Seed treatment with endophytic fungi has been regarded as an effective method for plant parasitic nematode control. Endophytic fungi from cucumber seedlings were isolated and screened for their potential to be used as seed treatment agents against Meloidogyne incognita. Among the 294 isolates screened, 23 significantly reduced galls formed by M. incognita in greenhouse test. The 10 most effective isolates were Fusarium (5), Trichoderma (1), Chaetomium (1), Acremonium (1), Paecilomyces (1), and Phyllosticta (1). Their control efficacies were repeatedly tested and their colonizations as well as in vitro activity against M. incognita were studied. They reduced the number of galls by 24.0%-58.4% in the first screening and 15.6%-44.3% in the repeated test, respectively. Phyllosticta Ph511 and Chaetomium Ch1001 had high colonizations on both the roots and the aboveground parts of cucumber seedlings. Fusarium isolates had colonization preference on the roots, their root colonizations ranging from 20.1% to 47.3% of the total root area. Trichoderma Tr882, Paecilomyces Pa972, and Acremonium Ac985 had low colonizations on both the roots and the aboveground parts. Acremonium Ac985, Chaetomium Ch1001, Paecilomyces Pa972, and Phyllosticta Ph511 produced compounds affecting motility of the second stage juveniles of M. incognita. Based on these results, Chaetomium Ch1001 was considered to have the highest potential as a seed treatment agent for M. incognita biocontrol.

Memristors offer great advantages as a new hardware solution for neuromorphic computing due to their fast and energy‐efficient matrix vector multiplication. However, the nonlinear weight updating property of memristors makes it difficult to be trained in a neural network learning process. Several compensation schemes have been proposed to mitigate the updating error caused by nonlinearity; nevertheless, they usually involve complex peripheral circuits design. Herein, stochastic and adaptive learning methods for weight updating are developed, in which the inaccuracy caused by the memristor nonlinearity can be effectively suppressed. In addition, compared with the traditional nonlinear stochastic gradient descent (SGD) updating algorithm or the piecewise linear (PL) method, which are most often used in memristor neural network, the design is more hardware friendly and energy efficient without the consideration of pulse numbers, duration, and directions. Effectiveness of the proposed method is investigated on the training of LeNet‐5 convolutional neural network. High accuracy, about 93.88%, on the Modified National Institute of Standards and Technology handwriting digits datasets is achieved (with typical memristor nonlinearity as ±1), which is close to the network with complex PL method (94.7%) and is higher than the original nonlinear SGD method (90.14%).

No abstract

No abstract