University of Luxembourg

About: University of Luxembourg is a education organization based out in Luxembourg, Luxembourg. It is known for research contribution in the topics: Context (language use) & Computer science. The organization has 4744 authors who have published 22175 publications receiving 381824 citations.

Systematic prediction of health-relevant human-microbial co-metabolism through a computational framework

Abstract: The gut microbiota is well known to affect host metabolic phenotypes. The systemic effects of the gut microbiota on host metabolism are generally evaluated via the comparison of germfree and conventional mice, which is impossible to perform for humans. Hence, it remains difficult to determine the impact of the gut microbiota on human metabolic phenotypes. We demonstrate that a constraint-based modeling framework that simulates “germfree” and “ex-germfree” human individuals can partially fill this gap and allow for in silico predictions of systemic human-microbial co-metabolism. To this end, we constructed the first constraint-based host-microbial community model, comprising the most comprehensive model of human metabolism and 11 manually curated, validated metabolic models of commensals, probiotics, pathogens, and opportunistic pathogens. We used this host-microbiota model to predict potential metabolic host-microbe interactions under 4 in silico dietary regimes. Our model predicts that gut microbes secre...

Pharmacological Characterization of an Antisense Knockdown Zebrafish Model of Dravet Syndrome: Inhibition of Epileptic Seizures by the Serotonin Agonist Fenfluramine

Abstract: Dravet syndrome (DS) is one of the most pharmacoresistant and devastating forms of childhood epilepsy syndromes. Distinct de novo mutations in the SCN1A gene are responsible for over 80% of DS cases. While DS is largely resistant to treatment with existing anti-epileptic drugs, promising results have been obtained in clinical trials with human patients treated with the serotonin agonist fenfluramine as an add-on therapeutic. We developed a zebrafish model of DS using morpholino antisense oligomers (MOs) targeting scn1Lab, the zebrafish ortholog of SCN1A. Zebrafish larvae with an antisense knockdown of scn1Lab (scn1Lab morphants) were characterized by automated behavioral tracking and high-resolution video imaging, in addition to measuring brain activity through local field potential recordings. Our findings reveal that scn1Lab morphants display hyperactivity, convulsive seizure-like behavior, loss of posture, repetitive jerking and a myoclonic seizure-like pattern. The occurrence of spontaneous seizures was confirmed by local field potential recordings of the forebrain, measuring epileptiform discharges. Furthermore, we show that these larvae are remarkably sensitive to hyperthermia, similar to what has been described for mouse models of DS, as well as for human DS patients. Pharmacological evaluation revealed that sodium valproate and fenfluramine significantly reduce epileptiform discharges in scn1Lab morphants. Our findings for this zebrafish model of DS are in accordance with clinical data for human DS patients. To our knowledge, this is the first study demonstrating effective seizure inhibition of fenfluramine in an animal model of Dravet syndrome. Moreover, these results provide a basis for identifying novel analogs with improved activity and significantly milder or no side effects.

Automated testing for SQL injection vulnerabilities: an input mutation approach

Abstract: Web services are increasingly adopted in various domains, from finance and e-government to social media. As they are built on top of the web technologies, they suffer also an unprecedented amount of attacks and exploitations like the Web. Among the attacks, those that target SQL injection vulnerabilities have consistently been top-ranked for the last years. Testing to detect such vulnerabilities before making web services public is crucial. We present in this paper an automated testing approach, namely μ4SQLi, and its underpinning set of mutation operators. μ4SQLi can produce effective inputs that lead to executable and harmful SQL statements. Executability is key as otherwise no injection vulnerability can be exploited. Our evaluation demonstrated that the approach is effective to detect SQL injection vulnerabilities and to produce inputs that bypass application firewalls, which is a common configuration in real world.

Internet gaming disorder should qualify as a mental disorder

Abstract: Australian & New Zealand Journal of Psychiatry, 00(0) In their recent ANZJP paper, Dullur and Starcevic (2018) argue that Internet gaming disorder (IGD) should not qualify as a mental disorder. They base this view on several arguments, including the notion that IGD does not fit the concept of a mental disorder, that IGD would pathologise normal gaming, that the addiction model for gaming is misleading and that a diagnosis is not necessary for treatment purposes. In this paper, we provide a critical appraisal of the authors’ points. While there are some aspects of their arguments which we support, there are many with which we disagree. We believe their views would be relevant to other behavioural addictions and would serve to undermine their validity, including with respect to gambling disorder.