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Impact of Soil Productivity on the Growth of Two Meyer Lemon Trees

Shen et al. | Dec 14, 2020

Impact of Soil Productivity on the Growth of Two Meyer Lemon Trees

Here, the authors aimed to apply home soil testing to identify the cause of the growth differences between two lemon trees. They hypothesized that differences in physical and chemical soil characteristics were influencing differences in soil productivity and plant growth. Overall, the study demonstrated the effectiveness of home soil testing to characterize soils and help homeowners solve common gardening problems.

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Utilizing a Wastewater-Based Medium for Engineered Saccharomyces cerevisiae for the Biological Production of Fatty Alcohols and Carboxylic Acids to Replace Petrochemicals

Ramesh et al. | Oct 02, 2019

Utilizing a Wastewater-Based Medium for Engineered <em>Saccharomyces cerevisiae</em> for the Biological Production of Fatty Alcohols and Carboxylic Acids to Replace Petrochemicals

Saccharomyces cerevisiae yeast is used to produce bioethanol, an alternative to fossil fuels. In this study, authors take advantage of this well studied yeast by genetically engineering them to increase fatty acid biosynthesis and culturing in a cost-effective wastewater based medium; potentially providing a sustainable alternative to petrochemicals.

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Improving Wound Healing by Breaking Down Biofilm Formation and Reducing Nosocomial Infections

DiStefano et al. | Jul 09, 2019

Improving Wound Healing by Breaking Down Biofilm Formation and Reducing Nosocomial Infections

In a 10-year period in the early 2000’s, hospital-based (nosocomial) infections increased by 123%, and this number is increasing as time goes on. The purpose of this experiment was to use hyaluronic acid, silver nanoparticles, and a bacteriophage cocktail to create a hydrogel that promotes wound healing by increasing cell proliferation while simultaneously disrupting biofilm formation and breaking down Staphylococcus aureus and Pseudomonas aeruginosa, which are two strains of bacteria that attribute to nosocomial infections and are increasing in antibiotic resistance.

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How does light affect the distribution of Euglena sp. and Tetrahymena pyriformis

Singh et al. | Mar 03, 2022

How does light affect the distribution of <em>Euglena sp.</em> and <em>Tetrahymena pyriformis</em>

In this article, the authors explored the locomotory movement of Euglena sp. and Tetrahymena pyriformis in response to light. Such research bears relevance to the migration and distribution patterns of both T. pyriformis and Euglena as they differ in their method of finding sustenance in their native environments. With little previous research done on the exploration of a potential response to photostimulation enacted by T. pyriformis, the authors found that T. pyriformis do not bias in distribution towards areas of light - unlike Euglena, which displayed an increased prevalence in areas of light.

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Effect of heme vs. non-heme iron supplements on gut microbiome fitness

Dogra et al. | Nov 07, 2022

Effect of heme vs. non-heme iron supplements on gut microbiome fitness

Here, based on identification of iron deficiencies of a majority of people around the world, the authors sought to understand how the two main forms of dietary iron, heme and non-heme, affect the bacteria found in the human gut. by using a cell plate study, they found that bacterial growth increased with increasing concentration os either form of iron, up until the point where the high iron content resulted in cytotoxicity. They suggest this evidence points to the potential dangers of overconsumption of iron.

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Using explainable artificial intelligence to identify patient-specific breast cancer subtypes

Suresh et al. | Jan 12, 2024

Using explainable artificial intelligence to identify patient-specific breast cancer subtypes

Breast cancer is the most common cancer in women, with approximately 300,000 diagnosed with breast cancer in 2023. It ranks second in cancer-related deaths for women, after lung cancer with nearly 50,000 deaths. Scientists have identified important genetic mutations in genes like BRCA1 and BRCA2 that lead to the development of breast cancer, but previous studies were limited as they focused on specific populations. To overcome limitations, diverse populations and powerful statistical methods like genome-wide association studies and whole-genome sequencing are needed. Explainable artificial intelligence (XAI) can be used in oncology and breast cancer research to overcome these limitations of specificity as it can analyze datasets of diagnosed patients by providing interpretable explanations for identified patterns and predictions. This project aims to achieve technological and medicinal goals by using advanced algorithms to identify breast cancer subtypes for faster diagnoses. Multiple methods were utilized to develop an efficient algorithm. We hypothesized that an XAI approach would be best as it can assign scores to genes, specifically with a 90% success rate. To test that, we ran multiple trials utilizing XAI methods through the identification of class-specific and patient-specific key genes. We found that the study demonstrated a pipeline that combines multiple XAI techniques to identify potential biomarker genes for breast cancer with a 95% success rate.

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