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Category: biological – Page 8

Lost Primal Eye Paradigm scientific review and update August 2026

A growing body of research across paleoneurology, evolutionary biology, and chronobiology now supports the core mechanisms of Steve Nichols’s Lost Primal Eye paradigm aka Median Vision Theory (MVT). This is a regular scientific review and update posted for The Posthuman University Journal on academia.edu August 2026 Palaeoneurology and Therapsid Evolution Benoit et al. (2016) (Acta Palaeontologica Polonica): Examined over 800 therapsid fossil skulls, documenting the convergent, gradual reduction and complete loss of the parietal foramen across Permo-Triassic eutheriodonts. The study directly links the degeneration of the physical pineal eye to the evolution of mammalian endothermy, nocturnal adaptation, and the transfer of photoreception to paired lateral eyes.

What if The Brain Doesn’t Create Consciousness? Scientist Proposes It Might Be The Fabric of Reality Itself

For decades, neuroscientists have searched the brain for the biological basis of consciousness, assuming our thoughts, emotions, and subjective experiences emerge from networks of neurons.

But what if that assumption is incomplete?

That’s the provocative question explored by neuroscientist Christof Koch of the Allen Institute in Seattle.

AI and ‘Ramanomics’ could eliminate a major obstacle to studying living cells

Fluorescent dyes have long been used in biological research to identify and visualize structures within living cells. Although effective, they have several drawbacks, including altering the cells under study, limiting the number of structures that can be examined at once and reducing measurement accuracy.

A team led by University at Buffalo researchers has developed a new method that draws on advances in artificial intelligence and Raman spectroscopy to overcome the limitations of dye-based imaging.

The approach combines AI with “Ramanomics,” a UB-pioneered optical technology that measures the biochemical makeup of cells without altering them. Rather than relying on fluorescent labels, which are dyes that bind to specific cellular components and glow under specialized lighting, it identifies cellular structures by their unique biochemical signatures.

Reversal of protein chemical aging by enzymatic deglycation

et al. used an innovative series of screening and directed evolution steps to produce a new enzyme (CMLase) which can remove a pernicious form of advanced glycation end product (AGE) linkage from proteins. AGEs contribute to biological aging, so CMLase may possess therapeutic potential to combat parts of the aging process.

Advanced glycation end products (AGEs) in proteins, a hallmark of aging, are considered irreversible. Here, authors report the development of CMLase — an enzyme that specifically oxidizes Nε-carboxymethyl-lysine (CML) and restores the native lysine residues in vitro and in human tissue samples.

Scientists Discover the Brain May Enter a New Biological Phase Between 50 and 75

Between midlife and older age, brain cells undergo unexpected shifts in inflammation and gene regulation. The findings suggest that brain aging may be an active biological remodeling process rather than a simple, gradual decline.

Can We Defeat Human Aging?

For all of human history, death has been the one certainty.

That certainty is now being challenged.

The biggest names in longevity research believe aging can be slowed, reversed, and one day eliminated. AI researchers think biology is simply another puzzle waiting to be solved. Together, they may be building the technology that changes civilization forever.

But extending life isn’t the real story.

The real story is what happens when humanity no longer has an expiration date.

This is the science, the debate, and the future of defeating aging.

Continue reading “Can We Defeat Human Aging?” | >

PRX Intelligence publishes its first papers

The American Physical Society’s newest highly selective, open access journal, PRX Intelligence, has published its inaugural papers. The studies demonstrate how artificial intelligence and machine learning methods can be used to advance scientific knowledge and capabilities across the physical sciences — from neural networks that streamline molecular simulations to data-driven learning schemes that accelerate quantum embedding workflows.

As AI and machine learning transform the physical sciences, PRX Intelligence is designed to provide a multidisciplinary platform for research pioneering the development and application of these approaches. Building on the foundation of Physical Review X, the journal publishes open access articles expected to have substantial and lasting impact. It welcomes studies that apply AI and machine learning across theory, simulation, and experimentation in physics and related fields — including computer science, mathematics, engineering, materials science, chemistry, biology, and earth and environmental sciences. Relevant topics include discovery and synthesis, physics-informed learning, data-driven approaches, machine learning pipelines for observational platforms, and more. Articles have flexible formats and lengths and may include research papers, perspectives, roadmaps, tutorials, and more.

PRX Intelligence will waive article publication charges for manuscripts submitted or transferred before Jan. 1, 2027. And like all other APS journals, it will always waive these charges for researchers in low-and middle-income countries. Sign up for email updates to keep up with the latest news from the journal.

Predicted Noncrystalline Structures Have Bonus Properties

Simulations reveal disordered structures that are also surprisingly resistant to impacts and cracks.

Metamaterials derive their unique properties from their tailored, macroscale structures, not from their chemical compositions or atomic-scale structures. Although designers often rely on regular, repeating architectures, many of nature’s toughest materials—from bone to spider silk—owe their resilience to structural disorder. Now, inspired by those biological examples, researchers have used machine learning to find new designs for disordered metamaterials [1]. These structures not only have the properties for which they were optimized, but they also resist deformation and fracture. The researchers have built a prototype car bumper based on their designs, and they propose uses in ballistic shields, helmets, and other protective equipment.

The design of functional metamaterials has conventionally focused on ordered structures, where the repeating nature of the patterns allows predictions of macroscopic behavior. Amorphous structures lack that periodicity, leaving an enormous number of possible disordered arrangements that are difficult to explore systematically. Yet disorder can also be an asset, enabling mechanical behaviors that are otherwise difficult or impossible to achieve. The main challenge has been to search the large number of potential structures efficiently enough to identify the rare ones that combine useful functionality with physical stability.

Voice AI Platform & Voice Agents

The end of artificial latency in enterprise voice.

Smallest.ai just secured $13 million in Series A funding to kill conversational lag in artificial intelligence.

As reported by TechCrunch, the late-2024 startup is abandoning massive, sluggish language models in favor of specialized, real-time voice architecture. The singular goal is absolute zero latency.

The round was led by Seligman Ventures. Sierra Ventures and 3one4 Capital participated, pushing total funding past $21 million. The model itself processes listening, thinking, and speaking simultaneously. When queried beyond its local knowledge base, the system places callers on a brief hold to ping an offline foundational model.

Existing clients already include Truecaller and RingCentral. strictly targets enterprise customer support platforms, actively competing against ElevenLabs, Cartesia, and regional players like Sarvam.

The Architectural Shift.

Massive models create massive delays. Text chat tolerates latency. Voice requires immediate biological pacing.

Continue reading “Voice AI Platform & Voice Agents” | >