Strain-dependent differences in corticolimbic processing of aversive or rewarding stimuli

Aberrations in the elaboration of both aversive and rewarding stimuli characterize several psychopathologies including anxiety, depression and addiction. Several studies suggest that different neurotrasmitters, within the corticolimbic system, are critically involved in the processing of positive and negative stimuli. Individual differences in this system, depending on genotype, have been shown to act as a liability factor for different psychopathologies. Inbred mouse strains are commonly used in preclinical studies of normal and pathological behaviors. In particular, C57BL/6J (C57) and DBA/2J (DBA) strains have permitted to disclose the impact of different genetic backgrounds over the corticolimbic system functions. Here, we summarize the main findings collected over the years in our laboratory, showing how the genetic background plays a critical role in modulating amminergic and GABAergic neurotransmission in prefrontal-accumbal-amygdala system response to different rewarding and aversive experiences, as well as to stress response. Finally, we propose a top-down model for the response to rewarding and aversive stimuli in which amminergic transmission in prefrontal cortex (PFC) controls accumbal and amygdala neurotransmitter response

Teachers as designers of GBL scenarios: Fostering creativity in the educational settings

This paper presents a research started in 2010 with the aim of fostering the creativity of teachers through the design of Game-Based Learning scenarios. The research has been carried out involving teachers and trainers in the co-design and implementation of digital games as educational resources. Based on the results grained from the research, this paper highlights successful factors of GBL, as well as constraints and boundaries that the introduction of innovative teaching and learning practices faces within educational settings

Intake O2 Concentration Estimation in a Turbocharged Diesel Engine through NOE

Diesel engines with their embedded control systems are becoming increasingly complex as the emission regulations tighten, especially concerning NOx pollutants. The combustion and emission formation processes are closely correlated to the intake manifold O2 concentration. Consequently, the performance of the engine controllers can be improved if a model-based or sensor-based estimation of the O2 concentration is available. The paper addresses the modeling of the O2 concentration in a turbocharged diesel engine. Dynamic models, compared to generally employed steady state maps, capture the dynamic effects occurring over transients, when the major deviations from the stationary maps are found. Dynamic models positively affect the control system making it more effective and, exploiting information coming from sensors, they provide a more robust prediction performance. Firstly, a Nonlinear Output Error model (NOE), with simulation focus, fed with four inputs is presented. The considered nonlinear function set is the one of neural networks. The inputs are engine BMEP, engine RPM and EGR and VGT valves position. Two distinct datasets are used for training and validation of the NOE model. These sets are generated using GT-Power simulation software implementing a fine model of the engine, previously validated on experimental measurements taken on the real engine. Besides the transient validation, the NOE model was tested against GT-Power outputs on step tests involving the EGR and VGT actuators. At last the network output is compared with an O2 steady state map over a transient in normal and faulty conditions. The performance of the model is satisfactory in both conditions. Secondly, the potential benefits of installing an O2 sensor in the intake manifold is presented: a Nonlinear Auto-Regressive with eXogenous input (NARX) model is considered and compared to the previously investigated NOE. The results prove that, exploiting the output coming from the O2 sensor, the model prediction capability significantly improves

Air path and combustion controls coordination in diesel engine

The tightening of the diesel pollutants emissions regulations has made the performances obtainable from steady-state map controls, commonly employed in Internal Combustion Engine (ICE) management, unsatisfactory. To overcome these performance limitations, control systems have to cope with the engine transient operation conditions, coupling between its subsystem dynamics, and the trade-off between different requirements to efficiently manage the engine. The work demonstrates the deployment of a reference generator that coordinates the air path and combustion control systems of a turbocharged diesel engine for heavy-duty applications. The control system coordinator is based on neural networks and allows to exploit the best performance of the two control systems. The key idea is to generate air path targets, intake O2 concentration and Intake MAnifold Pressure (IMAP), coherent with the ones of the combustion control system, engine load and engine-out Nitrogen Oxides (NOx). In this way, the air path control system provides the global conditions for the correct functioning of the engine, while, in cooperation, the combustion control will react to fast changes in the engine operating state and compensate for the remaining deviations with respect to load and NOx targets. Reference generator networks are suitable for further real-time implementation on rapid-prototyping hardware and their performance was overall good

Effects of lack of microRNA-34 on the neural circuitry underlying the stress response and anxiety

Stress-related psychiatric disorders, including anxiety, are complex diseases that have genetic, and environmental causes. Stressful experiences increase the release of prefrontal amygdala neurotransmitters, a response that is relevant to cognitive, emotional, and behavioral coping. Moreover, exposure to stress elicits anxiety-like behavior and dendritic remodeling in the amygdala. Members of the miR-34 family have been suggested to regulate synaptic plasticity and neurotransmission processes, which mediate stress-related disorders. Using mice that harbored targeted deletions of all 3 members of the miR-34-family (miR-34-TKO), we evaluated acute stress-induced basolateral amygdala (BLA)-GABAergic and medial prefrontal cortex (mpFC) aminergic outflow by intracerebral in vivo microdialysis. Moreover, we also examined fear conditioning/extinction, stress-induced anxiety, and dendritic remodeling in the BLA of stress-exposed TKO mice. We found that TKO mice showed resilience to stress-induced anxiety and facilitation in fear extinction. Accordingly, no significant increase was evident in aminergic prefrontal or amygdala GABA release, and no significant acute stress-induced amygdalar dendritic remodeling was observed in TKO mice. Differential GRM7, 5-HT2C, and CRFR1 mRNA expressionwas noted in the mpFC and BLA between TKO andWT mice. Our data demonstrate that the miR-34 has a critical function in regulating the behavioral and neurochemical response to acute stress and in inducing stress-related amygdala neuroplasticity

Prefrontal/accumbal catecholamine system processes high motivational salience

Motivational salience regulates the strength of goal seeking, the amount of risk taken, and the energy invested from mild to extreme. Highly motivational experiences promote highly persistent memories. Although this phenomenon is adaptive in normal conditions, experiences with extremely high levels of motivational salience can promote development of memories that can be re-experienced intrusively for long time resulting in maladaptive outcomes. Neural mechanisms mediating motivational salience attribution are, therefore, very important for individual and species survival and for well-being. However, these neural mechanisms could be implicated in attribution of abnormal motivational salience to different stimuli leading to maladaptive compulsive seeking or avoidance. We have offered the first evidence that prefrontal cortical norepinephrine (NE) transmission is a necessary condition for motivational salience attribution to highly salient stimuli, through modulation of dopamine (DA) in the nucleus accumbens (NAc), a brain area involved in all motivated behaviors. Moreover, we have shown that prefrontal-accumbal catecholamine (CA) system determines approach or avoidance responses to both reward- and aversion-related stimuli only when the salience of the unconditioned stimulus (UCS) is high enough to induce sustained CA activation, thus affirming that this system processes motivational salience attribution selectively to highly salient events

Default prior distributions from quasi- and quasi-profile likelihoods.

In some problems of practical interest, a standard Bayesian analysis can be difficult to perform. This is true, for example, when the class of sampling parametric models is unknown or if robustness with respect to data or to model misspecifications is required. These situations can be usefully handled by using a posterior distribution for the parameter of interest which is based on a pseudo-likelihood function derived from estimating equations, i.e. on a quasi-likelihood, and on a suitable prior distribution. The aim of this paper is to propose and discuss the construction of a default prior distribution for a scalar parameter of interest to be used together with a quasi-likelihood function. We show that the proposed default prior can be interpreted as a Jeffreys-type prior, since it is proportional to the square-root of the expected information derived from the quasi-likelihood. The frequentist coverage of the credible regions, based on the proposed procedure, is studied through Monte Carlo simulations in the context of robustness theory and of generalized linear models with overdispersion

Diesel Engine Cycle to Cycle Feed-forward plus Closed-loop Combustion Control

A model-based open-loop compensator has been combined with a cycle to cycle closed-loop controller with the aim of managing engine load and NOx (Nitrogen Oxides)emission. Both the control strategies employ a virtual sensor realized through a predictive combustion model calibrated on real test bench measurements. Thanks to the virtual sensor no direct in-cylinder pressure measurement is required. Injected fuel quantity and start of injection of the main pulse are regulated to target the desired engine load and NOx respectively. In the closed-loop control the regulation of the manipulated variables is performed by two separate loops implementing PI and lag regulators, one to control the engine load and the other the NOx. Both open-loop and closed-loop strategies have been tested separately and then in cooperation between them in order to improve the closed-loop controller time response. Model-in-the-Loop technique was exploited to develop and assess the three control strategies by co-simulation between Simulink and GT-Power executing a fast-running model of a light-duty FPT F1C Euro VI diesel engine. Simulations show promising results and real-time capacity, therefore the strategies are suitable for successive implementation on the real engine through rapid prototyping

Classification of cancer pathology reports: a large-scale comparative study

We report about the application of state-of-the-art deep learning techniques to the automatic and interpretable assignment of ICD-O3 topography and morphology codes to free-text cancer reports. We present results on a large dataset (more than 80 000 labeled and 1 500 000 unlabeled anonymized reports written in Italian and collected from hospitals in Tuscany over more than a decade) and with a large number of classes (134 morphological classes and 61 topographical classes). We compare alternative architectures in terms of prediction accuracy and interpretability and show that our best model achieves a multiclass accuracy of 90.3% on topography site assignment and 84.8% on morphology type assignment. We found that in this context hierarchical models are not better than flat models and that an element-wise maximum aggregator is slightly better than attentive models on site classification. Moreover, the maximum aggregator offers a way to interpret the classification process.Comment: 10 pages, 6 figures, 3 tables, accepted for publication in IEEE Journal of Biomedical and Health Informatics (J-BHI

Recensione di M. Marraffa, A. Paternoster, Sentirsi Esistere. Inconscio, coscienza, autocoscienza

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