Highly accurate real-space electron densities with neural networks.

Variational ab initio methods in quantum chemistry stand out among other methods in providing direct access to the wave function. This allows, in principle, straightforward extraction of any other observable of interest, besides the energy, but, in practice, this extraction is often technically difficult and computationally impractical. Here, we consider the electron density as a central observable in quantum chemistry and introduce a novel method to obtain accurate densities from real-space many-electron wave functions by representing the density with a neural network that captures known asymptotic properties and is trained from the wave function by score matching and noise-contrastive estimation.

Carborane-Decorated Siloles with Highly Efficient Solid-State Emissions - What Drives the Photophysical Properties?

New hybrids were synthesised by linking carboranes and siloles, both of which are known as aggregation-induced emission active units. Although most of the newly synthesised systems do not display notable quantum yield either in solution or in the aggregated state, they emit strongly in the solid-state, and a quantum yield of up to 100 % can be achieved. The tailorable quantum yield can be attributed to the packing of the molecules in the crystal lattice ruled by the carborane and phenyl moieties according to the SC-XRD data.

Multimodal hierarchical classification of CITE-seq data delineates immune cell states across lineages and tissues.

Single-cell RNA sequencing (scRNA-seq) is invaluable for profiling cellular heterogeneity and transcriptional states, but transcriptomic profiles do not always delineate subsets defined by surface proteins. Cellular indexing of transcriptomes and epitopes (CITE-seq) enables simultaneous profiling of single-cell transcriptomes and surface proteomes; however, accurate cell-type annotation requires a classifier that integrates multimodal data. Here, we describe multimodal classifier hierarchy (MMoCHi), a marker-based approach for accurate cell-type classification across multiple single-cell modalities that does not rely on reference atlases.

Biofilm Inhibition Activity of Fennel Honey, Fennel Essential Oil and Their Combination.

The eradication of bacterial biofilms remains a persistent challenge in medicine, particularly because an increasing number of biofilms exhibit resistance to conventional antibiotics. This underscores the importance of searching for novel compounds that present antibacterial and biofilm inhibition activity. Various types of honey and essential oil were proven to be effective against a number of biofilm-forming bacterial strains.