I saw this on Zhihu a long time ago. Whether you call it a joke or a tease, I'll try to retell it.

       Three people go to apply for a job at a chemical industry-related company: one is a major in analytical chemistry, one in organic chemistry, and one in computational chemistry.

       The first one interviewed was the analytical chemist. The interviewer asked, "What is produced when hydrogen ions and hydroxide ions are put together?" After some thought, the analytical chemist gave the answer: "They form water, H2O." The interviewer said, "Think about it again, what exactly can be produced?" The analytical chemist hesitated for a moment and said, "It just produces water, H2O." The interviewer finally said, "You can go back now."

       The second one interviewed was the organic chemist. The interviewer asked, "What is produced when hydrogen ions and hydroxide ions are put together?" The organic chemist also gave the answer after some thought: "They form water, H2O." The interviewer said, "Think about it again, what exactly can be produced?" The organic chemist thought some more and said with confusion, "Maybe there are some trace unknown impurities?" The interviewer finally said, "You can go back now."

       The third one interviewed was the computational chemist. The interviewer asked, "What is produced when hydrogen ions and hydroxide ions are put together?" The computational chemist gave the same answer after some thought: "They form water, H2O." The interviewer said, "Think about it again, what exactly can be produced?" The computational chemist glanced at the interviewer wickedly, walked to the window and drew the curtains without a sound, turned back to look at the tightly closed door, and whispered, "What would you like it to produce?"

I remember someone in the comments saying: "A veteran computational chemist, skilled at Photoshop and creating fitted curves."

What have we gained since computer tools entered the traditional scientific research industry? The first thing we gained was a sense of novelty and excitement; it opened up a path that had never been imagined before. Fresh blood was injected into the researchers' brains, and countless inspirations and possibilities burst forth in an instant. Many problems that were once difficult to solve, or problems that had to be bypassed, were met with tools to break through and solve them. Subsequently, a large number of scientific research achievements with brand-new, brilliant ideas appeared—some retrospective, some subversive, others pioneering new approaches. These achievements, cutting through thorns and thistles at the forefront, became lighthouses in the vast ocean of knowledge and the unknown, followed by countless successors. In the end, scientific research results that did not involve this new direction were viewed as too old-fashioned, traditional, and backward. Achievements that could have been graded A before now only get a C. Many people innovate for the sake of innovation, awkwardly and crudely utilizing these new technologies, and others have even organized standardized routines, or even exploited information gaps to rake in profits. They tell stories that are full of holes and that even they themselves don't understand, telling them to others who also don't understand. They call it: the livelihood of a million barge men.

A paper published in Nature in 2020

A local hospital participated in a big data learning project targeting a local urinary tract infection. The initial traditional treatment regimen was a combination of two antibiotics (A and B), which was later improved to a combination of three (A, B, and C) based on research findings and clinical trials. During this project, when clinicians prescribed treatment protocols, they attempted to reduce the use of one antibiotic at a time, tracking and recording patient follow-ups and treatment outcomes. Based on the results of over 3,000 cases, it was determined that antibiotic C no longer had any therapeutic effect on the bacteria causing this urinary tract infection, as the local bacteria had mutated and developed resistance. The study also estimated the timeline of when this resistance emerged.

The findings helped the local hospital refine its treatment protocols, reduce financial expenditure and economic burden for patients, and lower antibiotic usage, which contributes to overall public health. Furthermore, the results assist in developing evolutionary tracing research on antibiotic resistance in pathogens.

For a long time, hospital case resources have remained underutilized. In the era before big data, these cases eventually became obsolete paper records stored in archives. Yet, these detailed and valuable resources often contain keys to predicting the future and revealing the truths of life. Today, newly hired doctors generally possess the skills to conduct meta-analyses of cases, electronic medical record entry is now widespread, and physicians worldwide have an obligation and mission to share local cases into global databases. With the rapid growth and development of the computer industry, its influence and impact will ultimately benefit every industry below it, shining and emitting heat in these interdisciplinary fields. Future scientific development is almost certainly going to be networked and intersecting, requiring specialized skills and knowledge from all walks of life and all fields.

At 5:00 AM on January 20, 2022, a villager in Ertou Village was just about to go to sleep after finishing a day's work

Suddenly, he noticed the farm tools used for mixing ash in the square moving on their own; in his shock, he heard the tool say, 'Good morning, Ertou Village'

The villager looked toward the east, but there wasn't a single ray of sunlight, only a seventeen-day-old full moon in the sky

The villager was terrified and thought to himself, 'These tools must have become spirits. Being able to see the sun in the pitch black of night—could they be possessed by sun-worms?'

The villager dared not speak a word and hurried away, silently chanting, 'May Lord Gwyn protect me'

The teacher called me over this morning and we talked for a long time, and I even broke my mouse in the process. I ended up going to Xinjiekou in the afternoon to get it fixed just to clear my head, and by the time I made it there and back, it was already evening.

1. I need to redo experiments for my main project; there’s so much stuff to catch up on if I want to graduate on time. The teacher thinks the reason I'm in this situation is that I don't communicate with her and ended up doing a lot of useless work. I believe the work I did was something I wouldn't be able to avoid later anyway, so I figured I might as well get it done now and at least see what the results are. In the end, we reached a consensus: stop expanding the scope, put on hold the work that hasn't started yet, and just focus on fixing the current gaps. This way, my workload will be reduced, and I agreed to it.

2. The teacher set up a Feishu group; from now on, anything that comes up goes straight on there to avoid any lack of communication.

3. The main project won't be finished in time. We didn't talk about author ranking, but I definitely have to bring that up later, and it has to be as sole first author. This also kills my plan to use this paper to apply for a PhD this year. It's just an objective fact; the fact is that it won't get done.

4. I didn't take a break last summer, so the teacher is planning to give me 4 weeks off to go home to write my thesis and let me catch my breath.

5. I currently want to try out bioinformatics. If I can independently publish a paper in bioinformatics within a year, I'll consider switching fields. If not, it just proves I’m a piece of shit with no talent for it and should stop leaning in that direction.

5. I haven't left campus in a long time. It suddenly reminded me of a saying: everyone always says their own work is important, but then again, whose work in this world isn't important?

Continuing from yesterday

       Here I would like to explain the example I gave yesterday: why scientists, through rigorous and logical scientific research, arrive at absurd conclusions and yet treat them as gospel, is largely due to being overly optimistic about conclusions based on subjective assumptions.

       Looking from a God's-eye view, people are not on the street but in office buildings because of their different identities and professions (that is, these people have different functions and roles in society), so one cannot lump them together with people on the street; this is error number one. Looking from a God's-eye view, it is people sitting at desks working hard + men being prone to hair loss that led to the reduction of 'hair', or rather, what truly caused the reduction of 'hair' is the environment the individual is subjected to—all factors in this environment have only a correlation with the reduction of 'hair', not a causal relationship. Similar examples include 'smoking causes lung cancer', 'car exhaust causes global warming', or 'because I ate 10 ice creams at 2 AM yesterday and had a stomachache today, eating at 2 AM causes a stomachache the next day'; these are all cases of optimistically magnifying one of many factors and treating correlation as causation. This is error number two. Then, regarding the glowing head, we know it is because a lack of hair makes the top of the head reflect light more easily, but from the sequence of the researchers' discoveries, they found the 'glow' first, then discovered the 'lack of hair'. Although research proved the upstream/downstream order of 'glow' and 'hair', the real cause is determined by more fundamental physical laws, and according to the logic of the researchers in their 'God' perspective, they would never reach that level. Finally, we are very clear that making people unable to move is not a substitute for sitting inside a building; even if they are inside a building, it still requires male members to toil for months to see 'hair reduction' and 'glowing'. This is where researchers subjectively and arrogantly magnified the specific differences between two types of people. There are so many differences between people inside and outside, but the researchers deemed they had picked out the most core difference—'moving versus not moving'. These errors, layered on top of each other, eventually led to a conclusion that seems logical but is full of holes.

       Let's return to a concrete real-world example. One type of cell divides constantly without showing any signs of fatigue, exhibiting a 'long-lived' phenomenon; another type of cell shows obvious division inhibition after 5-10 generations, exhibiting 'short-lived' characteristics. First, can these two types of cells even be compared together? Is the huge difference they manifest a result caused by different 'lifespans', or the cause of the 'lifespan'? Does a different 'lifespan' really have anything to do with the characteristic differences we observe (cells maintaining division activity)? Are the characteristics we observe really the true differences between the different types of cells? Scientists actually have no good way to solve these cognitive problems. The most effective method we have is repeated verification, repeating the demonstration, everyone debating it together, and trying to form a 'common sense consensus', as well as constant falsification—trying every way to prove the viewpoint is wrong. Only when a viewpoint has become 'we all know it without needing to say it' and 'we cannot prove this is wrong', can we put aside our worries and say 'it is indeed so'. But even then, it is highly likely to be overturned in the future. This is also why scientists cannot say things like 'GMO food is harmless' or say early in the epidemic that 'COVID-19 is definitely transmissible from person to person', because even if the evidence is abundant, we still consider it insufficient.

PS1: The 'hair' example was inspired by a junior fellow student who has already gone bald; I solemnly declare here that I have no intention of offending anyone

PS2: When typing long text and hitting enter, the page doesn't automatically scroll down

PS3: My own 'hair' is even longer and denser than my avatar's

Biological research is essentially a form of reverse engineering. The Creator—that old bastard—wrote 3 billion letters of code but broke it into only 48 files without a single comment, leaving humans to just sit there and guess: what is this part for? What is that part for? Once they've studied enough and feel they've grasped some of the language and logic, they start trying to write some of their own for fun—for example, making a perfectly healthy, happy, and free bacterium do nothing but produce antibiotics, then slicing it open, extracting the goods, and disposing of the corpse.

      But the main work remains the deciphering. We call this "non-translatable" or "pure" basic research. Scientists usually debate a problem repeatedly, with extreme caution and obsession, going so far as to have a phobia-like distinction between correlation and causation. They never venture conclusions lightly, even filling their language with hedging to emphasize stability. Some friends might understand this approach but not fully support it, so I will give an example to help you understand why this caution is necessary.

      Let’s assume there is a graduate student under God who wants to study humans on Earth. One day, he shines a flashlight down, sees a group of black dots moving around with no apparent pattern, and concludes that humans are a group of freely moving black dots. This view immediately becomes popular in Heaven; everyone repeats the experiment, confirms the result at different times and locations, and validates it. Later, someone shines the flashlight into an office building full of bald programmers. When the light hits, it reflects! Great joy! This is a groundbreaking discovery! They immediately conclude: humans exist who can receive the energy of flashlight beams and emit light! Then, another round of experiments follows: they find that people in some office buildings can emit light, while others cannot, and even fewer people on the streets can do so. Moreover, people in the buildings don't move, while people on the streets do. Because of these contradictions, they reach a "cautious" conclusion: people sitting in office buildings might find it easier to emit light by staying stationary. Sounds ridiculous, doesn't it? Well, it's not over.

      As technology in heaven advances, they eventually become able to extract some of these people. They liquify those in the office buildings and analyze the components, only to find in amazement that these glowing office workers have a significantly reduced component known as "hair." Furthermore, they develop a technique to dump concrete from the sky to force people on the street to stay still—and are surprised to find that even when these people stop moving, they don't emit light. So, it turns out it wasn't "stillness causing light," but rather "light causing stillness." Ultimately, a top-tier journal in Heaven published a paper titled "Humans achieve a lack of movement and the ability to emit light by ceasing hair growth," and "rigorously" added: although our conclusion is sound and stands up to scrutiny, we have found that some people sitting in office buildings simply refuse to glow, which requires further research to explore and perfect our knowledge system.

2022.01.16

1. Reporting my summary tomorrow. I’ve gone through my major project from start to finish during this period; these things probably account for about 1/3 of my total workload. It’s been 3 years, I realized—this is all the proof I have for my 3 years of work.

2. Did a quick calculation: I’m still missing about 3-4 sets of experiments for the final thesis. Heading home on January 24th, returning on February 19th, and submitting the thesis for blind review in March. The plan is to finish half before January 24th, do the other half when I get back, and write the thesis while I’m home for the New Year.

3. Of course, that’s assuming everything goes smoothly. I’ve already fallen out with my junior supervisor, and I don’t know what tomorrow will bring or if they’ll dump another load of crap on me, but I’m not prepared to compromise on anything anymore. If they want to push it, I’ll push back—at worst, I just won’t get the degree.

4. At first, I thought my research direction was super badass, something no one in the academic world had ever done, and I really felt that “none but me” sense of destiny. Now that I’ve finished going through it, I just feel like it’s a pile of absolute shit. A person’s 3 golden years spent with so much taxpayer money to produce a pile of shit like this? Congratulations to the scientific community for getting their hands on a premium piece of golden shit.

5. I’m past caring now. I’m just this kind of person: “no respect for father or ruler, no regard for home or country.” If I want to, I can treat people’s face with zero respect, and throwing in the towel is purely up to my whim. Of course, if I really wanted to, I could be a willing workhorse with no complaints, but maybe that’s just the kind of labor supervisors hate the most.

6. I originally thought I’d be able to leave with 3 papers, but given my relationship with the junior supervisor, I expect they won't even let me have one. Well, that’s fine; if you don’t put my name on them, I’ll delete the raw data so you don’t get it either, and then I’ll report you for academic misconduct. Even if I don't enter academia, I’ll still make you sick enough to never get that associate professor title—you can just spend the rest of your life with your cat.

7. Looking back, it’s truly magical. When I was working on a short-term, fast-paced project with that junior supervisor, it went so smoothly, seamless integration. When problems arose that others couldn't solve, it felt like, “I guess it’s up to me to step in,” and the figures just came out one after another. The paper was slated early on, starting in August and basically finished by November—everyone was happy. I never could have imagined then that things would end up like this today.

8. Because I had to analyze gene correlations and 5-year survival rates, I used TCGA and GEO databases, which gave me, a computer-illiterate half-wit, a deep experience of how tough it is. I’ve also deeply realized that programmers are the real laborers who output value in today's world. Looking at my work, I’m actually thinking about switching to bioinformatics.

I'm wondering if making the cover image a bit more suggestive/racy would be better

Haven't checked in for a few days