Lifeboat Foundation

The motions within the molecule provide a new way to compare the structures and functions of similar proteins.

Proteins play a central role in nearly every biological process, and they often change shape as they function. Over the past decade, a research team has developed a method of analysis that can help make sense of the available atomic-scale structural data and reveal the key physical distortions that underlie protein functions. Now the team has shown that the technique provides a consistent way of comparing proteins from different species, demonstrating similar structural changes in many of them [1]. The researchers believe that the technique will help biologists better understand the cross-species variations among proteins.

Proteins are linear chains of amino acids that fold up into specific three-dimensional shapes. Although there are lots of atomic-scale data on the structural changes that protein molecules execute as they function, researchers have had few quantitative methods to extract insights from these data, says biophysicist Pablo Sartori of the Gulbenkian Institute of Science in Portugal. One challenge, he says, is the arbitrary choice one makes when comparing two similar protein structures, such as the structures of a protein in two different conformations. “If you align region A of the protein, then region B shows displacement. If you align region B, then region A shows displacement. If you align the average, then both are displaced a bit.” Another problem is that the relative displacement is often not the quantity that best reflects the structural changes associated with protein function.

The perplexing phenomenon of homochirality in life, where biomolecules exist in only one of two mirror-image forms, remains unexplained despite historical attention from scientific figures like Pasteur, Lord Kelvin, and Pierre Curie. Recent research suggests the combination of electric and magnetic fields might influence this preference through experiments showing enantioselective effects on chiral molecules interacting with magnetized surfaces, offering indirect evidence towards understanding this mystery.

The phenomenon known as homochirality of life, which refers to the exclusive presence of biomolecules in one of their two possible mirror-image configurations within living organisms, has intrigued several prominent figures in science. This includes Louis Pasteur, who first identified molecular chirality, William Thomson (also known as Lord Kelvin), and Pierre Curie, a Nobel Laureate.

A conclusive explanation is still lacking, as both forms have, for instance, the same chemical stability and do not differ from each other in their physicochemical properties. The hypothesis, however, that the interplay between electric and magnetic fields could explain the preference for one or the other mirror-image form of a molecule – so-called enantiomers – emerged early on.

Researchers have discovered a mechanism steering the evolution of multicellular life. They identify how altered protein folding drives multicellular evolution.

In a new study led by researchers from the University of Helsinki and the Georgia Institute of Technology, scientists turned to a tool called experimental evolution. In the ongoing Multicellularity Long Term Evolution Experiment (MuLTEE), laboratory yeast are evolving novel multicellular functions, enabling researchers to investigate how they arise.

The study, published in Science Advances, puts the spotlight on the regulation of proteins in understanding evolution.

Structured light, which encompasses various spatial patterns of light like donuts or flower petals, is crucial for a myriad of applications from precise measurements to communication systems.

The many properties of light allow it to be manipulated and used for applications that range from very sensitive measurements to communications and intelligent ways to interrogate objects. A compelling degree of freedom is the spatial pattern, called structured light, which can resemble shapes such as donuts and flower petals. For instance, patterns with different numbers of petals can represent letters of the alphabet, and when observed on the other side, deliver the message.

Unfortunately, what makes these patterns sensitive for measurements also make them susceptible to unwanted environmental factors such as air turbulence, aberrated optics, stressed fibers, or biological tissues doing their own “patterning” and distorting the structure. Here the distorted pattern can deteriorate to the point that the output pattern looks nothing like the input, rendering them ineffective.

Continue reading “Researchers develop method to manipulate structured light without distortion” »

Bats are an important group of mammals to understand the ecology, diversity, and transmission of associated microbes – including viruses, bacteria, and fungi.

Over the past two decades, research on bat-associated microbes such as viruses, bacteria and fungi has dramatically increased. Here, we synthesize themes from a conference symposium focused on advances in the research of bats and their microbes, including physiological, immunological, ecological and epidemiological research that has improved our understanding of bat infection dynamics at multiple biological scales. We first present metrics for measuring individual bat responses to infection and challenges associated with using these metrics. We next discuss infection dynamics within bat populations of the same species, before introducing complexities that arise in multi-species communities of bats, humans and/or livestock. Finally, we outline critical gaps and opportunities for future interdisciplinary work on topics involving bats and their microbes.

Studies of bat-associated microbes (i.e. microorganisms detected in or isolated from bats) date back to rabies virus investigations in the early 1900s [1]. In the past two decades, following the emergence of Severe Acute Respiratory Syndrome (SARS) coronavirus (CoV) in 2003 and SARS-CoV-2 in 2019, there has been a dramatic increase in research on bat-associated microbes, including viruses, bacteria, haemosporidians and fungi [2–5]. These microbes may or may not cause disease in bats, and thus we broadly use the term ‘microbes’ rather than ‘pathogens’ throughout this paper to acknowledge that detecting microorganisms in bats is distinct from the process of determining pathogenicity [6].

For more than a decade it has been possible for physicists to accurately measure the location of individual atoms to a precision smaller than one-thousandth of a millimeter using a special type of microscope. However, this method has so far only provided the x and y coordinates. Information on the vertical position of the atom is lacking.

A new method has now been developed that can determine all three spatial coordinates of an atom with one single image. This method—developed by the University of Bonn and University of Bristol—is based on an ingenious physical principle. The study is published in the journal Physical Review A.

Anyone who has used a microscope in a biology class to study a plant cell will probably be able to recall a similar situation. It is easy to tell that a certain chloroplast is located above and to the right of the nucleus.

Neuromorphic computing, inspired by the intricate architecture and functionality of the human brain, represents a departure from traditional computing paradigms. Unlike conventional von Neumann architectures, which rely on sequential processing and centralized memory, neuromorphic systems emulate the parallelism, event-driven processing, and adaptive learning capabilities of biological neural networks. By leveraging principles such as massive parallelism and event-driven modality, neuromorphic computing offers a more efficient and flexible approach to processing complex data in real-time.

Advantages of Neuromorphic Computing for IoT

The adoption of neuromorphic computing in IoT promises many benefits, ranging from enhanced processing power and energy efficiency to increased reliability and adaptability. Here are some key advantages:

Such as complex models of various areas of the cortex. The main novelty of this work is the abstraction of a neuromorphic architecture into clusters represented by minicolumns and hypercolumns, analogously to the fundamental structural units observed in neurobiology. Without this approach, simulating large-scale fully connected networks needs prohibitively large memory to store look-up tables for point-to-point connections. Instead, we use a novel architecture, based on the structural connectivity in the neocortex, such that all the required parameters and connections can be stored in on-chip memory. The cortex simulator can be easily reconfigured for simulating different neural networks without any change in hardware structure by programming the memory. A hierarchical communication scheme allows one neuron to have a fan-out of up to 200 k neurons. As a proof-of-concept, an implementation on one Altera Stratix V FPGA was able to simulate 20 million to 2.6 billion leaky-integrate-and-fire (LIF) neurons in real time. We verified the system by emulating a simplified auditory cortex (with 100 million neurons). This cortex simulator achieved a low power dissipation of 1.62 μW per neuron. With the advent of commercially available FPGA boards, our system offers an accessible and scalable tool for the design, real-time simulation, and analysis of large-scale spiking neural networks.

Our inability to simulate neural networks in software on a scale comparable to the human brain (10¹¹ neurons, 10¹⁴ synapses) is impeding our progress toward understanding the signal processing in large networks in the brain and toward building applications based on that understanding. A small-scale linear approximation of a large spiking neural network will not be capable of providing sufficient information about the global behavior of such highly nonlinear networks. Hence, in addition to smaller scale systems with detailed software or hardware neural models, it is necessary to develop a hardware architecture that is capable of simulating neural networks comparable to the human brain in terms of scale, with models with an intermediate level of biological detail, that can simulate these networks quickly, preferably in real time to allow interaction between the simulation and the environment.

Johns Hopkins electrical and computer engineers are pioneering a new approach to creating neural network chips—neuromorphic accelerators that could power energy-efficient, real-time machine intelligence for next-generation embodied systems like autonomous vehicles and robots.

Electrical and computer engineering graduate student Michael Tomlinson and undergraduate Joe Li—both members of the Andreou Lab—used natural language prompts and ChatGPT4 to produce detailed instructions to build a spiking neural network chip: one that operates much like the human brain.

Through step-by-step prompts to ChatGPT4, starting with mimicking a single biological neuron and then linking more to form a network, they generated a full chip design that could be fabricated.