Using computational tools and virtual screening, researchers at the Center for Redox Processes in Biomedicine (Redoxoma) have identified new inhibitors of the enzyme human 15-lipoxygenase-2 (h15-LOX-2). This protein plays an important role in inflammatory and metabolic processes and contributes to cellular homeostasis.
The discovery, described in the Journal of Medicinal Chemistry, could open up new avenues for investigating the biological and pathological functions of the enzyme and provide promising candidates for the development of new drugs.
“Although h15-LOX-2 is a potential biological target, it’s scarcely been explored for this purpose. Our work contributes to new inhibitors that have structural diversity among themselves and with respect to inhibitors already described in the literature. What’s more, they have similar drug properties according to predictions based on computational models,” says Lucas Gasparello Viviani, first author of the article.
Mass production is expected for the second half of the year, and TSMC promises it will represent a major step forward in performance and efficiency – potentially reshaping the technological landscape.
Microchips are the foundation of modern technology, found in nearly all electronic devices, from electric toothbrushes and smartphones to laptops and household appliances. They are made by layering and etching materials like silicon to create microscopic circuits containing billions of transistors.
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?
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.”
Quantum computers have the potential to solve certain problems far more efficiently than classical computers. In a recent development, researchers have designed a quantum algorithm to simulate systems of coupled masses and springs, known as coupled oscillators. These systems are fundamental in modeling a wide range of physical phenomena, from molecules to mechanical structures like bridges.
To simulate these systems, the researchers first translated the behavior of the coupled oscillators into a form of the Schrödinger equation, which describes how the quantum state of a system evolves over time. They then used advanced Hamiltonian simulation techniques to model the system on a quantum computer.
Hamiltonian methods provide a framework for understanding how physical systems evolve, connecting principles of classical mechanics with those of quantum mechanics. By leveraging these techniques, the researchers were able to represent the dynamics of N coupled oscillators using only about log(N) quantum bits (qubits), a significant reduction compared to the resources required by classical simulations.
This talk by Professor Thomas Polger (Professor of Philosophy at the Department of Philosophy University of Cincinnati) was given on Thursday 24 March 2022 as part of the Dutch Distinguished Lecture Series in Philosophy and Neuroscience (#DDLS).
Title: Thomas Polger “The Puzzling Resilience of Multiple Realization” (#DDLS).
Caption.
Abstract.
According to the multiple realization argument, mental states or processes can be realized in diverse and heterogeneous physical systems; and that fact implies that mental states or processes can not be identified with any one particular kind of physical state or process. In particular, mental processes can not be identified with of brain processes. Moreover, the argument provides a general model for the autonomy of the “special” sciences. The multiple realization argument is widely influential. But over the last thirty years it has also faced serious objections. Despite those objections, most philosophers regard that fact of multiple realization and the cogency of the multiple realization argument as obviously correct. Why is that? What is it about the multiple realization argument that makes it so resilient? One reason that the multiple realization argument is deeply intertwined with a view that minds are, in some sense, computational. But I argue that the sense in which minds are computational does not support the conclusion that they are obviously multiply realized. I argue that the sense in which brains compute does not imply that brains implement computational processes that are multiply realizable, and it does not provide a general model for the autonomy of the special sciences.
The mind is a lot like a computer — but what if this metaphor was more than just a metaphor? According to the philosopher Andy Clark, human minds aren’t just like computers, human minds are computers! In this video, we’ll get into the consequences of this seemingly radical framework and what it means for cognitive science as a whole.
What does it mean to claim that the mind extends beyond the brain in some meaningful way? Can objects in the external environment, such as a computer or even a notebook, combine with the traditional mind to create a cognitive \.
Quantum promises huge breakthroughs in drug discovery and last-mile delivery, but there are still unknowns about what a world in quantum’s hands will mean.
“I give you God’s view,” said Toby Cubitt, a physicist turned computer scientist at University College London and part of the vanguard of the current charge into the unknowable, and “you still can’t predict what it’s going to do.”
Eva Miranda, a mathematician at the Polytechnic University of Catalonia (UPC) in Spain, calls undecidability a “next-level chaotic thing.”
Undecidability means that certain questions simply cannot be answered. It’s an unfamiliar message for physicists, but it’s one that mathematicians and computer scientists know well. More than a century ago, they rigorously established that there are mathematical questions that can never be answered, true statements that can never be proved. Now physicists are connecting those unknowable mathematical systems with an increasing number of physical ones and thereby beginning to map out the hard boundary of knowability in their field as well.
