Isolation of isoprene degrading bacteria from soils, development of isoA gene probes and identification of the active isoprene degrading soil community using DNA-stable isotope probing

Emissions of biogenic volatile organic compounds (bVOCs), are an important element in the global carbon cycle, accounting for a significant proportion of fixed carbon. They contribute directly and indirectly to global warming and climate change and have a major effect on atmospheric chemistry. Plants emit isoprene to the atmosphere in similar quantities to emissions of methane from all sources and each account for approximately one third of total VOCs. Although methanotrophs, capable of growth on methane, have been intensively studied, we know little of isoprene biodegradation. Here we report the isolation of two isoprene-degrading strains from the terrestrial environment and describe the design and testing of PCR primers targeting isoA, the gene encoding the active-site component of the conserved isoprene monooxygenase, which are capable of retrieving isoA sequences from isoprene-enriched environmental samples. Stable isotope probing experiments, using biosynthesized 13C-labelled isoprene, identified the active isoprene-degrading bacteria in soil. This study identifies novel isoprene-degrading strains using both culture-dependent and, for the first time, culture-independent methods and provides the tools and foundations for continued investigation of the biogeography and molecular ecology of isoprene-degrading bacteria. This article is protected by copyright. All rights reserved

Encog: Library of Interchangeable Machine Learning Models for Java and C#

This paper introduces the Encog library for Java and C#, a scalable, adaptable, multiplatform machine learning framework that was 1st released in 2008. Encog allows a variety of machine learning models to be applied to datasets using regression, classification, and clustering. Various supported machine learning models can be used interchangeably with minimal recoding. Encog uses efficient multithreaded code to reduce training time by exploiting modern multicore processors. The current version of Encog can be downloaded from http://www.encog.org

FAST: FAST Analysis of Sequences Toolbox.

FAST (FAST Analysis of Sequences Toolbox) provides simple, powerful open source command-line tools to filter, transform, annotate and analyze biological sequence data. Modeled after the GNU (GNU's Not Unix) Textutils such as grep, cut, and tr, FAST tools such as fasgrep, fascut, and fastr make it easy to rapidly prototype expressive bioinformatic workflows in a compact and generic command vocabulary. Compact combinatorial encoding of data workflows with FAST commands can simplify the documentation and reproducibility of bioinformatic protocols, supporting better transparency in biological data science. Interface self-consistency and conformity with conventions of GNU, Matlab, Perl, BioPerl, R, and GenBank help make FAST easy and rewarding to learn. FAST automates numerical, taxonomic, and text-based sorting, selection and transformation of sequence records and alignment sites based on content, index ranges, descriptive tags, annotated features, and in-line calculated analytics, including composition and codon usage. Automated content- and feature-based extraction of sites and support for molecular population genetic statistics make FAST useful for molecular evolutionary analysis. FAST is portable, easy to install and secure thanks to the relative maturity of its Perl and BioPerl foundations, with stable releases posted to CPAN. Development as well as a publicly accessible Cookbook and Wiki are available on the FAST GitHub repository at https://github.com/tlawrence3/FAST. The default data exchange format in FAST is Multi-FastA (specifically, a restriction of BioPerl FastA format). Sanger and Illumina 1.8+ FastQ formatted files are also supported. FAST makes it easier for non-programmer biologists to interactively investigate and control biological data at the speed of thought

Developing "personality" taxonomies: Metatheoretical and methodological rationales underlying selection approaches, methods of data generation and reduction principles

Taxonomic "personality" models are widely used in research and applied fields. This article applies the Transdisciplinary Philosophy-of-Science Paradigm for Research on Individuals (TPS-Paradigm) to scrutinise the three methodological steps that are required for developing comprehensive “personality” taxonomies: 1) the approaches used to select the phenomena and events to be studied, 2) the methods used to generate data about the selected phenomena and events and 3) the reduction principles used to extract the “most important” individual-specific variations for constructing “personality” taxonomies. Analyses of some currently popular taxonomies reveal frequent mismatches between the researchers’ explicit and implicit metatheories about “personality” and the abilities of previous methodologies to capture the particular kinds of phenomena toward which they are targeted. Serious deficiencies that preclude scientific quantifications are identified in standardised questionnaires, psychology’s established standard method of investigation. These mismatches and deficiencies derive from the lack of an explicit formulation and critical reflection on the philosophical and metatheoretical assumptions being made by scientists and from the established practice of radically matching the methodological tools to researchers’ preconceived ideas and to pre-existing statistical theories rather than to the particular phenomena and individuals under study. These findings raise serious doubts about the ability of previous taxonomies to appropriately and comprehensively reflect the phenomena towards which they are targeted and the structures of individual-specificity occurring in them. The article elaborates and illustrates with empirical examples methodological principles that allow researchers to appropriately meet the metatheoretical requirements and that are suitable for comprehensively exploring individuals’ “personality”

Interpreting "personality" taxonomies: Why previous models cannot capture individual-specific experiencing, behaviour, functioning and development. Major taxonomic tasks still lay ahead

As science seeks to make generalisations, a science of individual peculiarities encounters intricate challenges. This article explores these challenges by applying the Transdisciplinary Philosophy-of-Science Paradigm for Research on Individuals (TPS-Paradigm) and by exploring taxonomic “personality” research as an example. Analyses of researchers’ interpretations of the taxonomic “personality” models, constructs and data that have been generated in the field reveal widespread erroneous assumptions about the abilities of previous methodologies to appropriately represent individual-specificity in the targeted phenomena. These assumptions, rooted in everyday thinking, fail to consider that individual-specificity and others’ minds cannot be directly perceived, that abstract descriptions cannot serve as causal explanations, that between-individual structures cannot be isomorphic to within-individual structures, and that knowledge of compositional structures cannot explain the process structures of their functioning and development. These erroneous assumptions and serious methodological deficiencies in widely used standardised questionnaires have effectively prevented psychologists from establishing taxonomies that can comprehensively model individual-specificity in most of the kinds of phenomena explored as “personality”, especially in experiencing and behaviour and in individuals' functioning and development. Contrary to previous assumptions, it is not universal models but rather different kinds of taxonomic models that are required for each of the different kinds of phenomena, variations and structures that are commonly conceived of as “personality”. Consequently, to comprehensively explore individual-specificity, researchers have to apply a portfolio of complementary methodologies and develop different kinds of taxonomies, most of which have yet to be developed. Closing, the article derives some meta-desiderata for future research on individuals' “personality”

Dimensions of Neural-symbolic Integration - A Structured Survey

Research on integrated neural-symbolic systems has made significant progress in the recent past. In particular the understanding of ways to deal with symbolic knowledge within connectionist systems (also called artificial neural networks) has reached a critical mass which enables the community to strive for applicable implementations and use cases. Recent work has covered a great variety of logics used in artificial intelligence and provides a multitude of techniques for dealing with them within the context of artificial neural networks. We present a comprehensive survey of the field of neural-symbolic integration, including a new classification of system according to their architectures and abilities.Comment: 28 page

Pacifier overuse and conceptual relations of abstract and emotional concepts

This study explores the impact of the extensive use of an oral device since infancy (pacifier) on the acquisition of concrete, abstract, and emotional concepts. While recent evidence showed a negative relation between pacifier use and children’s emotional competence (Niedenthal et al., 2012), the possible interaction between use of pacifier and processing of emotional and abstract language has not been investigated. According to recent theories, while all concepts are grounded in sensorimotor experience, abstract concepts activate linguistic and social information more than concrete ones. Specifically, the Words As Social Tools (WAT) proposal predicts that the simulation of their meaning leads to an activation of the mouth (Borghi and Binkofski, 2014; Borghi and Zarcone, 2016). Since the pacifier affects facial mimicry forcing mouth muscles into a static position, we hypothesize its possible interference on acquisition/consolidation of abstract emotional and abstract not-emotional concepts, which aremainly conveyed during social and linguistic interactions, than of concrete concepts. Fifty-nine first grade children, with a history of different frequency of pacifier use, provided oral definitions of the meaning of abstract not-emotional, abstract emotional, and concrete words. Main effect of concept type emerged, with higher accuracy in defining concrete and abstract emotional concepts with respect to abstract not-emotional concepts, independently from pacifier use. Accuracy in definitions was not influenced by the use of pacifier, butcorrespondence and hierarchical clustering analyses suggest that the use of pacifier differently modulates the conceptual relations elicited by abstract emotional and abstract not-emotional. While the majority of the children produced a similar pattern of conceptual relations, analyses on the few (6) children who overused the pacifier (for more than 3 years) showed that they tend to distinguish less clearly between concrete and abstract emotional concepts and between concrete and abstract not-emotional concepts than children who did not use it (5) or used it for short (17). As to the conceptual relations they produced, children who overused the pacifier tended to refer less to their experience and to social and emotional situations, usemore exemplifications and functional relations, and less free associations