Suppressive Effect of Soil Microbiomes Associated with Tropical Fruit Trees on Meloidogyne enterolobii.

Plant-parasitic nematodes are one of the main biotic factors limiting agricultural production worldwide, with root-knot nematodes (Meloidogyne spp.) being the most damaging group. This study was conducted to evaluate the efficacy of soil microbiomes, associated with various subtropical fruit trees, on the management of a Meloidogyne enterolobii population. Of 14 soil microbiomes tested for nematode suppression, 9 samples in the first experiment and 10 samples in the repeat experiment had significantly (p ≤ 0.05) lower numbers of eggs and J2 compared to the untreated control. The highest nematode suppression was recorded for SA12 extracted from a papaya orchard with a 38% reduction in the nematode population density. In addition, the presence of some bacteria (Bacillus aryabhattai, B. funiculus and B. simplex) and fungi (Metarhizium marquandii, Acremonium sp. and Mortierella sp.) was correlated to a higher suppression potential in some samples. Substantial variations were observed for the diversity of bacterial and fungal isolates among the samples collected from various crop hosts and regions. This suggests that the nematode suppression potential of different soil microbiomes highly depends on the abundance and diversity of fungal and bacterial strains present in the soil. The study confirmed that among all variables, soil dryness, pH, Fe, Zn, organic matter, altitude, and crop cultivar strongly influenced the soil microbial composition.

Screening of Rhizosphere Bacteria and Nematode Populations Associated with Soybean Roots in the Mpumalanga Highveld of South Africa.

Soybean is among South Africa's top crops in terms of production figures. Over the past few years there has been increasingly more damage caused to local soybean by plant-parasitic nematode infections. The presence of Meloidogyne (root-knot nematodes) and Pratylenchus spp. (root lesion nematodes) in soybean fields can cripple the country's production, however, little is known about the soil microbial communities associated with soybean in relation to different levels of Meloidogyne and Pratylenchus infestations, as well as the interaction(s) between them. Therefore, this study aimed to identify the nematode population assemblages and endemic rhizosphere bacteria associated with soybean using Next Generation Sequencing (NGS). The abundance of bacterial genera that were then identified as being significant using linear discriminant analysis (LDA) Effect Size (LEfSe) was compared to the abundance of the most prevalent plant-parasitic nematode genera found across all sampled sites, viz. Meloidogyne and Pratylenchus. While several bacterial genera were identified as significant using LEfSe, only two with increased abundance were associated with decreased abundance of Meloidogyne and Pratylenchus. However, six bacterial genera were associated with decreased Pratylenchus abundance. It is therefore possible that endemic bacterial strains can serve as an alternative method for reducing densities of plant-parasitic nematode genera and in this way reduce the damages caused to this economically important crop.

Effect of Photo-Selective Shade Nets on Pollination Process and Nut Development of Corylus avellana L.

Hazelnut (Corylus avellana L.) is one of the most appreciated nut crops, which is motivating the cultivation outside its historical production areas. Despite that, there is still limited knowledge about the floral biology of the species and its developmental fruiting stages under different environments. Adverse climatic conditions can threaten the pollination process and fruit development. In South Africa, the deciduous fruit industry identified the net shading as a tool to mitigate the effects of unfavorable abiotic events. The objective of this work was to investigate the effects of photo-selective nets on the pollination process and nut development of C. avellana. Mature hazelnut trees were maintained under netting and compared with the ones in open field. Microscopic examination of female flower and developing nuts were conducted in order to observe the pollen tube growth and the pattern of disodium fluorescein transport into the funiculus and ovule. The results showed differences in pollen tubes growth and timing between the treatments. Generally, trees under nets showed higher rate in pollen tubes developing and reaching the base of the style. On the contrary, the tests carried out in open field showed a higher ratio of pollen tubes arrested in the style. The results also indicated differences in ovules abortion. Developing fruits that showed an interruption point at the funicle level or at junction point of the ovule were classified as aborting fruits (blank nuts at harvest time). A higher rate of abortion was detected in open field compared to the plants under netting. In conclusion, the shade nets influenced the pollen tube growth and the nut development, principally due to micro-climate modification. Therefore, further investigations are needed to analyze the influence of light spectra and to determine the sustainability of photo-selective nets over several years.

A service-oriented distributed semantic mediator: integrating multiscale biomedical information.

Biomedical research continuously generates large amounts of heterogeneous and multimodal data spread over multiple data sources. These data, if appropriately shared and exploited, could dramatically improve the research practice itself, and ultimately the quality of health care delivered. This paper presents DISMED (DIstributed Semantic MEDiator), an open source semantic mediator that provides a unified view of a federated environment of multiscale biomedical data sources. DISMED is a Web-based software application to query and retrieve information distributed over a set of registered data sources, using semantic technologies. It also offers a userfriendly interface specifically designed to simplify the usage of these technologies by non-expert users. Although the architecture of the software mediator is generic and domain independent, in the context of this paper, DISMED has been evaluated for managing biomedical environments and facilitating research with respect to the handling of scientific data distributed in multiple heterogeneous data sources. As part of this contribution, a quantitative evaluation framework has been developed. It consist of a benchmarking scenario and the definition of five realistic use-cases. This framework, created entirely with public datasets, has been used to compare the performance of DISMED against other available mediators. It is also available to the scientific community in order to evaluate progress in the domain of semantic mediation, in a systematic and comparable manner. The results show an average improvement in the execution time by DISMED of 55% compared to the second best alternative in four out of the five use-cases of the experimental evaluation.

@neurIST: infrastructure for advanced disease management through integration of heterogeneous data, computing, and complex processing services.

The increasing volume of data describing human disease processes and the growing complexity of understanding, managing, and sharing such data presents a huge challenge for clinicians and medical researchers. This paper presents the @neurIST system, which provides an infrastructure for biomedical research while aiding clinical care, by bringing together heterogeneous data and complex processing and computing services. Although @neurIST targets the investigation and treatment of cerebral aneurysms, the system's architecture is generic enough that it could be adapted to the treatment of other diseases. Innovations in @neurIST include confining the patient data pertaining to aneurysms inside a single environment that offers clinicians the tools to analyze and interpret patient data and make use of knowledge-based guidance in planning their treatment. Medical researchers gain access to a critical mass of aneurysm related data due to the system's ability to federate distributed information sources. A semantically mediated grid infrastructure ensures that both clinicians and researchers are able to seamlessly access and work on data that is distributed across multiple sites in a secure way in addition to providing computing resources on demand for performing computationally intensive simulations for treatment planning and research.

@neuLink: a service-oriented application for biomedical knowledge discovery.

We introduce the architecture of @neuLink, a service-oriented environment for biomedical knowledge discovery which has been developed in the course of EU Integrated Project @neurIST. The application integrates data from databases with information extracted from unstructured text sources. Moreover, @neuLink supports the analysis of primary biomolecular data associated with individual patients and thus enables the interpretation of molecular data inside a clinical research environment. Based on an assembly of data services, @neuLink interacts with the complex @neurIST grid infrastructure through a dedicated data access and data mediation service. Data types integrated by @neuLink are covering the entire span of biomolecular entities: from gene names in text to entries in EntrezGene; from mentions of drugs to Drugbank, from information on allelic variants in scientific literature to entries in dbSNP. The architecture of @neuLink allows easy integration of other webservice-based applications and thus the spectrum of analysis capabilities of @neuLink can be extended following the requirements of the users of the @neurIST system.

A service-oriented grid infrastructure for biomedical data and compute services.

Service-oriented Grid technologies are increasingly utilized for the realization of future biomedical IT infrastructures since they offer unprecedented opportunities for the integration of advanced analysis and simulation applications as well as distributed heterogeneous data sources and information systems. The European Union's @neurIST project is developing a Grid-based IT infrastructure for the management of all processes linked to research, diagnosis, and treatment development for complex and multifactorial diseases encompassing data repositories, computational analysis services, and information systems handling multiscale, multimodal information at distributed sites. This paper provides an overview of the @neurIST Grid middleware and outlines the infrastructure offered for the provision of advanced compute and data services to support computationally demanding modeling and simulation tasks and to access heterogeneous distributed data sources through semantic integration.