Lifeboat Foundation

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.

(alternate spelling noösphere) is a philosophical concept developed and popularized by the biogeochemist Vladimir Vernadsky, and philosopher and Jesuit priest Pierre Teilhard de Chardin. Vernadsky defined the as the new state of the biosphere^[1] and described as the planetary “sphere of reason”.^[2][3] The represents the highest stage of biospheric development, that of humankind’s rational activities.^[4]

The word is derived from the Greek νόος (“nous, mind, reason”) and σφαῖρα (“sphere”), in lexical analogy to “atmosphere” and “biosphere”.^[5] The concept cannot be accredited to a single author. The founding authors Vernadsky and de Chardin developed two related but starkly different concepts, the former grounded in the geological sciences, and the latter in theology. Both conceptions of the share the common thesis that together human reason and scientific thought have created, and will continue to create, the next evolutionary geological layer. This geological layer is part of the evolutionary chain.^[6][7] Second-generation authors, predominantly of Russian origin, have further developed the Vernadskian concept, creating the related concepts: noocenosis and noocenology.^[8].

At the end of the 20th century, analog systems in computer science have been widely replaced by digital systems due to their higher computing power. Nevertheless, the question keeps being intriguing until now: is the brain analog or digital? Initially, the latter has been favored, considering it as a Turing machine that works like a digital computer. However, more recently, digital and analog processes have been combined to implant human behavior in robots, endowing them with artificial intelligence (AI). Therefore, we think it is timely to compare mathematical models with the biology of computation in the brain. To this end, digital and analog processes clearly identified in cellular and molecular interactions in the Central Nervous System are highlighted. But above that, we try to pinpoint reasons distinguishing in silico computation from salient features of biological computation. First, genuinely analog information processing has been observed in electrical synapses and through gap junctions, the latter both in neurons and astrocytes. Apparently opposed to that, neuronal action potentials (APs) or spikes represent clearly digital events, like the yes/no or 1/0 of a Turing machine. However, spikes are rarely uniform, but can vary in amplitude and widths, which has significant, differential effects on transmitter release at the presynaptic terminal, where notwithstanding the quantal (vesicular) release itself is digital. Conversely, at the dendritic site of the postsynaptic neuron, there are numerous analog events of computation. Moreover, synaptic transmission of information is not only neuronal, but heavily influenced by astrocytes tightly ensheathing the majority of synapses in brain (tripartite synapse). At least at this point, LTP and LTD modifying synaptic plasticity and believed to induce short and long-term memory processes including consolidation (equivalent to RAM and ROM in electronic devices) have to be discussed. The present knowledge of how the brain stores and retrieves memories includes a variety of options (e.g., neuronal network oscillations, engram cells, astrocytic syncytium). Also epigenetic features play crucial roles in memory formation and its consolidation, which necessarily guides to molecular events like gene transcription and translation. In conclusion, brain computation is not only digital or analog, or a combination of both, but encompasses features in parallel, and of higher orders of complexity.

Keywords: analog-digital computation; artificial and biological intelligence; bifurcations; cellular computation; engrams; learning and memory; molecular computation; network oscillations.

Copyright © 2023 Gebicke-Haerter.

Researchers at CHU Sainte-Justine and Université de Montréal have discovered a new mechanism involved in the expression of Down syndrome, one of the main causes of intellectual disability and congenital heart defects in children. The study’s findings were published today in Current Biology.

Down syndrome (SD), also called trisomy 21 syndrome, is a genetic condition that affects approximately one in every 800 children born in Canada. In these individuals, many genes are expressed abnormally at the same time, making it difficult to determine which genes contribute to which differences.

Professor Jannic Boehm’s research team focused on RCAN1, a gene that is overexpressed in the brains of fetuses with Down syndrome. The team’s work provides insights into how the gene influences the way the condition manifests itself.

Biomimetic robots, which mimic the movements and biological functions of living organisms, are a fascinating area of research that can not only lead to more efficient robots but also serve as a platform for understanding muscle biology.

Among these, biohybrid actuators, made up of soft materials and muscular cells that can replicate the forces of actual muscles, have the potential to achieve life-like movements and functions, including self-healing, high efficiency, and high power-to-weight ratio, which have been difficult for traditional bulky robots that require heavy energy sources.

One way to achieve these life-like movements is to arrange muscle cells in biohybrid actuators in an anisotropic manner. This involves aligning them in a specific pattern where they are oriented in different directions, like what is found in living organisms.