Determination of Serum Nucleotidase with Cytidine Monophosphate as Substrate. Part II: Improvement of the procedure

Peer Reviewe

Changes in circle area after gravity compensation training in chronic stroke patients

After a stroke, many people experience difficulties to selectively activate muscles. As a result many patients move the affected arm in stereotypical patterns. Shoulder abduction is often accompanied by elbow flexion, reducing the ability to extend the elbow. This involuntary coupling reduces the patient's active range of motion. Gravity compensation reduces the activation level of shoulder abductors which limits the amount of coupled elbow flexion. As a result, stroke patients can instantaneously increase their active range of motion [1]. The objective of the present study is to examine whether training in a gravity compensated environment can also lead to an increased range of motion in an unsupported environment. Parts of this work have been presented at EMBC2009, Minneapolis, USA

lassopack: Model selection and prediction with regularized regression in Stata

This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional setting where the number of predictors $p$ may be large and possibly greater than the number of observations, $n$. We offer three different approaches for selecting the penalization (`tuning') parameters: information criteria (implemented in lasso2), $K$-fold cross-validation and $h$-step ahead rolling cross-validation for cross-section, panel and time-series data (cvlasso), and theory-driven (`rigorous') penalization for the lasso and square-root lasso for cross-section and panel data (rlasso). We discuss the theoretical framework and practical considerations for each approach. We also present Monte Carlo results to compare the performance of the penalization approaches.Comment: 52 pages, 6 figures, 6 tables; submitted to Stata Journal; for more information see https://statalasso.github.io

Optimal Scaling transformations to model non-linear relations in GLMs with ordered and unordered predictors

In Generalized Linear Models (GLMs) it is assumed that there is a linear effect of the predictor variables on the outcome. However, this assumption is often too strict, because in many applications predictors have a nonlinear relation with the outcome. Optimal Scaling (OS) transformations combined with GLMs can deal with this type of relations. Transformations of the predictors have been integrated in GLMs before, e.g. in Generalized Additive Models. However, the OS methodology has several benefits. For example, the levels of categorical predictors are quantified directly, such that they can be included in the model without defining dummy variables. This approach enhances the interpretation and visualization of the effect of different levels on the outcome. Furthermore, monotonicity restrictions can be applied to the OS transformations such that the original ordering of the category values is preserved. This improves the interpretation of the effect and may prevent overfitting. The scaling level can be chosen for each individual predictor such that models can include mixed scaling levels. In this way, a suitable transformation can be found for each predictor in the model. The implementation of OS in logistic regression is demonstrated using three datasets that contain a binary outcome variable and a set of categorical and/or continuous predictor variables.Comment: 35 pages, 4 figure

Fisheries

This is the final version. Available from MCCIP via the DOI in this record

Combined measurements of prey availability explain habitat selection in foraging seabirds

Understanding links between habitat characteristics and foraging efficiency helps predict how environmental changes influence populations of top predators. This study examines whether measurements of prey (clupeids) availability varied over stratification gradients, and determined if any of those measurements coincided with aggregations of foraging seabirds (common guillemot Uria aalge and Manx shearwater Puffinus puffinus) in the Celtic Sea, UK. The probability of encountering foraging seabirds was highest around fronts between mixed and stratified water. Prey were denser and shallower in mixed water, whilst encounters with prey were most frequent in stratified water. Therefore, no single measurement of increased prey availability coincided with the location of fronts. However, when considered in combination, overall prey availability was highest in these areas. These results show that top predators may select foraging habitats by trading-off several measurements of prey availability. By showing that top predators select areas where prey switch between behaviours, these results also identify a mechanism that could explain the wider importance of edge habitats for these taxa. As offshore developments (e.g. marine renewable energy installations) change patterns of stratification, their construction may have consequences on the foraging efficiency of seabirds

Sub-Typing of Rheumatic Diseases Based on a Systems Diagnosis Questionnaire

The future of personalized medicine depends on advanced diagnostic tools to characterize responders and non-responders to treatment. Systems diagnosis is a new approach which aims to capture a large amount of symptom information from patients to characterize relevant sub-groups.49 patients with a rheumatic disease were characterized using a systems diagnosis questionnaire containing 106 questions based on Chinese and Western medicine symptoms. Categorical principal component analysis (CATPCA) was used to discover differences in symptom patterns between the patients. Two Chinese medicine experts where subsequently asked to rank the Cold and Heat status of all the patients based on the questionnaires. These rankings were used to study the Cold and Heat symptoms used by these practitioners.The CATPCA analysis results in three dimensions. The first dimension is a general factor (40.2% explained variance). In the second dimension (12.5% explained variance) 'anxious', 'worrying', 'uneasy feeling' and 'distressed' were interpreted as the Internal disease stage, and 'aggravate in wind', 'fear of wind' and 'aversion to cold' as the External disease stage. In the third dimension (10.4% explained variance) 'panting s', 'superficial breathing', 'shortness of breath s', 'shortness of breath f' and 'aversion to cold' were interpreted as Cold and 'restless', 'nervous', 'warm feeling', 'dry mouth s' and 'thirst' as Heat related. 'Aversion to cold', 'fear of wind' and 'pain aggravates with cold' are most related to the experts Cold rankings and 'aversion to heat', 'fullness of chest' and 'dry mouth' to the Heat rankings.This study shows that the presented systems diagnosis questionnaire is able to identify groups of symptoms that are relevant for sub-typing patients with a rheumatic disease

Insulating fcc YH3-ô stabilized by MgH2

We study the structural, optical, and electrical properties of MgzY1-z switchable mirrors upon hydrogenation. It is found that the alloys disproportionate into essentially pure YH3-δ and MgH2 with the crystal structure of YH3-δ dependent on the Mg concentration z. For 0~0.1 only cubic YH3-δ is present. Interestingly, cubic YH3-δ is expanded compared to YH2, in disagreement with theoretical predictions. From optical and electrical measurements we conclude that cubic YH3-δ is a transparent insulator with properties similar to hexagonal YH3-δ. Our results are inconsistent with calculations predicting fcc YH3-δ to be metallic, but they are in good agreement with recent GW calculations on both hcp and fcc YH3. Finally, we find an increase in the effective band gap of the hydrided MgzY1-z alloys with increasing z. Possibly this is due to quantum confinement effects in the small YH3 clusters