Variability in radiocarbon ages of biochemical compound classes of high molecular weight dissolved organic matter in estuaries

High molecular weight dissolved organic matter (HMW-DOM, > 1000 Da) represents a major fraction (> 30%) of dissolved organic carbon (DOC) in the ocean and thus plays an important role in the global biogeochemical cycling of carbon and many other elements. Its organic sources and formation mechanisms, however, are still not well understood especially in estuarine and coastal regions where multiple natural and anthropogenic sources contribute to total HMW-DOM. In this paper we report our measurements of natural radiocarbon (C-14) abundances and stable carbon isotope (C-13) compositions of the major biochemical compound classes: amino acids, carbohydrates and lipids separated from eight HMW-DOM samples collected from five US estuaries as part of our on-going study of sources, distribution and transport of chromophoric dissolved organic matter (CDOM) in estuarine and coastal waters. Distinct differences in both C-14 and C-13 values were found among the bulk HMW-DOM samples as well as the individual compound classes. Radiocarbon ages of the major compound classes varied by as much as 27,000 years in a single sample. The calculated average radiocarbon ages of the compound fractions of HMW-DOM indicate that the total lipid fraction is very "old", while the acid-insoluble fraction is slightly younger. Total amino acid and carbohydrate fractions, however, have relatively modern apparent C-14 ages. The significant variability in C-14 ages among the compound classes indicates not only multiple organic carbon sources but also different formation and turnover pathways controlling the cycling of different biochemical components of HMW-DOM in estuarine and coastal waters. (c) 2006 Elsevier Ltd. All rights reserved.

Caracterização dos solos e classes de terra para irrigação do Oeste da Bahia.

O trabalho identifica e estuda os solos do Oeste da Bahia, mais especificamente na área referente a de seis folhas do IBGE (Formosa do Rio Preto, Santa Rita de Cássia, Barreiras, Santana, São Domingos e Santa Maria da Vitória). O CD em anexo, contém os mapas: Levantamento Exploratório/Reconhecimento de Solos da Região Oeste da Bahia: legenda atualizada (Escala 1:1000.000); Classes de Terras para Irrigação: Região Oeste da Bahia (Escala 1:1000.000).

Linear Object Classes and Image Synthesis from a Single Example Image

The need to generate new views of a 3D object from a single real image arises in several fields, including graphics and object recognition. While the traditional approach relies on the use of 3D models, we have recently introduced techniques that are applicable under restricted conditions but simpler. The approach exploits image transformations that are specific to the relevant object class and learnable from example views of other "prototypical" objects of the same class. In this paper, we introduce such a new technique by extending the notion of linear class first proposed by Poggio and Vetter. For linear object classes it is shown that linear transformations can be learned exactly from a basis set of 2D prototypical views. We demonstrate the approach on artificial objects and then show preliminary evidence that the technique can effectively "rotate" high- resolution face images from a single 2D view.

Learning Classes Correlated to a Hierarchy

Trees are a common way of organizing large amounts of information by placing items with similar characteristics near one another in the tree. We introduce a classification problem where a given tree structure gives us information on the best way to label nearby elements. We suggest there are many practical problems that fall under this domain. We propose a way to map the classification problem onto a standard Bayesian inference problem. We also give a fast, specialized inference algorithm that incrementally updates relevant probabilities. We apply this algorithm to web-classification problems and show that our algorithm empirically works well.

Mapeamento digital de classes e atributos de solos: métodos, paradigmas e novas técnicas.

Panorama geral sobre os métodos de mapeamento de solos e/ou de suas propriedades, assim como sobre as principais técnicas quantitativas usadas.

Revisão sobre funções de Pedotransferência (PTFs) e novos métodos de predição de classes e atributos do solo.

Revisão sobre o uso das funções de pedotransferência e discussão sobre os vários tipos de PTFs. Diferentes abordagens e alguns princípios são considerados para desenvolver PTFs. Um conceito de sistema de inferência de solo é proposto (SINFERS), em que funções de pedotransferência são as regras do conhecimento, para serem usadas como ferramentas de inferência. É fornecida extensa bibliografia para consulta e expansão do conhecimento e uso da metodologia de pedotransferência.

Mapa de classes de terra pra irrigação do Projeto Jusante - área 3 - Glória/BA.

2008

Speeding up the learning of equivalence classes of Bayesian network structures

R. Daly, Q. Shen and S. Aitken. Speeding up the learning of equivalence classes of Bayesian network structures. Proceedings of the 10th International Conference on Artificial Intelligence and Soft Computing, pages 34-39.

Using ant colony optimisation in learning Bayesian network equivalence classes

R. Daly, Q. Shen and S. Aitken. Using ant colony optimisation in learning Bayesian network equivalence classes. Proceedings of the 2006 UK Workshop on Computational Intelligence, pages 111-118.

A Framework for the Scoring of Operators on the Search Space of Equivalence Classes of Bayesian Network Structures

R. Daly and Q. Shen. A Framework for the Scoring of Operators on the Search Space of Equivalence Classes of Bayesian Network Structures. Proceedings of the 2005 UK Workshop on Computational Intelligence, pages 67-74.

Locational distribution of gene functional classes in Arabidopsis thaliana

Riley, M. C., Clare, A., King, R. D. (2007). Locational distribution of gene functional classes in Arabidopsis thaliana. BMC Bioinformatics 8, Article No: 112 Sponsorship: EPSRC / RAEng

Restauração directa de classes II em pré-molares com compósitos

Tese de Doutoramento apresentada à Universidade Fernando Pessoa como parte dos requisitos para obtenção do grau de Doutor em em Biotecnologia e Saúde, Epidemiologia e Saúde Pública.

The Churchman's prayer manual : for use at prayer meetings, mission services, conventions, Bible classes, study circles and other occasions of united prayer, as well as for private use compiled by G. R. Bullock-Webster.

http://www.archive.org/details/churchmansprayer00bulluoft

A Characterization of First-Order Definable Subsets on Classes of Finite Total Orders

We give an explicit and easy-to-verify characterization for subsets in finite total orders (infinitely many of them in general) to be uniformly definable by a first-order formula. From this characterization we derive immediately that Beth's definability theorem does not hold in any class of finite total orders, as well as that McColm's first conjecture is true for all classes of finite total orders. Another consequence is a natural 0-1 law for definable subsets on finite total orders expressed as a statement about the possible densities of first-order definable subsets.

Detecting Instances of Shape Classes That Exhibit Variable Structure

This paper proposes a method for detecting shapes of variable structure in images with clutter. The term "variable structure" means that some shape parts can be repeated an arbitrary number of times, some parts can be optional, and some parts can have several alternative appearances. The particular variation of the shape structure that occurs in a given image is not known a priori. Existing computer vision methods, including deformable model methods, were not designed to detect shapes of variable structure; they may only be used to detect shapes that can be decomposed into a fixed, a priori known, number of parts. The proposed method can handle both variations in shape structure and variations in the appearance of individual shape parts. A new class of shape models is introduced, called Hidden State Shape Models, that can naturally represent shapes of variable structure. A detection algorithm is described that finds instances of such shapes in images with large amounts of clutter by finding globally optimal correspondences between image features and shape models. Experiments with real images demonstrate that our method can localize plant branches that consist of an a priori unknown number of leaves and can detect hands more accurately than a hand detector based on the chamfer distance.