Eliciting and encoding expert knowledge on variable selection into classical or Bayesian species distribution models

The quality of species distribution models (SDMs) relies to a large degree on the quality of the input data, from bioclimatic indices to environmental and habitat descriptors (Austin, 2002). Recent reviews of SDM techniques, have sought to optimize predictive performance e.g. Elith et al., 2006. In general SDMs employ one of three approaches to variable selection. The simplest approach relies on the expert to select the variables, as in environmental niche models Nix, 1986 or a generalized linear model without variable selection (Miller and Franklin, 2002). A second approach explicitly incorporates variable selection into model fitting, which allows examination of particular combinations of variables. Examples include generalized linear or additive models with variable selection (Hastie et al. 2002); or classification trees with complexity or model based pruning (Breiman et al., 1984, Zeileis, 2008). A third approach uses model averaging, to summarize the overall contribution of a variable, without considering particular combinations. Examples include neural networks, boosted or bagged regression trees and Maximum Entropy as compared in Elith et al. 2006. Typically, users of SDMs will either consider a small number of variable sets, via the first approach, or else supply all of the candidate variables (often numbering more than a hundred) to the second or third approaches. Bayesian SDMs exist, with several methods for eliciting and encoding priors on model parameters (see review in Low Choy et al. 2010). However few methods have been published for informative variable selection; one example is Bayesian trees (O’Leary 2008). Here we report an elicitation protocol that helps makes explicit a priori expert judgements on the quality of candidate variables. This protocol can be flexibly applied to any of the three approaches to variable selection, described above, Bayesian or otherwise. We demonstrate how this information can be obtained then used to guide variable selection in classical or machine learning SDMs, or to define priors within Bayesian SDMs.

Genetic burden associated with varying degrees of disease severity in endometriosis

Endometriosis is primarily characterized by the presence of tissue resembling endometrium outside the uterine cavity and is usually diagnosed by laparoscopy. The most commonly used classification of disease, the revised American Fertility Society (rAFS) system to grade endometriosis into different stages based on disease severity (I to IV), has been questioned as it does not correlate well with underlying symptoms, posing issues in diagnosis and choice of treatment. Using two independent European genome-wide association (GWA) datasets and top-level classification of the endometriosis cases based on rAFS [minimal or mild (Stage A) and moderate-to-severe (Stage B) disease], we previously showed that Stage B endometriosis has greater contribution of common genetic variation to its aetiology than Stage A disease. Herein, we extend our previous analysis to four endometriosis stages [minimal (Stage I), mild (Stage II), moderate (Stage III) and severe (Stage IV) disease] based on the rAFS classification system and compared the genetic burden across stages. Our results indicate that genetic burden increases from minimal to severe endometriosis. For the minimal disease, genetic factors may contribute to a lesser extent than other disease categories. Mild and moderate endometriosis appeared genetically similar, making it difficult to tease them apart. Consistent with our previous reports, moderate and severe endometriosis showed greater genetic burden than minimal or mild disease. Overall, our results provide new insights into the genetic architecture of endometriosis and further investigation in larger samples may help to understand better the aetiology of varying degrees of endometriosis, enabling improved diagnostic and treatment modalities.

Members of Chewra Kadischa (Burial Society) of the Klaus Synagogue, Mannheim, taken one day before the November Pogrom (Kristallnacht) Portraits; Groups; Men

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Isolation and characterization of riboflavin-binding protein from pregnant-rat serum

A high-affinity riboflavin -binding protein was isolated and characterized for the first time from pregnant-rat sera by affinity chromatography on a lumiflavin-agarose column. The purified protein was homogeneous by the criteria of analytical polyacrylamide-gel disc electrophoresis, gel-filtration chromatography on Sephadex G-100 and sodium dodecyl sulphate/polyacrylamide-gel electrophoresis. It had a molecular weight of 90000+/-5000 and interacted with [14C]riboflavin with a 1:1 molar ratio with a dissociation constant (Kd) of 0.42 micron.

Oscillation criteria for differential equations of second order

In this article, we give sufficient condition in the form of integral inequalities to establish the oscillatory nature of non linear homogeneous differential equations of the form where r, q, p, f and g are given data. We do this by separating the two cases f is monotonous and non monotonous.

Isohydric and anisohydric characterisation of vegetable crops. The classification of vegetables by their physiological responses to water stress

Research on the physiological response of crop plants to drying soils and subsequent water stress has grouped plant behaviours as isohydric and anisohydric. Drying soil conditions, and hence declining soil and root water potentials, cause chemical signals—the most studied being abscisic acid (ABA)—and hydraulic signals to be transmitted to the leaf via xylem pathways. Researchers have attempted to allocate crops as isohydric or anisohydric. However, different cultivars within crops, and even the same cultivars grown in different environments/climates, can exhibit both response types. Nevertheless, understanding which behaviours predominate in which crops and circumstances may be beneficial. This paper describes different physiological water stress responses, attempts to classify vegetable crops according to reported water stress responses, and also discusses implications for irrigation decision-making.

Microarrays in Molecular Profiling of Ewing Sarcoma Family of Tumors and CDKN2A Aberrations

Background: The Ewing sarcoma family of tumors (ESFT) are rare but highly malignant neoplasms that occur mainly in bone or but also in soft tissue. ESFT affects patients typically in their second decade of life, whereby children and adolescents bear the heaviest incidence burden. Despite recent advances in the clinical management of ESFT patients, their prognosis and survival are still disappointingly poor, especially in cases with metastasis. No targeted therapy for ESFT patients is currently available. Moreover, based merely on current clinical and biological characteristics, accurate classification of ESFT patients often fails at the time of diagnosis. Therefore, there is a constant need for novel molecular biomarkers to be applied in tandem with conventional parameters to further intensify ESFT risk-stratification and treatment selection, and ultimately to develop novel targeted therapies. In this context, a greater understanding of the genetics and immune characteristics of ESFT is needed. Aims: This study sought to open novel insights into gene copy number changes and gene expression in ESFT and, further, to enlighten the role of inflammation in ESFT. For this purpose, microarrays were used to provide gene-level information on a genomewide scale. In addition, this study focused on screening of 9p21.3 deletion sizes and frequencies in ESFT and, in another pediatric cancer, acute lymphocytic leukemia (ALL), in order to define more exact criteria for highrisk patient selection and to provide data for developing a more reliable diagnostic method to detect CDKN2A deletions. Results: In study I, 20 novel ESFT-associated suppressor genes and oncogenes were pinpointed using combined array CGH and expression analysis. In addition, interesting chromosomal rearrangements were identified: (1) Duplication of derivative chromosome der(22)(11;22) was detected in three ESFT patients. This duplication included the EWSR1-FLI1 fusion gene leading to increase in its copy number; (2) Cryptic amplifications on chromosomes 20 and 22 were detected, suggesting a novel translocation between chromosomes 20 and 22, which most probably produces a fusion between EWSR1 and NFATC2. In study II, bioinformatic analysis of ESFT expression profiles showed that inflammatory gene activation is detectable in ESFT patient samples and that the activation is characterized by macrophage gene expression. Most interestingly, ESFT patient samples were shown to express certain inflammatory genes that were prognostically significant. High local expression of C5 and JAK1 at the tumor site was shown to associate with favorable clinical outcome, whereas high local expression of IL8 was shown to be detrimental. Studies III and IV showed that the smallest overlapping region of deletion in 9p21.3 includes CDKN2A in all cases and that the length of this region is 12.2 kb in both Ewing sarcoma and ALL. Furthermore, our results showed that the most widely used commercial CDKN2A FISH probe creates false negative results in the narrowest microdeletion cases (<190 kb). Therefore, more accurate methods should be developed for the detection of deletions in the CDKN2A locus. Conclusions: This study provides novel insights into the genetic changes involved in the biology of ESFT, in the interaction between ESFT cells and immune system, and in the inactivation of CDKN2A. Novel ESFT biomarker genes identified in this study serve as a useful resource for future studies and in developing novel therapeutic strategies to improve the survival of patients with ESFT.

Interval Data Classification under Partial Information: A Chance-Constraint Approach

This paper presents a Chance-constraint Programming approach for constructing maximum-margin classifiers which are robust to interval-valued uncertainty in training examples. The methodology ensures that uncertain examples are classified correctly with high probability by employing chance-constraints. The main contribution of the paper is to pose the resultant optimization problem as a Second Order Cone Program by using large deviation inequalities, due to Bernstein. Apart from support and mean of the uncertain examples these Bernstein based relaxations make no further assumptions on the underlying uncertainty. Classifiers built using the proposed approach are less conservative, yield higher margins and hence are expected to generalize better than existing methods. Experimental results on synthetic and real-world datasets show that the proposed classifiers are better equipped to handle interval-valued uncertainty than state-of-the-art.

Estimating the economic implications for grazing properties in the Mackay Whitsunday catchments of practice changes to more sustainable landscapes.

In the Mackay Whitsunday region, the dominant grazing based operations are small intensive systems that heavily utilise soil, nutrient and chemical management practices. To improve water quality entering the Great Barrier Reef, graziers are being encouraged to adopt improved management practices. However, while there is good understanding of the management changes required to reach improved practice classification levels, there is poor understanding of the likely economic implications for a grazier seeking to move from a lower level classification to the higher level classifications. This paper provides analysis of the costs and benefits associated with adoption of intensive grazing best management practices to determine the effect on the profitability and economic sustainability of grazing enterprises, and the economic viability of capital investment to achieve best management. The results indicate that financial incentives are likely to be required to encourage smaller graziers to invest in changing their management practices, while larger graziers may only require incentives to balance the risk involved with the transition to better management practices.

Stability criteria for single pulse solutions of cw passive mode-locked lasers

The stability of the steady-state solutions of mode-locking of cw lasers by a fast saturable absorber is imvestigated. It is shown that the solutions are stable if the condition (Ps/Pa) = (2/3) (P0Pa) is satisfied, where (Ps/Pa) is the steady-state la ser power, (P0/Pa) is the power at mode-locking threshold, and Pa is the saturated power of the absorber.

Bovine cysticercosis-Development of a real-time PCR to enhance classification of suspect cysts identified at meat inspection

Laboratory confirmation methods are important in bovine cysticerosis diagnosis as other pathologies can result in morphologically similar lesions resulting in false identifications. We developed a probe-based real-time PCR assay to identify Taenia saginata in suspect cysts encountered at meat inspection and compared its use with the traditional method of identification, histology, as well as a published nested PCR. The assay simultaneously detects T. saginata DNA and a bovine internal control using the cytochrome c oxidase subunit 1 gene of each species and shows specificity against parasites causing lesions morphologically similar to those of T. saginata. The assay was sufficiently sensitive to detect 1 fg (Ct 35.09 +/- 0.95) of target DNA using serially-diluted plasmid DNA in reactions spiked with bovine DNA as well as in all viable and caseated positive control cysts. A loss in PCR sensitivity was observed with increasing cyst degeneration as seen in other molecular methods. In comparison to histology, the assay offered greater sensitivity and accuracy with 10/19 (53%) T. saginata positives detected by real-time PCR and none by histology. When the results were compared with the reference PCR, the assay was less sensitive but offered advantages of faster turnaround times and reduced contamination risk. Estimates of the assay's repeatability and reproducibility showed the assay is highly reliable with reliability coefficients greater than 0.94. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.

Migraine with aura and migraine without aura are not distinct entities: further evidence from a large Dutch population study

It is often debated whether migraine with aura (MA) and migraine without aura (MO) are etiologically distinct disorders. A previous study using latent class analysis (LCA) in Australian twins showed no evidence for separate subtypes of MO and MA. The aim of the present study was to replicate these results in a population of Dutch twins and their parents, siblings and partners (N = 10,144). Latent class analysis of International Headache Society (IHS)-based migraine symptoms resulted in the identification of 4 classes: a class of unaffected subjects (class 0), a mild form of nonmigrainous headache (class 1), a moderately severe type of migraine (class 2), typically without neurological symptoms or aura (8% reporting aura symptoms), and a severe type of migraine (class 3), typically with neurological symptoms, and aura symptoms in approximately half of the cases. Given the overlap of neurological symptoms and nonmutual exclusivity of aura symptoms, these results do not support the MO and MA subtypes as being etiologically distinct. The heritability in female twins of migraine based on LCA classification was estimated at .50 (95% confidence intervals [CI] .27 - .59), similar to IHS-based migraine diagnosis (h2 = .49, 95% CI .19-.57). However, using a dichotomous classification (affected-unaffected) decreased heritability for the IHS-based classification (h2 = .33, 95% CI .00-.60), but not the LCA-based classification (h2 = .51, 95% CI .23-.61). Importantly, use of the LCA-based classification increased the number of subjects classified as affected. The heritability of the screening question was similar to more detailed LCA and IHS classifications, suggesting that the screening procedure is an important determining factor in genetic studies of migraine.

Genomewide significant linkage to migrainous headache on chromosome 5q21

Familial typical migraine is a common, complex disorder that shows strong familial aggregation. Using latent-class analysis (LCA), we identified subgroups of people with migraine/severe headache in a community sample of 12,245 Australian twins (60% female), drawn from two cohorts of individuals aged 23-90 years who completed an interview based on International Headache Society criteria. We report results from genomewide linkage analyses involving 756 twin families containing a total of 790 independent sib pairs (130 affected concordant, 324 discordant, and 336 unaffected concordant for LCA-derived migraine). Quantitative-trait linkage analysis produced evidence of significant linkage on chromosome 5q21 and suggestive linkage on chromosomes 8, 10, and 13. In addition, we replicated previously reported typical-migraine susceptibility loci on chromosomes 6p12.2-p21.1 and 1q21-q23, the latter being within 3 cM of the rare autosomal dominant familial hemiplegic migraine gene (ATP1A2), a finding which potentially implicates ATP1A2 in familial typical migraine for the first time. Linkage analyses of individual migraine symptoms for our six most interesting chromosomes provide tantalizing hints of the phenotypic and genetic complexity of migraine. Specifically, the chromosome 1 locus is most associated with phonophobia; the chromosome 5 peak is predominantly associated with pulsating headache; the chromosome 6 locus is associated with activity-prohibiting headache and photophobia; the chromosome 8 locus is associated with nausea/vomiting and moderate/severe headache; the chromosome 10 peak is most associated with phonophobia and photophobia; and the chromosome 13 peak is completely due to association with photophobia. These results will prove to be invaluable in the design and analysis of future linkage and linkage disequilibrium studies of migraine.

Soil nitrogen—crop response calibration relationships and criteria for winter cereal crops grown in Australia

More than 1200 wheat and 120 barley experiments conducted in Australia to examine yield responses to applied nitrogen (N) fertiliser are contained in a national database of field crops nutrient research (BFDC National Database). The yield responses are accompanied by various pre-plant soil test data to quantify plant-available N and other indicators of soil fertility status or mineralisable N. A web application (BFDC Interrogator), developed to access the database, enables construction of calibrations between relative crop yield ((Y0/Ymax) × 100) and N soil test value. In this paper we report the critical soil test values for 90% RY (CV90) and the associated critical ranges (CR90, defined as the 70% confidence interval around that CV90) derived from analysis of various subsets of these winter cereal experiments. Experimental programs were conducted throughout Australia’s main grain-production regions in different eras, starting from the 1960s in Queensland through to Victoria during 2000s. Improved management practices adopted during the period were reflected in increasing potential yields with research era, increasing from an average Ymax of 2.2 t/ha in Queensland in the 1960s and 1970s, to 3.4 t/ha in South Australia (SA) in the 1980s, to 4.3 t/ha in New South Wales (NSW) in the 1990s, and 4.2 t/ha in Victoria in the 2000s. Various sampling depths (0.1–1.2 m) and methods of quantifying available N (nitrate-N or mineral-N) from pre-planting soil samples were used and provided useful guides to the need for supplementary N. The most regionally consistent relationships were established using nitrate-N (kg/ha) in the top 0.6 m of the soil profile, with regional and seasonal variation in CV90 largely accounted for through impacts on experimental Ymax. The CV90 for nitrate-N within the top 0.6 m of the soil profile for wheat crops increased from 36 to 110 kg nitrate-N/ha as Ymax increased over the range 1 to >5 t/ha. Apparent variation in CV90 with seasonal moisture availability was entirely consistent with impacts on experimental Ymax. Further analyses of wheat trials with available grain protein (~45% of all experiments) established that grain yield and not grain N content was the major driver of crop N demand and CV90. Subsets of data explored the impact of crop management practices such as crop rotation or fallow length on both pre-planting profile mineral-N and CV90. Analyses showed that while management practices influenced profile mineral-N at planting and the likelihood and size of yield response to applied N fertiliser, they had no significant impact on CV90. A level of risk is involved with the use of pre-plant testing to determine the need for supplementary N application in all Australian dryland systems. In southern and western regions, where crop performance is based almost entirely on in-crop rainfall, this risk is offset by the management opportunity to split N applications during crop growth in response to changing crop yield potential. In northern cropping systems, where stored soil moisture at sowing is indicative of minimum yield potential, erratic winter rainfall increases uncertainty about actual yield potential as well as reducing the opportunity for effective in-season applications.