As someone who has spent decades studying the evolution of nuclear energy, I’ve seen its emergence as a promising transformative technology, its stagnation as a consequence of dramatic accidents and its current re-emergence as a potential solution to ...
As someone who has spent decades studying the evolution of nuclear energy, I’ve seen its emergence as a promising transformative technology, its stagnation as a consequence of dramatic accidents and its current re-emergence as a potential solution to the challenges of global warming.
While the issues of global warming and sustainable energy strategies are among the most consequential in today’s society, it is difficult to find objective sources that elucidate these topics. Discourse on this subject is often positioned at one or another polemical extreme. Further complicating the flow of objective information is the involvement of advocates of vested interests as seen in the lobbying efforts of the coal, gas and oil industries. My goal has been to present nuclear energy’s potential role in a sustainable energy future—alongside renewables like wind and solar—without ideological baggage.
An additional hurdle that must be overcome in dealing with the pros and cons of nuclear energy is the psychological context in which fear of nuclear weapons and of radiation impedes rational analysis. The deep antipathy to nuclear phenomena is illustrated by what might be called the “Godzilla Complex” that developed after the crew of the Japanese fishing boat, the Lucky Dragon 5, was exposed to heavy radiation from a nuclear weapons test in 1954. Godzilla was conceived as a monster that emerged from the depths of the ocean due to radiation exposure. It has become an enduring concept that has been portrayed in nearly forty films in the United States and Japan and in numerous video games, novels, comic books and television shows.
It is not surprising that fear of nuclear reactor radiation has been widespread. In spite of the fact that there are no documented deaths due to nuclear reactor waste (in contrast to deaths from accidents), it is widely assumed that nuclear reactor waste is quite dangerous. In contrast, the fact that premature deaths attributable to the fossil-fuel component of air pollution worldwide exceeds more than 5 million annually generates little concern. Similarly, the total waste produced from nuclear energy can be stored on one acre in a building 50 feet high, whereas for every tonne of coal that is mined, 880 pounds of waste material remain. Furthermore, this waste contains toxic components. Yet public concern for nuclear waste clearly overshadows that for coal, despite these contrasting impacts.
After an in-depth review of the most significant nuclear accidents and recognition of the deep psychological antipathy to nuclear energy, I’ve become increasingly interested in the emergence of an international effort to develop safe, cost-effective nuclear energy known as the Generation IV Nuclear Initiative. This began in 2000 with nine participating countries and has since grown substantially.
In the early years, the Generation IV Nuclear Initiative took a systematic approach to identify reactor designs that could meet demanding criteria—including the key characteristic of being “fail safe”. Rather than depending upon add-on safety apparatus, “fail safe” designs rely on the laws of nature—such as gravity and fluid flow—to provide cooling in the event that the reactor overheats. Another high priority design feature is modular construction, allowing multiple units to be constructed in a timely and economical fashion.
After reviewing dozens of options, the Generation IV Nuclear Initiative settled on six designs that it found to be the most attainable and desirable. Since its initial efforts, countries that have embraced the goals of the Generation IV Nuclear Initiative have been pursuing additional designs including reactors that range in size from quite small to about one third the size of the typical one megawatt reactor.
In my book, I’ve focused my attention on four promising designs. These four designs eschew the vulnerabilities of using water as a coolant that proved so devastating at Chernobyl and Fukushima. The explosion at Chernobyl was due to steam and the three explosions at Fukushima were due to hydrogen gas that resulted from oxidation of fuel rods by overheated water. These were not nuclear explosions. Instead, the four designs I’ve highlighted use liquid sodium, liquid lead, molten salts and helium gas as coolants. Liquid sodium and liquid lead cooled reactors are operating successfully in Russia, while China incorporated a gas cooled reactor into its grid in 2023. In the United States, Kairos Power is constructing a molten salt cooled reactor, while the TerraPower company (founded by Bill Gates) has broken ground on construction of a sodium cooled reactor in Kemmerer, Wyoming. These are intended to be models for replacing coal fired power plants with Generation IV nuclear plants. Multiple implementations of this approach are planned through the early 2030s.
Given the world-wide interest in Generation IV reactor development and the many initiatives that are being pursued, it is likely that at least some of these projects will come to fruition in the near future. While success is not guaranteed, there is clearly a need for the general public and students to be kept informed of progress leading up to 2030 and beyond.
To help bridge the knowledge gap in this rapidly evolving domain, I’ve launched a newsletter on Substack called “Nuclear Tomorrow.” It’s written for anyone concerned with the intersection of public policy, energy generation, and its impact on global warming. I hope it serves as a resource for those seeking clarity in a complex and consequential field.
Animals caring for their young, such as a lioness carrying her cub by their scruff or a matriarchal elephant herd nursing young calves, are the kinds of behavior that many would pay good money to watch on a safari. However, fish, especially father fish, caring for their young has received limited popular attention, except maybe for the clownfish father-son duo featured in Finding Nemo. Findings published in the recent article “Parental care drives the evolution of male reproductive accessory glands across ray-finned fishes” in the journal Evolution by a group of scientists in Canada shed new light on the evolution of fish paternal care. Lucas Eckert (McGill University), along with his co-advisors Ben Bolker and Sigal Balshine (both at McMaster University) and their co-authors Jessica Miller and John Fitzpatrick, show that, among ray-finned fish, species in which fathers look after young evolved reproductive accessory organs six times faster than those without male care.
Ray-finned fish, bony fish with webbed fins supported by thin, long rays of bone, represent the vast majority of known fish species. Some of these species have reproductive accessory organs, which are parts analogous to prostate glands in humans. These organs are not directly involved in producing gametes, but they optimize reproductive potential through functions such as sperm storage and nourishment. They also produce fluids that increase the ability of sperm to move and fertilize eggs. Research on how these glands evolved has focused mostly on mammals and insects, with little known about their evolution in fish.
“Accessory reproductive glands are a bit of a ‘mystery organ’ when it comes to fish”, says Dr. Sigal Balshine, fish behavioral ecologist and co-principal investigator of this study. “Some fish have them while some don’t have them at all. We know of their existence only in a very few species out of nearly 30,000 fish species in the world. Even when they are present, accessory reproductive glands show bizarre diversity which has always made me think that there must be interesting evolutionary drivers shaping them. There was a lot we didn’t know regarding how or when they evolved, which is why we started collecting data on them”.
In certain groups of animals, sperms of multiple males compete to fertilize the eggs of a single female, a scenario known as sperm competition. Accessory glands produce secretions that enhance sperm performance, and scientists have long believed that they evolved as a weapon to aid in this post-copulatory war in organisms such as rodents and insects. Most fish biologists assumed fish reproductive accessory glands followed the same evolutionary trend. However, in their study, Eckert and colleagues shift the focus away from sperm competition towards parental care. These authors reconstruct the evolutionary history of reproductive accessory organs, testing whether parental care and/or mate competition among males contributed to their evolution.
“There was evidence that these organs were super important in the species that have them, in securing reproductive success and fitness through a variety of functions. In that context, when some species have them and some don’t, the most obvious question was what were the drivers that selected for their evolution in the first place.” says Lucas Eckert, PhD student and lead author of the study, and one of the many students who have been collecting these data since 2017.
The team approaches this question using a quantitative synthesis of phylogenetic, morphological, and behavioral trait data of ray-finned fish collected from published databases. A plethora of published research data is available on reproductive traits of fishes, owing to their remarkable diversity in reproductive organs and behaviors. However, previous studies mostly only describe these traits, without formally testing any hypotheses regarding their evolution. In this study, the authors compile reproductive trait data for over 600 fish species from research conducted over many decades, to quantify the influence that sperm competition and parental care have had in shaping the accessory glands.
In this study, we have been able to put existing data and methods together in ways that they have not been connected before”, says Dr. Ben Bolker, mathematical biologist and co-principal investigator of the study. “This study has been able to find ways to ask the question and find, how much sperm competition and parental care contribute to the evolution of accessory reproductive organs of ray-finned fish, rather than ask what exactly caused accessory glands to evolve, because in biology everything does everything”.
Left: Upside-down round goby (Neogobius melanostomus) male guarding his eggs. Photo by Sina Zarini. Right: Simplified phylogeny highlighting the main ray-finned fish groups in which accessory glands are present (red branches). Illustration by Lucas Eckert.
The special benefits accessory glands provide male fish for improving their reproductive success explains why they evolved faster in species with paternal care. Unlike in many other animal groups where mothers take care of their young, when it comes to fish, that duty was most commonly delegated to fathers by evolution: they had the resources to maximize the survival of fertilized eggs, such as territory, security and nutrition. Accessory glands produce secretions that protect fertilized eggs against microbial infections and increase sperm adhesiveness and the viable period of sperm after release. These secretions allow these stay-at-home fish dads to multi-task in keeping their sperm viable for newly spawning females even while taking care of their young and defending their nests.
Though the evolution of accessory glands is traditionally thought to be driven by sperm competition, this study uncovers a new angle on drivers of fish accessory gland evolution by considering parental care behaviors. The authors hope that these results will encourage researchers to take a closer look at these mysterious glands and consider their potential importance in the species that possess them.
Featured image: Goby eggs by Olivier Dugornay, via Wikimedia Commons (CC BY 4.0).
I often tell my students that biology has become a data-driven field. Certainly, there’s a general sense that methods related to biological sequences (that is methods in genomics and bioinformatics) have become very widespread. But what does that really mean?
To put a little flesh on those bones, I decided to look in detail at all the biology-related articles in a single issue of the journal Nature (issue 8069, the one that was current when I started). I’m focusing here on articles, representing novel peer reviewed research. By my count, 16 of the 26 papers in this issue are related to biology in one way or another. (Those 16 also include neuroscience and bio-engineering related papers).
For each of these articles I went through the methods looking for genomics and bioinformatics related approaches. I sorted what I found into a few categories. Here’s a short summary:
Four of the papers (25%) used high throughput DNA sequencing.
Four were doing phylogenetic reconstruction. (Two of these were doing both phylogenetic reconstruction and sequencing).
Four were doing RNA seq, that is high throughput sequencing of RNA to study gene expression.
Five used computational methods of sequence analysis (e.g. alignment or its derivatives).
My “other high throughput methods” category also contained five papers.
Considering all high throughput sequence-related methods together, I found that 10/16 papers fell into at least one of these categories. That is, just over 60% of biology papers in this issue were using one or another such method. Which is to say, these methods really are very common in modern research.
The papers in issue 8069 used these methods to study a huge diversity of questions. One paper used sequencing based approaches to better characterize variation in the pea plant studied by Gregor Mendel, using this to get insights into the basis of several of his traits (which had not previously been known). Another looked at deep phylogenetic relationships among eukaryotes. Still another compared patterns of methylation during development between eutherian and marsupial mammals. I could go on, but the message is that genomics and bioinformatics are used to answer many different kinds of questions.
The take-away is that these are foundational methods for modern biology. As such they should be basic training for any student interested in continuing with research in the biological sciences. This is not only so students can conduct research on their own, but also so they can understand papers they read in a deeper and more sophisticated way.
In our recent second edition of the book Concepts in Bioinformatics and Genomics, we try to balance biology, mathematics and programming, as well as build knowledge from the ground up. Topics range from RNA-Seq and genome-wide association studies to alignment and phylogenetic reconstruction. Our hope is that this approach will help students understand the research they encounter on a deeper level and prepare them to potentially participate in that enterprise.
Many criminal investigations, including “cold cases,” do not have a suspect but do have DNA evidence. In these cases, a genetic profile can be obtained from the forensic specimens at the crime scene and electronically compared to profiles listed in criminal DNA databases. If the genetic profile of a forensic specimen matches the profile of someone in the database, depending on other kinds of evidence, that individual may become the prime suspect in what was heretofore a suspect-less crime.
Searching DNA databases to identify potential suspects has become a critical part of criminal investigations ever since the FBI reported its first “cold hit” in July 1999, linking six sexual assault cases in Washington, D.C., with three sexual assault cases in Jacksonville, Florida. The match of the genetic profiles from the evidence samples with an individual in the national criminal database ultimately led to the identification and conviction of Leon Dundas.
How the statistical significance of a match obtained with a database search is presented to the jury should, in my view, be straightforward but, given the adversarial nature of our criminal justice system, remains contentious. One view is that if the profiles of the evidence and a suspect who had been identified by the database search match, then the estimated population frequency of that particular genetic profile (equivalent to the Random Match Probability in a non-database search case) is still the relevant statistic to be presented to the jury. The Random Match Probability (RMP) is an estimate of the probability that a randomly chosen individual in a given population would also match the evidence profile. The RMP is estimated as the population frequency of the specific genetic profile, which is calculated by multiplying the probabilities of a match at each individual genetic marker (the “Product Rule”).
An alternative view, often invoked by the defense, is that the size of the database should be multiplied by the RMP. For example, if the RMP is 1/100 million and the database that was searched is 1 million, this perspective argues that the number 1/100 is the one that should be presented to the jury. This calculation, however, represents the probability of getting a “hit” (match) with the database and not the probability of a coincidental match between the evidence and suspect (1/100 million), the more relevant metric for interpreting the probative significance of a DNA match. Although these arguments may seem arcane, the estimates that result from these different statistical metrics could be the difference between conviction and acquittal.
There are many different kinds of DNA databases. Ethnically defined population databases are used to calculate genotype frequencies and, thus, to estimate RMPs but are not useful for searching. The first DNA searches were of databases of convicted felons. In some jurisdictions, databases of arrestees have also been established and searched. These searches have recently been expanded to include “partial matches,” potentially implicating relatives of the individuals in the database. This strategy, known as “familial searching,” has been very effective but contentious, with discussions typically focused on the “trade-offs” between civil liberties and law enforcement. In some jurisdictions, the “trade-off” has been between two different controversial criminal database programs. In Maryland, for example, an arrestee database (albeit one specifying arraignment) was allowed but familial searching was outlawed. Familial searching has been critiqued as turning relatives of people in the database into “suspects.” A more accurate description is that these partial matches revealed by familial searching identify “persons of interest” and that they provide potential leads for investigation.
Recently, searching for partial matches in the investigation of suspect-less crimes has expanded from criminal databases to genealogy databases, as applied in the Golden State Killer case in 2018. These databases consist of genetic profiles from people seeking information about their ancestry or trying to find relatives. Genetic genealogy involves constructing a large family tree going back several generations based on the individuals identified in the database search and on genealogical records. Identifying several different individuals in the database whose profile shares a region of DNA with the evidence profile allows a family tree to be constructed. The shorter the shared region between two individuals or between the evidence and someone in the database, the more distant the relationship. This is because genetic recombination, the shuffling of DNA regions that occurs in each generation, reduces the length of shared DNA segments over time. So, in the construction of a family tree, the length of the shared region indicates how far back in time you have to go to locate the common ancestor. Tracing the descendants in this family tree who were in the area when the crime was committed identifies a set of potential suspects.
The DNA technologies used in investigative genetic genealogy (IGG) are different from those typically used in analyzing the evidence samples or the criminal database samples, which are based on around 25 short tandem repeat markers (STRs). The genotyping technology used to generate profiles in genealogy databases is based on analyzing thousands of single nucleotide polymorphisms (SNPs). With the recent implementation of Next Generation Sequencing technology to sequence the whole genome, even more informative searching for shared DNA regions can be accomplished. (Next Generation Sequencing of the whole genome is so powerful that it can now distinguish identical (monozygotic) twins!)
Investigative genetic genealogy (IGG) has completely upended the trade-offs and guidelines proposed for familial searching as well as many of the arguments. Many of the rationales justifying familial searching of criminal databases, such as the recidivism rate, and the presumed relinquishing by convicts of certain rights do not apply to genealogical databases. Also, the concerns about racial disparities in criminal databases don’t apply to these non-criminal databases either. In general, it’s very hard to draw lines in the sand when the sands are shifting so rapidly and the technology is evolving so quickly. And it is particularly difficult when dramatic successes in identifying the perpetrators of truly heinous unsolved crimes are lauded in the media, making celebrities of the forensic scientists who carried out the complex genealogical analyses that finally led to the arrest of the Golden State Killer and, shortly thereafter, to many others.
It’s still possible and desirable to set some guidelines for IGG, a complex and expensive procedure. It should be restricted to serious crimes. The profiles in the database should be restricted to those individuals who have consented to have their personal genomic data searched for law enforcement purposes. With the appropriate guidelines, the promise of DNA database searching to solve suspect-less crimes can truly transform our criminal justice system.
Claude Monet once said, “I perhaps owe having become a painter to flowers.” Perhaps he should have given bees equal credit for his occupation. Without them, the dialectical coevolutionary dance with flowers that has lasted 125 million years would not have produced the colorful landscapes he so cherished. For Darwin, it was an abominable mystery; for Monet, an endless inspiration.
Bees, like Monet, paint the landscape. Their tool kit, however, is not one of canvas, paint pigments, and brushes, but consists of special body parts and behavior. Their bodies, covered with branched hairs, trap pollen when they rub against floral anthers and transfer it to the stigma—pollination. Their visual spectrum is tuned to the color spectrum of flowers, not an adaptation of the bees to flowers but an adaptation of flowers to attract the pollinators. Insects evolved their color sensitivities long before flowering plants exploited them.
Monet’s ‘Le jardin de l’artiste à Giverny,’ 1900. Public Domain via Wikimedia Commons.
The behavioral toolkit of honey bees is expansive. Bees learn the diurnal nectar delivery rhythms of the flowers; they also learn their colors, shapes, odors, and where they are located. Honey bees are central-place foragers, meaning they have a stationary nest from which they explore their surroundings. They can travel more than 300 km2 in search of rewarding patches of flowers. To do this, they have a navigational tool kit. First, they need to know how far they have flown: an odometer. This they accomplish by measuring the optical flow that traverses the nearly 14,000 individual facets that make up their compound eyes, similar to us driving through a city and noting how much city flows by in our periphery. They calculate how far they have flown and the angle of their trajectory relative to the sun, requiring a knowledge of the sun’s location and a compass. Then they integrate the individual paths they took and determine a straight-line direction and distance from the nest. Equipped with this information, they return to the nest and tell their sisters the location of the bonanza they discovered.
Bee dance diagram. Emmanuel Boutet, CC BY-SA 2.5 via Wikimedia Commons.
Communication among honey bees is not done with airborne sounds, as they have no organs for detecting them. Information is conveyed through a dance performed by returning foragers on the vertical surface of a comb in a dark nest. New recruits gather on the comb dance floor, attend the dances, and learn the direction and distance to the patch of flowers. How they perceive the information in the dance is not known, but to us as observers, we can decipher the direction by the orientation of the dance, and the distance by timing one part of it. Because the dance is done on a vertical comb inside a dark cavity, perhaps a hollow tree or a box hive provided by a beekeeper, the forager has two challenges. First, she must perform a bit of analytical geometry and translate the angle of the food source relative to the location of the sun from a horizontal to a vertical plane, then she must represent the direction of the sun at the top of comb. This is a constant like north at the top of our topographical maps.
Walker canyon wildflowers. Mike’s Birds, CC BY-SA 2.0 via Wikimedia Commons.
Equipped with this information, recruits fly out of the nest in the direction of the resource for the distance indicated by the dance and seek the flowers. The flowers lure them in with attractive colors, shapes, odors, and sweet nectar that the bees imbibe and in the process transfer pollen onto the stigma, fertilizing the ova. The seeds develop, drop to the ground and wait until the following spring when the plants emerge and paint the fresh landscape with a kaleidoscope of colors that rivals Claude Monet.
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