Experimental tests on a pre-heated combustion chamber for ultra micro gas turbine device: air/fuel ratio evaluation

Current portable power generators are mainly based on internal combustion engine since they present higher values of efficiency comparing to other engines; the main reason why internal combustion engine is not convenient for micro power generation (5 - 30 kW) is because of their heaviness. Micro and ultra micro gas turbine devices, based on a micro compressor and a micro turbine installed on the same shaft, are more suitable for this scope for several reasons. Micro turbine systems have many advantages over reciprocating engine generators, such as higher power density (with respect to size and weight), extremely low emissions and few, or just one, moving part. Those designed with foil bearings and air-cooling operate without oil, coolants or other hazardous materials. Micro turbines also have the advantage of having the majority of their waste heat contained in their relatively high temperature exhaust. Micro turbines offer several potential advantages compared to other technologies for small-scale power generation, including: a small number of moving parts, compact size, lightweight, greater efficiency, lower emissions, lower electricity costs, and opportunities to utilize waste fuels. The object of this study is the experimental tests on a stand-alone gas turbine device with a pre-heated combustion chamber (CC), to validate the fuel consumption reduction, compared to an actual and commercial device, used on air models

Tourism as a tool for natural hazard protection and territory development: Civita di Bagnoregio (Viterbo, Italy) as a case study

The village of Civita di Bagnoregio (Viterbo, Italy) represents a surreal landscape generated by accelerated soil erosion. The active landslides and erosive phenomena which are affecting Civita contributed to the progressive reduction of its surface and to its depopulation and currently require advanced engineering solutions to mitigate their impact. Furthermore they contributed to internationally increase the village fame, resulting in an increasing number of visitors over last years. The increasing touristic pressure on the village has been evaluated by taking into account also possible rising due to the recent candidature of Civita di Bagnoregio to the UNESCO’s World Heritage List (WHL). The high touristic pressure is triggering new critical issues highlighting the absence of a proper management plan: the data analysis highlighted the need to develop appropriate tourist numbers management strategies, considering also a partial re-investment of entrance fees for activities aimed to safeguard the village. The present research highlight that effects of tourist flows attracted by Civita di Bagnoregio could substantially contribute to both the safeguard of the village and the economical development of the territory. Properly distributed in the area by planning tourism decentralization policies based on an integrated valorisation of the territory it would be also possible to expand benefits deriving from the tourism sector to the entire Teverina area, transforming a stress factor into a development vector for the whole territory and the local population

Combenefit: an interactive platform for the analysis and visualization of drug combinations.

MOTIVATION: Many drug combinations are routinely assessed to identify synergistic interactions in the attempt to develop novel treatment strategies. Appropriate software is required to analyze the results of these studies. RESULTS: We present Combenefit, new free software tool that enables the visualization, analysis and quantification of drug combination effects in terms of synergy and/or antagonism. Data from combinations assays can be processed using classical Synergy models (Loewe, Bliss, HSA), as single experiments or in batch for High Throughput Screens. This user-friendly tool provides laboratory scientists with an easy and systematic way to analyze their data. The companion package provides bioinformaticians with critical implementations of routines enabling the processing of combination data. AVAILABILITY AND IMPLEMENTATION: Combenefit is provided as a Matlab package but also as standalone software for Windows (http://sourceforge.net/projects/combenefit/). CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.This work has been supported by the Cancer Research UK grant C14303/A17197This is the final version of the article. It first appeared from Oxford University Press via http://dx.doi.org/10.1093/bioinformatics/btw23

Modelling of the cancer cell cycle as a tool for rational drug development: A systems pharmacology approach to cyclotherapy

The dynamic of cancer is intimately linked to a dysregulation of the cell cycle and signalling pathways. It has been argued that selectivity of treatments could exploit loss of checkpoint function in cancer cells, a concept termed "cyclotherapy". Quantitative approaches that describe these dysregulations can provide guidance in the design of novel or existing cancer therapies. We describe and illustrate this strategy via a mathematical model of the cell cycle that includes descriptions of the G1-S checkpoint and the spindle assembly checkpoint (SAC), the EGF signalling pathway and apoptosis. We incorporated sites of action of four drugs (palbociclib, gemcitabine, paclitaxel and actinomycin D) to illustrate potential applications of this approach. We show how drug effects on multiple cell populations can be simulated, facilitating simultaneous prediction of effects on normal and transformed cells. The consequences of aberrant signalling pathways or of altered expression of pro- or anti-apoptotic proteins can thus be compared. We suggest that this approach, particularly if used in conjunction with pharmacokinetic modelling, could be used to predict effects of specific oncogene expression patterns on drug response. The strategy could be used to search for synthetic lethality and optimise combination protocol designs

An automated fitting procedure and software for dose-response curves with multiphasic features.

In cancer pharmacology (and many other areas), most dose-response curves are satisfactorily described by a classical Hill equation (i.e. 4 parameters logistical). Nevertheless, there are instances where the marked presence of more than one point of inflection, or the presence of combined agonist and antagonist effects, prevents straight-forward modelling of the data via a standard Hill equation. Here we propose a modified model and automated fitting procedure to describe dose-response curves with multiphasic features. The resulting general model enables interpreting each phase of the dose-response as an independent dose-dependent process. We developed an algorithm which automatically generates and ranks dose-response models with varying degrees of multiphasic features. The algorithm was implemented in new freely available Dr Fit software (sourceforge.net/projects/drfit/). We show how our approach is successful in describing dose-response curves with multiphasic features. Additionally, we analysed a large cancer cell viability screen involving 11650 dose-response curves. Based on our algorithm, we found that 28% of cases were better described by a multiphasic model than by the Hill model. We thus provide a robust approach to fit dose-response curves with various degrees of complexity, which, together with the provided software implementation, should enable a wide audience to easily process their own data.This work was funded by Cancer Research UK grant C14303/A17197.This is the final version of the article. It first appeared from NPG via http://dx.doi.org/10.1038/srep1470

SynergyFinder : a web application for analyzing drug combination dose-response matrix data

Rational design of drug combinations has become a promising strategy to tackle the drug sensitivity and resistance problem in cancer treatment. To systematically evaluate the preclinical significance of pairwise drug combinations, functional screening assays that probe combination effects in a dose-response matrix assay are commonly used. To facilitate the analysis of such drug combination experiments, we implemented a web application that uses key functions of R-package SynergyFinder, and provides not only the flexibility of using multiple synergy scoring models, but also a user-friendly interface for visualizing the drug combination landscapes in an interactive manner.Peer reviewe

Role of cardiolipins, mitochondria, and autophagy in the differentiation process activated by all-trans retinoic acid in acute promyelocytic leukemia

The role played by lipids in the process of granulocytic differentiation activated by all-trans retinoic acid (ATRA) in Acute-Promyelocytic-Leukemia (APL) blasts is unknown. The process of granulocytic differentiation activated by ATRA in APL blasts is recapitulated in the NB4 cell-line, which is characterized by expression of the pathogenic PML-RARα fusion protein. In the present study, we used the NB4 model to define the effects exerted by ATRA on lipid homeostasis. Using a high-throughput lipidomic approach, we demonstrate that exposure of the APL-derived NB4 cell-line to ATRA causes an early reduction in the amounts of cardiolipins, a major lipid component of the mitochondrial membranes. The decrease in the levels of cardiolipins results in a concomitant inhibition of mitochondrial activity. These ATRA-dependent effects are causally involved in the granulocytic maturation process. In fact, the ATRA-induced decrease of cardiolipins and the concomitant dysfunction of mitochondria precede the differentiation of retinoid-sensitive NB4 cells and the two phenomena are not observed in the retinoid-resistant NB4.306 counterparts. In addition, ethanolamine induced rescue of the mitochondrial dysfunction activated by cardiolipin deficiency inhibits ATRA-dependent granulocytic differentiation and induction of the associated autophagic process. The RNA-seq studies performed in parental NB4 cells and a NB4-derived cell population, characterized by silencing of the autophagy mediator, ATG5, provide insights into the mechanisms underlying the differentiating action of ATRA. The results indicate that ATRA causes a significant down-regulation of CRLS1 (Cardiolipin-synthase-1) and LPCAT1 (Lysophosphatidylcholine-Acyltransferase-1) mRNAs which code for two enzymes catalyzing the last steps of cardiolipin synthesis. ATRA-dependent down-regulation of CRLS1 and LPCAT1 mRNAs is functionally relevant, as it is accompanied by a significant decrease in the amounts of the corresponding proteins. Furthermore, the decrease in CRLS1 and LPCAT1 levels requires activation of the autophagic process, as down-regulation of the two proteins is blocked in ATG5-silenced NB4-shATG5 cells

Combenefit: an interactive platform for the analysis and visualization of drug combinations.

Motivation: Many drug combinations are routinely assessed to identify synergistic interactions in the attempt to develop novel treatment strategies. Appropriate software is required to analyze the results of these studies. Results: We present Combenefit, new free software tool that enables the visualization, analysis and quantification of drug combination effects in terms of synergy and/or antagonism. Data from combinations assays can be processed using classical Synergy models (Loewe, Bliss, HSA), as single experiments or in batch for High Throughput Screens. This user-friendly tool provides laboratory scientists with an easy and systematic way to analyze their data. The companion package provides bioinformaticians with critical implementations of routines enabling the processing of combination data. Availability and Implementation: Combenefit is provided as a Matlab package but also as standalone software for Windows (http://sourceforge.net/projects/combenefit/). Contact: [email protected]. Supplementary information:Supplementary data are available at Bioinformatics online

Quantitative systems modeling approaches towards model-informed drug development: Perspective through case studies

Quantitative systems pharmacology (QSP) modeling has become an increasingly popular approach impacting our understanding of disease mechanisms and helping predict patients’ treatment responses to facilitate study design or development go/no-go decisions. In this paper, we highlight the notable contributions and opportunities that QSP approaches are to offer during the drug development process by sharing three examples that have facilitated internal decisions. The barriers to successful applications and the factors that facilitate the success of the modeling approach is discussed

Sinusoidal voltage protocols for rapid characterisation of ion channel kinetics

Understanding the roles of ion currents is crucial to predict the action of pharmaceuticals and mutations in different scenarios, and thereby to guide clinical interventions in the heart, brain and other electrophysiological systems. Our ability to predict how ion currents contribute to cellular electrophysiology is in turn critically dependent on our characterisation of ion channel kinetics - the voltage-dependent rates of transition between open, closed and inactivated channel states. We present a new method for rapidly exploring and characterising ion channel kinetics, applying it to the hERG potassium channel as an example, with the aim of generating a quantitatively predictive representation of the ion current. We fit a mathematical model to currents evoked by a novel 8 second sinusoidal voltage clamp in CHO cells over-expressing hERG1a. The model is then used to predict over 5 minutes of recordings in the same cell in response to further protocols: a series of traditional square step voltage clamps, and also a novel voltage clamp comprised of a collection of physiologically-relevant action potentials. We demonstrate that we can make predictive cell-specific models that outperform the use of averaged data from a number of different cells, and thereby examine which changes in gating are responsible for cell-cell variability in current kinetics. Our technique allows rapid collection of consistent and high quality data, from single cells, and produces more predictive mathematical ion channel models than traditional approaches