TITLE HERE Meet Our Staff Leaders:
By: XX ‘2X
June Liu ‘25: Co-Editor in Chief
Evana Jang ‘26: Co-Editor in Chief
Wei Li ‘25: Designer in Chief
Staff Writers:
Gabby Duncan ‘26
Olivia Cheon ‘27
Sofia Wu ‘27
Maryam Abdulkarim ‘25
Diane Ferragu ‘26
Nivrritii Mahesh ‘27
Guest Writer:
Eliza Mikheev ‘27
Helen Shearon ‘25: Section Editor Zaiyi Yang ‘26: Section Editor
Claire Lee ‘27: Section Editor
Grace Hu ‘27
Ejin Chung ‘28
Women in STEM
From Computers to Cosmos: The Career of Katherine Johnson
By: Gabby Duncan ’26
Katherine Johnson was a renowned American mathematician who made significant contributions to the field of mathematics and space exploration by calculating the trajectories of notable space explorations such as Apollo 11, Friendship 7, and Freedom 7. Born on August 26, 1918, in White Sulphur Springs, West Virginia, Johnson's love for mathematics started at a young age. She was a gifted student and skipped several grades in school, eventually graduating from West Virginia State College with a degree in mathematics and French (1).
In 1937, Johnson began her career as a teacher, still her passion for mathematics led her to take a job at the West Area Computing Unit for the National Advisory Committee for Aeronautics (NACA), which later became the National Aeronautics and Space Administration (NASA). There, Katherine became one of the first African American women to work at NASA. However, Johnson faced many biases and struggles at NASA due to her race and gender (3). One of the barriers she faced was segregation within the work office. Black and white workers were forced to occupy
1966 (4)
and use separate restrooms, eating spaces, and working areas. However, Katherine Johnson stood firmly against using those separate spaces and spoke up about those inequalities (2).
Despite facing racial discrimination and gender bias, Johnson didn’t falter and authored and co-
Katherine Johnson,
authored 26 research papers. Additionally, she contributed significantly to high-profile projects, such as calculating the trajectory for Alan Shepard's Freedom 7 mission in 1961 and John Glenn's Friendship 7 mission in 1962. However, her most notable achievement at NASA was her pivotal role in solving the calculations for flight paths and trajectory of the Apollo 11 mission to the moon in 1969 (3). Johnson’s accurate and precise calculations for the spacecraft lead to not only a successful orbit of the moon, but it also encouraged the need for more skilled mathematicians in aerospace engineering (2).
Katherine Johnson’s contributions to mathematics and space exploration were groundbreaking and pivotal. Her achievements not only advanced scientific knowledge but also helped pave the way for future generations of women and minorities in a predominantly white-dominated field. Thus, Johnson has received numerous awards and recognitions, including the Presidential Medal of Freedom (2015) and the Congressional Gold Medal (2019). In 2016, NASA named the Katherine G. Johnson Computational Research Facility after her. Additionally, a book written by Margot Lee Shetterly ‘Hidden Figures: The American Dream and the Untold Story of the Black Women Mathematicians Who Helped Win the Space Race’ was later made into a motion picture telling the story of 3 women, which included Katherine Johnson, who worked in the West Computers. Her captivating story continues to be told and motivates
people worldwide, cementing her impact for many years to come (4).
Works Cited
1. Deiss, H. (2020, February 24). Katherine Johnson: A Lifetime of STEM - NASA. NASA. https://www.nasa.gov/learningresources/katherine-johnson-a-lifetimeof-stem/
2. Life Story: Katherine Johnson. (n.d.). Women & the American Story. Retrieved November 1, 2024, from https://wams. nyhistory.org/growth-and-turmoil/coldwar-beginnings/katherine-johnson/
3. Shetterly, M. L. (2016, November 22). Katherine Johnson Biography. NASA; NASA. https://www.nasa.gov/centersand-facilities/langley/katherine-johnsonbiography/
4. The Editors of Encyclopedia Britannica. (2018). Katherine Johnson | Biography & Facts. In Encyclopædia Britannica. https:// www.britannica.com/biography/ Katherine-Johnson-mathematician
Image Courtesy of NASA
The Hidden Figure Behind the Discovery of DNA Molecule Structure:
TITLE HERE
Rosalind Franklin
By: Olivia Cheon ‘27
Rosalind Franklin was born on July 25, 1920, into an affluent Jewish family in England. From a young age, she showed exceptional intelligence, especially in science. In 1938, she enrolled at Newnham College, Cambridge, and studied chemistry. Despite receiving a fellowship to conduct research in physical chemistry at Cambridge in 1941, she gave up her fellowship the following year to investigate the physical chemistry of carbon and coal for the World War ll afare. From 1947 to 1950, she worked with Jacques Méring at the Laboratoire Central des Services Chimiques de l’Etat in Paris. He taught her X-ray diffraction, which afterward played an important role in her study of the DNA structure (1,2).
Franklin joined the Biophysical Lab-
oratory at King’s College, London, as a researcher in 1951 to study DNA. At the time, there was very little known about DNA’s chemical makeup or structure. However, by applying X-ray diffraction methods to study DNA, she soon found out about the density of DNA and its helical conformation(1). Franklin and her student Raymond Gosling took photos of DNA and found out that there were two forms that DNA exists in, a dry “A” form and a wet “B” form. One of the pictures from the B form called Photograph 51 played a crucial role in the evidence for Watson and Crick’s suggestion of the double-helix polymer structure of DNA (1,2).
After studying DNA structure, she returned to London and worked in the Crystallography Laboratory at Birkbeck College from 1953 to 1958. There, she finished her work on coals and DNA and made new discoveries on RNA by studying the molecular structure of the tobacco mosaic virus.
Unfortunately, 1958 would be Franklin’s last year working at the
lab because she died that year of cancer, halting all her studies forever (1).
There have been many controversies about whether Franklin should have been credited with the discovery of the double-helix structure of DNA because there were people who argued that she couldn’t even recognize the double-helix structure of her image for months when Watson understood it right away, while others insist otherwise(3). However, her contributions to science had crucial impacts on the studies afterward. She clearly differentiated the A and B forms of DNA, and her photograph 51 helped Watson and Crick prove the double-helix structure of DNA(3). She also laid the foundation for structural virology by her discoveries related to the structure of viruses(1). She was a scientist who helped to change the world through her pioneering experiments that brought deeper studies available in the science field.
Works Cited
1. Britannica, Rosalind Franklin, Last updated Oct 24, 2024. https:/ /www.britannica.com/biography/ Rosalind-Franklin
2. Biography, Rosalind Franklin Biography, Last updated Jun 15, 2020. https://www.biography. com/scientists/rosalind-franklin
3. Matthew Cobb& Nathaniel Comfort, What Rosalind Franklin truly contributed to the discovery of DNA’s structure, April 25, 2023. https://www.nature.com/articles/ d41586-023-01313-5
Image Courtesy of Imperial College London
The Astro-Violinist: Sarah Gillis
By: Claire Lee ’27
Sarah Gillis is an American SpaceX engineer best known for her work on the Polaris Mission and her project, Harmony of Resilience. She was born on January 1, 1994, and raised in Boulder Colorado. As a child, Gillis was surrounded by music and started learning the violin at a young age with her mother, Sue Levine, a violinist and teacher. Though she was raised to be a classical violinist, Sarah changed her aspirations when she was in high school, at Shining Mountain Waldorf School in Boulder, Colorado. Attending a lecture where she met Joe Tanner, a former NASA astronaut and her beloved high-school mentor, Gillis’ life took a turn as she was encouraged to pursue a degree in aerospace engineering (1).
Gillis graduated from the University of Colorado Boulder with degrees in both aerospace engineering and dance. As a college student, she began an internship at SpaceX, working
on the human-in-the-loop testing of the Dragon spacecraft for astronaut training. She eventually transitioned into the program full-time and is now overseeing the company’s astronaut training program as SpaceX's lead space operations engineer.. Her work includes the development of the mission-specific curriculum and executing training for both NASA and commercial astronauts (2). Gillis is recognized as an experienced mission control operator having trained the first International Space Station Demo-2 and Crew-1 missions as well as the Inspiration4 astronauts, the first all-civilian crew to go into orbit (2).
Most recently, she and three other astronauts, Jared Isaacman (commander), Scott Poteet (pilot), and Anna Menon (mission specialist) were members of the
Polaris Dawn mission. The four astronauts were aboard a SpaceX Crew Dragon Capsule, which launched from NASA’s Kennedy Space Center in Florida atop a Falcon9 rocket on September 10, 2024. Despite the mission only lasting five days, the Polaris Dawn crew had many duties to fulfill during this short period of time. The Polaris Dawn had four mission objectives: setting a new Earth-orbit altitude record, initiating the first commercial spacewalk, testing a new communication system called Starlink, and executing science experiments from twenty partner institutions (2). While in space, the crew worked on roughly forty science experiments that tested laser communications between Dragon and SpaceX’s Starlink satellites. During this mission, Gillis achieved many outstanding records such as being the youngest person to participate in a commercial space walk, becoming the woman who has traveled the furthest away from Earth alongside Menon, and most interestingly, being the first human to play violin in space (2).
While participating in the Polaris Mission, Gillis initiated her project, “Harmony of Resilience”, to symbolize the power of human resilience and the pursuit of extraordinary goals (3). In space, Gillis performed the solo violin part of Rey’s Theme by John Williams from the 2015 film, Star Wars: The Force Awakens. Her performance video was then successfully sent to Earth using SpaceX’s Starlink(2). Young musicians from many countries including Venezuela, the United States, Brazil
, Sweden, Uganda, and Haiti accompanied Gillis with the orchestration to raise money for St. Jude Children’s Research Hospital (1).
Gillis is a multi-talented individual whose work in science and music has touched communities worldwide. Her recent achievements with the Polaris Dawn Mission and the “Harmony for Resilience” project have pushed boundaries in various ways other than space technology. By participating in the first-ever commercial spacewalk and setting a record for women traveling farthest from Earth, Sarah Gillis not only inspires women in space science but also uses her stellar violin performance in space to convey hope and encourage future generations of young individuals to reach for the stars.
Works Cited
1. Niles, Laurie. 2024. “Astronaut-Violinist Sarah Gillis Performs from Space with Music Students on Earth.” Violinist.com. https://www.violinist.com/blog/laurie/ 20249/30128/.
2. Howell, Elizabeth. 2024. “Polaris Dawn Mission: Meet the Crew Taking 1st Commercial Spacewalk.” Space.com. Space. August 21. https://www.space. com/spacex-polaris-dawn-who-islaunching
3. “Harmony of Resilience - Polaris Program.” 2024. Polaris Program. September 13. https://polarisprogram. com/music/.
Fei-Fei Li
By: Sofia Wu ’27
Fei-Fei Li, the godmother of AI, has built a 1 billion dollar startup, World Labs, in less than 4 months (1). Her name– synonymous with AI development– and stories of her latest tech venture continue to fill news headlines.
Despite her rather recent outbreak of fame, she has been a pillar of the computer science community for decades. Li has worked tirelessly to rise above her humble beginnings in the US and has become a leader in her field as well as a champion of gender equality and racial diversity.
Over the years, she has held numerous important roles. Her
previous roles include serving as the Vice President at Google and Chief Scientist at Google Cloud. Currently, she is the inaugural Sequoia Professor in the computer science department at Stanford University. Her main research areas include machine learning, computer vision, and cognitive computational neuroscience (2).
Perhaps her love of STEM is inherited from her father, Li Shun, who worked in the computer department of a chemical plant. In China, they lived a prosperous life in Sichuan province. However, when her parents decided to take Fei-Fei Li to the US for better education, their immigrant life starkly contrasted with the way they had lived in China (3).
She faced many hardships as a young teenager, boarding a plane with her family from China to New Jersey with less than $20 (4). She struggled to learn English while keeping up in school, and spent her free time working in restaurants and at her parents’ drycleaning business to help the family stay afloat (4).
Despite these difficulties, according to the South China Morning Post, her love and talent for math and physics flourished in the US with the support of her high school mathematics teacher, Bob Sabella. After 3 years in
Image Courtesy of Wired
the US, Fei-Fei Li’s dedication earned her a scholarship to Princeton University and at the age of 29, she received a PhD from the California Institute of Technology (3).
During her doctoral studies, she made significant contributions to the one-shot learning technique, which can make predictions based on minimal data and is vital for computer vision and natural language processing (2).
Later, in 2006, she began conceptualizing the creation of the ImageNet database, which has been dubbed “the eye of AI” and laid the foundation for generative AI, such as ChatGPT (2).
According to Princeton Alumni, she believes that this current period is the ideal time to set up guardrails for how AI is developed, deployed, and governed. Similar to other tools that have enhanced human experiences like steam engines, electricity, and computers, Fei-Fei Li believes that “AI can be used for the public good, especially if we ensure that human agency always remains valued.”
Beyond her numerous contributions to AI, she serves as a model of using one’s influence to do good. She has worked to empower women and minority groups to join the field of computer science. These efforts have been most embodied through Li founding her non-profit educational organization AI4ALL. AI4ALL encourages young women and minority students to explore
computer science as a future research direction (2). In all of her ventures, including the publication of her book The Worlds I See, she is steadfast in using her influence to emphasize the importance of gender equality and racial diversity in this transformational industry. “I am a shy person, not good at expressing myself, but I insisted on publishing a book because the field of AI cannot lack female voices,” Li says (2).
Ultimately, Fei Fei Li is an inspiring leader who consistently uses her influence to encourage others to learn about the field she loves.
AI for AI4ALL aims for at least 60 percent of its students going through its program to be Black, Latinx or indigenous, as well as women or nonbinary. (Image Courtesy of Observer)
Works Cited
1. Tremayne-Pengelly, A. (2024, July 17). “Godmother of A.I.” FeiFei Li Has Created a $1B Startup in Less Than 4 Months. Observer. https://observer.com/2024/07/ai-godmother-fei-fei-li-1b-spatialintelligence-startup/
2. Downey, S. (2024). The Godmother of AI. Princeton Alumni. https://alumni.princeton.edu/stories/fei-fei-li-woodrow-wilsonaward
3. Zhang, Z. (2024, August 5). ‘Godmother of AI’, shy ChineseAmerican pioneer Fei-Fei Li seeks science chance for all. South China Morning Post. https://www.scmp.com/news/people-culture/ article/3272931/godmother-ai-shy-chinese-american-pioneer-feifei-li-seeks-science-chance-all
4. Fei-Fei Li: A Candid Look at a Young Immigrant’s Rise to AI Trailblazer. (2023, November 7). Stanford HAI; Stanford University. https://hai.stanford.edu/news/fei-fei-li-candid-look-youngimmigrants-rise-ai-trailblazer
STEM in News
AI in Scientific Literature Reviews: Balancing Speed and Substance
By: Maryam Abdulkarim ’25
We’ve been told over and over again about the consequences of Artificial Intelligence (AI) in writing. Its generated narratives aren’t creative, lack new thought, and can be repetitive. No matter how wonderful it might sound, it can be detected by an AI detector, proving its inhumanness. So AI lacks the human quality to write creative pieces, but what about science literature? A literature review is a summary and analysis of published work in a specific field of study. It should be precise and include the author's opinion, but clearly state its purpose, content, and significance, with advice for further studies. AI excels at summarizing information, identifying patterns, and maintaining consistency and precision in writing.
Many researchers are beginning to embrace AI as a valuable tool in literature reviews. One example is the AI research assistant created at the University of Iowa to help synthesize evidence and extract relevant texts based on userdefined research questions, making
the literature review process more manageable. They have found that generative AI can significantly expedite the processing of vast amounts of literature. Beyond merely saving time, AI can enhance the quality of literature reviews by examining citation patterns and keyword trends, revealing connections and insights that may not be immediately apparent to human researchers (1). This capability not only fosters a more comprehensive understanding of a field but also helps identify areas that warrant further exploration.
Image generated using Deep AI Image Generator using the prompt “an article titled ‘literature review’ being written by a robot”
Moreover, AI can mitigate biases that may inadvertently influence the selection and interpretation of studies, thereby promoting a more objective approach (2). AI systems can indeed exhibit biases similar to those of their creators, particularly in the context of literature reviews. This occurs because AI learns from the data it is trained on, which often reflects historical biases and societal inequities. However, while AI can reflect human biases, it also has the potential to promote a more objective approach in literature reviews by systematically analyzing large volumes of data without the emotional and cognitive biases that humans may have. It can also be used to identify biases in existing literature and research, helping researchers to recognize and address these biases in their work.
However, while these reviews can produce extensive lists of relevant factors, they often lack the depth and contextual understanding that human researchers bring to the table. In Mostafapour et al.'s comparative study, literature reviews generated by both ChatGPT-4 and human researchers were evaluated. They found that although GPT-4 was capable of quickly producing results and demonstrated a broad knowledge base, it struggled with contextual nuances and sometimes generated irrelevant or incorrect information. In contrast, human reviews not only provided accurate and relevant insights but also synthesized findings into coherent themes, offering a richer understanding of
the relational dynamics between physicians and patients (3).
While AI can serve as a valuable tool for conducting preliminary literature reviews by providing a rapid overview of topics, its limitations necessitate careful expert evaluation and refinement. Researchers are encouraged to view AI as an assistant rather than a substitute for human expertise, particularly in fields like health services and medical research, where contextual richness and accuracy are essential.
Work Cited
1. Huang, Jingshan, and Ming Tan. “The Role of ChatGPT in Scientific Communication: Writing Better Scientific Review Articles.” American Journal of Cancer Research, vol. 13, no. 4, 15 Apr. 2023, p. 1148, pmc.ncbi.nlm.nih.gov/ articles/PMC10164801/.
2. Nitin Simha Vihari, and Amandeep Kaur. “The Role of Generative AI-Assisted Literature Reviews in Transforming Academic Research.” Advances in Educational Technologies and Instructional Design Book Series, 12 Apr. 2024, pp. 77–87, www.igi-global.com/ chapter/the-role-of-generative-aiassisted-literature-reviews-intransforming-academic-research/346251, https://doi.org/10.4018/979-8-36931798-3.ch006.
3. Mehrnaz Mostafapour, et al. “ChatGPT vs. Scholars: A Comparative Examination of Literature Reviews Conducting by Humans and AI.” JMIR AI, vol. 3, 19 Aug. 2024, pp. e56537–e56537, https:// pubmed.ncbi.nlm.nih.gov/39159446/.
AI Predicts Early Signs of Alzheimer's: A Breakthrough in Early Diagnosis
By Diane Ferragu ’26
Alzheimer's disease, a progressive neurodegenerative disorder, remains one of the most challenging conditions to diagnose in its early stages. The disease is characterized by cognitive decline, memory loss, and eventual loss of independence (1). In recent years, artificial intelligence (AI) has emerged as a powerful tool in diagnosing Alzheimer’s disease earlier than ever before, potentially allowing for sooner interventions.
Machine learning, a subset of AI, is particularly effective in detecting early signs of Alzheimer's disease, specifically by forecasting the transition from mild cognitive impairment (MCI) to Alzheimer's.
MCI, a medical condition involving cognitive decline greater than expected from a person’s age though not severe enough to interfere significantly with daily life, is often considered a precursor to Alzheimer’s, with a substantial percentage of individuals diagnosed with MCI eventually progressing to Alzheimer’s. Predicting this transition is critical for early interventions. Several machine-learning models have been developed to analyze structural brain data from MRI and PET scans (2). These models focus on detecting minute changes in brain volume, connectivity, and amyloid accumulation—biomarkers linked to cognitive decline.
AI’s strength lies in its ability to process complex and multidimensional data, which refers to data that includes multiple variables. Complex analysis is able to analyze several variables simultaneously such as in neuroimaging where data capture, for example, structural, functional, and metabolic data at once. Machine learning algorithms trained on neuroimaging data can accurately predict the progression of Alzheimer’s by identifying patterns in brain degeneration (2). This allows for earlier detection compared to traditional diagnostic methods, which often rely on observable symptoms that manifest in the later stages of the disease.
Beyond neuroimaging, AI models are increasingly applied to digital health tools like wearable technology and mobile applications. These tools provide continuous monitoring of patients, collecting data on physical activity, sleep patterns, and other behavioral indicators that may signal cognitive decline. Wearable devices combined with AI algorithms enable continuous, real-time data collection to detect subtle changes in daily behavior like altered gait patterns or reduced physical activity—often early warning signs of Alzheimer’s (3).
The application of AI in digital health extends beyond monitoring. For example, AI-powered systems can now analyze speech patterns to detect early signs of cognitive decline. AI systems trained to
analyze speech could differentiate between healthy individuals and those in the early stages of Alzheimer’s (4). By examining subtle changes in language use, sentence structure, and word-finding difficulties, these systems offer a non-invasive way to screen for cognitive impairment.
In addition, using a remote, speechbased AI system that works via smartphones provides a highly accessible tool for early Alzheimer’s screening (5). This development is significant because it allows for widespread screening without requiring patients to visit a clinic, thus reaching a larger population, particularly in under-resourced areas.
Artificial intelligence is transforming diagnostic methods and advancing research on Alzheimer’s disease. AIdriven analysis accelerates research by processing large datasets from clinical trials, genomic studies, and biochemical research (6). AI models can uncover new biomarkers and disease mechanisms that might not be immediately apparent to human researchers. The ability to detect such patterns is crucial in advancing our understanding of Alzheimer's and identifying potential targets for therapeutic interventions.
Moreover, AI helps optimize drug development by streamlining the identification of patient subgroups that are more likely to respond to specific treatments (3). For instance, it revealed that patients in the early stages of Alzheimer’s tend to
respond well to cognitive training or memory exercises, while patients with the APOE4 allele or those showing rapid cognitive decline subgroups tend to respond well to treatments targeting amyloid plaques (3). This personalized approach to medicine could significantly improve the effectiveness of Alzheimer’s therapies, which currently offer limited benefits, especially in the advanced stages of the disease.
While AI offers tremendous promise in Alzheimer’s diagnostics, ethical concerns must be addressed, particularly regarding data privacy and the accuracy of AI predictions. Since AI systems rely on sensitive health information, ensuring patient data is securely managed is crucial (3). Furthermore, while AI has demonstrated high accuracy in detecting Alzheimer’s, there is still a risk of false positives or negatives, which could cause undue stress for patients or delay necessary treatments. Continuous validation and refinement of AI models are essential to ensure they perform reliably in clinical settings.
Despite these challenges, AI’s potential to revolutionize Alzheimer’s diagnosis and research is undeniable. AI’s role in the early detection of Alzheimer’s disease represents a significant breakthrough in the fight against this condition. By leveraging machine learning algorithms to analyze neuroimaging, speech patterns, and real-time behavioral data, AI is reshaping how
Alzheimer’s is diagnosed and managed. The early detection capabilities of AI not only offer hope for timely interventions but also provide a foundation for personalized treatment approaches that could slow the progression of the disease. As AI advances, its integration into Alzheimer’s diagnosis and treatment will likely become more prevalent, offering new opportunities for improving patient outcomes. The ongoing development of AI-driven innovations in healthcare underscores the potential to enhance diagnostic precision and intervention strategies, ultimately benefiting patients and their families in managing this complex neurodegenerative condition.
Image Courtesy of Vice
Works Cited
1. Alzheimer's Association. (2024). What is Alzheimer’s Disease? Alzheimer’s Disease and Dementia; Alzheimer’s Association. https://www.alz.org/ alzheimers-dementia/what-isalzheimers
2. Soraisam Gobinkumar Singh, Das, D., Barman, U., & Manob Jyoti Saikia. (2024). Early Alzheimer’s Disease Detection: A Review of Machine Learning Techniques for Forecasting Transition from Mild Cognitive Impairment. Diagnostics, 14(16), 1759–1759. https://doi.org/ 10.3390/diagnostics14161759
3. Lyall, D. M., Kormilitzin, A., Lancaster, C., Sousa, J., Petermann-Rocha, F., Buckley, C., Harshfield, E. L., Iveson, M. H., Madan, C. R., McArdle, R., Newby, D., Orgeta, V., Tang, E., Tamburin, S., Thakur, L. S., Lourida, I., Deep Dementia Phenotyping (DEMON) Network, Llewellyn, D. J., & Ranson, J. M. (2023). Artificial intelligence for dementia-Applied models and digital health. Alzheimer's & dementia : the journal of the Alzheimer's Association, 19(12), 5872–5884. https://doi.org/10.1002/alz.13391
6. Fristed, E., Skirrow, C., Meszaros, M., Lenain, R., Meepegama, U., Papp, K. V., Ropacki, M., & Weston, J. (2022). Leveraging speech and artificial intelligence to screen for early Alzheimer's disease and amyloid beta positivity. Brain communications, 4(5), fcac231. https://doi.org/ 10.1093/braincomms/fcac231
7. Fristed, E., Skirrow, C., Meszaros, M., Lenain, R., Meepegama, U., Cappa, S., Aarsland, D., & Weston, J. (2022). A remote speech-based AI system to screen for early Alzheimer's disease via smartphones. Alzheimer's & dementia (Amsterdam, Netherlands), 14(1), e12366. https://doi.org/10.1002/ dad2.12366
8. Zhang, W., Li, Y., Ren, W., & Liu, B. (2023). Artificial intelligence technology in Alzheimer's disease research. Intractable & rare diseases research, 12(4), 208–212. https://doi.org/10.5582/ irdr.2023.01091
Artificial Intelligence in Cardiology
By: Nivrritii Mahesh ’27
Our hearts are necessary to keep us alive during our busy days. It is essential to care for them, ensuring there aren’t any problems that can lead to fatal consequences in the future. As we turn towards a more technological future, it is vital to take the time to learn how our prospective cardiologic diagnosis and treatment may look with artificial intelligence. Cardiology involves the study of the heart, including diagnosis, diseases, and treatment. Artificial intelligence is growing more widespread and becoming a significant part of our future, including medical fields like cardiology.
Heart attacks and heart conditions, the leading causes of death globally, can be avoided through early detection and prescribing of medications with the help of artificial intelligence. The advancement of AI in cardiology signifies a new era, enabling doctors to provide more accuracy of 95.6% and assist with early diagnosis of heart diseases or strokes (1). This allows for personalized treatment, leading to greater efficiency and reduced bias in healthcare. AI can also analyze intricate heart scans, is likely to have fewer errors, and offers a more consistent approach to detection. Along with this technological method, attending to the compassionate aspect of medicine is crucial, allowing doctors to allocate more time to providing hands-on care and to spend additional time with their patients, enhancing the collaborative decisionmaking process. Furthermore, AI can help prevent future incidents by identifying hidden or impending diseases and assessing an individual's risk. Heart attacks and heart conditions are unpredictable and
may require a hospital visit when symptoms occur. For patients admitted to the hospital, waiting for test results can be critical, as their chances of survival diminish with each passing minute (2).
Artificial intelligence is a collected digital experience with a large body of data used to train a computer. The type of AI that is used in cardiology is called machine learning. Machine learning is an algorithm that learns large amounts of different data by recognizing patterns, which becomes more accurate over time as more data is fed. It can take in data such as numbers, images, and sounds from heart scans. Machine learning can also help doctors to diagnose heart disease efficiently and precisely. Many advanced AI medical tools used in cardiology are based on a type of machine learning called deep learning. Deep learning uses many layers of neural networks and is a method to process data inspired by how human brains process data (3).
Applying previous knowledge about artificial intelligence's purpose and how it works can be shown through evident cardiologic tools today. An example of a cardiologic tool is electrocardiograms (ECG), which scan the heart. Suppose somebody has a weak heart pump or, in other words, asymptomatic left ventricular dysfunction; a CT scan can pick it up earlier. If a computer or AI is exposed to many healthy and unhealthy ECGs, such as weak heart pumps, it can learn patterns and accurately diagnose the individual's heart disease (3).
Another example is a souped-up stethoscope, which uses AI to signal heart issues through sounds. This medical tool records the sound and the electrocardiogram to a phone, where the data runs through a neural network that examines the reading. It becomes an expert in noticing the subtle patterns to efficiently predict future possible heart problems in fifteen seconds (2).
As artificial intelligence, machine learning, and deep learning pave the way for many helpful medical tools, it is important to remember they are not perfect. Artificial intelligence can have biases as the data being fed carries biases. There can be representation bias as if not all racial groups are equally represented. These underrepresented groups may receive less care and be under-referred for imaging in the clinical setting. There is also measurement bias, which can result in a training dataset with inaccurate data or measurement, affecting the AI learning of the data of the heart scans. To overcome these biases, a wide range of variables should be used to represent patients with cardiovascular diseases across age, gender, race, ethnicity, socioeconomic status, and regional categories. Another way to overcome these biases is to employ a diverse research team composed of many voices, experiences, and backgrounds, with the idea of the importance of equity. Bias can be mitigated with guidance from diverse and equity-trained scientists who study bias and structural inequities in the healthcare system (4).
Works Cited
1. Singh et al. (2024). Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review. EClinicalMedicine, 73, 102660-. https://pubmed.ncbi.nlm.nih.gov/ 38846068/. PDF.
2. Mayo Foundation for Medical Education and Research. (2024, March 16). Cardiovascular medicine. Mayo Clinic. https://www.mayoclinic.org/departmentscenters/ai-cardiology/overview/ovc20486648
3. Corliss, J. (2022, August 1). Artificial Intelligence: Advancing into Cardiology. Harvard Health. http://www.health. harvard.eduwww.health.harvard.edu/ heart-health/artificial-intelligenceadvancing-into-cardiology.
4. Mitigating the risk of artificial intelligence bias in cardiovascular care. Mihan, Ariana et al. The Lancet Digital Health, Volume 6, Issue 10, e749 - e754. https://www.thelancet.com/journals/ landig/article/PIIS2589-7500(24)00155-9/ fulltexth. PDF
Artificial intelligence is quickly growing and making its debut in many aspects of our lives and is making impactful changes in cardiovascular care. This in cardiology medicine makes data processing consistent, provides early detection, betters treatments for patients with heart conditions, and lowers mortality rates. This gives doctors more time to do the essential part of connecting with their patients. Even though AI may have some biases and is still a new concept, there will always be room for improvements and changes. Some may think that artificial intelligence will replace doctors, but that’s untrue. AI is a powerful tool for doctors to find the disease earlier, present a treatment sooner to their patients, and continue making long-term, meaningful, impactful changes in cardiology medicine.
Memory Manipulation
By: June Liu ’25
Technology has plunged into the realm of science fiction. We have tools that can rewrite genomes, intelligence that can manifest whatever text or image we want, and now, we can manipulate memories. How far can we go, or rather, how far should we go?
In 2017, a research team from UCLA successfully erased memories from neurons of Aplysia, a type of sea snail. Led by Dr. David Glanzman, the team utilized a technique called RNA Interference (RNAi) to selectively delete a specific protein known to play a crucial role in the formation and storage of long-term memories called the cAMP response elementbinding protein 2 (CREB2). These RNAi target the messenger RNA of this protein, degrading it and thus preventing it from being translated into a protein (1).
Consequently, the researchers found that the snails were unable to recall a previously learned behavior, specifically the withdrawal of their siphon (the tube-like structure that helps movement) in response to a stimulus, suggesting that the memory had been erased. Interestingly, they also found that the erasure was reversible; once the expression of CREB2 was
restored, the snails were able to relearn the behavior (1).
The implications of this study were noted to be insightful, as they highlight neural mechanisms of memory and demonstrate the uses of RNA Interference.
That being said, how far can this technique be taken?
More recently, Dr. Glanzman and his team decided to experiment with snail memories again, and this time, something more radical—memory
Image Courtesy of Reddit
transplantation. They wanted to see if RNA could carry components of memory. To do this, they first trained a group of snails to associate electric shocks with a specific response. Then, they injected RNA from these trained snails into untrained ones.
Remarkably, the untrained snails began to display behaviors similar to those of the trained snails, such as retracting their siphons for a longer time in response to the shock. These results suggest a link between RNA and memory (2).
Memory erasure and now memory transplantation in snails begs the question: can the same thing be done in humans? There have been no studies about RNAi memory manipulation in humans for obvious reasons. Among the numerous ethical criticisms, the biggest one is the potential “off-target” effects in which a wrong protein gets targeted. Memory is highly associative, and selective memory modification poses a risk of weakening the emotional component of a memory held for the subject. This could potentially cause unwanted emotional or behavioral changes (3).
Sure, memory manipulation is incredible, but let’s not forget to consider the long-term consequences of such technology. It is time to take a step back and balance this promise of innovation with responsibility.
and lowers mortality rates. This gives doctors more time to do the essential part of connecting with their patients. Even though AI may have some biases and is still a new concept, there will always be room for improvements and changes. Some may think that artificial intelligence will replace doctors, but that’s untrue. AI is a powerful tool for doctors to find the disease earlier, present a treatment sooner to their patients, and continue making long-term, meaningful, impactful changes in cardiology medicine.
Works Cited
1. Kwon, D. (2017, June 28). Memories Erased from Snail Neurons. The Scientist Magazine®; The Scientist Magazine. https://www.the-scientist.com/memorieserased-from-snail-neurons-31309
2. Greenwood, V. (2018, May 15). Scientists Made Snails Remember Something That Never Happened to Them. The New York Times. https://www. nytimes.com/2018/05/15/science/ memory-transfer-snails.html
3. González-Márquez, C. (2023). Neuromodulation and memory: exploring ethical ramifications in memory modification treatment via implantable neurotechnologies. Frontiers in Psychology, 14. https://doi.org/10.3389/ fpsyg.2023.1282634
Image Courtesy of The Guardian
New Findings on Protein Mutations That Cause Rett Syndrome
By: Grace Hu ’27
In late August of 2024, scientists used a new approach in the research of the MeCP2 protein (methyl-CpG binding protein 2) and its relevance to DNA and protein modification. To introduce the protein, it’s a type of methylCpG-binding domain protein (MBD). The functional part of the structure consists of a methylcytosine-binding domain with 85 amino acids and a transcriptional repression domain which has 104 amino acids. When functioning, the MBD domain attaches to the DNA by forming a wedge while the TRD reacts to recruit enzyme histone deacetylases. Histone deacetylases are responsible for removing acetyl from histone, causing chromatin condensation and transcriptional repression.
The MeCP2 protein is translated from the MECP2 gene. One of its abilities is modifying chromatin, a compound with protein and DNA, that works to make DNA into chromosomes. Another purpose of MeCP2 is that it regulates the synapses of neurons, with high concentrations of the protein found in brain cells. Prior to this new finding, there was limited knowledge regarding MeCP2. It could be involved in cutting and rearranging mRNA, which controls the production of different
proteins, a process called alternative splicing.
The MeCP2 protein is extremely consequential, as it is responsible for regulating thousands of genes, and is “central to neural development” (1). Despite the many functions of the protein, it brings conflicting effects due to the lack of knowledge about it. MeCP2 duplication syndrome, a neurodevelopmental disorder, is caused by the overrepresentation of MeCP2. It affects mostly males, and results in symptoms including delay in speech and motor abilities, seizures, infantile hypotonia, etc. Rett syndrome, on the other hand, is caused by the lack of representation of MeCP2, which almost occurs exclusively in females, and is associated with symptoms like difficulty of speech, severe motor and mental retardation, seizures, etc.
Before the use of the new singlemolecule approach, it was believed that MeCP2 exclusively works on DNA modified with methylated
cytosines. There wasn’t a clear explanation as to why it doesn’t work on their unmethylated cytosines, as the protein can bind to both methylated and unmethylated DNA.
The method devised by researchers Liu, and others from Rockefeller University., the single molecule approach, revolves around the idea of observing a single piece of DNA being reacted upon by the MeCP2 protein. The method, in short, is as such. A single piece of DNA is taken and secured between micron-sized plastic beads. After which, MeCP2 proteins fluorescently labeled are added to the reaction. This method is significant because the singularity allows other potential factors that may impact the viewing of the reaction itself to be eliminated, and the labeling helps visualize how MeCP2 moves when processing on DNA.
Through the use of the single molecule method, Liu, et al., made the following findings. MeCP2 is shown to process faster on unmethylated DNA than methylated DNA. This is significant because, as Gabriella Chua, the graduate fellow and first author of the paper on this research, explains, ..., “this difference in motion may explain how the protein differentiates between the two”. These different dynamics were also shown to increase the efficiency of MeCP2 obtaining and using another regulatory protein to methylated DNA sites. This could help specify the protein’s ability to regulate genes in different parts of
the genome, “which may help direct MeCP2's gene regulatory functions towards specific locations within the genome” (1).
To conclude, this new method, along with the findings, is influential because it provides a completely different insight into viewing the MeCP2 protein and its related syndromes. Too little of MeCP2 can result in Rett syndrome, while too much of it causes MeCP2 duplication syndrome, both serious neurodevelopmental disorders extremely detrimental to one’s life. “There's no cure for Rett, [but]...our findings highlight how basic research can help the clinical community better understand a disease” (1).
Works Cited
1. Rockefeller University. (2024, August 27). New insight into the protein mutations that cause Rett syndrome. ScienceDaily. https://www. sciencedaily.com/releases/2024/08/240827140736.htm
2. Chua, G. N. L., Watters, J. W., Olinares, P. D. B., Begum, M., Vostal, L. E., Luo, J. A., Chait, B. T., & Liu, S. (2024, August 20). Differential dynamics specify mecp2 function at nucleosomes and methylated DNA. Nature News. https://www.nature.com/articles/s41594-024-01373-9
3. Mayo Foundation for Medical Education and Research. (2023, March 15). Rett syndrome. Mayo Clinic. https://www.mayoclinic.org/diseasesconditions/rett-syndrome/symptoms-causes/syc-20377227
4. National Institute of Neurological Disorders and Stroke. (2024, July 19). Rett syndrome | National Institute of Neurological Disorders and stroke. Rett Syndrome. https://www.ninds.nih.gov/health-information/ disorders/rett-syndrome
5. Rockefeller University. (2024, August 25). New insight into the protein mutations that cause Rett syndrome. News. https://www.rockefeller. edu/news/36409-new-insight-into-the-protein-mutations-that-causerett-syndrome/
6. MECP2 gene. (2017, March 1). https://medlineplus.gov/download/ genetics/gene/mecp2.pdf
7. Na, E. S., & Monteggia, L. M. (2011, May 31). The role of mecp2 in CNS development and function. Hormones and behavior. https://pmc. ncbi.nlm.nih.gov/articles/PMC3077534/
8. U.S. National Library of Medicine. (2024, September 19). MECP2 methyl-CPG binding protein 2 [homo sapiens (human)] - gene - NCBI. National Center for Biotechnology Information. https://www.ncbi.nlm. nih.gov/gene/4204
9. Esch, H. V. (2020, May 21). MECP2 duplication syndrome. GeneReviews® [Internet]. https://www.ncbi.nlm.nih.gov/books/ NBK1284/
Sea Robins
By: Eliza Mikheev ’27
A bizarre creature trekking across the depths of the ocean floor, with crab-like legs, the body of a fish, and the wings of a bird—sea robins are creatures worth the marvel. The usage of some distinct features of this animal provides a certain function to help them thrive in its environment. The unusual taste-bud-covered legs allow the sea robin to locate and uncover prey (1). As a tool to complete the tasks of procuring a meal, other animals follow behind in hopes of gathering the leftover discoveries. This fact was first discovered only a couple of years ago, by scientist David Kingsley and his team (1). Recently, studying these creatures has led to even more promising research regarding trait development (2).
When David first saw the fish at a small public aquarium, he was immediately interested in all of the unique features the fish possessed. He said, “The sea robins on display completely spun my head around because they had the body of a fish, the wings of a bird, and multiple legs like a crab” (1). Because of this, he decided to study the fish in a lab, where he and his team found research to show the different adaptations that this fish obtained based on its specific environment (2).
This spectacle of a creature can be used to study how adaptations within organisms can append an organ on itself. Also, studying the Sea Robin can let us learn more about ourselves. It was revealed that to dig, Sea Robins depend on the regulatory gene tbx3a to develop fin adaptations and papillae (1). This gene plays a crucial part in our limb development. Also, a lot of the genes that are in use for the taste buds of the sea robins are the genes that allow for our tongues to be created (1). Sea robins can be used as a model to compare specialized traits and how a specific organism can adapt to specific environments (2). Compare two types of sea robins to explore the genetic basis for these differences (2). Overall, learning about animals that may be more complicated than the eye can see, can allow us to better understand the connection between ourselves and the diverse organisms that surround us.
Works Cited:
1. Strickland, A. (2024, September 26). Sea robins are fish with ‘the wings of a bird and multiple legs like a crab.’ CNN. https://www. cnn.com/2024/09/26/science/sea-robinswalk-taste-seafloor/index.html
2. Zonarich, E. (2024, September 26). These fish use their ‘legs’ for more than walking. Harvard Gazette. https://news.harvard.edu/ gazette/story/2024/09/an-idea-with-legs/
Opinions
Performance Enhancing Technologies in Sports
By: Sofia Wu ’27
The combination of human achievement and scientific advancement has ushered in a transformative era in sports. New technologies have “changed the game”, allowing athletes to break records previously thought unattainable. However, sports governing bodies face a crucial decision: are these advancements elevating the caliber of competitions or exacerbating the divide between poor and wealthy athletes? I believe that these technologies should be banned unless all athletes have equal access. After all, competitions should test an athlete’s capabilities and serve as a way to showcase their skills. With the pervasiveness of technological
enhancements in sports, fair competition is undermined, sending a message that success depends on having superior equipment rather than on athletic performance. This message was intensified when some athletes dropped their sponsors to wear the LZR racer in 2008 and Nike Zoom Vapor Flys in 2020 and 2024.
The results of technology doping were best seen in the 2008 Olympics, when Michael Phelps won eight gold medals. This American swimmer holds the record as the most successful and decorated olympian of all time with a total of 28 medals. Previously, in an old-style swimsuit, he had missed out on two finals and barely qualified for a third. With the Speedo LZR Racer - developed in collaboration with NASA to reduce drag - he shattered world records. According to NASA, through testing fabrics using a wind tunnel and a water flume, this swimsuit was created, a product that reduced skin friction drag 24% more than previous Speedo racing suit fabrics. The swimsuit was later banned for providing an unfair advantage (1). Some may raise the question: why didn’t all swimmers just use this swimsuit? The LZR Racer, priced at 550 dollars, is
US swim team wears Speedo LZR racer suits at 2008 Beijing Olympics (Image Courtesy of CBS News)
beyond the financial reach of many athletes (2). The athletes who can afford these sports technologies often already benefit from better coaching and training facilities, compounding their advantage.
After the 2009 ruling that banned LZR racer suits, Speedo released a statement saying, "As a forwardthinking company that has invested millions in [research and development], we believe that technology -- properly monitored and adhering to guidelines -- does have a place in all sports. Any move that seems to take the sport back two decades -- such as a possible return to the traditional female swimsuit and male jammer -- is a retrograde step that could be detrimental to the future of swimming (3)." However, the Olympics and other events should not become a showcase for scientific innovations. Many believe it should remain the platform it has always been, one for athletes to showcase their talents without the influence of companies.
This issue is reexamined with the releases of Nike’s super shoes, the Nike Zoom Vaporfly, which have “redefined the Olympic marathon”. In fact, in December 2019, the New York Times found that runners wearing Vaporflys ran 4% to 5% faster than those wearing an average shoe (4). In a marathon, this is a difference of several minutes and the distinction between, for instance, podium and 6th place. But what’s the difference between these shoes and regular shoes that enable
its runners to shatter world records? According to Elliott Heath, product manager of Nike Running footwear, it comes down to two components: cushioning and propulsion. The cushioning protects runners’ legs and the propulsion helps runners propel off to the next stride, creating a shoe with lightweight foam and plate that amplifies the amount of energy stored and returned (4). The fact that some athletes are willing to abandon their sponsors to use these shoes for the Paris Olympics emphasizes the consequences of technology doping. When an athlete’s success relies on advanced footwear rather than talent and determination, the purpose of such competitions is challenged.
Ultimately, these ultra-high-tech innovations should be banned from competitions because they promote scientific advancements over athletic capability. The focus should remain on individual performance to ensure that athletes’ skills are properly reflected.
Runners wear Nike Zoom Vaporfly in men’s marathon at 2016 Rio Olympics (Image Courtesy of NBC News)
Robots vs. Astronauts
By: Ejin Chung ’28
As we push the limits of space, a fundamental question arises: Who should take the lead in space exploration? Should we keep sending astronauts, who have complex problem-solving skills, or should we shift toward industrious, cost-efficient robots that are less susceptible to the dangers of space?
Space is an extremely hostile environment for humans, especially with extreme temperatures and hazards of space. Venus and Mercury are hot and Jupiter is cold, which could lead to bone loss and muscle atrophy if the travel times get longer to these worlds. These temperatures and hazards are much less dangerous to the machine. Therefore, if a malfunction occurs in the robots, the loss of robots is less devastating than the loss of human life.
Robot exploration is less expensive than human exploration. To maintain the International Space Station (ISS), it costs 3 billion dollars per year to support astronauts’ lives in space since humans require life-support systems, food, and protection (3). If we shift towards robot exploration, we could use $3 billion to just develop a robot. On the other hand, robots do not require lifesupport systems, food, and protection, making them less expensive to maintain. For example, scientists estimate that human space exploration requires hundreds of billions of dollars. On the other hand, NASA’s lunar rover which investigated the moon for 100 days, costs $450 million as robots can be sent on longduration missions to explore
Image Courtesy of NSE
distant planets or moons without returning to Earth.
Robots are also more effective than humans. Robots are tailored to solve intricate space missions such as collecting and analyzing data which require repetition and precision. In addition, human astronauts have mental and physical limitations. They are more likely to make errors, especially in a state of unstable mentally or dangerous environments. Furthermore, it makes space missions more difficult to be successful as humans require sleep and psychological support. Contrarily, it only took 29 weeks to investigate Mars by using NASA’s Perseverance rover as robots are rarely influenced by physical and mental factors.
In conclusion, although astronauts are capable of solving complex problems and have played a significant role in pushing the boundaries of space historically, nowadays, robots are outstandingly developed. This includes favorable advantages of safety, expenditure, and efficiency. With these advantages, robots will unlock the various potentials of space and further push the boundaries of what is possible.
Works Cited
1. Goldsmith, D., & Rees, M. (2022, April 19). The End of Astronauts—and the Rise of Robots. Retrieved October 20, 2024, from WIRED. https://www. wired.com/story/end-of-astronautsrobots-space-exploration/
2. JGendron. (2019, February 21). Robots in Space: Past, Present, and Future. Retrieved October 20, 2024, from RobotShop Community. https:// community.robotshop.com/blog/show/ robots-in-space-past-present-andfuture
3. NASA Office of Inspector General Office of Audits NASA’S MANAGEMENT AND UTILIZATION OF THE INTERNATIONAL SPACE STATION. (2018). Retrieved from https://oig.nasa. gov/wp-content/uploads/2024/02/IG18-021.pdf
4. NSE. (2023, June 21). Astronauts vs Robots: Balancing Roles in the Space Economy. Retrieved October 20, 2024, from New Space Economy website: https://newspaceeconomy.ca/ 2023/06/21/astronauts-vs-robotsbalancing-roles-in-the-spaceeconomy/
5. Robots vs Astronauts. (2023, December). Retrieved October 20, 2024, from Pegasus Magazine. https:// www.ucf.edu/pegasus/opinion/
Image Courtesy of Dreamstime
