Retrospective Analysis of Wood Anatomical Traits Reveals a Recent Extension in Tree Cambial Activity in Two High-Elevation Conifers

The study of xylogenesis or wood formation is a powerful, yet labor intensive monitoring approach to investigate intra-annual tree growth responses to environmental factors. However, it seldom covers more than a few growing seasons, so is in contrast to the much longer lifespan of woody plants and the time scale of many environmental processes. Here we applied a novel retrospective approach to test the long-term (1926–2012) consistency in the timing of onset and ending of cambial activity, and in the maximum cambial cell division rate in two conifer species, European larch and Norway spruce at high-elevation in the Alps. We correlated daily temperature with time series of cell number and lumen area partitioned into intra-annual sectors. For both species, we found a good correspondence (1–10 days offset) between the periods when anatomical traits had significant correlations with temperature in recent decades (1969–2012) and available xylogenesis data (1996–2005), previously collected at the same site. Yet, results for the 1926–1968 period indicate a later onset and earlier ending of the cambial activity by 6–30 days. Conversely, the peak in the correlation between annual cell number and temperature, which should correspond to the peak in secondary growth rate, was quite stable over time, with just a minor advance of 4–5 days in the recent decades. Our analyses on time series of wood anatomical traits proved useful to infer on past long-term changes in xylogenetic phases. Combined with intensive continuous monitoring, our approach will improve the understanding of tree responses to climate variability in both the short- and long-term context

Transcriptional Response of Selenopolypeptide Genes and Selenocysteine Biosynthesis Machinery Genes in Escherichia coli during Selenite Reduction

This work was supported by a United States Department of Agriculture-Cooperative State Research, Education, and Extension Service grant (no. 2009-35318-05032), a Biotechnology Research grant (no. 2007-BRG-1223) from the North Carolina Biotechnology Center, and a startup fund from the Golden LEAF Foundation to the Biomanufacturing Research Institute and Technology Enterprise (BRITE).Bacteria can reduce toxic selenite into less toxic, elemental selenium (Se0), but the mechanism on how bacterial cells reduce selenite at molecular level is still not clear. We used Escherichia coli strain K12, a common bacterial strain, as a model to study its growth response to sodium selenite (Na2SeO3) treatment and then used quantitative real-time PCR (qRT-PCR) to quantify transcript levels of three E. coli selenopolypeptide genes and a set of machinery genes for selenocysteine (SeCys) biosynthesis and incorporation into polypeptides, whose involvements in the selenite reduction are largely unknown. We determined that 5 mM Na2SeO3 treatment inhibited growth by ∼50% while 0.001 to 0.01 mM treatments stimulated cell growth by ∼30%. Under 50% inhibitory or 30% stimulatory Na2SeO3 concentration, selenopolypeptide genes (fdnG, fdoG, and fdhF) whose products require SeCys but not SeCys biosynthesis machinery genes were found to be induced ≥2-fold. In addition, one sulfur (S) metabolic gene iscS and two previously reported selenite-responsive genes sodA and gutS were also induced ≥2-fold under 50% inhibitory concentration. Our findings provide insight about the detoxification of selenite in E. coli via induction of these genes involved in the selenite reduction process.Publisher PDFPeer reviewe

The Emergence of Canalization and Evolvability in an Open-Ended, Interactive Evolutionary System

Natural evolution has produced a tremendous diversity of functional organisms. Many believe an essential component of this process was the evolution of evolvability, whereby evolution speeds up its ability to innovate by generating a more adaptive pool of offspring. One hypothesized mechanism for evolvability is developmental canalization, wherein certain dimensions of variation become more likely to be traversed and others are prevented from being explored (e.g. offspring tend to have similarly sized legs, and mutations affect the length of both legs, not each leg individually). While ubiquitous in nature, canalization almost never evolves in computational simulations of evolution. Not only does that deprive us of in silico models in which to study the evolution of evolvability, but it also raises the question of which conditions give rise to this form of evolvability. Answering this question would shed light on why such evolvability emerged naturally and could accelerate engineering efforts to harness evolution to solve important engineering challenges. In this paper we reveal a unique system in which canalization did emerge in computational evolution. We document that genomes entrench certain dimensions of variation that were frequently explored during their evolutionary history. The genetic representation of these organisms also evolved to be highly modular and hierarchical, and we show that these organizational properties correlate with increased fitness. Interestingly, the type of computational evolutionary experiment that produced this evolvability was very different from traditional digital evolution in that there was no objective, suggesting that open-ended, divergent evolutionary processes may be necessary for the evolution of evolvability.Comment: SI can be found at: http://www.evolvingai.org/files/SI_0.zi

High-Throughput System for the Early Quantification of Major Architectural Traits in Olive Breeding Trials Using UAV Images and OBIA Techniques

The need for the olive farm modernization have encouraged the research of more efficient crop management strategies through cross-breeding programs to release new olive cultivars more suitable for mechanization and use in intensive orchards, with high quality production and resistance to biotic and abiotic stresses. The advancement of breeding programs are hampered by the lack of efficient phenotyping methods to quickly and accurately acquire crop traits such as morphological attributes (tree vigor and vegetative growth habits), which are key to identify desirable genotypes as early as possible. In this context, an UAV-based high-throughput system for olive breeding program applications was developed to extract tree traits in large-scale phenotyping studies under field conditions. The system consisted of UAV-flight configurations, in terms of flight altitude and image overlaps, and a novel, automatic, and accurate object-based image analysis (OBIA) algorithm based on point clouds, which was evaluated in two experimental trials in the framework of a table olive breeding program, with the aim to determine the earliest date for suitable quantifying of tree architectural traits. Two training systems (intensive and hedgerow) were evaluated at two very early stages of tree growth: 15 and 27 months after planting. Digital Terrain Models (DTMs) were automatically and accurately generated by the algorithm as well as every olive tree identified, independently of the training system and tree age. The architectural traits, specially tree height and crown area, were estimated with high accuracy in the second flight campaign, i.e. 27 months after planting. Differences in the quality of 3D crown reconstruction were found for the growth patterns derived from each training system. These key phenotyping traits could be used in several olive breeding programs, as well as to address some agronomical goals. In addition, this system is cost and time optimized, so that requested architectural traits could be provided in the same day as UAV flights. This high-throughput system may solve the actual bottleneck of plant phenotyping of "linking genotype and phenotype," considered a major challenge for crop research in the 21st century, and bring forward the crucial time of decision making for breeders