Lifeboat Foundation

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks.

Note: June 23 is Alan Turing’s birth anniversary.

Alan Turing wore many scientific hats in his lifetime: a code-breaker in World War II, a prophetic figure of artificial intelligence (AI), a pioneer of theoretical biology, and a founding figure of theoretical computer science. While the former of his roles continue to catch the fancy of popular culture, his fundamental contribution to the development of computing as a mathematical discipline is possibly where his significant scientific impact persists to date.

In a scientific first, researchers have used machine learning-powered AI to design de novo enzymes — never-before-existing proteins that accelerate biochemical reactions in living organisms. Enzymes drive a wide range of critical processes, from digestion to building muscle to breathing.

A team led by the University of Washington’s Institute for Protein Design, along with colleagues at UCLA and China’s Xi’an Jiaotong University, used their AI engine to create new enzymes of a kind called luciferases. Luciferases — as their name implies — catalyze chemical reactions that emit light; they’re what give fireflies their flare.

“Living organisms are remarkable chemists,” David Baker, a professor of biochemistry at UW and the study’s senior author, said.

For many years, the fields of physics and chemistry have held the belief that the properties of solid materials are fundamentally determined by the atoms and molecules they consist of. For instance, the crystalline nature of salt is credited to the ionic bond formed between sodium and chloride ions. Similarly, metals such as iron or copper owe their robustness to the metallic bonds between their respective atoms, and the elasticity of rubbers stems from the flexible bonds in the polymers that form them. This principle also applies to substances like fungi, bacteria, and wood.

Or so the story goes.

A new paper recently published in Nature upends that paradigm, and argues that the character of many biological materials is actually created by the water that permeates these materials. Water gives rise to a solid and goes on to define the properties of that solid, all the while maintaining its liquid characteristics.

Certain plant species.

A species is a group of living organisms that share a set of common characteristics and are able to breed and produce fertile offspring. The concept of a species is important in biology as it is used to classify and organize the diversity of life. There are different ways to define a species, but the most widely accepted one is the biological species concept, which defines a species as a group of organisms that can interbreed and produce viable offspring in nature. This definition is widely used in evolutionary biology and ecology to identify and classify living organisms.

During photosynthesis, a symphony of chemicals transforms light into the energy required for plant, algal, and some bacterial life. Scientists now know that this remarkable reaction requires the smallest possible amount of light – just one single photon – to begin.

A US team of researchers in quantum optics and biology showed that a lone photon can start photosynthesis in the purple bacterium Rhodobacter sphaeroides, and they are confident it works in plants and algae since all photosynthetic organisms share an evolutionary ancestor and similar processes.

The team says their findings bolster our knowledge of photosynthesis and will lead to a better understanding of the intersection of quantum physics in a wide range of complex biological, chemical, and physical systems, including renewable fuels.

The current understanding of the biology of aging is largely based on research aimed at identifying factors that influence lifespan. However, lifespan as a sole proxy measure of aging has limitations because it can be influenced by specific pathologies (not generalized physiological deterioration in old age). Hence, there is a great need to discuss and design experimental approaches that are well-suited for studies targeting the biology of aging, rather than the biology of specific pathologies that restrict the lifespan of a given species. For this purpose, we here review various perspectives on aging, discuss agreement and disagreement among researchers on the definition of aging, and show that while slightly different aspects are emphasized, a widely accepted feature, shared across many definitions, is that aging is accompanied by phenotypic changes that occur in a population over the course of an average lifespan. We then discuss experimental approaches that are in line with these considerations, including multidimensional analytical frameworks as well as designs that facilitate the proper assessment of intervention effects on aging rate. The proposed framework can guide discovery approaches to aging mechanisms in all key model organisms (e.g., mouse, fish models, D. melanogaster, C. elegans) as well as in humans.

Keywords: Aging; experimental design; lifespan; models; phenotypes.

Copyright © 2023 Elsevier B.V. All rights reserved.

The fascinating collective behaviors of biological systems have inspired extensive studies on shape assembly of robot swarms^6,7,8,9. One class of strategies widely studied in the literature are based on goal assignment in either centralized or distributed ways^10,11,12. Once a swarm of robots are assigned unique goal locations in a desired shape, the consequent task is simply to plan collision-free trajectories for the robots to reach their goal locations¹⁰ or conduct distributed formation control based on locally sensed information^6,13,14. It is notable that centralized goal assignment is inefficient to support large-scale swarms since the computational complexity increases rapidly as the number of robots increases^15,16. Moreover, when robots fail to function normally, additional algorithms for fault-tolerant detection and goal re-assignment are required to handle such situations¹⁷. As a comparison, distributed goal assignment can support large-scale swarms by decomposing the centralized assignment into multiple local ones^11,12. It also exhibits better robustness to robot faults. However, since distributed goal assignments are based on locally sensed information, conflicts among local assignments are inevitable and must be resolved by sophisticated algorithms such as local task swapping^11,12.

Another class of strategies for shape assembly that have also attracted extensive research attention are free of goal assignment^18,19,20,21. For instance, the method proposed in ref. ¹⁸ can assemble complex shapes using thousands of homogeneous robots. An interesting feature of this method is that it does not rely on external global positioning systems. Instead, it establishes a local positioning system based on a small number of pre-localized seed robots. As a consequence of the local positioning system, the proposed edge-following control method requires that only the robots on the edge of a swarm can move while those inside must stay stationary. The method in ref. ¹⁹ can generate swarm shapes spontaneously from a reaction-diffusion network similar to embryogenesis in nature. However, this method is not able to generate user-specified shapes precisely. The method in ref. ²¹ can aggregate robots on the frontier of shapes based on saliency detection. The user-defined shape is specified by a digital light projector. An interesting feature of this method is that it does not require centralized edge detectors. Instead, edge detection is realized in a distributed manner by fusing the beliefs of a robot with its neighbors. However, since the robots cannot self-localize themselves relative to the desired shape, they make use of random walks to search for the edges, which would lead to random trajectories. Another class of methods that do not require goal assignment is based on artificial potential fields^22,23,24,25. One limitation of this class of methods is that robots may easily get trapped in local minima, making it difficult to assemble nonconvex complex shapes.

Here, we propose a strategy for shape assembly of robot swarms based on the idea of mean-shift exploration: when a robot is surrounded by neighboring robots and unoccupied locations, it would actively give up its current location by exploring the highest density of nearby unoccupied locations in the desired shape. This idea does not rely on goal assignment. It is realized by adapting the mean-shift algorithm^26,27,28, which is an optimization technique widely used in machine learning for locating the maxima of a density function. Moreover, a distributed negotiation mechanism is designed to allow robots to negotiate the final desired shape with their neighbors in a distributed manner. This negotiation mechanism enables the swarm to maneuver while maintaining a desired shape based on a small number of informed robots. The proposed strategy empowers robot swarms to assemble nonconvex complex shapes with strong adaptability and high efficiency, as verified by numerical simulation results and real-world experiments with swarms of 50 ground robots. The strategy can be adapted to generate interesting behaviors including shape regeneration, cooperative cargo transportation, and complex environment exploration.

More apologism for aging and death.