Stochastic bifurcation in a driven laser system: Experiment and theory

We analyze the effects of stochastic perturbations in a physical example occurring as a higher-dimensional dynamical system. The physical model is that of a class-B laser, which is perturbed stochastically with finite noise. The effect of the noise perturbations on the dynamics is shown to change the qualitative nature of the dynamics experimentally from a stochastic periodic attractor to one of chaoslike behavior, or noise-induced chaos. To analyze the qualitative change, we apply the technique of the stochastic Frobenius-Perron operator [L. Billings et al., Phys. Rev. Lett. 88, 234101 (2002)] to a model of the experimental system. Our main result is the identification of a global mechanism to induce chaoslike behavior by adding stochastic perturbations in a realistic model system of an optics experiment. In quantifying the stochastic bifurcation, we have computed a transition matrix describing the probability of transport from one region of phase space to another, which approximates the stochastic Frobenius-Perron operator. This mechanism depends on both the standard deviation of the noise and the global topology of the system. Our result pinpoints regions of stochastic transport whereby topological deterministic dynamics subjected to sufficient noise results in noise-induced chaos in both theory and experiment

Automagically Encoding Adverse Drug Reactions in MedDRA

Abstract-Pharmacovigilance is the field of science devoted to the collection, analysis, and prevention of Adverse Drug Reactions (ADRs). Efficient strategies for the extraction of information about ADRs from free text sources are essential to support the important task of detecting and classifying unexpected pathologies, possibly related to (therapy-related) drug use. Narrative ADR descriptions may be collected in different ways, e.g., either by monitoring social networks or through the so called "spontaneous reporting, the main method pharmacovigilance adopts in order to identify ADRs. The encoding of free-text ADR descriptions according to MedDRA standard terminology is central for report analysis. It is a complex work, which has to be manually implemented by the pharmacovigilance experts. The manual encoding is expensive (in terms of time). Moreover, a problem about the accuracy of the encoding may occur, since the number of reports is growing up day by day. In this paper, we propose MagiCoder, an efficient Natural Language Processing algorithm able to automatically derive MedDRA terminologies from freetext ADR descriptions. MagiCoder is part of VigiWork, a web application for online ADR reporting and analysis. From a practical point of view, MagiCoder reduces the encoding time of ADR reports. Pharmacologists have simply to review and validate the MedDRA terms proposed by MagiCoder, instead of choosing the right terms among the 70K terms of MedDRA. Such improvement in the efficiency of pharmacologists' work has a relevant impact also on the quality of the following data analysis. Our proposal is based on a general approach, not depending on the considered language. Indeed, we developed MagiCoder for the Italian pharmacovigilance language, but preliminarily analyses show that it is robust to language and dictionary changes

Intelligenza artificiale e sicurezza: opportunità, rischi e raccomandazioni

L'IA (o intelligenza artificiale) è una disciplina in forte espansione negli ultimi anni e lo sarà sempre più nel prossimo futuro: tuttavia è dal 1956 che l’IA studia l’emulazione dell’intelligenza da parte delle macchine, intese come software e in certi casi hardware. L’IA è nata dall’idea di costruire macchine che - ispirandosi ai processi legati all’intelligenza umana - siano in grado di risolvere problemi complessi, per i quali solitamente si ritiene che sia necessario un qualche tipo di ragionamento intelligente. La principale area di ricerca e applicazione attuale dell’IA è il machine learning (algoritmi che imparano e si adattano in base ai dati che ricevono), che negli ultimi anni ha trovato ampie applicazioni grazie alle reti neurali (modelli matematici composti da neuroni artificiali) che a loro volta hanno consentito la nascita del deep learning (reti neurali di maggiore complessità). Appartengono al mondo dell’IA anche i sistemi esperti, la visione artificiale, il riconoscimento vocale, l’elaborazione del linguaggio naturale, la robotica avanzata e alcune soluzioni di cybersecurity. Quando si parla di IA c'è chi ne è entusiasta pensando alle opportunità, altri sono preoccupati poiché temono tecnologie futuristiche di un mondo in cui i robot sostituiranno l'uomo, gli toglieranno il lavoro e decideranno al suo posto. In realtà l'IA è ampiamente utilizzata già oggi in molti campi, ad esempio nei cellulari, negli oggetti smart (IoT), nelle industry 4.0, per le smart city, nei sistemi di sicurezza informatica, nei sistemi di guida autonoma (drive o parking assistant), nei chat bot di vari siti web; questi sono solo alcuni esempi basati tutti su algoritmi tipici dell’intelligenza artificiale. Grazie all'IA le aziende possono avere svariati vantaggi nel fornire servizi avanzati, personalizzati, prevedere trend, anticipare le scelte degli utenti, ecc. Ma non è tutto oro quel che luccica: ci sono talvolta problemi tecnici, interrogativi etici, rischi di sicurezza, norme e legislazioni non del tutto chiare. Le organizzazioni che già adottano soluzioni basate sull’IA, o quelle che intendono farlo, potrebbero beneficiare di questa pubblicazione per approfondirne le opportunità, i rischi e le relative contromisure. La Community for Security del Clusit si augura che questa pubblicazione possa fornire ai lettori un utile quadro d’insieme di una realtà, come l’intelligenza artificiale, che ci accompagnerà sempre più nella vita personale, sociale e lavorativa.AI (or artificial intelligence) is a booming discipline in recent years and will be increasingly so in the near future.However, it is since 1956 that AI has been studying the emulation of intelligence by machines, understood as software and in some cases hardware. AI arose from the idea of building machines that-inspired by processes related to human intelligence-are able to solve complex problems, for which it is usually believed that some kind of intelligent reasoning is required. The main current area of AI research and application is machine learning (algorithms that learn and adapt based on the data they receive), which has found wide applications in recent years thanks to neural networks (mathematical models composed of artificial neurons), which in turn have enabled the emergence of deep learning (neural networks of greater complexity). Also belonging to the AI world are expert systems, computer vision, speech recognition, natural language processing, advanced robotics and some cybersecurity solutions. When it comes to AI there are those who are enthusiastic about it thinking of the opportunities, others are concerned as they fear futuristic technologies of a world where robots will replace humans, take away their jobs and make decisions for them. In reality, AI is already widely used in many fields, for example, in cell phones, smart objects (IoT), industries 4.0, for smart cities, cybersecurity systems, autonomous driving systems (drive or parking assistant), chat bots on various websites; these are just a few examples all based on typical artificial intelligence algorithms. Thanks to AI, companies can have a variety of advantages in providing advanced, personalized services, predicting trends, anticipating user choices, etc. But not all that glitters is gold: there are sometimes technical problems, ethical questions, security risks, and standards and legislation that are not entirely clear. Organizations already adopting AI-based solutions, or those planning to do so, could benefit from this publication to learn more about the opportunities, risks, and related countermeasures. Clusit's Community for Security hopes that this publication will provide readers with a useful overview of a reality, such as artificial intelligence, that will increasingly accompany us in our personal, social and working lives

Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study

Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe

Simulation Alternatives for Modeling Networked Cyber-Physical Systems

Several embedded system applications are used to control physical processes. Sensing, computation and actuation are combined thus involving a set of highly heterogeneous components, i.e., digital and analog hardware, software, energy sources, and external environment. Moreover, the growing use of networks contributes to introduce a further level of heterogeneity. All these aspects should be taken into account in the design process to find highly optimized solutions; therefore a Cyber-Physical System approach is needed. In particular, simulation is a key technique in the different design stages. However, the heterogeneity of components, together with the presence of the network, forces to adopt complex and slow cosimulation techniques to carry on the simulation of the entire system. This work aims at proposing SystemC as unified framework to model and simulate Networked Cyber-Physical Systems. Concerning the modeling of continuous-time components and a specific class of discrete-time components, the different Models of Computation provided by the Analog/Mixed-Signal (AMS) extension of SystemC are used. Regarding the network, SystemC and the SystemC Network Simulation Library are used to model communications at different abstraction levels. The accuracy and speed of different simulation alternatives are compared by the application to a networked control system

Simulation Alternatives for the Verification of Networked Cyber-Physical Systems

Several embedded system applications are used to control physical processes thus leading to the concept of Cyber-Physical System. Sensing, computation and actuation are combined by involving a set of highly heterogeneous components, \ie{}, digital and analog hardware, software, energy sources, and external physical systems (\eg{}, a room to be heated, a mechanical component). Moreover, we see the growing use of networks to connect several sub-systems in a more efficient way; this contributes to introduce a further level of heterogeneity due to the presence of messages exchanged over communication channels. All these aspects should be taken into account in the design process to find highly optimized solutions; in particular, simulation is a key technique for their verification. However, the heterogeneity of components, together with the presence of the network, forces to adopt complex and slow co-simulation techniques to carry on the simulation of the entire system. This work aims at proposing SystemC as unified framework to model and simulate Networked Cyber-Physical Systems. Concerning the modeling of continuous-time components and a specific class of discrete-time components, the different Models of Computation provided by the Analog/Mixed-Signal (AMS) extension of SystemC are used. Regarding the network, SystemC and the SystemC Network Simulation Library are used to model communications at different abstraction levels. The approach is validated by considering a networked control system. First, the accuracy and speed of SystemC simulation is compared to Matlab/Simulink simulation. Second, different simulation alternatives are compared to co-simulation. Finally, we show that the simulation speed-up achieved by all-SystemC modeling allows to explore several design alternatives and the accurate modeling of the communication network can improve the design of the controller

Automagically Encoding Adverse Drug Reactions in MedDRA

Pharmacovigilance is the field of science devoted to the collection, analysis and prevention of Adverse Drug Reactions (ADRs). Efficient strategies for the extraction of information about ADRs from free text resources are essential to support the work of experts, employed in the crucial task of detecting and classifying unexpected pathologies possibly related to drug assumptions. Narrative ADR descriptions may be collected in several way, e.g. by monitoring social networks or through the so called spontaneous reporting, the main method pharmacovigi- lance adopts in order to identify ADRs. The encoding of free-text ADR descriptions according to MedDRA standard terminology is central for report analysis. It is a complex work, which has to be manually implemented by the pharmacovigilance experts. The manual encoding is expensive (in terms of time). Moreover, a problem about the accuracy of the encoding may occur, since the number of reports is growing up day by day. In this paper, we propose MagiCoder, an efficient Natural Language Processing al- gorithm able to automatically derive MedDRA terminologies from free-text ADR descriptions. MagiCoder is part of VigiWork, a web application for online ADR reporting and analysis. From a practical view-point, MagiCoder radically reduces the revision time of ADR reports: the pharmacologist has simply to revise and validate the automatic solution versus the hard task of choosing solutions in the 70k terms of MedDRA. This improvement of the expert work efficiency has a meaningful impact on the quality of data analysis. Moreover, our procedure is general purpose. We developed MagiCoder for the Italian pharmacovigilance language, but preliminarily analyses show that it is robust to language and dictionary changes

dMyc expression in the fat body affects DILP2 release and increases the expression of the fat desaturase Desat1 resulting in organismal growth

Drosophila dMyc (dMyc) is known for its role in cell-autonomous regulation of growth. Here we address its role in the fat body (FB), a metabolic tissue that functions as a sensor of circulating nutrients to control the release of Drosophila Insulin-like peptides (Dilps) from the brain influencing growth and development. Our results show that expression of dMyc in the FB affects development and animal size. Expression of dMyc, but not of CycD/cdk4 or Rheb, in the FB diminishes the ability to retain Drosophila Insulin-like peptide-2 (DILP2) in the brain during starvation, suggesting that expression of dMyc mimics the signal that remotely controls the release of Dilps into the hemolymph. dMyc also affects glucose metabolism and increases the transcription of Glucose-transporter-1 mRNA, and of Hexokinase and Pyruvate-Kinase mRNAs, key regulators of glycolysis. These animals are able to counteract the increased levels of circulating trehalose induced by a high sugar diet leading to the conclusion that dMyc activity in the FB promotes glucose disposal. dMyc expression induces cell autonomous accumulation of triglycerides, which correlates with increased levels of Fatty Acid Synthase and Acetyl CoA Carboxylase mRNAs, enzymes responsible for lipid synthesis. We also found the expression of Stearoyl-CoA desaturase, Desat1 mRNA significantly higher in FB overexpressing dMyc. Desat1 is an enzyme that is necessary for monosaturation and production of fatty acids, and its reduction affects dMyc's ability to induce fat storage and resistance to animal survival. In conclusion, here we present novel evidences for dMyc function in the Drosophila FB in controlling systemic growth. We discovered that dMyc expression triggers cell autonomous mechanisms that control glucose and lipid metabolism to favor the storage of nutrients (lipids and sugars). In addition, the regulation of Desat1 controls the synthesis of triglycerides in FB and this may affect the humoral signal that controls DILP2 release in the brain.</p