Tag Archives: Royal Society

Intelligent openness

Photo credit: Tom

As an engineer and an academic, my opinion as an expert is sought often informally but less frequently formally, perhaps because I am reluctant to offer the certainty and precision that is so often expected of experts and instead I tend to highlight the options and uncertainties [see ‘Forecasts and chimpanzees throwing darts’ on September 2nd 2020].  These options and uncertainties will likely change as more information and knowledge becomes available.  An expert, who changes their mind and cannot offer certainty and precision, tends not to be welcomed by society, and in particular the media, who want simple statements and explanations.  One problem with offering certainty and precision as an expert is that it might appear you are part of a technocratic subset seeking to impose their values on the rest of society, as Mary O’Brien has argued.  The philosopher Douglas Walton has suggested that it is improper for experts to proffer their opinion when there is a naked assertion that the expert’s identity warrants acceptance of their opinion or argument.  Both O’Brien and Walton have argued that expert authority is legitimate only when it can be challenged, which is akin to Popper’s approach to the falsification of scientific theories – if it is not refutable then it is not science.  An expert’s authority should be acceptable only when it can be challenged and Onora O’Neill has argued that trustworthiness requires intelligent openness.  Intelligent openness means that the information being used by the expert is accessible and useable; the expert’s decision or argument is understandable (clearly explained in plain language) and assessable by someone with the time, expertise and access to the detail so that they can attempt to refute the expert’s statements.  In other words, experts need to be  transparent and science needs to be an open enterprise.


Burgman MA, Trusting judgements: how to get the best out of experts, Cambridge: Cambridge University Press, 2016.

Harford T, How to make the world add up: 10 rules for thinking differently about numbers, London: Bridge Street Press, 2020.

O’Brien M, Making better environmental decisions: an alternative to risk assessment, Cambridge MA: MIT Press, 2000.

Walton D, Appeal to expert opinion: arguments from authority, University Park PA: Pennsylvania State University Press, 1997.

Royal Society, Science as an open enterprise, 2012: https://royalsociety.org/topics-policy/projects/science-public-enterprise/report/

500th post

Map of all readership distributionThis is the five hundredth post on this blog.  The first 21 posts were published randomly between July 11th, 2012, and January 4th, 2013; and the weekly posts only started on January 7th, 2013, so I have another 48 posts to publish before I can claim a decade of weekly posts.  Nevertheless, I feel it is worth shouting about 500 posts.

I am a little surprised to realise that I have written five hundred posts and it has made me pause to think about why I write them.  A number of answers came to mind, including because I enjoy writing – it empties my mind and allows me to move on to new thoughts or, on other occasions, it allows me to arrange my thoughts into some sort of order.  I also write posts to communicate ideas, to disseminate research, to entertain and to fulfill a commitment, initially to funding bodies (I started the blog as part of commitment to Royal Society Wolfson Research Merit Award) but increasingly to readers of the blog.  I am amazed that for the last five years the blog has been read in more 140 countries.  While I have a handful of statistics about the readership, beyond the small handful of readers who correspond with me or who I meet in person, I have no idea who reads the blog.  Most of time I do not give much thought to who is reading my posts and my intended reader is a rather vague fuzzy figure who barely exists in my mind.

The map shows the distribution of all readers over the 500 posts with the darker colour indicating more readers per country.

Nano biomechanical engineering of agent delivery to cells

figure 1 from [1] with text explanationWhile many of us are being jabbed in the arm to deliver an agent that stimulates our immune system to recognize the coronavirus SARS-CoV-2 as a threat and destroy it, my research group has been working, in collaboration with colleagues at the European Commission Joint Research Centre, on the dynamics of nanoparticles [1] [see ‘Size matters‘ on October 23rd, 2019] which could be used as carriers for the targeted delivery of therapeutic, diagnostic and imaging agents in the human body [2].  The use of nanoparticles to mechanically stimulate stem cells to activate signalling pathways and modulate their differentiation also has some potential [3]. In studies of the efficacy of nanoparticles in these biomedical applications, the concentration of nanoparticles interacting with the cell is a primary factor influencing both the positive and negative effects.  Such studies often involve exposing a monolayer of cultured cells adhered to the bottom of container to a dose of nanoparticles and monitoring the response over a period of time.  Often, the nominal concentration of the nanoparticles in biological medium supporting the cells is reported and used as the basis for determining the dose-response relationships.  However, we have shown that this approach is inaccurate and leads to misleading results because the nanoparticles in solution are subject to sedimentation due to gravity, Brownian motion [see ‘Slow moving nanoparticles‘ on December 13th, 2017] and inter-particle forces [see ‘ Going against the flow‘ on February 3rd, 2021] which affect their transport within the medium [see graphic] and the resultant concentration adjacent to the monolayer of cells.  Our experimental results using the optical method of caustics [see ‘Holes in fluids‘ on October 22nd, 2014] have shown that nanoparticle size, colloidal stability and solution temperature influence the distribution of nanoparticles in solution.  For particles larger than 60 nm in diameter (about one thousandth of the diameter of a human hair) the nominal dose differs significantly from the dose experienced by the cells.  We have developed and tested a theoretical model that accurately describes the settling dynamics and concentration profile of nanoparticles in solution which can be used to design in vitro experiments and compute dose-response relationships.


[1] Giorgi F, Macko P, Curran JM, Whelan M, Worth A & Patterson EA. 2021 Settling dynamics of nanoparticles in simple and biological media. Royal Society Open Science, 8:210068.

[2] Daraee H, Eatemadi A, Abbasi E, Aval SF, Kouhi M, & Akbarzadeh A. 2016 Application of gold nanoparticles in biomedical and drug delivery. Artif. Cells Nanomed. Biotechnol. 44, 410–422. (doi:10.3109/21691401.2014.955107)

[3] Wei M, Li S, & Le W. 2017 Nanomaterials modulate stem cell differentiation: biological
interaction and underlying mechanisms. J. Nanobiotechnol. 15, 75. (doi:10.1186/s12951-

From strain measurements to assessing El Niño events

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.


[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.