So I usually don’t provide news about me, but this is an opportunity to thank my awesome departmental and faculty colleagues, so I’ll do so here.
I found out today I was awarded a Distinguished Scholar’s Award by my Faculty (Mathematics and Sciences). When your colleagues start emailing you congratulations, I guess you begin to take notice that something is happening. As always, when you read the nice things people say about you, it causes some self-reflection and uncertainty, but I’ll run with the peer recognition and thank my colleagues and acknowledge my own students and various collaborators who are as much part of any recognition as I would be.
Also some good news, my departmental colleague, Dr. Lori MacNeil won recognition for her outstanding teaching, both from the faculty level (Distinguished Teaching Award) and from the students (Math and Science Council Excellence in Teaching & Student Engagement Award). Congratulations Lori. It’s nice when you work with someone and see how they teach and I can attest that this is well deserved!
These are challenging times for everyone, but news like this brightens the day, and I simply want to acknowledge that whomever nominated me for this has themselves to thank as well, since I am working alongside some supportive scholars who care about our students, our research, and our involvement in public work. Clearly, I owe somebody a beer or two.
We devised a simple way to prevent gaping (i.e. temporary and rapidly reversible) and examined how strongly this influenced thermoregulatory behaviours. Interestingly, although it did significantly lower thermal selection / thermal preference behaviour the effect was quite small. We also saw some interesting changes in heat orientation behaviour. Animals that were not able to gape behaved more randomly with respect to postural orientation, whereas the control lizards tend to shy away from orienting to hot temperatures (i.e. the definition of thermoregulation is to exhibit a corrective response when moving outside the set-point range).
Alas, we don’t have any cool images to share from this study, but consider looking at some of our other papers here and here where we have examined evaporative water loss and thermal imaging in bearded dragons.
The article is part of a special issue honouring Dr. Peter Frappell, a friend and colleague in respiratory and thermoregulatory physiology. Thanks Frapps for all your input and support!
Congratulations to Ian Black for getting this published and thanks to Dr. Laura Aedy for her early work on this project.
We’re happy to share news that our paper (shareable link) was recently accepted in Methods in Ecology and Evolution! As for the nitty gritty secrets of the study, that headline was to get your attention! I’ll point out some highlights here, with some visuals to summarize the main results.
The study resulted from a visiting PhD student (Núria Montmany-Playà, @NuriaBeachy) to the lab in January 2020, immediately prior to the pandemic and lock-down. Sadly, some of the research we planned to do was not possible due to travel restrictions and embassies immediately calling back their citizens. So, this represents about the only kind of research my lab is capable of doing during a lockdown.
So, what is the paper about? Technically, it’s a methods (thus the journal choice!) and resource paper (information on empirical measurements of emissivity which I often get from colleagues) with a warning to field thermographers to “check your distance“! (Actually, Faye et al 2016 already advised this, but given various interactions I have had over the past few years, some field thermographers might not be reading all the literature – so please read Faye et al’s paper cited at the bottom of this blog).
It has become increasing common in some animal thermography studies to capture the maximum temperature from a specific region of the body (often the face or the eye) and use this as an estimate of body temperature (it is not a good estimate!) or as a proxy for vasoreactivity related to stress (which it is a better indicator of, although depends on species and body part). This effect of distance was earlier studied by Faye et al (2016), although we focus on the challenges in animal thermography in the field where animals control how close you can get! Watch the video below for the effect in action, paying attention to the maximum temperature in the eye region and how it changes as this Trumpeter swan walks closer (starting from ~15 m) and closer (~2 m) to the camera:
Here are the results plotted over time as an animated plot:
As the bird gets closer, the maximum temperature of the same animal rises from a low of ~25C to reach closer to a more realistic value of ~33C. This is an enormous range and cannot reasonably be based on vasoreactivity, when the physics of thermal optics can explain this. Read the paper for the explanation behind how spot size and distance effects interact to produce fairly large errors in thermography. Controlling for distance and/or being aware of its influences is important to any field application of thermography. I doubt too many people are trying to image animals at 10 m away given the low resolution of many thermal cameras, but as prices come down and people take their devices into the field, I am sure researchers will run into these situations.
We measured the technical error using a blackbody calibration source placed at different distances from the camera, and tested under different conditions. Below, Tb refers to the blackbody temperature (i.e. true temperature) and Ta refers to prevailing ambient temperature. Delta T refers to how the thermal camera estimates the same black body temperature (where 0 refers to the closest distance measurement). We tested this with different camera/lens combinations to give insight into how the devices function even under well controlled conditions, far different from most field applications. You can see below that the error in temperature can be as poor as 6C below actual temperature at 10 m distance, and these results are for measurements of an electronic calibration source.
We also published some simple results on how the angle of incidence influences thermography measurements (see image above of the puffin). This is also well known by thermal imaging engineers and physicists, but not likely well appreciated by biologists, so we used a Leslie cube (see image below) and adhered various bits of biological materials to a copper surface, heated the surface up and painstakingly measured the temperature, allowing us to calculate the apparent emissivity. Objects at steep angles of incidence to the camera will have a lower emissivity, which means that the apparent radiation we measure is actually a lot of background reflected radiation, and thus is a source of possible error in field thermography.
So the main message of the paper is to keep good records of how far your camera is from your object of study. Correcting for this effect is complex and beyond the scope of our study, although we report that the potential error of being 10 meters away from an object can be as high as 4 to 6C, with similar errors for measuring objects at angles greater than 50 degrees incidence.
Many thanks to Núria for her hard work on this project. Without her visit, we would not have done this. And we also want to thank the two reviewers for their hard but fair questions but also for listening to our response.
Lockdown science had me borrowing plant material from my parents, Clifford and Brenda Tattersall, so their help was crucial to the final acceptance of the manuscript!
Montmany-Playà, N. and Tattersall, GJ. 2021. Spot size, distance, and emissivity errors in field applications of infrared thermography. Methods in Ecology and Evolution.https://doi.org/10.1111/2041-210X.13563
Please consider taking part of an open repository initiative of thermal images hosted at the following website: https://trench-ir.azurewebsites.net/. If you are acquiring thermal images of plants, animals, or their environment using FLIR cameras, we would like you to share your images as part of this initiative. We welcome images from research grade cameras or from hand-held mobile phone provided the images are radiometric jpgs.
Infrared imagery offers a unique opportunity to see biophysical properties in real time. We can watch organisms heat up, cool down, and generally transfer heat back and forth throughout their environment. In the TrEnCh-IR Project, we use infrared imagery to help people see the world from a thermal perspective because we believe it’s an intuitive first step to understanding microclimate and the impacts of warming.
The TrEnCh-IR project is part of a larger initiative interested in Translating Environmental Change into organismal responses. Our goal is to build case studies of how animals are impacted by climate change to improve our approach to climate change biology education, policy, and research.
FLIR cameras are extensively used, increasingly so with the availability of FLIR thermal cameras that attach to phones. However, the cameras produce images in a non-standard format (radiometric jpgs) and analyzing the images requires purchasing expensive FLIR software. Project collaborator Tattersall has produced an open source R package (ThermImage, https://github.com/gtatters/Thermimage) that converts the images into standard formats and extracts additional data to allow analysis in commonly used and open source software such as ImageJ. Our web service makes these tools more accessible. We aim to empower more people to view the world from a thermal perspective.
Our thermal image repository will allow researchers to analyze the surface temperatures of disparate organisms in diverse environments. Education and outreach resources promote understanding how organisms experience their environment. We aim to maintain the repository long term, but can not guarantee longevity at this point. An ongoing aim is to use initial AI algorithms, potentially combined with crowd sourced landmarking, to distinguish organisms, particular body parts, and backgrounds. Our interface will allow users to explore the images to understand how organisms interact with their environments.
Currently most analyses of the impacts of climate change on organisms are based on air temperatures, but body temperatures of ectotherms can differ from air temperatures by tens of degrees. Additionally, the characteristics and behaviours of organisms can result in their experiencing different body temperatures even in the same environment with repercussions for species interactions. Moving beyond air temperatures to consider body and surface temperatures may thus be essential to accurately forecasting climate change impacts. Thermal images provide compelling visual examples of why we need to move beyond air temperatures in examining climate change impacts as well as data that can inform approaches for modelling how organisms interact with their environment.
Dr. Lauren Buckley, University of Washington, Professor
Abigail Meyer, Lead Developer & University of Washington Research Scientist
Dr. Glenn Tattersall, Brock University Professor & Thermal Biologist
Site development based with University of Washington, in the Department of Biology.
This speaks to ~8 years of winter efforts to test this technique out in the lab and in the field! Hopefully we will publish soon. I’m not used to the CBC scooping us, but they don’t have to write the manuscripts and do the stats, so maybe I can excuse them.
These efforts are all related to a head starting project initiated by Anne Yagi on the Massasauga, taking physiological and behavioural data performed in careful lab experiments, testing these for 1-2 winters, then expanding to larger sample sizes in subsequent years to lead to the 9 minute videos above.
Many thanks to 8Trees Inc, Anne Yagi, Dr. Katharine Yagi and all of the animal care and field assistants that make Anne’s work possible (Theresa Bukovics, Tom Eles, Shawn Bukovac, Matt Jung, and many others).
At long last, resulting from herculean efforts of a number of former students, our paper is published. Out today in Royal Society Open Science, our paper entitled: “Hydrogen sulfide exposure reduces thermal set point in zebrafish” represents the efforts of two honours students (JC Shaw and CD Dobell) and the writing and analytical skills of a great PDF and colleague (DA Skandalis).
Here is a link to the study and full citation:
Skandalis DA, Dobell CD, Shaw JC, Tattersall GJ. 2020 Hydrogen sulfide exposure reduces thermal set point in zebrafish. R. Soc. Open Sci. 7: 200416.
We tested whether dissolved H2S in the water will alter thermal preference. Previously, work in mice has suggested that mice could be induced to adopt a “hibernation-like” state, although there was some doubt (in the literature) as to whether H2S signalled a change in thermoregulatory state or simply acted as a metabolic inhibitor. By testing this in zebrafish, we could test formally whether they prefer cooler temperatures with H2S exposure, and they did. Not only did they choose to cool down, but they continued to make thermoregulatory decisions, swimming back and forth between cool and warmer water, suggesting they are still making thermoregulatory decisions and not simply caught in the cold water. So…yeah, complicated. H2S might induce a behavioural anapyrexia (a lowered thermal set-point). We discuss the potential environmental and neurophysiological context in the paper for those interested. The rotten egg reference is to the smell of H2S gas.
To conduct this study, we used a system built by Brock University’s Technical Services and employed in our research lab that allows us to track fish in a two chamber thermal shuttle box:
This setup allows us to heat and cool a tank and track the fish’s choices over time. Here is a thermal image depicting an earlier version of the shuttle box (correcting the spill over of warm-water in the centre can be corrected using baffles and a circular chamber system, but I haven’t taken a new picture with the thermal camera during the pandemic lockdown):
There was some considerable interest in developing H2S as a therapeutic to put mammals and/or tissues/organs into a suspended state. It is intriguing that animals like zebrafish that can behaviourally regulate body temperature continue to do so under this exposure. Anaprexic stategies are commonly seen in ectotherms and perhaps by hijacking an innate signalling system, H2S evokes this response.
Congratulations, Nick! We just heard from the Faculty of Graduate Studies that Nick has received the 2020 Fall Distinguished Graduate Student Award for his MSc project. A well deserved award and a credit to Nick’s hard work.
Working from home during the COVID19 pandemic has proven a challenge for many of us. Our students are not allowed to pursue their research, and yet most of us are working as hard from home as we would be on campus.
Anyhow, at the beginning of the lockdown, I gathered what equipment I could from the lab and set up to research at home. Hardly a serious pursuit, but I was designing some training material for an overseas student and needed the equipment at home anyway.
What kind of research can you do on yourself on lockdown you might ask? A little bit of thermal imaging!
Maybe it is because imaging is appealing to people and it is compelling that a region of the face or head close to the eye always appears warm, so naturally people assume it might represent or correlate to core measurements of body temperature. Indeed, even in animal thermal biology, this is one of more common questions people ask me: “Can eye temperature be an estimate of core temperature?”. To which I quip, “No”, with caveats.
I am not aware of systematic studies demonstrating that these warm eye/head surface temperatures are really good at estimating core temperature, but we previously measured core and surface temperatures in a previous study of ours in ducklings across different times of day and during a period of fasting:
Core temperature rises and falls quite substantially (ranging from ~39 to ~42C) across these different states and time periods:
But the correlation between core temperature and the maximum eye region temperature is not that great. Indeed, you would expect the values to fall along or at least be parallel to the dotted line of equality below, but in the case of low air temperatures, the relationship is quite poor and the surface temperatures are much cooler than core.
So, it is easy to conclude that max eye temperature is not always a reliable indicator of core temperature in these ducks. Maybe we could derive an empirical calibration curve, taking into account air temperature, but the point is that this requires accurate temperature data and stable environmental conditions rarely present in the field.
So, what about the lockdown research mentioned earlier?
Having plenty of time to myself, I set up a thermal camera to capture a thermal image of my face at various times throughout the day to capture the natural variation in body temperature (no fever, per se, but my daily oral temperature measurements range from 35.6 to 36.9C). Here is a sample image, outlining the typical regions of skin surface measured:
It got too scary the longer I was in lockdown as my hair grew too long and disheveled, so I ceased the experiment after only a short period.
But the results (next 3 graphs) below show how poorly forehead assesses normal oral temperature, and even how maximum eye temperature is ~1C cooler than oral temperature and influenced by nearby ambient conditions (my garage was cold back in April so I set up there for a few measurements).
Conclusions: Ignoring the N=1 subject (due to the pandemic this seems justified), forehead is a poor measure of oral temperature (3.85C too low), maximum eye temperature a bit better (but still 1C too low and affected by air temperature), while simply pointing the camera in your mouth and getting maximum temperature yields a temperature ~0.5C higher than an oral thermometer.
So, why if you look at any images of companies and airports doing fever scanning they point devices at people’s foreheads or relying on a single pixel value from possibly the eye region?
The simple answer is that it is easy to do, but from a target accuracy perspective, it is terrible, especially if low accuracy devices are being employed inappropriately. In the one image above, the device is pointing at someone’s hair, which shields the skin and thus produces a cooler value. Cooler values will not trigger a fever detection even if it is there!
So, wherefore is the future of fever scanning? Intuitively, it seems it should work, but are we measuring the wrong thing? Why don’t we measure inside the mouth where normal oral thermometers do? At least this is better than crappy forehead measurements. Is this a privacy issue? Is it feasible to do in rapid scanning processes? Will it be feasible if we are all wearing masks?
I am not the first to write on this. This blog project was mostly a distraction in the early days of lockdown to keep my mind off the situation. I attach a few key articles and opinion pieces on the subject below that have commented more clearly on the connection (or lack) between fever and infection and why fever screening is not a panacea.
Links to further reading:
Scientific studies demonstrating reasonable predictive power for fever scanning:
I think this situation really needs another look by the physiology community. My anecdote here is simply based on self reflection/measurement but also based on years of experience with thermal imaging.
The first rule of thermal imaging in biological systems: “Surface Temperature is not equal to Core Temperature.” We can’t forget that. If you want to use surface temperature, you have to do a lot of calibration checks or have very good control over your subject.
In case a grad student locked down at home wants a writing project, here are a few key points that I know should impact the predictive power of max surface temperature measurements in the context of rapid fever scanning in public places:
Air temperature near skin
Air flow (convective heat exchange) over skin
Blood flow relationship with the skin surface
Camera user skills and training
Quality and accuracy of the thermal scanner (some scanners I see people using have accuracies of +-2 to 4C).
Pre-symptomatic people lack fever
Masking of fever with antipyretic drugs undetected by scanning
What is the precise correspondence of eye canthus temperature with core temperature measurement?
Although not an inspiring or catchy title, our study has just been published, demonstrating that fixed-frame video capture can provide a quantitative assessment of energetics (citation below).
Summary of the study
Infrared thermal imaging is a passive imaging technique that captures the emitted radiation from an object to estimate surface temperature, often for inference of heat transfer.
Infrared thermal imaging offers the potential to detect movement without the challenges of glare, shadows, or changes in lighting associated with visual digital imaging or active infrared imaging.
In this paper, we employ a frame subtraction algorithm for extracting the pixel-by-pixel relative change in signal from a fixed focus video file, tailored for use with thermal imaging videos.
By then cumulatively summing the sd for each frame across an entire video, we are able to assign quantitative activity assessments to thermal imaging data for comparison with simultaneous recordings of metabolic rates. We tested the accuracy and limits of this approach by analyzing movement of a metronome (see above) and provide an example of the approach to a study of Darwin’s finches.
In principle, this “Difference Imaging Thermography” (DIT) would allow for activity data to be standardized to energetic measurements and could be applied to any radiometric imaging system.
Fixed frame is required. Changing the reference frame or using a camera without a tripod would not work unless you do a lot of motion correction. Also, we used infrared thermal imaging because we were collecting data for a different purpose (still writing those up!), but we think that any sort of imaging should work, provided it produces a simple, ratiometric or radiometric image. Usually monochrome cameras or near infrared cameras produce a signal that is a simple greyscale image. Reflected light might interfere with the approach, so this is why we argued this might be an addition use of thermal imaging videos.
Behind the scenes
This paper took a long time to put together, but was the beginning of my lab’s journey into R, ImageJ, and open source coding. A lot has changed since I started the data analysis. Combing through 500 Gb of video files, extracting, processing, converting them into something manageable took the better part of 2 weeks on a supercomputer, until I realised that there were more efficient routes!
We have since created routines in ImageJ that help facilitate the conversions and have placed those routines in a github repository, where we will write them up as a methods paper in the future. The principles outlined in the paper are not themselves novel. Sliding average and frame subtraction routines are common in video processing software. Assessing whether the motion captured is correlated with meaningful biological information is what we hoped to capture with the study.