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The Cilium- and Centrosome-Associated Protein CCDC11 Is Required for Cytokinesis via Midbody Recruitment of the ESCRT- III Membrane Scission Complex Subunit CHMP2A

Ahmed et al. | Mar 14, 2018

The Cilium- and Centrosome-Associated Protein CCDC11 Is Required for Cytokinesis via Midbody Recruitment of the ESCRT- III Membrane Scission Complex Subunit CHMP2A

In order for cells to successfully multiply, a number of proteins are needed to correctly coordinate the replication and division process. In this study, students use fluorescence microscopy and molecular methods to study CCDC11, a protein critical in the formation of cilia. Interestingly, they uncover a new role for CCDC11, critical in the cell division across multiple human cell lines.

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Assessing the Efficacy of NOX Enzyme Inhibitors as Potential Treatments for Ischemic Stroke in silico

Vinay et al. | Sep 18, 2020

Assessing the Efficacy of NOX Enzyme Inhibitors as Potential Treatments for Ischemic Stroke <i>in silico</i>

Ischemic stroke occurs when blood flow to the brain is interrupted, causing brain damage. This study investigated the effectiveness of different NOX inhibitors as treatments for ischemic stroke in silico. The results help corroborate previous in vivo and in vitro studies in an in silico format, and can be used towards developing drugs to treat ischemic stroke.

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Open Source RNN designed for text generation is capable of composing music similar to Baroque composers

Goel et al. | May 05, 2021

Open Source RNN designed for text generation is capable of composing music similar to Baroque composers

Recurrent neural networks (RNNs) are useful for text generation since they can generate outputs in the context of previous ones. Baroque music and language are similar, as every word or note exists in context with others, and they both follow strict rules. The authors hypothesized that if we represent music in a text format, an RNN designed to generate language could train on it and create music structurally similar to Bach’s. They found that the music generated by our RNN shared a similar structure with Bach’s music in the input dataset, while Bachbot’s outputs are significantly different from this experiment’s outputs and thus are less similar to Bach’s repertoire compared to our algorithm.

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Can Green Tea Alleviate the Effects of Stress Related to Learning and Long-Term Memory in the Great Pond Snail (Lymnaea stagnalis)?

Elias et al. | Jan 30, 2021

Can Green Tea Alleviate the Effects of Stress Related to Learning and Long-Term Memory in the Great Pond Snail (<em>Lymnaea stagnalis</em>)?

Stress and anxiety have become more prevalent issues in recent years with teenagers especially at risk. Recent studies show that experiencing stress while learning can impair brain-cell communication thus negatively impacting learning. Green tea is believed to have the opposite effect, aiding in learning and memory retention. In this study, the authors used Lymnaea stagnalis , a pond snail, to explore the relationship between green tea and a stressor that impairs memory formation to determine the effects of both green tea and stress on the snails’ ability to learn, form, and retain memories. Using a conditioned taste aversion (CTA) assay, where snails are exposed to a sweet substance followed by a bitter taste with the number of biting responses being recorded, the authors found that stress was shown to be harmful to snail learning and memory for short-term, intermediate, and long-term memory.

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Isolation of Microbes From Common Household Surfaces

Gajanan et al. | Jan 27, 2013

Isolation of Microbes From Common Household Surfaces

Microorganisms such as bacteria and fungi live everywhere in the world around us. The authors here demonstrate that these predominantly harmless microbes can be isolated from many household locations that appear "clean." Further, they test the cleaning power of 70% ethanol and suggest that many "clean" surfaces are not in fact "sterile."

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Different volumes of acetic acid affect the oxygen production of spinach leaves during photosynthesis

Wang et al. | Feb 24, 2023

Different volumes of acetic acid affect the oxygen production of spinach leaves during photosynthesis

The burning of fossil fuels, leading to an increased amount of carbon emissions, is the main cause of acid rain. Acid rain affects the process of photosynthesis, which makes the topic valuable to investigate. Our group utilizes plants to further investigate the relationship between pH value and photosynthesis. In this experiment, our group hypothesized that rain with a lower pH will decrease the rate of photosynthesis, causing less oxygen to be produced in the reaction.

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The Role of Race in the Stereotyping of a Speaker’s Accent as Native or Non-native

Bhuvanagiri et al. | Jan 07, 2019

The Role of Race in the Stereotyping of a Speaker’s Accent as Native or Non-native

In the modern world, communication and mobility are no longer obstacles. A natural consequence is that people from all over the world are mixing like never before and national identity can no longer be determined simply by a person's appearance or manner of speech. In this article, the authors study how a person's accent interferes with the perception of a their national identity and proposes ways to eliminate such biases.

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Effect of the Herbal Formulation HF1 on the Expression of PD-L1 in PC3 cells

Imani et al. | Nov 15, 2019

Effect of the Herbal Formulation HF1 on the Expression of PD-L1 in PC3 cells

In this study, Imani et al. investigate whether a new proprietary herbal formulation, HF1, can inhibit expression of immune suppressor protein PD-L1. PD-L1 is a transmembrane protein that can be expressed by cancer cells to assist in their ability to avoid attacks from the immune system. Work from this study demonstrates that HF1 treatment can reduce expression of PD-L1 in cultured cancer cells, implicating HF1 as a potential new cancer therapy.

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Prediction of molecular energy using Coulomb matrix and Graph Neural Network

Hazra et al. | Feb 01, 2022

Prediction of molecular energy using Coulomb matrix and Graph Neural Network

With molecular energy being an integral element to the study of molecules and molecular interactions, computational methods to determine molecular energy are used for the preservation of time and resources. However, these computational methods have high demand for computer resources, limiting their widespread feasibility. The authors of this study employed machine learning to address this disadvantage, utilizing neural networks trained on different representations of molecules to predict molecular properties without the requirement of computationally-intensive processing. In their findings, the authors determined the Feedforward Neural Network, trained by two separate models, as capable of predicting molecular energy with limited prediction error.

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