This paper proposes a procedure which evaluates clusters of traffic accident and organizes them according to their significance. The standard kernel density estimation was extended by statistical significance testing of the resulting clusters of the traffic accidents. This allowed us to identify the most important clusters within each section. They represent places where the kernel density function exceeds the significance level corresponding to the 95th percentile level, which is estimated using the Monte Carlo simulations. To show only the most important clusters within a set of sections, we introduced the cluster strength and cluster stability evaluation procedures. The method was applied in the Southern Moravia Region of the Czech Republic.
- MeSH
- Accidents, Traffic statistics & numerical data MeSH
- Humans MeSH
- Monte Carlo Method MeSH
- Cluster Analysis MeSH
- Environment * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Czech Republic MeSH
Jednou z důležitých úloh při zkoumání struktury sledu akčních potenciálů je odhad tzv. firing rate funkce. Cílem článku je popsat možné zrychlení algoritmu, který může být pro tento odhad použit. Protože neexistuje jednoznačná metoda, kterak firing rate funkci odvodit, bylo navržené celé spektrum odlišných strategií. Jedna z populárních metod odhadu je konvoluce sledu akčních potenciálů gaussovským jádrem s patřičnou šířkou. Výběr konkrétního jádra a šířky je obvykle diskutabilní a autoři v nedávném článku [1] navrhují přesný algoritmus pro výpočet optimální šířky pro (nejen) gaussovská jádra. Pro rozsáhlejší množinu vstupních dat je elementární verze algoritmu bohužel neefektivní z hlediska času potřebného pro výpočet. V příspěvku navrhujeme vylepšenou implementaci algoritmu, která je efektivní i pro velká množství vstupních dat. Na konkrétních výsledcích implementovaného algoritmu bylo demonstrováno dosažené zrychlení, což potvrzuje vhodnost navrhované metody.
One of the important tasks in the spike train analysis is to estimate the underlying firing rate function. The aim of this article is to improve the time performance of an algorithm which can be used for the estimation. As there is no unique way how to infer the firing rate function, several different methods have been proposed. A popular method how to estimate this function is the convolution of a spike train with Gaussian kernel with appropriate kernel bandwidth. The definition of what “appropriate” means remains a matter of discussion and a recent paper [1] proposes a method how to exactly compute optimal bandwidth under certain conditions. For large sets of spike train data the elementary version of the algorithm is unfortunately too inefficient in terms of computational time complexity. We present a refined version of the algorithm which in turn allows us to use the original method even for large data sets. The achieved performance improvement is demonstrated on a particular results and shows usability of the proposed method.
- Keywords
- akční potenciál, sled akčních potenciálů, neurální kódování, firing rate, konvoluce, gaussovské jádro, šířka jádra, Brentova minimalizace, paralelní výpočet, MPI,
- MeSH
- Action Potentials physiology MeSH
- Algorithms MeSH
- Models, Neurological MeSH
- Neurons physiology MeSH
- Signal Transduction physiology MeSH
Background: Emergence of antibiotic-resistant bacteria makes exploration of natural antibacterial products imperative. Like other fruit processing industry by-products, date kernels, a waste from date processing industry is rich in its extractable polyphenols. The rich polyphenolic content suggests that date kernel extracts (DKE) can be a cost-effective source of antimicrobial agents, however, their antibacterial activity is poorly understood. Hence, a systematic review of available literature to establish DKE's antibacterial activity is warranted. Methods: A systematic PRISMA approach was employed, and relevant studies were identified using defined keywords from Google Scholar, Scopus, PubMed, and Web of Science databases. The search results were screened based on predefined eligibility criteria and data extraction, organization, pooling, and descriptive statistical analyses of original research records conducted. Results: A total of 888 published records were retrieved from databases. Preliminary screening by applying specific eligibility criteria reduced records to 96 which after full text screening further decreased to 14 records. Escherichia coli and Staphylococcus aureus were the most studied organisms. Results indicate moderate to highly active effect shown by the less polar solvent based DKE's against Gram-positive and by the aqueous based DKE's against Gram-negative bacteria. The review confirms antibacterial activity of DKE against both Gram-positive and -negative bacteria. Heterogeneity in reported polyphenolic content and antibacterial activity are due to differences in cultivars, extraction methods, test methods, model organisms, etc. Use of standardized protocols for isolation, characterization, testing of DKE's active polyphenols to elucidate its antibacterial activity is recommended to establish the clinical efficacy of natural antibacterial compounds from DKE. Conclusion: This review outlines the current knowledge regarding antibacterial activity of polyphenolic DKE, identifying gaps in information and provides key recommendations for future research directions.
- Publication type
- Systematic Review MeSH
In this comparative study, six causality detection methods were compared, namely, the Granger vector autoregressive test, the extended Granger test, the kernel version of the Granger test, the conditional mutual information (transfer entropy), the evaluation of cross mappings between state spaces, and an assessment of predictability improvement due to the use of mixed predictions. Seven test data sets were analyzed: linear coupling of autoregressive models, a unidirectional connection of two Hénon systems, a unidirectional connection of chaotic systems of Rössler and Lorenz type and of two different Rössler systems, an example of bidirectionally connected two-species systems, a fishery model as an example of two correlated observables without a causal relationship, and an example of mediated causality. We tested not only 20000 points long clean time series but also noisy and short variants of the data. The standard and the extended Granger tests worked only for the autoregressive models. The remaining methods were more successful with the more complex test examples, although they differed considerably in their capability to reveal the presence and the direction of coupling and to distinguish causality from mere correlation.
- MeSH
- Time Factors MeSH
- Causality MeSH
- Systems Analysis MeSH
- Models, Theoretical * MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
- Comparative Study MeSH
In order to analyze and improve the dental age estimation in children and adolescents for forensic purposes, 22 age estimation methods were compared to a sample of 976 orthopantomographs (662 males, 314 females) of healthy Czech children and adolescents aged between 2.7 and 20.5 years. All methods are compared in terms of the accuracy and complexity and are based on various data mining methods or on simple mathematical operations. The winning method is presented in detail. The comparison showed that only three methods provide the best accuracy while remaining user-friendly. These methods were used to build a tabular multiple linear regression model, an M5P tree model and support vector machine model with first-order polynomial kernel. All of them have mean absolute error (MAE) under 0.7 years for both males and females. The other well-performing data mining methods (RBF neural network, K-nearest neighbors, Kstar, etc.) have similar or slightly better accuracy, but they are not user-friendly as they require computing equipment and the implementation as computer program. The lowest estimation accuracy provides the traditional model based on age averages (MAE under 0.96 years). Different relevancy of various teeth for the age estimation was found. This finding also explains the lowest accuracy of the traditional averages-based model. In this paper, a technique for missing data replacement for the cases with missing teeth is presented in detail as well as the constrained tabular multiple regression model. Also, we provide free age prediction software based on this wining model.
- MeSH
- Data Mining MeSH
- Dentition, Permanent * MeSH
- Child MeSH
- Humans MeSH
- Linear Models MeSH
- Adolescent MeSH
- Young Adult MeSH
- Neural Networks, Computer MeSH
- Child, Preschool MeSH
- Radiography, Panoramic MeSH
- Decision Trees MeSH
- Software MeSH
- Support Vector Machine MeSH
- Age Determination by Teeth methods MeSH
- Tooth growth & development MeSH
- Check Tag
- Child MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Male MeSH
- Child, Preschool MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Comparative Study MeSH
Ten published DNA-based analytical methods aiming at detecting material of almond (Prunus dulcis) were in silico evaluated for potential cross-reactivity with other stone fruits (Prunus spp.), including peach, apricot, plum, cherry, sour cherry and Sargent cherry. For most assays, the analysis of nucleotide databases suggested none or insufficient discrimination of at least some stone fruits. On the other hand, the assay targeting non-specific lipid transfer protein (Röder et al., 2011, Anal Chim Acta 685:74-83) was sufficiently discriminative, judging from nucleotide alignments. Empirical evaluation was performed for three of the published methods, one modification of a commercial kit (SureFood allergen almond) and one attempted novel method targeting thaumatin-like protein gene. Samples of leaves and kernels were used in the experiments. The empirical results were favourable for the method from Röder et al. (2011) and a modification of SureFood allergen almond kit, both showing cross-reactivity <10(-3) compared to the model almond.
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
V rutinních systémech sledování nemocnosti na jednotlivé diagnózy je jedním z důležitých problé-mů analýza časového vývoje například týdenních počtů hlášení. Tento článek se zabývá metodikouuvedené problematiky. V praxi se ukazuje, že počty výskytů mnohých onemocnění jsou závislé naroční době. Samozřejmě je nutno vzít v úvahu i dlouhodobý vývoj počtu onemocnění. V článku sediskutuje o dvou často používaných přístupech. Jde jednak o Boxovu-Jenkinsovu analýzu časovýchřad, která modeluje „náhodnou chybu“, a jednak o metodu dekompozice trendu, která se pokoušírozložit pozorovaný počet případů na systematické složky (dlouhodobý trend a sezonní složku)a náhodné kolísání. V článku je popsána možnost vyhlazení odhadu časové řady pomocí modifi-kovaného jádrového odhadu. Pro ilustraci obou metod jsou použity týdenní údaje o celorepubliko-vých počtech nemocných s hepatitidou A, zarděnkami a salmonelózou.
In routine systems investigating the morbidity according to diagnosis it is very useful to analysethe development in time (for example the development of weekly reports). This paper is concernedwith the methodology of such analyses. In practice it appears that the number of cases depends onseason. It stands to reason, that it is necessary to consider also long-therm trends. In this paper twodifferent approaches are discussed – the Box-Jenkins analysis, which describes the random errorand the Method of Trend Decomposition which spread the number of cases into the systematiccomponent (long term trend and seasonal effect) and random variability. The authors describe themethod of smoothing the estimate of the time series by kernel estimate. In both approaches theyuse weekly reports from the whole Czech Republic of diagnoses viral hepatitis A, rubella andsalmonellosis.
BACKGROUND: Severe canopy-removing disturbances are native to many temperate forests and radically alter stand structure, but biotic legacies (surviving elements or patterns) can lend continuity to ecosystem function after such events. Poorly understood is the degree to which the structural complexity of an old-growth forest carries over to the next stand. We asked how pre-disturbance spatial pattern acts as a legacy to influence post-disturbance stand structure, and how this legacy influences the structural diversity within the early-seral stand. METHODS: Two stem-mapped one-hectare forest plots in the Czech Republic experienced a severe bark beetle outbreak, thus providing before-and-after data on spatial patterns in live and dead trees, crown projections, down logs, and herb cover. RESULTS: Post-disturbance stands were dominated by an advanced regeneration layer present before the disturbance. Both major species, Norway spruce (Picea abies) and rowan (Sorbus aucuparia), were strongly self-aggregated and also clustered to former canopy trees, pre-disturbance snags, stumps and logs, suggesting positive overstory to understory neighbourhood effects. Thus, although the disturbance dramatically reduced the stand's height profile with ~100% mortality of the canopy layer, the spatial structure of post-disturbance stands still closely reflected the pre-disturbance structure. The former upper tree layer influenced advanced regeneration through microsite and light limitation. Under formerly dense canopies, regeneration density was high but relatively homogeneous in height; while in former small gaps with greater herb cover, regeneration density was lower but with greater heterogeneity in heights. CONCLUSION: These findings suggest that pre-disturbance spatial patterns of forests can persist through severe canopy-removing disturbance, and determine the spatial structure of the succeeding stand. Such patterns constitute a subtle but key legacy effect, promoting structural complexity in early-seral forests as well as variable successional pathways and rates. This influence suggests a continuity in spatial ecosystem structure that may well persist through multiple forest generations.
During the reign of the first Ptolemaic kings in Egypt, mainly in the 3rd and 2nd centuries BCE, the Egyptian cults related to the divine couple of Isis and Sarapis (i.e. the Isiac cults) spread successfully from Egypt to ports and coastal cities of the ancient Mediterranean. The discussion on the topic of the factors involved in the process of the early spread of these cults outside Egypt is still open and, so far, the research in this area has been conducted mainly by using established historiographical methods. However, these methods are limited when dealing with the interplay among different variables involved in complex historical processes. This article aims to overcome these limits by using a quantitative spatial network analysis. The results of our previous published research, which focused on a quantitative evaluation of the impact of individual factors on the early spread of the Isiac cults across the ancient Aegean Islands, suggest that the process was promoted by military and commercial activities of the Ptolemaic dynasty, and that the Ptolemaic military operations were the most influential factor. Following these results, this article focuses on the early spread of the Isiac cults on the west coast of Hellenistic Asia Minor, i.e. the region which the Ptolemies attempted to control in the 3rd and 2nd centuries BCE. The statistically significant results presented in this article support the hypothesis that the Ptolemaic political engagement in Asia Minor had a positive impact on the early spread of the Isiac cults. The results also suggest that the activities of the Seleucid dynasty, a political rival of the Ptolemies, in the area of interest could have constituted an immunological factor limiting the spread of the Isiac cults further to the eastern parts of Asia Minor.
- MeSH
- Archaeology MeSH
- History, Ancient MeSH
- Transportation MeSH
- Humans MeSH
- Politics * MeSH
- Spatial Analysis MeSH
- Check Tag
- History, Ancient MeSH
- Humans MeSH
- Publication type
- Journal Article MeSH
- Historical Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- Geographicals
- Asia MeSH
- Egypt, Ancient MeSH
