Evaluation of Hungarian Wines for Resveratrol by Overpressured Layer Chromatography

A method, including solid phase extraction sample preparation, overpressured layer chromatographic separation and subsequent densitometric evaluation, was developed for measurement of total resveratrol (cis- and trans-isomers) content of wine. The amount of resveratrol was determined in wine samples from different winemaking regions of Hungary. The total resveratrol was high in Hungarian red wines (3.6–11 mg/L), and much lower in white ones (0.04–1.5 mg/L)

Fault diagnosis for uncertain networked systems

Fault diagnosis has been at the forefront of technological developments for several decades. Recent advances in many engineering fields have led to the networked interconnection of various systems. The increased complexity of modern systems leads to a larger number of sources of uncertainty which must be taken into consideration and addressed properly in the design of monitoring and fault diagnosis architectures. This chapter reviews a model-based distributed fault diagnosis approach for uncertain nonlinear large-scale networked systems to specifically address: (a) the presence of measurement noise by devising a filtering scheme for dampening the effect of noise; (b) the modeling of uncertainty by developing an adaptive learning scheme; (c) the uncertainty issues emerging when considering networked systems such as the presence of delays and packet dropouts in the communication networks. The proposed architecture considers in an integrated way the various components of complex distributed systems such as the physical environment, the sensor level, the fault diagnosers, and the communication networks. Finally, some actions taken after the detection of a fault, such as the identification of the fault location and its magnitude or the learning of the fault function, are illustrated

On the occurrence and implications of Jurassic primary continental boninite-like melts in the Zagros orogen

Ultramafic rocks, ranging from pyroxenites to hornblendites, are associated with granitoids of the Aligoodarz intrusive complex in the central Sanandaj–Sirjan Zone, representing the Mesozoic continental arc segment of the Zagros orogen. As inferred from the ultramafic whole rock composition and the most primitive clinopyroxene composition in pyroxenites, the geochemical signature of primary melt is significantly different from that of the continental arc basalts. In particular, primary melt is characterized by extremely low concentrations of incompatible elements and high concentrations of Mg and refractory elements typical of boninites. Amphibole is a late crystallizing mineral in these rocks and is in textural and chemical disequilibrium with olivine + orthopyroxene + clinopyroxene. Amphibole crystallized from a liquid underwent differentiation through a process of melt-rock reaction. In particular, early differentiated boninitic cumulates reacted with later melts with a strong crustal signature similar to Aligoodarz granodiorite. U-Pb zircon geochronology from ultramafic rocks and surrounding quartz-diorite yield similar ages and indicate that they are coeval with Aligoodarz granitoids (ca. 165–170 Ma). However, the occurrence of a marked negative Eu anomaly in zircon from the ultramafic rocks, which is absent in the boninitic primary melt, indicates that zircons crystallized from the infiltrating melt and in turn date the timing of melt infiltration. The interaction between ultramafic cumulates and infiltrated melt has generated a new liquid compositionally similar to high-Mg andesites and to the quartz-diorites hosting the ultramafic cumulates. The scenario that better account for the genesis of boninitic melts in the Sanandaj–Sirjan Zone is partial melting of a depleted mantle wedge in response to the onset of NeoTethys subduction. According to this hypothesis, middle Jurassic calc-alkaline magmatism in the Sanandaj–Sirjan Zone represents the mature stage of arc magmatism postdating boninite generation by about 10 Ma

On the occurrence and implications of Jurassic primary continental boninite-like melts in the Zagros orogen

Ultramafic rocks, ranging from pyroxenites to hornblendites, are associated with granitoids of the Aligoodarz intrusive complex in the central Sanandaj–Sirjan Zone, representing the Mesozoic continental arc segment of the Zagros orogen. As inferred from the ultramafic whole rock composition and the most primitive clinopyroxene composition in pyroxenites, the geochemical signature of primary melt is significantly different from that of the continental arc basalts. In particular, primary melt is characterized by extremely low concentrations of incompatible elements and high concentrations of Mg and refractory elements typical of boninites. Amphibole is a late crystallizing mineral in these rocks and is in textural and chemical disequilibrium with olivine + orthopyroxene + clinopyroxene. Amphibole crystallized from a liquid underwent differentiation through a process of melt-rock reaction. In particular, early differentiated boninitic cumulates reacted with later melts with a strong crustal signature similar to Aligoodarz granodiorite. U-Pb zircon geochronology from ultramafic rocks and surrounding quartz-diorite yield similar ages and indicate that they are coeval with Aligoodarz granitoids (ca. 165–170 Ma). However, the occurrence of a marked negative Eu anomaly in zircon from the ultramafic rocks, which is absent in the boninitic primary melt, indicates that zircons crystallized from the infiltrating melt and in turn date the timing of melt infiltration. The interaction between ultramafic cumulates and infiltrated melt has generated a new liquid compositionally similar to high-Mg andesites and to the quartz-diorites hosting the ultramafic cumulates. The scenario that better account for the genesis of boninitic melts in the Sanandaj–Sirjan Zone is partial melting of a depleted mantle wedge in response to the onset of NeoTethys subduction. According to this hypothesis, middle Jurassic calc-alkaline magmatism in the Sanandaj–Sirjan Zone represents the mature stage of arc magmatism postdating boninite generation by about 10 Ma

Using an optimal set of features with a machine learning-based approach to predict effector proteins for Legionella pneumophila

Type IV secretion systems exist in a number of bacterial pathogens and are used to secrete effector proteins directly into host cells in order to change their environment making the environment hospitable for the bacteria. In recent years, several machine learning algorithms have been developed to predict effector proteins, potentially facilitating experimental verification. However, inconsistencies exist between their results. Previously we analysed the disparate sets of predictive features used in these algorithms to determine an optimal set of 370 features for effector prediction. This study focuses on the best way to use these optimal features by designing three machine learning classifiers, comparing our results with those of others, and obtaining de novo results. We chose the pathogen Legionella pneumophila strain Philadelphia-1, a cause of Legionnaires’ disease, because it has many validated effector proteins and others have developed machine learning prediction tools for it. While all of our models give good results indicating that our optimal features are quite robust, Model 1, which uses all 370 features with a support vector machine, has slightly better accuracy. Moreover, Model 1 predicted 472 effector proteins that are deemed highly probable to be effectors and include 94% of known effectors. Although the results of our three models agree well with those of other researchers, their models only predicted 126 and 311 candidate effectors.Published copyAshari, Z., K.A. Brayton, and S. L. Broschat. (2019). Using an optimal set of features with a ma-chine learning-based approach to predict effector proteins for Legionella pneumophila. PLoS ONE, Vol. 14, No. 1, e0202312. doi:10.1371/journal.pone.0202312. PMCID: PMC6347213

Using an optimal set of features with a machine learning-based approach to predict effector proteins for Legionella pneumophila.

Type IV secretion systems exist in a number of bacterial pathogens and are used to secrete effector proteins directly into host cells in order to change their environment making the environment hospitable for the bacteria. In recent years, several machine learning algorithms have been developed to predict effector proteins, potentially facilitating experimental verification. However, inconsistencies exist between their results. Previously we analysed the disparate sets of predictive features used in these algorithms to determine an optimal set of 370 features for effector prediction. This study focuses on the best way to use these optimal features by designing three machine learning classifiers, comparing our results with those of others, and obtaining de novo results. We chose the pathogen Legionella pneumophila strain Philadelphia-1, a cause of Legionnaires' disease, because it has many validated effector proteins and others have developed machine learning prediction tools for it. While all of our models give good results indicating that our optimal features are quite robust, Model 1, which uses all 370 features with a support vector machine, has slightly better accuracy. Moreover, Model 1 predicted 472 effector proteins that are deemed highly probable to be effectors and include 94% of known effectors. Although the results of our three models agree well with those of other researchers, their models only predicted 126 and 311 candidate effectors

Prediction of T4SS Effector Proteins for Anaplasma phagocytophilum Using OPT4e, A New Software Tool

Type IV secretion systems (T4SS) are used by a number of bacterial pathogens to attack the host cell. The complex protein structure of the T4SS is used to directly translocate effector proteins into host cells, often causing fatal diseases in humans and animals. Identification of effector proteins is the first step in understanding how they function to cause virulence and pathogenicity. Accurate prediction of effector proteins via a machine learning approach can assist in the process of their identification. The main goal of this study is to predict a set of candidate effectors for the tick-borne pathogen Anaplasma phagocytophilum, the causative agent of anaplasmosis in humans. To our knowledge, we present the first computational study for effector prediction with a focus on A. phagocytophilum. In a previous study, we systematically selected a set of optimal features from more than 1,000 possible protein characteristics for predicting T4SS effector candidates. This was followed by a study of the features using the proteome of Legionella pneumophila strain Philadelphia deduced from its complete genome. In this manuscript we introduce the OPT4e software package for Optimal-features Predictor for T4SS Effector proteins. An earlier version of OPT4e was verified using cross-validation tests, accuracy tests, and comparison with previous results for L. pneumophila. We use OPT4e to predict candidate effectors from the proteomes of A. phagocytophilum strains HZ and HGE-1 and predict 48 and 46 candidates, respectively, with 16 and 18 deemed most probable as effectors. These latter include the three known validated effectors for A. phagocytophilum.Published copyEsna Ashari, Z., K.A. Brayton, and S.L. Broschat. (2019). Prediction of T4SS effector proteins for Anaplasma phagocytophilum using OPT4e, a new software tool. Frontiers in Microbiology, Vol.10. doi:10.3389/fmicb.2019.01391. PMCID: PMC6598457.First publication by Frontiers Medi