Search for: All records

Recent years have seen a surge in interest for leveraging neural networks to parameterize small-scale or fast processes in climate and turbulence models. In this short paper, we point out two fundamental issues in this endeavor. The first concerns the difficulties neural networks may experience in capturing rare events due to limitations in how data is sampled. The second arises from the inherent multiscale nature of these systems. They combine high-frequency components (like inertia-gravity waves) with slower, evolving processes (geostrophic motion). This multiscale nature creates a significant hurdle for neural network closures. To illustrate these challenges, we focus on the atmospheric 1980 Lorenz model, a simplified version of the Primitive Equations that drive climate models. This model serves as a compelling example because it captures the essence of these difficulties.

Doa10 (MARCHF6 in metazoans) is a large polytopic membrane-embedded E3 ubiquitin ligase in the endoplasmic reticulum (ER) that plays an important role in quality control of cytosolic and ER proteins. Although Doa10 is highly conserved across eukaryotes, it is not understood how Doa10 recognizes its substrates. Here, we define the substrate recognition mechanism of Doa10 by structural and functional analyses onSaccharomyces cerevisiaeDoa10 and its model substrates. Cryo-EM analysis shows that Doa10 has unusual architecture with a large lipid-filled central cavity, and its conserved middle domain forms an additional water-filled lateral tunnel open to the cytosol. Our biochemical data and molecular dynamics simulations suggest that the entrance of the substrate’s degron peptide into the lateral tunnel is required for efficient polyubiquitination. The N- and C-terminal membrane domains of Doa10 seem to form fence-like features to restrict polyubiquitination to those proteins that can access the central cavity and lateral tunnel. Our study reveals how extended hydrophobic sequences at the termini of substrate proteins are recognized by Doa10 as a signal for quality control.

The Macquart relation describes the correlation between the dispersion measure (DM) of fast radio bursts (FRBs) and the redshiftzof their host galaxies. The scatter of the Macquart relation is sensitive to the distribution of baryons in the intergalactic medium including those ejected from galactic halos through feedback processes. The variance of the distribution in DMs from the cosmic web (DM_cosmic) is parameterized by a fluctuation parameterF. In this work, we present a new measurement ofFusing 78 FRBs of which 21 have been localized to host galaxies. Our analysis simultaneously fits for the Hubble constantH₀and the DM distribution due to the FRB host galaxy. We find that the fluctuation parameter is degenerate with these parameters, most notablyH₀, and use a uniform prior onH₀to measure${\log }_{10}F>-0.86$at the 3σconfidence interval and a new constraint on the Hubble constant$H_0={85.3}_{-8.1}^{+9.4}\mathrm{km}{\mathrm{s}}^{-1}{\mathrm{Mpc}}^{-1}$. Using a synthetic sample of 100 localized FRBs, the constraint on the fluctuation parameter is improved by a factor of ∼2. Comparing ourFmeasurement to simulated predictions from cosmological simulation (IllustrisTNG), we find agreement between redshifts 0.4 <z andz< 2.0. However, atz< 0.4, the simulations underpredictF, which we attribute to the rapidly changing extragalactic DM excess distribution at low redshift.

Thermal Sn–C cleavage in the diarylstannylene Sn(Ar^iPr4)₂(Ar^iPr4= C₆H₃-2,6-(C₆H₃-2,6-iPr₂)₂) was used to generate ˙Sn(Ar^iPr4) and ˙Ar^iPr4radicals for alkyne arylstannylation.

Localized states in two-dimensional (2D) transition metal dichalcogenides (TMDCs) have been the subject of intense study, driven by potential applications in quantum information science. Despite the rapidly growing knowledge surrounding these emitters, their microscopic nature is still not fully understood, limiting their production and application. Motivated by this challenge, and by recent theoretical and experimental evidence showing that nanowrinkles generate strain-localized room-temperature emitters, we demonstrate a method to intentionally induce wrinkles with collections of stressors, showing that long-range wrinkle direction and position are controllable with patterned array design. Nano-photoluminescence (nano-PL) imaging combined with detailed strain modeling based on measured wrinkle topography establishes a correlation between wrinkle properties, particularly shear strain, and localized exciton emission. Beyond the array-induced wrinkles, nano-PL spatial maps further reveal that the strain environment around individual stressors is heterogeneous due to the presence of fine wrinkles that are less deterministic. At cryogenic temperatures, antibunched emission is observed, confirming that the nanocone-induced strain is sufficiently large for the formation of quantum emitters. At 300 K, detailed nanoscale hyperspectral images uncover a wide range of low-energy emission peaks originating from the fine wrinkles, and show that the states can be tightly confined to regions <10 nm, even in ambient conditions. These results establish a promising potential route towards realizing room temperature quantum emission in 2D TMDC systems.

Molecular targeting strategies have been used for years in order to control cancer progression and are often based on targeting various enzymes involved in metabolic pathways. Keeping this in mind, it is essential to determine the role of each enzyme in a particular metabolic pathway. In this review, we provide in-depth information on various enzymes such as ceramidase, sphingosine kinase, sphingomyelin synthase, dihydroceramide desaturase, and ceramide synthase which are associated with various types of cancers. We also discuss the physicochemical properties of well-studied inhibitors with natural product origins and their related structures in terms of these enzymes. Targeting ceramide metabolism exhibited promising mono- and combination therapies at preclinical stages in preventing cancer progression and cemented the significance of sphingolipid metabolism in cancer treatments. Targeting ceramide-metabolizing enzymes will help medicinal chemists design potent and selective small molecules for treating cancer progression at various levels.