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The excellent O-regioselectivity of the glycosidation of the ambident 2-O-substituted 5-fluorouracil (5-FU) via the silver salt method is computationally investigated at the MP2/6-311++G(2d,p):DZP//B3LYP/6-31+G(d):DZP level of theory. The reactions studied are those between 1-bromo-1-deoxy-2,3,4,6-tetra-O-acetyl-α-d-glucopyranose and the silver salts of 5-FU, 2-O-butyl-5-FU, and 2-O-benzyl-5-FU. Two pathways are considered as follows: (A) one where the silver and bromide ion do not interact, and (B) another where the silver and bromide ion interact in the transition states. Because the O-reaction barriers are much lower (by 13.3-22.2 kcal/mol) than N-reaction barriers in both pathways, the O-regioselectivity of the silver salt method can be satisfactorily explained by either path A or path B. Furthermore, path B, where Ag and Br interact consistently, has lower activation barriers than the corresponding path A (by 6.8-17.4 kcal/mol) in both N- and O-reactions. This computational result can be attributed to the following reasons: (1) the speeding-up effect in Koenigs-Knorr reactions due to the addition of silver carbonate into the reaction mixture; (2) the halogens being pulled away by silver ions from halides, as proposed by Kornblum and co-workers; and (3) the oxocarbenium ion involvement in the glycosidation reactions. The large energy difference between N- and O-transition states originates from the association between Ag and N-(O-) of the ambident unit (-N3-C4=O4) that shows significant covalent character so that the O-reaction transition states of the silver salt method benefit from favorable ionic interaction (C+···O-) and favorable covalent interaction (Ag···N). These two favorable interactions are in agreement with the hard and soft acids and bases principle; the former is a hard-hard interaction and the latter is a soft-soft interaction. © 2018 American Chemical Society.

The search for selective anticholinergic agents stems from varying cholinesterase levels as Alzheimer’s Disease progresses from the mid to late stage. In this computational study, we probed the selectivity of FDA-approved and metabolite compounds against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) with molecular-docking-based virtual screening. The results were evaluated using locally developed codes for the statistical methods. The docking-predicted selectivity for AChE and BChE was predominantly the consequence of differences in the volume of the active site and the narrower entrance to the bottom of the active site gorge of AChE. © 2024 by the authors.

The model reactions CH3X + (NH—CH=O)M ➔ CH3—NH—NH═O or NH═CH—O—CH3 + MX (M = none, Li, Na, K, Ag, Cu; X = F, Cl, Br) are investigated to demonstrate the feasibility of Marcus theory and the hard and soft acids and bases (HSAB) principle in predicting the reactivity of ambident nucleophiles. The delocalization indices (DI) are defined in the framework of the quantum theory of atoms in molecules (QT-AIM), and are used as the scale of softness in the HSAB principle. To react with the ambident nucleophile NH═CH—O−, the carbocation H3C+ from CH3X (F, Cl, Br) is actually a borderline acid according to the DI values of the forming C…N and C…O bonds in the transition states (between 0.25 and 0.49), while the counter ions are divided into three groups according to the DI values of weak interactions involving M (M…X, M…N, and M…O): group I (M = none, and Me4N) basically show zero DI values; group II species (M = Li, Na, and K) have noticeable DI values but the magnitudes are usually less than 0.15; and group III species (M = Ag and Cu(I)) have significant DI values (0.30–0.61). On a relative basis, H3C+ is a soft acid with respect to group I and group II counter ions, and a hard acid with respect to group III counter ions. Therefore, N-regioselectivity is found in the presence of group I and group II counter ions (M = Me4N, Li, Na, K), while O-regioselectivity is observed in the presence of the group III counter ions (M = Ag, and Cu(I)). The hardness of atoms, groups, and molecules is also calculated with new functions that depend on ionization potential (I) and electron affinity (A) and use the atomic charges obtained from localization indices (LI), so that the regioselectivity is explained by the atomic hardness of reactive nitrogen atoms in the transition states according to the maximum hardness principle (MHP). The exact Marcus equation is derived from the simple harmonic potential energy parabola, so that the concepts of activation free energy, intrinsic activation barrier, and reaction energy are completely connected. The required intrinsic activation barriers can be either estimated from ab initio calculations on reactant, transition state, and product of the model reactions, or calculated from identity reactions. The counter ions stabilize the reactant through bridging N- and O-site of reactant of identity reactions, so that the intrinsic barriers for the salts are higher than those for free ambident anions, which is explained by the increased reorganization parameter Δr. The proper application of Marcus theory should quantitatively consider all three terms of Marcus equation, and reliably represent the results with potential energy parabolas for reactants and all products. For the model reactions, both Marcus theory and HSAB principle/MHP principle predict the N-regioselectivity when M = none, Me4N, Li, Na, K, and the O-regioselectivity when M = Ag and Cu(I). © 2019 Wiley Periodicals, Inc. © 2019 Wiley Periodicals, Inc.

Deep learning is a promising approach for fine- grained disease severity classification for smart agriculture, as it avoids the labor-intensive feature engineering and segmentation-based threshold. In this work, we first propose a Densely Connected Convolutional Networks (DenseNet) based transfer learning method to detect the plant diseases, which expects to run on edge servers with augmented computing resources. Then, we propose a lightweight Deep Neural Networks (DNN) approach that can run on Internet of Things (IoT) devices with constrained resources. To reduce the size and computation cost of the model, we further simplify the DNN model and reduce the size of input sizes. The proposed models are trained with different image sizes to find the appropriate size of the input images. Experiment results are provided to evaluate the performance of the proposed models based on real- world dataset, which demonstrate the proposed models can accurately detect plant disease using low computational resources. © 2019 IEEE.

The same dataset can be analysed in different justifiable ways to answer the same research question, potentially challenging the robustness of empirical science1-3. In this crowd initiative, we investigated the degree to which research findings in the social and behavioural sciences are contingent on analysts' choices. We examined a stratified random sample of 100 studies published between 2009 and 2018, in which, for one claim per study, at least five reanalysts independently reanalysed the original data. The statistical appropriateness of the reanalyses was assessed in peer evaluations, and the robustness indicators were inspected along a range of research characteristics and study designs. We found that 34% of the independent reanalyses yielded the same result (within a tolerance region of ±0.05 Cohen's d) as the original report; with a four times broader tolerance region, this indicator increased to 57%. Of the reanalyses conducted, 74% reached the same conclusion as the original investigation, 24% yielded no effects or inconclusive results and 2% reported the opposite effect. This exploratory study indicates that the common single-path analyses in social and behavioural research should not be simply assumed to be robust to alternative analyses4. Therefore, we recommend the development and use of practices to explore and communicate this neglected source of uncertainty. © 2026. The Author(s), under exclusive licence to Springer Nature Limited.