Damping of liquid sloshing by foams

When a container is set in motion, the free surface of the liquid starts to oscillate or slosh. Such effects can be observed when a glass of water is handled carelessly and the fluid sloshes or even spills over the rims of the container. However, beer does not slosh as readily as water, which suggests that foam could be used to damp sloshing. In this work, we study experimentally the effect on sloshing of a liquid foam placed on top of a liquid bath. We generate a monodisperse two-dimensional liquid foam in a rectangular container and track the motion of the foam. The influence of the foam on the sloshing dynamics is experimentally characterized: only a few layers of bubbles are sufficient to significantly damp the oscillations. We rationalize our experimental findings with a model that describes the foam contribution to the damping coefficient through viscous dissipation on the walls of the container. Then we extend our study to confined three-dimensional liquid foam and observe that the behavior of 2D and confined 3D systems are very similar. Thus we conclude that only the bubbles close to the walls have a significant impact on the dissipation of energy. The possibility to damp liquid sloshing using foam is promising in numerous industrial applications such as the transport of liquefied gas in tankers or for propellants in rocket engines.Comment: 17 pages, accepted in Physics of Fluid

Big Data Pipelines on the Computing Continuum: Ecosystem and Use Cases Overview

Organisations possess and continuously generate huge amounts of static and stream data, especially with the proliferation of Internet of Things technologies. Collected but unused data, i.e., Dark Data, mean loss in value creation potential. In this respect, the concept of Computing Continuum extends the traditional more centralised Cloud Computing paradigm with Fog and Edge Computing in order to ensure low latency pre-processing and filtering close to the data sources. However, there are still major challenges to be addressed, in particular related to management of various phases of Big Data processing on the Computing Continuum. In this paper, we set forth an ecosystem for Big Data pipelines in the Computing Continuum and introduce five relevant real-life example use cases in the context of the proposed ecosystem

KidneyNetwork:Using kidney-derived gene expression data to predict and prioritize novel genes involved in kidney disease

Abstract: Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the disorder as potentially pathogenic variants can reside in genes that are not yet known to be involved in kidney disease. We have developed KidneyNetwork, that utilizes tissue-specific expression to inform candidate gene prioritization specifically for kidney diseases. KidneyNetwork is a novel method constructed by integrating a kidney RNA-sequencing co-expression network of 878 samples with a multi-tissue network of 31,499 samples. It uses expression patterns and established gene-phenotype associations to predict which genes could be related to what (disease) phenotypes in an unbiased manner. We applied KidneyNetwork to rare variants in exome sequencing data from 13 kidney disease patients without a genetic diagnosis to prioritize candidate genes. KidneyNetwork can accurately predict kidney-specific gene functions and (kidney disease) phenotypes for disease-associated genes. The intersection of prioritized genes with genes carrying rare variants in a patient with kidney and liver cysts identified ALG6 as plausible candidate gene. We strengthen this plausibility by identifying ALG6 variants in several cystic kidney and liver disease cases without alternative genetic explanation. We present KidneyNetwork, a publicly available kidney-specific co-expression network with optimized gene-phenotype predictions for kidney disease phenotypes. We designed an easy-to-use online interface that allows clinicians and researchers to use gene expression and co-regulation data and gene-phenotype connections to accelerate advances in hereditary kidney disease diagnosis and research. Translational statement: Genetic testing in patients with suspected hereditary kidney disease may not reveal the genetic cause for the patient’s disorder. Potentially pathogenic variants can reside in genes not yet known to be involved in kidney disease, making it difficult to interpret the relevance of these variants. This reveals a clear need for methods to predict the phenotypic consequences of genetic variation in an unbiased manner. Here we describe KidneyNetwork, a tool that utilizes tissue-specific expression to predict kidney-specific gene functions. Applying KidneyNetwork to a group of undiagnosed cases identified ALG6 as a candidate gene in cystic kidney and liver disease. In summary, KidneyNetwork can aid the interpretation of genetic variants and can therefore be of value in translational nephrogenetics and help improve the diagnostic yield in kidney disease patients.</p

Eyes Are Windows to the Chinese Soul: Evidence from the Detection of Real and Fake Smiles

How do people interpret the meaning of a smile? Previous studies with Westerners have found that both the eyes and the mouth are crucial in identifying and interpreting smiles, yet less is known about Easterners. Here we reported that when asking the Chinese to judge the Duchenne and non-Duchenne smiles as either real or fake, their accuracy and sensitivity were negatively correlated with their individualism scores but positively correlated with their collectivism scores. However, such correlations were found only for participants who stated the eyes to be the most useful references, but not for those who favored the mouth. Moreover, participants who favored the eyes were more accurate and sensitive than those who favored the mouth. Our results thus indicate that Chinese who follow the typical Eastern decoding process of using the eyes as diagnostic cues to identify and interpret others' facial expressions and social intentions, are particularly accurate and sensitive, the more they self-report greater collectivistic and lower individualistic values