Cosmic rays are high-energy astronomical particles originating from various sources across the universe. Here, The authors sought to understand how surface-level cosmic-ray muon flux is affected by atmospheric attenuation by measuring the variation in relative muon-flux rate relative to zenith angle, testing the hypothesis that muons follow an exponential attenuation model. The attenuation model predicts an attenuation length of 6.3 km. This result implies that only a maximum of 24% of muons can reach the Earth’s surface, due to both decay and atmospheric interactions.
Triggered largely by the warming and pollution of oceans, corals are experiencing bleaching and a variety of diseases caused by the spread of bacteria, fungi, and viruses. Identification of bleached/diseased corals enables implementation of measures to halt or retard disease. Benthic cover analysis, a standard metric used in large databases to assess live coral cover, as a standalone measure of reef health is insufficient for identification of coral bleaching/disease. Proposed herein is a solution that couples machine learning with crowd-sourced data – images from government archives, citizen science projects, and personal images collected by tourists – to build a model capable of identifying healthy, bleached, and/or diseased coral.
High-fructose diets consumed widely in modern societies predisposes to metabolic diseases such as diabetes. Using the worm C. elegans, the authors of this study investigated the effect of fructose on the worm's survival rates. They found that worms fed 15% fructose had a lower life expectancy than those on a fructose-free diet. These results suggest that, like in humans, fructose has a negative effect on worm survival, which makes them an easy, attractive model to study the effects of fructose on health.
Alzheimer's disease (AD) involves the reduction of cholinergic activity due to a decrease in neuronal levels of nAChR α7. In this work, Sanyal and Cuellar-Ortiz explore the role of the nAChR α7 in learning and memory retention, using Drosophila melanogaster as a model organism. The performance of mutant flies (PΔEY6) was analyzed in locomotive and olfactory-memory retention tests in comparison to wild type (WT) flies and an Alzheimer's disease model Arc-42 (Aβ-42). Their results suggest that the lack of the D. melanogaster-nAChR causes learning, memory, and locomotion impairments, similar to those observed in Alzheimer's models Arc-42.
Chronic alcohol consumption can cause cardiac myopathy, which afflicts about 500,000 Americans annually. Gunturi et al. wanted to understand the effects of alcohol on heart rate and confirm the role of nitric oxide (NO) signaling in heart rate regulation. Using the model organism Daphnia magna, a water crustacean with a large, transparent heart, they found that the heart rate of Daphnia magna was reduced after treatment with alcohol. This depression could be reversed after treatment with inhibitors of NO synthesis and signaling. Their work has important implications for how we understand alcohol-induced effects on heart rate and potential treatments to reverse heart rate depression as a result of alcohol consumption.
Alzheimer's disease is one of the leading causes of death in the United States and is characterized by neurodegeneration. Mishra et al. wanted to understand the role of two transport proteins, LRP1 and AQP4, in the neurodegeneration of Alzheimer's disease. They used a model organism for Alzheimer's disease, the nematode C. elegans, and genetic engineering to look at whether they would see a decrease in neurodegeneration if they increased the amount of these two transport proteins. They found that the best improvements were caused by increased expression of both transport proteins, with smaller improvements when just one of the proteins is overly expressed. Their work has important implications for how we understand neurodegeneration in Alzheimer's disease and what we can do to slow or prevent the progression of the disease.
The degeneration of nerve cells in the brain can lead to pathologies such as Parkinson’s disease. It has been suggested that neurons in humans may regenerate. In this study, the effect of different doses of caffeine on regeneration was explored in the planeria model. Caffeine has been shown to enhance dopamine production, and dopamine is found in high concentrations in regenerating planeria tissues. Higher doses of caffeine accelerated planeria regeneration following decapitation, indicating a potential role for caffeine as a treatment to stimulate regeneration.
Numerous organisms, including the marine bacterium Aliivibrio fischeri, produce light. This bioluminescence is involved in many important symbioses and may one day be an important source of light for humans. In this study, the authors investigated ways to increase bioluminescence production from the model organism E. coli.
Every year, around 40% of undergraduate students in the United States discontinue their studies, resulting in a loss of valuable education for students and a loss of money for colleges. Even so, colleges across the nation struggle to discover the underlying causes of these high dropout rates. In this paper, the authors discuss the use of machine learning to find correlations between the built environment factors and the retention rates of colleges. They hypothesized that one way for colleges to improve their retention rates could be to improve the physical characteristics of their campus to be more pleasing. The authors used image classification techniques to look at images of colleges and correlate certain features like colors, cars, and people to higher or lower retention rates. With three possible options of high, medium, and low retention rates, the probability that their models reached the right conclusion if they simply chose randomly was 33%. After finding that this 33%, or 0.33 mark, always fell outside of the 99% confidence intervals built around their models’ accuracies, the authors concluded that their machine learning techniques can be used to find correlations between certain environmental factors and retention rates.
Here, recognizing the recognizing the growing threat of non-biodegradable plastic waste, the authors investigated the ability to use a modified enzyme identified in bacteria to decompose polyethylene terephthalate (PET). They used simulations to screen and identify an optimized enzyme based on machine learning models. Ultimately, they identified a potential mutant PETases capable of decomposing PET with improved thermal stability.
