Tag: 研究

  • 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 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 events such as tongue extension, tail flicks, or ear movements.
    When does each event occur in the video, and how long does it last?
    Watching the video and logging everything in Excel each time quickly becomes overwhelming when you have defined many behaviors.
    With a tool like this, you can annotate the video directly and export the timing and event information.

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

    Python Video Annotator runs on modern PCs, retains the features you need, and can additionally be combined with external sensor data (such as pressure gauges) and behavior quantification tools like DeepLabCut, making it a very useful tool for researchers.

    How to install

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

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

    That said, once you have installed Anaconda on macOS and can use the conda command, the steps are essentially the same, so please refer to this guide once support arrives. (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 the various processes finish, the installation is complete.

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

    conda activate videoannotator
    start-video-annotator

    If the software starts up, you are all set.