A new data source for fisheries resource assessment: genetic estimates of the effective number of spawners. Final Report to the Fisheries Research and Development Corporation.

The development of innovative methods of stock assessment is a priority for State and Commonwealth fisheries agencies. It is driven by the need to facilitate sustainable exploitation of naturally occurring fisheries resources for the current and future economic, social and environmental well being of Australia. This project was initiated in this context and took advantage of considerable recent achievements in genomics that are shaping our comprehension of the DNA of humans and animals. The basic idea behind this project was that genetic estimates of effective population size, which can be made from empirical measurements of genetic drift, were equivalent to estimates of the successful number of spawners that is an important parameter in process of fisheries stock assessment. The broad objectives of this study were to 1. Critically evaluate a variety of mathematical methods of calculating effective spawner numbers (Ne) by a. conducting comprehensive computer simulations, and by b. analysis of empirical data collected from the Moreton Bay population of tiger prawns (P. esculentus). 2. Lay the groundwork for the application of the technology in the northern prawn fishery (NPF). 3. Produce software for the calculation of Ne, and to make it widely available. The project pulled together a range of mathematical models for estimating current effective population size from diverse sources. Some of them had been recently implemented with the latest statistical methods (eg. Bayesian framework Berthier, Beaumont et al. 2002), while others had lower profiles (eg. Pudovkin, Zaykin et al. 1996; Rousset and Raymond 1995). Computer code and later software with a user-friendly interface (NeEstimator) was produced to implement the methods. This was used as a basis for simulation experiments to evaluate the performance of the methods with an individual-based model of a prawn population. Following the guidelines suggested by computer simulations, the tiger prawn population in Moreton Bay (south-east Queensland) was sampled for genetic analysis with eight microsatellite loci in three successive spring spawning seasons in 2001, 2002 and 2003. As predicted by the simulations, the estimates had non-infinite upper confidence limits, which is a major achievement for the application of the method to a naturally-occurring, short generation, highly fecund invertebrate species. The genetic estimate of the number of successful spawners was around 1000 individuals in two consecutive years. This contrasts with about 500,000 prawns participating in spawning. It is not possible to distinguish successful from non-successful spawners so we suggest a high level of protection for the entire spawning population. We interpret the difference in numbers between successful and non-successful spawners as a large variation in the number of offspring per family that survive – a large number of families have no surviving offspring, while a few have a large number. We explored various ways in which Ne can be useful in fisheries management. It can be a surrogate for spawning population size, assuming the ratio between Ne and spawning population size has been previously calculated for that species. Alternatively, it can be a surrogate for recruitment, again assuming that the ratio between Ne and recruitment has been previously determined. The number of species that can be analysed in this way, however, is likely to be small because of species-specific life history requirements that need to be satisfied for accuracy. The most universal approach would be to integrate Ne with spawning stock-recruitment models, so that these models are more accurate when applied to fisheries populations. A pathway to achieve this was established in this project, which we predict will significantly improve fisheries sustainability in the future. Regardless of the success of integrating Ne into spawning stock-recruitment models, Ne could be used as a fisheries monitoring tool. Declines in spawning stock size or increases in natural or harvest mortality would be reflected by a decline in Ne. This would be good for data-poor fisheries and provides fishery independent information, however, we suggest a species-by-species approach. Some species may be too numerous or experiencing too much migration for the method to work. During the project two important theoretical studies of the simultaneous estimation of effective population size and migration were published (Vitalis and Couvet 2001b; Wang and Whitlock 2003). These methods, combined with collection of preliminary genetic data from the tiger prawn population in southern Gulf of Carpentaria population and a computer simulation study that evaluated the effect of differing reproductive strategies on genetic estimates, suggest that this technology could make an important contribution to the stock assessment process in the northern prawn fishery (NPF). Advances in the genomics world are rapid and already a cheaper, more reliable substitute for microsatellite loci in this technology is available. Digital data from single nucleotide polymorphisms (SNPs) are likely to super cede ‘analogue’ microsatellite data, making it cheaper and easier to apply the method to species with large population sizes.

How accurate are the marker orders in crop linkage maps generated from large marker datasets?

Marker ordering during linkage map construction is a critical component of QTL mapping research. In recent years, high-throughput genotyping methods have become widely used, and these methods may generate hundreds of markers for a single mapping population. This poses problems for linkage analysis software because the number of possible marker orders increases exponentially as the number of markers increases. In this paper, we tested the accuracy of linkage analyses on simulated recombinant inbred line data using the commonly used Map Manager QTX (Manly et al. 2001: Mammalian Genome 12, 930-932) software and RECORD (Van Os et al. 2005: Theoretical and Applied Genetics 112, 30-40). Accuracy was measured by calculating two scores: % correct marker positions, and a novel, weighted rank-based score derived from the sum of absolute values of true minus observed marker ranks divided by the total number of markers. The accuracy of maps generated using Map Manager QTX was considerably lower than those generated using RECORD. Differences in linkage maps were often observed when marker ordering was performed several times using the identical dataset. In order to test the effect of reducing marker numbers on the stability of marker order, we pruned marker datasets focusing on regions consisting of tightly linked clusters of markers, which included redundant markers. Marker pruning improved the accuracy and stability of linkage maps because a single unambiguous marker order was produced that was consistent across replications of analysis. Marker pruning was also applied to a real barley mapping population and QTL analysis was performed using different map versions produced by the different programs. While some QTLs were identified with both map versions, there were large differences in QTL mapping results. Differences included maximum LOD and R-2 values at QTL peaks and map positions, thus highlighting the importance of marker order for QTL mapping

Preliminary investigation into development of forage budget application and device

Forage budgeting, land condition monitoring and maintaining ground cover residuals are critical management practices for the long term sustainability of the northern grazing industry. The aim of this project is to do a preliminary investigation into industry need, feasibility and willingness to adopt a simple to use hand-held hardware device and compatible, integrated software applications that can be used in the paddock by producers, to assist in land condition monitoring and forage budgeting for better Grazing Land Management and to assist with compliance.

Spatially based data delivery and recording sys-Aust Sugar Cane industry

Establish an internet platform where spatially referenced data can be viewed, entered and stored.

Kalman Filtering in R

Support in R for state space estimation via Kalman filtering was limited to one package, until fairly recently. In the last five years, the situation has changed with no less than four additional packages offering general implementations of the Kalman filter, including in some cases smoothing, simulation smoothing and other functionality. This paper reviews some of the offerings in R to help the prospective user to make an informed choice.

Mateda-2.0: Estimation of Distribution Algorithms in MATLAB

This paper describes Mateda-2.0, a MATLAB package for estimation of distribution algorithms (EDAs). This package can be used to solve single and multi-objective discrete and continuous optimization problems using EDAs based on undirected and directed probabilistic graphical models. The implementation contains several methods commonly employed by EDAs. It is also conceived as an open package to allow users to incorporate different combinations of selection, learning, sampling, and local search procedures. Additionally, it includes methods to extract, process and visualize the structures learned by the probabilistic models. This way, it can unveil previously unknown information about the optimization problem domain. Mateda-2.0 also incorporates a module for creating and validating function models based on the probabilistic models learned by EDAs.

Imputação múltipla de dados faltantes: exemplo de aplicação no Estudo Pró-Saúde

Dados faltantes são um problema comum em estudos epidemiológicos e, dependendo da forma como ocorrem, as estimativas dos parâmetros de interesse podem estar enviesadas. A literatura aponta algumas técnicas para se lidar com a questão, e, a imputação múltipla vem recebendo destaque nos últimos anos. Esta dissertação apresenta os resultados da utilização da imputação múltipla de dados no contexto do Estudo Pró-Saúde, um estudo longitudinal entre funcionários técnico-administrativos de uma universidade no Rio de Janeiro. No primeiro estudo, após simulação da ocorrência de dados faltantes, imputou-se a variável cor/raça das participantes, e aplicou-se um modelo de análise de sobrevivência previamente estabelecido, tendo como desfecho a história auto-relatada de miomas uterinos. Houve replicação do procedimento (100 vezes) para se determinar a distribuição dos coeficientes e erros-padrão das estimativas da variável de interesse. Apesar da natureza transversal dos dados aqui utilizados (informações da linha de base do Estudo Pró-Saúde, coletadas em 1999 e 2001), buscou-se resgatar a história do seguimento das participantes por meio de seus relatos, criando uma situação na qual a utilização do modelo de riscos proporcionais de Cox era possível. Nos cenários avaliados, a imputação demonstrou resultados satisfatórios, inclusive quando da avaliação de performance realizada. A técnica demonstrou um bom desempenho quando o mecanismo de ocorrência dos dados faltantes era do tipo MAR (Missing At Random) e o percentual de não-resposta era de 10%. Ao se imputar os dados e combinar as estimativas obtidas nos 10 bancos (m=10) gerados, o viés das estimativas era de 0,0011 para a categoria preta e 0,0015 para pardas, corroborando a eficiência da imputação neste cenário. Demais configurações também apresentaram resultados semelhantes. No segundo artigo, desenvolve-se um tutorial para aplicação da imputação múltipla em estudos epidemiológicos, que deverá facilitar a utilização da técnica por pesquisadores brasileiros ainda não familiarizados com o procedimento. São apresentados os passos básicos e decisões necessárias para se imputar um banco de dados, e um dos cenários utilizados no primeiro estudo é apresentado como exemplo de aplicação da técnica. Todas as análises foram conduzidas no programa estatístico R, versão 2.15 e os scripts utilizados são apresentados ao final do texto.

Study of effect Rosmarinus officinalis oil and nisin on the growth of Streptococcus iniae in lab conditions and fillets of rainbow trout (Oncorhynchus mykiss)

Fish are an important part of a healthy diet since they contain high quality protein, but typically present a low fat percent when compared to other meats. Fish is an extremely perishable food commodity. On the other hand, food borne diseases are still a major problem in the world, even in well-developed countries. The increasing incidence of food borne diseases coupled with the resultant social and economic implications means there is a constant striving to produce safer food and to develop new antimicrobial agents concerns over the safety of some chemical preservatives and negative consumer reactions to preservatives they perceive as chemical and artificial, have prompted on increased interest in more ‘‘naturalgreen’’ alternatives for the maintenance or extension of product shelf-life. Particular interest has focused on the potential applications of plant essential oils. However, to establish the usefulness of natural antimicrobial preservatives, they must be evaluated alone and in combination with other preservation factors to determine whether there are synergistic effects and multiple hurdles can be devised. In this study, were evaluated the effects of different concentrations of Rosmarinus officinalis and nisin and storage time (15 days) on growth of Streptococcus iniae GQ850377 in a lab conditions and a food model system (fillets of rainbow trout) in 4 and 8 °C. In addition, we also studied multi factorial effects of four different concentration of rosemary, three different concentrations of nisin, two different levels of pH in 3 temperature 4,15 and 37 °C on log% of S.iniae during 43 days in BHI broth. The results on growth of S. iniae were evaluated using SPSS 20.0 statistical software and analyzed the logarithm of total count of the bacterial by Tukey Test. Results were considered statistically significant when P<0.05. MIC and MBC values of rosemary and nisin were 0.03, 0.075 % and 5, 40 μg/mL, respectively. The growth of S. iniae was effected significantly (P<0.05) by rosemary and nisin and also combination of rosemary and nisin in 4 and 8 °C. Samples treated with 0.135 and 0.405 % of rosemary showed a significant decrease on the growth of the bacteria compared with control sample(P<0.05). The most ١٤٦ inhibitory effects were seen in samples treated with 0.135 and 0.405% of rosemary until 9 days after storage. Also, the synergism effects of rosemary and nisin on the growth rate of bacteria was significant (P<0.05) compared with untreated samples and samples treated with the rosemary or nisin, only. Synergistic effects was observed at concentration of 0.405% rosemary and 0.75 μg/mL nisin in both temprature. Results of this study showed that different concentration of rosemary a significant inhibitory effect (P<0.05) on log% of S. iniae, in BHI broth in pH 5.5 and 7 in 4,15 and 37 °C during 43 days. In concentration of 0% rosemary (control) in pH 5.5 and 7 and 37°C, log% were 1.099 and 3.15, whereas in concentration of 0.015% rosemary were -4/241 and 1.454, respectively. The use of essential oils may improve food safety and overall microbial quality. If essential oils were to be more widely applied as antibacterials in foods, the organoleptic impact would be important. In addition, it is recommended to apply essential oils or their compounds as part of a hurdle system and to use it as an antimicrobial component along with other preservation techniques. Thus essential of R. officinalis with high antibacterial activity selected in this study could be a potential source for inhibitory substances against some food-borne pathogens and they may be candidates for using in foods or food-processing systems.

Effects of Zataria multiflora Boiss essential oil and nisin on the growth of Staphylococcus aureus in light salted silver carp fillet (Hypophthalmichthys molitrix)

There is an increasing demand in developing newer and safer methods in preserving food products.Among which herbal additives seem to attract evermore attention recently.the major advantage of herbal additives is due to their favorable aroma besides their antimicrobial effects and less expensive than chemical additives. Zataria multiflora Boiss is a native Iranian herb which is used vastly as a food preserver essential oils and also medical usage. Metabolites of harmless bacteria, such as Nisin are also known to be safe preservatives that have antimicrobial activity. However to establish the usefulness of natural antimicrobial preservatives, they must be evaluated alone and in combination with other preservation factors to determine whether there are synergistic effects in rigid media . In this study were evaluated the effects of different concentrations of Zataria multiflora (EO 0, 0.005, 0.015, 0.045, 0.135, 0.405 ,0.810 %) and Nisin(0, 0.15, 0.25, 0.75 μg/ml) and Storage time (up to 21 days) on growth of Staphylococcus aureus ATCC 6538 in a food model system(light salted fish of silver carp, Hypophthalmichthys molitrix). The results on growth of S. aureus were evaluated using SPSS 15.0 statistical software (SPSS 15.0 for windows, SPSS Inc.) and analyzed the logarithm of total count of the bacteria by Tukey Test. Results were considered statistically significant when P≤0.05. The growth of Staphylococcus aureus was affected significantly(P<0.05) by EO and Nisin and also combinations of EO and Nisin. Samples treated with 0.135, 0.405 and 0.810% of thyme essential oil showed a significant decrease on the growth of the bacteria compared with an treated samples(P<0.05). No significant difference was seen on the growth of S.aureus in samples treated with lower concentrations of Z.multiflora(below 0.045%) and untreated group(P>0.05). The most inhibitory effects were seen in samples treated with 0.405% and 0.810% of thyme essential oil until 9 and 12 days after storage,respectively. Also there was significant inhibtory effect(P<0.05) in different concentration of nisin on the organism compared with an treated samples. The synergism effects of the Eo and nisin on the growth rate of the bacteria was significant (P<0.05) compared with untreated samples and samples treated with the Eo or nisin, only. Synergismic effects was observed at concentration of 0.405 and 0.810% of Z. multiflora essential oil with 0.25 μg/ml Nisin, respectively until 15 days after storage. As expected it is preferred to apply the least possible amounts of additives in food preserving that not only are effective and safe but are economically justifiable.

Welfare Effort and Poverty from the Monetary and Capabilities Approach: A Cross-Country Analysis in Latin American and the Caribbean (1990-2010)

There is abundant empirical evidence on the negative relationship between welfare effort and poverty. However, poverty indicators traditionally used have been representative of the monetary approach, excluding its multidimensional reality from the analysis. Using three regression techniques for the period 1990-2010 and controlling for demographic and cyclical factors, this paper examines the relationship between social spending per capita —as the indicator of welfare effort— and poverty in up to 21 countries of the region. The proportion of the population with an income below its national basic basket of goods and services (PM1) and the proportion of population with an income below 50% of the median income per capita (PM2) were the two poverty indicators considered from the monetarist approach to measure poverty. From the capability approach the proportion of the population with food inadequacy (PC1) and the proportion of the population without access to improved water sources or sanitation facilities (PC2) were used. The fi ndings confi rm that social spending is actually useful to explain changes in poverty (PM1, PC1 and PC2), as there is a high negative and signifi cant correlation between the variables before and after controlling for demographic and cyclical factors. In two regression techniques, social spending per capita did not show a negative relationship with the PM2. Countries with greater welfare effort for the period 1990-2010 were not necessarily those with the lowest level of poverty. Ultimately social spending per capita was more useful to explain changes in poverty from the capability approach.

Psychology Statistics for Dummies

The quick, easy way to master all the statistics you'll ever need The bad news first: if you want a psychology degree you'll need to know statistics. Now for the good news: Psychology Statistics For Dummies. Featuring jargon-free explanations, step-by-step instructions and dozens of real-life examples, Psychology Statistics For Dummies makes the knotty world of statistics a lot less baffling. Rather than padding the text with concepts and procedures irrelevant to the task, the authors focus only on the statistics psychology students need to know. As an alternative to typical, lead-heavy statistics texts or supplements to assigned course reading, this is one book psychology students won't want to be without. Ease into statistics – start out with an introduction to how statistics are used by psychologists, including the types of variables they use and how they measure them Get your feet wet – quickly learn the basics of descriptive statistics, such as central tendency and measures of dispersion, along with common ways of graphically depicting information Meet your new best friend – learn the ins and outs of SPSS, the most popular statistics software package among psychology students, including how to input, manipulate and analyse data Analyse this – get up to speed on statistical analysis core concepts, such as probability and inference, hypothesis testing, distributions, Z-scores and effect sizes Correlate that – get the lowdown on common procedures for defining relationships between variables, including linear regressions, associations between categorical data and more Analyse by inference – master key methods in inferential statistics, including techniques for analysing independent groups designs and repeated-measures research designs Open the book and find: Ways to describe statistical data How to use SPSS statistical software Probability theory and statistical inference Descriptive statistics basics How to test hypotheses Correlations and other relationships between variables Core concepts in statistical analysis for psychology Analysing research designs Learn to: Use SPSS to analyse data Master statistical methods and procedures using psychology-based explanations and examples Create better reports Identify key concepts and pass your course

A Bayesian framework for age modeling of radiocarbon-dated peat deposits: Case studies from the Netherlands

Recently, Bayesian statistical software has been developed for age-depth modeling (wiggle-match dating) of sequences of densely spaced radiocarbon dates from peat cores. The method is described in non-statistical terms, and is compared with an alternative method of chronological ordering of 14C dates. Case studies include the dating of the start of agriculture in the northeastern part of the Netherlands, and of a possible Hekla-3 tephra layer in the same country. We discuss future enhancements in Bayesian age modeling.

Transition from Children’s to Adult Services forYoung People with Life-limiting Conditions: Findings from a Questionnaire Survey in Northern Ireland

Research aims: 
To describe service provision for the transition from children’s to adult services for young people with life-limiting conditions in Northern Ireland, and to identify organisational factors that promote or inhibit effective transition. 
Study population: 
Health, social, educational and charitable organisations providing transition services to young people with life-limiting conditions in Northern Ireland. 
Study design and methods: 
A questionnaire has been developed by the research team drawing on examples from the literature and the advice of an expert advisory group. The questionnaire was piloted with clinicians,academics and researchers in June 2013. The questionnaire focuses on components of practice which may promote continuity in the transition from child to adult care for young people with a life-limiting condition. The survey will be distributed throughout Northern Ireland to an estimated 75 organisations, following the Dillman total design survey method. Numerical data will be analysed using PASW Statistical software to generate descriptive statistics along with a thematic analysis of data generated by open-ended questions. 
Results and interpretations: 
The survey will provide a description of services, transition policies, approaches to managing transition, categories of service users, the ages at which transition starts and completes, experiences with minority ethnic groups, the input of service users to the process, organisational factors promoting or hindering effective transition, links between services, and service providers’ recommendations for improvements in services.The outcomes will be an overview of the transition services currently provided in Northern Ireland identifying models of good practice and the key factors influencing the quality, safety and continuity of care. Survey results are due early in 2014.

Using interviewer random effects to remove selection bias from HIV prevalence estimates

Background: Selection bias in HIV prevalence estimates occurs if non-participation in testing is correlated with HIV status. Longitudinal data suggests that individuals who know or suspect they are HIV positive are less likely to participate in testing in HIV surveys, in which case methods to correct for missing data which are based on imputation and observed characteristics will produce biased results. Methods: The identity of the HIV survey interviewer is typically associated with HIV testing participation, but is unlikely to be correlated with HIV status. Interviewer identity can thus be used as a selection variable allowing estimation of Heckman-type selection models. These models produce asymptotically unbiased HIV prevalence estimates, even when non-participation is correlated with unobserved characteristics, such as knowledge of HIV status. We introduce a new random effects method to these selection models which overcomes non-convergence caused by collinearity, small sample bias, and incorrect inference in existing approaches. Our method is easy to implement in standard statistical software, and allows the construction of bootstrapped standard errors which adjust for the fact that the relationship between testing and HIV status is uncertain and needs to be estimated. Results: Using nationally representative data from the Demographic and Health Surveys, we illustrate our approach with new point estimates and confidence intervals (CI) for HIV prevalence among men in Ghana (2003) and Zambia (2007). In Ghana, we find little evidence of selection bias as our selection model gives an HIV prevalence estimate of 1.4% (95% CI 1.2% – 1.6%), compared to 1.6% among those with a valid HIV test. In Zambia, our selection model gives an HIV prevalence estimate of 16.3% (95% CI 11.0% - 18.4%), compared to 12.1% among those with a valid HIV test. Therefore, those who decline to test in Zambia are found to be more likely to be HIV positive. Conclusions: Our approach corrects for selection bias in HIV prevalence estimates, is possible to implement even when HIV prevalence or non-participation is very high or very low, and provides a practical solution to account for both sampling and parameter uncertainty in the estimation of confidence intervals. The wide confidence intervals estimated in an example with high HIV prevalence indicate that it is difficult to correct statistically for the bias that may occur when a large proportion of people refuse to test.