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The dengue virus (DENV) is a mosquito-borne flavivirus transmitted through the infected mosquito, primarily of the Aedes genus. Over recent decades, the significant emergence of DENV infections has posed growing challenges for control efforts. Extensive research indicates that the dengue NS2B–NS3 protease, a non-structural viral protein, is the most promising target in dengue drug research. Therefore, our study synthesized two series of novel allosteric inhibitors of the NS2B–NS3 protease to explore previous allosteric inhibitors of the protease. Meanwhile, enzyme inhibitory activities of all compounds are measured and IC50 values of the optimized compounds are obtained by utilizing a fluorescence-based assay. Notably, compound A24 demonstrates the highest enzyme inhibitory activity with an IC50 value of 16 μM. Further enzyme kinetic experiments confirm that the optimized compound A24 functions as a non-competitive inhibitor of the NS2B–NS3 protease. Through molecular docking studies, compound A24 showed a binding energy of −5.6 kcal mol−1, comparable to the lead compounds, which preliminarily explains its similar inhibitory activity. In addition, compound A24 forms a stable complex with the protein primarily through hydrogen bonds and π–π interaction, with subsequent dynamics simulations confirming the stable binding mode. In conclusion, we designed and synthesized 59 novel scaffold compounds and identified compound A24 with the highest activity through pharmacological experiments and computer-aided drug design, which targets the allosteric sites and contributes significantly to the prevention of the dengue virus. [ABSTRACT FROM AUTHOR]

After a brief review of the history of pearlite structures in carbon steels, particularly on the pearlite formation mechanism, recent experimental investigations on the pearlite substructure are presented to express a distinct point of view. The water‐quenched high‐carbon pearlite substructure is investigated in detail by means of scanning electron microscopy and transmission electron microscopy. In the experimental observation results, it is shown that the cementite layer or ferrite layer in pearlite is composed of fine grains, which cannot be simply explained by traditional nucleation and grain growth mechanisms. However, the fine grain structure can be explained by the martensitic transformation products (twinned martensite with ultrafine grains of α–Fe and twinning boundaries ω–Fe (or ω–Fe3C)) and detwinning process. Upon tempering or detwinning, recrystallization of the ultrafine grains of both crystalline phases occurs to form the initial pearlite structure, while the grain size of both phases is still fine. The twinned martensite can be treated as the precursor of pearlite structure (pearlite nucleation stage), and the detwinning process can be regarded as the growth of the pearlite structure. Thus, the pearlite reaction can be described as follows: austenite → twinned martensite → pearlite. [ABSTRACT FROM AUTHOR]

To address the issue of low yield in the preparation of nanofiber materials using single-needle electrospinning technology, multi-needle electrospinning technology has emerged as a crucial solution for mass production. However, the mutual interference of multiple electric fields between the needles can cause significant randomness in the morphology of the produced nanofibers. To better predict the influence of electric field distribution on nanofiber morphology, simulation analysis of the multi-needle arrangement was conducted using finite element analysis (FEA) software. Nanofiber-coated yarn was produced continuously with the core yarn rotating. The water bath was utilized as the receiver of nanofibers on self-made water bath electrospinning equipment. The electric field distribution and mutual interference under seven different needle arrangements was simulated and analyzed by FEA software ANSYS Maxwell. The results indicated that when the needles were arranged diagonally in a staggered pattern and directly above the core yarn, the simulated electric field distribution was relatively uniform, with less mutual interference. The produced nanofibers exhibited a finer diameter and the diameter distribution was more concentrated. In addition, the nanofiber coating showed higher crystallinity and better mechanical properties. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have achieved breakthrough progress in various fields, largely owing to the support of large-scale datasets with manually annotated labels. However, obtaining such datasets is costly and time-consuming, making high-quality annotation a challenging task. In this work, we propose an improved noisy sample selection method, termed "sample selection framework", based on a mixup loss and recalibration strategy (SMR). This framework enhances the robustness and generalization abilities of models. First, we introduce a robust mixup loss function to pre-train two models with identical structures separately. This approach avoids additional hyperparameter adjustments and reduces the need for prior knowledge of noise types. Additionally, we use a Gaussian Mixture Model (GMM) to divide the entire training set into labeled and unlabeled subsets, followed by robust training using semi-supervised learning (SSL) techniques. Furthermore, we propose a recalibration strategy based on cross-entropy (CE) loss to prevent the models from converging to local optima during the SSL process, thus further improving performance. Ablation experiments on CIFAR-10 with 50% symmetric noise and 40% asymmetric noise demonstrate that the two modules introduced in this paper improve the accuracy of the baseline (i.e., DivideMix) by 1.5% and 0.5%, respectively. Moreover, the experimental results on multiple benchmark datasets demonstrate that our proposed method effectively mitigates the impact of noisy labels and significantly enhances the performance of DNNs on noisy datasets. For instance, on the WebVision dataset, our method improves the top-1 accuracy by 0.7% and 2.4% compared to the baseline method. [ABSTRACT FROM AUTHOR]

The development of high‐efficient and large‐scale non‐precious electrocatalysts to improve sluggish reaction kinetics plays a key role in enhancing electrocatalytic nitrogen reduction reaction (NRR) for ammonia production under mild condition. Herein, Fe3O4 and Fe supported by porous carbon (denoted as Fe/Fe3O4/PC‐800) composite with a high specific surface area of 1004.1 m2 g−1 was prepared via a simple template method. On one hand, the high surface area of Fe/Fe3O4/PC‐800 provides a large area to enhance N2 adsorption and promote more protons and electrons to accelerate the reaction, thereby greatly improving the dynamics. On the other hand, mesoporous Fe/Fe3O4/PC‐800 provides high electrochemically active surface area for promoting the occurrence of catalytic kinetics. As a result, Fe/Fe3O4/PC‐800 exhibited significantly enhanced NRR performance with an ammonia yield of 31.15 μg h−1 mg−1cat. and faraday efficiency of 22.26 % at −0.1 V vs. reversible hydrogen electrode (RHE). This study is expected to provide a new strategy for the synthesis of catalysts with large specific area and pave the way for the foundational research in NRR. [ABSTRACT FROM AUTHOR]

The electrocatalytic nitrogen reduction reaction (NRR) is known as a promising mean of nitrogen fixation to mitigate the energy crisis and facilitate fertilizer production under mild circumstances. For electrocatalytic reactions, the design of efficient catalysts is conducive to reducing activation energy and accelerating lethargic dynamics. Among them, hollow structural materials possess cavities in their structures, which can slack off the escape rate of N2 and reaction intermediates, prolong the residence time of N2, enrich the reaction intermediates' concentration, and shorten electron transportation path, thereby further enhancing their NRR activity. Here, the basic synthetic strategies of hollow structural materials are introduced first. Then, the recent breakthroughs in hollow structural materials as NRR catalysts are reviewed from the perspective of intrinsic, mesoscopic, and microscopic regulations, aiming to discuss how structures affect and improve the catalytic performance. Finally, the future research directions of hollow structural materials as NRR catalysts are discussed. This review is expected to provide an outlook for optimizing hollow structural NRR catalysts. [ABSTRACT FROM AUTHOR]