Tag: challenges

PhD life: Pushing it to the extreme, and its wonders

By Leila S. Lemos, PhD candidate in Wildlife Sciences, Fisheries and Wildlife Department

I already started my countdown: 57 days until my PhD defense date! Being so close to this date brings me a lot of excitement about sharing with the community the results of the project I’ve been working on the past 4.5 years, and that I am really proud of. It also brings me lots of excitement when thinking about the new things that will come in my next phase of life. But even though I am excited, I’ve also been stressed, anxious and under depression. There is a mix of feelings rushing inside of me right now.

For those who don’t know me, I am originally from Rio de Janeiro, Brazil. I’ve been spending the last years far from my family, friends, language and culture. My favorite hobby always was to go to the beach and swim in the warm ocean. I would do that at least twice a week. Brazil is a tropical place and we can go to the beach all year round.

Me and my nephew in one of my favorite places in Brazil: Buzios, Rio de Janeiro.

Being in Oregon is really different. Oregon is gorgeous and I love it here, especially during the summer. However, the fall season brings the rain. Lots of rain, and it only stops around March. The absence of sun (and vitamin D) also contributes to depression. Even during the summer, I cannot swim in the ocean as the water is still really cold.

In addition to all of these factors, a PhD comes with classes, exams, fieldwork, research project, lots of reading and learning, manuscript writing, deadlines and great responsibilities. When you don’t have a scholarship or when it runs out (in my case), you also need to find a way to fund yourself until it finishes. Since last September I have been a teaching assistant for the university to cover my tuition and health insurance costs, and to earn a monthly stipend. The work never ends, and you always have more and more things to do.

Source: Costanza (2015).

A PhD is a full-time job, even if you are still technically a student. Actually, a PhD is a 24-hour job. Even if you are not working, you are thinking about your experiments and/or deadlines. Even if you are not awake, you are dreaming about it. You feel guilty all the time if you are doing things that are not related to your work.

But, it turns out I am not alone. The more I talk to people about the struggles, disappointments, anxiety, impostor syndrome, insomnia, depression, exhaustion of graduate school, the more I find that it is more common than I first thought.  I have several friends facing the same problems right now.

I searched for some stats on this topic and I found a relatively recent study (Levecque et al. 2017) that evaluated the mental health of a sample of PhD students (N = 3659) from five different research discipline categories: sciences, biomedical sciences, applied sciences, humanities, and social sciences. PhD students were compared to other three groups: (1) highly educated individuals in the general population (N = 769), (2) highly educated employees (N = 592), and (3) higher education students (i.e., academic Bachelor, Master or Doctoral degree; N = 333). Research participants answered the web-based questionnaire that follows:

Table 1: Prevalence of common mental health problems in PhD students compared to three comparison groups.

Legend: RR: risk ratio adjusted for age and gender; CI: 95% confidence interval; GHQ2+: experienced at least two symptoms; GHQ3+: experienced at least three symptoms; GHQ4+: experienced at least four symptoms.
Source: Levecque et al. (2017)

It was alarming to me to see some of these results. Here are some of them:

  • A GHQ2+ score indicated psychological distress, and the prevalence was about twice as high in PhD students compared to the highly educated general population. PhD students were consistently more affected when compared to all of the other groups.
  • They found a significant relationship between psychological distress and the risk of having or developing a common psychiatric disorder (GHQ4+).
  • The odds of experiencing at least two psychological symptoms were 34% higher for female PhD students than for males.
  • No differences between scientific disciplines were found.

And here’s the funny thing: My PhD project researches stress in gray whales along the Oregon coast. I have been evaluating gray whale overall health by using different tools like photogrammetry, endocrinology and acoustics to monitor these individual whales. The more I read about stress and all the physiological response that occurs within the bodies of all vertebrates, the more I imagine it happening to me and all of the possible consequences. However, I do not consider myself a specialist on the theme yet, so I leave my mental health to a specialist. I have been seeing a psychiatrist and a psychologist and I have been learning that work-life balance is crucial, and it helps us maintain sanity. I have also been learning some “exercises” to help me with anxiety and impostor syndrome. This topic may not be an easy to talk about, but it is extremely important. If you are reading this and identify yourself, contact a professional who can help you. It has helped me.

Institutions should also increase their efforts to systematically map and monitor stressors and its outcomes in PhD students (Levecque et al. 2017). Identifying the problems and working towards solutions will benefit the institutions as students will do a better job.

Right now, I am just trying my best to achieve a work-life balance while I am still getting things done on time. All of my data has been analyzed and now I just need to write my chapters and prepare my defense presentation! It is hard to believe that in only 57 days I will be done.  

Source: Reddit (2019).

I feel like I have succeeded in painting a grim picture of life as a PhD student. If you were thinking of going to grad school and now you have doubts about it, stop right there! Grad school is challenging, but it is not impossible. There are many things that will bring you joy in grad school like a successful fieldwork season, a successful experiment, a good grade on an exam you studied really hard for, a compliment from your advisor, a R code that is finally running correctly, or an accepted manuscript in a relevant journal.

By the way… I just had a manuscript of my first thesis chapter accepted for publication and I could not be happier:

Getting a PhD is hard, but it is also rewarding. Also, any path you take in your career will have pros and cons. What determines your success is your resilience and how you deal with the challenges that come. You may be asking if I would still do a PhD if I could go back in time, right? The answer is yes! Even though I have been facing many (personal) challenges I am really proud of my PhD project findings and am glad to be contributing to the knowledge and conservation of these amazing animals.

But please, if you see me around don’t forget:

Source: Costanza (2015).

References:

Costanza T. 2015. 10 memes relate to PhD students. Available at: https://www.siliconrepublic. com/careers/10-memes-relate-to-phd-students. Date of assess: 01/20/2020

Reddit. 2019. Made a meme for my boyfriend who’s doing his PhD. Available at: https://www.reddit.com/r/memes/comments/9fq2pq/made_a_meme_for_my_boyfriend_whos_doing_his_phd/. Date of assess: 01/20/2020

Levecque, K., F. Anseel, A. Beuckelaer, J. V. Heyden, and L. Gisle. 2017. Work organization and mental health problems in PhD students. Research Policy 46:868–879.

Photogrammetry Insights

By Leila Lemos, PhD Candidate, Fisheries and Wildlife Department, Oregon State University

After three years of fieldwork and analyzing a large dataset, it is time to finally start compiling the results, create plots and see what the trends are. The first dataset I am analyzing is the photogrammetry data (more on our photogrammetry method here), which so far has been full of unexpected results.

Our first big expectation was to find a noticeable intra-year variation. Gray whales spend their winter in the warm waters of Baja California, Mexico, period while they are fasting. In the spring, they perform a big migration to higher latitudes. Only when they reach their summer feeding grounds, that extends from Northern California to the Bering and Chukchi seas, Alaska, do they start feeding and gaining enough calories to support their migration back to Mexico and subsequent fasting period.

 

Northeastern gray whale migration route along the NE Pacific Ocean.
Source: https://journeynorth.org/tm/gwhale/annual/map.html

 

Thus, we expected to see whales arriving along the Oregon coast with a skinny body condition that would gradually improve over the months, during the feeding season. Some exceptions are reasonable, such as a lactating mother or a debilitated individual. However, datasets can be more complex than we expect most of the times, and many variables can influence the results. Our photogrammetry dataset is no different!

In addition, I need to decide what are the best plots to display the results and how to make them. For years now I’ve been hearing about the wonders of R, but I’ve been skeptical about learning a whole new programming/coding language “just to make plots”, as I first thought. I have always used statistical programs such as SPSS or Prism to do my plots and they were so easy to work with. However, there is a lot more we can do in R than “just plots”. Also, it is not just because something seems hard that you won’t even try. We need to expose ourselves sometimes. So, I decided to give it a try (and I am proud of myself I did), and here are some of the results:

 

Plot 1: Body Area Index (BAI) vs Day of the Year (DOY)

 

In this plot, we wanted to assess the annual Body Area Index (BAI) trends that describe how skinny (low number) or fat (higher number) a whale is. BAI is a simplified version of the BMI (Body Mass Index) used for humans. If you are interested about this method we have developed at our lab in collaboration with the Aerial Information Systems Laboratory/OSU, you can read more about it in our publication.

The plots above are three versions of the same data displayed in different ways. The first plot on the left shows all the data points by year, with polynomial best fit lines, and the confidence intervals (in gray). There are many overlapping observation points, so for the middle plot I tried to “clean up the plot” by reducing the size of the points and taking out the gray confidence interval range around the lines. In the last plot on the right, I used a linear regression best fit line, instead of polynomial.

We can see a general trend that the BAI was considerably higher in 2016 (red line), when compared to the following years, which makes us question the accuracy of the dataset for that year. In 2016, we also didn’t sample in the month of July, which is causing the 2016 polynomial line to show a sharp decrease in this month (DOY: ~200-230). But it is also interesting to note that the increasing slope of the linear regression line in all three years is very similar, indicating that the whales gained weight at about the same rate in all years.

 

Plot 2: Body Area Index (BAI) vs Body Condition Score (BCS)

 

In addition to the photogrammetry method of assessing whale body condition, we have also performed a body condition scoring method for all the photos we have taken in the field (based on the method described by Bradford et al. 2012). Thus, with this second set of plots, we wanted to compare both methods of assessing whale body condition in order to evaluate when the methods agree or not, and which method would be best and in which situation. Our hypothesis was that whales with a ‘fair’ body condition would have a lower BAI than whales with a ‘good’ body condition.

The plots above illustrate two versions of the same data, with data in the left plot grouped by year, and the data in the right plot grouped by month. In general, we see that no whales were observed with a poor body condition in the last analysis months (August to October), with both methods agreeing to this fact. Additionally, there were many whales that still had a fair body condition in August and September, but less whales in the month of October, indicating that most whales gained weight over the foraging seasons and were ready to start their Southbound migration and another fasting period. This result is important information regarding monitoring and conservation issues.

However, the 2016 dataset is still a concern, since the whales appear to have considerable higher body condition (BAI) when compared to other years.

 

Plot 3:Temporal Body Area Index (BAI) for individual whales

 

In this last group of plots, we wanted to visualize BAI trends over the season (using day of year – DOY) on the x-axis) for individuals we measured more than once. Here we can see the temporal patterns for the whales “Bit”, “Clouds”, “Pearl”, “Scarback, “Pointy”, and “White Hole”.

We expected to see an overall gradual increase in body condition (BAI) over the seasons, such as what we can observe for Pointy in 2018. However, some whales decreased their condition, such as Bit in 2018. Could this trend be accurate? Furthermore, what about BAI measurements that are different from the trend, such as Scarback in 2017, where the last observation point shows a lower BAI than past observation points? In addition, we still observe a high BAI in 2016 at this individual level, when compared to the other years.

My next step will be to check the whole dataset again and search for inconsistencies. There is something causing these 2016 values to possibly be wrong and I need to find out what it is. The overall quality of the measured photogrammetry images was good and in focus, but other variables could be influencing the quality and accuracy of the measurements.

For instance, when measuring images, I often struggled with glare, water splash, water turbidity, ocean swell, and shadows, as you can see in the photos below. All of these variables caused the borders of the whale body to not be clearly visible/identifiable, which may have caused measurements to be wrong.

 

Examples of bad conditions for performing photogrammetry: (1) glare and water splash, (2) water turbidity, (3) ocean swell, and (4) a shadow created in one of the sides of the whale body.
Source: GEMM Lab. Taken under NMFS permit 16111 issued to John Calambokidis.

 

Thus, I will need to check all of these variables to identify the causes for bad measurements and “clean the dataset”. Only after this process will I be able to make these plots again to look at the trends (which will be easy since I already have my R code written!). Then I’ll move on to my next hypothesis that the BAI of individual whales varied by demographics including sex, age and reproductive state.

To carry out robust science that produces results we can trust, we can’t simply collect data, perform a basic analysis, create plots and believe everything we see. Data is often messy, especially when developing new methods like we have done here with drone based photogrammetry and the BAI. So, I need to spend some important time checking my data for accuracy and examining confounding variables that might affect the dataset. Science can be challenging, both when interpreting data or learning a new command language, but it is all worth it in the end when we produce results we know we can trust.

 

 

 

Challenges of fecal hormone analyses (Round 2): finally in Seattle!

By Leila Lemos, Ph.D. Student, Department of Fisheries and Wildlife, OSU

In a previous blog of mine, you could read about the challenges I have been facing while I am learning to analyze the hormone content in fecal samples of gray whales (Eschrichtius robustus). New challenges appeared along the way over the last month, while I was doing my training at the Seattle Aquarium (Fig. 1).

Figure 1: View of the Seattle Aquarium.

 

My training lasted a week and I am truly grateful to the energy and time our collaborators Shawn Larson (research coordinator), Amy Green and Angela Smith (laboratory technicians) contributed. They accompanied me throughout my training to ensure I would be able to conduct hormonal analysis in the future, and to handle possible problems along the way.

The first step was weighing all of the fecal samples (Fig. 2A). Subsequently, the samples were transferred to appropriate glass tubes (Figs. 2B & 2C) for the next laboratorial step.

Figure 2: Analytical processes: (A) Sample weighing; (B) Transference of the sample to a glass tube; (C) Result from the performed steps.

 

The second conducted step was the hormone extraction. The extraction began with the addition of an organic solvent, called methanol (CH3OH), to the sample tubes (Fig. 3A & 3B). Hormones leach out from the samples and dissolve in the methanol, due to their affinity for this polar solvent.

Tubes were then placed on a plate shaker (Fig. 3C) for 30 minutes, which is used to mix the substances, in order extract the hormones from the fecal samples. The next step was to place the tubes in a centrifuge (Fig. 3D) for 20 minutes. The centrifuge uses the sedimentation principle, causing denser substances or particles to settle to the bottom of the tube, while the less dense substances rise to the top.

Figure 3: Analytical processes: (A) Methanol addition; (B) Sample + methanol; (C) Plate shaker; (D) Centrifuge.

 

After this process, the two different densities were separated: the high-density particles of the feces were in the bottom of the tube, while the methanol containing the extracted hormones was at the top. The top phase (methanol + hormones) was then pipetted into a different tube (Fig. 4A). The solvent was then evaporated, by using an air dryer apparatus (Fig. 4B), with only the hormones remaining in the tube.

The third performed step was dilution. A specific amount of water, measured in correlation with sample weight and to the amount of the methanol mixed with each sample, was added to each tube (Fig. 4C). Since the hormones were concentrated in the methanol, the readings would exceed the measurement limits of the equipment (plate reader). Thus, in order to prepare the extracts for the immunoassays, different dilutions were made.

Figure 4: Analytical processes: (A) Methanol transference; (B) Methanol drying; (C) Water addition.

 

The fourth and final step was to finally conduct the assays. Each assay kit is specific to the hormone to be analyzed with specified instructions for each kit. Since we were analyzing four different hormones (cortisol, testosterone, progesterone, and triiodothyronine – T3) we followed four different processes accordingly.

First, a table was filled with the identification numbers of the samples to be analyzed in that specific kit (Fig. 5A). The kit (Fig. 5B) includes the plate reader and several solutions that are used in the process to prepare standard curves, to initiate or stop chemical reactions, among other functions.

A standard curve, also known as calibration curve, is a common procedure in laboratory analysis for determining the concentration of an element in an unknown sample. The concentration of the element is determined by comparison with a set of standard samples of known concentration.

The plate contains several wells (Fig. 5C & 5D), which are filled with the samples and/or these other solutions. When the plate is ready, (Fig.5D) it is carried to the microplate reader that measures the intensity of the color of each of the wells. The intensity of the color is inversely proportional to the concentration of the hormone in both the standards and the samples.

Figure 5: (A) Filling the assay table with the samples to be analyzed; (B) Assay kit to be used; (C) Preparation of the plate; (D) Plate ready to be read.

 

Since this is the first fecal hormone analysis being performed in gray whales, a validation process of the method is required. Two different tests (parallelism and accuracy) were performed with a pool of three different samples. Parallelism tests that the assay is measuring the antigen (hormone) of interest and also identifies the most appropriate dilution factor to be used for the samples. Accuracy tests that the assay measurement of hormone concentration corresponds to the true concentration of the sample (Brown et al. 2005).

This validation process only needs to be done once. Once good parallelism and accuracy results are obtained, and we have identified the correct dilution factor and approximate concentration of the samples, the samples are ready to be analyzed. Below you can see examples of a good parallelism test (parallel displacement; Fig. 6) and bad parallelism tests (Fig. 7) that indicate no displacement, low concentration or non-parallel displacement; and a good accuracy test (Fig. 8).

Figure 6: Example of a good parallelism test. The dark blue line indicates the standard curve; the pink line indicates a good parallelism test, showing a parallel displacement; and the ratios in black indicate the dilution factors.
Source: Brown et al. (2005)

 

Figure 7: Examples of bad parallelism tests. The dark blue line indicates the standard curve; the light blue line is an example of no displacement; the pink line is an example of low concentration of the sample; and the green line is an example of non-parallel displacement.
Source: Brown et al. (2005)

 

Figure 8: Example of a good accuracy test while analyzing hormone levels of pregnanediol glucuronide (Pdg) in elephant urine. The graph shows good linearity (R2 of 0.9986) and would allow for accurate concentration calculations.
Source: Brown et al. (2005)

 

After the validation tests returned reliable results, the samples were also analyzed. However, many complications were encountered during the assay preparations and important lessons were learned that I know will allow this work to proceed more smoothly and quickly in the future. For instance, I now know to try to buy assay kits of the same brand, and to be extremely careful while reading the manual of the process to be performed with the assay kit. With practice over the coming years, my goal is to master these assay preparations.

Now, the next step will be to analyze all of the results obtained in these analyses and start linking the multiple variables we have from each individual, such as age, sex and body condition. The results of this analysis will lead to a better understanding of how reproductive and stress hormones vary in gray whales, and also link these hormone variations to nutritional status and noise events, one of my PhD research goals.

 

Cited Literature:

Brown J, Walker S and Steinman K. 2005. Endocrine manual for reproductive assessment of domestic and non-domestic species. Smithsonian’s National Zoological Park, Conservation and Research Center, Virginia 1-69.