Publications by authors named "Xianpeng Wang"

The DNA mismatch repair (MMR) system plays a crucial role in repairing DNA damage and regulating cell cycle arrest induced by cadmium (Cd) stress. To elucidate the mechanism by which miRNAs target in regulating ' response to Cd stress, the wild-type , mutant, and three miRNA-overexpressing transgenic lines were grown hydroponically in half-strength MS solution containing cadmium (Cd) at concentrations of 0, 0.5, 1, 2, and 3 mg/L for 5 days.

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: The dopamine receptor D2 (DRD2), somatostatin receptor 2 (SSTR2), and oestrogen receptor 1 (ESR1) have been demonstrated to play a critical role in determining treatment response in pituitary neuroendocrine tumors (PitNETs). However, the identification of these receptors preoperative presented a significant challenge. The objective of this study was to develop a predictive model that employs both radiomics and deep learning features in conjunction with conventional magnetic resonance imaging (MRI) to predict the expression of these three receptors in PitNETs in a retrospective study.

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Cadmium is one of the most toxic heavy metal pollutants in the world, seriously affecting crop growth and human health. 2,4-Epibrassinolide (BRs) has been proven to promote plant growth, enhance abiotic stress resistance and improve crop quality and yield. In this study, adzuki bean (V.

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Melatonin (MT) has been found to mitigate cadmium (Cd) toxicity with negligible environmental risks. It remains poorly understood as to how MT mitigates Cd-induced growth repression and regulates RNA mA methylation. We aimed to elucidate the effect of MT on growth repression and RNA mA methylation in Arabidopsis () exposed to Cd stress.

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The reheating furnace is the key piece of equipment in the hot rolling process of steel production. In order to fully exploit all of the data recorded from the production process representing different information, this paper designs a process monitoring algorithm with multisource information fusion by integrating multiple information to comprehensively monitor the operating state of the reheating furnace. Multisource information fusion combines process variable data of the reheating furnace and heating process data of the slab.

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Plant height and flag leaf morphology critically affect plant yield because they determine above-ground plant biomass and photosynthate production. However, few genetic basis analyses and gene mining studies on plant height, flag leaf length, and flag leaf width have been performed, and there is little available information about the evolution and utilization of the underlying natural alleles. This study conducted a genome-wide association study (GWAS) using 689 rice accessions collected from diverse regions across the globe.

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Traditional direction of arrival (DOA) and polarization parameter estimation algorithms generally perform well under ideal conditions. However, their performance degrades significantly in practical scenarios due to mutual coupling effects among array elements. This work introduces an innovative method based on a distributed crossed dipole array to jointly estimate DOA and polarization parameters in the presence of mutual coupling effects.

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Photocatalytic upcycling of waste polyolefins into value-added chemicals provides promise in plastic waste management and resource utilization. Previous works demonstrate that polyolefins can be converted into carboxylic acids, with CO as the final oxidation product. It is still challenging to explore more transformation products, particularly mild-oxidation products such as alcohols, because of their instability compared with polymer substrates, which are prone to oxidation during catalytic reactions.

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Lodging reduces grain yield and quality in cereal crops. Lodging resistance is affected by the strength of the culm, which is influenced by the culm diameter, culm wall thickness, and cell wall composition. To explore the genetic architecture of culm diameter in rice (Oryza sativa), we conducted a genome-wide association study (GWAS).

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Propane dehydrogenation (PDH) is a highly efficient approach for industrial production of propylene, and the dual-atom catalysts (DACs) provide new pathways in advancing atomic catalysis for PDH with dual active sites. In this work, we have developed an efficient strategy to identify promising DACs for PDH reaction by combining high-throughput density functional theory (DFT) calculations and the machine-learning (ML) technique. By choosing the γ-AlO(100) surface as the substrate to anchor dual metal atoms, 435 kinds of DACs have been considered to evaluate their PDH catalytic activity.

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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.

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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.

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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.

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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.

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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.

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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).

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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.

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The paper proposes a fast method for the multidimensional parameter estimation of a polarization-sensitive array. Compared with conventional methods (e.g.

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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.

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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.

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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.

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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.

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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.

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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.

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Synopsis of recent research by authors named "Xianpeng Wang"

  • - Xianpeng Wang's recent research encompasses diverse fields including catalysis, neuroinflammation, sustainable materials, and sensor technology, reflecting a multidisciplinary approach to solving contemporary scientific challenges
  • - Notably, his work on dual-atom catalysts for propane dehydrogenation integrates machine learning with high-throughput DFT calculations, showcasing innovative strategies to enhance catalytic efficiency in industrial applications
  • - Additionally, his studies investigate the implications of inflammatory biomarkers on neurodegenerative diseases, the mechanical properties of concrete with biological aggregates, and advanced methods for signal processing in sensor arrays, contributing significant insights into both environmental sustainability and health-related topics