Metabotropic glutamate 2/3 receptors and epigenetic modifications in psychotic disorders: a review

Schizophrenia and Bipolar Disorder are chronic psychiatric disorders, both considered as "major psychosis"; they are thought to share some pathogenetic factors involving a dysfunctional gene x environment interaction. Alterations in the glutamatergic transmission have been suggested to be involved in the pathogenesis of psychosis. Our group developed an epigenetic model of schizophrenia originated by Prenatal Restraint Stress (PRS) paradigm in mice. PRS mice developed some behavioral alterations observed in schizophrenic patients and classic animal models of schizophrenia, i.e. deficits in social interaction, locomotor activity and prepulse inhibition. They also showed specific changes in promoter DNA methylation activity of genes related to schizophrenia such as reelin, BDNF and GAD67, and altered expression and function of mGlu2/3 receptors in the frontal cortex. Interestingly, behavioral and molecular alterations were reversed by treatment with mGlu2/3 agonists. Based on these findings, we speculate that pharmacological modulation of these receptors could have a great impact on early phase treatment of psychosis together with the possibility to modulate specific epigenetic key protein involved in the development of psychosis. In this review, we will discuss in more details the specific features of the PRS mice as a suitable epigenetic model for major psychosis. We will then focus on key proteins of chromatin remodeling machinery as potential target for new pharmacological treatment through the activation of metabotropic glutamate receptors

Learning Micromanipulation, Part 1: An Approach Based on Multidimensional Ability Inventories and Text Mining.

In the last decades, an effort has been made to improve the efficiency of high-level and academic education players. Nowadays, students’ preferences and habits are continuously evolving and so the educational institutions deal with important challenges, such as not losing attractiveness or preventing early abandonment during the programs. In many countries, some important universities are public, and so they receive national grants that are based on a variety of factors, on which the teaching efficiency has a great impact. This contribution presents a method to improve students commitment during traditional lessons and laboratory tests. The idea consists in planning some activities according to the students’ learning preferences, which were studied by means of two different approaches. The first one was based on Gardner’s multiple intelligence inventory, which is useful to highlight some peculiar characteristics of the students on the specific educational field. In the second method, direct interviews, voice recognition, and text mining were used to extract some interesting characteristics of the group of students who participated in the projects. The methods were applied in May 2018 to the students attending the course of Micro-Nano Sensors and Actuators for the postgraduate academic program dedicated to Industrial Nanotechnologies Engineering of the University of Rome La Sapienza. The present paper represents the first part of the investigation and it is dedicated essentially to the adopted methods. The second part of the work is presented in the companion paper dedicated to the presentation of the practical project that the students completed before the exam

Development of an efficient solver for detailed kinetics in reactive flows

The use of chemical kinetic mechanisms in CAE tools for reactive flow simulations is of high importance for studying and predicting pollutant formation. However, usage of complex reaction schemes is accompanied by high computational cost in both 1D and 3D-CFD frameworks. The combustion research community has addressed such challenge via two main approaches: 1) tailor made mechanism reduction strategies; 2) pre-tabulation of the chemistry process and look-up during run-time. The present work covers both topics, although much of the methodology development and validation efforts focused on tabulation.In the first eight months of the PhD work, an isomer lumping strategy based on thermodynamic data was developed and applied to a detailed three component reaction mechanism for n-decane, alpha-methylnaphthalene and methyl decanoate comprising 807 species and 7807 reactions. A total of 74 isomer groups were identified within the oxidation of n-decane and methyl-decanoate via analysis of the Gibbs free energy of the isomers. The lumping procedure led to a mechanism of 463 species and 7600 reactions which was compared against the detailed version over several reactor conditions and over a broad range of temperature, pressure and equivalence ratio. In all cases, very good agreement between the predictions obtained using the lumped and the detailed mechanism has been observed with an overall absolute error below 12%.In the second phase of the PhD work, a tabulated chemistry approach was developed, implemented and validated against an on-the-fly chemistry solver across different simulation frameworks. As a first attempt, a flamelet-based tabulation method for soot source terms was coupled to the stochastic reactor model (SRM) and tested against a well stirred reactor-based approach under Diesel engine conditions. The main purpose was to assess and quantify benefits of tabulation within the 0D-SRM framework with respect to soot formation only. Subsequently, a chemical enthalpy (ℎ298) based approach was developed and implemented within the SRM model to predict both combustion and emission formation. This approach was widely validated against the detailed on-the-fly solver solutions under 0D reactor conditions as well as Diesel engine conditions for a wide range of operating points. Good agreement was found between the two solvers and a remarkable speed-up was obtained by means of computational costs of the simulation. As a last step, the same tabulated chemistry solver was coupled to a commercial CFD solver (CONVERGE v. 2.4) via user defined functions and performances were assessed against the built-in on-the fly chemistry solver (SAGE) under Diesel engine sector simulations. The tabulated chemistry solver proved to be within an acceptable level of accuracy for engineering studies and showed a consistent speed-up in comparison to the SAGE solver.Across all the investigated frameworks, the developed tabulated chemistry solver was found to be a valid solution to speed-up simulation time without compromising accuracy of the solution for combustion and emissions predictions for Diesel engine applications. In fact, the much-reduced CPU times allowed the SRM to be included in broader engine development campaigns where multi-objective optimization methods where efficiently used to explore new engine designs

Development of an efficient solver for detailed kinetics in reactive flows

The use of chemical kinetic mechanisms in CAE tools for reactive flow simulations is of high importance for studying and predicting pollutant formation. However, usage of complex reaction schemes is accompanied by high computational cost in both 1D and 3-D CFD frameworks. The combustion research community has addressed such challenge via two main approaches: 1) tailor made mechanism reduction strategies; 2) pre-tabulation of the chemistry process and look-up during run-time. The present work covers both topics, although much of the methodology development and validation efforts focused on tabulation. In the first phase of the PhD work, an isomer lumping strategy based on thermodynamic data was developed and applied to a detailed three component reaction mechanism for n-decane, alpha-methylnaphthalene and methyl decanoate comprising 807 species and 7807 reactions. A total of 74 isomer groups were identified within the oxidation of n-decane and methyl decanoate via the assessment of the Gibbs free energy of the isomers. The lumping procedure led to a mechanism of 463 species and 7600 reactions, which was compared against the detailed version over several reactor conditions and over a broad range of temperature, pressure and equivalence ratio. In all cases, excellent agreement between the predictions obtained using the lumped and the detailed mechanism has been observed with an overall absolute error below 12%. In the second phase of the PhD work, a tabulated chemistry approach was developed, implemented and validated against an on-the-fly chemistry solver across different simulation frameworks. As a first attempt, a flamelet-based tabulation method for soot source terms was coupled to the stochastic reactor model and tested against a well stirred reactor-based approach under Diesel engine conditions. The main purpose was to assess and quantify benefits of tabulation within the 0-D SRM framework with respect to soot formation only. Subsequently, a latent enthalpy (h298) based approach was developed and implemented within the SRM model to predict both combustion and emission formation. This approach was widely validated against the detailed on-the-fly solver solutions under 0-D reactor conditions as well as Diesel engine conditions for a wide range of operating points. Good agreement was found between the two solvers and a remarkable speed-up was obtained in terms of computational costs of the simulation. As a last step, the same tabulated chemistry solver was coupled to a commercial CFD software via user defined functions and performances were assessed against the built-in on-the fly chemistry solver under Diesel engine sector simulations. The tabulated chemistry solver proved to be within an acceptable level of accuracy for engineering studies and showed a consistent speed-up in comparison to the online chemistry solver. Across all the investigated frameworks, the developed tabulated chemistry solver was found to be a valid solution to speed-up simulation time without compromising accuracy of the solution for combustion and emissions predictions for engine applications. In fact, the much-reduced CPU times allowed the SRM to be included in broader engine development campaigns where multi-objective optimization methods where efficiently used to explore new engine designs

How Valuable and Secure is Your Personal Data

This paper explores security and the value of everyday personal data. This paper will explore how major breaches can affect both companies and their clients. Corporate attitudes regarding the security of personal data will be discussed, what companies do with our data and lastly what strategies are employed by corporate organizations to maintain our data safe and secure. This is an important topic that needs more review and research because in the world today, security paradigms are failing on both the user and business side. The average citizen simply does not understand how important it is to keep information secure given the big advances in cyber crime as we move forward into a digital world of tomorrow. As well, businesses need to employ excellent cyber security expertise , to ensure the information they are required to maintain does not fall into the wrong hands

An a priori thermodynamic data analysis based chemical lumping method for the reduction of large and multi-component chemical kinetic mechanisms

A chemical species lumping approach for reduction of large hydrocarbons and oxygenated fuels is presented. The methodology is based on an a priori analysis of the Gibbs free energy of the isomer species which is then used as main criteria for the evaluation of lumped group. Isomers with similar Gibbs free energy are lumped assuming they present equal concentrations when applied to standard reactor conditions. Unlike several lumping approaches found in literature, no calculation results from the primary mechanism have been employed prior to the application of our chemical lumping strategy. An 807 species and 7807 individual reactions detailed mechanism comprising n-decane, alpha-methylnaphthalene and methyl decanoate has been used. The thermodynamic data have been analyzed and 74 isomer groups have been identified within the oxidation of n-decane and methyl decanoate. The mechanism reduction has led to a mechanism size of 463 species and 7600 reactions. Thereafter the lumped mechanism has been checked under several reactor conditions and over a broad range of temperature, pressure, and equivalence ratio in order to quantify the accuracy of the proposed approach. In all cases, very good agreement between the predictions obtained using the lumped and the detailed mechanism has been observed with an overall absolute error below 12%. Effects of the lumping procedure on sensitivities and on isomer concentrations were considered to further demonstrate the validity of the proposed approach

PHYSICAL ACTIVITY AND DEVELOPMENT OF EXECUTIVE FUNCTIONS IN DEVELOPMENTAL AGE: A SYSTEMATIC REVIEW

Physical activity (PA) has a positive role in the development of executive functions (EF) in children, improving mental and physical well-being. This systematic review aims to investigate the impact of PA on EF, in developmental age, within different educational environments. This review analysed studies from 2000 to 2025 on children 5-14 years old, following the PRISMA guidelines.The results confirm the fundamental role of PA in promoting the development of both cognitive and motor skills in children

Development of a Computationally Efficient Tabulated Chemistry Solver for Internal Combustion Engine Optimization Using Stochastic Reactor Models

The use of chemical kinetic mechanisms in computer aided engineering tools for internal combustion engine simulations is of high importance for studying and predicting pollutant formation of conventional and alternative fuels. However, usage of complex reaction schemes is accompanied by high computational cost in 0-D, 1-D and 3-D computational fluid dynamics frameworks. The present work aims to address this challenge and allow broader deployment of detailed chemistry-based simulations, such as in multi-objective engine optimization campaigns. A fast-running tabulated chemistry solver coupled to a 0-D probability density function-based approach for the modelling of compression and spark ignition engine combustion is proposed. A stochastic reactor engine model has been extended with a progress variable-based framework, allowing the use of pre-calculated auto-ignition tables instead of solving the chemical reactions on-the-fly. As a first validation step, the tabulated chemistry-based solver is assessed against the online chemistry solver under constant pressure reactor conditions. Secondly, performance and accuracy targets of the progress variable-based solver are verified using stochastic reactor models under compression and spark ignition engine conditions. Detailed multicomponent mechanisms comprising up to 475 species are employed in both the tabulated and online chemistry simulation campaigns. The proposed progress variable-based solver proved to be in good agreement with the detailed online chemistry one in terms of combustion performance as well as engine-out emission predictions (CO, CO2, NO and unburned hydrocarbons). Concerning computational performances, the newly proposed solver delivers remarkable speed-ups (up to four orders of magnitude) when compared to the online chemistry simulations. In turn, the new solver allows the stochastic reactor model to be computationally competitive with much lower order modeling approaches (i.e., Vibe-based models). It also makes the stochastic reactor model a feasible computer aided engineering framework of choice for multi-objective engine optimization campaigns