Exploring causal correlations of inflammatory biomarkers in idiopathic normal-pressure hydrocephalus: insights from bidirectional Mendelian randomization analysis.

Background and objective: Neuroinflammatory processes have been identified as playing a crucial role in the pathophysiology of various neurodegenerative diseases, including idiopathic normal-pressure hydrocephalus (iNPH). iNPH, defined as a common disease of cognitive impairment in older adults, poses major challenges for therapeutic interventions owing to the stringent methodological requirements of relevant studies, clinical heterogeneity, unclear etiology, and uncertain diagnostic criteria. This study aims to assess the relationship between circulating inflammatory biomarkers and iNPH risk using bidirectional two-sample Mendelian randomization (MR) combined with meta-analysis. Methods: In our bidirectional MR study, genetic data from a genome-wide association study (GWAS) involving 1,456 iNPH cases and 409,726 controls of European ancestry were employed. Single-nucleotide polymorphisms (SNPs) associated with exposures served as instrumental variables for estimating the causal relationships between iNPH and 132 types of circulating inflammatory biomarkers from corresponding GWAS data. Causal associations were primarily examined using the inverse variance-weighted method, supplemented by MR-Egger, weighted median, simple mode, and weighted mode analyses. In the results, heterogeneity was assessed using the Cochran Q test. Horizontal pleiotropy was evaluated through the MR-Egger intercept test and the MR pleiotropy residual sum and outliers test. Sensitivity analysis was conducted through leave-one-out analysis. Reverse MR analyses were performed to mitigate bias from reverse causality. Meta-analyses of identical inflammatory biomarkers from both data sources strengthened the findings. Results: Results indicated a genetically predicted association between Interleukin-16 (IL-16) [OR: 1.228, 95% CI: 1.049-1.439, p = 0.011], TNF-related apoptosis ligand (TRAIL) [OR: 1.111, 95% CI: 1.019-1.210, p = 0.017] and Urokinase-type plasminogen activator (uPA) [OR: 1.303, 95% CI: 1.025-1.658, p = 0.031] and the risk of iNPH. Additionally, changes in human Glial cell line-derived neurotrophic factor (hGDNF) [OR: 1.044, 95% CI: 1.006-1.084, p = 0.023], Matrix metalloproteinase-1 (MMP-1) [OR: 1.058, 95% CI: 1.020, 1.098, p = 0.003] and Interleukin-12p70 (IL-12p70) [OR: 0.897, 95% CI: 0.946-0.997, p = 0.037] levels were identified as possible consequences of iNPH. Conclusion: Our MR study of inflammatory biomarkers and iNPH, indicated that IL-16, TRAIL, and uPA contribute to iNPH pathogenesis. Furthermore, iNPH may influence the expression of hGDNF, MMP-1, and IL-12p70. Therefore, targeting specific inflammatory biomarkers could be promising strategy for future iNPH treatment and prevention.

Effect of biological shells aggregate on the mechanical properties and sustainability of concrete.

The recycling bio-waste shells problem has grown more and more serious in recent years and many efforts have been made to solve this problem. One possible solution is to put these bio-shells into concrete and recycle them as building materials using the aggregate matrix concrete approach. To verify the engineering feasibility, the mechanical properties of bio-shells aggregated concrete were invested via gradient substitution rates at 10%, 30%, and 50% with a total of 78 groups of specimens in this paper. Our results show that the mechanical properties of the concrete were enhanced in maximum flexural strength and maximum compressive. Economic performance was also analyzed and found that the costs of frame-shear structure, frame structure, and tube-in-tube structure were reduced by 10.2%, 10%, and 10.3%. The carbon environmental assessment also shows superiority in the carbon reduction of a single specimen with various rates of the shell. In summary, compared with ordinary concrete materials, it is very possible to use waste bio-shells as a substitute for aggregates to develop the sustainable recycling development of concrete materials.

Layered Double Hydroxide Derivatives for Polyolefin Upcycling.

While Ru-catalyzed hydrogenolysis holds significant promise in converting waste polyolefins into value-added alkane fuels, a major constraint is the high cost of noble metal catalysts. In this work, we propose, for the first time, that Co-based catalysts derived from CoAl-layered double hydroxide (LDH) are alternatives for efficient polyolefin hydrogenolysis. Leveraging the chemical flexibility of the LDH platform, we reveal that metallic Co species serve as highly efficient active sites for polyolefin hydrogenolysis. Furthermore, we introduced Ni into the Co framework to tackle the issue of restricted hydrogenation ability associated with contiguous Co-Co sites. In-situ analysis indicates that the integration of Ni induces electron transfer and facilitates hydrogen spillover. This dual effect synergistically enhances the hydrogenation/desorption of olefin intermediates, resulting in a significant reduction in the yield of low-value CH4 from 27.1 to 12.6%. Through leveraging the unique properties of LDH, we have developed efficient and cost-effective catalysts for the sustainable recycling and valorization of waste polyolefin materials.

Bulk Trapping Organic Semiconductor with Amino-Additive Enabling Ultrasensitive NO2 Sensors.

Organic semiconductor (OSC) gas sensors have garnered considerable attention due to their promising selectivity and inherent flexibility. Introducing a functional group or modification layer is an important route to modulate the doping/trapping state of the active layer and the gas absorption/desorption process. However, the majority of the functionalization lies in the surface/interface assembling process, which is difficult to control the functional group density. This in turn brings challenges for precise modulation of the charge transport and the doping/trapping density, which will affect the repeatability and reproducibility of sensing performance. Herein, we propose a facile bulk trapping strategy incorporating amino-terminated additive molecules via the vacuum deposition process, achieving ultrahigh sensitivity of ∼2000%/ppm at room temperature to NO2 gas and approaching ∼3000%/ppm at 50 °C. Additionally, the device exhibits commendable reproducibility, stability, and low concentration detection ability, reaching down to several ppb, indicating promising potential for future applications. Comprehensive analysis of electrical properties and density functional theory calculations reveals that these exceptional properties arise from the favorable electrical characteristics of the bulk trapping structure, the high mobility of C8-BTBT, and the elevated adsorption energy of NO2. This approach enables the construction of stable and reproducible sensitive sensors and helps to understand the sensing mechanism in OSC gas sensors.

Stable Interfacial Ruthenium Species for Highly Efficient Polyolefin Upcycling.

The present polyolefin hydrogenolysis recycling cases acknowledge that zerovalent Ru exhibits high catalytic activity. A pivotal rationale behind this assertion lies in the propensity of the majority of Ru species to undergo reduction to zerovalent Ru within the hydrogenolysis milieu. Nonetheless, the suitability of zerovalent Ru as an optimal structural configuration for accommodating multiple elementary reactions remains ambiguous. Here, we have constructed stable Ru0-Ruδ+ complex species, even under reaction conditions, through surface ligand engineering of commercially available Ru/C catalysts. Our findings unequivocally demonstrate that surface-ligated Ru species can be stabilized in the form of a Ruδ+ state, which, in turn, engenders a perturbation of the σ bond electron distribution within the polyolefin carbon chain, ultimately boosting the rate-determining step of C-C scission. The optimized catalysts reach a solid conversion rate of 609 g·gRu-1·h-1 for polyethylene. This achievement represents a 4.18-fold enhancement relative to the pristine Ru/C catalyst while concurrently preserving a remarkable 94% selectivity toward valued liquid alkanes. Of utmost significance, this surface ligand engineering can be extended to the gentle mixing of catalysts in ligand solution at room temperature, thus rendering it amenable for swift integration into industrial processes involving polyolefin degradation.

Influence of dynamic load and water on energy accumulation and dissipation in sandstone.

In various engineering projects such as mineral extraction, hydropower resource utilization, railway construction, and geological hazard mitigation, rock engineering is often encountered. Furthermore, dynamic loads and moisture content exert notable influence on the energy transformation processes within rocks. Yet, the specific interplay of dynamic loading and water's impact on the energy conversion mechanism within the sandstone remains unexplored. To address this gap, this study conducted impact loading experiments on sandstone, elucidating the rock's mechanical response under these conditions and unraveling the underlying energy conversion mechanisms. It was observed that the strength of sandstone exhibits a direct correlation with impact velocity. Moreover, employing energy calculation principles, we established a connection between moisture content and the sandstone's internal energy conversion properties. The study also delved into the microscopic fracture mechanisms within the sandstone, ultimately concluding that both water content and dynamic loading have a significant impact on these microscopic fracture mechanisms.

A HOOI-Based Fast Parameter Estimation Algorithm in UCA-UCFO Framework.

In this paper, we introduce a Reduced-Dimension Multiple-Signal Classification (RD-MUSIC) technique via Higher-Order Orthogonal Iteration (HOOI), which facilitates the estimation of the target range and angle for Frequency-Diverse Array Multiple-Input-Multiple-Output (FDA-MIMO) radars in the unfolded coprime array with unfolded coprime frequency offsets (UCA-UCFO) structure. The received signal undergoes tensor decomposition by the HOOI algorithm to get the core and factor matrices, then the 2D spectral function is built. The Lagrange multiplier method is used to obtain a one-dimensional spectral function, reducing complexity for estimating the direction of arrival (DOA). The vector of the transmitter is obtained by the partial derivatives of the Lagrangian function, and its rotational invariance facilitates target range estimation. The method demonstrates improved operation speed and decreased computational complexity with respect to the classic Higher-Order Singular-Value Decomposition (HOSVD) technique, and its effectiveness and superiority are confirmed by numerical simulations.

A Fast Method for Multidimensional Joint Parameter Estimation of Polarization-Sensitive Arrays.

The paper proposes a fast method for the multidimensional parameter estimation of a polarization-sensitive array. Compared with conventional methods (e.g., MUSIC algorithm), the proposed method applies an iterative approach based on Newton's method to obtain joint estimation results instead of a spectral search and dimension reduction. It also extends the original Newton method to the 4D scale using the Hessian matrix. To reduce the complexity of establishing the aim function, Nystrom's method is applied to process the covariance matrix. A new threshold is also proposed to select the results, which can accomplish the parameter estimation with a small number of iterations while guaranteeing a high estimation accuracy. Finally, the proposed algorithm is analyzed in detail and the numerical simulations of various algorithms are compared to verify its effectiveness.

Reweighted Off-Grid Sparse Spectrum Fitting for DOA Estimation in Sensor Array with Unknown Mutual Coupling.

In the environment of unknown mutual coupling, many works on direction-of-arrival (DOA) estimation with sensor array are prone to performance degradation or even failure. Moreover, there are few literatures on off-grid direction finding using regularized sparse recovery technology. Therefore, the scenario of off-grid DOA estimation in sensor array with unknown mutual coupling is investigated, and then a reweighted off-grid Sparse Spectrum Fitting (Re-OGSpSF) approach is developed in this article. Inspired by the selection matrix, an undisturbed array output is formed to remove the unknown mutual coupling effect. Subsequently, a refined off-grid SpSF (OGSpSF) recovery model is structured by integrating the off-grid error term obtained from the first-order Taylor approximation of the higher-order term into the underlying on-grid sparse representation model. After that, a novel Re-OGSpSF framework is formulated to recover the sparse vectors, where a weighted matrix is developed by the MUSIC-like spectrum function to enhance the solution's sparsity. Ultimately, off-grid DOA estimation can be realized with the help of the recovered sparse vectors. Thanks to the off-grid representation and reweighted strategy, the proposed method can effectively and efficiently achieve high-precision continuous DOA estimation, making it favorable for real-time direction finding. The simulation results validate the superiority of the proposed method.

Effect of Strain Rate on Tensile Properties of Carbon Fiber-Reinforced Epoxy Laminates with Different Stacking Sequences and Ply Orientations.

In practical application situations, a carbon fiber-reinforced polymer (CFRP) is often subjected to complex dynamic loadings. The effect of the strain rate on mechanical properties is very important for the CFRP design and product development. In this work, static and dynamic tensile properties of CFRP with different stacking sequences and ply orientations were investigated. The results showed that the tensile strengths of CFRP laminates were sensitive to the strain rate, while Young's modulus was independent of the strain rate. Moreover, the strain rate effect was related to the stacking sequences and ply orientations. The experimental results showed that the strain rate effects of the cross-ply laminates and quasi-isotropic-ply laminates were lower than that of the unidirectional-ply laminates. Finally, the failure modes of CFRP laminates were investigated. Failure morphology demonstrated that the differences in strain rate effects among cross-ply laminates, quasi-isotropic-ply laminates, and unidirectional-ply laminates were caused by the mismatch between the fiber and the matrix when the strain rate increased.

Mechanical Properties of Weld Lines in Injection-Molded Carbon Fiber-Reinforced Nylon (PA-CF) Composites.

Weld lines are a common defect generated in injection molding, which apparently affects the performance of final products, but the available reports on carbon fiber-reinforced thermoplastics are still rather few. In this study, the effects of injection temperature, injection pressure, and fiber content on the mechanical properties of weld lines were studied for carbon fiber-reinforced nylon (PA-CF) composites. The weld line coefficient was also calculated by comparing specimens with and without weld lines. The tensile and flexural properties of PA-CF composites significantly increased with the rise of fiber content for specimens without weld lines, while injection temperature and pressure demonstrated slight influences on mechanical properties. However, the existence of weld lines had negative influences on the mechanical properties of PA-CF composites due to poor fiber orientation in weld line regions. The weld line coefficient of PA-CF composites decreased as fiber content increased, indicating that the damage of weld lines to mechanical properties increased. The microstructure analysis showed that there were a large number of fibers distributed vertically to flow direction in weld lines regions, which could not play a reinforcing role. In addition, increasing injection temperature and pressure facilitated fiber orientation, which improved the mechanical properties of composites with low fiber content, while weakening composites with high fiber content instead. This article provides practical information for product design containing weld lines, which helps to optimize the forming process and formula design of PA-CF composites with weld lines.

Site-Selective Polyolefin Hydrogenolysis on Atomic Ru for Methanation Suppression and Liquid Fuel Production.

Catalytic hydrogenolysis of end-of-life polyolefins can produce value-added liquid fuels and therefore holds great promises in plastic waste reuse and environmental remediation. The major challenge limiting the recycling economic benefit is the severe methanation (usually >20%) induced by terminal C-C cleavage and fragmentation in polyolefin chains. Here, we overcome this challenge by demonstrating that Ru single-atom catalyst can effectively suppress methanation by inhibiting terminal C-C cleavage and preventing chain fragmentation that typically occurs on multi-Ru sites. The Ru single-atom catalyst supported on CeO2 shows an ultralow CH4 yield of 2.2% and a liquid fuel yield of over 94.5% with a production rate of 314.93 gfuels gRu -1 h-1 at 250 °C for 6 h. Such remarkable catalytic activity and selectivity of Ru single-atom catalyst in polyolefin hydrogenolysis offer immense opportunities for plastic upcycling.

Multi-Target Parameter Estimation of the FMCW-MIMO Radar Based on the Pseudo-Noise Resampling Method.

Subspace methods are widely used in FMCW-MIMO radars for target parameter estimations. However, the performances of the existing algorithms degrade rapidly in non-ideal situations. For example, a small number of snapshots may result in the distortion of the covariance matrix estimation and a low signal-to-noise ratio (SNR) can lead to subspace leakage problems, which affects the parameter estimation accuracy. In this paper, a joint DOA-range estimation algorithm is proposed to solve the above issues. Firstly, the improved unitary root-MUSIC algorithm is applied to reduce the influence of non-ideal terms in building the covariance matrix. Subsequently, the least squares method is employed to process the data and obtain paired range estimation. However, in a small number of snapshots and low SNR scenarios, even if the impact of non-ideal terms is reduced, there will still be cases where the estimators sometimes deviate from the true target. The estimators that deviate greatly from targets are regarded as outliers. Therefore, threshold detection is applied to determine whether outliers exist. After that, a pseudo-noise resampling (PR) technology is proposed to form a new data observation matrix, which further alleviates the error of the estimators. The proposed method overcomes performance degradation in a small number of snapshots or low SNRs simultaneously. Theoretical analyses and simulation results demonstrate the effectiveness and superiority.

Effect Observation of Optimized Individualized Nursing Care Applied to ICU Patients with Severe Pneumonia.

Purpose: This study aims to observe the effect of optimized individualized nursing care applied to intensive care unit (ICU) patients with severe pneumonia (SP). Methods: 440 patients with SP admitted to the ICU of our hospital from January 2019 to June 2020 were provided with routine nursing care (group A), and 550 patients with SP admitted from July 2020 to December 2021 were provided with optimized individualized nursing care (group B). The blood lactate index and acute physiology and chronic health evaluation (APACHE II) scores before and after care were compared between the two groups. The WBC count recovery time, mechanical ventilation time, antipyretic time, and length of hospital stay of the two groups were recorded. The complication rate of the two groups during the nursing care period was compared. The prognosis effect of the two groups after 6 and 12 months of discharge was followed up with the Seattle angina pectoris questionnaire (SAQ). Results: After care, the lactate level and lactate clearance rate were higher in both groups than before care, and the lactate level in group B was lower than that in group A and the lactate clearance rate was higher than that in group A (P < 0.05). After care, APACHE II scores were lower in both groups than before care, and lower in group B than in group A (P < 0.05). After care, the WBC count recovery time, mechanical ventilation time, antipyretic time, and length of hospital stay were shorter in group B than in group A (P < 0.05). During the nursing care period, the complication rate was lower in group B (5.82%) than in group A (11.59%) (P < 0.05). 6 and 12 months after discharge, the SAQ scores were higher in group B than in group A (P < 0.05). Conclusion: Optimized individualized nursing care applied to ICU SP patients can effectively improve the patients' physiological indicators, reduce complications, improve the prognosis of quality of life, and have a positive effect on the patients' speedy recovery.

Prediction of SiS2 and SiSe2 as promising anode materials for sodium-ion batteries.

In this work, we suggest SiS2 and SiSe2 as anode materials for sodium-ion batteries based on a first-principles prediction. Both SiS2 and SiSe2 have suitable adsorption energies (-1.01/-1.24 eV) and large diffusion constants (1.24 × 10-4/6.72 × 10-5 cm2 s-1) for Na ions at 300 K, resulting in low diffusion barriers (less than 0.1 eV) at high Na adsorption concentrations. As anode materials, SiS2 and SiSe2 exhibit excellent electrochemical stability with high theoretical capacities (517/864 mA h g-1) and desirable average voltages (0.19/0.25 V). Given these exceptional properties, SiS2 and SiSe2 are desired to be promising electrode materials for sodium-ion batteries.

A Multiobjective Evolutionary Nonlinear Ensemble Learning With Evolutionary Feature Selection for Silicon Prediction in Blast Furnace.

In the blast furnace ironmaking process, accurate prediction of silicon content in molten iron is of great significance for maintaining stable furnace conditions, improving hot metal quality, and reducing energy consumption. However, most of the current research works employ linear correlation coefficient methods to select input features in modeling, which may not fully take the nonlinear and coupling relationships between features into account. Therefore, this article considers the input feature selection issue of silicon content prediction model from a new perspective and proposes a multiobjective evolutionary nonlinear ensemble learning model with evolutionary feature selection mechanism (MOENE-EFS), in which extreme learning machine is adopted as the base learner. MOENE-EFS takes the input feature scheme of each base learner as well as their network structure and parameters as decision variables and proposes a modified nondominated sorting differential evolution algorithm to optimize two conflicting objectives, i.e., accuracy and diversity of base learners, simultaneously. Through the optimization, a set of Pareto optimal base learners with high accuracy and strong diversity can be obtained. Moreover, different from the linear ensemble methods commonly used in classical evolutionary ensemble learning, this article proposes a nonlinear ensemble method to combine the obtained base learners based on differential evolution. Experimental results indicate that the two proposed strategies, i.e., evolutionary feature selection and nonlinear ensemble, are very effective in improving the accuracy and stability of the prediction model. MOENE-EFS also outperforms the other prediction models in both benchmark data and practical industrial data. Furthermore, analysis on the input features of all Pareto optimal base learners shows that the evolutionary feature selection is capable of selecting essential features and is consistent with human experience, which indicates it is a promising method to deal with the input feature selection issue in silicon content prediction.

A Novel Unitary ESPRIT Algorithm for Monostatic FDA-MIMO Radar.

A novel unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, for the joint direction of arrival (DOA) and range estimation in a monostatic multiple-input multiple-output (MIMO) radar with a frequency diverse array (FDA), is proposed. Firstly, by utilizing the property of Centro-Hermitian of the received data, the extended real-valued data is constructed to improve estimation accuracy and reduce computational complexity via unitary transformation. Then, to avoid the coupling between the angle and range in the transmitting array steering vector, the DOA is estimated by using the rotation invariance of the receiving subarrays. Thereafter, an automatic pairing method is applied to estimate the range of the target. Since phase ambiguity is caused by the phase periodicity of the transmitting array steering vector, a removal method of phase ambiguity is proposed. Finally, the expression of Cramér-Rao Bound (CRB) is derived and the computational complexity of the proposed algorithm is compared with the ESPRIT algorithm. The effectiveness of the proposed algorithm is verified by simulation results.

Transplantation of Nurr1-overexpressing neural stem cells and microglia for treating parkinsonian rats.

BACKGROUND: Neural stem cells (NSCs) transplantation is considered a promising treatment for Parkinson's disease. But most NSCs are differentiated into glial cells rather than neurons, and only a few of them survive after transplantation due to the inflammatory environment. METHODS: In this study, neural stem cells (NSCs) and microglial cells both forced with the Nurr1 gene were transplanted into the striatum of the rat model of PD. The results were evaluated through reverse transcription polymerase chain reaction (RT-PCR), Western blot, and immunofluorescence analysis. RESULTS: The behavioral abnormalities of PD rats were improved by combined transplantation of NSCs and microglia, both forced with Nurr1. The number of tyrosine hydroxylase+ cells in the striatum of PD rats increased, and the number of Iba1+ cells decreased compared with the other groups. Moreover, the dopamine neurons differentiated from grafted NSCs could still be detected in the striatum of PD rats after 5 months. CONCLUSIONS: The results suggested that transplantation of Nurr1-overexpressing NSCs and microglia could improve the inhospitable host brain environments, which will be  a new potential strategy for the cell replacement therapy in PD.

Outage Probability Performance Prediction for Mobile Cooperative Communication Networks Based on Artificial Neural Network.

This paper investigates outage probability (OP) performance predictions using transmit antenna selection (TAS) and derives exact closed-form OP expressions for a TAS scheme. It uses Monte-Carlo simulations to evaluate OP performance and verify the analysis. A back-propagation (BP) neural network-based OP performance prediction algorithm is proposed and compared with extreme learning machine (ELM), locally weighted linear regression (LWLR), support vector machine (SVM), and BP neural network methods. The proposed method was found to have higher OP performance prediction results than the other prediction methods.