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.

The surface of the unicellular eukaryote, Tetrahymena pyriformis, is covered with thousands of hair-like cilia. These cilia are very similar to cilia of the human olfactory and respiratory tracts making them model organisms for studying cilia function and pathology. The authors of this study investigated the effect of voltage on T. pyriformis galvanotaxis, the movement towards an electrical stimulus. They observed galvanotaxis towards the cathode at voltages over 4V which plateau, indicating opening of voltage gated-ion channels to trigger movement.

Plastic pollution in the ocean is a major global concern. Remotely Operated Vehicles (ROVs) have promise for removing debris from the ocean, but more research is needed to achieve full effectiveness of the ROV technology. Wahlig and Gonzales tackle this issue by developing a deep learning model to distinguish trash from the environment in ROV images.

The Tor network allows individuals to secure their online identities by encrypting their traffic, however it is vulnerable to fingerprinting attacks that threaten users' online privacy. In this paper, the authors develop a new video fingerprinting model to explore how well video streaming can be fingerprinted in Tor. They found that their model could distinguish which one of 50 videos a user was hypothetically watching on the Tor network with 85% accuracy, demonstrating that video fingerprinting is a serious threat to the privacy of Tor users.

In this study, the authors created a survey to assess misconceptions and knowledge deficits regarding cardiovascular diseases exist among US adults and minors.

In this study, the authors address the current climate concern of high CO₂ levels by testing solid forms of hydroxide for CO₂ reduction and designing a drone to fly it in ambient air!

Here the authors sought to use three machine learning models to predict poverty levels in Cambodia based on available household data. They found teat multilayer perceptron outperformed the other models, with an accuracy of 87 %. They suggest that data-driven approaches such as these could be used more effectively target and alleviate poverty.

The authors analyze political endorsement patterns and impacts from the 2018 and 2020 midterm elections and find that such endorsements may be predictable based on the ideological and demographic factors of the endorser.

Metal-organic frameworks (MOFs) are promising new nanomaterials for use in the fight against climate change that can efficiently capture and convert CO₂ to other useful carbon products. This research used computational models to determine the reaction conditions under which MOFs can more efficiently capture and convert CO₂. In a cost-efficient manner, this analysis tested the hypothesis that pressure and temperature affect the efficacy of carbon capture and conversion, and contribute to understanding the optimal conditions for MOF performance to improve the use of MOFs for controlling greenhouse CO₂ emissions.

Scientist are investigating the use of methane-consuming bacteria to aid the growing problem of rising greenhouse gas emissions. While previous studies claim that low-frequency electromagnetic fields can accelerate the growth rate of these bacteria, Chu et al. demonstrate that this fundamental ideology is not on the same wavelength with their data.