Scientists have created eggs using the cells of male mice for the first time, leading to the birth of seven mice with two fathers, according to research Wednesday hailed as “revolutionary”.
The technique pioneered in the proof-of-concept experiment is a long way from potentially being used in humans, with obstacles including a low success rate, adaptation concerns and wide-ranging ethical considerations.
But the breakthrough raises the prospect of a raft of new reproductive possibilities, including that gay male couples —or even a single man—could have a biological child without needing a female egg.
OpenAI has released a new version of ChatGPT, claiming that the new language learning model is capable of passing – and even excelling in – a variety of academic exams.
ChatGPT-4, which will be available on Bing as well as the OpenAI website, is more reliable and more creative than its predecessor, according to OpenAI. The team tested the model on a number of exams designed for humans, from the bar exam to biology, using publicly available papers. While no additional training was given to the model ahead of the tests, it was able to perform well on most subjects, performing in the estimated 90th percentile for the bar exam and the 86th-100th in art history.
Just as the previous model was accused of being bad at math, this version struggled more with calculus, scoring in the 43rd-59th percentile.
Kim et al. used directed evolution methods to identify a high-fidelity SpCas9 variant, Sniper2L, which exhibits high general activity but maintains high specificity at a large number of target sites.
My name is Artem, I’m a computational neuroscience student and researcher.
In this video we will talk about the fundamental role of lognormal distribution in neuroscience. First, we will derive it through Central Limit Theorem, and then explore how it support brain operations on many scales — from cells to perception.
REFERENCES:
1. Buzsáki, G. & Mizuseki, K. The log-dynamic brain: how skewed distributions affect network operations. Nat Rev Neurosci 15264–278 (2014). 2. Ikegaya, Y. et al. Interpyramid Spike Transmission Stabilizes the Sparseness of Recurrent Network Activity. Cerebral Cortex 23293–304 (2013). 3. Loewenstein, Y., Kuras, A. & Rumpel, S. Multiplicative Dynamics Underlie the Emergence of the Log-Normal Distribution of Spine Sizes in the Neocortex In Vivo. Journal of Neuroscience 31, 9481–9488 (2011). 4. Morales-Gregorio, A., van Meegen, A. & van Albada, S. J. Ubiquitous lognormal distribution of neuron densities across mammalian cerebral cortex. http://biorxiv.org/lookup/doi/10.1101/2022.03.17.480842 (2022) doi:10.1101/2022.03.17.480842.
After debunking many myths around male and female brains, Gina Rippon’s research interests now include gender gaps in science and why they persist, even in allegedly gender-equal societies.
In vitro biological neural networks (BNNs) interconnected with robots, so-called BNN-based neurorobotic systems, can interact with the external world, so that they can present some preliminary intelligent behaviors, including learning, memory, robot control, etc.
This work aims to provide a comprehensive overview of the intelligent behaviors presented by the BNN-based neurorobotic systems, with a particular focus on those related to robot intelligence.
In this work, we first introduce the necessary biological background to understand the 2 characteristics of the BNNs: nonlinear computing capacity and network plasticity. Then, we describe the typical architecture of the BNN-based neurorobotic systems and outline the mainstream techniques to realize such an architecture from 2 aspects: from robots to BNNs and from BNNs to robots.
Researchers have developed a new model inspired by recent biological discoveries that shows enhanced memory performance. This was achieved by modifying a classical neural network.
Computer models play a crucial role in investigating the brain’s process of making and retaining memories and other intricate information. However, constructing such models is a delicate task. The intricate interplay of electrical and biochemical signals, as well as the web of connections between neurons and other cell types, creates the infrastructure for memories to be formed. Despite this, encoding the complex biology of the brain into a computer model for further study has proven to be a difficult task due to the limited understanding of the underlying biology of the brain.
Researchers at the Okinawa Institute of Science and Technology (OIST) have made improvements to a widely utilized computer model of memory, known as a Hopfield network, by incorporating insights from biology. The alteration has resulted in a network that not only better mirrors the way neurons and other cells are connected in the brain, but also has the capacity to store significantly more memories.
Studying the relationship between the arrangement of water molecules incorporated into layered materials like clays and the arrangement of ions within these materials has been a difficult experiment to conduct.
However, researchers have now succeeded in observing these interactions for the first time by utilizing a technique commonly used for measuring extremely small masses and molecular interactions at the nanoscale.
The nanoscale refers to a length scale that is extremely small, typically on the order of nanometers (nm), which is one billionth of a meter. At this scale, materials and systems exhibit unique properties and behaviors that are different from those observed at larger length scales. The prefix “nano-” is derived from the Greek word “nanos,” which means “dwarf” or “very small.” Nanoscale phenomena are relevant to many fields, including materials science, chemistry, biology, and physics.
Regeneration, Resuscitation & Biothreat Countermeasures — Commander Dr. Jean-Paul Chretien, MD, Ph.D., Program Manager, Biological Technology Office, DARPA
Commander Dr. Jean-Paul Chretien, MD, Ph.D. (https://www.darpa.mil/staff/cdr-jean-paul-chretien) is a Program Manager in the Biological Technology Office at DARPA, where his research interests include disease and injury prevention, operational medicine, and biothreat countermeasures. He is also responsible for running the DARPA Triage Challenge (https://triagechallenge.darpa.mil/).
Prior to coming to DARPA, CDR Dr. Chretien led the Pandemic Warning Team at the Defense Intelligence Agency’s National Center for Medical Intelligence, and as a naval medical officer, his previous assignments include senior policy advisor for biodefense in the White House Office of Science and Technology Policy; team lead for Innovation & Evaluation at the Armed Forces Health Surveillance Branch; and director of force health protection for U.S. and NATO forces in southwestern Afghanistan.
A proud mentor to nine graduate students and Oak Ridge Institute for Science and Education (ORISE) fellows, CDR Dr. Chretien received the Rising Star Award from the American College of Preventive Medicine, Best Publication of the Year Award from the International Society for Disease Surveillance, and Skelton Award for Public Service from the Harry S. Truman Scholarship Foundation. He has published over 50 peer-reviewed journal articles and 10 book chapters.
CDR Dr. Chretien earned a Bachelor of Science degree in political science from the United States Naval Academy, Master of Health Science in biostatistics and Doctor of Philosophy in genetic epidemiology degrees from the Johns Hopkins Bloomberg School of Public Health, and a Doctor of Medicine degree from the Johns Hopkins University School of Medicine. He completed his residency in general preventive medicine at the Walter Reed Army Institute of Research and fellowship in health sciences informatics at the Johns Hopkins University School of Medicine.
