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

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

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

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

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

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

CMS Forward-Backward MSGC milestone

The CMS MF1 milestone was set in order to evaluate system aspects of the CMS forward-backward MSGC tracker, to check the design and feasibility of mass production and to set up assembly and test procedures. We describe the construction and the experience gained with the operation of a system of 38 MSGC detectors assembled in six multi-substrate detector modules corresponding to the geometry of the forward-backward MSGC tracker in CMS. These modules were equipped with MSGCs mounted side by side, forming a continuous detector surface of about 0.2 m2. Different designs were tried for these modules. The problems encountered are presented with the proposed solutions. Operation conditions for the 38 MSGCs are reported from an exposure to a muon beam at the CERN SPS. Gain uniformity along the wedge-shaped strip pattern and across the detector modules are shown together with the detection efficiency, the spatial resolution, alignment and edge studies