The Toughest Year in Grad School

By Susan Sheng

Dear Third Year Self,

You did it! You finished all your coursework, successfully presented and defended your thesis proposal, and are now officially a PhD candidate! You’re probably tired but relieved to have finished your qualifying exam, and excited to get started on those experiments you proposed. Better get working so you can stick to that timeline, find some novel and interesting data to publish, and be able to graduate in 3.5 more years right? Let me offer a few words of advice as you get ready to start your third year.

 

First, add at least 6 months to those proposed dates on that timeline. Your PI is right, they are overly optimistic, and even the most straightforward-sounding experiments will take longer to troubleshoot and optimize than you think.

 

While you’re troubleshooting and optimizing, talk to people. Talk to labmates or colleagues in other labs who may have experience with your procedure. Reach out to the technical support staff at the companies producing your reagents. If for whatever reason you think your yield from commercial kits is not as high as expected, and even if you have scoured their websites without finding anything useful, call technical support and they might be able to give you some tweaks that will make all the difference.

 

I’m not going to lie, third year is going to be tough. You will go months without seeing any positive data. You will have some promising results, and then fail to be able to reproduce them. In the spring, when it’s time to submit poster abstracts to the department retreat and to the big annual conference, you will remember that time in second year when you remarked to your friends, “Next year I’ll have data to present!” and you will wonder where you went wrong.

 

This year, some of your classmates will leave with their Master’s degree and pursue other career paths, and you’ll wonder whether you should do the same. Take the time to look at job postings and attend career panels. Again, talk to people. Learn about what is out there, see what types of jobs interest you and find out what skills are needed for those positions. Maybe it will make more sense to leave with the Master’s degree, or maybe not. Maybe you will need to learn new skills; make a plan and figure out how to best acquire and demonstrate those skills (i.e. online or in-person classes, volunteering, etc.)

 

Cultivate your life outside the lab. Yes you will spend many hours working in the lab, but make sure you step away from the bench to get some fresh air. Connect with your classmates and commiserate over the struggles of grad school. Find some hobbies, maybe a local recreational sports league, or some fitness classes. Get out into nature (fun fact, a recent PNAS study has suggested that nature walks may help calm the brain. Take care of yourself so that you can go into lab rested and recharged.

 

Things will get better. You may need to switch gears and try different approaches and techniques to get at the same question. While you don’t want to juggle too many experiments and projects simultaneously, it might also not be ideal to focus solely on one single experiment, so try and find some balance. Having multiple experiments increases your chance of finding something that works, but you don’t want to split your time and attention too much.

 

Make sure you take time to read. You will be reading anyway as you troubleshoot, trying to see what conditions other people have published successfully, but take some time to read other papers relating to your project, your field, or just science in general. Step back and think about how your experiments fit into the bigger picture. Read about new discoveries and remind yourself why you were so excited about your project in the first place, and why you are in science to begin with. Remember, grad school is a marathon, not a sprint.

 

Signed,

Soon-to-be Fourth Year Self.

 

Measuring the Value of Science: Keeping Bias out of NIH Grant Review

 

By Rebecca Delker, PhD

Measuring the value of science has always been – and, likely, will always remain – a challenge. However, this task, with regard to federal funding via grants, has become increasingly more daunting as the number of biomedical researchers has grown substantially and the available funds contracted. As a result of this anti-correlation, funding rates for NIH grants, most notably, the R01, have dropped precipitously. The most troubling consequences of the current funding environment are (1) the concentration of government funds in the hands of older, established investigators at the cost of young researchers, (2) a shift in the focus of lab-heads toward securing sufficient funds to conduct research, rather than the research itself and (3) an expectation for substantial output, increasing the demands for preliminary experiments and discouraging the proposal of high-risk, high-reward projects. The federal grant system has a direct impact on how science is conducted and, in its current form, restricts intellectual freedom and creativity, promoting instead guaranteed, but incremental, scientific progress.

 

History has taught us that hindsight is the only reliable means of judging the importance of science. It was sixteen years after the death of Gregor Mendel – and thirty-five years after his seminal publication – before researchers acknowledged his work on genetic inheritance. The rapid advance of HIV research in the 1980s was made possible by years of retroviral research that occurred decades prior. Thus, to know the value of research prior, or even a handful of years after publication, is extremely difficult, if not impossible. Nonetheless, science is an innately forward-thinking endeavor and, as a nation, we must do our best to fairly distribute available government funds to the most promising research endeavors, while ensuring that creativity is not stifled. At the heart of this task lies a much more fundamental question – what is the best way to predict the value of scientific research?

 

In a paper published last month in Cell, Ronald Germain joins the conversation of grant reform and tackles this question by proposing a new NIH funding system that shifts the focus from project-oriented to investigator-oriented grants. He builds his new system on the notion that the track record of a scientist is the best predictor of future success and research value. By switching to a granting mechanism similar to privately funded groups like the HHMI, he asserts, the government can distribute funds more evenly, as well as free up time and space for creativity in research. Under the new plan, funding for new investigators would be directly tied to securing a faculty position by providing universities “block grants,” which are distributed to new hires. In parallel, individual grants for established investigators would be merged into one (or a few) grant(s), covering a wider range of research avenues. For both new and established investigators, the funding cycle would be increased to 5-7 years and – the most significant departure from the current system – grant renewal dependent primarily on a retrospective analysis of work completed during the prior years. The foundation for the proposed granting system relies on the assumption that past performance, with regard to output, predicts future performance. As Germain remarks, most established lab-heads trust a CV over a grant proposal when making funding decisions; but it is exactly this component of the proposal – of our current academic culture – that warrants a more in-depth discussion.

 

Germain is not the first to call into question the reliability of current NIH peer reviews. As he points out, funding decisions for project-oriented grants are greatly influenced by the inclusion of considerable preliminary data, as well as form and structure over content. Others go further and argue that the peer review process is only capable of weeding out bad proposals, but fails at accurately ranking the good. This conclusion is supported by studies, which establish a correlation between prior publication, not peer review score, and research outcome. (It should be noted that a recent study following the outcomes of greater than 100,000 funded R01 grants found that peer review scores are predictive of grant outcome, even when controlling for the effects of institute and investigator. The contradictory results of these two studies cannot yet be explained, though anecdotal evidence falls heavily in support of the former conclusions.)

 

Publication decisions are not without biases. Journals are businesses and, as such, benefit from publishing headline-grabbing science, creating an unintended bias against less trendy, but high quality, work. The more prestigious the journal, the higher its impact factor, the more this pressure seems to come into play. Further, just as there is a necessary skill set associated with successful grant writing that goes beyond the scientific ideas, publication success depends on more factors than the research itself. An element of “story-telling” can make research much more appealing; and human perception of the work during peer review can easily be influenced by name recognition of the investigator and/or institute. I think it is time to ask ourselves if past publication record is truly predictive of future potential, or, if it simply eases the way to additional papers.

 

In our modern academic culture, the quality of research and of scientists is often judged by quantitative measures that, at times, can mask true potential. Productivity, as measured by the number of papers published in a given period of time, is a standard gaining momentum in recent years to serve as a meaningful evaluation of the quality of a scientist. As Germain states, a “highly competent investigator” is unlikely “to fail to produce enough … to warrant a ‘passing grade’.” The interchangeability of competence and output has been taken to such extremes that pioneering physicist and Nobel Prize winner, Peter Higgs, has publicly stated that he would be overlooked in current academia because of the requirement to “keep churning out papers.” The demand for rapid productivity and high impact factor has caused an increase in the publication of poorly validated findings, as well as in retraction rates due to scientific misconduct. The metrics used currently to value science are just as, if not more, dangerous to the progress of science as the restrictions placed on research by current funding mechanisms.

 

I certainly do not have a fail-proof plan to fix the current funding problems; I don’t think anyone does. But, I do think that we need to look at grant reform in the context of the larger issues plaguing biomedical sciences. As a group of people who have chosen a line of work founded in doing/discovering/inventing the impossible, we have taken the easy way out when approached with measuring the value of research. Without the aid of hindsight, this task will never be objective and assigning quantitative measures like impact factor, productivity, and the h-index has proven only to generate greater bias in the system. We must embrace the subjectivity present in our review of scientific ideas while remaining careful not to vandalize scientific progress with bias. Measures to bring greater anonymity to the grant review process and greater emphasis on qualitative and descriptive assessments of past work and future ideas will help lessen the influence of human bias and make funding more fair. As our culture stands, a retrospective review process, as Germain proposes, with a focus on output runs the risk of adopting into the grant review process our flawed, and highly politicized, methods of judging the quality of science. I caution that in parallel to grant reform, we begin to initiate change in the metrics we use to measure the value of science.

 

Though NIH funding-related problems and the other systemic flaws of our culture seem at an all time high right now, the number of publications addressing these issues has also increased, especially in recent years. Now, more than ever, scientists at all stages recognize the immediacy of the problems and are engaging in conversations both in-person and online to brainstorm potential solutions. A new website  serves as a forum for all interested to join the discussion and contribute reform ideas – grant, or otherwise. With enough ideas and pilot experiments from the NIH we can ensure that the best science is funded and conducted. Onward and upward!

 

What's Keeping You Up at Night?

If you want to sleep, turn off your electronic device.

The light-emitting devices might be keeping you awake!

 

By Jesica Levingston Mac Leod, PhD

 

It is well established by now that staring at your phone, iPad or computer screen before going to sleep may delay your “real sleeping” time. The continued exposure to light excites the receptors in your eyes and therefore your brain, sending the signal that you must stay awake longer. This might not be a problem if you enjoy laying around in bed, tossing from side to side, but most people have to get to work early or have other commitments that haunt them the morning after a bad night of sleep. Insomnia is actually a serious disease; the lack of mindful dreaming can have a negative effect in your daytime life, and can result in poor performance at work. A recent study, published in the SLEEP journal, showed that reducing sleep from 8 hours to 4 hours makes memories less accessible in stressful situations.

 

Last December, a study in Boston added more evidence to the hypothesis that blue light negatively affects the secretion of melatonin, the hormone that helps regulate sleep and wake cycles. Dr. Chang and collaborators published in PNAS that the use of blue light emitting electronic devices before bedtime reduces a person’s alertness and interferes with their circadian rhythm. In this basic study they compared the effects of reading from a light emitting device verses from a paper book. They found that these e-readers delayed sleep for up to an hour compared to the old-fashioned paper books.

 

A recent study, published in the Journal of Biological Rhythms also tried to answer the question: can access to artificial light modify our sleeping patterns? Their answer was YES, it does! Sounds pretty legit, right?

 

Dr. De la Iglesia and collaborators studied two native communities in the north of Argentina: the Tobas and the Qom. These two indigenous communities share similar sociocultural and ethnic heritage, but one difference between them is that only the Tobas have access to electricity. Therefore, the Qom community regulates its lifestyle with natural light, like our ancestors before the almighty Mr. Edison’s invention.

 

The researchers provided the participants from both communities with motion-tracking wristbands to follow their activity during both summer and winter seasons. They found that in the summer season the Tobas had a tendency to get less daily sleep, about 43 min per day, than those living under natural light conditions. Not surprisingly, this was due to a later daily bedtime and sleep onset in the community with electricity, but a similar sleep offset and rise time in both communities. In the winter, the Qoms slept around 56 min per day more than those with access to electricity, and this was also related to earlier bedtimes and sleep onsets than the Tobas. They concluded: “The access to inexpensive sources of artificial light and the ability to create artificially lit environments must have been key factors in reducing sleep in industrialized human societies.”

 

But reading the conclusion you learn something else: the Toba community had TVs. This caused them to stay awake even later.

 

How do you get that pleasant sleep? To listen to lullabies… soft melodies ranging from 60 to 80 beats per minute. Take a warm bath, if body temperature drops before bedtime. Another option is to pay extra attention to your breath: focusing on how air moves through your body can relax you and can reduce stress. My favorite solution is to meditate! At least try – a lot of people accidentally fall asleep while trying to meditate anyways ;).

 

If you are a device-addicted insomniac, at least decrease the brightness of your screen. Tonight, have a nice encounter with Morpheus and remember that the rest of the human race will appreciate not dealing with a cranky sleepless person tomorrow.