Availability, collection and use of data on disability in the Caribbean subregion

Includes bibliography

Unbiased approach to diagnose the nutrient status of red guava (Psidium guajava)

Fertilization of guava relies on soil and tissue testing. The interpretation of tissue test is currently conducted by comparing nutrient concentrations or dual ratios with critical values or ranges. The critical value approach is affected by nutrient interactions. Nutrient interactions can be described by dual ratios where two nutrients are compressed into a single expression or a ternary diagrams where one redundant proportion can be computed by difference between 100% and the sum of the other two. There are D(D-1) possible dual ratios in a D-parts composition and most of them are thus redundant. Nutrients are components of a mixture that convey relative, not absolute information on the composition. There are D-1 balances between components or ingredients in any mixture. Compositional data are intrinsically redundant, scale dependent and non-normally distributed. Based on the principles of equilibrium and orthogonality, the nutrient balance concept projects D-1 isometric log ratio (ilr) coordinates into the Euclidean space. The D-1 balances between groups of nutrients are ordered to reflect knowledge in plant physiology, soil fertility and crop management. Our objective was to evaluate the ilr approach using nutrient data from a guava orchard survey and fertilizer trials across the state of São Paulo, Brazil. Cationic balances varied widely between orchards. We found that the Redfield N/P ratio of 13 was critical for high guava yield. We present guava yield maps in ternary diagrams. Although the ratio between nutrients changing in the same direction with time is often assumed to be stationary, most guava nutrient balances and dual ratios were found to be non-stationary. The ilr model provided an unbiased nutrient diagnosis of guava. © ISHS.

Knowledge and data for population in development: information activities of CELADE and the Latin American and Caribbean Population Information Network (IPALCA)

Includes bibliography

An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms

Wireless Sensor Networks (WSNs) can be used to monitor hazardous and inaccessible areas. In these situations, the power supply (e.g. battery) of each node cannot be easily replaced. One solution to deal with the limited capacity of current power supplies is to deploy a large number of sensor nodes, since the lifetime and dependability of the network will increase through cooperation among nodes. Applications on WSN may also have other concerns, such as meeting temporal deadlines on message transmissions and maximizing the quality of information. Data fusion is a well-known technique that can be useful for the enhancement of data quality and for the maximization of WSN lifetime. In this paper, we propose an approach that allows the implementation of parallel data fusion techniques in IEEE 802.15.4 networks. One of the main advantages of the proposed approach is that it enables a trade-off between different user-defined metrics through the use of a genetic machine learning algorithm. Simulations and field experiments performed in different communication scenarios highlight significant improvements when compared with, for instance, the Gur Game approach or the implementation of conventional periodic communication techniques over IEEE 802.15.4 networks. © 2013 Elsevier B.V. All rights reserved.

Venezuela: the Project Data Bank system in Venezuela

Includes bibliography

Geographically disaggregated census data for planning in developing countries

Includes bibliography

The plant ionome revisited by the nutrient balance concept

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Cell cultures of Maytenus ilicifolia Mart. are richer sources of quinone-methide triterpenoids than plant roots in natura

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Plant iononne diagnosis using sound balances: case study with mango (Mangifera Indica)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A Comparative Analysis of In-Medium Spectral Functions for N(940) and N (au)(1535) in Real-Time Thermal Field Theory

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Characterization of immobilized biomass by amplified rDNA restriction analysis (ARDRA) in an anaerobic sequencing-batch biofilm reactor (ASBBR) for the treatment of industrial wastewater

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

cDNA representational difference analysis used in the identification of genes expressed by Trichophyton rubrum during contact with keratin

Dermatophytes are adapted to infect skin, hair and nails by their ability to utilize keratin as a nutrient source. Trichophyton rubrum is an anthropophilic fungus, causing up to 90% of chronic cases of dermatophytosis. The understanding of the complex interactions between the fungus and its host should include the identification of genes expressed during infection. To identify the genes involved in the infection process, representational difference analysis (RDA) was applied to two cDNA populations from T. rubrum, one transcribed from the RNA of fungus cultured in the presence of keratin and the other from RNA generated during fungal growth in minimal medium. The analysis identified differentially expressed transcripts. Genes related to signal transduction, membrane protein, oxidative stress response, and some putative virulence factors were up-regulated during the contact of the fungus with keratin. The expression patterns of these genes were also verified by real-time PCR, in conidia of T. rubrum infecting primarily cultured human keratinocytes in vitro, revealing their potential role in the infective process. A better understanding of this interaction will contribute significantly to our knowledge of the process of dermatophyte infection.