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Conceptualization to utilize microbial composition as a prediction tool has been widely applied in human cohorts, yet the potential capacity of soil microbiota as a diagnostic tool to predict plant phenotype remains unknown. Here, we collected 130 soil samples which are 54 healthy controls and 76 ginseng rusty roots (GRRs). Alpha diversities including Shannon, Simpson, Chao1, and phylogenetic diversity were significantly decreased in GRR (P less then 0.05). Moreover, we identified 30 potential biomarkers. The optimized markers were obtained through fivefold cross-validation on a support vector machine and yielded a robust area under the curve of 0.856. Notably, evaluation of multi-index classification performance including accuracy, F1-score, and Kappa coefficient also showed robust discriminative capability (90.99%, 0.903, and 0.808). Taken together, our results suggest that the disease affects the microbial community and offers the potential ability of soil microbiota to identifying farms at the risk of GRR.We investigate the mechanism of energy transfer between ruthenium(II) (Ru) and osmium(II) (Os) polypyridyl complexes affixed to a polyfluorene backbone (PF-RuOs) using a combination of time-resolved emission spectroscopy and coarse-grained molecular dynamics (CG MD). Photoexcitation of a Ru chromophore initiates Dexter-style energy hopping along isoenergetic complexes followed by sensitization of a lower-energy Os trap. While we can determine the total energy transfer rate within an ensemble of solvated PF-RuOs from time-dependent Os* emission spectra, heterogeneity of the system and inherent polymer flexibility give rise to highly multiexponential kinetics. We developed a three-part computational kinetic model to supplement our spectroscopic results (1) CG MD model of PF-RuOs that simulates molecular motions out to 700 ns, (2) energy transfer kinetic simulations in CG MD PF-RuOs that produce time-resolved Ru and Os excited-state populations, and (3) computational experiments that interrogate the mechanisms by which motion aids energy transfer. Good agreement between simulated and experimental emission transients reveals that our kinetic model accurately simulates the molecular motion of PF-RuOs during energy transfer. Simulated results indicate that pendant flexibility allows 81% of the excited state to sensitize an Os trap compared to a 48% occupation when we treat pendants statically. Our computational experiments show how static pendants are only able to engage in local energy transfer. The excited state equilibrates across a domain of complexes proximal to the initial excitation and becomes trapped within that unique, frozen locality. Side-chain flexibility enables pendants to swing in and out of the original domain spreading the excited state out to ±30 pendant complexes away from the initial excitation.As prevalent cofactors in living organisms, iron-sulfur clusters participate in not only the electron-transfer processes but also the biosynthesis of other cofactors. Many synthetic iron-sulfur clusters have been used in model studies, aiming to mimic their biological functions and to gain mechanistic insight into the related biological systems. The smallest [2Fe-2S] clusters are typically used for one-electron processes because of their limited capacity. Our group is interested in functionalizing small iron-sulfur clusters with redox-active ligands to enhance their electron storage capacity, because such functionalized clusters can potentially mediate multielectron chemical transformations. Herein we report the synthesis, structural characterization, and catalytic activity of a diferric [2Fe-2S] cluster functionalized with two o-phenylenediamide ligands. The electrochemical and chemical reductions of such a cluster revealed rich redox chemistry. The functionalized diferric cluster can store up to four electrons reversibly, where the first two reduction events are ligand-based and the remainder metal-based. The diferric [2Fe-2S] cluster displays catalytic activity toward silylation of dinitrogen, affording up to 88 equiv of the amine product per iron center.Predictive modeling for toxicity can help reduce risks in a range of applications and potentially serve as the basis for regulatory decisions. However, the utility of these predictions can be limited if the associated uncertainty is not adequately quantified. With recent studies showing great promise for deep learning-based models also for toxicity predictions, we investigate the combination of deep learning-based predictors with the conformal prediction framework to generate highly predictive models with well-defined uncertainties. We use a range of deep feedforward neural networks and graph neural networks in a conformal prediction setting and evaluate their performance on data from the Tox21 challenge. We also compare the results from the conformal predictors to those of the underlying machine learning models. [https://www.selleckchem.com/products/CHIR-258.html TKI-258 in vitro] The results indicate that highly predictive models can be obtained that result in very efficient conformal predictors even at high confidence levels. Taken together, our results highlight the utility of conformal predictors as a convenient way to deliver toxicity predictions with confidence, adding both statistical guarantees on the model performance as well as better predictions of the minority class compared to the underlying models.Conventional lubricants decrease the wear and friction between rolling and sliding surfaces while raising environmental concerns. The thermodynamics and flow behavior of lubricants that contain environmentally friendly additives such as ionic liquids (ILs) are of interest to industries that require lubricants with superior tribological properties. Noncorrosive ILs are promising additives for conventional oil that not only further decrease friction but also create a less hazardous alternative to existing lubricants. In this study, thermodynamics, dynamics, and rheology of IL-oil mixtures are studied by using atomistically detailed molecular dynamics (MD) simulations. A combination of different imidazolium-based cations with linear ([CnC1Im]+[NTf2]-, n = 3, 7) and branched chains ([(n - 2)mCn-1C1Im]+, n = 3, 7) and bis[(trifluoroethane)sulfonyl]imide ([NTf2]-) anion are selected as the model IL which is suspended in bulk hexadecane. The effects of IL content, architecture, size of cation, and temperature are examined on the thermorheological and dynamics of the IL-hexadecane mixture.