New nanoparticle design for detecting tumors

Nano-sized particles have been engineered in a new way to improve detection of tumors within the body and in biopsy tissue, a research team reports. The advance could enable identifying early stage tumors with lower doses of radiation.

Credit: KTH Royal Institute of Technology

Researchers from the KTH Royal Institute of Technology have developed “core-shell nanoparticles” which may be used in the future for targeted diagnostics, instead of current methods that use optical or X-ray fluorescence contrast agents. The tests performed in the laboratory on mice have shown that the new particles are able to detect early-stage tumours of only a few millimetres in size. “Nanoparticles of different size, originating from the same material, don’t appear to be distributed in the blood in the same concentrations,” Muhammet Toprak, Professor of Materials Chemistry at KTH, says. “That’s because when they come into contact with your body, they’re quickly wrapped in various biological molecules — which gives them a new identity.”

Source (KTH, Royal Institute of Technology. “New nanoparticle design paves way for improved detection of tumors.” ScienceDaily. ScienceDaily, 2 June 2021.)

Original paper: Saladino, G.M., Vogt, C., Li, Y., Shaker, K., Brodin, B., Svenda, M., Hertz, H.M. and Toprak, M.S., 2021. Optical and X-ray Fluorescent Nanoparticles for Dual Mode Bioimaging. ACS nano15(3), pp.5077-5085.

Animating Pictures

Researchers have developed a method for producing looping videos from one image. The technique is specialized in fluid motion seen in water, smoke or clouds. After training a deep neural network on thousands of images, the framework is capable of estimating motion. The solution posed several challenges, from which the most difficult to overcome was the employment of the splatting technique. Through it, holes in the top part of the images appeared. Using the previously obtained motion, the authors created a symmetric splatting methodology that merges the flow bidirectionally. A presentation of the published work can be seen in the following video.

For an in-depth understanding of the paper, please see the following resources:

Source (University of Washington. “Researchers can turn a single photo into a video.” ScienceDaily. ScienceDaily, 15 June 2021.)

Original paper: Holynski, A., Curless, B., Seitz, S.M. and Szeliski, R., 2020. Animating Pictures with Eulerian Motion Fields. arXiv preprint arXiv:2011.15128.

A Score To Settle

New information on the animated series Arcane has been released, starting with a short teaser.

Co-creators Christian Linke and Alex Yee introduce briefly the story setting, presenting the motivation behind this project.


Not everything that is true can be proven. This discovery transformed infinity, changed the course of a world war and led to the modern computer.

Here is a short documentary on Conway’s Game of Life, beautifully explaining how the logic gates of the Turing machine were implemented to run inside GoL.

For more in-depth explanations, read: Rendell, P., 2014. Turing machine universality of the game of life (Doctoral dissertation, University of the West of England).

Turing Machine Diagram for the Game of Life, Credit: Rendell

Local Cosmic Web from Galaxies

Dark matter is an elusive substance that makes up 80% of the universe. It also provides the skeleton for what cosmologists call the cosmic web, the large-scale structure of the universe that, due to its gravitational influence, dictates the motion of galaxies and other cosmic material. However, the distribution of local dark matter is currently unknown because it cannot be measured directly. Researchers must instead infer its distribution based on its gravitational influence on other objects in the universe, like galaxies.

Credit: Sungwook E. Hong

Previous attempts to map the cosmic web started with a model of the early universe and then simulated the evolution of the model over billions of years. However, this method is computationally intensive and so far has not been able to produce results detailed enough to see the local universe. In the new study, the researchers took a completely different approach, using machine learning to build a model that uses information about the distribution and motion of galaxies to predict the distribution of dark matter.

The researchers built and trained their model using a large set of galaxy simulations, called Illustris-TNG, which includes galaxies, gasses, other visible matter, as well as dark matter. The team specifically selected simulated galaxies comparable to those in the Milky Way and ultimately identified which properties of galaxies are needed to predict the dark matter distribution. The research team then applied their model to real data from the local universe from the Cosmicflow-3 galaxy catalog. The catalog contains comprehensive data about the distribution and movement of more than 17 thousand galaxies in the vicinity of the Milky Way — within 200 megaparsecs.

The map successively reproduced known prominent structures in the local universe, including the “local sheet” — a region of space containing the Milky Way, nearby galaxies in the “local group,” and galaxies in the Virgo cluster — and the “local void” — a relatively empty region of space next to the local group. Additionally, it identified several new structures that require further investigation, including smaller filamentary structures that connect galaxies. For example, it has been suggested that the Milky Way and Andromeda galaxies may be slowly moving toward each other, but whether they may collide in many billions of years remains unclear. Studying the dark matter filaments connecting the two galaxies could provide important insights into their future.

Adapted and abridged from Source (Penn State. “Dark matter map reveals hidden bridges between galaxies.” ScienceDaily. ScienceDaily, 25 May 2021.)

Original paper: Sungwook E. Hong, Donghui Jeong, Ho Seong Hwang, Juhan Kim. Revealing the Local Cosmic Web from Galaxies by Deep Learning. The Astrophysical Journal, 2021; 913 (1): 76 DOI: 10.3847/1538-4357/abf040