Mycena Mushrooms In The Moss

FOTD #073 : apricot jelly! (guepinia helvelloides)

apricot jelly (AKA salmon salad & red jelly fungus) is a saprobic jelly fungus in the family exidiaceae. it often grows in small tufts in the soil :-) it is found in canada, the US, mexico, iran, turkey, brazil, puerto rico, china & most parts of europe.

the big question : can i bite it?? yes !! it is edible but bland.

g. helvelloides description :

"the fungus produces salmon-pink, ear-shaped, gelatinous fruit bodies that grow solitarily or in small tufted groups on soil, usually associated with buried rotting wood. the fruit bodies are 4–10 cm (1.6–3.9 in) tall & up to 17 cm (6.7 in) wide; the stalks are not well-differentiated from the cap."

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

Biology - blue

Credits under the cut

Keep reading

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

source

Physarum pulcherrimum + beetle

by Kim Fleming

Microglossum viride

to all my researchers, students and people in general who love learning: if you don't know this already, i'm about to give you a game changer

connectedpapers

the basic rundown is: you use the search bar to enter a topic, scientific paper name or DOI. the website then offers you a list of papers on the topic, and you choose the one you're looking for/most relevant one. from here, it makes a tree diagram of related papers that are clustered based on topic relatability and colour-coded by time they were produced!

for example: here i search "human B12"

i go ahead and choose the first paper, meaning my graph will be based around it and start from the topics of "b12 levels" and "fraility syndrome"

here is the graph output! you can scroll through all the papers included on the left, and clicking on each one shows you it's position on the chart + will pull up details on the paper on the right hand column (title, authors, citations, abstract/summary and links where the paper can be found)

you get a few free graphs a month before you have to sign up, and i think the free version gives you up to 5 a month. there are paid versions but it really depends how often you need to use this kinda thing.

virus pattern from Macrovector_official (source). Stay safe everyone!

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.

Continue Reading