A Multi-Agent Evolutionary algorIthm for Connector-Based Assembly Sequence Planning

AbstractSome Evolutionary algorithms for connector-based ASP have been researched. But those algorithms have lots of blind searching because individuals have little intelligence in making use of geometry and assembly process information of product assembly body. To improve individuals’ intelligence, A multi-agent evolutionary algorithm for connector-based ASP (MAEA-ASP) is presented which is integrated with the multi-agent systems. learning, competition and crossover -mutation are designed as the behaviors of agent which locate lattice-like structure environment. Experimental results show that MAEA-ASP can find an approximate solution faster compared with other evolutionary algorithms

Selecting a semantic similarity measure for concepts in two different CAD model data ontologies

Semantic similarity measure technology based approach is one of the most popular approaches aiming at implementing semantic mapping between two different CAD model data ontologies. The most important problem in this approach is how to measure the semantic similarities of concepts between two different ontologies. A number of measure methods focusing on this problem have been presented in recent years. Each method can work well between its specific ontologies. But it is unclear how accurate the measured semantic similarities in these methods are. Moreover, there is yet no evidence that any of the methods presented how to select a measure with high similarity calculation accuracy. To compensate for such deficiencies, this paper proposes a method for selecting a semantic similarity measure with high similarity calculation accuracy for concepts in two different CAD model data ontologies. In this method, the similarity calculation accuracy of each candidate measure is quantified using Pearson correlation coefficient or residual sum of squares. The measure with high similarity calculation accuracy is selected through a comparison of the Pearson correlation coefficients or the residual sums of squares of all candidate measures. The paper also reports an implementation of the proposed method, provides an example to show how the method works, and evaluates the method by theoretical and experimental comparisons. The evaluation result suggests that the measure selected by the proposed method has good human correlation and high similarity calculation accuracy

A Conceptual Framework for Mapping Quantitative Trait Loci Regulating Ontogenetic Allometry

Although ontogenetic changes in body shape and its associated allometry has been studied for over a century, essentially nothing is known about their underlying genetic and developmental mechanisms. One of the reasons for this ignorance is the unavailability of a conceptual framework to formulate the experimental design for data collection and statistical models for data analyses. We developed a framework model for unraveling the genetic machinery for ontogenetic changes of allometry. The model incorporates the mathematical aspects of ontogenetic growth and allometry into a maximum likelihood framework for quantitative trait locus (QTL) mapping. As a quantitative platform, the model allows for the testing of a number of biologically meaningful hypotheses to explore the pleiotropic basis of the QTL that regulate ontogeny and allometry. Simulation studies and real data analysis of a live example in soybean have been performed to investigate the statistical behavior of the model and validate its practical utilization. The statistical model proposed will help to study the genetic architecture of complex phenotypes and, therefore, gain better insights into the mechanistic regulation for developmental patterns and processes in organisms

Investigation of Tumor Suppressing Function of CACNA2D3 in Esophageal Squamous Cell Carcinoma.

Background: Deletion of 3p is one of the most frequent genetic alterations in esophageal squamous cell carcinoma (ESCC), suggesting the existence of one or more tumor suppressor genes (TSGs) within these regions. In this study, one TSG, CACNA2D3 at 3p21.1, was characterized. Methods: Expression of CACNA2D3 in ESCCs was tested by quantitative real-time PCR and tissue microarray. The mechanism of CACNA2D3 downregulation was investigated by methylation-specific polymerase chain reaction (MS-PCR). The tumor suppressive function of CACNA2D3 was characterized by both in vitro and in vivo tumorigenic assays, cell migration and invasion assays. Results: CACNA2D3 was frequently downregulated in ESCCs (24/48, 50%), which was significantly associated with promoter methylation and allele loss (P<0.05). Tissue microarray result showed that downregulation of CACNA2D3 was detected in (127/224, 56.7%) ESCCs, which was significantly associated with lymph node metastasis (P = 0.01), TNM staging (P = 0.003) and poor outcome of ESCC patients (P<0.05). Functional studies demonstrated that CACNA2D3 could inhibit tumorigenicity, cell motility and induce apoptosis. Mechanism study found that CACNA2D3 could arrest cell cycle at G1/S checkpoint by increasing expressions of p21 and p53 and decreasing expression of CDK2. In addition, CACNA2D3 could upregulate intracellular free cytosolic Ca2+ and subsequently induce apoptosis. Conclusion: CACNA2D3 is a novel TSG responsible to the 3p21 deletion event and plays a critical suppressing role in the development and progression of ESCC. © 2013 Li et al.link_to_OA_fulltex

Extensions of MADM (Mosaic Analysis with Double Markers) in Mice

Mosaic Analysis with Double Markers (MADM) is a method for generating genetically mosaic mice, in which sibling mutant and wild-type cells are labeled with different fluorescent markers. It is a powerful tool that enables analysis of gene function at the single cell level in vivo. It requires transgenic cassettes to be located between the centromere and the mutation in the gene of interest on the same chromosome. Here we compare procedures for introduction of MADM cassettes into new loci in the mouse genome, and describe new approaches for expanding the utility of MADM. We show that: 1) Targeted homologous recombination outperforms random transgenesis in generation of reliably expressed MADM cassettes, 2) MADM cassettes in new genomic loci need to be validated for biallelic and ubiquitous expression, 3) Recombination between MADM cassettes on different chromosomes can be used to study reciprocal chromosomal deletions/duplications, and 4) MADM can be modified to permit transgene expression by combining it with a binary expression system. The advances described in this study expand current, and enable new and more versatile applications of MADM

A Statistical Model for Estimating Maternal-Zygotic Interactions and Parent-of-Origin Effects of QTLs for Seed Development

Proper development of a seed requires coordinated exchanges of signals among the three components that develop side by side in the seed. One of these is the maternal integument that encloses the other two zygotic components, i.e., the diploid embryo and its nurturing annex, the triploid endosperm. Although the formation of the embryo and endosperm contains the contributions of both maternal and paternal parents, maternally and paternally derived alleles may be expressed differently, leading to a so-called parent-of-origin or imprinting effect. Currently, the nature of how genes from the maternal and zygotic genomes interact to affect seed development remains largely unknown. Here, we present a novel statistical model for estimating the main and interaction effects of quantitative trait loci (QTLs) that are derived from different genomes and further testing the imprinting effects of these QTLs on seed development. The experimental design used is based on reciprocal backcrosses toward both parents, so that the inheritance of parent-specific alleles could be traced. The computing model and algorithm were implemented with the maximum likelihood approach. The new strategy presented was applied to study the mode of inheritance for QTLs that control endoreduplication traits in maize endosperm. Monte Carlo simulation studies were performed to investigate the statistical properties of the new model with the data simulated under different imprinting degrees. The false positive rate of imprinting QTL discovery by the model was examined by analyzing the simulated data that contain no imprinting QTL. The reciprocal design and a series of analytical and testing strategies proposed provide a standard procedure for genomic mapping of QTLs involved in the genetic control of complex seed development traits in flowering plants

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A Financial Distress Prediction Model Based on Sparse Algorithm and Support Vector Machine

Classification learning is a very important issue in machine learning, which has been widely used in the field of financial distress warning. Some researches show that the prediction model framework based on sparse algorithm has better performance than the traditional model. In this paper, we explore the financial distress prediction based on grouping sparsity. Feature selection of sparse algorithm plays an important role in classification learning, because many redundant and irrelevant features will degrade performance. A good feature selection algorithm would reduce computational complexity and improve classification accuracy. In this study, we propose an algorithm for feature selection classification prediction based on feature attributes and data source grouping. The existing financial distress prediction model usually only uses the data from financial statement and ignores the timeliness of company sample in practice. Therefore, we propose a corporate financial distress prediction model that is better in line with the practice and combines the grouping sparse principal component analysis of financial data, corporate governance characteristics, and market transaction data with support vector machine. Experimental results show that this method can improve the prediction efficiency of financial distress with fewer characteristic variables