I'll Miss Micro Lab...

FOTD #114 : purple brittlegill! (russula atropurpurea)

the purple brittlegill (also blackish-purple russula) is a mycorrhizal fungus in the family russulaceae. it grows with both coniferous & deciduous trees !! it has been recorded in europe, asia & eastern north america. :-)

the big question : can i bite it?? yes, though it's not particularly recommended. it is said to taste.. hot?

r. atropurpurea description :

"the cap is 4–10 cm (1.5–4 in) in diameter. it is dark reddish purple, with a dark; sometimes almost black centre. at first it is convex, but later flattens, & often has a shallow depression. it can also be lighter in colour, or mottled yellowish. the stem is firm, white, & turns grey with age. it measures 3–6 cm in length & 1–2 cm in diameter. the closely set and fairly broad gills are adnexed to almost free, & pale cream, giving a spore print of the same colour."

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

Leafy Sea Dragon (Phycodurus Eques)

Back when I asked for some concepts in September on my Instagram, @ sammithyst suggested a mon based on Petrie duals, a term in topology that refers to a loop of edges that can split a 3D shape in half in a certain way. Although that was incorporated into the design of this mon (skew polygon "teeth on each dish"), the name reminded me of Petri dishes.

Petri dishes are shallow dishes with a cover that is used to grow all sorts of cells, like bacteria, fungi, and even human cells. Cells can be grown with the growth medium that is put into the dish, some sort of food like some agarose gel or a liquid mixture of nutrients.

Cantri (Poison/Psychic): When dormant, they reside completely inside their dish, only coming out when they run out of the food that dragged in. Despite having many protozoan-like cells in their body, they seem to be resistant to the antibiotics of this world.

Pictures I had forgotten I took.

ig: @antonio_eya

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

improbable.com

Past Ig Winners

Ig® Nobel Prize Winners For achievements that first make people LAUGH then make them THINK Winners by year: 2023 : 2022 : 2021 2020 : 2019 :

The Ig Nobel Prize, for scientific research that makes people laugh and then makes them think. This year's winners!

CHEMISTRY and GEOLOGY PRIZE [POLAND, UK] Jan Zalasiewicz, for explaining why many scientists like to lick rocks. REFERENCE: “Eating Fossils,” Jan Zalasiewicz, The Paleontological Association Newsletter, no. 96, November 2017. Eating fossils | The Palaeontological Association (palass.org) WHO TOOK PART IN THE CEREMONY: Jan Zalasiewicz

LITERATURE PRIZE [FRANCE, UK, MALAYSIA, FINLAND] Chris Moulin, Nicole Bell, Merita Turunen, Arina Baharin, and Akira O’Connor for studying the sensations people feel when they repeat a single word many, many, many, many, many, many, many times. REFERENCE: “The The The The Induction of Jamais Vu in the Laboratory: Word Alienation and Semantic Satiation,” Chris J. A. Moulin, Nicole Bell, Merita Turunen, Arina Baharin, and Akira R. O’Connor, Memory, vol. 29, no. 7, 2021, pp. 933-942. doi.org/10.1080/09658211.2020.1727519 WHO TOOK PART IN THE CEREMONY: Chris Moulin, Akira O’Connor

MECHANICAL ENGINEERING PRIZE [INDIA, CHINA, MALAYSIA, USA] Te Faye Yap, Zhen Liu, Anoop Rajappan, Trevor Shimokusu, and Daniel Preston, for re-animating dead spiders to use as mechanical gripping tools. REFERENCE: “Necrobotics: Biotic Materials as Ready-to-Use Actuators,” Te Faye Yap, Zhen Liu, Anoop Rajappan, Trevor J. Shimokusu, and Daniel J. Preston, Advanced Science, vol. 9, no. 29, 2022, article 2201174. doi.org/10.1002/advs.202201174 WHO TOOK PART IN THE CEREMONY: Te Faye Yap and Daniel Preston

PUBLIC HEALTH PRIZE [SOUTH KOREA, USA] Seung-min Park, for inventing the Stanford Toilet, a device that uses a variety of technologies — including a urinalysis dipstick test strip, a computer vision system for defecation analysis, an anal-print sensor paired with an identification camera, and a telecommunications link — to monitor and quickly analyze the substances that humans excrete. REFERENCE: “A Mountable Toilet System for Personalized Health Monitoring via the Analysis of Excreta,” Seung-min Park, Daeyoun D. Won, Brian J. Lee, Diego Escobedo, Andre Esteva, Amin Aalipour, T. Jessie Ge, et al., Nature Biomedical Engineering, vol. 4, no. 6, 2020, pp. 624-635. doi.org/10.1038/s41551-020-0534-9 REFERENCE: “Digital Biomarkers in Human Excreta,” Seung-min Park, T. Jessie Ge, Daeyoun D. Won, Jong Kyun Lee, and Joseph C. Liao, Nature Reviews Gastroenterology and Hepatology, vol. 18, no. 8, 2021, pp. 521-522. doi.org/10.1038/s41575-021-00462-0 REFERENCE: “Smart Toilets for Monitoring COVID-19 Surges: Passive Diagnostics and Public Health,” T. Jessie Ge, Carmel T. Chan, Brian J. Lee, Joseph C. Liao, and Seung-min Park, NPJ Digital Medicine, vol. 5, no. 1, 2022, article 39. doi.org/10.1038/s41746-022-00582-0 REFERENCE: “Passive Monitoring by Smart Toilets for Precision Health,” T. Jessie Ge, Vasiliki Nataly Rahimzadeh, Kevin Mintz, Walter G. Park, Nicole Martinez-Martin, Joseph C. Liao, and Seung-min Park, Science Translational Medicine, vol. 15, no. 681, 2023, article eabk3489. doi.org/10.1126/scitranslmed.abk3489 WHO TOOK PART IN THE CEREMONY: Seung-min Park

COMMUNICATION PRIZE [ARGENTINA, SPAIN, COLOMBIA, CHILE, CHINA, USA] María José Torres-Prioris, Diana López-Barroso, Estela Càmara, Sol Fittipaldi, Lucas Sedeño, Agustín Ibáñez, Marcelo Berthier, and Adolfo García, for studying the mental activities of people who are expert at speaking backward. REFERENCE: “Neurocognitive Signatures of Phonemic Sequencing in Expert Backward Speakers,” María José Torres-Prioris, Diana López-Barroso, Estela Càmara, Sol Fittipaldi, Lucas Sedeño, Agustín Ibáñez, Marcelo L. Berthier, and Adolfo M. García, Scientific Reports, vol. 10, no. 10621, 2020. doi.org/10.1038/s41598-020-67551-z WHO TOOK PART IN THE CEREMONY: María José Torres-Prioris, Adolfo García

MEDICINE PRIZE [USA, CANADA, MACEDONIA, IRAN, VIETNAM] Christine Pham, Bobak Hedayati, Kiana Hashemi, Ella Csuka, Tiana Mamaghani, Margit Juhasz, Jamie Wikenheiser, and Natasha Mesinkovska, for using cadavers to explore whether there is an equal number of hairs in each of a person’s two nostrils. REFERENCE: “The Quantification and Measurement of Nasal Hairs in a Cadaveric Population,” Christine Pham, Bobak Hedayati, Kiana Hashemi, Ella Csuka, Margit Juhasz, and Natasha Atanaskova Mesinkovska, Journal of The American Academy of Dermatology, vol. 83, no. 6, 2020, pp. AB202-AB202. doi.org/10.1016/j.jaad.2020.06.902 WHO TOOK PART IN THE CEREMONY: Christine Pham, Natasha Mesinkovska, Margit Juhasz, Kiana Hashemi, Tiana Mamaghani

NUTRITION PRIZE [JAPAN] Homei Miyashita and Hiromi Nakamura, for experiments to determine how electrified chopsticks and drinking straws can change the taste of food. REFERENCE: “Augmented Gustation Using Electricity,” Hiromi Nakamura and Homei Miyashita, Proceedings of the 2nd Augmented Human International Conference, March 2011, article 34. doi.org/10.1145/1959826.1959860 WHO TOOK PART IN THE CEREMONY: Homei Miyashita, Hiromi Nakamura

EDUCATION PRIZE [CHINA, CANADA, UK, THE NETHERLANDS, IRELAND, USA, JAPAN] Katy Tam, Cyanea Poon, Victoria Hui, Wijnand van Tilburg, Christy Wong, Vivian Kwong, Gigi Yuen, and Christian Chan, for methodically studying the boredom of teachers and students. REFERENCE: “Boredom Begets Boredom: An Experience Sampling Study on the Impact of Teacher Boredom on Student Boredom and Motivation,” Katy Y.Y. Tam, Cyanea Y. S. Poon, Victoria K.Y. Hui, Christy Y. F. Wong, Vivian W.Y. Kwong, Gigi W.C. Yuen, Christian S. Chan, British Journal of Educational Psychology, vol. 90, no. S1, June 2020, pp. 124-137. doi.org/10.1111/bjep.12549 REFERENCE: “Whatever Will Bore, Will Bore: The Mere Anticipation of Boredom Exacerbates its Occurrence in Lectures,” Katy Y.Y. Tam, Wijnand A.P. Van Tilburg, Christian S. Chan, British Journal of Educational Psychology, epub 2022. doi.org/10.1111/bjep.12549 WHO TOOK PART IN THE CEREMONY: Christian Chan, Katy Y.Y. Tam, Wijnand A.P. Van Tilburg

PSYCHOLOGY PRIZE [USA] Stanley Milgram, Leonard Bickman, and Lawrence Berkowitz for experiments on a city street to see how many passersby stop to look upward when they see strangers looking upward REFERENCE: “Note on the Drawing Power of Crowds of Different Size,” Stanley Milgram, Leonard Bickman, and Lawrence Berkowitz, Journal of Personality and Social Psychology, vol. 13, no. 2, 1969, pp. 79-82. psycnet.apa.org/doi/10.1037/h0028070 WHO TOOK PART IN THE CEREMONY: Len Bickman

PHYSICS PRIZE [SPAIN, GALICIA, SWITZERLAND, FRANCE, UK] Bieito Fernández Castro, Marian Peña, Enrique Nogueira, Miguel Gilcoto, Esperanza Broullón, Antonio Comesaña, Damien Bouffard, Alberto C. Naveira Garabato, and Beatriz Mouriño-Carballido, for measuring the extent to which ocean-water mixing is affected by the sexual activity of anchovies. REFERENCE: “Intense Upper Ocean Mixing Due to Large Aggregations of Spawning Fish,” Bieito Fernández Castro, Marian Peña, Enrique Nogueira, Miguel Gilcoto, Esperanza Broullón, Antonio Comesaña, Damien Bouffard, Alberto C. Naveira Garabato, and Beatriz Mouriño-Carballido, Nature Geoscience, vol. 15, 2022, pp. 287–292. doi.org/10.1038/s41561-022-00916-3 WHO TOOK PART IN THE CEREMONY: Bieito Fernandez Castro, Beatriz Mouriño-Carballido, Alberto Naveira Garabato, Esperanza Broullon, Miguel Gil Coto

BRAINLESS BOX JELLIES CAN LEARN BY EXPERIENCE

Mangrove box jellyfish (Tripedalia cystophora) is a small species of box jellyfish, native to the Caribbean Sea and the Central Indo-Pacific, presenting a simple nervous system. But despite tiny, researchers have demonstrated present the ability to learn by association. Although has no central brain, and being the size of the finger-tip, this box jelly can be trained to associate the sensation of bumping into something with a visual cue, and to use the information to avoid future collisions.

In the wild, the Mangrove box jellyfish forage for tiny crustaceans between the roots of mangroves. To mimic this environment, researchers placed the box jellies in cylindrical tanks that had either black and white or grey and white vertical stripes on the walls. To the jellyfish, the dark stripes looked like mangrove roots in either clear or murky water. In the ‘murky water’ tanks, the jellyfish bumped into the wall because their visual system couldn’t detect the grey stripes very clearly. But after a few minutes, they learnt to adjust their behaviour, pulsing rapidly to swim away from the wall when they got too close, this state learning is based on the combination of visual and mechanical stimuli in simple animals with no brain.

The learning process, in difference with vertebrate animals, doesnt occurs in a central neuronal organs, but instead in a small organs named rhopalial nervous system, which act as learning center, in which the jelly combines visual and mechanical stimuli during operant conditioning.

Main image: An adult specimen of the box jellyfish T. cystophora., showing where is located one of the four sensory structures named rhopalia, which includes two lens eyes. Each rhopalium also contains a visual information processing center.

Reference (Open Access): Bielecki et al., 2023. Associative learning in the box jellyfish Tripedalia cystophora. Current Biology.

source