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Software product line (SPL) engineering is a recent approach to software development where a set of software products are derived for a well defined target application domain, from a common set of core assets using analogous means of production (for instance, through Model Driven Engineering). Therefore, such family of products are built from reuse, instead of developed individually from scratch. SPL promise to lower the costs of development, increase the quality of software, give clients more flexibility and reduce time to market. These benefits come with a set of new problems and turn some older problems possibly more complex. One of these problems is traceability management. In the European AMPLE project we are creating a common traceability framework across the various activities of the SPL development. We identified four orthogonal traceability dimensions in SPL development, one of which is an extension of what is often considered as “traceability of variability”. This constitutes one of the two contributions of this paper. The second contribution is the specification of a metamodel for a repository of traceability links in the context of SPL and the implementation of a respective traceability framework. This framework enables fundamental traceability management operations, such as trace import and export, modification, query and visualization. The power of our framework is highlighted with an example scenario.

Modularity and efficiency are often contradicting requirements, such that programers have to trade one for the other. We analyze this dilemma in the context of programs operating on collections. Performance-critical code using collections need often to be hand-optimized, leading to non-modular, brittle, and redundant code. In principle, this dilemma could be avoided by automatic collection-specific optimizations, such as fusion of collection traversals, usage of indexing, or reordering of filters. Unfortunately, it is not obvious how to encode such optimizations in terms of ordinary collection APIs, because the program operating on the collections is not reified and hence cannot be analyzed. We propose SQuOpt, the Scala Query Optimizer--a deep embedding of the Scala collections API that allows such analyses and optimizations to be defined and executed within Scala, without relying on external tools or compiler extensions. SQuOpt provides the same \"look and feel\" (syntax and static typing guarantees) as the standard collections API. We evaluate SQuOpt by re-implementing several code analyses of the Findbugs tool using SQuOpt, show average speedups of 12x with a maximum of 12800x and hence demonstrate that SQuOpt can reconcile modularity and efficiency in real-world applications.

Polyene cyclizations are among the most complex and challenging transformations in biology. In a single reaction step, multiple carbon–carbon bonds, ring systems and stereogenic centres are constituted from simple, acyclic precursors 1 – 3 . Simultaneously achieving this kind of precise control over product distribution and stereochemistry poses a formidable task for chemists. In particular, the polyene cyclization of (3 E ,7 E )-homofarnesol to the valuable naturally occurring ambergris odorant (−)-ambrox is recognized as a longstanding challenge in chemical synthesis 1 , 4 – 7 . Here we report a diastereoselective and enantioselective synthesis of (−)-ambrox and the sesquiterpene lactone natural product (+)-sclareolide by a catalytic asymmetric polyene cyclization by using a highly Brønsted-acidic and confined imidodiphosphorimidate catalyst in the presence of fluorinated alcohols. Several experiments, including deuterium-labelling studies, suggest that the reaction predominantly proceeds through a concerted pathway in line with the Stork–Eschenmoser hypothesis 8 – 10 . Mechanistic studies show the importance of the enzyme-like microenvironment of the imidodiphosphorimidate catalyst for attaining exceptionally high selectivities, previously thought to be achievable only in enzyme-catalysed polyene cyclizations. The catalytic asymmetric polyene cyclization of homofarnesol to ambrox is achieved using a highly Brønsted-acidic and confined imidodiphosphorimidate catalyst.

Footwear properties can influence physiological and biomechanical variables, which may lead to positive changes in distance running performance. One innovative development in running shoe technology is adding carbon fiber plates to increase midsole bending stiffness. However, there are only a few studies investigating the influence of shoe conditions on both physiological and biomechanical variables, simultaneously, when running for longer than 5 min or for distances > 1 km. Hence, the purpose of the current study was to investigate the influence of different running shoe concepts with carbon fiber plates on physiological and biomechanical parameters during a 10 km treadmill run. Twenty-three athletes participated in the study, which comprised four measurement days for each subject. On the first day, subjects performed a treadmill exhaustion test to determine maximum oxygen uptake. On the second, third, and fourth days, each subject ran 10 km at 70% of their maximum oxygen uptake in one of three shoe models. Significant differences were found between the shoe conditions for the biomechanical parameters, but not for the physiological parameters. It seems that runners adjusted their running styles to the shoe conditions during the 10 km run to reduce the load on the lower extremities without compromising their endurance performance. These results may have practical implications for runners, coaches, and shoe manufacturers.

Dual targeting of proteins to more than one subcellular localization has been found in animals, in fungi and in plants. In the latter, ambiguous N-terminal targeting signals have been described that result in the protein being located in both mitochondria and plastids. We have developed ambiguous targeting predictor (ATP), a machine-learning implementation that classifies such ambiguous targeting signals. Ambiguous targeting predictor is based on a support vector machine implementation that makes use of 12 different amino acid features. Prediction results were validated using fluorescent protein fusion. Both in silico and in vivo evaluations demonstrate that ambiguous targeting predictor is useful for predicting dual targeting to mitochondria and plastids. Proteins that are targeted to both organelles by tandemly arrayed signals (so-called twin targeting) can be predicted by both ambiguous targeting predictor and a combination of single targeting prediction tools. Comparison of ambiguous targeting predictor with previous experimental approaches, as well as in silico approaches, shows good congruence. Based on the prediction results, land plant genomes are expected to encode, on average, > 400 proteins that are located in mitochondria and plastids. Ambiguous targeting predictor is helpful for functional genome annotation and can be used as a tool to further our understanding about dual protein targeting and its evolution.