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Quantitative analysis of diffusion‐weighted magnetic resonance imaging (DW‐MRI) has been explored for many clinical applications since its development. In particular, the intravoxel incoherent motion (IVIM) model for DW‐MRI has been commonly utilized in various organs. However, because of the presence of excessive noise, the IVIM parameter maps obtained from pixel‐wise fitting are often unreliable. In this study, we propose a kernelized total difference‐based curve‐fitting method to estimate the IVIM parameters. Simulated DW‐MRI data at five signal‐to‐noise ratios (i.e., 10, 20, 30, 50, and 100) and real abdominal DW‐MRI data acquired on a 1.5‐T MRI scanner with nine b‐values (i.e., 0, 10, 25, 50, 100, 200, 300, 400, and 500 s/mm2) and six diffusion‐encoding gradient directions were used to evaluate the performance of the proposed method. The results were compared with those obtained by three existing methods: trust‐region reflective (TRR) algorithm, Bayesian probability (BP), and deep neural network (DNN). Our simulation results showed that the proposed method outperformed the other three comparing methods in terms of root‐mean‐square error. Moreover, the proposed method could preserve small details in the estimated IVIM parameter maps. The experimental results showed that, compared with the TRR method, the proposed method as well as the BP (and DNN) method could reduce the overestimation of the pseudodiffusion coefficient and improve the quality of IVIM parameter maps. For all studied abdominal organs except the pancreas, both the proposed method and the BP method could provide IVIM parameter estimates close to the reference values; the former had higher precision. The kernelized total difference‐based curve‐fitting method has the potential to improve the reliability of IVIM parametric imaging. [ABSTRACT FROM AUTHOR]

Objectives: This study evaluated the occurrence of Schistosoma mansoni and soil‐transmitted helminths in an endemic area in the Eastern Brazilian Amazon, analysing prevalence and spatial distribution. Methods: The study was conducted in four localities of Primavera Municipality, in Pará state. Data was obtained from the Decit 40/2012 project and the participants were divided into five age range categories for evaluation: children, adolescents, young adults, adults and elderly individuals. For the diagnostic tests, Kato–Katz slides were prepared to detect S. mansoni and soil‐transmitted helminths eggs. The spatial distribution map and the Kernel Density Estimation were performed to assess the presence and location of infections. Results: Stool samples revealed the presence of hookworms, S. mansoni, Ascaris lumbricoides and Trichuris trichiura eggs. Mono‐, bi‐ and poly‐parasitic infections were observed, with a significant prevalence of hookworm monoparasitism. Conclusions: The high frequency of children infected with soil‐transmitted helminths confirms their significance as an ongoing public health problem in the poorest municipalities of Brazil. The Geographic Information System plays a crucial role in environmental surveillance and in the control of epidemics and endemic diseases, enabling accurate assessment and informed decision‐making for their control. [ABSTRACT FROM AUTHOR]

With an explosive increase of data produced annually, researchers have been attempting to develop solutions for systems that can effectively handle large amounts of data. Single‐operating‐system (OS) non‐uniform memory access (NUMA) abstraction technology is an important technology that ensures the compatibility of single‐node programming interfaces across multiple nodes owing to its higher cost efficiency compared with scale‐up systems. However, existing technologies have not been successful in optimizing user performance. In this paper, we introduce a single‐OS NUMA abstraction technology that ensures full compatibility with the existing OS while improving the performance at both hypervisor and guest levels. Benchmark results show that the proposed technique can improve performance by up to 4.74 × on average in terms of execution time compared with the existing state‐of‐the‐art open‐source technology. [ABSTRACT FROM AUTHOR]

The regression discontinuity (RD) design is a widely utilized approach for assessing treatment effects. It involves assigning treatment based on the value of an observed covariate in relation to a fixed threshold. Although the RD design has been widely employed across various problems, its application to specific data types has received limited attention. For instance, there has been little research on utilizing the RD design when the outcome variable exhibits zero‐inflation. This study introduces a novel RD estimator using local likelihood, which overcomes the limitations of the local linear regression model, a popular approach for estimating treatment effects in RD design, by considering the data type of the outcome variable. To determine the optimal bandwidth, we propose a modified Ludwig‐Miller cross validation method. A set of simulations is carried out, involving binary, count, and zero‐inflated outcome variables, to showcase the superior performance of the suggested method over local linear regression models. Subsequently, the proposed local likelihood model is employed on HIV care data, where antiretroviral therapy eligibility is determined by a CD4 count threshold. A comparison is made between the results obtained using the local likelihood model and those obtained using local linear regression. [ABSTRACT FROM AUTHOR]

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Interest in incorporating historical data in the clinical trial has increased with the rising cost of conducting clinical trials. The intervention arm for the current trial often requires prospective data to assess a novel treatment, and thus borrowing historical control data commensurate in distribution to current control data is motivated in order to increase the allocation ratio to the current intervention arm. Existing historical control borrowing adaptive designs adjust allocation ratios based on the commensurability assessed through study‐level summary statistics of the response agnostic of the distributions of the trial subject characteristics in the current and historical trials. This can lead to distributional imbalance of the current trial subject characteristics across the treatment arms as well as between current control data and borrowed historical control data. Such covariate imbalance may threaten the internal validity of the current trial by introducing confounding factors that affect study endpoints. In this article, we propose a Bayesian design which borrows and updates the treatment allocation ratios both covariate‐adaptively and commensurate to covariate dependently assessed similarity between the current and historical control data. We employ covariate‐dependent discrepancy parameters which are allowed to grow with the sample size and propose a regularized local regression procedure for the estimation of the parameters. The proposed design also permits the current and the historical controls to be similar to varying degree, depending on the subject level characteristics. We evaluate the proposed design extensively under the settings derived from two placebo‐controlled randomized trials on vertebral fracture risk in post‐menopausal women. [ABSTRACT FROM AUTHOR]

Carya illinoinensis (Wangenh.) K. Koch, nuts are a renowned health food. However, there are many cultivars of this nut tree, and their mature kernel lipid composition has not been thoroughly studied. Therefore, we used liquid chromatography–mass spectrometry (LC–MS) to analyze the lipid composition of mature nuts of five C. illinoinensis cultivars. In the mature kernels of all cultivars, there were 58 lipid types which were mainly composed of glycerolipids (c. 65%) and phospholipids (>30%). Triacylglycerol (TG) accounted for the largest proportion of mature nuts of all cultivars, exceeding 50%; and diacylglycerol (DG), ceramide (Cer), phosphatidylcholine (PC), and phosphatidylethanolamine (PE) were also relatively high. Additionally, nuts contain fatty acids, mainly oleic, linoleic, and linolenic acids. Our research provides a new perspective for the processing and utilization of plant and edible oils, and for the use of C. illinoinensis kernels in the development of medicine and food science. [ABSTRACT FROM AUTHOR]

Repeated/clustered data are becoming increasingly popular in many research studies. While there is a considerable literature in the estimation of the nonparametric component functions, the problem has been addressed by very few in statistical inference such as simultaneous confidence bands (SCBs). Based on the efficient kernel estimation incorporating within‐cluster error correlation, we provide asymptotic Wald‐type‐based SCBs for the nonparametric function when the repeated measures have the equal cluster size. The performance of the SCBs is evaluated by simulation studies which support the asymptotic theory. The proposed method is also applied to a dataset on the Junior School Project of test scores. [ABSTRACT FROM AUTHOR]

Locally stationary time series frequently appears in both finance and environmental sciences (e.g., daily air pollutant concentration or financial returns). However, constructing the multi-step-ahead prediction interval for such time series remains an open question. Hence, we extend the nonparametric regression model with autoregressive errors for equally spaced designs to the time series setup. We propose a B-spline estimator for the trend function and a kernel estimator for the variance function to implement the model. The prediction interval of multi-step-ahead future observations is also constructed after fitting the autoregressive model of errors and obtaining the quantile of prediction residuals. The proposed method is illustrated by various simulation studies and an example of air pollutant data, containing 8 years of daily air pollutant concentrations in Xi'an. Our results demonstrate that our method outperforms others owing to its higher prediction accuracy and versatility. [ABSTRACT FROM AUTHOR]

SUMMARY: The maize red1 (r1) locus regulates anthocyanin accumulation and is a classic model for allelic diversity; changes in regulatory regions are responsible for most of the variation in gene expression patterns. Here, an intrachromosomal rearrangement between the distal upstream region of r1 and the region of naked endosperm 2 (nkd2) upstream to the third exon generated a nkd2 null allele lacking the first three exons, and the R1‐st (stippled) allele with a novel r1 5′ promoter region homologous to 5′ regions from nkd2‐B73. R1‐sc:124 (an R1‐st derivative) shows increased and earlier expression than a standard R1‐g allele, as well as ectopic expression in the starchy endosperm compartment. Laser capture microdissection and RNA sequencing indicated that ectopic R1‐sc:124 expression impacted expression of genes associated with RNA modification. The expression of R1‐sc:124 resembled nkd2‐W22 expression, suggesting that nkd2 regulatory sequences may influence the expression of R1‐sc:124. The r1‐sc:m3 allele is derived from R1‐sc:124 by an insertion of a Ds6 transposon in intron 4. This insertion blocks anthocyanin regulation by causing mis‐splicing that eliminates exon 5 from the mRNA. This allele serves as an important launch site for Ac/Ds mutagenesis studies, and two Ds6 insertions believed to be associated with nkd2 mutant alleles were actually located in the r1 5′ region. Among annotated genomes of teosinte and maize varieties, the nkd2 and r1 loci showed conserved overall gene structures, similar to the B73 reference genome, suggesting that the nkd2‐r1 rearrangement may be a recent event. Significance Statement: The iconic maize R1 gene encodes a HLH transcription factor that conditions anthocyanin pigmentation in aleurone, and has been instrumental in studies of gene expression and transposons. The R1‐sc124 allele is foundational to important genome mutagenesis projects and was created by an intrachromosomal rearrangement that brought regulatory sequences from the nkd2 gene, 1 Mb away, to produce elevated and ectopic R1 expression, resulting in ectopic downstream gene regulation in starchy endosperm tissue. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) networks are designed to obtain the best possible end‐user experience. This experience is measured in terms of security and speed of operation. In order to achieve high security, encryption, and authentication algorithms are proposed by researchers. The speed of operation is generally measured using the network's response time, and it can be improved using light‐weighted algorithms for data processing and actuation. Most of these techniques are applied at the application level, making the network ineffective in its originally deployed form. Moreover, due to the addition of security and data processing layers, the overall IoT network's QoS reduces, reducing the end user's experience. In order to remove these issues, this work proposes a novel kernel‐level multi‐channel fuzzy‐authentication and aggregation framework (KMFA2). The KMFA2 framework can solve authentication issues by using a kernel‐level lightweight and secure authentication protocol for all IoT devices. KMFA2 also enables the IoT devices to possess multi‐channel capabilities, with inbuilt aggregation capabilities. The overall implementation is carried out on the NS2 tool. The proposed KMFA2 protocol is tested on different IoT networks, and it is observed that the protocol improves the QoS by almost 10% and simplifies security using the authentication framework. The comparisons were performed with state‐of‐the‐art IoT deployments. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to generalize the classical concept of a positive operator, and to develop a general extension theory, which overcomes not only the lack of a Hilbert space structure, but also the lack of a normable topology. The concept of anti‐duality carries an adequate structure to define positivity in a natural way, and is still general enough to cover numerous important areas where the Hilbert space theory cannot be applied. Our running example – illustrating the applicability of the general setting to spaces bearing poor geometrical features – comes from noncommutative integration theory. Namely, representable extension of linear functionals of involutive algebras will be governed by their induced operators. The main theorem, to which the vast majority of the results is built, gives a complete and constructive characterization of those operators that admit a continuous positive extension to the whole space. Various properties such as commutation, or minimality and maximality of special extensions will be studied in detail. [ABSTRACT FROM AUTHOR]

Purpose: To introduce, develop, and evaluate a novel denoising technique for diffusion MRI that leverages nonlinear redundancy in the data to boost the SNR while preserving signal information. Methods: We exploit nonlinear redundancy of the dMRI data by means of kernel principal component analysis (KPCA), a nonlinear generalization of PCA to reproducing kernel Hilbert spaces. By mapping the signal to a high‐dimensional space, a higher level of redundant information is exploited, thereby enabling better denoising than linear PCA. We implement KPCA with a Gaussian kernel, with parameters automatically selected from knowledge of the noise statistics, and validate it on realistic Monte Carlo simulations as well as with in vivo human brain submillimeter and low‐resolution dMRI data. We also demonstrate KPCA denoising on multi‐coil dMRI data. Results: SNR improvements up to 2.7× were obtained in real in vivo datasets denoised with KPCA, in comparison to SNR gains of up to 1.8× using a linear PCA denoising technique called Marchenko‐Pastur PCA (MPPCA). Compared to gold‐standard dataset references created from averaged data, we showed that lower normalized root mean squared error was achieved with KPCA compared to MPPCA. Statistical analysis of residuals shows that anatomical information is preserved and only noise is removed. Improvements in the estimation of diffusion model parameters such as fractional anisotropy, mean diffusivity, and fiber orientation distribution functions were also demonstrated. Conclusion: Nonlinear redundancy of the dMRI signal can be exploited with KPCA, which allows superior noise reduction/SNR improvements than the MPPCA method, without loss of signal information. [ABSTRACT FROM AUTHOR]

SUMMARY: High‐throughput phenotyping systems are powerful, dramatically changing our ability to document, measure, and detect biological phenomena. Here, we describe a cost‐effective combination of a custom‐built imaging platform and deep‐learning‐based computer vision pipeline. A minimal version of the maize (Zea mays) ear scanner was built with low‐cost and readily available parts. The scanner rotates a maize ear while a digital camera captures a video of the surface of the ear, which is then digitally flattened into a two‐dimensional projection. Segregating GFP and anthocyanin kernel phenotypes are clearly distinguishable in ear projections and can be manually annotated and analyzed using image analysis software. Increased throughput was attained by designing and implementing an automated kernel counting system using transfer learning and a deep learning object detection model. The computer vision model was able to rapidly assess over 390 000 kernels, identifying male‐specific transmission defects across a wide range of GFP‐marked mutant alleles. This includes a previously undescribed defect putatively associated with mutation of Zm00001d002824, a gene predicted to encode a vacuolar processing enzyme. Thus, by using this system, the quantification of transmission data and other ear and kernel phenotypes can be accelerated and scaled to generate large datasets for robust analyses. [ABSTRACT FROM AUTHOR]

Multiview spectral clustering aims to separate data into different clusters efficiently by the use of multiview information. Many studies learn the affinity matrix from the original high‐dimensional data, whose noise goes against the clustering results. Besides, some methods based on self‐representation subspace clustering have a high time complexity. In this paper, we propose a simple, yet effective, and efficient method named Kernel‐based Low‐rank Tensorized Multiview Spectral Clustering (KLTMSC) to address these issues. Instead of using the original data to get the affinity matrix, KLTMSC learns the affinity matrix from kernel representation of the high‐dimensional data to reduce the noisy information. Furthermore, to be robust to noise, the low‐rank tensor is learned in the process of exploring the high‐order correlations between data. Experiments on real‐world data sets show that our method not only yields better results but also is quite time‐saving compared with other state‐of‐the‐art models. [ABSTRACT FROM AUTHOR]

We consider a system of hyperbolic integro‐differential equations of SH waves in a visco‐elastic porous medium. In this work, it is assumed that the visco‐elastic porous medium has weakly horizontally inhomogeneity. The direct problem is the initial‐boundary problem: the initial data is equal to zero, and the Neumann‐type boundary condition is specified at the half‐plane boundary and is an impulse function. As additional information, the oscillation mode of the half‐plane line is given. It is assumed that the unknown kernel has the form K(x,t)=K0(t)+ϵxK1(t)+..., where ϵ is a small parameter. In this work, we construct a method for finding K0,K1 up to a correction of the order of O(ϵ2). [ABSTRACT FROM AUTHOR]

In this note, we continue to investigate common properties of the products in the setting of rings, bounded linear operators, or Banach algebras. We prove: (i) If a,b,c are elements in a unital associative ring R satisfying aba=aca, then von Neumann regularity (resp. generalized Fredholmness relative to an ideal I of R) of 1−ac is converted into that of 1−ba. (ii) If A,B,C are bounded linear operators satisfying ABA=ACA, then I−AC and I−BA share common complementability of kernels and ranges. (iii) If a,b,c are elements in a unital semisimple Banach algebra A satisfying aba=aca and I is a trace ideal of A such that soc(A)⊆I⊆ kh(soc(A)), then 1−ac and 1−ba share common Fredholmness relative to I and have the same abstract index. A similar result holds for B‐Fredholmness in primitive Banach algebra. [ABSTRACT FROM AUTHOR]

Two‐dimensional digital image correlation (2D‐DIC) is an experimental technique used to measure in‐plane displacement of a test specimen. Real‐time measurement of full‐field displacement data is challenging due to enormous computational load of the algorithm. In order to improve the computational speed, the focus of recent research works has been on the approach of parallelization across subsets within image pairs using graphics processing unit (GPU). But alternate GPU‐based parallelization approaches to improve the performance of this algorithm as per the order of data processing have not been explored. To address this research gap, our method utilizes parallelism within a subset as well as across subsets for each computation step in an iteration cycle. A heterogeneous (CPU‐GPU) framework in combination with a pyramid‐based initial values estimation for subsets (in parallel) is proposed in this work. The precompute steps of the proposed framework are implemented using CPU, whereas the main iterative steps are realized using GPU. It is demonstrated that the overall computational speed of the proposed heterogeneous framework improves by ≈9× compared to a sequential CPU‐based implementation for a pair of gray‐scale images with a resolution of 588×2,048 pixels. As an important milestone, feasibility to measure deformations in real time (≤ 1 s) is manifested in this study. [ABSTRACT FROM AUTHOR]

Elephant populations are in decline across the African continent, but recent aerial surveys show that populations in Uganda are increasing. However, threats such as poaching and habitat disturbance remain. Having a comprehensive knowledge of the ranging behaviour of Ugandan elephants is crucial to understanding where critical habitat for the species occurs. We investigated various aspects of ranging behaviour of 45 radio‐collared elephants (Loxodonta africana) in three areas—Queen Elizabeth Protected Area (QEPA), Murchison Falls (MFPA) Protected Area and Kidepo Valley (KVCA) Conservation Area. We also set Ugandan analyses in a continental context by comparison with home ranges reported in published literature. Elephants within KVCA had larger core ranges than elephants in QEPA or MFPA. Wet season ranges in KVCA were much larger than dry season ranges. The most important core areas in all three national parks were centred around water resources. Home range size was negatively correlated with net primary productivity (NPP) at Ugandan (N = 39 individuals) and continental (N = 17 sites) scales. This study indicates that, at a local scale, factors such as water source location are important in shaping elephant ranging behaviour. At larger scales, factors such as NPP are good predictors of elephant home range size. [ABSTRACT FROM AUTHOR]

Hypoattenuating ocular lenses on CT have been described with cataract formation in humans, however published studies are currently lacking regarding this finding in veterinary patients. The purpose of this retrospective and prospective study was to describe the varying CT appearances of the ocular lens in vivo, and investigate the causes for CT density variations in a population of cats and dogs. A total of 102 canine and feline patients with CT of the head acquired at the authors' hospital between May 2011 and March 2019 were included. A bilateral hypoattenuating halo surrounding an isoattenuating to mildly hypoattenuating core was described in the ocular lens center of every cat in which a Philips brand proprietary image construction filter was used. A similar but more varied hypoattenuating region was noted in the lenses of 45.8% of dogs where the same filter was applied, as well as 43.8% of dogs with a second, similar filter. Ophthalmic examination of three live cats and one dog with hypoattenuating lenses demonstrated normal lens translucency, excluding the presence of cataract. The effect of different proprietary filters on lens appearance was also described in three fresh cadavers with normal lenses identified on ophthalmic, macroscopic, and microscopic examination. Etiology of the hypoattenuating areas within the ocular lens was not conclusively determined. Recognition that such a variant may be seen in the absence of cataract is important, in order to prevent misdiagnosis. [ABSTRACT FROM AUTHOR]

In this article, β‐type fractional Sturm‐Liouville Coulomb operator is considered by Hilfer fractional derivative. Fundamental spectral theory is investigated for the aforementioned problem. In this context, it is shown that the operator is self‐adjoint, eigenfunctions correspond to the distinct eigenfunctions are orthogonal, and eigenvalues are real. Furthermore, applications of this problem are given by the Adomian decomposition method and the results are shown with visual graphs. [ABSTRACT FROM AUTHOR]

This article is concerned with asymptotic theory for local estimators based on Bregman divergence. We consider a localized version of Bregman divergence induced by a kernel weight and minimize it to obtain the local estimator. We provide a rigorous proof for the asymptotic consistency of the local estimator in a situation where both the sample size and the bandwidth involved in the kernel weight increase. Asymptotic normality of the local estimator is also developed under the same asymptotic scenario. Monte Carlo simulations are also performed to confirm the theoretical results. The Canadian Journal of Statistics 47: 628–652; 2019 © 2019 Statistical Society of Canada [ABSTRACT FROM AUTHOR]

Purpose: Numerous image reconstruction methodologies for positron emission tomography (PET) have been developed that incorporate magnetic resonance (MR) imaging structural information, producing reconstructed images with improved suppression of noise and reduced partial volume effects. However, the influence of MR structural information also increases the possibility of suppression or bias of structures present only in the PET data (PET‐unique regions). To address this, further developments for MR‐informed methods have been proposed, for example, through inclusion of the current reconstructed PET image, alongside the MR image, in the iterative reconstruction process. In this present work, a number of kernel and maximum a posteriori (MAP) methodologies are compared, with the aim of identifying methods that enable a favorable trade‐off between the suppression of noise and the retention of unique features present in the PET data. Methods: The reconstruction methods investigated were: the MR‐informed conventional and spatially compact kernel methods, referred to as KEM and KEM largest value sparsification (LVS) respectively; the MR‐informed Bowsher and Gaussian MR‐guided MAP methods; and the PET‐MR‐informed hybrid kernel and anato‐functional MAP methods. The trade‐off between improving the reconstruction of the whole brain region and the PET‐unique regions was investigated for all methods in comparison with postsmoothed maximum likelihood expectation maximization (MLEM), evaluated in terms of structural similarity index (SSIM), normalized root mean square error (NRMSE), bias, and standard deviation. Both simulated BrainWeb (10 noise realizations) and real [18F] fluorodeoxyglucose (FDG) three‐dimensional datasets were used. The real [18F]FDG dataset was augmented with simulated tumors to allow comparison of the reconstruction methodologies for the case of known regions of PET‐MR discrepancy and evaluated at full counts (100%) and at a reduced (10%) count level. Results: For the high‐count simulated and real data studies, the anato‐functional MAP method performed better than the other methods under investigation (MR‐informed, PET‐MR‐informed and postsmoothed MLEM), in terms of achieving the best trade‐off for the reconstruction of the whole brain and PET‐unique regions, assessed in terms of the SSIM, NRMSE, and bias vs standard deviation. The inclusion of PET information in the anato‐functional MAP method enables the reconstruction of PET‐unique regions to attain similarly low levels of bias as unsmoothed MLEM, while moderately improving the whole brain image quality for low levels of regularization. However, for low count simulated datasets the anato‐functional MAP method performs poorly, due to the inclusion of noisy PET information in the regularization term. For the low counts simulated dataset, KEM LVS and to a lesser extent, HKEM performed better than the other methods under investigation in terms of achieving the best trade‐off for the reconstruction of the whole brain and PET‐unique regions, assessed in terms of the SSIM, NRMSE, and bias vs standard deviation. Conclusion: For the reconstruction of noisy data, multiple MR‐informed methods produce favorable whole brain vs PET‐unique region trade‐off in terms of the image quality metrics of SSIM and NRMSE, comfortably outperforming the whole image denoising of postsmoothed MLEM. [ABSTRACT FROM AUTHOR]

Waxy barley referred to as low‐amylose or amylose‐free has special advantages in nutrition composition and food processing. Waxy gene encoding granule–bound starch synthase I (GBSSI) is responsible for amylose synthesis in barley. The G3935‐to‐T in Waxy gene has been previously found in amylose–free barley. In this study, G3935‐to‐T was proved to co‐segregate with the waxy phenotype of barley, but has no obvious effect on GBSSI catalytic activity and starch chain length distribution. However, recombinant inbred lines with G3935‐to‐T in Waxy gene are of significant modification in starch granules morphology and pasting properties, increase of grain β‐glucan content, and decrease of thousand kernel weight along with lower kernel width. A polymerase chain reaction with confronting two–pair primers marker was developed for economic and efficient screening of G3935‐to‐T. This study provides the basis for cultivar improvement of waxy barley then fully developing its potential value and utility in food processing. [ABSTRACT FROM AUTHOR]

Cophylogeny is the congruence of phylogenetic relationships between two different groups of organisms due to their long‐term interaction. We investigated the use of tree shape distance measures to quantify the degree of cophylogeny. We implemented a reverse‐time simulation model of pathogen phylogenies within a fixed host tree, given cospeciation probability, host switching, and pathogen speciation rates. We used this model to evaluate 18 distance measures between host and pathogen trees including two kernel distances that we developed for labeled and unlabeled trees, which use branch lengths and accommodate different size trees. Finally, we used these measures to revisit published cophylogenetic studies, where authors described the observed associations as representing a high or low degree of cophylogeny. Our simulations demonstrated that some measures are more informative than others with respect to specific coevolution parameters especially when these did not assume extreme values. For real datasets, trees' associations projection revealed clustering of high concordance studies suggesting that investigators are describing it in a consistent way. Our results support the hypothesis that measures can be useful for quantifying cophylogeny. This motivates their usage in the field of coevolution and supports the development of simulation‐based methods, i.e., approximate Bayesian computation, to estimate the underlying coevolutionary parameters. [ABSTRACT FROM AUTHOR]

The generalized method of moments (GMM) and empirical likelihood (EL) are popular methods for combining sample and auxiliary information. These methods are used in very diverse fields of research, where competing theories often suggest variables satisfying different moment conditions. Results in the literature have shown that the efficient‐GMM (GMME) and maximum empirical likelihood (MEL) estimators have the same asymptotic distribution to order n−1/2 and that both estimators are asymptotically semiparametric efficient. In this paper, we demonstrate that when data are missing at random from the sample, the utilization of some well‐known missing‐data handling approaches proposed in the literature can yield GMME and MEL estimators with nonidentical properties; in particular, it is shown that the GMME estimator is semiparametric efficient under all the missing‐data handling approaches considered but that the MEL estimator is not always efficient. A thorough examination of the reason for the nonequivalence of the two estimators is presented. A particularly strong feature of our analysis is that we do not assume smoothness in the underlying moment conditions. Our results are thus relevant to situations involving nonsmooth estimating functions, including quantile and rank regressions, robust estimation, the estimation of receiver operating characteristic (ROC) curves, and so on. [ABSTRACT FROM AUTHOR]

Purpose: Parametric images obtained from kinetic modeling of dynamic positron emission tomography (PET) data provide a new way of visualizing quantitative parameters of the tracer kinetics. However, due to the high noise level in pixel‐wise image‐driven time‐activity curves, parametric images often suffer from poor quality and accuracy. In this study, we propose an indirect parameter estimation framework which aims to improve the quality and quantitative accuracy of parametric images. Methods: Three different approaches related to noise reduction and advanced curve fitting algorithm are used in the proposed framework. First, dynamic PET images are denoised using a kernel‐based denoising method and the highly constrained backprojection technique. Second, gradient‐free curve fitting algorithms are exploited to improve the accuracy and precision of parameter estimates. Third, a kernel‐based post‐filtering method is applied to parametric images to further improve the quality of parametric images. Computer simulations were performed to evaluate the performance of the proposed framework. Results and conclusions: The simulation results showed that when compared to the Gaussian filtering, the proposed denoising method could provide better PET image quality, and consequentially improve the quality and quantitative accuracy of parametric images. In addition, gradient‐free optimization algorithms (i.e., pattern search) can result in better parametric images than the gradient‐based curve fitting algorithm (i.e., trust‐region‐reflective). Finally, our results showed that the proposed kernel‐based post‐filtering method could further improve the precision of parameter estimates while maintaining the accuracy of parameter estimates. [ABSTRACT FROM AUTHOR]

The integro‐differential system of viscoelasticity equations with a source of explosive type is considered. It is assumed that the coefficients of the equations depend only on one spatial variable. The problem of determining the matrix kernel included in the integral terms of the equations is studied. The solution of the problem is reduced to a series of inverse problems for scalar hyperbolic equations. For each case, the inverse problem is replaced by an equivalent system of integral equations for unknown functions. The principle of constricted mapping in the space of continuous functions with weighted norms to the latter is applied. The theorems of global unique solvability are proved, and the stability estimates of solution to the inverse problems are obtained. [ABSTRACT FROM AUTHOR]

We consider the problem of finding the piezoelectric module e(x3),x3>0, occurring in the system of integro‐differential electroviscoelasticity equations. The medium density and the Lamé parameters are assumed to be function of one variable. The integrand K(t),t ∈ [0,T ] is known. As additional information, the Fourier transform of the second component of the displacement vector function for x3=0 is specified. The theorems on the existence of a unique solution of the inverse problem and the stability theorem are the research results. [ABSTRACT FROM AUTHOR]

Summary: System diagnosis has been of a great interest for all aspects of industrial processes more precisely to gain in quality. It is based essentially on the analysis of the links between the variables of a system and more precisely on the changes of the relations between these variables, which testify to the presence of faults or anomalies. For that purpose, data modeling is the process of finding a mathematical expression that provides a good fit between given finite sample values of the independent variables and the associated values of the dependent variables of the process. The aim of this paper is to detect and, above all, localize faults affecting a system with nonlinear behavior, when its model is not known a priori. An important part of the presentation is dedicated to the construction of fault indicators capable of locating faults, ie, recognizing the input or output of a system affected by a fault. The first part of this paper is devoted to how to predict the output of a nonlinear behavior system. The second part proposes a way for the detection and isolation of measurement faults based on the proposed prediction model. The relevance of the proposed technique, for modeling and system diagnosis, is illustrated on a simulated example in the context of SIMO and MIMO systems. [ABSTRACT FROM AUTHOR]

Abstract: Bullock et al. (Journal of Ecology 105:6‐19, 2017) have suggested that the theory behind the Wald Analytical Long Distance (WALD) model for wind dispersal from a point source needs to be re‐examined. This is on the basis that an inverse Gaussian probability density function (pdf) does not provide the best fit to seed shadows around individual source plants known to be dispersed by wind. We present two reasons why we would not necessarily expect any of the standard mechanistically derived pdfs to fit real seed shadows any better than empirical functions. Firstly, the derivation of “off‐the‐shelf” pdfs such as the Gaussian, exponential and inverse Gaussian involves only one of the processes and factors that together generate a real seed shadow. It is implausible to expect that a single‐process model, no matter how sophisticated in detail, will capture the behaviour of an entire, complex system, which may involve a number of sequential random processes, or a superposition of parallel random processes, or both. Secondly, even if there is only one process involved and we have a perfect model for that process, the basic parameters of the model would be difficult to pin down precisely. Moreover, these parameters are unlikely to remain constant over a dispersal season, so that effectively we observe the outcome of a linear combination of dispersal events with different parameter values, constituting a form of averaging over the parameters of the distribution. Simple examples show that averaging a pdf over its parameters can lead to a pdf from an entirely different class. Synthesis. The failure of the inverse Gaussian model to fit seed shadow data is not in itself a reason to doubt the validity of the Wald Analytical Long Distance model for movement of particles through the air under specified environmental conditions. A greater awareness is needed of the differences between the Wald Analytical Long Distance and the inverse Gaussian (or Wald) and the purposes for which they are used. The complexity of dispersing populations of seeds means that any of the standard mechanistically derived pdfs will actually be merely empirical in this context. Shape and flexibility of a pdf is far more important for adequately describing data than some perceived higher status. [ABSTRACT FROM AUTHOR]

Summary: Starch is the most abundant storage carbohydrate in maize kernel. The content of amylose and amylopectin confers unique properties in food processing and industrial application. Thus, the resurgent interest has been switched to the study of individual amylose or amylopectin rather than total starch, whereas the enzymatic machinery for amylose synthesis remains elusive. We took advantage of the phenotype of amylose content and the genotype of 9,007,194 single nucleotide polymorphisms from 464 inbred maize lines. The genome‐wide association study identified 27 associated loci involving 39 candidate genes that were linked to amylose content including transcription factors, glycosyltransferases, glycosidases, as well as hydrolases. Except the waxy gene that encodes the granule‐bound starch synthase, the remaining candidate genes were located in the upstream pathway of amylose synthesis, while the downstream members were already known from prior studies. The linked candidate genes could be transferred to manipulate amylose content and thus add value to maize kernel in the breeding programme. [ABSTRACT FROM AUTHOR]

Abstract: We used Argos‐linked Fastloc‐Global Positioning System (Argos‐linked Fastloc‐GPS) satellite tags to investigate how loggerhead sea turtles use neritic foraging habitats at multiple scales. Out of 24 turtles, six individuals used more than one foraging site, with all sites being separated by >25 km. These six individuals used up to four sites, remaining at each site for a mean of 150 days and returning to the same site a minimum of 52 days later. The other 18 turtles remained in a single site. The area within sites was not used uniformly, with 15 out of 24 turtles exhibiting complex movement patterns within and amongst up to five focal patches, which were typically 0.1–5.0 km apart within a single site. Movements between sites and patches might sometimes have reflected overwintering behaviour; however, similar movement patterns occurred at multiple times of the year, suggesting other factors were also involved. Use of multiple sites and patches might be driven by differences in resource availability, such as food and/or night‐time refuges, competition, or exploratory movement to investigate or locate alternative patches. We confirmed competition via direct visual observations of aggressive interactions between individuals at one foraging patch. Our results illustrate the importance of standardizing data to the same number of locations per day and night to accurately delineate key areas used by turtles or for evidence‐based marine protected area planning. [ABSTRACT FROM AUTHOR]

To quantify the effect of the surrounding landscape context on a biological response at a site, most studies measure landscape variables within discs centred on this biological response (threshold-based method, TBM). This implicitly assumes that the effect of a unit area of the landscape is consistent up to a threshold distance beyond which it drops to zero. However, it seems more likely that the landscape effect declines with increasing distance from the biological response point. Here, we develop a method to quantify landscape context effects by weighting the landscape variables by functions that decrease with distance. We illustrate the method using abundance data on birds and insects, and compare the results to the threshold approach., We defined distance weighting functions by the function family (e.g. negative exponential, Gaussian...) and by the parameters for this function. We developed a method to simultaneously estimate the parameters characterizing the effect of the landscape variables and the parameters of the best weighting functions. For each test dataset, we determined which weighting function (family and parameters) had the most support, by optimizing the model AIC., The distance-weighted method (DWM) improved model support over the TBM in three of four datasets, with the exponential power function selected as the best weighing function in all three cases., The observed differences between estimations of landscape context effects by the distance-weighted and the threshold methods have significant implications for landscape management. For example, the DWM suggests that managing a landscape for 90% of its effect on a focal population requires an area over five times larger than the area estimated by the threshold method, a situation that might apply for priority conservation of few remnant populations of a severely endangered species. In contrast, management for 30% of the landscape effect requires only about half the area estimated using the threshold method, a situation that might apply to a management situation with limited resources or low political/societal support., The DWM is applicable to any species-habitat relationship. More comparisons are needed to determine the situations in which distance-weighted estimation of landscape context effects is warranted over the simpler threshold method. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this study was to quantitatively characterize the fundamental aspects of image quality ( IQ) associated with different computed tomography ( CT) reconstruction algorithms, the resolution, noise texture, noise magnitude per dose, and use those data to devise a methodology to match IQ between different CT systems. Methods and materials This study entailed a 3-step methodology involving (a) characterizing the noise magnitude, texture, and resolution for a CT system-reconstruction using the relationship between noise magnitude and Computed Tomography Dose Index ( CTDI), noise power spectrum ( NPS), and modulation transfer function ( MTF), (b) developing clinically relevant strategies of weighting the differences among system-reconstructions as a means to determine the best match (c) identifying for each target system-reconstruction, system-reconstructions with matched in terms of that minimum IQ differences. Images of the ACR CT phantom were acquired at two dose levels on each of two CT scanners. Images were reconstructed using all available reconstruction kernels and multiple iterative reconstruction ( IR) settings. Each reconstruction was characterized as described above. Percent changes for each IQ metric were calculated for every possible pair of system-reconstructions. Weighting functions, reflecting the human visual system's limit to discriminate between spatial frequencies with differences below 5%, were applied to the differences and the product of the weighted values was used to indicate the best match for each system-reconstruction. Results Noise texture and resolution are governed by choice of reconstruction kernel and IR strength, while noise magnitude is additionally dependent on dose. Harder kernels have better resolution, finer noise texture, and increase the required dose for a given noise magnitude, and vice versa. Increasing IR strength generally improves resolution, coarsens noise texture, and lowers the required dose. Seventy-one percent of Siemens matches for GE target reconstructions had percent changes in noise texture/resolution under 5%. Seventy-three percent of GE matches for Siemens target reconstructions had percent changes in noise texture/resolution under 5%. ACR phantom images for each matched reconstruction pair appeared similar in both noise magnitude and noise texture. Conclusion Matching image appearance in terms of resolution, noise magnitude, and noise texture provides a quantitative and reproducible strategy to improve consistency in image quality among different CT scanners and reconstructions. [ABSTRACT FROM AUTHOR]

We review Bayesian and classical approaches to nonparametric density and regression estimation and illustrate how these techniques can be used in economic applications. On the Bayesian side, density estimation is illustrated via finite Gaussian mixtures and a Dirichlet Process Mixture Model, while nonparametric regression is handled using priors that impose smoothness. From the frequentist perspective, kernel-based nonparametric regression techniques are presented for both density and regression problems. Both approaches are illustrated using a wage dataset from the Current Population Survey. WIREs Comput Stat 2017, 9:e1406. doi: 10.1002/wics.1406 For further resources related to this article, please visit the . [ABSTRACT FROM AUTHOR]

In this paper, the inclusion measures of convex bodies are studied. Some new isoperimetric-type inequalities are established by using the technique of inner parallel bodies and mixed volumes. These inequalities give the upper and lower bounds for the inclusion measures, and show the role of the form body of a convex body in these bounds. [ABSTRACT FROM AUTHOR]

The rising of global per capita food consumption has driven scientists and manufacturers into a search for new or renewed edible oil sources, while also improving their production towards maximum yield, quality, and economic feasibility. Hence, a new origin (Algerian) of Moringa oleifera seed oil was characterized in the present study in terms of its physical and chemical features on two consecutive crops. Moreover, aiming to ascertain if the industrial solvent extraction could be enhanced, extracted oils from shelled and unshelled seeds were compared. All oils under study presented a low oxidation status and an excellent resistance to oxidation. Additionally to the characteristic high average content of unsaturated fatty acids (71% oleic acid), microcomponent analyses revealed interesting amounts of phytosterols (4-5 g/kg), tocopherols (287-327 mg/kg), carotenoids (4-8 mg/kg), and phenolic compounds (11-17 mg/kg). These outcomes were within or better (stability and oxidative status) than reported data for M. oleifera seed oil from other origins, and without significant differences between crops. Apart from a slight color change and increased sterol and wax contents when extracted with shells, no other significant compositional changes were observed, including oil extraction yield, both equivalent to 37% on a kernel basis. This work sustains the high quality of Algerian M. oleifera seed oil, a still underutilized crop in this country. Also, it provides an insight on the potential use of grinded whole seeds directly for solvent extraction, reducing labor demand and cost for the industries, without significant variation on compositional and stability parameters. Practical applications: Regarding the growing demand on edible oils, and considering Algeria as a country with a large range of climatic conditions, M. oleifera cultivation for edible oil extraction could be of economic interests to the region. Based on the findings of this work, the oil extracted from M. oleifera grown in Algeria presents significantly high quality, stability, and consistency between crops. From a technological point of view, we also propose that solvent extraction can be performed directly on the powder of whole M. oleifera seeds, avoiding the shelling step, with proportional oil yield and equivalents quality, of major importance for the industry. Algerian Moringa oil presents high quality, stability, and consistency between crops. The presence of shells does not effect oil quality and yield, of major importance for the industry. [ABSTRACT FROM AUTHOR]

Changes in the starch digestibility and the properties of the starches isolated from waxy, low-, and high-amylose rice kernels subjected to heat-moisture treatment (HMT) at 100°C for 16 h at a moisture content of 25% were investigated. The results showed that amylose content is important in determining pasting properties for both HMTrice starches and in-kernel HMTrice starches. Amylose leaching, swelling power, and solubility index decreased in hydrothermal treatment of both isolated rice starches and rice kernels. The gelatinization temperatures of both hydrothermal-treated samples were affected by different treatments to different extents, and the changes were more pronounced for starches isolated from the treated rice kernels. The relative crystallinity increased in the starch from treated waxy and low-amylose rice kernels but decreased in the starch from treated high-amylose rice kernels. Starch digestibility study suggested that thermal treatment of rice kernels was more effective in producing low-glycemic-index starchbased foods than the same treatment of isolated starch. The study showed that HMT on rice kernels had significant impacts of physicochemical properties and starch digestibility of waxy, low-, and high-amylose rice starches to various extents. [ABSTRACT FROM AUTHOR]

Race conditions are difficult to detect because they usually occur only under specific execution interleavings. Numerous program analysis and testing techniques have been proposed to detect race conditions between threads on single applications. However, most of these techniques neglect races that occur at the process level due to complex system event interactions. This article presents a framework, SIMEXPLORER, that allows engineers to effectively test for process-level race conditions. SIMEXPLORER first uses dynamic analysis techniques to observe system execution, identify program locations of interest, and report faults related to oracles. Next, it uses virtualization to achieve the fine-grained controllability needed to exercise event interleavings that are likely to expose races. We evaluated the effectiveness of SIMEXPLORER on 24 real-world applications containing both known and unknown process-level race conditions. Our results show that SIMEXPLORER is effective at detecting these race conditions, while incurring an overhead that is acceptable given its effectiveness improvements. [ABSTRACT FROM AUTHOR]

The multiple linear model is used successfully to extend the linear model to nonlinear problems. However, the conventional multilinear models fail to learn the global structure of a training data set because the local linear models are independent of each other. Furthermore, the local linear transformations are learned in the original space. Therefore, the performance of multilinear methods is strongly dependent on the results of partition. This paper presents a kernel approach for the implementation of the local linear discriminant analysis for face recognition problems. In the original space, we utilize a set of local linear transformations with interpolation to approximate an optimal global nonlinear transformation. Based on the local linear models in the original space, we derive an explicit kernel mapping to map the training data into a high-dimensional transformed space. The optimal transformation is learned globally in the transformed space. Experimental results show that the proposed method is more robust to the partition results than the conventional multilinear methods. Compared with the general nonlinear kernels that utilize a black-box mapping, our proposed method can reduce the negative effects caused by the potential overfitting problem. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [ABSTRACT FROM AUTHOR]

In biomedical studies, it is often of interest to classify/predict a subject's disease status based on a variety of biomarker measurements. A commonly used classification criterion is based on area under the receiver operating characteristic curve (AUC). Many methods have been proposed to optimize approximated empirical AUC criteria, but there are two limitations to the existing methods. First, most methods are only designed to find the best linear combination of biomarkers, which may not perform well when there is strong nonlinearity in the data. Second, many existing linear combination methods use gradient-based algorithms to find the best marker combination, which often result in suboptimal local solutions. In this paper, we address these two problems by proposing a new kernel-based AUC optimization method called ramp AUC (RAUC). This method approximates the empirical AUC loss function with a ramp function and finds the best combination by a difference of convex functions algorithm. We show that as a linear combination method, RAUC leads to a consistent and asymptotically normal estimator of the linear marker combination when the data are generated from a semiparametric generalized linear model, just as the smoothed AUC method. Through simulation studies and real data examples, we demonstrate that RAUC outperforms smooth AUC in finding the best linear marker combinations, and can successfully capture nonlinear pattern in the data to achieve better classification performance. We illustrate our method with a dataset from a recent HIV vaccine trial. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT Prescribed fire is an often-used tool for managing or manipulating wildlife habitats. However, studies on the effects of prescribed fire on conservation-priority songbirds are limited. We examined effects of low-intensity prescribed burns on vegetation structure and composition, and on habitat use of 45 radiotagged male Swainson's warblers ( Limnothlypis swainsonii) during the 2008 and 2009 breeding seasons in the St. Francis National Forest in eastern Arkansas, USA. We found that home ranges in areas with extensive burning (>50% burned) were significantly larger ( [ABSTRACT FROM AUTHOR]

Information in the data often has far fewer degrees of freedom than the number of variables encoding the data. Dimensionality reduction attempts to reduce the number of variables used to describe the data. In this article, we shall survey some dimension reduction techniques that are robust. We consider linear dimension reduction first and describe robust principal component analysis ( PCA) using three approaches. The first approach uses a singular value decomposition of a robust covariance matrix. The second approach employs robust measures of dispersion to realize PCA as a robust projection pursuit. The third approach uses a low-rank plus sparse decomposition of the data matrix. We also survey robust approaches to nonlinear dimension reduction under a unifying framework of kernel PCA. By using a kernel trick, the robust methods available for PCA can be extended to nonlinear cases. WIREs Comput Stat 2015, 7:63-69. doi: 10.1002/wics.1331 For further resources related to this article, please visit the . Conflict of interest: The authors have declared no conflicts of interest for this article. [ABSTRACT FROM AUTHOR]

Recent advances in data mining have integrated kernel functions with Bayesian probabilistic analysis of Gaussian distributions. These machine-learning approaches can incorporate prior information with new data to calculate probabilistic rather than deterministic values for unknown parameters. This article extensively analyzes a specific Bayesian kernel model that uses a kernel function to calculate a posterior beta distribution that is conjugate to the prior beta distribution. Numerical testing of the beta kernel model on several benchmark datasets reveals that this model's accuracy is comparable with those of the support vector machine (SVM), relevance vector machine, naive Bayes, and logistic regression, and the model runs more quickly than all the other algorithms except for logistic regression. When one class occurs much more frequently than the other class, the beta kernel model often outperforms other strategies to handle imbalanced datasets, including under-sampling, over-sampling, and the Synthetic Minority Over-Sampling Technique. If data arrive sequentially over time, the beta kernel model easily and quickly updates the probability distribution, and this model is more accurate than an incremental SVM algorithm for online learning. [ABSTRACT FROM AUTHOR]

Fusarium poses food and feed safety problems because most species produce mycotoxins. To understand the epidemiology of the Fusarium disease, efforts must focus more precisely on how environmental variables affect disease presence. The objectives of the present study were to monitor the occurrence of Fusarium species in maize kernels in northwestern Spain to determine the risk of mycotoxin contamination and to identify environmental traits affecting the composition of the Fusarium species identified. A combination of 24 environments was evaluated. The percentage of kernels infected by F. verticillioides ranged from 33 to 99%, supporting the idea that fumonisin contamination is the main maize-based feed and food safety concern in this area. In this region, temperature and humidity primarily affected Fusarium spp. occurrence. Warmer temperatures during the later stages of kernel development and during kernel drying increased the frequency of F. verticillioides in maize kernels, while the presence of F. subglutinans was increased by higher relative humidity during the silking stage and cooler temperatures during kernel drying. [ABSTRACT FROM AUTHOR]

ABSTRACT Magnetic resonance image (MRI) segmentation refers to a process of assigning labels to set of pixels or multiple regions. It plays a major role in the field of biomedical applications as it is widely used by the radiologists to segment the medical images input into meaningful regions. In recent years, various brain tumor detection techniques are presented in the literature. In this article, we have developed an approach to brain tumor detection and severity analysis is done using the various measures. The proposed approach comprises of preprocessing, segmentation, feature extraction, and classification. In preprocessing steps, we need to perform skull stripping and then, anisotropic filtering is applied to make image suitable for extracting features. In feature extraction, we have modified the multi-texton histogram (MTH) technique to improve the feature extraction. In the classification stage, the hybrid kernel is designed and applied to training of support vector machine to perform automatic detection of tumor region in MRI images. For comparison analysis, our proposed approach is compared with the existing works using K-cross fold validation method. From the results, we can conclude that the modified multi-texton histogram with non-linear kernels has shown the accuracy of 86% but the MTH with non-linear kernels shows the accuracy of 83.8%. [ABSTRACT FROM AUTHOR]

Chemogenomic kinase-kernel virtual screening models interpolate between very accurate, empirically-trained Profile-QSAR models of the nearest binding-site homologues with IC50 assay data. Between them, activity has been predicted for the entire human kinome. [ABSTRACT FROM AUTHOR]

Background Since there is an increasing demand on the world market for alternative crops suitable for organic production, spelt wheat ( Triticum aestivum spp. spelta L.) is a highly attractive farming option. Alternaria species are widespread and infect a great variety of economically important crops. Certain species are known producers of mycotoxins. The aim of this study was to assess the protective effect of hulls covering the spelt kernels on Alternaria toxins. Results Alternariol ( AOH) and alternariol monomethyl ether ( AME) were evaluated in hulls and dehulled kernels after plant inoculation with one A. alternata and two different A. tenuissima isolates. Mycotoxins were determinated using high-performance liquid chromatography with dioade array detection. The detected levels of AOH and AME were four times higher in hulls compared to kernels in inoculation treatments. AOH was registered at levels ranging from 227 to 331 µg kg−1 in dehulled kernels and from 433 up to 1647 µg kg−1 in hulls. AME was predominant toxin detected in the range of 277 to 398 µg kg−1 in dehulled kernels and from 1844 to 2183 µg kg−1 in hulls, with highly significant difference to water control treatment. Conclusion Obtained results indicate the significantly higher concentrations of Alternaria toxins in hulls than in dehulled kernels which implicate the possible protective effect of spelt wheat hulls.© 2012 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Although dispersal distance plays a major role in determining whether organisms will reach new habitats, empirical data on the environmental factors that affect dispersal distance are lacking. Population density and kin competition are two factors theorised to increase dispersal distance. Using the two-spotted spider mite as a model species, we altered these two environmental conditions and measured the mean dispersal distance of individuals, as well as other attributes of the dispersal kernel. We find that both density and relatedness in the release patch increase dispersal distance. Relatedness, but not density, changes the shape of the dispersal kernel towards a more skewed and leptokurtic shape including a longer 'fat-tail'. This is the first experimental demonstration that kin competition can shape the whole distribution of dispersal distances in a population, and thus affect the geographical spread of dispersal phenotypes. [ABSTRACT FROM AUTHOR]