Influence of secondary neutrons induced by proton radiotherapy for cancer patients with implantable cardioverter defibrillators

<p>Abstract</p> <p>Background</p> <p>Although proton radiotherapy is a promising new approach for cancer patients, functional interference is a concern for patients with implantable cardioverter defibrillators (ICDs). The purpose of this study was to clarify the influence of secondary neutrons induced by proton radiotherapy on ICDs.</p> <p>Methods</p> <p>The experimental set-up simulated proton radiotherapy for a patient with an ICD. Four new ICDs were placed 0.3 cm laterally and 3 cm distally outside the radiation field in order to evaluate the influence of secondary neutrons. The cumulative in-field radiation dose was 107 Gy over 10 sessions of irradiation with a dose rate of 2 Gy/min and a field size of 10 × 10 cm². After each radiation fraction, interference with the ICD by the therapy was analyzed by an ICD programmer. The dose distributions of secondary neutrons were estimated by Monte-Carlo simulation.</p> <p>Results</p> <p>The frequency of the power-on reset, the most serious soft error where the programmed pacing mode changes temporarily to a safety back-up mode, was 1 per approximately 50 Gy. The total number of soft errors logged in all devices was 29, which was a rate of 1 soft error per approximately 15 Gy. No permanent device malfunctions were detected. The calculated dose of secondary neutrons per 1 Gy proton dose in the phantom was approximately 1.3-8.9 mSv/Gy.</p> <p>Conclusions</p> <p>With the present experimental settings, the probability was approximately 1 power-on reset per 50 Gy, which was below the dose level (60-80 Gy) generally used in proton radiotherapy. Further quantitative analysis in various settings is needed to establish guidelines regarding proton radiotherapy for cancer patients with ICDs.</p

The Escherichia coli transcriptome mostly consists of independently regulated modules

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome

Inference of gene regulatory networks from time series by Tsallis entropy

Background: The inference of gene regulatory networks (GRNs) from large-scale expression profiles is one of the most challenging problems of Systems Biology nowadays. Many techniques and models have been proposed for this task. However, it is not generally possible to recover the original topology with great accuracy, mainly due to the short time series data in face of the high complexity of the networks and the intrinsic noise of the expression measurements. In order to improve the accuracy of GRNs inference methods based on entropy (mutual information), a new criterion function is here proposed. Results: In this paper we introduce the use of generalized entropy proposed by Tsallis, for the inference of GRNs from time series expression profiles. The inference process is based on a feature selection approach and the conditional entropy is applied as criterion function. In order to assess the proposed methodology, the algorithm is applied to recover the network topology from temporal expressions generated by an artificial gene network (AGN) model as well as from the DREAM challenge. The adopted AGN is based on theoretical models of complex networks and its gene transference function is obtained from random drawing on the set of possible Boolean functions, thus creating its dynamics. On the other hand, DREAM time series data presents variation of network size and its topologies are based on real networks. The dynamics are generated by continuous differential equations with noise and perturbation. By adopting both data sources, it is possible to estimate the average quality of the inference with respect to different network topologies, transfer functions and network sizes. Conclusions: A remarkable improvement of accuracy was observed in the experimental results by reducing the number of false connections in the inferred topology by the non-Shannon entropy. The obtained best free parameter of the Tsallis entropy was on average in the range 2.5 <= q <= 3.5 (hence, subextensive entropy), which opens new perspectives for GRNs inference methods based on information theory and for investigation of the nonextensivity of such networks. The inference algorithm and criterion function proposed here were implemented and included in the DimReduction software, which is freely available at http://sourceforge.net/projects/dimreduction and http://code.google.com/p/dimreduction/.Fundacao de Amparo e Amparo a Pesquisa do Estado de Sao Paulo (FAPESP)Coordenacao de Aperfeicofamento de Pessoal de Nivel Superior (CAPES)Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq

Data-driven reverse engineering of signaling pathways using ensembles of dynamic models

Signaling pathways play a key role in complex diseases such as cancer, for which the development of novel therapies is a difficult, expensive and laborious task. Computational models that can predict the effect of a new combination of drugs without having to test it experimentally can help in accelerating this process. In particular, network-based dynamic models of these pathways hold promise to both understand and predict the effect of therapeutics. However, their use is currently hampered by limitations in our knowledge of the underlying biochemistry, as well as in the experimental and computational technologies used for calibrating the models. Thus, the results from such models need to be carefully interpreted and used in order to avoid biased predictions. Here we present a procedure that deals with this uncertainty by using experimental data to build an ensemble of dynamic models. The method incorporates steps to reduce overfitting and maximize predictive capability. We find that by combining the outputs of individual models in an ensemble it is possible to obtain a more robust prediction. We report results obtained with this method, which we call SELDOM (enSEmbLe of Dynamic lOgic-based Models), showing that it improves the predictions previously reported for several challenging problems.JRB and DH acknowledge funding from the EU FP7 project NICHE (ITN Grant number 289384). JRB acknowledges funding from the Spanish MINECO project SYNBIOFACTORY (grant number DPI2014-55276-C5-2-R). AFV acknowledges funding from the Galician government (Xunta de Galiza) through the I2C postdoctoral fellowship ED481B2014/133-0. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Fungal Planet description sheets : 951–1041

Novel species of fungi described in this study include those from various countries as follows: Antarctica,Apenidiella antarctica from permafrost, Cladosporium fildesense from an unidentified marine sponge. Argentina,Geastrum wrightii on humus in mixed forest. Australia, Golovinomyces glandulariae on Glandularia aristigera,Neoanungitea eucalyptorum on leaves of Eucalyptus grandis, Teratosphaeria corymbiicola on leaves of Corymbiaficifolia, Xylaria eucalypti on leaves of Eucalyptus radiata. Brazil, Bovista psammophila on soil, Fusarium awaxy onrotten stalks of Zea mays, Geastrum lanuginosum on leaf litter covered soil, Hermetothecium mikaniae-micranthae(incl. Hermetothecium gen. nov.) on Mikania micrantha, Penicillium reconvexovelosoi in soil, Stagonosporopsis vannacciifrom pod of Glycine max. British Virgin Isles, Lactifluus guanensis on soil. Canada, Sorocybe oblongisporaon resin of Picea rubens. Chile, Colletotrichum roseum on leaves of Lapageria rosea. China, Setophoma cavernafrom carbonatite in Karst cave. Colombia, Lareunionomyces eucalypticola on leaves of Eucalyptus grandis. CostaRica, Psathyrella pivae on wood. Cyprus, Clavulina iris on calcareous substrate. France, Chromosera ambiguaand Clavulina iris var. occidentalis on soil. French West Indies, Helminthosphaeria hispidissima on dead wood.Guatemala, Talaromyces guatemalensis in soil. Malaysia, Neotracylla pini (incl. Tracyllales ord. nov. and Neotracyllagen. nov.) and Vermiculariopsiella pini on needles of Pinus tecunumanii. New Zealand, Neoconiothyriumviticola on stems of Vitis vinifera, Parafenestella pittospori on Pittosporum tenuifolium, Pilidium novae-zelandiaeon Phoenix sp. Pakistan, Russula quercus-floribundae on forest floor. Portugal, Trichoderma aestuarinum fromsaline water. Russia, Pluteus liliputianus on fallen branch of deciduous tree, Pluteus spurius on decaying deciduous wood or soil. South Africa, Alloconiothyrium encephalarti, Phyllosticta encephalarticola and Neothyrostromaencephalarti (incl. Neothyrostroma gen. nov.) on leaves of Encephalartos sp., Chalara eucalypticola on leaf spots ofEucalyptus grandis x urophylla, Clypeosphaeria oleae on leaves of Olea capensis, Cylindrocladiella postalofficiumon leaf litter of Sideroxylon inerme, Cylindromonium eugeniicola (incl. Cylindromonium gen. nov.) on leaf litter ofEugenia capensis, Cyphellophora goniomatis on leaves of Gonioma kamassi, Nothodactylaria nephrolepidis (incl.Nothodactylaria gen. nov. and Nothodactylariaceae fam. nov.) on leaves of Nephrolepis exaltata, Falcocladiumeucalypti and Gyrothrix eucalypti on leaves of Eucalyptus sp., Gyrothrix oleae on leaves of Olea capensis subsp.macrocarpa, Harzia metro-sideri on leaf litter of Metrosideros sp., Hippopotamyces phragmitis (incl. Hippopotamycesgen. nov.) on leaves of Phragmites australis, Lectera philenopterae on Philenoptera violacea, Leptosilliamayteni on leaves of Maytenus heterophylla, Lithohypha aloicola and Neoplatysporoides aloes on leaves of Aloesp., Millesimomyces rhoicissi (incl. Millesimomyces gen. nov.) on leaves of Rhoicissus digitata, Neodevriesiastrelitziicola on leaf litter of Strelitzia nicolai, Neokirramyces syzygii (incl. Neokirramyces gen. nov.) on leaf spots of Syzygium sp., Nothoramichloridium perseae (incl. Nothoramichloridium gen. nov. and Anungitiomycetaceae fam.nov.) on leaves of Persea americana, Paramycosphaerella watsoniae on leaf spots of Watsonia sp., Penicilliumcuddlyae from dog food, Podocarpomyces knysnanus (incl. Podocarpomyces gen. nov.) on leaves of Podocarpusfalcatus, Pseudocercospora heteropyxidicola on leaf spots of Heteropyxis natalensis, Pseudopenidiella podocarpi,Scolecobasidium podocarpi and Ceramothyrium podocarpicola on leaves of Podocarpus latifolius, Scolecobasidiumblechni on leaves of Blechnum capense, Stomiopeltis syzygii on leaves of Syzygium chordatum, Strelitziomycesknysnanus (incl. Strelitziomyces gen. nov.) on leaves of Strelitzia alba, Talaromyces clemensii from rotting wood ingoldmine, Verrucocladosporium visseri on Carpobrotus edulis. Spain, Boletopsis mediterraneensis on soil, Calycinacortegadensisi on a living twig of Castanea sativa, Emmonsiellopsis tuberculata in fluvial sediments, Mollisia cortegadensison dead attached twig of Quercus robur, Psathyrella ovispora on soil, Pseudobeltrania lauri on leaf litterof Laurus azorica, Terfezia dunensis in soil, Tuber lucentum in soil, Venturia submersa on submerged plant debris.Thailand, Cordyceps jakajanicola on cicada nymph, Cordyceps kuiburiensis on spider, Distoseptispora caricis onleaves of Carex sp., Ophiocordyceps khonkaenensis on cicada nymph. USA, Cytosporella juncicola and Davidiellomycesjuncicola on culms of Juncus effusus, Monochaetia massachusettsianum from air sample, Neohelicomycesmelaleucae and Periconia neobrittanica on leaves of Melaleuca styphelioides x lanceolata, Pseudocamarosporiumeucalypti on leaves of Eucalyptus sp., Pseudogymnoascus lindneri from sediment in a mine, Pseudogymnoascusturneri from sediment in a railroad tunnel, Pulchroboletus sclerotiorum on soil, Zygosporium pseudomasonii onleaf of Serenoa repens. Vietnam, Boletus candidissimus and Veloporphyrellus vulpinus on soil. Morphological andculture characteristics are supported by DNA barcodes

Fungal planet description sheets: 951–1041

Novel species of fungi described in this study include those from various countries as follows: Antarctica , Apenidiella antarctica from permafrost, Cladosporium fildesense fromanunidentifiedmarinesponge. Argentina , Geastrum wrightii onhumusinmixedforest. Australia , Golovinomyces glandulariae on Glandularia aristigera, Neoanungitea eucalyptorum on leaves of Eucalyptus grandis, Teratosphaeria corymbiicola on leaves of Corymbia ficifolia, Xylaria eucalypti on leaves of Eucalyptus radiata. Brazil, Bovista psammophila on soil, Fusarium awaxy on rotten stalks of Zea mays, Geastrum lanuginosum on leaf litter covered soil, Hermetothecium mikaniae-micranthae (incl. Hermetothecium gen. nov.)on Mikania micrantha, Penicillium reconvexovelosoi in soil, Stagonosporopsis vannaccii from pod of Glycine max. British Virgin Isles , Lactifluus guanensis onsoil. Canada , Sorocybe oblongispora on resin of Picea rubens. Chile, Colletotrichum roseum on leaves of Lapageria rosea. China, Setophoma caverna fromcarbonatiteinKarstcave. Colombia , Lareunionomyces eucalypticola on leaves of Eucalyptus grandis. Costa Rica, Psathyrella pivae onwood. Cyprus , Clavulina iris oncalcareoussubstrate. France , Chromosera ambigua and Clavulina iris var. occidentalis onsoil. French West Indies , Helminthosphaeria hispidissima ondeadwood. Guatemala , Talaromyces guatemalensis insoil. Malaysia , Neotracylla pini (incl. Tracyllales ord. nov. and Neotra- cylla gen. nov.)and Vermiculariopsiella pini on needles of Pinus tecunumanii. New Zealand, Neoconiothyrium viticola on stems of Vitis vinifera, Parafenestella pittospori on Pittosporum tenuifolium, Pilidium novae-zelandiae on Phoenix sp. Pakistan , Russula quercus-floribundae onforestfloor. Portugal , Trichoderma aestuarinum from salinewater. Russia , Pluteus liliputianus on fallen branch of deciduous tree, Pluteus spurius on decaying deciduouswoodorsoil. South Africa , Alloconiothyrium encephalarti, Phyllosticta encephalarticola and Neothyrostroma encephalarti (incl. Neothyrostroma gen. nov.)onleavesof Encephalartos sp., Chalara eucalypticola on leaf spots of Eucalyptus grandis × urophylla, Clypeosphaeria oleae on leaves of Olea capensis, Cylindrocladiella postalofficium on leaf litter of Sideroxylon inerme , Cylindromonium eugeniicola (incl. Cylindromonium gen. nov.)onleaflitterof Eugenia capensis , Cyphellophora goniomatis on leaves of Gonioma kamassi , Nothodactylaria nephrolepidis (incl. Nothodactylaria gen. nov. and Nothodactylariaceae fam. nov.)onleavesof Nephrolepis exaltata , Falcocladium eucalypti and Gyrothrix eucalypti on leaves of Eucalyptus sp., Gyrothrix oleae on leaves of Olea capensis subsp. macrocarpa , Harzia metro sideri on leaf litter of Metrosideros sp., Hippopotamyces phragmitis (incl. Hippopota- myces gen. nov.)onleavesof Phragmites australis , Lectera philenopterae on Philenoptera violacea , Leptosillia mayteni on leaves of Maytenus heterophylla , Lithohypha aloicola and Neoplatysporoides aloes on leaves of Aloe sp., Millesimomyces rhoicissi (incl. Millesimomyces gen. nov.) on leaves of Rhoicissus digitata , Neodevriesia strelitziicola on leaf litter of Strelitzia nicolai , Neokirramyces syzygii (incl. Neokirramyces gen. nov.)onleafspots o