TheOriginsofMathematics: InventedorDiscovered?
ByAndrewWatts
OSMOSIS Science Magazine Spring 2023 Edition
Writers: Isabel Dilandro
Andrew Watts
Jack DuPuy
Ping Yen (Jeff) Tsai
Ryan Cvelbar
Editors: Lily Dickson
Joshua Pandian
Paxton Mills
Alex Robertson
Designers: Mikayla Quinn
Marianna Vrakas
CONGRATULATIONS GRADUATING SENIORS!
Lily Dickson
Mikayla Quinn
Joshua Pandian
Ryan Cvelbar
CoverImagefromhttps://blog.revolutionanalytics.com/2018/04/mathematical-art-in-r-.html
DearReader,
Thankyouforpickingupthissemester’sissueof OSMOSIS.Weareastudent-runmagazinefilledwith allthingsscience!Insidethesepages,youwillfind articlesexploringallaspectsofscience,fromstraight scientificargumentstophilosophicalquestions regardingmathematicsandlearninginaworldofAI. Wehaveatalentedgroupofwriters,editors,and designersthisyear,andIcannotwaittosharewith youourspringissue.Itisbittersweettoputtogether myfinalissueofOSMOSIS,andlittlemakesme prouderthanbeingapartofthisteam.Whatever articleyouchoosetoread,Ihopeyoufindsomething youenjoyasmuchasweenjoyedputtingitall together.
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SpringIssue2023
Table of Contents
ChatGPT: A Blessing or a Curse for Education? By
Jack DuPuy
The Origins of Mathmatics: Invented or Discovered? By Andrew
Watts
A Mini Spark: Casting Light on the Science of Candy By Isabel Dilandro
Seafood Mislabeling: Molecular Strategies to Identify Fish Species and Rethinking Public Outreach in Taiwan and the U.S.
By Ping Yen (Jeff) Tsai
Parkinson’s on the Radar, A Revolution in Disease Diagnosis By Ryan Cvelbar
ChatGPT: A Blessing or a Curse for Education?
By Jack DuPuy
I asked ChatGPT to give me a limerick about itself, and here are the results: There was an AI named ChatGPT, Whose language skills were pretty nifty, It could chat all day, In a human-like way, And answer your questions quite swiftly. So what is ChatGPT and how is it changing education for better or worse? ChatGPT is a large language model developed by OpenAI that uses artificial intelligence (AI) and machine learning to generate human-like responses to natural language questions and statements. It has been trained on a vast amount of data from a diverse range of sources including books, articles, and websites, allowing it to answer a wide range of questions on various topics.
ChatGPT has the potential to revolutionize education in several ways. Firstly, it can provide a personalized learning experience for students by answering their questions and providing them with information on topics they are interested in. This can help to make learning more engaging and enjoyable, as students can learn at their own pace and according to their individual needs. Additionally, ChatGPT can be used as a tool for teachers to facilitate learning in the classroom. By answering students' questions and providing them with relevant information, ChatGPT can help to support teachers in their role and provide them with more time to focus on other aspects of
teaching. Overall, ChatGPT has the potential to transform education by making learning more accessible, personalized, and engaging for students, and by providing teachers with a powerful tool to support their teaching. While ChatGPT can be a powerful tool for learning and education, there are also some potential risks and limitations that need to be considered. One concern is that ChatGPT may not always provide accurate or reliable information. The model's responses are generated based on the data it has been trained on, which may contain biases or inaccuracies. This means that students may be provided with incorrect or incomplete information, which could lead to misunderstandings and errors in their learning. Additionally, there is a risk that students may become overly reliant on ChatGPT and not develop critical thinking and problem-solving skills. If students rely too heavily on the model for answers, they may not be developing the skills they need to think critically and creatively, which could limit their ability to solve problems and come up with new ideas. Another concern is the potential for ChatGPT to be used for cheating. If students are able to use the model to quickly generate answers to questions without actually learning the material, this could undermine the integrity of assessments and lead to a devaluation of education.
I wish I could tell you that all of that analysis was my own, but in fact ChatGPT wrote all of that about itself. While this topic is not a super challenging one to write about,
it is still impressive that most of you believed that a human was writing words that were in reality produced by an algorithm. In the interest of transparency, I promise that everything from here on out is written by a human.
As you can see, this tool is revolutionary, and I believe that educators will not be able to escape it even if they want to. It can solve problems in math, computer science, and the natural sciences, provide summaries of books, movies, and arguments, and write decent papers on many different topics. Unless all work is completed in class in front of the teacher, grade-school students will have the opportunity to use ChatGPT as a tool for completing their work and getting good grades. As such, I believe that educators need to shift their focus away from busywork and grades and more towards learning. I don’t pretend to have all the answers for how that can be done, but I’m sure there are ways to implement changes in the work students do, the structure of the classes themselves, and how students are tested and graded on their understanding of the material that will encourage students to use ChatGPT as a way to learn rather than a way to get work done the easy way. Please also keep in mind that using ChatGPT without the explicit permission of your professor constitutes an honor violation here at UR. While I believe trying to ban the use of AI tools works to
stunt our growth as students, most of the education world has yet to adopt that view.
The Origins of Mathematics: Invented or Discovered?
By Andrew Watts
A divisive subject amongst students of all ages, Mathematics remains an integral part of each student’s educational journey. Whyisthat?you may wonder. The answer is that Mathematics lies at the center of the natural world. Among its innumerable practical applications, Mathematics is used to build the cars we drive and the houses we live in, it is even used to create your favorite video game. Perhaps you may now wonder, how it is that the seemingly trivial nature of Mathematics has such great ability to assist us in manipulating and modeling the natural world? Like you, many Mathematicians and philosophers have pondered this question and the answer to it appears to be even more complex than the subject itself. To address this question we must first investigate the fundamental nature of Mathematics and how it came to be. That is, wasMathematicsinventedordiscovered?To help me explore this quandary, I spoke to University of Richmond Mathematics Professor and Biomedical Mathematician, Dr. Lester Caudill, about the origins of Mathematics and the reasons that it endows us with such profound understanding of our world.
The earliest traces of Mathematics come thousands of years ago in the form of the counting numbers, which the Mathematical world agrees were invented. The counting numbers were needed to quantify things that existed in the natural world. Once this number system was established and agreed upon, the arithmetic operations were able to be deduced. For example, if an ancient farmer were
amount is added to each, they are still equal. This axiom then allows us to solve for unknowns, like x” in an equation that models the real world. Mathematicians use these axioms to define the realm in which they hope to explore. Dr. Caudill elaborated on the creation and selection of axioms, stating that “Mathematicians often work backwards to find axioms in order to derive Mathematical logic”. In essence, Mathematics is a deductive science as opposed to the traditional investigative process of natural sciences which utilize inductive reasoning and discovery. In Mathematics, theorems and axioms are set in place and the arithmetic that follows must be consistent with them; whereas, in a science like Chemistry or Biology, scientists observe phenomena and try to decipher why it occurs. While Mathematicians know why the Math they are using works– because the axioms say it does– their job is then to apply the Math to the world’s pressing issues. Therefore, the axioms of Mathematics appear to be an invention of human intellect, but the subsequent logic that can be deduced is discovered through experimentation with arithmetic. So, you may wonder Whatwould happeniftheaxiomsweretochange?Dr. Caudill believes that if the most fundamental axioms of Mathematics were changed, then Mathematics may be very different from the one we know now. If we were to define a new set of rules, the following logic that can be discovered must also change to
remain consistent with the rules – thus, elucidating the philosophical complexity of the argument. Mathematics is found in many places in the natural world. The Fibonacci sequence can be found in the spirals of hurricanes and galaxies or the petals of a flower. Fractal geometry is demonstrated by the intricate structures of snowflakes. Math can also be used to model occurrences in the natural world, like using differential equations to model disease spread or population growth, thus leading to the question: dotheseexamplessuggestthatMathematicsisnotonlyaninventionofhumanintellect,butthatitisaphysicalpropertyofthenaturalworld?Dr. Caudill does not believe so, but rather maintains that Mathematical logic was derived from the axiomatic truths put forth by Mathematicians. It is the application of this Mathematical logic to the natural world that leads us to find these patterns in nature. In his own research, Dr. Caudill relies on Mathematical models to understand deeper the mechanisms of bacterial infection in the human body. In creating these models, he must consider all of the factors that influence bacterial growth and how the body responds. I asked him, whatisitaboutMathematicsthatallowsustosoaccuratelypredictnatural occurrences? In his response he referenced the complexity of the model and the number of considerations it heeds. He concludes that the more factors that must be considered, the less reliable the model. Dr. Caudill’s logic is consistent as he maintains that Mathematical structures must first be invented before they can be applied to the real world, as they do not naturally exist in nature. Rather,
nature can be described using Mathematics, much like it can be with words. Spoken language and Mathematical language are similar in the way that they both give meaning to natural objects and occurrences, but in order for that meaning to be understood, the words, symbols, and structures of the two languages, themselves, must be clearly defined and universally understood.
In exploring this topic, I, myself, have become convinced that Mathematics was both invented and discovered. Ironically, a subject whose material is often regarded as rather unambiguous has conjured such a divisive debate– and even more paradoxical that the conclusion of two opposing statements can both be true. I have learned that Mathematics is as precise as it is mysterious, and I believe that to be the reason why it has captivated so many of the world’s greatest thinkers for so long – and will continue to do so for centuries to come.
By Isabel DiLandro
A Mini Spark: Casting Light on the Science of Candy
Every day, children experience something for the first time in their lives. In the mind of a child, many naturally occurring phenomena often feel like magic. So when my friend told me at our third grade sleepover she could show me a magic trick, I believed she really could. We stuffed ourselves in a closet and she pulled out something I could never have expected: a bag of Wint-O-Green Lifesaver mints. Unsure of what would happen next, I watched in anticipation and confusion as my friend pulled one out, placed it in her mouth, and bit down, hard. A fairly standard turn of events when a kid gets ahold of some candy, except for one part: the mint lit up in the dark like a tiny firework. Eureka, magic! Although unexplainable then, time has proved there is some rationale behind this fantastical spectacle.
The mastermind behind the whole operation is a phenomenon called triboluminescence from the Greek verb tribein, meaning “to rub”, and the Latin noun lumen, meaning “light”. As the name suggests, it is a form of light that occurs because of friction between molecules. In this case, when you bite into a Wint-OGreen Lifesaver, the force of impact crushes the sugar crystals in the mint and thrust electrons out of their atomic field and into free space. Then, those electrons bump into nitrogen molecules, the principal component of air. However, the presence of electrons is a finicky thing. Too few or, in this case, too
many electrons create a chemical imbalance which results in an unstable or more reactive atom. So when the number of electrons deviates from the standard, the disparity must be resolved so the atom can return to its conventional, most stable state. The electrons from the sugar crystals thus essentially supercharge the electrons from the nitrogen molecules, imparting energy onto them. In order to release all of the built-up energy, the nitrogen molecules generate light.
Theoretically this process can occur in all candies – and does, just on an extremely subtle scale – but is most noticeable in these specific Lifesavers because of an ingredient called methyl salicylate which gives the mints their specific wintergreen flavor. Methyl salicylate is fluorescent allowing it to absorb the light of shorter wavelengths emitted by compounds such as sugar and convert it into a longer wavelength which is observable to the human eye. So, the ultraviolet light set off by nitrogen is transformed into and reemitted as light on the visible spectrum.
So, if you want to give this a try for yourself, the experiment itself is incredibly straightforward. All you need are some Wint-O-Green Lifesaver hard mints and a fairly dark room; the darker the room, the easier it will be to see the results. Then, just bite down hard. Very illuminating to say the least!
Referances:
Anne Marie Helmenstine, P. D. (2020, January 14). Why wintergreen lifesavers spark in the dark. ThoughtCo. Retrieved February 26, 2023, from https://www.thoughtco.com/whywintergreen-lifesavers-spark-in-the-dark602179
Contributors, H. S. W. (2000, November 3). Why do wint-O-green life savers spark in the dark? HowStuffWorks. Retrieved February 26, 2023, from https://recipes.howstuffworks.com/question5 05.htm
Figure 1: Jablonski energy diagram of fluorescence.
by Ping Yen (Jeff) Tsai
Seafood Mislabeling: Molecular Strategies to Identify Fish Species and Rethinking Public Outreach in Taiwan and the U.S.
Swapping identical fish with substitutes has been a widespread issue across the globe. Despite the valiant efforts of policymakers, seafood misidentification continues to be pervasive among large and small vendors, from sushi venues to grocery stores in the United States. A customer could order a plate of local seafood with good intentions of supporting small businesses and later realize that they may have consumed lower-value products from poorly managed fisheries (Kroetz et al. 2020).
Dishonest vendors could substitute white tuna for escolar, which can cause severe gastrointestinal issues if consumers are sensitive to its toxin, gempylotoxin. In a 2013 study done by Oceana, 84% of the tuna samples collected nationwide were found to be escolar. In Taiwan, vendors in unregulated wet markets would continue to sell escolar as tuna to increase their profit margin. As a result, news channels began educating consumers on distinguishing the two fishes in the market by the color and texture of their fish skin, the elasticity of the meat, and bone sizes. Policymakers should continually enforce interdisciplinary measures to safeguard consumers’ health and prevent seafood fraud in the market.
To combat seafood mislabelling, researchers use polymerase chain reaction (PCR) to identify differences in the genetic sequences of fishes that are otherwise easily substituted on the market. Scientists use PCR to amplify many copies of the target DNA region, so they can later check whether different samples’ genetic sequences share similarities. First, the genetic material (DNA) is extracted from the fish. PCR then amplifies a target gene region with primers to copy the sequence for multiple cycles. The resulting DNA fragments are then visualized via gel electrophoresis to determine if the sample's base pairs are similar or different. The Basic Tool Alignment Search Tool (BLAST) can speed up the identification process by aligning
the targeted gene sequence of the sample with the GenBank sequence database. However, a large-scale search by Steinegger and Salzberg (2020) found more than 2 million contaminated entries in the database, posing a potential risk of misidentifying species. Blanco-Fernandez et al. (2021) found widespread mislabelling of overcaught Atlantic bluefin tuna (Thunnus thynnus) as albacore (Thunnus alalunga) in Spain. It turns out that 2-3% of bluefin tuna share identical mitochondrial DNA with albacore, "which has often misled mitochondrial-based phylogenetic inferences of the genus Thunnus" (Díaz-Arce and Rodríguez-Ezpeleta 2023). Although the 2021 study claims that certain T. thynnus products were sold as T. alalunga, their best BLAST hit is not evidence of mislabelling. It is due to mitochondrial introgression between T. alalunga and T. thynnus. Due to the presence of contaminated sequences in GenBank, BLAST can be unreliable when an identical species carries mitochondrial introgression. With molecular barcoding technology, researchers in Taiwan and the U.S. identified mislabelling in popular fishes such as tuna, salmon, snapper, halibut, and mackerel with deep microbiome profiling. DNA barcoding targets regions encoding the mitochondrial cytochrome c oxidase subunit I (MT-CO1) and the 16S ribosomal RNA (16S rRNA) gene. Most recently in Taiwan, the gross substitution rate of fish products is 18.9% among the 127 samples collected. "Snapper, cod, and surimi products are particularly vulnerable to fraudulent substitutions," according to Chen et al. (2020). In a similar study conducted in Los Angeles, California, the average substitution rate is higher at 40%, yet mislabeling is distinctive across all species. Willette et al. (2017) found that "all samples of red snapper were mislabelled (100%) [...] 89% of red snapper samples (Lutjanus campechanus) were replaced by eight different fish taxa," including the red seabream. The Taiwanese
study also discovered a whopping 84.6% mislabelling rate in snappers from sushi, sashimi, cooked dishes, and ingredients. As a result, we could pay to eat fish that wasn’t what we expected to receive.
References:
Identifying mislabeled seafood products can be difficult for consumers, especially when numerous fish products are prepackaged or processed for purchase. It could be particularly concerning for those who want to avoid fish like escolar or those with higher mercury content like mackerel. In Taiwan, seafood is regularly sold in regulated and unregulated wet markets. Vendors in my neighborhood start setting up their stands from 3 to 4 am, dumping bags of fresh ice into huge styrofoam boxes, and laying out the fresh catches. Since I visited these markets from a young age with my mom, I gradually realized how each fish looked different, despite many having identical white meat. Public outreach should focus on releasing pictorial seafood guides for consumers to distinguish between species and enhance transparency in venues on releasing information regarding the source regions of their seafood products. Although combating seafood misidentification is difficult, it is necessary to safeguard the health of many sensitive to allergens in different fish. Furthermore, mislabeling can undermine people’s confidence in sustainable fisheries if they do not receive seafood products that are high quality and nutritious as expected. After the pandemic, the global fish economy continues to dominate at USD 178.1 billion by total export revenue in 2022, according to the Food and Agriculture Organization (FAO). Despite regulations becoming stricter, the fishery industry still strives to maximize its financial gain as unethical actions go unnoticed and unpunished. The scale of global fishery commerce prompts policymakers to continue designing innovative interventions to minimize mislabeling. It will prevent illegally captured fish from entering legal trade. As Seafood fraud remains prevalent in our everyday life, policies should continuously enforce the correct labeling of fish once they enter the supply chain.
Blanco-Fernandez, C., A. Ardura, P. Masiá, N. Rodriguez, L. Voces, M. Fernandez-Raigoso, A. Roca, G. Machado-Schiaffino, E. Dopico & E. Garcia-Vazquez. 2021. Fraud in highly appreciated fish detected from DNA in Europe may undermine the Development Goal of sustainable fishing in Africa. Scientific Reports 11, 11423.
Chen, P.Y., C.W. Ho, A.C. Chen, C.Y. Huang, T.Y. Liu, K.H Liang. 2020. Investigating seafood substitution problems and consequences in Taiwan using molecular barcoding and deep microbiome profiling. Scientific Reports 10, 21997.
Díaz-Arce, N. and N. Rodríguez-Ezpeleta. Best BLAST hit alone cannot be used as evidence of fraud. 2023. Scientific Reports 13, 905.
Eastern Broadcasting Company News. 2016. Looking similar: Selling escolars as white tuna, be careful not be scammed (真的好像！「油魚當鱈魚賣」小心別 被坑了). Available online at https://news.ebc.net.tw/News/living/37984; last accessed February 28, 2023.
FAO. 2022. GLOBEFISH Highlights – International markets for fisheries and aquaculture products, second issue 2022, with January–December 2021 Statistics. FAO of the United Nations. Available online at https://doi.org/10.4060/cc1350en; last accessed February 28, 2023.
Kroetz, K., G.M. Luque, J.A. Gephart, S.L. Jardine, P. Lee, K.C. Moore, C. Cole, A. Steinkruger, C.J. Donlan. Consequences of seafood mislabeling for marine populations and fisheries management. 2020. Proceedings of the National Academy of Sciences 117, 30318–30323.
Warner, K., W. Timme, B. Lowell, M. Hirshfield. 2013. Oceana study reveals seafood fraud nationwide. Oceana. Available online at https://oceana.org/reports/oceana-study-revealsseafood-fraud-nationwide/; last accessed February 28, 2023.
Willette, D.A., S.E. Simmonds, S.H. Cheng, S. Esteves, T.L. Kane, H. Nuetzel, N. Pilaud, R. Rachmawati, P.H. Barber. Using DNA barcoding to track seafood mislabeling in Los Angeles restaurants. 2017. Conservation Biology 31, 1076–1085.
AParkinson’s on the Radar, A Revolution in Disease Diagnosis
By Ryan Cvelbar
ccording to the Parkinson’s Foundation, Parkinson’s disease is one of the fastest-growing neurological diseases in the world, second only to Alzheimer’s. The National Institute on Aging loosely defines Parkinson’s disease as a brain disorder that develops over the course of several years resulting from nerve cell death in the basal ganglia, a section of the brain that controls movement. These nerve cells are known to produce the neurotransmitter, dopamine, that is necessary for proper movement of your body. As more of these cells die, less dopamine is produced than normal and patients begin to experience symptoms.
The most well-known symptoms of Parkinson’s are motor in nature and include uncontrollable movements such as tremors, stiffness, and poor coordination. These symptoms begin to manifest years after onset of the disease once most of the disease’s irreversible damage has taken place. Parkinson’s disease also results in a loss of norepinephrine, the main signal used by the autonomic nervous system to control automatic functions like heart rate, blood pressure, and breathing, resulting in irregularities in autonomic functioning. Dr. James Parkinson, the first to diagnose Parkinson’s disease, identified this connection between irregularities in autonomic functioning, particularly breathing patterns, and the disease during his career as a surgeon. A recent advance in medical technology, and the topic of this article, has identified a way to exploit Dr. Parkinson’s observation and identify this biomarker of Parkinson’s disease years ahead of motor symptom development, helping patients to seek treatment before significant irreversible damage has occurred.
According to Forbes, Dina Katabi, principal investigator at MIT Jameel Clinic, is the first to develop a mechanism for capturing this biomarker.
The product of Katabi and her team’s work at MIT is a device called, “Emerald”. Emerald is a neural network that looks like a WiFi router and emits radio signals to its sleeping patient to track nocturnal breathing patterns. For those of you who are not familiar with what a neural network is; it is a series of algorithms that mimics the human brain by using data relationships to arrive at conclusions. Put more simply, you might think of the program functioning like a filter in Microsoft Excel that allows you to pinpoint similarities, differences, and trends in data to draw conclusions and execute decisions. The data
in this case being the patient’s breathing data that is compared against thousands of other patients’ data. Emerald works by measuring how radio waves bounce off the patient and back to the device. The rate and depth of rib cage expansion and contraction varies based on disease presence and the severity or degree of disease progression, resulting in a spectrum of frequencies with which the radio waves bounce off the patient and return to the device. The frequency, intensity, and pattern with which the waves reach the device are processed by the machine’s neural network that compares the breathing data of the patient to the artificial
Intelligence program’s standard breathing dataset. According to the National Institutes of Health (NIH), this standard breathing dataset was constructed by Katabi and her team by recording breathing patterns of 757 Parkinson’s patients and 6,914 healthy patients over the course of 11,964 nights consisting of more than 120,000 hours of nocturnal breathing signals. This data forms the basis of the neural network that the AI program uses to identify similarities in breathing to the 757 Parkinson’s patients and differences from the 6,914 healthy patients to diagnose Parkinson’s.
According to the NIH, Emerald is remarkably sensitive to nuances in breathing patterns as Emerald was able to correctly identify Parkinson’s 86% of the time with one night of breathing data and 95% of the time with 12 nights of data.
Dr. Charles Dinerstein, vascular surgeon and Director of Medicine at the American Council on Science and Health, further supports the efficacy of Emerald. He states that compared to digital mammography, widely used to screen and identify breast cancer, Emerald is just as specific and sensitive. He also cites Emerald’s 0.94 correlation with the MDSUnified Parkinson's Disease Rating Scale currently used by doctors to subjectively diagnose the disease, indicating the algorithm’s ability to accurately track disease progression. Dr. Dinerstein’s
final supporting evidence in favor of the device’s accuracy is the fact that the device managed to show a diagnostic accuracy of 90% when tested against data not used during the initial training process of the program’s neural network, which is usually never the case for AI-derived diagnostics. This advance in medical technology development is invaluable for the field of medicine because, according to the National Institute on Aging, there are currently no tests to diagnose Parkinson’s before motor symptom development. Current diagnoses consist of taking a medical history, cerebrospinal fluid extraction, or performing neuroimaging similar to an MRI. These expensive and invasive methods do not make sense for patients who have not yet developed symptoms of the disease and so are only used on suspected Parkinson’s patients years after disease onset when motor symptoms begin to appear. Additionally, there is no cure for Parkinson’s capable of stopping or reversing its progression, but treatments to slow progression do exist.
In addition to facilitating a jumpstart on slowing disease progression, Emerald is accelerating clinical trials of potential treatments and cures for Parkinson’s. Where once it was merely impossible to judge if Parkinson’s symptoms were improving or worsening during clinical trials, Emerald now
provides an accurate and systematic way of measuring a drug’s effect on disease progression with just a couple nights of breathing data. According to Katabi, Emerald is already being used by large pharmaceutical and biotech companies working on Parkinson’s treatments. The future of Emerald looks endless and green as it is already being investigated for use in earlier detection of heart disease and business experts foresee Emerald’s future commercialization, making it as common a household item as at-home detectors of atrial fibrillation, high blood pressure, and diabetes.
References
Fauzia, Miriam. “This Technology Listens to Your Breath to Diagnose Early-Stage Parkinson's Disease.” Inverse,Inverse,7Sept.2022,https:// www.inverse.com/innovation/ai-deviceparkinsons-diagnosis.
Fyler, Tony. “Medtech: AI Can Detect Parkinson's Disease at Home.” TechHQ,25Aug.2022, https://techhq.com/2022/08/medtech-ai-candetect-parkinsons-disease-from-night-breathing -patterns/.
Kite-Powell, Jennifer. “See How This AI Analyzes Breathing Patterns for Early Detection of Parkinson's.” Forbes,ForbesMagazine,26Aug.2022, https://www.forbes.com/sites/ jenniferhicks/2022/08/25/see-how-this-aianalyzes-breathing-patterns-for-early-detection -of-parkinsons/?sh=21696d518003.
“Night Breathing Patterns Identify People with Parkinson's Disease.” NationalInstitutesofHealth, U.S.DepartmentofHealthandHumanServices, 27Sept.2022,https://www.nih.gov/newsevents/nih-research-matters/night-breathingpatterns-identify-people-parkinson-s-disease.
“Parkinson's Disease Gets Diagnostic Help from Artificial Intelligence.” AmericanCouncilonScience andHealth,13Sept.2022,https://www.acsh.org/ news/2022/09/10/parkinson%E2%80%99sdisease-gets-diagnostic-help-artificialintelligence-16547.
“Parkinson's Disease: Causes, Symptoms, and Treatments.” NationalInstituteonAging,U.S.DepartmentofHealthandHumanServices,https:// www.nia.nih.gov/health/parkinsonsdisease#:~:text=Parkinson's%20disease%20is% 20a%20brain,have%20difficulty%20walking% 20and%20talking.
“Statistics.” Parkinson'sFoundation,https://
www.parkinson.org/understanding-parkinsons/ statistics#:~:text=Nearly%20one%20million% 20people%20in,diagnosed%20with%20PD% 20each%20year.
Tabikha, Kamal. “Syrian Scientist Develops AI System to Detect Parkinson's Early from Breathing Patterns.” TheNational,TheNational,14Oct.2022, https://www.thenationalnews.com/ mena/2022/10/14/syrian-scientist-develops-aisystem-to-detect-parkinsons-early-frombreathing-patterns/.
Verma, Pranshu. “A New Algorithm Could Spot Parkinson's Early. Will It Help?” TheWashington Post,WPCompany,4Sept.2022,https:// www.washingtonpost.com/ technology/2022/09/02/parkinsons-disease-aidiagnosis/.
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