Kristján Jónasson

Linkage of Osteoporosis to Chromosome 20p12 and Association to BMP2

Osteoporotic fractures are a major cause of morbidity and mortality in ageing populations. Osteoporosis, defined as low bone mineral density (BMD) and associated fractures, have significant genetic components that are largely unknown. Linkage analysis in a large number of extended osteoporosis families in Iceland, using a phenotype that combines osteoporotic fractures and BMD measurements, showed linkage to Chromosome 20p12.3 (multipoint allele-sharing LOD, 5.10; p value, 6.3 × 10−7), results that are statistically significant after adjusting for the number of phenotypes tested and the genome-wide search. A follow-up association analysis using closely spaced polymorphic markers was performed. Three variants in the bone morphogenetic protein 2 (BMP2) gene, a missense polymorphism and two anonymous single nucleotide polymorphism haplotypes, were determined to be associated with osteoporosis in the Icelandic patients. The association is seen with many definitions of an osteoporotic phenotype, including osteoporotic fractures as well as low BMD, both before and after menopause. A replication study with a Danish cohort of postmenopausal women was conducted to confirm the contribution of the three identified variants. In conclusion, we find that a region on the short arm of Chromosome 20 contains a gene or genes that appear to be a major risk factor for osteoporosis and osteoporotic fractures, and our evidence supports the view that BMP2 is at least one of these genes.

Gradual caldera collapse at Bárdarbunga volcano, Iceland, regulated by lateral magma outflow

Driven to collapse Volcanic eruptions occur frequently, but only rarely are they large enough to cause the top of the mountain to collapse and form a caldera. Gudmundsson et al. used a variety of geophysical tools to monitor the caldera formation that accompanied the 2014 Bárdarbunga volcanic eruption in Iceland. The volcanic edifice became unstable as magma from beneath Bárdarbunga spilled out into the nearby Holuhraun lava field. The timing of the gradual collapse revealed that it is the eruption that drives caldera formation and not the other way around. Science, this issue p. 262 Magma flow from under the Bárdarbunga volcano drove caldera collapse during the 2014 eruption. INTRODUCTION The Bárdarbunga caldera volcano in central Iceland collapsed from August 2014 to February 2015 during the largest eruption in Europe since 1784. An ice-filled subsidence bowl, 110 square kilometers (km2) in area and up to 65 meters (m) deep developed, while magma drained laterally for 48 km along a subterranean path and erupted as a major lava flow northeast of the volcano. Our data provide unprecedented insight into the workings of a collapsing caldera. RATIONALE Collapses of caldera volcanoes are, fortunately, not very frequent, because they are often associated with very large volcanic eruptions. On the other hand, the rarity of caldera collapses limits insight into this major geological hazard. Since the formation of Katmai caldera in 1912, during the 20th century’s largest eruption, only five caldera collapses are known to have occurred before that at Bárdarbunga. We used aircraft-based altimetry, satellite photogrammetry, radar interferometry, ground-based GPS, evolution of seismicity, radio-echo soundings of ice thickness, ice flow modeling, and geobarometry to describe and analyze the evolving subsidence geometry, its underlying cause, the amount of magma erupted, the geometry of the subsurface caldera ring faults, and the moment tensor solutions of the collapse-related earthquakes. RESULTS After initial lateral withdrawal of magma for some days though a magma-filled fracture propagating through Earth’s upper crust, preexisting ring faults under the volcano were reactivated over the period 20 to 24 August, marking the onset of collapse. On 31 August, the eruption started, and it terminated when the collapse stopped, having produced 1.5 km of basaltic lava. The subsidence of the caldera declined with time in a near-exponential manner, in phase with the lava flow rate. The volume of the subsidence bowl was about 1.8 km3. Using radio-echo soundings, we find that the subglacial bedrock surface after the collapse is down-sagged, with no indications of steep fault escarpments. Using geobarometry, we determined the depth of magma reservoir to be ~12 km, and modeling of geodetic observations gives a similar result. High-precision earthquake locations and moment tensor analysis of the remarkable magnitude M5 earthquake series are consistent with steeply dipping ring faults. Statistical analysis of seismicity reveals communication over tens of kilometers between the caldera and the dike. CONCLUSION We conclude that interaction between the pressure exerted by the subsiding reservoir roof and the physical properties of the subsurface flow path explain the gradual near-exponential decline of both the collapse rate and the intensity of the 180-day-long eruption. By combining our various data sets, we show that the onset of collapse was caused by outflow of magma from underneath the caldera when 12 to 20% of the total magma intruded and erupted had flowed from the magma reservoir. However, the continued subsidence was driven by a feedback between the pressure of the piston-like block overlying the reservoir and the 48-km-long magma outflow path. Our data provide better constraints on caldera mechanisms than previously available, demonstrating what caused the onset and how both the roof overburden and the flow path properties regulate the collapse. The Bárdarbunga caldera and the lateral magma flow path to the Holuhraun eruption site. (A) Aerial view of the ice-filled Bárdarbunga caldera on 24 October 2014, view from the north. (B) The effusive eruption in Holuhraun, about 40 km to the northeast of the caldera

Avalanche hazard zoning in Iceland based on individual risk

Abstract Avalanche hazard is a threat to many residential areas in Iceland. In 1995 two avalanche accidents, causing a total of 34 fatalities in areas thought to be safe, prompted research on avalanche hazard assessment. A new method was developed, and in 2000 a new regulation on avalanche hazard zoning was issued. The method and regulation are based on individual risk, or annual probability of death due to avalanches. The major components of the method are the estimation of avalanche frequency, run-out distribution and vulnerability. The frequency is estimated locally for each path under consideration, but the run-out distribution is based on data from many locations, employing the concept of transferring avalanches between slopes. Finally the vulnerability is estimated using data from the 1995 avalanches. Under the new regulation, new hazard maps have been prepared for six of the most vulnerable villages in Iceland. Hazard zones are delineated using risk levels of 0.2×10–4, 0.7×10–4 and 2×10–4 a–1, with risk less than 0.2×10–4 a–1 considered acceptable. When explaining the new zoning to the public, a measure of annual individual risk that allows comparison with other risks in society has proven advantageous.

Effects of knowledge, education, and experience on acceptance of first trimester screening for chromosomal anomalies

Objectives. To assess pregnant womens knowledge and understanding of first trimester prenatal screening (nuchal translucency, maternal serum free beta‐human chorionic gonadotrophin and pregnancy‐associated plasma‐protein‐A), to evaluate the impact of a new information booklet and investigate the effects of education and experiential knowledge of congenital disabilities on the perceived likelihood of accepting prenatal screening. Design. A quasi‐experimental quantitative study with a self‐completion questionnaire. Setting. Five different maternity care clinics in Iceland. Population. Expectant mothers in first trimester of pregnancy (n = 379). Material and methods. Expectant mothers were divided into two groups, an intervention and a control group, both receiving traditional care and information. The intervention group additionally received an information booklet about prenatal screening and diagnosis. Main outcome measures. Womens knowledge score of prenatal screening. The correlation between education, knowledge score, experiential knowledge of congenital disabilities, and the likelihood of accepting prenatal screening. Results. More than half of the women (57%) believed they received sufficient information to make an informed decision about screening. Knowledge scores were significantly higher for the intervention group (with mean 4.8 compared with 3.7 on a 0–8 scale, p < 0.0001). Those with higher scores were more likely to accept screening (p < 0.0001). Women with experiential knowledge of congenital anomalies in their own families were more likely to accept prenatal screening (p = 0.017). Conclusions. Various factors, e.g. experiential knowledge, education and information about prenatal screening affect the likelihood of participation in prenatal screening programs. More information results in better knowledge and higher uptake rate.

Controversy Corner: An empirical study of software architectures' effect on product quality

Abstract: Software architecture is concerned with the structure of software systems and is generally agreed to influence software quality. Even so, little empirical research has been performed on the relationship between software architecture and software quality. Based on 1141 open source Java projects, we calculate three software architecture metrics (measuring classes per package, normalized distance, and a new metric introduced by us concerning the excess of coupling degree) and analyze to which extent these metrics are related to product metrics (defect ratio, download rate, methods per class, and method complexity). We conclude that there are a number of significant relationships between product metrics and architecture metrics. In particular, the number of open defects depends significantly on all our architecture measures.

Genome-wide search for breast cancer linkage in large Icelandic non-BRCA1/2 families

Introduction:A significant proportion of high-risk breast cancer families are not explained by mutations in known genes. Recent genome-wide searches (GWS) have not revealed any single major locus reminiscent of BRCA1 and BRCA2, indicating that still unidentified genes may explain relatively few families each or interact in a way obscure to linkage analyses. This has drawn attention to possible benefits of studying populations where genetic heterogeneity might be reduced. We thus performed a GWS for linkage on nine Icelandic multiple-case non-BRCA1/2 families of desirable size for mapping highly penetrant loci. To follow up suggestive loci, an additional 13 families from other Nordic countries were genotyped for selected markers.Methods:GWS was performed using 811 microsatellite markers providing about five centiMorgan (cM) resolution. Multipoint logarithm of odds (LOD) scores were calculated using parametric and nonparametric methods. For selected markers and cases, tumour tissue was compared to normal tissue to look for allelic loss indicative of a tumour suppressor gene.Results:The three highest signals were located at chromosomes 6q, 2p and 14q. One family contributed suggestive LOD scores (LOD 2.63 to 3.03, dominant model) at all these regions, without consistent evidence of a tumour suppressor gene. Haplotypes in nine affected family members mapped the loci to 2p23.2 to p21, 6q14.2 to q23.2 and 14q21.3 to q24.3. No evidence of a highly penetrant locus was found among the remaining families. The heterogeneity LOD (HLOD) at the 6q, 2p and 14q loci in all families was 3.27, 1.66 and 1.24, respectively. The subset of 13 Nordic families showed supportive HLODs at chromosome 6q (ranging from 0.34 to 1.37 by country subset). The 2p and 14q loci overlap with regions indicated by large families in previous GWS studies of breast cancer.Conclusions:Chromosomes 2p, 6q and 14q are candidate sites for genes contributing together to high breast cancer risk. A polygenic model is supported, suggesting the joint effect of genes in contributing to breast cancer risk to be rather common in non-BRCA1/2 families. For genetic counselling it would seem important to resolve the mode of genetic interaction.

Smart data analytics methods for remote sensing applications

The big data analytics approach emerged that can be interpreted as extracting information from large quantities of scientific data in a systematic way. In order to have a more concrete understanding of this term we refer to its refinement as smart data analytics in order to examine large quantities of scientific data to uncover hidden patterns, unknown correlations, or to extract information in cases where there is no exact formula (e.g. known physical laws). Our concrete big data problem is the classification of classes of land cover types in image-based datasets that have been created using remote sensing technologies, because the resolution can be high (i.e. large volumes) and there are various types such as panchromatic or different used bands like red, green, blue, and nearly infrared (i.e. large variety). We investigate various smart data analytics methods that take advantage of machine learning algorithms (i.e. support vector machines) and state-of-the-art parallelization approaches in order to overcome limitations of big data processing using non-scalable serial approaches.

Corrected sequential linear programming for sparse minimax optimization

We present a new algorithm for nonlinear minimax optimization which is well suited for large and sparse problems. The method is based on trust regions and sequential linear programming. On each iteration a linear minimax problem is solved for a basic step. If necessary, this is followed by the determination of a minimum norm corrective step based on a first-order Taylor approximation. No Hessian information needs to be stored. Global convergence is proved. This new method has been extensively tested and compared with other methods, including two well known codes for nonlinear programming. The numerical tests indicate that in many cases the new method can find the solution in just as few iterations as methods based on approximate second-order information. The tests also show that for some problems the corrective steps give much faster convergence than for similar methods which do not employ such steps.

Evaluating exact VARMA likelihood and its gradient when data are incomplete

A detailed description of an algorithm for the evaluation and differentiation of the likelihood function for VARMA processes in the general case of missing values is presented. The method is based on combining the Cholesky decomposition method for complete data VARMA evaluation and the Sherman-Morrison-Woodbury formula. Potential saving for pure VAR processes is discussed and formulae for the estimation of missing values and shocks are provided. A theorem on the determinant of a low rank update is proved. Matlab implementation of the algorithm is in a companion article.

Algorithm 878: Exact VARMA likelihood and its gradient for complete and incomplete data with Matlab

Matlab functions for the evaluation of the exact log-likelihood of VAR and VARMA time series models are presented (vector autoregressive moving average). The functions accept incomplete data, and calculate analytical gradients, which may be used in parameter estimation with numerical likelihood maximization. Allowance is made for possible savings when estimating seasonal, structured or distributed lag models. Also provided is a function for creating simulated VARMA time series that have an accurate distribution from term one (they are spin-up free). The functions are accompanied by a a simple example driver, a program demonstrating their use for real parameter fitting, as well as a test suite for verifying their correctness and aid further development. The article concludes with description of numerical results obtained with the algorithm.