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Category: biological – Page 84

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Imagine having a building made of stacks of bricks connected by adaptable bridges. You pull a knob that modifies the bridges and the building changes functionality. Wouldn’t it be great?

A team of researchers led by Prof. Aitor Mugarza, from the Catalan Institute of Nanoscience and Nanotechnology (ICN2) and ICREA, together with Prof. Diego Peña from the Center for Research in Biological Chemistry and Molecular Materials of the University of Santiago de Campostela (CiQUS-USC), Dr. Cesar Moreno, formerly a member of ICN2’s team and currently a researcher at the University of Cantabria, and Dr. Aran Garcia-Lekue, from the Donostia International Physics Center (DIPC) and Ikerbasque Foundation, has done something analogous, but at the single-atom scale, with the aim of synthesizing new carbon-based materials with tunable properties.

As explained in a paper just published in the Journal of the American Chemical Society (JACS) and featured on the cover of the issue, this research is a significant breakthrough in the precise engineering of atomic-thin materials —called “2D materials” due to their reduced dimensionality. The proposed fabrication technique opens exciting new possibilities for , and, in particular, for application in advanced electronics and future solutions for sustainable energy.

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In recent decades, engineers have created a wide range of robotic systems inspired by animals, including four legged robots, as well as systems inspired by snakes, insects, squid and fish. Studies exploring the interactions between these robots and their biological counterparts, however, as still relatively rare.

Researchers at Peking University and China Agricultural University recently set out to explore what happens when live fish are placed in the same environment as a robotic fish. Their findings, published in Bioinspiration & Biomimetics, could both inform the development of fish-inspired robots and shed some new light on the behavior of real fish.

“Our research team has been focusing on the development of self-propelled robotic fish for a considerable amount of time,” Dr. Junzhi Yu, one of the researchers who carried out the study, told Tech Xplore. “During our , we observed an exciting phenomenon where live fish were observed following the swimming robotic fish. We are eager to further explore the underlying principles behind this phenomenon and gain a deeper understanding of this ‘fish following’ behavior.”

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Three university teams will explore and take initial steps to mitigate risks associated with manufacturing capabilities that rely on biological processes in space. The DARPA Biomanufacturing: Survival, Utility, and Reliability beyond Earth (B-SURE) program aims to address foundational scientific questions to determine how well industrial bio-manufacturing microorganisms perform in space conditions.

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In a new study in Nature Machine Intelligence, researchers Bojian Yin and Sander Bohté from the HBP partner Dutch National Research Institute for Mathematics and Computer Science (CWI) demonstrate a significant step towards artificial intelligence that can be used in local devices like smartphones and in VR-like applications, while protecting privacy.

They show how brain-like neurons combined with novel learning methods enable training fast and energy-efficient spiking on a large scale. Potential applications range from wearable AI to and Augmented Reality.

While modern artificial neural networks are the backbone of the current AI revolution, they are only loosely inspired by networks of real, biological neurons such as our brain. The brain however is a much larger network, much more energy-efficient, and can respond ultra-fast when triggered by external events. Spiking neural networks are special types of neural networks that more closely mimic the working of biological neurons: the neurons of our nervous system communicate by exchanging electrical pulses, and they do so only sparingly.

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Year 2013 😗😁

Shamees Aden, a British designer and scientist, has come up with a concept for a pair of self-repairing shoes of synthetic protocell materials.

Protocells are molecules that on their own are not alive, but when used with other types of protocells can mimic the properties of living organisms. They react to heat, light and pressure like live cells.

Aden’s concept for the shoes is that they could be 3D printed to the exact size of the wearer and when worn the shoes would react to heat and pressure to grow and expand in pressure points where more cushioning is needed. They would be kept in protocell fluid overnight to regenerate.

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Mathematicians have uncovered a universal explanatory framework that provides a “window into evolution.” This framework explains how molecules interact with each other in adapting to changing conditions while still maintaining tight control over essential properties that are crucial for survival.

According to Dr. Araujo from the QUT School of Mathematical Sciences, the research results provide a blueprint for the creation of signaling networks that are capable of adapting across all life forms and for the design of synthetic biological systems.

“Our study considers a process called robust perfect adaptation (RPA) whereby biological systems, from individual cells to entire organisms, maintain important molecules within narrow concentration ranges despite continually being bombarded with disturbances to the system,” Dr. Araujo said.

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Researchers have created atomically thin artificial neurons capable of processing both light and electric signals for computing. The material enables the simultaneous existence of separate feedforward and feedback paths within a neural network, boosting the ability to solve complex problems.

For decades, scientists have been investigating how to recreate the versatile computational capabilities of biological neurons to develop faster and more energy-efficient machine learning systems. One promising approach involves the use of memristors: capable of storing a value by modifying their conductance and then utilizing that value for in-memory processing.

However, a key challenge to replicating the complex processes of biological neurons and brains using memristors has been the difficulty in integrating both feedforward and feedback neuronal signals. These mechanisms underpin our cognitive ability to learn complex tasks, using rewards and errors.

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face_with_colon_three 2022 Microbes can now clean up plastics in lakes, streams, and oceans eventually.

Ultra-high resolution mass spectrometry revealed that plastic bags leach labile compounds. Bioassays performed in Scandinavian lakes indicated that these compounds are incorporated into biomass faster and more efficiently than natural organic matter.

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Neural networks are distributed computing structures inspired by the structure of a biological brain and aim to achieve cognitive performance comparable to that of humans but in a much shorter time.

These technologies now form the basis of machine learning and that can perceive the environment and adapt their own behavior by analyzing the effects of previous actions and working autonomously. They are used in many areas of application, such as speech and image recognition and synthesis, autonomous driving and augmented reality systems, bioinformatics, genetic and molecular sequencing, and high-performance computing technologies.

Compared to conventional computing approaches, in order to perform complex functions, neural networks need to be initially “trained” with a large amount of known information that the network then uses to adapt by learning from experience. Training is an extremely energy-intensive process and as computing power increases, the neural networks’ consumption grows very rapidly, doubling every six months or so.

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