CO2 lidar system for atmospheric studies

A lidar facility using a TEA CO2 laser source is being developed at the ENEA Laboratories for Atmospheric Studies. The different subsystems and the proposed experimental activities are described

A bootstrap approach for assessing the uncertainty of outcome probabilities when using a scoring system

Background: Scoring systems are a very attractive family of clinical predictive models, because the patient score can be calculated without using any data processing system. Their weakness lies in the difficulty of associating a reliable prognostic probability with each score. In this study a bootstrap approach for estimating confidence intervals of outcome probabilities is described and applied to design and optimize the performance of a scoring system for morbidity in intensive care units after heart surgery. Methods: The bias-corrected and accelerated bootstrap method was used to estimate the 95% confidence intervals of outcome probabilities associated with a scoring system. These confidence intervals were calculated for each score and each step of the scoring-system design by means of one thousand bootstrapped samples. 1090 consecutive adult patients who underwent coronary artery bypass graft were assigned at random to two groups of equal size, so as to define random training and testing sets with equal percentage morbidities. A collection of 78 preoperative, intraoperative and postoperative variables were considered as likely morbidity predictors. Results: Several competing scoring systems were compared on the basis of discrimination, generalization and uncertainty associated with the prognostic probabilities. The results showed that confidence intervals corresponding to different scores often overlapped, making it convenient to unite and thus reduce the score classes. After uniting two adjacent classes, a model with six score groups not only gave a satisfactory trade-off between discrimination and generalization, but also enabled patients to be allocated to classes, most of which were characterized by well separated confidence intervals of prognostic probabilities. Conclusions: Scoring systems are often designed solely on the basis of discrimination and generalization characteristics, to the detriment of prediction of a trustworthy outcome probability. The present example demonstrates that using a bootstrap method for the estimation of outcome-probability confidence intervals provides useful additional information about score-class statistics, guiding physicians towards the most convenient model for predicting morbidity outcomes in their clinical context

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part II: an illustrative example

<p>Abstract</p> <p>Background</p> <p>Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example.</p> <p>Methods</p> <p>Eight models were developed: Bayes linear and quadratic models, <it>k</it>-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively.</p> <p>Results</p> <p>Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and <it>k</it>-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, <it>k</it>-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results.</p> <p>Conclusion</p> <p>Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery.</p

The PLASMONX Project for advanced beam physics experiments

The Project PLASMONX is well progressing into its design phase and has entered as well its second phase of procurements for main components. The project foresees the installation at LNF of a Ti:Sa laser system (peak power > 170 TW), synchronized to the high brightness electron beam produced by the SPARC photo-injector. The advancement of the procurement of such a laser system is reported, as well as the construction plans of a new building at LNF to host a dedicated laboratory for high intensity photon beam experiments (High Intensity Laser Laboratory). Several experiments are foreseen using this complex facility, mainly in the high gradient plasma acceleration field and in the field of mono- chromatic ultra-fast X-ray pulse generation via Thomson back-scattering. Detailed numerical simulations have been carried out to study the generation of tightly focused electron bunches to collide with laser pulses in the Thomson source: results on the emitted spectra of X-rays are presented

Posttranscriptional regulation of angiotensin II type 1 receptor expression by glyceraldehyde 3-phosphate dehydrogenase

Regulation of angiotensin II type 1 receptor (AT1R) has a pathophysiological role in hypertension, atherosclerosis and heart failure. We started from an observation that the 3′-untranslated region (3′-UTR) of AT1R mRNA suppressed AT1R translation. Using affinity purification for the separation of 3′-UTR-binding proteins and mass spectrometry for their identification, we describe glyceraldehyde 3-phosphate dehydrogenase (GAPDH) as an AT1R 3′-UTR-binding protein. RNA electrophoretic mobility shift analysis with purified GAPDH further demonstrated a direct interaction with the 3′-UTR while GAPDH immunoprecipitation confirmed this interaction with endogenous AT1R mRNA. GAPDH-binding site was mapped to 1–100 of 3′-UTR. GAPDH-bound target mRNAs were identified by expression array hybridization. Analysis of secondary structures shared among GAPDH targets led to the identification of a RNA motif rich in adenines and uracils. Silencing of GAPDH increased the expression of both endogenous and transfected AT1R. Similarly, a decrease in GAPDH expression by H2O2 led to an increased level of AT1R expression. Consistent with GAPDH having a central role in H2O2-mediated AT1R regulation, both the deletion of GAPDH-binding site and GAPDH overexpression attenuated the effect of H2O2 on AT1R mRNA. Taken together, GAPDH is a translational suppressor of AT1R and mediates the effect of H2O2 on AT1R mRNA

A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery – Part I: model planning

<p>Abstract</p> <p>Background</p> <p>Different methods have recently been proposed for predicting morbidity in intensive care units (ICU). The aim of the present study was to critically review a number of approaches for developing models capable of estimating the probability of morbidity in ICU after heart surgery. The study is divided into two parts. In this first part, popular models used to estimate the probability of class membership are grouped into distinct categories according to their underlying mathematical principles. Modelling techniques and intrinsic strengths and weaknesses of each model are analysed and discussed from a theoretical point of view, in consideration of clinical applications.</p> <p>Methods</p> <p>Models based on Bayes rule, <it>k-</it>nearest neighbour algorithm, logistic regression, scoring systems and artificial neural networks are investigated. Key issues for model design are described. The mathematical treatment of some aspects of model structure is also included for readers interested in developing models, though a full understanding of mathematical relationships is not necessary if the reader is only interested in perceiving the practical meaning of model assumptions, weaknesses and strengths from a user point of view.</p> <p>Results</p> <p>Scoring systems are very attractive due to their simplicity of use, although this may undermine their predictive capacity. Logistic regression models are trustworthy tools, although they suffer from the principal limitations of most regression procedures. Bayesian models seem to be a good compromise between complexity and predictive performance, but model recalibration is generally necessary. <it>k</it>-nearest neighbour may be a valid non parametric technique, though computational cost and the need for large data storage are major weaknesses of this approach. Artificial neural networks have intrinsic advantages with respect to common statistical models, though the training process may be problematical.</p> <p>Conclusion</p> <p>Knowledge of model assumptions and the theoretical strengths and weaknesses of different approaches are fundamental for designing models for estimating the probability of morbidity after heart surgery. However, a rational choice also requires evaluation and comparison of actual performances of locally-developed competitive models in the clinical scenario to obtain satisfactory agreement between local needs and model response. In the second part of this study the above predictive models will therefore be tested on real data acquired in a specialized ICU.</p

Alpine ethnobotany in Italy: traditional knowledge of gastronomic and medicinal plants among the Occitans of the upper Varaita valley, Piedmont

A gastronomic and medical ethnobotanical study was conducted among the Occitan communities living in Blins/Bellino and Chianale, in the upper Val Varaita, in the Piedmontese Alps, North-Western Italy, and the traditional uses of 88 botanical taxa were recorded. Comparisons with and analysis of other ethnobotanical studies previously carried out in other Piemontese and surrounding areas, show that approximately one fourth of the botanical taxa quoted in this survey are also known in other surrounding Occitan valleys. It is also evident that traditional knowledge in the Varaita valley has been heavily eroded. This study also examined the local legal framework for the gathering of botanical taxa, and the potential utilization of the most quoted medicinal and food wild herbs in the local market, and suggests that the continuing widespread local collection from the wild of the aerial parts of Alpine wormwood for preparing liqueurs (Artemisia genipi, A. glacialis, and A. umbelliformis) should be seriously reconsidered in terms of sustainability, given the limited availability of these species, even though their collection is culturally salient in the entire study area

Digital strategies to a local cultural tourism development: Project e-Carnide

Digital humanities and smart economy strategies are being seen as an important link between tourism and cultural heritage, as they may contribute to differentiate the audiences and to provide different approaches. Carnide is a peripheral neighbourhood of Lisbon with an elderly population, visible traces of rurality, and strong cultural and religious traditions. The academic project e-Carnide concerns its tangible and intangible cultural heritage and the data dissemination through a website and a mobile app, with textual and visual information. The project aims to analyse the impact of technological solutions on cultural tourism development in a sub-region, involving interdisciplinary research in heritage, history of art, ethnography, design communication and software engineering and the collaboration between the university and local residents in a dynamic and innovative way. Framed by a theoretical approach about the role of smart economy for the cultural tourism development in peripheral areas, this paper focuses on a case study, dealing with documents, interviews and observations, in order to understand how the e-Carnide project evolves. The study comprises an analysis about the strengths, weaknesses, opportunities and threats (SWOT analysis) of the project in view to realize its social and cultural implications and to appreciate how it can be applied in other similar and enlarged projects. Results of the research indicates that the new technological strategies can promote the involvement of the population in the knowledge of its own heritage as a factor of cultural and creative tourism development centred on an authentic and immersive experience of the places