Lifeboat Foundation

Using an improved version of the gene editing tool CRISPR/Cas9, researchers knocked out up to twelve genes in plants in a single blow. Until now, this had only been possible for single or small groups of genes. The approach was developed by researchers at Martin Luther University Halle-Wittenberg (MLU) and the Leibniz Institute of Plant Biochemistry (IPB). The method makes it easier to investigate the interaction of various genes. The study appeared in The Plant Journal.

The inheritance of traits in plants is rarely as simple and straightforward as Gregor Mendel described. The monk, whose experiments in the 19th century on trait inheritance in peas laid the foundation of genetics, in fact got lucky. “In the traits that Mendel studied, the rule that only one gene determines a specific trait, for example the color of the peas, happened to apply,” says plant geneticist Dr. Johannes Stuttmann from the Institute of Biology at MLU. According to the researcher, things are often much more complicated. Frequently there are different genes that, through their interaction with one another, result in certain traits or they are partly redundant, in other words they result in the same trait. In this case, when only one of these genes is switched off, the effects are not visible in the plants.

The scientists at MLU and IPB have now developed a way to study this complex phenomenon in a more targeted way by improving CRISPR/Cas9. These gene editing tools can be used to cut the DNA of organisms at specific sites. The team built on the work of biologist Dr. Sylvestre Marillonnet who developed an optimized building block for the CRISPR/Cas9 system at the IPB. “This building block helps to produce significantly more Cas9 enzyme in the plants, which acts as a scissor for the genetic material,” explains Stuttmann. The researchers added up to 24 different guide RNAs which guide the scissor enzyme to the desired locations in the genetic material. Experiments on thale cress (Arabidopsis thaliana) and the wild tobacco plant Nicotiana benthamiana proved that the approach works. Up to eight genes could be switched off simultaneously in the tobacco plants while, in the thale cress, up to twelve genes could be switched off in some cases.

The FDA does not use modern updated science to retest chemicals in food. This study showed that a common food preservative can increase food allergies and damage the immune system.

New science suggests the FDA should test all food chemicals for safety.

A food preservative used to prolong the shelf life of Pop-Tarts, Rice Krispies Treats, Cheez-Its and almost 1250 other popular processed foods may harm the immune system, according to a new peer-reviewed study by Environmental Working Group.

Continue reading “Researchers Warn: Preservative Used in Hundreds of Popular Foods May Harm the Immune System” »

AuthorResearch paper: https://pubs.acs.org/doi/10.1021/acs.est.0c05984

https://scitechdaily.com/scientists-detect-55-chemicals-neve…e-unknown/

https://particleandfibretoxicology.biomedcentral.com/article…0385-9

Continue reading “55 Chemicals From Unknown Origin Discovered in Pregnant Women” »

Furthermore, it implies that defects in the repair process, not the DNA damage itself, can potentially lead to developmental or neurodegenerative diseases.

Researchers at the National Institutes of Health (NIH) have discovered specific regions within the DNA of neurons that accumulate a certain type of damage (called single-strand breaks or SSBs). This accumulation of SSBs appears to be unique to neurons, and it challenges what is generally understood about the cause of DNA damage and its potential implications in neurodegenerative diseases.

Because neurons require considerable amounts of oxygen to function properly, they are exposed to high levels of free radicals—toxic compounds that can damage DNA within cells. Normally, this damage occurs randomly. However, in this study, damage within neurons was often found within specific regions of DNA called “enhancers” that control the activity of nearby genes.

Continue reading “DNA damage ‘hot spots’ discovered within neurons” »

“The results were surprising, but convincing, says Yannick Pauchet, a molecular entomologist also at the Max Planck Institute for Chemical Ecology. ” According to the data they provide, horizontal gene transfer is the most parsimonious explanation,” he says.

But how the whitefly managed to swipe a plant gene is unclear. One possibility, says Turlings, is that a virus served as an intermediate, shuttling genetic material from a plant into the whitefly genome.

As researchers s… See More.

Continue reading “First known gene transfer from plant to insect identified” »

Plastics are one of the world’s largest polluters, taking hundreds of years to degrade in nature. A research team, led by YSE professor Yuan Yao and Liangbing Hu from the University of Maryland, has created a high-quality bioplastic from wood byproducts that they hope can solve one of the world’s most pressing environmental issues.

Efforts to shift from petrochemical plastics to renewable and biodegradable plastics have proven tricky — the production process can require toxic chemicals and is expensive, and the mechanical strength and water stability is often insufficient. But researchers have made a breakthrough, using wood byproducts, that shows promise for producing more durable and sustainable bioplastics.

A study published in Nature Sustainability, co-authored by Yuan Yao, assistant professor of industrial ecology and sustainable systems at Yale School of the Environment (YSE), outlines the process of deconstructing the porous matrix of natural wood into a slurry. The researchers say the resulting material shows a high mechanical strength, stability when holding liquids, and UV-light resistance. It can also be recycled or safely biodegraded in the natural environment, and has a lower life-cycle environmental impact when compared with petroleum-based plastics and other biodegradable plastics.

Artificial microswimmers that can replicate the complex behavior of active matter are often designed to mimic the self-propulsion of microscopic living organisms. However, compared with their living counterparts, artificial microswimmers have a limited ability to adapt to environmental signals or to retain a physical memory to yield optimized emergent behavior. Different from macroscopic living systems and robots, both microscopic living organisms and artificial microswimmers are subject to Brownian motion, which randomizes their position and propulsion direction. Here, we combine real-world artificial active particles with machine learning algorithms to explore their adaptive behavior in a noisy environment with reinforcement learning. We use a real-time control of self-thermophoretic active particles to demonstrate the solution of a simple standard navigation problem under the inevitable influence of Brownian motion at these length scales. We show that, with external control, collective learning is possible. Concerning the learning under noise, we find that noise decreases the learning speed, modifies the optimal behavior, and also increases the strength of the decisions made. As a consequence of time delay in the feedback loop controlling the particles, an optimum velocity, reminiscent of optimal run-and-tumble times of bacteria, is found for the system, which is conjectured to be a universal property of systems exhibiting delayed response in a noisy environment.

Living organisms adapt their behavior according to their environment to achieve a particular goal. Information about the state of the environment is sensed, processed, and encoded in biochemical processes in the organism to provide appropriate actions or properties. These learning or adaptive processes occur within the lifetime of a generation, over multiple generations, or over evolutionarily relevant time scales. They lead to specific behaviors of individuals and collectives. Swarms of fish or flocks of birds have developed collective strategies adapted to the existence of predators (1), and collective hunting may represent a more efficient foraging tactic (2). Birds learn how to use convective air flows (3). Sperm have evolved complex swimming patterns to explore chemical gradients in chemotaxis (4), and bacteria express specific shapes to follow gravity (5).

Inspired by these optimization processes, learning strategies that reduce the complexity of the physical and chemical processes in living matter to a mathematical procedure have been developed. Many of these learning strategies have been implemented into robotic systems (7–9). One particular framework is reinforcement learning (RL), in which an agent gains experience by interacting with its environment (10). The value of this experience relates to rewards (or penalties) connected to the states that the agent can occupy. The learning process then maximizes the cumulative reward for a chain of actions to obtain the so-called policy. This policy advises the agent which action to take. Recent computational studies, for example, reveal that RL can provide optimal strategies for the navigation of active particles through flows (11–13), the swarming of robots (14–16), the soaring of birds , or the development of collective motion (17).

Three-dimensional (3D), submillimeter-scale constructs of neural cells, known as cortical spheroids, are of rapidly growing importance in biological research because these systems reproduce complex features of the brain in vitro. Despite their great potential for studies of neurodevelopment and neurological disease modeling, 3D living objects cannot be studied easily using conventional approaches to neuromodulation, sensing, and manipulation. Here, we introduce classes of microfabricated 3D frameworks as compliant, multifunctional neural interfaces to spheroids and to assembloids. Electrical, optical, chemical, and thermal interfaces to cortical spheroids demonstrate some of the capabilities. Complex architectures and high-resolution features highlight the design versatility. Detailed studies of the spreading of coordinated bursting events across the surface of an isolated cortical spheroid and of the cascade of processes associated with formation and regrowth of bridging tissues across a pair of such spheroids represent two of the many opportunities in basic neuroscience research enabled by these platforms.

Progress in elucidating the development of the human brain increasingly relies on the use of biosystems produced by three-dimensional (3D) neural cultures, in the form of cortical spheroids, organoids, and assembloids (1–3). Precisely monitoring the physiological properties of these and other types of 3D biosystems, especially their electrophysiological behaviors, promises to enhance our understanding of the interactions associated with development of the nervous system, as well as the evolution and origins of aberrant behaviors and disease states (4–8). Conventional multielectrode array (MEA) technologies exist only in rigid, planar, and 2D formats, thereby limiting their functional interfaces to small areas of 3D cultures, typically confined to regions near the bottom contacting surfaces.

Discovery may offer clues to carbon’s role in planet and star formation. Much of the carbon in space is believed to exist in the form of large molecules called polycyclic aromatic hydrocarbons (PAHs). Since the 1980s, circumstantial evidence has indicated that these molecules are abundant in space.