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String Theory Emerges from “Almost Nothing”

Developed in the 1960s, string theory proposes that everything in the universe is made from invisible strings. The theory arose as a possible solution to the problem of “quantum gravity,” the quest to align quantum mechanics, which describes our world at the smallest scales, with the general theory of relativity, which explains how our universe works on the largest scales (and includes gravity). Researchers have tried to reconcile the two theories—asking, for example, how gravity behaves in the quantum realm—but their equations go berserk, or in mathematical terms, go to infinity.

String theory is a mathematical solution that tames the unruly infinities. It purports that all particles, including the graviton—the hypothetical particle believed to convey the force of gravity—are generated by very small vibrating strings. The math behind string theory requires the strings to vibrate in at least 10 dimensions, rather than the four we live in (three for space and one for time), which is one of the reasons some scientists are not convinced that string theory is correct. But perhaps the biggest challenge for the theory is the ultrahigh energies required for testing it: Such an experiment would require a particle collider the size of a galaxy.

What is a physicist to do? One way they can probe the theory is to turn to a “bootstrap” approach, in which researchers start with certain assumptions they believe to be true about the universe, and then see what laws emerge out of those assumptions. In a new paper titled “Strings from Almost Nothing,” accepted for publication in Physical Review Letters, Caltech researchers, and their colleagues at New York University and Institut de Fisica d’Altes Energies in Barcelona, have done just that. From a couple of basic assumptions about how particles should scatter off one another at very high energies, they derived the elements of string theory.

Amino Acid Patterns Help Scientists Distinguish Alien Life

Dr. Fabian Klenner: “We’re showing that life does not only produce molecules. Life also produces an organizational principle that we can see by applying statistics.” [ https://www.labroots.com/trending/space/30534/amino-acid-pat…ien-life-2](https://www.labroots.com/trending/space/30534/amino-acid-pat…ien-life-2)


What methods can scientists use to correctly identify biosignatures, aka signs of life beyond Earth? This is what a recent study Nature Astronomy hopes to address as a team of researchers from the University of California, Riverside (UC Riverside) and Israel investigated a new pattern-based method for identifying biosignatures. This study has the potential to help scientists develop new methods for finding life beyond Earth, which could narrow the scope for both how and where to find life.

For the study, the researchers used mathematics to suggest that instead of looking for specific molecules when searching for biosignatures scientists should instead look for organizational patterns. The primary motivation behind the study was to challenge longstanding methods regarding how to search for biosignatures, which have traditionally been focused on finding individual and specific molecules. In the end, the researchers found that the amino acids in biological (biotic) samples exhibited a much larger range of diversity compared to non-biological (abiotic) samples. These could shape a new generation of astrobiology, which is the study of searching for life beyond Earth.

“We’re showing that life does not only produce molecules,” said Dr. Fabian Klenner, who is an assistant professor of planetary sciences at UC Riverside and a co-author on the study. “Life also produces an organizational principle that we can see by applying statistics.”

Venus Clouds Driven by Solar System’s Largest Waves

“Up until now, we used a global circulation model (GCM) for Venus that is similar to Earth’s, but this model doesn’t include the hydraulic jump which we have now identified,” said Dr. Takeshi Imamura. [ https://www.labroots.com/trending/space/30535/venus-clouds-d…gest-waves](https://www.labroots.com/trending/space/30535/venus-clouds-d…gest-waves)


What explains the unique behavior of Venus’ clouds? This is what a recent study published in the Journal of Geophysical Research: Planets hopes to address as an international team of scientists led by Japan and included the United States and Spain investigated a longstanding conundrum regarding Venus’ meteorology, specifically cloud weather patterns. This study has the potential to help scientists better understand planetary cloud patterns and what this can teach us about planetary formation and evolution.

For the study, the researchers used a series of computer models to simulate Venus’ cloud weather patterns, specifically focusing on a 6,000-kilometer-wide (3,728-mile-wide) cloud front whose behavior has puzzled scientists for years. The primary puzzlement is the origin of the massive cloud wave, which current global climate models can’t explain. Along with the puzzlement, the motivation behind the study also comes from a knowledge gap in the formation of the lower cloud regions within Venus’ atmosphere.

In the end, the researchers found that a phenomenon known as a “hydraulic jump” was responsible for producing the massive cloud wave front. This jump is caused by changes in airflow in the lower cloud regions combined with a strong updraft, resulting in sulfuric acid vapor (which comprises Venus’ clouds) to condense, forming the massive cloud wave front. This study helps explain the connection between the Venusian atmosphere motion and clouds.

Cost-Effectiveness of Thrombectomy With or Without Alteplase in Large Vessel Occlusion StrokeA Meta-Analysis Considering Time-to-Treatment

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A neuropeptide regulates cell non-autonomous protein homeostasis

FLP-17’s role in stress resistance aligns with its established functions. FLP-17 belongs to an evolutionarily conserved class, FMRF-amide/RF-amide neuropeptides, that plays important roles in energy balance and reproduction across phyla.34,35 In C. elegans, FLP-17 is secreted from a pair of sensory neurons (BAG) in response to low oxygen and high carbon dioxide, which can be caused by unfavorable food conditions or pathogens.36,37 FLP-17 then acts through specific neurons to inhibit egg laying and initiate an aversion behavior until the animal has reached more favorable conditions.30,36 Interestingly, unfavorable food conditions and pathogens also threaten organismal protein homeostasis.33,38 Therefore, we speculate that FLP-17 evolved to simultaneously protect the animal from proteotoxic stress while facilitating a behavioral program to help the animal navigate to more favorable conditions.

To coordinate adaptive behavioral and metabolic responses, FLP-17 primarily signals through the GPCR EGL-6 in specific neurons.30,31 Therefore, we tested whether EGL-6 also mediates FLP-17’s role in UPRER activation and found that FLP-17-induced activation of the UPRER and ER stress resistance is partially dependent on EGL-6. Egl-6 expression is predominantly neuronal, evidenced by transcriptional reporters and single-cell RNA-seq datasets.30,39 However, low levels of egl-6 expression were detected in intestine-specific translation of ribosome-affinity purification, which may better reflect protein levels.40 This suggests that FLP-17 may signal either through an intermediate cell type (such as a neuron) or directly to the intestine to activate UPRER.30,39 Furthermore, the partial dependence, combined with persistent stress gene activation in egl-6 (lof) backgrounds (Figure 5 E), indicates that additional unidentified receptors and mechanisms likely contribute to FLP-17 phenotypes.

Although FLP-17 was sufficient to activate the UPRER, it was not required for cell non-autonomous activation of the UPRER by glial:: xbp-1s, as flp-17 null mutants did not suppress glial:: xbp-1s phenotypes. This likely reflects neuropeptide network redundancy. Supporting this hypothesis, flp-17 (lof)) resulted in modest upregulation of stress response genes (Figure S3G) and a slight increase in hsp-4p::GFP in the glial:: xbp-1s animals (Figure 2D), suggesting compensatory activation of stress signaling pathways when FLP-17 is absent. This compensation could occur through multiple mechanisms. First, glial:: xbp-1s may induce multiple neuropeptides that provide functionally redundant UPRER activation. While no other candidate from our neuropeptidomics screen was individually sufficient to induce UPRER, we cannot exclude compensation by peptides not detected in our analysis, such as insulin-like peptides.

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