Reconstructing gene regulatory networks (GRNs) plays an important role in identifying the complicated regulatory relationships, uncovering regulatory patterns in cells, and gaining a systematic view for biological processes. In order to reconstruct large-scale GRNs accurately, in this paper, we first use fuzzy cognitive maps (FCMs), which are a kind of cognition fuzzy influence graphs based on fuzzy logic and neural networks, to model GRNs. Then, a novel hybrid method is proposed to reconstruct GRNs from time series expression profiles using memetic algorithm (MA) combined with neural network (NN), which is labeled as MANNFCM-GRN. In MANNFCM-GRN, the MA is used to determine regulatory connections in GRNs and the NN is used to determine the interaction strength of the regulatory connections. In the experiments, the performance of MANNFCM-GRN is validated on both synthetic data and the benchmark dataset DREAM3 and DREAM4. The experimental results demonstrate the efficacy of MANNFCM-GRN and show that MANNFCM-GRN can reconstruct GRNs with high accuracy without expert knowledge. The comparison with existing algorithms also shows that MANNFCM-GRN outperforms ant colony optimization, non-linear Hebbian learning, and real-coded genetic algorithms.
Over recent years,catalytic materials of Fe-N-C species have been recognized being active for oxygen reduction reaction(ORR).However,the identification of active site remains challenging as it generally involves a pyrolysis process and mixed components being obtained.Herein Fe3C/C and Fe2N/C samples were synthesized by temperature programmed reduction of Fe precursors in 15%CH4/H2and pure NH3,respectively.By acid leaching of Fe2N/C sample,only single sites of FeN4species were presented,providing an ideal model for identification of catalytic functions of the single sites of FeN4in ORR.A correlation was conducted between the concentration of FeⅡN4in low spin state by Mossbauer spectra and the kinetic current density at 0.8 V in alkaline media,and such a structure-performance correlation assures the catalytic roles of low spin FeⅡN4 species as highly active sites for the ORR. 相似文献
Bismuth doped La2-xBixNiO4+δ (x = 0, 0.02 and 0.04) oxides are investigated as SOFC cathodes. The effects of Bi doping on the phase structure, thermal expansion, electrical conduction behavior as well as electrochemical performance are studied. All the samples exist as a tetragonal Ruddlesden-Popper structure. Bi-doped LBNO-0.02 and LBNO-0.04 have good chemical and thermal compatibility with LSGM electrolyte. The average TEC over 20–900°С was 13.4 × 10?6 and 14.2 × 10?6 K?1 for LBNO-0.02 and LBNO-0.04, respectively. The electrical conductivity was decreasing with the rise of Bi doping content. EIS measurement indicates Bi doping can decrease the ASR values. At 750 °C, the obtained ASR for LBNO-0.04 is 0.18 Ωcm2, which is 56% lower than that of the sample without Bi doping, suggesting Bi doping is beneficial to the electrochemical catalytic activity of LBNO cathodes. 相似文献
The effects of La2O3–Al2O3–SiO2 addition on the thermal conductivity, coefficient of thermal expansion (CTE), Young's modulus and cyclic thermal shock resistance of hot-pressed h-BN composite ceramics were investigated. The samples were heated to 1000 °C and then quenched to room temperature with 1–50 cycles, and the residual flexural strength was used to evaluate cyclic thermal shock resistance. h-BN composite ceramics containing 10 vol% La2O3–Al2O3 and 20 vol% SiO2 addition exhibited the highest flexural strength, thermal conductivity and relatively low CTE, which were beneficial to the excellent thermal shock resistance. In addition, the viscous amorphous phase of ternary La2O3–Al2O3–SiO2 system could accommodate and relax thermal stress contributing to the high thermal shock resistance. Therefore, the residual flexural strength still maintained the value of 234.3 MPa (86.9% of initial strength) after 50 cycles of thermal shock. 相似文献