49 research outputs found
On the stability of accelerating relativistic shock waves
We consider the corrugation instability of the self-similar flow with an
accelerating shock in the highly relativistic regime. We derive the correct
dispersion relation for the proper modes in the self-similar regime, and
conclude that this solution is unstable.Comment: 25 pages, 10 figures. Accepted for publication in the Astrophysical
Journa
Predicting symptom severity and contagiousness of respiratory viral infections
This work aims at predicting the symptom severity and contagiousness of a person infected with respiratory virus, using time series gene expression data. Four different respiratory viruses were studied – RSV, H1N1, H3N2 and Rhinovirus. Predictive models were built for each virus for each time point. Partial least squares discriminant analysis was used for feature selection and random forest was used for classification. Certain genes were identified as biomarkers in distinguishing the subjects. Gene enrichment analysis was performed on the differentially expressed genes. Prediction accuracy values were high even when expression data from early time points were analyzed. Significant genes were detected as early as 5 and 10 hours post infection, as compared to prior work that did so at 29 hours post infection. The potential biomarkers obtained with the proposed approach need to be investigated further
Confidence-Ranked Reconstruction of Census Microdata from Published Statistics
A reconstruction attack on a private dataset takes as input some publicly
accessible information about the dataset and produces a list of candidate
elements of . We introduce a new class of data reconstruction attacks based
on randomized methods for non-convex optimization. We empirically demonstrate
that our attacks can not only reconstruct full rows of from aggregate query
statistics , but can do so in a way that reliably ranks
reconstructed rows by their odds of appearing in the private data, providing a
signature that could be used for prioritizing reconstructed rows for further
actions such as identify theft or hate crime. We also design a sequence of
baselines for evaluating reconstruction attacks. Our attacks significantly
outperform those that are based only on access to a public distribution or
population from which the private dataset was sampled, demonstrating that
they are exploiting information in the aggregate statistics , and not
simply the overall structure of the distribution. In other words, the queries
are permitting reconstruction of elements of this dataset, not the
distribution from which was drawn. These findings are established both on
2010 U.S. decennial Census data and queries and Census-derived American
Community Survey datasets. Taken together, our methods and experiments
illustrate the risks in releasing numerically precise aggregate statistics of a
large dataset, and provide further motivation for the careful application of
provably private techniques such as differential privacy
An explainable model of host genetic interactions linked to COVID-19 severity
We employed a multifaceted computational strategy to identify the genetic factors contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing (WES) dataset of a cohort of 2000 Italian patients. We coupled a stratified k-fold screening, to rank variants more associated with severity, with the training of multiple supervised classifiers, to predict severity based on screened features. Feature importance analysis from tree-based models allowed us to identify 16 variants with the highest support which, together with age and gender covariates, were found to be most predictive of COVID-19 severity. When tested on a follow-up cohort, our ensemble of models predicted severity with high accuracy (ACC = 81.88%; AUCROC = 96%; MCC = 61.55%). Our model recapitulated a vast literature of emerging molecular mechanisms and genetic factors linked to COVID-19 response and extends previous landmark Genome-Wide Association Studies (GWAS). It revealed a network of interplaying genetic signatures converging on established immune system and inflammatory processes linked to viral infection response. It also identified additional processes cross-talking with immune pathways, such as GPCR signaling, which might offer additional opportunities for therapeutic intervention and patient stratification. Publicly available PheWAS datasets revealed that several variants were significantly associated with phenotypic traits such as "Respiratory or thoracic disease", supporting their link with COVID-19 severity outcome.A multifaceted computational strategy identifies 16 genetic variants contributing to increased risk of severe COVID-19 infection from a Whole Exome Sequencing dataset of a cohort of Italian patients