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Down-regulation of CD44 inhibits Wnt/β-catenin mediated cancer cell migration and invasion in gastric cancer

Baek et al. | May 10, 2021

Down-regulation of CD44 inhibits Wnt/β-catenin mediated cancer cell migration and invasion  in gastric cancer

In this study, we aimed to characterize CD44-mediated regulation of the Wnt/β-catenin signaling pathway, which promotes cancer invasion and metastasis. We hypothesized that CD44 down-regulation will inhibit gastric cancer cell migration and invasion by leading to down-regulation of Wnt/β-catenin signaling. We found that CD44 up-regulation was significantly related to poor prognosis in gastric cancer patients. We demonstrated the CD44 down-regulation decreased β-catenin protein expression level. Our results suggest that CD44 down-regulation inhibits cell migration and invasion by down-regulating β-catenin expression level.

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Transfer learning and data augmentation in osteosarcoma cancer detection

Chu et al. | Jun 03, 2023

Transfer learning and data augmentation in osteosarcoma cancer detection
Image credit: Chu and Khan 2023

Osteosarcoma is a type of bone cancer that affects young adults and children. Early diagnosis of osteosarcoma is crucial to successful treatment. The current methods of diagnosis, which include imaging tests and biopsy, are time consuming and prone to human error. Hence, we used deep learning to extract patterns and detect osteosarcoma from histological images. We hypothesized that the combination of two different technologies (transfer learning and data augmentation) would improve the efficacy of osteosarcoma detection in histological images. The dataset used for the study consisted of histological images for osteosarcoma and was quite imbalanced as it contained very few images with tumors. Since transfer learning uses existing knowledge for the purpose of classification and detection, we hypothesized it would be proficient on such an imbalanced dataset. To further improve our learning, we used data augmentation to include variations in the dataset. We further evaluated the efficacy of different convolutional neural network models on this task. We obtained an accuracy of 91.18% using the transfer learning model MobileNetV2 as the base model with various geometric transformations, outperforming the state-of-the-art convolutional neural network based approach.

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The Effect of Anubias barteri Plant Species on Limiting Freshwater Acidification

Ramanathan et al. | Jul 06, 2021

The Effect of <i>Anubias barteri</i> Plant Species on Limiting Freshwater Acidification

Research relating to freshwater acidification is minimal, so the impact of aquatic plants, Anubias barteri var. congensis and Anubias barteri var. nana, on minimizing changes in pH was explored in an ecosystem in Northern California. Creek water samples, with and without the aquatic plants, were exposed to dry ice to simulate carbon emissions and the pH was monitored over an eight-hour period. There was a 25% difference in the observed pH based on molar hydrogen ion concentration between the water samples with plants and those without plants, suggesting that aquatic plants have the potential to limit acidification to some extent. These findings can guide future research to explore the viable partial solution of aquatic plants in combating freshwater acidification.

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Presoaking Seeds with Vinegar Improves Seed Development and Drought Tolerance in Maize Plants

D'Agate et al. | Jul 24, 2020

Presoaking Seeds with Vinegar Improves Seed Development and Drought Tolerance in Maize Plants

Climate change has contributed to the increasing annual temperatures around the world and poses a grave threat to Maize crops. Two methods proven to help combat plant drought stress effects are presoaking seeds (seeds are soaked in a liquid before planting) and the application of Acetic Acid (vinegar) to soil. The purpose of this experiment was to explore if combining these two methods by presoaking seeds with a vinegar solution can improve the seed development and plant drought tolerance of Maize plants during drought conditions.

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Time-Efficient and Low-Cost Neural Network to detect plant disease on leaves and reduce food loss and waste

Singh et al. | Apr 24, 2023

Time-Efficient and Low-Cost Neural Network to detect plant disease on leaves and reduce food loss and waste

About 25% of the food grown never reaches consumers due to spoilage, and 11.5 billion pounds of produce from gardens are wasted every year. Current solutions involve farmers manually looking for and treating diseased crops. These methods of tending crops are neither time-efficient nor feasible. I used a convolutional neural network to identify signs of plant disease on leaves for garden owners and farmers.

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