Prediction of Chronic Inflammation for Inhaled Particles: the Impact of Material Cycling and Quarantining in the Lung Epithelium

On a daily basis, people are exposed to a multitude of health-hazardous airborne particulate matter with notable deposition in the fragile alveolar region of the lungs. Hence, there is a great need for identification and prediction of material-associated diseases, currently hindered due to the lack of in-depth understanding of causal relationships, in particular between acute exposures and chronic symptoms. By applying advanced microscopies and omics to in vitro and in vivo systems, together with in silico molecular modeling, it is determined herein that the long-lasting response to a single exposure can originate from the interplay between the newly discovered nanomaterial quarantining and nanomaterial cycling between different lung cell types. This new insight finally allows prediction of the spectrum of lung inflammation associated with materials of interest using only in vitro measurements and in silico modeling, potentially relating outcomes to material properties for a large number of materials, and thus boosting safe-by-design-based material development. Because of its profound implications for animal-free predictive toxicology, this work paves the way to a more efficient and hazard-free introduction of numerous new advanced materials into our lives.     

High-Yield Separation of Extracellular Vesicles Using Programmable Zwitterionic Coacervates

Programmable coacervates based on zwitterionic polymers are designed as dynamic materials for ion exchange bioseparation. These coacervates are proposed as promising materials for the purification of soft nanoparticles such as liposomes and extracellular vesicles (EVs). It is shown that the stimulus-responsiveness of the coacervates and the recruitment of desired molecules can be independently programmed by polymer design. Moreover, the polymeric coacervates can recruit and release intact liposomes, human EVs, and nanoalgosomes in high yields and separate vesicles from different types of impurities, including proteins and nucleic acids. This approach combines the speed and simplicity of precipitation methods and the programmability of chromatography with the gentleness of aqueous two-phase separation, thereby guaranteeing product stability. This material represents a promising alternative for providing a low-shear, gentle, and selective purification method for EVs.     

Serious storytelling – a first definition and review

Multimedia Tools and Applications - In human culture, storytelling is a long-established tradition. The reasons people tell stories are manifold: to entertain, to transfer knowledge between...     

Survey on Simulation for Mobile Ad-Hoc Communication for Disaster Scenarios

Mobile ad-hoc communication is a demonstrated solution to mitigate the impact of infrastructure failures during large-scale disasters. A very complex issue in this domain is the design validation of software applications that support decision-making and communication during natural disasters. Such disasters are irreproducible, highly unpredictable, and impossible to scale down, and thus extensive assessments cannot be led in situ. In this context, simulation constitutes the best approach towards the testing of software solutions for natural disaster responses. The present survey reviews mobility models, ad-hoc network architectures, routing protocols and network simulators. Our aim is to provide guidelines for software developers with regards to the performance evaluation of their applications by means of simulation.     

MASE-BDI: agent-based simulator for environmental land change with efficient and parallel auto-tuning

This paper presents an agent-based simulator for environmental land change that includes efficient and parallel auto-tuning. This simulator extends the Multi-Agent System for Environmental simulation (MASE) by introducing rationality to agents using a mentalistic approach—the Belief-Desire-Intention (BDI) model—and is thus named MASE-BDI. Because the manual tuning of simulation parameters is an error-prone, labour and computing intensive task, an auto-tuning approach with efficient multi-objective optimization algorithms is also introduced. Further, parallelization techniques are employed to speed up the auto-tuning process by deploying it in parallel systems. The MASE-BDI is compared to the MASE using the Brazilian Cerrado biome case. The MASE-BDI reduces the simulation execution times by at least 82 × and slightly improves the simulation quality. The auto-tuning algorithms, by evaluating less than 0.00115 % of a search space with 6 million parameter combinations, are able to quickly tune the simulation model, regardless of the objective used. Moreover, the experimental results show that executing the tuning in parallel leads to speedups of approximately 11 × compared to sequential execution in a hardware setting with 16-CPU cores.     

Performance of infiltration swales with regard to operation in winter times in an Alpine region

In cold climate regions winter conditions significantly influence the performance of stormwater infiltration devices. Frozen soil and water storage by snow changes their operation. In this paper winter operation of a grassed infiltration swale was investigated using on-site and laboratory measurements. The field investigation of a grassed swale at a parking place in an Alpine region showed that the swale fulfilled its function properly. Although the top layer was frozen for some time, the storage capacity of the swale was sufficient to store the precipitation until the conditions improved. The soil attenuated the air temperature, at 20 cm below ground surface the soil was only frozen for one week. winter maintenance proved to be a problem, together with the snow from the parking place a lot of gravel and fine particles were deposited at one end of the swale. This decreased the hydraulic conductivity at that point significantly. The laboratory tests with soil columns showed an increase of flow time through the soil column with decreasing soil moisture content. For soil temperatures below 0 degrees C the hydraulic conductivity was reduced for increasing initial soil moisture contents. All in all the hydraulic conductivity was best around 0 degrees C for all soil water contents. However, also at minus 5 degrees C the coefficient of hydraulic conductivity was always at least above 10(-6) m/s, thus within the range of tolerated hydraulic conductivity specified in the national guidelines. Nevertheless, the handling of the soil was found to have high influence on the results. The results indicate that in the Alpine region infiltration swales operate sufficiently under winter conditions although with decreased performance.     

Designing context-sensitive systems: An integrated approach

Context-sensitive systems (CSS) are computer systems that use context to provide more relevant services or information to support users performing their tasks, where context is any information that can be used to characterize the situation in which something exists or occurs. CSS demand that designers consider new aspects and challenges in comparison to traditional applications. In a preliminary experiment, we observed that developers find it difficult to include the concept of context in their applications. However, only few approaches offer integrated domain-independent support on developing CSS. This paper presents an integrated approach to assist the design of CSS. The originality of this work lies on the proposed way of thinking about context, on the proposed context metamodel and on the specification of a process for designing CSS. The metamodel supports building context models by making explicit the concepts related to context manipulation and by separating the context structure model from the CSS behavior model. The design process details the main activities related to context specification and the design of CSS, providing a systematic way to execute these tasks. This work also advances the state of the art related to the understanding of the concept of context (and its associated concepts). Three experimental studies were conducted to evaluate the proposal: its instantiation in the design of a context-sensitive Expert Recommender System, its usage by distinct designers on their CSS projects, and a qualitative evaluation of the overall proposal by experienced CSS developers. These studies showed a good acceptance of our approach and indicated the feasibility of using it on real projects.     

Objective and subjective evaluation of the acoustic comfort in classrooms

The acoustic comfort of classrooms in a Brazilian public school has been evaluated through interviews with 62 teachers and 464 pupils, measurements of background noise, reverberation time, and sound insulation. Acoustic measurements have revealed the poor acoustic quality of the classrooms. Results have shown that teachers and pupils consider the noise generated and the voice of the teacher in neighboring classrooms as the main sources of annoyance inside the classroom. Acoustic simulations resulted in the suggestion of placement of perforated plywood on the ceiling, for reduction in reverberation time and increase in the acoustic comfort of the classrooms.     

LearnMet: learning domain-specific distance metrics for plots of scientific functions

Scientific experimental results are often depicted as plots of functions to aid their visual analysis and comparison. In computationally comparing these plots using techniques such as similarity search and clustering, the notion of similarity is typically distance. However, it is seldom known which distance metric(s) best preserve(s) semantics in the respective domain. It is thus desirable to learn such domain-specific distance metrics for the comparison of plots. This paper describes a technique called LearnMet proposed to learn such metrics. The input to LearnMet is a training set with actual clusters of plots. These are iteratively compared with clusters over the same plots predicted using an arbitrary but fixed clustering algorithm. Using a guessed initial metric for clustering, adjustments are made to the metric in each epoch based on the error between the predicted and actual clusters until the error is minimal or below a given threshold. The metric giving the lowest error is output as the learned metric. The proposed LearnMet technique and its enhancements are discussed in detail in this paper. The primary application of LearnMet is clustering plots in the Heat Treating domain. Hence it is rigorously evaluated using Heat Treating data. Given distinct test sets for evaluation, clusters of plots predicted using the learned metrics are compared with given actual clusters over the same plots. The extent to which the predicted and actual clusters match each other denotes the accuracy of the learned metrics.