BACKGROUND: Pregnenolone sulfate (PregS) is known as a steroid conjugate positively modulating N-methyl-D-aspartate receptors on neuronal membranes. These receptors are responsible for permeability of calcium channels and activation of neuronal function. Neuroactivating effect of PregS is also exerted via non-competitive negative modulation of GABA(A) receptors regulating the chloride influx. Recently, a penetrability of blood-brain barrier for PregS was found in rat, but some experiments in agreement with this finding were reported even earlier. It is known that circulating levels of PregS in human are relatively high depending primarily on age and adrenal activity. METHODS: Concerning the neuromodulating effect of PregS, we recently evaluated age relationships of PregS in both sexes using polynomial regression models known to bring about the problems of multicollinearity, i.e., strong correlations among independent variables. Several criteria for the selection of suitable bias are demonstrated. Biased estimators based on the generalized principal component regression (GPCR) method avoiding multicollinearity problems are described. RESULTS: Significant differences were found between men and women in the course of the age dependence of PregS. In women, a significant maximum was found around the 30th year followed by a rapid decline, while the maximum in men was achieved almost 10 years earlier and changes were minor up to the 60th year. The investigation of gender differences and age dependencies in PregS could be of interest given its well-known neurostimulating effect, relatively high serum concentration, and the probable partial permeability of the blood-brain barrier for the steroid conjugate. CONCLUSIONS: GPCR in combination with the MEP (mean quadric error of prediction) criterion is extremely useful and appealing for constructing biased models. It can also be used for achieving such estimates with regard to keeping the model course corresponding to the data trend, especially in polynomial type regression models.

• MeSH
• dítě MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• lineární modely MeSH
• mladiství MeSH
• pohlavní dimorfismus * MeSH
• předškolní dítě MeSH
• pregnenolon krev   MeSH
• senioři MeSH
• stárnutí krev   MeSH
• zdraví * MeSH
• Check Tag
• dítě MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mužské pohlaví MeSH
• předškolní dítě MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• pregnenolon MeSH
• pregnenolone sulfate MeSH Prohlížeč

Sum fraction terms can approximate multi-variable functions on the basis of discrete observations, replacing a partial differential equation definition with polynomial elementary data relation descriptions. Artificial neural networks commonly transform the weighted sum of inputs to describe overall similarity relationships of trained and new testing input patterns. Differential polynomial neural networks form a new class of neural networks, which construct and solve an unknown general partial differential equation of a function of interest with selected substitution relative terms using non-linear multi-variable composite polynomials. The layers of the network generate simple and composite relative substitution terms whose convergent series combinations can describe partial dependent derivative changes of the input variables. This regression is based on trained generalized partial derivative data relations, decomposed into a multi-layer polynomial network structure. The sigmoidal function, commonly used as a nonlinear activation of artificial neurons, may transform some polynomial items together with the parameters with the aim to improve the polynomial derivative term series ability to approximate complicated periodic functions, as simple low order polynomials are not able to fully make up for the complete cycles. The similarity analysis facilitates substitutions for differential equations or can form dimensional units from data samples to describe real-world problems.

• Klíčová slova
• Differential polynomial neural network, General partial differential equation composition, Multi-variable function approximation, Sum derivative term substitution,
• MeSH
• algoritmy MeSH
• interpretace statistických dat MeSH
• matematika * MeSH
• nelineární dynamika MeSH
• neuronové sítě * MeSH
• počasí MeSH
• počítačová simulace MeSH
• strojové učení MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

The aims of this study were to create a regression model of the relationship between load and muscle power output and to determine an optimal load for maximum power output during a countermovement squat and a bench press. 55 males and 48 females performed power testing at 0, 10, 30, 50, 70, 90, and 100% of their individual one-repetition maximum (1-RM) in the countermovement squat and bench press exercises. Values for the maximum dynamic strength and load for each lift were used to develop a regression model in which the ratio of power was predicted from the ratio of the load for each type of lift. By optimizing the regression model, we predicted the optimal load for maximum muscle power. For the bench press and the countermovement squat, the mean optimal loads for maximum muscle output ranged from 50 to 70% of maximum dynamic strength. Optimal load in the acceleration phase of the upward movement of the two exercises appeared to be more important than over the full range of the movement. This model allows for specific determination of the optimal load for a pre-determined power output.

• MeSH
• biologické modely * MeSH
• dospělí MeSH
• fyzická vytrvalost fyziologie   MeSH
• kosterní svaly fyziologie   MeSH
• lidé MeSH
• počítačová simulace MeSH
• přenos energie fyziologie   MeSH
• regresní analýza MeSH
• statistické modely MeSH
• svalová kontrakce fyziologie   MeSH
• zatížení muskuloskeletálního systému fyziologie   MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Modeling the interrelationships between the input parameters and outputs (responses) in any machining processes is essential to understand the process behavior and material removal mechanism. The developed models can also act as effective prediction tools in envisaging the tentative values of the responses for given sets of input parameters. In this paper, the application potentialities of nine different regression models, such as linear regression (LR), polynomial regression (PR), support vector regression (SVR), principal component regression (PCR), quantile regression, median regression, ridge regression, lasso regression and elastic net regression are explored in accurately predicting response values during turning and drilling operations of composite materials. Their prediction performance is also contrasted using four statistical metrics, i.e., mean absolute percentage error, root mean squared percentage error, root mean squared logarithmic error and root relative squared error. Based on the lower values of those metrics and Friedman rank and aligned rank tests, SVR emerges out as the best performing model, whereas the prediction performance of median regression is worst. The results of the Wilcoxon test based on the drilling dataset identify the existence of statistically significant differences between the performances of LR and PCR, and PR and median regression models.

• Klíčová slova
• composite material, drilling, model, regression, turning,
• Publikační typ
• časopisecké články MeSH

Identifying outliers and high-leverage points is a fundamental step in the least-squares regression model building process. The examination of data quality involves the detection of influential points, outliers and high-leverages, which cause many problems in regression analysis. On the basis of a statistical analysis of the residuals (classical, normalized, standardized, jackknife, predicted and recursive) and diagonal elements of a projection matrix, diagnostic plots for influential points indication are formed. The identification of outliers and high leverage points are combined with graphs for the identification of influence type based on the likelihood distance. The powerful procedure for the computation of influential points characteristics written in S-Plus is demonstrated on the model predicting the metabolic clearance rate of glucose (MCRg) that represents the ratio of the amount of glucose supplied to maintain blood glucose levels during the euglycemic clamp and the blood glucose concentration from common laboratory and anthropometric indices. MCRg reflects insulin sensitivity filtering-off the effect of blood glucose. The prediction of clamp parameters should enable us to avoid the demanding clamp examination, which is connected with a higher load and risk for patients.

• MeSH
• algoritmy MeSH
• amenorea diagnóza   metabolismus   patofyziologie   MeSH
• globulin vázající pohlavní hormony analýza   MeSH
• glukosa farmakokinetika   farmakologie   MeSH
• glykemický clamp MeSH
• hyperandrogenismus diagnóza   metabolismus   patofyziologie   MeSH
• index tělesné hmotnosti MeSH
• interpretace statistických dat MeSH
• inzulin farmakologie   MeSH
• krevní glukóza účinky léků   metabolismus   MeSH
• lidé MeSH
• metabolická clearance účinky léků   MeSH
• metoda nejmenších čtverců MeSH
• počítačová grafika MeSH
• regresní analýza MeSH
• software MeSH
• statistické modely * MeSH
• triglyceridy krev   MeSH
• výběr pacientů MeSH
• Check Tag
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• globulin vázající pohlavní hormony MeSH
• glukosa MeSH
• inzulin MeSH
• krevní glukóza MeSH
• triglyceridy MeSH

Temperature drives development in insects and other ectotherms because their metabolic rate and growth depends directly on thermal conditions. However, relative durations of successive ontogenetic stages often remain nearly constant across a substantial range of temperatures. This pattern, termed 'developmental rate isomorphy' (DRI) in insects, appears to be widespread and reported departures from DRI are generally very small. We show that these conclusions may be due to the caveats hidden in the statistical methods currently used to study DRI. Because the DRI concept is inherently based on proportional data, we propose that Dirichlet regression applied to individual-level data is an appropriate statistical method to critically assess DRI. As a case study we analyze data on five aquatic and four terrestrial insect species. We find that results obtained by Dirichlet regression are consistent with DRI violation in at least eight of the studied species, although standard analysis detects significant departure from DRI in only four of them. Moreover, the departures from DRI detected by Dirichlet regression are consistently much larger than previously reported. The proposed framework can also be used to infer whether observed departures from DRI reflect life history adaptations to size- or stage-dependent effects of varying temperature. Our results indicate that the concept of DRI in insects and other ectotherms should be critically re-evaluated and put in a wider context, including the concept of 'equiproportional development' developed for copepods.

• MeSH
• biologické modely * MeSH
• hmyz růst a vývoj   MeSH
• stadia vývoje * MeSH
• statistické modely * MeSH
• teplota * MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

Classifying a measurable clinical outcome as a dichotomous variable often involves difficulty with borderline cases that could fairly be assigned either of the two binary class memberships. In such situations the indicated class membership is often highly subjective and subject to, for instance, a measurement error. In other situations the intermediate level of a three-level ordinal factor may sometimes be explicitly reserved for cases which could likely belong to either of the two binary classes. Such indefinite readings are often eliminated from the statistical analysis. In this article we review conceptual and methodological aspects of employing proportional odds logistic regression for a three level ordinal factor as a suitable alternative to ordinary logistic regression when dealing with limited uncertainty in classifying clinical outcome as a binary variable.

• MeSH
• ateroskleróza diagnostické zobrazování   MeSH
• cholesterol krev   MeSH
• interpretace statistických dat * MeSH
• kouření MeSH
• krevní glukóza metabolismus   MeSH
• lidé MeSH
• logistické modely * MeSH
• prediktivní hodnota testů MeSH
• statistické modely * MeSH
• ultrasonografie MeSH
• vápník krev   MeSH
• Check Tag
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Česká republika MeSH
• Názvy látek
• cholesterol MeSH
• krevní glukóza MeSH
• vápník MeSH

Response surface methodology (RSM) was employed to study the effect of the composition of the rice-glycerol complex medium on the production of lovastatin (Lvs) by the ascomycete Monascus ruber in mixed solid-liquid (or submerged) cultures at 25 degrees C. Four components (rice powder, peptone, glycerol, glucose) were studied to evaluate the approximate polynomial for all dependent variables, explaining their effects on the production of Lvs. The best composition derived from RSM regression was (in g/L) rice powder 34.4, peptone 10.8,, glucose 129, KNO3 8.0, MgSO4.7H2O 4.0 and glycerol 36.4 mL/L. With this composition, the Lvs production was 157 mg/L after 10 d of cultivation. In comparison with glycerol and glucose, the rice powder becomes a more suitable carbon source and represents a great potential for the production of Lvs.

• MeSH
• biologické modely MeSH
• glukosa metabolismus   MeSH
• glycerol metabolismus   MeSH
• lovastatin biosyntéza   metabolismus   MeSH
• Monascus metabolismus   MeSH
• peptony metabolismus   MeSH
• regresní analýza MeSH
• rýže (rod) metabolismus   MeSH
• statistické modely MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• glukosa MeSH
• glycerol MeSH
• lovastatin MeSH
• peptony MeSH

The accuracy of the received signal strength-based visible light positioning (VLP) system in indoor applications is constrained by the tilt angles of transmitters (Txs) and receivers as well as multipath reflections. In this paper, for the first time, we show that tilting the Tx can be beneficial in VLP systems considering both line of sight (LoS) and non-line of sight transmission paths. With the Txs oriented towards the center of the receiving plane (i.e., the pointing center F), the received power level is maximized due to the LoS components on F. We also show that the proposed scheme offers a significant accuracy improvement of up to ~66% compared with a typical non-tilted Tx VLP at a dedicated location within a room using a low complex linear least square algorithm with polynomial regression. The effect of tilting the Tx on the lighting uniformity is also investigated and results proved that the uniformity achieved complies with the European Standard EN 12464-1. Furthermore, we show that the accuracy of VLP can be further enhanced with a minimum positioning error of 8 mm by changing the height of F.

• Klíčová slova
• Tx’s tilting, linear least square, localization, polynomial regression, received signal strength, visible light communication, visible light positioning,
• Publikační typ
• časopisecké články MeSH

The REGDIA regression diagnostics algorithm in S-Plus is introduced in order to examine the accuracy of pK(a) predictions made with four updated programs: PALLAS, MARVIN, ACD/pKa and SPARC. This report reviews the current status of computational tools for predicting the pK(a) values of organic drug-like compounds. Outlier predicted pK(a) values correspond to molecules that are poorly characterized by the pK(a) prediction program concerned. The statistical detection of outliers can fail because of masking and swamping effects. The Williams graph was selected to give the most reliable detection of outliers. Six statistical characteristics (F(exp), R(2), R(P)(2), MEP, AIC, and s(e) in pK(a) units) of the results obtained when four selected pK(a) prediction algorithms were applied to three datasets were examined. The highest values of F(exp), R(2), R(P)(2), the lowest values of MEP and s(e), and the most negative AIC were found using the ACD/pK (a) algorithm for pK(a) prediction, so this algorithm achieves the best predictive power and the most accurate results. The proposed accuracy test performed by the REGDIA program can also be applied to test the accuracy of other predicted values, such as log P, log D, aqueous solubility or certain physicochemical properties of drug molecules.

• MeSH
• algoritmy * MeSH
• chemické modely * MeSH
• kyseliny chemie   MeSH
• léčivé přípravky chemie   MeSH
• molekulární struktura MeSH
• regresní analýza MeSH
• software MeSH
• statistické modely * MeSH
• validace softwaru MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• srovnávací studie MeSH
• Názvy látek
• kyseliny MeSH
• léčivé přípravky MeSH