Genetic analysis of GRIA2 and GRIA4 genes in migraine

Background Migraine is a brain disorder affecting ∼12% of the Caucasian population. Genes involved in neurological, vascular, and hormonal pathways have all been implicated in predisposing individuals to developing migraine. The migraineur presents with disabling head pain and varying symptoms of nausea, emesis, photophobia, phonophobia, and occasionally visual sensory disturbances. Biochemical and genetic studies have demonstrated dysfunction of neurotransmitters: serotonin, dopamine, and glutamate in migraine susceptibility. Glutamate mediates the transmission of excitatory signals in the mammalian central nervous system that affect normal brain function including cognition, memory and learning. The aim of this study was to investigate polymorphisms in the GRIA2 and GRIA4 genes, which encode subunits of the ionotropic AMPA receptor for association in an Australian Caucasian population. Methods Genotypes for each polymorphism were determined using high resolution melt analysis and the RFLP method. Results Statistical analysis showed no association between migraine and the GRIA2 and GRIA4 polymorphisms investigated. Conclusions Although the results of this study showed no significant association between the tested GRIA gene variants and migraine in our Australian Caucasian population further investigation of other components of the glutamatergic system may help to elucidate if there is a relationship between glutamatergic dysfunction and migraine.

Risk-management of UAS robust autonomy for its integration into civil aviation safety frameworks

This paper discusses a model of the civil aviation reg- ulation framework and shows how the current assess- ment of reliability and risk for piloted aircraft has limited applicability for Unmanned Aircraft Systems (UAS) with high levels of autonomous decision mak- ing. Then, a new framework for risk management of robust autonomy is proposed, which arises from combining quantified measures of risk with normative decision making. The term Robust Autonomy de- scribes the ability of an autonomous system to either continue or abort its operation whilst not breaching a minimum level of acceptable safety in the presence of anomalous conditions. The decision making associ- ated with risk management requires quantifying prob- abilities associated with the measures of risk and also consequences of outcomes related to the behaviour of autonomy. The probabilities are computed from an assessment under both nominal and anomalous sce- narios described by faults, which can be associated with the aircraft’s actuators, sensors, communication link, changes in dynamics, and the presence of other aircraft in the operational space. The consequences of outcomes are characterised by a loss function which rewards the certification decision

Effect of doping with Co and/or Cu on electronic structure and optical properties of ZnO

This paper reports on ab initio numerical simulations of the effect of Co and Cu dopings on the electronic structure and optical properties of ZnO, pursued to develop diluted magnetic semiconductors vitally needed for spintronic applications. The simulations are based upon the Perdew-Burke-Enzerh generalized gradient approximation on the density functional theory. It is revealed that the electrons with energies close to the Fermi level effectively transfer only between Cu and Co ions which substitute Zn atoms, and are located in the neighbor sites connected by an O ion. The simulation results are consistent with the experimental observations that addition of Cu helps achieve stable ferromagnetism of Co-doped ZnO. It is shown that simultaneous insertion of Co and Cu atoms leads to smaller energy band gap, redshift of the optical absorption edge, as well as significant changes in the reflectivity, dielectric function, refractive index, and electron energy loss function of ZnO as compared to the doping with either Co or Cu atoms. These highly unusual optical properties are explained in terms of the computed electronic structure and are promising for the development of the next-generation room-temperature ferromagnetic semiconductors for future spintronic devices on the existing semiconductor micromanufacturing platform.

A Fuzzy Approximation Scheme for Sequential Learning in Pattern Recognition

An adaptive learning scheme, based on a fuzzy approximation to the gradient descent method for training a pattern classifier using unlabeled samples, is described. The objective function defined for the fuzzy ISODATA clustering procedure is used as the loss function for computing the gradient. Learning is based on simultaneous fuzzy decisionmaking and estimation. It uses conditional fuzzy measures on unlabeled samples. An exponential membership function is assumed for each class, and the parameters constituting these membership functions are estimated, using the gradient, in a recursive fashion. The induced possibility of occurrence of each class is useful for estimation and is computed using 1) the membership of the new sample in that class and 2) the previously computed average possibility of occurrence of the same class. An inductive entropy measure is defined in terms of induced possibility distribution to measure the extent of learning. The method is illustrated with relevant examples.

Liver transplantation in children: Experience with the development of an Australian pilot programme. The Queensland Liver Transplantation Programme

Children with end-stage liver disease now form a major sub-group of patients considered suitable for liver transplantation (ltp), and enjoy better survival statistics after transplantation than do adults. Since June 1984, a paediatric ltp programme has been developed in Brisbane with an initial working relationship and ongoing close links with two USA centres (Pittsburgh, and the UCLA Medical Center). Fourteen children with end-stage liver disease have been referred to the Queensland Liver Transplantation Programme for formal assessment. Following frank, informed discussion with their parents, 10 of these children were offered the option of ltp. During the transition stage, two infants with biliary atresia were referred to UCLA at their parents' request and, subsequently, eight children aged from 9 months to 6 years have been placed on a transplant candidacy list in Brisbane. A donor procurement team with access to a Queensland Government jet has been available to cover all mainland States except Western Australia. Six of the children have now had orthotopic ltp (two children at the UCLA Medical Center; four children at the Royal Children's Hospital, Brisbane). One UCLA patient died with a non-functioning graft, and one Brisbane patient died 5 weeks post-transplant with rejection, hepatic artery thrombosis and sepsis. The other four children are alive and well, three with normal liver function and one with unexplained intrahepatic cholestasis, during the 1-20 month follow-up to date. Three further children have died of their liver disease without a donor of an appropriate blood group and size being found, and one patient still awaits a suitable donor. The experience of these authors suggests that ltp is a major advance in the treatment of paediatric liver disease, and that the procedure can be carried out successfully in Australia with initial results comparable with leading overseas centres. The procedure requires the full array of services of a major paediatric tertiary care facility, an intensive team effort with awareness of the special needs of children, and a widespread procurement capability. A major problem for Australia is the procurement of sufficient numbers of optimal paediatric donor livers.

IgA nephropathy and Henoch-Schönlein nephritis in adults : with special reference to factors affecting outcome

IgA nephropathy (IgAN) is the most common primary glomerulonephritis. In one third of the patients the disease progresses, and they eventually need renal replacement therapy. IgAN is in most cases a slowly progressing disease, and the prediction of progression has been difficult, and the results of studies have been conflicting. Henoch-Schönlein nephritis (HSN) is rare in adults, and prediction of the outcome is even more difficult than in IgAN. This study was conducted to evaluate the clinical and histopathological features and predictors of the outcome of IgAN and HSN diagnosed in one centre (313 IgAN patients and 38 HSN patients), and especially in patients with normal renal function at the time of renal biopsy. The study also aimed to evaluate whether there is a difference in the progression rates in four countries (259 patients from Finland, 112 from UK, 121 from Australia and 274 from Canada), and if so, can this be explained by differences in renal biopsy policy. The third aim was to measure urinary excretions of cytokines interleukin 1ß (IL-1ß) and interleukin 1 receptor antagonist (IL-1ra) in patients with IgAN and HSN and the correlations of excretion of these substances with histopathological damage and clinical factors. A large proportion of the patients diagnosed in Helsinki as having IgAN had normal renal function (161/313 patients). Four factors, (hypertension, higher amounts of urinary erythrocytes, severe arteriolosclerosis and a higher glomerular score) which independently predicted progression (logistic regression analysis), were identified in mild disease. There was geographic variability in renal survival in patients with IgAN. When age, levels of renal function, proteinuria and blood pressure were taken into account, it showed that the variability related mostly to lead-time bias and renal biopsy indications. Amount of proteinuria more than 0.4g/24h was the only factor that was significantly related to the progression of HSN. the Hypertension and the level of renal function were found to be factors predicting outcome in patients with normal renal function at the time of diagnosis. In IgAN patients, IL-1ra excretion into urine was found to be decreased as compared with HSN patients and healthy controls. Patients with a high IL-1ra/IL-1ß ratio had milder histopathological changes in renal biopsy than patients with a low/normal IL-1ra/IL-1ß ratio. It was also found that the excretion of IL-1ß and especially IL-1ra were significantly higher in women. In conclusion, it was shown that factors associated with outcome can reliably be identified even in mild cases of IgAN. Predicting outcome in adult HSN, however, remains difficult.

Support vector machine applied to settlement of shallow foundations on cohesionless soils

The determination of settlement of shallow foundations on cohesionless soil is an important task in geotechnical engineering. Available methods for the determination of settlement are not reliable. In this study, the support vector machine (SVM), a novel type of learning algorithm based on statistical theory, has been used to predict the settlement of shallow foundations on cohesionless soil. SVM uses a regression technique by introducing an ε – insensitive loss function. A thorough sensitive analysis has been made to ascertain which parameters are having maximum influence on settlement. The study shows that SVM has the potential to be a useful and practical tool for prediction of settlement of shallow foundation on cohesionless soil.

Applicability of Statistical Learning Algorithms for Spatial Variability of Rock Depth

Two algorithms are outlined, each of which has interesting features for modeling of spatial variability of rock depth. In this paper, reduced level of rock at Bangalore, India, is arrived from the 652 boreholes data in the area covering 220 sqa <.km. Support vector machine (SVM) and relevance vector machine (RVM) have been utilized to predict the reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth. The support vector machine (SVM) that is firmly based on the theory of statistical learning theory uses regression technique by introducing epsilon-insensitive loss function has been adopted. RVM is a probabilistic model similar to the widespread SVM, but where the training takes place in a Bayesian framework. Prediction results show the ability of learning machine to build accurate models for spatial variability of rock depth with strong predictive capabilities. The paper also highlights the capability ofRVM over the SVM model.

Analysis of EXAFS data of complex systems

Analysis of EXAFS data of complex systems containing more than one phase and one type of coordination, has been discussed. It is shown that a modified treatment of EXAFS function as well as the amplitude ratio plots provide useful means of obtaining valuable structural information. The systems investigated are: biphasic Ni+NiO mixture, NiAl2O4 with two coordinations for Ni, NiO+NiAl2O4 mixture, CoS+CoO system and Ni dispersed on Al2O3. The results obtained with these systems have been most satisfactory and serve to illustrate the utility and the applicability of the innovations described in this paper.

Noninvasive biomarkers for early smoking related lung disease

Chronic obstructive pulmonary disease (COPD) is a slowly progressive disease characterized by airway inflammation and largely irreversible airflow limitation. One major risk factor for COPD is cigarette smoking. Since the inflammatory process starts many years prior to the onset of clinical symptoms and still continues after smoking cessation, there is an urgent need to find simple non-invasive biomarkers that can be used in the early diagnosis of COPD and which could help in predicting the disease progression. The first aim of the present study was to evaluate the involvement of different oxidative/nitrosative stress markers, matrix metalloproteinases (MMPs) and their tissue inhibitor-1 (TIMP-1) in smokers and in COPD. Elevated numbers of inducible nitric oxide synthase (iNOS), nitrotyrosine, myeloperoxidase (MPO) and 4-hydroxy-2-nonenal (4-HNE) positive cells and increased levels of 8-isoprostane and lactoferrin were found in sputum of non-symptomatic smokers compared to non-smokers, and especially in subjects with stable mild to moderate COPD, and they correlated with the severity of airway obstruction. This suggests that an increased oxidant burden exists already in the airways of smokers with normal lung function values. However, none of these markers could differentiate healthy smokers from symptomatic smokers with normal lung function values i.e. those individuals who are at risk of developing COPD. In contrast what is known about asthma exhaled nitric oxide (FENO) was lower in smokers than in non-smokers, the reduced FENO value was significantly associated with neutrophilic inflammation and the elevated oxidant burden (positive cells for iNOS, nitrotyrosine and MPO). The levels of sputum MMP-8 and plasma MMP-12 appeared to differentiate subjects who have a risk for COPD development but these finding require further investigations. The levels of all studied MMPs correlated with the numbers of neutrophils, and MMP-8 and MMP-9 with markers of neutrophil activation (MPO, lactoferrin) suggesting that especially neutrophil derived oxidants may stimulate the tissue destructive MMPs already in lungs of smokers who are not yet experiencing any airflow limitation. When investigating the role of neutrophil proteases (neutrophil elastase, MMP-8, MMP-9) during COPD exacerbation and its recovery period, we found that levels of all these proteases were increased in sputum of patients with COPD exacerbation as compared to stable COPD and controls, and decreased during the one-month recovery period, giving evidence for a role of these enzymes in COPD exacerbations. In the last study, the effects of subject`s age and smoking habits were evaluated on the plasma levels of surfactant protein A (SP-A), SP-D, MMP-9 and TIMP-1. Long-term smoking increased the levels of all of these proteins. SP-A most clearly correlated with age, pack years and lung function decline (FEV1/FVC), and based on the receiver operating characteristic curve analysis, SP-A was the best marker for discriminating subjects with COPD from controls. In conclusion, these findings support the hypothesis that especially neutrophil derived oxidants may activate MMPs and induce an active remodeling process already in the lungs of smokers with normal lung function values. The marked increase of sputum levels of neutrophil proteases in smokers, stable COPD and/or during its exacerbations suggest that these enzymes play a role in the development and progression of COPD. Based on the comparison of various biomarkers, SP-A can be proposed to serve as sensitive biomarker in COPD development.

Nonlinear analysis of adhesively bonded lap joints considering viscoplasticity in adhesives

This paper presents nonlinear finite element analysis of adhesively bonded joints considering the elastoviscoplastic constitutive model of the adhesive material and the finite rotation of the joint. Though the adherends have been assumed to be linearly elastic, the yielding of the adhesive is represented by a pressure sensitive modified von Mises yield function. The stress-strain relation of the adhesive is represented by the Ramberg-Osgood relation. Geometric nonlinearity due to finite rotation in the joint is accounted for using the Green-Lagrange strain tensor and the second Piola-Kirchhoff stress tensor in a total Lagrangian formulation. Critical time steps have been calculated based on the eigenvalues of the transition matrices of the viscoplastic model of the adhesive. Stability of the viscoplastic solution and time dependent behaviour of the joints are examined. A parametric study has been carried out with particular reference to peel and shear stress along the interface. Critical zones for failure of joints have been identified. The study is of significance in the design of lap joints as well as on the characterization of adhesive strength. (C) 1999 Elsevier Science Ltd. All rights reserved.

A comparative study of ordinary kriging and support vector machine models for the spatial variability of rock depth in Bangalore

In this paper, reduced level of rock at Bangalore, India is arrived from the 652 boreholes data in the area covering 220 sq.km. In the context of prediction of reduced level of rock in the subsurface of Bangalore and to study the spatial variability of the rock depth, ordinary kriging and Support Vector Machine (SVM) models have been developed. In ordinary kriging, the knowledge of the semivariogram of the reduced level of rock from 652 points in Bangalore is used to predict the reduced level of rock at any point in the subsurface of Bangalore, where field measurements are not available. A cross validation (Q1 and Q2) analysis is also done for the developed ordinary kriging model. The SVM is a novel type of learning machine based on statistical learning theory, uses regression technique by introducing e-insensitive loss function has been used to predict the reduced level of rock from a large set of data. A comparison between ordinary kriging and SVM model demonstrates that the SVM is superior to ordinary kriging in predicting rock depth.

Familial Diarrhea Syndrome Caused by an Activating GUCY2C Mutation

BACKGROUND Familial diarrhea disorders are, in most cases, severe and caused by recessive mutations. We describe the cause of a novel dominant disease in 32 members of a Norwegian family. The affected members have chronic diarrhea that is of early onset, is relatively mild, and is associated with increased susceptibility to inflammatory bowel disease, small-bowel obstruction, and esophagitis. METHODS We used linkage analysis, based on arrays with single-nucleotide polymorphisms, to identify a candidate region on chromosome 12 and then sequenced GUCY2C, encoding guanylate cyclase C (GC-C), an intestinal receptor for bacterial heat-stable enterotoxins. We performed exome sequencing of the entire candidate region from three affected family members, to exclude the possibility that mutations in genes other than GUCY2C could cause or contribute to susceptibility to the disease. We carried out functional studies of mutant GC-C using HEK293T cells. RESULTS We identified a heterozygous missense mutation (c.2519G -> T) in GUCY2C in all affected family members and observed no other rare variants in the exons of genes in the candidate region. Exposure of the mutant receptor to its ligands resulted in markedly increased production of cyclic guanosine monophosphate (cGMP). This may cause hyperactivation of the cystic fibrosis transmembrane regulator (CFTR), leading to increased chloride and water secretion from the enterocytes, and may thus explain the chronic diarrhea in the affected family members. CONCLUSIONS Increased GC-C signaling disturbs normal bowel function and appears to have a proinflammatory effect, either through increased chloride secretion or additional effects of elevated cellular cGMP. Further investigation of the relevance of genetic variants affecting the GC-C-CFTR pathway to conditions such as Crohn's disease is warranted. (Funded by Helse Vest Western Norway Regional Health Authority] and the Department of Science and Technology, Government of India.)

Noise tolerance under risk minimization

In this paper, we explore noise-tolerant learning of classifiers. We formulate the problem as follows. We assume that there is an unobservable training set that is noise free. The actual training set given to the learning algorithm is obtained from this ideal data set by corrupting the class label of each example. The probability that the class label of an example is corrupted is a function of the feature vector of the example. This would account for most kinds of noisy data one encounters in practice. We say that a learning method is noise tolerant if the classifiers learnt with noise-free data and with noisy data, both have the same classification accuracy on the noise-free data. In this paper, we analyze the noise-tolerance properties of risk minimization (under different loss functions). We show that risk minimization under 0-1 loss function has impressive noise-tolerance properties and that under squared error loss is tolerant only to uniform noise; risk minimization under other loss functions is not noise tolerant. We conclude this paper with some discussion on the implications of these theoretical results.

Learning from positive and unlabelled examples using maximum margin clustering

Learning from Positive and Unlabelled examples (LPU) has emerged as an important problem in data mining and information retrieval applications. Existing techniques are not ideally suited for real world scenarios where the datasets are linearly inseparable, as they either build linear classifiers or the non-linear classifiers fail to achieve the desired performance. In this work, we propose to extend maximum margin clustering ideas and present an iterative procedure to design a non-linear classifier for LPU. In particular, we build a least squares support vector classifier, suitable for handling this problem due to symmetry of its loss function. Further, we present techniques for appropriately initializing the labels of unlabelled examples and for enforcing the ratio of positive to negative examples while obtaining these labels. Experiments on real-world datasets demonstrate that the non-linear classifier designed using the proposed approach gives significantly better generalization performance than the existing relevant approaches for LPU.