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IntroductionAs tick-borne diseases rise to become the second most prevalent arthropod-transmitted disease globally, the increasing investigations focus on ticks correspondingly. Factors contributed to this increase include anthropogenic influences, changes in vertebrate faunal composition, social-recreational shifts, and climatic variation. Employing the 16S gene sequence method in next-generation sequencing (NGS) allows comprehensive pathogen identification in samples, facilitating the development of refined approaches to tick research omnidirectionally.MethodsIn our survey, we compared the microbial richness and biological diversity of ticks in Wuwei City, Gansu province, differentiating between questing ticks found in grass and parasitic ticks collected from sheep based on 16S NGS method.ResultsThe results show Rickettsia, Coxiella, and Francisella were detected in all 50 Dermacentor nuttalli samples, suggesting that the co-infection may be linked to specific symbiotic bacteria in ticks. Our findings reveal significant differences in the composition and diversity of microorganisms, with the Friedmanniella and Bordetella genera existing more prevalent in parasitic ticks than in questing ticks (p 0.6. For instance, Francisella restrains the development of 10 other bacteria in parasitic ticks, whereas Phyllobacterium and Arthrobacter enhance colonization across all tick species.DiscussionBy leveraging NGS techniques, our study reveals a high degree of species and phylogenetic diversity within the tick microbiome. It further highlights the potential to investigate the interplay between bacterial genera in both parasitic and questing ticks residing in identical habitat environments.

Handwritten character recognition(HCR) is an important branch of image recognition,which recognizes the handwritten characters with the data mining and machine learning technologies.Currently,the HCR methods mainly focus on the improvements of different deep learning models,where the multiple-layer extreme learning machine(ML-ELM) has attracted the wide attention from the academia and industry due to its faster training speed and better recognition performance than deep belief net(DBN) and deep Boltzmann machine(DBM).However,the recognition performance of ML-ELM is severely influenced by the random weights when determining the input weights for each hidden-layer.This paper first proposes a decomposition ELM(DE-ELM) which is a shallow ELM training scheme based on the hidden-layer output matrix decomposition and then applies DE-ELM to deal with HCR problems,i.e.,handwritten digits in MNIST,handwritten digits and English letters in EMNIST,handwritten Japanese characters in KMNIST and K49-MNIST.In comparison with ML-ELM,DE-ELM reduces the randomness of ELM-based HCR model.Meanwhile,DE-ELM can obtain higher recognition accuracy than ML-ELM with the same training time and faster training speed than ML-ELM with the equal recognition accuracy.Experimental results demonstrate the feasibility and effectiveness of the proposed DE-ELM when dealing with HCR problems.