Quantitative Timed-Up-and-Go Parameters in Relation to Cognitive Parameters and Health-Related Quality of Life in Mild-to-Moderate Parkinson's Disease

IntroductionThe instrumented-Timed-Up-and-Go test (iTUG) provides detailed information about the following movement patterns: sit-to-walk (siwa), straight walking, turning and walk-to-sit (wasi). We were interested in the relative contributions of respective iTUG sub-phases to specific clinical deficits most relevant for daily life in Parkinson’s disease (PD). More specifically, we investigated which condition–fast speed (FS) or convenient speed (CS)–differentiates best between mild- to moderate-stage PD patients and controls, which parameters of the iTUG sub-phases are significantly different between PD patients and controls, and how the iTUG parameters associate with cognitive parameters (with particular focus on cognitive flexibility and working memory) and Health-Related-Quality of Life (HRQoL).MethodsTwenty-eight PD participants (65.1±7.1 years, H&Y stage 1–3, medication OFF state) and 20 controls (66.1±7.5 years) performed an iTUG (DynaPort^®, McRoberts BV, The Netherlands) under CS and FS conditions. The PD Questionnaire 39 (PDQ-39) was employed to assess HRQoL. General cognitive and executive functions were assessed using the Montreal Cognitive Assessment and the Trail Making Test.ResultsThe total iTUG duration and sub-phases durations under FS condition differentiated PD patients slightly better from controls, compared to the CS condition. The following sub-phases were responsible for the observed longer total duration PD patients needed to perform the iTUG: siwa, turn and wasi. None of the iTUG parameters correlated relevantly with general cognitive function. Turning duration and wasi maximum flexion velocity correlated strongest with executive function. Walking back duration correlated strongest with HRQoL.DiscussionThis study confirms that mild- to moderate-stage PD patients need more time to perform the iTUG than controls, and adds the following aspects to current literature: FS may be more powerful than CS to delineate subtle movement deficits in mild- to moderate-stage PD patients; correlation levels of intra-individual siwa and wasi parameters may be interesting surrogate markers for the level of automaticity of performed movements; and sub-phases and kinematic parameters of the iTUG may have the potential to reflect executive functioning and HRQoL aspects of PD patients.     

Risk Perception and Risk-Taking Behaviour during Adolescence: The Influence of Personality and Gender

This study investigated the influence of personality characteristics and gender on adolescents’ perception of risk and their risk-taking behaviour. Male and female participants (157 females: 116 males, aged 13–20) completed self-report measures on risk perception, risk-taking and personality. Male participants perceived behaviours as less risky, reportedly took more risks, were less sensitive to negative outcomes and less socially anxious than female participants. Path analysis identified a model in which age, behavioural inhibition and impulsiveness directly influenced risk perception, while age, social anxiety, impulsiveness, sensitivity to reward, behavioural inhibition and risk perception itself were directly or indirectly associated with risk-taking behaviour. Age and behavioural inhibition had direct relationships with social anxiety, and reward sensitivity was associated with impulsiveness. The model was representative for the whole sample and male and female groups separately. The observed relationship between age and social anxiety and the influence this may have on risk-taking behaviour could be key for reducing adolescent risk-taking behaviour. Even though adolescents may understand the riskiness of their behaviour and estimate their vulnerability to risk at a similar level to adults, factors such as anxiety regarding social situations, sensitivity to reward and impulsiveness may exert their influence and make these individuals prone to taking risks. If these associations are proven causal, these factors are, and will continue to be, important targets in prevention and intervention efforts.     

In Vitro Study of Taenia solium Postoncospheral Form

Background

The transitional period between the oncosphere and the cysticercus of Taenia solium is the postoncospheral (PO) form, which has not yet been completely characterized. The aim of this work was to standardize a method to obtain T. solium PO forms by in vitro cultivation. We studied the morphology of the PO form and compared the expression of antigenic proteins among the PO form, oncosphere, and cysticerci stages.

Methodology/Principal Findings

T. solium activated oncospheres were co-cultured with ten cell lines to obtain PO forms, which we studied at three stages of development–days 15, 30, and 60. A high percentage (32%) of PO forms was obtained using HCT-8 cells in comparison to the other cell lines. The morphology was observed by bright field, scanning, and transmission electron microscopy. Morphology of the PO form changed over time, with the six hooks commonly seen in the oncosphere stage disappearing in the PO forms, and vesicles and microtriches observed in the tegument. The PO forms grew as they aged, reaching a diameter of 2.5 mm at 60 days of culture. 15–30 day PO forms developed into mature cysticerci when inoculated into rats. Antigenic proteins expressed in the PO forms are also expressed by the oncosphere and cysticerci stages, with more cysticerci antigenic proteins expressed as the PO forms ages.

Conclusions/Significance

This is the first report of an in vitro production method of T. solium PO forms. The changes observed in protein expression may be useful in identifying new targets for vaccine development. In vitro culture of PO form will aid in understanding the host-parasite relationship, since the structural changes of the developing PO forms may reflect the parasite’s immunoprotective mechanisms. A wider application of this method could significantly reduce the use of animals, and thus the costs and time required for further experimental investigations.     

High Resolution Melting Analysis Targeting hsp70 as a Fast and Efficient Method for the Discrimination of Leishmania Species

Background

Protozoan parasites of the genus Leishmania cause a large spectrum of clinical manifestations known as Leishmaniases. These diseases are increasingly important public health problems in many countries both within and outside endemic regions. Thus, an accurate differential diagnosis is extremely relevant for understanding epidemiological profiles and for the administration of the best therapeutic protocol.

Methods/Principal Findings

Exploring the High Resolution Melting (HRM) dissociation profiles of two amplicons using real time polymerase chain reaction (real-time PCR) targeting heat-shock protein 70 coding gene (hsp70) revealed differences that allowed the discrimination of genomic DNA samples of eight Leishmania species found in the Americas, including Leishmania (Leishmania) infantum chagasi, L. (L.) amazonensis, L. (L.) mexicana, L. (Viannia) lainsoni, L. (V.) braziliensis, L. (V.) guyanensis, L. (V.) naiffi and L. (V.) shawi, and three species found in Eurasia and Africa, including L. (L.) tropica, L. (L.) donovani and L. (L.) major. In addition, we tested DNA samples obtained from standard promastigote culture, naturally infected phlebotomines, experimentally infected mice and clinical human samples to validate the proposed protocol.

Conclusions/Significance

HRM analysis of hsp70 amplicons is a fast and robust strategy that allowed for the detection and discrimination of all Leishmania species responsible for the Leishmaniases in Brazil and Eurasia/Africa with high sensitivity and accuracy. This method could detect less than one parasite per reaction, even in the presence of host DNA.     

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.