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Boron–nitrogen (BN) incorporation offers a versatile approach to modulate the electronic structure of polycyclic aromatic hydrocarbons by combining electron-deficient and electron-rich elements within a single π-framework. In this work, we examine the two-electron reduction of a BN-fused dipyridylanthracene (BDPA, 1), providing access to a family of alkali-metal dianion salts, M2-12– (M = Li, Na, K, and Rb). Single-crystal X-ray diffraction reveals cation-dependent ion-pairing motifs, with solvent-separated ion products for Li and K and contact ion products seen for Na and Rb. Computational analysis shows that product stabilization is governed by partial charge transfer from the reduced BDPA core to the metal centers. Structural analysis further reveals substantial bond-length reorganization and increased curvature of the π-framework upon reduction, consistent with the emergence of quinoidal electronic delocalization. This transformation is accompanied by a pronounced redistribution of aromaticity, involving a loss of aromatic character of the pyridyl groups and the central anthracene ring, and an enhancement of aromaticity in the terminal rings, as supported by HOMA and NICS analyses. A comparison of the electronic structure of the dianion 12– with those of isoelectronic dianionic all-carbon (M12–) and neutral BN-fused N,N-dihydrophenazine (M2) analogs demonstrates much more profound quinoidal character of 12–, signifying how the dearomatization and delocalization of charge is effectively supported by ‘B–N for C–C’ substitution. Together, these results demonstrate that two-electron reduction can be exploited to trigger dramatic changes in charge distribution and aromaticity, which directly impact the structural and spectroscopic properties of B–N Lewis pair functionalized PAHs. 

This study investigates the efficacy of a Writing-to-Learn Pedagogy (WTLP) intervention in fostering critical thinking among underrepresented STEM students at an Historically Black College and University (HBCU). Utilizing Ennis’s (1987) taxonomy, the research employed a qualitative descriptive design to evaluate shifts in dispositions and abilities through reflective journaling and writing-to-explain within a hybrid industrial electricity course. Findings from two cohorts of 23 students revealed significant growth in analytical thinking, inquisitiveness, and confidence in reasoning. However, the generalizability of these findings is limited by the small sample size and single-institution design. Notably, the integration of structured learning plans bridged the gap between metacognitive awareness and applied problem-solving performance. This research provides empirical evidence for asset-based instructional strategies implemented within the distinctive context of HBCU STEM education, contributing to the preparation of a competent and diverse STEM workforce. 

Abstract We report the results of the “UM‐TBM” and “Zheng” groups in CASP15 for protein monomer and complex structure prediction. These prediction sets were obtained using the D‐I‐TASSER and DMFold‐Multimer algorithms, respectively. For monomer structure prediction, D‐I‐TASSER introduced four new features during CASP15: (i) a multiple sequence alignment (MSA) generation protocol that combines multi‐source MSA searching and a structural modeling‐based MSA ranker; (ii) attention‐network based spatial restraints; (iii) a multi‐domain module containing domain partition and arrangement for domain‐level templates and spatial restraints; (iv) an optimized I‐TASSER‐based folding simulation system for full‐length model creation guided by a combination of deep learning restraints, threading alignments, and knowledge‐based potentials. For 47 free modeling targets in CASP15, the final models predicted by D‐I‐TASSER showed average TM‐score 19% higher than the standard AlphaFold2 program. We thus showed that traditional Monte Carlo‐based folding simulations, when appropriately coupled with deep learning algorithms, can generate models with improved accuracy over end‐to‐end deep learning methods alone. For protein complex structure prediction, DMFold‐Multimer generated models by integrating a new MSA generation algorithm (DeepMSA2) with the end‐to‐end modeling module from AlphaFold2‐Multimer. For the 38 complex targets, DMFold‐Multimer generated models with an average TM‐score of 0.83 and Interface Contact Score of 0.60, both significantly higher than those of competing complex prediction tools. Our analyses on complexes highlighted the critical role played by MSA generating, ranking, and pairing in protein complex structure prediction. We also discuss future room for improvement in the areas of viral protein modeling and complex model ranking. 

Abstract Leveraging iterative alignment search through genomic and metagenome sequence databases, we report the DeepMSA2 pipeline for uniform protein single- and multichain multiple-sequence alignment (MSA) construction. Large-scale benchmarks show that DeepMSA2 MSAs can remarkably increase the accuracy of protein tertiary and quaternary structure predictions compared with current state-of-the-art methods. An integrated pipeline with DeepMSA2 participated in the most recent CASP15 experiment and created complex structural models with considerably higher quality than the AlphaFold2-Multimer server (v.2.2.0). Detailed data analyses show that the major advantage of DeepMSA2 lies in its balanced alignment search and effective model selection, and in the power of integrating huge metagenomics databases. These results demonstrate a new avenue to improve deep learning protein structure prediction through advanced MSA construction and provide additional evidence that optimization of input information to deep learning-based structure prediction methods must be considered with as much care as the design of the predictor itself.