The present research was conducted to explore the potential of mango kernel starch from the Chaunsa variety to develop starch and starch nanoparticles (SNPs) based films. The investigation included starch isolation from mango kernel followed by the preparation of SNPs by acid hydrolysis and a thorough examination of various physicochemical properties for film formation. The properties of SNPs were found to be distinctly different from those of native starch. SNPs exhibited an aggregated form with an irregular surface, whereas native starch had an oval and elongated shape with a smooth surface. X-ray diffraction (XRD) analysis confirmed that the starch type in SNPs was of the A-type. Additionally, the pasting properties of SNPs were minimal due to the acid hydrolysis process. SNP-based composite film was developed with (5 %) SNP concentration added. This successful incorporation of SNPs enhanced biodegradability, with complete degradation occurring within three weeks. Moreover, the composite films displayed increased burst strength, measuring 1303.51 ± 73.7 g, and lower water vapor transmission rates (WVTR) at (7.40 ± 0.50) × 10-3 g per square meter per second and reduced water solubility at 35.32 ± 3.0 %. This development represents a significant advancement in the field of eco-friendly packaging materials.

• Klíčová slova
• Mango kernel starch, Nanoparticles films, Waste utilization,
• MeSH
• difrakce rentgenového záření MeSH
• hydrolýza MeSH
• Mangifera * chemie   MeSH
• nanočástice * chemie   MeSH
• obaly potravin metody   MeSH
• rozpustnost MeSH
• škrob * chemie   MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• škrob * MeSH

The analysis of functional magnetic resonance imaging (fMRI) data involves multiple stages of data pre-processing before the activation can be statistically detected. Spatial smoothing is a very common pre-processing step in the analysis of functional brain imaging data. This study presents a broad perspective on the influence of spatial smoothing on fMRI group activation results. The data obtained from 20 volunteers during a visual oddball task were used for this study. Spatial smoothing using an isotropic gaussian filter kernel with full width at half maximum (FWHM) sizes 2 to 30 mm with a step of 2 mm was applied in two levels - smoothing of fMRI data and/or smoothing of single-subject contrast files prior to general linear model random-effects group analysis generating statistical parametric maps. Five regions of interest were defined, and several parameters (coordinates of nearest local maxima, t value, corrected threshold, effect size, residual values, etc.) were evaluated to examine the effects of spatial smoothing. The optimal filter size for group analysis is discussed according to various criteria. For our experiment, the optimal FWHM is about 8 mm. We can conclude that for robust experiments and an adequate number of subjects in the study, the optimal FWHM for single-subject inference is similar to that for group inference (about 8 mm, according to spatial resolution). For less robust experiments and fewer subjects in the study, a higher FWHM would be optimal for group inference than for single-subject inferences.

• MeSH
• algoritmy MeSH
• artefakty MeSH
• design vybavení MeSH
• dospělí MeSH
• kontrastní látky MeSH
• lidé MeSH
• magnetická rezonanční tomografie metody   MeSH
• mapování mozku přístrojové vybavení   metody   MeSH
• mozek patologie   MeSH
• normální rozdělení MeSH
• počítačové zpracování obrazu MeSH
• reprodukovatelnost výsledků MeSH
• statistické modely MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• kontrastní látky MeSH

Neuronal firing rate is traditionally defined as the number of spikes per time window. The concept is essential for the rate coding hypothesis, which is still the most commonly investigated scenario in neuronal activity analysis. The estimation of dynamically changing firing rate from neural data can be challenging due to the variability of spike times, even under identical external conditions; hence a wide range of statistical measures have been employed to solve this particular problem. In this paper, we review established firing rate estimation methods, briefly summarize the technical aspects of each approach and discuss their practical applications.

• Klíčová slova
• Bayesian rule, Firing rate, Kernel smoothing, Spike train, Time histogram,
• MeSH
• akční potenciály * MeSH
• algoritmy MeSH
• Bayesova věta MeSH
• interpretace statistických dat MeSH
• lidé MeSH
• modely neurologické MeSH
• neurony fyziologie   MeSH
• pravděpodobnost MeSH
• stochastické procesy MeSH
• zvířata MeSH
• Check Tag
• lidé MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

The bacterial endosymbiont Wolbachia has been used to control insect pests owing to its ability to manipulate their life history and suppress infectious diseases. Therefore, knowledge on Wolbachia dynamics in natural populations is fundamental. The European cherry fruit fly, Rhagoletis cerasi, is infected with the Wolbachia strain wCer2, mainly present in southern and central European populations, and is currently spreading into wCer2-uninfected populations driven by high unidirectional cytoplasmic incompatibility. Here, we describe the distribution of wCer2 along two transition zones where the infection is spreading into wCer2-uninfected R. cerasi populations. Fine-scale sampling of 19 populations in the Czech Republic showed a smooth decrease of wCer2 frequency from south to north within a distance of less than 20 km. Sampling of 12 Hungarian populations, however, showed a sharp decline of wCer2 infection frequency within a few kilometres. We fitted a standard wave equation to our empirical data and estimated a Wolbachia wave speed of 1.9 km yr-1 in the Czech Republic and 1.0 km yr-1 in Hungary. Considering the univoltine life cycle and limited dispersal ability of R. cerasi, our study highlights a rapid Wolbachia spread in natural host populations.

• Klíčová slova
• European cherry fruit fly, cytoplasmic incompatibility, endosymbiont, modelling,
• MeSH
• prostorová analýza * MeSH
• Tephritidae mikrobiologie   MeSH
• Wolbachia fyziologie   MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Česká republika MeSH
• Maďarsko MeSH

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.

• Klíčová slova
• ANOVA model, LOESS, Serfling model, epidemic threshold, incidence, long-time trend, seasonal trend,
• MeSH
• epidemiologické metody MeSH
• epidemiologie * MeSH
• incidence MeSH
• lidé MeSH
• roční období * MeSH
• statistické modely * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

In routine systems investigating the morbidity according to diagnosis it is very useful to analyse the development in time (for example the development of weekly reports). This paper is concerned with the methodology of such analyses. In practice it appears that the number of cases depends on season. It stands to reason, that it is necessary to consider also long-term trends. In this paper two different approaches are discussed--the Box-Jenkins analysis, which describes the random error and the Method of Trend Decomposition which spread the number of cases into the systematic component (long term trend and seasonal effect) and random variability. The authors describe the method of smoothing the estimate of the time series by kernel estimate. In both approaches they use weekly reports from the whole Czech Republic of diagnoses viral hepatitis A, rubella and salmonellosis.

• MeSH
• časové faktory MeSH
• epidemiologické metody MeSH
• hepatitida A epidemiologie   MeSH
• interpretace statistických dat * MeSH
• lidé MeSH
• morbidita * MeSH
• salmonelóza epidemiologie   MeSH
• zarděnky epidemiologie   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• anglický abstrakt MeSH
• časopisecké články MeSH
• Geografické názvy
• Česká republika epidemiologie   MeSH

PURPOSE: We reviewed the survival time for patients with primary brain tumors undergoing treatment with stereotactic radiation methods at the Masaryk Memorial Cancer Institute Brno. We also identified risk factors and characteristics, and described their influence on survival time. METHODS: In summarizing survival data, there are two functions of principal interest, namely, the survival function and the hazard function. In practice, both of them can depend on some characteristics. We focused on nonparametric methods, propose a method based on kernel smoothing, and compared our estimates with the results of the Cox regression model. The hazard function is conditional to age and gross tumor volume and visualized as a color-coded surface. A multivariate Cox model was also designed. RESULTS: There were 88 patients with primary brain cancer, treated with stereotactic radiation. The median survival of our patient cohort was 47.8 months. The estimate of the hazard function has two peaks (about 10 months and about 40 months). The survival time of patients was significantly different for various diagnoses (p≪0.001), KI (p = 0.047) and stereotactic methods (p = 0.033). Patients with a greater GTV had higher risk of death. The suitable threshold for GTV is 20 cm3. Younger patients with a survival time of about 50 months had a higher risk of death. In the multivariate Cox regression model, the selected variables were age, GTV, sex, diagnosis, KI, location, and some of their interactions. CONCLUSION: Kernel methods give us the possibility to evaluate continuous risk variables and based on the results offer risk-prone patients a different treatment, and can be useful for verifying assumptions of the Cox model or for finding thresholds of continuous variables.

• MeSH
• dospělí MeSH
• Kaplanův-Meierův odhad MeSH
• lidé středního věku MeSH
• lidé MeSH
• meningeom mortalita   patologie   chirurgie   MeSH
• mladiství MeSH
• mladý dospělý MeSH
• nádory mozku mortalita   patologie   chirurgie   MeSH
• prognóza MeSH
• proporcionální rizikové modely MeSH
• radiochirurgie MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• tumor burden MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• srovnávací studie MeSH

BACKGROUND: Clusters of infection can indicate the underlying risk pattern of an endemic disease. Retrospective epidemiological data have been used to map the risk of tick-borne encephalitis (TBE) and Lyme borreliosis (LB) in the Central Bohemian region of the Czech Republic. METHODS: Both reported places of infection and patients' residences were entered in a geographical information system; their distance distribution and census data were used to model density of the population at risk. Point-pattern analysis and non-parametric kernel smoothing of points of infection were applied to compute the risk maps. Tick flagging and direct immunofluorescence assay were used to probe true LB-risk in the field. RESULTS: Tick-borne encephalitis infections proved to be more clustered than those of LB which was widespread; however, the most prominent clusters of both diseases largely correspond to each other. The estimated LB risk correlated well with tangible disease challenge as assessed from the tick abundance and Borrelia infection rates at 15 selected localities surveyed annually. CONCLUSION: The risk of LB is widely and smoothly distributed over the area studied, apparently following tick habitats wherever they occur, while TBE is confined to a subset of these locations.

• MeSH
• časoprostorové shlukování * MeSH
• incidence MeSH
• klíšťová encefalitida epidemiologie   MeSH
• lidé MeSH
• lymeská nemoc epidemiologie   MeSH
• retrospektivní studie MeSH
• rizikové faktory MeSH
• senzitivita a specificita MeSH
• statistické modely * MeSH
• zeměpis MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Česká republika epidemiologie   MeSH

BACKGROUND: Socioeconomic inequalities are increasingly recognised as an important public health issue, although their role in the leading causes of mortality in urban areas in Europe has not been fully evaluated. In this study, we used data from the INEQ-CITIES study to analyse inequalities in cause-specific mortality in 15 European cities at the beginning of the 21st century. METHODS: A cross-sectional ecological study was carried out to analyse 9 of the leading specific causes of death in small areas from 15 European cities. Using a hierarchical Bayesian spatial model, we estimated smoothed Standardized Mortality Ratios, relative risks and 95% credible intervals for cause-specific mortality in relation to a socioeconomic deprivation index, separately for men and women. RESULTS: We detected spatial socioeconomic inequalities for most causes of mortality studied, although these inequalities differed markedly between cities, being more pronounced in Northern and Central-Eastern Europe. In the majority of cities, most of these causes of death were positively associated with deprivation among men, with the exception of prostatic cancer. Among women, diabetes, ischaemic heart disease, chronic liver diseases and respiratory diseases were also positively associated with deprivation in most cities. Lung cancer mortality was positively associated with deprivation in Northern European cities and in Kosice, but this association was non-existent or even negative in Southern European cities. Finally, breast cancer risk was inversely associated with deprivation in three Southern European cities. CONCLUSIONS: The results confirm the existence of socioeconomic inequalities in many of the main causes of mortality, and reveal variations in their magnitude between different European cities.

• Klíčová slova
• MORTALITY, SOCIAL INEQUALITIES, SPATIAL ANALYSIS,
• MeSH
• Bayesova věta MeSH
• chudoba MeSH
• disparity zdravotního stavu * MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• příčina smrti trendy   MeSH
• prostorová analýza MeSH
• průřezové studie MeSH
• sociální determinanty zdraví * MeSH
• socioekonomické faktory MeSH
• stupeň vzdělání MeSH
• velkoměsta ekonomika   statistika a číselné údaje   MeSH
• zdraví ve městech ekonomika   statistika a číselné údaje   MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Evropa epidemiologie   MeSH
• velkoměsta ekonomika   statistika a číselné údaje   MeSH

The efficiency of machine learning algorithms for electronically excited states is far behind ground-state applications. One of the underlying problems is the insufficient smoothness of the fitted potential energy surfaces and other properties in the vicinity of state crossings and conical intersections, which is a prerequisite for an efficient regression. Smooth surfaces can be obtained by switching to the diabatic basis. However, diabatization itself is still an outstanding problem. We overcome these limitations by solving both problems at once. We use a machine learning approach combining clustering and regression techniques to correct for the deficiencies of property-based diabatization which, in return, provides us with smooth surfaces that can be easily fitted. Our approach extends the applicability of property-based diabatization to multidimensional systems. We utilize the proposed diabatization scheme to achieve higher prediction accuracy for adiabatic states and we show its performance by reconstructing global potential energy surfaces of excited states of nitrosyl fluoride and formaldehyde. While the proposed methodology is independent of the specific property-based diabatization and regression algorithm, we show its performance for kernel ridge regression and a very simple diabatization based on transition multipoles. Compared to most other algorithms based on machine learning, our approach needs only a small amount of training data.

• Publikační typ
• časopisecké články MeSH