Defence responses of native and invasive plants to the native generalist vine parasite Cassytha pubescens -anatomical and functional studies

© 2020 CSIRO. We investigated the responses of two invasive and two native host species to the parasitic vine Cassytha pubescens R.Br. using glasshouse experiments. We assessed growth of the parasite and its hosts, and anatomy and functionality of haustoria. Target hosts were infected using C. pubescens already established on a donor host. This enabled measurement of growth in target hosts that were detached (parasite connection severed) or not from the donor host. Haustorial connections to hosts were investigated using histological methods. We tested the functionality of haustoria in one invasive and one native host using radiolabelled phosphorus (32P). After it was severed from the donor host, C. pubescens grew poorly on the native host, Acacia myrtifolia (Sm.) Willd. This was likely due to a lack of effective functional haustorial development because although haustoria were firmly attached and morphologically alike those formed on the other hosts, their anatomy was different: their connections with the vascular system were not developed and there was no transfer of 32P from A. myrtifolia to the parasite. In contrast, the other three host species supported the growth of the parasite and had fully developed haustoria. Effective transfer of 32P from the invasive host to the parasite confirmed this. Our results suggest a range of defence mechanisms in C. pubescens hosts and are consistent with reports of strong detrimental effects on invasive hosts. Further, they amount to evidence for the potential use of a native parasite as biological control for invasive species

Using Cartesian Coordinate Systems to Create, Classify, and Retrieve Biomedical Time-Series: Applications to 24-hour Ambulatory Blood Pressure Monitoring

Background: Ambulatory Blood Pressure Measurement (ABPM) allows physicians to monitor blood pressure variability under everyday living conditions and predicts clinical outcomes better than conventional blood-pressure measurement. ABPM can demonstrate mean arterial pressure (MAP) behavior over 24 hours relevant to clinical practice, such as nocturnal hypertension or increased blood pressure variability. We hypothesized that individuals with the same cardiovascular health status would have the same MAP signal (MAPs) waveform. Methods: This study reutilizes a data subset from the IDACO Consortium to create 24-hour MAPs. We assigned all the MAPs to data matrix X, performed principal components analysis (PCA) to X, and calculated the percentage of the total variance explained by each of the 82 principal components (PC). The first three PC explained 85.03%, 9.47%, and 5.50% of the total variance. We used every MAP signal\u27s first three PC scores as their three-dimensional Euclidean Space (x, y, z) coordinates and assigned them to matrix C. Then, we calculated hierarchical clusters of the rows of C with Ward\u27s linkage minimum variance algorithm and a Euclidian metric and encoded this information on the agglomerative hierarchical cluster tree Z. We determined the gap statistic in Z to obtain the optimal number of clusters. We created seven agglomerative clusters from the linkages stored in Z, using Ward’s distance as the criterion for defining the clusters. Finally, we plotted and colored the mapped MAPs by their assigned cluster number at the locations specified by their (x, y, z) coordinates. Results: The MAPs cartesian representations show that MAPs with similar waveforms cluster in the same three-dimensional Euclidean subspace. These patterns identified individuals with dipping and non-dipping blood pressure behavior, which is relevant to clinical management. Conclusions: Mapping a set of physiological signals into a Euclidian space creates a mathematical formalism that provides a statistical framework to classify physiological signals by their waveform. By applying our method to existing electrophysiological and physiological databases, we can cluster any biomedical time-series (blood pressure, ECG, EEG, EMG, patch-clamp, single-unit recordings, etc.) by physiologic or pathological waveform, so further epidemiological and genetic studies can be conducted on the subjects or tissue samples sharing similar patterns

Effects of Land Crabs on Leaf Litter Distributions and Accumulations in a Mainland Tropical Rain Forest 1

The effect of the fossorial land crab Gecarcinus quadratus (Gecarcinidae) on patterns of accumulation and distribution of leaf litter was studied for two years in the coastal primary forests of Costa Rica's Corcovado National Park. Within this mainland forest, G, quadratus achieve densities up to 6 crabs/m 2 in populations extending along the Park's Pacific coastline and inland for ca 600 m. Crabs selectively forage for fallen leaf litter and relocate what they collect to burrow chambers that extend from 15 to 150 cm deep ( N = 44), averaging (±SE) 48.9 ± 3.0 cm. Preference trials suggested that leaf choice by crabs may be species-specific. Excavated crab burrows revealed maximum leaf collections of 11.75 g dry mass– 2.5 times more leaf litter than collected by square-meter leaf fall traps over several seven-day sampling periods. Additionally, experimental crab exclosures (25 m 2 ) were established using a repeated measures randomized block design to test for changes in leaf litter as a function of reduced crab density. Exclosures accumulated significantly more (5.6 ± 3.9 times) leaf litter than did control treatments during the wet, but not the dry, seasons over this two-year study. Such extensive litter relocation by land crabs may affect profiles of soil organic carbon, rooting, and seedling distributions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73250/1/j.1744-7429.2003.tb00590.x.pd

Nanoinformatics: developing new computing applications for nanomedicine

Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. While nanomaterials open up the possibility for developing new devices in many industrial and scientific areas, they also offer breakthrough perspectives for the prevention, diagnosis and treatment of diseases. In this paper, we analyze the different aspects of nanoinformatics and suggest five research topics to help catalyze new research and development in the area, particularly focused on nanomedicine. We also encompass the use of informatics to further the biological and clinical applications of basic research in nanoscience and nanotechnology, and the related concept of an extended ?nanotype? to coalesce information related to nanoparticles. We suggest how nanoinformatics could accelerate developments in nanomedicine, similarly to what happened with the Human Genome and other -omics projects, on issues like exchanging modeling and simulation methods and tools, linking toxicity information to clinical and personal databases or developing new approaches for scientific ontologies, among many others

Predicting the start week of respiratory syncytial virus outbreaks using real time weather variables

<p>Abstract</p> <p>Background</p> <p>Respiratory Syncytial Virus (RSV), a major cause of bronchiolitis, has a large impact on the census of pediatric hospitals during outbreak seasons. Reliable prediction of the week these outbreaks will start, based on readily available data, could help pediatric hospitals better prepare for large outbreaks.</p> <p>Methods</p> <p>Naïve Bayes (NB) classifier models were constructed using weather data from 1985-2008 considering only variables that are available in real time and that could be used to forecast the week in which an RSV outbreak will occur in Salt Lake County, Utah. Outbreak start dates were determined by a panel of experts using 32,509 records with ICD-9 coded RSV and bronchiolitis diagnoses from Intermountain Healthcare hospitals and clinics for the RSV seasons from 1985 to 2008.</p> <p>Results</p> <p>NB models predicted RSV outbreaks up to 3 weeks in advance with an estimated sensitivity of up to 67% and estimated specificities as high as 94% to 100%. Temperature and wind speed were the best overall predictors, but other weather variables also showed relevance depending on how far in advance the predictions were made. The weather conditions predictive of an RSV outbreak in our study were similar to those that lead to temperature inversions in the Salt Lake Valley.</p> <p>Conclusions</p> <p>We demonstrate that Naïve Bayes (NB) classifier models based on weather data available in real time have the potential to be used as effective predictive models. These models may be able to predict the week that an RSV outbreak will occur with clinical relevance. Their clinical usefulness will be field tested during the next five years.</p

Evidence for Enhanced Mutualism Hypothesis: Solidago canadensis Plants from Regular Soils Perform Better

The important roles of plant-soil microbe interactions have been documented in exotic plant invasion, but we know very little about how soil mutualists enhance this process (i.e. enhanced mutualism hypothesis). To test this hypothesis we conducted two greenhouse experiments with Solidago canadensis (hereafter Solidago), an invasive forb from North America, and Stipa bungeana (hereafter Stipa), a native Chinese grass. In a germination experiment, we found soil microbes from the rhizospheres of Solidago and Stipa exhibited much stronger facilitative effects on emergence of Solidago than that of Stipa. In a growth and competition experiment, we found that soil microbes strongly facilitated Solidago to outgrow Stipa, and greatly increased the competitive effects of Solidago on Stipa but decreased the competitive effects of Stipa on Solidago. These findings from two experiments suggest that in situ soil microbes enhance the recruitment potential of Solidago and its ability to outcompete native plants, thereby providing strong evidence for the enhanced mutualism hypothesis. On the other hand, to some extent this outperformance of Solidago in the presence of soil microbes seems to be unbeneficial to control its rapid expansion, particularly in some ranges where this enhanced mutualism dominates over other mechanisms

Diversity Effects on Productivity Are Stronger within than between Trophic Groups in the Arbuscular Mycorrhizal Symbiosis

The diversity of plants and arbuscular mycorrhizal fungi (AMF) has been experimentally shown to alter plant and AMF productivity. However, little is known about how plant and AMF diversity interact to shape their respective productivity.We co-manipulated the diversity of both AMF and plant communities in two greenhouse studies to determine whether the productivity of each trophic group is mainly influenced by plant or AMF diversity, respectively, and whether there is any interaction between plant and fungal diversity. In both experiments we compared the productivity of three different plant species monocultures, or their respective 3-species mixtures. Similarly, in both studies these plant treatments were crossed with an AMF diversity gradient that ranged from zero (non-mycorrhizal controls) to a maximum of three and five taxonomically distinct AMF taxa, respectively. We found that within both trophic groups productivity was significantly influenced by taxon identity, and increased with taxon richness. These main effects of AMF and plant diversity on their respective productivities did not depend on each other, even though we detected significant individual taxon effects across trophic groups.Our results indicate that similar ecological processes regulate diversity-productivity relationships within trophic groups. However, productivity-diversity relationships are not necessarily correlated across interacting trophic levels, leading to asymmetries and possible biotic feedbacks. Thus, biotic interactions within and across trophic groups should be considered in predictive models of community assembly

NMR-Based Structural Modeling of Graphite Oxide Using Multidimensional 13C Solid-State NMR and ab Initio Chemical Shift Calculations

Chemically modified graphenes and other graphite-based materials have attracted growing interest for their unique potential as lightweight electronic and structural nanomaterials. It is an important challenge to construct structural models of noncrystalline graphite-based materials on the basis of NMR or other spectroscopic data. To address this challenge, a solid-state NMR (SSNMR)-based structural modeling approach is presented on graphite oxide (GO), which is a prominent precursor and interesting benchmark system of modified graphene. An experimental 2D C-13 double-quantum/single-quantum correlation SSNMR spectrum of C-13-labeled GO was compared with spectra simulated for different structural models using ab initio geometry optimization and chemical shift calculations. The results show that the spectral features of the GO sample are best reproduced by a geometry-optimized structural model that is based on the Lerf-Klinowski model (Lerf, A. et al. Phys. Chem. B 1998, 102, 4477); this model is composed of interconnected sp(2), 1,2-epoxide, and COH carbons. This study also convincingly excludes the possibility of other previously proposed models, including the highly oxidized structures involving 1,3-epoxide carbons (Szabo, I. et al. Chem. Mater. 2006, 18, 2740). C-13 chemical shift anisotropy (CSA) patterns measured by a 2D C-13 CSA/isotropic shift correlation SSNMR were well reproduced by the chemical shift tensor obtained by the ab initio calculation for the former model. The approach presented here is likely to be applicable to other chemically modified graphenes and graphite-based systems

Report on the sixth blind test of organic crystal structure prediction methods.

The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and `best practices' for performing CSP calculations. All of the targets, apart from a single potentially disordered Z' = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms.The organisers and participants are very grateful to the crystallographers who supplied the candidate structures: Dr. Peter Horton (XXII), Dr. Brian Samas (XXIII), Prof. Bruce Foxman (XXIV), and Prof. Kraig Wheeler (XXV and XXVI). We are also grateful to Dr. Emma Sharp and colleagues at Johnson Matthey (Pharmorphix) for the polymorph screening of XXVI, as well as numerous colleagues at the CCDC for assistance in organising the blind test. Submission 2: We acknowledge Dr. Oliver Korb for numerous useful discussions. Submission 3: The Day group acknowledge the use of the IRIDIS High Performance Computing Facility, and associated support services at the University of Southampton, in the completion of this work. We acknowledge funding from the EPSRC (grants EP/J01110X/1 and EP/K018132/1) and the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC through grant agreements n. 307358 (ERC-stG- 2012-ANGLE) and n. 321156 (ERC-AG-PE5-ROBOT). Submission 4: I am grateful to Mikhail Kuzminskii for calculations of molecular structures on Gaussian 98 program in the Institute of Organic Chemistry RAS. The Russian Foundation for Basic Research is acknowledged for financial support (14-03-01091). Submission 5: Toine Schreurs provided computer facilities and assistance. I am grateful to Matthew Habgood at AWE company for providing a travel grant. Submission 6: We would like to acknowledge support of this work by GlaxoSmithKline, Merck, and Vertex. Submission 7: The research was financially supported by the VIDI Research Program 700.10.427, which is financed by The Netherlands Organisation for Scientific Research (NWO), and the European Research Council (ERC-2010-StG, grant agreement n. 259510-KISMOL). We acknowledge the support of the Foundation for Fundamental Research on Matter (FOM). Supercomputer facilities were provided by the National Computing Facilities Foundation (NCF). Submission 8: Computer resources were provided by the Center for High Performance Computing at the University of Utah and the Extreme Science and Engineering Discovery Environment (XSEDE), supported by NSF grant number ACI-1053575. MBF and GIP acknowledge the support from the University of Buenos Aires and the Argentinian Research Council. Submission 9: We thank Dr. Bouke van Eijck for his valuable advice on our predicted structure of XXV. We thank the promotion office for TUT programs on advanced simulation engineering (ADSIM), the leading program for training brain information architects (BRAIN), and the information and media center (IMC) at Toyohashi University of Technology for the use of the TUT supercomputer systems and application software. We also thank the ACCMS at Kyoto University for the use of their supercomputer. In addition, we wish to thank financial supports from Conflex Corp. and Ministry of Education, Culture, Sports, Science and Technology. Submission 12: We thank Leslie Leiserowitz from the Weizmann Institute of Science and Geoffrey Hutchinson from the University of Pittsburgh for helpful discussions. We thank Adam Scovel at the Argonne Leadership Computing Facility (ALCF) for technical support. Work at Tulane University was funded by the Louisiana Board of Regents Award # LEQSF(2014-17)-RD-A-10 “Toward Crystal Engineering from First Principles”, by the NSF award # EPS-1003897 “The Louisiana Alliance for Simulation-Guided Materials Applications (LA-SiGMA)”, and by the Tulane Committee on Research Summer Fellowship. Work at the Technical University of Munich was supported by the Solar Technologies Go Hybrid initiative of the State of Bavaria, Germany. Computer time was provided by the Argonne Leadership Computing Facility (ALCF), which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357. Submission 13: This work would not have been possible without funding from Khalifa University’s College of Engineering. I would like to acknowledge Prof. Robert Bennell and Prof. Bayan Sharif for supporting me in acquiring the resources needed to carry out this research. Dr. Louise Price is thanked for her guidance on the use of DMACRYS and NEIGHCRYS during the course of this research. She is also thanked for useful discussions and numerous e-mail exchanges concerning the blind test. Prof. Sarah Price is acknowledged for her support and guidance over many years and for providing access to DMACRYS and NEIGHCRYS. Submission 15: The work was supported by the United Kingdom’s Engineering and Physical Sciences Research Council (EPSRC) (EP/J003840/1, EP/J014958/1) and was made possible through access to computational resources and support from the High Performance Computing Cluster at Imperial College London. We are grateful to Professor Sarah L. Price for supplying the DMACRYS code for use within CrystalOptimizer, and to her and her research group for support with DMACRYS and feedback on CrystalPredictor and CrystalOptimizer. Submission 16: R. J. N. acknowledges financial support from the Engineering and Physical Sciences Research Council (EPSRC) of the U.K. [EP/J017639/1]. R. J. N. and C. J. P. acknowledge use of the Archer facilities of the U.K.’s national high-performance computing service (for which access was obtained via the UKCP consortium [EP/K014560/1]). C. J. P. also acknowledges a Leadership Fellowship Grant [EP/K013688/1]. B. M. acknowledges Robinson College, Cambridge, and the Cambridge Philosophical Society for a Henslow Research Fellowship. Submission 17: The work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1- 0387 and by the National Science Foundation Grant CHE-1152899. The work at the University of Silesia was supported by the Polish National Science Centre Grant No. DEC-2012/05/B/ST4/00086. Submission 18: We would like to thank Constantinos Pantelides, Claire Adjiman and Isaac Sugden of Imperial College for their support of our use of CrystalPredictor and CrystalOptimizer in this and Submission 19. The CSP work of the group is supported by EPSRC, though grant ESPRC EP/K039229/1, and Eli Lilly. The PhD students support: RKH by a joint UCL Max-Planck Society Magdeburg Impact studentship, REW by a UCL Impact studentship; LI by the Cambridge Crystallographic Data Centre and the M3S Centre for Doctoral Training (EPSRC EP/G036675/1). Submission 19: The potential generation work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1-0387 and by the National Science Foundation Grant CHE-1152899. Submission 20: The work at New York University was supported, in part, by the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF-13-1-0387 (MET and LV) and, in part, by the Materials Research Science and Engineering Center (MRSEC) program of the National Science Foundation under Award Number DMR-1420073 (MET and ES). The work at the University of Delaware was supported by the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF-13-1- 0387 and by the National Science Foundation Grant CHE-1152899. Submission 21: We thank the National Science Foundation (DMR-1231586), the Government of Russian Federation (Grant No. 14.A12.31.0003), the Foreign Talents Introduction and Academic Exchange Program (No. B08040) and the Russian Science Foundation, project no. 14-43-00052, base organization Photochemistry Center of the Russian Academy of Sciences. Calculations were performed on the Rurik supercomputer at Moscow Institute of Physics and Technology. Submission 22: The computational results presented have been achieved in part using the Vienna Scientific Cluster (VSC). Submission 24: The potential generation work at the University of Delaware was supported by the Army Research Office under Grant W911NF-13-1-0387 and by the National Science Foundation Grant CHE-1152899. Submission 25: J.H. and A.T. acknowledge the support from the Deutsche Forschungsgemeinschaft under the program DFG-SPP 1807. H-Y.K., R.A.D., and R.C. acknowledge support from the Department of Energy (DOE) under Grant Nos. DE-SC0008626. This research used resources of the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357. This research used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DEAC02-05CH11231. Additional computational resources were provided by the Terascale Infrastructure for Groundbreaking Research in Science and Engineering (TIGRESS) High Performance Computing Center and Visualization Laboratory at Princeton University.This is the final version of the article. It first appeared from Wiley via http://dx.doi.org/10.1107/S2052520616007447