NMR and Electron Microscopy Studies of Soybean Seeds, Soybean Proteins, and Oil for Improved Utilization in Foods and Feeds

NMR, NIR and EM techniques are suitable methods of measuring protein and oil content in soybeans. After completion of a measurement with NMR or NIR, a soybean sample could be either replanted or consummated. NMR techniques involved in the present work are based on the response of nuclei, as nuclear spin flips, when set in a 7.05T magnetic field and excited with rf pulses of 300 MHz. NIR spectroscopy is based on outer electron molecular vibrations, when excited with radiation of frequency 1012 -4×1014Hz, and =2.5Om-750 nm. 
TEM and ESEM techniques are bulk and surface analysis techniques, respectively, working with electron beams of 15KV, and image resolution in the range of nm’s. ESEM is based on the possibility of imaging with secondary emitted electrons from the samples, and TEM on the possibility to have image contrast based on the difference in electron transmission through elements of different atomic weight. Novel results are presented by NMR, FT-NIR spectroscopy and Electron Microscopy for intact, selected soybean seeds accessions, soybean oil and soybean proteins

Produktionsmittelbesteuerung in der Landwirtschaft: In Deutschland relativ hohe Belastung im Vergleich zu wichtigen EU-Konkurrenzländern

Das ifo Institut untersuchte im Auftrag der Bundesanstalt für Landwirtschaft und Ernährung (BLE) die Besteuerung bestimmter Produktionsmittel in ausgewählten EU-Mitgliedstaaten. Neben einem steuerrechtlichen Teil, in dem die unterschiedlichen Regelungen in den einzelnen Ländern dargelegt wurden, lag der Schwerpunkt der Untersuchung auf der Analyse der sich ergebenden Belastungen der landwirtschaftlichen Produktionsmittel für landwirtschaftliche Modellbetriebe. Von besonderem Interesse war dabei, die Verschiedenartigkeit der Belastung und die dadurch steuerlich bedingten Wettbewerbsverzerrungen zu erkennen. Darüber hinaus schafft das Gutachten eine Basis für die aktuelle Diskussion über die Chancen eines aktiven Beitrags des steuerpolitischen Instrumentariums zur Umstrukturierung der Landwirtschaft. Für die untersuchten Ländern wurde aufgezeigt, welche Staaten im Bereich der Landwirtschaft ökologisch orientierte Steuern auf Produktionsmittel einsetzen und welches Instrumentarium dabei primär verwendet wird. Im Ergebnis zeigte sich, dass die steuerlichen Regelungen für die landwirtschaftlichen Produktionsmittel deutlich unterschiedliche Belastungen ergeben. Dabei führen die deutschen Vorschriften, neben den steuerlichen Regelungen in Dänemark und Schweden, zu vergleichsweise hohen Belastungen - überraschenderweise weisen ökologisch orientierte Betriebe eine höhere steuerliche Belastung auf als konventionell orientierte Betriebe. Zudem konnten die mit dem Einsatz ökologisch motivierter Steuern auf landwirtschaftliche Produktionsmittel erhofften Effekte in der Empirie nur zum Teil nachgewiesen werden. Aber ohne Einsatz dieser Steuern würde beispielsweise die Belastung der landwirtschaftlichen Flächen mit Düngemittel und Pestiziden in einigen EU-Ländern über dem derzeitigen Niveau liegen.Produktionsfaktor, Landwirtschaft, Steuerbelastung, Wettbewerb, Vergleich, Deutschland, EU-Staaten

NIR Calibrations for Soybean Seeds and Soy Food Composition Analysis: Total Carbohydrates, Oil, Proteins and Water Contents [v.2]

Conventional chemical analysis techniques are expensive, time consuming, and often destructive. The non-invasive Near Infrared (NIR) technology was introduced over the last decades for wide-scale, inexpensive chemical analysis of food and crop seed composition (see Williams and Norris, 1987; Wilcox and Cavins, 1995; Buning and Diller, 2000 for reviews of the NIR technique development stage prior to 1998, when Diode Arrays were introduced to NIR). NIR spectroscopic measurements obey Lambert and Beer’s law, and quantitative measurements can be successfully made with high speed and ease of operation. NIR has been used in a great variety of food applications. General applications of products analyzed come from all sectors of the food industry including meats, grains, and dairy products (Shadow, 1998).
Novel NIR calibrations for rapid, reliable and accurate composition analysis of a variety of several soy based foods and bulk soybean seeds were developed and validated in a six-year collaborative project with a large number of different samples (N >~12, 000). The availability of such calibrations is important for establishing NIR as a secondary method for composition analysis of foods and soybeans both in applications and fundamental research

Comparative analysis of expressed sequence tags from three castes and two life stages of the termite Reticulitermes flavipes

<p>Abstract</p> <p>Background</p> <p>Termites (Isoptera) are eusocial insects whose colonies consist of morphologically and behaviorally specialized castes of sterile workers and soldiers, and reproductive alates. Previous studies on eusocial insects have indicated that caste differentiation and behavior are underlain by differential gene expression. Although much is known about gene expression in the honey bee, <it>Apis mellifera</it>, termites remain relatively understudied in this regard. Therefore, our objective was to assemble an expressed sequence tag (EST) data base for the eastern subterranean termite, <it>Reticulitermes flavipes</it>, for future gene expression studies.</p> <p>Results</p> <p>Soldier, worker, and alate caste and two larval cDNA libraries were constructed, and approximately 15,000 randomly chosen clones were sequenced to compile an EST data base. Putative gene functions were assigned based on a BLASTX Swissprot search. Categorical <it>in silico </it>expression patterns for each library were compared using the R-statistic. A significant proportion of the ESTs of each caste and life stages had no significant similarity to those in existing data bases. All cDNA libraries, including those of non-reproductive worker and soldier castes, contained sequences with putative reproductive functions. Genes that showed a potential expression bias among castes included a putative antibacterial humoral response and translation elongation protein in soldiers and a chemosensory protein in alates.</p> <p>Conclusions</p> <p>We have expanded upon the available sequences for <it>R. flavipes </it>and utilized an <it>in silico </it>method to compare gene expression in different castes of an eusocial insect. The <it>in silico </it>analysis allowed us to identify several genes which may be differentially expressed and involved in caste differences. These include a gene overrepresented in the alate cDNA library with a predicted function of neurotransmitter secretion or cholesterol absorption and a gene predicted to be involved in protein biosynthesis and ligase activity that was overrepresented in the late larval stage cDNA library. The EST data base and analyses reported here will be a valuable resource for future studies on the genomics of <it>R. flavipes </it>and other termites.</p

Partially Lazy Classification of Cardiovascular Risk via Multi-way Graph Cut Optimization

Cardiovascular disease (CVD) is considered a leading cause of human mortality with rising trends worldwide. Therefore, early identification of seemingly healthy subjects at risk is a priority. For this purpose, we propose a novel classification algorithm that provides a sound individual risk prediction, based on a non-invasive assessment of retinal vascular function. so-called lazy classification methods offer reduced time complexity by saving model construction time and better adapting to newly available instances, when compared to well-known eager methodS. Lazy methods are widely used due to their simplicity and competitive performance. However, traditional lazy approaches are more vulnerable to noise and outliers, due to their full reliance on the instances' local neighbourhood for classification. In this work, a learning method based on Graph Cut Optimization called GCO mine is proposed, which considers both the local arrangements and the global structure of the data, resulting in improved performance relative to traditional lazy methodS. We compare GCO mine coupled with genetic algorithms (hGCO mine) with established lazy and eager algorithms to predict cardiovascular risk based on Retinal Vessel Analysis (RVA) data. The highest accuracy of 99.52% is achieved by hGCO mine. The performance of GCO mine is additionally demonstrated on 12 benchmark medical datasets from the UCI repository. In 8 out of 12 datasets, GCO mine outperforms its counterpartS. GCO mine is recommended for studies where new instances are expected to be acquired over time, as it saves model creation time and allows for better generalization compared to state of the art methodS

The Corporate Tax Reform of 2008: Germany’s Answer to Globalisation – or just Patchwork?

ISSN:1612-066