Validation of simulations in multiphase flow metrology by comparison with experimental video observations

One important task in flow metrology is to evaluate the uncertainty in multiphase flow metering. A first important step towards this goal is to establish an accurate computational fluid dynamics (CFD) model of multiphase flows. In this contribution, results of multiphase flow simulations are validated by comparison with experimental data. For the evaluation and quantification of experimental observations, a tool for video analysis has been implemented. This tool extracts the liquid level over time, which is then used for further analysis and comparison with simulation data. Additional relevant parameters are obtained by frequency analysis, which is applied to both, experimental and simulation data. A comparison of the results shows good agreement between experiment and simulation

Deep learning based liquid level extraction from video observations of gas-liquid flows

The slug flow pattern is one of the most common gas–liquid flow patterns in multiphase transportation pipelines, particularly in the oil and gas industry. This flow pattern can cause severe problems for industrial processes. Hence, a detailed description of the spatial distribution of the different phases in the pipe is needed for automated process control and calibration of predictive models. In this paper, a deep-learning based image processing technique is presented that extracts the gas–liquid interface from video observations of multiphase flows in horizontal pipes. The supervised deep learning model consists of a convolutional neural network, which was trained and tested with video data from slug flow experiments. The consistency of the hand-labelled data and the predictions of the trained model have been evaluated in an inter-observer reliability test. The model was further tested with other data sets, which also included recordings of a different flow pattern. It is shown that the presented method provides accurate and reliable predictions of the gas–liquid interface for slug flow as well as for other separate flow patterns. Moreover, it is demonstrated how flow characteristics can be obtained from the results of the deep-learning based image processing technique

Accounting for Uncertainty in Cumulative Sediment Transport Using a Bayesian Approach

That sediment transport estimates have large uncertainty is widely acknowledged. When these estimates are used as the basis for a subsequent analysis, such as cumulative sediment loads or budgets, treatment of uncertainty requires careful consideration. The propagation of uncertainty is a problem that has been studied in many other scientific disciplines. In recent years, Bayesian statistical methods have been successfully used to this end in hydrology, ecology, climate science, and other disciplines where uncertainty plays a major role—their applications in sediment transport, however, have been few. Previous work demonstrated how deterministic sediment transport equations can be brought into a probabilistic framework using Bayesian methods. In this paper, we extend this basic model and apply it to sediment transport observations collected on the Snake River in Wyoming, USA. These data were used previously to develop a 50-year sediment budget below Jackson Lake dam. We revisit this example to demonstrate how viewing sediment transport probabilistically can help better characterize the propagation of uncertainty in the calculation of cumulative sediment transport. We present the development of probabilistic sediment rating curves that rely on deterministic sediment transport equations and then show how these can be used to compute the distribution of sediment input and output for each year from 1958 to 2007. The Bayesian approach described provides a robust way to quantify uncertainty and then propagate it through to subsequent analyses. Results show that transport uncertainty is quantified naturally in the Bayesian approach, making it unnecessary for modelers to assume some specified error rate (e.g., ± 5%) when developing estimates of cumulative transport. Further, we demonstrate that a Bayesian approach better constrains uncertainty and allows sediment deficit and surplus to be examined in terms of quantified risk

Evolution of the Genotype-to-Phenotype Map and the Cost of Pleiotropy in Mammals

Evolutionary studies have long emphasized that the genetic architecture of traits holds important microevolutionary consequences. Yet, studies comparing the genetic architecture of traits across species are rare, and discussions of the evolution of genetic systems are made on theoretical arguments rather than on empirical evidence. Here, we compared the genetic architecture of cranial traits in two different mammalian model organisms: the gray short-tailed opossum, Monodelphis domestica, and the laboratory mouse, Mus musculus. We show that both organisms share a highly polygenic genetic architecture for craniofacial traits, with many loci of small effect. However, these two model species differ significantly in the overall degree of pleiotropy, N, of the genotype-to-phenotype map, with opossums presenting a higher average N. They also diverge in their degree of genetic modularity, with opossums presenting less modular patterns of genetic association among traits. We argue that such differences highlight the context dependency of gene effects, with developmental systems shaping the variational properties of genetic systems. Finally, we also demonstrate based on the opossum data that current measurements for the relationship between the mutational effect size and N need to be re-evaluated in relation to the importance of the cost of pleiotropy for mammals

Differences in toxicity of anionic and cationic PAMAM and PPI dendrimers in zebrafish embryos and cancer cell lines

Dendrimers are an emerging class of polymeric nanoparticles with beneficial biomedical applications like early diagnostics, in vitro gene transfection or controlled drug delivery. However, the potential toxic impact of exposure on human health or the environment is often inadequately defined. Thus, polyamidoamine (PAMAM) dendrimers of generations G3.0, 3.5, 4.0, 4.5 and 5.0 and polypropylenimine (PPI) dendrimers G3.0, 4.0 and 5.0 were tested in zebrafish embryos for 96 h and human cancer cell lines for 24 h, to assess and compare developmental in vivo toxicity with cytotoxicity. The zebrafish embryo toxicity of cationic PAMAM and PPI dendrimers increased over time, with EC50 values ranging from 0.16 to just below 1.7 μM at 24 and 48 hpf. The predominant effects were mortality, plus reduced heartbeat and blood circulation for PPI dendrimers. Apoptosis in the embryos increased in line with the general toxicity concentration-dependently

Data - Genetics - Porto et al 2016

<div>## add_effect_vectors</div><div><br></div><div>folder containing the standardized additive effect vectors</div><div><br></div><div>## coded_marker_data</div><div>Genotypes for all markers, separated by chromosome. Genotype coding is described in the main text</div><div><br></div><div>## mouse_pleio_vectors</div><div>Mouse pleiotropy vectors used in the simulation. Values of 1 correspond to a trait being affected by that locus. Zero means no effect.</div><div>The manuscript corresponding to the mouse data is being submitted separately from this manuscript.</div><div><br></div><div>## phenotype_covariates</div><div>Phenotypes and covariates used when fitting the mixed models.</div><div><br></div><div>##sas_code </div><div>Code used to perform trait-specific QTL mapping</div><div><br></div><div>## matrices</div><div>G and P matrices calculated from the QTL data</div><div><br></div

mRNA vaccines against SARS-CoV-2 induce comparably low long-term IgG Fc galactosylation and sialylation levels but increasing long-term IgG4 responses compared to an adenovirus-based vaccine

Background: The new types of mRNA-containing lipid nanoparticle vaccines BNT162b2 and mRNA-1273 and the adenovirus-based vaccine AZD1222 were developed against SARS-CoV-2 and code for its spike (S) protein. Several studies have investigated short-term antibody (Ab) responses after vaccination. Objective: However, the impact of these new vaccine formats with unclear effects on the long-term Ab response - including isotype, subclass, and their type of Fc glycosylation - is less explored. Methods: Here, we analyzed anti-S Ab responses in blood serum and the saliva of SARS-CoV-2 naive and non-hospitalized pre-infected subjects upon two vaccinations with different mRNA- and adenovirus-based vaccine combinations up to day 270. Results: We show that the initially high mRNA vaccine-induced blood and salivary anti-S IgG levels, particularly IgG1, markedly decrease over time and approach the lower levels induced with the adenovirus-based vaccine. All three vaccines induced, contrary to the short-term anti-S IgG1 response with high sialylation and galactosylation levels, a long-term anti-S IgG1 response that was characterized by low sialylation and galactosylation with the latter being even below the corresponding total IgG1 galactosylation level. Instead, the mRNA, but not the adenovirus-based vaccines induced long-term IgG4 responses - the IgG subclass with inhibitory effector functions. Furthermore, salivary anti-S IgA levels were lower and decreased faster in naive as compared to pre-infected vaccinees. Predictively, age correlated with lower long-term anti-S IgG titers for the mRNA vaccines. Furthermore, higher total IgG1 galactosylation, sialylation, and bisection levels correlated with higher long-term anti-S IgG1 sialylation, galactosylation, and bisection levels, respectively, for all vaccine combinations. Conclusion: In summary, the study suggests a comparable "adjuvant" potential of the newly developed vaccines on the anti-S IgG Fc glycosylation, as reflected in relatively low long-term anti-S IgG1 galactosylation levels generated by the long-lived plasma cell pool, whose induction might be driven by a recently described T-H1-driven B cell response for all three vaccines. Instead, repeated immunization of naive individuals with the mRNA vaccines increased the proportion of the IgG4 subclass over time which might influence the long-term Ab effector functions. Taken together, these data shed light on these novel vaccine formats and might have potential implications for their long-term efficacy.</p