Insulin-Like Growth Factor 1 (IGF-1) in Parkinson's Disease: Potential as Trait-, Progression- and Prediction Marker and Confounding Factors

Introduction

Biomarkers indicating trait, progression and prediction of pathology and symptoms in Parkinson''s disease (PD) often lack specificity or reliability. Investigating biomarker variance between individuals and over time and the effect of confounding factors is essential for the evaluation of biomarkers in PD, such as insulin-like growth factor 1 (IGF-1).

Materials and Methods

IGF-1 serum levels were investigated in up to 8 biannual visits in 37 PD patients and 22 healthy controls (HC) in the longitudinal MODEP study. IGF-1 baseline levels and annual changes in IGF-1 were compared between PD patients and HC while accounting for baseline disease duration (19 early stage: ≤3.5 years; 18 moderate stage: >4 years), age, sex, body mass index (BMI) and common medical factors putatively modulating IGF-1. In addition, associations of baseline IGF-1 with annual changes of motor, cognitive and depressive symptoms and medication dose were investigated.

Results

PD patients in moderate (130±26 ng/mL; p = .004), but not early stages (115±19, p>.1), showed significantly increased baseline IGF-1 levels compared with HC (106±24 ng/mL; p = .017). Age had a significant negative correlation with IGF-1 levels in HC (r = -.47, p = .028) and no correlation in PD patients (r = -.06, p>.1). BMI was negatively correlated in the overall group (r = -.28, p = .034). The annual changes in IGF-1 did not differ significantly between groups and were not correlated with disease duration. Baseline IGF-1 levels were not associated with annual changes of clinical parameters.

Discussion

Elevated IGF-1 in serum might differentiate between patients in moderate PD stages and HC. However, the value of serum IGF-1 as a trait-, progression- and prediction marker in PD is limited as IGF-1 showed large inter- and intraindividual variability and may be modulated by several confounders.     

Inertia weight control strategies for particle swarm optimization

Particle swarm optimization (PSO) is a population-based, stochastic optimization technique inspired by the social dynamics of birds. The PSO algorithm is rather sensitive to the control parameters, and thus, there has been a significant amount of research effort devoted to the dynamic adaptation of these parameters. The focus of the adaptive approaches has largely revolved around adapting the inertia weight as it exhibits the clearest relationship with the exploration/exploitation balance of the PSO algorithm. However, despite the significant amount of research efforts, many inertia weight control strategies have not been thoroughly examined analytically nor empirically. Thus, there are a plethora of choices when selecting an inertia weight control strategy, but no study has been comprehensive enough to definitively guide the selection. This paper addresses these issues by first providing an overview of 18 inertia weight control strategies. Secondly, conditions required for the strategies to exhibit convergent behaviour are derived. Finally, the inertia weight control strategies are empirically examined on a suite of 60 benchmark problems. Results of the empirical investigation show that none of the examined strategies, with the exception of a randomly selected inertia weight, even perform on par with a constant inertia weight.     

Turing learning: a metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for ‘tricking’ the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product—the classifiers—that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.     

A taxonomic revision of <Emphasis Type="Italic">Bursera</Emphasis> subgen. <Emphasis Type="Italic">Bursera</Emphasis> in the Greater Antilles and the Bahamas,including a new species from Cuba

A revision of Bursera in the Greater Antilles and the Bahamas has confirmed that Commiphora does not occur in the region and that all but one species in the region belong to Bursera subgen. Bursera. Here we describe a new species, Bursera yaterensis; B. nashii is synonymized with B. glauca and B. ovata is synonymized with B. trinitensis; and we return five species from Commiphora to Bursera. A dichotomous key is provided using mostly vegetative characters due to the frequent lack of adequate reproductive material and the relative uniformity of most floral and fruit characters.     

Scale-dependent bi-trophic interactions in a semi-arid savanna: how herbivores eliminate benefits of nutrient patchiness to plants

The scale of resource heterogeneity may influence how resources are locally partitioned between co-existing large and small organisms such as trees and grasses in savannas. Scale-related plant responses may, in turn, influence herbivore use of the vegetation. To examine these scale-dependent bi-trophic interactions, we varied fertilizer [(nitrogen (N)/phosphorus (P)/potassium (K)] applications to patches to create different scales of nutrient patchiness (patch size 2 × 2 m, 10 × 10 m, or whole-plot 50 × 50 m) in a large field experiment in intact African savanna. Within-patch fertilizer concentration and the total fertilizer load per plot were independently varied. We found that fertilization increased the leaf N and P concentrations of trees and grasses, resulting in elevated utilization by browsers and grazers. Herbivory off-take was particularly considerable at higher nutrient concentrations. Scale-dependent effects were weak. The net effect of fertilization and herbivory was that plants in fertilized areas tended to grow less and develop smaller rather than larger standing biomass compared to plants growing in areas that remained unfertilized. When all of these effects were considered together at the community (plot) level, herbivory completely eliminated the positive effects of fertilization on the plant community. While this was true for all scales of fertilization, grasses tended to profit more from coarse-grained fertilization and trees from fine-grained fertilization. We conclude that in herbivore-dominated communities, such as the African savanna, nutrient patchiness results in the herbivore community profiting rather more than the plant community, irrespective of the scale of patchiness. At the community level, the allometric scaling theory’s prediction of plant—and probably also animal—production does not hold or may even be reversed as a result of complex bi-trophic interactions.     

Short-term partitioning of Cd recently taken up between sunflowers organs (<Emphasis Type="Italic">Helianthus annuus</Emphasis>) at flowering and grain filling stages: effect of plant transpiration and allometry

Aims

This work concentrated on understanding the allocation of Cd recently taken up between the organs of sunflower at early and middle reproductive growth stages. The roles of transpiration and allometry were investigated.

Methods

Sunflowers were grown hydroponically in greenhouse, being exposed to low concentrations of Cd (pCd²⁺ = 11.03). At flower bud and grain filling stages, plants were exposed for three days to ¹¹¹Cd and at the same time, subjected or not to fans to increase the transpiration. The partitioning of ¹¹¹Cd between plant organs measured by high resolution ICP-MS was then modelled.

Results

Although the use of fans increased the plant water uptake and transpiration by about 20%, there were no significant effects on the partitioning of recent Cd. Most of the recent Cd was recovered in roots (60%) and only 2.8% were found in seeds (0.8% for the husk and 2.0% for the almonds). The sequestration of recent Cd in a plant organ was successfully explained by its biomass and except for leaves, by the biomass of other organs acting as competitive sinks.

Conclusions

This work proposes a modelling approach for the partitioning of the labelled Cd between plant organs in sunflower.