INTRODUCTION: Impaired copy of intersecting pentagons from the Mini-Mental State Examination (MMSE), has been used to assess dementia in Parkinson's disease (PD). We used a digitizing tablet during the pentagon copying test (PCT) as a potential tool for evaluating early cognitive deficits in PD without major cognitive impairment. We also aimed to uncover the neural correlates of the identified parameters using whole-brain magnetic resonance imaging (MRI). METHODS: We enrolled 27 patients with PD without major cognitive impairment and 25 age-matched healthy controls (HC). We focused on drawing parameters using a digitizing tablet. Parameters with between-group differences were correlated with cognitive outcomes and were used as covariates in the whole-brain voxel-wise analysis using voxel-based morphometry; familywise error (FWE) threshold p < 0.001. RESULTS: PD patients differed from HC in attention domain z-scores (p < 0.0001). In terms of tablet parameters, the groups differed in Shannon entropy (horizontal in-air, p = 0.003), which quantifies the movements between two strokes. In PD, a correlation was found between the median of Shannon entropy (horizontal in-air) and attention z-scores (R = -0.55, p = 0.006). The VBM revealed an association between our drawing parameter of interest and gray matter (GM) volume variability in the right superior parietal lobe (SPL). CONCLUSION: Using a digitizing tablet during the PCT, we identified a novel entropy-based parameter that differed between the nondemented PD and HC groups. This in-air parameter correlated with the level of attention and was linked to GM volume variability of the region engaged in spatial attention.
- MeSH
- Entropy MeSH
- Cognitive Dysfunction * MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods MeSH
- Neuropsychological Tests MeSH
- Parkinson Disease * complications diagnostic imaging psychology MeSH
- Gray Matter MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Pohybová aktivita na úrovni anaerobního prahu (ANP) nachází uplatnění u velmi širokého spektra osob od vrcholových sportovců přes netrénované zdravé osoby až po nemocné osoby s kardiovaskulárními i jinými chorobami. Byla vyvinuta nová metoda určení ANP ze spiroergometrických parametrů, (10). Autoři tuto metodu transformovali do tabulkového procesoru MS Excel 97 jako softwarovou aplikaci. Metoda umožní vyšetření ANP i těm pracovištím, která nejsou vybavena nejmodernějšími analyzátory vydechovaných plynů ani nemají k dispozici specifický software k vyhodnoceníANR Jde o metodu, pomocí které je ANP vždy hodnotitelný, která je plně reprodukovaíelná a nezávislá na subjektivním faktoru.
Exercise activity within the anaerobic threshold (AT) intensity is used in a very wide spectrum of population; it can be used not only in top athletes or in healthy sedentary persons but also in the patients suffering from either cardiovascular or various other diseases. The authors developed a new method of AT assessment from common spiroergometric parameters and transformed the method to a software application within the spreadsheet Excel 97. The method provides fully reproducibte results which are independent on subjective factor.
Acta oto-laryngologica, ISSN 0365-5237 suppl. 353, 1977
37 s. : tab., grafy ; 26 cm
In epidemiology, it is very important to estimate the baseline incidence of infectious diseases. From this baseline, the epidemic threshold can be derived as a clue to recognize an excess incidence, i.e. to detect an epidemic by mathematical methods. Nevertheless, a problem is posed by the fact that the incidence may vary during the year, as a rule, in a season dependent manner. To model the incidence of a disease, some authors use seasonal trend models. For instance, Serfling applies the sine function with a phase shift and amplitude. A similar model based on the analysis of variance with kernel smoothing and Serfling's higher order models, i.e. models composed of multiple sine-cosine function pairs with a variably long period, will be presented below. Serfling's model uses a long-term linear trend, but the linearity may not be always acceptable. Therefore, a more complex, long-term trend estimation will also be addressed, using different smoothing methods. In addition, the issue of the time unit (mostly a week) used in describing the incidence is discussed.
- MeSH
- Epidemiologic Methods MeSH
- Epidemiology * MeSH
- Incidence MeSH
- Humans MeSH
- Seasons * MeSH
- Models, Statistical * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Lung cancer is the leading cause of cancer death in both men and women. Smoking causes 80-90% of cases of lung cancer. In this study, an attempt was made to assess the impact of cigarette smoking on the risk of lung cancer by the so-called threshold-specific energy model. This model allows to analyse the biological effects of radon daughter products on the lung tissue, and is based on the assumption that the biological effect (i.e. cell inactivation) will manifest itself after the threshold-specific energy z0 deposited in the sensitive volume of the cell is exceeded. Cigarette smoking causes, among others, an increase in the synthesis of the survivin protein that protects cells from apoptosis and thereby reduces their radiosensitivity. Based on these facts, an attempt was made to estimate the shape of the curves describing the increase in the oncological effect of radiation as a function of daily cigarette consumption.
- MeSH
- Smoking adverse effects MeSH
- Humans MeSH
- Lung Neoplasms etiology MeSH
- Neoplasms, Radiation-Induced etiology MeSH
- Air Pollutants, Radioactive adverse effects MeSH
- Radon adverse effects MeSH
- Models, Statistical * MeSH
- Environmental Exposure adverse effects MeSH
- Check Tag
- Humans MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
Flow data represent crucial input for reliable diagnostics of sewer functions and identification of potential problems such as unwanted inflow and infiltration. Flow estimates from pumping stations, which are an integral part of most separate sewer systems, might help in this regard. A robust model and an associated optimization procedure is proposed for estimating inflow to a pumping station using only registered water levels in the pump sump and power consumption. The model was successfully tested on one month of data from a single upstream station. The model is suitable for identification of pump capacity and volume thresholds for switching the pump on and off. These are parameters which are required for flow estimation during periods with high inflows or during periods with flow conditions triggering pump switching on and off at frequencies close to the temporal resolution of monitored data. The model is, however, sensitive within the transition states between emptying and filling to observation errors in volume and on inflow/outflow variability.
- MeSH
- Waste Disposal, Fluid statistics & numerical data MeSH
- Sewage * MeSH
- Models, Statistical * MeSH
- Water MeSH
- Publication type
- Journal Article MeSH
Normalized entropy as a measure of randomness is explored. It is employed to characterize those properties of neuronal firing that cannot be described by the first two statistical moments. We analyze randomness of firing of the Ornstein-Uhlenbeck (OU) neuronal model with respect either to the variability of interspike intervals (coefficient of variation) or the model parameters. A new form of the Siegert's equation for first-passage time of the OU process is given. The parametric space of the model is divided into two parts (sub-and supra-threshold) depending upon the neuron activity in the absence of noise. In the supra-threshold regime there are many similarities of the model with the Wiener process model. The sub-threshold behavior differs qualitatively both from the Wiener model and from the supra-threshold regime. For very low input the firing regularity increases (due to increase of noise) cannot be observed by employing the entropy, while it is clearly observable by employing the coefficient of variation. Finally, we introduce and quantify the converse effect of firing regularity decrease by employing the normalized entropy.
ABSTRACT: Background: Physical growth of children and adolescents depends on the interaction of genetic and environmental factors e.g. diet and living conditions. Aim: We aim to discuss the influence of socioeconomic situation, using income inequality and GDP per capita as indicators, on body height, body weight and the variability of height and weight in infants and juveniles. Material and methods: We re-analyzed data from 439 growth studies on height and weight published during the last 35 years. We added year- and country-matched GDP per capita (in current US$) and the Gini coefficient for each study. The data were divided into two age groups: infants (age 2) and juveniles (age 7). We used Pearson correlation and principal component analysis to investigate the data. Results: Gini coefficient negatively correlated with body height and body weight in infants and juveniles. GDP per capita showed a positive correlation with height and weight in both age groups. In infants the standard deviation of height increases with increasing Gini coefficient. The opposite is true for juveniles. A correlation of weight variability and socioeconomic indicators is absent in infants. In juveniles the variability of weight increases with declining Gini coefficient and increasing logGDP per capita. Discussion: Poverty and income inequality are generally associated with poor growth in height and weight. The analysis of the within-population height and weight variations however, shows that the associations between wealth, income, and anthropometric parameters are very complex and cannot be explained by common wisdom. They point towards an independent regulation of height and weight.
- MeSH
- Anthropology, Physical MeSH
- Anthropometry MeSH
- Poverty MeSH
- Child MeSH
- Infant MeSH
- Humans MeSH
- Child, Preschool MeSH
- Socioeconomic Factors * MeSH
- Models, Statistical * MeSH
- Body Weight physiology MeSH
- Body Height physiology MeSH
- Child Development physiology MeSH
- Check Tag
- Child MeSH
- Infant MeSH
- Humans MeSH
- Child, Preschool MeSH
- Publication type
- Journal Article MeSH
