Realistic modeling of leakage and intrusion flows through leak openings in pipes

The hydraulics of leakage and intrusion flows through leak openings in pipes is complicated by variations in the leak areas owing to changes in pressure. This paper argues that the pressure–area relationship can reasonably be assumed to be a linear function, and a modified orifice equation is proposed for more realistic modeling of leakage and intrusion flows. The properties of the modified orifice equation are explored for different classes of leak openings. The implications for the current practice of using a power equation to model leakage and intrusion flows are then investigated. A mathematical proof is proposed for an equation linking the parameters of the modified orifice and power equations using the concept of a dimensionless leakage number. The leakage exponent of a given leak opening is shown to generally not be constant with variations in pressure and to approach infinity when the leakage number approaches a value of minus one. Significant modeling errors may result if the power equation is extrapolated beyond its calibration pressure range or at high exponent values. It is concluded that the modified orifice equation and leakage number provide a more realistic description of leakage and intrusion flows, and it is recommended that this approach be adopted in modeling studies

Interplay of non-linear elasticity and dislocation-induced superfluidity in solid Helium-4

The mechanism of the roughening induced partial depinning of gliding dislocations from Helium-3 impurities is proposed as an alternative to the standard "boiling off". We give a strong argument that Helium-3 remains bound to dislocations even at large temperatures due to very long equilibration times. A scenario leading to the similarity between elastic and superfluid responses of solid Helium-4 is also discussed. Its main ingredient is a strong suppression of the superfluidity along dislocation cores by dislocation kinks (D. Aleinikava, et. al., arXiv:0812.0983). These kinks, on one hand, determine the temperature and Helium-3 dependencies of the generalized shear modulus and, on the other hand, control the superfluid response. Several proposals for theoretical and experimental studies of solid Helium-4 are suggested.Comment: final version accepted to the special JLTP issue on Supersolid, 16 pages, 6 figures: typos corrected, more explanations give

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.Peer Reviewe

Genetic studies of IgA nephropathy: past, present, and future

Immunoglobulin A nephropathy (IgAN) is the most common form of primary glomerulonephritis worldwide and an important cause of kidney disease in young adults. Highly variable clinical presentation and outcome of IgAN suggest that this diagnosis may encompass multiple subsets of disease that are not distinguishable by currently available clinical tools. Marked differences in disease prevalence between individuals of European, Asian, and African ancestry suggest the existence of susceptibility genes that are present at variable frequencies in these populations. Familial forms of IgAN have also been reported throughout the world but are probably underrecognized because associated urinary abnormalities are often intermittent in affected family members. Of the many pathogenic mechanisms reported, defects in IgA1 glycosylation that lead to formation of immune complexes have been consistently demonstrated. Recent data indicates that these IgA1 glycosylation defects are inherited and constitute a heritable risk factor for IgAN. Because of the complex genetic architecture of IgAN, the efforts to map disease susceptibility genes have been difficult, and no causative mutations have yet been identified. Linkage-based approaches have been hindered by disease heterogeneity and lack of a reliable noninvasive diagnostic test for screening family members at risk of IgAN. Many candidate-gene association studies have been published, but most suffer from small sample size and methodological problems, and none of the results have been convincingly validated. New genomic approaches, including genome-wide association studies currently under way, offer promising tools for elucidating the genetic basis of IgAN

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches

IntroductionThe COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. MethodsExtensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors.ResultsResults revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. DiscussionThe key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies