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Insecticides have been used for centuries to counteract widespread pest damage to valuable food crops. Eventually, over time, beetles, moths, flies and other insects develop genetic mutations that render the insecticide chemicals ineffective.

Escalating resistance by these mutants forces farmers and vector control specialists to ramp up use of poisonous compounds at increasing frequencies and concentrations, posing risks to human health and damage to the environment since most insecticides kill both ecologically important insects as well as pests.

To help counter these problems, researchers recently developed powerful technologies that genetically remove insecticide-resistant variant genes and replace them with genes that are susceptible to pesticides. These gene-drive technologies, based on CRISPR gene editing, have the potential to protect valuable crops and vastly reduce the amount of chemical pesticides required to eliminate pests.

New Curtin University-led research has uncovered what may be the oldest direct evidence of ancient hot water activity on Mars, revealing the planet may have been habitable at some point in its past.

The study analyzed a 4.45 billion-year-old grain from the famous Martian meteorite NWA7034, also known as Black Beauty, and found geochemical “fingerprints” of -rich fluids.

Study co-author Dr. Aaron Cavosie from Curtin’s School of Earth and Planetary Sciences said the discovery opened up new avenues for understanding ancient Martian hydrothermal systems associated with magmatism, as well as the planet’s past habitability.

Is the chemical toxic?

While the scientists are unsure about the toxicity of the chemical, it is concerning since chloronitramide anion bears resemblance to other chemicals that are toxic in nature. David Wahman, one of the study’s authors and a research environmental engineer at the Environmental Protection Agency, said, “It has similarity to other toxic molecules. We looked for it in 40 samples in 10 US chlorinated drinking water systems located in seven states. We did find it in all the samples.”

DNA can be damaged by normal cellular processes as well as external factors such as UV radiation and chemicals. Such damage can lead to breaks in the DNA strand. If DNA damage is not properly repaired, mutations can occur, which may result in diseases like cancer. Cells use repair systems to fix this damage, with specialized proteins locating and binding to the damaged regions. Now, researchers from the Kind Group at the Hubrecht Institute have mapped the activity of repair proteins in individual human cells. The study demonstrates how these proteins collaborate in so-called “hubs” to repair DNA damage. These findings may lead to new cancer therapies and other treatments where DNA repair is essential.

The researchers published their findings in Nature Communications in an article titled, “Genome-wide profiling of DNA repair proteins in single cells.”

“Accurate repair of DNA damage is critical for maintenance of genomic integrity and cellular viability,” the researchers wrote. “Because damage occurs non-uniformly across the genome, single-cell resolution is required for proper interrogation, but sensitive detection has remained challenging. Here, we present a comprehensive analysis of repair protein localization in single human cells using DamID and ChIC sequencing techniques.”

How can scientific discoveries based on large volumes of experimental data be accelerated by artificial intelligence (AI)? This can be achieved in heterogeneous catalysis, according to a recent study led by Prof. Weixue Li from the University of Science and Technology of China (USTC) of the Chinese Academy of Sciences, published in Science.

The researchers developed a comprehensive theory of metal-support interaction (MSI), a key aspect of catalysis, by combining interpretable AI with domain knowledge, experimental data, and first-principles simulations.

Supported metal catalysts are widely used in industrial chemical production, petrochemical refining, and environmental control systems like exhaust catalysts. MSI influences interfacial activities, such as charge transfer, chemical composition, perimeter sites, particle shape, and suboxide encapsulation, in addition to stabilizing dispersed catalysts. As a result, modifying MSI is one of the few ways to enhance catalyst performance.

However, Hassabis’ true breakthrough came just a month ago, when he and two colleagues from DeepMind won the Nobel Prize in Chemistry for their development of AlphaFold, an AI tool capable of predicting the structure of the 200 million known proteins. This achievement would have been nearly impossible without AI, and solidifies Hassabis’ belief that AI is set to become one of the main drivers of scientific progress in the coming years.

Hassabis — the son of a Greek-Cypriot father and a Singaporean mother — reflects on the early days of DeepMind, which he founded in 2010, when “nobody was working on AI.” Over time, machine learning techniques such as deep learning and reinforcement learning began to take shape, providing AI with a significant boost. In 2017, Google scientists introduced a new algorithmic architecture that enabled the development of AGI. “It took several years to figure out how to utilize that type of algorithm and then integrate it in hybrid systems like AlphaFold, which includes other components,” he explains.

“During our first years, we were working in a theoretical space. We focused on games and video games, which were never an end in themselves. It gave us a controlled environment in which to operate and ask questions. But my passion has always been to use AI to accelerate scientific understanding. We managed to scale up to solving a real-world problem, such as protein folding,” recalls the engineer and neuroscientist.

A large field-of-view, single-cell-resolution two-and…


Research on moscovium and nihonium shows they are more reactive than flerovium and subject to notable relativistic effects, broadening our understanding of superheavy elements and their potential uses.

An international team led by scientists from GSI/FAIR in Darmstadt, Johannes Gutenberg University Mainz, and the Helmholtz Institute Mainz has successfully determined the chemical properties of the artificially produced superheavy elements moscovium and nihonium (elements 115 and 113).

Moscovium is now the heaviest element ever to be chemically studied. Their research, published in Frontiers in Chemistry, reveals that both elements are more chemically reactive than flerovium (element 114), which was previously studied at GSI/FAIR.

In an era where AI and data are driving the scientific revolution, quantum computing technology is emerging as another game-changer in the development of new drugs and new materials.

Dr. Hyang-Tag Lim’s research team at the Center for Quantum Technology at the Korea Institute of Science and Technology (KIST) has implemented a quantum computing algorithm that can estimate interatomic bond distances and ground state energies with chemical accuracy using fewer resources than conventional methods, and has succeeded in performing accurate calculations without the need for additional quantum error mitigation techniques.

The work is published in the journal Science Advances.

A large number of 2D materials like graphene can have nanopores—small holes formed by missing atoms through which foreign substances can pass. The properties of these nanopores dictate many of the materials’ properties, enabling the latter to sense gases, filter out seawater, and even help in DNA sequencing.

“The problem is that these 2D materials have a wide distribution of nanopores, both in terms of shape and size,” says Ananth Govind Rajan, Assistant Professor at the Department of Chemical Engineering, Indian Institute of Science (IISc). “You don’t know what is going to form in the material, so it is very difficult to understand what the property of the resulting membrane will be.”

Machine learning models can be a powerful tool to analyze the structure of nanopores in order to uncover tantalizing new properties. But these models struggle to describe what a looks like.

Using various telescopes, an international team of astronomers has conducted a comprehensive study of a double-lined spectroscopic binary known as HD 34736. The study, published November 6 in the Monthly Notices of the Royal Astronomical Society, delivers important insights into the properties of this system.

So far, the majority of binaries have been detected by Doppler shifts in their , hence these systems are called spectroscopic binaries. Observations show that in some spectroscopic binaries, spectral lines from both stars are visible, and these lines are alternately double and single. These systems are known as double-lined spectroscopic binaries (SB2).

HD 34,736 is an SB2 system consisting of two chemically peculiar late B-type , located some 1,215 light years away. Previous of HD 34,736 have found that the system has an extraordinarily strong magnetic field exceeding 4.5 kG. The effective temperatures of the primary and secondary star were found to be 13,700 and 11,500 K, respectively.