2 Å atomic displacement) is attributed to magnetic interactions and bonding between A and B cations. These findings reveal universal A-site splitting in LiNbO3-type structures with mixed multivalent B/B', or anionic sites, and the splitting-atomic displacement can be strongly suppressed by magnetic interactions and/or hybridization of valence bands between d electrons of the A- and B-site cations.The "Franck-Condon" (FC) excited state is the first state created when a molecule absorbs a visible photon. Here we report Stark and visible absorption spectroscopies that interrogate the FC state of rigorously diamagnetic [M(bpy)3]2+ complexes, where bpy is 2,2'-bipyridine and M = Fe, Ru, and Os. Direct singlet-to-triplet metal-to-ligand charge transfer (MLCT) transitions are evident in the 550-750 nm region of the absorbance spectrum of [Os(bpy)3]2+, yet are poorly resolved or absent for [Ru(bpy)3]2+ and [Fe(bpy)3]2+. In the presence of a strong 0.4-0.8 MV/cm electric field, well-resolved transitions are observed for all the complexes in this same spectral region. In particular, an electroabsorption feature at 633 nm (15 800 cm-1) provides compelling evidence for the direct population of a high spin [Fe(bpy)3]2+* MLCT excited state.  https://www.selleckchem.com/products/monastrol.html  Group theoretical considerations and Liptay analysis of the Stark spectra revealed dramatic light-induced dipole moment changes in the range [Formula see text] = 3-9 D with the triplet transitions consistently showing shorter charge transfer distances. The finding that the spin of the initially populated FC excited state differs from that of the ground state, even with a relatively light first row transition metal, is relevant to emerging applications in energy up-conversion, dye sensitization, spintronics, photoredox catalysis, and organic light emitting diodes (OLEDs).Proteins in their native states can be represented as ensembles of conformers in dynamical equilibrium. Thermal fluctuations are responsible for transitions between these conformers. Normal modes analysis (NMA) using elastic network models (ENM) provides an efficient procedure to explore global dynamics of proteins commonly associated to conformational transitions. In the present work, we present an iterative approach to explore protein conformational spaces by introducing structural distortions according to their equilibrium dynamics at room temperature. The approach can be used either to perform unbiased explorations of conformational space or to explore guided pathways connecting two different conformations, e.g., apo and holo forms. In order to test its performance, four proteins with different magnitude of structural distortions upon ligand binding have been tested. In all cases, the conformational selection model has been confirmed and the conformational space between apo and holo forms has been encompassed. Different strategies have been tested that impact either on the efficiency to achieve a desired conformational change or to achieve a balanced exploration of the protein conformational multiplicity.Eight new neo-clerodane diterpenoids (1-8) were acquired from the aerial parts of Ajuga pantantha. Spectroscopic data analysis permitted the definition of their structures, and experimental and calculated electronic circular dichroism data were used to define their absolute configurations. Compounds 2 and 4-8 were found to have NO inhibitory effects with IC50 values of 20.2, 45.5, 34.0, 27.0, 45.0, and 25.8 μM, respectively. The more potent compounds 2, 6, and 8 were analyzed to establish their anti-inflammatory mechanism, including regulation of the expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) proteins as well as their binding interactions with the two proteins.Spectrometric methods with rapid biomarker detection capacity through untargeted metabolomics are becoming essential in the clinical cancer research. Liquid chromatography-mass spectrometry (LC-MS) is a rapidly developing metabolomic-based biomarker technique due to its high sensitivity, reproducibility, and separation efficiency. However, its translation to clinical diagnostics is often limited due to long data acquisition times (∼20 min/sample) and laborious sample extraction procedures when employed for large-scale metabolomics studies. Here, we developed a segmented flow approach coupled with high-resolution mass spectrometry (SF-HRMS) for untargeted metabolomics, which has the capability to acquire data in less than 1.5 min/sample with robustness and reproducibility relative to LC-HRMS. The SF-HRMS results demonstrate the capability for screening metabolite-based urinary biomarkers associated with prostate cancer (PCa). The study shows that SF-HRMS-based global metabolomics has the potential to evolve into a rapid biomarker screening tool for clinical research.Substituent effects play critical roles in both modulating reaction chemistry and supramolecular self-assembly processes. Using substituted terephthalate dianions (p-phthalic acid dianions; PTADAs), the effect of varying the type, number, and position of the substituents was explored in terms of their ability to regulate the inherent anion complexation features of a tetracationic macrocycle, cyclo[2](2,6-di(1H-imidazol-1-yl)pyridine)[2](1,4-dimethylenebenzene) (referred to as the Texas-sized molecular box; 14+), in the form of its tetrakis-PF6- salt in DMSO. Several of the tested substituents, including 2-OH, 2,5-di(OH), 2,5-di(NH2), 2,5-di(Me), 2,5-di(Cl), 2,5-di(Br), and 2,5-di(I), were found to promote pseudorotaxane formation in contrast to what was seen for the parent PTADA system. Other derivatives of PTADA, including those with 2,3-di(OH), 2,6-di(OH), 2,5-di(OMe), 2,3,5,6-tetra(Cl), and 2,3,5,6-tetra(F) substituents, led only to so-called outside binding, where the anion interacts with 14+ on the outsi the first interaction sphere.Quantitative predictions of reaction properties, such as activation energy, have been limited due to a lack of available training data. Such predictions would be useful for computer-assisted reaction mechanism generation and organic synthesis planning. We develop a template-free deep learning model to predict the activation energy given reactant and product graphs and train the model on a new, diverse data set of gas-phase quantum chemistry reactions. We demonstrate that our model achieves accurate predictions and agrees with an intuitive understanding of chemical reactivity. With the continued generation of quantitative chemical reaction data and the development of methods that leverage such data, we expect many more methods for reactivity prediction to become available in the near future.