What does it mean to genetically modify food?

I’ve seen a series of articles recently debating the pros and cons and really, the differences between genetically modified food and organic food. There’s a lot of confusion as to what happens when our crops and the resulting food has been modified in a way we don’t understand.

First, some definitions

Genetically modified food/organisms (GMO): altering the genetic makeup in some way, usually by adding in new genes from another organism or rearranging genes already present in the organism. Examples include delayed-ripening tomatoes, and herbicide- and pest-resistant crops (http://edis.ifas.ufl.edu/fs084)

Organic food: food grown without synthetic chemicals or irradiation (which are used to control weeds and pests). Natural variations such as mulch can be used.

So, genetically modified food can be organic or not, and organic food can be genetically modified or not. GMO’s have been altered on a genetic, internal level to enhance or suppress different characteristics or properties, and organic food has not been altered on the outside by environmental factors such as chemical pesticides.

So, what makes GMO crops different than conventional crops? Really, the speed at which changes to the crop can be made. In conventional crops, different plants are cross-bred one generation at a time, to try and produce new plants with desirable traits. For example, you may breed two plants that are both resistant to some type of bug that eats their leaves. The goal would be to develop a new plant strain that will not be affected by those bugs. With GMO plants, scientists can figure out which gene(s) is/are responsible for the resistance to those bugs and put it into the new plants directly. This save a lot of time and energy, compared to breeding plants one generation at a time, hoping the gene you want to passed down is present in the new crops.

Are GMOs safe? Yes. Are they the same as organic? No. However, GMOs may be helpful for creating new crops that don’t require chemicals or pesticides to flourish:


Further reading can be found here:



Socioeconomic status (SES) affects brain size

A recent study has come out demonstrating a relationship between household income and parental education (collectively called socioeconomic status, or SES) and brain size in children and adolescents.

I’m particularly excited by this work, since it comes from a group of researchers I work closely with and whose dataset is a partition of the same one I use: the Pediatric, Imaging, NeuroCognition and Genetics (PING) initiative (ping.ucsd.edu).

Depending on which headline you read, either poverty, stress, low parental income or education or some combination of those causes children to have smaller brains. This is not true – this study is associative, not causative. It seems natural to assume low income is causing smaller brains in children, but we don’t have evidence to say that (yet).

One thing I wanted to point out about this paper that has caused some confusion is controlling for subjects’ genetic ancestry factor (GAF). This is NOT the same as controlling for their whole genomic profile. GAF is calculated as a proportion of different lineages based on genetic profiles. Specifically, it is a calculated proportion of European, African, Native American, East Asian, Central Asian, and Oceanic decent based on genotype analysis (Fjell et al., 2012). It is meant to reflect the biologically and culturally relevant factors that tend to go along with genetic ancestry (subtle differences in head and brain size and shape and the cultural factors that could affect testing scores or measurements).

(To be continued)



Scientific American:






the Today Show:


UC San Diego:


I also found this thoughtful blog post by another scientists:


Women in Academia; Are We Equal Yet?


I read an article made me want to throw my laptop out the window and sail away to live on a deserted island for the rest of my days.

Academic science isn’t sexist: http://www.nytimes.com/2014/11/02/opinion/sunday/academic-science-isnt-sexist.html

This article is arguing that academic science is no longer sexist, and that most reports of sexism and the gender gap in academia stem from old data (well, the two articles I reference below are from 2014 and 2012, respectively). This just isn’t true. What I find even more disturbing is this bit:

“According to our research, the biggest culprits are rooted in women’s earlier educational choices, and in women’s occupational and lifestyle preferences.”………. “As children, girls tend to show more interest in living things (such as people and animals), while boys tend to prefer playing with machines and building things.”

So, they’re proposing that women are just choosing not to enter these fields, ignoring the glaring social constructs and implicit biases discouraging women from entering these fields. There are so many factors that influence whether or not a woman pursues a career in a STEM field (science, technology, engineering and math), this article takes an over simplistic view by proposing that women, in essence, just aren’t as interested in math and science.

However, their paper does contain a lot of information about women in higher academia and their positions in various fields. One major issue with the analyses in this paper is the focus on women only; there is a lack of any direct comparison to males. Every single one of the graphs depicted in the paper are showing the increase in percentage of women doing or getting “x” as a function of time (jobs in STEM fields, grants, tenure positions, etc.). We can infer the level of males from these graphs (i.e. if women make up 20% of doctoral students in math, males must be 80%), but if the goal is to prove there is no sexism in academic science, and therefore that men and women are equal, including both gender in the analyses and graphs would be crucial. The authors also don’t address the fact that the majority of their graphs (plotting percentage of women in different STEM fields) lie below 50% = the point at which males and females would have equal enrollment, employment, and funding.

One graph illustrates the percentage of PhD’s awarded to women in a variety of fields from 1970 to 2010. Notably, Psychology is the only field listed where women earn over 50% of PhDs. The social sciences and life sciences seem to reach to 50% mark around 2010, but the percentage of women earning PhD in fields of Geoscience, Economics, Engineering, Math and Computer Science and the Physical Sciences are anywhere from 20% to 40% in 2010 (up from their starting percentages of 0-10% in 1970).

As we move into higher levels of academia, the difference between men and women becomes even more pronounced. Among all the same fields listed above, when examining the percentage of females in tenture-track faculty positions, only Psychology is 50% female in 2010. Engineering barely reaches 10% women in tenure-track faculty positions by 2010. All of this information coming from the very same paper stating that there is no sexism in academic science.

I also did my own investigation. Using the NSF Survey of Earned Doctorates (SED), I looked at how many men and women earned their doctorate in a variety of disciplines in 2012.

Screen Shot 2014-12-18 at 4.18.51 PM

A couple points of interest:

Number of doctorate degrees in Engineering: Men = 6,527, Women = 1,883 (22%)

Number of doctorate degrees in Math and Computer Science: Men = 2,683, Women = 861 (24%)

Number of doctorate degrees in Psychology: Men = 1,047, Women = 2,566 (71%)

Overall, men earned more doctorates in Engineering, Physical Sciences, Geosciences, Math and Computer Science, Social Sciences, and Architecture & Environmental Design. Women earned more doctorate degrees in Life Sciences, Psychology, Humanities and Arts & Music. In those cases where women earn more degrees in a given field than men, the gap is much smaller on average than those fields where men earn more degrees. This seems to still be a far cry from equality.

The authors of the original article further qualify their position:

“That’s not to say that mistreatment doesn’t still occur — but when it does, it is largely anecdotal, or else overgeneralized from small studies. As we found, when the evidence of mistreatment goes beyond the anecdotal, it is limited to a small number of comparisons of men and women involving a single academic rank in a given field on a specific outcome.”

This is also not true. The article linked below, “Sex and race discrimination in academia starts even before grad school”, includes a sample of 6,548 professors in various disciplines, including STEM fields, at 259 public and private universities. The authors sent emails to professors, asking for an informal meeting to discuss future academic and educational opportunities. All that was altered between emails (the dependent variable) was the name of the “student” sending it. Names varied by gender and ethnicity; a stereotypical white male name was “Steven Smith” and a black female name was “Latoya Brown”. Response rates for women and non-white ethnicities was statistically lower for every field relative to white males (the exception being fine arts). Science, technology and business fields all showed large disparities between response rates for white males and every other group. Business showed the greatest disparity, with 87% of white males receiving a response compared to just 62% of female and minority students. This demonstrates that even before students enter graduate school, there is some implicit bias or predisposition for professors and academics to prefer to interact with and respond to men over women.

The problem isn’t just male professors either. The second article I reference below, “Science faulty’s subtle gender biases favor male”, demonstrates that both male and female professors show an implicit bias against female applicants, rating them as less competent than an identical male applicant. Again, some applications had a female name and others had a male name, although the content of the application was identical. Those applications will a male name were also offered a higher starting salary on average.

Taken together, these studies show that this implicit bias against female students isn’t restricted to one area of study or major and it reaches across professors and academics of all genders and races. This suggests that there is in fact some cultural bias against women in science and technology that is so pervasive it even effects tenure-track female professors, who have presumably suffered through years of the same biases themselves. It isn’t simply the case that women are “choosing” not to enter into STEM fields; there is a wider cultural bias in place affecting every aspect of a woman’s career choice.

I felt the need to write this article (which is slightly outside my intended focus of science news reporting) because of what seems to be a recent flux of articles either proving or disproving the gender gap in academia. I was also prompted by my own experiences as a female researcher in typically male-dominated STEM fields. It’s important that this issue be addressed; that we come together as a community to find ways to lessen (hopefully abolish) these implicit biases still present in our society so that the best and brightest can excel, regardless of gender or race.

Although there are dozens of articles illustrating the gender gap in academia, these two articles are particularly well done:

Sex and race discrimination in academia starts even before grad school: http://www.scientificamerican.com/article/sex-and-race-discrimination-in-academia-starts-even-before-grad-school/

Science faulty’s subtle gender biases favor male students: http://www.pnas.org/content/109/41/16474.abstract

Why do we really need sleep?

Although we generally accept the fact that we have to sleep, we’ve been unable to describe the specific reason(s) why. We have many hypotheses, of course; sleep saves energy because we aren’t moving around, it may help cool the brain, it may help consolidate memories, etc. There is evidence to support some of these hypotheses (for example, you will remember things you studied better after you sleep), but sleep isn’t necessary for any of these processes (you can still remember things you studied even if you don’t sleep right away). However, a new study may have found evidence for a very straightforward explanation of why we sleep: it cleans up our brain. Specifically, it removes potentially harmful neurotoxic waste that accumulates during normal brain function.

During the course of the day, your brain is constantly processing information around you, carrying out various processes that require energy. Your neurons are always working. Each neuron in your brain needs energy to fire signals and send information to other areas of the brain. These energy requirements come in the form of metabolites, chemicals, and other substances in your brain. The creation and use of the chemicals used to fuel the brain’s activity has some byproduct or waste, just like when you’re cooking and you end up with some extra rind, peels, etc. that you throw away.

The current research suggests that these byproducts or waste such as β-amyloid (a type of plaque build-up found in Alzheimer’s patients) may be cleaned up during sleep. The researchers were able to look at the brains of mice in real time using an imaging technique that uses light bounced through the surface of the brain. They were able to show that sleep (and anesthesia) increases space between cells and tissues, allowing cerebrospinal fluid to mix with the fluids in the cells. The movement of these fluids is what clears molecules like beta-amyloid out of the tissue. The researchers conclude that sleep allows for restoration because it removes potentially harmful waste products that build up during wakefulness.

So, another reason to make sure you get enough sleep!


Original article, published in Science:


News coverage:


Resurrecting a 700-year-old virus….. from poop

NPR, the Washington Post, Fox News, Science Magazine, IFLScience and many other sites recently reported on the ‘resurrection’ of a 700-year-old-virus, made possible by its preservation in Caribou poop. Fascinating, or horrifying?

The simple idea behind this discovery was that in very cold conditions, organic material should be preserved. Researchers unearthed some caribou poop that had been frozen for 700+ years in a piece of ice. They identified two unfamiliar viruses, one that was in good enough condition to be reconstituted in a lab and successfully infect a plant.

The researchers were able to synthesize the DNA of the virus and then sequence the resulting genome. There was no known record of this virus in present-day databases, but the researchers hypothesized that the virus was a type of geminivirus (a virus that attacks plants), based on similarity to existing plant viruses. The researchers then collected contemporary caribou fecal samples and tested for viruses. They found that the three contemporary caribou feces viruses that were identified were between 50% and 80% similar to the 700+ year old virus. This could give us some idea of how quickly viruses change and mutate over time.

The researchers then wanted to see if this old virus could still infect current-day plants. For this test, they used a tobacco plant that is known to be susceptible to a variety of viruses. They were able to show that the virus successfully infected the laboratory tobacco plant by examining the newly sprouted leaves, which germinated after the plant had been exposed to the virus. The experimenters noted that there were no obvious signs of infection, and the virus is most likely not dangerous.

A main point of significance for these findings is that previously dormant viruses may still be viable once they melt from the ice and enter the environment:

“As climate change accelerates the melting of arctic ice, it is possible that ancient viral particles and the associated nucleic acids could be released into the environment. Although low temperature might preserve the genetic signature of some plant- and insect-related viruses, it is currently unknown whether such particles remained infectious. If such virions are infectious, as recently claimed for a large nucleocytoplasmic DNA virus in permafrost (7), their release could contribute to the diversity of circulating viruses.”


Original Research Article: http://www.pnas.org/content/early/2014/10/23/1410429111

News Reports: http://www.foxnews.com/science/2014/10/29/scientist-resurrects-700-year-old-viruses-thanks-to-caribou-poop/




Is violent crime genetically determined?

Recent news reports have stated that researchers may have found the two genes responsible for violent crimes. Reported in BBC, Healthday News, Independent Co (UK) and others, this study may provide us with valuable information on the cause or explanation for violent crime and behavior. Depending on which article you read, between 700 and 900 violent criminals were genotyped in order to look for specific genes related to ‘extremely violent behavior’. The researchers reported that variants of two genes, MAOA and CDH13, were commonly associated with inmates categorized as ‘extremely violent’ but were not related to non-violent offenders.

First, a bit of background on genetics. Our genome contains sequences of DNA that ‘code’ different processes or functions in our body. It is estimated that humans have 20,000 to 25,000 genes, although we have a relatively poor understanding of what most of them do. Genes can interact with each other and the environment in complex ways, so although we are sometimes able to identify some genes related in a certain process, function or characteristic (i.e., genes related to height, eye color, or intelligence), they can often be expressed in different ways based on other genes and the environment.

The current study examined 794 inmates, spanning 19 prisons in Finland. Inmates were classified as nonviolent (215 inmates), violent offenders, having committed at least one violent crime (538 inmates), and extremely violent offends, having committed at least 10 violent crimes (84 inmates). Violent crimes were defined as “murder, attempted murder, manslaughter, attempted manslaughter, other types of homicide and battery.” Non-violent crimes included “drunk driving, drug-related crimes or property crimes.” These groups of inmates composed the “CRIME” cohort, or discovery cohort. A discovery cohort is needed in genetic studies to first identify possible related genes. A second replication cohort then confirms any related genes found in the discovery cohort. This ensures that whatever genes are found are less likely to be false discoveries, since they were replicated in a new subject population. Here, the replication cohort was a database of 114 violent offenders who had committed at least one homicide.

“Candidate” genes are those genes that have been identified as possibly being related to the behavior or characteristic under investigation and are usually identified in the discovery cohort.  This study identified the low-activity MAOA genotype as a candidate gene for violent behavior.  This genotype was more strongly related to the extremely violent offenders, with no difference between males and females.

A “Genome-Wide Association Study (GWAS)” is when the entire genotype is examined for any genes related to the behavior or characteristic (target) under investigation. These findings are driven solely by statistics; the researchers have no previous hypotheses about what genes may be related to their target so they adjust their level of significance so that only a small number of genes will pass. However, it is possible that some genes pass the level of significance solely by chance (a basic property of statistics and probability), so a replication cohort is used to see if any of those genes pass the level of significance again, suggesting that they are less likely to be spurious findings and more likely to be real. This study did a GWAS on both the violent offenders and the extremely violent offenders. Five of the most-significant genes in the GWAS findings were examined in the replication cohort. The two genes that showed the most powerful relationship to violent criminal behavior was the low-activity MAOA genotype (which is related to aggressive behavior) and the CDH13 gene (which is also related to ADHD).

This does not mean that anyone with these genotypes are predestined to become violent criminals. In fact, only about 5-10% of violent crimes can be attributed to these genotypes, meaning the majority of people with these genes will never commit a violent crime. Criminal behavior is a very complex phenomenon, and is influenced by both genetics and the environment. There are also many other traits that may go along with ‘extremely violent criminal behavior’, such as aggression, mental illness, history of abuse, etc., so these genes may in fact be representing some other aspect or influence of violent behavior. In addition, the number of extremely violent criminal offenders is very small compared to the overall population and is an extreme example of human behavior, so this limits our ability to get enough subjects and enough variability to draw strong conclusions.


Original Research Publication: http://www.nature.com/mp/journal/vaop/ncurrent/full/mp2014130a.html


News Reports: http://www.bbc.com/news/science-environment-29760212





Do you really only use 10% of your brain?

I’m going to start with a brain fact that’s often thrown around, but is wholly incorrect. Being in the neurosciences, when I get a new class of students, this is often one of the first myths that comes up.

No, we do not only use 10% of our brain.

This is the fundamental premise of many books and movies, which rely on the protagonist being able to unlock “all 100%” of the brain, either via some sort of drug or possibly surgery. Unsurprisingly, polls show around 50% of people believe this myth.

First, if you know anything about biology, you know that it takes energy to maintain a each and every part of a body. The brain is an especially expensive organ, using about 20% of the total energy the body needs while only accounting for 3-5% of the total body weight. There is no reason why we would have an expensive organ if we weren’t even using 90% of it. That’s a huge waste of energy, and energy is a valuable resource.

Second, why would we have such a large brain if we didn’t need it for something? We have a very large brain for our body size, relative to other primates and mammals. Strictly speaking, we have way more brain than we need to survive. We must have all this brain for a reason. Specifically, the reason seems to be for higher cognitive functions such as creativity, metacognition (thinking about thinking), and thinking about the future.

Third, the old adage “use it or lose it” applies to brain function. The brain is plastic, meaning it can change with new experiences or information. If a specific neuron connection is not used, it is lost. The brain isn’t going to spend energy maintaining something it doesn’t need. That energy could be used elsewhere, where new connections are being made. Every time your learn something or have a new experience, new neural connections in your brain are formed and strengthened.

14601014695_30cfe1972d_z  image: aboutmodafinil.com

This myth about only using 10% of your brain, which may have started as far back as the 1920s, often stems from the fact that not every area of the brain is active at the same time (if it was, you would be having a seizure). However, each and every area of the brain has a purpose and a function. There is absolutely no evidence suggesting that there is any part of your brain that is not used.

Another sometimes misinterpreted fact is that of brain localization, meaning certain areas are responsible for certain tasks (in a typical, healthy brain; things can get switched around in certain cases). There are areas of your brain specialized for processing visual information, spatial navigation, language, and sensory and motor processing. That does not mean that other areas are not involved in these functions, just that these areas seem to do the brunt of the work for a given task. However, no task is done in complete isolation. For example, when you’re talking to someone, you’re getting visual information, auditory information, and possibly sensory information. You’re also using motor areas of your brain to move your mouth and gesture. A “language” task involves many other systems.

While we’re discussing brain usage, it is also not true that some people are “left hemisphere dominant” or “right hemisphere dominant”. While its true that some functions occur primarily in one hemisphere or the other, that does not mean one hemisphere is dominant overall. They both serve many functions. Although things are slightly different in left-handed people. We’ll get to that in a later post.

Relevant source articles: