Amanita Flavoconia (I Think), Russula Sp, Cantharellus Cibarius And C. Minor

FOTD #065 : parrot waxcap! (gliophorus psittacinus)

the parrot waxcap / parrot toadstool is a mycorrhizal fungus in the family hygrophoraceae. it is widely distributed in the grasslands of western europe, the UK, iceland, greenland, the americas, south africa & japan.

the big question: can i bite it?? it is edible & has a mild taste !!

g. psittacinus description :

"the parrot toadstool is a small mushroom, with a convex to umbonate cap up to 4 centimetres (1.6 in) in diameter, which is green when young & later yellowish or even pinkish tinged. the stipe, measuring 2–8 cm (0.8–3.1 in) in length and 3–5 mm in width, is green to greenish yellow. the broad adnate gills are greenish with yellow edges and spore print white. the green colouring persists at the stem apex even in old specimens."

[images : source & source] [fungus description : source]

When I was in the hospital, they gave me a big bracelet that said ALLERGY, but like. I'm allergic to bees. Were they going to prescribe me bees in there.

NaH + H2O = NaOH + H2

Ode to the Microbe

Prints

FOTD #074 : slimy green waxcap! (gliophorus/hygrocybe graminicolor)

the slimy green waxcap is an agaric fungus from the family hygrophoraceae. it is found in australia & aotearoa :-) not much else is known about this mushroom.

the big question : can i bite it?? the edibility is unknown.

g./h. graminicolor description :

"the light green cap & stem of this small agaric are covered with a thick, slimy, glutinous coating. a waxy, grey-green, glutinous thread runs along the edges of white waxy gills. the convex cap becomes centrally depressed & ages to brown."

[images : source & source] [fungus description : source]

"GREEN BABY !! i couldn't find an exact measurement, but she's *small*. i love this mushroom so so so much<3"

Enzyme Function Prediction from Amino Acid Sequence: How AI is Leading the Way

New AI tool 'CLEAN' predicts enzyme functions from amino acid sequences, even for unstudied or poorly understood enzymes.

An innovative artificial intelligence program called CLEAN (contrastive learning–enabled enzyme annotation) has the ability to predict enzyme activities based on their amino acid sequences, even if the enzymes are unfamiliar or inadequately understood. The researchers have reported that CLEAN has surpassed the most advanced tools in terms of precision, consistency, and sensitivity. However, a deeper understanding of enzymes and their roles would be beneficial in a number of disciplines, including genetics, chemistry, pharmaceuticals, medicine, and industrial materials.

The scientists are using the protein language to forecast their performance, similar to how ChatGPT uses written language data to generate predictive phrases. Almost all scientists desire to comprehend the purpose of a protein as soon as they encounter a new protein sequence. Furthermore, this tool will aid researchers in promptly recognizing the suitable enzymes needed to manufacture chemicals and materials for various applications, be it in biology, medicine, or industry.

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Bacterial Morphology Chart. Poster of full color micrographs depicting basic bacterial shapes and arrangements. x

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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FUNGI: THE ROTTEN WORLD AROUND US [1983]

Blorbo family tree <3