Mutations in the gene encoding IFT dynein complex component WDR34 cause jeune asphyxiating thoracic dystrophy

Bidirectional (anterograde and retrograde) motor-based intraflagellar transport (IFT) governs cargo transport and delivery processes that are essential for primary cilia growth and maintenance and for hedgehog signaling functions. The IFT dynein-2 motor complex that regulates ciliary retrograde protein transport contains a heavy chain dynein ATPase/motor subunit, DYNC2H1, along with other less well functionally defined subunits. Deficiency of IFT proteins, including DYNC2H1, underlies a spectrum of skeletal ciliopathies. Here, by using exome sequencing and a targeted next-generation sequencing panel, we identified a total of 11 mutations in WDR34 in 9 families with the clinical diagnosis of Jeune syndrome (asphyxiating thoracic dystrophy). WDR34 encodes a WD40 repeat-containing protein orthologous to Chlamydomonas FAP133, a dynein intermediate chain associated with the retrograde intraflagellar transport motor. Three-dimensional protein modeling suggests that the identified mutations all affect residues critical for WDR34 protein-protein interactions. We find that WDR34 concentrates around the centrioles and basal bodies in mammalian cells, also showing axonemal staining. WDR34 coimmunoprecipitates with the dynein-1 light chain DYNLL1 in vitro, and mining of proteomics data suggests that WDR34 could represent a previously unrecognized link between the cytoplasmic dynein-1 and IFT dynein-2 motors. Together, these data show that WDR34 is critical for ciliary functions essential to normal development and survival, most probably as a previously unrecognized component of the mammalian dynein-IFT machinery.

An immunodiagnostic assay for quantitation of specific ige to the major pollen allergen component, pas n 1, of the subtropical bahia grass

Background Pollens of the Panicoideae subfamily of grasses including Bahia (Paspalum notatum) are important allergen sources in subtropical regions of the world. An assay for specific IgE to the major molecular allergenic component, Pas n 1, of Bahia grass pollen (BaGP) would have immunodiagnostic utility for patients with pollen allergy in these regions. Methods Biotinylated Pas n 1 purified from BaGP was coated onto streptavidin ImmunoCAPs. Subjects were assessed by clinical history of allergic rhinitis and skin prick test (SPT) to aeroallergens. Serum total, BaGP-specific and Pas n 1-specific IgE were measured. Results: Pas n 1 IgE concentrations were highly correlated with BaGP SPT (r = 0.795, p < 0.0001) and BaGP IgE (r = 0.915, p < 0.0001). At 0.23 kU/l Pas n 1 IgE, the diagnostic sensitivity (92.4%) and specificity (93.1%) for the detection of BaGP allergy was high (area under receiver operator curve 0.960, p < 0.0001). The median concentrations of Pas n 1 IgE in non-Atopic subjects (0.01 kU/l, n = 67) and those with other allergies (0.02 kU/l, n = 59) showed no inter-group difference, whilst grass pollen-Allergic patients with allergic rhinitis showed elevated Pas n 1 IgE (6.71 kU/l, n = 182, p < 0.0001). The inter-Assay coefficient of variation for the BaGP-Allergic serum pool was 6.92%. Conclusions Pas n 1 IgE appears to account for most of the BaGP-specific IgE. This molecular component immunoassay for Pas n 1 IgE has potential utility to improve the sensitivity and accuracy of diagnosis of BaGP allergy for patients in subtropical regions.

Introduction: Epitope mimicry as a component cause of autoimmune disease

The causes of autoimmune diseases have yet to be fully elucidated. Autoantibodies, autoreactive T cell responses, the presence of a predisposing major histocompatibility complex (MHC) haplotype and responsiveness to corticosteroids are features, and some are possibly contributory causes of autoimmune disease. The most challenging question is how autoimmune diseases are triggered. Molecular mimicry of host cell determinants by epitopes of infectious agents with ensuing cross-reactivity is one of the most popular yet still controversial theories for the initiation of autoimmune diseases [1]. Throughout the 1990s, hundreds of research articles focusing to various extents on epitope mimicry, as it is more accurately described in an immunological context, were published annually. Many of these articles presented data that were consistent with the hypothesis of mimicry but that did not actually prove the theory. Other equally convincing reports indicated that epitope mimicry was not the cause of the autoimmune disease despite sequence similarity between molecules of infectious agents and the host. Some 20 years ago, Rothman [2] proposed a model for disease causation and I have used this as a framework to examine the role of epitope mimicry in the development of autoimmune disease. The thesis of Rothman’s model is that an effect, in this instance autoimmune disease, arises as a result of a cause. In most cases, multiple-component causes contribute synergistically to yield the effect, and each of these components alone is insufficient as a cause. Logically, some component causes, such as the presence of a particular autoimmune response, are also necessary causes.

Robust principal component analysis in water quality index development

Some statistical procedures already available in literature are employed in developing the water quality index, WQI. The nature of complexity and interdependency that occur in physical and chemical processes of water could be easier explained if statistical approaches were applied to water quality indexing. The most popular statistical method used in developing WQI is the principal component analysis (PCA). In literature, the WQI development based on the classical PCA mostly used water quality data that have been transformed and normalized. Outliers may be considered in or eliminated from the analysis. However, the classical mean and sample covariance matrix used in classical PCA methodology is not reliable if the outliers exist in the data. Since the presence of outliers may affect the computation of the principal component, robust principal component analysis, RPCA should be used. Focusing in Langat River, the RPCA-WQI was introduced for the first time in this study to re-calculate the DOE-WQI. Results show that the RPCA-WQI is capable to capture similar distribution in the existing DOE-WQI.

Differentiation of cultivars of flos chrysanthemum with the use of high-performance liquid chromatography fingerprints and chemometrics

Flos Chrysanthemum is a generic name for a particular group of edible plants, which also have medicinal properties. There are, in fact, twenty to thirty different cultivars, which are commonly used in beverages and for medicinal purposes. In this work, four Flos Chrysanthemum cultivars, Hangju, Taiju, Gongju, and Boju, were collected and chromatographic fingerprints were used to distinguish and assess these cultivars for quality control purposes. Chromatography fingerprints contain chemical information but also often have baseline drifts and peak shifts, which complicate data processing, and adaptive iteratively reweighted, penalized least squares, and correlation optimized warping were applied to correct the fingerprint peaks. The adjusted data were submitted to unsupervised and supervised pattern recognition methods. Principal component analysis was used to qualitatively differentiate the Flos Chrysanthemum cultivars. Partial least squares, continuum power regression, and K-nearest neighbors were used to predict the unknown samples. Finally, the elliptic joint confidence region method was used to evaluate the prediction ability of these models. The partial least squares and continuum power regression methods were shown to best represent the experimental results.

Combined NIR/MIR analysis: A novel method for the classification of complex substances such as Illicium verum Hook. F. and its adulterants

A novel combined near- and mid-infrared (NIR and MIR) spectroscopic method has been researched and developed for the analysis of complex substances such as the Traditional Chinese Medicine (TCM), Illicium verum Hook. F. (IVHF), and its noxious adulterant, Iuicium lanceolatum A.C. Smith (ILACS). Three types of spectral matrix were submitted for classification with the use of the linear discriminant analysis (LDA) method. The data were pretreated with either the successive projections algorithm (SPA) or the discrete wavelet transform (DWT) method. The SPA method performed somewhat better, principally because it required less spectral features for its pretreatment model. Thus, NIR or MIR matrix as well as the combined NIR/MIR one, were pretreated by the SPA method, and then analysed by LDA. This approach enabled the prediction and classification of the IVHF, ILACS and mixed samples. The MIR spectral data produced somewhat better classification rates than the NIR data. However, the best results were obtained from the combined NIR/MIR data matrix with 95–100% correct classifications for calibration, validation and prediction. Principal component analysis (PCA) of the three types of spectral data supported the results obtained with the LDA classification method.

Frequency response function based damage identification using principal component analysis and pattern recognition technique

Pattern recognition is a promising approach for the identification of structural damage using measured dynamic data. Much of the research on pattern recognition has employed artificial neural networks (ANNs) and genetic algorithms as systematic ways of matching pattern features. The selection of a damage-sensitive and noise-insensitive pattern feature is important for all structural damage identification methods. Accordingly, a neural networks-based damage detection method using frequency response function (FRF) data is presented in this paper. This method can effectively consider uncertainties of measured data from which training patterns are generated. The proposed method reduces the dimension of the initial FRF data and transforms it into new damage indices and employs an ANN method for the actual damage localization and quantification using recognized damage patterns from the algorithm. In civil engineering applications, the measurement of dynamic response under field conditions always contains noise components from environmental factors. In order to evaluate the performance of the proposed strategy with noise polluted data, noise contaminated measurements are also introduced to the proposed algorithm. ANNs with optimal architecture give minimum training and testing errors and provide precise damage detection results. In order to maximize damage detection results, the optimal architecture of ANN is identified by defining the number of hidden layers and the number of neurons per hidden layer by a trial and error method. In real testing, the number of measurement points and the measurement locations to obtain the structure response are critical for damage detection. Therefore, optimal sensor placement to improve damage identification is also investigated herein. A finite element model of a two storey framed structure is used to train the neural network. It shows accurate performance and gives low error with simulated and noise-contaminated data for single and multiple damage cases. As a result, the proposed method can be used for structural health monitoring and damage detection, particularly for cases where the measurement data is very large. Furthermore, it is suggested that an optimal ANN architecture can detect damage occurrence with good accuracy and can provide damage quantification with reasonable accuracy under varying levels of damage.

The correlation between reading and mathematics ability at age twelve has a substantial genetic component

Dissecting how genetic and environmental influences impact on learning is helpful for maximizing numeracy and literacy. Here we show, using twin and genome-wide analysis, that there is a substantial genetic component to children’s ability in reading and mathematics, and estimate that around one half of the observed correlation in these traits is due to shared genetic effects (so-called Generalist Genes). Thus, our results highlight the potential role of the learning environment in contributing to differences in a child’s cognitive abilities at age twelve.

CCD: Connected Component Descriptor for Robust Mosaicing of Camera-Captured Document Images

We propose a robust method for mosaicing of document images using features derived from connected components. Each connected component is described using the Angular Radial Tran. form (ART). To ensure geometric consistency during feature matching, the ART coefficients of a connected component are augmented with those of its two nearest neighbors. The proposed method addresses two critical issues often encountered in correspondence matching: (i) The stability of features and (ii) Robustness against false matches due to the multiple instances of characters in a document image. The use of connected components guarantees a stable localization across images. The augmented features ensure a successful correspondence matching even in the presence of multiple similar regions within the page. We illustrate the effectiveness of the proposed method on camera captured document images exhibiting large variations in viewpoint, illumination and scale.

The Learning Component of Dynamic Allocation Indices

For a multiarmed bandit problem with exponential discounting the optimal allocation rule is defined by a dynamic allocation index defined for each arm on its space. The index for an arm is equal to the expected immediate reward from the arm, with an upward adjustment reflecting any uncertainty about the prospects of obtaining rewards from the arm, and the possibilities of resolving those uncertainties by selecting that arm. Thus the learning component of the index is defined to be the difference between the index and the expected immediate reward. For two arms with the same expected immediate reward the learning component should be larger for the arm for which the reward rate is more uncertain. This is shown to be true for arms based on independent samples from a fixed distribution with an unknown parameter in the cases of Bernoulli and normal distributions, and similar results are obtained in other cases.

Establishment of an Australian mild onion industry - the sensory component

In recent years, there has been increasing interest from growers, merchants, supermarkets and consumers in the establishment of a national mild onion industry. Imperative to the success of the emergent industry is the application of the National Mild Onion Certification Scheme that will establish standards and recommendations to be met by growers to allow them to declare their product as certified mild onions. The use of sensory evaluation techniques has played an imperative role throughout the project timeline that has also included varietal evaluation, evaluation of current agronomic practices and correlation of chemical analysis data. Raw onion consumer acceptance testing on five different onion varieties established preferences amongst the varieties for odour, appearance, flavour, texture and overall and differences in the level of pungency and aftertaste perceived. Demographic information was obtained regarding raw and cooked onion use, frequency of consumption and responses to the idea of a mild, less pungent onion. Additionally, focus groups were conducted to further investigate consumer attitudes to onions. Currently, a trained onion panel is being established to evaluate several odour, flavour and aftertaste attributes. Sample assessments will be conducted in January 2004 and correlated with chemical analyses that will hopefully provide the corner-stone for the anticipated Certification Scheme.

Characterization of tree-to-tree variation in morphological, nutritional and medicinal properties of Canarium indicum nuts

As part of a feasibility study of the commercialization potential of C. indicum nuts as Agroforestry Tree Products in Papua New Guinea, preliminary characterization studies have examined the tree-to-tree variation in morphological traits (nut and kernel mass and kernel:nut ratio), as well as nutritional (carbohydrate, fat, protein, sodium, vitamin E) and medicinal traits (anti-oxidant activity, anti-inflammatory activity and phenolic content) of kernels from 18 to 72 trees in a small number of different villages of Papua New Guinea (East New Britain Province). There was continuous variation in these traits indicating opportunities for multiple trait cultivar development targeted at food and pharmaceutical markets. Certain traits, for example anti-inflammatory activity, in which tree-to-tree variation was highly significant, present greater opportunities than others, such as saturated:unsaturated fatty acid ratio. This intraspecific variation was greater within populations than between populations. The data presented has allowed the development of a strategy to domesticate C. indicum for cultivation in homegardens and cocoa-coconut agroforests, using a participatory approach aimed at the production of agroforestry tree products (AFTPs) to empower small-holders and enhance their livelihoods and income.

Structure and properties of two component hydrogels comprising lithocholic acid and organic amines

We demonstrate the aptitude of supramolecular hydrogel formation using simple bile acid such as lithocholic acid in aqueous solution in the presence of various dimeric or oligomeric amines. By variation of the choice of the amines in such mixtures the gelation properties could be modulated. However, the replacement of lithocholic acid (LCA) by cholic acid or deoxycholic acid resulted in no hydrogel formation. FT-IR studies confirm that the carboxylate and ammonium residues of the two components are involved in the salt (ion-pair) formation. This promotes further assembly of the components reinforced by a continuous hydrogen bonded network leading to gelation. Electron microscopy shows the morphology of the internal organization of gels of two component systems which also depends significantly on the amine part. Variation of the amine component from the simple 1,2-ethanediamine (EDA) to oligomeric amines in such gels of lithocholic acid changes the morphology of the assembly from long one-dimensional nanotubes to three-dimensional complex structures. Single crystal X-ray diffraction analysis with one of the amine-LCA complexes suggested the motif of fiber formation where the amines interact with the carboxylate and hydroxyl moieties through electrostatic forces and hydrogen bonding. From small angle neutron scattering study, it becomes clear that the weak gel from LCA-EDA shows scattering oscillation due to the presence of non-interacting nanotubules while for gels of LCA with oligomeric amines the individual fibers come together to form complex three-dimensional organizations of higher length scale. The rheological properties of this class of two component system provide clear evidence that the flow behavior can be modulated varying the acid-amine ratio.