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Current Biology. They trained Caribbean box jellyfish (Tripedalia cystophora) to learn to spot and dodge obstacles. The study challenges previous notions that advanced learning requires a centralized brain and sheds light on the evolutionary roots of learning and memory.

No bigger than a fingernail, these seemingly simple jellies have a complex visual system with 24 eyes embedded in their bell-like body. Living in mangrove swamps, the animal uses its vision to steer through murky waters and swerve around underwater tree roots to snare prey. Scientists demonstrated that the jellies could acquire the ability to avoid obstacles through associative learning, a process through which organisms form mental connections between sensory stimulations and behaviors.

It lets researchers extract pixel-by-pixel information from nanoscale.

The nanoscale refers to a length scale that is extremely small, typically on the order of nanometers (nm), which is one billionth of a meter. At this scale, materials and systems exhibit unique properties and behaviors that are different from those observed at larger length scales. The prefix “nano-” is derived from the Greek word “nanos,” which means “dwarf” or “very small.” Nanoscale phenomena are relevant to many fields, including materials science, chemistry, biology, and physics.

The newly upgraded Linac Coherent Light Source (LCLS) X-ray free-electron laser (XFEL) at the Department of Energy’s SLAC National Accelerator Laboratory successfully produced its first X-rays, and researchers around the world are already lined up to kick off an ambitious science program.

The upgrade, called LCLS-II, creates unparalleled capabilities that will usher in a new era in research with X-rays.

Scientists will be able to examine the details of quantum materials with unprecedented resolution to drive new forms of computing and communications; reveal unpredictable and fleeting chemical events to teach us how to create more sustainable industries and clean energy technologies; study how biological molecules carry out life’s functions to develop new types of pharmaceuticals; and study the world on the fastest timescales to open up entirely new fields of scientific investigation.

By stepping outside the box of our usual way of thinking about numbers, my colleagues and I have recently shown that arithmetic has biological roots and is a natural consequence of how perception of the world around us is organized.

Our results explain why arithmetic is true and suggest that mathematics is a realization in symbols of the fundamental nature and creativity of the mind.

Thus, the miraculous correspondence between mathematics and physical reality that has been a source of wonder from the ancient Greeks to the present—as explored in astrophysicist Mario Livio’s book Is God a Mathematician?—suggests the mind and world are part of a common unity.

Many biological processes depend on chemical reactions that are localized in space and time and therefore require catalytic components that self-organize. The collective behavior of these active particles depends on their chemotactic movement—how they sense and respond to chemical gradients in the environment. Mixtures of such active catalysts generate complex reaction networks, and the process by which self-organization emerges in these networks presents a puzzle. Jaime Agudo-Canalejo of the Max Planck Institute for Dynamics and Self-Organization, Germany, and his colleagues now show that the phenomenon of self-organization depends strongly on the network topology [1]. The finding provides new insights for understanding microbiological systems and for engineering synthetic catalytic colloids.

In a biological metabolic network, catalysts convert substrates into products. The product of one catalyst species acts as the substrate for another species—and so on. Agudo-Canalejo and his team modeled a three-species system. First, building on a well-established continuum theory for catalytically active species that diffuse along chemical gradients, they showed that systems where each species responds chemotactically only to its own substrate cannot self-organize unless one species is self-attracting. Next, they developed a model that allowed species to respond to both their substrates and their products. Pair interactions between different species in this more complex model drove an instability that spread throughout the three-species system, causing the catalysts to clump together. Surprisingly, this self-organization process occurred even among particles that were individually self-repelling.

The researchers say that their discovery of the importance of network topology—which catalyst species affect and are affected by which substrates and products—could open new directions in studies of active matter, informing both origin-of-life research and the design of shape-shifting functional structures.

X-ray free-electron lasers (XFELs) first came into existence two decades ago. They have since enabled pioneering experiments that “see” both the ultrafast and the ultrasmall. Existing devices typically generate short and intense x-ray pulses at a rate of around 100 x-ray pulses per second. But one of these facilities, the Linac Coherent Light Source (LCLS) at the SLAC National Accelerator Laboratory in California, is set to eclipse this pulse rate. The LCLS Collaboration has now announced “first light” for its upgraded machine, LCLS-II. When it is fully up and running, LCLS-II is expected to fire one million pulses per second, making it the world’s most powerful x-ray laser.

The LCLS-II upgrade signifies a quantum leap in the machine’s potential for discovery, says Robert Schoenlein, the LCLS’s deputy director for science. Now, rather than “demonstration” experiments on simple, model systems, scientists will be able to explore complex, real-world systems, he adds. For example, experimenters could peer into biological systems at ambient temperatures and physiological conditions, study photochemical systems and catalysts under the conditions in which they operate, and monitor nanoscale fluctuations of the electronic and magnetic correlations thought to govern the behavior of quantum materials.

The XFEL was first proposed in 1992 to tackle the challenge of building an x-ray laser. Conventional laser schemes excite large numbers of atoms into states from which they emit light. But excited states with energies corresponding to x-ray wavelengths are too short-lived to build up a sizeable excited-state population. XFELs instead rely on electrons traveling at relativistic speed through a periodic magnetic array called an undulator. Moving in a bunch, the electrons wiggle through the undulator, emitting x-ray radiation that interacts multiple times with the bunch and becomes amplified. The result is a bright x-ray beam with laser coherence.

With up to a million X-ray flashes a second, the laser will help study mechanisms in physics, chemistry, and biology.

The US Department of Energy’s (DOE) SLAC National Accelerator Laboratory has fired the first X-rays using the upgraded Linac Coherent Light Source (LCLS) X-ray free-electron laser (XFEL), a press release said. The upgraded version, dubbed LCLS-II, was built for $1.1 billion.

Continue reading “World’s most powerful X-ray laser fired for the first time” »

Summary: The revolutionary field of bio-computing is making waves as DishBrain, a neural system combining 800,000 living brain cells, learns to play Pong. Recognizing the pressing need for ethical guidelines in this emerging domain, the pioneers behind DishBrain have joined forces with bioethicists in a study.

The research explores the moral considerations around biological computing systems and their potential consciousness. Beyond its innovation, the technology offers vast environmental benefits, potentially transforming the energy-consuming IT industry.

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Penn State researchers have recently harnessed the biological concept for application in artificial intelligence to develop the first artificial, multisensory integrated neuron, which may forever change the world of AI and robotics.

Associate professor of engineering science and mechanics at Penn State Saptarshi Das explains: “Robots make decisions based on the environment they are in, but their sensors do not generally talk to each other. A collective decision can be made through a sensor processing unit, but is that the most efficient or effective method? In the human brain, one sense can influence another and allow the person to better judge a situation.”