Synthesis and characterization of fully bio-based unsaturated polyester resins

The sustainable tomorrow for future generations lies with the present industrial development toward the proper utilization of various bio-based products. For a transition to a higher level of sustainability, it is necessary to form a new platform for advanced technology products. This paper reports the development of new fully bio-based unsaturated polyesters resins (UPRs). A series of prepolymers were synthesized by varying saturated diacids (oxalic, succinic and adipic acid), itaconic acid and 1,2-propandiol. Dimethyl itaconate was used as a reactive diluent (RD) in amounts of 30, 35 and 40 wt%. Rheological measurements showed that the obtained resins possessed viscosities (234-2226 mPa s) amenable to a variety of liquid molding techniques. The impact of composition variables-prepolymer structure and amount of RD-on the chemical, mechanical and thermal properties of the thermosets was examined by DMA, TA and tensile measurements and was discussed in detail. The tensile properties (37-52 MPa), glass transition temperature (60-97 A degrees C) and coefficient of thermal expansion (71-168 10(-6) A degrees C-1) of the cured resins were in the desired range for UPRs. This investigation showed that UPRs based on itaconic acid can be tailored during synthesis of the prepolymer to meet the needs of different property profiles

Digital imaging of root traits (DIRT): a high-throughput computing and collaboration platform for field-based root phenomics

Plant root systems are key drivers of plant function and yield. They are also under-explored targets to meet global food and energy demands. Many new technologies have been developed to characterize crop root system architecture (CRSA). These technologies have the potential to accelerate the progress in understanding the genetic control and environmental response of CRSA. Putting this potential into practice requires new methods and algorithms to analyze CRSA in digital images. Most prior approaches have solely focused on the estimation of root traits from images, yet no integrated platform exists that allows easy and intuitive access to trait extraction and analysis methods from images combined with storage solutions linked to metadata. Automated high-throughput phenotyping methods are increasingly used in laboratory-based efforts to link plant genotype with phenotype, whereas similar field-based studies remain predominantly manual low-throughput.DescriptionHere, we present an open-source phenomics platform “DIRT”, as a means to integrate scalable supercomputing architectures into field experiments and analysis pipelines. DIRT is an online platform that enables researchers to store images of plant roots, measure dicot and monocot root traits under field conditions, and share data and results within collaborative teams and the broader community. The DIRT platform seamlessly connects end-users with large-scale compute “commons” enabling the estimation and analysis of root phenotypes from field experiments of unprecedented size.ConclusionDIRT is an automated high-throughput computing and collaboration platform for field based crop root phenomics. The platform is accessible at http://​dirt.​iplantcollaborat​ive.​org/​ and hosted on the iPlant cyber-infrastructure using high-throughput grid computing resources of the Texas Advanced Computing Center (TACC). DIRT is a high volume central depository and high-throughput RSA trait computation platform for plant scientists working on crop roots. It enables scientists to store, manage and share crop root images with metadata and compute RSA traits from thousands of images in parallel. It makes high-throughput RSA trait computation available to the community with just a few button clicks. As such it enables plant scientists to spend more time on science rather than on technology. All stored and computed data is easily accessible to the public and broader scientific community. We hope that easy data accessibility will attract new tool developers and spur creative data usage that may even be applied to other fields of science

Phosphodiesterase 4D and 5-lipoxygenase activating protein genes and risk of ischemic stroke in Sardinians

Genetic factors contribute to the risk of ischemic stroke (IS). The phosphodiesterase-4D (PDE4D) and the 5-lipoxygenase activating protein (ALOX5AP) genes were identified as contributors to stroke in an Icelandic population. In an attempt to better define the contributory role of PDE4D and ALOX5AP genes to the risk of IS in humans, we carried out the present association study in a well-characterized, earlier published, genetically homogenous population from the island of Sardinia, Italy. In this cohort, including 294 cases and 235 controls, age, hypertension, hypercholesterolemia, and atrial fibrillation represent risk factors for IS. The PDE4D gene was evaluated by four single nucleotide polymorphisms (SNP32, SNP45, SNP83, SNP87) and by the microsatellite AC008818-1; the ALOX5AP gene was characterized by three SNPs (SG13S32, SG13S89, ALO2A). The results of our study provide no evidence of association between any single PDE4D and ALOX5AP gene variant with the risk of IS in the Sardinian cohort. Haplotype analysis, including that constructed with allele 0 of microsatellite AC008818-1 and SNP45 of the PDE4D gene, was also negative. In conclusion, we found no evidence of association between PDE4D and ALOX5AP genes and the risk of IS in a genetically homogenous population from Sardinia. European Journal of Human Genetics (2009) 17, 1448-1453; doi:10.1038/ejhg.2009.71; published online 6 May 200