Royal Society Publishing has recently published a special issue of Philosophical Transactions A: Biological fluid dynamics: emerging directions compiled and edited by Smitha Maretvadakethope, Marco Polin, David J Smith and Laurence G Wilson and the articles can be accessed directly at  www.bit.ly/TransA2304 

A print version is also available at the special price of £40.00 per issue from sales@royalsociety.org

About this issue
The microscopic world of algae, bacteria, spermatozoa and other swimming microorganisms is fundamental to life on Earth. Here, fluid dynamics follows very different physical laws from those familiar to us. Friction dominates, so cells have to squirm and corkscrew their way through fluid rather than glide. Microorganism have evolved to survive and thrive in the world of biologically active fluids, performing essential functions such as navigating, feeding, cooperating and reproducing. Long-range interactions in microscopic flow can cause beautiful collective effects, such as pattern formation and ‘active turbulence’. Driven by recent advances and touching on topics ranging from new medical technologies to the origins of life itself, this special issue presents contributions at the cutting edge of research in this field.

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Applications are open for 10 ECR funded places at The Company of Biologists Workshop on Novelty, Co-option and Divergence During Gene Network Evolution, organised byJames Hombría and Antónia Monteiro.

The Workshop will bring together researchers working on gene regulation, developmental biology, mathematical modelling and evolution. The invited researchers work on a variety of complex systems and are examining how these systems originated and have evolved over time. By comparing perspectives and experimental approaches to examine the evolution of their specific systems, we hope to draw common threads that may be applicable to most systems, and we aim to highlight these after the meeting in a Review article.

Deadline: Friday 5 December

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Hopeful monsters. Morphospace. Mutation. Natural variation. Mutagenesis screens. Polymorphism. Deformity. Phenotype. Disease. Adaptation. Anomaly. Variant. Error.  

What defines the distinction between defect and difference? Between natural and unnatural? Between right and wrong?

When you are disabled, you are made to feel that you are wrong.

In my evo-devo PhD, my study species was a species of cichlid fish. When people unfamiliar with them ask me what on earth a cichlid is and why on earth I would study it, I launch into explaining what an adaptive radiation is with “you know Darwin’s finches? Like that but fish, and instead of islands, it’s lakes. They’re all sorts of shapes and colours to suit all sorts of ecological niches.” What’s so interesting about them is their diversity. In paper introductions we try to convey the extent of that diversity with adjectives like “remarkable”, “unparalleled”, and “astonishing”. They are words full of wonder. Even with our range of species in the lab, it is something I only really appreciated personally rather than academically when I finally went diving in Lake Malawi/Nyasa.

All of that diversity in form is the product of varying developmental processes, tweaked by genetic differences from an ancestral set of developmental trajectories. So another thing we sometimes say is that cichlids represent a “natural mutagenic screen”¹. Nature has provided a set of morphological differences, so we can investigate the relationship between developmental processes and morphological outcome. Not only does this give us a better understanding of the role of development in evolutionary change, but also it may provide another window into the developmental processes that we still don’t fully understand. The implication is that by studying natural variation, you can better understand “normal” development. If you understand which genetic and developmental differences lead to natural biodiversity, you can gain insights into how genetic and developmental differences lead to pathological deviation from the norm². Take cichlid craniofacial shape diversity, for example. The variation between cichlids reflects adaptations to different feeding strategies. One of the key findings of evo-devo research is that the same developmental pathways and programmes are used across even distantly related organisms. So studying craniofacial development in one organism, such as a fish, can shed light on craniofacial development in another vertebrate, such as humans. By extension, studying how craniofacial development differs between cichlids can shed light on parallel defects in human craniofacial development³.

Who decides what is normal development? Who decides what is natural biodiversity or pathological deviation? Who defines the distinction between defect and difference?

Developmental biology and disability have been two parallel paths in my adult life. Rather, they seemed to be parallel, until more and more ideas from each began to collide. I first stumbled across an article that made me realise I am neurodivergent one evening while undertaking an undergraduate summer research project in a developmental biology lab. Four years later I made it to the top of the waiting list at the NHS clinic and was diagnosed, during my PhD. In the intervening time, I was developing a better understanding of perspectives on evolution and development. In that same intervening time, I read up on neurodiversity theory and disability justice.

Disabled is one of those adjectives that is secretly a verb. Like exhausted, frightened, excited, there is an implicit action that caused this state. I am exhausted – I was exhausted by the swim. I am frightened – I was frightened by the news. I am disabled. The question is: disabled by what? The medical model of disability locates the disability in the person’s body. The social model, on the other hand, locates the disability in the world around me. The world disables me. It disables me by not meeting my access needs. It does not pretend my physiological condition does not exist – but it identifies that the problems related to that condition have external causes. The social model is freeing because it is actionable. If you are shortsighted, you probably don’t consider yourself disabled, and that’s likely because you have prescription glasses or contacts that mean you are not cut off from engaging with parts of the world. The social model is freeing because it means disabled is not a dirty word: you no longer consider it to be an inherent character flaw. Disability is not shameful. Inaccessibility is shameful.

You can repeat those sentences like a mantra as much as you want, but the internalised ableism is hard to shake. It is hard to shake the conviction that you are somehow innately wrong. That it is your fault.

Developmental biology is in the business of understanding how forms are built. With that remit comes the study of how form-building goes awry. Sometimes that’s a tool in the developmental researcher’s toolbox: “Break it, and we understand how a sequence is necessary”⁴. Sometimes understanding how it goes awry by itself is the motivation: so you can suggest how you could fix it. This is often given as justification for the blue-sky discovery science, and is sometimes the central motivation for the work.

About the same time as my diagnosis, I began to lose my hearing – a condition involving excessive bone remodelling that may or may not be inherited, may or may not be exacerbated by oestrogen, but definitely isn’t environmentally induced and that’s about as much as we know. After having covid, I began severely struggling to keep up with my work and life, and about a year later admitted defeat and took long-term sick leave to recover, thankfully returning to finish my PhD despite my fears at the point of intermission. I am accruing disabilities. It is as if I am collecting them like shiny cards. Shiny, shameful, cards.

One of the key perspectives on evolution and development that I like to make clear to people in my non-science life is that there is no such thing as best adaptation. It is a pervasive popular misconception that evolution is a series of advancements and that some species are more evolved than others. The misconception is understandable, given the original scientific thinking on evolution was imbued with the same ideas of advancement and progress. But there’s no such thing as adaptation in a vacuum. Fitness is a concept only in relation to the environment. In my first year undergraduate lectures, we were shown representations of fitness landscapes, and I had fun picturing the landscapes shifting when the environment changed. We stepped through the maths of sickle cell allele fitness in situations with malaria and without malaria. Everything shifts with context, and everything depends on that flimsy, ephemeral balance between organism and environment.

To be glib, a human is no more evolved than a fish, but is better adapted to running long distances to tire out prey, while a fish is better adapted to living underwater. Having lungs instead of gills is a problem if you want to be underwater. To see the cichlids in their natural environment, I donned heavy equipment to take the air down with me. That wouldn’t be something I’d need to consider if I had been looking for finches instead.

The social model of disability is analogous to this argument of adaptation: everything depends on the environment. I’m not arguing that disabilities are an adaptation to worlds that we haven’t yet built. I’m highlighting that the environment determines whether a difference is a disadvantage.

It doesn’t matter if you have insensitive hearing if you are communicating with hand signs. It is not a disadvantage to be sensitive to fluorescent lights if you don’t work in an office rammed full of them. Crucially for the social model of disability, we construct our environment. Humans generally can’t see in UV, so we don’t make road signs with UV markings. If starlings were in charge, things might be different.

Who defines the distinction between defect and difference?

There are many, many aspects of human variation. There are many ways to be disabled. The neurodiversity paradigm makes the argument the most strongly that just as there is bio-diversity between species, within humans there is neuro-diversity. This analogy was arrived at by autistic people on web forums in the 1990s⁵. The differences are physiologically neutral, but disabling because the world is set up to cater by default to the neurotypical neurotype.

Not every neurodivergent person feels the same about these things, of course. And people disabled in other ways feel differently too: people with chronic illnesses, particularly those with chronic pain and energy-limiting conditions, tend to find the social model doesn’t capture the problems they face. In these cases, there is often a dearth of research behind the disabling lack of treatment options. Disabled people are not listened to about which conditions need more research. And in particular, disabled people are not listened to about which aspects of their conditions they would find it useful to have research address. The fact that this research is not done (and sometimes instead unwanted, harmful research is done instead) is an extension of the social model: the lack of appropriate research is itself disabling.

Note my qualifiers: appropriate research, aspects of their conditions. I’ve watched developmental biology presentations where a gene has been identified as of interest through one method or another, and a condition or a whole collection of conditions are listed next to the gene name on the slide. Presumably these have shown up on a human GWAS somewhere. Sometimes it’s physical conditions like spina bifida, or polydactyly. Sometimes it’s neurological conditions like autism, schizophrenia, dementia. It is an off-hand list written to say: ‘look, there could be useful implications to this work! We could be useful and eliminate all these conditions!’ It flattens all these conditions into inherently bad, bad in the same way, with no nuance into what people might actually want to see for improvements to their lives. It is a gut-punch every time. I came to see a seminar about neurons, and I was casually told it would be better if I didn’t exist.

My brilliant friend wrote an article about the futility and danger of the simplifications of identifying genetic associations to complicated human traits in BlueSci, on the search for ‘gay genes’⁶. Not only it is unsurprising that complex, multi-dimensional traits are highly polygenic, but also this pathologising approach encourages the eugenicist perspective: find the cause of difference so you can eradicate it. (And consider: back when homosexuality was in the DSM, would that have shown up on the list of conditions next to the gene of interest in those presentations?) Often, disability-related research similarly enables eugenicist goals. The more I have come to understand this, the more wary I have become of efforts to introduce human embryo gene editing and unwittingly or wittingly make those eugenicist goals possible. With the last of my pre-intermission spare energy, I wrote an article for the Keppel Health Review on autism research, and how autism research funding is often spent on attempts to find the cause of autism, in a way that implies that autism can and should then be eliminated⁷. Rather than listen to criticisms or even make good use of the lived-experience insights from autistic people, high-profile autism research has continued the same research directions while simply pasting inclusive language on top. (Not exactly good practice Public and Patient Involvement!) I was excitedly gearing up to write about the use of language about disability in developmental biology and the intertwined history of eugenics when, ironically, I had to pause working due to my long-term illness.

I’m not arguing that disabled people don’t want research, or new treatments or interventions. I’m highlighting that disability can’t be flattened into something that is uniformly and inherently wrong, for the scientist to swoop in to save us all from.  

Who defines the distinction between defect and difference?

Medical research can transform lives. We live in the age of miracles – I believe this genuinely and viscerally. Transformative medical advances can rest on developmental biology research. And more broadly, developmental and evolutionary biology concepts shape the way we see ourselves as human.

So, it is possible to do better. It is possible to engage with disabled people when you realise your development research is related to disability. Too often, attempts at engagement with marginalised groups relevant to research boils down to pasting inclusive language over the same narrow negative perspectives. Genuine engagement is as simple as listening. Listen to how disabled people feel about their conditions and their circumstances. Listen to what they would have change if they could. Listen to how they relate to their conditions and how they prefer to describe them. Listen to how they explain their experience of their conditions, because it might give you ideas and insights into what to research next. Listen to what kind of research they would find useful and what kind of research they would find harmful. Listen to people with the relevant conditions, because disability isn’t a singular experience. Listen to a range of people with those conditions because, again, disability isn’t a singular experience. And crucially, change your approach to be consistent with what you hear. Change your language, yes, but also interrogate what views underlie the language you were previously using and challenge those views. I think the field could be so much richer for it.

Who defines the distinction between defect and difference? Surely it should be those who live with those differences.

My favourite part of my PhD was any time I had the chance to look at my embryos under the microscope. They were strange, gorgeous things. Developmental biology is fundamentally beautiful. We are no less beautiful for our variation. Instead, perhaps we are more so. Perhaps we are remarkable. Perhaps we are full of wonder.

1. Kocher. (2004). Adaptive evolution and explosive speciation: the cichlid fish model. Nature Reviews Genetics 5, 288–298 https://doi.org/10.1038/nrg1316
2. Diogo, Guinard, Diaz Jr (2017). Dinosaurs, chameleons, humans, and evo-devo path: Linking Étienne Geoffroy’s teratology, Waddington’s homeorhesis, Alberch’s logic of “monsters,” and Goldschmidt hopeful “monsters”. J. Exp. Zool. (Mol. Dev. Evol.) 328B: 207–229 https://doi.org/10.1002/jez.b.22709
3. Powder, Albertson (2016). Cichlid fishes as a model to understand normal and clinical craniofacial variation. Developmental Biology 415, 338-346 https://doi.org/10.1016/j.ydbio.2015.12.018
4. Cooper (2024). The case against simplistic genetic explanations of evolution. Development 151 https://doi.org/10.1242/dev.203077
5. Martijn Dekker (2023). A correction on the origin of the term ‘neurodiversity’ https://www.inlv.org/2023/07/13/neurodiversity-origin.html
6. Chay Graham (2021). What have we learnt by searching for gay genes? BlueSci 52 https://www.bluesci.co.uk/posts/what-have-we-learnt-by-searching-for-gay-genes
7. Clark (2023). Beyond box-ticking: redressing communication power imbalances in autism research. Keppel Health Review. No longer available online; author copy at https://1drv.ms/b/c/de730b21dd0b2d0d/EX6XCAnx–hMo-_vuM-yF-4B4qjWEE5tZbKHYpgeVvjP5A?e=3Pqnix

(7 votes)

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Doctoral programs say they want the brightest, most creative, and motivated students. But once you enter, creativity often gets replaced by execution, independence by subordination, and discovery by survival. Why does this paradox exist, and what does it mean for the future of science?

The academic path looks simple on paper: PhD, to postdoc, to PI. In practice, most doctoral students never become principal investigators, and many who do spend more time chasing funding cycles than pursuing the questions that first drew them to science. Yet, in this high-pressure system, the bottleneck begins far earlier – during the PhD itself.

Doctoral training should be about developing the ability to ask new questions, test risky ideas, and learn through failure. Instead, many students are trained mainly as “hands”. They join ongoing projects, collect and analyze data, write papers, and keep the lab productive. That technical training is valuable, but it does not cultivate the creative independence required of a scientist.

This is a structural trap. A PhD student may be talented, motivated, and full of ideas, but the funding architecture rarely treats them as independent scientists. Instead, they are seen as extensions of their PI: useful hands within someone else’s grant, not originators of their own research. Their ability to explore depends entirely on whether a PI has the time, money, and open-mindedness to support side projects. Exploration in science rarely follows a straight line: when you begin working within a PI’s broader framework, small and unexpected findings often emerge. These fragments, seemingly minor at first, can combine later to sharpen or even overturn an initial hypothesis. But following up on them usually demands extra experiments, financial investment, and time – luxuries a student cannot access independently. Awarding small research grants directly to students could support such exploratory work, giving them the chance to refine an idea, craft a proposal, and navigate submission guidelines. This process itself is vital training in independence – not only in how to build a project, but also in how to cope with the inevitability of rejection and try again.

Funding systems reinforce the trap. In the US, there are prestigious opportunities such as the NIH F31 predoctoral fellowship or the NSF Graduate Research Fellowship Program; funding programs that allow students to pursue independently led scientific projects. These awards are fiercely competitive, but applying is itself a form of training: students must learn the system, engage with program officers, and craft proposals that stand a chance of success. Even without funding, the experience prepares them for future large-scale NIH or NSF applications. By contrast, in Europe such funding opportunities for student-led projects are scarce. Large initiatives like Marie Skłodowska-Curie Actions fund doctoral networks, but the money is formally awarded to the PI, not the student. Seed grants for doctoral candidates are rare, and existing options, such as EMBO or Company of Biologists travel grants, support mobility and training, but not the independent pursuit of a research project.

The result is a predictable cycle: new cohorts of doctoral students become experts in executing tools, presenting data, and meeting deadlines, but not necessarily in generating ideas that push science forward.

Lab culture compounds this problem. When the lab leader values only hierarchy, the PI’s ideas reign supreme. When junior researchers don’t feel safe or encouraged to voice critique, propose hypotheses, or share their own ideas – creativity is stifled. But in labs where every opinion is listened to, where mistakes are not punished but discussed, where funding applications from students are encouraged regardless of seniority – that is where scientific innovation grows.

This is not a problem of talent. PhD students are often able to push the frontiers of science, but only if given the resources and freedom to pursue new ideas. If doctoral training is to form scientists rather than technicians, then structures should be in place to make this possible: funding lines that support student-led discovery, PIs who act as co-mentors rather than gatekeepers, and programs that reward exploration as much as publication.

The paradox is clear. Doctoral programs attract creative minds, but the system too often suppresses the very qualities it claims to seek. And the consequence is equally clear: creative people leave academia for start-ups, biotech, and other environments where risky ideas are supported and failure is treated as progress. If this trend continues, academia risks not only losing its brightest people but also its role as the primary driver of scientific discovery.

(3 votes)

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A recent paper by Otsuki and colleagues investigates the molecular mechanisms driving limb regeneration in axolotl

The question

You may know the axolotl (Ambystoma mexicanum), its funny face and gills floating around its head.

What you may not know is that it is also a model organism for organ regeneration thanks to its ability to regenerate many body parts, including its limbs. This is possible because cells know and remember where they are and can use this knowledge to inform the regeneration process. Cells found at the front of the limb possess so-called anterior identity, while those at the back hold posterior identity information. After amputation, cells from the anterior and posterior parts of the stub meet and trigger correct limb regeneration.

But how do cells know to produce a new limb after limb amputation, and not a tail or head instead?

The molecular bit

A recent study by Otsuki and colleagues¹, highlighted in a News & Views article², investigates the process of limb regeneration in axolotl through transgenic lines, transcriptomics and grafting experiments.

Otsuki and colleagues found that posterior identity in axolotl is established and maintained by a positive feedback loop that involves Hand2, a protein that controls the expression of other genes, and Shh (Sonic hedgehog), a signalling protein involved in limb growth. During development, Hand2 is expressed in posterior cells, and it is present at a steady state in adults. During regeneration, Hand2 is necessary and sufficient to induce the expression of Shh, which in turn activates Hand2 expression in nearby cells, sustaining the establishment of posterior identity in the new limb. After regeneration, Shh expression stops but residual Hand2 ensures lasting positional memory.

The unexpected discovery and why it matters

Interestingly, Otsuki and colleagues were able to rewire anterior-posterior memory, but only during regeneration and in one direction: anterior cells can stably acquire posterior identity when placed in posterior zones (or upon transient Shh signalling), but the opposite leads to defective limb regeneration.

The results presented by Otsuki and colleagues represent an important step forward in the understanding and manipulation of organ regeneration, and future studies into therapeutic applications in humans will benefit from this important work.

References

1. Otsuki, L., Plattner, S. A., Taniguchi-Sugiura, Y., Falcon, F. & Tanaka, E. M. Molecular basis of positional memory in limb regeneration. Nature 1–9 (2025) doi:10.1038/s41586-025-09036-5.

2. Wu, S. Y. C. & Whited, J. L. How axolotl cells ‘remember’ development to rebuild a lost limb. Nature d41586-025-01447–8 (2025) doi:10.1038/d41586-025-01447-8.

(6 votes)

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The Kahneman Chronicles #1: When a Nobel Laureate Fixed Our Lab’s Scheduling Disasters

Daniel Kahneman (1934-2024) was a legendary psychologist who revolutionized our understanding of human decision-making and became known as the “grandfather of behavioral economics.” Awarded the 2002 Nobel Prize in Economics, Kahneman’s groundbreaking research with Amos Tversky revealed systematic biases and mental shortcuts leading people to make irrational choices. 

This article series imagines what would transpire when Daniel Kahneman took a sabbatical and worked in a fly lab. 
Part of “The Kahneman Chronicles: Lessons from a Fly Lab” – A report from our imaginary interdisciplinary fellowship program

On the day Nobel Laureate Prof. Daniel Kahneman arrived for his sabbatical, our Drosophila lab buzzed with nervous excitement. Here was the legend himself—extraordinary psychologist who’d won economics’ highest prize, revolutionizing our understanding of errors in decision-making.

The ghost of Thomas Morgan urged us to do our best. We’d prepared our most impressive experiments, polished our presentations, and practiced our pitch for explaining fly development.

What we hadn’t prepared for was Kahneman spending his entire first morning silently observing us work. Often he scribbled notes in a small black notebook with the focused intensity of Jane Goodall studying chimps.

Why do we spend so much time in the lab?

“I’ll just quickly mount these embryos—twenty minutes, tops,” announced postdoc Shweta. This became a two-hour odyssey involving broken coverslips and dried glue. Followed by an existential crisis, wondering if the fluorescent blob she saw was signal or autofluorescence from a properly developing embryo.

“Quick PCR setup, maybe thirty minutes,” declared grad student Fillip, before vanishing into an afternoon-long quest. Missing primers. Buffer math. Finding the thermal cycler waited on “infinite hold” since previous Tuesday. You know the drill.

“Fascinating,” Kahneman murmured after each wildly inaccurate prediction.

By day three, a pattern was undeniable. Every time estimate in our lab was spectacularly yet consistently wrong. “Simple” tasks morphed into epic quests.

The Intervention

Kahneman approached the whiteboard where we’d sketched our weekly schedule – optimistically planning seventeen different experiments into forty work hours.

“Let’s implement realistic time budgeting,” he announced with the calm authority of someone who’d spent decades studying how humans delude themselves. Our simple thirty-minute embryo injections were now allocated one-hour blocks.

The room erupted in protests. “But we’ve done these injections hundreds of times!” “We know exactly how long they take!”

Kahneman smiled. “You’re all victims of the planning fallacy. Your System 1 is wildly optimistic about everything. Your mind accounts only for quick needle preparations while forgetting inevitable moments someone drops the cover slip itself”

“Your intuitive mind,” he explained, “only remembers the core task—actual injection. It conveniently forgets the setup, troubleshooting, inevitable equipment malfunction, and time spent staring at embryos wondering if they are worth injecting at all.”

The Planning Fallacy: The tendency to underestimate time, costs, and risks of future actions while overestimating their benefits. Even when people know similar tasks have taken longer than expected in past, they still predict future tasks will take less time.

System 1 vs. System 2 : Kahneman’s framework for two modes of thought. System 1 is fast, automatic, and intuitive (like quickly estimating “this should take twenty minutes”). System 2 is slow, deliberate, and logical (like carefully calculating each step: needle prep, embryo collection, injection setup, actual injection, cleanup, and imaging).

The Kahneman Method in Action

His solution was deceptively simple: multiply every time estimate by two, then add buffer time for “unknown unknowns.” “There are things you know will probably go wrong—known unknowns, like occasional broken needle or contaminated sample,” he explained.

“But then there are unknown unknowns—the completely unexpected problems you can’t even anticipate. The incubator that dies on a weekend, the new batch of reagent that behaves differently, or the day your hands just won’t stop shaking. You can’t plan for specific unknown unknowns, but you can acknowledge they exist.”

He made us track everything for two weeks: actual injection times, PCR setup duration…and the data was humbling. Our “standard” twenty-minute procedure had a median time of 40 minutes, with some taking over 1.5 hours when equipment misbehaved.

We tried his interventions skeptically. To our disbelief, the results were miraculous and maddening in equal measure.

For the first time in lab history, experiments actually finished when scheduled. Postdocs stopped working until midnight to complete “quick afternoon experiments.” Stress level plummeted as people stopped running late for their next commitment.

“Your emotional attachment to each experiment makes you treat it as special,” Kahneman explained. “You think ‘this time will be different’ or ‘I’m more prepared now.’ But from a statistical perspective, today’s PCR is just another data point in the distribution of ‘times PCR has taken in this lab.’
The planning fallacy tricks you into believing you can beat the historical average through wishful thinking.”

The lesson was profound: scientists are ultimately human and prone to same cognitive biases that affect everyone else. We bring these same mental shortcuts to our labs, our experimental designs, and our data interpretations. The first step toward better science maybe a more nuanced use of an important equipment—our own minds.

Have you experienced similar planning fallacies and overcome them? Do share in the comments.

What else did the Prof. Kahneman advise us on? Stay tuned for the next article in the series.

Practical Applications: The Kahneman Time Revolution

1. Track Reality First: Record actual times for routine procedures for couple of weeks.

2. Use the 1.5x Rule: Multiply routine task estimates by 1.5.

3. Use the 3x Rule: Triple your estimate for novel experiments.

4. Build Break Points: Schedule natural stopping points in long experiments, to allow buffers for unknown unknowns.

5. Try the Three-Point Method: For familiar tasks, estimate your best-case time (everything goes perfectly) and worst-case time (multiple things go wrong). Then calculate the geometric mean (root of the product) for a realistic schedule estimate.

Example: Embryo injection times (Best case 20 minutes, worst case 1 hours), geometric mean√(20 × 60) = √1200 ≈ 34 minutes.

Sameer Thukral is a post doc in the lab of Yu-Chiun Wang at RIKEN-BDR, Kobe, Japan, where he loves discussing science in the healthy and respectful lab environment. He is a developmental biologist with a focus on mechanics of yolk-blastoderm interactions. He is also the co-founder of BDR-Launchpad, a post-doc network for supporting ECRs with the hidden curriculum of science.

The observations made here are his own and do not reflect the opinions of the employer. This article was written by Sameer Thukral, with formatting, structuring and framing support of Claude AI.

(5 votes)

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I am an avid podcast listener, especially at the gym. Instead of fueling my reps with anger-fueled lyricism or upbeat songs that raise my bpm to 120, I noticed feeding my brain with podcasts is a better way of enduring my hour and a half workout. After all, what is a better way to feel incentivized to squat to 60 kg than to listen about how women have been overlooked in science? In the recent season of the So Cultured podcast, tears of anger fell down my face at the cruel injustices faced by Brenda Milner, Tu Youyou, and Marie Curie. Besides anger, I also felt inspired. Worried about my own academic journey, wondering if I am good enough, I found comfort in their stories.

Conducting my own investigation, wondering whose story is not largely known, I bumped into Hilde Mangold (or Hilde Proescholdt at the time) and “The Organizer.” During her doctoral studies, she used two species of salamander with embryos that differ in pigmentation to perform 259 transplantations of the blastopore’s dorsal lip into the ventral region of a host gastrula^{1, 2}. This experiments catapulted Spemann for the Nobel Prize. In several significant cases, she observed that a secondary axis with neural system developed in the host embryos ³. Spemann and Hilde Mangold therefore concluded that the embryonic region of the dorsal blastopore lip was able to induce embryonic development and called this specific embryonic region an “organizer.”⁴

Hilde’s trajectory was impressive. At the age of 16, she attended the prestigious Gymnasium Ernestinum, at the time almost inaccessible to girls¹. But like most young girls at the time, she was sent to a private institute for young ladies to learn proper housekeeping and social etiquette right after. But her curiosity and intelligence granted her a place at the University of Jena, which eventually led her to Spemann. She possessed a great deal of skill. Perhaps this dexterity to perform and master minuscule surgical operations could be attributed to the time she spent sewing in school. In fact, she had a keen eye for detail, documenting her implants with drawings in her lab notebook⁵, a skill that may be lost in the upcoming years with digitalization.

Although it was Spemann’s quest started in 1903, with the production of identical twins from newt embryos using his daughter’s hair loop⁶. The completion of the pusruit to find the said “organizer,” couldn’t have been done without Hilde. She expanded Spemann’s techniques. Used thin glass needles, often heated, to cut certain parts from the embryos or to burn them away. She was critical in providing the empirical evidence needed, imagine your dissertation helps your professor win a Nobel. And aside from prizes, this experiments were monumental for the time, forging paths for theoretical and developmental biology, and cell to cell communication.

Hilde’s story was short lived. Now I wonder what would’ve she further achieved is were not for dying at the young age of 26 from an accident. The best we can do is to continue living for them, and as other women at the time, to persist and lead by curiosity and resilience. Perhaps our time as graduate students are not as fruitful as Hilde’s, still, as students we understimate the work we do in the lab, and forget to advocate for our contributions. We troubleshoot for months, have failed results, or no results at all. So although we must perform the 200 experiments, we must celebrate and give some grace even if only 6 are significant. At the end, we are in fact, the sum of our parts.

References

1. VAN Robays, J. (2016). Hilde Mangold-Pröscholdt (1898 – 1924): The Spemann-Mangold Organizer. Facts, Views & Vision in ObGyn, 8(1), 63–68.
2. De Robertis, E. M., Driever, W., & Mayor, R. (2024). Celebrating the centennial of the most famous experiment in embryology: Hilde Mangold, Hans Spemann and the organizer. Cells & Development, 178, 203921. https://doi.org/10.1016/j.cdev.2024.203921
3. Kumar, V., Park, S., Lee, U., & Kim, J. (2021). The Organizer and Its Signaling in Embryonic Development. Journal of Developmental Biology, 9(4), 47. https://doi.org/10.3390/jdb9040047
4. Spemann, H., & Mangold, H. (1924). Über Induktion von Embryonalanlagen durch Implantation artfremder Organisatoren. Archiv für Mikroskopische Anatomie und Entwicklungsmechanik, 100(3–4), 599–638. https://doi.org/10.1007/BF02108133
5. Driever, W., Holzschuh, J., Sommer, L., Nitschke, R., Naumann, A., Elmer, J., & Giere, P. (2024). Hilde Mangold: Original microscope slides and records of the gastrula organizer experiments. Cells & Development, 178, 203909. https://doi.org/10.1016/j.cdev.2024.203909
   
6. De Robertis, E. M. (2006). Spemann’s organizer and self-regulation in amphibian embryos. Nature Reviews Molecular Cell Biology, 7(4), 296–302. https://doi.org/10.1038/nrm1855
   
7. Von Bubnoff, A. (2024). The Spemann-Mangold organizer discovery and society. Cells & Development, 178, 203906. https://doi.org/10.1016/j.cdev.2024.203906
   
8. Brandt, C. (2022). Vitalism, Holism, and Metaphorical Dynamics of Hans Spemann’s “Organizer” in the Interwar Period. Journal of the History of Biology, 55(2), 285–320. https://doi.org/10.1007/s10739-022-09682-9
   

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Join us in mid-October to hear from two early-career researchers working on development across scales. Chaired by Development’s Executive Editor, Alex Eve.

Wednesday 15 October – 09:00 BST (UTC+1)

At the speakers’ discretion, the webinar will be recorded to view on demand. To see the other webinars scheduled in our series, and to catch up on previous talks, please visit: thenode.biologists.com/devpres

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I’m a big fan of podcasts, and one of my favorites is Tim Harford’s “Cautionary Tales.” It tells true stories about disasters and what we can learn from them. One episode particularly stuck with me—the story of Tenerife.

On March 27, 1977, two Boeing 747s collided on a foggy runway in Tenerife, killing 583 people. This wasn’t just about miscommunication, mechanical failure or bad weather. The investigation revealed something more profound: the captain was the airline’s Chief Flying Instructor, thus creating a steep “gradient” that prevented his first officer from challenging a fatal mistake.

When the first officer realized they didn’t have takeoff clearance, he saw disaster coming but couldn’t bring himself to forcefully challenge his superior.

This got me thinking: when power dynamics prevent people from speaking up, disaster follows. Does this also apply to academia?

A Pattern Worth Noticing

Tim Harford’s podcast reveals a disturbing pattern: many disasters across different fields stem from the same problem—people being unable to challenge authority when they see danger ahead. From naval catastrophes to medical errors, from financial crashes to engineering failures, a common thread is often authority gradients that silence dissenting voices.

To be clear, most academic labs aren’t disaster zones. Most PIs, including my own, are thoughtful mentors who genuinely care about their students’ growth and scientific development. Many labs operate with healthy dynamics where ideas flow freely and disagreement is welcomed.

But here’s a learning from other fields: even well-intentioned leaders can unknowingly create subtle power imbalances. And in science, our “disasters” aren’t plane crashes—they’re missed discoveries, delayed projects, unexplored hypotheses, and brilliant ideas that never see daylight.

The Academic Context

In academia, unlike most corporate environments, one person—your PI—has enormous influence over your career trajectory. As a PhD student or Post-doc, you commit years to one supervisor’s lab. They guide your research direction, allow your access to resources, and significantly influence your future opportunities.

This isn’t inherently problematic. Expertise matters, and experienced scientists rightfully guide newcomers. The challenge is when this necessary hierarchy inadvertently creates barriers to open scientific dialogue.

Even in the best labs, there might be subtle versions of this dynamic. A student hesitating to present data that contradicts the PI’s hypothesis. A postdoc avoiding questions that might seem to challenge established lab protocols. These aren’t dramatic confrontations—they’re quiet moments where respect for authority might overshadow respect for scientific inquiry.

The Free Resource We Maybe Missing

Of all the things science needs—expensive equipment, ample funding, and reagents—respect, costs nothing. Yet, it might be our most powerful tool. Every carefully planned experiment and every piece of expensive equipment depends on people thriving in an environment where they feel safe, heard, and valued.

Science thrives on disagreement. The best discoveries often come from questioning prevailing wisdom and challenging assumptions. But when subtle power dynamics make people hesitate to speak up, we miss out on breakthrough ideas.

The most productive labs may be doing something simple: they separate intellectual discussion from hierarchy. In these labs, everyone responds to contradictory data with curiosity, not defensiveness. Unexpected results are seen as learning opportunities, not failures.

A Quick Self-Check for the Lab

As an opportunity for reflection, PIs and mentees can ask themselves:How often do mentees feel comfortable disagreeing with an idea? If it’s rare, it may be worth examining why. Perhaps even create a “disagreement board” to make the act of questioning a hypothesis more salient and celebrated. What’s the atmosphere like when someone presents data that contradicts an expectation? Do people feel comfortable sharing results that go against the grain?

These aren’t accusations; they’re simply opportunities for growth and improvement. The goal isn’t to flatten hierarchies but to ensure that authority serves discovery, not ego. Sometimes, the most junior person in the lab has a game-changing insight. But they can only share it if they feel safe to do so.

The bottom line

Listening to cautionary tales from other fields reminded me that power dynamics are everywhere, often subtle, and worth examining. In science, where truth-seeking is our highest goal, creating space for respectful disagreement isn’t just good mentorship—it’s essential for discovery.

Sameer Thukral is a post doc in the lab of Yu-Chiun Wang at RIKEN-BDR, Kobe, Japan, where he loves discussing science in a healthy and respectful environment. He is developmental biologist with a focus on mechanics of yolk-blastoderm interactions. He is also the co-founder of BDR-Launchpad, a post-doc network for supporting ECRs with the hidden curriculum of science.

The observations made here are his own and do not reflect the opinions of the employer. This article was written by Sameer Thukral, with formatting, structuring and framing support of Claude AI.

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Our ‘Featured resource’ series aims to shine a light on the resources that support our research – the unsung heroes of the science world. In this post, we learn about the data and functionalities available at Facebase, and hear about new initiatives they are developing.

What is FaceBase?

FaceBase is a public data resource and repository dedicated to advancing basic and clinical research spanning the translational spectrum of dental, oral, and craniofacial (DOC) biology, as well as related systemic health and disease models throughout the data lifecycle. FaceBase realizes this mission by recruiting, transforming, and publicly sharing research and clinical data. 

This freely available and public resource currently hosts over 1,100 datasets, approximately 3,000 experiments, over 210,000 images, and more than 8,000 genomics files. FaceBase exemplifies FAIR (Findability, Accessibility, Interoperability and Reusability) and TRUST (Transparency, Responsibility, User focused, Sustainability, and Technology) principles of scientific data sharing, ensuring that its clean, well-structured datasets are not only easy to find and reuse, but are also inherently AI-ready for integration into modern computational workflows.

FaceBase hosts data from both human subjects and animal models, encompassing a wide array of experimental approaches, including multiple omics and imaging data types. This platform welcomes contributions of data from the community after going through a careful review process and quality assurance.

• 1,170 datasets, 2,984 experiments, 210,000+ images, 8,000+ genomics files
• Human subjects and animal model data (Current animal models include mouse, zebrafish, chimp and chick)
• Controlled-access and public data
• Genomic and phenotypic data from multiple species
• Most known types of genomics and imaging data
• Resources and strategies to enhance data reproducibility
• State-of-the-art data science methods to support cutting edge research
• Standards and educational resources for improving data management and sharing practices across the community

FaceBase demonstrated itself as a credible resource for the DOC research community through its CoreTrustSeal accreditation after a two-year approval process, as well as becoming one of a select number of NIH approved Controlled Access Data Repositories (CADRs) handling genomics and other sensitive data.

What inspired the development of FaceBase?

In 2009, National Institute of Dental and Craniofacial Research, National Institutes of Health (NIDCR, NIH) launched FaceBase in response to the need for more comprehensive analysis of craniofacial development. With the immense amount of craniofacial data being generated, there is a danger of relevant datasets being buried in the avalanche of genomic and other data.

The first five years (known as FaceBase 1) started with a spoke-and-hub of 10 spoke projects and resulted in almost 600 datasets and over 100 publications. The next phase of FaceBase (FaceBase 2) began in August 2014 with 10 spoke projects and a new hub that developed an updated data model allowing for more data integration and faceted searches with a new server interface. The third phase (FaceBase 3) dismantled the spoke-and-hub model in favor of a community-based model that opened submissions to any contributor. We also promoted the idea of self-curation which allowed us to scale up considerably: since opening up to community contributions, we have more than doubled the number of contributors and our dataset growth has kept pace with prior years.

How can scientists use FaceBase in their research?

For researchers and clinicians seeking to generate a hypothesis for a new grant, validate their own data by comparing with controls, or examine phenotypes in mutant models, the FaceBase Data Browser provides an intuitive interface. Data are represented as filtered records, with sidebar attributes that function similarly to filters on an online shopping site.

Find Data

You may begin your search with the BROWSE ALL DATASETS button on the homepage or you can use the DATA tab in the top navigation menu bar (available on all pages) to start with a particular model.

When you start searching the data browser you will see:

• Search results based on filters
• Faceted navigation sidebar on the left
• Search bar above the results

By default, the data is sorted to display the most recently released data first. On the left side is the faceted navigation based on characteristics of the data and experiments. Scroll down to see all the categories of filters available to narrow down your search.

Export Data

All open access data can be downloaded directly from the browser without requiring login. If you want to download a large amount of data, you can use our BDBag protocol-derived tool, which allows for reliable transfer of a “bag” of digital content – in this context, a group of files that you want to export in bulk. It is available as a GUI client and a command-line client.

For more information, see https://docs.facebase.org/docs/Exporting-Data-from-FaceBase/.

Fill out Data Management & Sharing (DMS) Plans

We also offer resources to help you include FaceBase in your Data Management Sharing (DMS) plan, including template text that you can copy and paste into your plan. You can find guidance on how to fill out the various fields here: https://www.facebase.org/contributing/dms/.

Who are the people behind the resource?

FaceBase is run by University of Southern California’s Center for Craniofacial Molecular Biology (CCMB) and Information Sciences Institute (ISI) in Los Angeles.

Our current leadership and staff include:

• Principal Investigators – Yang Chai (CCMB) and Carl Kesselman (ISI)

• Co-Investigators – Robert Schuler (ISI, technical lead) and Parish P. Sedghizadeh (Herman Ostrow School of Dentistry of USC)

• Scientific Curators – Jifan Feng, Tingwei Guo, and Thach Vu Ho (CCMB)

• Data Management Lead – Alejandro Bugacov (ISI)

• Collaborations and Communications Coordinator – Cris Williams (ISI)

• Project Manager – VyVy Nguyen (CCMB)

How can researchers help and contribute to the resource?

The most effective ways to support FaceBase are two pronged: 1) contribute data to improve the breadth and depth of our offerings and 2) cite any data you deposit or reuse by using the citation tools embedded in the platform.

Contribute data

FaceBase welcomes biomedical basic and clinical research across the translational spectrum related to the DOC domains as well as those from related systems. We are also an approved repository for the HEAL Initiative, an NIH-wide effort to speed scientific solutions to stem the national opioid public health crisis.

Our current funding phase expands our focus to accept research and data on relevant anatomical and biological health and disease models beyond DOC domains, for example the ear and eye or biomarkers that overlap with those found in DOC regions.

Interested researchers and clinicians simply fill out a short form (https://www.facebase.org/contributing/submitting/form.html) to submit their data for review.

After a review process from the FaceBase team and NIH program staff, approved projects will receive a one-hour one-to-one tutorial to learn how to curate their data using the online metadata forms and how to upload data. You can find more information about the process here: https://docs.facebase.org/docs/Data-Submission-Key-Concepts/.

Note that our focus is on high quality data that conforms to FAIR initiatives that bolster or expand existing data. Find more detailed descriptions of the types of data we are especially interested in here: https://www.facebase.org/contributing/data-priorities/. If you have any questions about whether your data is a good fit, please contact us at help@facebase.org.

Cite FaceBase data

FaceBase has been leading the charge on effective and transparent citation of data for many years. Every data record has its own unique, permanent identifier. In addition, every Dataset and Project page has a registered Digital Object Identifier (DOI) and a “Share and cite” button that provides citation text that you can simply copy and paste into your publication.

For more information and examples of citations, please go to: https://www.facebase.org/citing/

What are the next steps for FaceBase?

New collaborations and multi-tenant federation

EarBase: As part of our new focus to include research and data from relevant anatomical and biological health and disease models, FaceBase is collaborating with the National Institute on Deafness and Other Communication Disorders (NIDCD) to migrate 3D images of the temporal bone that were previously held in a private enclave.

CranioRate: Another new development is our collaboration with CranioRate, a user interface that is being launched in late 2025 to help surgeons and clinicians manage metopic craniosynostosis cases, a birth defect that affects the structure of the skull. In particular, FaceBase is supporting their open access human craniosynostosis image bank and working towards standardized vocabularies and ontologies to ensure the data’s FAIR-ness.

Integrating clinical elements from Electronic Health Records (EHR)

We are collaborating with clinician-scientists on a pilot project to integrate clinical data from patients with temporomandibular disorders (TMD) into FaceBase. Important directives of this pilot include ensuring clear patient consent for repository use (that specifically permit the use of identifiable health information for research without requiring re-authorization) and exploring the potential of AI/ML methods to analyze clinical notes and improve diagnostic accuracy.

Advanced computation and AI-ready analytics

By definition, aligning the data in the FaceBase repository with FAIR principles means that our data, which is clean, well-formatted, with structured metadata and provenance, is ready for a data scientist to pull into analytics platforms. In the future, we plan to continue to enhance the AI-readiness of our data, provide curated collections of “reference datasets” for training purposes, and enable interoperability with LLMs and lab notebooks and develop an AI-assisted curation bot for data contributors.

Interoperability with external data resources

We are also developing a pipeline to transform raw FaceBase data into a processed format that can be ingested by external resources, for example a cloud-based analytics platform.

Where can we find FaceBase?

You can find us at www.facebase.org and you can always contact us at help@facebase.org.

Other ways to connect:

• Attend one of our monthly Office Hours on the last Wednesday of each month via Zoom (requires registration).
• Join our mailing list by signing up for a FaceBase Users account.
• Follow us on Bluesky or X.

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