“These sites act like Velcro with different colors – designed so that only strands with matching ‘colors’ (in fact, complementary DNA sequences) can connect,” said Dr. Luu.
This method allows researchers to construct customizable, highly specific architectures that can perform intricate tasks at the molecular level.
One of the most promising applications of this technology is its ability to create nanorobots capable of delivering drugs directly to targeted areas within the body.
“Discovering liquid water oceans inside the moons of Uranus would transform our thinking about the range of possibilities for where life could exist,” said Dr. Douglas Hemingway.
Do the moons of Uranus have interior liquid oceans like the moons of Jupiter and Saturn? This is what a recent study published in Geophysical Research Letters hopes to address as a pair of researchers investigated the likelihood of five Uranus moons, Miranda, Ariel, Umbriel, Titania, and Oberon possessing interior oceans. This study holds the potential to not only help researchers better understand the compositions of these moons, but also establish a framework for sending a spacecraft to Uranus for the first time since NASA’s Voyager 2 in 1986.
For the study, the researchers used computer models to simulate changes in each moon’s wobble with the goal of estimating the potential amount of liquid water that each moon could be harboring. This technique could be used to detect liquid oceans within these moons, thus increasing the feasibility of a future spacecraft mission to Uranus.
In the end, the researchers found that oceans greater than 40 kilometers (25 miles) thick could be detectable, but ocean thickness less than that could prove difficult to detect without better resolution of the wobble calculations. Using these wobble calculations, the researchers estimate that a wobble of 300 feet could indicate an ocean 100 miles thick with an ice shell of 20 miles, using Ariel as an example.
Amazon just dropped Nova multimodal AI models that beat gpt-4o and sonnet 3.5 🤯
New state-of-the-art foundation models from Amazon deliver frontier intelligence and industry-leading price performance.
When it is solicited, the research emphases of E.9 Space Biology: Research Studies will fall under two broad categories: Precision Health and Space Crops.
For Precision Health-focused studies, investigators may propose to use any non-primate animal model system, and any appropriate cell/tissue culture/ microphysiological system/ organoid or microbial models, that are supported by the chosen platform. For Space Crop-focused studies, applicants may propose to use any plant model system, and when appropriate, any microbial or plant and microbial model systems that are supported by the chosen platform.
This opportunity will include five different Project Types: Research Investigations, Early Career Research Investigations, New NASA Investigators, GeneLab Analytical Investigations, and Tissue Sharing Investigations.
This is precisely the nature of our new understanding of biology, which has occurred over the past twenty years and is now sufficiently advanced to offer a new paradigm.
The previous paradigm was given in what is called the central dogma.
Engineers harness focused ultrasound to revolutionize CRISPR’s capabilities to treat countless diseases.
As described in that paper and henceforth, a transformer is a deep learning neural network architecture that processes sequential data, such as text or time-series information.
Now, MIT-birthed startup Liquid AI has introduced STAR (Synthesis of Tailored Architectures), an innovative framework designed to automate the generation and optimization of AI model architectures.
The STAR framework leverages evolutionary algorithms and a numerical encoding system to address the complex challenge of balancing quality and efficiency in deep learning models.
We take a look at the current best predictions for what the solar system will look like throughout the rest of the Sun’s life.
Did Venus have oceans in its ancient past and could they have supported life as we know it, or even as we don’t know it? This is what a recent study published in Nature Astronomy hopes to address as a team of researchers from the University of Cambridge investigated the climate history of Venus and whether it possessed liquid water oceans on its surface deep in its past. This study holds the potential to help scientists better understand past conditions on planetary bodies throughout the solar system and what this could mean for finding evidence of ancient life beyond Earth.
For the study, the researchers used computer models to estimate how fast the Venusian atmosphere is losing water, carbon dioxide, and carbonyl sulphide molecules, all of which are required to be replenished by volcanic gases so atmospheric stability can be maintained. Therefore, by studying how fast these molecules are leaving the atmosphere, scientists can estimate the amount of present and past volcanic activity on Venus, thus determining if Venus once had oceans of liquid water that might have supported life as we know it. In the end, the researchers determined that Venus is far too dry to have ever possessed bodies of liquid oceans on its surface.
“We won’t know for sure whether Venus can or did support life until we send probes at the end of this decade,” said Tereza Constantinou, who is a PhD student at Cambridge’s Institute of Astronomy and lead author of the study. “But given it likely never had oceans, it is hard to imagine Venus ever having supported Earth-like life, which requires liquid water.”
Evaluating the speed at which viruses spread and transmit across host populations is critical to mitigating disease outbreaks. A study published December 3 in PLOS Biology by Simon Dellicour at the University of Brussels (ULB), Belgium, and colleagues evaluate the performance of statistics measuring how viruses move across space and time in infected populations.
Genomic sequencing allows epidemiologists to examine the evolutionary history of pathogenic outbreaks and track the spatial movement of an outbreak. However, the sampling intensity of genomic sequences can potentially impact the accuracy of dispersal insights gained through these evolutionary approaches.
In order to assess the impact of the sampling size, researchers simulated the spread of several pathogens to evaluate three dispersal metrics estimated from the analysis of viral genomes: a lineage dispersal velocity (the speed at which lineages spread), a diffusion coefficient (how fast lineages invade space), and an isolation-by-distance signal (how genomic sequences of a population become less similar over geographic distance) metric.