A model analysis on nitrate leaching under different soil and climate conditions and use of catch crops

The use of crops and catch crops with deep rooting can strongly improve the possibility of retaining nitrate-N that will otherwise be leached to the deeper soil layers and end up in the surrounding environment. But will it always be an advantage for the farmer to grow a catch crop? This will depend on factors such as soil mineral nitrogen level, soil water holding capacity, winter precipitation, rooting depth and N demand of the scceeding crop. These factors interact, and it can be very difficult for farmers or advisors to use this information to decide whether growing a catch crop will be beneficial. To analyse the effect of catch crops under different Danish soil and precipitation conditions, we used the soil, plant and atmosphere model Daisy

Simulating Root Density Dynamics and Nitrogen Uptake -Field Trials and Root Model Approach in Denmark

Plant soil and atmosphere models are commonly used to predict crop yield and associated environmental consequences. Such models often include complex modelling of water movement, soil organic matter turnover and above ground plant growth. However, the root modelling in these models is often very simple, partly due to a limited access to experimental data. Here we propose a root model developed to describe root growth, root density and nitrogen uptake. The model focuses on annual crops, and attempts to model root growth of different crop species and row crops and its significance for nitrogen uptake from different parts of the soil volume

Simulating Root Density Dynamics and Nitrogen Uptake – Can a Simple Approach be Sufficient?

The modeling of root growth in many plant–soil models is simple and with few possibilities to adapt simulated root proliferation and depth distribution to that actually found with different crop species. Here we propose a root model, developed to describe root growth, root density and nitrogen uptake. The model focuses on annual crops, and attempts to model root growth of different crop species and row crops and its significance for nitrogen uptake from different parts of the soil volume

Prediction model for unsuccessful return to work after hospital-based intervention in low back pain patients

BACKGROUND: Many studies on low back pain (LBP) have identified prognostic factors, but prediction models for use in secondary health care are not available. The purpose of this cohort study, based on a randomised clinical study, was to identify risk factors for unsuccessful return to work (U-RTW) in sick-listed LBP patients with or without radiculopathy and to validate a prediction model for U-RTW. METHODS: 325 sick-listed LBP patients with or without radiculopathy were included in an intervention study and followed for one year. Afterwards, 117 other LBP patients were recruited similarly, included in a validation study and also followed for one year. All patients were subjected to identical procedures and interventions and received a brief intervention by the same rehabilitation doctor and physiotherapist. Half of them received case manager guidance within a multidisciplinary setting. At baseline, they completed a questionnaire and went through a clinical low-back examination. Sciatica was investigated by magnetic resonance imaging (MRI). U-RTW was registered in a national database both initially and at 1-year. RESULTS: Neither initial U-RTW (24.0%) nor one-year U-RTW (38.2%) were statistically significantly different in the two intervention groups nor in patients with and without radiculopathy. Multivariate logistic regression analysis identified two clinical and five psychosocial baseline predictors for one-year U-RTW (primary outcome). The clinical predictors included pain score (back+leg pain) and side-flexion. The five psychosocial predictors included ‘bodily distress’ ‘low expectations of RTW’, ‘blaming the work for pain’, ‘no home ownership’ and ‘drinking alcohol less than once/month’. These predictors were not statistically significantly different in patients with and without radiculopathy, and they also predicted initial U-RTW (secondary outcome). Obesity and older age were only supplementary predictors in patients with radiculopathy. A prediction model was established and tested in the validation study group. The model predicted one-year U-RWT in patients with intermediate and high risk, but only partially in patients with low risk. The model predicted all three risk categories in initial U-RTW. CONCLUSIONS: A prediction model combining baseline clinical and psychosocial risk factors predicted patients with low, intermediate and high risk for unsuccessful return to work, both initially and at 1-year

A comparison of two approaches for solving unconstrained influence diagrams

AbstractInfluence diagrams and decision trees represent the two most common frameworks for specifying and solving decision problems. As modeling languages, both of these frameworks require that the decision analyst specifies all possible sequences of observations and decisions (in influence diagrams, this requirement corresponds to the constraint that the decisions should be temporarily linearly ordered). Recently, the unconstrained influence diagram was proposed to address this drawback. In this framework, we may have a partial ordering of the decisions, and a solution to the decision problem therefore consists not only of a decision policy for the various decisions, but also of a conditional specification of what to do next. Relative to the complexity of solving an influence diagram, finding a solution to an unconstrained influence diagram may be computationally very demanding w.r.t. both time and space. Hence, there is a need for efficient algorithms that can deal with (and take advantage of) the idiosyncrasies of the language. In this paper we propose two such solution algorithms. One resembles the variable elimination technique from influence diagrams, whereas the other is based on conditioning and supports any-space inference. Finally, we present an empirical comparison of the proposed methods

A model for assessment of telemedicine applications: MAST

Objectives: Telemedicine applications could potentially solve many of the challenges faced by the healthcare sectors in Europe. However, a framework for assessment of these technologies is need by decision makers to assist them in choosing the most efficient and cost-effective technologies. Therefore in 2009 the European Commission initiated the development of a framework for assessing telemedicine applications, based on the users' need for information for decision making. This article presents the Model for ASsessment of Telemedicine applications (MAST) developed in this study. Methods: MAST was developed through workshops with users and stakeholders of telemedicine. Results: Based on the workshops and using the EUnetHTA Core HTA Model as a starting point a three-element model was developed, including: (i) preceding considerations, (ii) multidisciplinary assessment, and (iii) transferability assessment. In the multidisciplinary assessment, the outcomes of telemedicine applications comprise seven domains, based on the domains in the EUnetHTA model. Conclusions: MAST provides a structure for future assessment of telemedicine applications. MAST will be tested during 2010-13 in twenty studies of telemedicine applications in nine European countries in the EC project Renewing Health

A cross-sectional comparison of performance, neurophysiological and MRI outcomes of responders and non-responders to fampridine treatment in multiple sclerosis - An explorative study

OBJECTIVE: To compare baseline physical and cognitive performance, neurophysiological, and magnetic resonance imaging (MRI) outcomes and examinetheir interrelationship inparticipants with Multiple Sclerosis (MS), already established aseither responder or non-responder to Fampridine treatment, andto examine associationswiththe expanded disability status scale (EDSS) and 12-item MS walking scale (MSWS-12). METHODS: Baseline data from an explorative longitudinal observational study were analyzed. Participants underwent the Timed 25-Foot Walk Test (T25FW), Six Spot Step Test (SSST), Nine-Hole Peg Test, Five Times Sit-to-Stand Test, Symbol Digit Modalities Test (SDMT), neurophysiological testing, including central motor conduction time (CMCT), peripheral motor conduction time (PMCT), motor evoked potential (MEP) amplitudesand electroneuronographyof the lower extremities, and brain MRI (brain volume, number and volume of T2-weighted lesions and lesion load normalized to brain volume). RESULTS: 41 responders and 8 non-responders were examined. There were no intergroup differences inphysical performance, cognitive, neurophysiological, andMRI outcomes (p > 0.05).CMCT was associated withT25FW, SSST, EDSS, and MSWS-12,(p < 0.05). SDMT was associated with the number and volume of T2-weighted lesions, and lesion load normalized to brain volume (p < 0.05). CONCLUSION: No differences were identified between responders and non-responders to Fampridine treatment regarding physical and cognitive performance, neurophysiological or MRI outcomes. The results call for cautious interpretation and further large-scale studies are needed to expand ourunderstanding of underlying mechanisms discriminating Fampridine responders and non-responders.CMCT may be used as a marker of disability and walking impairment, while SDMT was associated with white matter lesions estimated by MRI. ClinicalTrials.gov identifier: NCT03401307