Exploring structural, magnetic, and catalytic diversity in tetranuclear {CuO} cubane cores and a dinuclear complex derived from closely related ligand systems.

The coordination compounds featuring a {CuO} core, typically bridged by hydroxo or alkoxo groups, are particularly intriguing due to their notable magnetic properties and catalytic activity. In this study, we explored the synthesis and characterization of four new Schiff base ligands and their subsequent complexation with Cu salts, which resulted in the formation of three tetranuclear complexes: [Cu(L)]·2HO (1), [Cu(L)(HL)](Cl)(NO)·5HO (2), and [Cu(L)] (3), as well as one dinuclear complex: [Cu(L)] (4). These tetranuclear complexes all feature a {CuO} core, but with differing coordination environments around the Cu centers.

TransConv: convolution-infused transformer for protein secondary structure prediction.

Context: Protein secondary structure prediction is essential for understanding protein function and characteristics and can also facilitate drug discovery. Traditional methods for experimentally determining protein structures are both time-consuming and costly. Computational biology offers a viable alternative by predicting protein structures from their sequences.

Supramolecular Dimeric Mn Complexes: Synthesis, Structure, Magnetic Properties, and Catalytic Oxidation Studies.

In this study, a tetradentate Schiff-base ligand (HL), synthesized by the condensation of ethylenediamine with 2-hydroxy-3-methoxy-5-methylbenzaldehyde, was reacted with either manganese salts or manganese salts in the presence of various pseudohalides in methanol. This reaction resulted in the formation of five mononuclear Mn complexes: [Mn(L)(HO)](NO)·1/2HO·1/2CHOH (), [Mn(L)(HO)](ClO)·HO (), [Mn(L)(N)(HO)]·1/3HO (), [Mn(L)(NCS)(HO)] (), and [Mn(L)(HO)](dca) () (where dca is dicyanamide ion). X-ray crystallography revealed that the Mn centers adopt a hexa-coordinate pseudo-octahedral geometry, where the equatorial plane is constructed with phenoxo oxygen and imine nitrogen atoms from the Schiff base ligand, while the axial positions are occupied by water molecules or a combination of water and pseudohalides.

Active Learning with Particle Swarm Optimization for Enhanced Skin Cancer Classification Utilizing Deep CNN Models.

Skin cancer is a critical global health issue, with millions of non-melanoma and melanoma cases diagnosed annually. Early detection is essential to improving patient outcomes, yet traditional deep learning models for skin cancer classification are often limited by the need for large, annotated datasets and extensive computational resources. The aim of this study is to address these limitations by proposing an efficient skin cancer classification framework that integrates active learning (AL) with particle swarm optimization (PSO).