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Single pollen parents in flowering plants may be more common than previously thought

While all seeds produced within a fruit have the same maternal genome, the paternal genomes of seeds can come from the pollen of one or more paternal parents. A common assumption about flowering plants is that the ovules are most often pollinated by multiple paternal parents at the flower level.

Various genomic conflicts can arise during the process of fertilization and in multiseed plants, including conflicts over nutritional resources between the maternal plant and its offspring, conflicts over nutritional resources among developing seeds, between paternal and maternal genomes over seed development and competition among paternal parents. The relationship between these genomic conflicts and single or multiple paternal parentage is unclear.

To shed some light on the prevalence of monogamy and polyandry in flowering plants, a group of researchers in India conducted a systematic literature review of studies from 1984 to 2024 and a of 63 flowering from diverse families. The study was recently published in the Proceedings of the National Academy of Sciences. The number of paternal parents was determined in the context of self-compatible vs. incompatible breeding, seed number, and phylogenetic relationships.

Direct visualization of quantum zero-point motion in complex molecule reveals eternal dance of atoms

Most of us find it difficult to grasp the quantum world. According to Heisenberg’s uncertainty principle, it’s like observing a dance without being able to see simultaneously exactly where someone is dancing and how fast they’re moving—you always must choose to focus on one.

And yet, this quantum dance is far from chaotic; the dancers follow a strict choreography. In , this strange behavior has another consequence: Even if a molecule should be completely frozen at absolute zero, it never truly comes to rest. The it is made of perform a constant, never-ending quiet dance driven by so-called zero-point energy.

Multiwavelength observations investigate the nature of a TeV gamma-ray binary

An international team of astronomers has performed multiwavelength observations of a gamma-ray binary system known as HESS J0632+057. Results of the observational campaign, presented July 31 on the pre-print server arXiv, shed more light on the nature of this binary.

Gamma-ray binaries consist of a massive OB-type star in orbit with a compact object like a neutron star or black hole. Some of them emit a significant amount of radiation in very high-energy (TeV) and are therefore known as TeV gamma-ray binaries (TGBs).

TGBs exhibit diverse high-energy phenomena driven by interactions between the two components. Given that these sources are extremely rare, astronomers are especially interested in exploring them in detail, which could help us better understand their mysterious nature.

OpenAI releases ChatGPT-5 as AI race accelerates

OpenAI released a keenly awaited new generation of its hallmark ChatGPT on Thursday, touting “significant” advancements in artificial intelligence capabilities as a global race over the technology accelerates.

ChatGPT-5 is rolling out free to all users of the AI tool, which is used by nearly 700 million people weekly, OpenAI said in a briefing with journalists.

Co-founder and chief executive Sam Altman touted this latest iteration as “clearly a model that is generally intelligent.”

Researchers overcome long-standing bottleneck in single photon detection with twisted 2D materials

The ability to detect single photons (the smallest energy packets constituting electromagnetic radiation) in the infrared range has become a pressing need across numerous fields, from medical imaging and astrophysics to emerging quantum technologies. In observational astronomy, for example, the light from distant celestial objects can be extremely faint and require exceptional sensitivity in the mid-infrared.

Similarly, in free-space quantum communication—where single photons need to travel across vast distances—operating in the mid-infrared can provide key advantages in signal clarity.

The widespread use of single-photon detectors in this range is limited by the need for large, costly, and energy-intensive cryogenic systems to keep the temperature below 1 Kelvin. This also hinders the integration of the resulting detectors into modern photonic circuits, the backbone of today’s information technologies.

Maximizing direct methanol fuel cell performance: Reinforcement learning enables real-time voltage control

Fuel cells are energy solutions that can convert the chemical energy in fuels into electricity via specific chemical reactions, instead of relying on combustion. Promising types of fuel cells are direct methanol fuel cells (DMFCs), devices specifically designed to convert the energy in methyl alcohol (i.e., methanol) into electrical energy.

Despite their potential for powering large electronics, vehicles and other systems requiring portable power, these methanol-based fuel cells still have significant limitations. Most notably, studies found that their performance tends to significantly degrade over time, because the materials used to catalyze reactions in the cells (i.e., electrocatalytic surfaces) gradually become less effective.

One approach to cleaning these surfaces and preventing the accumulation of poisoning products produced during chemical reactions entails the modulation of the voltage applied to the fuel cells. However, manually adjusting the voltage applied to the surfaces in effective ways, while also accounting for physical and chemical processes in the fuel cells, is impractical for .

The universe’s secret harvest: Shedding light on ‘the cosmic grapes’

Astronomers have discovered a remarkably clumpy rotating galaxy that existed just 900 million years after the Big Bang, shedding new light on how galaxies grew and evolved in the early universe. Nicknamed the “Cosmic Grapes,” the galaxy appears to be composed of at least 15 massive star-forming clumps—far more than current theoretical models predict could exist within a single rotating disk at this early time.

The discovery, published in Nature Astronomy, was made possible by an extraordinary combination of observations from the Atacama Large Millimeter/submillimeter Array (ALMA) and the James Webb Space Telescope (JWST), all focused on a single galaxy that happened to be perfectly magnified by a foreground galaxy cluster through gravitational lensing. In total, more than 100 hours of telescope time were dedicated to this single system, making it one of the most intensively studied galaxies from the .

Although the galaxy had appeared as a smooth, single disk-like object in previous Hubble images, the powerful resolution of ALMA and JWST, enhanced by , revealed a dramatically different picture: a rotating galaxy teeming with massive clumps, resembling a cluster of grapes. The finding marks the first time astronomers have linked small-scale and large-scale rotation in a typical galaxy at cosmic dawn, reaching spatial resolutions down to just 10 parsecs (about 30 light-years).

Programmable soft material bends, bounces and absorbs energy on demand

Scientists at Lawrence Livermore National Laboratory (LLNL) and their collaborators have created a new class of programmable soft materials that can absorb impacts like never before, while also changing shape when heated.

The research—which includes collaborators from Harvard University, the California Institute of Technology (Caltech), Sandia National Laboratories and Oregon State University—opens the door to smarter, lighter and more resilient materials that respond to the world around them. The research is published in the journal Advanced Materials.

Built from liquid crystal elastomers (LCEs)—rubbery polymers that shift in response to heat, light or stress—the team 3D-printed the materials into carefully engineered lattice structures. These lattices can be designed to absorb energy, stiffen, soften or even change shape, depending on their architecture and environmental conditions.

AI accelerates development of advanced heat-dissipating polymers

A machine learning method developed by researchers from the Institute of Science Tokyo, the Institute of Statistical Mathematics, and other institutions accurately predicts liquid crystallinity of polymers with 96% accuracy. They screened over 115,000 polyimides and selected six candidates with a high probability of exhibiting liquid crystallinity. Upon successful synthesis and experimental analyses, these liquid crystalline polyimides demonstrated thermal conductivities up to 1.26 W m⁻¹ K⁻¹, accelerating the discovery of efficient thermal materials for next-generation electronics.

Finding new polymer materials that can efficiently dissipate heat while maintaining high reliability is one of the biggest challenges in modern electronics. One promising solution is liquid crystalline polyimides, a special class of polymers whose molecules naturally align into highly ordered structures.

These ordered chains create pathways for heat flow, making liquid crystalline polyimides highly attractive for thermal management in semiconductors, flexible displays, and next-generation devices. However, designing these polymers has long relied on trial and error because researchers lacked clear design rules to predict whether a polymer would form a liquid crystalline phase.

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