With extraordinary optics, lighting reflexes and remarkable flight control, dragonflies can rotate 360 degrees for up to five times in a single second. Though the flight of their prey is often an unpredictable zig-zag pattern, dragonflies’ midair hunting success rate remains at around 95%. Comparably, the success rate of wolves is ~20%, ~30% for cats, and ~50% for Great White Sharks.
Their flight is just one of the many factors scientists are interested in understanding, with the plan to integrate their biological adaptations into human engineering.
Photo by Kulsawad
Inspiringly Precise Flight
The flight of this incredible insect has been studied in controlled experiments, mimicking a predator-prey environment using tiny beads at various velocities. It was observed that dragonflies consistently flew just one meter quicker than the bead. These findings demonstrated the minimal effort needed for such precise flight, attesting to their remarkable instincts. They can enhance humanity’s ongoing technological development, used specifically in the interception of drones and other flying objects.
Unforeseen Mercury Pollution Tracker?
ercury is a toxin that damages brain development in both humans and animals, and much of it comes from human activity – like the usage of fossil fuels in power plants and medical waste disposal. It accumulates in water, becoming Methylmercury, and if exposed to high levels of it, concerns in human fetal development arise. Due to the fact that dragonflies can thrive in nearly every aquatic habitat— even ones in the desert— their larvae are useful for collecting data on ecosystems.
Photo by David M. Restivo
Mercury contamination was previously thought to be low in desert ecosystems. However, the nationwide Dragonfly Mercury Project, led by the United States Geological and National Park Services, recently discovered that there are surprisingly high levels of mercury in dragonfly larvae found in deserts.
Summary: Nervous System and Machine Integration
Once the dragonfly’s nerve cell functions and neuronal processing was adapted to machine learning, neuroscientists, computer scientists, and mechanical engineers collaborated in the creation of an algorithm that mimicked the insect’s visual tracking ability. It could then be used in autonomous pursuit robots, making them 20 times faster than previous algorithms, while staying just as accurate!
Dragonflies. They really are as impressive as their name.
References Sherwin, F. Aerial Engineering and Physics of the Dragonfly. https://www.icr.org/article/13497 Communications and Publishing. Dragonflies Reveal Surprising INsights into Mercury Pollution. https://www.usgs.gov/dragonfliesandmercury New Hampshire Department of Environmental Services. Mercury: Sources, Transport, Deposition and Impacts. https://www.des.nh.gov/sites/g/files/ehbemt341/files/documents/2020-01/ard-28.pdf US Environmental Protection Agency. Health Effects of Exposures to Mercury. https://www.epa.gov/mercury/health-effects-exposures-mercury Forest and Rangeland Ecosystem Science Center. The Dragonfly Mercury Project. https://www.usgs.gov/centers/forest-and-rangeland-ecosystem-science-center/science/dragonfly-mercury-project Division of Research and Innovation, University of Adelaide. Dragonfly technology provides pinpoint accuracy and super speed. https://www.adelaide.edu.au/research/news/list/2019/08/16/dragonfly-technology-provides-pinpoint-accuracy-and-super-speed
In 1820, Hans Christian Ørsted conducted an experiment that redefined the worlds of both science and engineering. Upon passing a current through a wire suspended above a compass and observing the compass needle move, Ørsted was able to conclude that the electric and magnetic forces – both recognized as separate forces – were indeed related, and thus, unified. This observation was further expanded on by James Maxwell, who later asserted that changing electric currents can produce electromagnetic fields, thus establishing the existence of electromagnets. Since then, electromagnets have become the keystone to technological advancements that have contributed to the development of MRI devices in medicine, electrical circuitry used in numerous electrical applications, and maglev trains in transportation.
Since 1984, numerous countries worldwide have utilized maglev trains to transport people across large distances, the first of which being located in Great Britain. The most famous maglev train in the world, however, is the Shanghai Maglev, which connects the Pudong International Airport to various neighborhoods in Shanghai’s Pudong district. The train is able to reach record speeds of up to 501 km/h (301.3 mi/h) and was able to transport a record number of 53 million passengers in 2023.
While there are three primary types of maglev operating systems, most operate using a rudimentary principle that is discussed in the majority of high school science classes: principles of magnetic attraction and repulsion, though on a much larger scale. For example, the most common mechanism used on maglev trains utilizes cars that have four magnets on the bottom corners. The magnets beneath the train car have opposing poles to the ones on the bottom of the track; when electricity runs through these magnets, the trains begin to levitate. Moving trains forward requires another set of electromagnets. Electromagnets on the sides of the tracks are placed so that their poles alternate with respect to the ones on the train car, (ie.alternating between north and south), and therefore, pulling it forward using attractive and repulsive magnetic fields. While levitation helps the train gain speed, guide wheels are often used for stability, and ensure that the train remains on the tracks.
Figure 1, Source: (Whyte)
As the train accelerates, its levitation increases. Several trains can levitate up to 4 inches (10 mm) above train tracks at high speeds, which also serves to considerably reduce the amount of friction acting upon the train. This allows maglev trains to reach significantly higher speeds than their diesel or rail counterparts. While they still experience some friction from air resistance, there is less energy lost to heat and sound, which are common characteristics of traditional rail trains. Ultimately, as the majority of their energy is directed towards propelling the train forward and allowing them to reach higher speeds, maglev trains are praised for being an energy-efficient alternative to traditional trains.
Due to the fact that maglev trains employ the use of electricity, as opposed to fossil fuels as an energy source, they also produce fewer carbon emissions, making them a “greener” source of transportation that reduces traffic congestion in urban areas. According to Sayeed Mavani from the McGill Business Review, plans to develop a high-speed maglev rail system in the northeastern United States will connect various neighborhoods across northeastern states, while indirectly helping “reduce current rail energy consumption by 30 percent.” This project is also projected to increase the US Gross Domestic Product (GDP) by approximately “$254 million annually,” demonstrating that maglev trains have the potential to substantially boost the economy. While initial funding to build maglev systems from scratch may be expensive, the long term benefits offered are too great to ignore. As a highly efficient machine displaying a wide array of consistently positive benefits, maglev trains are a low energy, highly efficient method of transportation that should be implemented on a global scale to reduce carbon emissions.
Works Cited
BYJU’S. “Uses of Electromagnet with Its Applications in the Practical World.” BYJUS, 2023, byjus.com/physics/uses-of-electromagnet/. Accessed 27 August 2024.
“China: Number of Passengers on the Beijing-Shanghai High Speed Rail Line 2023 | Statista.” Statista, Statista, 2023, www.statista.com/statistics/1074379/china-number-of-passengers-transported-on-beijing-shanghai-high-speed-rail-line/. Accessed 28 Aug. 2024.
Li, Shiyi. “State of the Art and Future Development of Magnetic Levitation Technology.” Highlights in Science, Engineering and Technology, vol. 31, School of Electrical and Electronic Engineering, North China Electric Power University, Baoding, China, 2023. Accessed 26 August 2024.
Mavani, Sayeed. “Travelling Green: Is Maglev the Future of Eco-Friendly Transportation?” McGill Business Review, McGill Business Review, 23 August 2021, mcgillbusinessreview.com/articles/travelling-green-is-maglev-the-future-of-eco-friendly-transportation-1. Accessed 25 Aug. 2024.
Mitchell, Alanna. The Spinning Magnet. Penguin Random House, 2018. Accessed 22 August 2024.
Whyte, Chelsea. “How Maglev Works.” Energy.gov, US Department of Energy, 14 June 2016, www.energy.gov/articles/how-maglev-works. Accessed 27 August 2024.
Humans have identified, synthesized, and even tested millions of molecules thus far. However, out of the near-infinite possibilities, only a small fraction of molecules have been discovered thus far. But what if it was possible to exponentially accelerate this process?
Current System
Figure 1: leaders-mena.com
Generally, drug discovery is a long, arduous process; while thousands of molecular compounds and existing treatments are tested on a disease based on new insights, barely any are chosen for additional analysis. More specifically, approximately 10,000 drugs are tested, on average, in the discovery stage. Of this, around 250 continue to the preclinical stage, and only about 5 are deemed ready for further analysis in the clinical trials.
Therefore, with this current process, there is an approximate 0.05% chance of discovering a possibly effective drug. Even so, of the scarce amount that pass to the clinical trials, even fewer advance beyond that stage to reach commercial availability. With an average success rate of drugs in clinical trials at approximately 11.1%, it can be reasonably inferred that a staggeringly low 0.005%, or 1 in 20,000 molecules tested become an actual product.
Despite the current model’s inefficiency, until very recently there were no better alternatives. To put it into perspective, the US Food and Drug Administration (FDA) currently uses the previously described selective method. In the past few years, a more productive option has begun to emerge – artificial intelligence.
Digital Chemistry
Figure 2: bu.edu
In a 2022 MIT study, a machine learning model was trained to suggest molecular structures that have certain properties and can be synthesized. By feeding the model with a multitude of molecules, elements, and reactions that take place to form its structure, the model learns to synthesize desired molecules independently. The trained model is then inputted with a set of “building blocks” to create a molecule (list of chemicals and their possible reactions) to restrain the search to what is possible; then, a decision tree is employed to find the final molecule. Although the model is still in development, this methodology could revolutionize the drug discovery process, and open up doors for limitless innovation in the future.
One company that has also worked on utilizing artificial intelligence in drug development considerably, and currently holds the title for the “leading computational platform for molecular discovery and design” is Schrödinger. With a working procedure for molecular discovery, and several patents, this company has developed molecules that have passed the preclinical stage, and could likely be effective in treating diseases. For example, the drug SGR-1505 blocks a protein called MALT1, which is involved in growth of B-cell lymphomas (cancers of the immune system) and therefore, could be a promising way to treat those cancers. This drug was discovered through machine learning methods, and serves as a testament to the extent of the possibility in this field.
Conclusion
Traditional drug discovery, although foundational, is an extremely tedious and inefficient process that has considerable potential for improvement. To reduce the time and resources involved with this system, using machine learning procedures can significantly reduce the effort necessary to successfully achieve molecular discovery. Despite the old system still being in use, digital chemistry’s integration of machine learning and AI can potentially redefine the field entirely – a new era.
Despite the substantial ongoing research in cognitive neuroscience, several questions have yet to be answered. What is the neural basis of the self? How does one’s neurobiology result in a complex being, capable of experiencing emotion and engaging in self-reflection? At what point does the brain’s function shift from being purely biological to enabling introspection and conscious thought? Although there is not one fixed answer to these questions, many scientists over the past decade have advanced this field of study by identifying different parts of the human brain that make up one’s identity–one of which is the Default Mode Network.
The Default Mode Network (DMN) is a system of interconnected brain regions that are distributed throughout the cerebral cortex. When these regions work as one, their combined functions lead to creativity and self-reflection. This article will discuss the basic purposes of the DMNand how this intricate network shapes our personal experiences and identity.
What is the Default Mode Network?
The DMN is like a switch that turns on- and off based on whether or not a task is being performed. While it is usually inactive when one’s attention is required to complete a task, such as engaging in a conversation, solving a puzzle, or exercising, the DMN activates once tasks are complete—or when one is at “rest.” This encompasses all the moments when one is conscious, but not actively thinking about any specific topic (Vessel et al.).
When the DMN is active, the mind is likely focused on internal processes, such as self-reflection, imagining the future, or recalling personal experiences. If the DMN is not properly shut off when switching to an external task, an individual may be prone to “mind-wandering”, or becoming distracted from their goal by unrelated thoughts. Some of the other essential functions of the DMN include pondering about oneself, social cognition, memory of specific life events, and language.
Image from https://www.simplypsychology.org/what-is-the-default-mode-network.html
The major components of the DMN are the medial prefrontal cortex (mPFC), precuneus (PCUN), posterior cingulate cortex (PCC), and the angular gyrus (ANG); each region has a unique function. The vmPFC (ventro-medial prefrontal cortex) is involved in motivation, while the dmPFC (dorso-medial prefrontal cortex) is responsible for self-referential judgment and understanding the mental states of others (Viezzer). The PCC, PCUN, and ANG contribute to a process called “autobiographical memory”, a collection of key events resembling a life story. Apart from these networks, the DMN also includes other brain regions that are involved in memory and emotions, such as the hippocampus and the amygdala.
While the basic function of the DMN holds true for most scenarios, there are many exceptions when it is active even during task performance. An experiment done by researchers at New York University shows that the DMN can be activated when one is viewing artwork. Participants in this experiment were shown pieces of art and asked to evaluate how “moving” they were, which was measured by their emotional reaction. The results showed that the DMN activates upon viewing “highly-moving” art, meaning that art elicits a strong reaction (Vessel et. al). Why would the participants’ DMN be active while they are focused on an external stimulus? The answer to that question lies in the concept of self-discovery. The art that someone is drawn to often reflects a part of them. It is possible that the DMN helped participants identify parts of themselves that were represented in the artwork, aiding the process of introspection. The DMN’s role in self-reflection is also corroborated by the results of an experiment done by Davey et al., which revealed that the Default Mode Network is engaged with making self-referential judgements. Individuals were presented with various personal qualities (e.g. “skeptical”, “perfectionistic”) and asked how well they thought each described them. The fMRI data collected indicated that the DMN was more active when performing this self-referential task than an externally based activity (Davey et al.). Similarly to the previous experiment, this finding reinforces that the DMN is essential for shaping one’s identity. Moreover, neuroscience researcher Vinod Menon from Stanford University suggests that when the functions of DMN regions overlap, a unique “internal narrative” is created. The “internal narrative” consists of personal values and beliefs, memories of major events, facts about oneself and the surrounding world, as well as the ability to reason (Menon). In other words, the DMN’s functions configure patterns of thought in human-beings and are central to our perception.
Applications of the Default Mode Network to Creativity and Self-Discovery
The default mode network plays a major role in shaping one’s personal story. Whether an individual is reflecting on their dreams or contemplating their favorite music genres, the DMN serves as a gateway to understanding one’s deepest values and ambitions. This elaborate connection of brain regions can also serve as a starting point for creativity. The beliefs one collects throughout their life can easily seep into their thought process. Although one’s spontaneous thoughts are easily dismissed as insignificant distractions, the “stream of consciousness” has hidden potential, as spontaneous thoughts tend to be the most innovative. Taking the time to deliberately explore these thoughts can uncover hidden ideas, whether they hold an unconsidered perspective or a novel solution to a previously “impossible” problem. So, the next time the human mind decides to wander off, take a second to entertain those “distracting” thoughts–perhaps an innovative, fruitful idea may come to light.
Davey, Christopher G., et al. “Mapping the Self in the Brain’s Default Mode Network.” NeuroImage, vol. 132, May 2016, pp. 390–397, www.sciencedirect.com/science/article/pii/S1053811916001294, https://doi.org/10.1016/j.neuroimage.2016.02.022.
Menon, Vinod. “20 Years of the Default Mode Network: A Review and Synthesis.” Neuron, vol. 111, no. 16, May 2023, www.med.stanford.edu/content/dam/sm/scsnl/documents/Neuron_2023_Menon_20_years.pdf, https://doi.org/10.1016/j.neuron.2023.04.023.
Shofty, Ben, et al. “The Default Network Is Causally Linked to Creative Thinking.” Molecular Psychiatry, vol. 27, no. 3, 1 Jan. 2022, pp. 1848–1854, https://doi.org/10.1038/s41380-021-01403-8.
Vessel, Edward A., et al. “Art Reaches Within: Aesthetic Experience, the Self and the Default Mode Network.” Frontiers in Neuroscience, vol. 7, 2013, https://doi.org/10.3389/fnins.2013.00258.
Viezzer, Sara. “What Is the Default Mode Network?” Simply Psychology, 3 Mar. 2023, www.simplypsychology.org/what-is-the-default-mode-network.html.
