Atomic Insight into the Local Structure and Microenvironment of Isolated Co-Motifs in MFI Zeolite Frameworks for Propane Dehydrogenation.

Embedding metal species into zeolite frameworks can create framework-bond metal sites in a confined microenvironment. The metals sitting in the specific T sites of zeolites and their crystalline surroundings are both committed to the interaction with the reactant, participation in the activation, and transient state achievement during the whole catalytic process. Herein, we construct isolated Co-motifs into purely siliceous MFI zeolite frameworks (Co-MFI) and reveal the location and microenvironment of the isolated Co active center in the MFI zeolite framework particularly beneficial for propane dehydrogenation (PDH). The isolated Co-motif with the distorted tetrahedral structure ({(≡SiO)2Co(HO-Si≡)2}, two Co-O-Si bonds, and two pseudobridging hydroxyls (Co···OH-Si) is located at T1(7) and T3(9) sites of the MFI zeolite. DFT calculations and deuterium-labeling reactions verify that the isolated Co-motif together with the MFI microenvironment collectively promotes the PDH reaction by providing an exclusive microenvironment to preactivate C3H8, polarizing the oxygen in Co-O-Si bonds to accept H* ({(≡SiO)CoHδ- (Hδ+O-Si≡)3}), and a scaffold structure to stabilize the C3H7* intermediate. The Co-motif active center in Co-MFI goes through the dynamic evolutions and restoration in electronic states and coordination states in a continuous and repetitive way, which meets the requirements from the series of elementary steps in the PDH catalytic cycle and fulfills the successful catalysis like enzyme catalysis.

Multiclass retinal disease classification and lesion segmentation in OCT B-scan images using cascaded convolutional networks.

Disease classification and lesion segmentation of retinal optical coherence tomography images play important roles in ophthalmic computer-aided diagnosis. However, existing methods achieve the two tasks separately, which is insufficient for clinical application and ignores the internal relation of disease and lesion features. In this paper, a framework of cascaded convolutional networks is proposed to jointly classify retinal diseases and segment lesions. First, we adopt an auxiliary binary classification network to identify normal and abnormal images. Then a novel, to the best of our knowledge, U-shaped multi-task network, BDA-Net, combined with a bidirectional decoder and self-attention mechanism, is used to further analyze abnormal images. Experimental results show that the proposed method reaches an accuracy of 0.9913 in classification and achieves an improvement of around 3% in Dice compared to the baseline U-shaped model in segmentation.

Experimental research on a Raman-based distributed temperature sensor assisted by PCA for locating the temperature abnormal event of nuclear waste drums.

Aimed at locating the temperature abnormal event of nuclear waste drums in a nuclear waste temporary storage repository by a Raman-based distributed temperature sensor, a principal component analysis (PCA)-based method for application is proposed. The effectiveness of the proposed method is verified in the physical simulation device of the nuclear waste drums. First, some samples of the temperature abnormal event with known location are taken as the reference samples, and their features are extracted by PCA. Then, the features of the test sample data to be located are also extracted by PCA. The Euclidean distance is used to measure the similarity between the features of the test sample and the feature of each reference sample. Finally, we find the reference sample that is most similar to a test sample, the location of which is considered the location of the temperature anomaly event for the test sample. The experimental results show that the proposed method can accurately locate the temperature abnormal event of the nuclear waste drums, and the accuracy rate can reach 96%. The method that is proposed in this paper can reliably locate the temperature abnormal event generated by the nuclear waste temporary storage repository induced by external factors such as landslides or earthquakes, and provide technical support for nuclear safety.

Secular trends and urban-rural disparities in the median age at menarche among Chinese han girls from 1985 to 2019.

BACKGROUND: Menarche is a substantial milestone of female puberty. Timing of age at menarche is considered the key to understanding the potential linkages with women's health outcomes later in life. This study aimed to explore the secular trends and urban‒rural disparities in the median age at menarche among Chinese Han girls from 1985 to 2019. METHODS: Data were extracted from the 1985, 1995, 2005, 2014, and 2019 Chinese National Surveys on Students' Constitution and Health, which were nationally representative cross-sectional studies, and a total of 173,535 Han girls aged 9-18 years were examined. Girls were asked whether menarche had occurred. The median age at menarche was estimated by probit analysis. Z tests were used to compare the differences between survey years and between urban and rural areas. RESULTS: The median age at menarche among Chinese Han girls decreased from 13.37 years in 1985 to 12.00 years in 2019, and the overall decrease was more significant in rural areas (1.77 years) than in urban areas (0.99 years). The average five-year change in the decrease in the median age at menarche showed an accelerating and then slowing pace; and it was observed similarly in both urban and rural areas. The urban‒rural disparities shrank from 0.64 years in 1985 to 0.44 years in 1995, then to 0.27 years in 2005, 0.24 years in 2014, and finally to - 0.14 years in 2019. CONCLUSIONS: The median age at menarche among Chinese Han girls continued to decline from 1985 to 2019 but at a slowing pace in the last five years. Urban‒rural disparities gradually narrowed. Sexual and reproductive health education and interventions to prevent the decline in the age of menarche are needed, especially in rural areas.

PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions.

Graph node embedding aims at learning a vector representation for all nodes given a graph. It is a central problem in many machine learning tasks (e.g., node classification, recommendation, community detection). The key problem in graph node embedding lies in how to define the dependence to neighbors. Existing approaches specify (either explicitly or implicitly) certain dependencies on neighbors, which may lead to loss of subtle but important structural information within the graph and other dependencies among neighbors. This intrigues us to ask the question: can we design a model to give the adaptive flexibility of dependencies to each node's neighborhood. In this paper, we propose a novel graph node embedding method (named PINE) via a novel notion of partial permutation invariant set function, to capture any possible dependence. Our method 1) can learn an arbitrary form of the representation function from the neighborhood, without losing any potential dependence structures, and 2) is applicable to both homogeneous and heterogeneous graph embedding, the latter of which is challenged by the diversity of node types. Furthermore, we provide theoretical guarantee for the representation capability of our method for general homogeneous and heterogeneous graphs. Empirical evaluation results on benchmark data sets show that our proposed PINE method outperforms the state-of-the-art approaches on producing node vectors for various learning tasks of both homogeneous and heterogeneous graphs.

Bone marrow mesenchymal stem cell-derived extracellular vesicles containing miR-181d protect rats against renal fibrosis by inhibiting KLF6 and the NF-κB signaling pathway.

Recent studies have investigated the ability of extracellular vesicles (EVs) in regulating neighboring cells by transferring signaling molecules, such as microRNAs (miRs) in renal fibrosis. EVs released by bone marrow mesenchymal stem cells (BMSCs) contain miR-181d, which may represent a potential therapy for renal fibrosis. miR-181d has been speculated to regulate Krüppel-like factor 6 (KLF6), which activates the nuclear factor-kappa B (NF-κB) signaling pathway. Luciferase assays were performed to confirm the relationship between miR-181d and KLF6. Gain- and loss-of-function studies in vivo and in vitro were performed to assess the effect of BMSC-derived EVs (BMSC-EVs), which contained miR-181d, on KLF6, NF-κB, and renal fibrosis. Transforming growth factor-ß (TGF-ß)-induced renal tubular epithelial HK-2 cells were treated with EVs derived from BMSCs followed by evaluation of collagen type IV α1 (Col4α1), Collagen I and α-smooth muscle actin (α-SMA) as indicators of the extent of renal fibrosis. Renal fibrosis was induced in rats by unilateral ureteral obstruction (UUO) followed by the subsequent analysis of fibrotic markers. BMSC-EVs had higher miR-181d expression. Overexpression of miR-181d correlated with a decrease in KLF6 expression as well as the levels of IκBα phosphorylation, α-SMA, Col4α1, TGF-ßR1 and collagen I in HK-2 cells. In vivo, treatment with miR-181d-containing BMSC-derived EVs was able to restrict the progression of fibrosis in UUO-induced rats. Together, BMSC-EVs suppress fibrosis in vitro and in vivo by delivering miR-181d to neighboring cells, where it targets KLF6 and inhibits the NF-κB signaling pathway.

Iron-Salt Thermally Emitted Strategy to Prepare Graphene-like Carbon Nanosheets with Trapped Fe Species for an Efficient Electrocatalytic Oxygen Reduction Reaction in the All-pH Range.

Earth-abundant, highly active, and durable electrocatalysts toward oxygen reduction reaction (ORR) in the all-pH range are highly required for practical application of electrochemical energy conversion technologies. Here, non-noble-metal graphene-like carbon nanosheets with trapped Fe species (Fe-N/GPC) are developed by an iron-salt thermally emitted strategy, which integrates the modulation of the electronic structure for boosted intrinsic activity with the engineering of hierarchical porosity for enriched active sites. The ORR electrocatalytic performance of Fe-N/GPC-800 achieves the half-wave potentials of 0.86 and 0.77 V with limiting current densities of 6.1 and 4.7 mA cm-2 in 0.1 M KOH and 0.1 M PBS solutions, respectively, as well as respectable stability. Furthermore, Fe-N/GPC-800 also shows considerable ORR catalytic activity in acid media accompanied by stability superior to those of Pt/C catalysts. The as-prepared Fe-N/GPC-800, as a cathodic catalyst, is assessed in a Zn-air battery test and delivers an open-circuit voltage of 1.44 V with a power density of 134 mW cm-2 as well as the outstanding durability after 350 cycles at 10 mA cm-2, demonstrating appreciable promise in application of metal-air batteries. This work provides an enabling and versatile strategy for facile and scale-up preparation of high-performance non-noble-metal electrocatalysts.