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Alloferon improves the growth performance and developmental time of mealworms (Tenebrio molitor)

Shon et al. | Oct 20, 2023

Alloferon improves the growth performance and developmental time of mealworms <em>(Tenebrio molitor)</em>

Mealworms (Tenebrio molitor) are important food sources for reptiles, birds, and other organisms, as well as for humans. However, the slow growth and low survival rate of mealworms cause problems for mass production. Since alloferon, a synthetic peptide, showed long-term immunological effects on mealworms, we hypothesized that alloferon would function as a growth promoter to maximize mealworm production. We discovered that the overall weight of the alloferon-containing gelatin diet group was 39.5-90% heavier, and the development time of the experimental group was shortened up to 20.6-39.6% than the control group.

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Effect of Different Growth Media on Algae’s Ability for Carbon Dioxide Biofixation

Chaudhuri et al. | Oct 12, 2020

Effect of Different Growth Media on Algae’s Ability for Carbon Dioxide Biofixation

In this study, the authors investigate the effects of different algal growth media on algae's ability to perform carbon dioxide biofixation, or utilize carbon dioxide by fixing it into fatty acids within the cells. More specifically, carbon dioxide biofixation of Chlorella vulgaris was cultured in one of four media options and carbon dioxide was measured and compared to controls. The study results demonstrated that the use of media can enhance algae's capacity for biofixation and this has important implications for developing methods to reduce carbon dioxide in the environment.

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A comparative analysis of machine learning approaches for prediction of breast cancer

Nag et al. | May 11, 2021

A comparative analysis of machine learning approaches for prediction of breast cancer

Machine learning and deep learning techniques can be used to predict the early onset of breast cancer. The main objective of this analysis was to determine whether machine learning algorithms can be used to predict the onset of breast cancer with more than 90% accuracy. Based on research with supervised machine learning algorithms, Gaussian Naïve Bayes, K Nearest Algorithm, Random Forest, and Logistic Regression were considered because they offer a wide variety of classification methods and also provide high accuracy and performance. We hypothesized that all these algorithms would provide accurate results, and Random Forest and Logistic Regression would provide better accuracy and performance than Naïve Bayes and K Nearest Neighbor.

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Applying centrality analysis on a protein interaction network to predict colorectal cancer driver genes

Saha et al. | Nov 18, 2023

Applying centrality analysis on a protein interaction network to predict colorectal cancer driver genes

In this article the authors created an interaction map of proteins involved in colorectal cancer to look for driver vs. non-driver genes. That is they wanted to see if they could determine what genes are more likely to drive the development and progression in colorectal cancer and which are present in altered states but not necessarily driving disease progression.

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Modeling the effects of acid rain on bacterial growth

Shah et al. | Nov 17, 2020

Modeling the effects of acid rain on bacterial growth

Acid rain has caused devastating decreases in ecosystems across the globe. To mimic the effect of acid rain on the environment, the authors analyzed the growth of gram-negative (Escherichia coli) and gram-positive (Staphylococcus epidermidis) bacteria in agar solutions with different pH levels. Results show that in a given acidic environment there was a significant decrease in bacterial growth with an increase in vinegar concentration in the agar, suggesting that bacterial growth is impacted by the pH of the environment. Therefore, increased levels of acid rain could potentially harm the ecosystem by altering bacterial growth.

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Estimation of cytokines in PHA-activated mononuclear cells isolated from human peripheral and cord blood

Subbiah et al. | Mar 09, 2022

Estimation of cytokines in PHA-activated mononuclear cells isolated from human peripheral and cord blood

In this study, the authors investigated the time-dependent cytokine secretion ability of phyto-hemagglutinin (PHA)-activated T cells derived from human peripheral (PB) and cord blood (CB). They hypothesized that the anti-inflammatory cytokine, IL-10, and pro-inflammatory cytokine, TNFα, levels would be higher in PHA-activated T cells obtained from PB as compared to the levels obtained from CB and would decrease over time. Upon PHA-activation, the IL-10 levels were relatively high while the TNFα levels decreased, making these findings applicable in therapeutic treatments e.g., rheumatoid arthritis, psoriasis, and organ transplantation.

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Population Forecasting by Population Growth Models based on MATLAB Simulation

Li et al. | Aug 31, 2020

Population Forecasting by Population Growth Models based on MATLAB Simulation

In this work, the authors investigate the accuracy with which two different population growth models can predict population growth over time. They apply the Malthusian law or Logistic law to US population from 1951 until 2019. To assess how closely the growth model fits actual population data, a least-squared curve fit was applied and revealed that the Logistic law of population growth resulted in smaller sum of squared residuals. These findings are important for ensuring optimal population growth models are implemented to data as population forecasting affects a country's economic and social structure.

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