Tag: 研究

  • Writing My First Paper: A Personal Account

    Writing papers is one of the most important things a researcher does—it is how research gets reported to the world.

    Writing a paper takes far more time than you would imagine; it is unglamorous work that demands real persistence.
    That is exactly why the first paper you write yourself leaves such a strong 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 never publish a paper at all at that stage.

    I am grateful to have been given such a rare opportunity, and I want to write down that experience here so I don’t forget how it felt.

    The Time Scale of Writing a Paper

    In biology, it generally 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 from my own experience, the big difference 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 something like “this neuron contributes to this behavior in this way,” so it is not at all unusual for a paper to have more than five figures (each containing multiple graphs).

    For that reason, gathering all that data, writing the paper, 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 see whether the content makes sense.
    • English proofreading (about 1 month)
    • Submission!!! (2 days to over 4 months). A rejection can come back in two days, whereas if it goes out for review you may hear nothing for months.
    • Revision (1–6 months). You receive reviewer comments, add missing data, revise the content, and resubmit to the journal. Revisions come as major or minor, and you are often asked to make changes that alter the overall structure of the paper.
    • Acceptance!!! Once you have handled the revisions and the journal gives the green light, the paper is accepted. Only at this point does the work finally count as an achievement.
    • Formatting for publication (1 week to 1 month). This is the final round of work, getting the paper formatted for posting online or printing in the journal.
    • Publication, writing a press release, and so on. After acceptance, all kinds of things happen in quick succession and life gets busy.

    You don’t just write a paper and press a button to publish it. A paper is evaluated by many people, examined by third parties for logical soundness, and only then accepted and released to the world.

    My Own Experience

    First of all, the published paper is here.

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

    I started 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—an unusually smooth submission process.

    That March, I was thrilled to be told we would write a paper, and I was determined to finish it as fast as I could.
    But even though 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 writing up my own results.

    My English was weak, which didn’t help, and I kept getting sent back with “I don’t understand this—please fix it.”
    Rather than being handed a corrected version, I was told which parts were unclear and asked to rewrite them myself. At the time it was genuinely painful and I kept thinking, just teach me! But looking back, having to research and think through 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 will have many chances to write papers in the future, and if I write it for you, you’ll never be able to write one yourself—so write it yourself.

    Still, hard is hard. Around that summer I couldn’t eat, and my stomach was completely worn down.
    (Personally I found it puzzling—why would just writing text be so stressful?—but apparently my body felt it.)

    Why was it so tough? Because while you’re writing a paper, you basically stop doing experiments and focus entirely on writing.
    Meanwhile, conferences, classes, and personal life all keep coming at you.

    The paper isn’t going well and the experiments aren’t moving either, so you start to feel like you’re accomplishing nothing—especially when everyone around you is making progress.
    At the lab’s biannual progress meetings, I was the only one with nothing to report, which was nerve-wracking.

    And the final blow was having no one to talk to about it.
    Writing a paper as a first-year master’s student is unusual, and from the outside it looks enviable.
    Because of that, there was no one in the lab I could discuss the paper with, and people outside research just think “it’s only writing.” I felt misunderstood, as if I had to do it all alone.

    Around September, when things got especially hard, I turned to a programming-based project 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 research not moving forward, and because it was a short paper it was easier to write. I could feel myself improving, and the success gave me real confidence.

    With all that, I had finished a rough draft 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 read it too?”
    “Whaaat?!?! We’re not done yet??”
    — I still remember thinking exactly that.

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

    In December, I made the initial submission to iScience.

    iScience is not an extremely high-ranking journal, but it is by no means a low-ranking one either — it is a solid journal, good enough that doctoral work gets published there.

    For me, choosing iScience was a stretch goal; I figured the odds were 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 surprised at the same time.
    My advisor seemed surprised too.

    After responding to the reviewers’ comments, I resubmitted, and following some minor revisions the paper was formally accepted.
    After submission, things went fairly smoothly and simply followed the flow.

    Personally, the initial submission was the most exciting moment and the one that gave me the greatest sense of accomplishment.

    I realized how naive my thinking about the process of writing a paper had been, and I also came to feel that writing a paper is an extremely valuable experience for the research that follows.

    People say that if you want to become a researcher, the first thing you should do is write a paper — and now I understand why. It’s not about the results themselves. The process of writing taught me the importance of building the logic of my own research, thinking ahead, and moving forward with the perspective of someone who is going to write a paper.

    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.

    I’m also hoping to post something more practical later on about how I actually went about writing the paper, so please look forward to it.

  • 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.
    The usual picture is that a drug’s efficacy or structure is tested in these animals first, and only then does the work move on to clinical trials.

    That picture isn’t wrong, but there are many other organisms used in research.
    Medaka, zebrafish, nematodes, E. coli, yeast, Drosophila melanogaster, Xenopus laevis, Arabidopsis thaliana, silkworms, and so on…
    These organisms are called model organisms, and a huge variety of research is carried out with them.

    A model organism is one that the research community studies exhaustively, so that by coming to know that single organism 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 come as a surprise, but Drosophila melanogaster is probably the most unexpected of all.
    Wait, a fly? The kind that shows up in your kitchen?

    Exactly!
    The scientific name of the fruit fly is Drosophila melanogaster.
    It has been central to research recognized by several Nobel Prizes and is used widely across biology.

    Fly researchers gather at meetings such as the Japanese Drosophila Research Conference (JDRC), and there are also fly conferences in the Americas, Europe, and Asia. On top of that, meetings focused specifically on the fly nervous system, such as NeuroFly and Neurobiology of Drosophila, show just how global this research community is.

    The advantages of working with flies include:
    ・They have a central nervous system
    ・A rich toolkit of genetic techniques
    ・The existence of balancer chromosomes
    ・A short generation time
    ・They are easy to rear
    ・They show stereotyped behavioral patterns

    and many more. Because researchers around the world have converged on Drosophila melanogaster, enormous databases have been built, and in the brain nearly the entire network of connections between neurons 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 functions that wiring serves.

    Beyond that, in areas such as immunity and developmental patterning, there remain a great many open questions, including which molecules are involved.

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

    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) suggested the fly to another researcher (William Ernest Castle) as material for genetics.
    The reason: it is easy to rear in large numbers.

    Thomas Hunt Morgan later 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 homeotic genes, which have a profound influence on development.
    Fly 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 are advantages no other organism offered, which is exactly why the fly was such an outstanding material for genetics research.

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

    A few extra notes

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

    You might imagine flies to be dirty, but their food is a jelly-like medium containing yeast, not raw meat, so bacteria don’t proliferate.

    Flies are fairly hardy and relatively easy to keep.
    That is surely part of why they have been studied for so long.

    Dissections are done by hand with forceps under a stereomicroscope.
    You are dissecting a fly roughly 2.5 mm long with forceps.

    Behavioral experiments are possible too, and researchers have devised all sorts of ingenious apparatus for them.

    Another wonderful thing about flies is that stock centers exist in several places around the world. They maintain large numbers of fly lines carrying specific genetic manipulations made in labs everywhere, and you can order whatever line you need from them.

    That means you don’t have to build the line yourself and can start experiments right away.
    (Doing the genetic manipulation yourself would take at least three months.)
    Truly standing on the shoulders of giants.

    Biologists often say that once you start working on Drosophila, you can’t go back to any other organism, which says a great deal about how well suited the fly is to research.

  • How to Install Python Video Annotator

    How to Install Python Video Annotator

    What is Python Video Annotator?

    Python Video Annotator is an application that lets you analyze recorded videos and annotate events within them along a timeline.

    Researchers in neuroscience and ethology can use it to record videos of animals and then analyze and quantify their behavior.
    For example, suppose you are recording mouse behavior and want to score actions such as sticking out the tongue, wagging the tail, or moving the ears.
    When in the video does each action occur, and how long does it last?
    Watching the video and logging everything in Excel each time becomes overwhelming once you have defined many behaviors.
    With a tool like this, you can record annotations directly on the video and export the timing and event information.

    There used to be an open-source application called VCode for recording animal behavior.
    The problem, however, was that it no longer runs on current computer operating systems.

    Python Video Annotator works on modern PCs and retains the essential features, while also allowing you to combine it with external sensor data (such as pressure gauges) and behavior-quantification tools like DeepLabCut, making it an extremely useful tool for researchers.

    How to install

    The official website describes the installation procedure in detail, but I could not get it to install properly on my computer. (Installing it directly may have caused conflicts with packages that were already installed.)
    So instead I followed the approach described on the GitHub page, building a virtual environment with Anaconda and installing it there.

    It sounds complicated when described in words, but the steps are very simple.
    As of now (October 20, 2021) it does not appear to support the latest macOS (Big Sur 11.6), though this will probably be fixed soon.
    For that reason, I will use Windows as the example here.

    That said, on macOS the steps are basically the same once you have installed Anaconda and can use the conda command, so please refer to this guide once support is available. (For details, see my previous post.)

    Install Anaconda and open the Anaconda prompt.
    Then create a virtual environment and activate it.

    conda create -n videoannotator python=3.6
    conda activate videoannotator

    Next, install the required packages.

    pip install opencv-python-headless pyqt5==5.14.1 pyqtwebengine==5.14.0

    Then install Python Video Annotator.

    pip install python-video-annotator

    Once all the processing finishes, the installation is complete.

    To launch it, activate the virtual environment first and then run it.

    conda activate videoannotator
    start-video-annotator

    If the software starts up, you are all set.