Lifeboat Foundation

A research team headed by chemists at the University of California, Irvine has discovered a previously unknown way in which light interacts with matter, a finding that could lead to improved solar power systems, light-emitting diodes, semiconductor lasers and other technological advancements.

The Red Planet launches large bursts of plasma into space from its upper atmosphere, much like the sun’s coronal mass ejections, despite not having a global magnetic field.

By Alex Wilkins

Researchers have made a digital map showing a tiny chunk of a human brain in unprecedented detail.

Based on a brain tissue sample that had been surgically removed from a person, the map represents a cubic millimeter of brain—an area about half the size of a grain of rice. But even that tiny segment is overflowing with 1.4 million gigabytes of information—containing about 57,000 cells, 230 millimeters of blood vessels and 150 million synapses, the connections between neurons.

The researchers published their findings in the journal Science on Friday. They have made the data set freely available online and provided tools for analyzing and proofreading it.

Gary Marcus’ book Kluge is about the human brain and its workings. And I have been interested in how the brain works since my undergratuate days at Allegheny College working with Pete Elias and researching the learning of mice (1968) and especially into my doctoral work with Dick King at UNC-Chapel Hill. I actually think there is only modest improvement in some aspects of what we have learned about the brain since I graduated in 1977.

But we have come a long way… In ancient Greece, thinkers like Hippocrates and Aristotle grappled with the nature of the mind and its connection to the brain. While Hippocrates believed that the brain was the seat of intelligence and consciousness, Aristotle argued that the heart was the center of reason and emotion, with the brain serving merely as a cooling mechanism. We now know that the brain actually does have some impacts on thinking for most people. (grin)

I thought to share the AI book summary produced by Perplexity when I asked it to summarize the main ideas about how the brain evolved to produce this thing we can consciousness… I slightly edited the output. As Spock would say, “Fascinating.”

The new type of network that is making waves in the ML world.

Scientists have produced an enhanced, ultra-pure form of silicon that allows the construction of high-performance qubit devices. This fundamental component is crucial for paving the way towards scalable quantum computing.

The finding, published in the journal Communications Materials – Nature, could define and push forward the future of quantum computing.

The research was led by Professor Richard Curry from the Advanced Electronic Materials group at The University of Manchester, in collaboration with the University of Melbourne in Australia.

A collaborative research team from NIMS and Tokyo University of Science has successfully developed a cutting-edge artificial intelligence (AI) device that executes brain-like information processing through few-molecule reservoir computing. This innovation utilizes the molecular vibrations of a select number of organic molecules. By applying this device for the blood glucose level prediction in patients with diabetes, it has significantly outperformed existing AI devices in terms of prediction accuracy.

With the expansion of machine learning applications in various industries, there’s an escalating demand for AI devices that are not only highly computational but also feature low-power consumption and miniaturization. Research has shifted towards physical reservoir computing, leveraging physical phenomena presented by materials and devices for neural information processing. One challenge that remains is the relatively large size of the existing materials and devices.

Silicon spin qubits are promising for the realisation of scalable quantum computing platforms but their coherence times in natural silicon are limited by the non-zero nuclear spin of the 29Si isotope. Here, enriched 28 Si down to 2.3 ppm residual 29Si is obtained by focused ion beam implantation.

Presynapses locally recycle synaptic vesicles to efficiently communicate information. During use and recycling, proteins on the surface of synaptic vesicles break down and become less efficient. In order to maintain efficient presynaptic function and accommodate protein breakdown, new proteins are regularly produced in the soma and trafficked to presynaptic locations where they replace older protein-carrying vesicles. Maintaining a balance of new proteins and older proteins is thus essential for presynaptic maintenance and plasticity. While protein production and turnover have been extensively studied, it is still unclear how older synaptic vesicles are trafficked back to the soma for recycling in order to maintain balance. In the present study, we use a combination of fluorescence microscopy, hippocampal cell cultures, and computational analyses to determine the mechanisms that mediate older synaptic vesicle trafficking back to the soma. We show that synaptic vesicles, which have recently undergone exocytosis, can differentially utilize either the microtubule or the actin cytoskeleton networks. We show that axonally trafficked vesicles traveling with higher speeds utilize the microtubule network and are less likely to be captured by presynapses, while slower vesicles utilize the actin network and are more likely to be captured by presynapses. We also show that retrograde-driven vesicles are less likely to be captured by a neighboring presynapse than anterograde-driven vesicles. We show that the loss of synaptic vesicle with bound molecular motor myosin V is the mechanism that differentiates whether vesicles will utilize the microtubule or actin networks. Finally, we present a theoretical framework of how our experimentally observed retrograde vesicle trafficking bias maintains the balance with previously observed rates of new vesicle trafficking from the soma.

Cytoskeleton-based trafficking mechanics have long been explored because of their essential role in neuronal function and maintenance (Westrum et al., 1983; Okada et al., 1995; Sorra et al., 2006; Perlson and Holzbaur, 2007; Tao-Cheng, 2007; Hirokawa et al., 2009; Staras and Branco, 2010; Tang et al., 2013; Wu et al., 2013; Maeder et al., 2014; Guedes-Dias et al., 2019; Gramlich et al., 2021; Watson et al., 2023). Protein trafficking via cytoskeleton transport is essential for synaptogenesis (Perlson and Holzbaur, 2007; Santos et al., 2009; Klassen et al., 2010; Wu et al., 2013; Guedes-Dias et al., 2019; Guedes-Dias and Holzbaur, 2019; Kurshan and Shen, 2019; Watson et al., 2023) and to replace older proteins with newer proteins for efficient function (Cohen et al., 2013; Dörrbaum et al., 2018, 2020; Heo et al., 2018; Truckenbrodt et al., 2018; Jähne et al., 2021; Watson et al., 2023).