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The research on deep Web classification is an important area in large-scale deep Web integration, which is still at its early stage. Many deep Web sources are structured by providing structured query interfaces and results. Classifying such structured sources into domains is one of the critical steps toward the integration of heterogeneous Web sources. In this paper, we present an Ontology-based deep Web classification, which includes a category ontology model and a deep Web Vector Space Model (VSM). The experimental results show that we can get a good performance with average precision 91.6% and average recall 92.4%.

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The causal agent of pine wilt disease, pine wood nematode (PWN) (Bursaphelenchus xylophilus), revealed extended lifespan at low temperature. To discover the molecular mechanism of this phenomenon, we attempted to study the molecular characterization, transcript abundance, and functions of three genes of the cyclic guanosine monophosphate (cGMP) pathway from B. xylophilus. Three cGMP pathway genes were identified from B. xylophilus. Bioinformatic software was utilized to analyze the characteristics of the three putative proteins. Function of the three genes in cold tolerance was studied with RNA interference (RNAi). The results showed that the deduced protein of Bx-DAF-11 has an adenylate and guanylate cyclase catalytic domain, indicating an ability to bind to extracellular ligands and synthesizing cGMP. Both Bx-TAX-2 and Bx-TAX-4 have cyclic nucleotide-binding domains and ion transport protein domains, illustrating that they are cGMP-gated ion channels. The transcript level of Bx-daf-11, Bx-tax-2, and Bx-tax-4 increased at low temperature. The survival rates of three gene silenced B. xylophilus revealed a significant decrease at low temperature. This study illustrated that the cGMP pathway plays a key role in low-temperature-induced lifespan extension in B. xylophilus.

The MADS-box genes encode transcription factors with key roles in plant growth and development. A comprehensive analysis of the MADS-box gene family in bread wheat (Triticum aestivum) has not yet been conducted, and our understanding of their roles in stress is rather limited. Here, we report the identification and characterization of the MADS-box gene family in wheat. A total of 180 MADS-box genes classified as 32 Mα, 5 Mγ, 5 Mδ, and 138 MIKC types were identified. Evolutionary analysis of the orthologs among T. urartu, Aegilops tauschii and wheat as well as homeologous sequences analysis among the three sub-genomes in wheat revealed that gene loss and chromosomal rearrangements occurred during and/or after the origin of bread wheat. Forty wheat MADS-box genes that were expressed throughout the investigated tissues and development stages were identified. The genes that were regulated in response to both abiotic stresses (i.e., phosphorus deficiency, drought, heat, and combined drought and heat) and biotic stresses (i.e., Fusarium grami-nearum, Septoria tritici, stripe rust and powdery mildew) were detected as well. A few notable MADS-box genes were specifically expressed in a single tissue and those showed relatively higher expression differences between the stress and control treatment. The expression patterns of considerable MADS-box genes differed from those of their orthologs in Brachypodium, rice, and Arabidopsis. Collectively, the present study provides new insights into the possible roles of MADS-box genes in response to stresses and will be valuable for further functional studies of important candidate MADS-box genes.

In recent years, neural networks have been extensively deployed for computer vision tasks, particularly visual classification problems, where new algorithms reported to achieve or even surpass the human performance. Recent studies have shown that they are all vulnerable to the attack of adversarial examples. Small and often imperceptible perturbations to the input images are sufficient to fool the most powerful neural networks. Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle, PyTorch, Caffe2, MxNet, Keras, TensorFlow and it can benchmark the robustness of machine learning models.

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Hatching out from the zona pellucida (Zp) is a crucial step for blastocyst implantation and development. However, it is still unknown whether the location of the hatching site relative to the inner cell mass (ICM) affects embryo implantation and foetal development. Here, we classified hatching blastocysts into three categories, 0° ≤ θ ≤ 30°, 30° < θ ≤ 60°, and 60° < θ ≤ 90°, in which θ is determined based on the relative position of the hatching site to the arc midpoint of the icM. non-surgical embryo transfer (NSET) devices were employed to evaluate blastocyst implantation and embryo development. Of 1,827 hatching blastocysts, 43.84%, 30.60%, and 21.67% were categorized as 30° < θ ≤ 60°, 0° ≤ θ ≤ 30°, and 60° < θ ≤ 90°, respectively. Embryos with different hatching sites showed no distinct differences in blastocyst implantation; surrogate female pregnancy; embryo development to term; litter size, or offspring survival, gender, or body weight. Our results indicate that mouse blastocyst hatching site is not randomly distributed. embryo implantation and development are not correlated with the blastocyst hatching site in mice. thus, assessment of the blastocyst hatching site should not be recommended to evaluate mouse blastocyst implantation and developmental potential.