Simulating dynamic crop growth with an adapted land surface model – JULES-SUCROS: model development and validation

The increasing demand for ecosystem services, in conjunction with climate change, is expected to signif- icantly alter terrestrial ecosystems. In order to evaluate the sustainability of land and water resources, there is a need for a better understanding of the relationships between crop production, land surface characteristics and the energy and water cycles. These relationships are analysed using the Joint UK Land Environment Simulator (JULES). JULES includes the full hydrological cycle and vegetation effects on the energy, water, and carbon fluxes. However, this model currently only simulates land surface processes in natural ecosystems. An adapted version of JULES for agricultural ecosystems, called JULES-SUCROS has therefore been developed. In addition to overall model improvements, JULES-SUCROS includes a dynamic crop growth structure that fully fits within and builds upon the biogeochemical modelling framework for natural vegetation. Specific agro-ecosystem features such as the development of yield-bearing organs and the phenological cycle from sowing till harvest have been included in the model. This paper describes the structure of JULES-SUCROS and evaluates the fluxes simulated with this model against FLUXNET measurements at 6 European sites. We show that JULES-SUCROS significantly improves the correlation between simulated and observed fluxes over cropland and captures well the spatial and temporal vari- ability of the growth conditions in Europe. Simulations with JULES-SUCROS highlight the importance of vegetation structure and phenology, and the impact they have on land–atmosphere interactions.

Energy at work: a measurement validation and linkage to unit effectiveness

We introduce the notion that the energy of individuals can manifest as a higher-level, collective construct. To this end, we conducted four independent studies to investigate the viability and importance of the collective energy construct as assessed by a new survey instrument—the productive energy measure (PEM). Study 1 (n = 2208) included exploratory and confirmatory factor analyses to explore the underlying factor structure of PEM. Study 2 (n = 660) cross-validated the same factor structure in an independent sample. In study 3, we administered the PEM to more than 5000 employees from 145 departments located in five countries. Results from measurement invariance, statistical aggregation, convergent, and discriminant-validity assessments offered additional support for the construct validity of PEM. In terms of predictive and incremental validity, the PEM was positively associated with three collective attitudes—units' commitment to goals, the organization, and overall satisfaction. In study 4, we explored the relationship between the productive energy of firms and their overall performance. Using data from 92 firms (n = 5939employees), we found a positive relationship between the PEM (aggregated to the firm level) and the performance of those firms. Copyright © 2011 John Wiley & Sons, Ltd.

Secondary structure prediction with support vector machines

Motivation: A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem. Methods: Binary SVMs are trained to discriminate between two structural classes. The binary classifiers are combined in several ways to predict multi-class secondary structure. Results: The average three-state prediction accuracy per protein (Q3) is estimated by cross-validation to be 77.07 ± 0.26% with a segment overlap (Sov) score of 73.32 ± 0.39%. The SVM performs similarly to the 'state-of-the-art' PSIPRED prediction method on a non-homologous test set of 121 proteins despite being trained on substantially fewer examples. A simple consensus of the SVM, PSIPRED and PROFsec achieves significantly higher prediction accuracy than the individual methods. Availability: The SVM classifier is available from the authors. Work is in progress to make the method available on-line and to integrate the SVM predictions into the PSIPRED server.

Assessment of thought–shape fusion: initial validation of a short version of the trait thought–shape fusion scale

Objective: Thought–shape fusion (TSF) is a cognitive distortion that has been linked to eating pathology. Two studies were conducted to further explore this phenomenon and to establish the psychometric properties of a French short version of the TSF scale. Method: In Study 1, students (n 5 284) completed questionnaires assessing TSF and related psychopathology. In Study 2, the responses of women with eating disorders (n 5 22) and women with no history of an eating disorder (n 5 23) were compared. Results: The French short version of the TSF scale has a unifactorial structure, with convergent validity with measures of eating pathology, and good internal consistency. Depression, eating pathology, body dissatisfaction, and thought-action fusion emerged as predictors of TSF. Individuals with eating disorders have higher TSF, and more clinically relevant food-related thoughts than do women with no history of an eating disorder. Discussion: This research suggests that the shortened TSF scale can suitably measure this construct, and provides support for the notion that TSF is associated with eating pathology.

Development and validation of the Morphing Fear Questionnaire (MFQ)

Morphing fears (also called transformation obsessions) involve concerns that a person may become contaminated by and acquire undesirable characteristics of others. These symptoms are found in patients with OCD and are thought to be related to mental contamination. Given the high levels of distress and interference morphing fears can cause, a reliable and valid assessment measure is needed. This article describes the development and evaluation of the Morphing Fear Questionnaire (MFQ), a 13-item measure designed to assess for the presence and severity of morphing fears. A sample of 900 participants took part in the research. Of these, 140 reported having a current diagnosis of OCD (SR-OCD) and 760 reported never having had OCD (N-OCD; of whom 24 reported a diagnosis of an anxiety disorder and 23 reported a diagnosis of depression). Factor structure, reliability, and construct and criterion related validity were investigated. Exploratory and confirmatory factor analyses supported a one-factor structure replicable across the N-OCD and SR-OCD group. The MFQ was found to have high internal consistency and good temporal stability, and showed significantly greater associations with convergent measures (assessing obsessive-compulsive symptoms, mental contamination, thought-action fusion and magical thinking) than with divergent measures (assessing depression and anxiety). Moreover, the MFQ successfully discriminated between the SR-OCD sample and the N-OCD group, anxiety disorder sample, and depression sample. These findings suggest that the MFQ has sound psychometric properties and that it can be used to assess morphing fear. Clinical implications are discussed.

The BALTIMOS (BALTEX Integrated Model System) fi eld experiments: A comprehensive atmospheric boundary layer data set for model validation over the open and ice- covered Baltic Sea

A comprehensive atmospheric boundary layer (ABL) data set was collected in eight fi eld experiments (two during each season) over open water and sea ice in the Baltic Sea during 1998–2001 with the primary objective to validate the coupled atmospheric- ice-ocean-land surface model BALTIMOS (BALTEX Integrated Model System). Measurements were taken by aircraft, ships and surface stations and cover the mean and turbulent structure of the ABL including turbulent fl uxes, radiation fl uxes, and cloud conditions. Measurement examples of the spatial variability of the ABL over the ice edge zone and of the stable ABL over open water demonstrate the wide range of ABL conditions collected and the strength of the data set which can also be used to validate other regional models.