Review written by Alexandra Libby (PNI)
Cell division is one of the most important and well-studied biological processes. Organisms generate new cells in order to grow and reproduce (Figure 1); the types of cell division responsible for each of these goals are called mitosis and meiosis, respectively. Like many biological processes, cell division involves a well-timed, complex coordination of proteins and cellular machinery. Disrupted division can lead to a multitude of problems including genetic mutations, cell death, and cancer (Zhivotovsky and Orrenius, 2010).
Part II of our series into the phenomenon of phase separation that is changing how biologists understand cellular biology
Review written by Xinyang (David) Bing (LSI)
“For liquid-liquid phase separation, Princeton is the center of the universe, and my work benefited from collaborations and interactions with Cliff Brangwynne's lab.”
This is how Dr. Nicholas Treen, from the lab of Mike Levine, described his close working relationship with the neighboring Brangwynne lab. In his latest publication, he and his collaborators set out to describe a novel type of condensate formation in the nucleus involved in gene silencing.
The first cell divisions of a newly fertilized embryo are arguably the most instrumental events that occur throughout the life of an animal. During early embryonic development, an intricate web of processes must occur coordinately to lay the blueprint for the developing organism. Like a set of dominoes, every gene that is expressed during early developmental processes leads to consequences downstream during later developmental stages. Even slight errors may lead to a malfunctioning embryo and certain death of the animal. Therefore, all animals have their own set of developmental “blueprints” that necessitate massive numbers of genes be expressed in a tightly controlled manner, both in terms of timing and levels.
Continue reading "Life: Like oil and water? Part II"
Review written by Yinuo Zhang (ECON)
The tradeoff between social distancing and its potential adverse economic effects has been at the center of debates during COVID-19 in the United States. On one hand, it is crucial to practice social distancing to prevent further spread of COVID-19. On the other hand, economic activities plummeted due to the closing of non-essential businesses mandated by many states. As a result, initial unemployment claims reached an unprecedented number of over 6.8 million on March 28th, the highest since 1967. The rapid development of COVID-19 has called urgent attention to the impact of existing public health interventions and its consequences on the real economy. In particular, do non-pharmaceutical interventions (NPI) like social distancing further hinder economic activity on top of the ongoing pandemic? Does the tradeoff between social distancing and subdued economic activities exist?
Review written by Rebekah Rashford (PNI)
There is much consensus that negative stressful early life experiences impact the development of an individual. Numerous studies in humans have linked childhood adversity (e.g., loss of a caregiver, abuse, natural disaster, etc.) to an increased risk for depression and other psychiatric disorders in adulthood. In other words, the more an individual has experienced negative stressors in childhood, the more likely that individual is to develop depression or anxiety when they experience mild stressors in adulthood. This heightened sensitization and increased risk of mood disorders in humans has a parallel observation in rodents, specifically mice, which are used as model organisms in the discussed study. Principal Investigator Catherine Jensen Peña and colleagues at the Icahn School of Medicine at Mount Sinai were interested in exploring the epigenetic effects of such early life stressors on reward circuitry in the brain. Throughout this work the authors posit, as does much of the early life stress (ELS) field, that there could be epigenetic mechanisms at work leading to the aforementioned risk of mood disorder development.
Review written by Leon Mait (PSY)
In times of financial hardship, low-income individuals can often turn to their communities for support. Unfortunately, this buffer against financial difficulties provided by community resources can erode over time. One factor that may contribute to such erosion is economic inequality (which has been on the rise in the United States). This connection was recently found by a group of international researchers, including Princeton’s own Elke U. Weber, who holds joint appointments in Psychology, the School of Public and International Affairs, and Engineering.
“Rediscovery” of a decades-old physics idea reignites the fields of cellular and molecular biology
Review written by Xinyang (David) Bing (LSI)
Lava lamps are fluorescent mixtures of oil and water that are immiscible and, when heated, float around, generating hypnotizing patterns that lull you to sleep. Now, biologists are seriously considering the possibility that the same physics that govern lava lamps may also control almost everything that goes on inside our cells.1 Walk through the halls of MIT and Harvard, Oxford and Cambridge, or of course Princeton, and you would likely hear what everybody is talking about: liquid-liquid phase separation.
Review written by Jaydeep Singh (MAT)
Forecasting the trajectory of tropical cyclones (TC), which are known as hurricanes when they appear in the Atlantic, remains an urgent meteorological challenge. The difficulties in TC prediction were laid bare during 2019’s Hurricane Dorian, a devastating Category 5 storm that swept through the Bahamas and the Southeastern United States. Like many Atlantic storms before it, Dorian underwent the process of rapid intensification (RI), catching scientists off guard with consecutive daily wind strength increases of over 30mph. The effect? Dorian transformed from a Category 2 to Category 5 storm in only two days. The staggering intensification of Atlantic storms raises the following question: what is the science behind these RI events, and how predictable are they?
Review written by Rohini Majumdar (PSY)
Research on life trajectories is one of social science’s most popular exports to policy making. Predictive models for specific life outcomes have been applied to a number of settings, including criminal justice and child-protective services. Despite the real-world relevance and far-reaching impact of this research, not many high-quality datasets exist to support it. The Fragile Families and Child Wellbeing Study (FFCWS), however, is one such enterprise by Princeton’s Center for Research on Child Wellbeing and the Columbia Population Research Center. Over 900 studies have used the FFCWS’s rich longitudinal data, which follows the families of almost 5000 babies born in major US cities around the turn of the millennium. Most of these children were born to unmarried parents, putting these families at a greater risk of splitting up and living in poverty compared to traditional families. Although life trajectory research has been used to inform policy, whether it can also be used to accurately predict specific life outcomes for individuals remains undetermined. To find out, Princeton Sociology’s Matthew Salganik, Ian Lundberg, Alex Kindel, and Sara McLanahan spearheaded the Fragile Families Challenge (FFC), a mammoth scientific mass collaboration based on a research design called the common task method.
Review written by Shanka Subhra Mondal (ELE)
In recent years, machine learning (ML) has become commonplace in our software and devices. Its applications are varied, ranging from finance and marketing to healthcare and computer vision. ML already has the ability to out-perform humans on many tasks, such as video game competitions and image/object recognition, to name a few. At a high-level, ML comprises a set of algorithms that rely heavily on data (training data) to make decisions about another set of data (testing data) that it has not previously encountered. One of the sub-fields of computer vision where machine learning has proven particularly useful is in image classification, where the goal is to categorize objects in a given image. While this task might sound easy for humans, it can be challenging for an algorithm, particularly when the picture is blurred, not properly illuminated, or noisy. Robust image classification is not an easy task.
Review written by Jess Breda (PNI)
Have you ever wondered how information is transferred from one brain to another? This process can occur in a variety of ways, from verbal storytelling to simple hand gestures, and across different backgrounds, such as a flight attendant instructing a first time flyer, or a casual conversation with a friend. We gain information from others on a daily basis. However, to study this on a biological level requires the complicated task of recording from two brains experiencing the same stimuli and aligning their activity in time.