Comparative transcriptomic analysis reveals novel roles of transcription factors and hormones during the flowering induction and floral bud differentiation in sweet cherry trees (Prunus avium L. Cv. Bing)

In sweet cherry trees, flowering is commercially important because the flowers, after fertilization, will generate the fruits. In P. avium, the flowering induction and flower organogensis are the first developmental steps towards flower formation and they occur within specialized organs known as floral buds during the summer, nine months before blooming. During this period the number of floral buds per tree and the bud fruitfulness (number of flowers per bud) are stablished affecting the potential yield of orchards and the plant architecture. The floral bud development is sensitive to any type of stress and the hotter and drier summers will interfere with this process and are calling for new adapted cultivars. A better understanding of the underlying molecular and hormonal mechanisms would be of help, but unlike the model plant Arabidopsis, very little is known about floral induction in sweet cherry. To explore the molecular mechanism of floral bud differentiation, high-throughput RNA sequencing was used to detect differences in the gene expression of P. avium floral buds at five differentiation stages. We found 2,982 differentially expressed genes during floral bud development. We identified genes associated with floral initiation or floral organ identity that appear to be useful biomarkers of floral development and several transcription factor families (ERF, MYB, bHLH, MADS-box and NAC gene family) with novel potential roles during floral transition in this species. We analyzed in deep the MADS-box gene family and we shed light about their key role during floral bud and organs development in P. avium. Furthermore, the hormonal-related signatures in the gene regulatory networks and the dynamic changes of absicic acid, zeatin and indolacetic acid contents in buds suggest an important role for these hormones during floral bud differentiation in sweet cherry. These data provide a rich source of novel informacion for functional and evolutionary studies about floral bud development in sweet cherry and new tools for biotechnology and breeding.Fil: Villar, Luis. Comision Nacional de Investigacion Cientifica y Tecnologica. Centro de Investigacion Regional. Centro de Estudios Avanzados En Fruticultura.; ChileFil: Lienqueo, Ixia. Comision Nacional de Investigacion Cientifica y Tecnologica. Centro de Investigacion Regional. Centro de Estudios Avanzados En Fruticultura.; ChileFil: Llanes, Analia Susana. Universidad Nacional de Rio Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones Agrobiotecnológicas - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Agrobiotecnológicas; ArgentinaFil: Rojas, Pamela. Comision Nacional de Investigacion Cientifica y Tecnologica. Centro de Investigacion Regional. Centro de Estudios Avanzados En Fruticultura.; ChileFil: Perez, Jorge. Comision Nacional de Investigacion Cientifica y Tecnologica. Centro de Investigacion Regional. Centro de Estudios Avanzados En Fruticultura.; ChileFil: Correa, Francisco. Comision Nacional de Investigacion Cientifica y Tecnologica. Centro de Investigacion Regional. Centro de Estudios Avanzados En Fruticultura.; ChileFil: Sagredo, Boris. Comision Nacional de Investigacion Cientifica y Tecnologica. Centro de Investigacion Regional. Centro de Estudios Avanzados En Fruticultura.; ChileFil: Masciarelli, Oscar Alberto. Universidad Nacional de Rio Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones Agrobiotecnológicas - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Agrobiotecnológicas; ArgentinaFil: Luna, Maria Virginia. Universidad Nacional de Rio Cuarto. Facultad de Ciencias Exactas Fisicoquímicas y Naturales. Instituto de Investigaciones Agrobiotecnológicas - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones Agrobiotecnológicas; ArgentinaFil: Almada, Rubén. Comision Nacional de Investigacion Cientifica y Tecnologica. Centro de Investigacion Regional. Centro de Estudios Avanzados En Fruticultura.; Chil

Scalable Kernel Methods via Doubly Stochastic Gradients

The general perception is that kernel methods are not scalable, and neural nets are the methods of choice for nonlinear learning problems. Or have we simply not tried hard enough for kernel methods? Here we propose an approach that scales up kernel methods using a novel concept called "doubly stochastic functional gradients". Our approach relies on the fact that many kernel methods can be expressed as convex optimization problems, and we solve the problems by making two unbiased stochastic approximations to the functional gradient, one using random training points and another using random functions associated with the kernel, and then descending using this noisy functional gradient. We show that a function produced by this procedure after $t$ iterations converges to the optimal function in the reproducing kernel Hilbert space in rate $O(1/t)$, and achieves a generalization performance of $O(1/\sqrt{t})$. This doubly stochasticity also allows us to avoid keeping the support vectors and to implement the algorithm in a small memory footprint, which is linear in number of iterations and independent of data dimension. Our approach can readily scale kernel methods up to the regimes which are dominated by neural nets. We show that our method can achieve competitive performance to neural nets in datasets such as 8 million handwritten digits from MNIST, 2.3 million energy materials from MolecularSpace, and 1 million photos from ImageNet.Comment: 32 pages, 22 figure

GRASS: Generative Recursive Autoencoders for Shape Structures

We introduce a novel neural network architecture for encoding and synthesis of 3D shapes, particularly their structures. Our key insight is that 3D shapes are effectively characterized by their hierarchical organization of parts, which reflects fundamental intra-shape relationships such as adjacency and symmetry. We develop a recursive neural net (RvNN) based autoencoder to map a flat, unlabeled, arbitrary part layout to a compact code. The code effectively captures hierarchical structures of man-made 3D objects of varying structural complexities despite being fixed-dimensional: an associated decoder maps a code back to a full hierarchy. The learned bidirectional mapping is further tuned using an adversarial setup to yield a generative model of plausible structures, from which novel structures can be sampled. Finally, our structure synthesis framework is augmented by a second trained module that produces fine-grained part geometry, conditioned on global and local structural context, leading to a full generative pipeline for 3D shapes. We demonstrate that without supervision, our network learns meaningful structural hierarchies adhering to perceptual grouping principles, produces compact codes which enable applications such as shape classification and partial matching, and supports shape synthesis and interpolation with significant variations in topology and geometry.Comment: Corresponding author: Kai Xu ([email protected]

Data Classification and Its Application in Credit Card Approval

We are all now living in the information age. The amount of data being collected by businesses, companies and agencies is large. Recent advances in technologies to automate and improve data collection have increased the volumes of data. Lying hidden in all this data is potentially useful information that is rarely made explicit or taken advantage of. In this context, data mining has arisen as an important research area that helps to reveal the hidden useful information from the raw data collected. Many intensive researches have been conducted to enhance the capability of data mining solution in providing the intelligence so that different types of businesses can make informed decisions. This project demonstrates how data mining can address the need of business intelligence in the process of decision-making. An analysis on the field of data mining is done to show how data mining, especially data classification, can help in businesses such as targeted marketing, credit card approval, fraud detection, medical diagnosis, and scientific work. This project is involved with identification of the available algorithms used in data classification and the implementation of C4.5 decision tree induction algorithm in solving the data classifying task. Sample credit card approval dataset is used to demonstrate the functionality of a data mining solution prototype, which includes the typical tasks of a decision tree induction process: data selection, data preprocessing, decision tree induction, tree pruning, rules generation and validation. The result of this application using the sample credit card approval dataset includes a decision tree, a set of rules derived from the decision tree and its accuracy. These outputs help to identify the pattern of applicants who are more likely to be accepted or rejected. The set of rules can be used as part of the knowledge base in expert system or decision support system for financial institutions

Transcription profiling provides insights into gene pathways involved in horn and scurs development in cattle

<p>Abstract</p> <p>Background</p> <p>Two types of horns are evident in cattle - fixed horns attached to the skull and a variation called scurs, which refers to small loosely attached horns. Cattle lacking horns are referred to as polled. Although both the <it>Poll </it>and <it>Scurs </it>loci have been mapped to BTA1 and 19 respectively, the underlying genetic basis of these phenotypes is unknown, and so far, no candidate genes regulating these developmental processes have been described. This study is the first reported attempt at transcript profiling to identify genes and pathways contributing to horn and scurs development in Brahman cattle, relative to polled counterparts.</p> <p>Results</p> <p>Expression patterns in polled, horned and scurs tissues were obtained using the Agilent 44 k bovine array. The most notable feature when comparing transcriptional profiles of developing horn tissues against polled was the down regulation of genes coding for elements of the cadherin junction as well as those involved in epidermal development. We hypothesize this as a key event involved in keratinocyte migration and subsequent horn development. In the polled-scurs comparison, the most prevalent differentially expressed transcripts code for genes involved in extracellular matrix remodelling, which were up regulated in scurs tissues relative to polled.</p> <p>Conclusion</p> <p>For this first time we describe networks of genes involved in horn and scurs development. Interestingly, we did not observe differential expression in any of the genes present on the fine mapped region of BTA1 known to contain the <it>Poll </it>locus.</p