Bowdoin Science Journal

Science

New developments in understanding plankton transport

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By definition, plankton are small organisms that predominantly drift along with ocean currents. They typically swim slowly in relation to ocean currents. Plankton have been shown to exhibit gyrotaxis, which is directed locomotion to balance gravity and viscous torques, to passively alter their movements (Kessler, 1985). When performing this response, plankton experience net flow even in turbulence with no net flow by preferentially sampling turbulence fluctuations. In contrast to this original method of transport, Dibenedetto et al. theorized that plankton “surf” ocean currents by sensing and reorienting in response to the velocity gradient, doubling their net speed in turbulence (2025). 

In their experiment, Dibenedetto et al. studied Crepidula fornicata, which are slipper snails with approximately spherical planktonic larvae that use cilia for movement. They tend to swim upwards to prevent sinking because their bottom-heaviness makes them negatively buoyant. Both early-stage (2 day-old) and late-stage (12 day-old) larvae were observed in a jet-stirred turbulence tank in a random order of low, medium, and high turbulence. They found that C. fornicata actively rotate to oppose local vorticity, or fluid rotation, contrasting with the typical passive response that is assumed (Vorticity – an Overview | ScienceDirect Topics, n.d.). They then compared these results to simulations of passive gyrotaxis, which is characterized by a reduction in upward swimming, and active surfing, which is characterized by an increased rate in upward swimming, because these methods cannot be isolated experimentally. They again found that C. fornicata behavior more closely aligns with surfing (DiBenedetto et al., 2025).

Figure 1: Larval response to instantaneous vorticity in early-stage larvae (A), late-stage larvae (B), and simulation (C) at various turbulence levels.

Dibenedetto et al. found a strong anti-correlation between plankton horizontal relative velocity and fluid vorticity in both age stages at all levels of turbulence (Figure 1). This is consistent with active surfing behavior because the flow’s vorticity and gravitational torque would tilt larvae counterclockwise due to their bottom-heaviness, but they actively resisted these forces and instead tilted clockwise. This behavior was more consistent in late-stage larvae. Furthermore, their velocity more closely resembles that of surfing rather than gyrotaxis in the simulation.

Figure 2: Velocity sampling of early-stage and late-stage larvae at various dissipation rates.

Additionally, they found that late-stage larvae preferentially sampled upwelling vertical velocities relative to mean fluid velocity (Figure 2). This was especially prevalent in higher turbulence levels. Early-stage larvae did not exhibit this behavior, indicating that they are not as skilled at surfing as late-stage larvae. Overall, this study found that passive reorientation models to describe plankton response to turbulence are often insufficient, as active surfing was exhibited in C. fornicata to increase the speed of their upward transport. Further research on the interplay between passive and active responses to turbulence is necessary to fully understand plankton transport. Plankton transport is valuable in allowing for the dispersal of planktonic larvae and supporting global marine food webs, as many organisms rely on consuming plankton for energy.

References

DiBenedetto, M. H., Monthiller, R., Eloy, C., & Mullineaux, L. S. (2025). Plankton active response to turbulence enables efficient transport. Journal of Experimental Biology, 228(24), jeb251123. https://doi.org/10.1242/jeb.251123 

Kessler, J. O. (1985). Hydrodynamic focusing of motile algal cells. Nature, 313(5999), 218–220. https://doi.org/10.1038/313218a0 

Vorticity—An overview | ScienceDirect Topics. (n.d.). Retrieved February 22, 2026, from https://www.sciencedirect.com/topics/physics-and-astronomy/vorticity 

The Association Between Tooth Loss and Cognitive Decline

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Imaging of a vertical (coronal) slice through the brain of an Alzheimer patient (left) compared with a normal brain ( right).
“Could Magnetic Brain Stimulation Help People with Alzheimer’s? | Scientific American.” n.d. Accessed May 4, 2026. https://www.scientificamerican.com/article/could-magnetic-brain-stimulation-help-people-with-alzheimer-rsquo-s/.

Cognitive decline with age is a major concern in medicine and public health. In 2021, the World Health Organization reported 57 million people were affected by dementia worldwide (World Health Organization, 2023). Well-established risk factors include alcohol intake, lower education level, physical inactivity, obesity, and diabetes, and preventive strategies have developed steadily. However, one potential contributor is often overlooked in major dementia research: tooth loss. Galindo-Moreno et al. (2022) examined this relationship through a large-scale analysis of over 100,000 US Americans, making a case for oral health as an underrecognized factor in cognitive decline. 

Edentulism refers to the partial or complete loss of permanent teeth. Edentulism can be caused by a multitude of factors, including biological processes such as caries (tooth decay) and periodontal disease (infection or inflammation of gums and bone), pulpal pathologies (damage to nerves, tissue, and blood vessels in the center of a tooth), trauma, or oral cancer. In addition to biological causes, edentulism can result from factors affecting dental care: patient preference, access to care, treatment options, and health insurance (Felton 2009). A study found 37% of edentulism cases were due to extraction from caries, 29% from periodontal diseases, and 12% due to trauma (Al-Rafee 2020).  

Although oral health care has developed significantly in the last few decades, edentulism remains a prevalent and irreversible condition (Al-Rafee 2020). It can occur at all ages, but the highest incidence occurs between the ages of 75-79 [Figure 1] (Chen et al. 2025). Those most affected by tooth loss typically have a lower socioeconomic standing, which makes health care less affordable and accessible [Figure 2] (Vemulapalli et al. 2024)  

Graph of global incidence and prevalence of edentulism per 100,000 across all ages. Highest incidence rate at ages 75-79. Prevalence per 100,000 gradually increases as age increases.
Figure 1: Global prevalence and incidence rates of edentulism in 2021. Chen, Hui Min, Kuo Shen, Ling Ji, Colman McGrath, and Hui Chen. 2025. “Global and Regional Patterns in Edentulism (1990-2021) With Predictions to 2040.” International Dental Journal 75 (2): 735–43. https://doi.org/10.1016/j.identj.2024.11.022. December 31, 2024: 738

 

Prevalence rate of complete edentulism in US adults 65 years and older across different socio-economic status'. As income increases, the rate of complete edentulism decreases.
Figure 2: Prevalence rate of complete edentulism in US adults 65 years and older according to demographic characteristics: Behavioral Risk Factor Surveillance System 2012-2020. Income level. Vemulapalli, Abhilash, Surendra Reddy Mandapati, Anusha Kotha, Hemanth Rudraraju, and Subhash Aryal. 2024. “Prevalence of Complete Edentulism among US Adults 65 Years and Older.” The Journal of the American Dental Association 155 (5): 399–408. https://doi.org/10.1016/j.adaj.2024.02.002. May 6, 2024: 407

Galindo-Moreno et al. proposed multiple pathways by which tooth loss can lead to cognitive decline. Two that play directly into known factors are the “diet and nutrition mechanism” and the masticatory mechanism. The number of teeth and which teeth are present affect what we can eat and how we eat. Mastication — chewing of food (Xu et al. 2008) — is directly influenced by edentulism due to the reduced bite force one can exert with missing teeth or dentures (Galindo-Moreno et al. 2022; Weijenberg et al. 2011). Changes to mastication may impact cognition by decreasing sensory input, which would reduce cell growth and development, impairing the cholinergic neurotransmitter system responsible for regulating memory, muscles, and attention, and reducing the generation of new neurons triggered by exercise (Weijenberg et al. 2011). Mastication additionally restricts our diet and therefore directly plays into the diet and nutrition mechanism. Often, with altered dentition, chewing can be an immense hurdle, for which the solution is a softer yet less nutritious diet.Nutrients such as omega-3 fatty acids, B vitamins, and antioxidants have important neuroprotective properties that help preserve the blood brain barrier, an essential layer that prevents toxins from entering the brain,additionally reducing inflammation, lowering the risk of cognitive decline (Power et al. 2019). Both the masticatory and diet and nutrition mechanisms are intertwined with diabetes and obesity, which are known risk factors for cognitive decline (Galindo-Moreno et al. 2022). 

Another pathway this study mentions is the inflammation/infection mechanism. A leading cause of edentulism is periodontitis, a severe gum infection often driven by the bacterium Porphyromonas gingivalis. This bacterium induces the local release of cytokines, proinflammatory proteins (Galindo-Moreno et al. 2022). Once in the bloodstream, cytokines promote the production of amyloid-β, a peptide whose accumulation is associated with Alzheimer’s disease (Leira et al. 2020). Simultaneously, Porphyromonas gingivalis increases the permeability of the blood-brain barrier (Lei et al. 2023). The heightened permeability of the BBB causes accumulation of overproduced amyloid-β in the brain tissue [Figure 3] (Galindo-Moreno et al. 2022; Leira et al. 2020)  

Amyloid PET scan of patient with Alzheimer's Disease (right), and patient without Alzheimer's (left). Patient with Alzheimer's Disease shows higher detection of Amyloid plaques.
Figure 3: Amyloid PET scan comparison of healthy brain and Alzheimer’s disease. Chapleau, Marianne, Leonardo Iaccarino, David Soleimani-Meigooni, and Gil D. Rabinovici. 2022. “The Role of Amyloid PET in Imaging Neurodegenerative Disorders: A Review.” Clinical Investigation. Journal of Nuclear Medicine 63 (Supplement 1): 13S-19S. https://doi.org/10.2967/jnumed.121.263195.

To investigate the relationship between tooth loss and cognitive decline, the researchers analyzed data from over 100,000 Americans drawn from two large national health surveys, NHIS (2014-2017) and NHANES (2005-2018). The NHIS survey was particularly well-suited for assessing cognitive state, as it included four questions on concentration and memory. However, the survey included only one binary dental question asking whether the participants had a complete dentition or had lost ≥1 teeth. The NHANES survey complemented this with a thorough section on dental records. The exact number and location of lost teeth were documented. However, it assessed cognitive state with only one question on memory and confusion (Galindo-Moreno et al. 2022).   

Their primary statistical tool was multinomial logistic regression, a method used when an outcome has more than two categories. In this case, the categories were cognitive difficulty, ranging from “none” to “some” to “a lot.” By using this model, the researchers simultaneously accounted for other factors known to affect cognitive health, including age, income, education level, depression, anxiety, cardiovascular health, and lifestyle habits such as smoking and exercise, which were included in the health surveys. By modeling these variables together, the researchers could estimate the independent contribution of tooth loss to cognitive decline.  

The results were expressed as odds ratios (ORs), which indicate how much more likely a given outcome is in one group than in a reference group. Here, the reference was a fully toothed person reporting no cognitive difficulties. An OR above 1.0 indicated higher odds of cognitive problems among people with missing teeth. This held true even after the other variables were statistically accounted for. The researchers also used a technique called ROC curve analysis on the NHANES data that included exact tooth counts, allowing them to identify a meaningful threshold below which cognitive risk measurably increased (Galindo-Moreno et al. 2022).  

The researchers found that, overall, the presence of teeth was statistically associated with a better cognitive state. The NHIS data showed that people with edentulism (partial or complete) had an OR > 1 across all cognitive categories, especially memory, even after accounting for other risk factors. This trend was also observed across categories of gender, socio-economic status (SES), education, and cardiovascular risk — all of which negatively impact cognition. Notably, socioeconomic status emerged as one of the strongest predictors, alongside edentulism, reflecting how directly financial circumstances shape access to dental care and, through it, long-term cognitive health. 

Using ROC curve analysis of the NHANES data, they determined the threshold for cognitive risk to be 20.5 teeth, indicating that a person with fewer than 21 teeth has an increased risk of cognitive decline compared to a fully dentulous person (Galindo-Moreno et al. 2022). Importantly, the study analyzed the NHANES survey and found a gradient effect: the fewer teeth a person had, the worse their cognitive outcomes tended to be, which strengthens the case that the association is meaningful rather than coincidental. Furthermore, a threshold could be determined for each individual tooth category: 5.5, 5.5, 3.5, 4.5, respectively, for molars, premolars, canines, and incisors. The multinomial regression of the NHANES data determined molars had the highest OR. The researchers linked this to the masseter, an important masticatory muscle supported by molars, which may, through its activity, stimulate the release of neurotrophic factors that support brain health. 

The link between edentulism and cognitive decline is still scarcely researched. As of March 2026, there are only 66 results on PubMed, 142 on ScienceDirect, and 148 on Wiley on the correlation between edentulism and cognitive decline. To put this into perspective, there are 2,277 results on PubMed, 18,967 on ScienceDirect, and 10,546 on Wiley on the relationship between diet and cognitive decline. The discussed research article combines two USA national health surveys with diverse samples, NHIS and NHANES, making it one of the largest in scope to date on tooth loss and cognitive decline. Although Galindo-Moreno and his team compellingly demonstrate the correlation, they recognize that their findings cannot answer whether edentulism leads to poorer cognition or rather poor cognition leads to edentulism (Galindo-Moreno et al. 2022, 3498). Some of the issues the researchers faced were the binary assessment of dentition in the NHIS survey, the single question on cognitive condition in the NHANES survey, and the overall lack of records on the reasons for tooth loss (Galindo-Moreno et al. 2022).   

Nevertheless, this study is a step in the right direction. Galindo-Moreno et al. showed that edentulism is correlated with cognition, thereby providing meaningful epidemiological evidence for a relatively young field. Consequently, this study and further research could have great clinical implications for cognitive health, not only in cost-effective treatment and prevention, but also in an important personal impact for those struggling with cognitive impairments and dental hygiene. 

Al-Rafee, Mohammed A. 2020. “The Epidemiology of Edentulism and the Associated Factors: A Literature Review.” Journal of Family Medicine and Primary Care 9 (4): 1841–43. https://doi.org/10.4103/jfmpc.jfmpc_1181_19.  

Chapleau, Marianne, Leonardo Iaccarino, David Soleimani-Meigooni, and Gil D. Rabinovici. 2022. “The Role of Amyloid PET in Imaging Neurodegenerative Disorders: A Review.” Clinical Investigation. Journal of Nuclear Medicine63 (Supplement 1): 13S-19S. https://doi.org/10.2967/jnumed.121.263195.  

Chen, Hui Min, Kuo Shen, Ling Ji, Colman McGrath, and Hui Chen. 2025. “Global and Regional Patterns in Edentulism (1990-2021) With Predictions to 2040.” International Dental Journal 75 (2): 735–43. https://doi.org/10.1016/j.identj.2024.11.022.  

“Dementia.” n.d. Accessed March 27, 2026. https://www.who.int/news-room/fact-sheets/detail/dementia.  

Felton, David A. 2009. “Edentulism and Comorbid Factors.” Journal of Prosthodontics 18 (2): 88–96. https://doi.org/10.1111/j.1532-849X.2009.00437.x.  

Galindo-Moreno, Pablo, Lucia Lopez-Chaichio, Miguel Padial-Molina, et al. 2022. “The Impact of Tooth Loss on Cognitive Function.” Clinical Oral Investigations 26 (4): 3493–500. https://doi.org/10.1007/s00784-021-04318-4.  

Lei, Shuang, Jian Li, Jingjun Yu, et al. 2023. “Porphyromonas Gingivalis Bacteremia Increases the Permeability of the Blood-Brain Barrier via the Mfsd2a/Caveolin-1 Mediated Transcytosis Pathway.” International Journal of Oral Science15 (January): 3. https://doi.org/10.1038/s41368-022-00215-y.  

Leira, Yago, Álvaro Carballo, Marco Orlandi, et al. 2020. “Periodontitis and Systemic Markers of Neurodegeneration: A Case–Control Study.” Journal of Clinical Periodontology 47 (5): 561–71. https://doi.org/10.1111/jcpe.13267.  

Power, Rebecca, Alfonso Prado-Cabrero, Ríona Mulcahy, Alan Howard, and John M. Nolan. 2019. “The Role of Nutrition for the Aging Population: Implications for Cognition and Alzheimer’s Disease.” Annual Review of Food Science and Technology 10 (1): 619–39. https://doi.org/10.1146/annurev-food-030216-030125. 

Vemulapalli, Abhilash, Surendra Reddy Mandapati, Anusha Kotha, Hemanth Rudraraju, and Subhash Aryal. 2024. “Prevalence of Complete Edentulism among US Adults 65 Years and Older.” The Journal of the American Dental Association 155 (5): 399–408. https://doi.org/10.1016/j.adaj.2024.02.002.  

Weijenberg, R. A. F., E. J. A. Scherder, and F. Lobbezoo. 2011. “Mastication for the Mind—The Relationship between Mastication and Cognition in Ageing and Dementia.” Neuroscience & Biobehavioral Reviews 35 (3): 483–97. https://doi.org/10.1016/j.neubiorev.2010.06.002.  

World Health Organization. 2023. “Dementia” Fact Sheets. https://www.who.int/news-room/fact-sheets/detail/dementia 

Xu, W. L., J. E. Bronlund, J. Potgieter, et al. 2008. “Review of the Human Masticatory System and Masticatory Robotics.” Mechanism and Machine Theory 43 (11): 1353–75. https://doi.org/10.1016/j.mechmachtheory.2008.06.003. 

Epigenetic Signatures of Intergenerational Trauma in Three Generations of Syrian Refugees

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The effects of traumatic experiences are known to have various consequences on a person’s life. However, what is less commonly studied is the impact of maternal traumatic experiences or maternal stress on future generations. A recent 2025 study “Epigenetic signatures of intergenerational exposure to violence in three generations of Syrian refugees” conducted by Connie J. Mulligan et al. looks at maternal trauma, stress, and exposure to violence in three generations of Syrian mothers to study the epigenetic impact of violence on future offspring. Mothers exposed to traumatic violence while pregnant have the potential to genetically impact offspring through epigenetic modification. This type of genetic modification causes changes to cellular gene expression through the mechanism of DNA methylation (DNAm), which refers to the addition of a methyl group to a DNA sequence. For instance, DNAm plays a role in cellular differentiation and development, so that the function of cells is determined. This means that even though all your cells have the same genes, different expression in each cell means that you can have muscle cells and nerve cells (Centers for Disease Control and Prevention 2025). Relating to pregnancy, when a pregnant mother is lacking in nutrients, their baby could have different levels of DNAm, which could explain why they had an increased likelihood for certain diseases (Centers for Disease Control and Prevention 2025). Epigenetic modification can lead to increases or decreases in gene expression, which suggests that DNAm plays a role in controlling the impact of maternal trauma in offspring health outcomes.

In this study, maternal trauma is considered to be violent experiences that include being beaten, seeing someone else be beaten, shot, or killed. Additionally, maternal stressors include nutritional deficiencies, exposure to toxins, and psychosocial stressors like anxiety or trauma. These stressors can be transmitted from mother to offspring through cellular changes in the maternal and fetal stress response system, known as the HPA axes, and glucocorticoid metabolism, which allows the body to maintain and regulate stress hormones. The transmission of these stressors are associated with changes in newborn gene expression and epigenetic age acceleration or worse health outcomes. A developing fetus is characterized by high phenotypic plasticity, meaning that their genes are likely to produce different phenotypes, or physical characteristics determined by genetics, as a result of environmental factors. This allows a fetus to use environmental cues to determine an optimal phenotype to survive the postnatal environment, particularly if the mother is experiencing trauma or stressors.

This study specifically used the Developmental Origins of Health and Disease (DOHaD) hypothesis as a framework to look at epigenetic variation as a method of mediating the impact of psychosocial trauma on future generations. The DOHaD hypothesis states that early life adversity has an impact on later health outcomes; for instance, there are strong associations with low birthweight and adverse living conditions with an increased risk of cardiovascular disease in adulthood. This study looked at DNAm signatures of war-related violence across three generations of Syrian refugees by comparing germline, prenatal, and direct exposures to violence. The researchers proposed that there is a presence of differentially methylated positions (DMPs) in DNA that are sensitive to a psychosocial, and therefore violent, environment that are transmissible to future generations. In this study, the researchers propose the hypothesis that exposures to violence can lead to intergenerational epigenetic marks.

This study samples three groups of three-generation Syrian families with varying exposures to violence (Fig. 1). This study defined the exposure groups by using the regional conflicts of the Hama city massacre in 1980 and the Syrian uprising and armed conflicts beginning in 2011. The participants in this study were recruited in Jordan by snowball sampling, which is a method where research participants help researchers in finding other subjects (Oregon State University 2010). The Syrian women who participated had experienced violence and were pregnant during the 1980 or 2011 conflicts before fleeing to Jordan. Syrian families who moved to Jordan before 1980 and had not experienced exposure to violence were used as the control group.

Figure 1. Three groups of three-generation Syrian families with direct, prenatal, and germline exposure to violence. The control group is unexposed to violence. The direct exposure group refers to pregnant mothers who directly experienced the traumatic violence. In the 1980 group, the grandmothers were directly exposed to violence. In the 2011 group, the mothers and older children directly experienced violence. The prenatal exposure group refers to the fetus that was exposed to maternal stress in the womb as a result of the direct exposure to violence that their mother experienced. In the 1980 group, the prenatally exposed fetus in the F1 generation grew up to become a mother in the F2 generation. In the 2011 group, the prenatally exposed fetus in the F2 generation grew up to be a child in the F3 generation. The germline exposure group refers to a fetus whose mother prenatally experienced the violence, so the affected DNA in their reproductive egg cells were inherited by the fetus. In the 1980 group, the germline exposed fetus in the F2 generation grew up to be a child in the F3 generation.

Survey data and buccal swab samples were collected from the mothers and children for 10 families in the 1980 exposure group, 22 families in the 2011 exposure group and 16 families in the control group. A buccal swab is a method to non-invasively collect DNA from the cells inside of a person’s cheek. The buccal samples were collected using Transport swabs or DNA Buccal Swabs. The survey data consisted of an interview with the mothers and screening for traumatic events experienced by using the Traumatic Events Checklist. The researchers then calculated a trauma events score by counting the number of affirmative answers to the Traumatic Events Checklist. DNA methylation data was collected using a hybridizing technique to measure the genetic similarities between DNA sequences. Sensitivity analyses were used to test the robustness of their results to the distribution of age. Enrichment analysis was used to identify if any biological themes appeared more often than expected by chance, which would indicate what the modified genes would do. In addition, epigenetic age estimation was collected by analyzing DNAm patterns to determine biological age. To determine whether there was a linear relationship between DNAm and the amount of trauma events, DNAm was plotted against the cumulative number of violence trauma events.

This study had a three generation study design, which allowed for the DNAm signatures of various exposures to violence to be compared. The first exposure group was the 1980 group, which consisted of maternal grandmothers who were pregnant daughters (F2 generation) were prenatally exposed to violence and their grandchildren (F3 generation) were germline exposed to war violence. The 2011 exposure group included mothers (F2 generation) who were pregnant before fleeing Syria, so the fetus was prenatally exposed in utero and older children in the family (F3 generation) were directly exposed to violent conflict. The control group included Syrian mothers and grandmothers that lived in Jordan before 1980. 

To generate DNAm data, an epigenome-wide association study (EWAS) was conducted to identify differentially methylated positions (DMPs) that were associated with each exposure to violence. The researchers identified that the final set of 35 DMPs had 14 sites that were associated with germline exposure to violence and 21 sites that were associated with direct exposure to violence. No DMPs were associated with prenatal exposure to violence. Additionally, 32 DMPs had the same directionality (Fig. 2).

 

Forest plots and box plots depicting genome-wide significant differences in site-specific DNAm when comparing violence exposure groups and controls.
Figure 2. Genomic differences in site-specific DNAm when comparing direct exposure groups vs. control, prenatal exposure groups vs. control, and germline exposure groups vs. control. The control group experienced no exposure to violence.

The largest difference in DNAm compared to the control group was at a germline DMP at a site that produces keratin and has a potential role in some cancers. The highest DNAm was observed at the germline DNA and two directly associated DMPs, at a site with proteins that play a role in cell death. The germline-associated DMP showed a statistically significant reduction in DNAm in germline, direct, and prenatally exposed individuals. When the relationship between DNAm and the amount of violent trauma events was looked at, the plot suggested that most DMPs showed a dose-response relationship between DNAm and the amount of trauma events (Fig. 3).

Plots illustrating DNAm levels for individual Trauma Event scores for 14 gremlin exposure DMPs and 21 direct exposure DMPs.
Figure 3. DNAm levels of direct and germline exposure to violence groups compared to control group. This suggests that the number of trauma effects experienced causes shifts in DNAm.

After testing for epigenetic aging, there was a high correlation between epigenetic and chronological age in the study sample. Compared to children, the mothers had a higher variation in epigenetic age compared to chronological age. When analyzing the mothers, there was no significant association between epigenetic age acceleration and trauma exposure. However, when analyzing only the children, prenatal exposure to violence trauma was associated with epigenetic age acceleration.

This study suggests that the impacts of maternal stress and trauma can have effects on future generations through epigenetic mechanisms. The epigenetic marks on the DNA of mothers exposed to traumatic violent experiences, in the form of DNAm and DMPs, found in this study reveal that trauma has an effect on DNA expression. Thirty-two of all DMPs found showed a similar directionality of change in DNAm, which suggests that there is a common epigenetic signature of violence across germline, prenatal, and direct exposures to violence. The epigenetic marks found in this study may contribute to enhanced responses to future stressful experiences, which is also known as epigenetic “priming.” This means that genes are prepared to activate and quickly respond to future environmental cues.

Furthermore, the epigenetic marks could be used as biomarkers to identify individuals who could benefit from intervention programs. This study also identified that there is an association between epigenetic age acceleration and prenatal exposure to violence, which could be correlated with accelerated biological aging and with future health outcomes. For instance, environmental toxins may affect future generations more than those directly exposed (Korolenko et al. 2023).

To further investigate the effects of violence of intergenerational genetic marks, there needs to be research conducted on larger and more diverse population groups. There should also be studies that collect other types of body tissues or blood, since there can be tissue-dependent differences in DNAm. Additionally, it’s important to study other forms of epigenetic modifications, such as histone modifications or non-coding RNAs. This type of research is important because it allows refugees to be better understood and helps address the traumatic issues they face. Understanding the genetic mechanisms underlying trauma can encourage policymakers and humanitarian organizations to provide better resources to refugee populations.

 

 

 

Citations:

Centers for Disease Control and Prevention. 2025. Epigenetics, Health, and Disease. Genomics and Your Health. https://www.cdc.gov/genomics-and-health/epigenetics/index.html.

Korolenko AA, Noll SE, Skinner MK. 2023. Epigenetic Inheritance and Transgenerational Environmental Justice. The Yale Journal of Biology and Medicine. 96(2):241–250. doi:https://doi.org/10.59249/FKWS5176. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303257/.

Mulligan, C.J., Quinn, E.B., Hamadmad, D. et al. Epigenetic signatures of intergenerational exposure to violence in three generations of Syrian refugees. Sci Rep 15, 5945 (2025). https://doi.org/10.1038/s41598-025-89818-z

Oregon State University. 2010. Snowball Sampling | Division of Research and Innovation. Division of Research and Innovation. https://research.oregonstate.edu/ori/irb/policies-and-guidance-investigators/guidance/snowball-sampling.



When Distraction Helps: Music as a Tool for Focus in ADHD Cases

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Attention Deficit Hyperactivity Disorder (ADHD) is a developmental disorder that is characterized by inattention, hyperactivity, and impulsivity. This is one of the most common learning disorders diagnosed in children. ADHD not only takes a toll on an individual’s academic sphere but also their social sphere(Martin-Moratinos et al., 2023). From struggling to focus in class to having their impulsiveness affect their interpersonal relationships, symptoms permeate children’s worlds. The diagnosis process includes a variety of tests, interviews, questionnaires, and, in children, observation(ADHD Screening). For example, the Wechsler Intelligence Scale for Children-Revised (WISC-R), developed in 1974, is a test that attempts to measure intelligence through testing 10 abilities. This test, among others, such as the Wide Range Achievement Test-Revised (WRAT), was used in the diagnostic process for the participants in Abikoff et al. (1996). 

 

In 1983, Andries Frans Sanders proposed the underaroused theory in the Cognitive-Energetic Model. The theory stated that those with ADHD have abnormally low levels of physiological arousal and, in turn, seek out input via hyperactivity(Sanders, 1983). Abikoff et al. sought to use music as a high salience extra task stimulation to investigate this theory. This study assessed the impact of music on the arithmetic performance of a group of 40 second graders with ADHD. The theorized goal was to reach an optimal level of arousal(1996). They assessed this by giving the group of 40 boys an arithmetic test to match their ability and having them complete it in different conditions. Rather than just silence or music, researchers added a speech condition. Students were subjected to three arithmetic tasks of the same difficulty. The first test was done with 10 minutes of their 3 favorite songs on loop, the second was 10 minutes of background speech, and the third and final test was 10 minutes of silence. 

 

Through these results, researchers concluded that music was the ideal condition for kids with ADHD. This iteration of testing resulted in an accuracy of 82%, compared to 77% for speech and 79% for silence. Furthermore, when looking at the testing results of the control group, which was made up of non-disabled students at the same grade level, it was revealed that changes in condition did not affect their performance. A nuance that came to the surface through the analysis was that the effects of the conditions were order-dependent. Children who had music as their first condition had more than twice as many correct answers as those who had music as their second or third test condition. 

 

These results lend support to the under-arousal theory. Distractibility is often a characteristic thought to be heightened in those with ADHD, and is often attributed to extra stimuli that are not related to the work they are assigned. However, Abikoff et al. offer a counter to this. Using this study as a tool, focused facilitation strategies can be developed to better support students with ADHD. 

 

Despite the strengths of this study, it is also important to address some key limitations. It is worth glancing at the limitations of the study itself. Since this study took place in 1996, it is important to consider more recent developments in ADHD research. This could result in theories being disproved. However, even recent studies seem to support the idea that music can help with focus in ADHD patients (Martin-Moratinos et al., 2023). For example, Madjar et al showed improved reading scores in students with ADHD when exposed to music while reading(2020). Additionally, the presence of the following studies aids in the fact that the study had a relatively small sample size (40 participants), and they were all boys. Because Abikoff et al. (1996) only studied boys, it’s hard to know whether the same pattern would hold for girls with ADHD, especially since girls tend to show fewer outward hyperactive symptoms and more subtle, internalized ones. However, later work that did include girls—like Madjar et al. (2020), who tested mixed-gender preadolescents—also found that music boosted performance for students with ADHD. This suggests that the helpful effect of music isn’t limited to boys, even if the way it supports attention might look a little different across genders.

 

Ultimately, the management of ADHD in and out of the classroom requires an individualized, holistic approach. The demonstration of music as a coping mechanism can usher it into becoming a tool in treatment plans. In the same spirit, further development of this finding could lead to additional understandings of the impact of other types of stimuli (visual, tactile, or olfactory) on ADHD management. Overall, the study’s results open the door to using music not just as background noise, but as a strategic tool for cultivating focus in children with ADHD. As researchers expand this work—with larger, mixed-gender samples and broader types of sensory stimulation—we move closer to individualized interventions that address the whole child, both inside and outside the classroom. 

 

 

References:

Abikoff, H., Courtney, M. E., Szeibel, P. J., & Koplewicz, H. S. (1996). The effects of auditory stimulation on the arithmetic performance of children with ADHD and nondisabled children. Journal of Learning Disabilities, 29(3), 238–246. https://doi.org/10.1177/002221949602900302 

ADHD Screening: What To Expect. (n.d.). Cleveland Clinic. Retrieved December 24, 2025, from https://my.clevelandclinic.org/health/diagnostics/24758-adhd-screening 

Everything You Need to Know About ADHD. (n.d.). Retrieved December 24, 2025, from https://www.adhdevidence.org/blog/everything-you-need-to-know-about-adhd 

Madjar, N., Gazoli, R., Manor, I., & Shoval, G. (2020). Contrasting effects of music on reading comprehension in preadolescents with and without ADHD. Psychiatry Research, 291, 113207. https://doi.org/10.1016/j.psychres.2020.113207 

Martin-Moratinos, M., Bella-Fernández, M., & Blasco-Fontecilla, H. (2023). Effects of Music on Attention-Deficit/Hyperactivity Disorder (ADHD) and Potential Application in Serious Video Games: Systematic Review. Journal of Medical Internet Research, 25, e37742. https://doi.org/10.2196/37742 

Sanders, A. F. (1983). Towards a model of stress and human performance. Acta Psychologica, 53(1), 61–97. https://doi.org/10.1016/0001-6918(83)90016-1

The Science of When You Exercise

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People often think the most important aspect of how exercise affects your overall health is how hard you work, how much weight you can lift, or how far you can run. However, two recent studies have uncovered another factor that might be just as important for maximizing health benefits – when you exercise. Multiple studies have looked at the impact of how when you exercise affects your body. For example, one study looked at the impact of exercise timing in mice, focusing on the growth of muscle tissue, while another study looked at a large population of people and how their exercise habits affected sleep quality. Together, these studies show that when exercise takes place matters more than most people believe.

The first study, done by Liu et al. and published in Nature Communications, looked at how timing of exercise in mice affected long-term health (Liu et al., 2025). Mice, like people, have a circadian rhythm, which is a 24-hour internal clock in the body that regulates and affects energy, metabolism, and sleep. Muscles in the body also have internal clocks, which decide when to burn fat or sugar.

In the study, Liu et al. had two groups of mice run at a low intensity and low volume on treadmills at different times of day: one group exercised before sleep and the other exercised right after waking up. Training lasted for several months, and researchers measured the mice’s body weight throughout the study and measured the mice’s strength, endurance, and blood sugar before and after the study was conducted, all of which are indicators of long-term exercise results. The results of the study were quite clear. Mice who exercised before sleep showed increased physical and metabolic improvements after the period of consistent exercise, meaning they gained less fat, had more endurance, and showed better blood sugar control. The group of mice that exercised after waking saw less improvement in these areas (Liu et al., 2025).

The second study, done by Leota et al. and also published in Nature Communications, tracked the health data of over 14,000 human participants using fitness wearables over four million nights of sleep (Leota et al., 2025). The researchers wanted to see whether exercising in the evening, before bedtime, affected sleep quality.

The researchers found that the later and harder people worked out, the more their sleep was affected. When people exercised four or more hours before going to bed, their sleep was normal, regardless of the intensity of the workout. When people exercised two to four hours before going to bed, they took a longer time to fall asleep and slept less. When people exercised two hours or less before going to bed, especially at a high intensity, sleep noticeably got worse. Some took up to over an hour longer to fall asleep, slept about 40 minutes less overall, and had a higher heart rate throughout the night (Leota et al., 2025).

Although the mice study found that exercise before bed improved overall health, the human study found that the closer exercise got to bedtime, the worse sleep became, which is also known to negatively impact recovery and overall health. While these studies may seem contradictory, they actually align upon consideration of the factor of exercise intensity. High-intensity training in the evening negatively affects sleep, while low/moderate-intensity exercise in the evening is beneficial for muscle growth and recovery without impacting sleep.

Although both studies were different, they arrived at the same key conclusion: that the body works best when its internal cycles, like its circadian rhythm, are not disrupted. Exercise, such as heavy lifting or sprinting, activates the body’s sympathetic nervous system, which is the part of the nervous system responsible for the “fight or flight” response. Sleep, along with recovery, lowered heart rate, and relaxation, is activated by the parasympathetic nervous system, otherwise known as the “rest and digest” state. While the activation of the sympathetic nervous system is good for exercise and performance, it is not good when the body needs to sleep. Instead of letting the body settle down, activation of the sympathetic nervous system keeps your body revved up, lessening sleep time and quality, and therefore overall recovery and future performance.

Because of the busyness of daily life, it’s not always possible to perfectly time every workout. Evening workouts are often unavoidable due to the realities of many people’s daily schedules. However, the combination of results from these studies shows that evening workouts aren’t automatically bad for overall health. In fact, they can even improve the benefits of exercise as long as their intensities are adjusted according to their relation to bedtime. If working out in the evening more than four hours before bedtime, high-intensity exercise can take place without risk of impacting sleep quality and physical health. If working out four hours or less before bedtime, it is better to opt for lower-intensity exercise, which will allow you to sleep better and recover more quickly. In the end, both studies show that being slightly more intentional about when and how hard you train can make a real difference in your sleep, recovery, and overall performance.

 

Works Cited

Liu, J., Xiao, F., Choubey, A., Kumar S, U., Wang, Y., Hong, S., Yang, T., Otlu, H. G., Oturmaz, E. S., Loro, E., Sun, Y., Saha, P., Khurana, T. S., Chen, L., Hou, X., & Sun, Z. (2025). Muscle Rev-erb controls time-dependent adaptations to chronic exercise in mice. Nature Communications, 16(1), 5708. https://doi.org/10.1038/s41467-025-60520-y

Leota, J., Presby, D. M., Le, F., Czeisler, M. É., Mascaro, L., Capodilupo, E. R., Wiley, J. F., Drummond, S. P. A., Rajaratnam, S. M. W., & Facer-Childs, E. R. (2025). Dose-response relationship between evening exercise and sleep. Nature Communications, 16(1), 3297. https://doi.org/10.1038/s41467-025-58271-x

Cause of Sea Star Wasting Disease Epidemic Linked to Common Bacteria

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Fig. 1. Photo of a sunflower sea star (Pycnopodia helianthoides) in a kelp forest. (Mazza, Marco. The Independent, June 21, 2024.)

Since its emergence in 2013, sea star wasting disease (SSWD) has quickly spread along the west coast of North America, infecting dozens of sea star species from Mexico to Alaska and upending marine ecosystems. A variety of causes of SSWD have been proposed over the past decade, but no clear cause has been isolated for what is now considered one of the largest marine epidemics. Sunflower sea stars, or Pycnopodia helianthoides, are considered one of the most vulnerable species to SSWD, with billions dying from SSWD since its emergence. Although sunflower sea stars once inhabited the entirety of the west coast of North America, they are now considered functionally extinct in much of their southern range. Over 87% of the population has been lost in the remaining northern areas, earning the species a classification of critically endangered. The large-scale decline of sunflower sea stars due to SSWD has had a cascading effect on ecosystems, in which sea urchin populations have experienced uninhibited growth in the absence of predation. This ecological imbalance has led to the mass destruction of kelp forests and the creation of “urchin barrens” (locations where a previous kelp forest was destroyed by sea urchin overgrazing), demonstrating the profound impact SSWD has on kelp ecosystems and the species that rely on them.

After a series of exposure experiments and genetic sequencing tests of sunflower sea stars infected with SSWD, scientists identified the common bacterium Vibrio pectenicida as a causative agent (a pathogen that directly leads to disease, but may occur under the influence of other environmental or physical conditions) for SSWD. These findings may have lasting impacts on attempts to stem the spread and population losses caused by SSWD, including future efforts to recover the population of sunflower sea stars. 

Over the course of three years (2021-2024), scientists conducted a total of seven exposure experiments on sunflower sea stars. Using tissue extracts, coelomic fluid injections (an essential fluid similar to blood for sea stars that circulates immune system cells), and tank water from diseased sunflower sea stars, exposed sea stars were infected with SSWD. Healthy sunflower sea stars were collected in Washington state or raised at Friday Harbor Laboratories, and were first isolated in a 2-week quarantine period to ensure that collected stars did not develop SSWD after potential exposure in the wild. All exposure methods led to transmission of SSWD, with 92% (46/50) of exposed individuals displaying symptoms of SSWD. The disease stages were progressively categorized as “arm twisting,” “arm autonomy,” and “mortality.” Stars exposed to SSWD often died between 6 days to 2 weeks post exposure, usually within a week after showing the first symptoms of the disease. 

While using diseased coelomic fluid and tissue sample injections to infect healthy sea stars, scientists also utilized control samples, in which tissues or coelomic fluid from a diseased star were first treated with heat or filtered before injection into a healthy star. All 54 individuals injected with treated samples survived, with limited indications of SSWD. Most sea stars injected with untreated tissue (24 out of 26) or coelomic fluid (16 out of 18) samples from diseased stars contracted SSWD. The dramatic decrease in disease spread after heat treatment indicated that the causative agent (pathogen) of SSWD was likely cellular.

Fig. 2. Diagram of exposure experiment process using treated and untreated Vibrio pectenicida bacteria and diseased tissues. (Prentice et al., 2025)
Fig. 2. Diagram of exposure experimental process using treated and untreated Vibrio pectenicida bacteria and diseased tissues. (Prentice et al., 2025)

After identifying that the cause of SSWD was likely cellular, scientists genetically sequenced diseased sea star coelomic fluid and tissues from both in-lab sea stars and sea stars at field outbreak sites. Coelomic fluid from healthy stars and stars exposed to SSWD was also collected to contrast the microbes present in sea stars at all disease stages. After RNA and DNA analysis (particularly using 16S ribosomal RNA gene amplicon datasets), the most significant microbial difference between healthy and diseased groups was identified to be the bacterium V. pectenicida (r^2 ≥ 0.90), which was found in abundance in samples from stars with SSWD and was absent in samples from healthy stars. This difference in microbial presence allowed scientists to pinpoint V. pectenicida as a likely causative agent of SSWD. Small bacterial loads of V. pectenicida were found in healthy stars, leading scientists to propose that sea stars can remain healthy with low concentrations of V. pectenicida in ideal environmental conditions. This may indicate that outbreaks occur when environmental conditions (such as increasing temperatures) compromise the star’s immune system and allow the bacterium to flourish.

After genetic sequencing identified V. pectenicida as a candidate for the causative agent of SSWD, scientists conducted a series of exposure experiments using pure V. pectenicida cultures isolated from infected stars. When injected into healthy sea stars, V. pectenicida bacterium strains FHCF-3 and FHCF-5 cultures resulted in SSWD. Healthy sea stars were then injected with high (10^5 colony forming units) and low (10^3 c.f.u.) amounts of V. pectenicida strain FHCF-3 and heat-treated controls. 13 out of 14 stars injected with living bacteria all contracted SSWD and died, while all stars injected with heat treated (dead) bacteria survived. The disease progressed faster in stars injected with a higher concentration of V. pectenicida strain FHCF-3, with mortality occurring 6-11 days post exposure. Meanwhile, the group exposed to a lower concentration of live bacteria progressed through the disease more slowly, with mortality occurring 11-16 days post exposure.

Fig. 3. Chart of disease progression in sunflower sea stars using different methods of exposure to SSWD. Visual representations of disease symptoms are displayed below. (Prentice et al., 2025)
Fig. 3. Chart of disease progression in sunflower sea stars using different methods of exposure to SSWD. Visual representations of disease symptoms are displayed below. (Prentice et al., 2025)

After identifying V. pectenicida as a strong possible cause of SSWD, gene sampling was also conducted at field sites across British Columbia in May and October 2023. Although no individuals sampled at the five sites exhibited signs of SSWD or had V. pectenicida in May, V. pectenicida was identified in two outbreak populations in October. Vibrio pectenicida was found in 16% of healthy stars from visually unaffected sites, 74% of visually normal stars in outbreak sites, and 86% of diseased stars in outbreak sites. The analysis of a genetic database from southeast Alaska in 2016 during an SSWD outbreak also found V. pectenicida in both diseased and normal stars in outbreak sites but not healthy sites, suggesting that V. pectenicida also played a role in past outbreaks of SSWD. Scientists hypothesized that instances of Vibrio pectenicida in apparently disease-free stars may be due to exposure to other diseased stars in the wild. 

The discovery of V. pectenicida as a contributing cause of SSWD has strong implications for future research and conservation efforts for struggling sea star populations. V. pectenicida has been found globally (ranging from Australia to Asia to Europe to the US) from 2009-2019 in a variety of marine hosts, particularly in shellfish and bivalve aquaculture. Future research can focus on the mechanism of V. pectenicida as a pathogen, further distinguishing where the bacterium can be found, and modes of transmission both between sea stars and from prey shellfish populations. Scientists proposed that warming oceans due to climate change may make stars more vulnerable to outbreaks of V. pectenicida and other pathogens that thrive in warmer environments, which would support an observed trend between SSWD and warming water temperatures. Since sea stars respond to unfavorable environmental conditions (such as warming water) with similar symptoms to SSWD, it has been difficult to classify SSWD outbreaks. The discovery of V. pectenicida as a causative agent allows researchers to identify V. pectenicida as an indicator of SSWD in sampling, supporting the expansion of sampling across different environments and sea star species. This is essential for continuing to understand SSWD and crafting a response to protect struggling sea star populations and affected ecosystems. 

 

References:

Mazza, Marco. “How Sunflower Stars Can Save California’s Vanishing Kelp Forests.” The Independent, Santa Barbara Independent, 21 June 2024, https://www.independent.com/2024/06/21/how-sunflower-stars-can-save-californias-vanishing-kelp-forests/ 

Prentice, M.B., Crandall, G.A., Chan, A.M. et al. “Vibrio pectenicida strain FHCF-3 is a causative agent of sea star wasting disease.” Nat Ecol Evol 9, 1739–1751 (2025). https://doi.org/10.1038/s41559-025-02797-2

Pumping Without Pedaling: How Corners Turn Timing into Speed

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Watch a skilled rider enter a berm: they arrive tall, compress as the turn loads up, and rise on exit – no pedaling, yet they launch out faster. This isn’t magic; it’s timing that lets the ground do positive work on you. This reciprocal motion between the bike and the rider is called pumping, evident in three places: rollers, banked corners (berms), and jumps. This article focuses on the physics in berms and a recent model by Golembiewski and colleagues that computes an optimal pumping rhythm through corners. We finish with brief notes on extending the same logic to rollers and jumps.

Fig 1: Rider pumping through a berm at UCI World Championships (Velosolutions Global, 2024)

The Basic Physics in a Berm 

The ground must push hard on the bike to bend its path towards the center. That “heaviness” is the normal load N. A compact way to sketch the load you feel (or the “heaviness”) is:

The first term is gravity on a bank with tilt β , the second term is the centripetal demand of the turn (speed v, radius R), and the third term is what you add by moving your body normal to the surface (a rider)a: positive when you compress, negative when you unweight). Even if the radius R stays roughly constant through the main arc, N ramps up when you go from straight to arc (entry) and drops when you go from arc back to straight (exit). Those ramps are the windows that matter, when a sliver of the ground’s reaction force points forward along the bikes path (T). The instantaneous power is roughly P=Tv. You use this to gain speed.

The bike has two wheels, splitting the pumping motion into two time frames: a short bar press as the front hits the entry ramp, then a short pedal press as the rear reaches it. Those two brief pulses create two small forward pushes per berm. Note that this isn’t conservation of angular momentum (mvr) because the ground is doing external work but applying compressive forces at the right time. 

Why this matters. This turns “pump the berm” from a vibe into a repeatable rule you can coach, measure, and design for: press twice in the entry window, glide out, and you bank real, compounding speed—no pedaling required.

Inside the Research: A Two-Mass Model on a Banked Ribbon

The paper starts with a cartoon model where the bike and the rider are represented by two points—centers of mass xb and xr — joined by a massless link of length l(t)

Fig 2: Simplified Bike & Rider Model

To give these points a world to live in, they build a 3D banked surface, called S, using a set of parametric equations:

Think of g as a recipe that turns the pair “where you are around the track (Φ)” and “where you are across the bank (Θ)” into a 3-D position.  Rather than let the bike wander anywhere on S, the authors choose a riding line by prescribing as a function of Φ.

Subsequently, the author derives a position equation for the bike–rider system that depends only on Φ — the progress angle around the banked turn—under the riding line assumption that the rider holds an inner line on the straights and shifts toward the outer (higher) line near the apex.

Fig 3: Visualization of Riding Path

Fig 4: Two-mass model on torus surface

To simplify the problem, the author also introduces an upright constraint (Fig. 4): this means the imaginary line between the bike and the rider is always perpendicular to the track surface. The movement of the rider will only be orthogonally, no fore–aft lean—so l(t) is exactly “how much you squat or extend” relative to the bank. Under that constraint, an explicit expression for the rider position (g̃) is derived.

This equation takes Φ — the progress angle around the banked turn, and l (the distance between the bike’s COM and the rider’s COM) as input, and outputs a point in 3D space. 

Setting up an Equation of Motion

The authors model the bike–rider system with positions that vary over time. The bike position is xb(t) and the rider position is xr(t). Velocity is the time derivative of position (how fast the points move), and acceleration is the time derivative of velocity. A single “squat/extend” degree of freedom along the surface normal is captured by the body–bike separation l(t). In everyday terms, l̇  is how quickly you are moving up or down, and l̈ is how hard you accelerate that motion. This variable l̈(t) is the core of the study — it becomes the control input in the simulation. From the position formulas, the paper computes the speeds (kinetic energy)
and heights (potential energy) of both masses:

  • Kinetic energy K = (bike term) + (rider term)
  • Potential energy U = (gravity acting on each mass via its z-height)

Rather than listing every individual force, they use the standard energy approach to produce a single, compact equation that governs motion along the track. Written the way it appears in the paper, it’s an implicit ordinary differential equation (ODE) in the along-track angle φ(t) that also depends on your body motion l(t):

The terms mean:

  • M(φ, l) φ̈ — the effective inertia for turning the system around the track.
  • F(φ, l) φ̇2 — curvature/banking effects that grow with speed.
  • Q(φ, l, l̇) φ̇ — coupling between your height change and along-track motion.
  • P(φ, l, l̇, l̈) — the part driven by your deliberate squat/extend acceleration (l̈), i.e., the “pump.”

Intuition: When you accelerate your body normal to the surface while the berm sets the contact frame, the last term acts like a small forward push in the along-track equation. That is the mechanism the model quantifies.

Setting Up an Optimal Control Problem

The paper asks a simple question: If you’re not allowed to pedal, how should you squat and extend to get through a banked turn the fastest? To answer it, they turn riding into a decision-making problem a computer can solve. This is called an optimal control problem.

Fig 5: Optimal Control Setup

 State: Within the integral, x(t) is the state vector—it stores four numbers at every instant:

  • Where you are around the corner (an angle): φ(t).
  • How fast you’re sweeping around (angular rate; higher rate = higher speed): φ̇(t).
  • How tall you are above the bike along the bank’s normal (body–bike separation): l(t).
  • How quickly that height is changing (going up or down): l̇(t).

The researchers bundle these into a compact vector the computer updates over time, simulating the rider’s progress around the track:

Reward and punishment inside the integral: The cost being minimized adds a reward for making progress/speed and a penalty for harsh pumping:

  • qT x(t) (Fig 5) is a linear reward/penalty on the state. In the paper, q = [ -65, -65, 0, 0 ]T. Because we minimize J, those negative weights reward larger φ (angle covered) and φ̇ (speed). In short: the optimizer prefers going farther and faster.
  • u(t)2 penalizes violent pumping. Here u(t) = l̈(t) is the rider’s normal acceleration (how hard you compress or unweight). Sudden, large inputs make u2 jump, increasing the cost, so the optimizer favors smooth, well-timed pulses over thrashing.

Units intuition: the integrand is “cost per second.” Integrating over dt gives a total cost. Lower J means you went farther/faster while using less harsh acceleration.

Control (what you choose): Your single decision signal is how hard you accelerate your body up or down relative to the bike, along the bank’s normal direction—the essence of pumping:

  • Positive control: compress (drive yourself down)
  • Negative control: unweight (pop up)

They call this input u(t) = l̈(t)

The notation u(⋅) ∈ PC ([0,T], ℝ) (Fig 5) means the control is piecewise-continuous over the time window — mostly smooth with at most a few kinks.

Dynamics constraint (obeying physics): The model provides an equation tying together how the state changes when you pick a control, with a specified starting condition x(0)=x0:

The equation basically means: given the track shape and gravity, if you push this hard right now, this rule predicts how your position, speed, and body height will evolve next. The solver enforces this rule at every tiny time step so it never “cheats.”

Setting Realistic Constraints

The researchers then determine the realistic bounds for length between the bike and rider and acceleration between the bike and rider by doing a motion capture of a real setup.

Fig 6: Experiment Setup
Fig 7: Camera image with marked pints

 

 

 

 

They use motion trackers to track 46 markers at 100 Hz and infer rider CoM and bike reference points to measure what a human can actually do. The result were the graphs below:

Fig 8: Absolute distance between CoM rider and bike
Fig 9: Acceleration of the riders CoM relative to the bike

 

 

 

 

 

We can see the graph roughly mirrors the motion you feel in real life:

  1. Riders enter the berm pushing the bike down and extending length
  2. Riders maintain pressure throughout the berm and compresses near the end
  3. When riders exit the berm they push the bike down again and re-extend.

In this real-world experiment, researchers observed the range of body-bike length to be(0.27803m  ≤ l(t) ≤  0.59559  m) and body-bike acceleration (pumping) to be (-8.6648 m/s2 ≤ l̈(t) ≤ 30.1478 m/s2)

The researchers then substituted these bounds into their optimal control problem to determine the optimal pumping technique mathematically. 

What the Model Predicts (and how it matches good riding)

The researchers solved the optimal-control problem for a 5-second ride segment that includes two steep corners and two short straights. They start the rider in a neutral body position, traveling at an angular speed of φ̇ = π/3 rad/s (about 9.43 m/s bike speed),
entering at the beginning section between two opposing corners. They then plug the physical parameters into the dynamics (equation of motion):

mb mr ggrav R r λ
15 kg 80 kg 9.8067 m/s2 3 m 1 m 3

The track constants R, r, and λ determine the sharpness and banking of the corner (here R = 3 m, r = 1 m, λ = 3). Finally, using MATLAB (via CasADi) and IPOPT the researchers solve the optimal-control problem, successfully simulating a complete cycle through the track, producing the graphs below:

Fig 10: Simulation results

What the optimal solution does:

  • Kinematics (Fig. 10a):ϕ(T) ends close to 2π — a full pass through the track section including both steep curves.
  • Pose evolution (Fig. 10b):The optimal profile drives the body high at entry (near lmax), then compresses toward lmin across each corner, and re-extends later. In short: enter tall, compress through the berm, then re-extend for each berm. We can also see this visualized in Fig 3
  • Speed gain (Fig. 10c):Remarkably, between t = 0 and t ≈ 2.8 s (the first berm), the bike speed increases by vb ≈ 1.49 m/s — generated without pedaling, purely by the reciprocal mass motion (squat/extend).
  • Control signal (Fig. 10d):The input u*(t) = l̈(t) shows short downward-acceleration bursts (negative then positive spikes) clustered in each corner. These spikes are the timed “pump” impulses that increase normal load precisely when the track’s orientation gives a small forward component — at corner entry and exit — producing useful forward work.

They ran a comparison without pumping (set u(t) = 0, but tested several starting heights l(0)). The fastest no-input case took 6.13 s to reach the same terminal angle φ(T). With the optimal pumping u*(t), the time dropped to 5.00 s. That’s a time saving of Δt = 1.13 s — about 18.43% faster for the same path segment.

Bottom line

Within the paper’s simplified two-mass, upright model (with experiment-derived bounds), pumping through a berm means spending your limited normal-acceleration budget inside the corner (to harvest speed) and avoiding payback at the exit. The solver’s best answer matches what skilled riders do: enter tall, compress through the berm, re-extend later. The numbers quantify the payoff: roughly ≈ 1.5 m/s speed gain per corner and ≈ 18% reduction in lap time for the segment.

From Model to Trail: a Real-World Translation in Riding Technique

The paper’s takeaway is: spend your limited “normal-acceleration budget” inside the corner and don’t pay it back on exit. In practice, that means arrive tall, compress through the corner, and re-extend later (ideally once you’re back on a straight).

What the model doesn’t capture (and how real riders adapt) is:

  • Only normal motion. The paper restricts the rider to move orthogonal to the track’s surface (no fore–aft pump). On dirt, riders can pump slightly forward/back as well. That can shift the pattern earlier, often giving a high → low → high within a single corner, instead of the model’s high → low (then high on the straight).
  • One fixed line. The solver rides a prescribed line (inner on straights, drifting outward at apex). Outside the lab you can pick lines that change banking and gravity use:
    • High → low line: drop from high entry to lower apex/exit to cash in gravitational energy while you compress.
    • Low → high line: for traction or setup, at the cost of more input work.
  • Two contacts, richer phasing. With front and rear wheels you get two timed opportunities per corner (bar press as the front enters the load ramp, pedal press as the rear reaches it). Skilled riders also unweight the front earlier to keep exit smooth.

Beyond Berms: Brief Notes on Rollers and Jumps

Rollers (smooth waving undulations): You speed up by placing two brief load pulses around each crest — bar press as the front rolls over the crest, pedal press as the rear follows — then staying light on the upslope so you don’t give the energy back. These two pulses creates a small forward push each. Add them up, subtract gravity and drag, and you get your acceleration

  1. Front wheel at crest (arms compact, legs half-extended). You’re coiling up at the exact moment curvature flips.
  2. Front on downside; rear crosses crest (arm push grows to full extension; legs fully compressed). Two quick injections: a bar press as the front tips over (raising front normal load just as the ground points forward), then a pedal press as the rear crests. Each creates a small forward contact component
  3. Front in ravine; rear on downside (arms finish extension, legs extend down the back face). Keep pedal pressure while the rear is still on the back face. begin to unweight the bars as the front meets the upslope to avoid negative work.
  4. Front on upslope; rear in ravine (legs reach full extension; arms half-compressed; front unweighted). Now the ground would slow you (upslope). You keep the front light and use the up-kick to pop your mass upward—maintaining speed while the bike climbs under you.

Jumps: Preload on the run-up, then choose: extend on the lip at the point of maximum normal force where m · v2 · κ is maximum (you can look at my personal research if interested) to trade speed for height or stay light to preserve forward speed. The same “press-when-helpful, light-when-hurtful” rule applies, just with a vertical-energy trade at takeoff.

Conclusion

Pumping is a control problem you solve with your body: press exactly while the turn makes you heavy, and rise while it makes you light. The research formalizes this with a minimal two-mass model and an optimizer that times a compress–extend input to harvest the berm’s geometry—a clean physics story for the “free speed” riders feel in corners.

Bibliography

Velosolutions Global. (2024, March 6). 2024 qualifier events announced for UCI Pump Track World Championships. Pinkbike. https://www.pinkbike.com/news/2024-qualifier-events-announced-for-uci-pump-track-world-championships.html

Golembiewski, J., Schmidt, M., Terschluse, B., Jaitner, T., Liebig, T., & Faulwasser, T. (2023). The dynamics of a bicycle on a pump track—First results on modeling and optimal control (arXiv Preprint No. 2311.07251). arXiv. https://doi.org/10.48550/arXiv.2311.07251

Effect of Dental Malocclusions on Posture in Children

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It is estimated that over six million patients seek orthodontic treatment every year to improve their malocclusion, or misalignment of teeth (Hung et al. 2023). Seeing as many people value this treatment, it is not surprising to learn that the way our teeth fit into one another affects the way we eat, talk, breathe, and even our posture. Musculoskeletal (shoulders, spine, muscles) and stomatognathic (teeth, jaws, chewing muscles, tongue, lips) are separate systems of our bodies that interact in intricate ways. For example, a misalignment of teeth alters the muscle-use patterns in our cheeks to compensate for this disparity, which in turn affects the neck muscles which are connected to our face muscles. Through a slight discrepancy in teeth-alignment, the whole head can shift into a different position, impacting one’s health (Bardellini et al. 2022). Unfortunately, the intersection of posture and dental malocclusions is a scarcely researched field. Seeing how impactful dental alignment is to the rest of the body, it is important to research and understand the factors that influence it.    

One study published in 2022 by a group of Italian researchers (Bardellini et al.) examined how these systems work together, and the effects of correcting dental malocclusions through orthodontic treatment on the posture of children. While there are many different classifications and types of dental malocclusions, this article specifically analyzes patients using Angle’s classification. Angle’s classification shows three types of malocclusions: class I, II, or III (Fig. 1). Each is described by the position of the lower (mandible) and upper (maxillary) molars. Class I is defined as the molars fitting together in a standard way, however, malocclusions are still present in other teeth besides the molars. In Angle’s class II, the lower molar is farther back (distal) than the upper molar. Lastly, class III shows the lower molar too far in front of the upper molar (Campbell and Goldstein 2021).  

Angle's classification of occlusion illustrated with dental diagrams and hand analogies: normal occlusion, Class I, Class II, and Class III malocclusions.
Figure 1: Simulate Angle’s classification of malocclusion by hands. Xie, Zhiwei, Fuying Yang, Sujuan Liu, and Min Zong. 2023. “The ‘Hand as Foot’ Teaching Method in Angle’s Classification of Malocclusion.” Asian Journal of Surgery 46 (2): 1063

The patients that participated in the study were assessed by two clinicians who evaluated their dental occlusions according to Angle’s classification. While deciding which patients to include in the study, the type of dental-skeletal malocclusion within Angle’s classification did not play a role. Most patients observed in this study exhibited a class II malocclusion, followed by class I and III. Patients that had scoliosis, required physical therapy, chronic diseases affecting balance, macro trauma, cleft lip or palate were excluded to ensure that the improvement in posture depended only on malocclusions and orthodontic treatment. Since this study aimed to find a connection between misalignment of teeth and posture in children, the patients belonged to the age group of 9-12 (Bardellini et al. 2022).    

Bardellini and her team investigated the postures and weight distribution of patients before and after the treatment using multiple methods, such as vertical laser line (VLL) and stabilo-baropodometric analysis.   

To examine the posture through VLL, the patients were positioned in a standardized position (relaxed posture and arms at side) in front of a white wall. A singular vertical laser line (VLL) was projected onto the patients (Bardellini et al. 2022). The posture was then examined for two factors, the position of the head in relation to the VLL and an excess of extension or flexion. A standard position means the head is centered so that it crosses the tragus—the pointy piece of cartilage close to the cheek (Fig. 2).

Anatomical illustration of human ear with labeled pin above the tragus.
Figure 2: Tragus – anatomical structures. Source: IMAIOS, “Tragus – Anatomical Structures,” accessed November 14, 2025.

If the cartilage did not cross the VLL, the patients’ head was either in a forward or backwards position. Extension and flexion were examined by asking the patients to open their mouths as wide as possible. If the head moved away from the VLL line, it indicated either excess of extension—head bent backwards—or of flexion—the head bent forwards (Fig. 3, Bardellini et al. 2022).

Orthodontic treatment outcomes displayed as paired lateral profile photographs of six patients labeled a through f. Each pair shows pre-treatment (left) and post-treatment (right) views with a vertical line for reference. Arrows on some cases indicate anterior or posterior shifts in facial profile. The images demonstrate improvements in head posture following treatment.
Figure 3: Improvement of the head position (evidenced with the “open mouth test”) in six patients (a-b-c-d-e-f). Bardellini, Elena, Maria Gabriella Gulino, Stefania Fontana, Francesca Amadori, Massimo Febbrari, and Alessandra Majorana. 2022. “Can the Treatment of Dental Malocclusions Affect the Posture in Children?” May 1, 2022: 245

The VLL test indicated that 16 out of 60 patients had a backwards position of the head, 29 a forward position, 10 showed excess of extension while opening their mouths, and 31 an excess of flexion. Only seven patients already had a correct position, meaning that in 75% of patients, dental misalignment influenced head position in relation to VLL line, and 68.33% either flexion or extension.  

After determining the posture of the head, the researchers then examined the weight distribution of the participants using a stabilo-paropodometric platform. The patients were asked to stand on a carpet under which a stabilo-paropodometric platform (40x40cm) was placed. The platform measured the typology of the foot and weight distribution across the two feet. The typology of feet can be divided into three kinds: normal, cavus (extreme arch), or flat (underdeveloped arch). Typology can differ between feet, with either both feet showing the same type or different types. The ideal distribution of body weight between feet should be symmetrical at about 50% on each foot (Bardellini et al. 2022).  

Through measurements obtained with the stabilo-baropodometric platform, the study found 45 cases (both or one side) with cavus feet, and 6 with flat feet (both sides). Hence, 85% of patients had a typology that incorrectly supported their body. Additionally, about 70% of patients had an unequal weight distribution between their two feet, exacerbating bad posture. An incorrect spread of body weight can be identical on both feet—either too much pressure on the ball of the foot or heel—or it can vary between feet (i.e. one foot shows increased pressure at heel, and the other at the ball of the foot) (Bardellini et al. 2022).  

After the classification of malocclusion was identified and the posture (VLL) and weight distributions (Stabilo-baropodometric platform) were measured, the patients were treated with an individually prepared Mouth Slow Balance (Fig. 4), which works by repositioning the tongue, widening the maxilla (upper jaw), and keeping the mandible’s (lower jaw) relation to the maxilla (Bardellini et al. 2019, Bardellini et al. 2022). They describe the MSB device as a “evolution of the Binator”, a retainer like appliance adjusting the bite (Fig. 5, Bardellini et al. 2019 p. 243).  

Photograph of a Mouth Slow Balance (MSB) device, an orthodontic appliance made of acrylic resin and wire that resembles a traditional retainer.
Figure 4: The MBS (mouth slow balance) Class III device Bardellini, E., M. G. Gulino, S. Fontana, J. Merlo, M. Febbrari, and A. Majorana. 2019. “Long-term evaluation of the efficacy on the podalic support and postural control of a new elastic functional orthopaedic device for the correction of Class III malocclusion.” European Journal of Paediatric Dentistry, no. 3: 200.
Photograph of a Binator device, an orthodontic appliance made of acrylic resin and wire that resembles a traditional retainer.
Figure 5: The Binator appliance. Pakshir, Hamidreza, Ali Mokhtar, Alireza Darnahal, Zinat Kamali, Mohammad Hadi Behesti, and Abdolreza Jamilian. 2017. “Effect of Bionator and Farmand Appliance on the Treatment of Mandibular Deficiency in Prepubertal Stage.” Turkish Journal of Orthodontics 30 (1): 16

The patients were observed during their treatments for four years (2014-2018), and by the end, 51 out of 60 patients exhibited a correction of malocclusions, either fully aligned or class I (Bardellini et al. 2022). Other patients either dropped out of the study (3 patients) or reached a correction after the observed time frame (6 patients). 

Of the 53 patients, 23 obtained the ideal position and 19 saw an improvement but did not complete correction of head-position. In 10 cases, patients were found to have been overcorrected.  In the beginning of the four-year observation period, 15 patients had a correct position regarding VLL posture assessment. After treatment, 7 kept their correct position, while 8 now developed a forward position. Additionally, two patients that showed a backwards position before treatment developed a forward position by the end (Bardellini et al. 2022).  

Bardellini et al. (2022) also found significant improvements of the posture in VLL open mouth exams. 53.3% now kept their tragus on the laser line while opening their mouths, when they used to hyper-extend or –flex.  

53 participants (88%) improved their foot typology, of which 17 achieved a complete correction. Before treatment, only 15% of participants had a “normal” typology, which increased to 28% after treatment. However, weight distribution that varied between feet significantly increased from 18 to 37, of which seven patients developed a weight distribution imbalance they previously didn’t show. Overall, cases also exhibited an improvement without complete correction which decreased the median of support discrepancies over the course of the treatment (Bardellini et al. 2022).  

These findings provide evidence for Bardellini et al.’s hypothesis that posture is in fact altered by dental malocclusions. They explain that through a complex chain of muscles across different systems, muscles alter their patterns which disturb the posture, specifically in the position of the head and support of feet.  Muscles around our cheeks (masticatory) and neck (cervical) were already discovered to have a connection in previous research (Bardellini et al. 2022). Furthermore, trunk muscles (abdomen, chest, back) are also connected to these muscles. Since the misalignment of teeth affects the so-called mandibular elevator muscles that are a part of our cheek muscles, this change flows over into other muscle systems (cervical and trunk) acting on our posture. Our strategies for balancing are primarily spread across the trunk, head, and pelvis, which means that the misposition of the head leads our body to try and balance it using other methods (trunk and pelvis) (Bardellini et al. 2022). So, the wrong posture shifts the center of gravity. 

Although Bardellini et al. have found significant evidence that there is a correlation between dental malocclusions and posture, they acknowledge that they are one of few studies that focus on this specific alteration in posture, hence emphasizing that more research needs to be done.  

Furthermore, the results may have been skewed because the team did not consider that the natural changes occurring in growing children may also influence their posture, weight distribution, and more. However, for this specific study it would have been unethical to have a control group of untreated children to compare the effects of treatment vs no treatment (Bardellini et al. 2022).   

Bardellini and her team are one of the few trailblazing research articles that examine the impact of malocclusions on posture, specifically targeting the head and feet. As mentioned before, not much research has been done in this field that examines this topic especially, yet it can prove to be vital for child development. Correcting posture early on can improve a person’s life-quality for the rest of their lives, impacting everyday tasks. Hopefully, in the future more researchers will recognize the importance of this subject and contribute new findings.

References:

Bardellini, E., M. G. Gulino, S. Fontana, J. Merlo, M. Febbrari, and A. Majorana. 2019. “Long-term evaluation of the efficacy on the podalic support and postural control of a new elastic functional orthopaedic device for the correction of Class III malocclusion.” European Journal of Paediatric Dentistry, no. 3: 199–203. https://doi.org/10.23804/ejpd.2019.20.03.06. 

Bardellini, Elena, Maria Gabriella Gulino, Stefania Fontana, Francesca Amadori, Massimo Febbrari, and Alessandra Majorana. 2022. “Can the Treatment of Dental Malocclusions Affect the Posture in Children?” May 1. DOI: 10.17796/1053-4625-46.3.11 

Campbell, Stephen, and Gary Goldstein. 2021. “Angle’s Classification–A Prosthodontic Consideration: Best Evidence Consensus Statement.” Journal of Prosthodontics (United States) 30 (S1): 67–71. https://doi.org/10.1111/jopr.13307. 

Hung, Man, Golnoush Zakeri, Sharon Su, and Amir Mohajeri. 2023. “Profile of Orthodontic Use across Demographics.” Dentistry Journal 11 (12): 291. https://doi.org/10.3390/dj11120291. 

IMAIOS. “Tragus.” e-Anatomy, accessed November 20, 2025. https://www.imaios.com/en/e-anatomy/anatomical-structures/tragus-1536888748. 

Pakshir, Hamidreza, Ali Mokhtar, Alireza Darnahal, Zinat Kamali, Mohammad Hadi Behesti, and Abdolreza Jamilian. 2017. “Effect of Bionator and Farmand Appliance on the Treatment of Mandibular Deficiency in Prepubertal Stage.” Turkish Journal of Orthodontics 30 (1): 15–20. https://doi.org/10.5152/TurkJOrthod.2017.1604. 

Xie, Zhiwei, Fuying Yang, Sujuan Liu, and Min Zong. 2023. “The ‘Hand as Foot’ Teaching Method in Angle’s Classification of Malocclusion.” Asian Journal of Surgery 46 (2): 1062–64. https://doi.org/10.1016/j.asjsur.2022.07.130. 

Ethical ramifications of AI-powered medical diagnoses

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Incredible advancements in artificial intelligence (AI) have recently paved the way for the use of AI in healthcare settings. Implementation of AI has the potential to address worker shortages in the medical field, lead to discovery of new drugs, or improve diagnoses (Bajwa et al., 2021). A writer for the American Medical Association, Benji Feldheim applauds AI for restoring the “human side” in medicine. For example, AI scribes in particular ease the documentation burden doctors face—reducing burnout and improving doctors’ interactions with patients as a result (Feldheim, 2025). Another example is the AI model developed by Shmatko et al. (2025), known as Delphi-2M, which is capable of accurately predicting a patient’s next 20 years of disease burden (i.e., what diseases they would contract and when). Evidently, AI is a very promising technology already capable of improving lives, however, there are reasons to be skeptical. While these advances are promising, these uses of AI also raise concerns about fairness and clinical safety. After a brief synopsis of Shmatko et al.’s Delphi-2M, I evaluate the ethical ramifications of AI-powered diagnoses and related clinical tools.

Delphi-2M is an AI model trained on over 400,000 patient histories from a UK database to forecast an individual’s 20-year disease trajectory. Similar to chatbots like ChatGPT, Delphi-2M is a large language model (LLM), a type of AI that can recognize and reproduce patterns from large amounts of data. Similar to how chatbots pick up on what words are likely to appear with other words in order to form sentences, Delphi-2M learns from its vast training set of medical records to predict a patient’s disease trajectory from realworld patterns. As Yonghui Wu puts it in her summary of Shmatko et al.’s work, it’s just how becoming a smoker may be followed by a future diagnosis of lung cancer—these are patterns Delphi-2M recognize. To do this, Delphi-2M is fed “tokens” that link diseases or health factors to specific times in a person’s life, like chickenpox at age 2 or smoking at age 41 (Figure 1). Then, Delphi-2M outputs new tokens that predict what diseases and when they will occur in an individual’s life, like the onset of respiratory disorders at age 71 as a result of smoking. Delphi-2M, after being trained, was tested by predicting the medical histories of 1.9 million patients not included in the original training set. Shmatko et al. demonstrate this AI to have great success in accurately predicting disease trajectory, as it partially predicts patterns in individuals’ diagnoses in 97% of cases.

Visualization of Delphi-2M input and output (Wu, 2025).

Nonetheless, we must hold AI used to diagnose patients to a higher level of scrutiny compared to AI used commercially. LLMs are not perfect as they are subject to algorithmic bias and misuse, beginning before their creation. Shmatko et al. (2025), for example, address some shortcomings of the training data used for Delphi-2M. Notably, they explain the data from a mostly-white, older subset of the UK population isn’t entirely generalizable to very different demographics. Though Shmatko et al. found successes testing the model against a Danish database after training it on UK patients, I’m still concerned how Delphi-2M would perform on non-European and younger demographics, or those underrepresented in training data. Facial recognition is a prime example of where AI underperforms when training datasets lack diverse representation. AI designed to recognize faces historically underperform on individuals with feminine features or darker skin due to unrepresentative training data (Hardesty, 2018). With this in mind, it’s important that training data for diagnostic AI is representative of all demographics prior to widespread implementation.

Furthermore, Cabitza et al. (2017) wrote on some of the unintended consequences of machine learning in healthcare, postulating that widespread implementation of these tools also has the potential to reduce the skill of physicians. Though convenient in the short run, Cabitza et al. raise concerns with overreliance on AI—as studies show physicians aided by AI were less sensitive and accurate in diagnosing patients. Mammogram readers, for instance, were 14% less sensitive in their diagnostics when presented with images marked by computer-aided detection (Povyakalo et al., 2013). Though this study focused on image diagnoses, it’s clear how widespread use of Delphi-2M would lead to the same problems of deskilling in physicians. Delphi-2M is also exclusively a text-based model, which as Cabitza et al. detail, means that these diagnosis algorithms do not incorporate crucial contextual elements that are “psychological, relational, social, and organizational” in nature. A realworld example that Cabitza et al. described was an instance in which an AI model predicted a lower mortality risk for patients with pneumonia and asthma compared to those with pneumonia and without asthma. Understanding that asthma is not a protective factor for pneumonia patients, the involved researchers found the discrepant AI output was the result of hospital procedures that admitted pneumonia patients with asthma directly to intensive care, giving them better health outcomes. This missing piece of crucial information, which was difficult to represent in these prognostic models, led to an error a physician would not make. Thus, AI is limited in what information it can train on.

Though these new advancements in healthcare AI are promising, they have their limits. Tools like Delphi-2M spot patterns across vast clinical histories that no single clinician could feasibly track, yet the benefits depend on who is represented in the data, how predictions are explained and used, and whether safeguards are in place when they fail. Before AI is implemented in healthcare, we must demand representative training sets, validation across diverse populations, clear disclosures of uncertainty and limitations, and constant human involvement in the process that resists automation bias and deskilling. In short, diagnostic AI should supplmenent—not replace—clinical judgment, and it should be developed with privacy, equity, and patient trust at the forefront. Only then will these systems reliably improve care rather than merely appear to.

 

References

Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthcare Journal, 8(2), e188–e194. https://doi.org/10.7861/fhj.2021-0095

Cabitza, F., Rasoini, R., & Gensini, G. F. (2017). Unintended consequences of machine learning in medicine. JAMA, 318(6), 517. https://doi.org/10.1001/jama.2017.7797

Feldheim, B. (2025, June 12). AI scribes save 15,000 hours—and restore the human side of medicine. American Medical Association. https://www.ama-assn.org/practice-management/digital-health/ai-scribes-save-15000-hours-and-restore-human-side-medicine

Hardesty, L. (2018, February 11). Study finds gender and skin-type bias in commercial artificial-intelligence systems. MIT News. https://news.mit.edu/2018/study-finds-gender-skin-type-bias-artificial-intelligence-systems-0212

Povyakalo, A. A., Alberdi, E., Strigini, L., & Ayton, P. (2013). How to Discriminate between Computer-Aided and Computer-Hindered Decisions. Medical Decision Making, 33(1), 98–107. https://doi.org/10.1177/0272989×12465490

Wu, Y. (2025). AI uses medical records to accurately predict onset of disease 20 years into the future. Nature, 647(8088), 44–45. https://doi.org/10.1038/d41586-025-02971-3

Phytoplankton and Ocean Warming: Uneven Adaptations at the Base of the Marine Food Web

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Global warming is steadily transforming Earth’s oceans. Between 1901 and 2023, sea surface temperatures have increased at an average rate of 0.14℉ per decade (US EPA, 2016). This seemingly small thermal shift is enough to disrupt circulation patterns, alter nutrient availability, and restructure entire marine communities. As oceans absorb over 90% of excess atmospheric heat, they become both a buffer against and a victim of climate change (Climate Change, 2025). Among the many organisms affected by these changes, phytoplankton—the microscopic, photosynthetic organisms that drift near the ocean’s surface—serve as a critical case study. These single-celled producers are responsible for about half of Earth’s oxygen production, and they form the foundation of aquatic food webs, converting sunlight into chemical energy that sustains nearly all marine life (Hook, 2023). Therefore, understanding how phytoplankton respond to warming is essential for predicting the future of marine ecosystems.

Phytoplankton are highly sensitive to temperature fluctuations. Since their metabolic processes, growth rates, and enzymatic activities are temperature-dependent, even minor thermal changes can reshape their abundance and distribution. When waters warm beyond a species’ thermal tolerance, populations may decline or shift toward cooler regions (Barton et al., 2016). At the microscopic level, these shifts can cascade upward through the food web, reducing food availability for zooplankton, fish, and the higher-level predators that feed on them, such as sharks, whales, and seals. However,  one key question remains: can phytoplankton adapt to rising temperatures, or will their thermal limits determine the structure of future marine ecosystems?

Huertas et al. (2011) directly addressed this question through controlled laboratory experiments designed to measure the capacity of phytoplankton to evolve under warming. The researchers selected twelve species representing a range of environments—freshwater, coastal, open-ocean, and coral symbiotic systems—to test whether thermal tolerance varied among ecological types. To simulate long-term warming, they employed a “ratchet technique,” in which phytoplankton populations were gradually exposed to higher temperatures. Each population started from a single cloned cell to remove preexisting genetic variation. Then, the cell cultures were repeatedly grown and transferred into warmer conditions, forcing the populations to either adapt to the changes through genetic mutations or face extinction.

The results revealed striking differences among species. Freshwater species, such as Scenedesmus intermedius, exhibited remarkable resilience, adapting to temperatures as high as 40°C. Coastal species like Tetraselmis suecica and Dictyosphaerium chlorelloides tolerated up to 35°C, while open-ocean species such as Emiliania huxleyi and Monochrysis lutheri showed little to no capacity for adaptation. Coral symbionts (Symbiodinium species) demonstrated limited but detectable resistance, reflecting the thermal stress already observed in coral reef environments. Importantly, adaptation was not simply a case of short-term acclimation. The researchers found that resistant populations arose at different times across replicate cultures. This serves as evidence that adaptation stemmed from rare, spontaneous genetic mutations instead of physiological flexibility. Growth rates of adapted populations diverged significantly from their ancestral strains, confirming that true evolutionary change had occurred.

These findings carry major implications for understanding the ecological future of the oceans. If phytoplankton species differ so widely in their ability to adapt, warming will likely reorganize marine communities from the bottom up. Species capable of rapid genetic adaptation may dominate, while others could decline or disappear. This uneven resilience could favor smaller, faster-growing species, altering nutrient cycling and potentially weakening the ocean’s ability to sequester carbon. Because phytoplankton drive roughly half of global primary production, any restructuring of these communities could ripple through food webs, climate regulation, and fisheries.

While Huertas et al. focused on individual species in controlled conditions, Poloczanska et al. (2016) broadens this picture to the scale of global ecosystems. Their review synthesized nearly 2,000 observations of marine organisms responding to climate change, confirming that uneven adaptation is already occurring across taxa and ocean regions. On average, species distributions are shifting towards the north and south poles by about 72 kilometers per decade, and spring life-cycle events such as breeding or migration are advancing by four days per decade. Warm-water species are becoming more abundant, while cold-water species decline. Coral calcification, the process by which corals take in calcium and carbonate ions to build their exoskeletons, is weakening under combined warming and acidification stress. These patterns mirror the interspecific variability observed by Huertas et al.; some organisms adjust successfully to changing conditions, while others falter. Here, the broader conclusion is that climate change does not affect marine life uniformly—it selectively reshapes communities based on biological flexibility, dispersal ability, and evolutionary potential.

Fig 1. Global distribution of documented marine biological responses to climate change across major ocean regions (Poloczanska et al., 2016). Bars show the proportion of observed responses as consistent (dark blue), equivocal (light blue), or no change (yellow). Numbers indicate total observations per region; symbols identify taxa with ≥10 observations. Background colors represent regional sea-surface warming from 1950–2009 (yellow: low; orange: medium; red: high). Regions are defined by ecological structure and oceanographic features. eveal that climate-driven shifts in abundance, distribution, and phenology vary sharply across ocean basins—mirroring the uneven adaptive capacities described by Huertas et al. (2011).

Together, these studies illustrate both the mechanisms and the consequences of ocean warming. Huertas et al. provides mechanistic insight—showing that adaptation in phytoplankton depends on genetic change, and that some species are inherently more capable than others. Building off of this, Poloczanska et al. reveals how these species-level differences scale up, driving global shifts in abundance, distribution, and ecosystem structure. The two perspectives complement one another; laboratory experiments explain how adaptation might occur, while global syntheses show where and to what extent it already has.

As climate change accelerates, understanding the adaptability of foundational organisms like phytoplankton becomes increasingly urgent. Their evolutionary potential will determine not only the structure of marine ecosystems, but also the ocean’s capacity to regulate the planet’s climate. By linking experimental evidence with global ecological trends, researchers are beginning to map out a future ocean defined by winners and losers—a mosaic of adaptation, migration, and loss. The challenge ahead lies in predicting how these microscopic shifts will ripple through the web of life that depends on them.

References:

Barton, A. D., Irwin, A. J., Finkel, Z. V., & Stock, C. A. (2016). Anthropogenic climate change drives shift and shuffle in North Atlantic phytoplankton communities. Proceedings of the National Academy of Sciences, 113(11), 2964–2969. https://doi.org/10.1073/pnas.1519080113 

Climate Change: Ocean Heat Content | NOAA Climate.gov. (2025, June 26). https://www.climate.gov/news-features/understanding-climate/climate-change-ocean-heat-content 

Hook, B. (2023, May 31). Phenomenal Phytoplankton: Scientists Uncover Cellular Process Behind Oxygen Production | Scripps Institution of Oceanography. https://scripps.ucsd.edu/news/phenomenal-phytoplankton-scientists-uncover-cellular-process-behind-oxygen-production 

Huertas, I. E., Rouco, M., López-Rodas, V., & Costas, E. (2011). Warming will affect phytoplankton differently: Evidence through a mechanistic approach. Proceedings of the Royal Society B: Biological Sciences, 278(1724), 3534–3543. https://doi.org/10.1098/rspb.2011.0160 

Poloczanska, E. S., Burrows, M. T., Brown, C. J., García Molinos, J., Halpern, B. S., Hoegh-Guldberg, O., Kappel, C. V., Moore, P. J., Richardson, A. J., Schoeman, D. S., & Sydeman, W. J. (2016). Responses of Marine Organisms to Climate Change across Oceans. Frontiers in Marine Science, 3. https://doi.org/10.3389/fmars.2016.00062 

US EPA, O. (2016, June 27). Climate Change Indicators: Sea Surface Temperature [Reports and Assessments]. https://www.epa.gov/climate-indicators/climate-change-indicators-sea-surface-temperature