Testing for Neglected Nonlinearity in Cointegrating Relationships

This paper proposes pure significance tests for the absence of nonlinearity in cointegrating relationships. No assumption of the functional form of the nonlinearity is made. It is envisaged that the application of such tests could form the first step towards specifying a nonlinear cointegrating relationship for empirical modelling. The asymptotic and small sample properties of our tests are investigated, where special attention is paid to the role of nuisance parameters and a potential resolution using the bootstrap.Cointegration, Nonlinearity, Neural networks, Bootstrap

Boosting Estimation of RBF Neural Networks for Dependent Data

This paper develops theoretical results for the estimation of radial basis function neural network specifications, for dependent data, that do not require iterative estimation techniques. Use of the properties of regression based boosting algorithms is made. Both consistency and rate results are derived. An application to nonparametric specification testing illustrates the usefulness of the results.Neural Networks, Boosting

Testing for ARCH in the Presence of Nonlinearity of Unknown Form in the Conditional Mean

Tests of ARCH are a routine diagnostic in empirical econometric and financial analysis. However, it is well known that misspecification of the conditional mean may lead to spurious rejections of the null hypothesis of no ARCH. Nonlinearity is a prime example of this phenomenon. There is little work on the extent of the effect of neglected nonlinearity on the properties of ARCH tests. This paper provides some such evidence and also new ARCH testing procedures that are robust to the presence of neglected nonlinearity. Monte Carlo evidence shows that the problem is serious and that the new methods alleviate this problem to a very large extent.Nonlinearity, ARCH, Neural networks

Testing the Martingale Difference Hypothesis Using Neural Network Approximations

The martingale difference restriction is an outcome of many theoretical analyses in economics and finance. A large body of econometric literature deals with tests of that restriction. We provide new tests based on radial basis function neural networks. Our work is based on the test design of Blake and Kapetanios (2000, 2003a,b). However, unlike that work we can provide a formal theoretical justification for the validity of these tests using approximation results from Kapetanios and Blake (2007). These results take advantage of the link between the algorithms of Blake and Kapetanios (2000, 2003a,b) and boosting. We carry out a Monte Carlo study of the properties of the new tests and find that they have superior power performance to all existing tests of the martingale difference hypothesis we consider. An empirical application to the S&P500 constituents illustrates the usefulness of our new test.Martingale difference hypothesis, Neural networks, Boosting

MAGNITUDE ESTIMATION: AN APPLICATION TO FARMERS' RISK-INCOME PREFERENCES

Magnitude estimation, a technique developed by psychology for obtaining ratio scaled values, was used to derive risk-income preferences of ninety-one central Indiana farmers. Both variability-income and bankruptcy-income measures were developed and related to farmers' socio-economic attributes. Wealth and education had limited effects compared with off-farm employment, percent debt and expected levels of income, percent debt and net worth growth. Magnitude estimation provided reliable estimates of preferences. Farmers gave greater importance to the bankruptcy-income measure of risk-income preferences, but only a small portion of the variation of either measure could be explained.Farm Management, Risk and Uncertainty,

Expression of a bacterial 3-dehydroshikimate dehydratase reduces lignin content and improves biomass saccharification efficiency.

Lignin confers recalcitrance to plant biomass used as feedstocks in agro-processing industries or as source of renewable sugars for the production of bioproducts. The metabolic steps for the synthesis of lignin building blocks belong to the shikimate and phenylpropanoid pathways. Genetic engineering efforts to reduce lignin content typically employ gene knockout or gene silencing techniques to constitutively repress one of these metabolic pathways. Recently, new strategies have emerged offering better spatiotemporal control of lignin deposition, including the expression of enzymes that interfere with the normal process for cell wall lignification. In this study, we report that expression of a 3-dehydroshikimate dehydratase (QsuB from Corynebacterium glutamicum) reduces lignin deposition in Arabidopsis cell walls. QsuB was targeted to the plastids to convert 3-dehydroshikimate - an intermediate of the shikimate pathway - into protocatechuate. Compared to wild-type plants, lines expressing QsuB contain higher amounts of protocatechuate, p-coumarate, p-coumaraldehyde and p-coumaryl alcohol, and lower amounts of coniferaldehyde, coniferyl alcohol, sinapaldehyde and sinapyl alcohol. 2D-NMR spectroscopy and pyrolysis-gas chromatography/mass spectrometry (pyro-GC/MS) reveal an increase of p-hydroxyphenyl units and a reduction of guaiacyl units in the lignin of QsuB lines. Size-exclusion chromatography indicates a lower degree of lignin polymerization in the transgenic lines. Therefore, our data show that the expression of QsuB primarily affects the lignin biosynthetic pathway. Finally, biomass from these lines exhibits more than a twofold improvement in saccharification efficiency. We conclude that the expression of QsuB in plants, in combination with specific promoters, is a promising gain-of-function strategy for spatiotemporal reduction of lignin in plant biomass

New statistical method identifes cytokines that distinguish stool microbiomes

Regressing an outcome or dependent variable onto a set of input or independent variables allows the analyst to measure associations between the two so that changes in the outcome can be described by and predicted by changes in the inputs. While there are many ways of doing this in classical statistics, where the dependent variable has certain properties (e.g., a scalar, survival time, count), little progress on regression where the dependent variable are microbiome taxa counts has been made that do not impose extremely strict conditions on the data. In this paper, we propose and apply a new regression model combining the Dirichlet-multinomial distribution with recursive partitioning providing a fully non-parametric regression model. This model, called DM-RPart, is applied to cytokine data and microbiome taxa count data and is applicable to any microbiome taxa count/metadata, is automatically fit, and intuitively interpretable. This is a model which can be applied to any microbiome or other compositional data and software (R package HMP) available through the R CRAN website

Herschel observations of EXtra-Ordinary Sources: Analysis of the HIFI 1.2 THz Wide Spectral Survey Toward Orion KL II. Chemical Implications

We present chemical implications arising from spectral models fit to the Herschel/HIFI spectral survey toward the Orion Kleinmann-Low nebula (Orion KL). We focus our discussion on the eight complex organics detected within the HIFI survey utilizing a novel technique to identify those molecules emitting in the hottest gas. In particular, we find the complex nitrogen bearing species CH$_{3}$CN, C$_{2}$H$_{3}$CN, C$_{2}$H$_{5}$CN, and NH$_{2}$CHO systematically trace hotter gas than the oxygen bearing organics CH$_{3}$OH, C$_{2}$H$_{5}$OH, CH$_{3}$OCH$_{3}$, and CH$_{3}$OCHO, which do not contain nitrogen. If these complex species form predominantly on grain surfaces, this may indicate N-bearing organics are more difficult to remove from grain surfaces than O-bearing species. Another possibility is that hot (T$_{\rm kin}$$\sim$300 K) gas phase chemistry naturally produces higher complex cyanide abundances while suppressing the formation of O-bearing complex organics. We compare our derived rotation temperatures and molecular abundances to chemical models, which include gas-phase and grain surface pathways. Abundances for a majority of the detected complex organics can be reproduced over timescales $\gtrsim$ 10$^{5}$ years, with several species being under predicted by less than 3$\sigma$. Derived rotation temperatures for most organics, furthermore, agree reasonably well with the predicted temperatures at peak abundance. We also find that sulfur bearing molecules which also contain oxygen (i.e. SO, SO$_{2}$, and OCS) tend to probe the hottest gas toward Orion KL indicating the formation pathways for these species are most efficient at high temperatures.Comment: 31 pages, 6 figures, 1 Table, accepted to the Astrophysical Journa

The Magnetic Electron Ion Spectrometer (MagEIS) Instruments Aboard the Radiation Belt Storm Probes (RBSP) Spacecraft

This paper describes the Magnetic Electron Ion Spectrometer (MagEIS) instruments aboard the RBSP spacecraft from an instrumentation and engineering point of view. There are four magnetic spectrometers aboard each of the two spacecraft, one low-energy unit (20–240 keV), two medium-energy units (80–1200 keV), and a high-energy unit (800–4800 keV). The high unit also contains a proton telescope (55 keV–20 MeV). The magnetic spectrometers focus electrons within a selected energy pass band upon a focal plane of several silicon detectors where pulse-height analysis is used to determine if the energy of the incident electron is appropriate for the electron momentum selected by the magnet. Thus each event is a two-parameter analysis, an approach leading to a greatly reduced background. The physics of these instruments are described in detail followed by the engineering implementation. The data outputs are described, and examples of the calibration results and early flight data presented

A Novel Sensitive Immunoassay Targeting the 5-Methylthio-d-Xylofuranose-Lipoarabinomannan Epitope Meets the WHO's Performance Target for Tuberculosis Diagnosis.

The only currently commercialized point-of-care assay for tuberculosis (TB) that measures lipoarabinomannan (LAM) in urine (Alere LF-LAM) has insufficient sensitivity. We evaluated the potential of 100 novel monoclonal antibody pairs targeting a variety of LAM epitopes on a sensitive electrochemiluminescence platform to improve the diagnostic accuracy. In the screening, many antibody pairs showed high reactivity to purified LAM but performed poorly at detecting urinary LAM in clinical samples, suggesting differences in antigen structure and immunoreactivity of the different LAM sources. The 12 best antibody pairs from the screening were tested in a retrospective case-control study with urine samples from 75 adults with presumptive TB. The best antibody pair reached femtomolar analytical sensitivity for LAM detection and an overall clinical sensitivity of 93% (confidence interval [CI], 80% to 97%) and specificity of 97% (CI, 85% to 100%). Importantly, in HIV-negative subjects positive for TB by sputum smear microscopy, the test achieved a sensitivity of 80% (CI, 55% to 93%). This compares to an overall sensitivity of 33% (CI, 20% to 48%) of the Alere LF-LAM and a sensitivity of 13% (CI, 4% to 38%) in HIV-negative subjects in the same sample set. The capture antibody targets a unique 5-methylthio-d-xylofuranose (MTX)-dependent epitope in LAM that is specific to the Mycobacterium tuberculosis complex and shows no cross-reactivity with fast-growing mycobacteria or other bacteria. The present study provides evidence that improved assay methods and reagents lead to increased diagnostic accuracy. The results of this work have informed the development of a sensitive and specific novel LAM point-of-care assay with the aim to meet the WHO's performance target for TB diagnosis