Lifeboat Foundation

BMIs using intracortical electrodes, such as Utah arrays, are particularly adept at sensing fast changing (millisecond-scale) neural activity from spatially localized regions (1 cm) during behavior or stimulation that is correlated to activity in such spatially specific regions, for example, M1 for motor and V1 for vision. Intracortical electrodes, however, struggle to track individual neurons over longer periods of time, for example, between subsequent recording sessions^15,16. Consequently, decoders are typically retrained every day¹⁵. A similar neural population identification problem is also present with an ultrasound device, including from shifts in the field of view between experiment sessions. In the current study, we demonstrated an alignment method that stabilizes image-based BMIs across more than a month and decodes from the same neurovascular populations with minimal, if any, retraining. This is a critical development that enables easy alignment of a previous days’ models to a new day’s data and allows decoding to begin with minimal to no new training data. Much effort has focused on ways to recalibrate intracortical BMIs across days that do not require extensive new data^{18,19,20,21,22,23}. Most of these methods require identification of manifolds and/or latent dynamical parameters and collecting new neural and behavioral data to align to these manifolds/parameters. These techniques are, to date, tailored to each research group’s specific applications with varying requirements, such as hyperparameter tuning of the model²³ or a consistent temporal structure of data²². They are also susceptible to changes in function in addition to anatomy. For example, ‘out-of-manifold’ learning/plasticity alters the manifold²⁴ in ways that many alignment techniques struggle to address. Finally, some of the algorithms are computationally expensive and/or difficult to implement in online use²².

Contrasting these manifold-based methods, our decoder alignment algorithm leverages the intrinsic spatial resolution and field of view provided by fUS neuroimaging to perform decoder stabilization in a way that is intuitive, repeatable and performant. We used a single fUS frame (∼ 500 ms) to generate an image of the current session’s anatomy and aligned a previous session’s field of view to this single image. Notably, this did not require any additional behavior for the alignment. Because we only relied upon the anatomy, our decoder alignment is robust, can use any off-the-shelf alignment tool and is a valid technique so long as the anatomy and mesoscopic encoding of relevant variables do not change drastically between sessions.

It remains an open question as to how much the precise positioning of the ultrasound transducer during each session matters for decoder performance, especially out-of-plane shifts or rotations. In these current experiments, we used linear decoders that assumed a given image pixel is the same brain voxel across all aligned data sessions. To minimize disruptions to this pixel–voxel relationship, we performed image alignment within the 2D plane. As we could only image a 2D recording plane, we did not correct for any out-of-plane brain shifts between sessions that would have disrupted the pixel–voxel mapping assumption. Future fUS-BMI decoders may benefit from three-dimensional (3D) models of the neurovasculature, such as registering the 2D field of view to a 3D volume^25,26,27 to better maintain a consistent pixel–voxel mapping.

A recent study discovered that for individuals who often feel lonely, the distinction between actual friends and beloved fictional characters gets blurred in the part of the brain that is active when thinking about others, a new study found.

The study involved brain scans of “Game of Thrones” enthusiasts as they reflected on different characters from the series and their real-life friends. Prior to the study, all participants had undergone a loneliness assessment.

The difference between those who scored highest on loneliness and those who scored lowest was stark, said Dylan Wagner, co-author of the study and associate professor of psychology at The Ohio State University.

OAK RIDGE, Tenn. — At Oak Ridge National Laboratory, the government-funded science research facility nestled between Tennessee’s Great Smoky Mountains and Cumberland Plateau that is perhaps best known for its role in the Manhattan Project, two supercomputers are currently rattling away, speedily making calculations meant to help tackle some of the biggest problems facing humanity.

You wouldn’t be able to tell from looking at them. A supercomputer called Summit mostly comprises hundreds of black cabinets filled with cords, flashing lights and powerful graphics processing units, or GPUs. The sound of tens of thousands of spinning disks on the computer’s file systems, and air cooling technology for ancillary equipment, make the device sound somewhat like a wind turbine — and, at least to the naked eye, the contraption doesn’t look much different from any other corporate data center. Its next-door neighbor, Frontier, is set up in a similar manner across the hall, though it’s a little quieter and the cabinets have a different design.

Yet inside those arrays of cabinets are powerful specialty chips and components capable of, collectively, training some of the largest AI models known. Frontier is currently the world’s fastest supercomputer, and Summit is the world’s seventh-fastest supercomputer, according to rankings published earlier this month. Now, as the Biden administration boosts its focus on artificial intelligence and touts a new executive order for the technology, there’s growing interest in using these supercomputers to their full AI potential.

Whole-genome alignment of 239 primate species reveals noncoding regulatory elements that are under selective constraint in primates but not in other placental mammals, that are enriched for variants that affect human gene expression and complex traits in diseases.

Protein-like aggregates known as amyloids can bind to molecules of genetic material. It is possible that these two types of molecules stabilized each other during the development of life—and that this might even have paved the way for the genetic code.

How organisms develop from inanimate matter is one of the biggest questions in science. Although many possible explanations have been proposed, there are no definitive answers. That’s no surprise: these processes took place 3 billion to 4 billion years ago, when the conditions on Earth were completely different from today.

“Over this vast period of time, evolution has thoroughly obliterated the traces that lead back to the origins of life,” says Roland Riek, professor of physical chemistry and associate director of ETH Zurich’s new interdisciplinary Center for Origin and Prevalence of Life. Science has no choice but to formulate hypotheses—and to substantiate them as thoroughly as possible with experimental data.

A tiny but prolific world of microbes encompasses everything around us, both inside and out. Microbiomes, which are comprised of diverse communities of microbes, play a pivotal role in shaping human health, yet the intricacies of how different microbial compositions influence our well-being remain largely unknown.

In a recent study published in Proceedings of the National Academy of Sciences, researchers at the University of Illinois Urbana-Champaign describe a new framework they have created to predict how species within microbiomes interact with each other to create unique compositions.

“Microbes can be used in medicine, aka ‘bugs as drugs,’ and these microbial therapeutics hold the possibility of being the answer to many of the diseases we face today,” said Shreya Arya, a graduate student in the O’Dwyer lab.

Limits of large language models in precision medicine. Treating cancer is becoming increasingly complex, but also offers more and more possibilities. After all, the better a tumor’s biology and genetic features are understood, the more treatment approaches there are. To be able to offer patients personalized therapies tailored to their disease, laborious and time-consuming analysis and interpretation of various data is required. Researchers at Charité — Universitätsmedizin Berlin and Humboldt-Universität zu Berlin have now studied whether generative artificial intelligence (AI) tools such as ChatGPT can help with this step. This is one of many projects at Charité analyzing the opportunities unlocked by AI in patient care.

If the body can no longer repair certain genetic mutations itself, cells begin to grow unchecked, producing a tumor.

The crucial factor in this phenomenon is an imbalance of growth-inducing and growth-inhibiting factors, which can result from changes in oncogenes — genes with the potential to cause cancer — for example.

AI can be just as discriminatory in the workplace as any human manager and the law needs to catch up to this new reality.

By associate professor alysia blackham, university of melbourne.

Procurement professionals face challenges more daunting than ever. Recent years’ supply chain disruptions and rising costs, deeply familiar to consumers, have had an outsize impact on business buying. At the same time, procurement teams are under increasing pressure to supply their businesses while also contributing to business growth and profitability.

Deloitte’s 2023 Global Chief Procurement Officer Survey reveals that procurement teams are now being called upon to address a broader range of enterprise priorities. These range from driving operational efficiency (74% of respondents) and enhancing corporate social responsibility (72%) to improving margins via cost reduction (71%).