Magne Aldrin

Predicting survival from microarray data—a comparative study

MOTIVATION Survival prediction from gene expression data and other high-dimensional genomic data has been subject to much research during the last years. These kinds of data are associated with the methodological problem of having many more gene expression values than individuals. In addition, the responses are censored survival times. Most of the proposed methods handle this by using Coxs proportional hazards model and obtain parameter estimates by some dimension reduction or parameter shrinkage estimation technique. Using three well-known microarray gene expression data sets, we compare the prediction performance of seven such methods: univariate selection, forward stepwise selection, principal components regression (PCR), supervised principal components regression, partial least squares regression (PLS), ridge regression and the lasso. RESULTS Statistical learning from subsets should be repeated several times in order to get a fair comparison between methods. Methods using coefficient shrinkage or linear combinations of the gene expression values have much better performance than the simple variable selection methods. For our data sets, ridge regression has the overall best performance. AVAILABILITY Matlab and R code for the prediction methods are available at http://www.med.uio.no/imb/stat/bmms/software/microsurv/.

Prevalence of alcohol and drugs among Norwegian motor vehicle drivers: A roadside survey

The objective of the study was to determine the prevalence of alcohol, psychoactive medicinal drugs and illegal drugs among drivers in Norwegian road traffic. Drivers of motor vehicles were selected from April 2005 to April 2006 in the south-eastern part of Norway, surrounding, but not including the capital, Oslo. A stratified two-stage cluster sampling procedure was used. In the first stage, random road sites and time intervals were selected, and in the second stage, drivers were stopped by random at those sites and time intervals. Altogether about 12,000 drivers were asked to provide a sample of oral fluid (saliva) and answer a few questions. Samples of oral fluid were obtained from 88% of the drivers, of whom 30% were females and 70% males. The prevalence of each drug was estimated by a weighted average using weights adjusted for under- or over-sampling compared to traffic statistics. Alcohol or drugs were found in oral fluid samples of 4.5% of the drivers; alcohol in 0.4%, psychoactive medicinal drugs in 3.4%, and illegal drugs in 1.0%. Illegal drugs were found more frequently in samples from younger drivers, while psychoactive medicinal drugs were more frequently found in samples from older drivers. Psychoactive medicinal drugs were more prevalent among females than males, among drivers stopped on working days rather than weekends, and among those who reported annual driving distances less than 16,000 km. The drugs found most frequently were zopiclone (1.4%), benzodiazepines (1.4%), codeine (0.8%), tetrahydrocannabinol (0.6%) and amphetamines (0.3%). Two or more drugs were found in 0.6% of the samples, corresponding to 15% of the drug-positive drivers.

Sea lice as a density-dependent constraint to salmonid farming

Fisheries catches worldwide have shown no increase over the last two decades, while aquaculture has been booming. To cover the demand for fish in the growing human population, continued high growth rates in aquaculture are needed. A potential constraint to such growth is infectious diseases, as disease transmission rates are expected to increase with increasing densities of farmed fish. Using an extensive dataset from all farms growing salmonids along the Norwegian coast, we document that densities of farmed salmonids surrounding individual farms have a strong effect on farm levels of parasitic sea lice and efforts to control sea lice infections. Furthermore, increased intervention efforts have been unsuccessful in controlling elevated infection levels in high salmonid density areas in 2009–2010. Our results emphasize host density effects of farmed salmonids on the population dynamics of sea lice and suggest that parasitic sea lice represent a potent negative feedback mechanism that may limit sustainable spatial densities of farmed salmonids.

A Controlled Experiment Comparing the Maintainability of Programs Designed with and without Design Patterns—A Replication in a Real Programming Environment

Software “design patterns” seek to package proven solutions to design problems in a form that makes it possible to find, adapt and reuse them. To support the industrial use of design patterns, this research investigates when, and how, using patterns is beneficial, and whether some patterns are more difficult to use than others. This paper describes a replication of an earlier controlled experiment on design patterns in maintenance, with major extensions. Experimental realism was increased by using a real programming environment instead of pen and paper, and paid professionals from multiple major consultancy companies as subjects. Measurements of elapsed time and correctness were analyzed using regression models and an estimation method that took into account the correlations present in the raw data. Together with on-line logging of the subjects’ work, this made possible a better qualitative understanding of the results. The results indicate quite strongly that some patterns are much easier to understand and use than others. In particular, the Visitor pattern caused much confusion. Conversely, the patterns Observer and, to a certain extent, Decorator were grasped and used intuitively, even by subjects with little or no knowledge of patterns. The implication is that design patterns are not universally good or bad, but must be used in a way that matches the problem and the people. When approaching a program with documented design patterns, even basic training can improve both the speed and quality of maintenance activities.

A stochastic model for the assessment of the transmission pathways of heart and skeleton muscle inflammation, pancreas disease and infectious salmon anaemia in marine fish farms in Norway.

Salmon farming is threatened, economically and ecologically, by infectious diseases. To reduce the risk of epidemics, authorities have developed regulations. These are based on quantitative understanding of pathways of infection, representing disease specific risks. A stochastic model was fitted to historical data, to estimate risk factors associated with competing spread mechanisms. Three infectious diseases were compared, heart and skeletal muscle inflammation (HSMI), pancreas disease (PD) and infectious salmon anaemia (ISA). This study was based on space-time data, from Norway from 2003 to 2007, describing the susceptible fish cohorts and the reported infections. Particular interest was given to seaway distances between farms and their local management organisation. The parameter measuring the effect of distance to an infectious fish farm was positive and significant for all diseases, implying that the risk involved with proximate infectious fish farms increased with decreasing distance. For HSMI and PD there was a significant effect of sharing a contact network with an infectious farm. For HSMI, but not for PD or ISA, there was a significant effect of previous infected cohorts on the same farm. The relative contribution of each transmission pathway was dominated by seaway distance for PD and HSMI, while other non-defined pathways dominated for ISA. This comparative study highlights that the three diseases have different patterns of spread, with important consequences for disease prevention and management.

The influence of missing value imputation on detection of differentially expressed genes from microarray data

MOTIVATION Missing values are problematic for the analysis of microarray data. Imputation methods have been compared in terms of the similarity between imputed and true values in simulation experiments and not of their influence on the final analysis. The focus has been on missing at random, while entries are missing also not at random. RESULTS We investigate the influence of imputation on the detection of differentially expressed genes from cDNA microarray data. We apply ANOVA for microarrays and SAM and look to the differentially expressed genes that are lost because of imputation. We show that this new measure provides useful information that the traditional root mean squared error cannot capture. We also show that the type of missingness matters: imputing 5% missing not at random has the same effect as imputing 10-30% missing at random. We propose a new method for imputation (LinImp), fitting a simple linear model for each channel separately, and compare it with the widely used KNNimpute method. For 10% missing at random, KNNimpute leads to twice as many lost differentially expressed genes as LinImp. AVAILABILITY The R package for LinImp is available at http://folk.uio.no/idasch/imp.

A stochastic model for infectious salmon anemia (ISA) in Atlantic salmon farming

Infectious salmon anemia (ISA) is one of the main infectious diseases in Atlantic salmon farming with major economical implications. Despite the strong regulatory interventions, the ISA epidemic is not under control, worldwide. We study the data covering salmon farming in Norway from 2002 to 2005 and propose a stochastic space-time model for the transmission of the virus. We model seaway transmission between farm sites, transmission through shared management and infrastructure, biomass effects and other potential pathways within the farming industry. We find that biomass has an effect on infectiousness, the local contact network and seaway distance of 5 km represent similar risks, but a large component of risk originates from other sources, among which are possibly infected salmon smolt and boat traffic.

Modelling the spread of infectious salmon anaemia among salmon farms based on seaway distances between farms and genetic relationships between infectious salmon anaemia virus isolates

Infectious salmon anaemia (ISA) is an important infectious disease in Atlantic salmon farming causing recurrent epidemic outbreaks worldwide. The focus of this paper is on tracing the spread of ISA among Norwegian salmon farms. To trace transmission pathways for the ISA virus (ISAV), we use phylogenetic relationships between virus isolates in combination with space–time data on disease occurrences. The rate of ISA infection of salmon farms is modelled stochastically, where seaway distances between farms and genetic distances between ISAV isolates from infected farms play prominent roles. The model was fitted to data covering all cohorts of farmed salmon and the history of all farms with ISA between 2003 and summer 2009. Both seaway and genetic distances were significantly associated with the rate of ISA infection. The fitted model predicts that the risk of infection from a neighbourhood infectious farm decreases with increasing seaway distance between the two farms. Furthermore, for a given infected farm with a given ISAV genotype, the source of infection is significantly more likely to be ISAV of a small genetic distance than of moderate or large genetic distances. Nearly half of the farms with ISA in the investigated period are predicted to have been infected by an infectious farm in their neighbourhood, whereas the remaining half of the infected farms had unknown sources. For many of the neighbourhood infected farms, it was possible to point out one or a few infectious farms as the most probable sources of infection. This makes it possible to map probable infection pathways.

Space-time modelling of the spread of salmon lice between and within Norwegian marine salmon farms.

Parasitic salmon lice are potentially harmful to salmonid hosts and farm produced lice pose a threat to wild salmonids. To control salmon lice infections in Norwegian salmonid farming, numbers of lice are regularly counted and lice abundance is reported from all salmonid farms every month. We have developed a stochastic space-time model where monthly lice abundance is modelled simultaneously for all farms. The set of farms is regarded as a network where the degree of contact between farms depends on their seaway distance. The expected lice abundance at each farm is modelled as a function of i) lice abundance in previous months at the same farm, ii) at neighbourhood farms, and iii) other, unspecified sources. In addition, the model includes explanatory variables such as seawater temperature and farm-numbers of fish. The model gives insight into factors that affect salmon lice abundance and contributing sources of infection. New findings in this study were that 66% of the expected salmon lice abundance was attributed to infection within farms, 28% was attributed to infection from neighbourhood farms and 6% to non-specified sources of infection. Furthermore, we present the relative risk of infection between neighbourhood farms as a function of seaway distance, which can be viewed as a between farm transmission kernel for salmon lice. The present modelling framework lays the foundation for development of future scenario simulation tools for examining the spread and abundance of salmon lice on farmed salmonids under different control regimes.

Behavior of lactating Holstein-Friesian cows during spontaneous cycles of estrus.

The objectives of the present study were to describe, in detail, behavior associated with standing estrus (STE) in lactating dairy cows and behavioral changes during complete estrous cycles. Estrus signs were monitored by continuous video recording of 20 Holstein-Friesian (HF) cows housed on an outdoor wood-chip pad during 1 estrous cycle (22 d). Other social behavior was recorded during STE and, for comparison, during 1 selected day when none of the cows were in estrus. Standing stationary when mounted was defined as the primary estrus sign. Anogenital sniff, chin rest, attempt to mount, and mount were defined as secondary estrus signs. Ovarian cyclicity was confirmed by progesterone measurements. This study reports short mean duration of STE (7.1±1.44h) and estrus (mount period; 12.9±1.84h) of the 13 cows expressing these signs. All mounting activities involved at least one cow in, or within 4h of, STE. The most frequent sign during STE was anogenital sniff initiated, followed by chin rest received, chin rest initiated, chase up initiated, anogenital sniff received, mount initiated, head butt, mount received, attempt to mount initiated, push away received, play rub, attempt to mount received, follow initiated, threat received, flehmen, avoid, bellow, and social lick received. Standing and mounting activity in HF cows was inconsistent during estrus, indicating that other signs could be of greater use. The frequency of secondary estrus signs initiated and received increased gradually during the last 12h before STE, revealing significant differences between periods from 4 to 6 and 1 to 3h before STE. A considerable increase in receptive behavior (secondary estrus signs received) was identified between 1 to 3h prior to STE and STE. Both frequent initiated and received behaviors were associated with STE. A significant decrease in the frequency of secondary estrus signs initiated and received occurred 3h after STE. Cows in STE simultaneously predominantly chose the other standing cow as mate and expressed secondary estrus signs more frequently. Based on the results of this study, we suggest that chase up could be regarded as a reliable indicator of estrus and that the changes in proceptive (initiated) and receptive (received) behavior could be used as predictors of different stages in estrus. Knowledge of these behavioral signs may improve heat detection rates and the ability to predict the optimum breeding time for dairy cows.