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Investigating the Role of the Novel ESCRT-III Recruitment Factor CCDC11 in HIV Budding: A Potential Target for Antiviral Therapy

Takemaru et al. | Feb 24, 2020

Investigating the Role of the Novel ESCRT-III Recruitment Factor CCDC11 in HIV Budding: A Potential Target for Antiviral Therapy

Acquired immunodeficiency syndrome (AIDS) is a life-threatening condition caused by the human immunodeficiency virus (HIV). In this work, Takemaru et al explored the role of Coiled-Coil Domain-Containing 11 (CCDC11) in HIV-1 budding. Their results suggest that CCDC11 is critical for efficient HIV-1 budding, potentially indicating CCDC11 a viable target for antiviral therapeutics without major side effects.

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Effects of Ocean Acidification on the Photosynthetic Ability of Chaetoceros gracilis in the Monterey Bay

Harvell et al. | Jan 16, 2020

Effects of Ocean Acidification on the Photosynthetic Ability of <i>Chaetoceros gracilis</i> in the Monterey Bay

In this article, Harvell and Nicholson hypothesized that increased ocean acidity would decrease the photosynthetic ability of Chaetoceros gracilis, a diatom prolific in Monterey Bay, because of the usually corrosive effects of carbonic acid on both seashells and cells’ internal structures. They altered pH of algae environments and measured the photosynthetic ability of diatoms over four days by spectrophotometer. Overall, their findings indicate that C. gracilis may become more abundant in Monterey Bay as the pH of the ocean continues to drop, potentially contributing to harmful algal blooms.

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The Effect of Varying Training on Neural Network Weights and Visualizations

Fountain et al. | Dec 04, 2019

The Effect of Varying Training on Neural Network Weights and Visualizations

Neural networks are used throughout modern society to solve many problems commonly thought of as impossible for computers. Fountain and Rasmus designed a convolutional neural network and ran it with varying levels of training to see if consistent, accurate, and precise changes or patterns could be observed. They found that training introduced and strengthened patterns in the weights and visualizations, the patterns observed may not be consistent between all neural networks.

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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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QuitPuff: A Simple Method Using Saliva to Assess the Risk of Oral Pre-Cancerous Lesions and Oral Squamous Cell Carcinoma in Chronic Smokers

Shamsher et al. | Mar 27, 2019

QuitPuff: A Simple Method Using Saliva to Assess the Risk of Oral Pre-Cancerous Lesions and Oral Squamous Cell Carcinoma in Chronic Smokers

Smoking generates free radicals and reactive oxygen species which induce cell damage and lipid peroxidation. This is linked to the development of oral cancer in chronic smokers. The authors of this study developed Quitpuff, simple colorimetric test to measure the extent of lipid peroxidation in saliva samples. This test detected salivary lipid peroxidation with 96% accuracy in test subjects and could serve as an inexpensive, non-invasive test for smokers to measure degree of salivary lipid peroxidation and potential risk of oral cancer.

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Characterization of Drought Tolerance in Arabidopsis Mutant fry1-6

Kim et al. | Jan 07, 2019

Characterization of Drought Tolerance in Arabidopsis Mutant  fry1-6

In a world where water shortage is becoming an increasing concern, and where population increase seems inevitable, food shortage is an overwhelming concern for many. In this paper, the authors aim to characterize a drought-resistant strain of A. thaliana, investigating the cause for its water resistance. These and similar studies help us learn how plants could be engineered to improve their ability to flourish in a changing climate.

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Optimizing AI-generated image detection using a Convolutional Neural Network model with Fast Fourier Transform

Gupta et al. | Oct 24, 2025

Optimizing AI-generated image detection using a Convolutional Neural Network model with Fast Fourier Transform

Recent advances in generative AI have made it increasingly hard to distinguish real images from AI-generated ones. Traditional detection models using CNNs or U-net architectures lack precision because they overlook key spatial and frequency domain details. This study introduced a hybrid model combining Convolutional Neural Networks (CNN) with Fast Fourier Transform (FFT) to better capture subtle edge and texture patterns.

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