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Motivation: Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results: We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an autoencoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 90% and 78% cell-matching accuracy for each one of the samples, respectively., Bioinformatics, 36 (S2), ISSN:1367-4803, ISSN:1460-2059

Motivation Recent technological advances have led to an increase in the production and availability of single-cell data. The ability to integrate a set of multi-technology measurements would allow the identification of biologically or clinically meaningful observations through the unification of the perspectives afforded by each technology. In most cases, however, profiling technologies consume the used cells and thus pairwise correspondences between datasets are lost. Due to the sheer size single-cell datasets can acquire, scalable algorithms that are able to universally match single-cell measurements carried out in one cell to its corresponding sibling in another technology are needed. Results We propose Single-Cell data Integration via Matching (SCIM), a scalable approach to recover such correspondences in two or more technologies. SCIM assumes that cells share a common (low-dimensional) underlying structure and that the underlying cell distribution is approximately constant across technologies. It constructs a technology-invariant latent space using an auto-encoder framework with an adversarial objective. Multi-modal datasets are integrated by pairing cells across technologies using a bipartite matching scheme that operates on the low-dimensional latent representations. We evaluate SCIM on a simulated cellular branching process and show that the cell-to-cell matches derived by SCIM reflect the same pseudotime on the simulated dataset. Moreover, we apply our method to two real-world scenarios, a melanoma tumor sample and a human bone marrow sample, where we pair cells from a scRNA dataset to their sibling cells in a CyTOF dataset achieving 93% and 84% cell-matching accuracy for each one of the samples respectively. Availability https://github.com/ratschlab/scim, bioRxiv

The main objective of the Offshore Code Comparison Collaboration Continuation, with Correlation (OC5) project, is validation of aero-hydro-servo-elastic simulation tools for offshore wind turbines (OWTs) through comparison of simulated results to the response data of physical systems. Phase III of the OC5 project analyzes the Senvion 5M wind turbine supported by the OWEC Quattropod from the alpha ventus offshore wind farm. This paper shows results of the verification of the OWT models (code-to-code comparison). A subsequent publication will focus on their validation (comparison of simulated results to measured physical system response data). Based on the available data, the participants of Phase III set up numerical models of the OWT in their simulation tools. It was necessary to verify and to tune these models. The verification and tuning were performed against an OWT model available at the University of Stuttgart – Stuttgart Wind Energy (SWE) and documentation provided by Senvion and OWEC Tower. A very good match was achieved between the results from the reference SWE model and models set up by OC5 Phase III participants. Copyright © 2018 by ASME

The open-source, aero-hydro-servo-elastic wind turbine simulation software FAST v8 (created by the National Renewable Energy Laboratory) was recently coupled to two newly developed mooring dynamics modules: MoorDyn and FEAMooring. MoorDyn is a lumped-mass-based mooring dynamics module developed by the University of Maine, and FEAMooring is a finite-element-based mooring dynamics module developed by Texas A&M University. This paper summarizes the work performed to verify and validate these modules compared to other mooring models and measured test data to assess their reliability and accuracy. The quality of the fairlead load predictions by the open-source mooring modules MoorDyn and FEAMooring appears to be largely equivalent to that predicted by the commercial tool OrcaFlex. Both mooring dynamic model predictions agree well with the experimental data considering the given limitations in the accuracy of the platform hydrodynamic load calculation and the quality of the measurement data.

The quasi-static and dynamic mooring modules of the open-source aero-hydro-servo-elastic wind turbine simulation software, FAST v8, have previously been verified and validated, but only for mooring arrangements consisting of single lines connecting each fairlead and anchor. This paper extends the previous verification and validation efforts to focus on the multisegmented mooring capability of the FAST v8 modules: MAP++, MoorDyn, and the OrcaFlex interface. The OC3-Hywind spar buoy system tested by the DeepCwind consortium at the MARIN ocean basin, which includes a multisegmented bridle layout of the mooring system, was used for the verification and validation activities. This paper focuses on free-decay tests because the influence of the multisegmented moorings is most important for yaw motion, which is not excited by waves. All of the listed modules are able to represent the platform motion observed in the experimental data to a satisfactory degree. MoorDyn and the benchmark tool (OrcaFlex) yield almost indistinguishable results. But due to limitations of the system tested and data obtained, further work is needed to truly validate the multisegmented capability.