Lifeboat Foundation

Researchers from the Moscow Institute of Physics and Technology, joined by a colleague from Argonne National Laboratory, U.S., have implemented an advanced quantum algorithm for measuring physical quantities using simple optical tools. Published in Scientific Reports, their study takes us a step closer to affordable linear optics-based sensors with high performance characteristics. Such tools are sought after in diverse research fields, from astronomy to biology.

Maximizing the sensitivity of measurement tools is crucial for any field of science and technology. Astronomers seek to detect remote cosmic phenomena, biologists need to discern exceedingly tiny organic structures, and engineers have to measure the positions and velocities of objects, to name a few examples.

Until recently, no measurement tool could ensure precision above the so-called shot noise limit, which has to do with the statistical features inherent in classical observations. Quantum technology has provided a way around this, boosting precision to the fundamental Heisenberg limit, stemming from the basic principles of quantum mechanics. The LIGO experiment, which detected gravitational waves for the first time in 2016, shows it is possible to achieve Heisenberg-limited sensitivity by combining complex optical interference schemes and quantum techniques.

Richard Dawkins is one of the world’s most famous atheists. An evolutionary biology at Oxford and best-selling author of The God Delusion — his new book ‘Outgrowing God — A Beginner’s Guide’ aims to inform young people about religion and atheism. He talks to Krishnan about why he wrote it, his passion for scientific truth and whether he thinks there’s life outside of Earth.

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Scientists have figured out how microbes can suck energy from rocks. Such life-forms might be more widespread than anyone anticipated.

Researchers from the Faculty of Physics at the University of Warsaw, ETH in Zurich and the University of Cambridge have synthesized and analysed active microparticles self-propelling in a fluid and reversing their propulsion direction depending on the wavelength of illuminating light. A research article summarising their work has recently been published in Nature Communications.

Active matter encompasses systems with self-propelling elements that draw energy from the environment and convert it into kinetic energy. This is currently a lively discipline in physics, spanning across many time and length scales, concerning, e.g., the behaviour of birds in flocks (such as murmurations of starlings), schools of fish (as a form of protection against predators), and also bacteria in biofilms and other aquatic microswimmers. It focuses both on the behaviour of individual elements and understanding their mechanisms of energy conversion, interaction and coupling with the environment so important for the survival, and on the collective effects and emergence of new phenomena in large populations. Both can be successfully described on different levels of precision, starting from simplistic minimal coarse-grained models, and up to refined numerical simulations.

Bacteria, algae, spermatozoa, ciliates and other unicellular organisms are an important group of active swimmers. Exploring the physical basis of their dynamics is often complicated by their immense diversity, biological complexity, and high sensitivity to external conditions. The aquatic microworld is, however, governed by the universal laws of fluid dynamics, which put limitations on all organisms.

Researchers have long sought to understand the origins of life on Earth. A new study conducted by scientists at the Institute for Advanced Study, the Earth-Life Science Institute (ELSI), and the University of New South Wales, among other participating institutions, marks an important step forward in the effort to understand the chemical origins of life. The findings of this study demonstrate how “continuous reaction networks” are capable of producing RNA precursors and possibly ultimately RNA itself — a critical bridge to life.

The paper is published in the Proceedings of the National Academy of Sciences.

While many of the mechanisms that propagate life are well understood, the transition from a prebiotic Earth to the era of biology remains shrouded in mystery. Previous experiments have demonstrated that simple organic compounds can be produced from the reactions of chemicals understood to exist in the primitive Earth environment. However, many of these experiments relied on coordinated experimenter interventions. This study goes further by employing a model that is minimally manipulated to most accurately simulate a natural environment.

Plant biologists have long sought a deeper understanding of foundational processes involving kinases, enzymes that catalyze key biological activities in proteins. Analyzing the processes underlying kinases in plants takes on greater urgency in today’s environment increasingly altered by climate warming.

Certain “SnRK2” kinases (sucrose-non-fermenting-1-related protein kinase-2s) are essential since they are known to be activated in response to drought conditions, triggering the protective closure of small pores on leaf surfaces known as stoma. These pores allow carbon dioxide to enter leaves, but plants also lose more than 90 percent of their water by evaporation through them. Pore opening and closing functions help optimize growth and drought tolerance in response to changes in the environment.

Now, plant biologists at the University of California San Diego have developed a new nanosensor that allows researchers to monitor SnRK2 protein kinase activity in live plant cells. The SnRK2 activity sensor, or “SNACS,” is described in the journal eLife.

Predictive biology is the next great chapter in synthetic and systems biology, particularly for microorganisms. Tasks that once seemed infeasible are increasingly being realized such as designing and implementing intricate synthetic gene circuits that perform complex sensing and actuation functions, and assembling multi-species bacterial communities with specific, predefined compositions. These achievements have been made possible by the integration of diverse expertise across biology, physics and engineering, resulting in an emerging, quantitative understanding of biological design. As ever-expanding multi-omic data sets become available, their potential utility in transforming theory into practice remains firmly rooted in the underlying quantitative principles that govern biological systems. In this Review, we discuss key areas of predictive biology that are of growing interest to microbiology, the challenges associated with the innate complexity of microorganisms and the value of quantitative methods in making microbiology more predictable.

There aren’t many computer chips that you have to build a life support system for.

But when you’re combining actual living brain cells with inorganic silicon chips, you can’t feed them just electricity. You actually need to supply everything they would normally get in a fully biological body.

Why bother?

Like biological brains, artificial neural networks may depend on slow-wave sleep for learning.