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Category: biological – Page 4

Prebiotic molecules central to life’s earliest metabolic processes—chemical reactions in cells that change food into energy—may have been born in deep space long before Earth existed, according to new research from the University of Hawaiʻi at Mānoa Department of Chemistry.

Scientists in the W. M. Keck Research Laboratory in Astrochemistry have recreated the found in dense interstellar clouds and discovered a way for the complete set of complex carboxylic acids—critical ingredients in modern metabolism—to form without life on timescales equivalent to a few million years.

The study, published in the Proceedings of the National Academy of Sciences, focused on molecules such as those in the Krebs cycle, a fundamental metabolic pathway used by nearly all living organisms. These molecules, which help break down nutrients to release energy, may have , forming in the icy, low-temperature environments of interstellar space.

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AI and Human Enhancement:

A groundbreaking new AI system is exploring the limits of human potential, developing technologies that can enhance our physical and cognitive abilities. 🤖 By analyzing biological data and applying advanced engineering principles, the AI can identify ways to improve human performance.

How AI Enhances Human Abilities:

AI-powered human enhancement technologies can:

Enhance Physical Abilities: Increase strength, speed, and endurance.
Improve Cognitive Abilities: Enhance memory, intelligence, and creativity.
Extend Lifespan: Slow down the aging process and increase lifespan.
The Ethical Implications:

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Asymmetric interactions between molecules may serve as a stabilizing factor for biological systems. A new model by researchers in the Department of Living Matter Physics at the Max Planck Institute for Dynamics and Self-Organization (MPI-DS) reveals the regulatory role of non-reciprocity.

The scientists aim to understand the physical principles based on which particles and molecules are able to form living beings, and eventually, organisms. The work is published in the journal Physical Review Letters.

Most organizations, including companies, societies, or nations, function best when each member carries out their assigned role. Moreover, this efficiency often relies on spatial organization, which arose due to rules or emerged naturally via learning and . At the , cells operate in a similar way, with different components handling .

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This essay advances a speculative yet empirically-grounded hypothesis: that microtubular cytoskeletal structures constitute proto-cognitive architectures in unicellular organisms, thereby establishing an evolutionary substrate for cognition that predates neural systems. Drawing upon converging evidence from molecular biology, quantum biophysics, phenomenological philosophy, and biosemiotic theory, I propose a cytoskeletal epistemology wherein cognition emerges not exclusively from neural networks, but from the dynamic, embodied information-processing capacities inherent in cellular organization itself. This framework challenges neurocentric accounts of mind while suggesting new avenues for investigating the biological foundations of knowing.

Contemporary cognitive science predominantly situates the genesis of mind within neural tissue, tacitly assuming that cognition emerges exclusively from the electrochemical dynamics of neurons and their synaptic interconnections. Yet this neurocentric paradigm, while experimentally productive, encounters both conceptual and empirical limitations when confronted with fundamental questions regarding the biological preconditions for epistemic capacities. As Thompson (2007) observes, “Life and mind share a set of basic organizational properties, and the organizational properties distinctive of mind are an enriched version of those fundamental to life” (p. 128). This suggests a profound continuity between biological and cognitive processes — a continuity that invites investigation into pre-neural substrates of cognition.

The present inquiry examines the hypothesis that the microtubule — a foundational cytoskeletal element ubiquitous across eukaryotic cells — functions not merely as mechanical infrastructure but as an evolutionary precursor to cognitive architecture, instantiating proto-epistemic capacities in unicellular and pre-neural multicellular organisms. This hypothesis emerges at the intersection of multiple research programs, including quantum approaches to consciousness (Hameroff & Penrose, 2014), autopoietic theories of cognition (Maturana & Varela, 1980), and recent advances in cytoskeletal biology (Pirino et al., 2022).

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Characterizing the intelligence of biological organisms is challenging yet crucial. This paper demonstrates the capacity of canonical neural networks to autonomously generate diverse intelligent algorithms by leveraging an equivalence between concepts from three areas of cognitive computation: neural network-based dynamical systems, statistical inference, and Turing machines.

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After confirming the potential historic observation, the results were evaluated for several possible errors. The work was also analyzed independently. Each time, the team came back to the conclusion that they may have found the first potential signs of life outside our solar system.

“It was an incredible realisation seeing the results emerge and remain consistent throughout the extensive independent analyses and robustness tests,” said co-author Måns Holmberg, a researcher at the Space Telescope Science Institute in Baltimore.

Notably, the concentrations of either DMS or DMDS spotted by JWST were thousands of times higher than concentrations found on Earth. According to the Cambridge astronomers, detecting high levels of either of these chemicals on Hycean (ocean) worlds due to large amounts of biological activity was previously predicted.

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Real-world social cognition requires processing and adapting to multiple dynamic information streams. Interpreting neural activity in such ecological conditions remains a key challenge for neuroscience. This study leverages advancements in de-noising techniques and multivariate modeling to extract interpretable EEG signals from pairs of participants (male-male, female-female, and male-female) engaged in spontaneous dyadic dance. Using multivariate temporal response functions (mTRFs), we investigated how music acoustics, self-generated kinematics, other-generated kinematics, and social coordination uniquely contributed to EEG activity. Electromyogram recordings from ocular, face, and neck muscles were also modeled to control for artifacts. The mTRFs effectively disentangled neural signals associated with four processes: (I) auditory tracking of music, (II) control of self-generated movements, (III) visual monitoring of partner movements, and (IV) visual tracking of social coordination. We show that the first three neural signals are driven by event-related potentials: the P50-N100-P200 triggered by acoustic events, the central lateralized movement-related cortical potentials triggered by movement initiation, and the occipital N170 triggered by movement observation. Notably, the (previously unknown) neural marker of social coordination encodes the spatiotemporal alignment between dancers, surpassing the encoding of self-or partner-related kinematics taken alone. This marker emerges when partners can see each other, exhibits a topographical distribution over occipital areas, and is specifically driven by movement observation rather than initiation. Using data-driven kinematic decomposition, we further show that vertical bounce movements best drive observers’ EEG activity. These findings highlight the potential of real-world neuroimaging, combined with multivariate modeling, to uncover the mechanisms underlying complex yet natural social behaviors.

Significance statement Real-world brain function involves integrating multiple information streams simultaneously. However, due to a shortfall of computational methods, laboratory-based neuroscience often examines neural processes in isolation. Using multivariate modeling of EEG data from pairs of participants freely dancing to music, we demonstrate that it is possible to tease apart physiologically established neural processes associated with music perception, motor control, and observation of a partner’s movement. Crucially, we identify a previously unknown neural marker of social coordination that encodes the spatiotemporal alignment between dancers, beyond self-or partner-related kinematics alone. These findings highlight the potential of computational neuroscience to uncover the biological mechanisms underlying real-world social and motor behaviors, advancing our understanding of how the brain supports dynamic and interactive activities.

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It’s no wonder engineers have long dreamed of harnessing these powers in human-made structures. Now, scientists have combined fungus and bacteria to create a living material that stays alive for up to a month and can form bone-like structures. The researchers say this approach could one day be used to create structural components that repair themselves.

“We are excited about our results and look forward to engineering more complex and larger structures,” Chelsea Heveran at Montana State University, who led the study, told New Scientist. “When viability is sufficiently high, we could start really imparting lasting biological characteristics to the material that we care about, such as self-healing, sensing, or environmental remediation.”

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An international collaboration between four scientists from Mainz, Valencia, Madrid, and Zurich has published new research in the Proceedings of the National Academy of Sciences, shedding light on the most significant increase in complexity in the history of life’s evolution on Earth: the origin of the eukaryotic cell.

While the endosymbiotic theory is widely accepted, the billions of years that have passed since the fusion of an archaea and a bacteria have resulted in a lack of evolutionary intermediates in the phylogenetic tree until the emergence of the eukaryotic cell. It is a gap in our knowledge, referred to as the black hole at the heart of biology.

“The new study is a blend of theoretical and observational approaches that quantitatively understands how the genetic architecture of life was transformed to allow such an increase in complexity,” stated Dr. Enrique M. Muro, representative of Johannes Gutenberg University Mainz (JGU) in this project.

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