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Magnesium and nickel were recovered from nickel-rich serpentine through sulfation roasting and water leaching. The factors affecting the extraction percentages of Mg and Ni were discussed. Under the conditions of the ratio of acid to ore of 0.8:1 and roasting temperature of 650 °C for 120 min, 91.6% of Mg and 88.7% of Ni but only 4.8% of Fe were extracted. The roasting kinetics of Mg and Ni were investigated. The results showed that the roasting stage was governed by internal diffusion in the temperature range of 350–650 °C, and the activation energy of nickel and magnesium were different in the time ranges of 0–30 min and 60–120 min, with 17.45 kJ⋅mol−1 (0–30 min) and 14.12 kJ⋅mol−1 (60–120 min) for magnesium and 15.48 kJ⋅mol−1 (0–30 min) and 12.46 kJ⋅mol−1 (60–120 min) for nickel. The kinetic equations were obtained.

CD146, an endothelial biomarker, has been shown to be aberrantly upregulated during pathological angiogenesis and functions as a coreceptor for vascular endothelial growth factor receptor 2 (VEGFR-2) to promote disease progression. However, the regulatory mechanisms of CD146 expression during angiogenesis remain unclear. Using a microRNA screening approach, we identified a novel negative regulator of angiogenesis, microRNA 329 (miR-329), that directly targeted CD146 and inhibited CD146- mediated angiogenesis in vitro and in vivo. Endogenous miR-329 expression was downregulated by VEGF and tumor necrosis factor alpha (TNF-ɑ), resulting in the elevation of CD146 in endothelial cells. Upregulation of CD146 facilitated an endothelial response to VEGF-induced SRC kinase family (SKF)/p38 mitogen-activated protein kinase (MAPK)/NF-ɑB activation and consequently promoted endothelial cell migration and tube formation. Our animal experiments showed that treatment with miR-329 repressed excessive CD146 expression on blood vessels and significantly attenuated neovascularization in a mouse model of pathological angiogenesis. Our findings provide the first evidence that CD146 expression in angiogenesis is regulated by miR- 329 and suggest that miR-329 could present a potential therapeutic tool for the treatment of angiogenic diseases. [ABSTRACT FROM AUTHOR]

The epithelial-mesenchymal transition (EMT) plays an important role in breast cancer metastasis, especially in the most aggressive and lethal subtype, “triple-negative breast cancer” (TNBC). Here, we report that CD146 is a unique activator of EMTs and significantly correlates with TNBC. In epithelial breast cancer cells, overexpression of CD146 down-regulated epithelial markers and up-regulated mesenchymal markers, significantly promoted cell migration and invasion, and induced cancer stem cell-like properties. We further found that RhoA pathways positively regulated CD146-induced EMTs via the key EMT transcriptional factor Slug. An orthotopic breast tumor model demonstrated that CD146-overexpressing breast tumors showed a poorly differentiated phenotype and displayed increased tumor invasion and metastasis. We confirmed these findings by conducting an immunohistochemical analysis of 505 human primary breast tumor tissues and found that CD146 expression was significantly associated with high tumor stage, poor prognosis, and TNBC. CD146 was expressed at abnormally high levels (68.9%), and was strongly associated with E-cadherin down-regulation in TNBC samples. Taken together, these findings provide unique evidence that CD146 promotes breast cancer progression by induction of EMTs via the activation of RhoA and up-regulation of Slug. Thus, CD146 could be a therapeutic target for breast cancer, especially for TNBC. [ABSTRACT FROM AUTHOR]

Research on the uncertainty of wind power has a significant influence on power system planning and decision-making. This paper proposes a novel method for wind power interval forecasting based on rough sets theory, weighted Markov chain, and kernel density estimation (KDE) method. Since the wind power prediction is significantly correlated to its historical record, this method first applies the Markov chain method to predict the power at different steps based on historical power data, and then the overall power is calculated via rough set weighted summation. Finally, the obtained forecasting power is fed into the KDE forecasting model to obtain both upper and lower bounds of the probability interval of the wind power at a certain confidence level. The predicted interval coverage probability and average bandwidth are two of the criterions used to evaluate the proposed method. Moreover, the simulation results obtained via the Markov chain-KDE method and the weighted Markov chain-KDE method are compared against the results of the proposed method. These comparisons show that the proposed method based on rough set theory and weighted Markov chain KDE method offers unique advantages over the other methods for probability interval prediction of wind power, which are higher coverage, narrower average bandwidth, and a more accurate result.

Nanoparticles containing magnetic materials, such as magnetite (Fe3O4), are particularly useful for imaging and separation techniques. As these nanoparticles are generally considered to be biologically and chemically inert, they are typically coated with metal catalysts, antibodies or enzymes to increase their functionality as separation agents. Here, we report that magnetite nanoparticles in fact possess an intrinsic enzyme mimetic activity similar to that found in natural peroxidases, which are widely used to oxidize organic substrates in the treatment of wastewater or as detection tools. Based on this finding, we have developed a novel immunoassay in which antibody-modified magnetite nanoparticles provide three functions: capture, separation and detection. The stability, ease of production and versatility of these nanoparticles makes them a powerful tool for a wide range of potential applications in medicine, biotechnology and environmental chemistry. [ABSTRACT FROM AUTHOR]

Our previous study has demonstrated that CD146 molecule is a biomarker on vascular endothelium, which is involved in angiogenesis and tumor growth. However the mechanism behind is not clear. Here we have for the first time developed a novel CD146 blockade system using CD146 siRNA to study its function on endothelial cells. Our data showed that CD146 siRNA specifically blocked the expression of CD146 on both mRNA and protein levels, leading to the significant suppression of HUVEC proliferation, adhesion and migration. These results demonstrate that CD146 plays a key role in vascular endothelial cell activity and angiogenesis, and CD146 siRNA can be used as a new inhibitor for anti-angiogenesis therapy.Cell Research (2006) 16: 313–318. doi:10.1038/sj.cr.7310039; published online 16 March 2006 [ABSTRACT FROM AUTHOR]

RNA interference (RNAi) represents a promising gene-specific therapy against tumors. However, its clinical translation is impeded by poor performance of lysosomal escape and tumor targeting. This challenge is especially prominent in glioblastoma (GBM) therapy, necessitating the penetration of the blood-brain barrier (BBB). Leveraging the intrinsic tumor-targeting and BBB traversing capability of human H-ferritin, we designed a series of ferritin variants with positively charged cavity and truncated carboxyl terminus, termed tHFn(+). These nanocarriers respond to weak acid and disassemble in endosomal compartments, exposing the internal positive charges to facilitate the lysosomal escape of loaded small interfering RNA (siRNA). Functioning as universal siRNA nanocarriers, tHFn(+) significantly enhanced the uptake of different siRNAs and suppressed gene expressions associated with GBM progression. Furthermore, tHFn(+) traversed the BBB and targeted glioma in vivo by binding to its receptors (e.g., transferrin receptor 1). tHFn(+)-delivered siRNAs exhibited exceptional therapeutic effects against glioma in vivo, advancing RNAi therapeutics beyond GBM for the treatment of various diseases. [ABSTRACT FROM AUTHOR]

Acute alcohol intoxication could cause multiorgan damage, including nervous, digestive, and cardiovascular systems, and in particular, irreversible damage to the brain and liver. Emerging studies have revealed that the endogenous multienzymatic antioxidant defense system (MEAODS) plays a central role in preventing oxidative stress and other toxicological compounds produced by alcohol. However, few available drugs could quickly regulate MEAODS. Herein, we report a nanosized iron sulfide (nFeS) that can rapidly release polysulfide species in gastric juice. The released hydrogen polysulfide activates the Keap1/Nrf2 pathway via S-persulfidation of cysteine residues in Keap1, which promotes the expression of antioxidant enzymes and glutathione synthesis-related enzymes, thus potentiating MEAODS. Results indicate that the activated MEAODS not only alleviates oxidative stress and inflammation in the brain and liver but also mitigates movement dysfunction after only 2.5 hours of oral nFeS treatment. Collectively, this study provides a MEAODS-regulated strategy with nFeS and may aid the prevention of acute alcoholic injury. [ABSTRACT FROM AUTHOR]

The icing problem of wind turbine blades in northern China has a serious impact on the normal and safe operation of the unit. In order to effectively predict the icing conditions of wind turbine blades, a deep fully connected neural network optimized by machine learning (ML) algorithms based on big data from the wind farm is proposed to diagnose the icing conditions of wind turbine blades. This study first uses the random forest model to reduce the features of the supervisory control and data acquisition (SCADA) data that affect blade icing, and then uses the K-nearest neighbor (KNN) algorithm to enhance the active power feature. The features after the random forest reduction and the active power mean square error (MSE) feature enhanced by the KNN algorithm are combined and used as the input of the fully connected neural network (FCNN) to perform and an empirical analysis for the diagnosis of blade icing. The simulation results show that the proposed model has better diagnostic accuracy than the ordinary back propagation (BP) neural network and other methods.

The intermittency and uncertainty of wind power result in challenges for large-scale wind power integration. Accurate wind power prediction is becoming increasingly important for power system planning and operation. In this paper, a probabilistic interval prediction method for wind power based on deep learning and particle swarm optimization (PSO) is proposed. Variational mode decomposition (VMD) and phase space reconstruction are used to pre-process the original wind power data to obtain additional details and uncover hidden information in the data. Subsequently, a bi-level convolutional neural network is used to learn nonlinear features in the pre-processed wind power data for wind power forecasting. PSO is used to determine the uncertainty of the point-based wind power prediction and to obtain the probabilistic prediction interval of the wind power. Wind power data from a Chinese wind farm and modeled wind power data provided by the United States Renewable Energy Laboratory are used to conduct extensive tests of the proposed method. The results show that the proposed method has competitive advantages for the point-based and probabilistic interval prediction of wind power.

Peroxidases are proteins that contain heme and are widely active in plants, microorganisms, and animals. In this review study on plant peroxidases, in addition to introducing them, how to measure the activity of the enzyme, checking the number of isozymes, and tracking the structural changes of the enzyme have also been examined. This two-substrate enzyme, which converts hydrogen peroxide into water, oxidizes many organic and inorganic substrates during its catalysis, all of which can be used to measure enzyme activity. However, its specific substrate is still hydrogen peroxide. The presence of calcium and at least four disulfide bonds in the structure of peroxidases have been proven to help the formation and strength of the three-dimensional structure of the molecule. Peroxidases of plants have several roles, including involvement in lignin biosynthesis, auxin metabolism, cell growth, creation of transverse connections of the cell wall, and especially response to environmental stresses. It has been proven that in all physiological processes and phenological changes of the plant, hydrogen peroxide is produced in tissues, and peroxidase catalase cleans the cell from this toxic substance in two different ways. Therefore, peroxidases are considered a good option to follow the path of the cell's confrontation with stress-causing factors and to face situations such as oxidative stress. Due to the advances in biological sciences and the production of pure samples of peroxidases, this molecule is also used for ligand-protein studies in pharmaceutical research. [ABSTRACT FROM AUTHOR]

Based on an analysis of the working principles of the hydraulic variable pitch system of a wind turbine, a novel Petri net model and reliability evaluation method are proposed. First, Petri net theory is adopted to build a model for each discrete state of the operation of the hydraulic pitch system of the wind turbine and at the same time a fault Petri net model is established. Then through qualitative analysis and quantitative calculations based on the fault Petri net, the system reliability indexes are obtained. During the qualitative analysis process, in order to more conveniently find the minimal cut sets of the fault Petri net, a Visual C++ 6.0-based algorithm is compiled and the minimal cut sets are tested correctly with another method. During the quantitative calculation process, the fault probability has been obtained from the equations according to the fault probability of libraries and transitions between different states. Not only does the proposed Petri net describe the structure, function and operation of the hydraulic pitch system with a graphic language, but the fault Petri net model can also clearly express the logical relations among faults. The novel Petri net model offers simple calculations and the prospect of broad applicability and the new reliability evaluation method provides an important reference for the performance evaluation of these systems.

Damaged wind turbine (WT) blades have an imbalanced load and abnormal vibration, which affects their safe and stable operation or even results in blade rupture. To solve this problem, this study proposes a new method to detect damage in WT blades using wavelet packet energy spectrum analysis and operational modal analysis. First, a wavelet packet transform is used to analyze the tip displacement of the blades to obtain the energy spectrum. The damage is detected preliminarily based on the energy change in different frequency bands. Subsequently, an operational modal analysis method is used to obtain the modal parameters of the blade sections and the damage is located based on the modal strain energy change ratio (MSECR). Finally, the professional WT simulation software GH (Garrad Hassan) Bladed is used to simulate the blade damage and the results are verified by developing an online fault diagnosis platform integrated with MATLAB. The results show that the proposed method is able to diagnose and locate the damage accurately and provide a basis for further research of online damage diagnosis for WT blades.

Wind turbine condition-monitoring and fault diagnosis have important practical value for wind farms to reduce maintenance cost and improve operating level. Due to the special distribution law of the operating parameters of similar turbines, this paper compares the instantaneous operation parameters of four 1.5 MW turbines with strong correlation of a wind farm. The temperature-power distribution of the gearbox bearings is analyzed to find out the main trend of the turbines and the deviations of individual turbine parameters. At the same time, for the huge amount of data caused by the increase of turbines number and monitoring parameters, this paper uses the huge neural network and multi-hidden layer of a convolutional neural network to model historical data. Finally, the rapid warning and judgment of gearbox bearing over-temperature faults proves that the monitoring method is of great significance for large-scale wind farms.

Intermittency and uncertainty pose great challenges to the large-scale integration of wind power, so research on the probabilistic interval forecasting of wind power is becoming more and more important for power system planning and operation. In this paper, a Naive Bayesian wind power prediction interval model, combining rough set (RS) theory and particle swarm optimization (PSO), is proposed to further improve wind power prediction performance. First, in the designed prediction interval model, the input variables are identified based on attribute significance using rough set theory. Next, the Naive Bayesian Classifier (NBC) is established to obtain the prediction power class. Finally, the upper and lower output weights of NBC are optimized segmentally by PSO, and are used to calculate the upper and lower bounds of the optimal prediction intervals. The superiority of the proposed approach is demonstrated by comparison with a Naive Bayesian model with fixed output weight, and a rough set-Naive Bayesian model with fixed output weight. It is shown that the proposed rough set-Naive Bayesian-particle swarm optimization method has higher coverage of the probabilistic prediction intervals and a narrower average bandwidth under different confidence levels.

Functionalized tetrapodal ZnO nanostructures are tested in plasmid DNA experiments (1) as a solid-phase adsorbent for plasmid DNA purification, (2) as improving reagents in a polymerase chain reaction (PCR) and (3) as novel carriers for gene delivery. The amino-modification, the tetrapod-like shape of the nanostructure and its high biocompatibility all contribute to measurements showing promise for applications. A sol-gel method is used for silica coating and amino-modification. Plasmid DNA is purified through reversible conjugations of amino-modified ZnO tetrapods with DNA. Also, as additional reagents, functionalized tetrapods are shown to improve the amount of PCR product. For transfection, ZnO tetrapods provide some protection against deoxyribonuclease cleavage of plasmid DNA and deliver plasmid DNA into cells with little cytotoxicity. [ABSTRACT FROM AUTHOR]

CD146 is a novel endothelial blomarker and plays an essential role in angiogen-esis; however, its role in the molecular mechanism underlying angiogenesis re-mains poorly understood. In the present study, we show that CD146 interacts di-rectly with VEGFR-2 on endothelial cells and at the molecular level and identify the structural basis of CD146 binding to VEGFR-2. In addition, we show that CD146 is required in VEGF-induced VEGFR-2 phosphorylation, AKT/p38 MAPKS/NF-κB activation, and thus promotion of endo-thelial cell migration and microvascular formation. Furthermore, we show that anti-CD146 AA98 or CD146 sIRNA abro-gates all VEGFR-2 activation induced by VEGF. An in vivo angiogenesis assay showed that VEGF-promoted microvascu-lar formation was impaired in the endothe-lial conditional knockout of CD146 (CD146EC-KO). Our animal experiments demonstrated that anti-CD146 (AA98) and anti-VEGF (bevacizumab) have an addi-tive inhibitory effect on xenografted hu-man pancreatic and melanoma tumors. The results of the present study suggest that CD146 is a new coreceptor for VEGFR-2 and is therefore a promising target for blocking tumor-related angio-genesis. [ABSTRACT FROM AUTHOR]