Mushrooms Releasing Spores Into The Wind. Captured By Paul Stamets

Hydra (genus)

©️nonotnow photography.

🔬~microscope: biolam r11~

📸iPhone 11

"Microbial Rainbow" (detail), Tal Danino, 2018

source

Petri Dishes

Although they are scientific I think they make really cool art pieces i like to use them as reference images when practicing how to use colored pencils

Recent Research Unveils New Genomic Landscape of the Human Gut Microbiome

BGI-Research introduced CGR2, the expanded cultivated genome reference landscape for the human gut microbiome.

Scientists from BGI-Research developed a new version of the Cultivated Genome Reference (CGR), a repository of high-quality draft genomes of the human gut microbiome. The current version of CGR, which is CGR2, has been further expanded to incorporate numerous high-quality draft genomes generated from cultivated bacteria. CGR2 classifies previously unidentified species and uncovers the functional and genomic diversity of bacterial strains. An in-depth analysis of carbohydrate-active enzymes (CAzymes) reveals the phyla with the largest and most diverse repertoires of these enzymes. CGR2 also enabled the identification of genes involved in the synthesis of secondary metabolites in the gut microbiome. The unraveling of the gut microbiome genomic landscape will enable the development of therapeutics and provide a deep insight into the evolution of the human gut microbiome.

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this is the content i'm in the microscopy subreddit for

Broad's Scientist Introduce a Machine Learning Model to Identify Genetic Factors for Cardiovascular Diseases

A machine learning model has been introduced by Broad's Scientists to identify genetic factors associated with cardiovascular diseases.

Using the heart as an investigational model, scientists at the Broad Institute of MIT and Harvard have designed an autoencoder-based machine-learning pipeline that can effectively predict a patient’s heart condition based on image data from ECGs and MRIs. The approach could also be used to detect markers related to cardiovascular diseases.

Nearly all areas of medical science have utilized artificial intelligence (AI) over the years. It has been effectively diagnosing diseases and predicting their transmission and prognosis. AI has been used to design therapeutic approaches effectively and has been helpful in the field of drug design. The use of AI in studying cardiovascular diseases has come a long way, especially machine learning-based systems. AI-based algorithms can be trained to predict cardiovascular disease outcomes using available diagnostic imaging technology.

Currently, the field of cardiology uses a variety of imaging technologies, such as ultrasound imaging, magnetic resonance imaging (MRI), computed tomography (CT), etc. The Electrocardiogram (ECG) is a widely used test to monitor the heart’s rhythm. These technologies generate a lot of data that can be utilized to analyze the condition of a person’s heart. The availability of several diagnostic modalities has raised the need for standardized tools for analyzing imaging data effectively. A multi-modal framework built on machine learning techniques has been suggested by researchers from The Broad Institute of MIT and Harvard. The proposed system can help doctors to understand the cardiovascular state of a person using data from MRIs and ECGs. In practice, clinicians can use data generated from the machine learning program to diagnose a patient appropriately.

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A microscopic spectacle: these diatoms (Bacillaria paxillifer) slide parallel to each other in large colonies. I can only speculate as to why, but I imagine it is a method to access sunlight for photosynthesis while also providing a quick route to safety. 250x magnification, 4x speed.

lil chemistry moodboard for motivation 🧪🥼

orange slime mold plasmodium by Jerry Yap, Malaysia

A mushroom cultivated in a petri dish on Agar Agar shows it’s actual shape, the essential part of the mushroom - the Mycel, is usually hidden from the eye. By Aimee Cornwell