Francesc X. Prenafeta-Boldú

Bio: Francesc X. Prenafeta-Boldú is an academic researcher from Stellenbosch University. The author has contributed to research in topics: Energy source & Manure. The author has an hindex of 25, co-authored 66 publications receiving 3452 citations. Previous affiliations of Francesc X. Prenafeta-Boldú include Polytechnic University of Catalonia & Wageningen University and Research Centre.

Fungi growing on aromatic hydrocarbons: biotechnology's unexpected encounter with biohazard?

Abstract: The biodegradation of aromatic hydrocarbons by fungi has traditionally been considered to be of a cometabolic nature. Recently, however, an increasing number of fungi isolated from air biofilters exposed to hydrocarbon-polluted gas streams have been shown to assimilate volatile aromatic hydrocarbons as the sole source of carbon and energy. The biosystematics, ecology, and metabolism of such fungi are reviewed here, based in part on re-evaluation of a collection of published hydrocarbon-degrading isolates obtained from authors around the world. Incorrect or outdated identifications in original publications are corrected by ribosomal DNA sequence analysis. The data show that many volatile-hydrocarbon-degrading strains are closely related to, or in some cases clearly conspecific with, the very restricted number of human-pathogenic fungal species causing severe mycoses, especially neurological infections, in immunocompetent individuals. Neurochemistry features a distinctive array of phenolic and aliphatic compounds that are related to molecules involved in the metabolism of aromatic hydrocarbons. Hence, there may be physiological connections between hydrocarbon assimilation and certain patterns of mammalian infection.

A review of the use of convolutional neural networks in agriculture

Abstract: Deep learning (DL) constitutes a modern technique for image processing, with large potential. Having been successfully applied in various areas, it has recently also entered the domain of agriculture. In the current paper, a survey was conducted of research efforts that employ convolutional neural networks (CNN), which constitute a specific class of DL, applied to various agricultural and food production challenges. The paper examines agricultural problems under study, models employed, sources of data used and the overall precision achieved according to the performance metrics used by the authors. Convolutional neural networks are compared with other existing techniques, and the advantages and disadvantages of using CNN in agriculture are listed. Moreover, the future potential of this technique is discussed, together with the authors’ personal experiences after employing CNN to approximate a problem of identifying missing vegetation from a sugar cane plantation in Costa Rica. The overall findings indicate that CNN constitutes a promising technique with high performance in terms of precision and classification accuracy, outperforming existing commonly used image-processing techniques. However, the success of each CNN model is highly dependent on the quality of the data set used.

Agri-IoT: A semantic framework for Internet of Things-enabled smart farming applications

Abstract: With the recent advancement of the Internet of Things (IoT), it is now possible to process a large number of sensor data streams using different large-scale IoT platforms. These IoT frameworks are used to collect, process and analyse data streams in real-time and facilitate provision of smart solutions designed to provide decision support. Existing IoT-based solutions are mainly domain-dependent, providing stream processing and analytics focusing on specific areas (smart cities, healthcare etc.). In the context of agri-food industry, a variety of external parameters belonging to different domains (e.g. weather conditions, regulations etc.) have a major influence over the food supply chain, while flexible and adaptive IoT frameworks, essential to truly realize the concept of smart farming, are currently inexistent. In this paper, we propose Agri-IoT, a semantic framework for IoT-based smart farming applications, which supports reasoning over various heterogeneous sensor data streams in real-time. Agri-IoT can integrate multiple cross-domain data streams, providing a complete semantic processing pipeline, offering a common framework for smart farming applications. Agri-IoT supports large-scale data analytics and event detection, ensuring seamless interoperability among sensors, services, processes, operations, farmers and other relevant actors, including online information sources and linked open datasets and streams available on the Web.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

Bacterial decolorization and degradation of azo dyes: a review.

Abstract: A variety of synthetic dyestuffs released by the textile industry pose a threat to environmental safety. Azo dyes account for the majority of all dyestuffs, produced because they are extensively used in the textile, paper, food, leather, cosmetics and pharmaceutical industries. Existing effluent treatment procedures are unable to remove recalcitrant azo dyes completely from effluents because of their color fastness, stability and resistance to degradation. Bacterial decolorization and degradation of azo dyes under certain environmental conditions has gained momentum as a method of treatment, as these are inexpensive, eco-friendly and can be applied to wide range of such dyes. This review mainly focuses on the different mechanisms of decolorization and discusses the effect of various physicochemical parameters on the dye removal efficiency of different bacteria. The enzymatic mechanisms involved in the bacterial degradation of azo dyes, the identification of metabolites by using various analytical techniques, and the nature of their toxicity has been investigated. This review provides an overview of bacterial decolorization/degradation of azo dyes and emphasizes the application of these processes for the treatment of azo dye-containing wastewaters.

Basic and applied aspects in the microbial degradation of azo dyes

Abstract: Azo dyes are the most important group of synthetic colorants. They are generally considered as xenobiotic compounds that are very recalcitrant against biodegradative processes. Nevertheless, during the last few years it has been demonstrated that several microorganisms are able, under certain environmental conditions, to transform azo dyes to non-colored products or even to completely mineralize them. Thus, various lignolytic fungi were shown to decolorize azo dyes using ligninases, manganese peroxidases or laccases. For some model dyes, the degradative pathways have been investigated and a true mineralization to carbon dioxide has been shown. The bacterial metabolism of azo dyes is initiated in most cases by a reductive cleavage of the azo bond, which results in the formation of (usually colorless) amines. These reductive processes have been described for some aerobic bacteria, which can grow with (rather simple) azo compounds. These specifically adapted microorganisms synthesize true azoreductases, which reductively cleave the azo group in the presence of molecular oxygen. Much more common is the reductive cleavage of azo dyes under anaerobic conditions. These reactions usually occur with rather low specific activities but are extremely unspecific with regard to the organisms involved and the dyes converted. In these unspecific anaerobic processes, low-molecular weight redox mediators (e.g. flavins or quinones) which are enzymatically reduced by the cells (or chemically by bulk reductants in the environment) are very often involved. These reduced mediator compounds reduce the azo group in a purely chemical reaction. The (sulfonated) amines that are formed in the course of these reactions may be degraded aerobically. Therefore, several (laboratory-scale) continuous anaerobic/aerobic processes for the treatment of wastewaters containing azo dyes have recently been described.