The very first moments of the Universe can be reconstructed mathematically even though they cannot be observed directly. Physicists from the Universities of Göttingen and Auckland (New Zealand) have greatly improved the ability of complex computer simulations to describe this early epoch. They discovered that a complex network of structures can form in the first trillionth of a second after the Big Bang. The behavior of these objects mimics the distribution of galaxies in today's Universe. In contrast to today, however, these primordial structures are microscopically small. Typical clumps have masses of only a few grams and fit into volumes much smaller than present-day elementary particles. The results of the study have been published in the journal Physical Review D.

The researchers were able to observe the development of regions of higher density that are held together by their own gravity. "The physical space represented by our simulation would fit into a single proton a million times over," says Professor Jens Niemeyer, head of the Astrophysical Cosmology Group at the University of Göttingen. "It is probably the largest simulation of the smallest area of the Universe that has been carried out so far." These simulations make it possible to calculate more precise predictions for the properties of these vestiges from the very beginnings of the Universe. 

Although the computer-simulated structures would be very short-lived and eventually "vaporize" into standard elementary particles, traces of this extreme early phase may be detectable in future experiments. "The formation of such structures, as well as their movements and interactions, must have generated a background noise of gravitational waves," says Benedikt Eggemeier, a Ph.D. student in Niemeyer's group and first author of the study. "With the help of our simulations, we can calculate the strength of this gravitational wave signal, which might be measurable in the future."

It is also conceivable that tiny black holes could form if these structures undergo runaway collapse. If this happens they could have observable consequences today or form part of the mysterious dark matter in the Universe. "On the other hand," says Professor Easther, "If the simulations predict black holes form, and we don't see them, then we will have found a new way to test models of the infant Universe."

Researchers led by the University of Missouri have created a new method for analyzing large amounts of biological data to help scientists draw faster conclusions for possible treatments.

Studying genetic material on a cellular level, such as single-cell RNA-sequencing, can provide scientists with a detailed, high-resolution view of biological processes at work. This level of detail helps scientists determine the health of tissues and organs, and better understand the development of diseases such as Alzheimer's that impacts millions of people. However, a lot of data is also generated and leads to the need for an efficient, easy-to-use way to analyze it.

Now, a team of engineers and scientists from the University of Missouri and the Ohio State University have created a new way to analyze data from single-cell RNA-sequencing by using a computer method called "machine learning." This method uses the power of high-performance computers to intelligently analyze large amounts of data and help scientists draw faster conclusions and move to the next stage of the research. Their methodology is detailed in a new paper published by an academic journal. 

"Single-cell genetic profiling is on the cutting edge of today's technological advances because it measures how many genes are present and how they are expressed from the level of an individual biological cell," said Dong Xu, a professor in the MU College of Engineering. "At a minimum, there could be tens of thousands of cells being analyzed in this manner, so there ends up being a huge amount of data collected. Currently, determining conclusions from this type of data can be challenging because a lot of data must be filtered through in order to find what researchers are looking for. So, we applied one of the newest machine-learning methods to tackle this problem -- a graph neural network."

After the supercomputer intelligently analyzes the data through a machine learning process, the graph neural network then takes the results and creates a visual representation of the data to help easily identify patterns. The graph is made up of dots -- each dot representative of a cell -- and similar types of cells are color-coded for easy recognition. Xu said precision medicine is a good example of how single-cell RNA-sequencing can be used.

"With this data, scientists can study the interactions between cells within the micro-environment of a cancerous tissue, or watch the T-cells, B-cells and immune cells all try to attack the cancerous cells," Xu said. "Therefore, in cases where a person has a strong immune system, and cancer hasn't fully developed yet, we can learn how cancer can possibly be killed at an early stage, and we have our results sooner because of machine learning, which leads us to a viable treatment faster." 

Xu believes this is a great example of how engineers and biologists can work together to study problems or issues in biology. He hopes this method can be used by biologists as a new tool to help solve complex biological questions, such as a possible treatment for Alzheimer's disease.