Besides, it exhibited terrific electrochemical performance for NADH oxidation and sensing by greatly boosting the response and lowering the oxidation overpotential. It could also work on biomimetic cofactors with even higher activity. Finally, xylose dehydrogenase was immobilized with the nanozyme to constitute a hybrid bioelectrode for xylose sensing. The biosensor had a xylose detecting range of 5-400 μM with the limit of detection as low as 1 μM and can retain its performance after being reused several times. Our results suggest that the PtNPs@MWCNTs characterized as a NADH oxidase nanozyme hold great promise in the applications of biocatalysis and biosensing, which intensively deal with dehydrogenases and natural or biomimetic cofactors.The aromaticity in porphyrinoids results from the π conjugation through two different annular perimeters the macrocyclic ring and the local heterocyclic rings appended to it. Analyses, based on aromatic stabilization energies (ASE), indicate that the local circuits (6π) are responsible for the significant aromatic stabilization of these systems. This local aromaticity can be coupled with the one from 4n + 2π macrocyclic circuit. It can either compensate for the destabilization due to a 4n π macrocyclic circuit, or be the only source of aromatic stabilization in porphyrinoids with macrocycles without π-conjugated bonds. This “multifaceted” aromatic character of porphyrinoids makes it challenging to analyze their aromaticity using magnetic descriptors because of the intricate interaction of local versus macro-cyclic circulation. In this contribution, we show that the analysis of the bifurcation of the induced magnetic field, Bind, allows clear identification and quantification of both local, and macrocyclic aromaticity, in a representative group of porphyrinioids. In porphyrin, bifurcation values accurately predict the local and macrocyclic contribution rate to overall aromatic stabilization determined by ASE.Bacterial adhesion to surfaces is a crucial step in initial biofilm formation. In a combined experimental and computational approach, we studied the adhesion of the pathogenic bacterium Staphylococcus aureus to hydrophilic and hydrophobic surfaces. We used atomic force microscopy-based single-cell force spectroscopy and Monte Carlo simulations to investigate the similarities and differences of adhesion to hydrophilic and hydrophobic surfaces. Our results reveal that binding to both types of surfaces is mediated by thermally fluctuating cell wall macromolecules that behave differently on each type of substrate on hydrophobic surfaces, many macromolecules are involved in adhesion, yet only weakly tethered, leading to high variance between individual bacteria, but low variance between repetitions with the same bacterium. On hydrophilic surfaces, however, only few macromolecules tether strongly to the surface. Since during every repetition with the same bacterium different macromolecules bind, we observe a comparable variance between repetitions and different bacteria. We expect these findings to be of importance for the understanding of the adhesion behaviour of many bacterial species as well as other microorganisms and even nanoparticles with soft, macromolecular coatings, used e.g. for biological diagnostics.The design and discovery of small molecule medicines has largely been focused on a small number of druggable protein families. A new paradigm is emerging, however, in which small molecules exert a biological effect by interacting with RNA, both to study human disease biology and provide lead therapeutic modalities. Due to this potential for expanding target pipelines and treating a larger number of human diseases, robust platforms for the rational design and optimization of small molecules interacting with RNAs (SMIRNAs) are in high demand. This review highlights three major pillars in this area. First, the transcriptome-wide identification and validation of structured RNA elements, or motifs, within disease-causing RNAs directly from sequence is presented. Second, Ceritinib chemical structure provide an overview of high-throughput screening approaches to identify SMIRNAs as well as discuss the lead identification strategy, Inforna, which decodes the three-dimensional (3D) conformation of RNA motifs with small molecule binding partners, directly from sequence. An emphasis is placed on target validation methods to study the causality between modulating the RNA motif in vitro and the phenotypic outcome in cells. Third, emergent modalities that convert occupancy-driven mode of action SMIRNAs into event-driven small molecule chemical probes, such as RNA cleavers and degraders, are presented. Finally, the future of the small molecule RNA therapeutics field is discussed, as well as hurdles to overcome to develop potent and selective RNA-centric chemical probes.A direct optimization method for obtaining excited electronic states using density functionals is presented. It involves selective convergence on saddle points on the energy surface representing the variation of the energy as a function of the electronic degrees of freedom, thereby avoiding convergence to a minimum and corresponding variational collapse to the ground electronic state. The method is based on an exponential transformation of the molecular orbitals, making it possible to use efficient quasi-Newton optimization approaches. #link# Direct convergence on a target nth-order saddle point is guided by an appropriate preconditioner for the optimization as well as the maximum overlap method. Results of benchmark calculations of 52 excited states of molecules indicate that the method is more robust than a standard self-consistent field (SCF) approach especially when degenerate or quasi-degenerate orbitals are involved. The method can overcome challenges arising from rearrangement of closely spaced orbitals in a charge-transfer excitation of the nitrobenzene molecule, a case where the SCF fails to converge. The formulation of the method is general and can be applied to non-unitary invariant functionals, such as self-interaction corrected functionals.Inverse problems continue to garner immense interest in the physical sciences, particularly in the context of controlling desired phenomena in non-equilibrium systems. In this work, we utilize a series of deep neural networks for predicting time-dependent optimal control fields, E(t), that enable desired electronic transitions in reduced-dimensional quantum dynamical systems. To solve this inverse problem, we investigated two independent machine learning approaches (1) a feedforward neural network for predicting the frequency and amplitude content of the power spectrum in the frequency domain (i.e., the Fourier transform of E(t)), and (2) a cross-correlation neural network approach for directly predicting E(t) in the time domain. Both of these machine learning methods give complementary approaches for probing the underlying quantum dynamics and also exhibit impressive performance in accurately predicting both the frequency and strength of the optimal control field. We provide detailed architectures and hyperparameters for these deep neural networks as well as performance metrics for each of our machine-learned models.