Spin-polarised atomic ensembles probed by light based on the Faraday interaction are a versatile platform for numerous applications in quantum metrology and quantum information processing. Here we consider an ensemble of Alkali atoms that are continuously optically pumped and probed. Due to the collective scattering of photons at large optical depth, the steady state of atoms does not correspond to an uncorrelated tensor-product state, as is usually assumed. We introduce a self-consistent method to approximate the steady state including the pair correlations, taking into account the multilevel structure of atoms. We find and characterize regimes of Raman lasing, akin to the model of a superradiant laser. We determine the spectrum of the collectively scattered photons, which also characterises the coherence time of the collective spin excitations on top of the stationary correlated mean-field state, as relevant for applications in metrology and quantum information.

Gas bubble nucleation and its control is one of the most important parameters in industrial foaming applications defining the physical and chemical properties of the end product. It is possible to enhance this process by adding gas bubble nucleation supporting agents. In this work, the potency of native corn and potato starch as bubble nucleating agents for low-temperature high pressure (HP) foaming applications have been evaluated at 30 barg. In a first work step, the physical properties of the starches were assessed using scanning electron microscopy (SEM), Washburn rise method, nitrogen adsorption, Hg porosimetry as well as light scattering and compared to those of talcum, a well know and widely used nucleating agent in non-food systems. Secondly, the effect of the addition of these starch particles on CO2 gas bubble nucleation in highly viscous watery hydroxy-methyl-propyl-cellulose dispersions was determined applying HP rheology. Results of the surface properties evaluation suggested that the investigated native starch particles are suitable natural nucleating agents for HP foaming applications but are less efficient than talcum particles. The critical supersaturation value of a 1 wt% HPMC dispersion was reduced from 2.9±0.4 to 1.1±0.7 and 1.3±0.6 after the addition of 1 wt% corn and potato starch particles, respectively. The applied HP rheology technique developed to measure the critical supersaturation revealed, that the starches can compete with the talcum. These findings also allowed to validate the suitability of HP rheology to investigate gas bubble nucleation under defined shear conditions thus, enabling new insights into the mechanism of this process for low-temperature foaming applications in food and pharmaceutical product systems.

Engineering languages for model-driven development (MDD) highly rely on code generators that systematically and efficiently generate source code from abstract models. Although code generation is an essential technique, there is still a lot of ad hoc mechanisms in use that prevent an efficient and reliable use and especially reuse of code generators. The first part of the paper focuses on general mechanisms necessary to really allow reuse of flexible code generators. Based on these general considerations, we present a code generator infrastructure, that allows to easily develop a generator, but especially allows to adapt existing generators to different technology stacks and thus widely supports reusability, customizability, and flexibility. In the second part of the paper, we present an integrated template- and transformation-based code generation approach. It enables efficient code generation of object-oriented code and retains the benefits of both approaches. Even more, its synergetic use improves usability beyond using just a single approach. Internally, an intermediate representation (IR) and a separation of the code generation process into three main steps is used. First, the input model is processed and transformed to the IR. Second, elements in the IR are manipulated by source and target language independent transformations. Target language specific implementations are added by templates, which are attached to IR elements. Third, the resulting IR is used by a template engine to generate code with a predefined set of default templates for a particular target language. The overall goal of this paper is to show how to address necessary code generator considerations to effectively and efficiently use engineering languages in MDD.

While genetic variants have been reported to be associated with obsessive-compulsive disorder (OCD), the small effect sizes suggest that epigenetic mechanisms such as DNA methylation may also be relevant. The serotonin transporter (SLC6A4) gene has been extensively investigated in relation to OCD, since serotonin reuptake inhibitors are the pharmacological treatment of choice for the disorder. The current study set three questions: Firstly, whether the high expressing loci of the SLC6A4 polymorphisms, 5-HTTLPR + rs25531, rs25532 and rs16965628 are associated with family-based (n = 164 trios) and case-control OCD (n = 186, 152, respectively). This was also examined by a meta-analysis. Secondly, whether DNA methylation and RNA levels of the SLC6A4 differ in saliva and blood of a subset of samples from pediatric and adult OCD patients and matched controls. And lastly, whether morning awakening cortisol levels correlate with the above. A meta-analysis confirmed the association of the LA-allele with OCD (OR = 1.21, p = 0.00018), maintaining significance in the early-onset OCD subgroup (OR = 1.21, p = 0.022). There was no association between rs25532 or rs16965628 and OCD. Our preliminary data showed that SLC6A4 DNA methylation levels in an amplicon located at the beginning of the first intron were significantly higher in the saliva of pediatric OCD patients compared to controls and adult patients with OCD, but no alterations in RNA levels or in polymorphism interactions were observed. Morning awakening salivary cortisol levels positively correlated with methylation levels, and negatively correlated with RNA levels. This study further supports the involvement of the SLC6A4 gene in OCD through both genetic and epigenetic mechanisms. This finding needs to be explored further in an independent large sample.

Regular physical activity and physical fitness are closely related to a positive health status in humans. In this context, the muscle becomes more important due to its function as an endocrine organ. Muscle tissue secretes "myokines" in response to physical activity and it is speculated that these myokines are involved in physical activity induced positive health effects. Recently, the newly discovered myokine Irisin thought to be secreted by the muscle in response to physical activity and might be related to the health inducing effect by inducing browning of white adipose tissue. Speculating that myokines at least partly mediate exercise related health effects one would assume that regular physical activity and physical fitness are associated with resting Irisin concentrations in healthy humans. To investigate the association between resting Irisin concentration and either short-term physical activity, habitual physical activity, or physical fitness, data of 300 healthy participants from the cross-sectional KarMeN-study were analyzed. By applying different activity measurements we determined short-term and habitual physical activity, as well as physical fitness. Fasting serum samples were collected to determine resting Irisin concentrations by Enzyme-linked Immunosorbent Assay. Multivariate linear regression analysis served to investigate associations of the individual physical activity parameters with Irisin concentrations. Therefore, lean body mass and total fat mass (both determined by dual-energy X-ray absorptiometry) as well as age and parameters of glucose metabolism were included as confounders in multivariate linear regression analysis. Results showed that Irisin serum concentrations were not related to measures of physical activity and physical fitness in healthy humans under resting conditions, irrespective of the applied methods. Therefore we assume that if physical activity related effects are partly induced by myokines, permanently increased Irisin serum concentration may not be necessary to induce health-related exercise effects.

Physiological and functional parameters, such as body composition, or physical fitness are known to differ between men and women and to change with age. The goal of this study was to investigate how sex and age-related physiological conditions are reflected in the metabolome of healthy humans and whether sex and age can be predicted based on the plasma and urine metabolite profiles.In the cross-sectional KarMeN (Karlsruhe Metabolomics and Nutrition) study 301 healthy men and women aged 18-80 years were recruited. Participants were characterized in detail applying standard operating procedures for all measurements including anthropometric, clinical, and functional parameters. Fasting blood and 24 h urine samples were analyzed by targeted and untargeted metabolomics approaches, namely by mass spectrometry coupled to one-or comprehensive two-dimensional gas chromatography or liquid chromatography, and by nuclear magnetic resonance spectroscopy. This yielded in total more than 400 analytes in plasma and over 500 analytes in urine. Predictive modelling was applied on the metabolomics data set using different machine learning algorithms.Based on metabolite profiles from urine and plasma, it was possible to identify metabolite patterns which classify participants according to sex with > 90% accuracy. Plasma metabolites important for the correct classification included creatinine, branched-chain amino acids, and sarcosine. Prediction of age was also possible based on metabolite profiles for men and women, separately. Several metabolites important for this prediction could be identified including choline in plasma and sedoheptulose in urine. For women, classification according to their menopausal status was possible from metabolome data with > 80% accuracy.The metabolite profile of human urine and plasma allows the prediction of sex and age with high accuracy, which means that sex and age are associated with a discriminatory metabolite signature in healthy humans and therefore should always be considered in metabolomics studies.

Software for self-driving vehicles requires intensive testing to avoid fatal accidents and to allow correct operation in real-world environments. Simulation frameworks allow to imitate the behaviour of complex systems such as autonomous vehicles using simplified models of the real world. Hence, they are important tools allowing to extend component and functional tests to address interconnections between sensors, actuators, and controllers in virtual and predefined environments. Existing simulators can be separated into high-level and low-level ones. Both are designed for very specific scenarios and are not suitable for addressing all driving situations. While high-level simulators are suitable for mastering large testing environments such as cities, they lack fine-grained simulation capabilities, e.g., turning of wheels. In contrast, low-level simulators provide a high level of detail with realistic motion profiles. This is usually only possible in small testing environments. In this paper, we present an approach that combines the benefits of both high-level and lowlevel simulators to execute component and connector models. Vehicle and traffic engineers can choose the most suitable level of detail for their application and integrate real-world environment data from OpenStreetMap. Moreover, the simulator allows for adaptations and extensions of the physical vehicle configuration including new sensors, actuators and control systems. Another feature of our simulator is its automated testing support and its ability to visualize 3D simulations in a browser.

OBJECTIVE:The objective of the study was to identify predictors of BMI in German adults by considering the BMI distribution and to determine whether the association between BMI and its predictors varies along the BMI distribution.METHODS:The sample included 9,214 adults aged 18-80 years from the German National Nutrition Survey II (NVS II). Quantile regression analyses were conducted to examine the association between BMI and the following predictors: age, sports activities, socio-economic status (SES), healthy eating index-NVS II (HEI-NVS II), dietary knowledge, sleeping duration and energy intake as well as status of smoking, partner relationship and self-reported health.RESULTS:Age, SES, self-reported health status, sports activities and energy intake were the strongest predictors of BMI. The important outcome of this study is that the association between BMI and its predictors varies along the BMI distribution. Especially, energy intake, health status and SES were marginally associated with BMI in normal-weight subjects; this relationships became stronger in the range of overweight, and were strongest in the range of obesity.CONCLUSIONS:Predictors of BMI and the strength of these associations vary across the BMI distribution in German adults. Consequently, to identify predictors of BMI, the entire BMI distribution should be considered.

Modern cloud-based service architectures have to cope with requirements arising from handling big data such as integrating heterogeneous data sources (variety), storing the large amount of data (volume), keeping up with the frequency of data (velocity), and tolerating errors and faults within the data (veracity). Development of new services must be fast and efficient by reusing already existing services. Reuse and composition of services enable value-added services. Incorporating individual end-user preferences in the service design is important, but raises new challenges regarding privacy. In fact, reuse, composition to value-added services, and fulfilling privacy requirements are hindered by multiple challenges and necessary, yet expensive and error-prone, repetitive tasks. Integration of heterogeneous data sources demands for the identification of a common data model and integration of, typically heterogeneous third-party, data sources. This usually entails that users have to provide much of their personal data to the service, thus losing control over their data. Apart from the loss of control, users also have to give the same content to different services multiple times. In case the content changes they have to update it in several places. The developer has to deal with these problems for every new service under development. We discuss the technical constituents of modern cloud-based service architectures. For each constituent of the modern cloud-based service architecture, we identify target technology-specific parts, parts containing the services' logic, and parts specific to the using service. For those, we identify the possibilities for a generative approach. This classification of parts enables us to analyze what parts of such a modern cloud-service architecture can be automatically generated or at least systematically derived.

Comparison of food consumption, nutrient intake and underreporting of diet history interviews, 24-h recalls and weighed food records to gain further insight into specific strength and limitations of each method and to support the choice of the adequate dietary assessment method.

Reinforcement learning is a sub-field of machine learning where an agent aims to learn a behavior or a policy maximizing a reward function by trial and error. The approach is particularly interesting for the design of autonomous cyber-physical systems such as self-driving cars. In this work we present a generative, domain-specific modeling framework for the design, training and integration of reinforcement learning systems. It consists of a neural network modeling language which is used to design the models to be trained, e.g. actor and critic networks, and a training language used to describe the training procedure and set the corresponding hyperparameters. The underlying component model allows the modeler to embed the trained networks in larger component & connector architectures. We illustrate our framework by the example of a self-driving racing car.

The increasing complexity of modern systems development demands for specific modeling languages capturing the various aspects to be tackled. However, engineering of comfortable modeling languages as well as their tooling is a challenging endeavor. Far too often, new languages are built from scratch. We shed light into the advances of modeling language engineering that facilitates reuse, modularity, compositionality and derivation of new languages based on language components. We discuss ways to design, combine, and derive modeling languages in all their relevant aspects. For each of these activities, we illustrate their application for the model-driven development of a data exploration tool. The tool itself uses a set of meta-information, namely the structural model to derive all necessary software components that help to gather, store, visualize and navigate the data.

In many development projects models are core artifacts used to generate concrete implementations from them. However, for many systems it is impossible or not useful to generate the complete software system from models alone. Hence, developers need mechanisms for integrating generated and handwritten code. Applying such mechanisms without considering their effects can cause issues in projects, where model and code artifacts are essential. Thus, a sound approach for the integration of both forms of code is needed.In this paper, we provide an overview of mechanisms for integrating handwritten and generated object-oriented code. To compare these mechanisms, we define and apply a set of criteria. The results are intended to help model-driven development (MDD) tool developers in choosing an appropriate integration mechanism. In this extended version, we additionally discuss essential integration aspects including the protection of generated code and elaborate on how to use action languages to extend generated code.

Natural genetic variation is the raw material of evolution and influences disease development and progression. An important question is how this genetic variation translates into variation in protein abundance. To analyze the effects of the genetic background on gene and protein expression in the nematode Caenorhabditis elegans, we quantitatively compared the two genetically highly divergent wild-type strains N2 and CB4856. Gene expression was analyzed by microarray assays, and proteins were quantified using stable isotope labeling by amino acids in cell culture. Among all transcribed genes, we found 1,532 genes to be differentially transcribed between the two wild types. Of the total 3,238 quantified proteins, 129 proteins were significantly differentially expressed between N2 and CB4856. The differentially expressed proteins were enriched for genes that function in insulin-signaling and stress-response pathways, underlining strong divergence of these pathways in nematodes. The protein abundance of the two wild-type strains correlates more strongly than protein abundance versus transcript abundance within each wild type. Our findings indicate that in C. elegans only a fraction of the changes in protein abundance can be explained by the changes in mRNA abundance. These findings corroborate with the observations made across species.

Ein medizinischer Behandlungsprozess setzt sich aus zumeist vereinheitlichten Abläufen und verschiedenen Entscheidungen zusammen. Um einen optimalen Behandlungsablauf für bestimmte Krankheitsbilder und Symptomkomplexe zu gewährleisten, werden klinikinterne Standard Operating Procedures, Verfahrensanweisungen oder übergeordnete Behandlungspfade festgelegt. Allerdings müssen diese Behandlungsanweisungen die aktuell geltenden medizinischen ERC-Leitlinien berücksichtigen. Damit die Anzahl an Fehlbehandlungen auf ein Minimum reduziert wird, überarbeiten Gremien unter Berücksichtigung aktuellster wissenschaftlicher Erkenntnisse regelmäßig diese Leitlinien. Zur Vermeidung von Fehlanpassungen bei der manuellen Co-Evolution der Standard Operating Procedures bzgl. den aktualisierten Leitlinien, erarbeitet dieses Paper wichtige informatikrelevante Forschungsfragen, um die Kliniken in diesem Aspekt mittels Validierung und Automatisierung in Zukunft unterstützen zu können.

Generating software from abstract models is a prime activity in model-driven engineering. Adaptable and extendable code generators are important to address changing technologies as well as user needs. However, they are less established, as variability is often designed as configuration options of monolithic systems. Thus, code generation is often tied to a fixed set of features, hardly reusable in different contexts, and without means for configuration of variants. In this paper, we present an approach for developing product lines of template-based code generators. This approach applies concepts from feature-oriented programming to make variability explicit and manageable. Moreover, it relies on explicit variability regions (VR) in a code generator’s templates, refinements of VRs, and the aggregation of templates and refinements into reusable layers. A concrete product is defined by selecting one or multiple layers. If necessary, additional layers required due to VR refinements are automatically selected.