my blog

1D heat flow in a metal (mini-lecture)

I have recently presented a mini-lecture about thermal physics. It was part of an interview and rather off-topic relatively to my research. I had fun diving back into ‘pure’ physics and I thought to share the material I presented.


I also built and brought with me in the auditorium this 1m copper rod to visualize the ‘heat wave’ moving from the hot to the cold side of the rod.

Hyperlapse movie clip showing a copper rod heated at the right end. Heat flow is shown with a change of temperature visualized by a thermochromic paint, from green to red tones.

I was also able to shoot a clip with a thermal camera mounted on my phone. I did this really at the last moment in a hotel room (yes, I had to clarify I was not going to do anything strange in the room when I checked in with equipment 🙂

Hyperlapse movie clip showing a copper rod heated at the right end. Heat flow is shown with a change of temperature visualized with a thermal camera.

Finally, I prepared a handout. Again, this was rushed because I did not have much time and it was just a mock lecture, not a real one, but while I was making notes for myself while studying a topic I had studied a quarter of a century earlier, I thought to write them now.

A virtual tour of my labs at the MRC Cancer Unit

All photos are 3D pictures, it was fun to try out the technology.

Regrettably, the MRC defunded the MRC Cancer Unit and the School of Clinical Medicine could no longer support our Department. In the current academic job market, I am experiencing some uncertainties about where I will relocate. This is thus one of my last opportunities to show where the discoveries we have done over the last years (in 2022 hopefully you will see much more in print).

Biolabs and 3d printing. Most people know me for my work in microscopy. However, more than half of my group is dedicated to cancer cell biology. This is the 3D picture of one of my wet lab bays. Opposite Suzan’s and Anna’s workplaces, we have our rapid prototyping workshop. Some of you might notice an old Maker Bot Replicator 2. My first introduction to 3D printing. But the most notable printer is the Form3B by Formlabs in our custom (orange) enclosure, with which we print both biocompatible scaffolds and chambers for our light-sheet microscope. Then the Ultimaker, a workhorse for all the needs. The last entry is a CellInk BioX, for 3D bioprinting that we are still integrating into our 3D culture workflow.

Laser lab. Ready for more tech? This is the room that bridges past and future developments. This laser lab was the first lab I could call ‘my lab’, where I started to work in 2010 as an EPSRC LSI fellow on my own. All new tech I produced over the last 12 years came from this room, at least initially. One side of the room serves as our electronics and optical workshop and then we have two optical tables dedicated to prototyping. Certainly a crowded room. The development of our SIM/FLIM system (ATLAS.ONE) was delayed by the pandemics and now we disassembled it getting ready for relocating. Before ATLAS.ONE, this room hosted the various iterations of confocal spectropolarimetry I developed over the years. The centrepiece of this room is now an open-top light-sheet microscope (ATLAS.TWO – CRUK funded). This is just the first preview of a system that hopefully will be the protagonist of many future papers.

Optogenetics lab. After a major infrastructural refurbishment, I was able to get a second laboratory, which I dedicated to optogenetics. Here, we prep samples in a (blue-light or red-light) darkroom. We also have small incubators to ensure keeping cells in light-controlled areas. There up on the shelves, there is the skeleton of our first OptoFarm, a system to culture cells under tightly controlled light (biochemical) protocols. Now, this is discontinued and replaced by a much simpler and more flexible system that we’ll publish soon. And yes, of course, you see also our workhorse for single-cell fluorescence dynamics, integrated with multiple cameras, photoactivation capabilities, multiple light sources, and microfluidics. This commercial system was bought and then modified with an MRF grant and despite being very temperamental, it gave us a lot of good data!

Biophotonics lab. I hear you asking… what about FLIM. Of course, we are almost there. Here, we dive into one of the rooms of the imaging facilities where I customized a multi-photon / confocal microscope with time-resolved technologies. In this front view, you see the Leica SP5 and the Chameleon Vision 2 (to the right). Two instruments that gave me a lot of satisfaction. The blue boxes are a custom-built FLIM system (ELIS) that I built when FLIM was still relatively slow. But now commercial systems are also super-fast and I have packed ELIS for good. To be unpacked, once I will have new laboratories, the PicoQuant rapid FLIM of the latest generation.

Let’s go around the table because this room is full of tech. In this room, I hosted several generations of HDIM systems. Some published, others not. The black box on the table is a streamlined and efficient version of HDIM, a time-resolved spectropolarimeter. Coupled with the multi-photon microscope, we get high efficiency in detecting fluorescence with 16 concurrent spectral channels, 2 polarizations and 64 time-bins. Under the table, is the amazing SPC152 – the heart of the system by Becker&Hickl.

Yes, a lot of boxes around because we need to pack up! The back of the table hosts a pulse-picker we used with SPAD arrays, beam conditioning optics and HDIM Gen 4 (I think!) 🙂 I stopped its development because of COVID but soon or late I will resume. Hopefully, this will be fully automated also in its alignment and will integrate fast FLIM electronics.

This first virtual tour ends here. Hopefully, the next tour – with a bit of luck – will be from my new labs.

On the misuse of case studies: a case study

Our organization is committed to equality, diversity and inclusiveness. For example, Dr Clara Madeup benefitted from our ‘return to work’ programme that permitted her to come back to work after an extended 2 years maternity break. Clara is now a tenure track associate professor leading in the field of biotechnology.

How many Claras and Johns showcase success stories across our industry? More often than ever, we need to submit case studies during assessment processes, so much so that it is not unlikely to receive negative feedback if we describe our actions and outcomes carefully but without illustrating case studies.

Which is the likelihood that an organization does not have good case studies to showcase? And how likely is it that an organization decided to illustrate a failure in a case study? How representative success stories are of an organization, particularly organizations that are based on high staff turnover and competition? In fact, a few handpicked case studies can conceal otherwise worrisome statistics available within a document right alongside nice case studies.

Of course, the exclusive use of positive case studies in our websites, the brochures we use to describe how great we are, or at least we want to be, is absolutely obvious and legitimate.

I have seen case studies related to negative events within my organization only in two cases. First, introductory courses for health&safety that often provides plenty of examples of incidents with few cases discussed in detail. They are very informative because in the utter boredom of a long H&S course they actually tell you the story of not what can go wrong but what did go wrong in a lab like yours, maybe next door. Second, I had volunteered for a course designed to inform how to help victims of rape and sexual harassment. Instead of dwelling on how good our organization is, we went deep in describing which problems we have to deal with, how problematic communication can be, and how both academic and justice systems can easily fail victims. Very different situations but the illustration of what CAN and what DOES go wrong was absolutely instructive and helped focus on what we should do to prevent incidents.

During management meetings, we usually discuss what we can improve. Obviously, we do not speak about positive things only, quite the contrary. However, we do this often through rather unevocative statistics and get excited when we see progress compared to the past, or we are better than other organizations in the same area. I wished, however, organizations would focus more on the investigation of negative case studies during management meetings, of course, anonymized and taking any necessary precautions or even with the consent of colleagues involved, so that we could understand more deeply the consequences of our failures and identify better strategies to eliminate or mitigate our shortcomings.

I think we should bring a bit of the scientific method we experimentalists are so accustomed to deal with. We often learn a lot from experiments that fail for no apparent reason, and we showcase our failures to colleagues to get help and to teach less experienced how to identify solutions.

I am not really sure about how often ‘negative’ case studies are used in academic management to inform executive decisions in the broader community. In my experience not enough, probably, because the ‘negative’ case studies we should analyze are often just simply buried, swiped under the carpet, a topic for more specialist discussions reserved for those that make issues disappear.

I hope organizations will adopt more the use of ‘negative’ case studies as a tool to improve and fully understand the suffering of those who find themself in challenging situations. And I hope we are asked to produce case studies to illustrate success stories and good practices less frequently during an assessment, reserving these to public brochures.

Against (online) abuse

English football has announced a three days boycott of social media to raise awareness against online abuse. I am no footballer and I even do not follow football but I follow Formula One, and thanks to Lewis Hamilton engagement against racism I got aware of this initiative. Sport should be all about coming together in a joyful way and transforming the instinct to compete and fight into a game based on fairness and respect. However, far too often sport – and football in particular being so popular – makes itself a vector of abuse, online or in person, verbal or physical.

Abuse is part of our society and – like all of the human shortcomings – it will be never fully eradicated. However, abuse should still rise indignity from all us, either if online or not. Sadly, influential people have contributed to normalize online abuse, attitudes that are then percolating back into the streets. Admittingly, every person might have a different sensitivity and personal judgment about what ‘abuse’ is beyond the strict legal definition. This should not be, however, used as a free pass even just to be unkind, certainly not to be, well… you guessed… abusive.

Then, as someone active on social media, despite not being an athlete for the past quarter of a century, I’ll turn my social media off until Monday night in solidarity with this initiative and any victim of abuse.

A brief journey to India, and into models of carcinogenesis

In early 2016, I was asked if I wished to speak at the discussion meeting “Conflict and Competition in Cellular Populations” in Bangalore, India organized by Dr Sandeep Krishna and Dr Sunil Laxman (NCBS). The title sounded so intriguing that I accepted without even checking the actual topic of the meeting. Then an adventure begun, that now concluded (did it?) in 2021 with a small paper entitled “Cooperation of partially transformed clones: an invisible force behind the early stages of carcinogenesis” published in the journal of the Royal Society, Open Science (10.1098/rsos.201532). Let me tell you the story of this journey that, perhaps, might inspire you to adventure outside of your field.

For brevity, I’ll skip the details about the actual trip. It was of course exciting to experience a culture I am often exposed but I never lived. The food, the people, the contrasts of India, a small glimpse into a complex galaxy of humanity. My short trip to India started with a sleep-deprived-me trying to explain to the border police that the conference Conflict and Competition in Cellular Populations, nicknamed CCCP, which poster was written in pseudo-Cyrillic, was not a political conference (I would have needed a different visa in that case!) and concluded back in Cambridge a week later with a slightly embellished bedtime story for my 3 years old daughter about the animals I saw in the park that hosts NCBS, a story that I am still telling now and then to her.

But of course, here I focus on the science. The conference hosted a good number of great speakers (referring to others) on the topic of ecology (er, yes, the title made sense). Suddenly it dawned on me I was ‘a bit’ off-topic. However, I loved talk after talk learning a bit about ecology, including its mathematical foundations. I really enjoyed the meeting, so much so I could not stop thinking about its relevance for my work that back then was focused on non-genetic heterogeneity in cell decisions, carcinogenesis and the DNA damage response.

The study of cancer as an ecological problem is not new, of course. Something very specific started to bug me though, something I could not find literature about. We know that different clones of cancer cells cooperate and compete in tumours but what happens during the very early steps of carcinogenesis? I was queuing to board the airplane when I succeeded to download the paper “Evolution of cooperation among tumor cells” published ten years earlier by Axelrod and colleagues in PNAS. It was a nice in-flight read, but the flight from Bangalore to London is long and I started to obsess about a very simple mathematical fact.

For a moment, let’s imagine you dream of establishing a business but you need £1M to start it. However, you are a bit of an odd person and decide to do it only if you win the lottery which jackpot is £500k. You clearly make strange decisions but I am not here to judge… the oddest thing is, however, that you bet on winning the lottery not just once but twice. Then you have an idea You agree with your village of similarly odd-minded people that if anyone wins the lottery, you will pool the money together to invest in this start-up. This is still an unlikely strategy, and certainly one that has a tiny probability to succeed, but it is definitely more likely to work out than waiting to win the jackpot twice alone.

Back to carcinogenesis. Every day, each cell has a certain probability to mutate because of exposure to radiation, chemicals or simply the chance of errors of biochemical machineries. Mutation after mutation in the right genes, a cell might grow into cancer. A very unlikely series of events that, however, with trillion of cells in our bodies, over one’s lifespan is likely to happen. We know that certain mutations occurs in cells that eventually lead to cancer. We know that one cell wins the macabre lottery of disease multiple times before leading to cancer. We then know that many cells will get mutations within an otherwise healthy tissue.

We usually consider that all these other mutant cells will either accrue neutral mutations (i.e., mutations that will not change the fitness of the cell, nor confer a cancer phenotype), or deleterious mutations that will be purged by tumour suppressive mechanisms. However, cells within a tissue communicate and mutations occurs also in genes responsible of cell-to-cell communication. In my recent work I propose a ‘toy model’ with which I explore the possibility that the gene- and cell- centric mutational process should be reconsidered in the context of an overall tissue where cell-to-cell communication might reshape the early steps of carcinogenesis. I am not the first one doing so, but I try to emphasize with simple modelling how the mutational process should be seen in the context of a collective of cells rather than in a gene- or cell- centric fashion.

What did I learn beyond what I have written in the paper (i.e. in addition to the science)?

First I had really fun, something that over time does not happen with every paper, even those more important ones where we invest major resources in. I even had fun during the revision process. As many of us experience, I often got half of the referees very supportive of my work and half rather dismissive. But those very supportive have been often extraordinary kind and helpful, either defining the manuscript ‘a refreshing read different from what I usually read in this field‘ (earlier submission in a different journal) to ‘the models presented here make the point in a clear and dramatic manner‘. The last referee of the last submission now published was particularly helpful. Not only they critically review the manuscript but also invested time to describe a discrete time Markov chain model that I could have integrated in the manuscript. This suggestion permitted me to learn a bit of maths I did not practise before, and to improve the work… this is what refereeing should be.

Second, alongside the enthusiasm of adventuring in a rather different field from my already eclectic research interests, I also felt the pain of being an outsider; a pain I feel often but that it was made sharper by the fact I was a single author. This was really a ‘pet project’. I got convinced to shape my notes in a manuscript only after I attended a seminar by Prof. Allan Balmain in 2018 related to the Nat Cell Biol article “Multicolour lineage tracing reveals clonal dynamics of squamous carcinoma evolution from initiation to metastasis“. It was a great talk and somehow relevant to the notes I had written since my trip to India. I decided to try to publish my ideas after reading the commentary by Prof. Kornelia Polyak and Prof. Michalina Janiszewska where they state: “One possible explanation is that there is a cooperative interaction between the streak and bulk tumour cell populations; an intriguing hypothesis that warrants further investigation but was not tested by Reeves et al.5. The streak pattern observed by Reeves et al. is reminiscent of the streaks generated by non-mutualistic budding yeast analysed by Muller et al.13.” Eventually, I am not sure the work I had put in this manuscript was worth the pain.

Then, do I advise others to adventure so wildly in other territories? As I have written before, it is rarely rewarding career-wise and never easy. But, once in a while, let’s just follow the passion and enthusiasm for something new, with no regrets. Any adventure comes with some pain but the fun of exploring, eventually, makes the experience worth living overall.

I wish that this small new paper can really provoke some thoughts, or inspire some young scientist to adventure… perhaps not too much and not alone as exploring comes with its perils.

Changing of the Guard

I more excited than other times for a talk I will deliver next week. When invited, I read the list of speakers and I noticed so many names of people whose science I follow very closely. This time something is different though. I read their papers since I am a student, papers they published perhaps when they were students or young postdocs, in fact many of them are my generation. I grew with their science as if they were well-established academics as I never paid attention to affiliations or titles. Some of those I had recognised early in my career disappeared from the field or academia, others are fully established by now. This made me thing about my attitude towards generational change… a great contradiction of thoughts.

Missing the Old Guard. Several scientists I really respect have retired or are about to. I have been privileged to meet so many, particularly in the area of biochemical/biophysical imaging. Scientists who contributed so much, inspiring figures who shaped contemporary science, often without hype or even recognition in the broader community. Wait, am I missing the Old Guard? This feeling contrast so much with another one. In time, old ideas become an obstacle to progress and a generational change is desirable. You might indeed know the popular concept that ‘science advance one funeral at a time’. I do think there is an element of truth in it. So, why do I have such profound contradiction in my feelings?

Loving the New Guard. I am active in the area of biophotonics since an undergrad student, and having swapped discipline a few times, it is simpler for me to use microscopy as an example. The super-resolution revolution has been inspirational although I have observed it from the outside. In a few years, a new generation of stars begun to shine and a constellation of younger scientists who broke with the past was born alongside. Also in biochemical imaging I see great changes, the consolidation of certain ideas that once were considered heresy or simply very very niche. And yes, this get me rather excited. Wait, do I really love the New Guard? I see so much I do not like in science, and this is not just something imposed or inherited by previous generations. There are so many colleagues* with whom I might disagree about science and often on how Academia should be run. Disagreement is ok but sometime this is a much more profound divide.

OK, I got it wrong. Today, I have suddenly realised how wrong I was in interpreting my own feelings about generational change in Academia. While the majority of us would agree that generational change is necessary to avoid science stagnating, perhaps we do not really understand why**.

I love challenging established ideas on the basis of logic and experiment, I love discussing alternative interpretations that are not mainstream (but still scientific!), I love risk-taking in science (not in life although sometimes it is difficult to keep them separate), I love intellectual change (not so much change in my everyday life). Generational change might help the things I like to emerge but old generations do not have exclusivity in being dogmatic or risk-adverse, indeed those I admire are not. The issue is that too often also the younger generations accept dogmas (not just critically incorporating established theories and models in their thinking), they would guard an old ‘truth’ no matter what. But when they lose their authority of reference because of generational change, somehow their confidence or power is weakened, leaving space for positive change.

Hence, I now realise I am merely recognising a new generation of scientists with whom I might share a vision and I am excited that new people now replace those who retire for whom I had the same affinity and respect. Generational divides are much less important than an open attitude to change.

So, perhaps, I do not like guards in science at all because in science the fewer cages or palaces we have the better it is.

And after this lucubration, I will thoroughly enjoy my next talk in any case 🙂

NOTES

* I use the term colleague very loosely to refer scientists in related fields.

** I just had a glance to this paper by Azoulay et al., interesting concepts

COVID | data analysis (new CAAT 4.3 release)

WARNING. I am not a medical doctor nor an epidemiologist. The analysis I am sharing here is only for the data geeks around that are curious. Please follow the advice of your national authorities and health system.

DOI

I have just published a new release of CAAT, Matlab code to analyse Johns Hopkins dataset on the SARS-CoV-2 pandemics. The usual caveat is that data is likely to be underestimates. Underestimation does not occur only because of lack of transparency but most of the times because of differences in definitions of COVID-related fatalities and efficiency of reporting systems across countries. For example, using the excess mortality statistics we know that about 30-50% under reporting is rather physiological because of deaths occurring outside the hospital settings or because people might die positive to COVID but not for COVID. This also accounted to a significant adjustment of statistics we have noticed in the past in Hubei. However, changes over time within a country might be more reliable and, therefore, there is still something to learn from this data.

I started analyzing data when the UK government decided to drop the policies set up for containment of SARS-CoV-2. Now that data has been extensively discussed in the UK, I curate CAAT only for others that would like to explore the data. However, at this release it is worth mentioning just the main observation. While the first acute phase of the pandemics is subsiding in Europe and Northern America, it is now flaring in South America at worrying pace.

Relative fatalities in the population at risk for each country. This is a new visualization I provide. Both bar graphs are ordered according to the values shown to the right, i.e. the fraction of the population at risk who has already died. At the right, the weekly rate of fatalities during the current week and the preceding two weeks. Those countries that, so far, has experienced high casualties are showing a significant reduction of weekly fatalities showing the positive effects of policies aimed to contain the virus.

Commenting out line 362 ‘d_ord = p_ord;’, you can order the weekly rate of fatalities according to the last week. This might reveal which are the countries at most risk now. Several South American countries are topping this list. In Brazil, Ecuador and Peru, about 0.5% of the population at risk died during the last week and, sadly is several countries this rate is accelerating.
Let’s also focus on some good news. This is one of the several plots hard coded in CAAT, comparing Italy, UK, Germany, Denmark and Sweden. As it is well established by now, new fatalities are dropping significantly and lock-down measures are gradually abandoned in favor of social distancing measures. The individual outbreaks started at different times and at different rates. Comparisons between countries are therefore difficult but the effectiveness of policies within countries can be evaluated. If you were interested in different graphs or comments, let me know but I will not elaborate more on this at this stage.
This graph is a bit of a mess but I present it for completeness. In North America lock-down measures are having a clear effect. In South America we see alarming increases. As I do not follow South American politics and specific news, I can’t draw conclusions, but it is evident that – assuming no change in reporting occurred, Argentina has slowed down the outbreak but not yet put it under control.

Although I have explained this before, I should probably clarify how I evaluate the population at risk. To estimate how many people might die in each country in the (unrealistic) scenario where everyone would get ill, I used the age-dependent fatality rates published in The Lancet by Ferguson’s group and multiplied these values with the demographics of each country as reported by the UN.

Reviewer 3 | A semi-serious discussion

I guess that if you landed here, you know what I am referring to, but let me clarify the subject of this post for the benefit of the youngest scientists. During peer-review, we get good and bad feedback, either deserved or not. We can then respond and revise our work. However, it is not rare to get the reports from the mythological reviewer 3 (also known by a different number), one that dismisses your hard work in ways that you will find particularly unfair and difficult – if possible at all – to rebut. There are various flavours of Reviewer 3, but common traits – not necessarily all present in an individual report – might be the condescending tone, vague unreferenced criticisms, the request of impossible experiments, a deep misunderstanding of the manuscript, accusations of various type. The important aspect of referee 3 is that, generally, responding to their critique is either factually impossible or would not improve the quality of your work. Of course, there is a lot of subjective interpretation here, and some of referee 3’s suggestions might be proper, or some report that at first glance are good (negative but well done) might be written by reviewer 3 in incognito. In any case, most scientists agree that Reviewer 3 does exist and, some of us asked for an independent assessment of a controversial refereeing report, might even know the identity of some of them, however ever-shifting they are.

***

Why Reviewer 3? Well, this is very anecdotal and indeed people might do the same ‘joke’ but changing the numbering. The argumentation I am going to elaborate on (I beg you, Reviewer 3, if you are reading, please remember this is not a completely serious discussion), does not depend on precise numbers, certainly not by the cardinality of the referee. In any profession, there are very skilled and bad professionals; this applies also to the academic world, of course. However, referee 3 does not have to be particularly bad scientifically, they might be the smartest of all, but for the scope of the refereeing process, referee 3 is doing this particular job and at that particular time, particularly badly, perhaps for lack of time, hubris, a particular emotional state, ignorance or for a genuine misunderstanding: it does not matter. It exists. Then, let’s take the anecdotal report of Referee 3, for a moment, at face value.

***

Let’s say that each time an editor nominates a reviewer, it is like the toss of an unfair coin (i.e., the probability of heads is different from the probability of tails) – either we get a Referee 3 or we do not. The probability to get at least one referee 3, is then the complement of the probability to not get one at all, i.e. p1=1-(1-p0)^n, with n the number of referees nominated by an editor. Which is the probability p0 for referee to be referee 3?

There might be some data out there, but as data is relatively unimportant both to make my point and to reviewer 3, I will assume that as Reviewer 3 is often called Reviewer… THREE, it is a frequent occurrence to observe one out of three referees been, well you got it, referee three. Then, after ‘careful consideration’, I assumed that one out of three is the most frequent occurrence.  The mode of a binomial distribution is floor((n+1)p0)=1 or ceil((n+1)p0-1=1. We can thus infer that between 1/4 to 1/2 of all referees could provide a Reviewer3-like  response. Hence, which is the probability to get at least one Referee 3 for your submission? Well, although a rare occurrence, if the editor asks the opinion of just one expert (perhaps as a preliminary inquiry) this probability is somewhere between 1/4 and 1/2, of course, identical to p0. For two referees, we will get a 43-75% probability and for three (the most common case), almost a 60-90% probability. Therefore, getting a Referee 3 report might be a rather obvious outcome of the peer-review process.

Now, let’s do another outrageous assumption. Let’s assume that also the editor, when handling a manuscript, could make the same mistakes as a referee 3 and that the journal has a very high bar for a manuscript to be accepted, i.e. any substantial negative feedback causes a rejection. In this case, the probability that the Referee 3 syndrome might negatively affect your submission is between 70-95%. Unrealistic? Maybe.

***

Now that several weeks passed, the referees’ reports are back in the hand of the editor. This is a very complex stage where so many objective and subjective factors might change how referee 3 is handled.

One possible outcome is that you get two Referee 3s… a rare outcome… isn’t it? If three referees have sent reports in, the probability to get at least two Referee 3s is actually between 15-50% Let’s say that – on average – a quarter of papers could be rejected because of Referee 3s, as if you get at least two of them any editor would, legitimately, dismiss the idea that those are ‘bad’ referees.

Let’s assume now you got just one referee 3 report. Again, with no intention to be accurate, these are the possible outcomes I can think about:

  1. The Editor considers Referee 3’s points valid and the paper is rejected. Unexperienced authors will give up this submission at this stage, address any valid point raised during the refereeing and move to a second journal. Keep in mind now, that at the next journal, you will get the same probability of getting a Referee 3. However, if Referees one and two were positive with a few criticisms that could be addressed with new data, the experienced author would appeal. Until recently, I did not realize that Editors are quite open to this option assuming they find the manuscript interesting and that you get only a single problematic referee. Unfortunately, journals have mechanisms to discourage this path. However, if you can disregard emotions and humbly reassess your work on the basis of the Referees’ critique and you still find that the main issue is a Referee 3, engage – positively – the Editor. In most cases, you will find nice people trying to help out.
  2. The Editor considers Referee 3’s points invalid and in one way or another, you will be allowed to address only the solid scientific point of Referee 3. It is very rare this will be written to you explicitly. I still find difficult to handle this situation. In most cases, this is the more likely situation you will get published even with a Referee 3 in the cohorts of referees. My suggestion is to speak with a senior colleague to decide how to proceed, or again to engage in a polite and proactive way the Editor.
  3. The Editor considers Referee 3’s points invalid and asks for the opinion of Referee 4. This is the most sympathetic and proactive response that an Editor can have. However, this is also a situation that does not protect you from Referee 3, as the shapeshifting nature of Referee 3 might make them reappear with a differently numbered T-shirt. You will have between 25-50% to get another Referee 3 and being rejected not on merit. On a positive side, you might have up to 75% probability to replace a Referee 3 with a more objective peer.

***

Which is the point of this post? As I stated in the title, this is not a serious and quantitative analysis of peer-review. But I wished to address with outrageous simplifications a basic issue. Does the attitude of Referee 3 play an important role in peer-review? There are several reports showing how peer-review, despite its importance and the several mechanisms to establish a formal and objective process, give rise to a high degree of randomness in the outcome. Here, I just wished to point out that the probability to get a random and unfair report might be high. I leave to others the study of how high this value really is. However, while very experienced Editors and Authors might know how to handle the situation, there are two issues that concern me:

  1. We are accustomed to harsh criticism. Often, a solid scientific debate is confused with been tough, and assertiveness is confused with freedom to not be polite. Who manage peer-review, academic or professional Editors, or managers in funding agencies, might consider this the natural and obvious rules of the game. Being a scientist has become something of a high-pressure job and it seems everyone has to accept this. Most of us are good and well-intentioned people, but the gears of this heavy machinery that is science are difficult to change, at least while the machine is in action.
  2. The authors, or grant applicants, should have a very balanced approach. On the one hand, they should always make an effort to learn from criticism, even unfair criticism. This is a bit tricky with Referee 3. However, we always have to dissect Referee 3 to identify any useful critique. This is the trivial advise, trivial as it should be obvious. There is something more about this, that if you are a younger scientist with no proper mentoring, you might not know. Referee 3s can have a huge psychological impact on you. I’ve seen this happening to group leaders, and I have experienced this on my own.

*** UPDATE ***

After the publication of this blog-post, Reviewer 3 contacted me privately with the following message.

  1. The assumptions the author does are all wrong and WordPress should not have allowed the submission of this article
  2. The conclusions of the authors are clearly impossible as they conflict with a large body of literature
  3. The authors do not cite any literature, but particularly the papers I published in 1965 that clearly and unequivocally demonstrate the opposite finding or the same findings.
  4. The article is written in English, Latin would be the preferred choice for this field
  5. Even if the authors could address these shortcomings with a major revision, this article should not be even posted on LinkedIn
  6. Moreover, the article is poorly written, for instance, for instance ‘my own’ is not Korektly PhrammatiKalleee

*** UPDATE 2 ***

Hi Donald,

yes, that is sarcasm… not, you know…

Take care,

A.

Managing risk in the lab at the times of coronavirus

In the UK, we are waiting for good news to reopen our laboratories. Well, not ‘waiting’ but getting ready. It might be in two weeks or two months but we have to be ready because if we will be ‘back to normal’, we will have new outbreaks. In science, we are lucky as we are already trained to manage risk. However, most scientists in the UK have a conflictual relationship with health and safety procedures that are often perceived (probably rightly) as overly bureaucratic and can drive people away from good practice. I am lucky as I deal with wonderful people both locally at the Cancer Unit and centrally at University on the regard to safety, at least in those areas I have responsibilities.

In my opinion, this is the moment to restructure how we handle safety. On the one hand, formal risk assessment is very important to identify the source of hazards. It is easy to imagine we can work safely but there are some topics that are very tricky. For example, we are discussing how to deal with fire doors… we can’t keep them wedged open because of fire safety but it would be better to not close them to avoid touching surfaces unnecessarily. There are perhaps solutions that avoid any risk, for example providing hand sanitizers on either side of the door or reverting previous rules and enforce the use of gloves in any area of the labs, or install automatic (fire) doors as soon as possible. What about air conditioning? We need it in a modern building with sensitive equipment but should we do any change? Are they safe? What about cell culture? The other day I joked about infected cell cultures in CL1 laboratories? Wait… it was a joke but then – out of curiosity – I realized that coronaviruses, this included, can propagate in several mammalian cell lines (they express ACE2 and most of them are not killed by the virus). Is this an issue? I assume it is not as it is unlikely we contaminate cultures (we work in aseptic conditions) and cells do not generate aerosols we can breathe. But I wished to mention this just to make a point: it is worth thinking deeply about how work will be when we return in the laboratories to identify possible issues, without paranoia and without panicking, but proactively and scientifically.

However, paperwork never protected us. It helps to identify issues and to protect us legally. There is a set of rules that have been gradually abandoned in favour of PPEs and engineering measures to manage risk and I believe we have to retrain people using those rules. It will be impossible to make the world 100% safe from coronavirus, certainly in the short term. We can, however, manage risk by changing behaviours to make it negligible but we need to be prepared and everyone has to comply.

Let me do two examples, not specific to viral work. Even just twenty years ago, for some of us laser safety was just removing any reflecting surfaces from your body and the environment (no rings, badges, other jewellery, no wall mirrors in an optics lab etc) and changing your behaviour: never align your eyes with the likely direction of the laser. This meant, for example, that a researcher would mature the instinct of turning the head always away from the optical table when picking something from the floor. Those were the times when accidents would still happen at a certain frequency because good laboratory practises without PPE relies on a person never do a mistake. PPEs should protect us from our mistakes but once you wear protective gear, once you feel shielded from the hazards, behaviour will change back to normal.

Another example is tissue culture. It is a fair amount of years I do not do TC work in person but, sometimes, when I get a coffee at the Hutch canteen and I pay, I pass on top of my mug and my brain signal me not to do it. Under hood, we avoid to pass over open flasks to minimize the risk of contamination (of the cultures). Again, some of us might have worked perfectly aseptically and safely with no PPE in the past.

I DO NOT advise to drop PPEs or risk assessments, do not misunderstand. The only point I want to make is that changes in behaviour such as social distancing and enhanced hand hygiene will be very important, more important than anything else to come back to work safely. We need to be careful in retraining ourselves. Again, without paranoia or panic. Other than doing ridiculous elbow bumps to replace shaking hands, a smile and a greeting will do. Giving way to people to maintain distance in close environments, planning how to move around cramped laboratories, how to reach instrumentation, when and how to clean hands or use PPE, but also very practical and trivial things such as the use of toilets or where and when to have a lunch or a break, how to reach the workplace might be more challenging. Challenging – not impossible, at least in most cases.

I have been very supportive of lockdowns. Among other things, this period is permitting us to exercise social distancing and train on how to handle materials we buy or we get delivered at home. This is valuable time if and only if you are actually using this opportunity to actively prepare for a life with COVID. We all hope that this virus will burn itself out soon. However, at the moment it seems unlikely and therefore the keyword is one: preparedness.

Do not do the mistakes that several people in leadership have done. They were not prepared to manage this pandemic despite they knew it would happen soon or late. They were not prepared to instruct us for timely societal changes. Are they now really prepared for the next phase, i.e. the management of life with SARS-CoV-2 endemic? I hope in more clarity and transparency. However, to be fair, it would have been difficult not to do mistakes.

If you did not do this already, brainstorm with your team and communicate to your managers what you might want to plan. I also advise having clear and shared rules. As safety will be based quite significantly on behavioural changes, conflicts at the workplace are also likely. Although we are all feeling closer to each other and more helpful, there are always the zealots and the neglectful. Those that are worried about anything and those that are worried about nothing. We need to reassure the former and letting them perhaps working only off the lab (whenever possible) if they cannot handle the situation. We should dialogue with the latter to explain we have to abide by a set of shared rules and, if they do not comply, we should get them off the lab. And in any case, help categories at risk and colleagues that might struggle with mental health.

If you did not do this already, it is time to prepare. Not business as usual but with a new norm as soon as the government will permit us to resume work until this virus will be defeated or at least tamed. For the same reason that most of us are staying at home, helping keyworkers to do their job, we’ll soon be called back to work. Not just for ourselves but again for those amazing people who have kept working in difficult situations in the streets, hospitals, care homes, shops. In fact, it is our duty to share the burden of a society that cannot remain on the shoulders of only a fraction of us. However, we shall do this not in irresponsible ways, but with absolute preparedness. This applies to governments and public institutions but it does apply also to each of us.

Covid (v3) – data visualizations

WARNING. I am not a medical doctor nor an epidemiologist. The analysis I am sharing here is only for the data geeks around that are curious. Please follow the advice of your national authorities and health system.

Let me publish this short update on the COVID pandemic to share some of the most interesting visualizations I found online and the last update of Matlab code for those of you who would like to play with the data. For some of us, keeping an eye on the data is a way to diffuse anxiety about the pandemic and it is in this spirit I keep posting, now and then, comments on COVID. If data, trajectories, comments on the pandemics are stressing you and you are experiencing a COVID overload, perhaps you would like to read Prof. Aisha Ahmad’s article – particularly if you work in higher education. And… not read this far less useful post of mine.

First, NextStrain is a tool that permits to monitor the spread of various infectious diseases, including CoV-2019 (thanks to Quentin to share the link). You can appreciate how the virus is mutating and how different clusters/mutations are geographically spread. I should note that, to my knowledge, there is no evidence for differences in the severity of the disease depending on mutations identified so far. These differences are currently used just as simple fingerprints, or ‘paternity tests’ to identify the evolution and the spread of the virus. I avoid interpretations of this data but you can find reports on NextStrain. I assume that these maps are biased significantly according to the amount of sequencing that each country is doing. You might appreciate the central role of, of course, China, but also of the UK as a hub of transmission. This might be because of London, an important aviation hub, and the high numbers of sequencing happening in the UK, but neglecting the number of samples, if the timing/trajectory of the spread will be confirmed in due time, it will be very interesting to understand the implications.

Before showing a bit of new Matlab code I worked on in the little spare time I had lately, I would like also to introduce the work that inspired my new analysis. There is this very nice tool by Aatish Bhatia and a great introductory YouTube clip by the same author.

Like most non-expert people, I am interested in understanding the efficacy of public health measures taken in different countries. As confirmed cases are rather unreliable, and the likely cause of the big differences in mortality rates across countries (see previous posts), I currently focus only on reported fatalities. It should be noted that this data is more reliable, but again it depends on the reporting criteria of each individual country and these might change. Hence, the true impact on public health will be clear only retrospectively in a year or so, but for the time being, we can appreciate trends to get an impression if containment measures are working or not.

First, I would like to start with the raw data. Here I present groups of selected countries based on my own interest but with the Matlab code available on GitHub you can generate the same graphs for the groups of countries of your choice using the Johns Hopkins CSSE dataset. To analyse just the raw data Bahtia’s tool is easier to use, but I show here the results to introduce my next analyses. Please note that the data is averaged over a sliding window of three days and we usually get data with 1 day of delay. Therefore, the curves are representative of the situation as it was 2-4 days ago.

These graphs are difficult to interpret as countries not only are experiencing different types of epidemics (for example, more or less localised) but do have also rather different population sizes. However, these trends allow us to understand – broadly speaking – if the policies enacted by individual governments are providing the expected results.

You might notice that I present two entries for China: one for China overall, and another just for the Hubei province. Here, the two graphs are almost identical because the Chinese outbreak started in Wuhan, within the Hubei province, and there caused many more fatalities. The black line is just shown as a reference to guide the eye. When the traces significantly deviate from this line, eventually tracing a horizontal line, confirmed deaths stop increasing exponentially. In that phase, the epidemic is still causing fatalities but it becomes more manageable and predictable. Most importantly, for those countries in lockdown, it is the clear sign that the strategy is effective. For those countries that are not in lockdown, for example, the Republic of Korea (aka South Korea), such trend imply that the epidemics is not resolved but controlled to strike a balance between socioeconomic sustainability and control of the epidemics.

The second gallery is a collection of the very same plots but normalized to the total population of each country. You might appreciate here why I report data for both Hubei alone and mainland China overall. I heard – amazingly even from virologists – that the data from China is not correct because China cannot possibly have so few fatalities being such a large country. At this stage, we can’t trust data as fully reliable from any country, but it should be clear that the epidemics was initially localized in Wuhan, Hubei. There it went out of control but in the rest of China a combination of the lockdown and tracking of patients made possible to avoid an uncontrolled epidemics. This is why Hubei, with its 60M inhabitants, should be considered as a reference and not all mainland China and why I am reporting the two curves.

Normalizing fatality by population size, we can now appreciate how some countries are in much similar state at the moment in Europe with, of course, plenty of exceptions. Another note on the reliability of data. All of us, even the non-specialists like me, have learnt how ‘confirmed cases’ of covid are a rather unreliable indicator because of different capacities in each country to test, particularly in different stages of the epidemics. Reported fatalities are a more robust indicator. However, different countries might adopt different methodologies to report ‘confirmed’ cases. Some countries make a distinction between people who died with COVID and people who died of COVID, but others do not. Some countries are faster than others in reporting and deaths outside hospitals – anywhere – are likely to be counted with a significant delay, or sometimes not reported at all because of lack of testing. Therefore, keep in mind that data from any country is not rock solid, and we will discover the real impact of COVID only at a later stage.

Most importantly, this is not a race between countries. Each of these numbers is a human life cut short and a complex network of relations broken. Therefore, my comments are provided as a means to try to understand what is happening from the standpoint of the layman I am in this context, with the deepest respect of what ‘fatalities’ means.

This noted, I wished to present the last gallery. Different countries have different demographics and the risks, as we know, of COVID are age-dependent. Therefore, I used the age-dependent mortalities inferred from mainland China (Hubei excluded – this represents a best-case scenario) to provide a rough estimate of the population at risk in each country. For example, once adjusted for the different demographics, an average fatality rate in China would amount to about ~1% but ~2% in Italy and 1.7% in the UK. Notably, the mortality rate in Hubei was higher (~4%) and using this value for Italy we would expect ~9% mortality. However, many have noted how these values are unrealistic and heavily depends on testing capacity, that is constrained during phases when a health system is overwhelmed. Most studies estimate fatality rates around 0.5% to 1%. If 1% is the real mortality rate, to evaluate the population at risk in Hubei, we can simply take 1% of the ~60M inhabitant as the population at risk. For all other countries, this proportion is made considering the different number of inhabitants in different age bands.

The take-home message is that the actions that governments decided to take are having the desired effects. While I might disagree with one policy or another, I thus invite people to follow the guidance provided from each country. However, it will be interesting to keep comparing the Netherlands and Sweden with other European countries, and China with other countries in South East Asia as, by choice of necessity, different strategies have been employed. Data from the UK and Sweden (and others) is rather noisy, with some temporal variation that might depend on the way data is reported. Therefore, it is to early to tell how the situation is developing in these two countries, but over the next week, a picture will be rather clear. We have now to watch out for the US and also the many countries that I did not study so far but that will play an equally important role in the evolution of this disease.

Personally, I had supported a fast initial response. While trying to shrink the epidemics, I hope that countries now will cooperate to share resources to save as many lives as possible. At the same time, I hope that countries will also cooperate in rebooting the world economy and productivity as soon as possible. We should not rush to not waste all the work done but we should have clear plans to remerge from lockdowns.

We are still adapting to this new reality. However, while supporting our societies in passing through this public health issue, as soon as we’ll see those trajectories dropping (or before if you can), we will have to quench the pandemics of hate that might break out between countries. We can do that only by resisting the populist trends we had been already experiencing, dark energies that might be getting stronger than ever.

I hope, instead, that we will feel closer to each other. In our streets, in our nations, with our neihboring countries but also with those far far away who suffered or will suffer like any of us. Together, we can build a brighter future. Against each other, more and more lifes will be lost.