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How antibiotics work inhibiting RNA polymerase?

Scientists have long been fascinated by two promising classes of antibiotics that disable RNA polymerase (RNAP). They knew that these drugs could grind gene expression to a halt in several pathogens, including the bacterium behind tuberculosis, by binding to specific locations in the RNAP. But despite decades of study, a key question remained: what process are the drugs actually targeting?

Now, a new paper in PNAS solves that mystery and simultaneously uncovers a new element of basic biology. Using these antibiotics as tools to clarify the finer points of RNAP function, the researchers discovered that the enzyme works only if a certain moving part briefly swings into place to stabilize RNA synthesis—and that these drugs disable the enzyme by preventing that motion. The findings reveal a previously unknown mechanism of RNA synthesis shared across diverse forms of life and lay the groundwork for developing next-generation antibiotics.

“It’s sort of a two-for-one,” says the senior author. “We now know how these inhibitors work, and the inhibitors also revealed a conformational change in the active site that we didn’t know was important.”

Neural network approach makes AI uncertainty checks far more efficient

McGill University researchers have developed a more energy-efficient method of building AI systems that are better at measuring—and indicating—their own uncertainty. This will help users determine when human oversight is needed, when additional data should be collected and when a model is being asked to work beyond the conditions it was trained for, the researchers said.

“Artificial intelligence systems now play a central role in daily life, from medical diagnosis and content moderation to autonomous driving and AI agents that act on our behalf,” said Mame Diarra Touré, lead author and Ph.D. candidate in the Department of Mathematics and Statistics. “As these systems take on more responsibility, they need to become more trustworthy. They should recognize when they are uncertain, rather than giving confident answers in situations where they may be wrong.”

The research was supervised by David A. Stephens, professor in the Department of Mathematics and Statistics. “Singular Bayesian Neural Networks,” by Mame Diarra Touré and Stephens, was presented at the Forty-Third International Conference on Machine Learning (ICML 2026).

Why Doesn’t the Federation Use Replicators to Become Infinitely Rich?

If the Federation can replicate food, clothing, furniture, spare parts, and even medical supplies, then why doesn’t it simply use replicators to become infinitely rich?

The answer is much more interesting than simply saying “money doesn’t matter in Star Trek.”

Replicators don’t create infinite wealth — they create abundance.

Once almost anyone can produce a particular object on demand, that object stops being scarce. And when scarcity disappears, so does much of its economic value.

But replicators can’t eliminate every kind of scarcity.

You can’t replicate land. You can’t replicate a historic location. You can’t instantly replicate decades of human experience, expertise, creativity, reputation, or time.

Abstract: 6 Department of Urology, Mayo Clinic, Rochester, Minnesota, USA

6 Department of Urology, Mayo Clinic, Rochester, Minnesota, USA.

7Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, Alberta, Canada.

Address correspondence to: Zhenkun Lou or Robert W. Mutter, Mayo Clinic, Kellen Building 401,200 First Street SW, Rochester, Minnesota 55,905, USA. Phone: 507.284.2702; Email: [email protected] (ZL). Phone: 507.284.3261; Email: [email protected] (RWM).

People Who Live Past 100 Have a Surprising Abundance of Cancer-Killing Immune Cells

Today, the average human lives for about 71 years.

A rare few people will see their 100th birthday, earning the title of centenarian, and even fewer will live past 110.

We call these folks supercentenarians.

New research published in Cell Press reveals one possible explanation for how these superagers might be dodging the mounting risk of cancer and illness as the years progress.

Cognition and consciousness arise from analog computations, says new theory

A new theory from neuroscientists at MIT’s Picower Institute proposes that cognition and consciousness may depend not only on neurons and synaptic connections, but also on the traveling electrical waves generated by neural activity.

The traditional “brain as circuitry” analogy captures an important part of neuroscience: synaptic connections store and transmit information. But Earl Miller and colleagues argue that synapses alone may be too slow and inflexible to explain how the brain rapidly assembles and reorganizes neural networks from moment to moment.

Their proposal centers on brain waves as a dynamic control system.

Slower alpha and beta oscillations, associated with internal information such as memories, goals and expectations, may regulate faster gamma activity associated with incoming sensory information. Because these waves can travel across the cortex, they could rapidly determine which populations of neurons participate in processing at a particular place and time.

The researchers describe this as “spatiotemporal computing.” Where electrical waves interact, their amplitudes can add or subtract, potentially allowing the brain to perform a form of analog computation through wave interference rather than relying entirely on sequential, digital-like operations.

The theory also incorporates ephaptic coupling—the possibility that electrical fields generated by populations of neurons can directly influence the firing of nearby neurons, providing another rapid mechanism for coordinating neural activity.

The authors extend the idea to consciousness, proposing that conscious awareness emerges when these wave dynamics organize widespread cortical activity into a coherent, globally integrated state. Supporting evidence includes anesthesia research showing that drugs with very different molecular mechanisms can all produce unconsciousness while disrupting large-scale brain-wave organization.

Gentle chemical glow helps scientists capture sharper images of living cells

We reach for brighter, better lighting for sharper pictures, whether we’re photographing a puppy or a microscopic cell. In most cases, the light comes from outside the object being photographed. A recent study explored a different approach, using the cells’ own chemical glow to illuminate their internal structures. This new experimental framework, called REID, lets microscopes capture super-sharp, high-resolution details of cell structures by bypassing the need for harsh external lasers that can sometimes damage the cells being imaged.

Chemical reactions inside cells are normally very dim, but researchers discovered that swapping a common chemical co-reactant for a biological buffer called Bis-Tris boosted the cellular glow by 1,000 times. This let them capture sharp, unblurred images in just 20 milliseconds. The REID framework combined computer algorithms with electricity-triggered, chemical and biological (BL) luminescence to sharpen images down to 100 nanometers.

The team tested REID’s sensitivity, or how little of a biomarker it could still detect, using the cancer marker CEA, and compared it with standard fluorescence microscopy. They found that although the REID setup was less efficient at producing light, it was eight times more sensitive at detecting the cancer marker than the standard technique. The findings are published in Nature.

Cancerassociated fibroblast subtypes differentially modulate natural killer cells in cancer

Cancer-associated fibroblasts (CAFs) represent an abundant and heterogeneous component of pancreatic ductal adenocarcinoma (PDAC) but their interplay with natural killer (NK) cells is largely understudied. Rodrigues et al. show that CAFs, particularly myofibroblastic (my)CAF modulate NK anti-tumor capacity. This is mediated partially by prostaglandin E2, affecting granzyme B expression.

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