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Five Aphelenchoides besseyi isolates collected from bird’s-nest ferns or rice possess different parasitic capacities in bird’s-nest fern. Two different glycoside hydrolase (GH) 45 genes were identified in the fern isolates, and only one was found in the rice isolates. A Abe GH5-1 gene containing an SCP-like family domain was found only in the fern isolates. Abe GH5-1 gene has five introns suggesting a eukaryotic origin. A maximum likelihood phylogeny revealed that Abe GH5-1 is part of the nematode monophyletic group that can be clearly distinguished from those of other eukaryotic and bacterial GH5 sequences with high bootstrap support values. The fern A. besseyi isolates were the first parasitic plant nematode found to possess both GH5 and GH45 genes. Surveying the genome of the five A. besseyi isolates by Southern blotting using an 834 bp probe targeting the GH5 domain suggests the presence of at least two copies in the fern-origin isolates but none in the rice-origin isolates. The in situ hybridization shows that the Abe GH5-1 gene is expressed in the nematode ovary and testis. Our study provides insights into the diversity of GH in isolates of plant parasitic nematodes of different host origins.

Semi-supervised learning (SSL) is widely-used to explore the vast amount of unlabeled data in the world. Over the decade, graph-based SSL becomes popular in automatic image annotation due to its power of learning globally based on local similarity. However, recent studies have shown that the emergence of large-scale datasets challenges the traditional methods. On the other hand, most previous works have concentrated on single-label annotation, which may not describe image contents well. To remedy the deficiencies, this paper proposes a new graph-based SSL technique with multi-label propagation, leveraging the distributed computing power of the MapReduce programming model. For high learning performance, the paper further presents both a multi-layer learning structure and a tag refinement approach, where the former unifies both visual and textual information of image data during learning, while the latter simultaneously suppresses noisy tags and emphasizes the other tags after learning. Experimental results based on a medium-scale and a large-scale image datasets show the effectiveness of the proposed methods.

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Retrieving relevant videos from a large corpus on mobile devices is a vital challenge. This paper addresses two key issues for mobile search on user-generated videos. The first is the lack of good relevance measurement, due to the unconstrained nature of online videos, for learning semantic-rich representations. The second is due to the limited resource on mobile devices, stringent bandwidth, and delay requirement between the device and the video server. We propose a knowledge-embedded sparse projection learning approach. To alleviate the need for expensive annotation for hash learning, we investigate varying approaches for pseudo label mining, where explicit semantic analysis leverages Wikipedia and performs the best. In addition, we propose a novel sparse projection method to address the efficiency challenge. It learns a discriminative compact representation that drastically reduces transmission cost. With less than 10% non-zero element in the projection matrix, it also reduces computational and storage cost. The experimental results on 100K videos show that our proposed algorithm is competitive in the performance to the prior state-of-the-art hashing methods which are not applicable for mobiles and solely rely on costly manual annotations. The average query time on 100K videos consumes only 0.592 seconds.