Light measurement and the terminology of low

Abstract. This paper (i) considers the measurement of light in terms of the flow of radiant energy or of particles in a vector field; (ii) derives terminology and units for the specification of light as a flow of radiant energy or of photons which satisfy both the requirements of SI and are physically consistent and (iii) recommends that this terminology is adopted by all plant scientists to avoid ambiguity, confusion and the proliferation of synonyms when reporting light measurements.     

Evaluating the efficiency of enzyme accelerated CO2 capture: chemical kinetics modelling for interpreting measurement results

In this paper, the efficiency of the carbonic anhydrase (CA) enzyme in accelerating the hydration of CO₂ is evaluated using a measurement system which consists of a vessel in which a gaseous flow of mixtures of nitrogen and CO₂ is bubbled into water or water solutions containing a known quantity of CA enzyme. The pH value of the solution and the CO₂ concentration at the measurement system gas exhaust are continuously monitored. The measured CO₂ level allows for assessing the quantity of CO₂, which, subtracted from the gaseous phase, is dissolved into the liquid phase and/or hydrated to bicarbonate. The measurement procedure consists of inducing a transient and observing and modelling the different kinetics involved in the steady-state recovery with and without CA. The main contribution of this work is exploiting dynamical system theory and chemical kinetics modelling for interpreting measurement results for characterising the activity of CA enzymes. The data for model fitting are obtained from a standard bioreactor, in principle equal to standard two-phase bioreactors described in the literature, in which two different techniques can be used to move the process itself away from the steady-state, inducing transients.     

Imputation of environmental variables for vegetation plots based on compositional similarity

Question: Large databases contain many plots, but few subsets with measured environmental data. To obtain broader datasets, researchers use expert‐based indicator values as surrogates; alternatively, these can be estimated by imputation. Does imputation provide more exact approximations than indicator values? Location: West Carpathians (Slovakia, Poland, Czech Republic) and Bulgaria. Methods: We developed a simple imputation method based on plot similarity that estimates missing environmental variables for plots –MOSS (mean of similar samples). This was tested for pH and conductivity, important environmental factors influencing vegetation composition and structure within wetlands, on two datasets of 485 (West Carpathians) and 118 (Bulgaria) plots for which directly measured values were available. The West Carpathian dataset was used for calibration. Imputation was based on calculating mean of the measured factor from a group of most similar plots. Using pre‐defined similarity criteria, we selected subsets of both datasets and compared estimated and measured values. Using root mean‐squared error of prediction, we compared predictive power of MOSS with Ellenberg indicator values and other recent methods. Results: Within one study region, MOSS predicts sample pH and conductivity more precisely than Ellenberg and similar calibration methods. Predictive power slightly decreased when MOSS was transferred to a distant region. Conclusions: Imputation using MOSS appears to accurately predict pH and conductivity from existing composition data within a single geographical region, and thus increases number of replicates. MOSS does not require expert‐based indicator values, which may be imprecise. We provide examples where MOSS can be utilised without risk of circular reasoning or introducing pseudo‐replications.     

Use of signal quality measurements to gain efficiency in the analysis of cDNA microarray data

This research provides a new way to measure error in microarray data in order to improve gene expression analysis.Microarray data contains many sources of error.In order to glean information about mRNA expression levels,the true signal must first be segregated from noise.This research focuses on the variation that can be captured at the spot level in cDNA microarray images.Variation at other levels,due to differences at the array,dye,and block levels,can be corrected for by a variety of existing normalization procedures.Two signal quality estimates that capture the reliability of each spot printed on a microarray are described.A parametric estimate of within-spot vari ance,referred to here as σ s2pot,assumes that pixels follow a normal distribution and are spatially correlated.A non-parametric estimate of error,called the mean square prediction error(MSPE),assumes that spots of high quality possess pixels that are similar to their neighbors.This paper will provide a framework to use either spot quality measure in downstream analysis,specifically as weights in regression models.Using these spot quality estimates as weights can result in greater efficiency,in a statistical sense,when modeling microarray data.     

Muscle cross-section measurement by magnetic resonance imaging

Muscle cross-section areas were measured by magnetic resonance imaging (MRI) in the thigh of a human cadaver, the results being compared with those obtained by photography of corresponding anatomic macroslices. A close correlation was found between MRI and photographic evaluation, differences between the methods ranging from nil to 9.5%, depending on the scan position and the muscle groups. In vivo MRI measurements were performed on 12 female and 16 male students, the objectivity, the test-retest reliability and the variability of the MRI measurements being studied by fixing the scan position either manually or by coronary scan. The latter method appeared to be more objective and reliable. The coefficients of variation for muscle cross-section areas measured by MRI were in the range of those for the planimetry of given cross-section areas. Allowing for differentiation between several small muscle bundles in a given area, MRI proved to be a suitable method to quantify muscle cross-sections for intra- and interindividual analysis of muscle size.     

Estimation in the Cox survival regression model with covariate measurement error and a changepoint

The Cox regression model is a popular model for analyzing the relationship between a covariate vector and a survival endpoint. The standard Cox model assumes a constant covariate effect across the entire covariate domain. However, in many epidemiological and other applications, the covariate of main interest is subject to a threshold effect: a change in the slope at a certain point within the covariate domain. Often, the covariate of interest is subject to some degree of measurement error. In this paper, we study measurement error correction in the case where the threshold is known. Several bias correction methods are examined: two versions of regression calibration (RC1 and RC2, the latter of which is new), two methods based on the induced relative risk under a rare event assumption (RR1 and RR2, the latter of which is new), a maximum pseudo-partial likelihood estimator (MPPLE), and simulation-extrapolation (SIMEX). We develop the theory, present simulations comparing the methods, and illustrate their use on data concerning the relationship between chronic air pollution exposure to particulate matter PM₁₀ and fatal myocardial infarction (Nurses Health Study (NHS)), and on data concerning the effect of a subject's long-term underlying systolic blood pressure level on the risk of cardiovascular disease death (Framingham Heart Study (FHS)). The simulations indicate that the best methods are RR2 and MPPLE.     

Brief Measures of the Animal Attitude Scale

ABSTRACT

There is increasing interest among social and behavioral scientists in brief measures of attitudes and personality traits. Based on factor analysis and using an existing data set (n = 400 adults), we constructed a 5- and a 10-item version of the widely used animal attitude Scale (AAS). Both versions were highly correlated with the original 20-item AAS (rs > 0.95, p < 0.001), and both versions demonstrated acceptable reliability and validity. The AAS-5 and the AAS-10 have excellent psychometric properties and offer alternatives for researchers who need convenient and short measures of attitudes related to animal welfare.     

Development and Validation of the Food-Craving Inventory

Objective: The primary aim of this study was to develop and validate the Food-Craving Inventory (FCI), a self-report measure of specific food cravings. Research Methods and Procedures: In a preliminary study, participants (n = 474) completed the initial version of the FCI. The results from this study were used in developing the revised FCI. Participants (n = 379) completed the revised FCI in the primary study designed to develop a self-report measure of specific food cravings. Results: Common factor analysis yielded four conceptual factors (subscales) that were interpreted as high fats, sweets, carbohydrates/starches, and fast-food fats. Confirmatory factor analysis found that the four factors could be modeled as dimensions (or first-order factors) of a higher order construct—food craving. Test–retest and internal consistency analyses indicated good reliability for the total score and each of the subscales. Subscale scores were compared with scores on the Three Factor Eating Questionnaire and a conceptual measure of food craving. We found support for the content, concurrent, construct, and discriminant validity of the FCI. Discussion: The FCI was found to be a reliable and valid measure of general and specific food cravings. The FCI can be used in research related to overeating and binge eating. Also, it may be useful in treatment studies that target obesity and/or food cravings.