All known natural phenomena fit into just a few categories and unifying them all is quantum field theory, says physicist Matt Strassler.
Scientists have grown small but complex models of human organs from live fetus cells for the first time, giving experts new insight into our development and potential treatments for malformations while in the womb.
These organoids aren’t full replicas of organs, but they’re close enough to the real deal that they can be used to study disease and other aspects of human biology that are difficult to investigate in living people.
In a new study carried out by an international team of researchers, lung, kidney, and intestine organoids were grown from living stem cells in amniotic fluid. This fluid helps to protect the growing baby and feed it with nutrients, and is taken from the mother without harming her baby as part of regular pregnancy tests.
Universal behavior is a central property of phase transitions, which can be seen, for example, in magnets that are no longer magnetic above a certain temperature. A team of researchers from Kaiserslautern, Berlin and Hainan, China, has succeeded for the first time in observing such universal behavior in the temporal development of an open quantum system, a single cesium atom in a bath of rubidium atoms.
This finding helps to understand how quantum systems reach equilibrium. This is of interest to the development of quantum technologies, for example. The study has been published in Nature Communications.
Phase transitions in chemistry and physics are changes in the state of a substance, for example, the change from a liquid to a gaseous phase, when an external parameter such as temperature or pressure is changed.
Alexander Borst, Max-Planck-Institute for Biological Intelligence, Martinsried, GermanyAbstract: Detecting the direction of image motion is important for vis…
Unlike computers, cells in the brain use digital and analog signals at the same time to communicate with each other, researchers have found.
The finding contradicts the belief that nerve cells in the brain communicate with each other using digital code only.
In an analog system, signals can vary continuously, while digital systems represent signals by a series of pulses. The brain uses a mixture of the two to transmit signals among cells, researchers say.
At the end of the 20th century, analog systems in computer science have been widely replaced by digital systems due to their higher computing power. Nevertheless, the question keeps being intriguing until now: is the brain analog or digital? Initially, the latter has been favored, considering it as a Turing machine that works like a digital computer. However, more recently, digital and analog processes have been combined to implant human behavior in robots, endowing them with artificial intelligence (AI). Therefore, we think it is timely to compare mathematical models with the biology of computation in the brain. To this end, digital and analog processes clearly identified in cellular and molecular interactions in the Central Nervous System are highlighted. But above that, we try to pinpoint reasons distinguishing in silico computation from salient features of biological computation. First, genuinely analog information processing has been observed in electrical synapses and through gap junctions, the latter both in neurons and astrocytes. Apparently opposed to that, neuronal action potentials (APs) or spikes represent clearly digital events, like the yes/no or 1/0 of a Turing machine. However, spikes are rarely uniform, but can vary in amplitude and widths, which has significant, differential effects on transmitter release at the presynaptic terminal, where notwithstanding the quantal (vesicular) release itself is digital. Conversely, at the dendritic site of the postsynaptic neuron, there are numerous analog events of computation. Moreover, synaptic transmission of information is not only neuronal, but heavily influenced by astrocytes tightly ensheathing the majority of synapses in brain (tripartite synapse). At least at this point, LTP and LTD modifying synaptic plasticity and believed to induce short and long-term memory processes including consolidation (equivalent to RAM and ROM in electronic devices) have to be discussed. The present knowledge of how the brain stores and retrieves memories includes a variety of options (e.g., neuronal network oscillations, engram cells, astrocytic syncytium). Also epigenetic features play crucial roles in memory formation and its consolidation, which necessarily guides to molecular events like gene transcription and translation. In conclusion, brain computation is not only digital or analog, or a combination of both, but encompasses features in parallel, and of higher orders of complexity.
Keywords: analog-digital computation; artificial and biological intelligence; bifurcations; cellular computation; engrams; learning and memory; molecular computation; network oscillations.
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Catalysts unlock pathways for chemical reactions to unfold at faster and more efficient rates, and the development of new catalytic technologies is a critical part of the green energy transition.
The Rice University lab of nanotechnology pioneer Naomi Halas has uncovered a transformative approach to harnessing the catalytic power of aluminum nanoparticles by annealing them in various gas atmospheres at high temperatures.
According to a study published in the Proceedings of the National Academy of Sciences, Rice researchers and collaborators showed that changing the structure of the oxide layer that coats the particles modifies their catalytic properties, making them a versatile tool that can be tailored to suit the needs of different contexts of use from the production of sustainable fuels to water-based reactions.
A research team at KAIST has developed the world’s first AI semiconductor capable of processing a large language model (LLM) with ultra-low power consumption using neuromorphic computing technology.
The technology aims to develop integrated circuits mimicking the human nervous system so that chips could be able to perform more sophisticated tasks that require adaption and reasoning with far less energy consumption.
Scientific Reports — Complex hybrid weighted pruning method for accelerating deep convolutional neural networks. arXiv preprint arXiv:1808.06866 (2018).
