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IntroductionCerebral ischemic injury is associated with long-term disability. Dexmedetomidine (Dex) can exert neuroprotective effects on cerebral ischemic/reperfusion injury. The present study explored the mechanism of Dex in cerebral ischemic injury.Materials and methodsTo this end, the permanent middle cerebral artery occlusion (p-MCAO) mouse model was established and treated with Dex or/and Nrf2 inhibitor ML385. Subsequently, microglia were subjected to oxygen–glucose deprivation (OGD) in sugar-free environment and thereafter treated with Dex, Nrf2 inhibitor, and NLRP3 lentiviral overexpression vector, respectively.ResultsDex alleviated the neurobehavioral deficit of p-MCAO mice, reduced brain water content, relieved pathological changes, and reduced cerebral infarction size. Dex promoted the polarization of microglia from M1 to M2, thus ameliorating oxidative stress and inflammatory responses. Our results showed that Dex promoted M2-polarization of microglia in vivo and in vitro by promoting HO-1 expression via Nrf2 nuclear import. Moreover, the Nrf2/HO-1 axis inhibited the activation of NLRP2 inflammasome and NLRP3 overexpression reversed the effect of Dex.ConclusionIn conclusion, Dex promoted M2-polarization of microglia and attenuated oxidative stress and inflammation, and thus protected against cerebral ischemic injury by activating the Nrf2/HO-1 pathway and inhibiting NLRP3 inflammasome.

In recent decades, the explosion of the waste generation rate and corresponding environmental impacts worldwide have turned waste management into one of the most vital services in urban areas to alleviate the waste-related issues. In this study, a novel integrated model is developed to improve the municipal solid waste system by considering the facility location, shift scheduling, and vehicle routing decisions. The problem is formulated as a tri-objective mixed-integer linear programming model, striving to optimize the sustainable development goals in the waste system. These objectives encompass the total profit, air pollution emissions, citizen satisfaction, and social risk factors. The findings from this study illustrate that the proposed integrated framework empowers decision makers to maintain the resilience of the municipal solid waste system by concurrently addressing three critical sustainability aspects.

Undevelopable stiffened curved shells have been widely used in engineering fields. The shape of the undevelopable curved surface is generally characterized with the non-straight generatrix and variable cross sections, which makes it challenging to automatically model and optimize stiffeners on the undevelopable curved surface. Therefore, the data-driven modelling and optimization framework are proposed for undevelopable stiffened curved shells in this paper. Firstly, a novel mesh deformation method is developed for the data-driven modelling of undevelopable stiffened curved shells based on RBF neural network machine learning method. Its main idea is to firstly define a developable curved shell (background mesh domain) having similar topological characteristics with the undevelopable curved shell (target mesh domain), and then train the mapping relationship between the background mesh domain and the target mesh domain by RBF neural network, and finally the complicated modelling problem of the undevelopable stiffened curved shell can be transformed into a simple modelling problem of developable stiffened curved shell by means of the mapping relationship. Moreover, based on the efficient global optimization (EGO) surrogate method, a data-driven layout optimization method is established for minimizing the structural weight of undevelopable stiffened curved shells. Finally, three representative optimization examples are carried out, including modelling and optimization of stiffeners on hyperbolic parabolic curved surfaces, blade-shaped curved surfaces and S-shaped variable cross-sectional curved surfaces. Optimal results indicate that the structural weight of undevelopable stiffened curved shells decreases significantly after the optimization, indicating the effectiveness of the proposed modelling and optimization framework.

The objective of this study is to investigate the fate and transport of per-polyfluoroalkyl substances (PFAS) through a high-density polyethylene (HDPE) geomembrane (GM) that is commonly used in landfill composite liner systems. Tests were conducted to measure the sorption and diffusion of four per-polyfluoroalkyl substances (PFAS) with varying numbers of carbons in chain and functional groups on HDPE GM. Perfluoroalkyl carboxyl acids (PFCAs), and perfluoroalkyl sulphonic acids (PFSAs) were investigated in this study. The partition coefficients (K d ) on HDPE GM ranged from 5 to 12 L/Kg. K d showed an increasing trend with chain length and were found to be sensitive to functional groups of PFAS. Molecular weight directly affected the K d . The diffusion coefficients (D g ) of PFCAs and PFSAs through 0.1-mm HDPE GM were found to be in the orders of 10 -18 to 10 -17 m 2 /s. The D g decreased with increasing molar mass and were also observed to be dependent on the functional group. D g of PFSAs was lower than that of PFCAs for a similar number of carbons in the chain.

A nationwide investigation was carried out to evaluate the geochemical characteristics and environmental impacts of red mud and leachates from the major alumina plants in China. The chemical and mineralogical compositions of red mud were investigated, and major, minor, and trace elements in the leachates were analyzed. The mineral and chemical compositions of red mud vary over refining processes (i.e., Bayer, sintering, and combined methods) and parental bauxites. The main minerals in the red mud are quartz, calcite, dolomite, hematite, hibschite, sodalite, anhydrite, cancrinite, and gibbsite. The major chemical compositions of red mud are Al, Fe, Si, Ca, Ti, and hydroxides. The associated red mud leachate is hyperalkaline (pH > 12), which can be toxic to aquatic life. The concentrations of Al, Cl−, F−, Na, NO32−, and SO42− in the leachate exceed the recommended groundwater quality standard of China by up to 6637 times. These ions are likely to increase the salinization of the soil and groundwater. The minor elements in red mud leachate include As, B, Ba, Cr, Cu, Fe, Ni, Mn, Mo, Ti, V, and Zn, and the trace elements in red mud leachate include Ag, Be, Cd, Co, Hg, Li, Pb, Sb, Se, Sr, and Tl. Some of these elements have the concentration up to 272 times higher than those of the groundwater quality standard and are toxic to the environment and human health. Therefore, scientific guidance is needed for red mud management, especially for the design of the containment system of the facilities.

Stem cell therapy has been explored for the treatment of cerebral stroke. Several types of stem cells have been investigated to ensure the safety and efficacy in clinical trials. Cryopreserved umbilical cord blood (UCB) mononuclear cells (MNCs) obtained from healthy donors have a more stabilized quality, thereby ensuring a successful therapy. A phase I study was conducted on patients aged 45–80 years who sustained acute ischemic stroke. An UCB unit was obtained from a public cord blood bank based on ABO/Rh blood type, HLA matching score (6/6), and cell dose (total MNC count of 0.5–5 × 107 cells/kg). In addition, to facilitate blood brain barrier penetration of UCB, 4 doses of 100 mL mannitol was administered intravenously after 30 min after UCB transplantation and every 4 h thereafter. The primary outcomes were the number of disease (GVHD) within 100 days after transfusion. The secondary outcomes were changes in the National Institutes of Health Stroke Scale (NIHSS), Barthel index, and Berg Balance Scale scores. A 46-year-old male patient with identical ABO/Rh blood type, HLA matching score of 6/6, and MNC count of 2.63 × 108 cells/kg was enrolled. The patient did not present with serious AEs or GVHD during the 12-month study period. The patient’s NIHSS score decreased from 9 to 1. Moreover, the Berg Balance Scale score increased from 0 to 48 and the Barthel index score from 0 to 90. This preliminary study showed that an adult patient with hemiplegia due to ischemic stroke completely recovered within 12 months after receiving allogeneic UCB therapy.

Stiffened plates are widely used in aerospace structures as load-bearing components. In order to obtain a novel design of stiffened structures with excellent performance, a generative design method of stiffened plates (GDMSP) based on the homogenization method is proposed in this paper, which optimizes the stiffener layout based on an equivalent model. Then, the detailed model can then be obtained by extracting the stiffener path from the discrete distribution of stiffener angles. Moreover, the optimized design can be obtained by size optimization based on the detailed model. Two examples are used to illustrate the proposed framework, including the stiffness maximization of a rectangle stiffener plate and the buckling load maximization of a square stiffener plate. The optimized stiffener configurations are characterized by streamlines and uniform lines, respectively. For the first example, the stiffness of the stiffener design has an improvement of 17%. For the second example, the optimized design improves the buckling load by 35%. Results indicate that the proposed method can effectively provide a novel generative design for stiffened plates. Moreover, the obtained results have a clear stiffener path and have a noticeable improvement in performance, which can be directly used to establish a detailed model.

Since the high-fidelity model (HFM) of hierarchical stiffened shells is time-consuming, the sampling points based on HFM are generally few, which would result in a certain randomness of the sampling process. In some cases, the prediction accuracy of the variable-fidelity surrogate model (VFSM) is prone to be not robust and reliable. In order to improve the robustness of the prediction accuracy of VFSM, a two-step adaptive updating approach is proposed for the robust establishment of VFSM. In the first step, the leave-one-out (LOO) cross validation is carried out for sampling points of the low-fidelity model (LFM), aiming at finding out those with large prediction error. Then, these points are evaluated by HFM and then added into the original HFM set. In the second step, another LOO cross validation is performed on sampling points of the hybrid bridge function linking HFM and LFM. Based on the Voronoi diagram method, new updating points are chosen from where the largest prediction error of the bridge function lies, and then the VFSM is updated. After above two-step updating process, the VFSM is established. Three simple examples of test functions are firstly presented to verify the effectiveness and efficiency of the proposed method. Further, the proposed method is applied to an engineering example of hierarchical stiffened shells. In order to provide evaluation indexes for prediction accuracy and robustness of VFSM, the VFSM is established by multiple times, and the mean value and the standard deviation of the relative root mean square error ( RRMSE ) values of the multiple sets of VFSM are calculated. Results indicate that, under the similar computational cost, the mean value and the standard deviation of the RRMSE values of the proposed method decrease by 24.1% and 82.0% than those of the traditional VFSM based on the direct sampling method, respectively. Therefore, the high prediction accuracy and robustness of the proposed method is verified. Additionally, the total computational time of the proposed VFSM decreases by 70% than that of the surrogate model based on HFM when achieving the similar prediction accuracy, indicating the high prediction efficiency of the proposed VFSM.

Hypergraphs have become an accurate and natural expression of high-order coupling relationships in complex systems. However, applying high-order information from networks to vital node identification tasks still poses significant challenges. This paper proposes a von Neumann entropy-based hypergraph vital node identification method (HVC) that integrates high-order information as well as its optimized version (semi-SAVC). HVC is based on the high-order line graph structure of hypergraphs and measures changes in network complexity using von Neumann entropy. It integrates s-line graph information to quantify node importance in the hypergraph by mapping hyperedges to nodes. In contrast, semi-SAVC uses a quadratic approximation of von Neumann entropy to measure network complexity and considers only half of the maximum order of the hypergraph’s s-line graph to balance accuracy and efficiency. Compared to the baseline methods of hyperdegree centrality, closeness centrality, vector centrality, and sub-hypergraph centrality, the new methods demonstrated superior identification of vital nodes that promote the maximum influence and maintain network connectivity in empirical hypergraph data, considering the influence and robustness factors. The correlation and monotonicity of the identification results were quantitatively analyzed and comprehensive experimental results demonstrate the superiority of the new methods. At the same time, a key non-trivial phenomenon was discovered: influence does not increase linearly as the s-line graph orders increase. We call this the saturation effect of high-order line graph information in hypergraph node identification. When the order reaches its saturation value, the addition of high-order information often acts as noise and affects propagation.

PurposeTo evaluate the effectiveness and safety of prophylactic intraoperative uterine artery embolization (UAE) performed immediately after fetal delivery during planned cesarean section or cesarean hysterectomy in patients with placenta accreta spectrum disorder or placenta previa.MethodsA systematic search was conducted on Ovid MEDLINE and Embase, PubMed, Web of Science, and Cochrane databases. Studies were selected using the Population/Intervention/Comparison/Outcomes (PICO) strategy. The intraoperative blood loss and the rate of emergent peripartum hysterectomy (EPH) were the primary outcomes, whereas the length of hospital stay and volume of blood transfused were the secondary outcomes. A random-effects model was employed to pool each effect size. The cumulative values of the primary outcomes were calculated using the generic inverse variance method.ResultsEleven retrospective cohort studies and five case series were included, recruiting 421 women who underwent prophylactic intraoperative UAE (UAE group) and 374 women who did not (control group). Compared with the control group, the UAE group had significantly reduced intraoperative blood loss (p = 0.020) during cesarean section or cesarean hysterectomy. Furthermore, the EPH rate was also significantly decreased (p = 0.020; cumulative rate: 19.65%), but not the length of hospital stay (p = 0.850) and volume of pRBC transfused (p = 0.140), after cesarean section in the UAE group. The incidence of major complications was low (3.33%), despite two patients with uterine necrosis.ConclusionThe currently available data provides encouraging evidence that prophylactic intraoperative UAE may contribute to hemorrhage control and fertility preservation in women with abnormal placentation.RegistrationPROSPERO registration code: CRD42021230581. https://clinicaltrials.gov/ct2/show/CRD42021230581Level of EvidenceLevel 2a, systematic review of retrospective cohort studies.