PhotosynQ Website Update and New Features

We’ve updated the PhotosynQ website and added many new features to improve your user experience!

Some of the new features include: expanded plotting, mapping and graphing data options, improving the user page to help users better track their PhotosynQ projects, protocols and macros, and adding more content to the tutorials and help center.

We will be detailing these new features in blog posts over the next two weeks, but let me briefly explain a couple of the updates:

Tutorials

Two area’s where new PhotosynQ users seem to struggle are with creating robust projects and cleaning up their data prior to data analysis.

Project Creation

Since we began shipping the MultispeQ v1.0’s over 9 months ago we have seen a drastic increase in the number of PhotosynQ projects that are being created. Which is really exciting! Occasionally, we’ll check out new projects to figure out how people are doing with the platform and try to come up with ways to improve the PhotosynQ experience. We have noticed that setting up projects has been a problem for many users. The most common problems are choosing the wrong measurement protocol or not asking good project questions, which then makes filtering, analyzing and interpreting data difficult.

So we have added a Project creation tutorial to better guide users through the process.

Data Quality

Recently we published a blog post about how to Flag data to improve data quality. This is an important step in getting the most out of your PhotosynQ project. In our default Leaf Photosynthesis MultispeQ v1.0 and Photosynthesis RIDES protocols we add issue warnings that help identify measurements that may have technical issues, but many people are unfamiliar with what these mean. So we have also added a Data Quality tutorial to explain issue warnings and data flagging.

Help Center

If you have ever visited the help center you may have noticed a little poll at the bottom of each post asking if the article was helpful or not. Well, believe it or not, clicking those buttons actually matters! We have updated a number of help articles to be more helpful and have added numerous articles.

That’s it for today, but stay tuned! More details about new features will be coming soon!

 

Flagging Data to Improve Data Quality

Improve your PhotosynQ projects by getting rid of low quality measurements with the flagging data feature.

 

Whether you are taking measurements in the field or in the lab, it is not uncommon for a handful of measurements to be returned to you with an issue warning. There are a variety of warnings you might receive, red issue warnings suggest that a measurement might be bad, while yellow and blue issue messages simply provide information about the measurement. You may find it is necessary to remove the measurements with red issue warnings from a project before you can properly analyze your data.

WHY

Bad measures can happen for many reasons, maybe someone had shaky hands or the leaf was dead. Or maybe the leaf was not totally covering the light guide, many  of these things can be attended to and fixed if you follow our Best Measurement Practices. It could be our fault, your fault, or maybe natures. All of these will return issue warnings that something went wrong during your measurement. You always have the choice to discard and redo the measurement in the field, however this can be easy to overlook. If you go through your experiment, accept everything and later realize you may have made some mistakes, we allow you to flag this data later in the Data Explorer. Flagging data DOES NOT delete the measurements from the website, but rather hides them. Flagged data can always be viewed by clicking the “Include flagged datasets” box in the Add Series tab, so don’t worry!

FlagDataBlogpic2Including flagged data in your data series

HOW

Flagging your data could not be any easier in the Data Viewer at photosynq.org. All you will have to do is select a single suspect point (1), either from a scatter plot or the table. Look at the data point in the single data view, and see if there were any issue warnings (2). If you determine this point is no good and you’d like to flag it, under the name of the person who submitted the data point, you will see a tab for Issues. Click here, write up your issue in the Reason for Flagging dialog box and click Submit to quickly flag a data point (3). It is important to remember that when you flag a data point you are hiding it from your analysis, NOT deleting it entirely, don’t worry, we never delete your data.

CIRCLEBADBADBAD(1) This measurement’s SPAD (relative chlorophyll content) is very low, we can take a closer look at it by clicking on the data point

BADTRACE

(2) Here we can see that the macro output an issue warning when the measurement was taken. Even if there is no issue warning, you may spot a problem with the measurement by examining the measurement trace. In this case it appears that someone failed to completely cover their entire light guide!

GOODTRACE

For reference, this is a good, average trace for the Leaf Photosynthesis MultispeQ V1.0

owowoww

(3)Flagging data requires a reason, this helps everyone involved be on the same page

The Difference Flagging Makes

When you a flag a data point, you are essentially putting them aside, not to be considered when you run analysis, so it is easy to see how your outcomes might be different depending on if you flagged your data or not.

phi2AVGwflag.png

First, let’s look at data for a project that includes every data point. This is what you might see in your project after a day in the field and no work done to your data yet. You can see for the Phi2 parameter that the standard error is very large, even reaching into the negatives. Since plants can’t have a negative Phi2, it indicates some work on the data needs to be done before we can be confident in the results. If you went through and flagged bad measurements, you will end up with a better graph like the one below.

phi2AVGnoflag

After flagging all of the measurements that either had issue warnings or had obvious problems, by looking at the measurement traces, we get a very different graph. Even after flagging the poor quality measurements we still see large standard error bars. Remember, photosynthetic parameters are highly dependent on ambient conditions such as light intensity, time of day and temperature. We recommend using multivariate analysis to account for these factors, for more tips on data analysis, check out our tutorials here.

Issue warnings can occur on exceptional leaves, such as ones with a very dark deep green colour, and still be fine, so it is important to examine them, rather than just flag whole chunks. Flagging your data where appropriate will make your data tighter and more true to what was observed in your experiment. This is useful when interpreting your results because you are not being bogged down by faulty measurements, which in turn should make statistical analysis of your results much better!

Read More »

New PhotosynQ Related Publication

Check out the new publication in the American Journal of Plant Sciences, using the MultispeQ and PhotosynQ Platform (10.4236/ajps.2017.84050)

Response of Cowpea Genotypes to Drought Stress in Uganda

Saul Eric Mwale, Mildred Ochwo-Ssemakula, Kassim Sadik, Esther Achola, Valentor Okul, Paul Gibson, Richard Edema, Wales Singini, Patrick Rubaihayo

Moisture stress is a challenge to cowpea production in the drought prone areas of eastern and north eastern Uganda, with yield losses of up to 50% reported. Genotypes grown by farmers are not drought tolerant. This study was therefore, undertaken at Makerere University Agricultural Research Institute Kabanyolo to identify cowpea genotypes tolerant to drought. Thirty cowpea accessions comprising of Ugandan landraces and released varieties, Brazilian lines, Makerere University breeding lines, elite IITA germplasm and seven IITA drought tolerant lines as checks were screened for drought tolerance at vegetative and reproductive stages. The experiment was designed as a 2 × 37 factorial and laid out in a split-plot arrangement, 37 genotypes of cowpea at two soil moisture stress levels (T1, no stress and T2, severe stress) with all factorial combinations replicated two times in a screen house. The genotypes showed considerable variability in tolerance to drought. Genotypes were significantly different for chlorophyll content (P ≤ 0.01), efficiency of photosystem II (P ≤ 0.05), non-photochemical quenching (P ≤ 0.05), recovery (P ≤ 0.01), delayed leaf senescence (P ≤ 0.01), grain yield (P ≤ 0.01), 100 seed weight (P ≤ 0.05), number of pods per plant and number of seeds per pod (P ≤ 0.001). There was a highly significant positive correlation between chlorophyll content and efficiency of photosystem II (r = 0.75, P ≤ 0.001) implying that chlorophyll content and efficiency of photosystem II could be used as efficient reference indicators in the selection of drought tolerant genotypes. Genotypes SECOW 5T, SECOW 3B, SECOW 4W, WC 30 and MU 24 C gave relatively high yields under stress and no stress conditions, maintained above mean chlorophyll content, efficiency of photosystem II and had good recovery scores from stress and thus were tolerant to drought stress induced at both vegetative and reproductive stages.


More PhotosynQ related publications are available here

Building Strong Research Collaborations

If you have ever visited the PhotosynQ webpage (you’re reading the PhotosynQ blog, so I’ll assume you have), you know that the banner across our homepage reads “Truly Collaborative Plant Research.”

We have always aspired to making PhotosynQ a flexible platform to accommodate many forms of collaboration. For example, we hope the open nature of PhotosynQ data combined with built-in discussion tools will foster communication and collaboration between researchers across the globe.

I recently returned from Uganda, where I conducted some training workshops and (hopefully) established a long-term collaboration between PhotosynQ and the Makerere University Regional Centre for Crop Improvement (MaRCCI). Before my trip, MaRCCI had a few MultispeQ devices, a few students had used PhotosynQ, and they have even published a few papers. However, until now, there has been little direct communication between MaRCCI and PhotosynQ.

After spending 2 days together, learning how to create robust projects and collect, analyze and interpret quality photosynthesis data, we hope to develop a much stronger collaboration.

MaRCCI pic for blog

What does ‘stronger’ collaboration mean?

MaRCCI already has access to PhotosynQ’s low-cost, cutting edge phenotyping technologies and platform for data storage, visualization and management. Building a stronger collaboration means giving MaRCCI students and faculty the opportunity to work directly with the PhotosynQ team to analyze the links between complex photosynthesis phenotypes and crop outcomes (this requires sharing of outcome data such as yield, disease resistance, etc). It also brings MaRCCI into the PhotosynQ development workflow. So, as we continue to work on automating advanced analytical tools like multivariate analysis, prediction and QTL mapping we will work closely with MaRCCI students and faculty to make sure that what we are developing will solve their problems.

On the flip side, collaboration with MaRCCI offers PhotosynQ some great benefits. MaRCCI recently received support from the World Bank as an “African Higher Education Centre of Excellence” in plant breeding and related activities. This means that they are positioned to be a hub of plant breeding training for breeding programs throughout sub-Saharan Africa. They receive Masters and PhD students from 20 different countries, and while I was there I met students from Benin, Burundi and Tanzania, just to name a few. Such a diverse group of students allows us to disseminate PhotosynQ technology over a wide geographical area, and have much greater impact.

The end result, we hope, will be a long-term partnership that improves the education and capacity of young plant breeders across Africa and helps PhotosynQ continue to evolve as an advanced phenotyping platform.

Personally, I look forward to continuing the work with a great institution with enthusiastic students and faculty.

 

 

New PhotosynQ Related Publication

Check out the new publication in Photosynthesis Research, using the MultispeQ and PhotosynQ Platform (10.1007/s11120-017-0449-9)

Faster photosynthetic induction in tobacco by expressing cyanobacterial flavodiiron proteins in chloroplasts

Rodrigo GómezNéstor Carrillo, María P. Morelli, Suresh Tula, Fahimeh Shahinnia, Mohammad-Reza Hajirezaei, Anabella F. Lodeyro

Plants grown in the field experience sharp changes in irradiation due to shading effects caused by clouds, other leaves, etc. The excess of absorbed light energy is dissipated by a number of mechanisms including cyclic electron transport, photorespiration, and Mehler-type reactions. This protection is essential for survival but decreases photosynthetic efficiency. All phototrophs except angiosperms harbor flavodiiron proteins (Flvs) which relieve the excess of excitation energy on the photosynthetic electron transport chain by reducing oxygen directly to water. Introduction of cyanobacterial Flv1/Flv3 in tobacco chloroplasts resulted in transgenic plants that showed similar photosynthetic performance under steady-state illumination, but displayed faster recovery of various photosynthetic parameters, including electron transport and non-photochemical quenching during dark–light transitions. They also kept the electron transport chain in a more oxidized state and enhanced the proton motive force of dark-adapted leaves. The results indicate that, by acting as electron sinks during light transitions, Flvs contribute to increase photosynthesis protection and efficiency under changing environmental conditions as those found by plants in the field.


More PhotosynQ related publications are available here

Intelligent Information Technologies in Education and Science – Ukraine

 

On October 18, 2017, the Interdisciplinary Workshop on the dissemination of knowledge on “Intellectual Information Technologies in Education and Science” took place at the Faculty of Chemistry and Biology of the Ternopil National Pedagogical University (TNPU).

The co-organizers of this event were Andriy and Natalia Hertz, employees of the Department of General Biology and Methodology of Natural Sciences Teaching and the Department of Botany and Zoology (Faculty of Chemistry and Biology of TNPU).

According to the program, a demonstration of the possibilities of IT solutions in biological, educational and pedagogical research took place. 

In particular, the on-line PhotosynQ platform was presented as a web tool for an integrated assessment of the physiological state of plants. 

Information was disseminated on how the MultispeQ can measure, collect and analyze photosynthesis data in field and laboratory conditions.

The focus was on the openness and flexibility of the PhotosynQ platform and the development of educational tools through it, and more.

The students and faculty all wished to have the opportunity to work with MultispeQ and PhotosynQ and to evaluate the condition of plants for themselves.

More Info [English] | More Info [Original]

PhotosynQ measures more than just plants: A history of our forays into measuring soils

Over the past several years there has been quite a bit of interest in measuring soil properties, which makes sense given that most of the plants we care about grow in soil. In response to that interest, we have developed numerous PhotosynQ prototypes, protocols and macros to measure soils over the past two years.

We started by using the MultispeQ to measure soil active C using potassium permanganate. This method used a cuvette to measure a color change in solution. The problem with using colorimetry, however, is that it requires users to do wet chemistry in the field. We would rather avoid that.

Another approach was to measure in situ C mineralization as an indicator of soil health. We have built multiple iterations of in situ soil C chambers (below). In general, the results from these chambers were positive, but there were a few drawbacks. One is that the results were highly dependent on soil moisture content and temperature. Therefore, we would need to collect a lot of data at different moisture and temperature conditions to account for these variations, much like we need to collect photosynthesis data at multiple light intensities to account for the effect of light on Phi2, PhiNPQ and PhiNO. The second problem is that the prototypes were quite clunky, and generated a lot of funny looks around campus when they were half-buried with random wires hanging out. If enough people were interested in using the in situ chambers, we could make a few mechanical changes to make them look less like an IED.

Soil chambers

Going back to the drawing board, we brainstormed different ideas to simply assess soil health without having to take a lot of measurements or have multiple devices. This led us to develop a simple tool for measuring soil C mineralized from a sealed container. Using a “24-hour C mineralization burst” we can control for different temperature and moisture conditions by first air-drying, and then rewetting soil samples in quart jars. Then we use a syringe to sample headspace in the jar and inject it into a pass-through CO2 sensor.

C min

The technology is pretty simple, just a CO2 sensor connected to a microcontroller, loaded with PhotosynQ firmware. To demonstrate our new SoilspeQ, we worked with a professor at MSU and took soil samples from a field where she was testing the effect of cover cropping on soil quality and maize productivity.  We took many samples from different areas including soil that had mixed cover crops, soils without cover crops and some soil from the bare ground bordering the field plots. We also collected the soil from 1-5 cm and 5-10 cm deep, so we could see if there were differences between them. Check out the results here.

Our final approach to measuring soils is still in its early stages, but we are looking forward to see where it goes. We have teamed up with a professor at Colorado State University to develop microfluidics cards. The goal is to use reagent embedded cards to reduce in field wet chemistry and accurately measure key soil properties. We then use the MultispeQ or a version of the CoralspeQ to measure the color change. Our initial test, using Al3+ in solution at different concentrations is shown below.  

Al Fig