DaViz: Visualization for Android Malware Datasets

National audienceWith millions of Android malware samples available, researchers have a large amount of data to perform malware detection and classification, specially with the help of machine learning. Thus far, visualization tools focus on single samples or one-to-many comparison, but not a many-to-many approach. In order to exploit the quantity of data from various datasets to obtain meaningful information, we propose DaViz, a visualization tool for Android malware datasets. With the aid of multiple chart types and interactive sample filtering, users can explore different application datasets and compare them. This new tool allows to get a better understanding of the datasets at hand, and help to continue research by narrowing the samples to those of interest based on selected characteristics

Genetic Heterogeneity Underlying Phenotypes with Early-Onset Cerebellar Atrophy

Cerebellar atrophy (CA) is a frequent neuroimaging finding in paediatric neurology, usually associated with cerebellar ataxia. The list of genes involved in hereditary forms of CA is continuously growing and reveals its genetic complexity. We investigated ten cases with early-onset cerebellar involvement with and without ataxia by exome sequencing or by a targeted panel with 363 genes involved in ataxia or spastic paraplegia. Novel variants were investigated by in silico or experimental approaches. Seven probands carry causative variants in well-known genes associated with CA or cerebellar hypoplasia: SETX, CACNA1G, CACNA1A, CLN6, CPLANE1, and TBCD. The remaining three cases deserve special attention; they harbour variants in MAST1, PI4KA and CLK2 genes. MAST1 is responsible for an ultrarare condition characterised by global developmental delay and cognitive decline; our index case added ataxia to the list of concomitant associated symptoms. PIK4A is mainly related to hypomyelinating leukodystrophy; our proband presented with pure spastic paraplegia and normal intellectual capacity. Finally, in a patient who suffers from mild ataxia with oculomotor apraxia, the de novo novel CLK2 c.1120T>C variant was found. The protein expression of the mutated protein was reduced, which may indicate instability that would affect its kinase activity

Role of age and comorbidities in mortality of patients with infective endocarditis

[Purpose]: The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. [Methods]: Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015.Patients were stratified into three age groups:<65 years,65 to 80 years,and ≥ 80 years.The area under the receiver-operating characteristic (AUROC) curve was calculated to quantify the diagnostic accuracy of the CCI to predict mortality risk. [Results]: A total of 3120 patients with IE (1327 < 65 years;1291 65-80 years;502 ≥ 80 years) were enrolled.Fever and heart failure were the most common presentations of IE, with no differences among age groups.Patients ≥80 years who underwent surgery were significantly lower compared with other age groups (14.3%,65 years; 20.5%,65-79 years; 31.3%,≥80 years). In-hospital mortality was lower in the <65-year group (20.3%,<65 years;30.1%,65-79 years;34.7%,≥80 years;p < 0.001) as well as 1-year mortality (3.2%, <65 years; 5.5%, 65-80 years;7.6%,≥80 years; p = 0.003).Independent predictors of mortality were age ≥ 80 years (hazard ratio [HR]:2.78;95% confidence interval [CI]:2.32–3.34), CCI ≥ 3 (HR:1.62; 95% CI:1.39–1.88),and non-performed surgery (HR:1.64;95% CI:11.16–1.58).When the three age groups were compared,the AUROC curve for CCI was significantly larger for patients aged <65 years(p < 0.001) for both in-hospital and 1-year mortality. [Conclusion]: There were no differences in the clinical presentation of IE between the groups. Age ≥ 80 years, high comorbidity (measured by CCI),and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in the <65-year group

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

100 años investigando el mar. El IEO en su centenario (1914-2014).

Se trata de un libro que pretende divulgar a la sociedad las principales investigaciones multidisciplinares llevadas a cabo por el Instituto Español de Oceanografía durante su primer siglo de vida, y dar a conocer la historia del organismo, de su Sede Central y de los nueve centros oceanográficos repartidos por los litorales mediterráneo y atlántico, en la península y archipiélagos.Kongsberg 20

Orchestrating Android Malware Experiments

International audienc

DaViz: Visualization for Android Malware Datasets

National audienceWith millions of Android malware samples available, researchers have a large amount of data to perform malware detection and classification, specially with the help of machine learning. Thus far, visualization tools focus on single samples or one-to-many comparison, but not a many-to-many approach. In order to exploit the quantity of data from various datasets to obtain meaningful information, we propose DaViz, a visualization tool for Android malware datasets. With the aid of multiple chart types and interactive sample filtering, users can explore different application datasets and compare them. This new tool allows to get a better understanding of the datasets at hand, and help to continue research by narrowing the samples to those of interest based on selected characteristics

DaViz: Visualization for Android Malware Datasets

National audienceWith millions of Android malware samples available, researchers have a large amount of data to perform malware detection and classification, specially with the help of machine learning. Thus far, visualization tools focus on single samples or one-to-many comparison, but not a many-to-many approach. In order to exploit the quantity of data from various datasets to obtain meaningful information, we propose DaViz, a visualization tool for Android malware datasets. With the aid of multiple chart types and interactive sample filtering, users can explore different application datasets and compare them. This new tool allows to get a better understanding of the datasets at hand, and help to continue research by narrowing the samples to those of interest based on selected characteristics

Debiasing Android Malware Datasets: How Can I Trust Your Results If Your Dataset Is Biased?

International audienceAndroid security has received a lot of attention over the last decade, especially malware investigation. Researchers attempt to highlight applications' security-relevant characteristics to better understand malware and effectively distinguish malware from benign applications. The accuracy and the completeness of their proposals are evaluated experimentally on malware and goodware datasets. Thus, the quality of these datasets is of critical importance: if the datasets are outdated or not representative of the studied population, the conclusions may be flawed. We specify different types of experimental scenarios. Some of them require unlabeled but representative datasets of the entire population. Others require datasets labeled with valuable characteristics that may be difficult to compute, such as malware datasets. We discuss the irregularities of datasets used in experiments, questioning the validity of the performances reported in the literature. This article focuses on providing guidelines for designing debiased datasets. First, we propose guidelines for building representative datasets from unlabeled ones. Second, we propose and experiment a debiasing algorithm that, given a biased labeled dataset and a target representative dataset, builds a representative and labeled dataset. Finally, from the previous debiased datasets, we produce datasets for experiments on Android malware detection or classification with machine learning algorithms. Experiments show that debiased datasets perform better when classifying with machine learning algorithms

Isolating malicious code in Android malware in the wild

International audienc