Lifeboat Foundation

Changes in the brain caused by Alzheimer’s disease are associated with shortening of the telomeres—the protective caps on the ends of chromosomes that shorten as cells age—according to a new study led by Anya Topiwala of Oxford Population Health, part of the University of Oxford, UK, published March 22 in the open-access journal PLOS ONE.

Telomeres on chromosomes protect DNA from degrading, but every time a cell divides, the telomeres lose some of their length. Short telomeres are a sign of stress and cellular aging, and are also associated with a higher risk of neurological and psychiatric disorders. Currently, little is known about the links between telomere length and changes that occur in the brains of people with neurological conditions. Understanding those relationships could offer insights into the biological mechanisms that cause neurodegenerative disorders.

In the new study, researchers compared telomere length in white blood cells to results from brain MRIs and electronic health records from more than 31,000 participants in the UK Biobank, a large-scale biomedical database and research resource containing anonymized genetic, lifestyle and health information from half a million UK participants.

Researchers have shown for the first time exactly how immature neutrophils—white blood cells that are an important part of the immune system—are hijacked by pancreatic cancers to drive immunosuppression and treatment resistance. The study, led by investigators at Sylvester Comprehensive Cancer Center at the University of Miami Miller School of Medicine, is published in Cancer Discovery, a journal of the American Association for Cancer Research.

The paper describes a previously unrecognized signaling circuit in pancreatic cancer that instigates immunosuppression and tumor-promoting inflammation in the pancreatic tumor microenvironment, ultimately creating treatment resistance. The central regulator of this treatment resistance is neutrophil-derived TNF signaling. TNF, or tumor necrosis factor, is a substance in the body that causes inflammation.

This is the first study to implicate immunosuppressive signaling from immature neutrophils—the earliest sentinels in developing pancreatic cancer—in this process, said Jashodeep Datta, M.D., associate director of Translational Research at the Sylvester Pancreatic Cancer Research Institute. Datta, whose laboratory at Sylvester led the research, is senior author of the multicenter study.

Argentinian-born mathematician Luis Caffarelli has won the 2023 Abel Prize — one of the most coveted awards in mathematics — for his work on equations that are important for describing physical phenomena, such as how ice melts and fluids flow. He is the first person born in South America to win the award.

Caffarelli’s results “are technically virtuous, covering many different areas of mathematics and its applications”, says a statement by Helge Holden, a mathematician at the Norwegian University of Science and Technology in Trondheim who chairs the Abel Committee.

The winner says receiving the news was an emotional moment, because “it shows that people have some appreciation for me and for my science”.

It’s no secret that OpenAI’s ChatGPT has some incredible capabilities—for instance, the chatbot can write poetry that resembles Shakespearean sonnets or debug code for a computer program. These abilities are made possible by the massive machine-learning model that ChatGPT is built upon. Researchers have found that when these types of models become large enough, extraordinary capabilities emerge.

But bigger models also require more time and money to train. The training process involves showing hundreds of billions of examples to a model. Gathering so much data is an involved process in itself. Then come the monetary and environmental costs of running many powerful computers for days or weeks to train a model that may have billions of parameters.

“It’s been estimated that training models at the scale of what ChatGPT is hypothesized to run on could take millions of dollars, just for a single training run. Can we improve the efficiency of these training methods, so we can still get good models in less time and for less money? We propose to do this by leveraging smaller language models that have previously been trained,” says Yoon Kim, an assistant professor in MIT’s Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL).

Presented by Intel.

Every day around the world, companies leverage artificial intelligence to accelerate scientific discovery, and transform consumer and business services. Regrettably, the employment of AI is not occurring evenly. McKinsey’s ‘The State of AI in 2022’ report documents that adoption of AI by organizations has stalled at 50%. AI leaders are pulling ahead of the pack. One reason is 53% of AI projects fail to get to production. As the benefits of AI to everyone are too great and the issues with AI being in the hands of only a few are too concerning, that it is an opportune time to survey the challenges of going from concept to deployment.

For the first time, scientists at the University of Sydney.

The University of Sydney is a public research university located in Sydney, New South Wales, Australia. Founded in 1,850, it is the oldest university in Australia and is consistently ranked among the top universities in the world. The University of Sydney has a strong focus on research and offers a wide range of undergraduate and postgraduate programs across a variety of disciplines, including arts, business, engineering, law, medicine, and science.

If you go running over a trail in the woods or a grassy field, there are countless bumps and dips in the terrain, each with the potential to trip you up. But typically, runners manage just fine. It’s a remarkable physical feat that we tend to take for granted. A team of researchers set out to better understand it.

With a specially made running track and mathematical modeling, the lab of Madhusudhan Venkadesan found that when running on uneven terrain, humans mostly rely on the body’s mechanical response for stability rather than consciously plot out their footsteps to find level ground. Further, they found that the runners were just as efficient in their movements and physical exertion as when running on flat ground. The results are published in eLife.

Even without occasional hazards like steep drops, runners must contend with gentler, but still uneven, ground that can be destabilizing. So why aren’t trails typically littered with toppled runners? One possibility is that visual cues allow runners to carefully observe the land to step on mostly level areas. On the other hand, running played a huge role in human evolution, particularly in how it benefited humans in hunting. That means sight cannot be devoted solely to find areas to step on; it’s also needed to watch out for the prey, trees or other obstacles to avoid, and decide which path to take.

Chipmaker Nvidia has announced a new service that will allow users to access supercomputer level computing power from web browsers. The service, called Nvidia DGX Cloud, is an AI supercomputing service that allows enterprises to run workloads on the company’s A100 and H100 chips remotely.

“DGX Cloud provides dedicated clusters of NVIDIA DGX AI supercomputing, paired with NVIDIA AI software. The service makes it possible for every enterprise to access its own AI supercomputer using a simple web browser, removing the complexity of acquiring, deploying and managing on-premises infrastructure,” the company said in a blog post.

As reported by Reuters, however, the service isn’t cheap. Nvidia is charging $37,000 per month for access to eight of the A100 or H100 chips — the company’s flagship chips, which are both designed for AI computing. “Each instance of DGX Cloud features eight Nvidia H100 or A100 80GB Tensor Core GPUs for a total of 640GB of GPU memory per node. A high-performance, low-latency fabric built with Nvidia Networking ensures workloads can scale across clusters of interconnected systems, allowing multiple instances to act as one massive GPU to meet the performance requirements of advanced AI training,” Nvidia’s blog post explained.

Data analytics teams around the world are failing to bring value to their organisations. According to a survey by market research firm Gartner published on Wednesday, most data and analytics leaders reported that their teams do not provide effective value to the organisation. This is despite increased interest from the industry in investing in data and analytics to improve efficiency in business.

The research report noted that while the lack of available talent is the top impediment for which data and analytics teams are failing to add value to their firms, as reported by 39% of executives, other roadblocks, such as lack of resources and funding to support the programmes and lack of support from top management are also hindering data and analytics projects. Besides, culture challenges to accept change and overall poor data literacy in organisations are among other reasons why many of these projects continue to suffer.

At a time when technology companies are already facing the brunt of layoffs in the face of uncertain economic conditions, teams not performing well or failing to add value to the organisation can face dire consequences, believe experts.