PRDM9 has recently been identified as a likely trans regulator of meiotic recombination hot spots in humans and mice. PRDM9 contains a zinc finger array that, in humans, can recognize a short sequence motif associated with hot spots, with binding to this motif possibly triggering hot-spot activity via chromatin remodeling. We now report that human genetic variation at the PRDM9 locus has a strong effect on sperm hot-spot activity, even at hot spots lacking the sequence motif. Subtle changes within the zinc finger array can create hot-spot nonactivating or enhancing variants and can even trigger the appearance of a new hot spot, suggesting that PRDM9 is a major global regulator of hot spots in humans. Variation at the PRDM9 locus also influences aspects of genome instability-specifically, a megabase-scale rearrangement underlying two genomic disorders as well as minisatellite instability-implicating PRDM9 as a risk factor for some pathological genome rearrangements.
This study expands traditional reaction-diffusion models by incorporating hyperbolic dynamics to explore the effects of inertial delays on pattern formation. The kinetic system considers a harvested predator-prey model where predator and prey populations gather in herds. Diffusion and inertial effects are subsequently introduced.
A highly sensitive aptamer sensor (aptasensor) is proposed based on metal-organic frameworks-silver nanoparticles (AgNPs@MOF) to detect sulfadimethoxine (SDM) by surface-enhanced Raman spectroscopy (SERS). AgNPs@MOF with SERS activity was successfully fabricated by synthesizing AgNPs in situ on the surface of MIL-101(Fe), and SDM aptamer and Raman reporter 4-aminophenthiophenol (4-ATP) were selected as specific recognition elements and signal probes, respectively. When SDM was absent, the SDM aptamers were effectively adsorbed on the surface of AgNPs@MOF, thus keeping AgNPs@MOF in a dispersed state, resulting in a weakened SERS signal of 4-ATP.
Background: Diabetes has emerged as the foremost public health challenge of the 21st century, with a notable shift towards managing it through an inflammatory lens. This study seeks to investigate the role of NLRP3 infalmmasome in diabetes over the past ten years, leveraging bibliometric analysis to pinpoint prevailing trends, underscore critical focal points, and establish a roadmap for subsequent research endeavors.
Method: A literature search was conducted based on the SCI-E database, and all recorded results were downloaded in plain text format for subsequent analysis.
Complex-Langevin field-theoretic simulations (CL-FTSs) provide an approximation-free method of calculating fluctuation corrections to the self-consistent field theory (SCFT) of block copolymer melts. However, the complex fields are prone to the formation of hot spots, which causes the method to fail. This problem has been attributed to an invariance under complex translations, which allows the system to drift away from the real-valued saddle-point of SCFT.
Modeling and predicting mutations are critical for COVID-19 and similar pandemic preparedness. However, existing predictive models have yet to integrate the regularity and randomness of viral mutations with minimal data requirements. Here, we develop a non-demanding language model utilizing both regularity and randomness to predict candidate SARS-CoV-2 variants and mutations that might prevail.
