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Blog – Page 624

At the recent annual International AIDS Conference, a startling presentation about the newest wonder drug in HIV prevention brought a raucous standing ovation.

But some of us in the public health community are now starting to wonder what all the cheering was about. Although the scientific results were impeccable, the process for translating those results into action for young women in Africa has been left to our imaginations. And if history is any guide, this could be a nightmare.

When the results first came out, Gilead, the manufacturer of lenacapavir, stated it was too early to discuss licensing and offering vague plans about its production and availability in Africa. Just recently, a second study among men who have sex with men and predominantly conducted in the Northern Hemisphere showed similarly promising results. While Gilead now says they have sufficient data to move ahead with licensing and manufacturing worldwide, they have offered no timeline to do so. Urgency to report trial results has not been mirrored by the urgency to provide access. Unanswered questions remain about why another study was needed to move ahead with approvals for use in African women, and if and when lenacapavir will be made available at an affordable price in the African region.

The drug, which has a manufacturing cost estimated at about $40 per year, is currently licensed as an HIV treatment for more than $42,000 per year in the United States. In South Africa, health care expenditures in the public sector are approximately $230 per person per year. Advocates and the study scientists have strongly urged Gilead to make lenacapavir swiftly available in sub-Saharan Africa at an affordable price. But with over 3,000 women infected with HIV each week in the region according to UNAIDS estimates, there is no time to waste.

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Neural networks have a remarkable ability to learn specific tasks, such as identifying handwritten digits. However, these models often experience “catastrophic forgetting” when taught additional tasks: They can successfully learn the new assignments, but “forget” how to complete the original. For many artificial neural networks, like those that guide self-driving cars, learning additional tasks thus requires being fully reprogrammed.

Biological brains, on the other hand, are remarkably flexible. Humans and animals can easily learn how to play a new game, for instance, without having to re-learn how to walk and talk.

Inspired by the flexibility of human and animal brains, Caltech researchers have now developed a new type of that enables neural networks to be continuously updated with new data that they are able to learn from without having to start from scratch. The algorithm, called a functionally invariant path (FIP) algorithm, has wide-ranging applications from improving recommendations on online stores to fine-tuning self-driving cars.

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Be it water, light or sound: waves usually propagate in the same way forwards as in the backward direction. As a consequence, when we are speaking to someone standing some distance away from us, that person can hear us as well as we can hear them. This is useful when having a conversation, but in some technical applications one would prefer the waves to be able to travel only in one direction – for instance, in order to avoid unwanted reflections of light or microwaves.

For sound waves, ten years ago researchers succeeded in suppressing their propagation in the backward direction; however, this also attenuated the waves travelling forwards. A team of researchers at ETH Zurich led by Nicolas Noiray, professor for Combustion, Acoustics and Flow Physics, in collaboration with Romain Fleury at EPFL, has now developed a method for preventing sound waves from travelling backwards without deteriorating their propagation in the forward direction. In the future, this method, which has recently been published in the scientific journal external page Nature Communications, could also be applied to electromagnetic waves.

The basis of this one-way street for sound waves are self-oscillations, in which a dynamical system periodically repeats its behaviour. “I’ve actually spent a good part of my career preventing such phenomena”, says Noiray. Amongst other things, he studies how self-sustaining thermo-acoustic oscillations can arise from the interplay between sound waves and flames in the combustion chamber of an aircraft engine, which can lead to dangerous vibrations. In the worst case, these vibrations can destroy the engine.

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A nanotube researcher in Japan has earned 13 retractions, with more to come, after an extensive investigation by the country’s National Institute of Advanced Industrial Science and Technology (AIST) revealed widespread misconduct in his work.

AIST’s investigation found Naohiro Kameta, senior principal researcher at the Nanomaterials Research Institute located in AIST’s Ibaraki campus, fabricated and falsified dozens of studies. He was apparently dismissed from his role following the findings.

The institute first learned of the problems in Kameta’s work in November 2022, according to a translated version of the investigation report. Initially, they looked into five papers, but eventually expanded their scrutiny to 61 articles on which Kameta was the lead or responsible author.

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SpaceX announced a new capability for the Dragon spacecraft on Sept. 27 in the unlikely event of a parachute failure. Dragon now has built-in redundancy to propulsively land using its SuperDraco thrusters, saving the vehicle and potential crew from a rough landing or imminent danger.

SpaceX introduced the concept of a propulsive landing Dragon over ten years ago. When SpaceX revealed Dragon 2, it was marketed as capable of propulsively landing anywhere on Earth and was initially designed to land exclusively with the SuperDracos. However, SpaceX ultimately pursued the use of parachutes as the main form of recovery for Dragon 2 missions.

Much has had to change with Dragon 2 since May 30, 2014, to make it the reliable crewed spacecraft we know it as today. Now, SpaceX has decided to bring back one of the main capabilities that was believed to have been left behind in development.

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