It’s tiring looking at yourself

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Picture of the authorFor many people Zoom fatigue is very real. It has been studied by Jeremy Bailenson at Stanford University [see ‘‘Zoom fatigue’ brought into focus by Stanford study‘ in the FT on February 26th, 2021]. He found that one source of tiredness was the high level of self-evaluation that arises from continually having to look at a video of yourself. Of course, it is easy to fix by choosing not to display your own video on your screen. We all have two egos: one is our subjectivity or the physical sensations registered by our body via our senses; and, the other is our reputation or the reflection of ourselves which forms our social identity [see ‘A reflection on existentialism‘ on December 20th, 2017]. Gloria Orrigi describes this second self as not a simple reflection but one that is ‘warped, amplified, redacted and multiplied in the eyes of others’. Perhaps it is hardly surprising that being constantly exposed to the view of ourselves being seen by others over a video conference raises our level of fatigue as we subconsciously and constantly review the impression being made on others. Orrigi describes our reputation as being like the trail left by snails has they slither over surfaces. Our social interactions with others leave deposits in their minds that become an information trail that we cannot erase and can only partially control. The pandemic has forced many of our social interactions to be via the internet which means they also leave electronic trails over which we have little control and cannot erase; however, we probably worry less about these traces than we do those left in the minds of others. Perhaps that is why our conversations lack spontaneity when conducted via a Zoom call [see ‘Distancing ourselves from each other‘ on January 13th, 2021] or maybe it’s just because it’s very difficult to gossip on a video conference even using the ‘chat’ function. Robin Dunbar has suggested that the real reason language evolved in humans is to allow us to gossip and thus maintain the social cohesion. If we are suffering from a loss of social cohesion caused by a lack of gossip then it is likely our stress levels would be raised causing us further fatigue. So, maybe we should be picking up our phones and calling people for a chat instead of scheduling meetings on Zoom.

The picture is the photograph of me that others see when I switch off my camera in an internet call.  It’s a selfie with which I am happy, for the moment anyway.

Sources:

Robin Dunbar, Grooming, gossip and the evolution of language, London: Faber and Faber, 1996.

Gloria Origgi, Reputation: what it is and why it matters, Princeton: Princeton University Press, 2018.

 

Collegiality as a defence against pandemic burnout

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photograph of a flower for decorative purposes onlyMany of my less experienced colleagues ask, ‘what is collegiality?’  Collegiality is the glue that holds universities together according to Neeta Baporikar.  While Roland S. Barth suggested that if students are to learn and develop, then their teachers must also learn and develop and collegiality is the set of practices and culture that support this adult growth.  In this context, Thomas Hoerr has proposed that collegiality has five components: (i) teachers talking about students with teachers; (ii) teachers working together to develop education programmes; (iii) teachers observing one another; (iv) teachers teaching each other; and (v) teachers talking about education and working together on committees.  Neeta Baporikar echoes this view by concluding that if we hope to teach students to participate, examine issues, collaborate, think critically and synthesise new approaches then we should be their model.  

In an environment where research is a priority, it is possible to substitute ‘researcher’ for ‘teacher’ in the descriptions above.  Then collegiality becomes researchers talking about [research] students, researchers working together to develop research programmes, researchers observing one another, researchers teaching each other, and researchers talking about research and working together on committees.  The idea that collegiality is a strategy for excellence holds as well as for research as it does for teaching.

The pressures on early career academics in a research university can be intense and the temptation to focus exclusively on delivering teaching and performing research can lead individuals to work in isolation and to neglect the opportunities provided by active engagement with their colleagues.  However, leaders must also take responsibility for creating an environment in which collegiality can thrive and encouraging active participation – it is part our service to the academic community as leaders to create and maintain a culture of scholarship and excellence [see ‘Clueless on leadership style’ on June 14th, 2017].  Neeta Baporikar provides steps that heads of departments can take to nurture collegiality, including providing a vision, encouraging collaborative participation, listening to diverse opinions, building on people’s strengths, and being aware of the world outside the department.  This is similar to the shepherding approach to leadership that I wrote about in May 2017 [‘Leadership is like shepherding’ on May 10th, 2017].  However, it has all become much more difficult in a pandemic – both collegiality and leadership.  Last week an article in Nature suggested that pandemic burnout is rife amongst academics working long hours in isolation to transpose and deliver their teaching materials online, to maintain their research without the spontaneity of face-to-face discussions with their team or collaborators, and to support the well-being and mental health of students who are also at risk of burnout.  It is suggested that burnout can be managed by finding a forum to express your feelings, creating ways to detach from stress, prioritizing and normalizing conversations about mental health, and fighting the isolation through meeting with peers.  These steps are a combination of traditional collegiality and the five ways to well-being: connect, be active, take notice, keep learning and give [see graphic in ‘On the impact of writing on well-being’ on March 3rd, 2021].

References

Neeta Baporikar, Collegiality as a strategy for excellence in academia, IJ Strategic Change Management, 6(1), 2015.

Roland Barth, Improving schools from within, Jossey-Bass, 2010.

Virginia Gewin, Pandemic burnout is rampant in academia, Nature, 591: 489-491, 2021.

Thomas R. Hoerr, Principal Connection: The Juggler’s Guide to Collegiality, Communication Skills for Leaders, 72(7): 88 -89, 2015.

From strain measurements to assessing El Niño events

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Figure 11 from RSOS 201086One of the exciting aspects of leading a university research group is that you can never be quite sure where the research is going next.  We published a nice example of this unpredictability last week in Royal Society Open Science in a paper called ‘Transformation of measurement uncertainties into low-dimensional feature space‘ [1].  While the title is an accurate description of the contents, it does not give much away and certainly does not reveal that we proposed a new method for assessing the occurrence of El Niño events.  For some time we have been working with massive datasets of measurements from arrays of sensors and representing them by fitting polynomials in a process known as image decomposition [see ‘Recognising strain‘ on October 28th, 2015]. The relatively small number of coefficients from these polynomials can be collated into a feature vector which facilitates comparison with other datasets [see for example, ‘Out of the valley of death into a hype cycle‘ on February 24th, 2021].  Our recent paper provides a solution to the issue of representing the measurement uncertainty in the same space as the feature vector which is roughly what we set out to do.  We demonstrated our new method for representing the measurement uncertainty by calibrating and validating a computational model of a simple beam in bending using data from an earlier study in a EU-funded project called VANESSA [2] — so no surprises there.  However, then my co-author and PhD student, Antonis Alexiadis went looking for other interesting datasets with which to demonstrate the new method.  He found a set of spatially-varying uncertainties associated with a metamodel of soil moisture in a river basin in China [3] and global oceanographic temperature fields collected monthly over 11 years from 2002 to 2012 [4].  We used the latter set of data to develop a new technique for assessing the occurrence of El-Niño events in the Pacific Ocean.  Our technique is based on global ocean dynamics rather than on the small region in the Pacific Ocean which is usually used and has the added advantages of providing a confidence level on the assessment as well as enabling straightforward comparisons of predictions and measurements.  The comparison of predictions and measurements is a recurring theme in our current research but I did not expect it to lead into ocean dynamics.

Image is Figure 11 from [1] showing convex hulls fitted to the cloud of points representing the uncertainty intervals for the ocean temperature measurements for each month in 2002 using only the three most significant principal components . The lack of overlap between hulls can be interpreted as implying a significant difference in the temperature between months.

References:

[1] Alexiadis, A. and Ferson, S. and  Patterson, E.A., , 2021. Transformation of measurement uncertainties into low-dimensional feature vector space. Royal Society Open Science, 8(3): 201086.

[2] Lampeas G, Pasialis V, Lin X, Patterson EA. 2015.  On the validation of solid mechanics models using optical measurements and data decomposition. Simulation Modelling Practice and Theory 52, 92-107.

[3] Kang J, Jin R, Li X, Zhang Y. 2017, Block Kriging with measurement errors: a case study of the spatial prediction of soil moisture in the middle reaches of Heihe River Basin. IEEE Geoscience and Remote Sensing Letters, 14, 87-91.

[4] Gaillard F, Reynaud T, Thierry V, Kolodziejczyk N, von Schuckmann K. 2016. In situ-based reanalysis of the global ocean temperature and salinity with ISAS: variability of the heat content and steric height. J. Climate. 29, 1305-1323.

Seeing things with nanoparticles

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Photograph showing optical microscope and ancilliary equipment set up on an optical benchLast week brought excitement and disappointment in approximately equal measures for my research on tracking nanoparticles [see ‘Slow moving nanoparticles‘ on December 13th, 2017 and ‘Going against the flow‘ on February 3rd, 2021]. The disappointment was that our grant proposal on ‘Optical tracking of virus-cell interaction’ was not ranked highly enough to receive funding from Engineering and Physical Sciences Research Council. Rejection is an occupational hazard for academics seeking to win grants and you learn to accept it, learn from the constructive criticism and look for ways of reworking the ideas into a new proposal. If you don’t compete then you can’t win. The excitement was that we have moved our apparatus for tracking nanoparticles into a new laboratory, which has been set up for it, so that we can start work on a pilot study looking at the ‘Interaction of bacteria and viruses with cellular and hard surfaces’.  We are also advertising for a PhD student to start in September 2021 to work on ‘Developing pre-clinical models to optimise nanoparticle based drug delivery for the treatment of diabetic retinopathy‘.  This is an exciting development because it represents our first step from fundamental research on tracking nanoparticles in biological media towards clinical applications of the technology. Diabetic retinopathy is an age-related condition that threatens your sight and currently is managed by delivery of drugs to the inside of the eye which requires frequent visits to a clinic for injections into the vitreous fluid of the eye.  There is potential to use nanoparticles to deliver drugs more efficiently and to support these developments we plan that the PhD student will use our real-time, non-invasive, label-free tracking technology to quantify nanoparticle motion through the vitreous fluid and the interaction of nanoparticles with the cells of the retina.