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.

Gust Response of Rotor and Propeller Systems

The influence of nonstationary turbulence on rotor and propeller systems is discussed. The review is made from a common analytical basis of nonstationary approach, with emphasis on concepts rather than on details. The necessity of such an approach and its feasibility for predicting a complete set of gust and response statistics together with correlations with somewhat limited test data are appraised.

Purification and kinetic mechanism of 5,10-metliyienetetrahydrofolate reductase from sheep liver

5,10-Methylenetetrahydrofolate reductase (EC 1.1.1.68) was purified from the cytosolic fraction of sheep liver by (NH4)2 SO4 fractionation, acid precipitation, DEAE-Sephacel chromatography and Blue Sepharose affinity chromatography. The homogeneity of the enzyme was established by sodium dodecyl sulphate-polyacrylamide gel electrophoresis, ultracentrifugation and Ouchterlony immunodiffusion test. The enzyme was a dimer of molecular weight 1,66,000 ± 5,000 with a subunit molecular weight of 87,000 ±5,000. The enzyme showed hyperbolic saturation pattern with 5-methyltetrahydrofolate.K 0.5 values for 5-methyltetrahydrofolate menadione and NADPH were determined to be 132 ΜM, 2.45 ΜM and 16 ΜM. The parallel set of lines in the Lineweaver-Burk plot, when either NADPH or menadione was varied at different fixed concentrations of the other substrate; non-competitive inhibition, when NADPH was varied at different fixed concentrations of NADP; competitive inhibition, when menadione was varied at different fixed concentrations of NADP and the absence of inhibition by NADP at saturating concentration of menadione, clearly established that the kinetic mechanism of the reaction catalyzed by this enzyme was ping-pong.

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.

Managing resistance to chemical treatments in stored products pests

This review focuses on key trends in resistance to chemical treatments in stored product pests, and advances in resistance management, with an emphasis on resistance to the fumigant phosphine. Findings: Phosphine resistance continues to be a major concern. In particular, phosphine resistance in Cryptolestes ferrugineus has emerged as a serious issue, with some populations exhibiting the strongest level detected so far for this fumigant. In response, a 'quick knock down test' has been established to deliver industry and scientists 'same day' advice on the resistance status of field samples; sulfuryl fluoride is being developed as a 'resistance breaker' and phosphine dosages are being revised to manage this problem. There has been major progress in identifying the genes responsible for phosphine resistance and the development of molecular resistance diagnostics for key pests. Several studies on Rhyzopertha dominica have demonstrated that molecular screening can be used to determine the frequency of resistance alleles in samples collected from farm storages. Despite on-going research in several pests, there is no definitive answer to the question of whether there is a fitness cost associated phosphine resistance, with some studies showing a clear cost and others none. Evidence continues to emerge of resistance to grain protectants, including the juvenile hormone analogue methoprene. The development and adoption of spinosad, as a next generation 'green' treatment, and the use of protectant combinations provides opportunities to counter the problem of protectant resistance.Directions for future research: A uniform set of protocols should be developed for phosphine resistance detection for all major species. It should combine 'quick tests' and molecular diagnostics to be adopted internationally. Research is required on the establishment of a decision making system that integrates newly developed grain protectants and fumigants, other alternative control methods, as well as an accurate and rapid resistance detection system for early warning of the emergence of new resistances.

Efficient search for statistically significant dependency rules in binary data

Analyzing statistical dependencies is a fundamental problem in all empirical science. Dependencies help us understand causes and effects, create new scientific theories, and invent cures to problems. Nowadays, large amounts of data is available, but efficient computational tools for analyzing the data are missing. In this research, we develop efficient algorithms for a commonly occurring search problem - searching for the statistically most significant dependency rules in binary data. We consider dependency rules of the form X->A or X->not A, where X is a set of positive-valued attributes and A is a single attribute. Such rules describe which factors either increase or decrease the probability of the consequent A. A classical example are genetic and environmental factors, which can either cause or prevent a disease. The emphasis in this research is that the discovered dependencies should be genuine - i.e. they should also hold in future data. This is an important distinction from the traditional association rules, which - in spite of their name and a similar appearance to dependency rules - do not necessarily represent statistical dependencies at all or represent only spurious connections, which occur by chance. Therefore, the principal objective is to search for the rules with statistical significance measures. Another important objective is to search for only non-redundant rules, which express the real causes of dependence, without any occasional extra factors. The extra factors do not add any new information on the dependence, but can only blur it and make it less accurate in future data. The problem is computationally very demanding, because the number of all possible rules increases exponentially with the number of attributes. In addition, neither the statistical dependency nor the statistical significance are monotonic properties, which means that the traditional pruning techniques do not work. As a solution, we first derive the mathematical basis for pruning the search space with any well-behaving statistical significance measures. The mathematical theory is complemented by a new algorithmic invention, which enables an efficient search without any heuristic restrictions. The resulting algorithm can be used to search for both positive and negative dependencies with any commonly used statistical measures, like Fisher's exact test, the chi-squared measure, mutual information, and z scores. According to our experiments, the algorithm is well-scalable, especially with Fisher's exact test. It can easily handle even the densest data sets with 10000-20000 attributes. Still, the results are globally optimal, which is a remarkable improvement over the existing solutions. In practice, this means that the user does not have to worry whether the dependencies hold in future data or if the data still contains better, but undiscovered dependencies.

Location of concentrators in a computer communication network: a stochastic automation search method

The following problem is considered. Given the locations of the Central Processing Unit (ar;the terminals which have to communicate with it, to determine the number and locations of the concentrators and to assign the terminals to the concentrators in such a way that the total cost is minimized. There is alao a fixed cost associated with each concentrator. There is ail upper limit to the number of terminals which can be connected to a concentrator. The terminals can be connected directly to the CPU also In this paper it is assumed that the concentrators can bo located anywhere in the area A containing the CPU and the terminals. Then this becomes a multimodal optimization problem. In the proposed algorithm a stochastic automaton is used as a search device to locate the minimum of the multimodal cost function . The proposed algorithm involves the following. The area A containing the CPU and the terminals is divided into an arbitrary number of regions (say K). An approximate value for the number of concentrators is assumed (say m). The optimum number is determined by iteration later The m concentrators can be assigned to the K regions in (mk) ways (m > K) or (km) ways (K>m).(All possible assignments are feasible, i.e. a region can contain 0,1,…, to concentrators). Each possible assignment is assumed to represent a state of the stochastic variable structure automaton. To start with, all the states are assigned equal probabilities. At each stage of the search the automaton visits a state according to the current probability distribution. At each visit the automaton selects a 'point' inside that state with uniform probability. The cost associated with that point is calculated and the average cost of that state is updated. Then the probabilities of all the states are updated. The probabilities are taken to bo inversely proportional to the average cost of the states After a certain number of searches the search probabilities become stationary and the automaton visits a particular state again and again. Then the automaton is said to have converged to that state Then by conducting a local gradient search within that state the exact locations of the concentrators are determined This algorithm was applied to a set of test problems and the results were compared with those given by Cooper's (1964, 1967) EAC algorithm and on the average it was found that the proposed algorithm performs better.

Degenerate surface modes in a magnetized plasma

Electromagnetic surface waves propagating along the plasma-vacuum interface parallel to an applied magnetic field are studied. New modes for which the field components are degenerate, not reported in the earlier investigation of Kotsarenko and Fedorchenko (1969), are found and discussed. These modes, which propagate up to the plasma frequency for all values of the magnetic field, start as forward waves at low frequency but smoothly change into the backward mode as the frequency increases.

Development and psychometric properties of the DCDDaily: a new test for clinical assessment of capacity in activities of daily living in children with developmental coordination disorder

Objective To develop the DCDDaily, an instrument for objective and standardized clinical assessment of capacity in activities of daily living (ADL) in children with developmental coordination disorder (DCD), and to investigate its usability, reliability, and validity. Subjects Five to eight-year-old children with and without DCD. Main measures The DCDDaily was developed based on thorough review of the literature and extensive expert involvement. To investigate the usability (assessment time and feasibility), reliability (internal consistency and repeatability), and validity (concurrent and discriminant validity) of the DCDDaily, children were assessed with the DCDDaily and the Movement Assessment Battery for Children-2 Test, and their parents filled in the Movement Assessment Battery for Children-2 Checklist and Developmental Coordination Disorder Questionnaire. Results 459 children were assessed (DCD group, n = 55; normative reference group, n = 404). Assessment was possible within 30 minutes and in any clinical setting. For internal consistency, Cronbach’s α = 0.83. Intraclass correlation = 0.87 for test–retest reliability and 0.89 for inter-rater reliability. Concurrent correlations with Movement Assessment Battery for Children-2 Test and questionnaires were ρ = −0.494, 0.239, and −0.284, p < 0.001. Discriminant validity measures showed significantly worse performance in the DCD group than in the control group (mean (SD) score 33 (5.6) versus 26 (4.3), p < 0.001). The area under curve characteristic = 0.872, sensitivity and specificity were 80%. Conclusions The DCDDaily is a valid and reliable instrument for clinical assessment of capacity in ADL, that is feasible for use in clinical practice.

Analytical method for solving a set of inhomogeneous infinite simultaneous equations

It is shown that a method based on the principle of analytic continuation can be used to solve a set of inhomogeneous infinite simultaneous equations encountered in the analysis of surface acoustic wave propagation along the periodically perturbed surface of a piezoelectric medium.

Analytical method for solving a set of infinite simultaneous equations

It is shown that a method based on the principle of analytic continuation can be used to solve a set of infinite simultaneous equations encountered in solving for the electric field of a periodic electrode structure.

Low-frequency degenerate surface modes of an electron-hole plasma

The computations of Flahive and Quinn1 of the dispersion curves of low frequency degenerate surface (DS) modes propagating along the magnetic field in an electron-hole plasma are extended to higher values of the wavenumber. We find that beyond a certain value of the wavenumber the DS mode re-enters the allowed region of surface wave propagation and tends to an asymptotic frequency ωR (<ωLH). These low frequency resonances of an electron-hole plasma are discussed with reference to the experimental observations.

Literacy leadership and accountability practices: Holding onto ethics in ways that count

Despite the rhetoric of schools serving the needs of specific communities, it is evident that the work of teachers and principals is shaped by government imperatives to demonstrate success according to a set of standard ‘benchmarks’. In this chapter, we draw from our current study of new forms of educational leadership emerging in South Australian public primary schools to explore the ways in which test-based accountability requirements are being mediated by principals in schools that serve high poverty communities. Taking an institutional ethnography approach we focus on the everyday work of a principal and a literacy leader in one suburban primary school to show the complexity of the impact of national testing on practices of literacy leadership. We elaborate on the inescapable textual framings and tasks faced by the principal and literacy leader, and those that they create and modify – such as a common literacy agreement and ‘literacy chats’ between a literacy leader and classroom teacher – in order to ‘hold on to ethics’. We argue that while leaders’ and teachers’ everyday work is regulated by ‘ruling relations’ (Smith, 1999), it is also organic and responsive to the local context. We conclude with a reflection on the important situated work that school leaders do in mediating trans-local policies that might otherwise close down possibilities for engaging ethically with students and their learning in a particular school.

Pheo-type: A diagnostic gene-expression assay for the classification of pheochromocytoma and paraganglioma

Context: Pheochromocytomas and paragangliomas (PPGLs) are heritable neoplasms that can be classified into gene-expression subtypes corresponding to their underlying specific genetic drivers. Objective: This study aimed to develop a diagnostic and research tool (Pheo-type) capable of classifying PPGL tumors into gene-expression subtypes that could be used to guide and interpret genetic testing, determine surveillance programs, and aid in elucidation of PPGL biology. Design: A compendium of published microarray data representing 205 PPGL tumors was used for the selection of subtype-specific genes that were then translated to the Nanostring gene-expression platform. A support vector machine was trained on the microarray dataset and then tested on an independent Nanostring dataset representing 38 familial and sporadic cases of PPGL of known genotype (RET, NF1, TMEM127, MAX, HRAS, VHL, and SDHx). Different classifier models involving between three and six subtypes were compared for their discrimination potential. Results: A gene set of 46 genes and six endogenous controls was selected representing six known PPGL subtypes; RTK1–3 (RET, NF1, TMEM127, and HRAS), MAX-like, VHL, and SDHx. Of 38 test cases, 34 (90%) were correctly predicted to six subtypes based on the known genotype to gene-expression subtype association. Removal of the RTK2 subtype from training, characterized by an admixture of tumor and normal adrenal cortex, improved the classification accuracy (35/38). Consolidation of RTK and pseudohypoxic PPGL subtypes to four- and then three-class architectures improved the classification accuracy for clinical application. Conclusions: The Pheo-type gene-expression assay is a reliable method for predicting PPGL genotype using routine diagnostic tumor samples.

Beyond Gauss: Image-set matching on the Riemannian manifold of PDFs

State-of-the-art image-set matching techniques typically implicitly model each image-set with a Gaussian distribution. Here, we propose to go beyond these representations and model image-sets as probability distribution functions (PDFs) using kernel density estimators. To compare and match image-sets, we exploit Csiszar´ f-divergences, which bear strong connections to the geodesic distance defined on the space of PDFs, i.e., the statistical manifold. Furthermore, we introduce valid positive definite kernels on the statistical manifold, which let us make use of more powerful classification schemes to match image-sets. Finally, we introduce a supervised dimensionality reduction technique that learns a latent space where f-divergences reflect the class labels of the data. Our experiments on diverse problems, such as video-based face recognition and dynamic texture classification, evidence the benefits of our approach over the state-of-the-art image-set matching methods.