Did I commit suicide?

If I had continued to be in IISER, I would have retired by this month end. I decided to quit academia exactly four years ago and taking some time to wind up, my last day in a formal academic institute was 31st march 2019.

When I decided and declared my plan to quit academia, there were a wide variety of responses. I know that many would have felt happy but did not say that, at least to me. The responses that reached me were sad, surprised, shocked, disagreeing, pursuing me to change my mind, wishing for a better future and all.  An old friend called and told me that I was committing suicide. I will have no future in science by quitting academics. Perhaps he, like many others, thought that science can survive only in academia. “Leaving academia is like killing oneself and one’s science.” he said. I could only reply “Let’s us experiment and see”. So my suicide was experimental. Now after almost 4 years of the continued experiment, I can ask myself, “Did I really commit suicide?” Was it an end of my creative, active, productive years in science measured by any standard?

Let us begin with the stereotyped conventional standard measures, in spite of my repeatedly expressed view that these are useless and often counterproductive measures. That is, the number of publications, impact factors etc. In the less than four years of non-academic, non-career focused pursuit of science, I (and my loosely knit and highly dynamic team) published 17 papers, 5 more are in preprints and under review, revision or resubmission. Seventeen more are in various stages of development out of which 11 have been penned down partially (list of all the 34 at the end). Quite often new ideas come from interactions with students or others and they jump up to high priority. The 17 that currently fall in line might be bypassed by more novel and attractive concepts. My average outcome of papers in the last 4 years is slightly greater than my own average when in service. Recently I came across this article in Science (https://www.science.org/doi/10.1126/sciadv.abq7056) that gives the average output of a faculty in the US. My average output as a citizen outside mainstream academia is 2-3 fold greater than that. For those who believe in impact factors I published in fairly good impact journals including Behavioral and Brain Sciences, Conservation Biology, Evolution, Scientific Reports-Nature and PLOS One. The only limiting factor was author charges, which puts a limit on how many papers I can publish in such journals. In spite of this limit, going by conventional standards I did not perform lower than the faculty average anywhere in the world.

But not everything is captured by the conventional standards. What I value more is the range of subjects that I could explore, some of which was possible ONLY because I did NOT work in an Institute or did not identify myself with the mainstream academics. The mainstream has three major hurdles or limitations. One is the heavy peer pressure owing to which it is too tough to think out of the box and consider possibilities other than the current trends in the mainstream. I feel the difference since I have experienced working both within and out of the system. In my experience, being free from peer pressure opens up the mind far wide and encourages alternative ideas and visions. Even more important than that, I can afford to be more honest now. Being in the system it is extremely important to worry about political correctness and not to hurt your potential funders, editors and reviewers. I feel free now to expose and criticize whatever I see as wrong, unfair or unethical. An academic aspiring for a successful career needs to inculcate cowardice. Success is not possible without that.

The second limitation is the bureaucratic framework in which you need to fit all your research. The bureaucratic procedures are made for a certain type of, mainly lab oriented research. Other areas of research have different needs. The institutional structure is intended to support science and therefore should have sufficient flexibility to support different needs of different types of research. But in reality the institutions say, “This is our framework and it shall nor bend. You bend your science to fit in, otherwise fXXX XXX”. (That’s what I actually did when faced with such a situation.) Having no funding is often better than having funding already allotted to something. Research can take unexpected paths anytime and you need to change everything when it does so. A pre-budgeted funding doesn’t allow you to do so.

The third barrier is the formalism; only things done a certain way are called science. The same thing done or written in a slightly different format is not considered science. This package of limitations comes along with the institutional support, so the support itself becomes the prison. I clearly experienced that being out of the prison improved my thinking, opened up the skies, eliminated rituals and reduced the conflicts of interest, biases and prejudices typical of mainstream science.

Of course having no money in hand puts a different set of limitations. So for obvious reasons, the nature of my research changed. I have no lab to do any desk work now. But the fields and forests out there and the human society are two open labs of huge dimensions. You don’t need any committee pre-approvals, pre-registration or any formality to start working on anything. Developing concepts and making simple models addressing novel questions and exploring new ideas does not need money. In addition, in today’s science huge amount of data are lying in the public domain largely unused and un-interpreted or more often misinterpreted. Most institutional scientists are data generators, not insight generators. Generating useful insights from already available data also does not need any funding. Anyone outside the academia can certainly do more insightful science using data generated by the academic labor contractors.

There is a neo-brahminical notion in the mainstream that only whatever is published in peer reviewed journals is science. This notion persists in spite of so many studies and experiments showing the flaws, biases, power play and academic racism in the peer review system. The science under the open sky is free of such flaws. I continued to publish in peer reviewed journals at times because the short sighted academics won’t understand the science outside. But now I also write original research findings directly for laypeople.  In the last 4 years I wrote three science books in Marathi and 6 more book concepts are developing. These books are not only science simplified in local languages. It has novel research outputs and novel concepts being discussed for the first time anywhere. I have lost count of articles I wrote in news papers and magazines.

Why publish in Marathi? For the simple reason that the kind of science I do can be better expressed in people’s language. A language comes with its own set of ideas, imaginations, fantasies and metaphors not necessarily shared by other languages. Fantasies are important for science, as exemplified by the August Kekule’s story about the origin of the concept of cyclic compounds. I have myself used very carefully concepts from Indian mythology in churning out novel interpretations of experimental data. Diversity of cultures, languages, mythologies will enrich the input of ideas in science further. So it makes sense to do original science in different languages. I am using Marathi more frequently now. It may eventually get translated in English if and when anyone wants them in that language.

Whom did I work with? One person can hardly do anything. Team work is essential. Obviously I had no PhD students registering with me anymore. I am no more teaching any undergrad courses. But somehow a few undergraduate students as well as PhD students registered with someone else kept on coming to me to enjoy working together. The advantage now is that they come from multiple colleges and institutions. My former co-workers continued to be with me independent of their jobs. I also worked with social workers, NGOs, farmers and tribesmen; having no formal training in science; some being illiterate or marginally literate and they have been my co-authors in papers published in prestigious journals too. Being illiterate should not prevent one from being a productive researcher and I could demonstrate this happening in reality. This would have been impossible in an institutional framework that relies on paper qualifications rather than the inner capabilities and drives of a candidate while recruiting research personnel. So I presume there will not be dearth of co-workers in pursuing science outside academia. In fact you get better personnel outside because there is no formal qualification requirement.

I am not writing this as a self appraisal. A self appraisal was necessary as a part of the academic routine in IISER, which I hated like most others. So why should I do it now when there is no compulsion. I am not writing this to say how great I am. I am not; I only followed a different path. It’s only meant to demonstrate that good quality science can be done outside mainstream academia. I will urge science personnel more talented than me to try out this model of doing science. I am sure there are more capable researchers than myself and they can make this model even more productive. The monopoly of academia needs to be broken. I don’t mean that we wind up all Universities and Institutions but we need to take active research beyond the bounds of academia and we should see more of common people contributing to important and novel areas of science that the academia just cannot reach.

In academia, we see a trend in the reverse direction. Scientific misconduct, reproducibility crisis, race- gender and other discriminations, peer review biases, embarrassingly growing retractions, stress-anxiety-suicide in students are increasingly coming to light. No sound, effective and durable solutions to such problems are coming forward. This is mainly because of badly designed academic systems. Systems thinking is rare in the field although there is intensive research in systems thinking focused on fields other than academia. These researchers do not seem to be doing anything to mend their own house. Changing the academic systems seems too improbable in near future mainly because people in academics have never experienced alternative systems. So they remain short sighted. I see one effective way out. I have experimented on it and demonstrated that it works. Liberating science from academia is my solution. This is badly needed for ensuring a future of more inclusive, more open minded and less prejudiced healthy science. 

Papers published after quitting formal academics:

I can say that in 7 out of the 17, my earlier time at IISER has some contribution.

  1. Shinde, S., Patwardhan, A. & Watve, M. (2022) The ratio versus difference optimization and its implications for optimality theory. Evolution, 762272-2280.  https://doi.org/10.1111/evo.14605
  2. Ojha, A., Watve, M. (2022). The predictive value of Kuhn’s anomaly and crisis: the case of type 2 diabetes. Academia Letters, Article 5494. https://doi.org/10.20935/AL5494.
  3. Watve M, Watve M. (2022) Tradition–invention dichotomy and optimization in the field of science. Behavioral and Brain Sciences Nov 10;45:e272. doi: 10.1017/S0140525X22001236.
  1. Kharate, R.; Watve, M. (2022) Covid 19: Did Preventive Restrictions Work? Curr Sci, 122, 1081-85.
  2. Vidwans Harshada, Kharate Rohini and Watve Milind (2021) Probability ratio or difference: How do people perceive risk? Resonance, 26, 1559-1565.
  3. Ulfat Baig et al (2021) Phylogenetic diversity and activity screening of cultivable actinobacteria isolated from marine sponges and associated environments from the western coast of India. Access Microbiology, 2021;3:000242. DOI 10.1099/acmi.0.000242.
  4. Shinde S, Ranade P, Watve M. (2021). Evaluating alternative hypotheses to explain the downward trend in the indices of the COVID-19 pandemic death rate. PeerJ 9:e11150 DOI 10.7717/peerj.11150.
  5. Poorva Joshi, Neelesh Dahanukar, Shankar Bharade, Vijay Dethe, Smita Dethe, Neha Bhandare and Milind Watve. (2021) Combining payment for crop damages and reward for productivity to address wildlife conflict. Conservation Biology, 2021, 1- https://doi.org/10.1111/cobi.13746. (This paper became the Editor’s pick for Conserv. Biol. Dec 2021 issue). This work also received an award from Society for Conservation Biology.
  6. Patil P, Lalwani P, Vidwans H, Kulkarni S, Bais D, Diwekar-Joshi M, et al. (2021) A multidimensional functional fitness score has a stronger association with type 2 diabetes than obesity parameters in cross sectional data. PLoS ONE 16(2): e0245093. https://doi.org/10.1371/ journal.pone.0245093
  7. Diwekar-Joshi M, Watve M (2020) Driver versus navigator causation in biology: the case of insulin and fasting glucose. PeerJ 8:e10396  https://doi.org/10.7717/peerj.10396

11.  Baig, U., Vidya Lakshmi., Ojha, A., & Watve, M. (2020). Geriatric infections: Decreased immunity or evolved opportunists?. Journal of biosciences45(1), 57. https://doi.org/10.1007/s12038-020-0025-x.

12.  Vibishan B. and Milind Watve. (2020) Context-dependent selection as the keystone in somatic evolution of cancer. Nature Sci Rep,10, 4223.

13.  Anagha Pund, Ketaki Holkar, Milind Watve and Ulfat Baig. (2020) Predator, prey and the third beneficiary. Matters 201911000003

14. Watve Milind and Ojas S. V. (2020) Difference, Division & Desi: How people’s innate intuitive economics decides the outcome of an operation. Economic and Political Weekly, Feb 22nd, Vol 8, 28-32.

  1. Ulfat Baig, Lavanya Lokhande, Poortata Lalwani, Suraj Chawla, Milind Watve (2019) Foraging theory and the propensity to be obese: an alternative to thrift. HOMO J.Comp Human Biol, 70(3), 193-216.
  2. Harshada Vidwans, Anagha Pund, Milind Watve (2019) The other side of Statistical significance, JAMA network https://jamanetwork.com/journals/jama/fullarticle/2730486

17.  Watve M. (2020) Challenges to human nutrition research: shall we learn from history? Science, e-letters https://science.sciencemag.org/content/367/6484/1298/tab-e-letters

In preprints and under review/resubmission:

1.    Srashti Bajpai and Milind Watve Evolution of new variants of SARS-Cov-2: mutation limited or selection limited? https://biorxiv.org/cgi/content/short/2022.09.22.509013v1

2.    Akanksha Ojha, Harshada Vidwans, Milind Watve  Does sugar control arrest complications in type 2 diabetes? Examining rigor in statistical and causal inference in clinical trials https://doi.org/10.1101/2022.08.02.22278347

  1. Watve M. G. and Bhisikar H. Epidemiology: Gray immunity models give qualitatively different predictions. https://www.preprints.org/manuscript/202109.0162/v1
  1. Milind Watve. The evolutionary psychology of scientific publishing: Cost-benefit optimization of players in the game. https://ecoevorxiv.org/nvpe2/
  1. Akankasha Ojha and Milind Watve. Hyperglycemia in type 2 diabetes: Physiological and clinical implications of a brain centered model.  https://www.biorxiv.org/content/10.1101/2022.01.19.477014v1
Themes at various stages of development that are expected to make publishable papers in near future:

(The first 11 are already partly written down. There are non-academic co-authors for many of them)

  1. Somatic evolution of cancer: A new synthesis
  2. Fitness interventions are more effective than anti-hyperglycemic interventions in preventing diabetic complications: A comparative meta-analysis
  3. Virulence management: shaping natural selection on virus in ongoing epidemics.
  4. Need for ingenious behavior based systems design: learning from good and bad systems
  5. Principles of behavior based systems design
  6. A behavior based farmer policy for India
  7. Potential applications of Support cum Reward system in Agriculture.
  8. Multiple context specific models of cost-benefit optimization in human behavior
  9. Additive and multiplicative fitness components and multi level selection.
  10. Potential behavioral conflicts and possible mitigation in community forest rights and other community management programs
  11. The evolutionary psychology of scientific publishing: a behavior based system model for science publishing
  12. The evolutionary psychology of research: Optimization of innovation and appropriate design of an academic support system.
  13. Poor correlation between different components of functional fitness and the trend with age.
  14. Inferring causation from cross sectional correlations in non-equilibrium systems
  15. Optimizing flower sex ratio for maximizing pollination
  16. Heart versus head in decision making: Biological and cultural evolution of the perceived dichotomy.
  17. Issue based democracy versus leader/party based democracy: How to design a behavior based constitution for a democratic country.

Is evidence not a high priority for BMJ’s Evidence Based Medicine?

Our manuscript was rejected by BMJ Evidence Based Medicine. The paper was examining data from 6 large clinical trials, evaluating the trial design, statistical rigor and inferential logic used to reach conclusions. We also reexamined the methodology used in published meta-analyses of these trials. The result was astonishing. Together there was no evidence that regulating glucose reduces the incidence of diabetic complications. This is important because glucose regulation is the main, perhaps the only focus of diabetes treatment at present. Our analysis showed that the entire line of treatment which has currently something like a trillion dollar turnover is without a sound evidence base. The preprint of Medrxiv is here https://www.medrxiv.org/content/10.1101/2022.08.02.22278347v1.

The journal rejected the manuscript within about 6 hours of submission saying that it did not achieve “a high priority score”. They say the rejection is not based on the quality of the paper. Only on a judgment of priority. This has interesting and far reaching implications for the field of type 2 diabetes on the one hand and for the science publishing system on the other.

What our analysis mainly found was the following

  1. All the papers have multiple and serious statistical flaws. The main being that when a large number of statistical comparisons are made, some are bound to come out to be significant by chance alone. For this a correction called Bonferroni correction needs to be made. None of the trials do this. If we apply this correction to their data, nothing remains statistically significant. Bonferroni correction is more conservative. Therefore we also suggested an alternative based on simulations, but even with this approach, none of the claimed benefits of treatment turn out to be really statistically significant.
  2. The clinical trial that is believed to have shown the benefits of sugar regulation, the UKPDS does not have a placebo control. Other trials that have placebo or blinding of some kind do not show as many benefits as the UKPDS. Therefore the assumed positive effects of glucose regulation are likely to be placebo alone. Then there is a second level of placebo in trials with surrogate end points such as glucose. The feeling that my glucose is in better control is likely to exert a placebo effect at a different level and none of the trials has appropriate controls for this.
  3. Even if we assume that the marginal benefits are true, it cannot be inferred that it was because of sugar normalization. Insulin has so  many other functions in the body. Some of the anti-diabetic drugs also have other sites of action independent of glucose. So the inference that these marginal benefits are because of sugar normalization has no support.
  4. The magnitude of difference is so small that it is clinically meaningless. Even if we take only the favorable results and assume them to be true, 10 patients will have to be treated for 25 years each in order to prevent one diabetic complication in one person.

All this is crystal clear from the data and it is high time we give up glucose normalization as the main focus of diabetes treatment. But beliefs appear to matter more than data in medicine. Since the analysis showed something against their belief, how can they publish it? Whatever the quality of data, analysis and arguments!!

Now since I have no formal career in science, the rejection will not affect me. On the contrary I am more delighted for having one more sample to understand how the secretive editorial machinery works. The rejection was so fast that the chance that anything in the manuscript would have been read seriously is simply out of question. The important thing to be read is only from where the paper comes. If the authors and their affiliation is obscure, there is no need to read anything further.

But what is more interesting is the reason given for the rejection. By saying, this was not a high priority issue, the journal admits that either type 2 diabetes is not a high priority disease or being statistically sound is not at a high priority, being critical about the nature of evidence  is not a high priority issue or questioning a line of treatment based on absence of supportive evidence is not a high priority concern for the journal called EVIDENCE BASED MEDICINE.  

The actual correspondence is pasted as it is below.


Dear Dr. Watve,

Manuscript ID bmjebm-2022-112095 – “Does sugar control arrest complications in type 2 diabetes? Examining rigor in statistical and causal inference in clinical trials.”

I write you in regards to the manuscript above.

We are sorry to say that we are unable to accept it for publication, as it did not achieve a high enough priority score to enable it to be published in BMJ Evidence-Based Medicine. We have not sent this manuscript for external peer review as in our experience this is unlikely to alter the chances of ultimate acceptance. We are keen to provide authors with a prompt decision to allow them to submit elsewhere without unnecessary delay.

Our decision may be disappointing, especially in view of the lack of a detailed critique. This decision must be based not only on quality but also timeliness and priority against other subject areas.

Thank you for considering BMJ Evidence-Based Medicine for the publication of your research. I hope the outcome of this specific submission will not discourage you from the submission of future manuscripts.

Best regards,

Dr. Juan Franco

Editor in Chief, BMJ Evidence-Based Medicine

Dec 8, 2022

Dear Dr. Franco

Thanks for your prompt response. I just have one request. I would like to have your consent to quote your reply in any article/blog/comments on preprints. 

Let me also tell you in what context I would like to quote your reply. 

Our paper pointed out many statistical and inferential flaws in a series of clinical trials on glucose normalization treatment to prevent diabetic complications. The nature of the flaws is such that their conclusions become completely invalid. The current practice of type 2 diabetes treatment becomes questionable.  All this is supported by sound analysis of data from systematically selected 6 large clinical trials. 

Your response says our arguments “did not achieve a high enough priority score” which implies that

1. Being statistically sound is not a high priority for you.

2. Being critical about the evidence base is not a high priority for you.

3. Questioning a line of treatment based on absence of supportive evidence is not a high priority for you. 

Kindly let me know your consent to quote your email in this context. 

I believe in complete transparency of the editorial process and therefore expect cooperation from you in this regard. 

Thanking you. 


(Dr. Milind Watve)


जुस्ते हक़ की रहगुजर में जो सियाही है, मेरी है

उस मजाज़े आराइश में तेरा ही बस हो तो क्या है

The darkness on the path to truth is my homeground. If the dazzling lights in the rest of the world are under your command, why should I care!!

I received no reply to this later.

The eLife experiment is welcome, but …….

The journal eLife has declared its new peer review policy which is a bold experiment in science communication. In a recent editorial (https://elifesciences.org/articles/83889) they declare that eLife will not use peer reviews for a dichotomous decision of accept-reject. Instead they will publish every paper that they choose to review along with the reviewers’ comments. Further they say that the authors will have sufficient freedom to use this peer review to publish elsewhere etc.

To some extent, this is precisely what I had been saying for quite some time (but with important differences). Of course, many others would have thought that way. As the article says, “Nobody who interacts with the current publishing system thinks it works well, and we all recognize that the way we use it impedes scientific progress”. Since there appears to be a consensus that the current system of science publishing is deeply flawed, there need to be alternative models and there have been some experiments on alternative publishing models. But one thing is lacking.

Science is a human endeavor and therefore is clearly subject to principles of human behavior. Any new system being designed needs to be based on our understanding of behavior. If it is based only on ideology, but ignores behavior, it is bound to fail in realizing its objectives. It may become stable and popular but that is not the measure of success. How far it serves the original purpose should be the measure of success. The central question is how to design a system that will serve the purpose, given the behavioral choices of all stakeholders in the field. The reason why the existing system is flawed lies in people’s behavior and a new system can also get easily corrupt for the same reason. It is therefore necessary to analyze the reasons behind the problems in the current system and see whether we have been addressing these problems in designing a new system. I have written a detailed article which is available as a preprint for the last 5 years (https://ecoevorxiv.org/nvpe2/). As expected in the article itself, this couldn’t have been published by a traditional journal, and the prediction has turned out to be correct so far. In these 5 years my analysis of the behavior of scientific community has gone much ahead. Here I will only mention a couple of behaviorally important points relevant to the eLife experiment that has begun.

The committees that decide recruitments, promotions or funding look at where a candidate has published rather than what is published. This is not without reason. The journal names and impact factors save them the cost of reading. Reading incurs substantial cost. IFs are popular only because they save the cost of reading. There can be an inexpensive pretense of evaluation without evaluating anything. So although IFs are not scientifically sound, they are behaviorally profitable and therefore the committees will not give up on them easily. The eLife’s stand of replacing the accept reject-decision by publishing peer reviews will compel the committee members to read research, and they will be most reluctant to do so. For over 2-3 decades, committee members are addicted to the ‘evaluate without reading’ package and de-addiction is not going to be easy.

The accept-reject decision cannot be replaced as long as the prestige of journal matters. The more prestigious journals will be overburdened with submissions and they can review only a limited number. So desk rejection will become even more important and there all the biases caused by the dichotomous decision will return in perhaps a worst form. eLife itself says “We will publish every paper that we send out for review”, which means a large number will be rejected without giving reason at some one’s vim. This decision is bound to be guided by private cost benefits of the editor which is not going to eliminate the existing biases.

There is one more potential contradiction. The elites of science control most of the prestigious journals. Therefore they are not so unhappy about conventional peer review systems. Peer reviews have biases by gender, country, race, reputation etc. So the underprivileged class of science will find the open peer review system more beneficial. But mostly the underprivileged are also poorly funded. They will not be able to afford the author charges of 2000 dollars per paper. So the change may not benefit the ones who are looking for a change. The journal has a facility of waiving charges, but how efficiently it works will decide everything. It is quite likely that the profitability or even sustainability of the journal will be compromised if waivers are really given to everyone who needs. There are more problems with the suggested change. But nevertheless, any experiment is welcome. The risk in doing such experiments without sufficient thinking is that failure of such experiments will further strengthen the flawed system once again. Therefore it is necessary to design behavior based systems right away. In economics and management, designing behavior informed systems is not a new concept. There is substantial research on it. Why not utilize this in science? And if the field of science itself fails to use novel scientific concepts, who else will?

A smarter way to suppress inconvenient science

After a delay of 6 months, the journal PLOS One returned our manuscript saying that they could not find an academic editor and reviewers for our manuscript. PLOS One is a fairly open minded journal and has a team of editors representing wide diversity of fields. That’s why this kind of response is quite surprising. This is only for the second time in my life I received this response. Earlier incident was with the journal Biology Direct. Are the two incidents only a matter of rare chance? Or are there any specific reasons to it?

One thing common about both is that both were about diabetes, highlighting models that are at substantial deviation from the prevalent mainstream thinking in the field. I think there lays the reason.

What was our paper about? It pointed out a large number of anomalies in the prevalent theory of glucose dysregulation in type 2 diabetes. It listed dozens of mismatches between the theory and an array of reproducible experimental or epidemiological findings. It also suggested an alternative model that could account for almost every anomaly in a coherent thread of logic. Classically type 2 diabetes is believed to result from an elusive concept of “insulin resistance” and inadequate compensatory insulin response. We, on the other hand assumed with sufficient evidence in hand that diabetes begins with vasculopathy. Because of deficient vasculature there is inadequate and defective glucose transport to the brain which makes the brain deficient in glucose. Deprived of sufficient glucose, the brain instructs the liver to release more glucose in blood. Vasculopathy is long known to be a characteristic of diabetes but the thinking was that chronic rise in glucose is the cause of vasculopathy. We are saying the reverse, vasculopathy the cause of rise in sugar. There is clear demonstration that transport of glucose from blood to brain is reduced prior to hyperglycemia. Further, ALL the experimental and epidemiological patterns not explained by the insulin resistance theory are explained with complete coherence by the “vasculopathy first” model. Therefore the alternative model looks more promising. There also exists published evidence that early signs of vasculopathy are seen much prior to hyperglycemia.

The catch is, if we accept the alternative model, the entire line of treatment of diabetes will become completely redundant. That would lead to collapse of a trillion dollar business. But that is much ahead in the sequence. Right now we are not over-claiming. We only say in this paper that the alternative model explains almost all the anomalies and therefore needs to be considered seriously and trigger research on a new line.

How do researchers in a field react to a finding, hypothesis, model or synthesis that directly contradicts the prevalent theory? You would expect them to critically view the new finding, may be find flaws in the argument, aggressively criticize, debate and so on. I am ready to believe that a welcome response is highly unlikely. It would be natural to expect heavy criticism. This might happen if the new argument is inherently flawed and it is easy to find the flaws in it. But what if the prevalent theory itself is flawed and the new argument it substantially stronger and sound in terms of logic, mathematics and evidence?

From repeated experience I know what a typical response of scientists is, particularly from the field of biomedicine. They prefer to keep mum. They neither accept nor reject any disruptive thinking or evidence. They pretend that they just haven’t heard of it. Criticism can be replied to. A debate is likely to take a logical path so that ultimately truth will prevail with a good chance, if not every time. But the strategy that always defeats novel thinking is “silence”. When the giants in a community have vested interests in a prevalent theory and someone makes a sound case that it is wrong, they just keep mum, pretend that nobody said anything; they did not hear anyone saying anything. In the days of hierarchical structure of science publishing this strategy can perhaps never be defeated. The giants in the field can block the new thought from getting published in the flagship journals of the field. They don’t care if it gets published anywhere else because they know nobody reads research anyway. Research is propagated only through a handful of journals; that too only the through the titles and abstracts. Rarely if ever, research papers are read completely.  So often the data in the paper contradicts the statements in the abstract. But everyone reads only the abstract and therefore truth remains masked. If we point out stark difference in the data and the conclusions in a paper, the journal is guaranteed to not respond.

This is not different in principle, from the responses of researchers to a disruptive idea described by Thomas Kuhn, albeit two major differences. One is that of difference in culture of the research fields. Kuhn mostly talked about physics in which ideas are debated. Debate is not in the culture of biomedicine. They have smarter ways to suppress alternative thinking. The second difference is that Kuhn wrote when peer review was not a mandatory norm in science publishing. Now peer review is another weapon by which any upcoming thought can be swiftly killed. And you need not waste any time in reading and commenting as well. Just decline to handle the manuscript and that is enough!! Here is our manuscript in a preprint form (https://www.biorxiv.org/content/10.1101/2022.01.19.477014v1) and see below the correspondence with the editors.

PLOS ONEFri, Aug 12, 6:17 PM
to Milind

Dear Dr. Watve,

I am writing with the difficult news that we have not been able to secure an Academic Editor to handle your manuscript “Hyperglycemia in type 2 diabetes: physiological and clinical implications of a brain centered model” (PONE-D-22-04305). Additionally, we have been unable to secure feedback from peer reviewers. We have therefore reluctantly decided that we must return your manuscript to you without review.

I recognize that this decision will be frustrating — it is our desire to provide every suitable manuscript the opportunity for review and evaluation by experts in the research community — and I sincerely apologize that we have not been able to do so in this case. We have exhausted the pool of potential PLOS ONE Academic Editors qualified to handle your manuscript but have not been able to secure a commitment to handle the submission. We have also invited a number of peer reviewers with relevant expertise, but we have not been able to secure the reviews required to support an editorial decision. We are withdrawing your manuscript from consideration to prevent further delays in the assessment of your submission, and so that you can move forward immediately if you choose to submit your work elsewhere.

Again, I am very sorry not to have more positive news for you. I wish you the best in finding an alternative venue for this work.

Best regards,
Emily Chenette

Milind Watve <milindwatve@gmail.com>Sun, Aug 14, 10:23 AM
to PLOS, bcc: Akanksha

Dear Emily,

I understand the agonies of editors. No issues. But I have one request. 

I would like to have your consent to post this letter in the public domain. It is very likely to be a remarkable event in the history of science and students of the history and philosophy of science need to have access to this information. How people in a field react to a paper challenging an existing dogma is a very important question in the history and philosophy of science and making this letter public is extremely essential. Therefore I want to append it to the preprint, as well as write an article about it on my blog on which I have often written about problems in science and science publishing. Link here if you want to view it (https://milindwatve.in/)

Awaiting your response. 


(Dr. Milind Watve)


जुस्ते हक़ की रहगुजर में जो सियाही है, मेरी है

उस मजाज़े आराइश में तेरा ही बस हो तो क्या है

The darkness on the path to truth is my homeground. If the dazzling lights in the rest of the world are under your command, why should I care!!

PLOS ONE <plosjournals@plos.org>Sun, Aug 14, 10:24 AM
to me

Dear Milind Watve

Thank you for contacting PLOS ONE. We will reply to your query as soon as we are able.

In the meantime, please take a look at the following links for more information about our processes:

A message to our community regarding COVID-19 https://blogs.plos.org/plos/2020/03/a-message-to-our-community-regarding-covid-19/
Submission Guidelines http://journals.plos.org/plosone/s/submission-guidelines
Reviewer Guidelines http://journals.plos.org/plosone/s/reviewer-guidelines
Publication Criteria http://journals.plos.org/plosone/s/criteria-for-publication
Editorial & Peer Review Process http://journals.plos.org/plosone/s/editorial-and-peer-review-process
PLOS ONE Video Shorts https://www.youtube.com/playlist?list=PL_O2Hm19V2gEUZoyf7J4nva9W9u1XhJXN

We appreciate you reaching out and will be back in touch shortly.

All the best,


Case 07687026

Milind Watve <milindwatve@gmail.com>Mon, Aug 29, 9:20 PM (13 hours ago)
to plosone

Dear Editor,

This is to inform you that since I did not get any reply from you for over two weeks, I am assuming that you have no objection if I publish your letter in any appropriate context, in a respectful manner. 


(Dr. Milind Watve)


जुस्ते हक़ की रहगुजर में जो सियाही है, मेरी है

उस मजाज़े आराइश में तेरा ही बस हो तो क्या है

The darkness on the path to truth is my homeground. If the dazzling lights in the rest of the world are under your command, why should I care!!

A welcome rejection:

I am happy to receive a rejection to my manuscript. I wrote a MS about the biases in peer reviews, how some basic principles of human behaviour create these biases and suggested a behaviour based system design for scientific publishing that would minimize, if not eliminate biases. Anticipating that criticising peer reviews would create controversies, I communicated this MS to the Journal of Controversial Ideas (JCI). After almost one year I received a rejection. One of the main reasons for the rejection is that this is not a controversial issue at all. “The idea that peer review is flawed and creates bad incentives is widely held by academics.”

This is unique experience. The paper is rejected because the peer reviewers agree that peer review is itself a bad idea. The paper is rejected because peer reviewers agree with one of my main arguments. They do contradict and strongly disagree with some of my other arguments (and still say that there is no controversial idea in this). I must say that this is one of the rare instances of a thorough and thoughtful peer review I received. I don’t agree with all that the reviewers say, which is fine. But I certainly have much to learn from what they say and this is not a very common experience. Out of the nearly 100 peer reviewed papers I published (which means those many acceptances along with a greater number of rejections) between 20-30 times I thought I received comments that would really improve the quality and rigor of the paper. This rejection is certainly one of them. This means that at least some times peer reviews rally help. The percentage in my experience was about 10 %.

Whether now I would communicate the paper to some other peer reviewed journal or not, I haven’t decided. But I am not too keen for obvious reasons. If everyone agrees that peer reviews are weird and flawed, why should we consider only peer reviewed publications as science? Peer reviews actually have no relevance to science. No doubt they have a relevance to making a career in science because there is a ritual of listing and counting peer reviewed papers. Every selection, appointment, promotion etc has to go through this ritual. Now I am not in the race of making a bright career. So I suffer no loss by getting my papers rejected.

But a curious observer in me is not dead. It won’t be until I remain cognitively healthy (by medical definitions). So I have a number of questions. If the flawedness of the peer review system is universally accepted and there is no controversy about it, why do we still depend so much on it? If the main pillar of science, that is publication of the outcome, is so flawed why we fail to see that it makes the entire field of science flawed? Why the attempts to change the system are so half hearted, ephemeral and almost always a failure until now? While new fields like behaviour based policy making are thriving, why don’t we apply them to science publishing? I did my own behavioural analysis of different players in scientific publishing and designed an alternative system. It is not necessary that everyone agrees with it. But doesn’t it deserve a debate? Shouldn’t my ideas be published in order to generate a debate? Why are people of science running away from addressing the fundamental flaws in the field?

Perhaps I know the answer. There is a in-power group which decides the protocols of science publishing as well as funding. The group that already enjoys the power does not suffer by the flaws. People who actually suffer by the unfair systems have no say in changing the system. This is a vicious cycle and the powerful people of science are either dumb enough not to see it or they actually want the flaws to perpetuate in order to retain their power. I am open to both the possibilities. If there is a third one that you can think of kindly let me know.

Here are the links where you can access my original manuscript along with one of the reviewers who has directly commented on it.

Another reviewer’s comments are on this link.


I leave it open for the readers to make their own opinions. Any comments are also welcome.

Science versus career: a trade-off

Within the last 24 hours I heard two complaints from two different students from very different institutions. I received a call last night from an agriculture student. He was in a tricky situation and wanted my advice. His dissertation work had ‘failed’. He wanted to develop a technology, a machine with some agricultural application, but it didn’t work. He had himself put in tens of thousands of rupees to make a machine, but it failed to work. Now his mentor was asking him to cook up some data and pretend that it worked, without which he said the degree won’t come. The application of the machine being season dependent, if he had to rework with refinement, he would have to wait till next year. The student was reluctant to give false data but simultaneously was terribly upset because not only he would lose the money he had put in, but also lose one full academic year.  

The other case was of a girl having completed her PhD desk work, was awaiting a paper to get accepted and preparing to complete the thesis. Typically at this stage, the post doc applications begin. The girl’s complaint was that during some phase of her work she had a major difference of opinion with her boss which strained their relations. Now she wasn’t sure whether her boss will give her a “good” letter of recommendation, without which she though she couldn’t get a post doc.

In both the cases I could sense that the students were terrified by the thought that their career could be completely destroyed in one stroke by their supervisor. In the first case the boy talked to me directly on phone and I tried to find out how thorough he was at his work. If the experiment failed, did he analyze and try to reason out why it failed? He said he was confident about his data and could say why their earlier thinking was wrong and why things cannot be done that way. I said, “I am ignorant about your field the only thing I would suggest a science student is this. You stand firm on your position and complete your report. Don’t cook up data only to please your boss. An analytical view of a failure is also a contribution to science. Try to convince your boss that you have sufficient work to make a dissertation, but avoid the temptation of cooking up data.”

The girl in the other case was not even ready to discuss her problems personally with anyone in academia. She was afraid that the ‘anyone’ may turn out to be a friend of her boss. If I get any chance to talk with her I will tell her more or less the same thing. If you think you are right, remain firm on your stand and don’t compromise your science for someone’s whim. If it is about post doc, communicate your problems frankly to your potential post doc mentor. It might work. There are alternative ways of doing good science and finding a career.

Frankly speaking I am not sure my solution will protect the careers of the two students, my solution is primarily to protect science from misconduct. Clearly in both the contexts there is an unfortunate tradeoff between doing good science and doing a successful science career. The two are way different and in instances like this, diametrically opposite.

My main concern goes much beyond the two cases. In science, one needs to be careful about experimental designs, accuracy, errors, biases, logical traps etc., but being confident about having done well on this front, it is necessary to stand firm on one’s position. Compromising on it to please someone like the mentor, the thesis examiner, the reviewer of manuscript or some established elite in the field is against the spirit of science. Conformity bias is a major hurdle in the progress of science and in both the cases this is exactly what is getting encouraged.

Now if the students’ fear is true, and complying with what their boss says is the only way of getting on to the career path, then the system is selecting against scientific spirit. My personal advice to the two students would be that if you are afraid of someone ruining your career, you are actually not fit to do science. You should find some other career for you, perhaps better than this. But what actually matters for them is ground reality and not my utopian advice. If taking a firm stand prevents them from getting their degrees or further positions, the system is filtering out at an early stage the right kind of mindset for science. If students are made to compromise their own stand in order to make a successful career, the future of science is bound to be bleak. With this trend academia would become completely devoid of the scientific spirit in no time.

I had related earlier one of my experiences as a reviewer (https://milindwatve.in/2020/09/17/how-peer-reviews-are-degrading-the-spirit-of-science/). In this case I differed from the authors in the interpretation of their experiment. Difference of opinion is fine. It’s a milestone in the path of healthy science. The authors could have counter-argued and defended their interpretation more clearly during revision and rebuttal. But I was the “reviewer god” of that moment. They didn’t agree with me but also didn’t want to displease the reviewer God. So in effect, they muddled the entire argument further and the residual clarity was also lost. I would have been happier to see them say “We beg to differ on this issue and we defend our stand with better arguments in the revised manuscript”. I would have recommend acceptance on such a stand, even if I didn’t agree. But the peer review culture is so degraded that many authors avoid any argument with the reviewer. This tendency makes publications easier but science more difficult.  Today’s science institutions as well as science publishing is taking such a shape that if you want to make a successful career in science then you need to compromise with the spirit of science.

The problem lies with the institutional culture, individual mindset and the norms of career path. Clearly there are individual mentors that encourage students to think and be independent at an early stage. But this is left to individuals. There is nothing in the system to ensure this. Solving individual cases or helping individual victims is not sufficient. The system and the norms of a typical career path need a rethinking.

Crowd-funding appeal to support farmers

using a novel ‘game theory’ based approach:

For the first time in my blog, I am making an appeal to contribute to crowd funding to support the pilot implementation of a novel scientific concept that has a direct application to solve a major problem in human-wildlife conflict. We play here a game with real money which solves a real life problem!!

The problem: In India, with its high population density and rich wildlife, there are large areas of inevitable human-wildlife co-existence. Strengthening agriculture in and around protected areas boosts conservation since it reduces people’s direct dependence on forest resources. Therefore farmer support needs to be a high priority conservation action. A major but largely overlooked problem is crop damage by wild herbivores. While compensating farmers for the damage caused by wild animals is an accepted principle in India, the process of assessing and compensating damage is fraught with a large number of practical difficulties and therefore has been ineffective.

A Novel solution: We came up with an alternative to traditional concept of compensation with a novel game theory based concept called “support cum reward” (SuR). The SuR amount to be paid to farmers is calculated as percentage based on the average loss in productivity over a group of farmers. Each farmer receives SuR as the percentage on his own productivity. Therefore while each farmer is supported by the average, a farmer showing high productivity in spite of the damage gets a higher reward. The data are self reported by farmers and endorsed by neighboring farmers. The unique game theory components ensure that honest self-reporting gets maximum returns. Since honest behavior is the most selfish behavior, there is little chance of anyone defecting from cooperation. This is a new game that is immune to cheating by any variant behavior. As a result the entire process can be automated through farmer friendly mobile apps so that the system becomes community operated with little need for central administration, auditing or policing. The design of the system has several built-in features of cross verifying data, automated detection and prevention of fraud, self-auditing and self-correcting.

A Prior small scale trial: SuR was implemented on a small scale (75 farmers) for three years in which the average farmer’s productivity increased over 2.5 fold. In this trial the SuR inputs of Rs 1.6 million increased the productivity by about Rs. 7 million. This phase of research was funded by Vidarbh Development Board, DeFries Bajpai Foundatin and NAAM foundation. This concept is appreciated in the scientific world as reflected by publication in one of the top journals in the field, and an international award by the Society for Conservation Biology.

Towards scale up: Now, the Forest Department of Maharashtra, has shown interest in conducting a medium scale pilot trial for 8 to 10 villages near Tadoba. We have signed a MoU with the Field Director TATR and the executive director of Tadoba Andhari Tiger Reserve Conservation Foundation. While the foundation takes the responsibility of managing the finance and partially funding the pilot, we still need to raise over Rs. 1.5 crore through crowd funding, CSR and other means. The medium scale implementation will pave the way for the Government to adopt a new policy to handle human-wildlife interaction.

Why citizen science: The nature of research is difficult to fit in the bureaucratic systems of Indian Institutions and universities going by my prior experience. Therefore myself and my team will work independently in collaboration with the farmers on the one hand and the Foundation on the other. This is a unique example of doing high quality science with direct community involvement, establishing a non-institutional model of doing science. Beyond handling people’s problem it is an attempt to raise an alternative model in which farmers including the illiterate ones turn into researchers and address their own problems themselves. It is not about ‘taking’ science to people it is about doing science with people.  Personally for me, this is going to be perhaps my career best science. You can donate starting from a few hundred dollars of few thousand rupees (no upper limit) at the link- https://mytadoba.org/donations/. Remember to go to the “other” option in the Type of donation window and write “For farmer support cum reward (SuR) fund”.

For more detailed information about SuR see links-






Covid 19: Why can’t they do simple mathematics?

For a student of science like me, whatever is happening with the pandemic and whatever is being said reveals how human mind works, how we perceive science, how we ask questions and what satisfies our questions.

A number of statements are being made by the mainstream Covid epidemiologists which are actually not supported by data. The same data can be interpreted differently. The different possible interpretations need to be treated as competing hypotheses and tested by making additional differential testable predictions. This is the core method of science. But the human mind has not evolved to follow the scientific method and pursue truth. It has evolved to make stories. The stories need to satisfy some audience. The attempt of an investigator is to satisfy his/her present audience with minimum effort. Different audiences have different satisfaction thresholds. If your story satisfies your audience, you stop there. You stop looking for alternative interpretations. You don’t need to test them. You don’t need to recheck whether your story is consistent enough with evidence. Whether there are any anomalies and whether they are serious enough to raise doubt on your story is no more a concern as long as nobody challenges your story. If someone challenges the story the first line of defence is to say the person is not credible, ignore him or better brand him as ‘anti-science’. Such branding works most of the time, so that you don’t really have to take trouble to pursue scientific method further. Scientific solutions are costlier. Social solutions are cheaper.

Take the example of the new wave which is said to be caused by the variant omicron. Is this a well tested hypothesis? By the methods of science that we teach undergraduates, any hypothesis can be tested against a null hypothesis. Different variants keep on arising and vanishing in a population owing to chance as well as by selection, and selection can happen due to not one but multiple reasons. In viruses, there is selection within a cell, during cell to cell transmission, there is selection on dose of the virus being transmitted, time for which the host remains infective, time for which viral particles survive outside the host, how the virus elicits immune response, whether and how virus evades host immunity and so on. The selection is necessarily multi-level with additive as well as multiplicative components and is really a challenge to selection dynamics. Further there can be trade-offs between any of these. Natural selection on viruses is much more complex than the prevalent naive thinking that a more infectious virus will win the race.

As the wave goes up and down, new variants keep on arising. Many variants increase their abundance while the wave is downwards. So new variants keep on arising during any part of the wave and by chance some variant may happen to ride a rising wave. This should be the null hypothesis, only by rejecting which we can say that a given variant is certainly responsible for a wave. I could not find any such analysis in literature but a story seems to be accepted that the new wave is because of omicron. The increase in proportion of omicron is not always accompanied with increased transmission. For example, in Russia, between 29th November 2021 and 11th Jan 2022, the Omicron proportion increased from negligible to 50 % while mean number of cases per day came down by more than half. Omicron accompanied a downward and not upward trend in total number of cases.

Even in countries where omicron accompanied a rise in the wave, omicron does not account for the rise. In India, for example between Dec 29th and Jan 20th the incidence rose about 40 fold and omicron proportion increased from negligible to 77%. By simple arithmetic, a rise to 77% can explain only about   4 fold increase in total incidence. During this time the delta variant incidence also increased by over 9 fold. If the new wave was caused by omicron, why did we see the delta cases going up 9 fold? This is simple arithmetic that we teach in secondary schools. Perhaps for experts in this field using simple mathematics is too below their dignity. They have to use sophisticated models to impress everyone. Simple arithmetic poses a major anomaly for the hypothesis that the new wave is “caused” by omicron. Data only show that omicron is associated with the new wave, that too in some countries, not everywhere. It is equally likely that the selective environment during the new wave allowed the spread of omicron. So the wave is the cause and omicron the consequence. Alternatively the association is only coincidental and there is no causal association between the two. Unless such alternatives are considered as competing hypotheses and ruled out, ‘omicron caused the new wave’ is not a scientific statement, by the core principles of science. It’s only a story that has convinced most people, therefore it is considered scientific as of today.

This is how science works most of the time. It is a complex social process that sometimes, particularly when convenient, uses the fundamental principles and methods of science. The methods of science is the tool used by people whose prejudices, interests and agendas decide the emerging inferences.

Then what caused the new wave? There are several alternative possible reasons. We showed with a model earlier that the wave pattern is possible by considering immunity as a continuous rather than a binary variable. In reality, immunity IS graded and not binary. By this model, waves arise and wane even without a new variant. So it is very much possible that waves arose as a part of the population dynamics of what we called in the model as ‘small immunity effects’. Alternatively it is also possible that the vaccines give only systemic immunity, but respiratory mucosal immunity has a different set of mechanisms which the vaccine administered by injection does not strengthen much. As a result when the low tide lingers around for sufficiently long period people tend to lose mucosal immunity, but systemic immunity is still there. Therefore a new wave begins with whatever variant is around, but does not lead to serious symptoms among the vaccinated. If this hypothesis is true, we should see increase in cases of all prevalent variants, though not to the same degree because different variants have different competitive abilities. Also even where the incidence is declining, the more competitive variant will become commoner. I am not saying this IS the reason. I am saying that being open to different possibilities and testing them with differential predictions is how science should work, but during Covid times scientists themselves seem to have forgotten the methods of science. I am happy that the Covid epidemiologists are providing a science teacher like me several examples to demonstrate how science should be and should not be done. It is also enriching in me the curious student of social psychology of science. It is interesting to see how the community of scientists actually works besides the principles of science.

Covid 19: Evolutionary interactions between vaccine, immunity and virulence. 

The Covid-19 picture is changing rapidly and if viewed with an unprejudiced mind, keeping several alternative possibilities open, a clear interpretation is emerging. There is a new wave spreading world-wide with unprecedented numbers but at the same time the proportion of hospitalizations and deaths are far less than earlier waves. Why is it so? There are two prevalent explanations which are mutually contradictory. On the one hand this is said to be an epidemic among the unvaccinated; on the other hand the reduced severity of symptoms and lower hospitalization and mortality rates are also credited to the vaccines. Both cannot be simultaneously true, if we do a simple calculation.

The global trend in the ratio of number of deaths and number of cases – a seven day running average.

Take the case of UK. The ratio of new cases to new deaths has come down from an average of above 10% between April and June 2020 to an average of 0.15 % between December 2021 and today. That is two orders of magnitude. If we have to ascribe the credit for reduced severity to vaccines alone, we will have to assume that in the unvaccinated, the severity remains unchanged. Going by this assumption, and given that 70% of the population of UK is fully vaccinated, for bringing down the mortality by over 50 fold it is necessary that vaccinated people are about 20 times more likely to be infected than unvaccinated people. Since this is absurd, we need to accept that the severity of infection in the unvaccinated has also come down substantially. This can either happen because the unvaccinated have also become immune by natural infection, or because the virus has mostly lost its virulence.

In order to differentiate between these two, we can have a look at Australia which also has about 70 % population vaccinated. But Australia had successfully kept the infection away until recently and therefore the unvaccinated are unlikely to be immunized by natural infection. When the number of cases is small, calculation of death rate is subject to large stochastic fluctuations. So we take only the period in which there were more than 100 deaths per week. This was the situation in Aug-Sept 2020 when the ratio in Australia was over 10 %. Then there was a long time in which there were zero or negligible Covid cases and deaths in Australia. So the population had little chance of acquiring immunity by natural infection. This situation changed again only after October 2021, but now the death proportion was much lower, about 0.6 % and between October 2021 and today it further declined to 0.05 %. Similar to UK if this is to be explained by vaccination alone, we will have to assume that vaccinated people are about 70 times more likely to get infected. Since this is unlikely, it is clear that the reduction in severity and mortality is not explained by vaccination and naturally acquired immunity together. The virus has indeed evolved towards reduced virulence. It was round about 20 times more severe than flu in the beginning which has come down to about 2 to 3 times. It is still more severe than common cold and flu but is moving rapidly to become just another.

Evolution will reduce the virulence of the virus was my clear prediction right from May-June 2020. The literature of evolution of virulence is full of mutually contradicting and confusing arguments. But the case with Covid has been very clear. Multiple studies showed that there was poor correlation between severity of symptoms and viral load. Virulence can give a selective advantage to a virus only if it is tightly correlated to the number of virus particles being shed by the host. If correlation with numbers is poor, there is no selective advantage in being virulent. On the contrary, virulent variants are more likely to face quarantine and thereby restrict their transmission. A milder strain allows the host to move around in the population and thereby spread it more widely. The reduction in severity of symptoms, hospitalization rates and mortality was a conspicuous trait in the beginning. But then it appeared to stagnate and had some hick ups for some time. I was perplexed by this trend but a couple of possible reasons soon became apparent.

There is another, more subtle contributor to selection for lower virulence. The immune status of the host exerts a strong selective pressure on pathogen virulence and this has received little appreciation in virulence literature. Immune response is costly in terms of resource allocation as well as the potential damage to tissues through heightened inflammatory and oxidative components of the process. Therefore it is not wise for the body to launch an all out immune response for every pathogen encountered. At times, particularly for milder pathogens, the cost of the immune response might be greater than the cost of being infected. The host therefore should make a judgment of the invisibility or virulence and accordingly optimize the immune response. The response given to a highly virulent pathogen must be of high intensity, but that given to a milder pathogen should be of minimum necessary intensity. There is some evidence that this is what the body actually does. It gives a high intensity immune response if and when a normal inhabitant turns virulent. For opportunistic pathogens living on the body, exposure to the organism was always there, but what changed was the level of invasion. Even in Covid data, the immune response obtained after a severe infection has been shown to be more intense than an asymptomatic one.

This exerts a differential selective pressure. If the host has a good immune infrastructure and possibly immune memory already existing against the pathogen, a virulent variant will evoke a strong immune response and thereby cause its own destruction. A mild variant on the other hand, may not evoke a strong immune response and thereby may get away causing a mild infection and spreading to a few more individuals. Therefore as the population immunity increases, the pathogen evolves to be milder. This can happen by naturally acquired as well as vaccine induced immunity. Therefore we witness that after a substantial population got vaccinated, the downward trend in virulence got steeper once again.

The concept of optimizing immune response can potentially answer one more question. Why in Covid the vaccine induced immunity appears to be short lived, contrasting with small pox. Corona viruses are seldom that virulent. Most are mild. Pox viruses can be very deadly. It is possible that our systems have evolved to invest less in immunity against Corona viruses and more against poxviruses. This hypothesis is worth exploring further.

But why did the declining virulence appear to stagnate in the middle stages of the pandemic? I think the possible reason is that we blunted natural selection by our own preventive strategies. Selection for milder strain is strong if the severe cases are effectively quarantined and milder ones are allowed to mix in the society. But owing to the contact tracing approach, we tried to quarantine everyone exposed, reducing seriously the selective advantage for the milder varieties. We also reduced our general immunity levels by the extra precautions taken, including masks and sanitizers. There have been reports that in countries where the preventive measures were very successful initially, infections by common endemic and seasonal mild strains of viruses suddenly started needing hospitalization. This is an indication that the preventive measures have actually been creating an immune bankruptcy or what has been called in published literature an immunological debt over time. What can be beneficial in the short run can become counterproductive in the long run. Note that ALL studies showing the beneficial effects of masks are short term studies 3 or at the most 5 months. Nobody has followed the long term effects of regular use of masks. This has never been a part of the mainstream thinking in preventive medicine, but evidence for preventive measures undermining immunity has been published in the context of many different diseases multiple times.

So, the vaccines have been useful in an unexpected way. They don’t seem to have prevented the spread of infection very efficiently. But they appear to have helped in creating a stronger selective pressure for decline in virulence. On the other hand it is quite possible that masks, that were helpful in the short run, may have turned counterproductive by changing the intra-host selective environment. Ultimately Covid is bound to become just another common cold virus and the progress in that direction is clearly visible. There is no other likely fate of the pandemic. The question is whether we could have facilitated the rate of this evolution. My gut feeling is yes, we could have, by implementing better designed and carefully optimized preventive measures. But not only data needed for this optimization is absent, even such a concept is absent, so systematic studies in that direction are not even expected to happen. Evolution today is rich in molecular data but that comes as a cost of deteriorating insights into natural selection. During the Covid saga, there were plenty of talks and huge data about the mutations and the variants and their transmission, but little insights into how selection worked on the mutants being generated. There were some half baked ‘evolutionary’ statements. Someone said that by the preventive restrictions you can keep the viral population limited and thereby minimize mutants arising. But the question whether viral evolution is mutation limited or selection limited was never critically examined. Sound evolutionary thinking needs being open to alternative possibilities, insights into possible selective forces and a keen eye on the patterns in data. If such thinking becomes a part of preventive medicine, I am sure it will make handling of future epidemics more efficient.

The epidemic of scientific misconduct: an innocent question

It’s not a new virus. Various types of frauds have been there throughout the history of science. The fake fossil human skull famous by the name Piltdown man created its own history. Some experimental results by great personalities such as Gregor Mendel and Jacques Monod are said to be “too good to be true”, i.e. so well fitting their hypothesis that such a result is statistically highly unlikely given the inherent biological variability. It is possible therefore that they themselves, or someone obliging them manipulated results or cherry picked only the favorable ones. There can be many more examples that never got exposed. But in spite of such examples the core spirit of science has largely survived and maintained its reputation.

However, the rate at which misconduct is mounting today seems to be unprecedented. There is reproducibility crisis, doubts raised about designs and conduct of clinical trials, statistical analysis twisted to support the favored hypothesis, manipulated images and cooked up data. It is difficult to decide whether the frequency of scientific misconduct has increased or only the rate of getting exposed has. The rate at which image manipulations are getting detected is due to the efforts of groups like ‘pubpeer’ (https://undark.org/2020/07/23/cracking-down-on-research-fraud/, https://blog.pubpeer.com/). Image manipulation is only one type of fraud. Currently most of the flags raised are based on detecting image manipulation. Many other types of frauds may have just escaped because we don’t have tools to detect them. If this is true, the actual frequency of misconduct must be substantially higher. ‘Retraction Watch’ (https://retractionwatch.com/) makes data on over 30,000 retractions and the reasons behind them accessible to people. Retractions on getting the misconduct exposed include papers by reputed laboratories and mentors. Occasionally the responsible people have to pay the cost of the deed through their nose but many others appear to escape more or less unhurt. The culprits may not pay the cost but science may be paying a big cost in terms of its reputation. Certainly I find it hard to prevent myself from losing faith in published science and I am not the only one. Richard Smith, the former editor of British Medical Journal, remarks that it is time to assume that all health research is fraudulent until proven otherwise!! (https://blogs.bmj.com/bmj/2021/07/05/time-to-assume-that-health-research-is-fraudulent-until-proved-otherwise/).

There have been many discussions on how to prevent the scientific frauds of various kinds. People have been debating the pros and cons of better ethics education, institutional mechanisms of vigil , strict action and punishment as deterrent and so on. But I am troubled more by another innocent but unpleasant question that keeps on peeping in my mind in spite of my repeated attempts to suppress.

The high frequency of retractions makes me wonder about how science works today. I thought science works by building on prior knowledge and working the way ahead. In my mental model of science, every piece of evidence, every bit of data, any new concept, model, analysis is crucial for progress. One published piece of work lays the foundation of further work and so on. If this was true, any paper retracted would have made some concept collapse, some paradigms failed, some lines of work given up, some technologies defunct and so on. But I am surprised that following over 3000 retractions per year there is hardly any collapse seen, no fundamental change in the direction of work, no reconsideration of the existing paradigm, no rethinking of any prevalent theory. If retraction of thousands of papers has no major effect on science, it only means that these papers never had any relevance to science. Their being in a state of ‘published’ or ‘retracted’ makes no difference to the field. Perhaps they are being published only for the benefit of the authors in building their CVs and getting better positions. If they really made a difference to the field of science, their retraction should have affected the field quite badly, but that is not the impression one gets after looking at the work of pub-peers and retraction watch. Thousands of papers have been retracted but no scientific theory has collapsed or no technology has been withdrawn.

If papers are irrelevant, so must be the career of a researcher. If in judging the career of a researcher, we go by the number of such publications and weigh by impact factors and so on, we are counting the irrelevant. This way we are building more and more irrelevant science in our institutions. No wonder if the institutions themselves become irrelevant soon. They already have to a large extent, as the apathy of common man, government and media shows.

I still hope and believe that interesting, fundamental, relevant and important science is being pursued somewhere. But it may not be with the big people working in the prime institutions of science and publishing in big journals. It may be happening in some obscure lab somewhere, some teacher with a handful of undergraduate students, some farmer, some uneducated, illiterate, humble, unnoticed individuals in some corner of a third world addressing a basic question. May be that kind of science will count ultimately, some day, some time. Let us wait till them.

What else can we do?  Tell me if you know.