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Researchers at Kyushu University have revealed how spatial distance between specific regions of DNA is linked to bursts of gene activity. Using advanced cell imaging techniques and computer modeling, the researchers showed that the folding and movement of DNA, as well as the accumulation of certain proteins, changes depending on whether a gene is active or inactive.

The study, published on December 6 in Science Advances, sheds insight into the complicated world of gene expression and could lead to new therapeutic techniques for diseases caused by improper regulation of gene expression.

Gene expression is a fundamental process that occurs within cells, with two main phases: transcription, where DNA is copied into RNA, and translation, where the RNA is used to make proteins. For each cell to carry out its specific functions in the body, or to respond to changing conditions, the right amount of a protein must be produced at the right time, meaning genes must be carefully switched on and off.

Researchers from Linköping University together with colleagues from Poland and Chile have confirmed a theory that proposes a connection between the complementarity principle and entropic uncertainty. Their study is published in the journal Science Advances.

“Our results have no clear or direct application right now. It’s basic research that lays the foundation for future technologies in quantum information and quantum computers. There’s enormous potential for completely new discoveries in many different research fields,” says Guilherme B Xavier, researcher in quantum communication at Linköping University, Sweden.

But to understand what the researchers have shown, we need to start at the beginning.

Major findings on the inner workings of a brittle star’s ability to reversibly control the pliability of its tissues will help researchers solve the puzzle of mutable collagenous tissue (MCT) and potentially inspire new “smart” biomaterials for human health applications.

The work is directed by Denis Jacob Machado—assistant professor in Bioinformatics at The University of North Carolina at Charlotte Center for Computational Intelligence to Predict Health and Environmental Risks (CIPHER)—and Vladimir Mashanov, staff scientist at Wake Forest Institute for Regenerative Medicine.

In “Unveiling putative modulators of mutable collagenous tissue in the brittle star Ophiomastix wendtii: an RNA-Seq analysis,” published recently in BMC Genomics, the researchers describe using advanced transmission electron microscopy (TEM), RNA sequencing, and other bioinformatics methods to identify 16 potential MCT modulator genes. This research offers a breakthrough towards understanding precisely how echinoderms quickly and drastically transform their collagenous tissue. The first author of the paper, Reyhaneh Nouri, is a Ph.D. student in UNC Charlotte’s Department of Bioinformatics and Genomics.

It requires 1 billion times less energy than our current systems.

Light-emitting diodes (LEDs), semiconductor-based devices that emit light when an electric current flows through them, are key building blocks of numerous electronic devices. LEDs are used to light up smartphone, computer, and TV displays, as well as light sources for indoor and outdoor environments.

Past studies consistently observed a decline in the performance and efficiency of LED devices based on two-dimensional (2D) materials at high current densities. This loss of efficiency at high current densities has been linked to high levels of interaction between excitons, which cause a process known as exciton-exciton annihilation (EEA).

Essentially, the properties of some 2D materials prompt excitons to strongly interact with each other, causing excitons to “deactivate” one another. This results in a significant waste of energy that could otherwise contribute to the lighting of LEDs.

In a recent study published in Nature Communications, a team of researchers at the Carl R. Woese Institute for Genomic Biology reports a new, robust computational toolset to extract biological relationships from large transcriptomics datasets. These efforts will help scientists better investigate cellular processes.

Living organisms are governed by their genome—an instruction manual written in the language of DNA that dictates how an organism grows, survives, and reproduces. By regulating the abundance of different RNA transcripts, cells control their protein expression level, thereby shaping their functions and responses to the environment.

Transcriptomics is the study of gene expression through cataloging the presence and abundance levels of active RNA transcripts generated from the genome under different conditions. Through the lens of RNA, transcriptomics technologies allow scientists to study the complex interactions that enable life and cause disease, as well as assess the biological effects of therapeutics.

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Cell–cell alignment and a background of stationary cells together shape the emergence of cellular clusters in a primary tumor.

In a cancer patient, tumor cells that circulate throughout the body in clusters pose a greater threat of metastasis than those that circulate individually. Those clusters are thought to come together while the cells are still within the primary tumor, but researchers still don’t understand the formation mechanism. Quirine Braat at Eindhoven University of Technology in the Netherlands and her colleagues have now used computer simulations to identify some of the factors at play [1].

The team used a computational lattice model of cells and tissues (the cellular Potts model) to examine a 2D layer of two types of cells—one motile (able to move) and one nonmotile. The tendency of the motile cells to migrate was represented in the model by an external force applied to each one. For a given cell, this force could align strongly or weakly with the forces acting on its neighboring cells.

A joint research team led by Yuuki Kubo and Shiji Tsuneyuki of the University of Tokyo has developed a new computational method that can efficiently determine the crystal structures of multiphase materials, powders that contain more than one type of crystal structures. The method can predict the structure directly from powder X-ray diffraction patterns, the patterns of X-rays passing through crystals roughly the same size as instant coffee particles.

Unlike conventional methods, this approach does not require the use of “lattice constants” and can be applied to existing experimental data that could not be analyzed until now. Thus, the new method is a crucial asset for discovering new material phases and developing new materials. The findings are published in The Journal of Chemical Physics.

Many materials can have several crystal structures, “phases,” even in the same solid state. Determining the underlying crystal structures of materials is essential for understanding their properties and formulating strategies to develop new materials. However, conventional methods make calculations using the “lattice constant,” a property of the crystal being investigated.