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A joint research team from the LKS Faculty of Medicine (HKUMed) and the Faculty of Science at the University of Hong Kong has uncovered an unexpected interaction between chemotherapeutic agents and a crucial efficacy marker.

We’ve yet to see a falling piece of space debris strike an airplane, but if it happens, the consequences would almost certainly be catastrophic – and according to a new study, the danger posed to planes is only rising.

The researchers behind the study, from the University of British Columbia in Canada, looked at worldwide flight data to model the distribution of planes in the sky, then compared this to records of uncontrolled rocket body reentries.

The increasing risk is also being driven in part by the mass deployment of satellites, like SpaceX’s Starlink, which will eventually reenter our airspace.

Extreme precipitation events in Antarctica, which are mostly dominated by snowfall due to sub-zero temperatures, also include rainfall, according to new research.

BAS scientists studying atmospheric rivers—narrow bands of concentrated moisture in the atmosphere or “rivers in the sky”—have discovered that these phenomena bring not only snow but also rain to parts of Antarctica, even during the continent’s cold winter months. The results are published in the journal The Cryosphere.

Using cutting-edge regional climate models (RCMs) at a of just one kilometer, the researchers explored how atmospheric rivers interact with Antarctica’s rugged terrain to deliver significant precipitation to key areas, including the Thwaites and Pine Island Ice Shelves in West Antarctica. These are areas known for their ongoing retreat and contribution to global sea-level rise.

Sylvain Lesné, a neuroscientist accused of image manipulation in a seminal Alzheimer’s disease paper in, resigned last week from his tenured professorship at the University of Minnesota Twin Cities (UMN). The move follows a 2.5-year investigation in which the university found problems with several other papers on which Lesné is an author. The study has already been pulled, but the school has asked that four more of Lesné’s papers be retracted.

The resignation, effective 1 March, was first reported by the. Lesné did not respond to a request for comment. UMN spokesperson Jake Ricker said, “The university has identified data integrity concerns impacting several publications and has been in touch with those journals to recommend retraction of the publications, where appropriate.”

As a postdoc, Lesné worked in the lab of neuroscientist Karen Ashe. In 2006, they published a study in that seemed to show a cause-effect relationship between a protein—amyloid-beta *56—and memory loss in rats. Those symptoms seemed to resemble the memory problems that afflict Alzheimer’s patients.

Scattering takes place across the universe at large and miniscule scales. Billiard balls clank off each other in bars, the nuclei of atoms collide to power the stars and create heavy elements, and even sound waves deviate from their original trajectory when they hit particles in the air.

Understanding such scattering can lead to discoveries about the forces that govern the universe. In a recent publication in Physical Review C, researchers from Lawrence Livermore National Laboratory (LLNL), the InQubator for Quantum Simulations and the University of Trento developed an algorithm for a quantum computer that accurately simulates scattering.

“Scattering experiments help us probe and their interactions,” said LLNL scientist Sofia Quaglioni. “The scattering of particles in matter [materials, atoms, molecules, nuclei] helps us understand how that matter is organized at a .”

To identify signs of particles like the Higgs boson, CERN researchers work with mountains of data generated by LHC collisions.

Hunting for evidence of an object whose behavior is predicted by existing theories is one thing. But having successfully observed the elusive boson, identifying new and unexpected particles and interactions is an entirely different matter.

To speed up their analysis, physicists feed data from the billions of collisions that occur in LHC experiments into machine learning algorithms. These models are then trained to identify anomalous patterns.