Molecular criteria for discriminating adipose fat and milk from different species by NanoESI MS and MS/MS of their triacylglycerols: application to archaeological remains

A new multistep analytical methodology is described in this paper for the precise identification of triacylglycerols, which are biomarkers of dairy products and subcutaneous fats, that may be chemically identified in archaeological pottery. It consists of the analysis of the total lipid extract from different kinds of fats by high-temperature gas chromatography, performed in order to select the ceramic vessels in which animal fats are well preserved, followed by nanoelectrospray QqTOF mass spectrometry that allows for distinguishing the specific origins of the lipids detected (namely, cow, sheep, or goat). The analysis of model samples, cow and goat dairy products and cow and sheep adipose fats, was successfully achieved. The fatty acid composition of each triacylglycerol was identified, which allowed for the discrimination of subcutaneous fats and dairy fats and distinguishing between cow and goat milk. This methodology was then applied to archaeological samples, and the presence of goat milk, cow milk, and possibly sheep subcutaneous fat was assessed based on the discriminating criteria found on modern fats.     

Low-temperature fabrication of layered self-organized Ge clusters by RF-sputtering

In this article, we present an investigation of (Ge + SiO₂)/SiO₂ multilayers deposited by magnetron sputtering and subsequently annealed at different temperatures. The structural properties were investigated by transmission electron microscopy, grazing incidence small angles X-ray scattering, Rutherford backscattering spectrometry, Raman, and X-ray photoelectron spectroscopies. We show a formation of self-assembled Ge clusters during the deposition at 250°C. The clusters are ordered in a three-dimensional lattice, and they have very small sizes (about 3 nm) and narrow size distribution. The crystallization of the clusters was achieved at annealing temperature of 700°C.     

Genetic Encoding of a Bicyclo[6.1.0]nonyne‐Charged Amino Acid Enables Fast Cellular Protein Imaging by Metal‐Free Ligation

Visualizing biomolecules by fluorescent tagging is a powerful method for studying their behaviour and function inside cells. We prepared and genetically encoded an unnatural amino acid (UAA) that features a bicyclononyne moiety. This UAA offered exceptional reactivity in strain‐promoted azide–alkyne cycloadditions. Kinetic measurements revealed that the UAA reacted also remarkably fast in the inverse‐electron‐demand Diels–Alder cycloaddition with tetrazine‐conjugated dyes. Genetic encoding of the new UAA inside mammalian cells and its subsequent selective labeling at low dye concentrations demonstrate the usefulness of the new amino acid for future imaging studies.     

Development of the Connecting Piece in ODF1-Deficient Mouse Spermatids

ODF1 is a major protein of the accessory fibres of the mammalian sperm tail. In addition, ODF1 is found in the connecting piece, a complex structure located at the posterior end of the nucleus that connects the sperm head and tail. The tight coupling of the sperm head and tail is critical for the progressive motility of the sperm to reach the oocyte for fertilisation. The depletion of ODF1 by homologous recombination in mice led to male infertility. Although sperm tails were present in the epididymis, no intact spermatozoa were found. Instead, the depletion of ODF1 resulted in sperm decapitation, suggesting that ODF1 is essential for the formation of the coupling apparatus and the tight linkage of the sperm head and tail. However, the development of the linkage complex in the absence of ODF1 has never been investigated. Here, I analysed the fine structure of the developing connecting piece by transmission electron microscopy. I show that the connecting piece develops as in wild-type spermatids. Structural abnormalities were not observed when ODF1 was absent. Thus, ODF1 is dispensable for the development of the connecting piece. However, the decapitation of ODF1-deficient spermatozoa indicates that the heads and tails of the spermatozoa are not linked, so that they separate when force is applied.     

A flow-based tabu search algorithm for the RCPSP with transfer times

In this paper, we propose a tabu search algorithm for the resource-constrained project scheduling problem with transfer times. Solutions are represented by resource flows extending the disjunctive graph model for shop scheduling problems. Neighborhoods are defined by parallel and serial modifications rerouting or reversing flow on certain arcs. This approach is evaluated from a theoretical and experimental point of view. Besides studying the connectivity of different neighborhoods, computational results are presented for benchmark instances with and without transfer times.     

Dissolved oxygen, COD, nitrogen and phosphorus profiles in a continuous sand filter used for WWTP effluent reclamation

Continuous sand filtration (CSF) offers interesting potential for the extensive treatment of wastewater treatment plant (WWTP) effluents for water reclamation and/or restrictive discharge. Research on concentration profiles over the height of the CSF shows that most bacteriological conversions are restricted to the lower part of the filter bed. Dissolved oxygen (DO) rapidly decreases to below 1 mg/L in the first 0.4 m of the filter bed, applying hydraulic velocities of 12.9 ～ 14.9 m/h and 10 ～ 20 mm/min sand velocities, independent of the methanol dosage. The DO decrease agrees with the observed decrease in chemical oxygen demand (COD). At the given operational conditions, NO(x)-N and N-total removal is dedicated to the first 0.9 m of the filter bed. Results show that by optimising the CSF operational conditions the very restrictive effluent N and P values of 2.2 and 0.15 mg/L, respectively, as described in the European Water Framework Directive, can be met.     

Automated bug assignment: Ensemble-based machine learning in large scale industrial contexts

Bug report assignment is an important part of software maintenance. In particular, incorrect assignments of bug reports to development teams can be very expensive in large software development projects. Several studies propose automating bug assignment techniques using machine learning in open source software contexts, but no study exists for large-scale proprietary projects in industry. The goal of this study is to evaluate automated bug assignment techniques that are based on machine learning classification. In particular, we study the state-of-the-art ensemble learner Stacked Generalization (SG) that combines several classifiers. We collect more than 50,000 bug reports from five development projects from two companies in different domains. We implement automated bug assignment and evaluate the performance in a set of controlled experiments. We show that SG scales to large scale industrial application and that it outperforms the use of individual classifiers for bug assignment, reaching prediction accuracies from 50 % to 89 % when large training sets are used. In addition, we show how old training data can decrease the prediction accuracy of bug assignment. We advice industry to use SG for bug assignment in proprietary contexts, using at least 2,000 bug reports for training. Finally, we highlight the importance of not solely relying on results from cross-validation when evaluating automated bug assignment.