Platyphylloside: Metabolism and digestibility reductionin vitro

The metabolism of platyphylloside [(5S)-5-hydroxy-1,7-bis-(4-hydroxyphenyl)-3-heptanone-5-O--d-glucopyranosidel]—known to reduce digestibility—was studiedin vitro in sheep rumen liquor. Platyphylloside is hydrolyzed to 5-hydroxy-3-platyphyllone [(5S)-5-hydroxy-1,7-bis-(4-hydroxyphenyl)-3-heptanone], which is reduced to centrolobol [1,7-bis-(4-hydroxyphenyl)-3-heptanol], via 3-platyphyllone [7-bis-(4-hydroxyphenyl)-3-heptanone]. The digestibility-reducing effect was shown to be correlated with the concentration of centrolobol.     

Uniqueness and multiplicity criteria for an nth order chemical reaction

New and very strong criteria are presented for a priori prediction of the conditions for which the steady-state lumped parameter model of an nth order chemical reaction (n ≥ 0) in an adiabatic CSTR has either a unique or multiple solutions. The criteria show that the higher the order of the reaction the smaller is the region in the parameters space for which multiplicity can occur.New uniqueness and multiplicity criteria are developed also for an nth order reaction in a porous catalyst using a model, which accounts for intraparticle concentration gradients, while assuming a uniform intraparticle temperature different from the ambient one. The region in the parameters space for which steady state multiplicity can occur for this model is smaller than that for a corresponding lumped model, which ignores the intraparticle concentration gradients.     

The challenge of predicting spinnability: Investigating benefits of adding lignin to cellulose solutions in air-gap spinning

In this study, the underlying mechanism for improved spinnability when mixing lignin and cellulose in solution was investigated. Co-processing of lignin and cellulose has previously been identified as a potential route for production of inexpensive and bio-based carbon fibers. The molecular order of cellulose contributes to the strength of the fibers and the high carbon content of lignin improves the yield during conversion to carbon fibers. The current work presents an additional benefit of combining lignin and cellulose; solutions that contain both lignin and cellulose could be air-gap spun at substantially higher draw ratios than pure cellulose solutions, that is, lignin improved the spinnability. Fibers were spun from solutions containing different ratios of lignin, from 0 to 70 wt%, and the critical draw ratio was determined at various temperatures of solution. The observations were followed by characterization of the solutions with shear and elongational viscosity and surface tension, but none of these methods could explain the beneficial effect of lignin on the spinnability. However, by measuring the take-up force it was found that lignin seems to stabilize against diameter fluctuations during spinning, and plausible explanations are discussed.     

Multiphoton fluorescent images with a spatially varying background signal: a ML deconvolution method

By means of multiphoton laser scanning microscopy, neuroscientists can look inside the brain deeper than has ever been possible before. Multiphoton fluorescent images, as all optical images, suffer from degradation caused by a variety of sources (e.g. light dispersion and absorption in the tissue, laser fluctuations, spurious photodetection and staining deficiency). From a modelling perspective, such degradations can be considered the sum of stochastic noise and a background signal. Among the methods proposed in the literature to perform image deconvolution in either confocal or multiphoton fluorescent microscopy, Vicidomini et al. (2009) were the first to incorporate models for noise (a Poisson process) and background signal (spatially constant) in the context of regularized inverse problems. Unfortunately, the so-called split-gradient deconvolution method (SGM) they used did not consider possible spatial variations in the background signal. In this paper, we extend the SGM by adding a maximum-likelihood estimation step for the determination of a spatially varying background signal. We demonstrate that the assumption of a constant background is not always valid in multiphoton laser microscopy and by using synthetic and actual multiphoton fluorescent images, we evaluate the face of validity of the proposed method, and compare its accuracy with the previously introduced SGM algorithm.     

Application of the Soil and Water Assessment Tool for six watersheds of Lake Erie: Model parameterization and calibration

The Soil and Water Assessment Tool (SWAT), a physically-based watershed-scale model, holds promise as a means to predict tributary sediment and nutrient loads to the Laurentian Great Lakes. In the present study, model performance is compared across six watersheds draining into Lake Erie to determine the applicability of SWAT to watersheds of differing characteristics. After initial model parameterization, the Huron, Raisin, Maumee, Sandusky, Cuyahoga, and Grand SWAT models were calibrated (1998-2001) and confirmed, or validated (2002-2005), individually for stream water discharge, sediment loads, and nutrient loads (total P, soluble reactive P, total N, and nitrate) based on available datasets. SWAT effectively predicted hydrology and sediments across a range of watershed characteristics. SWAT estimation of nutrient loads was weaker although still satisfactory at least two-thirds of the time across all nutrient parameters and watersheds. SWAT model performance was most satisfactory in agricultural and forested watersheds, and was less so in urbanized settings. Model performance was influenced by the availability of observational data with high sampling frequency and long duration for calibration and confirmation evaluation. In some instances, it appeared that parameter adjustments that improved calibration of hydrology negatively affected subsequent sediment and nutrient calibration, suggesting trade-offs in calibrating for hydrologic vs. water quality model performance. Despite these considerations, SWAT accurately predicted average stream discharge, sediment loads, and nutrient loads for the Raisin, Maumee, Sandusky, and Grand watersheds such that future use of these SWAT models for various scenario testing is reasonable and warranted.     

Flow profile measurement in microchannel using the optical feedback interferometry sensing technique

The need to accurately measure flow profiles in microfluidic channels is well recognised. In this work, we present a new optical feedback interferometry (OFI) flow sensor that accurately measures local velocity in fluids and enables reconstruction of a velocity profile inside a microchannel. OFI is a self-aligned interferometric technique that uses the laser as both the transmitter and the receiver thus offering high sensitivity, fast response, and a simple and compact optical design. The system described here is based on a commercial semiconductor laser and has been designed to achieve a micrometer-range spatial resolution. The sensor performance was validated by reconstructing the velocity profile inside a circular cross-section flow-channel with 320  $\upmu $ m internal diameter, with a relative error smaller than 1.8 %. The local flow velocity is directly measured, thus avoiding the need for model based profile calculation and uncertainties inherent to this approach. The system was validated by successfully extracting the flow profiles in both Newtonian and shear-thinning liquids.     

Making a Clean Sweep of Cultural Heritage

Digitization brings about new ways of analyzing data from cultural heritage areas. Automatic error detection, as input to semiautomatic error correction, is one type of analysis that can be found high on the priority list of cultural heritage data managers and researchers. We describe a general approach to cleaning cultural heritage databases. We present four case studies on databases from different cultural heritage institutions, and describe an information system in which we embed our error detector in a larger framework, enabling researchers to access, check, and correct their data more easily than before.     

Achieving traceability in large scale continuous integration and delivery deployment,usage and validation of the eiffel framework

The importance of traceability in software development has long been recognized, not only for reasons of legality and certification, but also to enable the development itself. At the same time, organizations are known to struggle to live up to traceability requirements, and there is an identified lack of studies on traceability practices in the industry, not least in the area of tooling and infrastructure. This paper presents, investigates and discusses Eiffel, an industry developed solution designed to provide real time traceability in continuous integration and delivery. The traceability needs of industry professionals are also investigated through interviews, providing context to that solution. It is then validated through further interviews, a comparison with previous traceability methods and a review of literature. It is found to address the identified traceability needs and found in some cases to reduce traceability data acquisition times from days to minutes, while at the same time alternatives offering comparable functionality are lacking. In this work, traceability is shown not only to be an important concern to engineers, but also regarded as a prerequisite to successful large scale continuous integration and delivery. At the same time, promising developments in technical infrastructure are documented and clear differences in traceability mindset between separate industry projects is revealed.     

Learning effective color features for content based image retrieval in dermatology

We investigate the extraction of effective color features for a content-based image retrieval (CBIR) application in dermatology. Effectiveness is measured by the rate of correct retrieval of images from four color classes of skin lesions. We employ and compare two different methods to learn favorable feature representations for this special application: limited rank matrix learning vector quantization (LiRaM LVQ) and a Large Margin Nearest Neighbor (LMNN) approach. Both methods use labeled training data and provide a discriminant linear transformation of the original features, potentially to a lower dimensional space. The extracted color features are used to retrieve images from a database by a k-nearest neighbor search. We perform a comparison of retrieval rates achieved with extracted and original features for eight different standard color spaces. We achieved significant improvements in every examined color space. The increase of the mean correct retrieval rate lies between 10% and 27% in the range of k=1-25 retrieved images, and the correct retrieval rate lies between 84% and 64%. We present explicit combinations of RGB and CIE-Lab color features corresponding to healthy and lesion skin. LiRaM LVQ and the computationally more expensive LMNN give comparable results for large values of the method parameter κ of LMNN (κ≥25) while LiRaM LVQ outperforms LMNN for smaller values of κ. We conclude that feature extraction by LiRaM LVQ leads to considerable improvement in color-based retrieval of dermatologic images.