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.

• Klíčová slova
• antibacterial, antimicrobial, date kernel extract, ethnopharmacology, medicinal plants, minimum bactericidal concentration, minimum inhibitory concentration, polyphenols,
• Publikační typ
• časopisecké články MeSH
• systematický přehled MeSH

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
• dopravní nehody statistika a číselné údaje   MeSH
• lidé MeSH
• metoda Monte Carlo MeSH
• shluková analýza MeSH
• životní prostředí * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Geografické názvy
• Česká republika MeSH

Swarm behaviors offer scalability and robustness to failure through a decentralized and distributed design. When designing coherent group motion as in swarm flocking, virtual potential functions are a widely used mechanism to ensure the aforementioned properties. However, arbitrating through different virtual potential sources in real-time has proven to be difficult. Such arbitration is often affected by fine tuning of the control parameters used to select among the different sources and by manually set cut-offs used to achieve a balance between stability and velocity. A reliance on parameter tuning makes these methods not ideal for field operations of aerial drones which are characterized by fast non-linear dynamics hindering the stability of potential functions designed for slower dynamics. A situation that is further exacerbated by parameters that are fine-tuned in the lab is often not appropriate to achieve satisfying performances on the field. In this work, we investigate the problem of dynamic tuning of local interactions in a swarm of aerial vehicles with the objective of tackling the stability-velocity trade-off. We let the focal agent autonomously and adaptively decide which source of local information to prioritize and at which degree-for example, which neighbor interaction or goal direction. The main novelty of the proposed method lies in a Gaussian kernel used to regulate the importance of each element in the swarm scheme. Each agent in the swarm relies on such a mechanism at every algorithmic iteration and uses it to tune the final output velocities. We show that the presented approach can achieve cohesive flocking while at the same time navigating through a set of way-points at speed. In addition, the proposed method allows to achieve other desired field properties such as automatic group splitting and joining over long distances. The aforementioned properties have been empirically proven by an extensive set of simulated and field experiments, in communication-full and communication-less scenarios. Moreover, the presented approach has been proven to be robust to failures, intermittent communication, and noisy perceptions.

• Klíčová slova
• field experiments and simulations, flocking, swarm (methodology), swarm robot control, unmanned aerial vehicle,
• Publikační typ
• časopisecké články 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

BACKGROUND: In India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation. RESULTS: We generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties1. Both methods predicted the kernel mass with R2 > 0.93 (XRT: R2 = 0.93 and mean error estimate (MAE) = 0.17, CNN: R2 = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R2 = 0.91, MAE = 0.09) compared to XRT (R2 = 0.78; MAE = 0.08). CONCLUSION: Our study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future.

• Klíčová slova
• Convolutional neural network (CNN), Kernel weight, Peanut production, Shelling percentage, Technology-driven system transformation, X-ray,
• Publikační typ
• časopisecké články 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.

• Publikační typ
• časopisecké články MeSH

Population numbers of Kordofan giraffe (Giraffa camelopardalis antiquorum) have declined throughout its range by more than 85% in the last three decades, including in the isolated easternmost population found in the Garamba National Park (NP) in the Democratic Republic of Congo.We provide new data on the conservation status and ecology of Kordofan giraffe in Garamba NP, specifically on the current population dynamics, distribution patterns, and spatial ecology for informed conservation management decisions.Data were gathered between September 26, 2016, and August 17, 2017, through direct observation and from eight GPS satellite collars deployed in early 2016. Movements, distribution patterns, and autocorrelated kernel density home ranges were estimated using the Continuous-Time Movement Modeling (CTMM) framework. We then compared results with home ranges calculated using the kernel density estimation (95% KDE) method.The Garamba NP population was estimated to be 45 giraffe with a female-dominated sex ratio (35% males; 65% females), and adult-dominated age class ratio (11.2% juveniles; 17.7% subadults; 71.1% adults). The giraffe's distribution was limited to the south-central sector of the Park, and giraffe were divided over different areas with some degree of connectivity. The average giraffe home range size was 934.3 km2 using AKDE and 268.8 km2 using KDE. Both methods have shown surprisingly large home ranges despite of the relatively high humidity of Garamba NP.Based on the outcomes of this research, urgent conservation action is needed to protect Garamba's remaining giraffe population.

• Klíčová slova
• Democratic Republic of Congo, GIS, Giraffa, Haut‐Uele, Kordofan giraffe, autocorrelated kernel density estimation, giraffe, home range, population structure,
• Publikační typ
• časopisecké články MeSH

This paper deals with the vulnerability of machine learning models to adversarial examples and its implication for robustness and generalization properties. We propose an evolutionary algorithm that can generate adversarial examples for any machine learning model in the black-box attack scenario. This way, we can find adversarial examples without access to model's parameters, only by querying the model at hand. We have tested a range of machine learning models including deep and shallow neural networks. Our experiments have shown that the vulnerability to adversarial examples is not only the problem of deep networks, but it spreads through various machine learning architectures. Rather, it depends on the type of computational units. Local units, such as Gaussian kernels, are less vulnerable to adversarial examples.

• Klíčová slova
• Adversarial examples, Genetic algorithms, Kernel methods, Neural networks, Supervised learning,
• MeSH
• algoritmy MeSH
• lidé MeSH
• neuronové sítě * MeSH
• řízené strojové učení * trendy   MeSH
• rozpoznávání automatizované metody   trendy   MeSH
• strojové učení trendy   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

BACKGROUND: Population health is vital to a nation's overall well-being and development. To achieve sustainable human development, a reduction in health inequalities and an increase in interstate convergence in health indicators is necessary. Evaluation of the convergence patterns can aid the government in monitoring the health progress across the Indian states. This study investigates the progressive changes in the convergence and divergence patterns in health status across major states of India from 1990 to 2018. METHODS: Sigma plots (σ), kernel density plots, and log t-test methods are used to test the convergence, divergence, and club convergence patterns in the health indicators at the state level. RESULTS: The result of the sigma convergence suggests that life expectancy at birth has converged across all states. After 2006, however, the infant mortality rate, neonatal mortality rate, and total fertility rate experienced a divergence pattern. The study's findings indicate that life expectancy at birth converges in the same direction across all states, falling into the same club (Club One). However, considerable cross-state variations and evidence of clubs' convergence and divergence are observed in the domains of infant mortality rate, neonatal death rate, and total fertility rate. As suggested by the kernel density estimates, life expectancy at birth stratifies, polarizes, and becomes unimodal over time, although with a single stable state. A bimodal distribution was found for infant, neonatal, and total fertility rates. CONCLUSIONS: Therefore, healthcare strategies must consider each club's transition path while focusing on divergence states to reduce health variations and improve health outcomes for each group of individuals.

• Klíčová slova
• Club convergence, Health status, Indian states, Kernel density,
• MeSH
• hodnocení výsledků zdravotní péče MeSH
• kojenec MeSH
• kojenecká mortalita * MeSH
• lidé MeSH
• naděje dožití * MeSH
• novorozenec MeSH
• porodnost MeSH
• zdravotní stav MeSH
• Check Tag
• kojenec MeSH
• lidé MeSH
• novorozenec MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

BACKGROUND: Fine-grained recognition of plants from images is a challenging computer vision task, due to the diverse appearance and complex structure of plants, high intra-class variability and small inter-class differences. We review the state-of-the-art and discuss plant recognition tasks, from identification of plants from specific plant organs to general plant recognition "in the wild". RESULTS: We propose texture analysis and deep learning methods for different plant recognition tasks. The methods are evaluated and compared them to the state-of-the-art. Texture analysis is only applied to images with unambiguous segmentation (bark and leaf recognition), whereas CNNs are only applied when sufficiently large datasets are available. The results provide an insight in the complexity of different plant recognition tasks. The proposed methods outperform the state-of-the-art in leaf and bark classification and achieve very competitive results in plant recognition "in the wild". CONCLUSIONS: The results suggest that recognition of segmented leaves is practically a solved problem, when high volumes of training data are available. The generality and higher capacity of state-of-the-art CNNs makes them suitable for plant recognition "in the wild" where the views on plant organs or plants vary significantly and the difficulty is increased by occlusions and background clutter.

• Klíčová slova
• Bark, Computer vision, Convolutional neural networks, Deep learning, Kernel maps, Leaves, Plants, SVM, Texture,
• Publikační typ
• časopisecké články MeSH