ServeNet: A Deep Neural Network for Web Services Classification

Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this paper, we present a novel deep neural network to automatically abstract low-level representation of both service name and service description to high-level merged features without feature engineering and the length limitation, and then predict service classification on 50 service categories. To demonstrate the effectiveness of our approach, we conduct a comprehensive experimental study by comparing 10 machine learning methods on 10,000 real-world web services. The result shows that the proposed deep neural network can achieve higher accuracy in classification and more robust than other machine learning methods.Comment: Accepted by ICWS'2

Dissecting the genome-wide evolution and function of R2R3-MYB transcription factor family in Rosa chinensis

Rosa chinensis, an important ancestor species of Rosa hybrida, the most popular ornamental plant species worldwide, produces flowers with diverse colors and fragrances. The R2R3-MYB transcription factor family controls a wide variety of plant-specific metabolic processes, especially phenylpropanoid metabolism. Despite their importance for the ornamental value of flowers, the evolution of R2R3-MYB genes in plants has not been comprehensively characterized. In this study, 121 predicted R2R3-MYB gene sequences were identified in the rose genome. Additionally, a phylogenomic synteny network (synnet) was applied for the R2R3-MYB gene families in 35 complete plant genomes. We also analyzed the R2R3-MYB genes regarding their genomic locations, Ka/Ks ratio, encoded conserved motifs, and spatiotemporal expression. Our results indicated that R2R3-MYBs have multiple synteny clusters. The RcMYB114a gene was included in the Rosaceae-specific Cluster 54, with independent evolutionary patterns. On the basis of these results and an analysis of RcMYB114a-overexpressing tobacco leaf samples, we predicted that RcMYB114a functions in the phenylpropanoid pathway. We clarified the relationship between R2R3-MYB gene evolution and function from a new perspective. Our study data may be relevant for elucidating the regulation of floral metabolism in roses at the transcript level

Genome-wide DNA polymorphisms in two cultivars of mei (Prunus mume sieb. et zucc.)

BACKGROUND: Mei (Prunus mume Sieb. et Zucc.) is a famous ornamental plant and fruit crop grown in East Asian countries. Limited genetic resources, especially molecular markers, have hindered the progress of mei breeding projects. Here, we performed low-depth whole-genome sequencing of Prunus mume ‘Fenban’ and Prunus mume ‘Kouzi Yudie’ to identify high-quality polymorphic markers between the two cultivars on a large scale. RESULTS: A total of 1464.1 Mb and 1422.1 Mb of ‘Fenban’ and ‘Kouzi Yudie’ sequencing data were uniquely mapped to the mei reference genome with about 6-fold coverage, respectively. We detected a large number of putative polymorphic markers from the 196.9 Mb of sequencing data shared by the two cultivars, which together contained 200,627 SNPs, 4,900 InDels, and 7,063 SSRs. Among these markers, 38,773 SNPs, 174 InDels, and 418 SSRs were distributed in the 22.4 Mb CDS region, and 63.0% of these marker-containing CDS sequences were assigned to GO terms. Subsequently, 670 selected SNPs were validated using an Agilent’s SureSelect solution phase hybridization assay. A subset of 599 SNPs was used to assess the genetic similarity of a panel of mei germplasm samples and a plum (P. salicina) cultivar, producing a set of informative diversity data. We also analyzed the frequency and distribution of detected InDels and SSRs in mei genome and validated their usefulness as DNA markers. These markers were successfully amplified in the cultivars and in their segregating progeny. CONCLUSIONS: A large set of high-quality polymorphic SNPs, InDels, and SSRs were identified in parallel between ‘Fenban’ and ‘Kouzi Yudie’ using low-depth whole-genome sequencing. The study presents extensive data on these polymorphic markers, which can be useful for constructing high-resolution genetic maps, performing genome-wide association studies, and designing genomic selection strategies in mei

Compound Kushen Injection suppresses human breast cancer stem-like cells by down-regulating the canonical Wnt/β-catenin pathway

<p>Abstract</p> <p>Background</p> <p>Cancer stem cells (CSCs) play an important role in cancer initiation, relapse and metastasis. To date, no specific medicine has been found to target CSCs as they are resistant to most conventional therapies and proliferate indefinitely. Compound Kushen Injection (CKI) has been widely used for cancer patients with remarkable therapeutic effects in Chinese clinical settings for many years. This study focused on whether CKI could inhibit MCF-7 SP cells in vitro and in vivo.</p> <p>Methods</p> <p>The analysis of CKI on SP population and the main genes of Wnt signaling pathway were studied first. Then we studied the tumorigenicity of SP cells and the effects of CKI on SP cells in vivo. The mice inoculated with 10,000 SP cells were randomly divided into three groups (6 in each group) and treated with CKI, cisplatin and saline (as a control) respectively for 7 weeks. The tumor formation rates of each group were compared. The main genes and proteins of the Wnt signaling pathway were analyzed by RT-PCR and western blot.</p> <p>Results</p> <p>CKI suppressed the size of SP population (approximately 90%), and down-regulated the main genes of Wnt signaling pathway. We also determined that MCF-7 SP cells were more tumorigenic than non-SP and unsorted cells. The Wnt signaling pathway was up-regulated in tumors derived from SP cells compared with that in tumors from non-SP cells. The tumor formation rate of the CKI Group was 33% (2/6, <it>P </it>< 0.05), and that of Cisplatin Group was 50%(3/6, <it>P </it>< 0.05), whereas that of the Control Group was 100% (6/6).The RT-PCR and western blot results indicated that CKI suppressed tumor growth by down-regulating the Wnt/β-catenin pathway, while cisplatin activated the Wnt/β-catenin pathway and might spare SP cells.</p> <p>Conclusions</p> <p>It suggested that CKI may serve as a novel drug targeting cancer stem-like cells, though further studies are recommended.</p

Statistical Model of Accurately Estimating Service Delay Behavior in Saturated IEEE 802.11 Networks Based on 2-D Markov Chain

To accurately estimate the service delay behavior of IEEE 802.11 networks, this paper comprehensively considers four main factors that affect the performance of IEEE 802.11 networks and establishes a service delay model with statistical characteristics. We analyzed the operation mechanism of 802.11 DCF, using the backoff stage and the backoff counter to portray the dynamic change characteristics of the system regarding the data frame transmission states. Afterward, we calculated the one-step transition probability of these states, establishing a 2-D Markov model, including the ICS procedure and the backoff procedure. Based on this model, we constructed steady-state equations to derive a relationship between the transmission probability and collision probability for each node transmission queue. By analyzing the ICS delay and the backoff delay, we obtained the probability generating function (PGF) of the average idle time. The analytical expressions of other service delays, such as the successful transmission time and collided transmission time, were derived to obtain the PGF of the total service delay. In the numerical simulation, we compared the first two statistical moments of the PGF with the Nav model, and it was found that our delay evaluation results were significantly better than the traditional evaluation results. The average service delay of the Nav model in all the scenarios was larger than that of the proposed model due to the lack of the ICS procedure in the Nav model. Since a DIFS duration is generally much shorter than a random backoff duration, our model saves the bandwidth and improves transmission efficiency

DeepOCL: A deep neural network for Object Constraint Language generation from unrestricted nature language

Abstract Object Constraint Language (OCL) is one kind of lightweight formal specification, which is widely used for software verification and validation in NASA and Object Management Group projects. Although OCL provides a simple expressive syntax, it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first‐order logic, which is approximately half accurate at the first stage of development. A deep neural network named DeepOCL is proposed, which takes the unrestricted natural language as inputs and automatically outputs the best‐scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule‐based generation approaches. To demonstrate the validity of our proposed approach, ablation experiments were conducted on a new sentence‐aligned dataset named OCLPairs. The experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation, scored 74.30 on BLEU, and greatly outperformed experienced developers by 35.19%. The proposed approach is the first deep learning approach to generate the OCL expression from the natural language. It can be further developed as a CASE tool for the software industry

Transcriptomics Investigation into the Mechanisms of Self-Incompatibility between Pin and Thrum Morphs of Primula maximowiczii

Heteromorphic self-incompatibility (SI) is an important system for preventing inbreeding in the genus Primula. However, investigations into the molecular mechanisms of Primula SI are lacking. To explore the mechanisms of SI in Primula maximowiczii, the pollen germination and fruiting rates of self- and cross-pollinations between pin and thrum morphs were investigated, and transcriptomics analyses of the pistils after pollination were performed to assess gene expression patterns in pin and thrum SI. The results indicated that P. maximowiczii exhibits strong SI and that the mechanisms of pollen tube inhibition differ between pin and thrum morphs. While self-pollen tubes of the pin morph were able to occasionally, though rarely, enter the style, those of the thrum morph were never observed to enter the style. The transcriptomics analysis of the pistils revealed 1311 and 1048 differentially expressed genes (DEGs) that were identified by comparing pin self-pollination (PS) vs. pin cross-pollination (PT) and thrum self-pollination (TS) vs. thrum cross-pollination (TP). Notably, about 90% of these DEGs exhibited different expression patterns in the two comparisons. Moreover, pin and thrum DEGs were associated with different Gene Ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways following enrichment analyses. Based on our results, the molecular mechanisms underlying the pin and thrum SI in P. maximowiczii appear to be distinct. Furthermore, the genes involved in the SI processes are commonly associated with carbohydrate metabolism and environmental adaptation. These results provide new insight into the molecular mechanisms of Primula SI