The role of Candida tropicalis biofilms on human urinary bladder cells

Candida tropicalis has been reported to be one of the Candida species which is most likely to cause urinary tract infections in hospitalized patients mainly associated with biofilm formation on catheters. Objectives: The aim of this work was to evaluate the effect of C. tropicalis' biofilms, formed on silicone coupons using artificial urine, on human urinary bladder cells. Enzymatic activity (haemolysin and proteinase) of biofilm cells was also assessed. Methods: Two C. tropicalis clinical isolates (from candiduria) and a reference strain were used. Biofilms (24-120 h) were formed in artificial urine on silicone coupons, and quantified by crystal violet (CV) staining and colony formation units (CFU). Haemolysin and proteinase activity of biofilm cells were evaluated in agar plates containing blod sheep and bovine serum albumin, respectively. The biofilm effect on urinary epithelial cells was performed incubating the silicone coupons with pre-formed biofilms with a confluent layer of human urinary bladder cells. The extent of adhesion was evaluated after 2h of incubation using the CV staining method; cell viability was evaluated by MTS and cytotoxicity by LDH production. Results: 24h biofilms showed differences among strains, with C. tropicalis ATCC 750 presenting a higher number of biofilm cells than the clinical isolates although having lower biofilm biomass. After 48h all strains reached a plateau of viable cells (≈ 1x106 CFU/cm2). C. tropicalis biofilm cells were able to express total haemolytic activity and high proteinase activity. Additionally, C. tropicalis biofilms adhered in higher extent to epithelial cells than their planktonic counterparts. Moreover, epithelial cells showed more damage when in contact with biofilms. Conclusions: Thus, it is possible to conclude that C. tropicalis were able to cause more epithelial cell damage when in biofilm form, than planktonic cells. This highlights the importance of biofilm formation when associated to the use of urinary catheters, on C. tropicalis virulence

Imaging philosophy

Human beings experience the world first of all by seeing and imagining it. They can get close to the world and to their own nature by reflecting on illuminating intuitions and ideas. These are evident truths, partly recorded by some philosophers (by Plato, for example) since the antiquity. These truths, however, have never been put, simply and immediately, at the centre of human beings’ practical and theoretical speculations. The main philosophical investigations, on the contrary, above all in the current age, have become more and more strictly verbal. The notes contained in this article are an attempt to stimulate the philosophical mind carefully to study images – that is, to study images as locatable or quasi locatable experiences (and even images as locatable or quasi locatable experiences that are the basis of non locatable experiences)

Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary

We propose a method to transfer knowledge across neural machine translation (NMT) models by means of a shared dynamic vocabulary. Our approach allows to extend an initial model for a given language pair to cover new languages by adapting its vocabulary as long as new data become available (i.e., introducing new vocabulary items if they are not included in the initial model). The parameter transfer mechanism is evaluated in two scenarios: i) to adapt a trained single language NMT system to work with a new language pair and ii) to continuously add new language pairs to grow to a multilingual NMT system. In both the scenarios our goal is to improve the translation performance, while minimizing the training convergence time. Preliminary experiments spanning five languages with different training data sizes (i.e., 5k and 50k parallel sentences) show a significant performance gain ranging from +3.85 up to +13.63 BLEU in different language directions. Moreover, when compared with training an NMT model from scratch, our transfer-learning approach allows us to reach higher performance after training up to 4% of the total training steps.Comment: Published at the International Workshop on Spoken Language Translation (IWSLT), 201

Cut-free Calculi and Relational Semantics for Temporal STIT Logics

We present cut-free labelled sequent calculi for a central formalism in logics of agency: STIT logics with temporal operators. These include sequent systems for Ldm , Tstit and Xstit. All calculi presented possess essential structural properties such as contraction- and cut-admissibility. The labelled calculi G3Ldm and G3Tstit are shown sound and complete relative to irreflexive temporal frames. Additionally, we extend current results by showing that also Xstit can be characterized through relational frames, omitting the use of BT+AC frames

Direct Speech-to-Text Translation Models as Students of Text-to-Text Models

Direct speech-to-text translation (ST) is an emerging approach that consists in performing the ST task with a single neural model. Although this paradigm comes with the promise to outperform the traditional pipeline systems, its rise is still limited by the paucity of speech-translation paired corpora compared to the large amount of speech-transcript and parallel bilingual corpora available to train previous solutions. As such, the research community focused on techniques to transfer knowledge from automatic speech recognition (ASR) and machine translation (MT) models trained on huge datasets. In this paper, we extend and integrate our recent work (Gaido, Gangi, et al. 2020) analysing the best performing approach to transfer learning from MT, which is represented by knowledge distillation (KD) in sequence-to-sequence models. After the comparison of the different KD methods to understand which one is the most effective, we extend our previous analysis of the effects – both in terms of benefits and drawbacks – to different language pairs in high-resource conditions, ensuring the generalisability of our findings. Altogether, these extensions complement and complete our investigation on KD for speech translation leading to the following overall findings: i) the best training recipe involves a word-level KD training followed by a fine-tuning step on the ST task, ii) word-level KD from MT can be detrimental for gender translation and can lead to output truncation (though these problems are alleviated by the fine-tuning on the ST task), and iii) the quality of the ST student model strongly depends on the quality of the MT teacher model, although the correlation is not linear

Idmb: a tool for navigating the Inspire data model and generating an Inspire SQL database and WFS Configuration

The Inspire Data Model Browser (IDMB) is a free tool that performs the following functions: (i) it presents the Inspire UML Data Model as a tree-based structure, which is complementary to the UML diagrams; (ii) it generates a Postgis SQL Script for creating an INSPIRE compliant SQL database (Inspire Database) and a configuration file for the Deegree tool that enables the access to the Inspire Database through a Web Feature Service (WFS) producing GML according to the Inspire XML Schemas

What factors affect the selection of industrial wastewater treatment configuration?

Industrial wastewater treatment is gaining significance in literature due to stricter environmental policies and increased environmental awareness. The selection of the wastewater configuration encompasses both the treatment as well as several decisions around wastewater collection and disposal pertaining industrial decision-making sphere. However, so far in the wastewater literature, research has mostly discussed either technical features of wastewater technologies, or wastewater policy issues at broader level, without focusing on the industrial decision-making issues and driving factors leading to the selection of a specific configuration. Starting from a literature review, the present study provides an innovative framework of the possible options for wastewater system configuration, as well as major adoption factors by industrial decision-makers. The factors have been classified according to 7 categories, namely: influent-related, technological, economic/financial, internal socio-cultural, external socio-cultural, regulation, site characteristics. The framework, validated with acknowledgeable experts, policy makers and firms, has been preliminarily applied to Italian and Australian food firms. Our investigation reveals that the framework was able to include all relevant problems faced by industries in the selection of a treatment system configuration; besides, the relative importance of factors has been assessed: legal requirements emerge as the most critical factors, followed by volume and discharge fee, the latter particularly interesting for policy makers purposes, since it may guide the decision-making process. Further, the wastewater volume seems to play a key role in our exploratory investigation, with smaller firms preferring a complete off-site treatment to reduce the complexity, whilst larger firms preferring instead more partial or complete on-site treatment configurations for compliance costs reduction. In conclusion, we have provided policy and managerial implications stemming from the study as well as sketched interesting future research avenues