Hayato M. Yamanouchi

Category: Research & Academia

  • What Exactly Is Systems Ethology?

    What Exactly Is Systems Ethology?

    Introduction

    Systems Biology was first proposed in 1998.
    In recent years, I feel that the concept of systems biology has gradually gained acceptance.

    For an excellent and very accessible overview of systems biology, see Tetsuya J. Kobayashi’s article “What exactly was systems biology?“.

    I apologize if I am mistaken, but I think systems biology can be summed up in a single phrase: “understanding life as a system.”

    My sense is that this introduced a new perspective across biology as a whole, and especially into a wide range of fields such as molecular biology, developmental biology, and chronobiology.

    My own research is in neuroethology, or more broadly ethology.
    In recent years, computational studies that treat animals as systems—neural simulations, connectome data analysis, and machine learning tools for analyzing animal behavior—have been widely published in neuroethology as well.

    Honestly, I do not know whether this kind of impact on neuroethology was anticipated when systems biology was first proposed, or whether the current situation falls within the scope of Systems Ethology.

    Still, it is a fact that this trend is advancing in neuroethology too, and in that sense I feel that the proposal of systems biology was remarkably prescient.

    The beginnings of Systems Ethology

    One question that naturally arises is: if the current trends in ethology are already being absorbed into systems biology, why bother to call it Systems Ethology?

    That is a fair point, and I do wonder about it myself. Even so, there were reasons that led me to deliberately propose Systems Ethology.

    This comes largely from a personal wish of mine: I wanted a place where researchers who study behavior could come together.

    Behavioral research today is pursued from many different perspectives. Alongside the central goal of neuroethology—understanding how neural circuits control behavior—there is ecology, which asks what function a behavior serves in its environment; evolutionary biology, which asks how a behavior evolved; and information science, which asks what strategies behavior implements and what kind of system it constitutes. Behavior is being studied across disciplinary boundaries.

    Even within neuroethology, the range is broad—from circuit-level work in model organisms such as fruit flies and mice to studies of unique behavioral mechanisms in non-model organisms—and it goes by different names, such as neuroethology or behavioral neurobiology.

    Adding to this, the approaches used to study behavior have also diversified: genetic manipulation of neural activity, recording of neural activity by electrophysiology and calcium imaging, behavioral quantification using machine learning, mathematical descriptions of behavior, exploration of behavioral strategies with reinforcement learning, and more classical detailed observation of behavior. Here too, methods cross disciplinary lines.

    As a result, my personal impression is that each of us attends whichever conference happens to be closest to the scope of our own research, and somewhere along the way I found myself wondering: where exactly is my home conference?

    Behavioral research is being pursued from ever more diverse perspectives, and the current reality is that we often want to learn about other groups’ methods but have no way to do so.
    It struck me as a great missed opportunity, and I imagined that if there were a venue where all of these came together, behavioral research might advance even further. That was the intent behind creating such a place.

    This is purely my own view, and I do not assume that those collaborating with me share the same motivation, nor that this rests on my own agenda alone—but the desire for a venue where everyone could gather was something I heard again and again in conversations with many people at conferences.

    And so, in order to create such a venue, I set out to propose Systems Ethology. In September 2024, at SWARM2024, I submitted a paper titled

    Toward Understanding the Principles of Animal Behaviors: Systems Ethology
    Hayato M Yamanouchi, Yusuke Notomi, Ryoya Tanaka, Shumpei Hisamoto, Shigeto Dobata

    and

    Systems Ethology: Toward Elucidating the Design Principles of Animal Behavior

    and organized the following Organized Session.

    The following is the abstract of this OS, quoted from the SWARM2024 website.

    The exploration of biology plays a crucial role in elucidating the behavioral mechanisms of individual agents and their collective behavior as swarms, owing to the complex and diverse nature of animal behavior. In particular, recent remarkable advances in information processing technology have helped to elucidate their complex behavioral patterns. Furthermore, these technological innovations also enable detailed investigations into non-model species where established research tools are lacking, thereby contributing to a broader understanding of various biological phenomena. 
    　Currently, methods for animal behavior analysis are highly diversified, necessitating opportunities for integrated discussions where specialists with cutting-edge knowledge can share their techniques. We therefore propose a framework called “Systems Ethology” to elucidate the design principles of animal behavior. With the overarching goal of understanding animal behavior as a system, we aim to facilitate the exchange of information regarding various approaches to elucidating the mechanisms underlying each behavior. Such cross-disciplinary interactions among researchers can assist in performing more efficient and meaningful research.
    　In this session, we aim to gather biological insights from various disciplines such as ecology, ethology, and neuroscience. Additionally, proposals for diverse behavioral analysis approaches, incorporating insights from information science and engineering, are also encouraged.

    For this OS we invited researchers studying behavior from a wide range of perspectives, and we were able to put together a very well-attended session.

    We are now planning what has long been a hope of mine: launching a Systems Ethology research meeting.

    The ideas behind Systems Ethology

    Next, I would like to discuss what Systems Ethology actually is and how we define it as a discipline.

    Ethology has long been centered on “Tinbergen’s four questions,” proposed by Niko Tinbergen.

    There are various ways of phrasing and framing them, but here we take the four perspectives to be “survival value,” “ontogeny,” “evolution,” and “causation.”

    Niko Tinbergen argued that behavior must be examined from these four perspectives, and they remain an important guiding framework for ethology today, and by extension for neuroethology.

    Today, however, each of these perspectives is pursued by research that focuses on it alone.

    To take a clear example, neuroethology focuses on how neurons control behavior from the standpoint of causation, ecology focuses on survival value, and evolutionary biology focuses on evolution.

    Answering each of these questions matters, but these perspectives have tended to exist separately, with anything outside a given focus treated as a black box.

    In other words, I feel there has never been a discipline that focuses on the causation and the survival value of behavior at the same time, covering both scopes. (Of course, at the level of individual studies, work that combines these views is becoming more common.)

    Truly understanding behavior requires all four perspectives—“survival value,” “ontogeny,” “evolution,” and “causation”—yet they have rarely been integrated. No one can say for certain why, but one factor is surely how difficult it is for these perspectives to understand one another.

    Put the other way around: what if we simply made mutual understanding possible?
    Here, the notion of a system may serve as the medium that solves this problem.

    That is, if the system becomes a kind of shared language linking these perspectives, each side can incorporate the others’ ideas.

    Here, I define this system as the behavioral system.

    There are many ways to conceive of behavior, and no single definition fits them all, but here I take a simpler view, defining behavior as:

    The internally coordinated responses (actions or inactions) of whole living organisms (individuals or groups) to internal and/or external stimuli, excluding responses that are more easily understood as developmental changes

    Japanese translation: 生物全体（個体または群れ）が内部および／または外部からの刺激に対して内部的に調整した反応（行動または不行動）であり、発達上の変化としてより容易に理解できる反応は除く。
    ~Source: Levitis et al. (2009)~

    That is the definition I adopt.

    Recast in terms of a system, behavior corresponds to the system’s output.
    The parts of the system responsible for input and processing are therefore treated as the behavioral system.

    A behavioral system can be viewed at multiple levels: with the individual as the unit, the neuron as the unit, or the molecule as the unit.
    Given this diversity of levels, understanding behavior requires working across them and making sense of behavior at several levels at once.

    Because this makes things so complex, however, fully understanding a single system within one study becomes impossible.
    That is precisely why we need a venue for sharing diverse approaches and insights across disciplinary boundaries.

    Returning to “Tinbergen’s four questions,” Systems Ethology proposes the following four guiding principles for answering them.

    1) System structure: Static structure represented by nodes and edges. It includes discovering the behavior and the pathways by which the behavior is formed from inputs. It also elucidates the system’s components and how they are connected. 

    2) System dynamics: The dynamic structure is represented by nodes and edges. This perspective includes dynamics of the system (structure changes of the systems) and dynamics on the system (inner-statements transition of the nodes in the system). Dynamic system structure changes as a result of learning, development, and other parameters. 

    3) The control method: Methods for controlling the system’s states and behaviors. This method includes behavioral changes caused by intervention on the system’s nodes or edges. 

    4) The design method: Meaning and principles of the system. This method includes understanding the system’s adaptive and evolutionary significance. 

    ~Source: Yamanouchi et al. SWARM2024, (2024)~

    These principles indicate which aspects of a system we set out to clarify, and they are based on the four principles of systems biology.

    No one is certain where Systems Ethology will go from here, and as things stand the details have yet to be unified.

    That is exactly why I want to build Systems Ethology as a venue for thinking about the future of the study of behavior.

    The future of Systems Ethology

    At present, Systems Ethology is a newly proposed framework, and one that exists only as a conference paper.

    Eventually, I hope to present it to the wider community in a form such as an opinion article.

    My more immediate hope is to realize the original goal: a place where people who study behavior can come together.

    To start, I would like to organize a Systems Ethology study group here in Japan.

    Closing remarks

    This article reflects my personal views and is not meant to define Systems Ethology definitively.

    Please keep in mind that others may see things differently, and that I may well be mistaken on some points.

  • What Exactly Is YORU, a New Behavior Analysis Tool Powered by Object Detection?

    What Exactly Is YORU, a New Behavior Analysis Tool Powered by Object Detection?

    In November 2024, a preprint was posted on bioRxiv describing YORU, a behavior analysis tool that takes a different approach from existing behavior analysis tools.
    In this post, I would like to explain what kind of tool it is.

    YORU preprint

    YORU documentation

    What is YORU?

    YORU is a deep learning-based tool for analyzing animal behavior.

    YORUのサイトより引用


    Many deep learning-based tools, such as DeepLabCut and SLEAP, have already been released.
    These tools use deep learning to track animal body parts and estimate posture (here I will call them tracking tools).

    By estimating posture, they reveal what posture an animal is in and use this for behavior analysis.

    YORU, by contrast, recognizes behavior using an object detection algorithm.
    In short, it does not track body parts; instead, it defines behavior directly from the appearance of the behaving animal.

    When we distinguish an apple from an orange, we rely on cues such as shape and color.
    Object detection excels at classifying objects, so it makes exactly this kind of appearance-based distinction.

    Applying this to animal behavior analysis means classifying animal behavior from its appearance.
    Within YORU, this is referred to as a “behavior object.”

    Whereas tracking tools represent behavior as points and lines, YORU analyzes behavior by enclosing it in a bounding box.

    Advantages of defining behavior with object detection

    The advantages of object detection-based behavior analysis include:

    ・Errors are less likely even as the number of individuals increases

    ・Less affected by the animal’s orientation

    ・Fast analysis speed

    ・Can capture behaviors that are hard to detect by tracking (such as a mouse crouching)

    and so on.

    It overcomes a weakness of tracking tools by making multi-animal behavior analysis simple and far less computationally costly.
    It also makes it easy to analyze behaviors that are difficult to define from body part coordinates.

    Consider, for example, the actions of opening and closing a hand.
    With a tracking tool, you would track the fingertips with a camera and define the hand as open or closed, but if the fingertips become hidden when the hand closes, tracking fails and the positional information can no longer be computed accurately.

    With object detection, however, the model looks at the overall shape of the open and closed hand, so it can still make an accurate call even when part of the hand is occluded.

    As for robustness to increasing numbers of individuals: with a tracking tool, as the number of animals grows you must assign each body part to the correct individual in order to capture each animal’s behavior accurately, whereas YORU analyzes only the shape of each individual, so no identity assignment or part-to-individual matching is required.

    Because analysis is also fast, real-time analysis is possible—you can trigger device control when a particular behavior occurs, enabling automation of behavioral experiments.

    Disadvantages of defining behavior with object detection

    There are, of course, drawbacks as well:

    ・Cannot capture a behavioral sequence

    ・Cannot identify individuals

    ・Cannot capture unknown behaviors

    ・Cannot capture behaviors that are not visually distinctive

    ・Provides no fine-grained detail about the defined behavior

    These are precisely the strengths of tracking tools. Rather than one approach being better than the other, the two complement each other’s advantages and disadvantages.

    Experimenters therefore need to choose the approach that best suits their situation.

    What makes YORU remarkable?

    Having covered the advantages and disadvantages of the object detection-based behavior analysis that underlies YORU, what exactly makes YORU itself stand out?

    Everything runs through a GUI

    YORU lets you perform behavior analysis without writing any code.

    The same is true of tools such as DeepLabCut and SLEAP, and it goes a long way toward making these analyses accessible to biologists.

    Design


    Unlike previous tools, YORU’s design doesn’t really look like that of a research tool.

    Built-in real-time analysis

    Unusually for tools of this kind, YORU comes with a full GUI for real-time analysis.

    Building a real-time analysis system is not that hard if you can program to some degree, but otherwise the barrier is quite high.

    With YORU, however, you can carry out real-time analysis—and even fire external triggers—entirely without programming.

    Adaptable to a wide range of experimental setups

    The biggest hurdle in real-time analysis is figuring out how to combine the code that drives your own experimental apparatus with the analysis itself.

    YORU adopts a plugin system that lets you choose the program used to control your external devices.

    In other words, you simply select the plugin that matches your own system.

    You can also write your own plugins: simply turning your device’s control program into a plugin makes it easy to link that hardware to YORU’s real-time analysis.

    This makes for a remarkably user-friendly system that no other tool currently offers.

    Closing thoughts

    YORU has only just been released, so there are still rough edges in usability and clear gaps in the documentation.

    That said, as these are addressed over time, I am very much looking forward to seeing how people put it to use.

  • Making Your JSPS Research Fellowship (DC1, DC2) Application Even Better [Advice from a Successful FY2024 DC Applicant]

    The results for the JSPS Research Fellowships (DC1 and DC2) were announced at 14:00 on Wednesday, September 27, 2023.

    I was one of the students who applied, and I am happy to report that I was selected.
    On X (formerly Twitter), people were saying that this year’s acceptance rate was lower than usual.

    Apparently the initial acceptance rate was about 14%, or around 17% including the second round of selections.

    I had heard that it is usually close to 20%, so when I saw this year’s rate my first thought was, “Seriously?”

    Through this application process—reading many other people’s JSPS applications and writing my own—I’ve put together some points worth keeping in mind to improve your chances even slightly.

    I think this advice applies not only to JSPS applications but to any writing meant to be read by others, such as grant proposals and job application essays, so I hope you’ll read on.

    What matters lies beyond the application itself

    First, the key to writing an application is conveying what you want to say within a limited space.

    An application explains your research and argues for its importance: this is who I am, these are the things I can do, and therefore I can achieve this goal. So please fund me.

    In other words, you need to keep in mind what lies beyond the writing itself.

    While writing, it’s easy to get caught up in immediate concerns—producing elegant prose, or writing something your advisor won’t complain about—and lose sight of what an “application” fundamentally is.

    It isn’t self-satisfaction: someone reads it, evaluates it, and scores you on that basis.
    This is clearly different from an exam, and precisely for that reason there is no single correct answer.

    My impression is that many people who have spent their lives taking tests with defined answers suddenly hit a wall when faced with an application, unsure of what to write or what “correct” even means.

    Even while writing, you need to keep asking who the application is for.
    Writing a few applications for other funding bodies before the JSPS one and having people read them makes a striking difference in readability.

    The keywords are “logic” and “specificity”

    Logic

    The two things I paid the most attention to when writing my JSPS application were logic and specificity.

    Logic means that your argument is consistent and that each topic connects to the next.
    You need to consider these connections at multiple scales: between sentences, between paragraphs, and across the application as a whole.

    To put it in more practical terms,
    a logical piece of writing is one in which the reader finds the information they want, in the order they want it.
    When this is done well, the content flows effortlessly into the reader’s mind.

    The point is to judge from the reader’s perspective.
    No matter how logical you think your argument is, it may not come across that way to someone else.

    Guides on writing JSPS applications often recommend inserting phrases like “in other words” or “the details are described below,” but the underlying purpose is to give the reader signposts that make the structure easy to follow.

    Opinions differ sharply on whether such phrases are necessary, but one thing is clear: if omitting them makes your structure hard to follow, you should include them even at the cost of precious space.

    Specificity

    Specificity means, quite literally, providing exactly as much information as the reader needs.

    You’ve probably often heard people say, about applications or research talks,
    “My research needs so much background that I just can’t fit it into a short format.”

    I’ve felt the same way myself—facing a 10-minute conference talk and thinking there was no way I could finish in 10 minutes.

    But it often turns out that even a talk you confidently packed with content contains plenty of information the audience didn’t need, and almost none of what they actually wanted to know.

    In other words, you haven’t decided what to keep and what to cut.

    To make those decisions, first determine what you most want to convey.
    Then keep what is necessary to explain that core message and discard what isn’t. That is how you achieve specificity.

    In applications like the JSPS fellowship, you have to describe your research and yourself in a very small amount of text.
    People often suggest dividing the fellowship funding by the number of characters in the application to calculate the value of a single sentence.

    I think the point of that exercise is to make you ask: is this sentence really worth that much money? If not, rewrite it.

    Background sections describe the field from a third-party perspective and are necessary to make your own work stand out, but since they describe other people’s research, their intrinsic value is low.
    Whether you keep them to a minimum or find some other value in them is up to you.

    Revising in this way is how you refine your content.

    So far I’ve talked about specificity while only discussing what to cut, but the real point is to condense the content and make each sentence as specific as it needs to be—no more, no less.

    Throughout the process, I kept logic and specificity constantly in mind, periodically stepping back to reread and rewrite.

    Show it to others early

    One of the most important things about an application is getting other people to read it early.

    Some people wait until it’s finished and they’re satisfied with it, and only show it a week before the deadline—which is honestly a waste.

    Showing your draft to others means both checking whether you’re heading in the right direction and getting feedback.
    It isn’t just about corrections.

    As the saying goes, the quality of a JSPS application depends on how many people have read it: feedback brings new insights and can even change your fundamental way of thinking.

    Ideally, aim to have someone other than your supervisor read it at least three weeks before the deadline.

    Don’t stare at your application nonstop

    When application season came around, I often saw people cutting back on experiments to focus entirely on their applications.
    It’s true that these documents require a fair amount of time, and revising them over and over eats up an enormous number of hours.

    That said, once you’ve written a reasonable draft, there’s no point in reading it over and over and burning time for nothing.
    In fact, you should put some distance between drafting and revising before you read it again.

    An application is an important document with your future riding on it.
    But it doesn’t improve in proportion to the time you pour into it. The real challenge is producing the best possible version as efficiently as you can.

    Rather than being glued to your computer, keep your experiments moving along, and even take breaks to refresh yourself. Writing that way lets you keep a level-headed perspective.

    I didn’t write only at the university. I would take my laptop to cafés and other places, changing my surroundings often as I wrote.

    Closing thoughts

    This year’s provisional acceptance rate is said to be about 14% at the first-stage screening.
    To put it the other way around, 86% of applicants were not selected for a JSPS fellowship.

    Since the vast majority of applicants are not selected, there’s no need to be more discouraged than necessary if you weren’t.

    At the same time, those who are selected have, in effect, secured their place ahead of many other people’s dreams and livelihoods.
    The award does reflect a genuine evaluation of your research as it stands, but beyond that it carries the responsibility to work even harder from here on.

    I want to keep that in mind myself and continue giving my research everything I have.

  • How to Search for Papers in the Life Sciences [A Graduate Student’s Guide]

    Once you join a lab, you will almost certainly be asked at some point to present a paper at a journal club.

    Journal club formats vary from lab to lab, but in many cases you are expected to find a paper yourself, read it yourself, and present it.
    Even outside of journal clubs, searching the literature is essential to research, since you need to know what has already been done.

    Here I would like to introduce some tools that come in handy for this.

    Google Scholar

    The first one is Google Scholar.

    It is so well known that there is probably no researcher who has not heard of it.

    Basically you can search just as you would on Google, but everything that comes up is bibliographic data such as papers and books.

    It is the tool you reach for when you want to search broadly, so I suspect it is the most widely used for literature searches.
    I also use Google Scholar when I just want a quick look: I type in some rough keywords and use it as a screening step.

    The flip side of being able to search broadly is that there are few options for specifying details.

    Still, the main filters are there, such as publication date and relevance, so it covers most needs.
    And when you want to restrict the search to a particular journal, you can just put the journal name in the keywords—exactly the same approach as a Google search.

    PubMed

    The next one is PubMed.

    PubMed is probably the best-known literature search tool in the life sciences.

    What makes this tool remarkable is the sheer variety of ways you can search for papers.

    You can search by author, journal, editor—pretty much any field you can think of.

    It is extremely useful when you already know the journal, the author, or any other details you want to search on.

    Web of Science

    The last one is Web of Science.

    It is a paid service, but if you belong to a university you can generally access it through the campus Wi-Fi, so students can use it for free.

    Where this tool really shines is that it lets you look up the impact factor (IF).

    The IF is a kind of journal ranking—a value indicating how strong a journal is.

    If you search Google for “journal name IF,” an impact factor will come up, but it is often not the official value.

    Web of Science is the organization that officially publishes IFs, so you can find the correct value there.

    I use Web of Science as an IF lookup tool when deciding which journal to submit to, or when I want to get a sense of how impressive a professor’s papers are.

    Final thoughts

    I use Google Scholar most of the time, but knowing about the other tools is handy when you need to look something up in a more specific way.


  • Writing My First Paper (A Personal Account)

    Writing papers is a crucial part of a researcher’s job—it is how we report our work to the world.

    Writing a paper takes far more time than you might imagine; it is unglamorous work that demands real persistence.
    That is precisely why the first paper you write yourself leaves such a lasting impression.

    I myself had the experience of writing a paper from scratch in my first year of the master’s program, under my supervisor’s guidance.
    It varies by field, but in biology it is quite rare for a master’s student to write the text of a paper themselves.
    After all, most students don’t even publish a paper at that stage.

    I’m grateful to have had such a rare opportunity, and I want to write down what it was like so that I don’t forget how I felt at the time.

    The Timeline of Writing a Paper

    In biology, it typically takes about a year from deciding to write a paper to actually seeing it published.
    It depends on the format and scale of the paper, but my sense is that what differs most from fields like chemistry and engineering is the sheer volume of content a paper must contain.

    Unlike reporting the discovery of a compound or a protein, neuroethology—the field I work in—requires demonstrating that “this neuron contributes to this behavior in this particular way,” so it is common for a paper to have more than five figures (each containing multiple graphs).

    For that reason, gathering all that data, writing it up, and getting it published is said to take longer than in other fields.

    Below is a rough outline of the steps leading up to submission.

    • Write the manuscript (1 month to over a year). All sorts of things can happen while writing, so it often doesn’t go smoothly.
    • Have others read the manuscript (1–3 months). This step happens sometimes and not others: you ask a colleague who isn’t involved in the project to read it and check whether the content makes sense.
    • English proofreading (about 1 month)
    • Submission!!! (2 days to over 4 months). A rejection can come back in as little as two days, while a manuscript sent out for review may go months without any word.
    • Handling revisions (1–6 months). You receive comments from the reviewers, collect any missing data, revise the content, and send it back to the journal. Revisions come as major or minor, and it’s not unusual to be asked for changes that alter the overall structure of the paper.
    • Acceptance!!! Once you’ve addressed the revisions and the journal gives the green light, the paper is finally accepted. Only at this point does the work count as a result.
    • Formatting for publication (1 week to 1 month). This is the final round of polishing so the paper is ready to appear online and in the journal.
    • Publication, preparing a press release, and so on. After acceptance, all kinds of things happen in quick succession and life gets busy.

    A paper isn’t published simply because you wrote it and pressed a button. It is evaluated by many people, examined by third parties to check whether its logic holds up, and only then approved and released to the world.

    My Own Experience

    First, here is the published paper: link.

    After presenting my undergraduate thesis in my fourth year, my supervisor suggested at the end of February that we write a paper.

    I began writing in March, submitted to the journal iScience in December, and the paper was formally accepted in April.
    It was published almost exactly a year after we decided to write it—a remarkably smooth process.

    I was thrilled that March when I was told we’d be writing a paper, and I was determined to finish it as quickly as possible.
    But although I was told to go write it, I had no idea how, so I looked up published papers and imitated them as best I could while drafting my own content.

    On top of that, English is not my strong suit, so I kept revising as I was repeatedly told, “I don’t understand this—please fix it.”
    At the time, being told to rewrite a passage rather than receiving a corrected version back was genuinely painful; I remember thinking, “Just teach me already.” Looking back, though, having to research and figure out how to write on my own is exactly what serves me today.

    What my supervisor told me was this: if you intend to become a researcher, you’ll have plenty of chances to write papers in the future, and if you don’t write this one yourself, you won’t be able to write them then.

    Still, hard is hard. Around the summer I couldn’t eat properly and my stomach was in bad shape.
    (Personally I found it puzzling—why should just writing text be so stressful?—but apparently it took a real toll on my body.)

    Why was it so hard? While writing a paper, you basically stop doing experiments and focus entirely on the writing.
    Meanwhile, conferences, classes, and everyday life all keep coming at you.

    The writing wasn’t going well and the experiments weren’t progressing, so I started to feel like I was accomplishing nothing at all—especially when everyone around me seemed to be moving forward.
    At the lab’s twice-yearly progress meetings, I was the only one with nothing to report, and the anxiety was intense.

    Worst of all was having no one to talk to.
    Writing a paper as a first-year master’s student is unusual, and from other people’s perspective it’s something to envy.
    Because of that, there was no one in the lab I could discuss the paper with, and people outside research would just think, “It’s only writing.” I felt misunderstood and completely alone in the work.

    Around September, when things got especially rough, I turned to my programming-based research as a distraction and wrote a short paper for an international conference.
    That turned out to be a great change of pace: it eased my anxiety about my research stalling, and because it was a short paper it was easier to write. I could feel myself improving, and the sense of a small success gave me real confidence.

    With all that going on, I had finished most of the manuscript by around October.
    I thought we were ready to submit, but then my advisor said,
    “Shall we have some other faculty members look at it too?”
    “Whaaaat!!!!???? We’re not done????”
    — I still remember thinking exactly that.

    The faculty members who read it gave me extremely apt advice, and the paper improved enormously.
    I learned firsthand that having other people read your manuscript is a hugely important part of the process, and that this applies not just to papers but to research in general.

    Then, in December, we made our initial submission to iScience.

    As journals go, iScience is not at the very top tier, but it is by no means low-ranked either — it is a very good journal, the kind of place where doctoral work gets published.

    For me, choosing iScience was a bit of a stretch goal; I put the odds at 7:3 against acceptance.
    So I was waiting for the rejection notice, but nothing came, and about a week later I was told the manuscript had been sent out for review. I was thrilled and astonished at the same time.
    My advisor seemed surprised too.

    After responding to the reviewers’ comments, we resubmitted, and following some minor revisions the paper was formally accepted.
    Once submitted, things moved along smoothly and pretty much followed their natural course.

    Personally, the initial submission was the moment when I was most excited and felt the greatest sense of accomplishment.

    I realized just how naive my assumptions about the process of writing a paper had been, and at the same time I came to appreciate that writing a paper is invaluable experience for the research that follows.

    People say that if you want to be a researcher, the first thing to do is write a paper. The reason, I now understand, has nothing to do with the results themselves: the act of writing a paper teaches you the importance of structuring your own research logically, thinking ahead, and moving forward with the perspective of someone who will have to write it up.

    It was an experience from which I learned a great deal, and I think it was a wonderful way to begin my life as a researcher.

    At some point I hope to post something more practical about how I actually went about writing the paper, so please stay tuned.

  • Why Are Fruit Flies Used in Research?

    What is a model organism?

    When you think of animals used in research, mice and guinea pigs probably come to mind first.
    You may picture researchers testing a drug’s efficacy or examining structures in these animals before moving on to actual clinical trials.

    That picture isn’t wrong, but there are many other organisms used in research.
    Medaka, zebrafish, nematodes, E. coli, yeast, Drosophila melanogaster, the African clawed frog, Arabidopsis thaliana, the silkworm, and so on…
    These organisms are called model organisms, and a wide range of research is carried out with them.

    A model organism is a species that the research community studies exhaustively, so that by coming to know it inside and out we can understand features and principles shared with other organisms.

    What is Drosophila melanogaster?

    Some of the organisms on that list may surprise you, but Drosophila melanogaster is probably the odd one out.
    Wait, a fly? The kind that shows up in your kitchen?

    Exactly!
    The scientific name of this fruit fly is Drosophila melanogaster.
    It has featured in a number of Nobel Prize–winning studies and is used widely in research.

    Besides the Japan Drosophila Research Conference (JDRC), where fly researchers gather, there are fly meetings in the United States, Europe, and Asia, along with conferences focused on the fly nervous system such as NeuroFly and Neurobiology of Drosophila—research is happening on a global scale.

    The advantages of working with flies include:
    ・they have a central nervous system
    ・a rich set of genetic tools
    ・the existence of balancer chromosomes
    ・a short generation time
    ・ease of rearing
    ・stereotyped behavioral patterns

    and many more.
    Because researchers around the world all study Drosophila melanogaster, enormous databases have been built up, and in the brain nearly the entire network of neuron-to-neuron connections is now known.

    At this point you might wonder whether there is anything left to study. It’s true that we know the wiring, but in many cases we still don’t know what function that wiring serves.

    On top of that, in areas such as immunity and developmental patterning, there remain a huge number of open questions, including which molecules are involved.

    You might ask what all this effort is for—but it is precisely by going this far that we can uncover principles universal to living things!
    That is what a model organism is.

    And these mechanisms can be applied to drug development, safe genetic engineering, and more.

    A brief history of Drosophila research

    The story goes that it all began in 1901, when a well-known figure (Charles W. Woodworth) recommended the fly to someone (William Ernest Castle) as a material for genetics.
    The reason: it was easy to rear in large numbers.

    Later, Thomas Hunt Morgan became famous for his genetic studies using Drosophila. His discovery of mutants and his demonstration that genes reside on chromosomes had an enormous impact on genetics.

    Another landmark was the discovery of the homeotic genes, which have a major influence on development.
    Drosophila work also played a major role in the discovery of clock genes.

    Being rearable in large numbers, being an insect, and allowing mutants to be generated easily were advantages no other organism offered, which is exactly why the fly was such an outstanding material for genetic research.

    Today, applications of the GAL4/UAS system, balancer chromosomes, and a wide variety of other tools have all been developed.

    A few extra notes

    In labs that study Drosophila, the flies are kept in cylindrical containers called vials, about 3 cm in diameter and 10 cm tall.
    Most labs have a dedicated fly room where large numbers of these vials are stored.

    Some people worry that flies must be dirty, but their food is a jelly-like medium containing yeast—not raw meat or anything like that—so bacteria don’t proliferate.

    They are fairly hardy and relatively easy to rear.
    That is probably why they have been studied for so long.

    Dissections are done by hand with forceps under a stereomicroscope.
    We dissect flies that are only about 2.5 mm long using forceps.

    Behavioral experiments are also possible, and all sorts of research is done with cleverly designed apparatus.

    Another wonderful thing about flies is that there are stock centers in several places around the world, holding large numbers of fly lines carrying specific genetic manipulations created in labs elsewhere, from which you can obtain whatever strain you need.

    That means you don’t have to build fly lines yourself and can start experiments right away.
    (Making a genetic manipulation yourself takes at least three months.)
    Truly standing on the shoulders of giants.

    Biologists often say that once you start working on Drosophila you can never work on anything else—a testament to just how well suited the fly is to research.

  • How to Write a JST Application (Internal University Application): A Memorandum

    How to Write a JST Application (Internal University Application): A Memorandum

    Introduction

    One task that follows researchers everywhere is the grant application. Anyone aiming for a research career cannot avoid it, yet it is often overlooked.

    I suspect many people struggle with application forms.
    I am one of them. I went into the School of Science precisely because I was bad at language arts, and yet, of all things, writing turned out to be the obstacle standing in my way….

    In recent years, support for PhD students has become substantial. Beyond the JSPS Research Fellowship (DC), there are university-internal grants funded by JST programs, WISE (Doctoral Program for World-leading Innovative & Smart Education) programs, JASSO, and more. Existing schemes have been expanded and new ones continue to be added.
    If you can secure this kind of support, it will cover most of your living expenses during the PhD, and you can also receive help with tuition.

    I myself have faced and submitted several university-internal applications.

    How they differ from the JSPS fellowship application

    University-internal applications are, for the most part, quite similar to the JSPS (Gakushin) application.
    The reason is simple: getting the JSPS fellowship is the ultimate goal.
    Also, requiring a different format would only add to the student’s workload, so most programs adopt the JSPS format and build in a process that lets you refine the same document.

    The selection criteria center on the research content and on the applicant’s aptitude and future potential.
    What matters is presenting these clearly so that they get across to the reader.
    There is absolutely no need to write elegant, literary prose.

    Although internal applications share much with the JSPS application, there are differences.
    Chief among them is that the kind of person an internal program is looking for differs somewhat from what JSPS is looking for.

    The call itself states what kind of applicant the program is seeking, so you need to write in a way that matches that.

    The biggest difference from the JSPS application, however, is who reads it.
    The JSPS application is assigned to a field, so it is read by someone with at least some relevant expertise.
    Internal applications, by contrast, are basically read by people within your own university. Even within the life sciences, a plant researcher may end up reading your animal research proposal.
    On top of that, each reviewer often has to read more internal applications than JSPS ones.

    In other words, keep the content accessible so that its appeal comes across even to someone in a different field.
    And make sure the content and its appeal register at a glance.

    Fundamentally, you should assume reviewers have no desire to read your application, and write one that catches their interest and makes them read it.
    Use figures and highlighting appropriately so that the important points are obvious at a glance.

    Tips for writing

    The keys to a good application are logical flow and concreteness.

    Logical flow means giving readers the information they want, at the moment and in the order they want it.
    In other words, you create logical flow by connecting each sentence to the next and by writing what the reader is already vaguely anticipating.

    For example:
    “During my undergraduate years I belonged to several student organizations and helped run each of them. Through this I developed leadership and the ability to work smoothly with others.”

    Running an organization → leadership and the ability to work smoothly with others

    When sentences connect like this, without friction or gaps, we can say the writing is logical.

    Next, concreteness. Adding it to the example above gives:
    “During my undergraduate years I belonged to several student organizations that planned events and social gatherings for students, and in each one I took the lead in running those events. Through this I developed leadership and the ability to work smoothly with others.”

    Adding concrete detail strengthens the logic of what exactly you did in the organization, how you were involved, and what abilities you gained as a result.

    The critical obstacle to achieving both, however, is the character limit.
    These limits are set quite tightly, and once you try to describe things concretely you run out of space.

    That is precisely why, in the rush to cram in content, these two elements tend to get neglected.

    Summarize the content concisely and clearly.
    The tension between that demand and the demands of logical flow and concreteness is the central problem in writing an application.

    The key to striking this balance is understanding what kind of person the program wants to select.
    Most calls specify which items they want you to address and what kind of content they expect.

    The reader must be able to see clearly where in your text each of those items is addressed.
    Don’t make them infer or read between the lines; the distinction should be obvious enough that they simply cannot miss it.

    If you keep this front and center, it becomes clear which sentences are necessary and which are not.

    An approach to writing

    When writing an application, start by listing the points you want to make as bullet points, then write out the prose without worrying about length.
    Then flesh out the content while keeping logical flow and concreteness in mind.

    At this stage you will usually be well over the limit, so the next step is cutting the text down.
    Rephrase and reword until you are only slightly over the character limit.

    At that point, have someone else look at your application.
    This stage is extremely important.
    Getting feedback early and often keeps you from drifting far off course and helps you develop your own way of writing applications.

    Showing your work to others is daunting, and pride often gets in the way, but this is the moment to set that pride aside and share it.
    In my experience, the stronger a student’s academic record, the more they tend to agonize over the application alone and delay showing it to anyone.

    Once you receive feedback, reread your own draft in light of it, recognize what needed improving, and rewrite.

    Having others read your draft is the single most important stage.
    Nobody writes a perfect application from the start, and criticism is not a rejection of you as a person; take it as advice that you are capable of more.

    Summary

    An application exists to be read by someone else.
    Never fall into the complacent assumption that reviewers will read it carefully, or that it would be unreasonable of them not to.

    If people won’t read it, then write something that makes them read it.
    No matter how good the content is, it means nothing unless people actually read it and the message gets across.

    A common piece of advice is to take the total amount of funding a grant application would bring in, divide it by the number of lines in the application, and work out how much each single line is worth.

    This gives you a clear standard for judging whether what you have written is worth that much to the reader.
    If you don’t think a sentence carries that much value, it may need to be rewritten.

    Further reading

  • Side Effects of COVID-19 Vaccines: A Science Student’s Serious Review of the Evidence — Based on Japan’s Ministry of Health, Labour and Welfare Website

    Side Effects of COVID-19 Vaccines: A Science Student’s Serious Review of the Evidence — Based on Japan’s Ministry of Health, Labour and Welfare Website

    With misinformation circulating about the side effects of COVID-19 vaccines and media coverage that often stokes anxiety, it can be hard to know which information to trust.

    In Japan, the most reliable source of information on COVID-19 vaccines is, I believe, the content posted on the Ministry of Health, Labour and Welfare website.
    However, materials such as the health status surveys are quite difficult to follow, so few people are likely to read them.

    The Ministry’s Q&A on COVID-19 vaccines is written very clearly, so I will cite it here.

    Basic facts about the vaccines

    First, two COVID-19 vaccines are currently being administered in Japan: the Pfizer vaccine and the Moderna vaccine.

    Both are mRNA vaccines that work by essentially the same mechanism, and their efficacy in preventing symptomatic COVID-19 is nearly identical—about 95% for Pfizer and about 94% for Moderna.

    Both are given as intramuscular injections.
    Intramuscular injection may look painful because the needle goes in fairly deep, but in fact most people around me said it hurt about as much as a subcutaneous injection such as a flu shot, or even less.

    There are a few other minor differences, but both vaccines are basically equally effective, and current data show no clear advantage of one over the other.

    Common side effects

    Side effects that may occur with both the Pfizer and Moderna vaccines include fever, headache, joint and muscle pain, pain at the injection site, fatigue, and chills.
    Pain at the injection site often appears the day after vaccination rather than immediately, and side effects can be delayed, so pay close attention to how you feel on the day after your shot.

    Rarely, serious reactions such as shock or anaphylaxis (a severe allergic reaction) may occur.
    Very rarely, mild myocarditis and pericarditis have been reported after vaccination.

    If you experience health damage from side effects after vaccination, you can apply to the Relief System for Injury to Health with Vaccination, so contact your local government office.

    A side effect commonly seen with the Moderna vaccine is what is known as “Moderna arm.”
    This refers to pain and swelling at the injection site appearing about a week after vaccination.

    In general, side effects subside within one to several days.

    Side effects are also said to be more likely after the second dose than the first.

    Health survey reported by the Ministry of Health, Labour and Welfare (as of July 7)

    The link to the health survey is here

    Pfizer vaccine

    Please see the link for the detailed data.
    Here I will give a rough summary of what the data show.

    As for local reactions at the injection site, the proportion of people with redness or swelling did not differ much between the first and second doses, at roughly 10–12%.

    However, about 90% of people reported pain at the injection site after both the first and second doses, so nearly everyone experiences some pain.
    The proportion reporting a feeling of warmth at the injection site was about 10% after the first dose and about 16% after the second.

    These symptoms peak on the day after vaccination and then roughly halve on day 3 and again on day 4.

    As for systemic reactions, fever above 37.5°C occurred in under 10% of people after the first dose, compared with about 40% after the second dose.
    Fever is more common in younger people (about 50% among those in their 20s), and about 5 percentage points more common in women than in men.

    Fever peaks on day 2, but by day 3 the proportion drops to about a quarter or less, and by day 4 it has resolved in nearly everyone—so it clears up quickly for most people.

    General fatigue occurred in about 25% of people after the first dose and about 70% after the second, while headache occurred in about 20% after the first dose and about 55% after the second. As with fever, the trend is that younger people are more likely to experience these effects, and women more so than men.

    The Moderna vaccine

    The rates of side effects for the Moderna vaccine were largely the same as for Pfizer, and the symptoms were similar as well.

    As for “Moderna arm,” which is characteristic of the Moderna vaccine, it occurred in about 5% of people after the first dose.
    Five percent may sound like a rare symptom, but it amounts to one in twenty people, so many of us probably know one or two people who have experienced it.

    Summary

    I have covered a lot of ground on the rates of side effects, so let me summarize here.

    • Pain at the injection site occurs in almost everyone
    • Local symptoms such as pain, redness, and swelling at the injection site occur at almost the same rate after the first and second doses
    • Systemic reactions occur at a higher rate after the second dose than after the first
    • Fever and fatigue may occur in a large proportion of people
    • Side effects are more likely in younger people, and more likely in women than in men
    • Side effects peak the day after vaccination, and in most people the symptoms subside within one to three days

    A great deal of media coverage is framed in a way that fuels anxiety.
    By looking at the actual data for yourself, you may get a clearer sense of just how much there really is to worry about with the COVID-19 vaccine.

    I hope this article proves helpful to you.

  • From a Talk People Sit Through to a Talk People Listen To!

    From a Talk People Sit Through to a Talk People Listen To!

    One thing you cannot avoid in university life is giving presentations.
    Not only in classes, but also in the lab, in seminars, and in club activities, there are many occasions when you have to stand up in front of people and present.

    From elementary school through high school, you mostly just sit and listen in class, and there are few chances to give a long presentation. Yet
    as soon as you enter university, you are suddenly put in front of an audience again and again.

    That said, it is also true that many people around us are bad at presenting, or simply dislike it,
    and unfortunately there are plenty of presentations that leave the audience thinking “that was poorly done” or “that was boring.”

    So I plan to keep sharing the presentation know-how I have built up during my time at university.

    This time, I want to talk about the mindset behind giving a presentation.

    A typical trait of people who are bad at presenting is that they give a talk that the audience is made to sit through.
    For example:
    ・spending too long on a single slide
    ・leaving dead air during the talk
    ・using too many slides
    ・failing to make the connections between points clear
    and so on.

    As a rule, you should assume that the people listening have almost no desire to hear your talk.
    Nobody starts out interested in your presentation.

    The mood of the audience is like sitting in a school gymnasium listening to the principal talk for thirty minutes.
    Few of us listened to those speeches with genuine curiosity every time.

    If you give a self-centered talk to an audience with no desire to listen, you may satisfy yourself, but your audience will be bored.

    Some people, seeing that the audience looks unengaged, is checking their phones, or is dozing off, will fall into self-loathing and conclude that their presentation was hopeless.

    To avoid that, you need to give a talk that people want to listen to.

    Presentation techniques matter for this, but what matters most is your own attitude toward the talk.
    Once that attitude changes, your presentation will naturally become audience-oriented.

    The keys to a talk people want to listen to are:
    ・be clear about the single most important message of your talk
    ・keep the presentation simple and minimal
    ・eliminate anything that you yourself find confusing or distracting

    Suppose your talk contains ten points.
    If you deliver all ten at the same intensity, nothing will stay with the audience. But if you present just one of them as clearly important, the other points may leave nothing behind while that one will at least stick in their minds.

    Narrow down what you want to say, keep it short and to the point, and make it easy to follow.

    Aim for a talk that people listen to willingly, not one that forces your views on them.
    If you keep that in mind, your presentations will naturally improve.

    If you know someone who is a good presenter, ask them all sorts of questions.
    If you don’t, watch talks by the people often held up as models, such as Steve Jobs or President Obama.
    They can reach a huge audience with words alone.
    Their technique is impressive, but above all, they are absolutely clear about what they want to say.

Hayato M. Yamanouchi

PhD student, Kamikouchi Laboratory, Nagoya University / JSPS Research Fellow (DC1)
Systems Ethology (Neuroethology × Informatics)

© 2026 Hayato M. Yamanouchi

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