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Controlled by the base level change cycle, the carbonates exposed from the periodically shallowed water body at the top of the high-frequency sequence develops multiple sets of thin reservoirs. Such reservoir identification has always been tricky in the prediction of carbonate reservoirs. Based on the Middle Permian strata from the Sichuan Basin, this paper established a forecasting approach of high-frequency-sequence-controlled carbonate thin reservoir. The steps are as follows: (1) Combine the measured results of samples from the typical outcrops with changes of carbon and oxygen isotopes, thus to construct a division program for the 3rd-order sequence of the Middle Permian strata. (2) Compare the carbon and oxygen isotope changes and the global sea level change trend, determine the controlling factors of the 3rd-order sequences. (3) Carry out spectrum analysis based on the natural gamma energy spectrum logging (ln (Th/K)) curves of Wells Long-17 and Anping-1 to establish a high-frequency sequence framework division program, and then apply the logging curve characteristics to correct the top interface positions of the high-frequency sequences, which is the potential location of carbonate reservoir. The research results suggest that the Middle Permian in the Sichuan Basin can be divided into three 3rd-order sequences, which are PSQ1, PSQ2 and PSQ3 from bottom to top. Among which, PSQ1 is mainly affected by global sea level (GSL) changes, and the changes originated from its tuning curve can be directly applied to the division of high-frequency sequences and reservoir prediction. PSQ2 and PSQ3 are jointly controlled by global sea level changes and regional tectonics. In cope with the influence of regional tectonics, the positions of the four-level sequence interface established according to the tuning curve need to be fully considered, and good results were achieved through systematical correction. This method can effectively reduce the risk of reservoir prediction caused by marine carbonate heterogeneity, and provide scientific foundation for the prediction of high-frequency cyclic reservoirs of platform facies and platform margin subfacies in other marine carbonate sedimentary basins.

Abstract Cognitive decline is a critical hallmark of brain aging. Although aging is a natural process, there is significant heterogeneity in cognition levels among individuals; however, the underlying mechanisms remain uncertain. In our study, we classified aged male Sprague‒Dawley rats into aged cognition-unimpaired (AU) group and aged cognition-impaired (AI) group by using an attentional set-shifting task. The transcriptome sequencing results of medial prefrontal cortex (mPFC) demonstrated significant differences in microglial activation and inflammatory response pathways between the two groups. Specifically, compared to AU rats, AI rats exhibited a greater presence of CD86-positive microglia and major histocompatibility complex class II (MHC-II)-positive microglia, along with elevated inflammatory molecules, in mPFC. Conversely, AI rats exhibited a reduction in the percentage of microglia expressing CD200R and the anti-inflammatory molecules Arg-1 and TGF-β. Additionally, peripheral blood analysis of AI rats demonstrated elevated levels of Th17 and Th1 cells, along with proinflammatory molecules; however, decreased levels of Treg cells, along with anti-inflammatory molecules, were observed in AI rats. Our research suggested that peripheral Th17/Treg cells and central microglial activation were associated with cognitive heterogeneity in aged rats. These findings may provide a new target for healthy aging.

Abstract The hydroboration of alkynes into vinylboronate esters is a vital transformation, but achieving high chemoselectivity of targeted functional groups and an appreciable turnover number is a considerable challenge. Herein, we develop two dynamically regulating dual-catalytic-site copper clusters (Cu4NC and Cu8NC) bearing N-heterocyclic thione ligands that endow Cu4NC and Cu8NC catalysts with performance. In particular, the performance of microcrystalline Cu4NC in hydroboration is characterized by a high turnover number (77786), a high chemoselectivity, high recovery and reusability under mild conditions. Mechanistic studies and density functional theory calculations reveal that, compared with the Cu8NC catalyst, the Cu4NC catalyst has a lower activation energy for hydroboration, accounting for its high catalytic activity. This work reveals that precisely constructed cluster catalysts with dual catalytic sites may provide a way to substantially improve catalytic properties by fully leveraging synergistic interactions and dynamic ligand effects, thus promoting the development of cluster catalysts.

Abstract Ground settlement prediction for highway subgrades is crucial in related engineering projects. When predicting the ground settlement, sparse sample data are often encountered in practice, which greatly affects the prediction accuracy. However, this has been seldom explored in previous studies. To resolve it, this paper proposes a regression Kriging (RK)—based method for ground settlement prediction with sparse data. Under the framework of RK, the stationarity of sample residual and trend structure are key factors for prediction accuracy. It is found that the use of Box–Cox transformation, which can help to achieve stationarity of sample residual, leads to significant increase of the prediction accuracy with sparse data. Specifically, the various evaluation metrics (i.e., root mean square error (RMSE), mean absolute error (MAE), mean arctangent absolute percent error (MAAPE) and scatter index (SCI)) are significantly decreased when the Box–Cox transformation is incorporated. In addition, the first-order polynomial trend structure is found to be more appropriate than those with higher orders for predicting settlements resulting from primary consolidation. Moreover, comparative study is conducted among the proposed RK method, classical prediction methods and back propagation neural network (BPNN). It is found that the evaluation metrics obtained by the RK method are significantly smaller than those obtained by the other methods, indicating its highest accuracy. By contrast, BPNN has the worst performance among the various methods, because the sparse data are inadequate to establish a satisfactory BPNN model.

Abstract Letermovir (LTV) prophylaxis is effective in reducing the incidence of clinically significant cytomegalovirus (CMV) infection (cs CMVi) after allogeneic haematopoietic stem cell transplantation (allo-HSCT). Since our centre began administering LTV prophylaxis in June 2022, we have observed a certain increase in the incidence of Epstein–Barr virus (EBV) reactivation after haploidentical HSCT. We retrospectively analysed 230 consecutive patients who underwent haploidentical HSCT with rabbit anti-thymocyte globulin (ATG) from October 2022 to June 2023. The LTV group included 133 patients who received LTV prophylaxis, and the control group included 97 patients who did not receive LTV prophylaxis. At 1 year after HSCT, EBV reactivation was observed in 36 patients (27%) in the LTV group and 13 patients (13%) in the control group (p = 0.012). All patients with EBV reactivation had EBV-DNAemia, and one patient in each group developed EBV-associated posttransplantation lymphoproliferative disorder (PTLD). The proportion of patients with low EBV-DNA loads (> 5 × 102 to

C17H12FN3O, orthorhombic, Pbca (no. 61), a = 14.7608(7) Å, b = 7.2802(4) Å, c = 24.4842(13) Å, V = 2,631.1(2) Å3, Z = 8, Rgt(F) = 0.0900, wRref(F2) = 0.1841, T = 170 K.