Hydrogen storage of nanocrystalline Mg–Ni alloy processed by equal-channel angular pressing and cold rolling

Ball-milled nanocrystalline Mg₂Ni powders were subjected to intense plastic straining by cold rolling or equal-channel angular pressing. Morphological and microstructural evolution during these processes has been investigated by scanning electron-microscopy and X-ray diffraction line profile analysis, respectively. Complementary hydrogen absorption experiments in a Sieverts' type apparatus revealed that there exists some correlation between the micro- and nanostructure and hydrogen storage properties of the severely deformed materials.     

New Insights into the Determinants of Specificity in Human Type I Arginase: Generation of a Mutant That Is Only Active with Agmatine as Substrate

Arginase catalyzes the hydrolysis of L-arginine into L-ornithine and urea. This enzyme has several analogies with agmatinase, which catalyzes the hydrolysis of agmatine into putrescine and urea. However, this contrasts with the highlighted specificity that each one presents for their respective substrate. A comparison of available crystal structures for arginases reveals an important difference in the extension of two loops located in the entrance of the active site. The first, denominated loop A (I129-L140) contains the residues that interact with the alpha carboxyl group or arginine of arginase, and the loop B (D181-P184) contains the residues that interact with the alpha amino group of arginine. In this work, to determine the importance of these loops in the specificity of arginase, single, double, and triple arginase mutants in these loops were constructed, as well as chimeras between type I human arginase and E. coli agmatinase. In previous studies, the substitution of N130D in arginase (in loop A) generated a species capable of hydrolyzing arginine and agmatine. Now, the specificity of arginase is completely altered, generating a chimeric species that is only active with agmatine as a substrate, by substituting I129T, N130Y, and T131A together with the elimination of residues P132, L133, and T134. In addition, Quantum Mechanic/Molecular Mechanic (QM/MM) calculations were carried out to study the accommodation of the substrates in in the active site of this chimera. With these results it is concluded that this loop is decisive to discriminate the type of substrate susceptible to be hydrolyzed by arginase. Evidence was also obtained to define the loop B as a structural determinant for substrate affinity. Concretely, the double mutation D181T and V182E generate an enzyme with an essentially unaltered k_cat value, but with a significantly increased K_m value for arginine and a significant decrease in affinity for its product ornithine.     

Assessment of in situ degradation of chlorinated ethenes and bacterial community structure in a complex contaminated groundwater system

The occurrence of in situ degradation of chlorinated ethenes was investigated using an integrated approach in a complex groundwater system consisting of several geological units. The assessment of hydrogeochemistry and chlorinated ethenes distribution using principal component analysis (PCA) in combination with carbon stable isotope analysis revealed that chlorinated ethenes were subjected to substantial biodegradation. Shifts in isotopic values up to 20.4 per thousand, 13.9 per thousand, 20.1 per thousand and 31.4 per thousand were observed between geological units for tetrachloroethene (PCE), trichloroethene (TCE), cis-dichloroethene (cDCE) and vinyl chloride (VC), respectively. The use of specific biomarkers (16S rRNA gene) indicated the presence of Dehalococcoides sp. DNA in 20 of the 33 evaluated samples. In parallel, the analysis of changes in the bacterial community composition in the aquifers using canonical correspondence analysis (CCA) indicated the predominant influence of the chlorinated ethene concentrations (56.3% of the variance, P=0.005). The integrated approach may open new prospects for the assessment of spatial and temporal functioning of bioattenuation in contaminated groundwater systems.     

Sample preparation for the analysis of complex carbohydrates by multicapillary gel electrophoresis with light-emitting diode induced fluorescence detection

This paper evaluates various sample preparation methods for multicapillary gel electrophoresis based glycan analysis to support electrokinetic injection. First the removal of excess derivatization reagent is discussed. Although the Sephadex G10 filled multiscreen 96-well filter plate and Sephadex G10 filled pipet tips enabled increased analysis sensitivity, polyamide DPA-6S pipet tips worked particularly well. In this latter case an automated liquid handling system was used to increase purification throughput, necessary to feed the multicapillary electrophoresis unit. Problems associated with the high glucose content of such biological samples as normal human plasma were solved by applying ultrafiltration. Finally, a volatile buffer system was developed for exoglycosidase-based carbohydrate analysis.     

Novel Polyurethane Scaffolds Containing Sucrose Crosslinker for Dental Application

In this paper, the synthesis, characterization, and properties of crosslinked poly(ε-caprolactone)-based polyurethanes as potential tissue replacement materials are reported. The polyurethane prepolymers were prepared from poly(ε-caprolactone)diol (PCD), polyethylene glycol (PEG)/polylactic acid diol (PLAD), and 1,6-hexamethylene diisocyanate (HDI). In these segmented polyurethanes, the role of PEG/PLAD was to tune the hydrophobic/hydrophilic character of the resulting polymer while sucrose served as a crosslinking agent. PLAD was synthesized by the polycondensation reaction of D,L-lactic acid and investigated by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and nuclear magnetic resonance spectroscopy (NMR). The crosslinked polyurethane samples (SUPURs) obtained were characterized by attenuated total reflectance Fourier-transform infrared spectroscopy (AT-FT-IR), swelling, and mechanical (uniaxial tensile tests) experiments. The thermo and thermomechanical behavior were studied by differential scanning calorimetry (DSC) and dynamical mechanical analysis (DMA). The viability of dental pulp stem cells was investigated in the case of polyurethanes composed of fully biocompatible elements. In our studies, none of our polymers showed toxicity to stem cells (DPSCs).     

A notion of task relatedness yielding provable multiple-task learning guarantees

The approach of learning multiple “related” tasks simultaneously has proven quite successful in practice; however, theoretical justification for this success has remained elusive. The starting point for previous work on multiple task learning has been that the tasks to be learned jointly are somehow “algorithmically related”, in the sense that the results of applying a specific learning algorithm to these tasks are assumed to be similar. We offer an alternative approach, defining relatedness of tasks on the basis of similarity between the example generating distributions that underlie these tasks. We provide a formal framework for this notion of task relatedness, which captures a sub-domain of the wide scope of issues in which one may apply a multiple task learning approach. Our notion of task similarity is relevant to a variety of real life multitask learning scenarios and allows the formal derivation of generalization bounds that are strictly stronger than the previously known bounds for both the learning-to-learn and the multitask learning scenarios. We give precise conditions under which our bounds guarantee generalization on the basis of smaller sample sizes than the standard single-task approach. Editors: Daniel Silver, Kristin Bennett, Richard Caruana. A preliminary version of this paper appears in the proceedings of COLT’03, (Ben-David and Schuller 2003).     

Handling dataset dependence with model ensembles for skin lesion classification from dermoscopic and clinical images

Dataset dependence affects many real-life applications of machine learning: the performance of a model trained on a dataset is significantly worse on samples from another dataset than on new, unseen samples from the original one. This issue is particularly acute for small and somewhat specific databases in medical applications; the automated recognition of melanoma from skin lesion images is a prime example. We document dataset dependence in dermoscopic skin lesion image classification using three publicly available medium size datasets. Standard machine learning techniques aimed at improving the predictive power of a model might enhance performance slightly, but the gain is small, the dataset dependence is not reduced, and the best combination depends on model details. We demonstrate that simple differences in image statistics account for only 5% of the dataset dependence. We suggest a solution with two essential ingredients: using an ensemble of heterogeneous models, and training on a heterogeneous dataset. Our ensemble consists of 29 convolutional networks, some of which are trained on features considered important by dermatologists; the networks' output is fused by a trained committee machine. The combined International Skin Imaging Collaboration dataset is suitable for training, as it is multi-source, produced by a collaboration of a number of clinics over the world. Building on the strengths of the ensemble, it is applied to a related problem as well: recognizing melanoma based on clinical (non-dermoscopic) images. This is a harder problem as both the image quality is lower than those of the dermoscopic ones and the available public datasets are smaller and scarcer. We explored various training strategies and showed that 79% balanced accuracy can be achieved for binary classification averaged over three clinical datasets.     

Highly sensitive pyrogen detection on medical devices by the monocyte activation test

Pyrogens are components of microorganisms, like bacteria, viruses or fungi, which can induce a complex inflammatory response in the human body. Pyrogen contamination on medical devices prior operation is still critical and associated with severe complications for the patients. The aim of our study was to develop a reliable test, which allows detection of pyrogen contamination on the surface of medical devices. After in vitro pyrogen contamination of different medical devices and incubation in a rotation model, the human whole blood monocyte activation test (MAT), which is based on an IL-1β-specific ELISA, was employed. Our results show that when combining a modified MAT protocol and a dynamic incubation system, even smallest amounts of pyrogens can be directly detected on the surface of medical devices. Therefore, screening of medical devices prior clinical application using our novel assay, has the potential to significantly reduce complications associated with pyrogen-contaminated medical devices.