High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials.

The Effect of Silica and Manure Addition into Suppressive and Conducive Soil on the Incidence of Fusarium Wilt Disease of Banana

Fusarium wilt, caused by Fusarium oxysporum f. sp. cubense (Foc), is one of the most destructive diseases of banana. One potential method to manage fusarium wilt of banana is by manipulating the nutrient status in the soil. This study was conducted to determine the quality of Foc suppressive and conducive soil, the influence of soil application of silica and manure on the incidence of fusarium wilt of banana. Surveys were conducted in five banana plantations in three provinces in Indonesia: Lampung-Sumatra, West Java and Central Java. From the five locations, one location (Sala-man-Central Java) was heavily infected by Foc, another location (NTF Lampung-Sumatera) was slightly infected by Foc, while the rest (Sarampad-West Java, Talaga-West Java and GGP Lampung-Sumatra) were healthy banana plantations without Foc infection. Labile carbon analysis showed that the Foc suppressive soil had greater labile carbon content than conducive soil. Also, the analysis of fluorescein diacetate hydrolysis (FDA) and ?-glucosidase showed greater microbial activity in suppressive soil than the conducive soil. Observations of the incidence of necrotic rhizome of Foc susceptible 'Ambon Kuning' (AAA) banana cultivar showed that in the suppressive soil taken from Sarampad West Java, the application of silica and manure helped suppress fusarium wilt disease development. In the conducive soil taken from Salaman-Central Java, silica and manure applications were not able to suppress disease incidence. The result of this study indicated that in suppressive soil, the application of silica can increase plant resistance to Foc infection, while manure application can increase soil microbial activity, and suppress Foc development.

Smoking behaviour modifies IL23r-associated disease risk in patients with Crohn's disease

Background and Aim The etiology of Crohn's disease (CD) implicates both genetic and environmental factors. Smoking behavior is one environmental risk factor to play a role in the development of CD. The study aimed to assess the contribution of the interleukin 23 receptor (IL23R) in determining disease susceptibility in two independent cohorts of CD, and to investigate the interactions between IL23R variants, smoking behavior, and CD-associated genes, NOD2 and ATG16L1. Methods Ten IL23R single-nucleotide polymorphisms (SNPs) were genotyped in 675 CD cases, and 1255 controls from Brisbane, Australia (dataset 1). Six of these SNPs were genotyped in 318 CD cases and 533 controls from Canterbury, New Zealand (dataset 2). Case–control analysis of genotype and allele frequencies, and haplotype analysis for all SNPs was conducted. Results We demonstrate a strong increased CD risk for smokers in both datasets (odds ratio 3.77, 95% confidence interval 2.88–4.94), and an additive interaction between IL23R SNPs and cigarette smoking. Ileal involvement was a consistent marker of strong SNP–CD association (P ≤ 0.001), while the lowest minor allele frequencies for location were found in those with colonic CD (L2). Three haplotype blocks were identified across the 10 IL23R SNPs conferring different risk of CD. Haplotypes conferred no further risk of CD when compared with single SNP analyses. Conclusion IL23R gene variants determine CD susceptibility in the Australian and New Zealand population, particularly ileal CD. A strong additive interaction exists between IL23R SNPs and smoking behavior resulting in a dramatic increase in disease risk depending upon specific genetic background.

Disease Diplomacy

In the age of air travel and globalized trade, pathogens that once took months or even years to spread beyond their regions of origin can now circumnavigate the globe in a matter of hours. Amid growing concerns about such epidemics as Ebola, SARS, MERS, and H1N1, disease diplomacy has emerged as a key foreign and security policy concern as countries work to collectively strengthen the global systems of disease surveillance and control. The revision of the International Health Regulations (IHR), eventually adopted by the World Health Organization’s member states in 2005, was the foremost manifestation of this novel diplomacy. The new regulations heralded a profound shift in international norms surrounding global health security, significantly expanding what is expected of states in the face of public health emergencies and requiring them to improve their capacity to detect and contain outbreaks. Drawing on Martha Finnemore and Kathryn Sikkink’s "norm life cycle" framework and based on extensive documentary analysis and key informant interviews, Disease Diplomacy traces the emergence of these new norms of global health security, the extent to which they have been internalized by states, and the political and technical constraints governments confront in attempting to comply with their new international obligations. The authors also examine in detail the background, drafting, adoption, and implementation of the IHR while arguing that the very existence of these regulations reveals an important new understanding: that infectious disease outbreaks and their management are critical to national and international security. The book will be of great interest to academic researchers, postgraduate students, and advanced undergraduates in the fields of global public health, international relations, and public policy, as well as health professionals, diplomats, and practitioners with a professional interest in global health security.

The Politics of Surveillance and Response to Disease Outbreaks: The New Frontier for States and Non-state Actors

The capacity to conduct international disease outbreak surveillance and share information about outbreaks quickly has empowered both State and Non-State Actors to take an active role in stopping the spread of disease by generating new technical means to identify potential pandemics through the creation of shared reporting platforms. Despite all the rhetoric about the importance of infectious disease surveillance, the concept itself has received relatively little critical attention from academics, practitioners, and policymakers. This book asks leading contributors in the field to engage with five key issues attached to international disease outbreak surveillance - transparency, local engagement, practical needs, integration, and appeal - to illuminate the political effect of these technologies on those who use surveillance, those who respond to surveillance, and those being monitored.

The politics of disease surveillance

The capacity to conduct international disease outbreak surveillance and share information about outbreaks quickly has empowered both State and Non-State Actors to take an active role in stopping the spread of disease by generating new technical means to identify potential pandemics through the creation of shared reporting platforms. Despite all the rhetoric about the importance of infectious disease surveillance, the concept itself has received relatively little critical attention from academics, practitioners, and policymakers. This book asks leading contributors in the field to engage with five key issues attached to international disease outbreak surveillance - transparency, local engagement, practical needs, integration, and appeal - to illuminate the political effect of these technologies on those who use surveillance, those who respond to surveillance, and those being monitored.

On probability-based inference under data missing by design

Whether a statistician wants to complement a probability model for observed data with a prior distribution and carry out fully probabilistic inference, or base the inference only on the likelihood function, may be a fundamental question in theory, but in practice it may well be of less importance if the likelihood contains much more information than the prior. Maximum likelihood inference can be justified as a Gaussian approximation at the posterior mode, using flat priors. However, in situations where parametric assumptions in standard statistical models would be too rigid, more flexible model formulation, combined with fully probabilistic inference, can be achieved using hierarchical Bayesian parametrization. This work includes five articles, all of which apply probability modeling under various problems involving incomplete observation. Three of the papers apply maximum likelihood estimation and two of them hierarchical Bayesian modeling. Because maximum likelihood may be presented as a special case of Bayesian inference, but not the other way round, in the introductory part of this work we present a framework for probability-based inference using only Bayesian concepts. We also re-derive some results presented in the original articles using the toolbox equipped herein, to show that they are also justifiable under this more general framework. Here the assumption of exchangeability and de Finetti's representation theorem are applied repeatedly for justifying the use of standard parametric probability models with conditionally independent likelihood contributions. It is argued that this same reasoning can be applied also under sampling from a finite population. The main emphasis here is in probability-based inference under incomplete observation due to study design. This is illustrated using a generic two-phase cohort sampling design as an example. The alternative approaches presented for analysis of such a design are full likelihood, which utilizes all observed information, and conditional likelihood, which is restricted to a completely observed set, conditioning on the rule that generated that set. Conditional likelihood inference is also applied for a joint analysis of prevalence and incidence data, a situation subject to both left censoring and left truncation. Other topics covered are model uncertainty and causal inference using posterior predictive distributions. We formulate a non-parametric monotonic regression model for one or more covariates and a Bayesian estimation procedure, and apply the model in the context of optimal sequential treatment regimes, demonstrating that inference based on posterior predictive distributions is feasible also in this case.

Modeling Asymmetries in Financial Data with Multiplicative Error Models

This thesis addresses modeling of financial time series, especially stock market returns and daily price ranges. Modeling data of this kind can be approached with so-called multiplicative error models (MEM). These models nest several well known time series models such as GARCH, ACD and CARR models. They are able to capture many well established features of financial time series including volatility clustering and leptokurtosis. In contrast to these phenomena, different kinds of asymmetries have received relatively little attention in the existing literature. In this thesis asymmetries arise from various sources. They are observed in both conditional and unconditional distributions, for variables with non-negative values and for variables that have values on the real line. In the multivariate context asymmetries can be observed in the marginal distributions as well as in the relationships of the variables modeled. New methods for all these cases are proposed. Chapter 2 considers GARCH models and modeling of returns of two stock market indices. The chapter introduces the so-called generalized hyperbolic (GH) GARCH model to account for asymmetries in both conditional and unconditional distribution. In particular, two special cases of the GARCH-GH model which describe the data most accurately are proposed. They are found to improve the fit of the model when compared to symmetric GARCH models. The advantages of accounting for asymmetries are also observed through Value-at-Risk applications. Both theoretical and empirical contributions are provided in Chapter 3 of the thesis. In this chapter the so-called mixture conditional autoregressive range (MCARR) model is introduced, examined and applied to daily price ranges of the Hang Seng Index. The conditions for the strict and weak stationarity of the model as well as an expression for the autocorrelation function are obtained by writing the MCARR model as a first order autoregressive process with random coefficients. The chapter also introduces inverse gamma (IG) distribution to CARR models. The advantages of CARR-IG and MCARR-IG specifications over conventional CARR models are found in the empirical application both in- and out-of-sample. Chapter 4 discusses the simultaneous modeling of absolute returns and daily price ranges. In this part of the thesis a vector multiplicative error model (VMEM) with asymmetric Gumbel copula is found to provide substantial benefits over the existing VMEM models based on elliptical copulas. The proposed specification is able to capture the highly asymmetric dependence of the modeled variables thereby improving the performance of the model considerably. The economic significance of the results obtained is established when the information content of the volatility forecasts derived is examined.

Pythiogeton ramosum, a new pathogen of soft rot disease of ginger (Zingiber officinale) at high temperatures in Australia

Pythium soft rot (PSR) of ginger caused by a number of Pythium species is of the most concern worldwide. In Australia, PSR outbreaks associated with Pythium myriotylum was recorded in 2007. Our recent pathogenicity tests in Petri dishes conducted on ginger rhizomes and pot trials on ginger plants showed that Pythiogeton (Py.) ramosum, an uncommon studied oomycete in Pythiaceae, was also pathogenic to ginger at high temperature (30–35 °C). Ginger sticks excised from the rhizomes were colonised by Py. ramosum which caused soft rot and browning lesions. Ginger plants inoculated with Py. ramosum showed initial symptoms of wilting and leave yellowing, which were indistinguishable from those of Pythium soft rot of ginger, at 10 days after inoculation. In addition, morphological and phylogenetic studies indicated that isolates of Py. ramosum were quite variable and our isolates obtained from soft rot ginger were divided into two groups based on these variations. This is also for the first time Py. ramosum is reported as a pathogen on ginger at high temperatures.

Projecting future expansion of invasive species: Comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships with four modeling methods run with multiple scenarios of (1) sources of occurrences and geographically isolated background ranges for absences, (2) approaches to drawing background (absence) points, and (3) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved by using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e. into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g. boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post-hoc test conducted on a new Partenium dataset from Nepal validated excellent predictive performance of our “best” model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for Parthenium hysterophorus L. (Asteraceae; parthenium). However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. This article is protected by copyright. All rights reserved.

Making the most of spatial information in health: A tutorial in Bayesian disease mapping for areal data

Disease maps are effective tools for explaining and predicting patterns of disease outcomes across geographical space, identifying areas of potentially elevated risk, and formulating and validating aetiological hypotheses for a disease. Bayesian models have become a standard approach to disease mapping in recent decades. This article aims to provide a basic understanding of the key concepts involved in Bayesian disease mapping methods for areal data. It is anticipated that this will help in interpretation of published maps, and provide a useful starting point for anyone interested in running disease mapping methods for areal data. The article provides detailed motivation and descriptions on disease mapping methods by explaining the concepts, defining the technical terms, and illustrating the utility of disease mapping for epidemiological research by demonstrating various ways of visualising model outputs using a case study. The target audience includes spatial scientists in health and other fields, policy or decision makers, health geographers, spatial analysts, public health professionals, and epidemiologists.

Studies on "Sappe" disease of the silkworm, Bombyx mori : II. Effect of age of larvae on the manifestation of the disease

The effect of age of the larvae on the manifestation of the "Sappe" disease of the silkworm by oral inoculation of different pathogens, viz., Aerobacter cloacae, Pseudomonas boreopolis, Escherichia freundii, Achromobacter delmarvae, A. Superficialis, Pseudomonas ovalis, and Staphylococcus albus was tested. It was found that the reaction of the larva to the pathogen was influenced by its age. Some, e.g., Escherichia freundii, were more lethal when introduced at early stages whereas certain others, e.g., Aerobacter cloacae and Staphylococcus albus, caused maximum damage when invading older larvae. Irrespective of the age of infection, death of the worms mainly occurred during molting and before spinning. The studies also indicated that growth and mortality of the larvae were affected differentially by the pathogens.