Low nasal carriage of drug-resistant bacteria among medical students in Vienna

Background: Multi-drug resistant bacteria are increasing and remain a major public health challenge worldwide. In order to understand the potential role of medical students as a reservoir for circulating pathogenic bacteria and their transmission, we analysed the nasal colonisation among 86 clinically exposed medical students of the Medical University of Vienna, which is integrated into General Hospital of Vienna

A Demonstration of Hierarchical Structure Usage in Expressive Timing Analysis by Model Selection Tests

© 2018 Technical Committee on Control Theory, Chinese Association of Automation. Analysing expressive timing in performed music can help machine to perform various perceptual tasks such as identifying performers and understand music structures in classical music. A hierarchical structure is commonly used for expressive timing analysis. This paper provides a statistical demonstration to support the use of hierarchical structure in expressive timing analysis by presenting two groups of model selection tests. The first model selection test uses expressive timing to determine the location of music structure boundaries. The second model selection test is matching a piece of performance with the same performer playing another given piece. Comparing the results of model selection tests, the preferred hierarchical structures in these two model selection tests are not the same. While determining music structure boundaries demands a hierarchical structure with more levels in the expressive timing analysis, a hierarchical structure with less levels helps identifying the dedicated performer in most cases

Immunogenicity and Tolerability after Two Doses of Non-Adjuvanted, Whole-Virion Pandemic Influenza A (H1N1) Vaccine in HIV-Infected Individuals

BACKGROUND: During the influenza pandemic of 2009/10, the whole-virion, Vero-cell-derived, inactivated, pandemic influenza A (H1N1) vaccine Celvapan® (Baxter) was used in Austria. Celvapan® is adjuvant-free and was the only such vaccine at that time in Europe. The objective of this observational, non-interventional, prospective single-center study was to evaluate the immunogenicity and tolerability of two intramuscular doses of this novel vaccine in HIV-positive individuals. METHODS AND FINDINGS: A standard hemagglutination inhibition (HAI) assay was used for evaluation of the seroconversion rate and seroprotection against the pandemic H1N1 strain. In addition, H1N1-specific IgG antibodies were measured using a recently developed ELISA and compared with the HAI results. Tolerability of vaccination was evaluated up to one month after the second dose. A total of 79 HIV-infected adults with an indication for H1N1 vaccination were evaluated. At baseline, 55 of the 79 participants had an HAI titer ≥1:40 and two patients showed a positive IgG ELISA. The seroconversion rate was 31% after the first vaccination, increasing to 41% after the second; the corresponding seroprotection rates were 92% and 83% respectively. ELISA IgG levels were positive in 25% after the first vaccination and in 37% after the second. Among the participants with baseline HAI titers <1:40, 63% seroconverted. Young age was clearly associated with lower HAI titers at baseline and with higher seroconversion rates, whereas none of the seven patients >60 years of age had a baseline HAI titer <1:40 or seroconverted after vaccination. The vaccine was well tolerated. CONCLUSION: The non-adjuvanted pandemic influenza A (H1N1) vaccine was well tolerated and induced a measurable immune response in a sample of HIV-infected individuals

Susceptibility of Candida albicans and Candida glabrata biofilms to silver nanoparticles in intermediate and mature development phases

Purpose: Silver nanoparticles (SN) have been studied as antibiofilm agents, to prevent Candida-associated denture stomatitis. Consequently, the aim of this study was to investigate the influence of the intermediate and maturation stages of Candida albicans and Candida glabrata biofilms development in the susceptibility to SN. Methods: SN (5 nm) suspensions were synthesized via reduction of silver nitrate by a solution of sodium citrate. These suspensions were used to treat, during 5 h, Candida biofilms grown on acrylic surfaces for 24- (intermediate stage) and 48-h (maturation stage), and their efficacy was determined by total biomass (using crystal violet staining) and colony forming units (CFU) quantification. Results: SN promoted significant reductions (p 0.05), when the different stages of biofilm development (24 or 48 h) were exposed to SN. Comparing the number of CFUs between 24- and 48-h biofilms treated with SN, a significant difference (p < 0.05) was found only for the strain C. albicans 324LA/94. Conclusions: It was concluded that, in general, the intermediate and maturation stages of biofilm development do not interfere in the susceptibility of C. albicans and C. glabrata biofilms to SN regarding. These findings are essential for the deployment of new therapies aimed at preventing denture stomatitis.This study was supported by the Sao Paulo Research Foundation (FAPESP), Brazil, process 2009/15146-5. The authors are indebted to LIEC-CMDMC and INCTMN/FAPESP-CNPq in the name of Andressa Kubo for preparing and characterizing the colloidal suspensions of silver nanoparticles. We also thank Dr. David Williams (Cardiff University, Cardiff, UK) for providing the strain 324LA/94, and George Duchow for the English review

Machine learning of symbolic compositional rules with genetic programming: dissonance treatment in Palestrina

We describe a method for automatically extracting symbolic compositional rules from music corpora. Resulting rules are expressed by a combination of logic and numeric relations, and they can therefore be studied by humans. These rules can also be used for algorithmic composition, where they can be combined with each other and with manually programmed rules. We chose genetic programming (GP) as our machine learning technique, because it is capable of learning formulas consisting of both logic and numeric relations. GP was never used for this purpose to our knowledge. We therefore investigate a well understood case in this study: dissonance treatment in Palestrina’s music. We label dissonances with a custom algorithm, automatically cluster melodic fragments with labelled dissonances into different dissonance categories (passing tone, suspension etc.) with the DBSCAN algorithm, and then learn rules describing the dissonance treatment of each category with GP. Learning is based on the requirement that rules must be broad enough to cover positive examples, but narrow enough to exclude negative examples. Dissonances from a given category are used as positive examples, while dissonances from other categories, melodic fragments without dissonances, purely random melodic fragments, and slight random transformations of positive examples, are used as negative examples

Bacterial vaginosis biofilms: challenges to current therapies and emerging solutions

Bacterial vaginosis (BV) is the most common genital tract infection in women during their reproductive years and it has been associated with serious health complications, such as preterm delivery and acquisition or transmission of several sexually transmitted agents. BV is characterized by a reduction of beneficial lactobacilli and a significant increase in number of anaerobic bacteria, including Gardnerella vaginalis, Atopobium vaginae, Mobiluncus spp., Bacteroides spp. and Prevotella spp.. Being polymicrobial in nature, BV etiology remains unclear. However, it is certain that BV involves the presence of a thick vaginal multi-species biofilm, where G. vaginalis is the predominant species. Similar to what happens in many other biofilm-related infections, standard antibiotics, like metronidazole, are unable to fully eradicate the vaginal biofilm, which can explain the high recurrence rates of BV. Furthermore, antibiotic therapy can also cause a negative impact on the healthy vaginal microflora. These issues sparked the interest in developing alternative therapeutic strategies. This review provides a quick synopsis of the currently approved and available antibiotics for BV treatment while presenting an overview of novel strategies that are being explored for the treatment of this disorder, with special focus on natural compounds that are able to overcome biofilm-associated antibiotic resistance.Research on BV biofilms in NC laboratory is supported by funding from the Fundação para a Ciência e a Tecnologia (FCT) strategic project of unit UID/BIO/04469/2013. DM and JC acknowledge the FCT fellowships SFRH/BD/87569/2012 and SFRH/BD/93963/2013 respectively. NC is an Investigador FCT

Distance based learning in a relational setting and its application to expressive music performance

Zsfassung in dt. SpracheDiese Arbeit beschreibt Forschungen im Bereich des Machinellen Lernens und deren Anwendungen auf musikwissenschaftlichen Fragen. Es wird ein neuer Lernalgorithmus namens DISTALL präsentiert sowie, darauf aufbauend, ein automatisches Lernsystem, das ein schwer fassbares Phänomen in der Musik analysieren und modellieren soll, nämlich ausdrucksvolle Musikinterpretation.Der Lernalgorithmus DISTALL ist im Bereich des relationalen `Instance-based Learning' (IBL) angesiedelt. Obwohl distanz- und instanzbasierte Lernalgorithmen im Bereich des Maschinellen Lernens immer sehr beliebt waren -- vor allem mit propositionalen, attributbasierten Repräsentationssprachen --, ist IBL in mächtigeren relationalen (auf Prädikatenlogik basierenden) Repräsentationen wesentlich schwieriger und weniger erforscht. In der Dissertation werden diese beiden Lernformalismen diskutiert, Vorteile des relationalen Lernens aufgezeigt und die These aufgestellt, dass der kritischste Teil eines relationalen IBL-Lernalgorithmus sein Ähnlichkeitsmaß (zwischen Mengen von Termen) ist.Verschiedene Mengen- Ähnlichkeitsmaße werden rekapituliert, und es wird der Schluss gezogen, dass ein Ähnlichkeitsmaß, das auf optimalem Matching zwischen Mengen basiert, aus zwei wesentlichen Gründen am vielversprechendsten ist: (1) wegen seiner intuitiv überzeugenden und nachvollziehbaren Aspekte und (2) wegen seine klaren theoretischen Eigenschaften. Auf einem solchen Ähnlichkeitsmaß aufbauend wird DISTALL implementiert und im Detail beschrieben.DISTALL wird sodann auf ein schwieriges Lernproblem aus dem Forschungsgebiet der Musikwissenschaft angewendet: Ausgehend von einer großen Zahl von Interpretationen (Aufnahmen) von Konzertpianisten soll der Computer lernen, Musik ausdrucksvoll zu spielen. Die Aufgabe wird als Mehrebenen-Dekompositions- und Vorhersage-Problem modelliert, und es wird gezeigt, dass dieses als relationales Lernproblem darstellbar ist und mittels relationalem IBL bewältigt werden kann. Experimente mit realen Daten, die aus einer beachtlichen Anzahl von Interpretationen eines Wiener Konzertpianisten gewonnen wurden, deuten die Brauchbarkeit unserer Methode an. Speziell wird gezeigt, dass die Vorhersagegenauigkeit von DISTALL die eines herkömmlichen propositionalen k-NN-Algorithmus übertrifft. Direkte experimentellen Vergleiche mit RIBL, einem modernen relationalen Lernalgorithmus, demonstrieren auch die klare Überlegenheit von DISTALL gegenüber RIBL bei dieser Lernaufgabe.Verschiedene weitere Verbesserungen des Lernsystem werden vorgestellt.Eine der Verbesserungen -- die explizite Darstellung zeitlicher Beziehungen -- demonstriert deutlich die Mächtigkeit des relationalen Lernformalismus.In qualitativer Hinsicht stellt sich heraus, dass unser Lernsystem zumindest zum Teil erstaunlich gute Vorhersagen macht. Einige nach dem Lernen vom Konzertpianisten von DISTALL generierte `Aufführungen' weisen erhebliche musikalische Qualität auf: eine davon gewann sogar einen Preis bei einem internationalen `Computer Music Performance'-Wettbewerb.Zwei weitere Anwendungen von DISTALL und seinem Ähnlichkeitsmaß werden sodann in der Dissertation vorgestellt: (1) Wir versuchen festzustellen, mit welchem Niveau an stilistischer `Konsistenz' ein Wiener Konzertpianst verschiedene Mozartsonaten spielt. Mit Hilfe des Ähnlichkeitmaßes von DISTALL kann ein Konzept stilistischer Übereinstimmung definiert werden, das über einfache Notentext- Wiederholungen hinausgeht, und darauf aufbauend wird ein quantitatives Maß von Aufführungs-Übereinstimmung zwischen beliebigen Musikphrasen realisiert, das systematische quantitative Experimente zulässt.(2) Wir studieren eine der interessantesten Probleme, das in dieser Art von Forschung formuliert werden kann: Kann eine Maschine ein formales, prädiktives Modell des Spielstils eines berühmten Pianisten lernen? Wir erkunden, inwieweit die Maschine `expressive Profile' großer Pianisten automatisch bilden kann, nur mit Hilfe von aus Audio-CDs gewonnenen Minimalinformationen und des Notentextes der gespielten Musik. Es stellt sich heraus, dass das auf DISTALL basierende Lernsystem tatsächlich in der Lage ist, `ausdrucksvolle' Interpretationen neuer Musikstücke zu generieren, die zur echten Interpretation des `Trainingspianisten' deutlich ähnlicher sind als zu den Interpretationen aller anderen Pianisten. Eine weitere interessante Anwendung unseres Lernalgorithmus wird schlussendlich noch besprochen: die automatische Erkennung berühmter Pianisten anhand ihres Spielstils. Wie die Experimente zeigen, sind auch bei diesem schwierigen Problem erstaunlich gute Resultate möglich.This thesis reports about work in the field of machine learning and its applications in musicology. We present a new machine learning algorithm called DISTALL and describe an automated learning system targeting one of the most elusive phenomena in music: to learn to play music expressively.DISTALL is situated in the field of relational instance-based learning (IBL). Although distance- and instance-based learning methods have always been popular in machine learning, IBL in a richer, relational setting is more difficult and has been less explored. We contrast IBL in a propositional setting to relational IBL, discuss advantages of relational representations, and argue that the most critical part of a relational IBL is its set distance measure. After different set distance measures are discussed, we conclude that the set distance measure based on optimal matching is the most appealing for both its intuitive properties and strong theoretical aspects. We construct DISTALL, our new relational IBL algorithm around the optimal matching set distance measure and discuss its algorithmic implementation in detail.DISTALL is applied to a difficult real-world learning task from expressive music performance research: learning, from large numbers of complex performances by concert pianists, to play music expressively. We model the problem as a multi-level decomposition and prediction task. We show that this is a fundamentally relational learning problem, and argue that relational IBL is indeed appropriate to address it. Experiments with data derived from a substantial number of Mozart piano sonata recordings by a skilled concert pianist demonstrate that the approach is viable. We show that DISTALL operating on structured, relational data outperforms a propositional k-NN algorithm. Experiments with a direct comparison to the state of the art relational learner RIBL clearly show DISTALL's superiority to RIBL on this learning task. Various improvements to the learning system are proposed, one of them -- temporal representation -- nicely demonstrating the power of relational formalisms. In qualitative terms, we end up with a system which at least partly makes surprisingly good predictions. Some of the piano performances produced by DISTALL after learning from human artist are of substantial musical quality; one even won a prize in an international `computer music performance' contest.Two further applications of DISTALL and its distance measure are presented: (1) We will try to assess the level of `consistency' of a Viennese concert pianist in playing different Mozart sonatas. With the help of DISTALL's similarity measure we are able to define a concept of consistency which goes beyond simple score repetitions. The level of performer consistency will be assessed between any tho phrases, regardless of similarity~/dissimilarity of the pieces they belong to.(2) We address one of the most interesting questions one can consider in this kind of research: Can a machine build a formal model of the playing style of great pianists? We investigate to what extent a machine can automatically build `expressive profiles' of famous pianists using only minimal performance information extracted from audio CD recordings by these pianists and the printed score of the music. It turns out that the learning system built around DISTALL is able to generate expressive performances on unseen pieces which are substantially closer to the real performances of the `trainer' pianist than those of all others. Finally, another interesting application is discussed: recognizing pianists from their style of playing - a difficult learning problem tackled in the recent literature. We show that surprisingly high accuracy rates can be achieved by using expressive performance profiles predicted by DISTALL for artist recognition.11