5AtRabD2b and AtRabD2c have overlapping functions in pollen development and pollen tube growth

<p>Abstract</p> <p>Background</p> <p>Rab GTPases are important regulators of endomembrane trafficking, regulating exocytosis, endocytosis and membrane recycling. Many Rab-like proteins exist in plants, but only a subset have been functionally characterized.</p> <p>Results</p> <p>Here we report that AtRabD2b and AtRabD2c play important roles in pollen development, germination and tube elongation. <it>AtrabD2b </it>and <it>AtrabD2c </it>single mutants have no obvious morphological changes compared with wild-type plants across a variety of growth conditions. An <it>AtrabD2b/2c </it>double mutant is also indistinguishable from wild-type plants during vegetative growth; however its siliques are shorter than those in wild-type plants. Compared with wild-type plants, <it>AtrabD2b/2c </it>mutants produce deformed pollen with swollen and branched pollen tube tips. The shorter siliques in the <it>AtrabD2b/2c </it>double mutant were found to be primarily due to the pollen defects. <it>AtRabD2b </it>and <it>AtRabD2c </it>have different but overlapping expression patterns, and they are both highly expressed in pollen. Both AtRabD2b and AtRabD2c protein localize to Golgi bodies.</p> <p>Conclusions</p> <p>These findings support a partially redundant role for AtRabD2b and AtRabD2c in vesicle trafficking during pollen tube growth that cannot be fulfilled by the remaining AtRabD family members.</p

Articulation of three core metabolic processes in Arabidopsis: Fatty acid biosynthesis, leucine catabolism and starch metabolism

<p>Abstract</p> <p>Background</p> <p>Elucidating metabolic network structures and functions in multicellular organisms is an emerging goal of functional genomics. We describe the co-expression network of three core metabolic processes in the genetic model plant <it>Arabidopsis thaliana</it>: fatty acid biosynthesis, starch metabolism and amino acid (leucine) catabolism.</p> <p>Results</p> <p>These co-expression networks form modules populated by genes coding for enzymes that represent the reactions generally considered to define each pathway. However, the modules also incorporate a wider set of genes that encode transporters, cofactor biosynthetic enzymes, precursor-producing enzymes, and regulatory molecules. We tested experimentally the hypothesis that one of the genes tightly co-expressed with starch metabolism module, a putative kinase AtPERK10, will have a role in this process. Indeed, knockout lines of AtPERK10 have an altered starch accumulation. In addition, the co-expression data define a novel hierarchical transcript-level structure associated with catabolism, in which genes performing smaller, more specific tasks appear to be recruited into higher-order modules with a broader catabolic function.</p> <p>Conclusion</p> <p>Each of these core metabolic pathways is structured as a module of co-expressed transcripts that co-accumulate over a wide range of environmental and genetic perturbations and developmental stages, and represent an expanded set of macromolecules associated with the common task of supporting the functionality of each metabolic pathway. As experimentally demonstrated, co-expression analysis can provide a rich approach towards understanding gene function.</p

Latent factor transition for dynamic collaborative filtering

User preferences change over time and capturing such changes is essential for developing accurate recom-mender systems. Despite its importance, only a few works in collaborative filtering have addressed this is-sue. In this paper, we consider evolving preferences and we model user dynamics by introducing and learning a transition matrix for each user’s latent vectors between consecutive time windows. Intuitively, the transition matrix for a user summarizes the time-invariant pat-tern of the evolution for the user. We first extend the conventional probabilistic matrix factorization and then improve upon this solution through its fully Bayesian model. These solutions take advantage of the model complexity and scalability of conventional Bayesian ma-trix factorization, yet adapt dynamically to user’s evolv-ing preferences. We evaluate the effectiveness of these solutions through empirical studies on six large-scale real life data sets

Are features equally representative? A feature-centric recommendation

Typically a user prefers an item (e.g., a movie) because she likes certain features of the item (e.g., director, genre, pro-ducer). This observation motivates us to consider a feature-centric recommendation approach to item recommendation: instead of directly predicting the rating on items, we predict the rating on the features of items, and use such ratings to derive the rating on an item. This approach offers several ad-vantages over the traditional item-centric approach: it incor-porates more information about why a user chooses an item, it generalizes better due to the denser feature rating data, it explains the prediction of item ratings through the predicted feature ratings. Another contribution is turning a principled item-centric solution into a feature-centric solution, instead of inventing a new algorithm that is feature-centric. This ap-proach maximally leverages previous research. We demon-strate this approach by turning the traditional item-centric la-tent factor model into a feature-centric solution and demon-strate its superiority over item-centric approaches.

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

LeafletAnalyzer, an Automated Software for Quantifying, Comparing and Classifying Blade and Serration Features of Compound Leaves during Development, and among Induced Mutants and Natural Variants in the Legume Medicago truncatula

Diverse leaf forms ranging from simple to compound leaves are found in plants. It is known that the final leaf size and shape vary greatly in response to developmental and environmental changes. However, changes in leaf size and shape have been quantitatively characterized only in a limited number of species. Here, we report development of LeafletAnalyzer, an automated image analysis and classification software to analyze and classify blade and serration characteristics of trifoliate leaves in Medicago truncatula. The software processes high quality leaf images in an automated or manual fashion to generate size and shape parameters for both blades and serrations. In addition, it generates spectral components for each leaflets using elliptic Fourier transformation. Reconstruction studies show that the spectral components can be reliably used to rebuild the original leaflet images, with low, and middle and high frequency spectral components corresponding to the outline and serration of leaflets, respectively. The software uses artificial neutral network or k-means classification method to classify leaflet groups that are developed either on successive nodes of stems within a genotype or among genotypes such as natural variants and developmental mutants. The automated feature of the software allows analysis of thousands of leaf samples within a short period of time, thus facilitating identification, comparison and classification of leaf groups based on leaflet size, shape and tooth features during leaf development, and among induced mutants and natural variants

Research on Construction Workers’ Safety Risk Sharing in Tunneling Projects Based on a Two-Mode Network: A Case Study of the Shangwu Tunnel

There is a high level of construction safety risk shared among construction workers in tunneling projects due to collaboration on the narrow and semi-enclosed construction site. However, no one has reported on this. Therefore, this paper proposes a new network model to explore risk-sharing features among construction workers based on a two-mode network. That model represents a new personnel safety management tool to provide suitable risk mitigation for tunneling projects. First, the work breakdown structure (WBS)–risk breakdown structure (RBS) method was employed to identify construction activities, risk resources, and construction safety risk factors (CSRFs). Subsequently, the two-mode WBS–RBS matrix was further established. The construction workers’ sets were determined based on the organization breakdown structure (OBS)–WBS method and a two-mode OBS–WBS matrix was established. By applying the construction activities in the WBS tree carrying the CSRFs as the link, a two-mode OBS–RBS network was established by converting the two-mode WBS–RBS and OBS–WBS matrices. Hence, taking CSRFs allocated by several construction workers as a basis for network generation, the construction workers’ risk-sharing network was further established. Centrality analysis identified the network characteristics and determined the most important construction workers in risk network. For example, this model was employed to explore the whole network characteristics of the Shangwu Tunnel and identify the workers in key positions in the risk-sharing network. Expert interviews demonstrated the model’s rationality and practicality. The results show that each construction worker’s safety risk-sharing degree in the Shangwu tunnel differed and reached varying levels. However, the staff from the engineering management department were in the key position of the risk-sharing network. Collectively, this model can help construction workers understand their risk-sharing degree to improve their safety awareness and adjust their attitude toward safety accordingly. Moreover, this strategy provides project managers with the necessary information to more effectively allocate safety resources and to be cognizant of the safety quality of each construction worker according to the different risk-sharing degrees

A Pd-Cu2O nanocomposite as an effective synergistic catalyst for selective semi-hydrogenation of the terminal alkynes only

A new type lead-free catalyst of a Pd-Cu2O nanocomposite was developed for highly selective semi-hydrogenation of alkynes. With unprecedented selectivity for the semi-hydrogenation of terminal alkynes to alkenes, we show for the first time that the catalyst only hydrogenated the terminal alkynes, i.e. did not hydrogenate the internal alkynes