Abstract. Provenance of scientific data is a key piece of the metadata record for the data's ongoing discovery and reuse. Provenance collection systems capture provenance on the fly, however, the protocol between application and provenance tool may not be reliable. Consequently, the provenance record can be partial, partitioned, and simply inaccurate. We use a workflow emulator that models faults to construct a large 10GB database of provenance that we know is noisy (that is, has errors). We discuss the process of generating the provenance database, and show early results on the kinds of provenance analysis enabled by the large provenance.

Qualitative user research is a human-intensive approach that draws upon ethnographic methods from social sciences to develop insights about work practices to inform software design and development. Recent advances in data science, and in particular, natural language processing (NLP), enables the derivation of machine-generated insights to augment existing techniques. Our work describes our prototype framework based in Jupyter, a software tool that supports interactive data science and scientific computing, that leverages NLP techniques to make sense of transcribed texts from user interviews. This work also serves as a starting point for incorporating data science techniques in the qualitative analyses process.

It can be natural to believe that many of the traditional issues of scale have been eliminated or at least greatly reduced via cloud computing. That is, if one can create a seemingly wellfunctioning cloud application that operates correctly on small or moderate-sized problems, then the very nature of cloud programming abstractions means that the same application will run as well on potentially significantly larger problems. In this paper, we present our experiences taking MODISAzure, our satellite data processing system built on the Windows Azure cloud computing platform, from the proof-of-concept stage to a point of being able to run on significantly larger problem sizes (e.g., from national-scale data sizes to global-scale data sizes). To our knowledge, this is the longest-running eScience application on the nascent Windows Azure platform. We found that while many infrastructure-level issues were thankfully masked from us by the cloud infrastructure, it was valuable to design additional redundancy and fault-tolerance capabilities such as transparent idempotent task retry and logging to support debugging of user code encountering unanticipated data issues. Further, we found that using a commercial cloud means anticipating inconsistent performance and black-box behavior of virtualized compute instances, as well as leveraging changing platform capabilities over time. We believe that the experiences presented in this paper can help future eScience cloud application developers on Windows Azure and other commercial cloud providers.

Building on our previous works that compared the efficacy of terpolymers vs. ternary blends in improving the performance of bulk heterojunction organic solar cells, the final piece of this series of studies focuses on comparing terpolymer and ternary blends constructed with two polymers with structurally similar backbones (monoCNTAZ and FTAZ) yet markedly different open circuit voltage (Voc) values. Terpolymers and ternary blends of five different ratios were studied and the results demonstrate that while the overall performance of both the systems is similar, the ternary blends exhibit higher short circuit current (Jsc) values, while the terpolymers exhibit higher Voc values. Investigation of the charge transfer state using low-energy external quantum efficiency (EQE) indicates that the ternary blends are governed by a parallel-like mechanism, while the terpolymer does not follow this mechanism. The key morphological difference between the systems, as elucidated by resonance soft X-ray scattering (RSoXS), is the slightly smaller size (∼60 nm) of domains in the ternary blends compared to that of the terpolymer (∼80 nm), which may affect exciton harvesting in the terpolymer system and lead to lower Jsc values. In addition, a lower driving force for the formation of charge transfer (CT) state is also likely to contribute to the lower Jsc values in the terpolymer system. All together, the data show that structurally similar (perhaps even miscible) polymers still exhibit key differences in performance when paired in terpolymers vs. ternary blends and allow us to further illuminate the underlying mechanisms of such complex systems.

Ion exchange membranes (IEMs) are a key component of electrochemical processes that purify water, generate clean energy, and treat waste. Most conventional polymer IEMs are covalently cross-linked, which results in a challenging tradeoff relationship between two desirable properties─high permselectivity and high conductivity─in which one property cannot be changed without negatively affecting the other. In an attempt to overcome this limitation, in this work we synthesized a series of anion exchange membranes containing non-covalent cross-links formed by a hydrogen bond donor (methacrylic acid) and a hydrogen bond acceptor (dimethylacrylamide). We show that these monomers act synergistically to improve both membrane permselectivity and conductivity relative to a control membrane without non-covalent cross-links. Furthermore, we show that the hydrogen bond donor and acceptor loading can be used to tune permselectivity and conductivity relatively independently of one another, escaping the tradeoff observed in conventional membranes.

Background

Few studies have examined prognostic outcomes-associated molecular signatures other than overall survival (OS) for gastric cancer (GC). We aimed to identify DNA methylation biomarkers associated with multiple prognostic outcomes of GC in an epigenome-wide association study.

Methods

Based on the Cancer Genome Atlas (TCGA), DNA methylation loci associated with OS (n = 381), disease-specific survival (DSS, n = 372), and progression-free interval (PFI, n = 383) were discovered in training set subjects (false discovery rates < 0.05) randomly selected for each prognostic outcome and were then validated in remaining subjects (P-values < 0.05). Key CpGs simultaneously validated for OS, DSS, and PFI were further assessed for disease-free interval (DFI, n = 247). Gene set enrichment analyses were conducted to explore the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways simultaneously enriched for multiple GC prognostic outcomes. Methylation correlated blocks (MCBs) were identified for co-methylation patterns associated with GC prognosis. Based on key CpGs, risk score models were established to predict four prognostic outcomes. Spearman correlation analyses were performed between key CpG sites and their host gene mRNA expression.

Results

We newly identified DNA methylation of seven CpGs significantly associated with OS, DSS, and PFI of GC, including cg10399824 (GRK5), cg05275153 (RGS12), cg24406668 (MMP9), cg14719951(DSC3), and cg25117092 (MED12L), and two in intergenic regions (cg11348188 and cg11671115). Except cg10399824 and cg24406668, five of them were also significantly associated with DFI of GC. Neuroactive ligand-receptor interaction pathway was suggested to play a key role in the effect of DNA methylation on GC prognosis. Consistent with individual CpG-level association, three MCBs involving cg11671115, cg14719951, and cg24406668 were significantly associated with multiple prognostic outcomes of GC. Integrating key CpG loci, two risk score models performed well in predicting GC prognosis. Gene body DNA methylation of cg14719951, cg10399824, and cg25117092 was associated with their host gene expression, whereas no significant associations between their host gene expression and four clinical prognostic outcomes of GC were observed.

Conclusions

We newly identified seven CpGs associated with OS, DSS, and PFI of GC, with five of them also associated with DFI, which might inform patient stratification in clinical practices.

The Carbon Capture Simulation Initiative (CCSI) project has developed and deployed scientific infrastructure called the CCSI Toolset. The CCSI Toolset provides state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. Carbon capture technologies have the potential to dramatically reduce the carbon emissions from power plants. The CCSI Toolset provides end users in industry with a comprehensive, integrated suite of leading-edge, scientifically validated models with simulation, uncertainty quantification, optimization, risk analysis and decision making support. The CCSI Toolset has at its core an integrated framework that enables execution of simulations and workflows including optimization and uncertainty parameter sweeps using a wide variety of computing platforms including desktops, clusters, Clouds, and HPC systems. The integration framework enables the running of a variety of commercial process simulation packages as well as custom simulators. Moreover, the framework enables scientists to run and manage thousands of concurrent simulations to perform optimizations and uncertainty quantification. Components of the CCSI Toolset are connected through the use of a data management system that stores data to a repository and enables the tracking of provenance for each simulation as well as its associated components. The data management system tracks all the configurations, models, simulations, and results created during the design of a carbon capture system and supports the design life-cycle as well as decision making. The primary contribution of this paper is thus the design and implementation of the integration framework within the CCSI Toolset, which provides both data management and simulation support for CCSI. This integration framework has been deployed and is in use by several groups of researchers and commercial entities.

Recent emphasis and requirements for open data publication have led to significant increases in data availability in the Earth sciences, which is critical to long-tail data integration. Currently, data are often published in a repository with an identifier and citation, similar to those for papers. Subsequent publications that use the data are expected to provide a citation in the reference section of the paper. However, the format of the data citation is still evolving, particularly with regards to citing dynamic data, subsets, and collections of data. Considering the motivations of both data producers and consumers, the most pressing need is to create user-friendly solutions that provide credit for data producers and enable accurate citation of data, particularly integrated data. Providing easy-to-use data citations is a critical foundation that is required to address the socio-technical challenges around data integration. Studies that integrate data from dozens or hundreds of datasets must often include data citations in supplementary material due to page limits. However, citations in the supplementary material are not indexed, making it difficult to track citations and thus giving credit to the data producer. In this paper, we discuss our experiences and the challenges we have encountered with current citation guidance. We also review the relative merits of the currently available mechanisms designed to enable compact citation of collections of data, such as data collections, data papers, and dynamic data citations. We consider these options for three data producer scenarios: a domain-specific data collection, a data repository, and a large-scale, multidisciplinary project. We posit that a new mechanism is also needed to enable citation of multiple datasets and credit to data producers.