If you go walking in the wild, you might expect that what you’re seeing is natural. All around you are trees, shrubs and grasses growing in their natural habitat.
But there’s something here that doesn’t add up. Across the world, there are large areas of habitat which would suit native plant species just fine. But very often, they’re simply absent.
Our new research gauges the scale of this problem, known as “dark diversity”. Our international team of 200 scientists examined plant species in thousands of sites worldwide.
Artificial Intelligence (AI) and Neuroscience are two fields, but they are closely related to each other. Artificial intelligence can provide powerful tools for neuroscience research, and its application in neurological diseases is of great importance. The convergence of AI and neuroscience has sparked a paradigm shift in our understanding of the brain and its intricate mechanisms.
Here, Creative Biolabs explores the remarkable impact of AI in neuroscience research, highlighting its potential to unlock new frontiers in our quest to unravel the mysteries of the brain.
Neuroscience research generates vast amounts of complex data, ranging from molecular and cellular information to data generated by large-scale brain activity. For researchers, analyzing and decoding this wealth of data is a major challenge. AI technology steps in to address just this problem.
In this episode, we welcome Prof. Dr.-Ing. Maurits Ortmanns, a leading expert in ASIC design and professor at the University of Ulm, Germany. With a distinguished career in microelectronics, Dr. Ortmanns has contributed extensively to the development of integrated circuits for biomedical applications. He shares insights into the critical role of ASIC (Application-Specific Integrated Circuit) design in advancing neurotech implants, focusing on low-power, high-speed circuits that are essential for optimizing the performance and reliability of these devices. Dr. Ortmanns also discusses the challenges and future of circuit integration in neurotechnology.
Top 3 Takeaways:
“Each ASIC is very low in cost because the development cost is spread across millions of units. The actual production cost is minimal; the primary expense lies in the development time until the first chips are produced and ready for manufacturing.” “For an inexperienced engineer, it typically takes about six months to a year to design the blueprint for the chip. Then, depending on the manufacturer, it takes an additional four to six months for the actual fabrication of the ASIC. Finally, you would need another one to two months for testing, so the total turnaround time for a small chip is approximately one and a half years.” “Let’s take the example of a neuromodulator. You need recordings or data from neurons and stimulation data going to the neurons, so you essentially have these two components. Then, you encounter challenges like stimulation artifacts. One person might focus on eliminating the stimulation artifact in the recording channel. That requires additional algorithms or hardware, and the data needs to be digitized, which is another task. You may also have someone working on a compression algorithm and building digital circuitry to compress the raw input data. Then, there’s the data interface, power management, and wireless energy delivery. Each person works on their specific innovation, and if everything is well-planned and lucky, all these pieces can come together to create a complete system. However, sometimes you simply don’t have a breakthrough idea for power management or communication.” 0:45 Do you want to introduce yourself better than I just did?
3:15 What is integrated circuit design?
7:30 What are ASIC’s? How are they used in neurotech?
10:15 How does the million dollar fab cost get split into each chip?
Main episode with Garry Nolan: https://youtu.be/g3bk1UXjKLIAs a listener of TOE you can get a special 20% off discount to The Economist and all it has to off…
Rao et al. found that lactylation stimulates the proteasomal degradation of cGAS independent of ubiquitin, which is compromised by phosphorylation of PSMA4 via disrupting its association with cGAS. Lactylation rewires PIK3CB activity and impairs ULK1-driven phosphorylation of PSMA4. Consequently, lactylation of cGAS sustains tumor growth and indicates the prognosis of LUAD.
Among the mountains of evidence that climate change is warming Earth faster than any other point in recorded history is the fact that most glaciers around the world are shrinking or disappearing. Melting glaciers and ice sheets are already the biggest contributors to global sea level rise, and according to the World Glacier Monitoring Service, ice loss rates have increased each decade since 1970. Yet, of the approximately 200,000 glaciers in the world currently, no database exists to identify which glaciers have disappeared, and when. The Global Land Ice Measurements from Space (GLIMS) initiative, an international project designed to monitor the world’s glaciers primarily using data from optical satellite instruments, aims to change that.
“Glaciers are indicators of climate change because they grow and shrink on longer timescales than rapidly changing weather, so they give a clearer signal about climate,” said Bruce Raup, a senior associate scientist at the National Snow and Ice Data Center (NSIDC) and director of the GLIMS initiative. “We know that glaciers are disappearing, but we’ve had no way to show that to people. So, we are making an effort to document glaciers that have disappeared and approximately when they disappeared.”
