Directed Evolution of Phi Class Glutathione Transferases Involved in Multiple-Herbicide Resistance of Grass Weeds and Crops

The extensive application of herbicides in crop cultivation has indisputably led to the emergence of weed populations characterized by multiple herbicide resistance (MHR). This phenomenon is associated with the enhanced metabolism and detoxifying ability of endogenous enzymes, such as phi class glutathione transferases (GSTFs). In the present work, a library of mutant GSTFs was created by in vitro directed evolution via DNA shuffling. Selected gstf genes from the weeds Alopecurus myosuroides and Lolium rigidum, and the cereal crops Triticum durum and Hordeum vulgare were recombined to forge a library of novel chimeric GSTFs. The library was activity screened and the best-performing enzyme variants were purified and characterized. The work allowed the identification of enzyme variants that exhibit an eight-fold improvement in their catalytic efficiency, higher thermal stability (8.3 °C) and three-times higher inhibition sensitivity towards the herbicide butachlor. The crystal structures of the best-performing enzyme variants were determined by X-ray crystallography. Structural analysis allowed the identification of specific structural elements that are responsible for k_cat regulation, thermal stability and inhibition potency. These improved novel enzymes hold the potential for utilization in biocatalysis and green biotechnology applications. The results of the present work contribute significantly to our knowledge of the structure and function of phi class plant GSTs and shed light on their involvement in the mechanisms of MHR.     

Active learning of user’s preferences estimation towards a personalized 3D navigation of geo-referenced scenes

The current technological evolutions enter 3D geo-informatics into their digital age, enabling new potential applications in the field of virtual tourism, pleasure, entertainment and cultural heritage. It is argued that 3D information provides the natural way of navigation. However, personalization is a key aspect in a navigation system, since a route that incorporates user preferences is ultimately more suitable than the route with the shortest distance or travel time. Usually, user’s preferences are expressed as a set of weights that regulate the degree of importance of the scene metadata on the route selection process. These weights, however, are defined by the users, setting the complexity to the user’s side, which makes personalization an arduous task. In this paper, we propose an alternative approach in which metadata weights are estimated implicitly and transparently to the users, transferring the complexity to the system side. This is achieved by introducing a relevance feedback on-line learning strategy which automatically adjusts metadata weights by exploiting information fed back to the system about the relevance of user’s preferences judgments given in a form of pair-wise comparisons. Practically implementing a relevance feedback algorithm presents the limitation that several pair-wise comparisons (samples) are required to converge to a set of reliable metadata weights. For this reason, we propose in this paper a weight rectification strategy that improves weight estimation by exploiting metadata interrelations defined through an ontology. In the sequel, a genetic optimization algorithm is incorporated to select the most user preferred routes based on a multi-criteria minimization approach. To increase the degree of personalization in 3D navigation, we have also introduced an efficient algorithm for estimating 3D trajectories around objects of interest by merging best selected 2D projected views that contain faces which are mostly preferred by the users. We have conducted simulations and comparisons with other approaches either in the field of on-line learning or route selection using objective metrics in terms of precision and recall values. The results indicate that our system yields on average a 13.76 % improvement of precision as regards the learning strategy and an improvement of 8.75 % regarding route selection. In addition, we conclude that the ontology driven weight rectification strategy can reduce the number of samples (pair-wise comparisons) required of 76 % to achieve the same precision. Qualitative comparisons have been also performed using a use case route scenario in the city of Athens.     

BETaaS: A Platform for Development and Execution of Machine-to-Machine Applications in the Internet of Things

Wireless Personal Communications - The integration of everyday objects into the Internet represents the foundation of the forthcoming Internet of Things (IoT). Smart objects will be the building...     

Altered Distribution and Expression of Syndecan-1 and -4 as an Additional Hallmark in Psoriasis

Syndecans act as independent co-receptors to exert biological activities and their altered function is associated with many pathophysiological conditions. Here, syndecan-1 and -4 were examined in lesional skin of patients with psoriasis. Immunohistochemical staining confirmed altered syndecan-1 distribution and revealed absence of syndecan-4 expression in the epidermis. Fibronectin (FN)—known to influence inflammation and keratinocyte hyperproliferation via α5β1 integrin in psoriasis—was also decreased. Syndecan-1 and -4 expression was analyzed in freshly isolated lesional psoriatic human keratinocytes (PHK) characterized based on their proliferation and differentiation properties. mRNA levels of syndecan-1 were similar between healthy and PHK, while syndecan-4 was significantly decreased. Cell growth and release of the pro-inflammatory Tumor Necrosis Factor-alpha (TNFα) were selectively and significantly induced in PHKs plated on FN. Results from co-culture of healthy keratinocytes and psoriatic fibroblasts led to the speculation that at least one factor released by fibroblasts down-regulate syndecan-1 expression in PHK plated on FN. To assay if biological treatments for psoriasis target keratinocyte proliferation, gelatin-based patches enriched with inteleukin (IL)-17α or TNFα blockers were prepared and tested using a full-thickness healthy epidermal model (Phenion^®). Immunohistochemistry analysis showed that both blockers impacted the localisation of syndecan-1 within the refined epidermis. These results provide evidence that syndecans expression are modified in psoriasis, suggesting that they may represent markers of interest in this pathology.     

Carbon‐Nanotube–PDMS Composite Coatings on Optical Fibers for All‐Optical Ultrasound Imaging

Polymer–carbon nanotube composite coatings have properties that are desirable for a wide range of applications. However, fabrication of these coatings onto submillimeter structures with the efficient use of nanotubes has been challenging. Polydimethylsiloxane (PDMS)–carbon nanotube composite coatings are of particular interest for optical ultrasound transmission, which shows promise for biomedical imaging and therapeutic applications. In this study, methods for fabricating composite coatings comprising PDMS and multiwalled carbon nanotubes (MWCNTs) with submicrometer thickness are developed and used to coat the distal ends of optical fibers. These methods include creating a MWCNT organogel using two solvents, dip coating of this organogel, and subsequent overcoating with PDMS. These coated fibers are used as all‐optical ultrasound transmitters that achieve high ultrasound pressures (up to 21.5 MPa peak‐to‐peak) and broad frequency bandwidths (up to 39.8 MHz). Their clinical potential is demonstrated with all‐optical pulse‐echo ultrasound imaging of an aorta. The fabrication methods in this paper allow for the creation of thin, uniform carbon nanotube composites on miniature or temperature‐sensitive surfaces, to enable a wide range of advanced sensing capabilities.     

Mapping commuter cycling risk in urban areas

Cycling is becoming an increasingly important transportation option for commuters. Cycling offers exercise opportunities and reduces the burden of motor vehicle travel on society. Mapping the risk of collision between cyclists and motor vehicles in urban areas is important to understanding safe cyclist route opportunities, making informed transportation planning decisions, and exploring patterns of injury epidemiology. To date, many geographic analyses and representations of cyclist risk have not taken the concept of exposure into account. Instead, risk is either expressed as a rate per capita, or as a count of events. Using data associated with the City of Hamilton, Canada, we illustrate a method for mapping commuter cyclist collision risk per distance travelled. This measure can be used to more realistically represent the underlying geography of cycling risk, and provide more geographically and empirically meaningful information to those interested in understanding how cycling safety varies over space.     

An artificial neural network-based multiscale method for hybrid atomistic-continuum simulations

This paper presents an artificial neural network-based multiscale method for coupling continuum and molecular simulations. Molecular dynamics modelling is employed as a local “high resolution” refinement of computational data required by the continuum computational fluid dynamics solver. The coupling between atomistic and continuum simulations is obtained by an artificial neural network (ANN) methodology. The ANN aims to optimise the transfer of information through minimisation of (1) the computational cost by avoiding repetitive atomistic simulations of nearly identical states, and (2) the fluctuation strength of the atomistic outputs that are fed back to the continuum solver. Results are presented for prototype flows such as the isothermal Couette flow with slip boundary conditions and the slip Couette flow with heat transfer.     

Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content

Online reviews have received much attention recently in the literature, as their visibility has been proven to play an important role during the purchase process. Furthermore, recent theoretical insight argue that the votes casted on how helpful an online review is (review helpfulness) are of particular importance, since they constitute a focal point for examining consumer decision making during the purchase process. In this paper, we explore the interplay between online review helpfulness, rating score and the qualitative characteristics of the review text as measured by readability tests. We construct a theoretical model based on three elements: conformity, understandability and expressiveness and we investigate the directional relationship between the qualitative characteristics of the review text, review helpfulness and the impact of review helpfulness on the review score. Furthermore, we examine whether this relation holds for extreme and moderate review scores. To validate this model we applied four basic readability measures to a dataset containing 37,221 reviews collected from Amazon UK, in order to determine the relationship between the percentage of helpful votes awarded to a review and the review text’s stylistic elements. We also investigated the interrelationships between extremely helpful and unhelpful reviews, as well as absolutely positive and negative reviews using intergroup comparisons. We found that review readability had a greater effect on the helpfulness ratio of a review than its length; in addition, extremely helpful reviews received a higher score than those considered less helpful. The present study contributes to the ever growing literature on on-line reviews by showing that readability tests demonstrate a directional relationship with average length reviews and their helpfulness and that this relationship holds both for moderate and extreme review scores.