soil surface. In addition to sensor construction, steps for quality control, as well as for calibration for various substrates, are explained. The sensors incorporate a Type E thermocouple to measure temperature and assess soil moisture by measuring the resistance between two gold-plated metal probes at the end of the sensor at a depth of 5 mm. The methods presented here can be altered to customize probes for different depths or substrates. These sensors have been effective in a variety of environments and have endured months of heavy rains in tropical forests as well as intense solar radiation in deserts of the southwestern U.S. Results demonstrate the effectiveness of these sensors for evaluating warming, drying, and freezing of the soil surface in a global change experiment.

Accurately measuring temperature and water content of the upper 5 mm of the soil surface can improve our understanding of environmental controls on biological, chemical, and physical processes. Here we describe a protocol for manufacturing, calibrating, and conducting measurements with soil surface temperature and moisture sensors.

Quantifying temperature and moisture at the soil surface is essential for understanding how soil surface biota respond to changes in the environment. ...