An Optimization Clustering Algorithm Based on Texture Feature Fusion for Color Image Segmentation

We introduce a multi-feature optimization clustering algorithm for color image segmentation. The local binary pattern, the mean of the min-max difference, and the color components are combined as feature vectors to describe the magnitude change of grey value and the contrastive information of neighbor pixels. In clustering stage, it gets the initial clustering center and avoids getting into local optimization by adding mutation operator of genetic algorithm to particle swarm optimization. Compared with well-known methods, the proposed method has an overall better segmentation performance and can segment image more accurately by evaluating the ratio of misclassification.© 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Preparation of Copper Oxide/TiO2 Composite Films by Mechanical Ball Milling and Investigated Photocatalytic Activity

The Cu/Ti composite coatings were prepared by the mechanical ball milling, the CuO/TiO2 and Cu2O/TiO2 composite photocatalytic films were obtained by the subsequent oxidation process. The microstructure of the composite films was analyzed by X-ray Diffraction (XRD）and scanning election microscope (SEM). The photocatalytic activity was evaluated, the effects of ball milling time on the formation of the Cu/Ti coatings were investigated, and the effects of the oxidation temperature and oxidation atmosphere on microstructure and photocatalytic activity of the films were studied. The results illustrate that the ball milling time has significant effects on the formation of the coatings and the coatings are continuous and compact by ball milling for 15 h. The photocatalytic activity of the CuO/TiO2 composite films is increased first and then decreased with the oxidation temperature increases, and the photocatalytic activity is the best at 500 °C. The CuO/TiO2 composite films are obtained by the oxidation of Cu/Ti coatings at 500 °C for 15 h in the air, while the Cu2O/TiO2 composite films are oxidized in carbon atmosphere. Photocatalysis efficiency of the films is obviously enhanced with the help of the p-n junction heterostructure in the Cu2O/TiO2 composite films

Stacking-Dependent Interlayer Phonons in 3R and 2H MoS2_{2}

We have investigated the interlayer shear and breathing phonon modes in MoS$_{2}$ with pure 3R and 2H stacking order by using polarization-dependent ultralow-frequency Raman spectroscopy. We observe up to three shear branches and four breathing branches in MoS$_{2}$ with thickness from 2 to 13 layers. The breathing modes show the same Raman activity behavior for both polytypes, but the 2H breathing frequencies are consistently several wavenumbers higher than the 3R breathing frequencies, signifying that 2H MoS$_{2}$ has slightly stronger interlayer lattice coupling than 3R MoS$_{2}$. In contrast, the shear-mode Raman spectra are strikingly different for 2H and 3R MoS$_{2}$. While the strongest shear mode corresponds to the highest-frequency branch in the 2H structure, it corresponds to the lowest-frequency branch in the 3R structure. Such distinct and complementary Raman spectra of the 3R and 2H polytypes allow us to survey a broad range of shear modes in MoS$_{2}$, from the highest to lowest branch. By combining the linear chain model, group theory, effective bond polarizability model and first-principles calculations, we can account for all the major observations in our experiment.Comment: 18 pages, 8 figures (supplemental material: 23 pages, 13 figures). 2D Materials, Accepted Manuscript online 24 January 201

An efficient fuzzy optimization algorithm based on convolutional neural network

The paper proposes a method based on dense-sparse-dense optimization algorithm. It uses sparsity to tune network weights. By adding fuzzy membership, the optimization strategy can enhance the feature information with larger weights and weaken the feature information with less weight. Through accurate cutting of network weights, parameters in network are effectively reduced. The experimental results show that the performance of this method is better than the existing method

An efficient fuzzy optimization algorithm based on convolutional neural network

The paper proposes a method based on dense-sparse-dense optimization algorithm. It uses sparsity to tune network weights. By adding fuzzy membership, the optimization strategy can enhance the feature information with larger weights and weaken the feature information with less weight. Through accurate cutting of network weights, parameters in network are effectively reduced. The experimental results show that the performance of this method is better than the existing method

An improved non-local means filter for color image denoising

Non-local means filter is a special case of non-linear filter. It performs well for filtering Gaussian noise while preserving edges and details of the original images. In this paper, we propose an improved filter for color image denoising based on combining the advantages of non-local means filter and bilateral filter. To compare the similarity of patches, a new weight value is computed by adding texture information into weights. The experimental results of color image filtering show that the proposed method has a better performance for reducing Gaussian noise and mixture noise

Identification of Candidate Expansin Genes Associated with Seed Weight in Pomegranate (<i>Punica granatum</i> L.)

Seed weight is an important target trait in pomegranate breeding and culture. Expansins act by loosening plant cell walls and cellulosic materials, permitting turgor-driven cell enlargement. However, the role of expansin genes (EXPs) in pomegranate seed weight remains elusive. A total of 29 PgrEXPs were identified in the ‘Dabenzi’ genome. These genes were classified into four subfamilies and 14 subgroups, including 22 PgrEXPAs, 5 PgrEXPBs, 1 PgrEXPLA, and 1 PgrEXPLB. Transcriptome analysis of PgrEXPs in different tissues (root, leaf, flower, peel, and seed testa) in ‘Dabenzi’, and the seed testa of the hard-seeded pomegranate cultivar ‘Dabenzi’ and soft-seeded cultivar ‘Tunisia’ at three development stages showed that three PgrEXPs (PgrEXPA11, PgrEXPA22, PgrEXPA6) were highly expressed throughout seed development, especially in the sarcotesta. SNP/Indel markers of these PgrEXPs were developed and used to genotype 101 pomegranate accessions. The association of polymorphic PgrEXPs with seed weight-related traits (100-seed weight, 100-kernel weight, 100-sarcotesta weight, and the percentage of 100-sarcotesta to 100-seed weight) were analyzed. PgrEXP22 was significantly associated with 100-seed weight and 100-sarcotesta weight and is a likely candidate for regulating seed weight and sarcotesta development in particular. This study provides an effective tool for the genetic improvement of seed weight in pomegranate breeding programs

Characterization and Expression Analysis of the UDP Glycosyltransferase Family in Pomegranate (<i>Punica granatum</i> L.)

UDP glycosyltransferases (UGTs) play an indispensable role in regulating signaling pathways and intracellular homeostasis in plants by catalyzing the glycosylation of metabolites. To date, the molecular characteristics and potential biological functions of the UGT gene family in pomegranate (Punica granatum L.) remain elusive. In this study, a total of 120 PgUGT genes were identified in the pomegranate genome. Phylogenetic analysis revealed that these PgUGTs were clustered into 15 groups: 13 conserved groups (A–J and L–N) and two newly discovered groups (P and R). Structural analysis showed that most members in the same evolutionary branch shared similar motifs and gene structures. Gene duplication analysis demonstrated that tandem duplication and fragment duplication were the primary driving force for the expansion of the PgUGT family. Expression analysis based on RNA-seq data indicated that PgUGTs exhibited various expression profiles in different pomegranate tissues. We further analyzed the expression patterns of the PgUGTs of groups E and L in the seed coat of the hard-seeded cultivar ‘Dabenzi’ and the soft-seeded cultivar ‘Tunisia’ at different developmental stages. There were eight PgUGTs with high expression levels in the seed coat of both cultivars: PgUGTE10 was highly expressed in inner and outer seed coats; PgUGTE20, PgUGTE21, PgUGTL6, PgUGTL11, and PgUGTL12 were mainly expressed in the inner seed coat; and PgUGTE12 and PgUGTL13 were mainly expressed in the outer seed coat. Interestingly, the relative expression levels of PgUGTE10 and PgUGTL11 in ‘Tunisia’ were higher than in ‘Dabenzi’. In the seedlings, quantitative real-time PCR analysis showed that the expression level of PgUGTE10 was induced by brassinolide treatment, while the expression of PgUGTL11 was up-regulated both by indole-3-acetic acid and the brassinolide treatment. In addition, the expressions of PgUGTE10 and PgUGTL11 were highly correlated with the expression of genes involved in hormone signaling and lignin biosynthesis pathways. These results suggested that PgUGTE10 and PgUGTL11 are potential candidate genes involved in seed hardness development by catalyzing the glycosylation of specific substrates

A Framework for Business Rule Presentation

A business rule is a statement that defines or constrains some aspects of a business. There has been a growing interest in developing techniques to support the extraction of business rules buried in legacy systems. However, little has been done so far to help understand the semantics of extracted business rules. We propose a framework to support the comprehension of business rules extracted from legacy systems. The framework consists of two levels: a representation level and a presentation level. At the representation level, we proposed a language, BRL, to express business rules. We also perform logical inferences over the set of business rules at this level. This helps to recover some properties that may not be explicitly available from the extracted business rules, but are essential to their understanding by users. The presentation level, on the other hand, is concerned with how to convey the semantics of business rules to different users. We believe that the expressiveness and reasoning power of our proposed approach significantly improve previous techniques in helping users to comprehend extracted business rule

An Anti-Cancer Peptide LVTX-8 Inhibits the Proliferation and Migration of Lung Tumor Cells by Regulating Causal Genes’ Expression in p53-Related Pathways

Spider venom has been found to show its anticancer activity in a variety of human malignancies, including lung cancer. In this study, we investigated the anti-cancer peptide toxin LVTX-8, with linear amphipathic alpha-helical conformation, designed and synthesized from the cDNA library of spider Lycosa vittata. Multiple cellular methods, such as CCK-8 assay, flow cytometry, colony formation assay, Transwell invasion and migration assay, were performed to detect peptide-induced cell growth inhibition and anti-metastasis in lung cancer cells. Our results demonstrated that LVTX-8 displayed strong cytotoxicity and anti-metastasis towards lung cancer in vitro. Furthermore, LVTX-8 could suppress the growth and metastasis of lung cancer cells (A549 and H460) in nude mouse models. Transcriptomics, integrated with multiple bioinformatics analysis, suggested that the molecular basis of the LVTX-8-mediated inhibition of cancer cell growth and metastasis manifested in two aspects: Firstly, it could restrain the activity of cancer cell division and migration through the functional pathways, including &ldquo;p53 hypoxia pathway&rdquo; and &ldquo;integrin signaling&rdquo;. Secondly, it could regulate the expression level of apoptotic-related proteins, which may account for programmed apoptosis of cancer cells. Taken together, as an anticancer peptide with high efficiency and acceptable specificity, LVTX-8 may become a potential precursor of a therapeutic agent for lung cancer in the future