Why Omphalotus illudens (Schwein.) Bresinsky et Besl i an independent species

Per qué Omphalotus illudens (Schwein.) Bresinsky et Besi és una espècie independent. El gènere Omphalotus viu des de la zona tropical a la temperada de tot el món. Actualment, hi ha vuit espècies descrites. Dues, O. Olearius i O. Illudens viuen a Europa. Tenint en compte que aquestes dues espècies són morfològicament molt semblants, el seu estatus taxonòmic és encara matèria de controvèrsia. Aquest estudi es basa en una revisió detallada d'aquest problema: s' hi evaluen dades morfol ògiques, quimiotaxonòmiques, moleculars, i també experiments d'encreuament. Els resultats ens porten a la conclusió que O. olearius i O. Illudens són dues espècies diferents.Por qué Omphalotus illudens (Schwein.) Bresinsky et Besi es una especie independiente. El género Omplialotus es conocido de la zona tropical a la templada, en todo el mundo. Actualmente, hay ocho especies descritas. Dos de ellas, O. olearius y O. Illudens viven en Europa. Ya que estas dos especies son morfológicamentc muy similares, aún existe discusión sobre su estatus taxonómico. Este estudio proporciona una revisión detallada de este problema: los datos morfológicos, quimiotaxonómicos, moleculares, al igual que experimentos de cruzamiento, son sometidos a evaluación. Los resultados llevan a la conclusión de que O. olearius y O. Illudens son dos especies distintas.Why Omphalotus illudens (Schwein.) Bresinsky et Besi is an independent species. The genus Omphalotus is known from tropical or temperate areas all over the world. Currently, eight species are described. Two of them, O. olearius and O. illudens. occur in Europe. Since these two species are morphologically very similar, their taxonomical status is still controversial. This study provides a careful review of the problem: morphological, chemotaxo nomical, molecular data as well as data from mating experiments are evaluated. The results point to the conclusion that O. olearius and O. illudens are distinct species

Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters

Assay interference caused by small molecules continues to pose a significant challenge for early drug discovery. A number of rule-based and similarity-based approaches have been derived that allow the flagging of potentially “badly behaving compounds”, “bad actors”, or “nuisance compounds”. These compounds are typically aggregators, reactive compounds, and/or pan-assay interference compounds (PAINS), and many of them are frequent hitters. Hit Dexter is a recently introduced machine learning approach that predicts frequent hitters independent of the underlying physicochemical mechanisms (including also the binding of compounds based on “privileged scaffolds” to multiple binding sites). Here we report on the development of a second generation of machine learning models which now covers both primary screening assays and confirmatory dose–response assays. Protein sequence clustering was newly introduced to minimize the overrepresentation of structurally and functionally related proteins. The models correctly classified compounds of large independent test sets as (highly) promiscuous or nonpromiscuous with Matthews correlation coefficient (MCC) values of up to 0.64 and area under the receiver operating characteristic curve (AUC) values of up to 0.96. The models were also utilized to characterize sets of compounds with specific biological and physicochemical properties, such as dark chemical matter, aggregators, compounds from a high-throughput screening library, drug-like compounds, approved drugs, potential PAINS, and natural products. Among the most interesting outcomes is that the new Hit Dexter models predict the presence of large fractions of (highly) promiscuous compounds among approved drugs. Importantly, predictions of the individual Hit Dexter models are generally in good agreement and consistent with those of Badapple, an established statistical model for the prediction of frequent hitters. The new Hit Dexter 2.0 web service, available at http://hitdexter2.zbh.uni-hamburg.de, not only provides user-friendly access to all machine learning models presented in this work but also to similarity-based methods for the prediction of aggregators and dark chemical matter as well as a comprehensive collection of available rule sets for flagging frequent hitters and compounds including undesired substructures.acceptedVersio

Global ecological predictors of the soil priming effect.

Identifying the global drivers of soil priming is essential to understanding C cycling in terrestrial ecosystems. We conducted a survey of soils across 86 globally-distributed locations, spanning a wide range of climates, biotic communities, and soil conditions, and evaluated the apparent soil priming effect using 13C-glucose labeling. Here we show that the magnitude of the positive apparent priming effect (increase in CO2 release through accelerated microbial biomass turnover) was negatively associated with SOC content and microbial respiration rates. Our statistical modeling suggests that apparent priming effects tend to be negative in more mesic sites associated with higher SOC contents. In contrast, a single-input of labile C causes positive apparent priming effects in more arid locations with low SOC contents. Our results provide solid evidence that SOC content plays a critical role in regulating apparent priming effects, with important implications for the improvement of C cycling models under global change scenarios

Quantum dynamics of simultaneously measured non-commuting observables.

In quantum mechanics, measurements cause wavefunction collapse that yields precise outcomes, whereas for non-commuting observables such as position and momentum Heisenberg's uncertainty principle limits the intrinsic precision of a state. Although theoretical work has demonstrated that it should be possible to perform simultaneous non-commuting measurements and has revealed the limits on measurement outcomes, only recently has the dynamics of the quantum state been discussed. To realize this unexplored regime, we simultaneously apply two continuous quantum non-demolition probes of non-commuting observables to a superconducting qubit. We implement multiple readout channels by coupling the qubit to multiple modes of a cavity. To control the measurement observables, we implement a 'single quadrature' measurement by driving the qubit and applying cavity sidebands with a relative phase that sets the observable. Here, we use this approach to show that the uncertainty principle governs the dynamics of the wavefunction by enforcing a lower bound on the measurement-induced disturbance. Consequently, as we transition from measuring identical to measuring non-commuting observables, the dynamics make a smooth transition from standard wavefunction collapse to localized persistent diffusion and then to isotropic persistent diffusion. Although the evolution of the state differs markedly from that of a conventional measurement, information about both non-commuting observables is extracted by keeping track of the time ordering of the measurement record, enabling quantum state tomography without alternating measurements. Our work creates novel capabilities for quantum control, including rapid state purification, adaptive measurement, measurement-based state steering and continuous quantum error correction. As physical systems often interact continuously with their environment via non-commuting degrees of freedom, our work offers a way to study how notions of contemporary quantum foundations arise in such settings

Vanin-1 Pantetheinase Drives Smooth Muscle Cell Activation in Post-Arterial Injury Neointimal Hyperplasia

The pantetheinase vanin-1 generates cysteamine, which inhibits reduced glutathione (GSH) synthesis. Vanin-1 promotes inflammation and tissue injury partly by inducing oxidative stress, and partly by peroxisome proliferator-activated receptor gamma (PPARγ) expression. Vascular smooth muscle cells (SMCs) contribute to neointimal hyperplasia in response to injury, by multiple mechanisms including modulation of oxidative stress and PPARγ. Therefore, we tested the hypothesis that vanin-1 drives SMC activation and neointimal hyperplasia. We studied reactive oxygen species (ROS) generation and functional responses to platelet-derived growth factor (PDGF) and the pro-oxidant diamide in cultured mouse aortic SMCs, and also assessed neointima formation after carotid artery ligation in vanin-1 deficiency. Vnn1−/− SMCs demonstrated decreased oxidative stress, proliferation, migration, and matrix metalloproteinase 9 (MMP-9) activity in response to PDGF and/or diamide, with the effects on proliferation linked, in these studies, to both increased GSH levels and PPARγ expression. Vnn1−/− mice displayed markedly decreased neointima formation in response to carotid artery ligation, including decreased intima:media ratio and cross-sectional area of the neointima. We conclude that vanin-1, via dual modulation of GSH and PPARγ, critically regulates the activation of cultured SMCs and development of neointimal hyperplasia in response to carotid artery ligation. Vanin-1 is a novel potential therapeutic target for neointimal hyperplasia following revascularization

Predicting the protein-protein interactions using primary structures with predicted protein surface

<p>Abstract</p> <p>Background</p> <p>Many biological functions involve various protein-protein interactions (PPIs). Elucidating such interactions is crucial for understanding general principles of cellular systems. Previous studies have shown the potential of predicting PPIs based on only sequence information. Compared to approaches that require other auxiliary information, these sequence-based approaches can be applied to a broader range of applications.</p> <p>Results</p> <p>This study presents a novel sequence-based method based on the assumption that protein-protein interactions are more related to amino acids at the surface than those at the core. The present method considers surface information and maintains the advantage of relying on only sequence data by including an accessible surface area (ASA) predictor recently proposed by the authors. This study also reports the experiments conducted to evaluate a) the performance of PPI prediction achieved by including the predicted surface and b) the quality of the predicted surface in comparison with the surface obtained from structures. The experimental results show that surface information helps to predict interacting protein pairs. Furthermore, the prediction performance achieved by using the surface estimated with the ASA predictor is close to that using the surface obtained from protein structures.</p> <p>Conclusion</p> <p>This work presents a sequence-based method that takes into account surface information for predicting PPIs. The proposed procedure of surface identification improves the prediction performance with an <it>F-measure </it>of 5.1%. The extracted surfaces are also valuable in other biomedical applications that require similar information.</p

Optimal assignment methods for ligand-based virtual screening

<p>Abstract</p> <p>Background</p> <p>Ligand-based virtual screening experiments are an important task in the early drug discovery stage. An ambitious aim in each experiment is to disclose active structures based on new scaffolds. To perform these "scaffold-hoppings" for individual problems and targets, a plethora of different similarity methods based on diverse techniques were published in the last years. The optimal assignment approach on molecular graphs, a successful method in the field of quantitative structure-activity relationships, has not been tested as a ligand-based virtual screening method so far.</p> <p>Results</p> <p>We evaluated two already published and two new optimal assignment methods on various data sets. To emphasize the "scaffold-hopping" ability, we used the information of chemotype clustering analyses in our evaluation metrics. Comparisons with literature results show an improved early recognition performance and comparable results over the complete data set. A new method based on two different assignment steps shows an increased "scaffold-hopping" behavior together with a good early recognition performance.</p> <p>Conclusion</p> <p>The presented methods show a good combination of chemotype discovery and enrichment of active structures. Additionally, the optimal assignment on molecular graphs has the advantage to investigate and interpret the mappings, allowing precise modifications of internal parameters of the similarity measure for specific targets. All methods have low computation times which make them applicable to screen large data sets.</p

Application of 3D Zernike descriptors to shape-based ligand similarity searching

Background: The identification of promising drug leads from a large database of compounds is an important step in the preliminary stages of drug design. Although shape is known to play a key role in the molecular recognition process, its application to virtual screening poses significant hurdles both in terms of the encoding scheme and speed. Results: In this study, we have examined the efficacy of the alignment independent three-dimensional Zernike descriptor (3DZD) for fast shape based similarity searching. Performance of this approach was compared with several other methods including the statistical moments based ultrafast shape recognition scheme (USR) and SIMCOMP, a graph matching algorithm that compares atom environments. Three benchmark datasets are used to thoroughly test the methods in terms of their ability for molecular classification, retrieval rate, and performance under the situation that simulates actual virtual screening tasks over a large pharmaceutical database. The 3DZD performed better than or comparable to the other methods examined, depending on the datasets and evaluation metrics used. Reasons for the success and the failure of the shape based methods for specific cases are investigated. Based on the results for the three datasets, general conclusions are drawn with regard to their efficiency and applicability