THIS week news broke that declining fertility rates could affect most countries in a quarter of a century.
The U.S. Sun spoke with one scientist who thinks humanoids will fill this gap in future populations.
According to The Financial Times, 75 percent of nations are predicted to fall beneath population replacement birthrates by 2050.
Neuromorphic computing is an emerging solution for companies specializing in small, energy-efficient edge computing devices and robotics, striving to improve their products. There has been a paradigm shift in computing since the advent of neuromorphic chips. With the potential to unlock new levels of processing speed, energy efficiency, and adaptability, neuromorphic chips are here to stay. Industries from robotics to healthcare are exploring the potential of neuromorphic chips in various applications.
What is Neuromorphic Computing?
Neuromorphic computing is a field within computer science and engineering that draws inspiration from the structure and operation of the human brain. Its goal is to create computational systems, including custom hardware replicating the neural networks and synapses in biological brains. These custom computational systems are commonly known as neuromorphic chips or neuromorphic hardware.
Neutron star mergers are a treasure trove for new physics signals, with implications for determining the true nature of dark matter, according to research from Washington University in St. Louis.
On Aug. 17, 2017, the Laser Interferometer Gravitational-wave Observatory (LIGO), in the United States, and Virgo, a detector in Italy, detected gravitational waves from the collision of two neutron stars. For the first time, this astronomical event was not only heard in gravitational waves but also seen in light by dozens of telescopes on the ground and in space.
Physicist Bhupal Dev in Arts & Sciences used observations from this neutron star merger — an event identified in astronomical circles as GW170817 — to derive new constraints on axion-like particles. These hypothetical particles have not been directly observed, but they appear in many extensions of the standard model of physics.
A research team led by Professor Yuanliang ZHAI at the School of Biological Sciences, The University of Hong Kong (HKU) collaborating with Professor Ning GAO and Professor Qing LI from Peking University (PKU), as well as Professor Bik-Kwoon TYE from Cornell University, has recently made a significant breakthrough in understanding how the DNA copying machine helps pass on epigenetic information to maintain gene traits at each cell division. Understanding how this coupled mechanism could lead to new treatments for cancer and other epigenetic diseases by targeting specific changes in gene activity. Their findings have recently been published in Nature.
Background of the Research.
Our bodies are composed of many differentiated cell types. Genetic information is stored within our DNA which serves as a blueprint guiding the functions and development of our cells. However, not all parts of our DNA are active at all times. In fact, every cell type in our body contains the same DNA, but only specific portions are active, leading to distinct cellular functions. For example, identical twins share nearly identical genetic material but exhibit variations in physical characteristics, behaviours and disease susceptibility due to the influence of epigenetics. Epigenetics functions as a set of molecular switches that can turn genes on or off without altering the DNA sequence. These switches are influenced by various environmental factors, such as nutrition, stress, lifestyle, and environmental exposures.
For the first time, scientists have developed artificial nucleotides, the building blocks of DNA, with several additional properties in the laboratory. The DNA carries the genetic information of all living organisms and consists of only four different building blocks, the nucleotides. Nucleotides are composed of three distinctive parts: a sugar molecule, a phosphate group and one of the four nucleobases adenine, thymine, guanine and cytosine. The nucleotides are lined up millions of times and form the DNA double helix, similar to a spiral staircase. Scientists from the UoC’s Department of Chemistry have now shown that the structure of nucleotides can be modified to a great extent in the laboratory.
The researchers developed so-called threofuranosyl nucleic acid (TNA) with a new, additional base pair. These are the first steps on the way to fully artificial nucleic acids with enhanced chemical functionalities. The study ‘Expanding the Horizon of the Xeno Nucleic Acid Space: Threose Nucleic Acids with Increased Information Storage’ was published in the Journal of the American Chemical Society.
Artificial nucleic acids differ in structure from their originals.
Ok, that was an unexpected turn on my feed. Just had to share. Cool, portable robot that fits in a backpack.
Conquer the Wild | LimX Dynamics’ Biped Robot P1 ventured into Tanglang Mountain Based on Reinforcement Learning ⛰️
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Artificial human chromosomes that function within human cells hold the potential to revolutionize gene therapies, including treatments for certain cancers, and have numerous laboratory uses. However, significant technical challenges have impeded their progress.
Now a team led by researchers at the Perelman School of Medicine at the University of Pennsylvania has made a significant breakthrough in this field that effectively bypasses a common stumbling block.
In a study recently published in Science, the researchers explained how they devised an efficient technique for making HACs from single, long constructs of designer DNA. Prior methods for making HACs have been limited by the fact that the DNA constructs used to make them tend to join together—“multimerize”—in unpredictably long series and with unpredictable rearrangements. The new method allows HACs to be crafted more quickly and precisely, which, in turn, will directly speed up the rate at which DNA research can be done. In time, with an effective delivery system, this technique could lead to better-engineered cell therapies for diseases like cancer.
This new Blackwell chip essentially can operate at a staggering 20 petaflops and a trillion parameters for AI development.
Twenty petaflops of AI performance, says Nvidia.
Gene therapy and correction can be used in almost all diseases and pathological conditions because of recent advancements, but some questions still have yet to be answered in this field of advancement. Based on the requirements and compatibility, gene therapy is divided into two parts: somatic gene therapy and germline gene therapy. If the transfer of DNA segments is done to cells that will affect the next generation, this is called somatic gene therapy. Somatic gene therapy is currently more efficient in research due to its less ethical issue and less complexity. The toughest task for curing diabetes with gene therapy is to have glucose responsiveness to insulin transgene expression. So studies were carried out to decrease obesity by gene therapy to decrease type 2 diabetes prevalence. Gene therapy using viral vectors remains risky and is still under scrutiny to ensure safety and efficacy during clinical trials.
