California-based company Mojo has announced its latest prototype Mojo Lens. This new version includes the world’s smallest and densest display for projecting augmented reality (AR) imagery.
Category: biotech/medical – Page 1,324
Officials in Tokyo are concerned about the more transmissible BA.2 Omicron coronavirus subvariant, which is increasingly dominant among infections.
The capital’s expert panel says its screening indicates the BA.2 subvariant accounted for nearly 68 percent of new cases in the week through March 28. The rate has climbed nearly 30 points in the past two weeks.
Tohoku Medical and Pharmaceutical University Professor Kaku Mitsuo said, “We are at a critical moment where infections will rapidly spread or not.” He added, “We have to take measures to prevent it from happening.”
At roughly 70 years human age, the mice looked elderly and unremarkable. Yet hidden underneath was a youthful cellular clock, turned back in time based on a Nobel-Prize-winning strategy. It’s also the latest bet for finding the fountain of youth, backed by heavy-hitter anti-aging startups in Silicon Valley.
At the center is partial cellular reprogramming. The technique, a sort of gene therapy, forces cells to make four proteins, collectively dubbed the Yamanaka factors. Like erasers, the factors wipe a cell’s genetic history clean, reverting adult cells—for example, skin cells—to a stem cell-like identity, giving them back the superpower to turn into almost any type of cell.
The process isn’t all-or-nothing. In a twist, scientists recently found that they can use the factors to rewind a cell’s genetic history tape rather than destroying it altogether. And if they stop at the right point, the cell dramatically loses its age, becoming more youthful but retaining its identity. The results spurred a wave of interest in moving the therapy to humans, with Calico Life Sciences—a sister company to Google—and Altos Labs, backed by Jeff Bezos, in the race.
Psilocybin is illegal in Canada, but people can apply for exemptions for end-of-life anxiety.
AI algorithms prompt robot to interrogate, select, decision-make to create a painting.
The study also developed an automated diagnostic pipeline to streamline the genomic data— including the millions of variants present in each genome—for clinical interpretation. Variants unlikely to contribute to the presenting disease are removed, potentially causative variants are identified, and the most likely candidates prioritized. For its pipeline, the researchers and clinicians used Exomiser, a software tool that Robinson co-developed in 2014. To assist with the diagnostic process, Exomiser uses a phenotype matching algorithm to identify and prioritize gene variants revealed through sequencing. It thus automates the process of finding rare, segregating and predicted pathogenic variants in genes in which the patient phenotypes match previously referenced knowledge from human disease or model organism databases. The use of Exomiser was noted in the paper as having greatly increased the number of successful diagnoses made.
The genomic future.
Not surprisingly, the paper concludes that the findings from the pilot study support the case for using whole genome sequencing for diagnosing rare disease patients. Indeed, in patients with specific disorders such as intellectual disability, genome sequencing is now the first-line test within the NHS. The paper also emphasizes the importance of using the HPO to establish a standardized, computable clinical vocabulary, which provides a solid foundation for all genomics-based diagnoses, not just those for rare disease. As the 100,000 Genomes Project continues its work, the HPO will continue to be an essential part of improving patient prognoses through genomics.
Cancer cells thrive by competing with normal cells for survival. Now, researchers are employing living bacteria to fight back against the cancer. This so-called bacteriotherapy – the deployment of bacteria to fight cancer – has sparked interest in the fields of immunotherapy and bioengineering.
Summary: 15 newly discovered “hotspots” in the genome that either speed up or slow down brain aging could be new targets for the development of Alzheimer’s medications and therapies for other brain disorders.
Source: USC
Researchers from a USC-led consortium have discovered 15 “hotspots” in the genome that either speed up brain aging or slow it down—a finding that could provide new drug targets to resist Alzheimer’s disease and other degenerative brain disorders, as well as developmental delays.
In machine learning, understanding why a model makes certain decisions is often just as important as whether those decisions are correct. For instance, a machine-learning model might correctly predict that a skin lesion is cancerous, but it could have done so using an unrelated blip on a clinical photo.
While tools exist to help experts make sense of a model’s reasoning, often these methods only provide insights on one decision at a time, and each must be manually evaluated. Models are commonly trained using millions of data inputs, making it almost impossible for a human to evaluate enough decisions to identify patterns.
Now, researchers at MIT and IBM Research have created a method that enables a user to aggregate, sort, and rank these individual explanations to rapidly analyze a machine-learning model’s behavior. Their technique, called Shared Interest, incorporates quantifiable metrics that compare how well a model’s reasoning matches that of a human.
Whether a computer could ever pass for a living thing is one of the key challenges for researchers in the field of Artificial Intelligence. There have been vast advancements in AI since Alan Turing first created what is now called the Turing Test—whether a machine could exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. However, machines still struggle with one of the fundamental skills that is second nature for humans and other life forms: lifelong learning. That is, learning and adapting while we’re doing a task without forgetting previous tasks, or intuitively transferring knowledge gleaned from one task to a different area.
Now, with the support of the DARPA Lifelong Learning Machines (L2M) program, USC Viterbi researchers have collaborated with colleagues at institutions from around the U.S. and the world on a new resource for the future of AI learning, defining how artificial systems can successfully think, act and adapt in the real world, in the same way that living creatures do.
The paper, co-authored by Dean’s Professor of Electrical and Computer Engineering Alice Parker and Professor of Biomedical Engineering, and of Biokinesiology and Physical Therapy, Francisco Valero-Cuevas and their research teams, was published in Nature Machine Intelligence, in collaboration with Professor Dhireesha Kudithipudi at the University of Texas at San Antonio, along with 22 other universities.