Advantage of single-trial models for response to selection in wheat breeding multi-environment trials

An investigation was conducted to evaluate the impact of experimental designs and spatial analyses (single-trial models) of the response to selection for grain yield in the northern grains region of Australia (Queensland and northern New South Wales). Two sets of multi-environment experiments were considered. One set, based on 33 trials conducted from 1994 to 1996, was used to represent the testing system of the wheat breeding program and is referred to as the multi-environment trial (MET). The second set, based on 47 trials conducted from 1986 to 1993, sampled a more diverse set of years and management regimes and was used to represent the target population of environments (TPE). There were 18 genotypes in common between the MET and TPE sets of trials. From indirect selection theory, the phenotypic correlation coefficient between the MET and TPE single-trial adjusted genotype means [r(p(MT))] was used to determine the effect of the single-trial model on the expected indirect response to selection for grain yield in the TPE based on selection in the MET. Five single-trial models were considered: randomised complete block (RCB), incomplete block (IB), spatial analysis (SS), spatial analysis with a measurement error (SSM) and a combination of spatial analysis and experimental design information to identify the preferred (PF) model. Bootstrap-resampling methodology was used to construct multiple MET data sets, ranging in size from 2 to 20 environments per MET sample. The size and environmental composition of the MET and the single-trial model influenced the r(p(MT)). On average, the PF model resulted in a higher r(p(MT)) than the IB, SS and SSM models, which were in turn superior to the RCB model for MET sizes based on fewer than ten environments. For METs based on ten or more environments, the r(p(MT)) was similar for all single-trial models.

Some results on the design of field experiments for comparing unreplicated treatments

In early generation variety trials, large numbers of new breeders' lines need to be compared, and usually there is little seed available for each new line. A so-called unreplicated trial has each new line on just one plot at a site, but includes several (often around five) replicated check or control (or standard) varieties. The total proportion of check plots is usually between 10% and 20%. The aim of the trial is to choose some good performing lines (usually around 1/3 of those tested) to go on for further testing, rather than precise estimation of their mean yield. Now that spatial analyses of data from field experiments are becoming more common, there is interest in an efficient layout of an experiment given a proposed spatial analysis. Some possible design criteria are discussed, and efficient layouts under spatial dependence are considered.

Efficient experimental designs when most treatments are unreplicated

In early generation variety trials, large numbers of new breeders' lines (varieties) may be compared, with each having little seed available. A so-called unreplicated trial has each new variety on just one plot at a site, but includes several replicated control varieties, making up around 10% and 20% of the trial. The aim of the trial is to choose some (usually around one third) good performing new varieties to go on for further testing, rather than precise estimation of their mean yields. Now that spatial analyses of data from field experiments are becoming more common, there is interest in an efficient layout of an experiment given a proposed spatial analysis and an efficiency criterion. Common optimal design criteria values depend on the usual C-matrix, which is very large, and hence it is time consuming to calculate its inverse. Since most varieties are unreplicated, the variety incidence matrix has a simple form, and some matrix manipulations can dramatically reduce the computation needed. However, there are many designs to compare, and numerical optimisation lacks insight into good design features. Some possible design criteria are discussed, and approximations to their values considered. These allow the features of efficient layouts under spatial dependence to be given and compared. (c) 2006 Elsevier Inc. All rights reserved.

Spatially clustered associations in health GIS

Overlaying maps using a desktop GIS is often the first step of a multivariate spatial analysis. The potential of this operation has increased considerably as data sources and Web services to manipulate them are becoming widely available via the Internet. Standards from the OGC enable such geospatial mashups to be seamless and user driven, involving discovery of thematic data. The user is naturally inclined to look for spatial clusters and correlation of outcomes. Using classical cluster detection scan methods to identify multivariate associations can be problematic in this context, because of a lack of control on or knowledge about background populations. For public health and epidemiological mapping, this limiting factor can be critical but often the focus is on spatial identification of risk factors associated with health or clinical status. Spatial entropy index HSu for the ScankOO analysis of the hypothetical dataset using a vicinity which is fixed by the number of points without distinction between their labels. (The size of the labels is proportional to the inverse of the index) In this article we point out that this association itself can ensure some control on underlying populations, and develop an exploratory scan statistic framework for multivariate associations. Inference using statistical map methodologies can be used to test the clustered associations. The approach is illustrated with a hypothetical data example and an epidemiological study on community MRSA. Scenarios of potential use for online mashups are introduced but full implementation is left for further research.

Representation and parameterisation issues in genetic algorithms

A multi-chromosome GA (Multi-GA) was developed, based upon concepts from the natural world, allowing improved flexibility in a number of areas including representation, genetic operators, their parameter rates and real world multi-dimensional applications. A series of experiments were conducted, comparing the performance of the Multi-GA to a traditional GA on a number of recognised and increasingly complex test optimisation surfaces, with promising results. Further experiments demonstrated the Multi-GA's flexibility through the use of non-binary chromosome representations and its applicability to dynamic parameterisation. A number of alternative and new methods of dynamic parameterisation were investigated, in addition to a new non-binary 'Quotient crossover' mechanism. Finally, the Multi-GA was applied to two real world problems, demonstrating its ability to handle mixed type chromosomes within an individual, the limited use of a chromosome level fitness function, the introduction of new genetic operators for structural self-adaptation and its viability as a serious real world analysis tool. The first problem involved optimum placement of computers within a building, allowing the Multi-GA to use multiple chromosomes with different type representations and different operators in a single individual. The second problem, commonly associated with Geographical Information Systems (GIS), required a spatial analysis location of the optimum number and distribution of retail sites over two different population grids. In applying the Multi-GA, two new genetic operators (addition and deletion) were developed and explored, resulting in the definition of a mechanism for self-modification of genetic material within the Multi-GA structure and a study of this behaviour.

Spatially clustered associations in health related geospatial data

Overlaying maps using a desktop GIS is often the first step of a multivariate spatial analysis. The potential of this operation has increased considerably as data sources an dWeb services to manipulate them are becoming widely available via the Internet. Standards from the OGC enable such geospatial ‘mashups’ to be seamless and user driven, involving discovery of thematic data. The user is naturally inclined to look for spatial clusters and ‘correlation’ of outcomes. Using classical cluster detection scan methods to identify multivariate associations can be problematic in this context, because of a lack of control on or knowledge about background populations. For public health and epidemiological mapping, this limiting factor can be critical but often the focus is on spatial identification of risk factors associated with health or clinical status. In this article we point out that this association itself can ensure some control on underlying populations, and develop an exploratory scan statistic framework for multivariate associations. Inference using statistical map methodologies can be used to test the clustered associations. The approach is illustrated with a hypothetical data example and an epidemiological study on community MRSA. Scenarios of potential use for online mashups are introduced but full implementation is left for further research.

Regional variation in written American English

The first study of its kind, Regional Variation in Written American English takes a corpus-based approach to map over a hundred grammatical alternation variables across the United States. A multivariate spatial analysis of these maps shows that grammatical alternation variables follow a relatively small number of common regional patterns in American English, which can be explained based on both linguistic and extra-linguistic factors. Based on this rigorous analysis of extensive data, Grieve identifies five primary modern American dialect regions, demonstrating that regional variation is far more pervasive and complex in natural language than is generally assumed. The wealth of maps and data and the groundbreaking implications of this volume make it essential reading for students and researchers in linguistics, English language, geography, computer science, sociology and communication studies.

System-on-a-Chip (SOC) based environmental monitoring platform for the detection of hazardous materials

The purpose of this research is design considerations for environmental monitoring platforms for the detection of hazardous materials using System-on-a-Chip (SoC) design. Design considerations focus on improving key areas such as: (1) sampling methodology; (2) context awareness; and (3) sensor placement. These design considerations for environmental monitoring platforms using wireless sensor networks (WSN) is applied to the detection of methylmercury (MeHg) and environmental parameters affecting its formation (methylation) and deformation (demethylation). ^ The sampling methodology investigates a proof-of-concept for the monitoring of MeHg using three primary components: (1) chemical derivatization; (2) preconcentration using the purge-and-trap (P&T) method; and (3) sensing using Quartz Crystal Microbalance (QCM) sensors. This study focuses on the measurement of inorganic mercury (Hg) (e.g., Hg2+) and applies lessons learned to organic Hg (e.g., MeHg) detection. ^ Context awareness of a WSN and sampling strategies is enhanced by using spatial analysis techniques, namely geostatistical analysis (i.e., classical variography and ordinary point kriging), to help predict the phenomena of interest in unmonitored locations (i.e., locations without sensors). This aids in making more informed decisions on control of the WSN (e.g., communications strategy, power management, resource allocation, sampling rate and strategy, etc.). This methodology improves the precision of controllability by adding potentially significant information of unmonitored locations.^ There are two types of sensors that are investigated in this study for near-optimal placement in a WSN: (1) environmental (e.g., humidity, moisture, temperature, etc.) and (2) visual (e.g., camera) sensors. The near-optimal placement of environmental sensors is found utilizing a strategy which minimizes the variance of spatial analysis based on randomly chosen points representing the sensor locations. Spatial analysis is employed using geostatistical analysis and optimization occurs with Monte Carlo analysis. Visual sensor placement is accomplished for omnidirectional cameras operating in a WSN using an optimal placement metric (OPM) which is calculated for each grid point based on line-of-site (LOS) in a defined number of directions where known obstacles are taken into consideration. Optimal areas of camera placement are determined based on areas generating the largest OPMs. Statistical analysis is examined by using Monte Carlo analysis with varying number of obstacles and cameras in a defined space. ^

Evidence of Recent Phosphorus Enrichment in Surface Soils of Taylor Slough and Northeast Everglades National Park

Everglades National Park (ENP) is the last hydrologic unit in the series of impounded marsh units that make up the present-day Everglades. The ENP receives water from upstream Water Conservation Areas via canals and water control structures that are highly regulated for flood control, water supply, wildlife management, concerns about poor water quality and the potential for downstream ecosystem degradation. Recent surveys of surface soils in ENP, designed for random sampling for spatial analysis of soil nutrients, did not sample proximate to inflow structures and thus did not detect increased soil phosphorus associated with these water conveyances. This study specifically addressed these areas in a focused sampling effort at three key inflow points in northeast ENP which revealed elevated soil TP proximate to inflows. Two transects extending down Shark River Slough and one down Taylor Slough (a natural watershed of particular ecological value) were found to have soil TP levels in excess of 500 mg kg−1—a threshold above which P enrichment is indicated. These findings suggest the negative impact of elevated water (P) from surface flows and support the assertion that significant soil TP enrichment is occurring in Taylor Slough and other areas of northeastern ENP.

Metacommunity Structure Along Resource and Disturbance Gradients in Everglades Wetlands

We evaluated metacommunity hypotheses of landscape arrangement (indicative of dispersal limitation) and environmental gradients (hydroperiod and nutrients) in structuring macroinvertebrate and fish communities in the southern Everglades. We used samples collected at sites from the eastern boundary of the southern Everglades and from Shark River Slough, to evaluate the role of these factors in metacommunity structure. We used eigenfunction spatial analysis to model community structure among sites and distance-based redundancy analysis to partition the variability in communities between spatial and environmental filters. For most animal communities, hydrological parameters had a greater influence on structure than nutrient enrichment, however both had large effects. The influence of spatial effects indicative of dispersal limitation was weak and only periphyton infauna appeared to be limited by regional dispersal. At the landscape scale, communities were well-mixed, but strongly influenced by hydrology. Local-scale species dominance was influenced by water-permanence and nutrient enrichment. Nutrient enrichment is limited to water inflow points associated with canals, which may explain its impact in this data set. Hydroperiod and nutrient enrichment are controlled by water managers; our analysis indicates that the decisions they make have strong effects on the communities at the base of the Everglades food web.

HIV/AIDS: um perfil epidemiológico do vírus e uma análise das práticas seguras, conhecimento e percepção de mulheres

In recent decades, the debate surrounding the consequences of the HIV has passed by great changes. Earlier, prevention campaigns focused risk groups then risk behaviors and ultimately vulnerability. Furthermore, over the years, the dimensions of HIV that emerged in the social environment are these: internalization, heterosexualization, impoverishment and feminization. Based on these contexts, the composition of this study comprises two papers: the former has the overall objective to analyze the epidemiology and incidence of HIV in Brazilian regions in the period from 1980 to 2012; the latter, it aims to find out whether there is the relationship among safe practices, knowledge and perception of women residents in Manaus and Boa Vista cities on the infection by HIV. In paper 1, it was used information from the Health Ministry, as a data source. Besides, it was developed an exploratory and spatial analysis of incidence rates and relative proportion of notified cases. In paper 2, was used as a source of data, the research "Evaluating the process of spatial and epidemic diffusion of HIV in the federal units of Brazil-Northern Region" in 2008. Furthermore, Statistical Techniques of Cluster Analysis, Analysis of Variance, Chi-Square and Logistic Regression were applied. In this paper, it was found that, in Brazilian Regions, the prevalence of reported cases occurred among heterosexuals in men 20-40 year age group and residing in metropolitan areas. It was observed a significant spatial correlation of the incidence rate of reported cases of HIV. It was also noted by the results that have good knowledge and awareness about HIV does not imply, essentially, in a safe sexual intercourse. These results have shown the need public policies geared to the guiding of society, based in educational strategies aiming both information about the virus and its prevention, as well as public awareness for safe sex practices or in stable or not intercourses

A desigualdade espacial do Baixo Peso ao Nascer no Brasil

Low birth weight (LBW) is a risk factor for neonatal and infant morbidity and mortality. In Brazil the highest percentages of low birth weight occur in regions of higher socio-economic status. The scope of this article is to ascertain the spatial distribution of low birth weight rates and the correlation with social and service indicators. The scale is ecological taking all the Brazilian states as units of analysis. The spatial analysis technique is the methodology used together with data from SINASC, IPEA and IBGE for 2009. Higher rates of low birth weight are found in the south/southeastern states (Global Moran: 0.267, p = 0.02). Clusters of the high-high type in the Southeast and of the low-low variety in states in the Amazon region are detected. The spatial inequality of low birth weight reflects the socio-economic conditions of the states. More developed regions have higher rates of low birth weight, therefore, the presence of the service and its use decrease infant mortality and increase LBW.