Biochemical and Transcriptome Analysis Reveals Pigment Biosynthesis Influenced Chlorina Leaf Formation in Anoectochilus roxburghii (Wall.) Lindl.

Anoectochilus roxburghii (Wall.) Lindl is a perennial herb of the Orchidaceae family; a yellow-green mutant and a yellow mutant were obtained from the wild type, thereby providing good material for the study of leaf color variation. Pigment content analysis revealed that chlorophyll, carotenoids, and anthocyanin were lower in the yellow-green and yellow mutants than in the wild type. Transcriptome analysis of the yellow mutant and wild type revealed that 78,712 unigenes were obtained, and 599 differentially expressed genes (120 upregulated and 479 downregulated) were identified. Using the Kyoto Encyclopedia of Genes and Genomes pathway analysis, candidate genes involved in the anthocyanin biosynthetic pathway (five unigenes) and the chlorophyll metabolic pathway (two unigenes) were identified. Meanwhile, the low expression of the chlorophyll and anthocyanin biosynthetic genes resulted in the absence of chlorophylls and anthocyanins in the yellow mutant. This study provides a basis for similar research in other closely related species.

Endometrioid Adenocarcinoma With Solitary Metastasis to the Appendix, Mimicking Primary Appendiceal Adenocarcinoma: A Case Report and Literature Review.

Metastatic tumors of the appendix are rare. Endometrial cancer tends to metastasize by directly invading neighboring structures; the lung, liver, bones, and brain are common sites of distant metastasis. Herein, we present a case of a solitary endometrial metastatic tumor in the appendiceal mucosa without serosal involvement that mimicked a primary adenocarcinoma of the appendix. The patient who had undergone a radical hysterectomy for an endometrioid adenocarcinoma 3 years earlier presented to the hospital with a history of persistent right-lower abdominal pain. Physical examination showed tension of the abdominal muscles, tenderness, and rebounding pain on the McBurney's point. Open appendectomy for suspected appendicitis revealed a perforation of the distal appendiceal tip. Opening of the surgical specimen showed a mass that was located in the mucosa of the appendix near the appendicular root and resembled a primary tumor of the appendix. Microscopically, the adenocarcinoma of the appendiceal mucosa showed a transitional relationship with the normal mucosa, involving the submucosa and muscle but not invading the serosa. Based on the patient's medical history and the results of immunohistochemical staining, we made a diagnosis of metastatic endometrioid adenocarcinoma. The gross anatomy and histologic features of solitary metastatic tumors can mimic those of primary tumors. A correct diagnosis should be made by combining the patient's medical history with morphologic and immunohistochemical test results.

Game-Based Memetic Algorithm to the Vertex Cover of Networks.

The minimum vertex cover (MVC) is a well-known combinatorial optimization problem. A game-based memetic algorithm (GMA-MVC) is provided, in which the local search is an asynchronous updating snowdrift game and the global search is an evolutionary algorithm (EA). The game-based local search can implement (k,l)-exchanges for various numbers of k and l to remove k vertices from and add l vertices into the solution set, thus is much better than the previous (1,0)-exchange. Beyond that, the proposed local search is able to deal with the constraint, such that the crossover operator can be very simple and efficient. Degree-based initialization method is also provided which is much better than the previous uniform random initialization. Each individual of the GMA-MVC is designed as a snowdrift game state of the network. Each vertex is treated as an intelligent agent playing the snowdrift game with its neighbors, which is the local refinement process. The game is designed such that its strict Nash equilibrium (SNE) is always a vertex cover of the network. Most of the SNEs are only local optima of the problem. Then an EA is employed to guide the game to escape from those local optimal Nash equilibriums to reach a better Nash equilibrium. From comparison with the state of the art algorithms in experiments on various networks, the proposed algorithm always obtains the best solutions.

Transcriptome analysis of Polianthes tuberosa during floral scent formation.

Polianthes tuberosa is a popular ornamental plant. Its floral scent volatiles mainly consist of terpenes and benzenoids that emit a charming fragrance. However, our understanding of the molecular mechanism responsible for the floral scent of P. tuberosa is limited. Using transcriptome sequencing and de novo assembly, a total of 228,706,703 high-quality reads were obtained, which resulted in the identification of 96,906 unigenes (SRA Accession Number SRP126470, TSA Acc. No. GGEA00000000). Approximately 41.85% of the unigenes were functionally annotated using public databases. A total of 4,694 differentially expressed genes (DEGs)were discovered during flowering. Gas chromatography-mass spectrometry analysis revealed that the majority of the volatiles comprised benzenoids and small amounts of terpenes. Homology analysis identified 13 and 17 candidate genes associated with terpene and benzenoid biosynthesis, respectively. Among these, PtTPS1, PtDAHPSs, PtPAL1, and PtBCMT2 might play important roles in regulating the formation of floral volatiles. The data generated by transcriptome sequencing provide a critical resource for exploring concrete characteristics as well as for supporting functional genomics studies. The results of the present study also lay the foundation for the elucidation of the molecular mechanism underlying the regulation of floral scents in monocots.

Link prediction in complex networks via matrix perturbation and decomposition.

Link prediction in complex networks aims at predicting the missing links from available datasets which are always incomplete and subject to interfering noises. To obtain high prediction accuracy one should try to complete the missing information and at the same time eliminate the interfering noise from the datasets. Given that the global topological information of the networks can be exploited by the adjacent matrix, the missing information can be completed by generalizing the observed structure according to some consistency rule, and the noise can be eliminated by some proper decomposition techniques. Recently, two related works have been done that focused on each of the individual aspect and obtained satisfactory performances. Motivated by their complementary nature, here we proposed a new link prediction method that combines them together. Moreover, by extracting the symmetric part of the adjacent matrix, we also generalized the original perturbation method and extended our new method to weighted directed networks. Experimental studies on real networks from disparate fields indicate that the prediction accuracy of our method was considerably improved compared with either of the individual method as well as some other typical local indices.

Clustering dynamics of complex discrete-time networks and its application in community detection.

The clustering phenomenon is common in real world networks. A discrete-time network model is proposed firstly in this paper, and then the phase clustering dynamics of the networks are studied carefully. The proposed model acts as a bridge between the dynamic phenomenon and the topology of a modular network. On one hand, phase clustering phenomenon will occur for a modular network by the proposed model; on the other hand, the communities can be identified from the clustering phenomenon. Beyond the phases' information, it is found that the frequencies of phases can be applied to community detection also with the proposed model. In specific, communities are identified from the information of phases and their frequencies of the nodes. Detailed algorithm for community detection is provided. Experiments show that the performance and efficiency of the dynamics based algorithm are competitive with recent modularity based algorithms in large scale networks.

MOEA/D with adaptive weight adjustment.

Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has achieved great success in the field of evolutionary multi-objective optimization and has attracted a lot of attention. It decomposes a multi-objective optimization problem (MOP) into a set of scalar subproblems using uniformly distributed aggregation weight vectors and provides an excellent general algorithmic framework of evolutionary multi-objective optimization. Generally, the uniformity of weight vectors in MOEA/D can ensure the diversity of the Pareto optimal solutions, however, it cannot work as well when the target MOP has a complex Pareto front (PF; i.e., discontinuous PF or PF with sharp peak or low tail). To remedy this, we propose an improved MOEA/D with adaptive weight vector adjustment (MOEA/D-AWA). According to the analysis of the geometric relationship between the weight vectors and the optimal solutions under the Chebyshev decomposition scheme, a new weight vector initialization method and an adaptive weight vector adjustment strategy are introduced in MOEA/D-AWA. The weights are adjusted periodically so that the weights of subproblems can be redistributed adaptively to obtain better uniformity of solutions. Meanwhile, computing efforts devoted to subproblems with duplicate optimal solution can be saved. Moreover, an external elite population is introduced to help adding new subproblems into real sparse regions rather than pseudo sparse regions of the complex PF, that is, discontinuous regions of the PF. MOEA/D-AWA has been compared with four state of the art MOEAs, namely the original MOEA/D, Adaptive-MOEA/D, [Formula: see text]-MOEA/D, and NSGA-II on 10 widely used test problems, two newly constructed complex problems, and two many-objective problems. Experimental results indicate that MOEA/D-AWA outperforms the benchmark algorithms in terms of the IGD metric, particularly when the PF of the MOP is complex.

CT image sequence restoration based on sparse and low-rank decomposition.

Blurry organ boundaries and soft tissue structures present a major challenge in biomedical image restoration. In this paper, we propose a low-rank decomposition-based method for computed tomography (CT) image sequence restoration, where the CT image sequence is decomposed into a sparse component and a low-rank component. A new point spread function of Weiner filter is employed to efficiently remove blur in the sparse component; a wiener filtering with the Gaussian PSF is used to recover the average image of the low-rank component. And then we get the recovered CT image sequence by combining the recovery low-rank image with all recovery sparse image sequence. Our method achieves restoration results with higher contrast, sharper organ boundaries and richer soft tissue structure information, compared with existing CT image restoration methods. The robustness of our method was assessed with numerical experiments using three different low-rank models: Robust Principle Component Analysis (RPCA), Linearized Alternating Direction Method with Adaptive Penalty (LADMAP) and Go Decomposition (GoDec). Experimental results demonstrated that the RPCA model was the most suitable for the small noise CT images whereas the GoDec model was the best for the large noisy CT images.

Overlapping community detection via network dynamics.

The modular structure of a network is closely related to the dynamics toward clustering. In this paper, a method for community detection is proposed via the clustering dynamics of a network. The initial phases of the nodes in the network are given randomly, and then they evolve according to a set of dedicatedly designed differential equations. The phases of the nodes are naturally separated into several clusters after a period of evolution, and each cluster corresponds to a community in the network. For the networks with overlapping communities, the phases of the overlapping nodes will evolve to the interspace of the two communities. The proposed method is illustrated with applications to both synthetically generated and real-world complex networks.