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The effects of Helianthus Annuus on Amyotrophic Lateral Sclerosis using Drosophila Melanogaster

Srinivasan et al. | Oct 13, 2022

The effects of <em>Helianthus Annuus</em> on Amyotrophic Lateral Sclerosis using <em>Drosophila Melanogaster</em>

Amyotrophic lateral sclerosis (ALS) affects nearly 200,000 people worldwide and there is currently no cure. The purpose of the study was to determine if Helianthus annuus seeds helped reduce nerve degeneration and increase locomotion using Drosophila melanogaster as the model organism. Through this experiment, we found a general trend suggesting that H. annuus helped increase the mobility of the D. melanogaster suggesting it could be a viable supplement for patients with ALS.

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Comparing Virulence of Three T4 Bacteriophage Strains on Ampicillin-Resistant and Sensitive E. coli Bacteria

Hudanich et al. | Dec 09, 2020

Comparing Virulence of Three T4 Bacteriophage Strains on Ampicillin-Resistant and Sensitive <em>E. coli</em> Bacteria

In this study, the authors investigate an alternative way to kill bacteria other than the use of antibiotics, which is useful when considering antibiotic-resistance bacteria. They use bacteriophages, which are are viruses that can infect bacteria, and measure cell lysis. They make some important findings that these bacteriophage can lyse both antibiotic-resistant and non-resistant bacteria.

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Assessing and Improving Machine Learning Model Predictions of Polymer Glass Transition Temperatures

Ramprasad et al. | Mar 18, 2020

Assessing and Improving Machine Learning Model Predictions of Polymer Glass Transition Temperatures

In this study, the authors test whether providing a larger dataset of glass transition temperatures (Tg) to train the machine-learning platform Polymer Genome would improve its accuracy. Polymer Genome is a machine learning based data-driven informatics platform for polymer property prediction and Tg is one property needed to design new polymers in silico. They found that training the model with their larger, curated dataset improved the algorithm's Tg, providing valuable improvements to this useful platform.

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Exponential regression analysis of the Canadian Zero Emission Vehicle market’s effects on climate emissions in 2030

Ajay et al. | Feb 25, 2023

Exponential regression analysis of the Canadian Zero Emission Vehicle market’s effects on climate emissions in 2030
Image credit: Andrew Roberts

Here, the authors explored how the sale and use of electric vehicles could reduce emissions from the transport industry in Canada. By fitting the sale of total of electric vehicles with an exponential model, the authors predicted the number of electric vehicle sales through 2030 and related that to the average emission for such vehicles. Ultimately, they found that the sale and use of electric vehicles alone would likely not meet the 45% reduction in emissions from the transport industry suggested by the Canadian government

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The Effect of the Stomatal Index on the Net Rate of Photosynthesis in the Leaves of Spinacia oleracea, Vinca minor, Rhododendron spp, Epipremnum aureum, and Hedera spp

Segev et al. | Nov 15, 2015

The Effect of the Stomatal Index on the Net Rate of Photosynthesis in the Leaves of <i>Spinacia oleracea</i>, <i>Vinca minor</i>, <i>Rhododendron spp</i>, <i>Epipremnum aureum</i>, and <i>Hedera spp</i>

The density of stomata, or stomatal index, in plant leaves is correlated with the plant's rate of photosynthesis, and affected by the plant's climate. In this paper, authors measure the stomatal index of five plant species to derive their rates of photosynthesis. These results could help track changes in plants' photosynthetic rates with changing climate.

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A novel deep learning model for visibility correction of environmental factors in autonomous vehicles

Dey et al. | Oct 31, 2022

A novel deep learning model for visibility correction of environmental factors in autonomous vehicles

Intelligent vehicles utilize a combination of video-enabled object detection and radar data to traverse safely through surrounding environments. However, since the most momentary missteps in these systems can cause devastating collisions, the margin of error in the software for these systems is small. In this paper, we hypothesized that a novel object detection system that improves detection accuracy and speed of detection during adverse weather conditions would outperform industry alternatives in an average comparison.

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