Novel Rigidochromic and Anti-Kasha Dual Emission Fluorophores Based on D-π-A Dyads as the Promising Materials for Potential Applications Ranging from Optoelectronics and Optical Sensing to Biophotonics and Medicine.

Today we see an increasing demand for new fluorescent materials exhibiting various sensory abilities due to their broad applicability ranging from the construction of flexible devices to bioimaging. In this paper, we report on the new fluorescent pigments AntTCNE, PyrTCNE, and PerTCNE which consist of 3-5 fused aromatic rings substituted with tricyanoethylene fragments forming D-π-A diad. Our studies reveal that all three compounds exhibit pronounced rigidochromic properties, i.

The economic potential of metalliferous sub-volcanic brines.

The transition to a low-carbon economy will increase demand for a wide range of metals, notably copper, which is used extensively in power generation and in electric vehicles. Increased demand will require new, sustainable approaches to copper exploration and extraction. Conventional copper mining entails energy-intensive extraction of relatively low-grade ore from large open pits or underground mines and subsequent ore refining.

Generalising better: Applying deep learning to integrate deleteriousness prediction scores for whole-exome SNV studies.

Many automatic classifiers were introduced to aid inference of phenotypical effects of uncategorised nsSNVs (nonsynonymous Single Nucleotide Variations) in theoretical and medical applications. Lately, several meta-estimators have been proposed that combine different predictors, such as PolyPhen and SIFT, to integrate more information in a single score. Although many advances have been made in feature design and machine learning algorithms used, the shortage of high-quality reference data along with the bias towards intensively studied in vitro models call for improved generalisation ability in order to further increase classification accuracy and handle records with insufficient data.