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Evaluating the antimicrobial activity of maitake mushroom extract against Staphylococcus epidermidis

Shamsudeen et al. | Oct 06, 2024

Evaluating the antimicrobial activity of maitake mushroom extract against <i>Staphylococcus epidermidis</i>
Image credit: Guido Blokker

Here, seeking to explore new antimicrobial therapies, the authors investigated the antimicrobial activity of Maitake mushroom extract against Staphylococcus epidermidis, a common cause of antibiotic resistant hospital-acquired infections. They found that Maitake extract showed potent antimicrobial activity, with higher concentrations showing inhibition comparable to tetracycline.

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Identifying shark species using an AlexNet CNN model

Sarwal et al. | Sep 23, 2024

Identifying shark species using an AlexNet CNN model

The challenge of accurately identifying shark species is crucial for biodiversity monitoring but is often hindered by time-consuming and labor-intensive manual methods. To address this, SharkNet, a CNN model based on AlexNet, achieved 93% accuracy in classifying shark species using a limited dataset of 1,400 images across 14 species. SharkNet offers a more efficient and reliable solution for marine biologists and conservationists in species identification and environmental monitoring.

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Intra and interspecies control of bacterial growth through extracellular extracts

Howe et al. | Jun 07, 2024

Intra and interspecies control of bacterial growth through extracellular extracts

The study discusses the relationship between bacterial species and the human gut microbiome, emphasizing the role of quorum sensing molecules in bacterial communication and its implications for health. Authors investigated the impact of bacterial supernatants from Escherichia coli (E. coli) on the growth of new E. coli and Enterobacter aerogenes (E. aerogenes) cultures.

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Modeling and optimization of epidemiological control policies through reinforcement learning

Rao et al. | May 23, 2023

Modeling and optimization of epidemiological control policies through reinforcement learning

Pandemics involve the high transmission of a disease that impacts global and local health and economic patterns. Epidemiological models help propose pandemic control strategies based on non-pharmaceutical interventions such as social distancing, curfews, and lockdowns, reducing the economic impact of these restrictions. In this research, we utilized an epidemiological Susceptible, Exposed, Infected, Recovered, Deceased (SEIRD) model – a compartmental model for virtually simulating a pandemic day by day.

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Can the nucleotide content of a DNA sequence predict the sequence accessibility?

Balachandran et al. | Mar 10, 2023

Can the nucleotide content of a DNA sequence predict the sequence accessibility?
Image credit: Warren Umoh

Sequence accessibility is an important factor affecting gene expression. Sequence accessibility or openness impacts the likelihood that a gene is transcribed and translated into a protein and performs functions and manifests traits. There are many potential factors that affect the accessibility of a gene. In this study, our hypothesis was that the content of nucleotides in a genetic sequence predicts its accessibility. Using a machine learning linear regression model, we studied the relationship between nucleotide content and accessibility.

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